Systems and methods for modeling pharmacokinetic endpoints using BTK inhibitors

Abstract

Systems and methods for modeling an endpoint responsive to treatment with a BTK inhibitor are provided. A model generating a representation of the endpoint in a subject responsive to BTK inhibitor treatment is obtained. Using the model, an iteration frequency and unpartitioned quantum of the BTK inhibitor are identified based upon a filtering criterion for the endpoint. The iteration frequency includes iteration intervals, each iteration interval including a corresponding amount of the unpartitioned quantum. The filtering criterion includes, for each iteration interval, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for a threshold duration, where the threshold occupancy is at least 50% and the threshold duration is at least 40% of an iteration interval. When the iteration frequency and the unpartitioned quantum satisfy the filtering criterion, an application regimen is identified based upon the iteration frequency and corresponding amounts of the unpartitioned quantum.

Description

Attorney Ref. No.: 126569-5019-WOSYSTEMS AND METHODS FOR MODELING PHARMACOKINETIC ENDPOINTS USING BTK INHIBITORSCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 716,551, filed November 5, 2024, and claims priority to U.S. Provisional Patent Application Serial No. 63 / 740.673, filed December 31, 2024, each of which is hereby incorporated by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates to systems and methods for modeling endpoints responsive to treatment with a Bruton’s Tyrosine Kinase (BTK) inhibitor. The present disclosure further relates to systems and methods for selecting dosage regimens for BTK inhibitors based upon target endpoints.BACKGROUND

[0003] Bruton’s Tyrosine Kinase (BTK) is a non-receptor tyrosine kinase that belongs to the Tec family and has an important function in several benign and malignant cells of the hematopoietic system. Moreover, recent clinical studies with irreversible oral BTK inhibitors, acalabrutinib and ibrutinib, have demonstrated clinical activity and tolerability against a variety of B-cell malignancies including: chronic lymphocytic leukemia (CLL), mantle cell lymphoma (MCL), Waldenstrom macroglobulinemia (WM), marginal zone lymphoma (MZL) and diffuse large B-cell lymphoma (DLBCL). The mechanism of action of BTK inhibitors is multifactorial, w ith a significant component of its function in lymphoid malignancies involving the disruption of the tumor cell and the microenvironment that protects it. Inhibition of BTK has been shown to regulate CLL, MCL and malignant myeloid cell migration in acute myeloid leukemia by inhibiting CXCR4-CXCL12 induced cell trafficking, homing and integrin adhesion by downregulating expression of numerous vascular adhesion molecules (Zaitseva (2014) Oncotarget 5, 9930-9938).1DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOSUMMARY

[0004] The present disclosure addresses the need in the art by providing systems and methods for modeling target endpoints responsive to treatment with BTK inhibitors (e.g., PK / PD modeling under different conditions and assumptions) and / or selecting dosage regimens for treating BTK-mediated conditions based on modeled target endpoints.

[0005] One aspect of the present disclosure provides a computer system for selecting a dosage regimen for treating a BTK-mediated condition using a BTK inhibitor, the computer system comprising a memory and a processor, the memory storing a plurality7of instructions executable by the processor. In some embodiments, the plurality7of instructions includes instructions for obtaining a model for predicting a BTK occupancy and duration. In some embodiments, the instructions include generating, using the model, an iteration frequency, and a total amount of the BTK inhibitor for administration based upon a filtering criterion. In some embodiments, the iteration frequency comprises one or more iteration interv als; each respective iteration interval in the one or more iteration intervals comprises a corresponding portion of the BTK inhibitor to be administered; and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, the BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the instructions include selecting a dosage regimen in accordance with a determination that the iteration frequency and the total amount of the BTK inhibitor satisfy the filtering criterion, where the dosage regimen includes the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding portion of the BTK inhibitor.

[0006] In some embodiments, the plurality of instructions further comprises instructions for obtaining, responsive to inputting the iteration frequency and the total amount of the BTK inhibitor to the model, as output from the model, one or more simulated values for the BTK occupancy.

[0007] In some embodiments, the instructions for obtaining the model further comprise instructions for training the model using, for each respective training subject in a plurality7of2DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO training subjects, a corresponding one or more measurements of the BTK occupancy levels in the respective training subject, and instructions for obtaining, responsive to inputting the iteration frequency and the total amount of the BTK inhibitor to the model, as output from the model, one or more simulated values for the BTK occupancy.

[0008] In some embodiments, each iteration interval consists of a 6, 8, 12, or 24-hour period. In some embodiments, one or more iteration intervals consists of from 1 to 4 iteration intervals. In some embodiments, one or more iteration intervals consists of 2 iteration intervals.

[0009] In some embodiments, the corresponding portion of the BTK inhibitor of each respective iteration interval in the one or more iteration intervals is the same. In some embodiments, the corresponding portion of the BTK inhibitor for a first respective iteration interval in the one or more iteration intervals is different from the corresponding portion of the BTK inhibitor for a second respective iteration interval in the one or more iteration intervals.

[0010] In some embodiments, the threshold occupancy is at least 60%, at least 80%, or at least 90%. In some embodiments, the threshold occupancy is from 50% to 100%. In some embodiments, the threshold duration comprises at least 50%, at least 60%, at least 70%, at least 80%, at 90%, or at least 95% of each iteration interval in the one or more iteration intervals. In some embodiments, the threshold duration consists of from 50% to 100% of each iteration interval in the one or more iteration intervals.

[0011] In some embodiments, the dosage regimen comprises administering the BTK inhibitor to a subject at the iteration frequency such that the total amount of the BTK inhibitor is fully administered to the subject over the one or more iteration intervals.

[0012] In some embodiments, the dosage regimen further comprises repeating the administering over a treatment period. In some embodiments, the treatment period comprises at least 1 week, at least two weeks, at least three weeks, at least four weeks, at least one month, at least two months, at least three months, at least four months, at least five months, at least six months, at least seven months, at least eight months, at least nine months, at least ten months, at least eleven months, at least one year, at least two years, or at least three years.3DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0013] In some embodiments, the BTK-mediated condition comprises a mast cell disease. In some embodiments, the mast cell disease comprises indolent systemic mastocytosis (ISM), mast cell activation syndrome (MCAS), eosinophilic esophagitis (EoE), or a food allergy. In some embodiments, the BTK-mediated condition comprises myeloproliferative neoplasm (MPN) including polycythemia vera (PV), essential thrombocythemia (ET), myelofibrosis (MF), dry eye disease, allergic conjunctivitis, or geographic atrophy (GA).

[0014] In some embodiments, the mast cell disease is ISM and the BTK inhibitor is l-(4- (((6-amino-5-(4-phenoxyphenyl)pyrimidin-4-yl)amino)methyl)-4-fluoropiperidin-l-yl)prop- 2-en-l-one or a pharmaceutically acceptable salt thereof; wherein the BTK inhibitor inhibits at least 90% of basophil activation following administration as measured by basophil CD63 expression in the human subject as described in Example 10.

[0015] Another aspect of the present disclosure provides a non-transitory computer readable storage medium, the non-transitory computer readable storage medium comprising a plurality of instructions, which when executed by a computer system, cause the computer system to perform a method for selecting a dosage regimen for treating a BTK-mediated condition using a Bruton’s Tyrosine Kinase (BTK) inhibitor. In some embodiments, the plurality of instructions includes instructions for obtaining a model for predicting a BTK occupancy and duration thereof. In some embodiments, the instructions include generating, using the model, an iteration frequency and a total amount of the BTK inhibitor for administration based upon a filtering criterion. In some embodiments, the iteration frequency comprises one or more iteration intervals; each respective iteration interval in the one or more iteration intervals comprises a corresponding portion of the BTK inhibitor to be administered; and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, the BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the instructions include selecting a dosage regimen in accordance with a determination that the iteration frequency and the total amount of the BTK inhibitor satisfy the filtering criterion, where the dosage regimen includes the iteration frequency and, for4DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO each respective iteration interval in the one or more iteration intervals, the corresponding portion of the BTK inhibitor.

[0016] Another aspect of the present disclosure provides a method for selecting a dosage regimen for treating a BTK-mediated condition using a Bruton’s Tyrosine Kinase (BTK) inhibitor. In some embodiments, the method includes obtaining a model for predicting a BTK occupancy and duration. In some embodiments, the method further includes generating, using the model, an iteration frequency and a total amount of the BTK inhibitor for administration based upon a filtering criterion. In some embodiments, the iteration frequency comprises one or more iteration intervals, and each respective iteration interval in the one or more iteration intervals comprises a corresponding portion of the BTK inhibitor. In some embodiments the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, the BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the method further includes selecting a dosage regimen in accordance with a determination that the iteration frequency and the total amount of the BTK inhibitor satisfy the filtering criterion, where the dosage regimen includes the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding portion of the BTK inhibitor.

[0017] Another aspect of the present disclosure provides a computer system for modeling a target endpoint responsive to treatment with a Bruton’s Tyrosine Kinase (BTK) inhibitor, the computer system comprising a memory7and a processor, the memory storing a plurality7of instructions executable by the processor. In some embodiments, the plurality of instructions includes instructions for obtaining a model of the target endpoint in a subject having a BTK- mediated condition, where the model generates, as output, a representation of the target endpoint in the subject responsive to treatment with the BTK inhibitor. In some embodiments, the instructions include generating, using the model, an iteration frequency and an unpartitioned quantum of the BTK inhibitor based upon a filtering criterion for the target endpoint. In some embodiments, the iteration frequency comprises one or more iteration intervals; each respective iteration interval in the one or more iteration intervals comprises a corresponding amount of the unpartitioned quantum of the BTK inhibitor to be administered;5DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the instructions further include determining an application regimen in accordance with a determination that the iteration frequency and the unpartitioned quantum of the BTK inhibitor satisfy the filtering criterion, where the application regimen includes the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding amount of the unpartitioned quantum of the BTK inhibitor.

[0018] In some embodiments, the model is obtained using one or more measurements of the target endpoint in the subject, the plurality of instructions further comprising instructions for obtaining, responsive to inputting the iteration frequency and the unpartitioned quantum of the BTK inhibitor to the model, as output from the model, one or more simulated values for the target endpoint in the subject. In some embodiments, the instructions for obtaining the model further comprise instructions for training the model using, for each respective training subject in a plurality of training subjects, a corresponding one or more measurements of the target endpoint in the respective training subj ect, and instructions for obtaining, responsive to inputting the iteration frequency and the unpartitioned quantum to the model, as output from the model, one or more simulated values for the target endpoint in the subject.

[0019] In some embodiments, each iteration interval consists of a 6, 8, 12, or 24-hour period. In some embodiments, the one or more iteration intervals consists of from 1 to 4 iteration intervals. In some embodiments, the one or more iteration intervals consists of 2 iteration intervals.

[0020] In some embodiments, the corresponding amount of the unpartitioned quantum of each respective iteration interval in the one or more iteration intervals is the same. In some embodiments, the corresponding amount of the unpartitioned quantum for a first respective iteration interval in the one or more iteration intervals is different from the corresponding amount of the unpartitioned quantum for a second respective iteration interval in the one or more iteration intervals.6DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0021] In some embodiments, the threshold occupancy is at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%. In some embodiments, the threshold occupancy is from 50% to 100%. In some embodiments, the threshold duration comprises at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% of each iteration interval in the one or more iteration intervals. In some embodiments, the threshold duration consists of from 50% to 100% of each iteration interval in the one or more iteration intervals.

[0022] In some embodiments, the application regimen comprises administering the BTK inhibitor to the subject at the iteration frequency such that the unpartitioned quantum of the BTK inhibitor is administered to the subject over the one or more iteration intervals.

[0023] In some embodiments, the application regimen further comprises repeating the administering over a treatment period. In some embodiments, the treatment period comprises at least 1 week, at least two weeks, at least three weeks, at least four weeks, at least one month, at least two months, at least three months, at least four months, at least five months, at least six months, at least seven months, at least eight months, at least nine months, at least ten months, at least eleven months, at least one year, at least two years, or at least three years.

[0024] In some embodiments, the BTK-mediated disease comprises a mast cell disease. In some embodiments, the mast cell disease comprises indolent systemic mastocytosis (ISM), mast cell activation syndrome (MCAS), eosinophilic esophagitis (EoE), or a food allergy. In some embodiments, the BTK-mediated disease comprises myeloproliferative neoplasm (MPN), dry' eye disease, allergic conjunctivitis, or geographic atrophy (GA).

[0025] Another aspect of the present disclosure provides a non-transitory computer readable storage medium, the non-transitory computer readable storage medium comprising a plurality of instructions, which when executed by a computer system, cause the computer system to perform a method for modeling a target endpoint responsive to treatment with a Bruton’s Tyrosine Kinase (BTK) inhibitor. In some embodiments, the plurality of instructions includes instructions for obtaining a model of the target endpoint in a subject having a BTK-mediated condition, where the model generates, as output, a representation of the target endpoint in the subject responsive to treatment with the BTK inhibitor. In some embodiments, the instructions further include generating, using the model, an iteration frequency and an unpartitioned quantum of the BTK inhibitor based upon a filtering criterion7DBl / 163759882.1Attorney Ref. No.; 126569-5019-WO for the target endpoint. In some embodiments, the iteration frequency comprises one or more iteration intervals; each respective iteration interval in the one or more iteration intervals comprises a corresponding amount of the unpartitioned quantum of the BTK inhibitor to be administered; and the fdtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the instructions further include determining an application regimen in accordance with a determination that the iteration frequency and the unpartitioned quantum of the BTK inhibitor satisfy the filtering criterion, where the application regimen includes the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding amount of the unpartitioned quantum of the BTK inhibitor.

[0026] Another aspect of the present disclosure provides a method for modeling a target endpoint responsive to treatment with a Bruton’s Tyrosine Kinase (BTK) inhibitor, comprising obtaining a model of the target endpoint in a subject having a BTK-mediated condition, where the model generates, as output, a representation of the target endpoint in the subject responsive to treatment with the BTK inhibitor. In some embodiments, the method further includes generating, using the model, an iteration frequency and an unpartitioned quantum of the BTK inhibitor based upon a filtering criterion for the target endpoint. In some embodiments, the iteration frequency comprises one or more iteration intervals; each respective iteration interval in the one or more iteration intervals comprises a corresponding amount of the unpartitioned quantum of the BTK inhibitor to be administered; and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the method further includes determining an application regimen in accordance with a determination that the iteration frequency and the unpartitioned quantum of the BTK inhibitor satisfy the filtering criterion, where the application regimen includes the iteration frequency and, for8DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO each respective iteration interval in the one or more iteration intervals, the corresponding amount of the unpartitioned quantum of the BTK inhibitor.

[0027] Yet another aspect of the present disclosure includes a non-transitory computer readable storage medium having stored thereon program code instructions for modeling a target endpoint responsive to treatment with a BTK inhibitor that, when executed by a processor, cause the processor to perform any of the methods and / or embodiments disclosed above. Still another aspect of the present disclosure includes a method for modeling a target endpoint responsive to treatment with a BTK inhibitor, the method comprising any of the methods and / or embodiments disclosed above.

[0028] Yet another aspect of the present disclosure includes a non-transitory computer readable storage medium having stored thereon program code instructions for selecting a dosage regimen for treating a BTK-mediated condition using a BTK inhibitor that, when executed by a processor, cause the processor to perform any of the methods and / or embodiments disclosed above. Still another aspect of the present disclosure includes a method for selecting a dosage regimen for treating a BTK-mediated condition using a BTK inhibitor, the method comprising any of the methods and / or embodiments disclosed above.BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also “figure" and “FIG." herein), of which:

[0030] Figures 1 A and IB collectively illustrate an exemplary system topology for modeling a target endpoint responsive to treatment with a BTK inhibitor, in accordance with some embodiments of the present disclosure.

[0031] Figures 2A, 2B, and 2C collectively provide a flow chart of processes and features for modeling a target endpoint responsive to treatment with a BTK inhibitor, in which optional steps are indicated by dashed lines, in accordance with some embodiments of the present disclosure.9DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0032] Figure 3 illustrates a mean plasma concentration-time profile for a BTK inhibitor in a first study population, in accordance with an embodiment of the present disclosure.

[0033] Figure 4 illustrates mean plasma concentration-time profiles for a BTK inhibitor in a second study population, in accordance with an embodiment of the present disclosure.

[0034] Figure 5 illustrates a schematic representation of a base population pharmacokinetics (PK) model, in accordance with an embodiment of the present disclosure. Abbreviations: CL = clearance; DI = duration of zero-order release; F = bioavailabi li ty ; Frei = relative bioavailability; Ka = first-order absorption rate constant; Q = inter-compartmental clearance; Vc = central volume of distribution; and Vp = peripheral volume of distribution.

[0035] Figure 6 illustrates goodness-of-fit plots for the base population PK model of FIG. 5, in accordance with an embodiment of the present disclosure. Open circles are individual data points. Thicker weight solid lines are LOESS smooth regression lines. Medium weight solid lines are lines of identity for the observations versus predictions and quantile-quantile plots, and y = 0 for the plots of CWRES. Abbreviations: CWRES = conditional weighted residual; LOESS = locally estimated scatter plot smoothing.

[0036] Figure 7 illustrates goodness-of-fit plots for a final population PK model, in accordance wi th an embodiment of the present disclosure. Open circles are individual data points. Thicker weight solid lines are LOESS smooth regression lines. Medium weight solid lines are lines of identity for the observations versus predictions and quantile-quantile plots, and y = 0 for the plots of CWRES. Abbreviations: CWRES = conditional weighted residual; LOESS = locally estimated scatter plot smoothing.

[0037] Figure 8 illustrates a prediction-corrected visual predictive check of the final population PK model of FIG. 7. in accordance with an embodiment of the present disclosure. Observed data are shown as dots with their quartiles in indicated lines (5%, 50%, and 95%). Predicted data are shown as dotted, dashed, and solid black lines. The shaded area labeled “Lower” and “Higher” represents the lower 5%, and higher 95% modeled predicted range of data with their respective 5% to 95% percentiles. The shaded area labeled “Med” represents the 50% median modeled predicted range of data with its respective 5% to 95% percentiles.10DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0038] Figure 9 illustrates BTK target occupancy-time profiles when administered with or without omeprazole.

[0039] Figure 10 illustrates an example schematic for the structure of a pharmacokinetic- pharmacodynamic (PK / PD) model, in accordance with an embodiment of the present disclosure, where Kin = l*KOut. Abbreviations: BTK = Bruton’s tyrosine kinase; Cp = Compound 128 plasma concentration; Kout = first-order degradation rate constant of BTK protein; Kin = zero-order BTK synthesis rate; Kr = second-order irreversible binding rate constant; free BTK = time-dependent fraction of unoccupied BTK with baseline unoccupied BTK set to 1.

[0040] Figure 11 illustrates goodness -of-fit plots for the PK / PD model of FIG. 10, in accordance with an embodiment of the present disclosure. Open circles are individual data points. Thicker weight solid lines are LOESS smooth regression lines. Medium weight solid lines are lines of identity7for the observations versus predictions and quantile-quantile plots, and y = 0 for the plots of CWRES. Abbreviations: CWRES = conditional weighted residual; LOESS = locally estimated scatter plot smoothing.

[0041] Figure 12 illustrates a prediction-corrected visual predictive check of the PK / PD model of FIG. 10 for the first study population, in accordance with an embodiment of the present disclosure. Open circles represent observed data points. The solid line is the observed median, and the dashed lines are the observed 5th and 95th percentiles. The middle shaded area is the 95% PI of the simulated median, and the upper and lower shaded areas are the 95% PI of the simulated 5th and 95th percentiles. Abbreviations: PI = prediction interval.

[0042] Figure 13 illustrates simulated mean concentration-time and BTK occupancy profiles for a BTK inhibitor in healthy subjects using a 150 mg bis in die (BID: twice a day) dose regimen and a 300 mg quaque die (QD: once a day) dose regimen, in accordance with an embodiment of the present disclosure. Simulations were performed with consideration of fixed effect estimated (typical parameter values) and inter-individual variabilities (IIV estimates) with virtual subjects of N = 500 for each scenario. The black line is the median value, and the shared area is the 95% prediction interval. Abbreviations: BID = twice daily; BTK = Bruton’s ty rosine kinase; PD = pharmacodynamic; PK = pharmacokinetic; QD = once daily.11DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0043] Figure 14 illustrates simulated steady-state BTK occupancy at Ctrough and duration of BTK occupancy of greater than or equal to 95% over 24 hours for total daily doses of BTK inhibitor from 50 mg to 900 mg, when given as i) a divided dose 12 hours apart (upper panel) or ii) a single daily dose (lower panel). Data points indicate the median values, and error bars represent 2.5thto 97.5thpercentiles. Abbreviations: BTK = Bruton’s tyrosine kinase; %BTK occupancy at Ctrough (or Trough) = %BTK occupancy at end of each dose interval, i.e., at trough (at 12 h or 24 h for BID and QD dosing, respectively); Dur95BID (top panel) = Duration of BTK occupancy >95% within the 12 hours between doses at steady state;Dur95QD (bottom panel) = Duration of BTK occupancy >95% within the 24 hours between doses at steady state.

[0044] Figure 15 illustrates simulated BTK occupancy profiles of a BTK inhibitor at a 150 mg BID dose regimen and varying BTK resynthesis rates, in accordance with an embodiment of the present disclosure. Abbreviations: BID = twice daily; HL = recovery halflife (h); PD = pharmacodynamic. BTK occupancy simulated effect-time profiles with 10 days of Compound 128 BID dose administration were simulated based on data from the PK / PD model. Legend indicates different BTK resynthesis rates measured by the half-life (h) of BTK occupancy. Lower BTK occupancy resynthesis half-life is indicative of more rapid BTK turnover. The simulations were performed with consideration of fixed effect estimated (typical parameter values) but not the inter-individual variabilities (IIV estimates).

[0045] Figure 16 illustrates simulated duration of BTK occupancy of greater than or equal to 95% over 12 hours at steady state in healthy subjects with var ing BTK resynthesis rates, in accordance with an embodiment of the present disclosure. Data points indicate median values and error bars represent 2.5thto 97.5thpercentiles. Abbreviations: BID = twice daily; BTK = Bruton’s tyrosine kinase; Dur95BID = Duration of BTK occupancy >95% within the 12 hours betw een doses at steady state.

[0046] Figure 17 illustrates simulated mean BTK inhibitor concentration-time profiles for an intravenous BTK inhibitor infusion ending at 1.5, 3, or 6 hours that achieves 3000 ng*h / mL AUC0-24 with different Tmax, in accordance with an embodiment of the present disclosure. Abbreviations: AUC = area under the concentration-time curve, Cmax = maximum plasma concentration, Tmax = Time at maximum plasma concentration.12DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0047] Figure 18 illustrates simulated BTK occupancy profiles at AUCO-24 3000 ng*h / mL with different Tmax, in accordance with an embodiment of the present disclosure. Abbreviations: PD = pharmacodynamic.

[0048] Figure 19 is a graph show ing percent of activated basophils (CD63+) 2 hours, 24 hours, and 48 hours after exposure to different BTK inhibitor (Compound 128) concentrations across multiple healthy blood donors, in accordance with an embodiment of the present disclosure.

[0049] Figure 20A is a summary' graph showing potency of basophil activation inhibition over different incubation times after BTK inhibitor (Compound 128) addition, in accordance with an embodiment of the present disclosure. Figure 20B shows ECso and EC90 over time after BTK inhibitor (Compound 128) addition, in accordance with an embodiment of the present disclosure.

[0050] Figure 21 shows the change in total symptom score based on the ISM-SAF survey for ISM patients after treatment with avapritinib 25 mg QD at Week 24, in accordance with some embodiments of the present disclosure. The total symptom score is reduced by 5.7 points, compared to the placebo.

[0051] Figure 22 shows the change in total symptom score based on the ISM-TSAF survey (Table 2) for ISM patients after treatment with Compound 128 100 mg BID or 150 mg BID at Week 12, in accordance with some embodiments of the present disclosure. The total symptom score is reduced by 28.0 points, compared to the placebo.

[0052] Figure 23 shows the change in total symptom score based on the SISM-TSAF survey (Table 3) for ISM patients after treatment with Compound 128 100 mg BID or 150 mg BID at Week 12, in accordance with some embodiments of the present disclosure. The total symptom score is reduced by 22.7 points, compared to the placebo.

[0053] Figure 24 shows the average reduction of total symptom score for ISM patients after 12-week treatment with Compound 128 100 mg BID or 150 mg BID, in accordance with some embodiments of the present disclosure. The average reduction of total symptom score is 55% compared to the baseline.13DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0054] Figure 25 shows the reduction in serum tryptase for ISM patients after treatment with Compound 128 100 mg BID or 150 mg BID, in accordance with some embodiments of the present disclosure.

[0055] Figure 26 shows the average basophil activation inhibition for ISM patients after treatment with Compound 128 100 mg BID or 150 mg BID, in accordance with some embodiments of the present disclosure. “C1D1” refers treatment cycle one. day one; “C1D8” refers to treatment cycle one, day eight; “C2D1” refers to treatment cycle 2, day one. “Pre’’ refers to predose (30 minutes before the morning dose). “Post” refers to two hours after the morning dose.

[0056] Like reference numerals refer to corresponding parts throughout the several views of the drawings.DETAILED DESCRIPTION

[0057] Given the above background, there is an unmet need for improved therapies for patients with BTK-mediated conditions, including but not limited to MPNs, such as MF and lymphoid malignancies, in particular using BTK inhibitors.

[0058] A key challenge in the pharmaceutical industry is the high cost of drug development, covering every thing from initial research and development (R&D) to costly clinical trials, manufacturing, and post-market surveillance. The mean cost of developing a new drug in 2000-2018 was $172.7 million dollars (2018 dollars). Factoring in the cost of drug failures, the cost of development jumps to $515.8 million and further to $879.3 million (2018 dollars) when considering capital costs . See, e.g., Sertkaya, A. et al., “Costs of Drug Development and Research Development Intensity in the US,” 2000-2018, JAMA Network Open, pp. 1-13, 2024. Another study considering all R&D costs including post approval R&D saw cost estimates jump to ~$2.87 billion (2013 dollars). See, e.g., DiMasi, JA. et al., “Innovation in the Pharmaceutical Industry: New Estimates of R&D Costs,” J. Health Econ, pp 20-33, 2016. ft should also be recognized that drug development timelines range from 5 to over 20 years with a median of 8.3 years. See, e.g.. Brown, D.G. et al., “Clinical Development Times for Innovative Drugs,” Nat Rev Drug Discov, pp 793-794, 2022. For instance , the median cost of development for 10 oncology' drugs for companies with no drugs14DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO on the market is $648.00 million with a median development time of 7.3 years. See, e.g., Prasad. V. et al., “Research and Development Spending to Bring a Single Cancer Drug to Market and Revenues After Approval,” JAMA Intern Med, pp. 1569-1575, 2017. Moreover, costs have likely increased significantly in recent years, particularly in light of current inflation, medical staff shortages and supply shortages.

[0059] Advantageously, the present disclosure addresses the need in the art by providing systems and methods for modeling a target endpoint (e.g, a clinical endpoint) responsive to treatment with a BTK inhibitor and / or selecting a dosage regimen for treating a BTK- mediated condition in a subject using a BTK inhibitor. The presently disclosed systems and methods include obtaining a model (e.g., a pharmacokinetics-pharmacodynamics (PK / PD) model) generating a representation of the target endpoint in a subject responsive to BTK inhibitor treatment. Using the model, an iteration frequency (e.g, a treatment frequency) and unpartitioned quantum (e.g., total dosage) for the BTK inhibitor are identified based upon a filtering criterion for the target endpoint. The iteration frequency includes iteration interv als, each iteration interval including a corresponding amount of the unpartitioned quantum. The filtering criterion includes, for each iteration interval, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for a threshold duration, where the threshold occupancy is at least 50% and the threshold duration is at least 40% of an iteration interval. An application regimen (e.g., a dosage regimen) is identified on the basis that the iteration frequency and the unpartitioned quantum (e.g., the total dosage) satisfy the filtering critenon, where the application regimen (e.g, the dosage regimen) includes the iteration frequency and, for each iteration interval, the corresponding amount of the unpartitioned quantum.

[0060] Advantageously, by utilizing modeling data, the presently disclosed systems and methods can be used to greatly decrease drug development costs through shorter timelines, expedited development, and quicker optimizations. Numerous studies have proven that PK / PD modeling data can successfully be utilized to model clinical trial results in both retrospective and prospective studies. See, e.g., Grant J. et al., “Mechanistic PK / PD modeling to Address Early-Stage Biotherapeutic Feasibility Questions,” pp. 1-11, 2022. Computational models can be built using real world data and then extrapolated onto a larger population under different assumptions (e.g, covariates, fed versus fasting, patient kinetics, dosing regimen such as interval, dose amount, etc. ), thus allowing for wide applicability and15DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO adaptability of the model. The outcomes of the models can then be directly applied to real- world decision-making, helping clinicians with identifying the optimal dosage, dosing regimens for a compound, and / or any potential risks involved. Moreover, models significantly reduce the need for expensive clinical trials that could otherwise take years to complete under all the conditions modeled. The present disclosure advantageously addresses the unmet need in the art by providing improved treatments through systems and methods for modeling target endpoints (e.g., PK / PD modeling) of BTK inhibitors under different conditions and assumptions and / or selecting dosage regimens for treating BTK-mediated conditions based on modeled target endpoints.

