Systems, methods, and devices for predicting the pharmacokinetic effects on ketogenesis following tricaprylin administration

Abstract

A system, method, and apparatus predicts pharmacokinetic parameters following administration of a drug product, such as medium chain triglycerides (MCTs). The method includes generating training data indicative of a human metabolic outcome of the drug product. The training data includes a gastric emptying parameter, a digestion rate, an absorption constant, or a conversion rate, and the training target variable is a ketone concentration or a plasma free fatty acid concentration. The method also includes training a physiologically based biopharmaceutical (PBB) model with the training data, and predicting at least one of a target plasma free fatty acid concentration or a target ketone concentration using the PBB model while using the patient data. Furthermore, as part of a parameter sensitivity analysis of the PBB model, the method varies input parameters of the training data in light of a simulated mean plasma free fatty acid profile or a simulated mean ketone plasma profile.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 323,305, filed March 24, 2022, entitled "METHODS FOR PREDICTING PHARMACOKINETIC INFLUENCES ON KETOGENESIS FOLLOWING ADMINISTRATION OF TRICAPRILIN TO HUMANS," and U.S. Provisional Patent Application No. 63 / 423,349, filed November 7, 2022, entitled "METHODS FOR PREDICTING PHARMACOKINETIC INFLUENCES ON KETOGENESIS FOLLOWING ADMINISTRATION OF TRICAPRILIN TO HUMANS," each of which is incorporated by reference in its entirety in this specification.

[0002] Aspects of the technology disclosed herein relate generally to systems and methods for predicting pharmacokinetics, and more specifically, to systems and methods for predicting ketogenic parameters in patients with Alzheimer's disease. [Background technology]

[0003]

[0003] Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common cause of dementia, accounting for approximately 60-80% of all cases. In the United States alone, an estimated 6.2 million patients aged 65 years or older are affected. Currently, there are several theories regarding the causes and potential mechanisms of AD. Such theories include the formation of amyloid-β plaques, the formation of neurofibrillary tangles in neurons, and chronic inflammation of brain tissue. Many pathophysiological pathways that contribute to the disease are still under investigation.

[0004]

[0004] Studies have confirmed a reduced expression of glucose transporters in the central nervous system (CNS) of Alzheimer's disease patients. Enhancing glucose metabolism in patients may also contribute to increased lifespan. In this context, ketones, acetoacetate and β-hydroxybutyrate, serve as primary energy sources, providing up to 80% of the brain's energy when glucose is not available. This has led to the development of the ketogenic diet to improve energy supply and achieve functional improvement in AD patients.

[0005]

[0005] It is with inter alia these considerations in mind that various aspects of the present disclosure have been conceived and developed. Summary of the Invention [Means for solving the problem]

[0006]

[0006] The embodiments described and claimed herein may address the above problems by providing a method for predicting a pharmacokinetic parameter after administration of a formulation. The method may include generating training data indicative of a human metabolic outcome of the formulation, training a physiologically based biopharmaceutics (PBB) model with the training data by one or more computing devices executing computer readable instructions, and / or modifying input parameters of the training data in light of a simulated mean plasma free fatty acid profile (e.g., octanoic acid) or a simulated mean ketone plasma profile as part of a parameter sensitivity analysis of the PBB model. Additionally, the method may include obtaining, by one or more computing devices, patient data corresponding to a patient receiving oral administration of the formulation, and predicting at least one of a target plasma free fatty acid concentration or a target ketone concentration using the PBB model using the patient data.

[0007]

[0007] In some examples, the formulation includes a medium chain triglyceride (MCT). The MCT can be tricaprylin. The training data can also include a data set based on a gastric emptying model. For example, the gastric emptying model can include a signature algorithm corresponding to a rhythmic emptying pattern. Additionally, the gastric emptying model can include a primary pathway for solid foods and a secondary pathway for liquid foods, and / or a gastric amplitude parameter, a complete emptying parameter, or a gastric periodicity parameter.

[0008]

[0008] In some cases, predicting at least one of the target plasma free fatty acid concentration or the target ketone concentration includes determining a digestion rate constant obtained by approximating the release profile of the formulation in the presence of lipase, assuming a first-order kinetic process. The training data may include a digestion rate and / or a bioavailability digestibility as an in vitro parameter. The training data may also include one or more of the following in vivo parameters: fraction metabolized, absorption constant, conversion rate, excretion rate, volume distribution of free fatty acids, or volume distribution of ketone bodies. Furthermore, the patient data may include one or more of the patient's physiological parameters or formulation administration information.

[0009]

[0009] In some examples, the method may further include modeling a digestion rate using the release profile of the formulation and may include obtaining a digestion rate constant based on the modeling of the digestion rate, where predicting at least one