[0061] Furthermore, the presently disclosed systems and methods have a practical application in facilitating the identification, development, prescription, and / or administration of therapeutic regimens to subjects in need thereof, such as subjects having BTK-mediated conditions. In particular, the presently disclosed systems and methods provide for the selection and / or administration of BTK inhibitor dosage regimens to subjects in need thereof, based upon the application of a filtering criterion to modeled or predicted target endpoints (e.g., BTK occupancy and duration thereof relative to threshold occupancy and threshold duration).

[0062] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0063] Definitions

[0064] Unless defined otherwise, all technical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to which this invention belongs. As used herein, the following terms have the meanings ascribed to them below, unless specified otherwise.16DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0065] As used herein, the term “about’' or “approximately"’ means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, e.g, the limitations of the measurement system. For example, “about” can mean within 3 or more than 3 standard deviations, per the practice in the art. Alternatively, “about” can mean a range of up to 20%, e.g., up to 10%, up to 5%, or up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, e.g., within 5-fold, or within 2-fold, of a value.

[0066] As used herein, a “BTK inhibitor” refers to a molecule that inhibits BTK catalytic activity or reduces or eliminates the amount of BTK by proteolytic degradation. The term “BTK inhibitor” includes, but is not limited to, a BTK degrader.

[0067] As used herein, the term “subject” refers to any animal (e.g., a mammal), including, but not limited to, humans, and non-human animals (including, but not limited to, non-human primates, dogs, cats, rodents, horses, cows, pigs, mice, rats, hamsters, rabbits, and the like (e.g.. which is to be the recipient of a particular treatment, or from whom cells are harvested). Tn certain embodiments, the subject is a human.

[0068] As used herein, the term “treating” or “treatment” refers to clinical intervention in an attempt to alter the disease course of the individual or cell being treated and can be performed either for prophylaxis or during the course of clinical pathology. Therapeutic effects of treatment include, without limitation, preventing occurrence or recurrence of disease, alleviation of symptoms, diminishment of any direct or indirect pathological consequences of the disease, reducing risk of anaphylaxis, decreasing the rate of disease progression or transformation, amelioration or palliation of the disease state, and remission or improved prognosis. By preventing progression or transformation of a disease or disorder, a treatment can prevent deterioration due to a disorder in an affected or diagnosed subject or a subject suspected of having the disorder, but also a treatment may prevent the onset of the disorder or a symptom of the disorder in a subject at risk for the disorder or suspected of having the disorder.

[0069] As used herein, an “effective amount” or “therapeutically effective amount” is an amount sufficient to affect a beneficial or desired clinical result upon treatment. An effective17DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO amount can be administered to a subject in one or more doses. In terms of treatment, an effective amount is an amount that is sufficient to palliate, ameliorate, stabilize, reverse, or slow the progression of the disease, or otherwise reduce the pathological consequences of the disease. The effective amount is generally determined by the physician on a case-by-case basis and is within the skill of one in the art. Several factors are typically taken into account when determining an appropriate dosage to achieve an effective amount. These factors include age, sex and weight of the subject, the condition being treated, the severity of the condition and the form and effective concentration of the immunoresponsive cells administered.

[0070] As used herein, the term “diagnosis’" or “diagnosed" refers to a determination as to whether a subject is likely affected by a given disease, disorder or dysfunction. The skilled artisan will appreciate that a diagnosis can be made on the basis of one or more diagnostic indicators including but not limited to, for example, biomarkers, images, and / or symptoms, the presence, absence, or amount of which is indicative of the presence or absence of the disease, disorder or dysfunction.

[0071] As used herein, the term “monomeric IgE” refers to IgE binding to the FceRl in absence of an antigen or not cross-linked by an antigen or other process.

[0072] As used herein, the term “systemic mastocytosis” refers to a type of mast cell disease, a rare disorder affecting both children and adults caused by the accumulation of functionally defective mast cells (also called mastocytes) and CD34+ mast cell precursors. People affected by mastocytosis are susceptible to a variety of symptoms, including gastrointestinal, skin, cardiovascular, neuropsychiatric, allergic, musculoskeletal, respiratory and systemic symptoms caused by the release of histamine and other pro-inflammatory’ substances from mast cells. When mast cells undergo degranulation, the substances (histamines, prostaglandins, leukotrienes) that are released, and subsequent proinfl ammatory cytokines that are produced, can cause a number of symptoms that can vary' over time and can range in intensity from mild to severe. Because mast cells play a role in allergic reactions, the symptoms of mastocytosis often are similar to the symptoms of an allergic reaction. Signs and symptoms may include, but are not limited to fatigue, skin lesions (urticaria pigmentosa), pruritus (itching), flushing and dermatographic urticaria (skin writing), abdominal18DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO discomfort, nausea and vomiting, diarrhea, olfactive intolerance, ear / nose / throat inflammation, anaphylaxis (shock from allergic or immune causes), episodes of very low blood pressure (including shock) and faintness / dizziness, bone or muscle pain, decreased bone density or increased bone density (osteoporosis or osteosclerosis), migraine / headache, depression, ocular discomfort, increased stomach acid production causing peptic ulcers (increased stimulation of enterochromaffin cell and direct histamine stimulation on parietal cell), malabsorption (due to inactivation of pancreatic enzymes by increased acid), hepatosplenomegaly, brain fog, heart palpitations, dyspnea and wheezing. Signs and symptoms may include, those identified in Valent (2017) Blood 129, 1420-1427. Suitable embodiments for systemic mastocytosis contemplated for use in the present disclosure are further described in the section entitled "‘Indolent Systemic Mastocytosis / ’ below.

[0073] As used herein, the term “indolent systemic mastocytosis” (ISM) refers to a form of systemic mastocytosis that mainly affects adults and typically presents with skin lesions (ISM+), usually in the form of urticaria pigmentosa (UP), while only a few patients have no skin lesions (ISM-). In addition, ISM patients frequently suffer from mast cell (MC) mediator-related symptoms, including brain fog, migraines, anxiety, bone and muscle pain, osteoporosis, dyspnea, congestion, throat swelling, wheezing, syncope, dizziness, palpitations, hy potensive anaphy laxis, diarrhea, nausea, vomiting, abdominal pain, pruritus, urticaria pigmentosa, extreme flushing, fatigue, and malaise. Isolated bone marrow mastocytosis (BMM) is a provisional subcategory of ISM typically characterized by absence of cutaneous lesions of ISM and normal to slightly elevated basal tryptase levels. A bone marrow (BM) smear typically reveals small-sized clusters and aggregates of MCs. In both ISM and BMM, patients have a high risk to develop severe anaphylactic reactions to various exogenous substances (triggers / allergens) such as insect bites. Presentation of severe osteoporosis or even spontaneous fractures is also possible. Although the etiology of ISM is not fully understood, an activating mutation of KIT, usually KIT D816V, is found in the MCs of almost all ISM cases. This mutation probably accounts for the abnormal accumulation of MCs in organ(s) / tissue(s). Also, the mutation is responsible for the lowered threshold to FcsRl -mediated degranulation of mast cells, leading to disease signs and symptoms even when abnormal accumulation has not occurred. In some cases, the mutation is found primarily in the neoplastic MC compartment. Suitable embodiments for indolent systemic19DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO mastocytosis contemplated for use in the present disclosure are further described in the section entitled ‘Indolent Systemic Mastocytosis;’ below.

[0074] As used herein, the term ’‘Mast Cell Activation Syndrome (MCAS)” is characterized by chronic systemic symptoms related to mast cell activation. In subjects with MCAS, mast cell degranulation may occur in response to innocuous stimuli or occur excessively in response to allergens. In addition to the presence of mast cell-related symptoms, MCAS is defined by elevated serum tryptase levels, or urine metabolites associated with mast cell activation. MCAS includes both monoclonal (M-MCAS) and non-monoclonal MCAS (NM-MCAS). M-MCAS is identified by the presence of clonal mast cells with an activating mutation in the KIT gene (usually D816V), and / or expression of CD25, a marker of mast cell clonality. Patients with NM-MCAS meet criteria for MCAS without findings of clonal mast cells. Signs and symptoms associated with mast cell activation range from nausea to abdominal cramping and diarrhea, and from mild pruritus to anaphylaxis and life-threatening hypotension. MCAS patients experience symptoms that impact multiple organ systems, potentially affecting skin, gastrointestinal, cardiovascular, and respiratory function. In patients with MCAS, chronic systemic symptoms can substantially and negatively impact quality of life and may progress to loss of consciousness and life-threatening hypotension.

[0075] As used herein, the term “QD” means quaque die, once a day, or once daily. As used herein, the term “BID” means bis in die, twice a day, or twice daily. As used herein, the term “TID” means ter in die, three times a day, or three times daily. As used herein, the term “QID” means quater in die, four times a day, or four times daily.

[0076] As used herein, the term “twitchy” refers to cells with reduced threshold for degranulation, including mast cells, basophils, monocytes, eosinophils, neutrophils, T cells and NK cells.

[0077] BTK inhibitor compounds of the disclosure also include crystalline and amorphous forms of the any of the compounds in Table 1, including, for example, polymorphs, pseudopolymorphs, solvates, hydrates, unsolvated polymorphs (including anhydrates), conformational polymorphs, and amorphous forms of the compounds, as well as mixtures thereof. “Crystalline form” and “polymorph” are intended to include all crystalline and amorphous forms of the compound, including, for example, polymorphs,20DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO pseudopolymorphs, solvates, hydrates, unsolvated polymorphs (including anhydrates), conformational polymorphs, and amorphous forms, as well as mixtures thereof, unless a particular crystalline or amorphous form is referred to herein.

[0078] As used interchangeably herein, the term “classifier” or “model” refers to a machine learning model or algorithm.

[0079] In some embodiments, a model is an unsupervised learning algorithm. One example of an unsupervised learning algorithm is cluster analysis.

[0080] In some embodiments, a model is supervised machine learning. Nonlimiting examples of supervised learning algorithms include, but are not limited to, logistic regression, neural networks, support vector machines. Naive Bayes algorithms, nearest neighbor algorithms, random forest algorithms, decision tree algorithms, boosted trees algorithms, multinomial logistic regression algorithms, linear models, linear regression, GradientBoosting, mixture models, hidden Markov models, Gaussian NB algorithms, linear discriminant analysis, or any combinations thereof. In some embodiments, a model is a multinomial classifier algorithm. In some embodiments, a model is a 2-stage stochastic gradient descent (SGD) model. In some embodiments, a model is a deep neural network (e.g., a deep-and-wide sample-level classifier).

[0081] Neural networks. In some embodiments, the model is a neural network (e.g, a convolutional neural network and / or a residual neural network). Neural network algorithms, also known as artificial neural networks (ANNs), include convolutional and / or residual neural network algorithms (deep learning algorithms). Neural networks can be machine learning algorithms that may be trained to map an input data set to an output data set, where the neural network comprises an interconnected group of nodes organized into multiple layers of nodes. For example, the neural network architecture may comprise at least an input layer, one or more hidden layers, and an output layer. The neural network may comprise any total number of layers, and any number of hidden layers, where the hidden layers function as trainable feature extractors that allow mapping of a set of input data to an output value or set of output values. As used herein, a deep learning algorithm (DNN) can be a neural network comprising a plurality of hidden layers, e.g., two or more hidden layers. Each layer of the neural network can comprise a number of nodes (or “neurons”). A node can receive input that comes either21DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO directly from the input data or the output of nodes in previous layers, and perform a specific operation, e.g., a summation operation. In some embodiments, a connection from an input to anode is associated with a parameter (e.g., a weight and / or weighting factor). In some embodiments, the node may sum up the products of all pairs of inputs, xi, and their associated parameters. In some embodiments, the weighted sum is offset with a bias, b. In some embodiments, the output of a node or neuron may be gated using a threshold or activation function, f, which may be a linear or non-linear function. The activation function may be, for example, a rectified linear unit (ReLU) activation function, a Leaky ReLU activation function, or other function such as a saturating hyperbolic tangent, identity, binary step, logistic, arcTan, softsign, parametric rectified linear unit, exponential linear unit, softPlus, bent identity. softExponential. Sinusoid. Sine. Gaussian, or sigmoid function, or any combination thereof.

[0082] The weighting factors, bias values, and threshold values, or other computational parameters of the neural network, may be "taught" or “learned’' in a training phase using one or more sets of training data. For example, the parameters may be trained using the input data from a training data set and a gradient descent or backward propagation method so that the output value(s) that the ANN computes are consistent with the examples included in the training data set. The parameters may be obtained from a back propagation neural network training process.

[0083] Any of a variety of neural networks may be suitable for use in the present disclosure. Examples can include, but are not limited to, feedforward neural networks, radial basis function networks, recurrent neural networks, residual neural networks, convolutional neural networks, residual convolutional neural networks, and the like, or any combination thereof. In some embodiments, the machine learning makes use of a pre-trained and / or transfer-learned ANN or deep learning architecture. Convolutional and / or residual neural networks can be used for analyzing an image of a subj ect in accordance with the present disclosure.

[0084] For instance, a deep neural network classifier comprises an input layer, a plurality of individually parameterized (e.g., weighted) convolutional layers, and an output scorer. The parameters (e.g., weights) of each of the convolutional layers as well as the input layer22DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO contribute to the plurality of parameters (e.g., weights) associated with the deep neural network classifier. In some embodiments, at least 100 parameters, at least 1000 parameters, at least 2000 parameters or at least 5000 parameters are associated with the deep neural network classifier. As such, deep neural network classifiers require a computer to be used because they cannot be mentally solved. In other words, given an input to the classifier, the classifier output needs to be determined using a computer rather than mentally in such embodiments. See, for example, Krizhevsky et al., 2012, "Imagenet classification with deep convolutional neural netw orks." in Advances in Neural Information Processing Systems 1, Pereira, Burges, Bottou, Weinberger, eds., pp. 1097-1105, Curran Associates, Inc.; Zeiler, 2012 “ADADELTA: an adaptive learning rate method,'’ CoRR, vol. abs / 1212.5701; and Rumelhart et al., 1988, '‘Neurocomputing: Foundations of research,” ch. Learning Representations by Back-propagating Errors, pp. 696-699, Cambridge, MA, USA: MIT Press, each of which is hereby incorporated by reference.

[0085] Neural network algorithms, including convolutional neural netw ork algorithms, suitable for use as classifiers are disclosed in, for example, Vincent et al., 2010, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” J Mach Learn Res 11, pp. 3371-3408; Larochelle et al., 2009, “Exploring strategies for training deep neural networks,” J Mach Learn Res 10, pp. 1-40; and Hassoun. 1995, Fundamentals of Artificial Neural Networks, Massachusetts Institute of Technology, each of which is hereby incorporated by reference. Additional example neural networks suitable for use as classifiers are disclosed m ' Duda etal., 2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York; and Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New- York, each of which is hereby incorporated by reference in its entirety. Additional example neural networks suitable for use as classifiers are also described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall / CRC; and Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory- Press, Cold Spring Harbor, New York, each of which is hereby incorporated by reference in its entirety.

[0086] Support vector machines. In some embodiments, the model is a support vector machine (SVM). SVM algorithms suitable for use as models are described in, for example, Cristianini and Shawe-Taylor, 2000, “An Introduction to Support Vector Machines,”23DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOCambridge University Press. Cambridge; Boser c / o / .. 1992, “A training algorithm for optimal margin classifiers.” in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, Pa., pp. 142-152; Vapnik, 1998, Statistical Learning Theory, Wiley, New York; Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.; Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc., pp. 259, 262-265; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; and Furey et al., 2000, Bioinformatics 16, 906-914, each of which is hereby incorporated by reference in its entirety. When used for classification, SVMs separate a given set of binary labeled data with a hyper-plane that is maximally distant from the labeled data. For cases in which no linear separation is possible, SVMs can work in combination with the technique of kernels', which automatically realizes a non-linear mapping to a feature space. The hyper-plane found by the SVM in feature space can correspond to a non-linear decision boundary in the input space. In some embodiments, the plurality of parameters (e.g., weights) associated with the SVM define the hyper-plane. In some embodiments, the hyper-plane is defined by at least 10, at least 20, at least 50, or at least 100 parameters and the SVM classifier requires a computer to calculate because it cannot be mentally solved.

[0087] Naive Bayes algorithms. In some embodiments, the model is a Naive Bayes algorithm. Naive Bayes models suitable for use as classifiers are disclosed, for example, in Ng et al., 2002, “On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes,” Advances in Neural Information Processing Systems, 14, which is hereby incorporated by reference. A Naive Bayes classifier is any classifier in a family of “probabilistic classifiers” based on applying Bayes' theorem with strong (naive) independence assumptions between the features. In some embodiments, they are coupled with Kernel density estimation. See, for example, Hastie et al., 2001, The elements of statistical learning : data mining, inference, and prediction, eds. Tibshirani and Friedman, Springer, New York, which is hereby incorporated by reference.

[0088] Nearest neighbor algorithms. In some embodiments, a model is a nearest neighbor algorithm. Nearest neighbor classifiers can be memory-based and include no classifier to be fit. For nearest neighbors, given a query point xo (a test subject), the k training points X(r), r, ... , k (here the training subjects) closest in distance to xo are identified and then the point xo is24DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO classified using the k nearest neighbors. Here, the distance to these neighbors is a function of the abundance values of the discriminating gene set. In some embodiments, Euclidean distance in feature space is used to determine distance asTypically, when the nearest neighbor algorithm is used, the abundance data used to compute the linear discriminant is standardized to have mean zero and variance 1. The nearest neighbor rule can be refined to address issues of unequal class priors, differential misclassification costs, and feature selection. Many of these refinements involve some form of weighted voting for the neighbors. For more information on nearest neighbor analysis, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York, each of which is hereby incorporated by reference.

[0089] A k-nearest neighbor model is a non-parametric machine learning method in which the input consists of the k closest training examples in feature space. The output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. See, Duda et al., 2001, Pattern Classification, Second Edition, John Wiley & Sons, which is hereby incorporated by reference. In some embodiments, the number of distance calculations needed to solve the k-nearest neighbor model is such that a computer is used to solve the classifier for a given input because it cannot be mentally performed.

[0090] Random forest, decision tree, and boosted tree algorithms. In some embodiments, the model is a decision tree. Decision trees suitable for use as models are described generally by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 395-396, which is hereby incorporated by reference. Tree-based methods partition the feature space into a set of rectangles, and then fit a model (like a constant) in each one. In some embodiments, the decision tree is random forest regression. One specific algorithm that can be used is a classification and regression tree (CART). Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and Random Forests. CART, ID3, and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 396-408 and pp. 411-412, which is hereby incorporated by reference. CART,25DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOMART, and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag. New York. Chapter 9, which is hereby incorporated by reference in its entirety. Random Forests are described in Breiman, 1999, “Random Forests— Random Features,” Technical Report 567, Statistics Department, U.C. Berkeley, September 1999, which is hereby incorporated by reference in its entirety. In some embodiments, the decision tree classifier includes at least 10, at least 20, at least 50, or at least 100 parameters (e.g., weights and / or decisions) and requires a computer to calculate because it cannot be mentally solved.

[0091] Regression. In some embodiments, the model uses a regression algorithm. A regression algorithm can be any type of regression. For example, in some embodiments, the regression algorithm is logistic regression. In some embodiments, the regression algorithm is logistic regression with lasso, L2 or elastic net regularization. In some embodiments, those extracted features that have a corresponding regression coefficient that fails to satisfy a threshold value are pruned (removed from) consideration. In some embodiments, a generalization of the logistic regression model that handles multicategory responses is used as the classifier. Logistic regression algorithms are disclosed in Agresti, An Introduction to Categorical Data Analysis, 1996, Chapter 5, pp. 103-144, John Wiley & Son, New York, which is hereby incorporated by reference. In some embodiments, the classifier makes use of a regression model disclosed in Hastie et al., 2001, The Elements of Statistical Learning. Springer-Verlag, New York. In some embodiments, the logistic regression model includes at least 10, at least 20, at least 50, at least 100, or at least 1000 parameters (e.g., weights) and requires a computer to calculate because it cannot be mentally solved.

[0092] Linear discriminant analysis algorithms. Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis can be a generalization of Fisher’s linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination can be used as the model (linear classifier) in some embodiments of the present disclosure.

[0093] Mixture model and Hidden Markov model. In some embodiments, the model is a mixture model, such as that described in McLachlan et al., Bioinformatics 18(3):413-422,26DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO2002. In some embodiments, in particular, those embodiments including a temporal component, the model is a hidden Markov model such as described by Schliep et al.. 2003, Bioinformatics 19(l):i255-i263.

[0094] Clustering. In some embodiments, the model is an unsupervised clustering model. In some embodiments, the model is a supervised clustering model. Clustering algorithms suitable for use as models are described, for example, at pages 211-256 of Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York, (hereinafter "Duda 1973") which is hereby incorporated by reference in its entirety. The clustering problem can be described as one of finding natural groupings in a dataset. To identify natural groupings, two issues can be addressed. First, a way to measure similarity (or dissimilarity) between two samples can be determined. This metric (e.g., similarity measure) can be used to ensure that the samples in one cluster are more like one another than they are to samples in other clusters. Second, a mechanism for partitioning the data into clusters using the similarity measure can be determined. One way to begin a clustering investigation can be to define a distance function and to compute the matrix of distances between all pairs of samples in the training set. If distance is a good measure of similarity, then the distance between reference entities in the same cluster can be significantly less than the distance between the reference entities in different clusters. However, clustering may not use a distance metric. For example, a nonmetric similarity function s(x. x') can be used to compare two vectors x and x'. s(x. x') can be a symmetric function whose value is large when x and x' are somehow “similar.’’ Once a method for measuring “similarity” or “dissimilarity” between points in a dataset has been selected, clustering can use a criterion function that measures the clustering quality of any partition of the data. Partitions of the data set that extremize the criterion function can be used to cluster the data. Particular exemplary clustering techniques that can be used in the present disclosure can include, but are not limited to, hierarchical clustering (agglomerative clustering using a nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering. In some embodiments, the clustering comprises unsupervised clustering (e.g., with no preconceived number of clusters and / or no predetermination of cluster assignments).27DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0095] Ensembles of models and boosting. In some embodiments, an ensemble (two or more) of model is used. In some embodiments, a boosting technique such as AdaBoost is used in conjunction with many other types of learning algorithms to improve the performance of the classifier. In this approach, the output of any of the models disclosed herein, or their equivalents, is combined into a weighted sum that represents the final output of the boosted classifier. In some embodiments, the plurality of outputs from the models is combined using any measure of central tendency known in the art, including but not limited to a mean, median, mode, a weighted mean, weighted median, weighted mode, etc. In some embodiments, the plurality of outputs is combined using a voting method. In some embodiments, a respective model in the ensemble of models is weighted or unweighted.

[0096] The term "classification” can refer to any number(s) or other characters(s) that are associated with a particular property of a sample. For example, a “+” symbol (or the word “positive”) can signify that a sample is classified as having a desired outcome or characteristic. In another example, the term “classification” refers to a respective outcome or characteristic (e.g.. closed, open, partial open). In some embodiments, the classification is binary (e.g., positive or negative) or has more levels of classification (e.g., a scale from 1 to 10 or 0 to 1). In some embodiments, the terms “cutoff’ and “threshold” refer to predetermined numbers used in an operation. In one example, a cutoff value refers to a value above which results are excluded. In some embodiments, a threshold value is a value above or below which a particular classification applies. Either of these terms can be used in either of these contexts.

[0097] As used herein, the term “parameter” refers to any coefficient or, similarly, any value of an internal or external element (e.g., a weight and / or a hyperparameter) in an algorithm, model, regressor, and / or classifier that can affect (e.g., modify, tailor, and / or adjust) one or more inputs, outputs, and / or functions in the algorithm, model, regressor and / or classifier. For example, in some embodiments, a parameter refers to any coefficient, weight, and / or hyperparameter that can be used to control, modify, tailor, and / or adjust the behavior, learning, and / or performance of an algorithm, model, regressor, and / or classifier. In some instances, a parameter is used to increase or decrease the influence of an input (e.g., a feature) to an algorithm, model, regressor, and / or classifier. As a nonlimiting example, in some embodiments, a parameter is used to increase or decrease the influence of a node (e.g., of a28DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO neural network), where the node includes one or more activation functions. Assignment of parameters to specific inputs, outputs, and / or functions is not limited to any one paradigm for a given algorithm, model, regressor, and / or classifier but can be used in any suitable algorithm, model, regressor, and / or classifier architecture for a desired performance. In some embodiments, a parameter has a fixed value. In some embodiments, a value of a parameter is manually and / or automatically adjustable. In some embodiments, a value of a parameter is modified by a validation and / or training process for an algorithm, model, regressor, and / or classifier (e.g., by error minimization and / or backpropagation methods). In some embodiments, an algorithm, model, regressor, and / or classifier of the present disclosure includes a plurality of parameters. In some embodiments, the plurality' of parameters is n parameters, where: n > 2; n > 5; n > 10; n > 25; n > 40; n > 50; n > 75; n > 100; n > 125; n > 150; n > 200; n > 225; n > 250; n > 350; n > 500; n > 600; n > 750; n > 1,000; n > 2,000; n > 4,000; n > 5,000; n > 7,500; n > 10,000; n > 20,000; n > 40,000; n > 75,000; n > 100,000; n > 200,000; n > 500,000, n > 1 x 106, n > 5 x 106, or n > 1 x 107. As such, the algorithms, models, regressors, and / or classifiers of the present disclosure cannot be mentally performed. In some embodiments n is between 10,000 and 1 x 107, between 100,000 and 5 x 106, or between 500,000 and 1 x 106. In some embodiments, the algorithms, models, regressors, and / or classifier of the present disclosure operate in a k-dimensional space, where k is a positive integer of 5 or greater (e.g, 5, 6, 7, 8, 9, 10, e / c.). As such, the algorithms, models, regressors, and / or classifiers of the present disclosure cannot be mentally performed.

[0098] As used herein, unless otherwise specified, the following abbreviations refer to the following terms: BID = twice daily; BTK = Bruton’s Tyrosine Kinase; BTKTO = BTK target occupancy: CL = clearance; Cp = Compound 128 plasma concentration (e.g. central compartment concentration); DI = duration of zero-order release; E-R = exposure-response; FOCE ELS = first-order conditional estimation with an extended least-squares; F = bioavailability; Frei = relative bioavailability; Free BTK = time-dependent fraction of unoccupied BTK w ith baseline unoccupied BTK set to 1; h = hour; HV= healthy volunteer; Ka = first-order absorption rate constant; Ka = absorption rate; Kin = zero-order BTK synthesis rate; Kr = second-order irreversible binding rate constant; Kout, first-order degradation rate constant of BTK protein; Q = inter-compartmental clearance; QD = once daily; PBMC = peripheral blood mononuclear cells; pcVPCs = prediction-corrected visual29DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO predictive checks; PD, pharmacodynamics; PK = pharmacokinetics; PPI = proton pump inhibitor; RSE = relative standard error; Vc = central volume of distribution; and Vp = peripheral volume of distribution.

[0099] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first subject could be termed a second subject, and, similarly, a second subject could be termed a first subject, without departing from the scope of the present disclosure. The first subject and the second subject are both subjects, but they are not the same subject. Furthermore, the terms “subject,'’ “user,"’ and “patient” are used interchangeably herein.

[0100] The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the description of the invention and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0101] As used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining"’ or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean "‘upon determining” or "‘in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.

[0102] Example Systems for Modeling a Target Endpoint

[0103] One aspect of the present disclosure provides systems for modeling a target endpoint in a subject having a BTK-mediated condition, responsive to treatment with a BTK30DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO inhibitor. A detailed description of a system 100 for modeling behaviors of target endpoints responsive to treatment with a BTK inhibitor, in accordance with the present disclosure, is described in conjunction with FIGs. 1A-B. As such, FIGs. 1 A-B collectively illustrate the topology of the system in accordance with the present disclosure. In some embodiments, the BTK-mediated condition comprises a mast cell disease. In some embodiments, the BTK- mediated condition is ISM. In some embodiments, the BTK-mediated condition is MCAS.

[0104] Referring to FIGs. 1A-B, in some embodiments, the system 100 receives data directly or indirectly through radio-frequency signals. In some embodiments such signals are in accordance with an 802.11 (WiFi), Bluetooth, or ZigBee standard. In some embodiments, the system 100 receives data across one or more communications networks 16.

[0105] Examples of networks 16 include, but are not limited to, the World Wide Web (WWW), an intranet and / or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and / or a metropolitan area network (MAN), and other devices by wireless communication. The wireless communication optionally uses any of a plurality of communications standards, protocols and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11ac, IEEE 802.11ax, IEEE 802.11b, IEEE 802.11g and / or IEEE 802.11n), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e-mail (e.g., Internet message access protocol (IMAP) and / or post office protocol (POP), instant messaging (e.g, extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS), and / or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of the present disclosure.

[0106] Of course, other topologies of the system 100 of FIGs. 1 A-B are possible. For instance, rather than relying on a communications network 16, information may be sent31DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO directly to the system 100. Further, the system 100 may constitute a portable electronic device, a server computer, or in fact constitute several computers that are linked together in a network or be a virtual machine in a cloud computing context. As such, the exemplary topology shown in FIGs. 1 A-B merely serves to describe the features of an embodiment of the present disclosure in a manner that will be readily understood to one of skill in the art.

[0107] Referring to FIGs. 1A-B. in typical embodiments, the system 100 comprises one or more computers. For purposes of illustration in FIGs. 1 A-B, the system 100 is represented as a single computer that includes all of the functionality for modeling a target endpoint in a subject having a BTK-mediated condition, responsive to treatment with a BTK inhibitor. However, the disclosure is not so limited. In some embodiments, the functionality is spread across any number of networked computers and / or resides on each of several networked computers and / or is hosted on one or more virtual machines at a remote location accessible across the communications netw ork 16. One of skill in the art will appreciate that any of a wide array of different computer topologies are used for the application and all such topologies are within the scope of the present disclosure.

[0108] Turning to FIGs. 1 A-B with the foregoing in mind, an exemplary system 100 for modeling a target endpoint in a subject having a BTK-mediated condition, responsive to treatment with a BTK inhibitor, comprises one or more processing units (CPUs) 74, a network or other communications interface 84, a memory 92 (e.g, random access memory), one or more magnetic disk storage and / or persistent devices 90 optionally accessed by one or more controllers 88, one or more communication busses 13 for interconnecting the aforementioned components, a user interface 78, the user interface 78 including a display 82 and input 80 (e.g, keyboard, keypad, touch screen), and a power supply 76 for powering the aforementioned components. In some embodiments, the input 80 is a touch-sensitive display, such as a touch-sensitive surface. In some embodiments, the user interface 78 includes one or more soft keyboard embodiments. The soft keyboard embodiments may include standard (QWERTY) and / or non-standard configurations of symbols on the displayed icons. In some embodiments, data in memory 92 is seamlessly shared with non-volatile memory 90 using known computing techniques such as caching. In some embodiments, memory 92 and / or memory 90 includes mass storage that is remotely located with respect to the central processing unit(s) 74. In other words, some data stored in memory' 92 and / or memory' 90 may32DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO in fact be hosted on computers that are external to the system 100 but that can be electronically accessed by the system 100 over an Internet, intranet, or other form of network or electronic cable (e.g., illustrated as element 16) using network interface 84.

[0109] In some embodiments, the memory 92 of the system 100 for modeling a target endpoint responsive to treatment with a BTK inhibitor includes:• an optional operating system 116 that includes procedures for handling various basic system services;• an optional network communication module 1 18 for connecting the system 100 with other devices, or a communication network;• a model construct 120, optionally including, for a subject 122 (e.g., 122-1,... 122-K) having a BTK-mediated condition 124 (e.g., 124-1), a target endpoint 126 (e.g., 126- 1-1, 126-1-2,. . . 126-1-N) or a representation thereof, responsive to treatment with the BTK inhibitor;• a target endpoint data store 130, optionally including, for a BTK inhibitor 132 (e.g., 132-1,... 132-B): o an unpartitioned quantum 134 for the BTK inhibitor (e.g, 134-1), and o an iteration frequency 136 (e.g., 136-1), where the iteration frequency comprises one or more iteration intervals 138 (e.g., 138-1-1,... 138-1 -Q), and where each respective iteration interval in the one or more iteration intervals comprises a corresponding portion of the unpartitioned quantum 140 (e.g., 140-1-1); and• a filtering module 150. optionally including at least a first filter 152 (e.g.. 152-1) comprising, for each respective iteration interval 138 in the one or more iteration intervals, a BTK occupancy by the BTK inhibitor 132 that satisfies a threshold occupancy 154 for at least a threshold duration 156;• an application construct 160, optionally for determining, in accordance with a determination that the iteration frequency 136 and the unpartitioned quantum 134 satisfy the first filter 152, an application regimen 162 that includes the iteration33DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO frequency 136 and the corresponding portion of the unpartitioned quantum 140 for each respective iteration interval 138 in the one or more iteration intervals.

[0110] In some implementations, one or more of the above identified data elements or modules of the system 100 are stored in one or more of the previously described memory devices, and correspond to a set of instructions for performing a function described above. The above-identified data, modules, or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various implementations. In some implementations, the memory 92 and / or 90 optionally stores a subset of the modules and data structures identified above. Furthermore, in some embodiments the memory 92 and / or 90 stores additional modules and data structures not described above. Further still, in some embodiments, the system 100 stores data for two or more subjects, five or more subjects, one hundred or more subjects, or 1000 or more subjects.

[0111] In some embodiments, the system 100 comprises a smart phone (e.g., an iPhone), laptop, tablet computer, desktop computer, or other form of electronic device (e.g., a gaming console). In some embodiments, the system 100 is not mobile. In some embodiments, the system 100 is mobile. In some embodiments, the system 100 includes a remote device, which is operated by a user for inputting and communicating response scores for evaluation features.

[0112] It should be appreciated that the system 100 illustrated in FIGs. 1A-B is only one example of a device that may be used for modeling a target endpoint responsive to treatment with a BTK inhibitor, and that the system 100 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown in FIGs. 1A-B are implemented in hardware, software, firmware, or a combination thereof, including one or more signal processing and / or application specific integrated circuits.

[0113] In some embodiments, the system 100 has any or all of the circuitry, hardware components, and software components found in the system 100 depicted in FIGs. 1 A-B. In the interest of brevity’ and clarity, only a few of the possible components of the system 10034DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO are shown in order to better emphasize the additional software modules that are installed on the system 100.

[0114] While the system 100 disclosed in FIGs. 1 A-B can work standalone, in some embodiments it can also be linked with electronic medical records to exchange information in any way.

[0115] Example Embodiments for Modeling an Outcome for a Target Condition

[0116] Now that details of a system 100 for modeling a target endpoint responsive to treatment with a BTK inhibitor have been disclosed, details regarding a flow chart of processes and features, that optionally use the system 100, in accordance with an embodiment of the present disclosure, are disclosed with reference to FIGs. 2A-C.

[0117] FIGs. 2A-C collectively illustrate a method 200 for modeling a target endpoint 126 in a subject 122 having a BTK-mediated condition 124, responsive to a candidate (e.g., predicted) treatment with a BTK inhibitor 132. Alternatively or additionally, in some embodiments, method 200 of FIGs. 2A-C is used for selecting or determining a dosage regimen 162 for treating a BTK-mediated condition 124 in a subject 122 using a BTK inhibitor 132. Alternatively or additionally, in some embodiments, method 200 of FIGs. 2A- C is used for obtaining and / or training a model for modeling a target endpoint 126 in a subject 122 having a BTK-mediated condition 124, or for selecting or determining a dosage regimen 162 for treating a BTK-mediated condition 124. Alternatively or additionally, in some embodiments, method 200 of FIGs. 2A-C is used for obtaining a dosage regimen 162 for administering, to a subject 122, a treatment for a BTK-mediated condition 124 using a BTK inhibitor 132. In some embodiments, the method 200 is performed at a computer system 100 comprising a memory and a processor, the memory storing a plurality of instructions executable by the processor. In some embodiments, such processes and features are conducted by the system 100 illustrated in FIGs. 1A-B. In some embodiments, the processes and features referenced in FIGs. 2A-C are conducted without the use of the system illustrated in FIGs. 1A-B.35DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0118] Subject conditions.

[0119] In some embodiments, the subject comprises a target condition, such as a clinical or biological condition. In some embodiments, the condition is a BTK-mediated condition.

[0120] Referring to Block 202, in some embodiments, the BTK-mediated condition 124 comprises a mast cell disease. Referring to Block 204, in some embodiments, the mast cell disease comprises indolent systemic mastocytosis (ISM), mast cell activation syndrome (MCAS), eosinophilic esophagitis (EoE), or a food allergy.

[0121] Referring to Block 206, in some embodiments, the BTK-mediated condition 124 comprises myeloproliferative neoplasm (MPN), dry eye disease, allergic conjunctivitis, or geographic atrophy (GA).

[0122] In some embodiments, the BTK inhibitor targets a neoplastic mast cell. In some embodiments, the BTK inhibitor targets an aberrant mast cell. In some embodiments, the BTK inhibitor targets mast cells with a low threshold for activation, such as in ISM or MCAS with D816V KIT mutation.

[0123] In some embodiments, the target condition comprises one or more symptom (e.g, of indolent systemic mastocytosis) selected from: fatigue, abdominal pain, diarrhea, nausea, skin spots, itching, flushing, brain fog. headache, dizziness, bone pain, and combinations thereof. In some embodiments, the target condition is a symptom (e.g., of indolent systemic mastocytosis) selected from muscle pain, difficulty concentrating, difficulty remembering, red spots, runny nose, nasal congestion, wheezing, shortness of breath, throat itching, heart palpitations, diarrhea, and combinations thereof. In some embodiments, the target condition is a symptom (e.g. of indolent systemic mastocytosis) selected from itching, skin redness, skin swelling, flushing, diarrhea, loose stools, fatigue, exhaustion, headache, muscle pain, joint pain, difficulty concentrating, difficulty remembering, red spots, and combinations thereof.

[0124] In some embodiments, the target condition is a symptom associated with systemic mastocytosis or indolent systemic mastocytosis, including, but not limited to. fatigue, skin lesions (urticaria pigmentosa), itching, and dermatographic urticaria (skin writing), abdominal discomfort, nausea and vomiting, diarrhea, olfactive intolerance, ear / nose / throat36DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO inflammation, anaphylaxis (shock from allergic or immune causes), episodes of very low blood pressure (including shock) and faintness, bone or muscle pain, decreased bone density or increased bone density (osteoporosis or osteosclerosis), headache, depression, ocular discomfort, increased stomach acid production causing peptic ulcers (increased stimulation of enterochromaffin cell and direct histamine stimulation on parietal cell), malabsorption (due to inactivation of pancreatic enzymes by increased acid) and / or hepatosplenomegaly.

[0125] In some embodiments, the subject is selected from a plurality of subjects. In some embodiments, the method includes selecting a dosage regimen for each subject in a plurality of subjects. Alternatively or additionally, in some embodiments, the method includes modeling a target endpoint in each respective subject in a plurality of subjects, where each respective subject in the plurality of subjects has a BTK-mediated condition.

[0126] In some embodiments, the plurality of subjects comprises at least 10, at least 20, at least 50, at least 100, at least 200, at least 500, at least 1000, at least 2000, at least 5000, or at least 10,000 subjects. In some embodiments, the plurality of subjects comprises no more than 100.000, no more than 10,000, no more than 5000. no more than 2000, no more than 1000, no more than 500, no more than 100, or no more than 50 subjects. In some embodiments, the plurality of subjects consists of from 10 to 100, from 80 to 500, from 200 to 1000, from 1000 to 10,000, or from 10,000 to 100,000 subjects. In some embodiments, the plurality of subjects falls within another range starting no lower than 10 subjects and ending no higher than 100,000 subjects.

[0127] BTK inhibitors.

[0128] BTK is a cytoplasmic kinase expressed in both mature and immature forms of selected hematopoietic cell types including immune cells such as B cells, macrophages, neutrophils and granulocytes, including mast cells, basophils and eosinophils. Schimdt (2004) Int. Arch. Allergy Immunol. 134, 65-78. BTK inhibitors offer a novel therapeutic approach for diseases characterized by aberrant signaling in cell types which rely on BTK expression and signal transduction to support their propagation, including mastocytosis (mast cell driven hematologic neoplasm). BTK inhibitors have already shown evidence of clinical activity in other diseases which implicate aberrant B-cell behavior including rheumatoid arthritis (RA), systemic lupus ery thematosus (SLE) and multiple sclerosis (MS). Specifically, on mast cells37DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO and basophils, BTK is located downstream of the high-affinity' IgE receptor (also known as FceRl) and cross-linking of FceRl promptly activates BTK, downstream of Lyn (tyrosineprotein kinase Lyn) and Syk (spleen ty rosine kinase). Studies have shown that the release of histamine and other preformed mediators as well as inflammatory cytokines by mast cells and basophils were reduced in BTK-null mice and patients with BTK deficiency. Hata (1998) J. Exp. Med. 187, 1235-1247. Inhibition of BTK blocks mast cell degranulation and inflammation through inhibition of FceRl -induced calcium release and immune response. Dispenza (2020) J. Clin. Invest. 130, 4759-4770.

[0129] BTK is a key' kinase that activates PLCy. BTK phosphory lates PLCy, a process mediated by the adaptor proteins LAT1 and LAT2 (also known as non-T cell adaptor linker, NT AL). Phosphorylated PLCy hydrolyzes phosphatidylinositol-4,5-bisphosphate to make diacylglycerol (DAG), and inositol 1,4,5-triphosphate (IP3), which respectively, result in the activation of PKC and the mobilization of calcium from the endoplasmic reticulum followed by an influx of external calcium triggering mast cell degranulation. Calcium release activates and causes NFKB to translocate to the nucleus of the cell, which results in transcription of multiple cytokines, included but not limited to IL-6, TNFa, and IL-13. Krystel-Whittemore (2016) Frontiers in immunology7, 6, 620.

[0130] Separately, dysregulated KIT signaling, through the D816V and other KIT mutations observed in ISM, amplifies FceRl -mediated degranulation and cytokine production by phosphorylation of LAT2 and the recruitment of PLCy. BTK activation of PLCy amplifies the signal cascade resulting in mast cell degranulation. LAT2 is central to linking FceRl and KIT signaling pathway s, and the enhancement of PLCy activity . The interconnection between these two signaling pathways in mutated KIT (D816V) mast cells lowers the antigen-mediated threshold for cell degranulation, resulting in "tw itchy” mast cells. Importantly, binding of ISM mast cell FceRl receptors to circulating monomeric IgE (i.e., IgE antibodies in the absence of antigen, allergen, or other crosslinking agent) may be sufficient to cause constitutively activated mast cells in patients with ISM, which in turn explains the continuous mast cell degranulation and cytokine production resulting in constitutional symptoms observed in the majority of patients with ISM. The BTK signaling pathway downstream of the FcsRl / IgE interaction is therefore critical to the activation,38DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO degranulation and cytokine-driven inflammatory' cascade triggered by mast cells and / or by basophils.

[0131] As a disease of aberrant mast cell signaling, where mast cells are too easily triggered to degranulate and may become activated by either monomeric IgE (IgE binding in the absence of antigen) or antigen-bound IgE, ISM represents a disease where BTK inhibition may offer a novel therapeutic approach. The literature indicates that the KIT D816V mutation drives ISM and its symptoms, which are not believed to be BTK-dependent, as KIT signaling is distinct from the FcsRl pathway. However, LAT2 connects KIT to FCER I . which in turn activates BTK and triggers the allergic-like symptoms experienced by most ISM patients. Therefore, it was unexpected, as disclosed herein, that BTK serves as a central hub for modulating ISM symptoms. Therapeutically targeting BTK with a BTK inhibitor may therefore hinder mast cell and basophil degranulation, subsequent histamine, tryptase release and cytokine-driven inflammation thought to be causing the significant ISM-related symptoms.

[0132] Mast cells are located throughout the body in areas below the epithelium in connective tissues surrounding blood cells, smooth muscle, mucosa and hair follicles. They are particularly abundant in tissues with frequent contact with the surrounding environment such as the skin, the linings of the esophagus, stomach and intestine (gastrointestinal tract), respiratory epithelium and certain ocular compartments (conjunctiva, choroid, uveal tract). They play an important role in the immune defense against bacteria and parasites. By releasing chemical “alarms” such as histamine, mast cells attract other key players of the immune defense sy stem to areas of the body where they are needed. In healthy individuals, the symptoms associated with mast cell degranulation are often relatively mild and shortlived (e.g, tenderness, redness and histamine-driven itching around a healing dermal injury). Mast cells express a cell surface receptor, KIT (CD117), which is the receptor for stem cell factor (SCF), a mast cell grow th factor. In laboratory studies, SCF appears to be important for the proliferation of mast cells. Mutations of the gene coding for the KIT receptor (e.g., D816V), leading to constitutive signaling through the receptor is found in >95% of patients with systemic mastocytosis. KIT signaling amplifies FcsRl signaling by inducing NT AL phosphorylation, creating docking sites for cytosolic adapter molecules and the signaling enzymes, PLCv and phosphoinositide (PI) 3-kinase. This leads to KIT signaling acting in39DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO synergy w ith FceRl signaling to enhance intracellular calcium levels and PKC activation, resulting in greater degranulation and histamine release, which may be an underlying pathogenic driver of ISM symptoms. Inhibition of BTK potently inhibits PLCy and calcium release leading to a block in mast cell degranulation with potential to neutralize the mast cell activating effects of KIT.

[0133] BTK is also a key component of FceRl signaling in basophils and thus represents an attractive target for preventing IgE-mediated responses in these cells. Basophils augment the signaling process initiated by mast cells. Basophils express a complete FceRl, the surface expression of w hich directly correlates with free IgE concentration. Aggregation of FceRl bound to IgE by multivalent antigen leads to basophil activation, granule exocytosis, and mediator release. As stated above, the BTK signaling pathw ay downstream of the FceRl / IgE interaction is therefore critical to the activation, degranulation and cytokine-driven inflammatory cascade triggered propagated by basophils.

[0134] Administration of BTK inhibitors.

[0135] In some embodiments, the method further includes administering a BTK inhibitor to a human subject in need thereof. In some embodiments, the method includes administering to a human subject in need thereof a BTK inhibitor compound or a pharmaceutically acceptable salt thereof.

[0136] In some embodiments, the BTK inhibitor is any of the compounds in Table 1 or a pharmaceutically acceptable salt thereof. In an embodiment, the method comprises the step of administering to said human subject a therapeutically effective amount of a BTK inhibitor or a pharmaceutically acceptable salt thereof, where the BTK inhibitor is a compound selected from Table 1.

[0137] In some embodiments, the BTK inhibitor is l-(4-(((6-amino-5-(4- phenoxyphenyl)pyrimidin-4-yl)amino)methyl)-4-fluoropiperidin-l-yl)prop-2-en-l-one or a pharmaceutically acceptable salt thereof.

[0138] Table 1 : BTK Inhibitors40DBl / 163759882.1Atorney Ref. No.: 126569-5019-WO41DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO42DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO43DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO44DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO45DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO46DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO47DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO48DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO49DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO50DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO51DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO52DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO53DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO54DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO55DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO56DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO57DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO58DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO59DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO60DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO61DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO62DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO63DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO64DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO65DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO66DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO67DB1 / 163759882.1Atorney Ref. No.: 126569-5019-WO68DB1 / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0139] In some embodiments, the BTK inhibitor is administered in an amount sufficient to inhibit histamine release in a human subject. In some embodiments, the BTK inhibitor is administered in an amount sufficient to inhibit tryptase release in a human subject. In some embodiments, the BTK inhibitor is administered in an amount sufficient to reduce one or more of: release of histamine, production of cytokines, and release of cytokines. In some embodiments, the BTK inhibitor is administered in an amount sufficient to inhibit release or production of pro-inflammatory cytokines. In some embodiments, histamine or cytokines are derived from mast cells, basophil cells, B cells, and / or monocytes, among others. In some embodiments, administering the BTK inhibitor reduces mast cell activation or basophil cell activation. In some embodiments, the BTK inhibitor is administered in an amount sufficient69DB1 / 163759882.1Attorney Ref. No.: 126569-5019-WO to reduce cytokine expression by mast cells and basophils. In some embodiments, the BTK inhibitor is administered in an amount sufficient reduce one or more secondary (e.g., distal) inflammatory processes. As used herein, a secondary inflammatory process occurs at a site different from an initial inflammatory process. Such reductions in secondary inflammatory processes can be measured by any means described herein and known in the art. Pro- inflammatory cytokines include IL-la, P, IL-2, IL-3, IL-6, IL-7, IL-9, IL-12, IL-17, IL-18, IL-23, TNF-a, LT, LIF, Oncostatin, and IFNcla, 3, y. In some embodiments, pro- inflammatory cytokines include IL-1, IL-6, TNF-alpha, IL-8, IL-12, IL-17, and IFN-gamma.

[0140] In some embodiments, the BTK inhibitor is administered in an amount sufficient to reduce degranulation of mast cells and / or basophil cells in a human subject. Such inhibition of degranulation blocks the release of histamine, tryptases, prostaglandins, leukotrienes, kinins, serotonin, heparin and serine proteases. In some embodiments, the BTK inhibitor is administered in an amount sufficient to activate monocytes. In some embodiments, the BTK inhibitor is administered in an amount sufficient to reduce release of leukotrienes. In some embodiments, the BTK inhibitor is administered in an amount sufficient to reduce release of prostaglandins. In some embodiments, the BTK inhibitor is administered in an amount sufficient to reduce release of kinins, serotonin, heparin and serine proteases.

[0141] In some embodiments, the BTK inhibitor is administered in an amount sufficient to inhibit FcsRI -mediated calcium signaling associated with mast cell degranulation in a human subject. In some embodiments, the IgE-mediated FceRl activity is associated with antigen binding to IgE. In mast cells, elevation in cytosolic calcium activates a cascade of downstream events that trigger degranulation, which is responsible for the release of inflammatory mediators associated with antigen binding to IgE bound to FceRl. Without wishing to be bound by theory, it is believed that BTK inhibitors block mast cell degranulation through the inhibition of calcium signaling, halting acute and late phase cytokine-driven inflammation. In an embodiment, the BTK inhibitor is administered in an amount sufficient to reduce IgE-mediated FceRl activity. In some embodiments, the BTK inhibitor is administered in an amount sufficient to reduce IgE-mediated FceRl activity. In some embodiments, the BTK inhibitor is administered in an amount sufficient to reduce non- IgE-mediated FceRl activity . In some embodiments, the BTK inhibitor is administered in an70DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO amount sufficient to inhibit non-FcsR I -mediated calcium signaling pathway. In some embodiments, the IgE-mediated FceRl activity is regulated by BTK activity. In some embodiments, the IgE-mediated FceRl activity is associated with monomeric IgE binding to FceRl.

[0142] In some embodiments, monomeric IgE (e.g., not bound to antigen) is already bound to FceRl on a mast cell, priming the mast cell for activation upon binding of antigen. Thus, mast cells expressing FceRl which is bound to IgE may be present in the skin or gastrointestinal tract, and produce the inflammatory response associated with ISM. In an embodiment, the BTK inhibitor, such as l-(4-(((6-amino-5-(4-phenoxyphenyl)pyrimidin-4- yl)amino)methyl)-4-fluoropiperidin-l-yl)prop-2-en-l-one or a pharmaceutically acceptable salt thereof, is administered in an amount sufficient to inhibit this activation of mast cells in the skin and / or gastrointestinal tract. Further, treatment with a BTK inhibitor over time, in some embodiments, reduces the amount of these primed mast cells at the inflammatory site (e.g., the skin and / or gastrointestinal tract). In some embodiments, simply the binding of monomeric IgE results in activation of the mast cells without the binding of antigen to IgE. See Cruse (2005) Eur. Respir. J. 25, 858-863. In some embodiments, the mast cells contain a mutation in KIT (e.g., D816V) which constitutively activates the mast cell resulting a level of degranulation which occurs in the absence of antigen binging to IgE. In some embodiments, the inhibition of BTK mediates sequestration of mast cells in the bone marrow. This sequestration of mast cells in the bone marrow results in decreased numbers of mast cells at potential peripheral tissue inflammatory sites (e.g., the skin and / or gastrointestinal tract). Hence, in some embodiments, this reduction of mast cells reduces the inflammatory response at the site of antigen exposure because there are fewer mast cells and / or basophils in the peripheral tissue to recruit and sustain an inflammatory response.

[0143] In some embodiments, the non-IgE-mediated FcsRl activity is a reduction of cytokine production. In some embodiments, the non-IgE-mediated FcsRl activity is MRGPRX2 mediated mast cell activation. In some embodiments, the BTK inhibitor is administered in an amount sufficient to reduce release of cytokines selected from the group consisting of IL-1, IL-6, TNF-alpha, IL-8, IL-12, IL-17, IFN-gamma, and combinations thereof. In some embodiments, the cytokines are released by activated mast cells, basophils, and monocytes. In some embodiments, the cytokine are released by secondary induction of71DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO cytokines via infiltrating cells (monocytes, neutrophils, mast cells, dendritic cells, NK cells, B lymphocytes, or T lymphocytes).

[0144] In some embodiments, administering the BTK inhibitor reduces trafficking of aberrant immature mast cells from bone marrow to skin and gastrointestinal tract, thereby reducing the number of aberrant mast cells in the skin and gastrointestinal tract. In some embodiments, administering the BTK inhibitor reduces trafficking of neoplastic mast cells. In some embodiments, administering the BTK inhibitor reduces signaling through a cognate receptor complex formed from FCER I . FcsRly, and other transmembrane molecules, including FceRI 0 or MS4A6A, in complex with docking molecules known to transmit cellular activation signals. This leads to the inhibition of phosphorylation and activation of downstream signaling enzymes that drive mast cell degranulation, cytokine production, chemokine and inflammatory mediator release, proliferation, and chemotaxis. Administration of a selective BTK inhibitor also reduces the ability of these signaling cascades to upregulate inducible receptor components, including FcsR I . FcsRy. FcsR 10. and MS4A6A, thereby decreasing mast cell hypersensitivity to FceRI -mediated signals.

[0145] In some embodiments, the subject has a ATTD816V mutation. For example, in a vast majority of cases with systemic mastocytosis, the clonal nature of the disease can be established through demonstration of a somatic A to T missense mutation at position 2447 of the coding sequence in the TT gene. Orfao et al., 2007, Br. J. Haematol. 138: 12-30. Without wishing to be bound by theory, the resulting substitution of aspartate (D) to valine (V) at amino acid position 816 in the kinase domain can lead to autoactivation of the KIT receptor tyrosine kinase and cause systemic mastocytosis. In some embodiments, the KIT D816V mutation results in constitutive activation of KIT. In some embodiments, the constitutive activation of ATT results in an aggregation of mast cells in bone marrow and / or peripheral tissue of the one or more subjects which degranulate upon exposure to a lower antigenic stimulus than healthy mast cells. In some embodiments, the subject does not have a KIT D816V mutation.

[0146] In some embodiments, the BTK inhibitor is administered once daily at a dose selected from the group consisting of 10 mg, 25 mg, 50 mg, 75 mg, 90 mg, 100 mg, 125 mg, 150 mg, 200 mg, 250 mg, and 300 mg. In some embodiments, the BTK inhibitor is72DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO administered once daily at a dose of from 10 mg to 200 mg. In some embodiments, the BTK inhibitor is administered twice daily at a dose selected from the group consisting of 10 mg. 25 mg, 50 mg, 75 mg, 90 mg, 100 mg, 125 mg, 150 mg, 200 mg, 250 mg, and 300 mg. In some embodiments, the BTK inhibitor is administered twice daily at a dose from 10 mg to 300 mg.

[0147] In some embodiments, the BTK inhibitor is orally administered.

[0148] In some embodiments, the method includes administering to the subject a therapeutically effective amount of a BTK inhibitor compound selected from Table 1 or a pharmaceutically acceptable salt thereof, in a dosage selected from the group consisting of at least 10 mg QD, at least 15 mg QD, at least 25 mg QD, at least 30 mg QD, at least 50 mg QD, at least 60 mg QD, at least 75 mg QD, at least 90 mg QD, at least 100 mg QD, at least 120 mg QD, at least 150 mg QD, at least 175 mg QD, at least 180 mg QD, at least 200 mg QD, at least 225 mg QD, at least 240 mg QD, at least 250 mg QD, at least 275 mg QD, at least 300 mg QD, at least 325 mg QD, at least 350 mg QD, at least 360 mg QD, at least 375 mg QD, or at least 480 mg QD. In some embodiments, the administering is in a dosage selected from the group consisting of no more than 600 mg QD, no more than 480 mg QD, no more than 400 mg QD, no more than 300 mg QD, no more than 250 mg QD, no more than 200 mg QD, no more than 150 mg QD, no more than 100 mg QD, no more than 50 mg QD, or no more than 15 mg QD. In some embodiments, the administering is in a dosage selected from the group consisting of from 10 to 50 mg QD, from 25 to 100 mg QD, from 75 to 200 mg QD, from 100 to 300 mg QD, from 250 to 400 mg QD, from 300 to 480 mg QD, or from 400 to 600 mg QD. In some embodiments, the administering is in a dosage that falls within another range starting no lower than 10 mg QD and ending no higher than 600 mg QD.

[0149] In some embodiments, the method includes administering to the subject a therapeutically effective amount of a BTK inhibitor compound selected from Table 1 or a pharmaceutically acceptable salt thereof, in a dosage selected from the group consisting of at least 10 mg BID, at least 15 mg BID, at least 25 mg BID, at least 30 mg BID, at least 50 mg BID, at least 60 mg BID, at least 75 mg BID, at least 90 mg BID, at least 100 mg BID, at least 120 mg BID, at least 150 mg BID, at least 175 mg BID, at least 180 mg BID. at least 200 mg BID, at least 225 mg BID, at least 240 mg BID, at least 250 mg BID, at least 275 mg BID, at least 300 mg BID, at least 325 mg BID, at least 350 mg BID, at least 360 mg BID, at73DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO least 375 mg BID, or at least 480 mg BID. In some embodiments, the administering is in a dosage selected from the group consisting of no more than 600 mg BID, no more than 480 mg BID, no more than 400 mg BID, no more than 300 mg BID, no more than 250 mg BID, no more than 200 mg BID, no more than 150 mg BID, no more than 100 mg BID, no more than 50 mg BID, or no more than 15 mg BID. In some embodiments, the administering is in a dosage selected from the group consisting of from 10 to 50 mg BID, from 25 to 100 mg BID, from 75 to 200 mg BID, from 100 to 300 mg BID, from 250 to 400 mg BID, from 300 to 480 mg BID, or from 400 to 600 mg BID. In some embodiments, the administering is in a dosage that falls within another range starting no lower than 10 mg BID and ending no higher than 600 mg BID.

[0150] In some embodiments, the method includes administering to the subject a therapeutically effective amount of a BTK inhibitor compound selected from Table 1 or a pharmaceutically acceptable salt thereof, in a dosage selected from the group consisting of at least 10 mg TID, at least 15 mg TID, at least 25 mg TID, at least 30 mg TID, at least 50 mg TID, at least 60 mg TID. at least 75 mg TID, at least 90 mg TID, at least 100 mg TID, at least 120 mg TID, at least 150 mg TID, at least 175 mg TID, at least 180 mg TID, at least 200 mg TID, at least 225 mg TID, at least 240 mg TID, at least 250 mg TID, at least 275 mg TID, at least 300 mg TID, at least 325 mg TID, at least 350 mg TID, at least 360 mg TID, at least 375 mg TID, or at least 480 mg TID. In some embodiments, the administering is in a dosage selected from the group consisting of no more than 600 mg TID, no more than 480 mg TID, no more than 400 mg TID, no more than 300 mg TID, no more than 250 mg TID, no more than 200 mg TID, no more than 150 mg TID, no more than 100 mg TID, no more than 50 mg TID, or no more than 15 mg TID. In some embodiments, the administering is in a dosage selected from the group consisting of from 10 to 50 mg TID, from 25 to 100 mg TID, from 75 to 200 mg TID, from 100 to 300 mg TID, from 250 to 400 mg TID, from 300 to 480 mg TID, or from 400 to 600 mg TID. In some embodiments, the administering is in a dosage that falls within another range starting no lower than 10 mg TID and ending no higher than 600 mg TID.

[0151] In some embodiments, the method includes administering to the subject a therapeutically effective amount of a BTK inhibitor compound selected from Table 1 or a pharmaceutically acceptable salt thereof, in a dosage selected from the group consisting of at74DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO least 10 mg QID, at least 15 mg QID, at least 25 mg QID, at least 30 mg QID, at least 50 mg QID, at least 60 mg QID, at least 75 mg QID, at least 90 mg QID, at least 100 mg QID, at least 120 mg QID, at least 150 mg QID, at least 175 mg QID, at least 180 mg QID, at least 200 mg QID, at least 225 mg QID, at least 240 mg QID, at least 250 mg QID, at least 275 mg QID, at least 300 mg QID, at least 325 mg QID, at least 350 mg QID, at least 360 mg QID, at least 375 mg QID, or at least 480 mg QID. In some embodiments, the administering is in a dosage selected from the group consisting of no more than 600 mg QID, no more than 480 mg QID, no more than 400 mg QID, no more than 300 mg QID, no more than 250 mg QID, no more than 200 mg QID, no more than 150 mg QID, no more than 100 mg QID, no more than 50 mg QID, or no more than 15 mg QID. In some embodiments, the administering is in a dosage selected from the group consisting of from 10 to 50 mg QID, from 25 to 100 mg QID, from 75 to 200 mg QID, from 100 to 300 mg QID, from 250 to 400 mg QID, from 300 to 480 mg QID, or from 400 to 600 mg QID. In some embodiments, the administering is in a dosage that falls within another range starting no lower than 10 mg QID and ending no higher than 600 mg QID.

[0152] Suitable embodiments for administering the BTK inhibitor to a subject contemplated for use in the present disclosure are described further elsewhere herein, as in the section entitled “Dosages and Dosing Regimens,” below.

[0153] In some embodiments, the subject has previously failed a prior treatment for ISM. In some embodiments, the subject is a non-responder to prior treatment for ISM. In some embodiments, the subject is treatment-resistant to prior treatment for ISM. In some embodiments, the prior treatment for ISM was poorly tolerated or treatment-emergent adverse effects led to treatment discontinuation. In some embodiments, the prior treatment for ISM is a KIT inhibitor, a KIT antibody, a P13K inhibitor (e.g., P13K delta inhibitor), or a PKC inhibitor. In some embodiments, the prior treatment for ISM is selected from the group consisting of: histamine Hl blockers, histamine H2 blockers, leukotriene inhibitors, cromolyn sodium, corticosteroids and omalizumab. In some embodiments, the prior treatment for ISM is a KIT inhibitor selected from the group consisting of regorafenib, sorafenib, imatinib, ilorasertib, sunitinib, pazopanib, lenvatinib, dasatinib, bezuclastinib, exarafenib, nintedanib, telatinib, avapritinib, TPX-0022, crenolanib, midostaurin, nilotinib, and pharmaceutically acceptable salts thereof. In some embodiments, the prior treatment for ISM is a SYK inhibitor75DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO selected from the group consisting of fostamatinib, entospletinib, cerdulatinib, TAK-659, and a pharmacologically acceptable salt thereof. In some embodiments, the prior treatment for ISM is a PKC inhibitor selected from the group consisting of bisindolylmaleimides, staurosporine, midostaurin, UCN-01, sotrastaurin, enzastaurin, ruboxistaurine, tivantinib, enzastaurin, riluzole, balanol, lestaurtinib, stauprimide, CEP-701, Arcyriaflavin A, chelerythrine chloride, bisindolylmaleimids I-XII. and a pharmacologically acceptable salt thereof. In some embodiments, the prior treatment is a MRGPRX2 inhibitor. In some embodiments, the MRGPRX2 inhibitor is EVO756, QWF, isoliquiritigenin, shikonin, imperatorin, roxithromysin, paeoniflorin, quercetin, genistein, aptamer-X35, or combinations thereof. In some embodiments, the prior treatment for ISM is a PI3K inhibitor selected from the group consisting of idelalisib, copanlisib, duvelisib, umbralisib. leniolisib, parsaclisib, zandelisib, eganelisib, linperlisib, nemiralisib, pilaralisib, seletalisib, tenalisib, AZD8186, AZD8835, CAL263, TG100-115, ZSTK474, and a pharmacologically acceptable salt thereof. In some embodiments, the prior treatment for ISM is HT-004 or HT-KIT. In some embodiments, the subject is ISM treatment-naive. In some embodiments, the BTK inhibitor is administered in combination with a supportive care regimen for ISM. In some embodiments, the BTK inhibitor is administered as a monotherapy. In some embodiments, the subject is resistant to a supportive care regimen for ISM.

[0154] ISM

[0155] Mastocytosis is a rare disorder characterized by the accumulation and activation of clonal mast cells in the skin and / or other tissues. See, Akin C, Metcalfe DD. Systemic mastocytosis. Annu Rev Med. 2004;55:419-32. ISM, characterized by relatively low mast cell (MC) burden and the absence of MC-related organ damage, is the most common subtype. Patients with ISM present with chronic skin, Gl, musculoskeletal, neurologic, and global systemic complaints, including recurrent episodes of anaphylaxis, which significantly and negatively impact quality of life. See, Worobec AS. Treatment of systemic mast cell disorders. Hematol Oncol Clin North Am. Jun 2000;14(3):659-87, vii.

[0156] Treatment of ISM is first directed at symptom management with H l and H2 antihistamines, antileukotriene agents, corticosteroids, and cromolyn. See, Worobec AS. Treatment of systemic mast cell disorders. Hematol Oncol Clin North Am. Jun76DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO2000;14(3):659-87, vii. Symptoms that cannot be controlled with symptoms-directed therapy may result in treatment with cytoreductive agents. While these agents may improve symptoms, they are associated with toxicities, including bone marrow suppression, and modest improvement in symptoms. See, Gotlib J, Castells M, Elberink HO, et al., “Avapritinib versus Placebo in Indolent Systemic Mastocytosis. NEJM Evidence,’' 2023; 2(6): EVIDoa2200339. Therefore, improved agents are needed to improve the quality of life for ISM patients with persistent and poorly controlled relevant symptom burden.

[0157] The clonal nature of mastocytosis can be established in almost all cases through demonstration of gain-of-function mutations involving the tyrosine kinase domain of KIT (a receptor on the surface of mast cells) in lesional skin and / or bone marrow (BM) cells. Nagata (1995) Proc. Natl. Acad. Sci. USA, 92: 10560-10564. ISM is characterized as an accumulation of "‘neoplastic” mature mast cells that typically harbor a KIT D816V mutation. This and other disease features have been proposed as consensus criteria (WHO 2016) for the diagnosis and classification of different subtypes of mastocytosis. Valent (2017) Blood 129, 1420-1427. Aberrant mast cells in ISM patients are often described as “twitchy” as they have a much lower threshold to activate / degranulate via their main signaling high affinity receptor (Fc epsilon Receptor One [FcsRl]) on account of their consti tutively-acti vale 77 D816V receptor. Furthermore, the excessive reactivity of ISM mast cell FcsRl receptors upon binding to circulating monomeric immunoglobulin E (IgE, i.e., without requirement for FceRl cross-linking by antigen, allergen or an autoimmune mechanism), coupled with activated KIT signaling through KIT mutations (e.g, D816V+ and others) may be the cause of constitutively activated ISM mast cells explaining the chronic, highly debilitating constitutional symptoms in the majority of patients with ISM.

[0158] In some embodiments, a symptom associated with ISM can be fatigue, abdominal pain, diarrhea, nausea, skin spots, itching, flushing, brain fog, headache, dizziness, bone pain, or a combination thereof. In some embodiments, the severity of each symptom associated with ISM, such as fatigue, abdominal pain, diarrhea, nausea, skin spots, itching, flushing, brain fog, headache, dizziness, bone pain via a symptom assessment form where patients rate the severity’ of symptoms experienced over the last 24 hours on a zero to ten score, with zero indicating an absence of the symptom and ten indicating extreme severity’ of such symptom.77DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0159] In some embodiments, a symptom associated with ISM can be muscle pain, difficulty concentrating, difficulty remembering, red spots, runny nose, nasal congestion, wheezing, shortness of breath, throat itching, heart palpitations, diarrhea, or a combination thereof. In some embodiments, the severity of each symptom associated with ISM, such as muscle pain, difficulty concentrating, difficulty7remembering, red spots, runny nose, nasal congestion, wheezing, shortness of breath, throat itching, heart palpitations, diarrhea is quantified via symptom assessment form where patients rate the severity of symptoms experienced over the last 24 hours on a zero to ten score, with zero indicating an absence of the symptom and ten indicating extreme severity7of such symptom.

[0160] In some embodiments, the symptom persists for at least one day prior to administration of the BTK inhibitor, such as at least two days, at least three days, at least four days, at least five days, at least six days, or at least seven days prior to administration of the BTK inhibitor.

[0161] In some embodiments, severity of the symptom is determined by self-assessment. In some embodiments, severity of the symptom is determined by self-reporting via a handheld device (e.g., cell phone).

[0162] In some embodiments, the severity7of a symptom is assessed by assigning a numerical value (which may also be referred to as a score) to the severity of the symptom, for example, by assigning a value on a scale of zero to ten, wherein a value of zero indicates that a symptom is absent and a value of ten indicates that a symptom’s severity is the most severe to the human subject. In some embodiments, a total symptom score is determined by summing the scores for each individual symptom. In some embodiments, the scores of two, three, four, five, six, seven, eight, nine, ten or eleven symptoms are summed to obtain a total symptom score. In some embodiments, a symptom score or a total symptom score is obtained by reporting a score (for example, on a scale of zero to 10) in response to one, two, three, four, five, six, seven, eight, nine, ten, or eleven of the following questions relating to the presence and severity7of symptoms associated with ISM over past 24 hours.Table 2: Symptom Assessment Questionnaire78DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0163] In some embodiments, the human subject is considered symptomatic and has a total symptom score equaling a sum of the scores of each of the eleven questions in Table 2, wherein each question is scored on a scale of zero to ten with zero indicating the symptom is absent and ten indicating the symptom is the most severe, of at least 14, such at least 15, at least 16, at least 18, at least 20, at least 22, at least 24, at least 25, at least 26, at least 28, at least 30, at least 32, at least 34, at least 35, at least 36. at least 38. at least 40, at least 42, at least 44, at least 45, at least 46, at least 48, and / or at least 50.

[0164] In some embodiments, a symptom score or a total symptom score is obtained by reporting a score (for example, on a scale of zero to ten) in response to one, two, three, four, five, six, seven, eight, or nine questions listed in Table 3 regarding the severity of the following symptoms over the past 24 hours (excluding question 9).Table 3: Supplemental Symptom Assessment Questionnaire79DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0165] In some embodiments, the human subject is considered symptomatic and has a total symptom score equaling a sum of the scores of each of questions 1-8 (excluding question 9) in Table 3, wherein each question 1-8 is scored on a scale of zero to ten with zero indicating the symptom is absent and ten indicating the symptom is the most severe, of at least 12, such at least 14, at least 15, at least 16, at least 18, at least 20, at least 22, at least 24, at least 25, at least 26, at least 28, at least 30, at least 32, at least 34, at least 35. at least 36, at least 38, at least 40, at least 42, at least 44, at least 45, at least 46, at least 48, and / or at least 50.

[0166] In some embodiments, a symptom is reduced if the symptom’s score is reduced by at least 10%, such as at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% compared to a baseline score for the symptom. In some embodiments, the baseline score for a symptom is the symptom’s score before administering a BTK inhibitor as described herein. In some embodiments, the disclosure relates to a reduction in a total symptom score. In some embodiments, the total symptom score is calculated based on the answers to the questions listed in Table 2. In some embodiments, the total symptom score is calculated based on the answers to the questions listed in Table 3. In some embodiments, the total symptom score is calculated based on the answers to the questions listed in Table 2 and Table 3. In some embodiments, a total symptom score is reduced if the total symptom score is reduced by at least 50%, such as at least 60%, at least 70%, at least 75%, at least 80%, and / or at least 90% compared to a baseline total symptom score. In some embodiments, the baseline total symptom score is the total symptom score before administering a BTK inhibitor as described80DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO herein. In some embodiments, the total symptom score refers to an average of daily total symptom scores, such as a 7-day average, 14-day average, 21-day average, or 28-day average of daily total symptom scores. In some embodiments, a reduction in a symptom and / or in a total symptom score is obtained after administering a BTK inhibitor as described herein for a period of time of at least 8 weeks, at least 12 weeks, at least 16 weeks, at least 20 weeks, at least 24 weeks, at least 28 weeks, at least 32 weeks, at least 36 weeks, at least 40 weeks, at least 44 weeks, at least 48 weeks, and / or at least 52 weeks.

[0167] MCAS

[0168] MCAS is characterized by recurrent, severe, and chronic sy stemic symptoms related to mast cell activation (Akin et al.. J Allergy Clin Immunol. Dec 2010; 126(6): 1099- 104 e4). In subjects with MCAS, mast cell degranulation may occur in response to innocuous stimuli or occur excessively in response to allergens. In addition to the presence of mast cell- related symptoms, MCAS is defined by elevated serum tryptase levels (or urine metabolites associated with mast cell activation) levels during symptomatic periods, and clinical response to medications that reduce mast cell activation or downstream signaling.

[0169] Signs and symptoms associated with mast cell activation range from nausea to abdominal cramping and diarrhea, and from mild pruritus to anaphylaxis and life-threatening hypotension (Akin et al.. J Allergy Clin Immunol. Aug 2017;140(2):349-355). During MCAS episodes, patients experience symptoms that impact multiple organ systems, potentially affecting skin, gastrointestinal, cardiovascular, and respiratory function. In subjects with MCAS, chronic systemic symptoms and severe, repeated, and unpredictable symptomatic episodes can substantially and negatively impact quality of life. Episodes may last for a few minutes to several hours, and symptoms may progress to loss of consciousness and lifethreatening hypotension. Thirty percent of patients with MCAS report anaphylaxis requiring emergency epinephrine treatment three or more times a year, and 85% report daily life is interrupted moderately or severely by symptoms.

[0170] In some embodiments, severity of the symptom is determined by self-assessment. In some embodiments, severity of the symptom is determined by self-reporting via a handheld device (e.g., cell phone).81DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0171] In some embodiments, the severity of a symptom is assessed by assigning a numerical value (which may also be referred to as a score) to the severity of the symptom, for example, by assigning a value on a scale of zero to ten, wherein a value of zero indicates that a symptom is absent and a value of ten indicates that a symptom’s severity is the most severe to the human subject. In some embodiments, a total symptom score is determined bysumming the scores for each individual symptom. In some embodiments, the scores of two, three, four, five, six, seven, eight, nine, ten or eleven symptoms are summed to obtain a total symptom score. In some embodiments, a symptom score or a total symptom score is obtained by reporting a score (for example, on a scale of zero to 10) in response to one, two, three, four, five, six, seven, eight, nine, ten, or eleven of the questions listed in Table 4 relating to the presence and severity of symptoms associated with MCAS over the past 24 hours.

[0172] Table 4: MCAS Symptom Assessment Questionnaire

[0173] In some embodiments, the human subject is considered symptomatic and has a total symptom score equaling a sum of the scores of each of the eleven questions in Table 4, wherein each question is scored on a scale of zero to ten with zero indicating the symptom is absent and ten indicating the symptom is the most severe, of at least 14, such at least 15, at least 16, at least 18, at least 20, at least 22, at least 24, at least 25, at least 26, at least 28, at82DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO least 30, at least 32, at least 34, at least 35, at least 36, at least 38, at least 40, at least 42, at least 44, at least 45, at least 46, at least 48, and / or at least 50.

[0174] In some embodiments, a symptom score or a total symptom score is obtained by reporting a score (for example, on a scale of zero to 10) in response to one, two, three, four, five, six, or seven of the questions listed in Table 5 relating to the presence and severity' of symptoms associated with MCAS over the past 24 hours.

[0175] Table 5: MCAS Supplemental Symptom Assessment Questionnaire

[0176] In some embodiments, the human subject is considered symptomatic and has a total symptom score equaling a sum of the scores of each of questions 1-7 in Table 5, wherein each question 1-7 is scored on a scale of zero to ten with zero indicating the symptom is absent and ten indicating the symptom is the most severe, of at least 12, such at least 14, at least 15, at least 16, at least 18, at least 20, at least 22, at least 24, at least 25, at least 26, at least 28, at least 30, at least 32, at least 34, at least 35, at least 36, at least 38, at least 40, at least 42, at least 44, at least 45, at least 46, at least 48. and / or at least 50.

[0177] In some embodiments, a symptom is reduced if the symptom’s score is reduced by at least 10%, such as at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% compared to a baseline score for the symptom. In some embodiments, the baseline score for a symptom is the symptom’s score before administering a BTK inhibitor as described herein. In some embodiments, the disclosure relates to a reduction in a total symptom score. In some83DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO embodiments, the total symptom score is calculated based on the answers to the questions listed in Table 4. In some embodiments, the total symptom score is calculated based on the answers to the questions listed in Table 5. In some embodiments, the total symptom score is calculated based on the answers to the questions listed in Table 4 and Table 5. In some embodiments, a total symptom score is reduced if the total symptom score is reduced by at least 30%, at least 40%, at least 50%, such as at least 60%, at least 70%, at least 75%, at least 80%, and / or at least 90% compared to a baseline total symptom score. In some embodiments, the baseline total symptom score is the total symptom score before administering a BTK inhibitor as described herein. In some embodiments, the total symptom score refers to an average of daily total symptom scores, such as a 7-day average, 14-day average, 21-day average, or 28-day average of daily total symptom scores. In some embodiments, the total symptom score refers to an average of rolling daily total symptom scores, such as a 7-day average, 14-day average, 21-day average, or 28-day average of daily total symptom scores. In some embodiments, a reduction in a symptom and / or in a total symptom score is obtained after administering a BTK inhibitor as described herein for a period of time of at least 8 weeks, at least 12 weeks, at least 16 weeks, at least 20 weeks, at least 24 weeks, at least 28 weeks, at least 32 weeks, at least 36 weeks, at least 40 weeks, at least 44 weeks, at least 48 weeks, and / or at least 52 weeks.

[0178] Models for endpoint prediction.

[0179] Referring again to FIGs. 2A-C, in Block 208, in some embodiments, methods and systems disclosed herein further include obtaining a model 120 of the target endpoint 126 in a subject 122 having a BTK-mediated condition 124. The model 120 generates, as output, a representation of the target endpoint 126 (e.g, a BTK occupancy and duration thereof) in the subject 122 responsive to a candidate treatment with the BTK inhibitor 132.

[0180] In some embodiments, the model is a pharmacokinetics-pharmacodynamics (PK / PD) model. In some embodiments, the model comprises a first component and a second component, wherein the first component is a PK model and the second component is a PD model. In some embodiments, the PK model is a two-compartment model with sequential zero-order release and first-order absorption, absorption lag time, and linear elimination, and84DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO the PD model comprises a zero-order BTK synthesis rate, a first-order degradation rate constant for BTK, and a second-order irreversible binding rate constant.

[0181] In some embodiments, the first component is a PK model comprising a two- compartment model with a sequential zero-order release and first-order absorption. In some embodiments, the model comprises a first-order distribution process and a first-order elimination process.

[0182] Without being limited to any one theory of operation, a zero-order process has no dynamic behavior and produces a constant output, while a first-order process gradually reaches equilibrium with an exponential response. For instance, a zero-order process is one in which the output of the system remains constant over time, regardless of changes in the input, and / or where the rate of change of the output is zero. A first-order process is one in which the rate of change of the output is proportional to the difference between the output and the equilibrium or desired value. In some embodiments, a first-order process responds to an input with a time delay and a gradual approach to a steady state or equilibrium value. In other words, the system gradually approaches equilibrium or a setpoint over time at a rate governed by the time constant r. The "first-order" refers to the fact that the system's response rate is proportional to the first derivative of the output with respect to time. See, for instance, Borowy and Ashurst, "Physiology. Zero and First Order Kinetics,” StatPearls 2022, available on the Internet at ncbi.nlm.nih.gov / books / NBK499866 / .

[0183] In some embodiments, the model further comprises one or more pharmacokinetic hyperparameters, including, but not limited to, absorption, bioavailability, distribution, volume of distribution, protein binding, metabolism, excretion, clearance, half-life, and / or drug kinetics.

[0184] In some embodiments, absorption refers to the process by which a drug enters the bloodstream from its site of administration, typically influenced by factors like the drug's form, route, and solubility. In some embodiments, bioavailability refers to the fraction of an administered drug that reaches the systemic circulation in an unchanged form, which is influenced by absorption and first-pass metabolism. In some embodiments, distribution refers to the process by which a drug spreads throughout the body’s tissues and organs after entering the bloodstream, influenced by factors like blood flow and tissue permeability. In85DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO some embodiments, the volume of distribution (Vd) refers to a theoretical volume that represents the extent of drug distribution in the body, calculated by dividing the total drug amount by the plasma concentration. In some embodiments, protein binding refers to the extent to which drugs bind to plasma proteins, affecting the free (active) drug concentration and thus the drug's effect and elimination. In some embodiments, metabolism refers to the biotransformation of a drug into a more water-soluble form, often occurring in the liver, to facilitate its elimination from the body. In some embodiments, excretion refers to the process of removing a drug from the body, primarily through the kidneys (urine), liver (bile), or other routes like sweat or feces. In some embodiments, clearance refers to a measure of the body’s ability to eliminate a drug, representing the volume of plasma cleared of the drug per unit time, and is typically influenced by liver and kidney function. In some embodiments, half-life (t* / 2) refers to the time it takes for the plasma concentration of a drug to decrease by half, which reflects the drug's elimination rate and is used to determine dosing intervals. In some embodiments, drug kinetics refers to the study of the absorption, distribution, metabolism, and excretion (ADME) of drugs, describing how the drug concentration in the body changes over time. Pharmacokinetic hyperparameters contemplated for use in the present disclosure are further described, for example, in Grogan and Preuss, “Pharmacokinetics,” StatPearls 2023, available on the Internet at ncbi.nlm.nih.gov / books / NBK557744 / . In some embodiments, the pharmacokinetic hyperparameters comprise values determined for the BTK inhibitor and / or for a concomitant medication (e.g, a clearance rate, volume of distribution, half-life, etc., of a BTK inhibitor and / or a concomitant medication).

[0185] In some embodiments, the model further comprises one or more random effects hyperparameters including, but not limited to, inter-individual random effects, intraindividual random effects, inter-occasion random effects, and / or residual error. In some embodiments, a random effect hyperparameter follows a log-normal distribution. In some embodiments, the model further includes a covariance matrix structure comprising one or more random effects hyperparameters (e.g., a covariance matrix Q).

[0186] Without being limited to any one theory of operation, in some embodiments, interindividual random effects refer to variations in drug response or pharmacokinetic parameters (such as absorption, metabolism, and clearance) between different individuals in a population. These differences arise from genetic, environmental, and physiological factors86DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO that cause individuals to respond differently to the same dose of a drug. In pharmacokinetic modeling, these random effects are typically modeled as variability around population averages, helping to account for individual differences in drug behavior. Intra-individual random effects reflect the variability within the same individual across different occasions or doses, which may arise due to factors like biological fluctuations, circadian rhythms, or temporary changes in health status. Inter-occasion random effects are variations observed between different dosing occasions for the same individual, which may result from factors like changes in drug absorption, metabolism, or disease state between treatment events. Residual error is the unexplained variability in drug concentrations or responses after accounting for all measured factors (e.g., individual characteristics and dosing) and reflects the random fluctuations in data that cannot be attributed to known sources of variability. Random effects contemplated for use in the present disclosure are further described, for example, in Shen and Liu, “Population Pharmacokinetics Studies with Nonlinear Mixed Effects Modeling,” SAS Global Forum 2007, available on the Internet at support, sas.com / resources / papers / proceedings / proceedings / forum2007 / 148-2007. pdf.

[0187] In some embodiments, the inter-individual random effects are modeled assuming a log-normal distribution as given by the following expression:9ki = 6k x e^ki

[0188] where Okt denotes the parameter value for the ithsubject, ft denotes the typical parameter value, and rjkt denotes the inter-individual random effect for the Ithsubject, assumed to have a mean of 0 and a variance a>k2.

[0189] In some embodiments, the residual error structure is assumed to follow an additive, proportional, or combined additive and proportional error model desenbed by the following expression:Yij=Clj X (1 + El if) + Eli]

[0190] where Ytj is the jthobserved concentration for the ithsubject, Cy is the corresponding predicted concentration, and sly (proportional) and £2 (additive) are the residual errors under the assumption that E~N (0, o2).87DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0191] FIG. 5 illustrates an example schematic of a PK model, in accordance with some embodiments of the present disclosure, the PK model comprising, for a given dosage, a sequential zero-order release (DI: duration of zero-order release) and absorption compartment, including a first-order absorption rate constant (Ka). Optionally, the absorption compartment further includes a relative bi oavai 1 ability hyperparameter (Frei). FIG. 5 further illustrates an optional central compartment comprising clearance (CL), bioavailability (F), and central volume of distribution (Vc) hyperparameters, as well as an optional peripheral compartment comprising inter-compartmental clearance (Q), bioavailability (F), and peripheral volume of distribution (Vp) hyperparameters.

[0192] In some embodiments, the PK model further includes one or more covariate model components. In some embodiments, a covariate model component includes one or more covariates, where covariates include continuous covariates and / or categorical covariates. In some embodiments, the one or more covariates are incorporated into the PK model using a proportional structure as shown below:ContinuousCategoricalPkt = 9k x ( 1 + 9j )Xi>

[0193] where Pkt is the population estimate of the parameter Pk for subject z, Xy is the value of continuous covariate ) for subject i (or an indicator variable for subject i for categorical covariate ) with values of 1 for the nonreference category and 0 for the reference category), M(Xj) is the median of covariate Xj in the analysis dataset, k is the typical value of the parameter Pk, and 9, is a coefficient that reflects the effect of covariate Xj on the parameter.

[0194] In some embodiments, the one or more covariates are selected from any of the embodiments for covariates disclosed elsewhere herein, for instance, in the section entitled “Covariates and cotreatments,” below.88DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0195] In some embodiments, the pharmacokinetics-pharmacodynamics (PK / PD) model including the first component and the second component is an irreversible turnover model that describes the relationship between a concentration of the BTK inhibitor and a BTK target occupancy. In some embodiments, the concentration of the BTK inhibitor is a plasma or blood concentration. In some embodiments, the irreversible turnover model describes the behavior of the BTK target occupancy under i) a constant rate of production, ii) a degradation or elimination rate that is proportional to its current amount, and iii) no re-synthesis once eliminated.

[0196] FIG. 10 depicts an example structure of the PK / PD model. In particular, FIG. 10 illustrates the BTK target as Free BTK (e.g., time-dependent fraction of unoccupied BTK with baseline unoccupied BTK set to I). BTK rate of production is shown as Kin (zero-order BTK synthesis rate), degradation or elimination rate is shown as Kout (first-order degradation rate constant of BTK protein), and BTK target occupancy is shown as Kr*Cp, where Cp refers to the BTK inhibitor plasma concentration (e.g., central compartment concentration), and Kr refers to a second-order irreversible binding rate constant.

[0197] In some embodiments, the model comprises a plurality of parameters. In some embodiments, the plurality' of parameters comprises at least 2, at least 3, at least 5, at least 10, at least 20, at least 50, at least 100, at least 500, at least 1000, at least 5000, at least 10,000, at least 100,000, or at least 1.000,000 parameters. In some embodiments, the plurality of parameters comprises no more than 10,000,000, no more than 1,000,000, no more than 100,000, no more than 10,000, no more than 1000, no more than 100, no more than 50, or no more than 10 parameters. In some embodiments, the plurality of parameters consists of from 2 to 10, from 5 to 50, from 10 to 200, from 100 to 1000, from 1000 to 10,000, from 10,000 to 1,000,000, or from 1,000,000 to 10,000,000 parameters. In some embodiments, the plurality of parameters falls within another range starting no lower than 2 parameters and ending no higher than 10,000,000 parameters.

[0198] In some embodiments, the model comprises any of the models and / or embodiments disclosed elsewhere herein, for example, in the section entitled ’'Definitions: Models,” above, and in Examples 2 and 4. In some embodiments, the model is fitted to a training dataset for a plurality of training subjects, as described in the section entitled89DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO“Obtaining models,’' below. In some embodiments, the training dataset includes, for each respective training subject in the plurality of training subjects, (i) a corresponding total dosage for the BTK inhibitor, (ii) a corresponding dosage frequency, and (iii) corresponding training values for the one or more target endpoints responsive to treatment with the BTK inhibitor at the corresponding total dosage and the corresponding dosage frequency. In some embodiments, the model is obtained using any of the methods and / or embodiments disclosed elsewhere herein, including, for example, the section entitled “Obtaining models,” below.

[0199] Candidate dosages.

[0200] In some embodiments, the model 120 generates the target endpoint 126, or a representation thereof, based upon a candidate unpartitioned quantum (e.g, total dosage) 134 for the BTK inhibitor at a candidate iteration frequency (e.g., dosage frequency) 136. For instance, in some embodiments, the model generates, as output, a prediction of the target endpoint 126 in a subject 122, thereby predicting a response to administration with the candidate unpartitioned quantum (e.g, total dosage) 134 for the BTK inhibitor at the candidate iteration frequency (e.g., dosage frequency) 136.

[0201] As used interchangeably herein, in some embodiments, an unpartitioned quantum of the BTK inhibitor refers to a total dosage amount of the BTK inhibitor.

[0202] In some embodiments, a total dosage amount will vary depending on the selection of a predetermined time period for application or administration to a subject. In some embodiments, the total dosage amount is based upon administration over a single day. In some embodiments, the predetermined time period comprises 6, 12, or 24 hours. In some embodiments, the predetermined time period comprises 1, 2, 3, 4, 5, 6, or 7 days. In some embodiments, the predetermined time period comprises 1 week, 2 weeks, 3 weeks, or 4 weeks. In some embodiments, the predetermined time period comprises at least 6 hours, at least 12 hours, at least 24 hours, at least 1 day, at least 2 days, at least 1 week, or at least 1 month. In some embodiments, the predetermined time period comprises no more than 2 months, no more than 1 month, no more than 1 week, no more than 2 days, no more than 1 day, or no more than 12 hours. In some embodiments, the predetermined time period consists of from 6 hours to 24 hours, from 12 hours to 2 days, from 1 day to 1 week, or from 1 week90DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO to 2 months. In some embodiments, the predetermined time period falls within another range starting no lower than 6 hours and ending no higher than 2 months.

[0203] In some embodiments, the candidate unpartitioned quantum (e.g., the total dosage amount) of the BTK inhibitor comprises at least 5 mg, at least 10 mg, at least 20 mg, at least 50 mg, at least 100 mg, at least 200 mg, at least 300 mg, at least 400 mg, at least 500 mg, or at least 1000 mg. In some embodiments, the candidate unpartitioned quantum comprises no more than 5000 mg, no more than 1000 mg, no more than 600 mg, no more than 500 mg, no more than 400 mg, no more than 300 mg, no more than 200 mg, no more than 100 mg, no more than 50 mg, or no more than 10 mg. In some embodiments, the candidate unpartitioned quantum consists of from 5 mg to 50 mg, from 10 mg to 200 mg, from 100 to 500 mg, from 500 mg to 1000 mg, from 20 mg to 1000 mg, or from 1000 mg to 5000 mg. In some embodiments, the candidate unpartitioned quantum falls within another range starting no lower than 5 mg and ending no higher than 5000 mg.

[0204] In some embodiments, the unpartitioned quantum (e.g, the total dosage) of the BTK inhibitor will vary depending of the BTK inhibitor selected for use. as will be apparent to one skilled in the art. Suitable BTK inhibitors contemplated for use in the present disclosure include, but are not limited to, any of the BTK inhibitors disclosed elsewhere herein. See, for example, the section entitled “BTK inhibitors,” above.

[0205] As used interchangeably herein, in some embodiments, an iteration frequency or dosage frequency refers to a frequency at which the BTK inhibitor is applied or administered to a subject. In some embodiments, the administration of the BTK inhibitor is an actual or candidate (e.g., predicted) administration. In some embodiments, the iteration frequency indicates a number of dosages, or treatments, of the BTK inhibitor for any of the predetermined time periods disclosed above.

[0206] In some embodiments, the predetermined time period is one day, and the candidate iteration frequency is once daily (QD), twice daily (BID) three times daily (TID), or four times daily (QID). In some embodiments, the candidate dosage frequency is more than four times daily.91DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0207] In some embodiments, the candidate iteration frequency is at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 20. or at least 50 times within a predetermined time period. In some embodiments, the candidate iteration frequency is no more than 100, no more than 50, no more than 20, no more than 10, or no more than 5 times within a predetermined time period. In some embodiments, the candidate iteration frequency is from 1 to 5, from 2 to 10, from 5 to 50, or from 20 to 100 times within a predetermined time period. In some embodiments, the candidate iteration frequency falls within another range starting no lower than 1 and ending no higher than 100 times within a predetermined time period.

[0208] Referring to Block 209, in some embodiments, the iteration frequency (e.g., candidate dosage frequency) 136 comprises one or more iteration intervals 138. Each respective iteration interval (e.g, dosage interval) 138 in the one or more iteration intervals comprises a corresponding amount 140 of the unpartitioned quantum.

[0209] In some embodiments, as used interchangeably herein, an iteration interval or dosage interval refers to an interval (e.g., a dosage or treatment interval) within which all or a portion of an unpartitioned quantum of a BTK inhibitor is applied or administered to a subject. In some embodiments, the number of iteration intervals is determined by the iteration frequency.

[0210] For instance, in some embodiments, the iteration frequency is 1, and the one or more iteration intervals consists of 1 iteration interval. In some embodiments, the iteration frequency is 2, and the one or more iteration intervals consists of 2 iteration intervals. In some embodiments, the one or more iteration intervals consists of from 1 to 4 iteration intervals. In some embodiments, the one or more iteration intervals is a plurality of iteration intervals.

[0211] In some embodiments, the one or more iteration intervals comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 20, or at least 50 iteration intervals. In some embodiments, the one or more iteration intervals comprises no more than 100, no more than 50, no more than 20, no more than 10, or no more than 5 iteration intervals. In some embodiments, the one or more iteration intervals consists of from 1 to 5, from 2 to 10, from 5 to 50, or from 20 to 100 iteration intervals. In some embodiments, the one or more iteration intervals falls within another range starting no lower than 1 iteration interval and92DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO ending no higher than 100 iteration intervals. Referring to Block 212. in some embodiments, the one or more iteration intervals 138 consists of 2 iteration intervals.

[0212] In some embodiments, a duration of each dosage interval in the one or more dosage intervals is the same. In some embodiments, a duration of a first dosage interval in a plurality of dosage interv als is different from a duration of a second dosage interval in the plurality of dosage interv als. In some embodiments, each respective iteration interv al in a plurality of iteration intervals spans the same amount of time, such that the plurality of iteration intervals are equally spaced. For example, in some embodiments, two intervals in a time period of 24 hours are spaced 12 hours apart. Alternatively or additionally, in some embodiments, two or more iteration intervals in a plurality of iteration intervals span different amounts of time, such that the two or more iteration intervals are not equally spaced. For example, in some embodiments, a first interval in a time period of 24 hours spans 6 hours and a second interval in the time period of 24 hours spans 18 hours, such that the iteration intervals span different amounts of time.

[0213] In some embodiments, the candidate dosage frequency is once daily (QD) and the one or more dosage intervals consists of a single dosage interval having a duration of 24 hours. In some embodiments, the candidate dosage frequency is twice daily (BID) and the one or more dosage intervals consists of two dosage intervals each having a duration of 12 hours. In some embodiments, the candidate dosage frequency is three times daily (TID) and the one or more dosage intervals consists of three dosage intervals each having a duration of eight hours. In some embodiments, the candidate dosage frequency is four times daily (QID) and the one or more dosage intervals consists of four dosage intervals each having a duration of six hours.

[0214] Referring to Block 210, in some embodiments, each iteration interval 138 consists of a 6, 8, 12, or 24-hour period. In some embodiments, the duration of an iteration interval comprises at least 10 minutes, at least 30 minutes, at least 1 hour, at least 2 hours, at least 6 hours, at least 12 hours, at least 24 hours, at least 48 hours, or at least 72 hours. In some embodiments, the duration of an iteration interval comprises no more than 1 week, no more than 72 hours, no more than 24 hours, no more than 12 hours, no more than 6 hours, or no more than 2 hours. In some embodiments, the duration of an iteration interval consists of93DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO from 10 minutes to 2 hours, from 1 hour to 6 hours, from 6 hours to 12 hours, from 12 hours to 24 hours, or from 24 hours to 1 week. In some embodiments, the duration of an iteration interval falls within another range starting no lower than 10 minutes and ending no higher than 1 week.

[0215] In some embodiments, the unpartitioned quantum is divided into a plurality of portions (e.g, amounts), for instance, by dividing the unpartitioned quantum over a plurality of iteration intervals. In some embodiments, a portion or amount of the unpartitioned quantum (e.g, a portion of the total dosage) of the BTK inhibitor refers to an amount of the BTK inhibitor applied or administered to the subject at each iteration interval in the one or more iteration intervals.

[0216] In some embodiments, the sum of each amount of the unpartitioned quantum of the BTK inhibitor (e.g, the sum of the corresponding portion of the total dosage of the BTK inhibitor) for each respective iteration interval in the one or more iteration intervals is equal to the unpartitioned quantum (e.g., the total dosage) of the BTK inhibitor. In some such embodiments, the unpartitioned quantum (e.g, total dosage) of the BTK inhibitor is completely administered across the one or more iteration intervals. In some embodiments, the sum of each amount of the unpartitioned quantum of the BTK inhibitor for each respective iteration interval in the one or more iteration intervals is less than the unpartitioned quantum of the BTK inhibitor. In some such embodiments, the unpartitioned quantum (e.g, the total dosage) of the BTK inhibitor is not completely administered across the one or more iteration intervals.

[0217] Alternatively or additionally, referring to Block 214, in some embodiments, the corresponding amount 140 of the unpartitioned quantum 134 of each respective iteration interval 138 in the one or more iteration intervals is the same. In some embodiments, the unpartitioned quantum (e.g., total dosage) of the BTK inhibitor is equally divided across the one or more iteration intervals. For instance, in some embodiments, a total dosage amount of 300 mg is divided into two equal portions of 150 mg, across two iteration intervals.

[0218] Referring to Block 216, in some embodiments, the corresponding amount 140 of the unpartitioned quantum 134 for a first respective iteration interval 138 in the one or more iteration intervals is different from the corresponding amount 140 of the unpartitioned94DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO quantum 134 for a second respective iteration interval 138 in the one or more iteration intervals. In some embodiments, the unpartitioned quantum (e.g., total dosage) of the BTK inhibitor is not equally divided across the one or more iteration intervals. For instance, in some embodiments, the candidate dosage frequency comprises one or more dosage intervals, wherein each respective dosage interval in the one or more dosage intervals comprises a corresponding portion of the candidate total dosage for the BTK inhibitor.

[0219] In some embodiments, the candidate dosage frequency is at least twice daily, the one or more dosage intervals comprises at least two dosage intervals, and for each respective dosage interval in the at least two dosage intervals, the corresponding portion of the candidate total dosage for the BTK inhibitor is the same. In some embodiments, the candidate dosage frequency is at least twice daily, the one or more dosage intervals comprises at least two dosage interv als, and a first portion of the candidate total dosage corresponding to a first dosage interval is different from a second portion of the candidate total dosage corresponding to a second dosage interval, in the at least two dosage intervals.

[0220] In some embodiments, the predetermined time period for administration, iteration frequency, number of iteration intervals, and / or amounts of the unpartitioned quantum (e.g., total dosage) of the BTK inhibitor will vary depending on the selection of any one or more of the above factors, as will be apparent to one skilled in the art. For instance, in some embodiments, an iteration frequency may result in shorter intervals if the predetermined time period of administration is shorter (e.g, 2 intervals of 6 hours each in a 12 hour period), whereas a same iteration frequency may result in longer intervals when the predetermined time period of administration is longer (e.g, 2 intervals of 12 hours each in a 24 hour period). In some embodiments, the predetermined time period of administration, iteration frequency, number of iteration intervals, and / or amounts of the unpartitioned quantum (e.g., total dosage) of the BTK inhibitor comprises any of the methods and / or embodiments disclosed elsewhere herein, for instance, in the section entitled “Dosages and dosing regimens,” below.

[0221] In some embodiments, method 200 includes inputting, to the model, one or more candidate total dosages and / or one or more candidate frequencies. In some embodiments, for each respective candidate unpartitioned quantum in a plurality of candidate unpartitioned quanta, for each respective candidate iteration frequency in a plurality of candidate iteration95DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO frequencies, the model 120 generates one or more target endpoints, based upon the respective candidate unpartitioned quantum and the respective candidate iteration frequency.

[0222] In some embodiments, the plurality of candidate unpartitioned quanta comprises at least 2, at least 3, at least 4, at least 5, at least 10, at least 20, or at least 50 unpartitioned quanta. In some embodiments, the plurality of candidate unpartitioned quanta comprises no more than 100. no more than 50. no more than 20, no more than 10, or no more than 5 unpartitioned quanta. In some embodiments, the plurality of candidate unpartitioned quanta consists of from 2 to 10, from 5 to 50, or from 20 to 100 unpartitioned quanta. In some embodiments, the plurality' of candidate unpartitioned quanta falls within another range starting no lower than 2 unpartitioned quanta and ending no higher than 100 unpartitioned quanta. In some embodiments, the plurality of candidate iteration frequencies comprises at least 2, at least 3, at least 4, at least 5, at least 10, at least 20, or at least 50 iteration frequencies. In some embodiments, the plurality' of candidate iteration frequencies comprises no more than 100, no more than 50, no more than 20, no more than 10, or no more than 5 iteration frequencies. In some embodiments, the plurality of candidate iteration frequencies consists of from 2 to 10, from 5 to 50, or from 20 to 100 iteration frequencies. In some embodiments, the plurality7of candidate iteration frequencies falls within another range starting no lower than 2 iteration frequencies and ending no higher than 100 iteration frequencies.

[0223] Methods for inputting to the model one or more candidate total dosages and / or one or more candidate frequencies contemplated for use in the present disclosure are described in greater detail elsewhere herein, for instance, in the sections entitled ‘‘Candidate dosages,” above, and “Dosage regimens,” below.

[0224] Covariates and cotreatments.

[0225] In some embodiments, systems and methods disclosed herein further include inputting, to the model, one or more covariates and / or cotreatments for use in determining target endpoint predictions.96DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0226] In some embodiments, the model further receives, as input, one or more of subject age, body weight, sex, race, H. pylori status, ethnicity, and / or food status (e.g., fed. unfed, low-fat, and / or high-fat).

[0227] Additionally or alternatively, in some embodiments, the model further receives, as input, one or more of subject cotreatments with a concomitant medication. In some embodiments, the concomitant medication is omeprazole and / or famotidine. In some embodiments, non-limiting concomitant medications contemplated for use in the present disclosure are disclosed elsewhere herein, for example, in the section entitled “Control compositions,’' below.

[0228] Alternatively or additionally, in some embodiments, the model further receives, as input, information for one or more subject kinetics. In some embodiments, subject kinetics include, but are not limited to, BTK resynthesis rate.

[0229] In some embodiments, systems and methods disclosed herein further include inputting, to the model, any of the co variates and / or cotreatments disclosed elsewhere herein, for example, in the section entitled “Obtaining models,” below.

[0230] Generating target endpoints.

[0231] Referring again to Block 208, in some embodiments, methods and systems disclosed herein further include generating, as output, one or more target endpoints 126 or a representation thereof, thereby generating a prediction of the one or more target endpoints in a subject 122 responsive to a candidate (e.g., predicted) administration with the BTK inhibitor 132. In some embodiments, the model 120 generates one or more target endpoints, based upon the candidate unpartitioned quantum and the candidate iteration frequency.

[0232] As described above, in some embodiments, the model predicts, as one or more target endpoints, a BTK occupancy and duration thereof.

[0233] In some embodiments, the BTK occupancy refers to an amount, ratio, proportion, or percentage of target BTK that is occupied by BTK inhibitor binding.97DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0234] In some embodiments, the BTK occupancy duration is determined as a proportion of an iteration (e.g, dosage) interval during which the predicted value for the BTK occupancy satisfies a threshold BTK occupancy.

[0235] For instance, as illustrated in FIG. 13, in some embodiments, an iteration interval consists of 12 hours, the threshold BTK occupancy is 90%, and the occupancy duration is the amount of time that the BTK occupancy exceeds 90% during the 12-hour iteration interval. In some embodiments, an iteration interval consists of 12 hours, the threshold BTK occupancy is 95%, and the occupancy duration is the amount of time that the BTK occupancy exceeds 95% during the 12-hour iteration interval. In some embodiments, an iteration interval consists of 24 hours, the threshold BTK occupancy is 90%, and the occupancy duration is the amount of time that the BTK occupancy exceeds 90% during the 24-hour iteration interval. In some embodiments, an iteration interval consists of 24 hours, the threshold BTK occupancy is 95%, and the occupancy duration is the amount of time that the BTK occupancy exceeds 95% during the 24-hour iteration interval.

[0236] In some embodiments, the one or more target endpoints further comprises a BTK inhibitor concentration. In some embodiments, a predicted value for the BTK inhibitor concentration comprises a maximum concentration. In some embodiments, a predicted value for the BTK inhibitor concentration comprises a minimum concentration.

[0237] In some embodiments, a target endpoint comprises any desired biological or clinical endpoint, as will be apparent to one skilled in the art. In some embodiments, the one or more target endpoints is selected from the group consisting of: BTK occupancy, duration of BTK occupancy above a threshold occupancy within a given iteration interval (e.g. , Dur90, Dur95), duration of BTK occupancy > 90% within the 12 hours between doses at steady state (e.g, Dur90BID), duration of BTK occupancy > 95% within the 12 hours between doses at steady state (e.g., Dur95BID), duration of BTK occupancy > 90% within the 24 hours between doses at steady state (e.g, Dur90QD), duration of BTK occupancy > 95% within the 24 hours between doses at steady state (e.g., Dur95QD), maximum concentration (e.g, Cmax) (e.g. maximum plasma concentration), concentration prior to BTK inhibitor administration (e.g, Ctrough), time at maximum plasma concentration (e.g.98DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOTmax), and / or %BTK occupancy before the next BTK inhibitor administration at the end of an iteration interval (%BTK Occupancy at Trough).

[0238] In some embodiments, a target endpoint, or a representation thereof, comprises an absolute value, a relative value, a percentage, a graph, and / or a time course.

[0239] In some embodiments, methods and systems disclosed herein further include using the model to predict the BTK occupancy at one or more candidate timepoints in a plurality of candidate timepoints, responsive to a candidate treatment with the candidate total dosage for the BTK inhibitor at the candidate dosage frequency. In some embodiments, the plurality of candidate timepoints comprises at least 2, at least 3, at least 5, at least 10, at least 20, at least 50, at least 100, or at least 500 timepoints. In some embodiments, the plurality of candidate timepoints comprises no more than 1000, no more than 100, no more than 50, or no more than 10 timepoints. In some embodiments, the plurality of candidate timepoints consists of from 2 to 10, from 5 to 50, from 10 to 200, or from 100 to 1000 timepoints. In some embodiments, the plurality of candidate timepoints falls within another range starting no lower than 2 timepoints and ending no higher than 1000 timepoints.

[0240] In some embodiments, the plurality of candidate timepoints comprises BTK occupancy at peak and BTK occupancy at trough.

[0241] In some embodiments, methods and systems disclosed herein further include using the model to predict a time course of the BTK occupancy across the plurality of candidate timepoints. For instance, FIG. 13 illustrates an example of target endpoints BTK inhibitor concentration and BTK occupancy across a plurality' of timepoints.

[0242] Filtering criteria.

[0243] In some embodiments, methods and systems disclosed herein further include using the model to determine an iteration frequency7and an unpartitioned quantum that satisfy a filtering criterion.

[0244] In some embodiments, the iteration frequency and unpartitioned quantum are determined by: inputting, into the model, a candidate unpartitioned quantum (e.g, candidate total dosage) for the BTK inhibitor and a candidate iteration frequency (e.g, candidate99DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO dosage frequency); obtaining, from the model, a prediction of one or more target endpoints, or representations thereof, for the candidate unpartitioned quantum and the candidate iteration frequency; and comparing the filtering criterion to the one or more predicted target endpoints. In some embodiments, the one or more target endpoints includes a predicted BTK occupancy and a predicted BTK occupancy duration, and the filtering criterion is a threshold BTK occupancy and a threshold occupancy duration. In some embodiments, the method further includes determining, after the comparing, that the iteration frequency and the unpartitioned quantum satisfy the filtering criterion.

[0245] Referring again to Block 209, for instance, in some embodiments, systems and methods disclosed herein further include generating, using the model 120, an iteration frequency 136 and an unpartitioned quantum (e.g, a total dosage) 134 for the BTK inhibitor 132 based upon a filtering criterion (e.g, a selection criterion) for the target endpoint 126. The iteration frequency (e.g., candidate dosage frequency) 136 comprises one or more iteration intervals 138. Each respective iteration interval (e.g., dosage interval) 138 in the one or more iteration intervals comprises a corresponding amount 140 of the unpartitioned quantum. The filtering criterion comprises at least a first filter 152 comprising, for each respective iteration interval 138 in the one or more iteration intervals, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy 154 for at least a threshold duration 156.

[0246] In some embodiments, the iteration frequency and unpartitioned quantum satisfying the filtering criterion are used to obtain an administration regimen (e.g, a dosage regimen) for the BTK inhibitor.

[0247] As used interchangeably herein, in some embodiments, a filtering criterion or selection criterion includes one or more filters for any of the target endpoints disclosed elsewhere herein, for example, in the section entitled “Generating target endpoints,” above.

[0248] In some embodiments, the one or more filters includes, but is not limited to, a threshold BTK occupancy, threshold duration of BTK occupancy above a threshold occupancy within a given iteration interval (e.g, Dur90, Dur95), threshold duration of BTK occupancy > 90% within the 12 hours between doses at steady state (e.g., Dur90BID), threshold duration of BTK occupancy > 95% within the 12 hours between doses at steady100DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO state (e.g., Dur95BID), threshold duration of BTK occupancy > 90% within the 24 hours between doses at steady state (e.g, Dur90QD), threshold duration of BTK occupancy > 95% within the 24 hours between doses at steady state (e.g, Dur95QD), threshold maximum concentration (e.g., Cmax) (e.g., maximum plasma concentration), threshold concentration prior to BTK inhibitor administration (e.g., Ctrough), threshold time at maximum plasma concentration (e.g, Tmax), and / or threshold %BTK occupancy before the next BTK inhibitor administration at the end of an iteration interval (%BTK Occupancy at Trough).

[0249] In some embodiments, the filtering criterion comprises at least 1, at least 2, at least 3, at least 4, at least 5, or at least 10 filters. In some embodiments, the filtering criterion comprises no more than 20, no more than 10, or no more than 5 filters. In some embodiments, the filtering criterion consists of from 1 to 5, from, 2 to 10, or from 5 to 20 filters. In some embodiments, the filtering criterion falls within another range starting no lower than 1 filter and ending no higher than 20 filters.

[0250] Referring to Block 218, in some embodiments, the threshold occupancy 154 is at least 50% and the threshold occupancy duration 156 comprises at least 40% of an iteration interval 138 in the one or more iteration intervals.

[0251] In some embodiments, the threshold occupancy is at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%. In some embodiments, the threshold occupancy is no more than 100%, no more than 95%, no more than 90%, no more than 80%, no more than 70%, no more than 60%, no more than 50%, or no more than 40%. In some embodiments, the threshold occupancy is from 30% to 50%, from 40% to 80%, from 60% to 95%, from 90% to 95%, or from 50% to 100%. In some embodiments, the threshold occupancy falls within another range starting no lower than 30% and ending no higher than 100%.

[0252] Alternatively or additionally, in some embodiments, the threshold duration is at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95% of each iteration interval in the one or more iteration intervals. In some embodiments, the threshold duration is no more than 100%, no more than 95%, no more than 90%, no more than 80%, no more than 70%, no more than 60%, no more than 50%, or no more than 40% of each iteration interval in the one or more iteration intervals. In some101DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO embodiments, the threshold duration is from 30% to 50%, from 40% to 80%, from 60% to 95%, from 90% to 95%, or from 50% to 100% of each iteration interval in the one or more iteration intervals. In some embodiments, the threshold duration falls within another range starting no lower than 30% and ending no higher than 100% of each iteration interval in the one or more iteration intervals.

[0253] In some embodiments, the threshold occupancy 154 is at least 60%. at least 70%. or at least 80%, and the threshold occupancy duration 156 comprises at least 40% of an iteration interval 138 in the one or more iteration intervals. In some embodiments, the threshold occupancy 154 is at least 90% or at least 95%, and the threshold occupancy duration 156 comprises at least 50% of an iteration interval 138 in the one or more iteration intervals.

[0254] In some embodiments, the predicted values for the one or more target endpoints satisfy the selection criterion in accordance with a determination that the predicted value for the BTK occupancy is greater than or equal to the threshold BTK occupancy, and / or the predicted value for the occupancy duration is greater than or equal to the threshold occupancy duration.

[0255] In some embodiments, the predicted values for the one or more target endpoints satisfy the selection criterion in accordance with a determination that the predicted value for the BTK occupancy is less than or equal to the threshold BTK occupancy, and / or the predicted value for the occupancy duration is less than or equal to the threshold occupancy duration.

[0256] Dosage regimen.

[0257] Refernng to Block 222, in some embodiments, methods and systems disclosed herein further include determining, in accordance with a determination that the iteration frequency 136 and the unpartitioned quantum (e.g., total dosage of the BTK inhibitor) 134 satisfy the fdtering criterion, an application regimen (e.g. a dosage regimen) 162 that includes the iteration frequency 136 and the corresponding amount 140 of the unpartitioned quantum (e.g., total dosage) 134 for each respective iteration interval 138 in the one or more iteration intervals.102DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0258] As used interchangeably herein, in some embodiments, an application regimen or dosage regimen is a treatment regimen for administering the BTK inhibitor to the subject, including a total dosage of the BTK inhibitor to be administered and a frequency at which all or portions of the total dosage BTK inhibitor is administered.

[0259] In some embodiments, the method includes using the model to determine whether a candidate total dosage amount for the BTK inhibitor and a candidate dosage frequency that satisfies a selection criterion. In some such embodiments, the method further includes selecting, as a dosage regimen, the total dosage amount and dosage frequency that satisfies the selection criterion. In some embodiments, the candidate total dosage amount for the BTK inhibitor is administered daily, every two days, every three days, every four days, every five days, every six days, or weekly.

[0260] Without being limited to any one theory of operation, in some embodiments, a candidate dosage regimen including a candidate total dosage and a candidate dosage frequency that satisfies a particular threshold is selected as being effective for treating the BTK-mediated condition. In some embodiments, the total dosage of the BTK inhibitor, administered at the iteration frequency, is considered to be therapeutically effective on the basis that the total dosage and the iteration frequency achieves one or more target endpoints that satisfy at least the filtering criterion. In some embodiments, a total dosage of the BTK inhibitor and a dosage frequency for administration of the BTK inhibitor is selected as a dosage regimen when the model generates a prediction that a predicted BTK occupancy and a predicted occupancy duration satisfies at least a threshold BTK occupancy and a threshold occupancy duration. In some embodiments, a dosage regimen is selected from one or more candidate regimens on the basis that a total dosage of the BTK inhibitor and a dosage frequency for administration of the BTK inhibitor in a respective candidate regimen satisfies at least a threshold BTK occupancy and a threshold occupancy duration.

[0261] In some embodiments, methods and systems disclosed herein further include inputting to the model a plurality of candidate regimens, each respective candidate regimen in the plurality of candidate regimens comprising a respective candidate total dosage for the BTK inhibitor and a respective candidate dosage frequency; using the model to obtain, as output, for each respective candidate regimen, the corresponding predicted values for the one103DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO or more target endpoints responsive to the respective candidate total dosage and the respective candidate dosage frequency corresponding to the respective candidate regimen; and selecting, from the plurality of candidate regimens, one or more candidate regimens that satisfy the selection criterion.

[0262] In some embodiments, the plurality' of candidate regimens comprises at least 2, at least 3, at least 5, at least 10. or at least 20 candidate regimens. In some embodiments, the plurality of candidate regimens comprises no more than 30, no more than 20, no more than 10, or no more than 5 fdters. In some embodiments, the plurality of candidate regimens consists of from 2 to 5, from, 2 to 10, or from 5 to 30 candidate regimens. In some embodiments, the plurality of candidate regimens falls within another range starting no lower than 2 candidate regimens and ending no higher than 30 candidate regimens.

[0263] In some embodiments, a first candidate regimen and a second candidate regimen in the plurality7of candidate regimens comprises the same corresponding candidate total dosage and different candidate dosage frequencies. In some embodiments, a first candidate regimen and a second candidate regimen in the plurality of candidate regimens comprises the same candidate dosage frequency and different corresponding candidate total dosages. In some embodiments, a first candidate regimen and a second candidate regimen in the plurality7of candidate regimens comprises different candidate total dosages and different candidate dosage frequencies. In some embodiments, the plurality of candidate regimens comprises any of the methods and / or embodiments disclosed elsew here herein, for instance, in the section entitled “Dosages and dosing regimens,” below.

[0264] In some embodiments, referring to Block 224, in some embodiments, the application regimen (e.g, dosage regimen) 162 comprises administering the BTK inhibitor 132 to the subject 122 at the iteration frequency 136 such that the unpartitioned quantum (e.g., total dosage) 134 of the BTK inhibitor 132 is administered to the subject 122 over the one or more iteration intervals 138. In some embodiments, systems and methods disclosed herein further include administering the BTK inhibitor to a test subject using the dosage regimen such that the test subject is administered with the total dosage for the BTK inhibitor over a number of dosages determined by the dosage frequency.104DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0265] Referring to Block 226, in some embodiments, the application regimen (e.g., dosage regimen) 162 further comprises repeating the administering over a treatment period. In some embodiments, the treatment period comprises at least 1 week, at least two weeks, at least three weeks, at least four weeks, at least one month, at least two months, at least three months, at least four months, at least five months, at least six months, at least seven months, at least eight months, at least nine months, at least ten months, at least eleven months, at least one year, at least two years, or at least three years. In some embodiments, the treatment period comprises no more than 5 years, no more than 1 year, no more than 6 months, no more than 1 month, or no more than 1 week. In some embodiments, the treatment period consists of from 2 days to 1 week, from 1 week to 1 month, from 1 month to 1 year, or from 1 year to 5 years. In some embodiments, the treatment period falls within another ranges starting no lower than 2 days and ending no higher than 5 years.

[0266] In some embodiments, methods and systems disclosed herein further include administering the BTK inhibitor to a test subject using the dosage regimen, wherein the BTK inhibitor is administered with food. In some embodiments, methods and systems disclosed herein further include administering the BTK inhibitor to a test subject using the dosage regimen, wherein the BTK inhibitor is administered without food. In some embodiments, methods and systems disclosed herein further include administering the BTK inhibitor to a test subject using the dosage regimen, wherein the BTK inhibitor is administered with concomitant medication (e.g., any of the concomitant medications disclosed elsewhere herein, see, e.g., "Covariates and cotreatments,” above). In some embodiments, methods and systems disclosed herein further include administering the BTK inhibitor to a test subject using the dosage regimen, wherein the BTK inhibitor is administered without concomitant medication.

[0267] Obtaining models.

[0268] In some embodiments, the one or more target endpoints are determined based on measurements from subject data (e.g., clinical trial data). In some embodiments, the one or more target endpoints are determined using a predictive model (e.g., a PK / PD model). In some embodiments, the predictive model is generated using subject data, such as from a105DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO clinical trial. In some embodiments, the one or more target endpoints are determined using a machine learning model.

[0269] Referring to Block 219, in some embodiments, the model 120 is obtained using one or more measurements of the target endpoint 126 (<?.g., the BTK occupancy and duration thereof) in the subject 122. In some embodiments, methods and systems disclosed herein further include obtaining, responsive to inputting an iteration frequency 136 and an unpartitioned quantum (e.g., a total dosage) 134 of the BTK inhibitor 132 to the model 120, as output from the model, one or more simulated values for the target endpoint 126 in the subject 122.

[0270] In some embodiments, the measurements of one or more target endpoints are obtained by measuring metrics for the one or more target endpoints in a patient. In some embodiments, the measurements are obtained over a plurality of timepoints. In some embodiments, the measurements are used to extrapolate output values for one or more predicted target endpoints, and / or to update treatment parameters for a predictive model.

[0271] In some embodiments, the model is a predictive model that is fitted to subject data, such as clinical trial data. Referring to Block 220, in some embodiments, obtaining the model further includes training the model 120 using, for each respective training subject in a plurality of training subjects, a corresponding one or more measurements of the target endpoint (e.g., the BTK occupancy and duration thereof) f26 in the respective training subject. In some such embodiments, the model generates responsive to inputting the iteration frequency 136 and an unpartitioned quantum (e.g., a total dosage) 134 of the BTK inhibitor 132 to the model 120, as output from the model, one or more simulated values for the target endpoint 126 in the subject 122.

[0272] In some embodiments, methods and systems disclosed herein further include generating the model by fitting an untrained or partially trained model to a training dataset comprising, for each respective training subject in the plurality of training subjects, (i) a corresponding total dosage for the BTK inhibitor, (ii) a corresponding dosage frequency, and (iii) corresponding training values for the one or more target endpoints responsive to treatment with the BTK inhibitor at the corresponding total dosage and the corresponding dosage frequency.106DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0273] In some embodiments, training subject data is obtained by administering the BTK inhibitor to each respective training subject in a plurality of training subjects, where the administering is performed using a corresponding total dosage for the BTK inhibitor and a corresponding dosage frequency; and obtaining, for each respective training subject in the plurality of training subjects, corresponding training values for one or more target endpoints responsive to treatment with the BTK inhibitor at a corresponding total dosage and a corresponding dosage frequency, where the one or more target endpoints comprises a BTK occupancy and an occupancy duration.

[0274] In some embodiments, the plurality7of training subjects comprises at least 10 training subjects. In some embodiments, the plurality of training subjects comprises at least 20 training subjects. In some embodiments, the plurality of training subjects comprises at least 50, at least 100, or at least 200 training subjects. In some embodiments, the plurality of training subjects comprises at least 10, at least 20, at least 50, at least 100, at least 200, at least 500, at least 1000, at least 2000, at least 5000, or at least 10,000 training subjects. In some embodiments, the plurality of training subjects comprises no more than 100,000, no more than 10,000, no more than 5000, no more than 2000, no more than 1000, no more than 500, no more than 100, or no more than 50 training subjects. In some embodiments, the plurality7of training subjects consists of from 10 to 100, from 80 to 500, from 200 to 1000, from 1000 to 10,000, or from 10,000 to 100,000 training subjects. In some embodiments, the plurality of training subjects falls within another range starting no lower than 10 training subjects and ending no higher than 100,000 training subjects.

[0275] In some embodiments, the plurality7of training subjects comprises subjects that are enrolled in a clinical trial. In some embodiments, the plurality of training subjects comprises one or more subjects in an experimental arm for administration of a BTK inhibitor. In some embodiments, the plurality7of training subjects comprises one or more subjects in a control arm for administration of a control composition and / or a placebo. In some embodiments, each respective training subject in the plurality of training subjects is a healthy subject. In some embodiments, a respective training subject in the plurality of training subjects is a healthy subject or has a BTK-mediated condition. Control compositions contemplated for use in the present disclosure are disclosed elsewhere herein, for example, in the section entitled “Control compositions,” below.107DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0276] In some embodiments, for a respective training subject in the plurality of training subjects, the corresponding total dosage for the BTK inhibitor is at least 20 mg, at least 50 mg, at least 100 mg, at least 200 mg, at least 300 mg, or at least 400 mg.

[0277] In some embodiments, the corresponding total dosage comprises at least 5 mg, at least 10 mg, at least 20 mg, at least 50 mg, at least 100 mg, at least 200 mg, at least 300 mg, at least 400 mg, at least 500 mg, or at least 1000 mg. In some embodiments, the corresponding total dosage comprises no more than 5000 mg, no more than 1000 mg, no more than 600 mg, no more than 500 mg, no more than 400 mg, no more than 300 mg, no more than 200 mg, no more than 100 mg, no more than 50 mg, or no more than 10 mg. In some embodiments, the corresponding total dosage consists of from 5 mg to 50 mg, from 10 mg to 200 mg, from 100 to 500 mg, from 500 mg to 1000 mg, from 20 mg to 1000 mg, or from 1000 mg to 5000 mg. In some embodiments, the corresponding total dosage falls within another range starting no lower than 5 mg and ending no higher than 5000 mg. In some embodiments, the total dosage amount for the BTK inhibitor is administered daily, every two days, every three days, every four days, every five days, every six days, or weekly.

[0278] In some embodiments, for a respective training subject in the plurality of training subjects, the corresponding dosage frequency is at least once daily, at least twice daily, at least three times daily, or at least four times daily.

[0279] In some embodiments, for each respective training subject in the plurality of training subjects, the corresponding training values for the one or more target endpoints responsive to treatment with the BTK inhibitor are obtained from a biological sample of the respective training subject. In some embodiments, the biological sample comprises peripheral blood mononuclear cells. In some embodiments, the biological sample comprises cells, tissue, blood, whole blood, plasma, serum, urine, cerebrospinal fluid, fecal, saliva, sweat, tears, pleural fluid, pericardial fluid, or peritoneal fluid. In some embodiments, the biological sample is obtained at one or more timepoints in a plurality' of timepoints prior, during, or after treatment with the BTK inhibitor.

[0280] In some embodiments, the plurality of training subjects comprises a first subset of training subjects, and, for each respective training subject in the first subset of training subjects, the treatment with the BTK inhibitor is administered with food. In some108DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO embodiments, the plurality of training subjects comprises a third subset of training subjects, and, for each respective training subject in the third subset of training subjects, the treatment with the BTK inhibitor is administered without food. In some embodiments, the lurality of training subjects comprises a second subset of training subjects, and, for each respective training subject in the second subset of training subjects, the treatment with the BTK inhibitor is administered with a concomitant medication. In some embodiments, the plurali ty of training subjects comprises a fourth subset of training subjects, and, for each respective training subject in the fourth subset of training subjects, the treatment with the BTK inhibitor is administered without concomitant medication. In some embodiments, the concomitant medication is omeprazole. In some embodiments, the concomitant medication is famotidine.

[0281] In some embodiments, the plurality of parameters further reflects, for each respective training subject in the plurality of training subjects, a corresponding BTK resynthesis rate. In some embodiments, the corresponding BTK resynthesis rate is determined based on a BTK half-life value and an inter-individual variability estimate for the plurality of training subjects. In some embodiments, the BTK half-life value is between 40 and 70 hours. In some embodiments, the plurality of parameters further reflects, for each respective training subject in the plurality of training subjects, any of the covariates disclosed herein (see, e.g., the section entitled “Covariates and cotreatments,'’ above).

[0282] For instance, in some embodiments, methods and systems disclosed herein further include administering the BTK inhibitor to each respective training subject in the plurality of training subjects, where: for each respective training subject in a first subset of the plurality of training subjects, the BTK inhibitor is administered with food, the corresponding total dosage for the BTK inhibitor is 300 mg, and the corresponding dosage frequency is once daily, for each respective training subject in a second subset of the plurality’ of training subjects, the BTK inhibitor is administered with a concomitant medication, the corresponding total dosage for the BTK inhibitor is 150 mg, and the corresponding dosage frequency is once daily, and for each respective training subject in a third subset of the plurality of training subjects, the BTK inhibitor is administered without concomitant mediation, the corresponding total dosage for the BTK inhibitor is 150 mg, and the corresponding dosage frequency is once daily. In some embodiments, the concomitant mediation is omeprazole, further comprising administering the concomitant mediation at 40 mg once daily. In some109DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO embodiments, the concomitant medication is famotidine, further comprising administering the concomitant medication at 20 mg twice daily.

[0283] Control compositions.

[0284] In some embodiments, the control composition comprises dasatinib, bosutinib, midostaurin, nilotinib, avapritinib, bezuclastinib, or a pharmacologically acceptable salt thereof. In some embodiments, the control composition comprises a BTK inhibitor selected from the group consisting of ibrutinib, acalabrutinib, remibrutinib, zanubrutinib, rilzabrutinib, tirabrutinib and a pharmacologically acceptable salt thereof. In some embodiments, the control composition comprises a KIT inhibitor selected from the group consisting of regorafenib, sorafenib, imatinib, ilorasertib, sunitinib. pazopanib, lenvatinib, dasatinib. elenestinib, BLU-808, masitinib, barzolvolimab, briquilimab, and a pharmacologically acceptable salt thereof. In some embodiments, the control composition comprises a KIT inhibitor selected from the group consisting of regorafenib, sorafenib, imatinib, ilorasertib, sunitinib. pazopanib, lenvatinib, dasatinib, bezuclastinib, exarafenib, nintedanib, telatinib, avapritinib, TPX-0022, crenolanib, midostaurin, nilotinib, and pharmaceutically acceptable salts thereof. In some embodiments, the control composition comprises a SYK inhibitor selected from the group consisting of fostamatinib, entospletinib, cerdulatinib, TAK-659, and a pharmacologically acceptable salt thereof. In some embodiments, the control composition comprises a PKC inhibitor selected from the group consisting of bisindolylmal eimides, staurosporine, midostaurin, UCN-01, sotrastaurin, enzastaurin, ruboxistaurine, tivantinib, enzastaurin, riluzole, balanol, lestaurtinib, stauprimide, CEP-701, Arcyriaflavin A, chelery thrine chloride, bisindolylmaleimids I-XII, and a pharmacologically acceptable salt thereof. In some embodiments, the control composition is a MRGPRX2 inhibitor. In some embodiments, the MRGPRX2 inhibitor is EVO756, QWF, isoliquiritigenin, shikonin, imperatorin, roxithromysin, paeoniflorin, quercetin, genistein, aptamer-X35, or combinations thereof. In some embodiments, the control composition comprises a PI3K inhibitor selected from the group consisting of idelalisib, copanlisib, duvelisib, umbralisib, leniolisib, parsaclisib. zandelisib. eganelisib, linperlisib, nemiralisib, pilaralisib. seletalisib, tenalisib, AZD8186, AZD8835, CAL263, TG100-1 15, ZSTK474, and a pharmacologically acceptable salt thereof. In some embodiments, the control composition for ISM is HT-004 or HT-KIT. In some embodiments, the control composition is an inhibitor of tyrosine kinase activity'. In110DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO some embodiments, the control composition targets a KIT D816V mutation or a KIT D816V- independent signaling molecule. In some embodiments, the KIT D816V-independent signaling molecule is Lyn or BTK. In some embodiments, the control composition targets SYK, PKC, PI3K, PI3K-delta, SIGLEC, or MRGPRX2. In some embodiments, the control composition comprises placebo. In some embodiments, the control composition comprises one or more best supportive care.

[0285] In some cases, the best supportive care includes one or more administration of one or more agents selected from the table below.111DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0286] Dosages and dosing regimens.

[0287] In some embodiments, the amount of a BTK inhibitor or a pharmaceutically acceptable salt thereof administered will be dependent on the human subject being treated, the severity of the disorder or condition, the rate of administration, the disposition of the compounds and the discretion of the prescribing physician. However, without being limited to any one theory' of operation, in some implementations, an effective dosage is in the range of about 0.001 to about 100 mg per kg body weight per day, such as about 1 to about 35 mg / kg / day. in single or divided doses. For a 70 kg human, this would amount to about 0.05 to 7 g / day, such as about 0.05 to about 2.5 g / day. In some instances, dosage levels below the lower limit of the previously mentioned range may be more than adequate, while in other cases still larger doses may be employed without causing any harmful side effect - e.g., by dividing such larger doses into several small doses for administration throughout the day.

[0288] In some embodiments, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered in a single dose. Multiple daily doses are also embodied, for example, twice daily. Typically, such administration will be oral. However, other routes may be used as appropriate.

[0289] In an embodiment, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered in multiple doses. In an embodiment, dosing may be once, twice, three times, or four times per day. In an embodiment, dosing may be selected from the group consisting of once a day. twice a day, three times a day. or four times a day, once every other day, once weekly, twice weekly, three times weekly, four times weekly, biweekly, and monthly. In112DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO other embodiments, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered about once per day to about four times per day. In some embodiments a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered once daily, while in other embodiments a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered twice daily, and in other embodiments a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered three times daily. In some embodiments a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered three times a week, including every Monday, Wednesday, and Friday.

[0290] Administration of a BTK inhibitor or a pharmaceutically acceptable salts thereof may continue as long as necessary. In some embodiments, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered for more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 or more days. In some embodiments, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered for less than 28, 14, 7, 6, 5, 4, 3, 2, or 1 day. In some embodiments, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered for about 14 days, about 21 days, about 28 days, about 35 days, about 42 days, about 49 days, or about 56 days. In some embodiments, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered chronically on an ongoing basis - e.g., for the reducing or alleviating of chronic effects. In another embodiment the administration of a BTK inhibitor or a pharmaceutically acceptable salt thereof continues for less than about 7 days. In yet another embodiment the administration continues for more than about 6, 10, 14, 28 days, two months, three months, four months, five months, six months, seven months, eight months, nine months, ten months, eleven months or one year. In some embodiments, the administration continues for more than about one year, two years, three years, four years, or five years. In some embodiments, continuous dosing is achieved and maintained for as long as necessary.

[0291] In some embodiments, an effective dosage of a BTK inhibitor or a pharmaceutically acceptable salt thereof is in the range of about 1 mg to about 600 mg, about 10 mg to about 500 mg, about 20 mg to about 450 mg, about 25 mg to about 200 mg, about 10 mg to about 200 mg, about 20 mg to about 150 mg, about 30 mg to about 120 mg, about 10 mg to about 90 mg, about 20 mg to about 80 mg, about 30 mg to about 70 mg, about 40 mg to about 60 mg, about 45 mg to about 55 mg, about 48 mg to about 52 mg, about 50 mg113DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO to about 150 mg, about 60 mg to about 140 mg, about 70 mg to about 130 mg, about 80 mg to about 120 mg. about 90 mg to about 110 mg, about 95 mg to about 105 mg, about 150 mg to about 250 mg, about 160 mg to about 240 mg, about 170 mg to about 230 mg, about 180 mg to about 220 mg, about 190 mg to about 210 mg, about 195 mg to about 205 mg, or about 198 to about 202 mg. In some embodiments, an effective dosage of a BTK inhibitor or a pharmaceutically acceptable salt thereof is about 15 mg, about 25 mg, about 30 mg, about 50 mg, about 50 mg, about 75 mg, about 90 mg, about 100 mg, about 120 mg, about 125 mg, about 150 mg, about 175 mg, about 180 mg, about 200 mg, about 225 mg, about 240 mg, about 250 mg, about 275 mg, about 300 mg, about 325 mg, about 350 mg, about 360 mg, about 375 mg, about 400 mg, about 425 mg, about 450 mg, about 475 mg, about 480 mg, or about 500 mg. In some embodiments, an effective dosage of a BTK inhibitor or a pharmaceutically acceptable salt thereof is 15 mg, 25 mg, 30 mg, 50 mg, 60 mg, 75 mg, 90 mg, 100 mg, 120 mg, 150 mg, 175 mg, 180 mg, 200 mg, 225 mg, 240 mg, 250 mg, 275 mg, 300 mg, 325 mg, 350 mg, 360 mg, 375 mg, and 480 mg.

[0292] In some embodiments, an effective dosage of a BTK inhibitor or a pharmaceutically acceptable salt thereof is in the range of about 0.01 mg / kg to about 4.3 mg / kg, about 0. 15 mg / kg to about 3.6 mg / kg, about 0.3 mg / kg to about 3.2 mg / kg, about 0.35 mg / kg to about 2.85 mg / kg, about 0.15 mg / kg to about 2.85 mg / kg, about 0.3 mg to about 2.15 mg / kg, about 0.45 mg / kg to about 1.7 mg / kg, about 0.15 mg / kg to about 1.3 mg / kg. about 0.3 mg / kg to about 1.15 mg / kg, about 0.45 mg / kg to about 1 mg / kg, about 0.55 mg / kg to about 0.85 mg / kg, about 0.65 mg / kg to about 0.8 mg / kg, about 0.7 mg / kg to about 0.75 mg / kg, about 0.7 mg / kg to about 2.15 mg / kg, about 0.85 mg / kg to about 2 mg / kg, about 1 mg / kg to about 1.85 mg / kg, about 1.15 mg / kg to about 1.7 mg / kg, about 1.3 mg / kg mg to about 1.6 mg / kg. about 1.35 mg / kg to about 1.5 mg / kg, about 2. 15 mg / kg to about 3.6 mg / kg, about 2.3 mg / kg to about 3.4 mg / kg, about 2.4 mg / kg to about 3.3 mg / kg, about 2.6 mg / kg to about 3. 15 mg / kg, about 2.7 mg / kg to about 3 mg / kg, about 2.8 mg / kg to about 3 mg / kg, or about 2.85 mg / kg to about 2.95 mg / kg. In some embodiments, an effective dosage of a BTK inhibitor or a pharmaceutically acceptable salt thereof is about 0.35 mg / kg. about 0.7 mg / kg. about 1 mg / kg, about 1.4 mg / kg, about 1.8 mg / kg, about 2.1 mg / kg, about 2.5 mg / kg, about 2.85 mg / kg, about 3.2 mg / kg, or about 3.6 mg / kg.114DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0293] In some embodiments, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered at a dosage of 10 to 500 mg BID, including a dosage of 15 mg, 25 mg, 30 mg, 50 mg, 60 mg, 75 mg, 90 mg, 100 mg, 120 mg, 150 mg, 175 mg, 180 mg, 200 mg, 225 mg, 240 mg, 250 mg, 275 mg, 300 mg, 325 mg, 350 mg, 360 mg, 375 mg, and 480 mg BID.

[0294] In some embodiments, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered at a dosage of 10 to 600 mg QD, including a dosage of 15 mg, 25 mg, 30 mg, 50 mg, 60 mg, 75 mg, 90 mg, 100 mg, 120 mg, 150 mg, 175 mg, 180 mg, 200 mg, 225 mg, 240 mg, 250 mg, 275 mg, 300 mg, 325 mg, 350 mg, 360 mg, 375 mg, and 480 mg QD.

[0295] An effective amount of a BTK inhibitor or a pharmaceutically acceptable salt thereof, in some embodiments, is administered in either single or multiple doses by any of the accepted modes of administration of agents having similar utilities, including buccal, sublingual, and transdermal routes, by intra-arterial injection, intravenously, parenterally, intramuscularly, subcutaneously or orally.

[0296] In some embodiments, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered to a subject intermittently, known as intermittent administration. By “intermittent administration"’ it is meant a period of administration of a therapeutically effective dose of a BTK inhibitor or a pharmaceutically acceptable salt thereof, followed by a time period of discontinuance, which is then followed by another administration period and so on. In each administration period, the dosing frequency can be independently selected from three times daily, twice daily, daily, once weekly, twice weekly, three times weekly, four times weekly, five times weekly, six times weekly or monthly. In an embodiment, the BTK inhibitor is a compound selected from Table 1 or a pharmaceutically acceptable salt thereof.

[0297] By “period of discontinuance” or “discontinuance period” or “rest period,” it is meant to the length of time when discontinuing of the administration of a BTK inhibitor or a pharmaceutically acceptable salt thereof. The time period of discontinuance may be longer or shorter than the administration period or the same as the administration period. During the discontinuance period, other therapeutic agents other than a BTK inhibitor or a115DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO pharmaceutically acceptable salt thereof may be administered. The discontinuance period is, in some embodiments, necessary to alleviate any toxic effects associated with a particular BTK inhibitor compound.

[0298] In an embodiment, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered to a human subject in need thereof for treating ISM and / or for reducing or alleviating a symptom of ISM for a first administration period, then followed by a discontinuance period, then followed by a second administration period, and so on. The first administration period, the second administration period, and the discontinuance period are independently selected from the group consisting of more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24. 25, 26, 27, 28, 29, one month, five weeks, six weeks, seven weeks, two months, nine weeks, ten weeks, eleven weeks, three months, thirteen weeks, fourteen weeks, fifteen weeks, four months, and more days, in which a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered to a subject three times daily, twice daily, daily, once weekly, twice weekly, three times weekly, four times weekly, five times weekly, six times weekly or monthly. In an embodiment, the first administration period is at same length as the second administration period. In an embodiment, the first administration period is shorter than the second administration period. In an embodiment, the first administration period is longer than the second administration period. In an embodiment, the first administration period and the second administration period are about one week, in w hich a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered to a subject daily; and the discontinuance period is about two weeks. In an embodiment, the first administration period and the second administration period are about three weeks, in which a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered to a subject daily; and the discontinuance period is about two weeks. In an embodiment, the first administration period and the second administration period are about three w eeks, in which a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered to a subject w eekly; and the discontinuance period is about two weeks. In an embodiment, the first administration period and the second administration period are about four weeks, in which a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered to a subject daily; and the discontinuance period is about two weeks. In an embodiment, the first administration period and the second administration period are about four weeks, in w hich a BTK inhibitor or a116DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO pharmaceutically acceptable salt thereof is administered to a subject weekly; and the discontinuance period is about two weeks. In an embodiment, the BTK inhibitor is a compound selected from Table 1 or a pharmaceutically acceptable salt thereof.

[0299] In an embodiment, a BTK inhibitor or a pharmaceutically acceptable salt thereof is administered to a subject in need thereof for treating ISM and / or for reducing or alleviating a symptom of ISM for a period selected from 3 weeks, 6 weeks, 9 weeks. 12 weeks, 15 weeks, 18 weeks, 21 weeks, 24 weeks, 27 weeks, 30 weeks, 33 weeks, 36 weeks, 39 weeks, 42 weeks, 45 weeks, 48 weeks, 51 weeks, 54 weeks, 57 weeks, 60 weeks, 63 weeks, 66 weeks, 69 weeks, 72 weeks, 75 weeks, 78 weeks, 81 weeks, 84 weeks, 87 weeks, 90 weeks, 93 weeks, 96 weeks, 99 weeks, 102 weeks, 105 weeks, 108 weeks, 111 weeks, 114 weeks, 117 weeks, 120 weeks, 123 weeks, 126 weeks, 129 weeks, 132 weeks, 135 weeks, 138 weeks, 141 weeks, 144 weeks, 147 weeks, 150 weeks, 153 weeks, and 156 weeks, wherein the BTK inhibitor is selected from any of the compounds in Table 1 or a pharmaceutically acceptable salt thereof. In an embodiment, the BTK inhibitor is orally administered at a dose of 100 mg twice a day.

[0300] Additional Embodiments

[0301] Another aspect of the present disclosure provides a computer system for modeling a target endpoint responsive to treatment with a Bruton's Tyrosine Kinase (BTK) inhibitor, the computer system comprising a memory and a processor, the memory storing a plurality of instructions executable by the processor. In some embodiments, the plurality of instructions includes instructions for obtaining a model of the target endpoint in a subject having a BTK- mediated condition, where the model generates, as output, a representation of the target endpoint in the subject responsive to treatment with the BTK inhibitor. In some embodiments, the instructions further include generating, using the model, an iteration frequency and an unpartitioned quantum of the BTK inhibitor based upon a filtering criterion for the target endpoint, w here the iteration frequency comprises one or more iteration intervals; each respective iteration interval in the one or more iteration intervals comprises a corresponding amount of the unpartitioned quantum of the BTK inhibitor to be administered; and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, a BTK occupancy by the BTK117DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the instructions further include determining an application regimen in accordance with a determination that the iteration frequency and the unpartitioned quantum satisfy the filtering criterion, where the application regimen includes the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding amount of the unpartitioned quantum of the BTK inhibitor.

[0302] Another aspect of the present disclosure provides a non-transitory computer readable storage medium, the non-transitory computer readable storage medium comprising a plurality of instructions, which when executed by a computer system, cause the computer system to perform a method for modeling a target endpoint responsive to treatment with a Bruton’s Tyrosine Kinase (BTK) inhibitor. In some embodiments, the plurality of instructions includes instructions for obtaining a model of the target endpoint in a subject having a BTK-mediated condition, where the model generates, as output, a representation of the target endpoint in the subject responsive to treatment with the BTK inhibitor. In some embodiments, the instructions further include generating, using the model, an iteration frequency and an unpartitioned quantum of the BTK inhibitor based upon a filtering criterion for the target endpoint, where: the iteration frequency comprises one or more iteration intervals; each respective iteration interval in the one or more iteration intervals comprises a corresponding amount of the unpartitioned quantum of the BTK inhibitor to be administered; and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the instructions further include determining an application regimen in accordance with a determination that the iteration frequency and the unpartitioned quantum of the BTK inhibitor satisfy the filtering criterion, where the application regimen includes the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding amount of the unpartitioned quantum of the BTK inhibitor.118DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0303] Another aspect of the present disclosure provides a computer system for selecting a dosage regimen for treating a Bruton's Tyrosine Kinase (BTK)-mediated condition using a BTK inhibitor, the computer system comprising a memory and a processor, the memory storing a plurality of instructions executable by the processor. In some embodiments, the plurality7of instructions include instructions for obtaining a model for predicting a BTK occupancy and duration thereof. In some embodiments, the instructions further include generating, using the model, an iteration frequency and a total amount of the BTK inhibitor for administration based upon a filtering criterion, where: the iteration frequency comprises one or more iteration intervals; each respective iteration interval in the one or more iteration intervals comprises a corresponding portion of the BTK inhibitor to be administered; and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, the BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the instructions further include selecting a dosage regimen in accordance with a determination that the iteration frequency and the total amount of the BTK inhibitor satisfy the filtering criterion, where the dosage regimen includes the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding portion of the BTK inhibitor.

[0304] In some embodiments, the instructions further include obtaining, responsive to inputting the iteration frequency and the total amount of the BTK inhibitor to the model, as output from the model, one or more simulated values for the BTK occupancy. In some embodiments, the instructions further include training the model using, for each respective training subject in a plurality of training subjects, a corresponding one or more measurements of the BTK occupancy in the respective training subject, and obtaining, responsive to inputting the iteration frequency and the total amount of the BTK inhibitor to the model, as output from the model, one or more simulated values for the BTK occupancy.

[0305] In some embodiments, each iteration interval consists of a 6, 8, 12, or 24-hour period. In some embodiments, the one or more iteration intervals consists of from 1 to 4 iteration intervals. In some embodiments, the one or more iteration intervals consists of 2 iteration inter als.119DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0306] In some embodiments, the corresponding portion of the BTK inhibitor of each respective iteration interval in the one or more iteration intervals is the same. In some embodiments, the corresponding portion of the BTK inhibitor for a first respective iteration interval in the one or more iteration intervals is different from the corresponding portion of the BTK inhibitor for a second respective iteration interval in the one or more iteration intervals.

[0307] In some embodiments, the threshold occupancy is at least 60%, at least 80%, or at least 90%. In some embodiments, the threshold occupancy is from 50% to 100%. In some embodiments, the threshold duration comprises at least 50%, at least 60%, at least 70%, at least 80%, at 90%, or at least 95% of each iteration interval in the one or more iteration intervals. In some embodiments, the threshold duration consists of from 50% to 100% of each iteration interval in the one or more iteration intervals.

[0308] In some embodiments, the dosage regimen comprises administering the BTK inhibitor to a subject at the iteration frequency such that the total amount of the BTK inhibitor is fully administered to the subject over the one or more iteration intervals. In some embodiments, the dosage regimen further comprises repeating the administering over a treatment period. In some embodiments, the treatment period comprises at least 1 week, at least two weeks, at least three weeks, at least four weeks, at least one month, at least two months, at least three months, at least four months, at least five months, at least six months, at least seven months, at least eight months, at least nine months, at least ten months, at least eleven months, at least one year, at least two years, or at least three years.

[0309] In some embodiments, the BTK-mediated condition comprises a mast cell disease. In some embodiments, the mast cell disease comprises indolent systemic mastocytosis (ISM), mast cell activation syndrome (MCAS), eosinophilic esophagitis (EoE), or a food allergy. In some embodiments, the BTK-mediated condition comprises myeloproliferative neoplasm (MPN), dry eye disease, allergic conjunctivitis, or geographic atrophy (GA).

[0310] Another aspect of the present disclosure provides a method for selecting a dosage regimen for treating a Bruton’s Tyrosine Kinase (BTK)-mediated condition using a BTK inhibitor. In some embodiments, the method includes obtaining a model for predicting a BTK occupancy and duration thereof. In some embodiments, the method includes generating,120DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO using the model, an iteration frequency and a total amount of the BTK inhibitor for administration based upon a filtering criterion, where: the iteration frequency comprises one or more iteration intervals; at each respective iteration interval in the one or more iteration intervals a corresponding portion of the BTK inhibitor is administered; and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, the BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the method includes selecting a dosage regimen in accordance with a determination that the iteration frequency and the total amount of the BTK inhibitor satisfy the filtering criterion, where the dosage regimen includes the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding portion of the BTK inhibitor.

[0311] Another aspect of the present disclosure provides a non-transitoiy computer readable storage medium, the non-transitory computer readable storage medium comprising a plurality of instructions, which when executed by a computer system, cause the computer system to perform a method for selecting a dosage regimen for treating a Bruton’s Tyrosine Kinase (BTK)-mediated condition using a BTK inhibitor. In some embodiments, the plurality7of instructions includes instructions for obtaining a model for predicting a BTK occupancy and duration thereof. In some embodiments, the plurality of instructions further includes generating, using the model, an iteration frequency and a total amount of the BTK inhibitor for administration based upon a filtering criterion, where: the iteration frequency comprises one or more iteration intervals; each respective iteration interval in the one or more iteration intervals comprises a corresponding portion of the BTK inhibitor to be administered; and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, the BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration. In some embodiments, the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals. In some embodiments, the plurality of instructions further includes selecting a dosage regimen in accordance with a determination that the iteration frequency and the total amount of the BTK inhibitor satisfy7the filtering121DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO criterion, where the dosage regimen includes the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding portion of the BTK inhibitor

[0312] Yet another aspect of the present disclosure includes a method for modeling a target endpoint responsive to treatment with a BTK inhibitor and / or selecting a dosage regimen for treating a BTK-mediated condition using a BTK inhibitor, the method comprising any of the methods and / or embodiments disclosed herein. Still another aspect of the present disclosure includes a system including a memory; one or more processors; and one or more modules stored in the memory and configured for execution by the one or more processors, the one or more modules including instructions for performing any of the methods disclosed herein. Another aspect of the present disclosure includes a non-transitory computer readable storage medium, the non-transitory computer readable storage medium storing one or more programs for execution by one or more processors of a computer system, the one or more computer programs including instructions for performing any of the methods disclosed herein.

[0313] EXAMPLES

[0314] The embodiments encompassed herein are now described with reference to the following examples. These examples are provided for the purpose of illustration only and the disclosure encompassed herein should in no way be construed as being limited to these examples, but rather should be construed to encompass any and all variations which become evident as a result of the teachings provided herein.

[0315] Example 1: Subject datasets for development of a population pharmacokinetic (PK) model for BTK inhibitor.

[0316] A population pharmacokinetic (popPK) model was developed to characterize the pharmacokinetics (PK) of a BTK inhibitor (Compound 128) and evaluate the impact of covariates of interest. Compound 128 individual post hoc PK estimates generated with the popPK model were used as input for the development of a population pharmacokineticspharmacodynamics (PK / PD) model (pop PK / PD) to describe the relationship between Compound 128 concentration and BTK target occupancy. The final PK / PD model was used122DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO to simulate single dose and steady-state pharmacokinetics of Compound 128 under a range of Compound 128 dosing conditions. BTK occupancy at different BTK resynthesis rates and BTK occupancy for fast and slow Compound 128 absorption profiles.

[0317] Briefly, the objectives of the analysis were to characterize the PK of Compound 128 through population PK modeling and assess sources of PK variability'; quantify the Compound 128 PK time course and variability’ yvith a Compound 128 tablet formulation administered under fed conditions; and generate steady-state predictions of Compound 128 PK concentration versus time profiles and PK parameters for various dosing regimens.

[0318] Dataset Preparation. Two study populations were used to obtain clinical data for the analysis and model development. The first study population (Compound 128-204; ■‘population 1”) and the second study population (MS200662-0017; “population 2”) were obtained as described in Table 6.

[0319] Table 6: Study Data Included in Analysis123DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOAbbreviations: DDI = drug-drug interactions; N = number of subjects with available data; PD = pharmacodynamics; PK = pharmacokinetics.

[0320] Data Programming and Quality Control. Assembly of the popPK datasets was performed using R (version 3.6.3 or higher). Once assembled, the analysis datasets underwent a formal quality control (QC) review by an analyst other than the data programmer.

[0321] Evaluable Subjects. The population PK analysis consisted of all subjects who had at least 1 post dose Compound 128 concentration sample greater than the lower limit of quantitation (LLOQ) and an associated dosing record and blood sampling record for that post-dose sample. The data included in the population PK analysis dataset are summarized in Table 7. There were 1276 quantifiable plasma Compound 128 concentrations from 43 subjects included in the initial model development. After fitting the base model structure to the dataset, no observations had high residuals ((conditional weighted residuals (CWRES)| > 4), so no quantifiable observations were excluded from the main analysis. Therefore, all the 1276 quantifiable observations were included in the base and final models.

[0322] Table 7: Population PK Data Summan'Abbreviations: BLQ = below the limit of quantification; Cones. = concentrations; No. = number; PK = pharmacokinetic.

[0323] Briefly, the objectives of the analysis were to characterize the PK of Compound 128 through population PK modeling and assess sources of PK variability; quantity' the Compound 128 PK time course and variability' with a Compound 128 tablet formulation administered under fed conditions; and generate steady-state predictions of Compound 128 PK concentration versus time profiles and PK parameters for various dosing regimens.124DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0324] Handling of Missing and Erroneous Data. Concentration samples missing corresponding dosing data were excluded from the analysis, as were samples with missing time or date information. In the PK and PK / PD analyses, concentration samples that were found to be below the limit of assay quantitation (BLQ; <1 ng / mL) were treated as missing and excluded from the analysis. Prior to model building, exploratory7graphical analyses were performed to identify unusual patterns and / or data points.

[0325] Exploratory PK Data Analysis . Compound 128 plasma concentration profiles from study populations 1 (Compound 128-204) and 2 (MS200662-0017) included in the current analysis are presented in FIGs. 3 and 4, respectively. For study population 2, mean concentrations increased up to 1.5 hours and decreased thereafter, while Tmaxfor study population 1 ranged from 1.5 to 3.5 hours. In the fed state, dose normalized Compound 128 Cmax was 1.59-fold higher in study population 2 than that of study population 1 at 817 ng / mL (300 mg dose with food) and 257 ng / mL (150 mg dose with a Low-Fat meal), respectively. In study population 1, mean plasma Compound 128 concentrations were similar following the administration of 150 mg Compound 128 with a Low Fat meal, a High Fat meal, a Low Fat meal with staggered 20 mg Famotidine (2 hours after and 10 hours before Compound 128). Mean plasma Compound 128 concentrations were lower following the administration of Compound 128 with a Low7Fat meal and steady state Omeprazole 40 mg compared to all other treatments.

[0326] Example 2: Development of a base PK model for characterizing target endpoints of BTK inhibitor.

[0327] A model was obtained to characterize the PK of BTK inhibitor Compound 128 through population PK modeling.

[0328] Software and Computational Approach. The population PK analysis methods were based on guidelines proposed by the Food and Drug Administration [1], Nonlinear mixed-effects modeling software (Phoenix NLME version 8.2) was used for popPK modeling and simulations. First-order conditional estimation with an extended least-squares (FOCE ELS) method with between-subject variability (BSV) and residual variability interaction of Phoenix NLME was used for PK model development. Nonlinear mixed-effects modeling software (NONMEM; version 7.4.3), a software package for nonlinear mixed-effects analysis125DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO(ICON, Hanover, MD, US), was used for PK / PD modeling. R was used for data explorations prior to modeling, model diagnostics and simulations.

[0329] Model Development. Based on a previous PK analysis of plasma Compound 128 [2], the PK model structure was a two-compartment model with zero-order release into the depot compartment followed by first-order, hereafter referred to as a sequential zero- and first-order absorption model. This model structure served as the starting point for development of the BTK inhibitor component population PK model. Distribution and elimination were modeled as first-order processes. The model was parameterized in terms of clearances and volumes of distribution. Because omeprazole had a large effect on the plasma Compound 128 concentration-time profiles, omeprazole effects on the absorption parameters (first-order absorption rate constant [Ka], bioavailability [Fl], and duration of zero-order release [DI]) were tested.

[0330] The inter-individual random effects on the parameters were introduced and retained when their inclusion did not cause model instability and when their estimates are not close to 0. They were modeled assuming a log-normal distribution as given by the following expression:9ki 9k. x e^1

[0331] where 6kt denoted the parameter value for the Ithsubject. 6k denoted the typical parameter value, anddenoted the inter-individual random effect for the ithsubject, assumed to have a mean of 0 and a variance a>k2.

[0332] Collectively, the vector of random effects (across the parameters indexed by k) had the covariance matrix Q. Covariance matrix structures, including diagonal and blocked diagonal structures, were evaluated after the completion of covariate model building. When the multiple study periods are presented in the dataset, random effects for inter-occasion variability were evaluated and were also assumed to follow a log-normal distribution [5], When the individual parameters needed restriction to certain values (e.g., bioavailability between 0 and 1), a logit transformation was applied.

[0333] The residual error structure was assumed to follow an additive, proportional, or combined additive and proportional error model described by the following expression:126DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOYij — Cij X (1 + Eli / ) + fi2i /

[0334] where Yy is the71observed concentration for the Ithsubject. Cy is the corresponding predicted concentration, and sly (proportional) and s2y (additive) are the residual errors under the assumption that s~N (0, o2).

[0335] The residual error model was optimized until no trends were visible in residual plots (in particular, absolute values of conditional weighted residuals (CWRES) versus individual predicted concentrations (IPRED)). When necessary, the data were log- transformed prior to analysis.

[0336] FIG. 5 illustrates the structure of the base PK model for BTK inhibitor Compound 128, including a two-compartment model with linear elimination and zero-order drug release into the absorption compartment preceded by a lag time (Tiag) and followed by the first-order absorption into the central compartment. The effects of omeprazole coadministration on F and Ka were also incorporated into the base model. Parameter estimates for the base model are presented in Table 8. All parameters were precisely estimated. Goodness-of-fit plots for the base model are shown in FIG. 6, indicating an adequate model fit to the data. No marked systematic trends were seen in the residual diagnostic plots.

[0337] Table 8: Compound 128 Population Base Model Parameter Estimates127DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0338] Example 3: Selection of covariates for the characterizing target endpoints of BTK inhibitor.

[0339] Additional inputs to the model were determined by assessing and selecting covariates for use in characterizing target endpoints of BTK inhibitors. In particular, covariates were examined to assess sources of PK variability.

[0340] The evaluation of the impact of covariates on the Compound 128 population PK model was focused primarily on the most clinically relevant covariates. Covariates that were tested for the population PK model are listed in Table 9. The subset of covariate-parameter relationships included were selected based on exploratory graphical analysis, mechanistic plausibility', and scientific and clinical interest.

[0341] Table 9: Baseline Covariates for Evaluation in the Population PK Model128DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0342] For a covariate to be included in the formal covariate analysis, the following conditions applied:• The covariate was available in at least 80% of subjects.• If covariates showed a correlation of >0.5. only one of the correlated covariates was included in the formal analysis. This was either the covariate with the strongest influence, as determined by exploratory graphical analysis, or the variable that was most meaningful from a clinical, biological, or practical perspective. Continuous covariates were preferred over categorized covariates with the same meaning.

[0343] Covariates that did not fulfill these criteria were evaluated graphically in an exploratory’ manner. However, graphical covariate analysis can be relied on only if p- shrinkage in the respective population model parameters is low [6],

[0344] A univariate screening process was used to select parameter-covariate relationships that were tested in the stepwise covariate search. Each parameter-covariate relationship was added one at a time to the structural model, and only parameter-covariate relationships significant at the 0.05 level in this univariate testing step or that are deemed to be of clinical interest (e.g.. renal and hepatic functions) were taken forward into the full stepwise covariate search process.

[0345] Covariates that fulfilled the above criteria were tested in a stepw ise process [7], The covariate selection was performed using a forward addition process followed bybackward elimination. The likelihood ratio test was used to evaluate the significance of incorporating or removing fixed effects into the population model based on significance levels that are set a priori. For forward addition and backward elimination, significance levels of 0.01 and 0.001 were utilized, respectively. The improvement of the model relative to the base model w as compared when each of the covariates is added univariately, and the model with the largest improvement was kept for the next evaluation step, given that there was an overall statistical significance supporting the inclusion of the respective covariate. During the backw ard elimination process, covariates w ere removed from the model one at a time if their deletion leads to insignificant model deterioration. The most insignificant covariate was129DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO removed first, and the procedure was repeated until no further insignificant covariate relationship was detected.

[0346] Continuous covariates were incorporated into the population model using a scaled structure based on either the median value of the covariate in the population or a standard reference value for the covariate. This approach ensured that covariate effects were relative to an individual in the middle of the population distribution for that co variate. Categorical covariates were initially incorporated into the population model using a proportional structure with either the most common level of the covariate being the reference or a level specific to the analysis (e.g., healthy subject versus patient). This approach ensured that categorical covariate effects were evaluated relative to a reference group or category. The mathematical structures of the co variate models are shown below:ContinuousPki= ekx ( Xij / M(Xj) )eiCategoricalPkt = 9k x ( 1 + 9j )Xii

[0347] where Pki is the population estimate of the parameter Pk for subject i, X is the value of continuous covariate A) for subject i (or an indicator variable for subject i for categorical covariate ) with values of 1 for the nonreference category and 0 for the reference category), M(Xj) is the median of covariate A) in the analysis dataset, Ok is the typical value of the parameter Pk, and 0j is a coefficient that reflects the effect of covariate ) on the parameter.

[0348] When these parameterizations proved inadequate to capture the observed relationship, other parameterizations were evaluated. Such alternatives included dichotomization, discretization, or truncation; censoring of continuous covariates; and / or linear or piecewise linear functions. Other continuous functions were also considered when deemed appropriate based on plots of random effects versus the covariate of interest. To ensure that the model that emerged from the covariate testing process did not neglect any important covariate effects, random effects from the tentative final model were plotted versus130DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO potential covariates and evaluated for residual trends in parameter-covariate relationships. Model re-parameterization was required for stability of the full model.

[0349] The final selection of PK covariates are summarized in Tables 10 and 11, which include baseline characteristics for subjects in the popPK analysis dataset. Body weight and age distributions were similar across studies, with an overall body weight median of 78.0 kg and age median of 44.0 years. Male subjects (N=24) constituted 55.8% of the dataset. Most subjects were white (88.4%), and all subjects from study population 2 (MS200662-0017) were white. H. Pylori status was collected at baseline in study population 1 (Compound 128- 204) to assess its effect on Compound 128 exposure (detected in 46% of subjects).

[0350] Table 10: Continuous Covariates of Subjects in the Population PK AnalysisAbbreviations: CV = coefficient of variation; Max = maximum; Min = minimum; N = number of subjects; SD = standard deviation.

[0351] Table 11 : Categorical Covariates of Subjects in the Population PK Analysis131DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOAbbreviations: N = number of subjects.

[0352] Example 4: Development of a final PK model for characterizing target endpoints of BTK inhibitor.

[0353] The base model of Example 2 was further used in conjunction with selected covariates to obtain a final PK model to characterize the PK of BTK inhibitor Compound 128 through population PK modeling. The final population PK model consisted of a two- compartment model for Compound 128 with linear elimination, sequential zero-order and first-order absorption process with Tiagand with combined proportional and additive residual errors. H. pylori infection effect on the duration of zero-order release (DI) was included into the final model. Body weight, sex, race, ethnicity, an age were not significant covariates.

[0354] Parameters for the final population PK model are presented in Table 12. Parameters, including covariate effects, were generally precisely estimated with relative standard errors (RSEs) < 10%, except for apparent peripheral volume of distribution (Vp / F) (RSE = 32.0%). The objective function value (OFV) was 11133, which decreased the OFV by 38 points. Combination with omeprazole decreased Compound 128 bioavailability (F) by 50.0% and decreased absorption rate (Ka) by 76.0% compared to administration of Compound 128 alone. The subjects with H. pylori infection had a 2-fold higher absorption duration of release than those who were H. pylori-negative.132DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0355] Table 12: Final Population PK Model Parameter Estimates for Compound 128

[0356] The goodness-of-fit (GOF) plots for the final population PK model are presented in FIG. 7. The GOF plots indicate an adequate model fit to the data with no marked systematic trends seen in the residual diagnostic plots. A slight underprediction of high concentrations is apparent in the observations (DV) versus individual predicted concentrations (IPRED) and conditional weighted residuals (CWRES) versus time after dose (TAD) plots, mostly due to the effect of omeprazole.133DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0357] Example 5: Qualification of the final PK model for characterizing target endpoints ofBTK inhibitor.

[0358] Internal qualification of the PK model of Example 4 was performed through a prediction-corrected visual predictive check (pcVPC) and was created to show the time course of the predicted mean and spread of concentrations (5thto 95thpercentile) versus the observed data [8], A total of at least 500 trial replicates were simulated using the observed covariates and dose regimens for each subject, the final model parameter estimates, and simulated individual-specific estimates and residual errors. The pcVPC were also used to evaluate the predictability of the final model. The pcVPC for the final population PK model is presented in FIG. 8. The pcVPC demonstrated that the model generally adequately described the central tendency and overall distribution of the observations.

[0359] Example 6: Development and qualification of a pharmacokinetic- pharmacodynamic (PK Pl)j model for predicting target endpoints in a subject responsive to treatment with BTK inhibitor.

[0360] Exploratory PD data analysis was performed on intracellular peripheral blood mononuclear cells (PBMC). Assembly of the pop PK / PD datasets was performed in the same manner as the popPK datasets described in Example 1. The data included in the PK / PD analysis dataset are summarized in Table 13. There were 520 quantifiable %BTK target occupancy from 26 subjects included in the PK / PD model. The PK / PD analysis consisted of all subjects who had at least 1 measurable %BTK target occupancy data and at least 1 administration of Compound 128 prior to each measurable %BTK target occupancy observation.

[0361] Table 13: %BTK Occupancy Information in Datasets for Model Development

[0362] The goodness-of-fit (GOF) plots for the final population PK model are presented in FIG. 7. The GOF plots indicate an adequate model fit to the data with no marked134DBl / 163759882.1Attorney Ref. No.; 126569-5019-WO systematic trends seen in the residual diagnostic plots. A slight underprediction of high concentrations is apparent in the observations (DV) versus individual predicted concentrations (IPRED) and conditional weighted residuals (CWRES) versus time after dose (TAD) plots, mostly due to the effect of omeprazole.

[0363] The intracellular (PBMC) BTK target occupancy (TO) profiles from study population 1 (Compound 128-204) are presented in FIG. 9. The extent of the Compound 128 PD effect was measured by AUEC0-12 and AUEC0-24. AUEC0-12 refers to area under the BTK%TO effect-time curve from time 0 to 12 hours; AUEC0-24 refers to area under the BTK%TO effect-time curve from time 0 to 24 hours after Compound 128 QD dosing and is equal to 2 x AUECo- for BID administration; and BTK%TO refers to percent BTK target occupancy.

[0364] AUECo-12 and AUEC0-24 was lower following BTK inhibitor treatment with concomitant medication omeprazole (Treatment D) compared to BTK inhibitor treatment with no concomitant medications (Treatment A). Median BTK%TO was lower following Treatment D compared to Treatment A at 2. 6, 12, 24. and 48 hours post dose. The BTK%TOmax was approximately 8% lower in Treatment D compared to Treatment A at 88% and 96%, respectively [9], As a result, the impact of omeprazole as a covariate was evaluated.

[0365] Population PK-PD Model. An irreversible turnover model was developed to describe the relationship between Compound 128 plasma concentration and BTK target occupancy [3, 4], The Compound 128 individual post hoc PK estimates generated with the population PK model were used as PK inputs for the PK / PD model, while the structure of the intracellular (PBMC) %BTK target occupancy component of the model was informed by graphical explorations of the observed data (e.g, %BTK occupancy -time plots, etc.). FIG. 10 depicts an example structure of the PK / PD model. The covariate effect of omeprazole was evaluated in the PK / PD model and no significant impact was found. Parameter estimates for the PK / PD model are presented in Table 14. The parameters were precisely estimated, with RSEs <24.0%. The goodness-of-fit (GOF) plots are presented in FIG. 11 and indicate an adequate model fit to the data with no marked systematic trends seen in the residual diagnostic plots.135DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0366] Table 14: PK / PD Model Parameter EstimatesKin = l*Kout; Abbreviations: BSV = between-subject variability; CV = coefficient of variation; RSE = relative standard error; SD = standard deviation; Kout = first-order degradation rate of BTK protein; Kin = zero-order BTK synthesis rate; Kr = the second-order irreversible binding rate constant.

[0367] Model Qualification. The pcVPCs for the PK / PD model are presented in FIG. 12.The pcVPCs demonstrated that the models adequately described the central tendency and overall distribution of the observations for most time points.

[0368] Example 7: Simulations of Steady-State PK and PD Metrics in Healthy Subjects.

[0369] A further analysis was performed to describe the relationship between BTK inhibitor (e.g, Compound 128) concentration and %BTK target occupancy, using the PK / PD model described in Examples 1-6. In particular, the final population PK and population PK / PD models determined in Examples 4 and 6 were used to predict steady state PK and %BTK target occupancy for various dosing regimens.

[0370] As described above in Examples 1-5, a population pharmacokinetic (popPK) model was developed to characterize Compound 128 PK and evaluate the impact of covariates of interest. The Compound 128 individual post hoc PK estimates generated with136DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO the popPK model were used as input for the development of a PK / PD model to describe the relationship between Compound 128 concentration and BTK target occupancy as described in Example 6. The final PK / PD model was used to simulate single dose and steady-state pharmacokinetics (PK) of Compound 128 under a range of Compound 128 dosing conditions, BTK occupancy at different BTK resy nthesis rates and BTK occupancy for fast and slow Compound 128 absorption profiles. The population PK analysis methods were based on guidelines proposed by the FDA. There were 1276 quantifiable plasma Compound 128 concentrations from 43 subjects included in the initial model development. The 43 healthy individuals consisted of samples from Part 2 Period 1 (tablet fed treatment) of study population 2 (MS200662-0017) and all subjects from study population 1 (Compound 128- 204). as described in. The samples used to create the model were as described in Tables 6, 7. 10, and 11.

[0371] Simulated effects of BTK inhibitor Compound 128 were determined using a 2- compartment population PK model with zero-order and first-order absorption with lag time and first-order elimination. The following covariate effects on Compound 128 PK were identified: lower bioavailability by 50% in healthy subjects for coadministration of Compound 128 with omeprazole relative to administration of Compound 128 alone; slower absorption (i.e., lower Ka) for coadministration of Compound 128 with omeprazole relative to administration of Compound 128 alone; and longer duration of absorption thus lower Cmax without affecting the AUC in H. pylori infection-positive subjects relative to H-pylori- negative subjects. Abbreviations: QD = Once Daily; BID = Twice a Day; TID = Three Times a Day; QID = Four Times a Day; Cmax = Maximum (or peak) serum concentration; and Tmax = Time when Cmax is observed.

[0372] The population PK / PD model was developed using 491 BTK occupancy observations from 26 healthy subjects in study population 1 (Compound 128-204). The PK / PD relationship comprised an irreversible turnover model and was used to simulate steady state PK profiles to determine PK parameters and %BTK occupancy parameters for total daily doses of 50 to 900 mg per day using the BTK resynthesis rate observed in healthy subjects from study population 1 (Compound 128-204). Simulations of Compound 128 concentration-time and intracellular (PMBC) BTK occupancy profiles in healthy subjects with a measured BTK resynthesis rate (half-life) of 55 hours at steady state were carried out137DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO for QD (once daily interval) and BID (twice a day interval) dosing regimens with total daily doses ranging from 50 to 900 mg. Administration of Compound 128 with a low-fat meal was assumed. The Compound 128 concentration-time and intracellular (PMBC) BTK occupancy profiles for 150 mg BID and 300 mg QD doses are presented in FIG. 13. The simulated profiles show that PK and PD steady state are achieved after the first BID dose. The BTK occupancy by the BTK inhibitor stays above -85% BTK occupancy for both BID and QD dosages at the various intervals as seen in FIG. 13. When BTK occupancy is investigated during Ctrough (lowest concentration of compound before next dosage) the occupancy rates stay above 83% as seen in FIG. 14 and at various dosages / concentrations as shown in Table 15.

[0373] Table 15: Summary of Simulated Steady-State Compound 128 Exposures and BTK Occupancy Parameters in Healthy Subjects Following Total Daily Doses of 50 to 900 mg138DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOAbbreviations: AUC = area under the concentration-time curve; AUCO-24 = AUCO-12 x 2 for BID regimens; AUEC = area under the %BTK occupancy-time curve; AUECO-24 = AUECO-12 x 2 for BID regimens; BID = twice daily; Cmax = maximum plasma concentration; Ctrough = concentration before the next Compound 128 administration; Dur90BID = Duration of BTK occupancy >90% within the 12 hours between doses at steady state; Dur95BID = Duration of BTK occupancy >95% within the 12 hours between doses at steady state; Dur90QD = Duration of BTK occupancy >90% within the 24 hours between doses at steady state; Dur95QD = Duration of BTK occupancy >95% within the 24 hours betw een doses at steady state; Tmax = Time at maximum plasma concentration; %BTK Occupancy at Trough = %BTK occupancy before the next Compound 128 BID or QD administration at the end of the dosing interval; QD = once daily.139DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0374] The simulations were performed with consideration of fixed effect estimated (typical parameter values) and inter-individual variabilities (IIV estimates) with virtual subjects of N=500 for each scenario. Results are reported as median [2.5thpercentile, 97.5thpercentile]. Calculated Dur90 or Dur95 for BID administration (Dur90BID and Dur95BID, respectively) refer to a single 12 h steady state dose interval and are 50% of the modeled Dur90 and Dur95 value for QD administration (Dur90QD and Dur95QD, respectively).

[0375] FIG. 14 and Table 15 illustrate simulated steady-state BTK occupancy at trough and the duration of BTK occupancy >90% and >95% (Dur90 and Dur95), for total daily Compound 128 doses from 50 to 900 mg, given as a divided dose 12 h apart or given daily. Steady state 24 h simulations of QD and BID Compound 128 doses showed that BTK occupancy at the end of each dose interval, i.e., at trough (at 12 h or 24 h for BID and QD dosing, respectively) and target coverage defined as Dur90 and Dur95 within the 12 or 24 hours betw een doses at steady state, increased with increasing dose.

[0376] The increases in BTK coverage and Trough BTK Occupancy with dose were saturable, with increasingly limited gains above a dose of 150 mg BID (e.g, 300 mg total daily dose), in accord with a covalent mechanism of action and near complete BTK receptor occupancy. Longer BTK coverage >90% or >95% was a clear benefit of BID vs QD dosing, as follows:• a 75 mg BID dose maintained median BTK occupancy > 90% over the entire 12-h dosing interval and > 95% for 6.8 h of the 12 h dosing interval, with median trough %BTK occupancy (at 12 h post-dose) of 91.1%. The 150 mg QD dose maintained median BTK occupancy > 90 % for 14 h of 24 h and > 95% for 8 h out of 24 h. with median trough %BTK occupancy (at 24 h post-dose) of 83. 1%.• a 150 mg BID dose maintained median BTK occupancy > 90 % over the entire 12-h dosing interval and > 95% for 9 h of the 12 h dosing interval, with median trough %BTK occupancy (at 12 h post-dose) of 93.5%. The 300 mg QD dose maintained median BTK occupancy > 90 % for 17.4 h of 24 h and > 95% for 10 h of 24 h, with median trough %BTK occupancy (at 24 h post-dose) of 86.4%.140DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO• a 300 mg BID dose maintained median BTK occupancy > 90 % over the entire 12-h dosing interval and > 95% for 11.2 h of the 12 h dosing interval, with median trough %BTK occupancy (at 12 h post-dose) of 94.8%. The 600 mg QD dose maintained median BTK occupancy > 90 % over 24 h and > 95% for 13 h of 24 h, with median trough %BTK occupancy (at 24 h post-dose) of 90.5%.• a 450 mg BID dose maintained median BTK occupancy > 90 % over the entire 12-h dosing interval and > 95% for 11.9 h of the 12 h dosing interval, with median trough %BTK occupancy (at 12 h post-dose) at 12 h of 95.8%. The 900 mg QD dose maintained median BTK occupancy > 90 % over 24 h and > 95% for 15.4 h out of 24 h, with median trough %BTK occupancy (at 24 h post-dose) of 92.2%.

[0377] Example 8: Modeling Effect of BTK Resynthesis Rate on BTK Occupancy.

[0378] The final PK / PD model determined in Example 6 was used to assess the effect of BTK resynthesis rate on BTK occupancy, in particular, to estimate the duration of steady state BTK target coverage for different dosing regimens and BTK resynthesis rates.

[0379] Simulations models were conducted for Compound 128 concentration-time and intracellular BTK occupancy profiles at steady state for dose regimens of 25 to 450 mg BID, while varying BTK resynthesis rates to achieve BTK turnover half-lives from 6 h (very fast) to 55 h - the resynthesis rate observed in healthy subjects from study population 1 (Compound 128-204). The objective of this analysis was to assess the duration of BTK target coverage at different BID dose levels at faster BTK resynthesis rates. The BTK occupancy model-estimated typical value profiles for 150 mg BID dosing with different resynthesis rates are presented in FIG. 15. The model simulations showed that at any given dose level, the duration of median BTK occupancy above 95% declined when BTK resynthesis rates were faster. In 4 out of 5 of the models the % BTK occupancy never dropped below 50% and only in the 6-hour resynthesis model does the % BTK briefly drop below 50% for 2 out of 19 of the interval iterations but clearly stays above 50% occupancy for more than 40% of the iteration.

[0380] The duration of BTK occupancy >95% over 24 h at the different BTK resynthesis rates is show n for all simulated BID doses in FIG. 16. The simulation demonstrates that the141DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO duration of BTK coverage >95% erodes as BTK resynthesis rate increases. At a healthy subject BTK resynthesis half-life of 55 h, and a Compound 128 dose of 150 mg BID, median BTK occupancy >95% is maintained for 9 h out of the 12 h dosing interval. At a faster BTK resynthesis half-life of 48 h, and a Compound 128 dose of 150 mg BID, median BTK occupancy >95% is maintained over approximately 7.5 h of the 12 h dosing interval. With a relatively rapid BTK resynthesis half-life of 24 h, a Compound 128 dose of 300 mg BID is needed to keep median BTK occupancy >95% approximately 6 h of the 12 h dosing interval.

[0381] Example 9: Prediction of target endpoints using model simulations to explore the impact of Cmax and Tmax on BTK occupancy.

[0382] The final PK / PD models determined in Example 6 was further utilized to determine how different PK profiles (e.g., high versus low Cmax, early versus late Tmax) affected the duration of sustained steady state BTK target occupancy >95%.

[0383] Since administration with food resulted in PK profiles with later Tmax and lower Cmax, the impact of delayed Tmax and lower Cmax on %BTK occupancy was investigated using the PK / PD model to ensure proper occupancy levels. Steady state QD PK / PD simulations of three hypothetical Compound 128 PK profiles were performed using a one compartment PK model with linear elimination. Simulation of a constant rate intravenous infusion of a Compound 128 dose equivalent to an AUC0-24 of 3000 ng*h / mL was done for once a day (QD) infusion durations of 1.5 h, 3 h and 6 h, yielding the three distinct PK profiles in FIG. 17. The three simulations were repeated for an AUC0-24 of 750 ng*h / mL and 1 8 ng*h / mL as outlined in Table 16.

[0384] The model-estimated typical values of apparent clearance (CL / F) and apparent central volume of distribution (Vc / F) were used to represent Compound 128 IV clearance (CL) and volume of distribution (V), respectively in the simulations, and IIV was not included. The administered dose required to achieve a desired steady-state AUC0-24 was calculated as:AUCO-24 = Dose / CL

[0385] The infusion duration was set to be equal to the desired Tmax, and the resulting end-of-infusion concentrations were the Cmax values. The impact of a later Tmax and lower142DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOCmax on %BTK occupancy were assessed while maintaining the same AUC. The simulated PK profiles and the resultant %BTK occupancy were compared across scenarios.

[0386] Table 16: Simulation ScenariosAbbreviations: AUC = area under the concentration-time curve, Cmax = maximum plasma concentration, Tmax = Time at maximum plasma concentration, PK = pharmacokinetics.

[0387] The models assumed a “high Cmax / fast Tmax7’, “medium Cmax / medium Tmax” and a “low Cmax / slow Tmax” under the simulated assumption of a mean Compound 128 concentration time profiles for an IV Compound 128 infusion ending at 1.5, 3 or 6 h which achieved a 3000 ng*h / mL AUC0-24 with different Tmax are shown in FIGs. 17 and 18. Increasing Tmax led to reduced Cmax, as expected. Simulated BTK occupancy profiles at AUC 3000 ng-h / mL with different Tmax are shown in FIG. 18. Three distinct PK profiles are illustrated with occupancy above 80% during the dosing time period.

[0388] Simulation of a constant rate intravenous infusion of a Compound 128 dose equivalent to an AUCO-24 of 3000 ng*h / mL, 750 ng-h / mL and 188 ng-h / mL were done with infusion durations of 1.5 h, 3 h and 6 h, as seen in Table 17. The resultant Cmax and simulated target coverage (duration of BTK target occupancy >95%) was compared across scenarios.143DBl / 163759882.1Attorney Ref. No.: 126569-5019-WOAt the same drug AUC exposure, shifting Tmax from 1.5 to 6 h almost doubled target coverage duration Specifically, the duration of BTK >95% shifted as follows:• AUCO-24 of 3000 ng-h / mL: from 13 to 24 h, respectively;• AUCO-24 of 750 ng-h / mL: from 9 to 18 h, respectively; and• AUCO-24 of 188 ng-h / mL: median %BTK target occupancy did not reach 95% under any scenario.

[0389] Table 17. Predicted Duration of BTK Occupancy >95% for Different PK Profiles for Steady State QD administration.The duration of infusion was set to Tmax. Abbreviations: AUC = area under the concentration-time curve; Cmax = maximum plasma concentration; Tmax = Time of maximum plasma concentration. PK = pharmacokinetics.

[0390] A flatter PK profile such those observed in a fed state versus a fasted state (e.g. , with a delayed Tmax) conferred a longer steady state %BTK target occupancy >95%. A key144DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO characteristic of covalent kinase inhibitors was a relatively short-lived exposure, e.g, an early Tmax and high Cmax with rapid subsequent elimination that drives a relatively longer duration of PD target coverage, and good therapeutic activity

[0010] , These modeled PK / PD data indicate that, for any given AUC, a later Tmax and lower Cmax drives exposure later in the dose interval that blocks resurgent BTK resynthesis that would otherwise erode the duration of target occupancy >90% or >95% over the dose interval. This factor may become more relevant at shorter BTK resynthesis half-lives.

[0391] Example 10: Basophil Activation Test (BAT).

[0392] The BAT was conducted as set forth in Santos (2021) Allergy, 76:2420-2432. Blood from six healthy donors was drawn and Compound 128 was added at various concentrations (10 pM, 3.16 pM, 1 pM, 0.3 pM, 0.1 pM, 0.03 pM, or 0 pM). Blood samples were gently rocked at room temperature for 2 hours, 24 hours, or 48 hours. The BAT assay using the Flow Cast Basophil Activation Kit (Buhlmann Diagnostics; Catalog # B-CCR- STCON) was then conducted following manufacturer instructions by (1) stimulating the blood with anti-FcsRl stimulation control in stimulation buffer with IL-3 and staining cells for CCR3 and CD63 for 15 minutes at 37°C, (2) lysing red blood cells and fixing cells with Lysing Reagent at room temperature for 10 minutes, (3) washing cells with Wash Buffer, and (4) acquiring data on a flow cytometer. Activated basophils were defined as CCR3+, low- side scatter and CD63+. Percent of activated basophils was calculated as number of activated basophils over the total number of basophils. A four-parameter logistic regression was applied to determine the ECso and EC90. Figure 19 shows the percent of activated basophils (CD63+). The results demonstrated that Compound 128 more potently inhibits basophil activation after longer drug exposures. Figure 20 shows the potency of basophil activation inhibition over time after Compound 128 addition. The results demonstrated that Compound 128 inhibits anti-FcsRl induced basophil activation and Compound 128 potency increased approximately 4-fold after 24 hours drug exposure. The average EC50 calculated for Compound 128 after a 2-hour, 24-hour and 48-hour drug exposure was 0.5, 0. 12 and 0.1 pM, respectively.

[0393] Example 11: Clinical Study to Evaluate Efficacy of BTK Inhibitors in Indolent Systemic Mastocytosis Patients Who are Resistant to Best Available Therapy.145DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0394] Efficacy of Compound 128 will be evaluated among adult subjects diagnosed with ISM as defined by WHO diagnostic criteria. The subjects have moderate-to-severe symptoms based on minimum mean total symptom scores (TSS) of ISM-TSAF over the 14-day eligibility screening period for assessment of TSS of ISM-TSAF. Minimum baseline TSS of ISM-TSAF for eligibility is > 28. Subjects will receive either 50, 75, 100 or 150 mg BID of Compound 128 plus best supportive care. Subject will be treated for twenty-four weeks and will be evaluated at twelve weeks and twenty-four weeks. Evaluation will be based on the 14- day rolling average TSS of ISM-TSAF or SISM-TSAF. The TSS of ISM-TSAF is based on patient reporting on eleven separate symptoms, measuring the severity of a broad range of mastocytosis-related symptoms over the prior 24 hours, based on a score from zero (no symptoms) to ten (worst possible symptoms experienced) scale for a TSS between 0 - 110. The eleven symptoms to be scored by the subject are fatigue, abdominal pain, diarrhea, skin spots, itching, flushing, brain fog, headache, dizziness, and bone pain. The TSS of SISM- TSAF is based on patient reporting on nine separate symptoms based on a score from zero (no symptoms) to ten (worst possible symptoms experienced). The nine symptoms to be scored by the subject are muscle pain, runny nose, nasal congestion, wheezing, shortness of breath, throat itching, heart palpitations, difficulty7concentrating, and diarrhea. The first eight items measure the severity of a broad range of mastocytosis-related symptoms over the prior 24 hours. Each of the first eight symptom is assessed on a 0 (None) to 10 (Worst Imaginable) scale for a TSS between 0 - 80. The 9th item measures incidence of diarrhea over the prior 24 hours. Endpoints will also be based on mean change in TSS of ISM-TSAF or SISM-TSAF based on 14-day average of patient-reported severity of ISM symptoms; mean change in serum tryptase level, KIT D816V VAF, change in best supportive care medications and change in number of anaphylactic episodes.

[0395] Five ISM patients were treated with Compound 128 at 100 mg or 150 mg BID. At week 12, the TSS of ISM-TSAF was reduced by 28 points compared to the placebo group (Figure 22), and the TSS of SISM-TSAF was reduced by 22.7 points, compared to the placebo (Figure 23). In contrast, avapritinib 25 mg QD reduced the TSS of ISM-TSAF by 5.7 points at week 24 (Figure 21). The average reduction of total symptom score of ISM-TSAF was 55% compared to the baseline while the average reduction of total symptom score of ISM-TSAF was 38% compared to the placebo group (Figure 24).146DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO

[0396] Additionally, blood samples were collected at the following timepoints: Cycle 1, Day 1: pre-dose (within 30 minutes prior to the morning dose), 0.5. 1.0, 2.0, 3.0, 4.0. and 6.0 hours (± 15 minutes) post-moming dose. Cycle 1, Day 8: pre-dose (within 30 minutes prior to the morning dose) and 2 hours (± 15 minutes) post-moming dose. Serum tryptase tests were conducted. The average reduction in serum try ptase is shown in Figure 25, demonstrating that Compound 128 at 100 mg or 150 mg BID is effective in reducing serum tryptase levels. The inhibition of basophil activation was also evaluated following the procedures illustrated in Example 10, as shown in Figure 26, demonstrating that Compound 128 at 100 mg or 150 mg BID is effective in inhibiting basophil activation.

[0397] References:1. Food and Drug Administration. Population Pharmacokinetics Guidance for Industry, July 2019. Available on the Internet at fda.gov / media / 128793 / download (accessed on 16 June 2021).2. Report T2021010-CP: TL-895 Population Pharmacokinetic Model and Simulations Based on Pharmacokinetics Following Administration of a 300 mg TL-895 Tablet Dose with a Standard Meal in Study MS200662-0017. July 2021.3. Haselmayer P, Camps M, Liu-Bujalski L, Nguyen N, Morandi F, Head J, et al. 2019. Efficacy and Pharmacodynamic Modeling of the BTK Inhibitor Evobrutinib in Autoimmune Disease Models. The Journal of Immunology, 2019, 202: 2888-2906.4. Abelo,A, Eriksson, MO. Karlsson, H. Larsson, and J. Gabrielsson. 2000. A turnover model of irreversible inhibition of gastric acid secretion by omeprazole in the dog. J. Pharmacol. Exp. Ther. 295: 662-669.5. Nguyen TH, Mouksassi MS, Holford N, et al. Model Evaluation Group of the International Society of Pharmacometrics (ISoP) Best Practice Committee. Model evaluation of continuous data pharmacometric models: metrics and graphics. CPT Pharmacometrics Syst Pharmacol. 2017 Feb;6(2):87-109.6. Karlsson MO, Savic RM. Diagnosing model diagnostics. Clin Pharmacol Ther. 2007 Jul;82(l): 17-20. Review.147DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO7. Jonsson EN, Karlsson MO. Automated covariate model building within NONMEM. Pharm Res. 1998 Sep;15(9): 1463-8.8. Bergstrand, M, Hooker AC, Wallin JE, Karlsson MO. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. The AAPS Journal, 2011; 13: 143-151.9. CA32669 TL-895-204 Clinical study report: A Phase 1, Single-Dose, Open Label Study to Evaluate the Effect of a High-Fat Meal, a Proton Pump Inhibitor, and an H2-receptor Antagonist on the Pharmacokinetics, Pharmacodynamics, and Safety of TL-895 Tablets in Healthy Subjects. 2022.10. Barf T. Kaptein A Irreversible protein kinase inhibitors: balancing the benefits and risks. J Med Chem. 2012 Jul 26;55(14): 6243-62.

[0398] CONCLUSION

[0399] All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.

[0400] The present invention can be implemented as a computer program product that comprises a computer program mechanism embedded in a non-transitory computer readable storage medium. For instance, the computer program product could contain the program modules show n in any combination of FIGs. 1A-C or 2A-C. These program modules can be stored on a CD-ROM, DVD, magnetic disk storage product, USB key, or any other non- transitory computer readable data or program storage product.

[0401] Many modifications and variations of this invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are offered by way of example only. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use148DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO contemplated. The invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled.149DBl / 163759882.1

Claims

Attorney Ref. No.: 126569-5019-WOWhat is claimed is:1 . A computer system for selecting a dosage regimen for treating a Bruton’s Tyrosine Kinase (BTK)-mediated condition using a BTK inhibitor, the computer system comprising a memory' and a processor, the memory storing a plurality' of instructions executable by the processor, the plurality of instructions comprising: instructions for obtaining a model for predicting a BTK occupancy and duration thereof; instructions for generating, using the model, an iteration frequency and a total amount of the BTK inhibitor for administration based upon a filtering criterion, wherein: the iteration frequency comprises one or more iteration intervals; each respective iteration interval in the one or more iteration intervals comprises a corresponding portion of the BTK inhibitor to be administered; and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, the BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration, wherein: the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals; and instructions for selecting a dosage regimen in accordance with a determination that the iteration frequency and the total amount of the BTK inhibitor satisfy the filtering criterion, wherein the dosage regimen comprises the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding portion of the BTK inhibitor.

2. The computer system of claim 1. the plurality of instructions further comprising: instructions for obtaining, responsive to inputting the iteration frequency and the total amount of the BTK inhibitor to the model, as output from the model, one or more simulated values for the BTK occupancy.

3. The computer system of claim 1, wherein the instructions for obtaining the model further comprise:150DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO instructions for training the model using, for each respective training subject in a plurality of training subjects, a corresponding one or more measurements of the BTK occupancy in the respective training subject, and instructions for obtaining, responsive to inputting the iteration frequency and the total amount of the BTK inhibitor to the model, as output from the model, one or more simulated values for the BTK occupancy.

4. The computer system of any one of claims 1-3, wherein each iteration interval consists of a 6, 8, 12, or 24-hour period.

5. The computer system of any one of claims 1-4, wherein the one or more iteration intervals consists of from 1 to 4 iteration intervals.

6. The computer system of any one of claims 1-5, wherein the one or more iteration intervals consists of 2 iteration intervals.

7. The computer system of any one of claims 1-6, wherein the corresponding portion of the BTK inhibitor of each respective iteration interval in the one or more iteration intervals is the same.

8. The computer system of any one of claims 1-6, wherein the corresponding portion of the BTK inhibitor for a first respective iteration interval in the one or more iteration intervals is different from the corresponding portion of the BTK inhibitor for a second respective iteration interval in the one or more iteration intervals.

9. The computer system of any one of claims 1-8, wherein the threshold occupancy is at least 60%, at least 80%, or at least 90%.

10. The computer system of any one of claims 1-9, wherein the threshold occupancy is from 50% to 100%.1 1 . The computer system of any one of claims 1 -10, wherein the threshold duration comprises at least 50%, at least 60%, at least 70%, at least 80%, at 90%, or at least 95% of each iteration interval in the one or more iteration intervals.151DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO12. The computer system of any one of claims 1-11, wherein the threshold duration consists of from 50% to 100% of each iteration interval in the one or more iteration intervals.

13. The computer system of any one of claims 1-12, wherein the dosage regimen comprises: administering the BTK inhibitor to a subject at the iteration frequency such that the total amount of the BTK inhibitor is fully administered to the subject over the one or more iteration intervals.

14. The computer system of claim 13, wherein the dosage regimen further comprises repeating the administering over a treatment period.

15. The computer system of claim 14, wherein the treatment period comprises at least 1 week, at least two weeks, at least three weeks, at least four weeks, at least one month, at least two months, at least three months, at least four months, at least five months, at least six months, at least seven months, at least eight months, at least nine months, at least ten months, at least eleven months, at least one year, at least two years, or at least three years.

16. The computer system of any one of claims 1-15. wherein the BTK-mediated condition comprises a mast cell disease.

17. The computer system of claim 16, wherein the mast cell disease comprises indolent systemic mastocytosis (ISM), mast cell activation syndrome (MCAS), eosinophilic esophagitis (EoE), or a food allergy.

18. The computer system of claim 17, wherein the mast cell disease is ISM and the BTK inhibitor is 1 -(4-(((6-amino-5-(4-phenoxyphenyl)pyrimidin-4-yl)amino)methyl)-4- fluoropiperidin-l-yl)prop-2-en-l-one or a pharmaceutically acceptable salt thereof; wherein the BTK inhibitor inhibits at least 90% of basophil activation following administration as measured by basophil CD63 expression in the human subject, thereby treating ISM in the human subject.152DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO19. The computer system of any one of claims 1-15, wherein the BTK-mediated condition comprises myeloproliferative neoplasm (MPN). dry eye disease, allergic conjunctivitis, or geographic atrophy (GA).

20. A non-transitory computer readable storage medium, the non-transitory computer readable storage medium comprising a plurality of instructions, which when executed by a computer system, cause the computer system to perform a method for selecting a dosage regimen for treating a Bruton’s Tyrosine Kinase (BTK)-mediated condition using a BTK inhibitor, the plurality of instructions comprising: instructions for obtaining a model for predicting a BTK occupancy and duration thereof; instructions for generating, using the model, an iteration frequency and a total amount of the BTK inhibitor for administration based upon a filtering criterion, wherein: the iteration frequency comprises one or more iteration intervals, each respective iteration interval in the one or more iteration intervals comprises a corresponding portion of the BTK inhibitor to be administered, and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, the BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration, wherein: the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals; and instructions for selecting a dosage regimen in accordance with a determination that the iteration frequency and the total amount of the BTK inhibitor satisfy the filtering criterion, wherein the dosage regimen comprises the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding portion of the BTK inhibitor.

21. A method for selecting a dosage regimen for treating a Bruton’s Tyrosine Kinase (BTK)-mediated condition using a BTK inhibitor, comprising: obtaining a model for predicting a BTK occupancy and duration thereof;153DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO generating, using the model, an iteration frequency and a total amount of the BTK inhibitor for administration based upon a filtering criterion, wherein: the iteration frequency comprises one or more iteration intervals, each respective iteration interval in the one or more iteration intervals comprises a corresponding portion of the BTK inhibitor to be administered, and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, the BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration, wherein: the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals; and selecting a dosage regimen in accordance with a determination that the iteration frequency and the total amount of the BTK inhibitor satisfy the filtering criterion, wherein the dosage regimen comprises the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding portion of the BTK inhibitor.

22. A computer system for modeling a target endpoint responsive to treatment with a Bruton’s Tyrosine Kinase (BTK) inhibitor, the computer system comprising a memory and a processor, the memory storing a plurality of instructions executable by the processor, the plurality of instructions comprising: instructions for obtaining a model of the target endpoint in a subject having a BTK- mediated condition, wherein the model generates, as output, a representation of the target endpoint in the subject responsive to treatment with the BTK inhibitor; instructions for generating, using the model, an iteration frequency and an unpartitioned quantum of the BTK inhibitor based upon a filtering criterion for the target endpoint, wherein: the iteration frequency comprises one or more iteration intervals, each respective iteration interval in the one or more iteration intervals comprises a corresponding amount of the unpartitioned quantum of the BTK inhibitor to be administered, and154DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration, wherein: the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals; and instructions for determining an application regimen in accordance with a determination that the iteration frequency and the unpartitioned quantum satisfy the filtering criterion, wherein the application regimen comprises the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding amount of the unpartitioned quantum.

23. The computer system of claim 22, wherein the model is obtained using one or more measurements of the target endpoint in the subject, the plurality of instructions further comprising: instructions for obtaining, responsive to inputting the iteration frequency and the unpartitioned quantum to the model, as output from the model, one or more simulated values for the target endpoint in the subject.

24. The computer system of claim 22, wherein the instructions for obtaining the model further comprise: instructions for training the model using, for each respective training subject in a plurality of training subjects, a corresponding one or more measurements of the target endpoint in the respective training subject, and instructions for obtaining, responsive to inputting the iteration frequency and the unpartitioned quantum to the model, as output from the model, one or more simulated values for the target endpoint in the subject.

25. The computer system of any one of claims 22-24, wherein each iteration interval consists of a 6, 8, 12. or 24-hour period.155DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO26. The computer system of any one of claims 22-25, wherein the one or more iteration intervals consists of from 1 to 4 iteration intervals.

27. The computer system of any one of claims 22-26, wherein the one or more iteration intervals consists of 2 iteration intervals.

28. The computer system of any one of claims 22-27, wherein the corresponding amount of the unpartitioned quantum of each respective iteration interval in the one or more iteration intervals is the same.

29. The computer system of any one of claims 22-27, wherein the corresponding amount of the unpartitioned quantum for a first respective iteration interval in the one or more iteration intervals is different from the corresponding amount of the unpartitioned quantum for a second respective iteration interval in the one or more iteration intervals.

30. The computer system of any one of claims 22-29, wherein the threshold occupancy is at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%.

31. The computer system of any one of claims 22-30, wherein the threshold occupancy is from 50% to 100%.

32. The computer system of any one of claims 22-31, wherein the threshold duration comprises at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% of each iteration interval in the one or more iteration intervals.

33. The computer system of any one of claims 22-32, wherein the threshold duration consists of from 50% to 100% of each iteration interval in the one or more iteration intervals.

34. The computer system of any one of claims 22-33, wherein the application regimen comprises: administering the BTK inhibitor to the subject at the iteration frequency such that the unpartitioned quantum of the BTK inhibitor is administered to the subject over the one or more iteration intervals.156DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO35. The computer system of claim 34, wherein the application regimen further comprises repeating the administering over a treatment period.

36. The computer system of claim 35, wherein the treatment period comprises at least 1 week, at least two weeks, at least three weeks, at least four weeks, at least one month, at least two months, at least three months, at least four months, at least five months, at least six months, at least seven months, at least eight months, at least nine months, at least ten months, at least eleven months, at least one year, at least two years, or at least three years.

37. The computer system of any one of claims 22-36, wherein the BTK-mediated disease comprises a mast cell disease.

38. The computer system of claim 37, wherein the mast cell disease comprises indolent systemic mastocytosis (ISM), mast cell activation syndrome (MCAS), eosinophilic esophagitis (EoE), or a food allergy.

39. The computer system of claim 38, wherein the mast cell disease is ISM and the BTK inhibitor is 1 -(4-(((6-amino-5-(4-phenoxy pheny l)pyrimidin-4-yl)amino)methyl)-4- fluoropiperidin-l-yl)prop-2-en-l-one or a pharmaceutically acceptable salt thereof; wherein the BTK inhibitor inhibits at least 90% of basophil activation following administration as measured by basophil CD63 expression in the human subject, thereby treating ISM in the human subject.

40. The computer system of any one of claims 22-36, wherein the BTK-mediated disease comprises myeloproliferative neoplasm (MPN), dry' eye disease, allergic conjunctivitis, or geographic atrophy (GA).

41. A non-transitory computer readable storage medium, the non -transitory computer readable storage medium comprising a plurality of instructions, which when executed by a computer system, cause the computer system to perform a method for modeling a target endpoint responsive to treatment with a Bruton’s Tyrosine Kinase (BTK) inhibitor, the plurality of instructions comprising:157DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO instructions for obtaining a model of the target endpoint in a subject having a BTK- mediated condition, wherein the model generates, as output, a representation of the target endpoint in the subject responsive to treatment with the BTK inhibitor; instructions for generating, using the model, an iteration frequency and an unpartitioned quantum of the BTK inhibitor based upon a filtering criterion for the target endpoint, wherein: the iteration frequency comprises one or more iteration intervals, each respective iteration interval in the one or more iteration intervals comprises a corresponding amount of the unpartitioned quantum, and the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration, wherein: the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals; and instructions for determining an application regimen in accordance with a determination that the iteration frequency and the unpartitioned quantum satisfy the filtering criterion, wherein the application regimen comprises the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding amount of the unpartitioned quantum.

42. A method for modeling a target endpoint responsive to treatment with a Bruton’s Tyrosine Kinase (BTK) inhibitor, comprising: obtaining a model of the target endpoint in a subject having a BTK-mediated condition, wherein the model generates, as output, a representation of the target endpoint in the subject responsive to treatment with the BTK inhibitor; generating, using the model, an iteration frequency and an unpartitioned quantum of the BTK inhibitor based upon a filtering criterion for the target endpoint, wherein: the iteration frequency comprises one or more iteration intervals, each respective iteration interval in the one or more iteration intervals comprises a corresponding amount of the unpartitioned quantum, and158DBl / 163759882.1Attorney Ref. No.: 126569-5019-WO the filtering criterion comprises at least a first filter comprising, for each respective iteration interval in the one or more iteration intervals, a BTK occupancy by the BTK inhibitor that satisfies a threshold occupancy for at least a threshold duration, wherein: the threshold occupancy is at least 50% and the threshold duration comprises at least 40% of an iteration interval in the one or more iteration intervals; and determining an application regimen in accordance with a determination that the iteration frequency and the unpartitioned quantum satisfy the filtering criterion, wherein the application regimen comprises the iteration frequency and, for each respective iteration interval in the one or more iteration intervals, the corresponding amount of the unpartitioned quantum.159DBl / 163759882.1

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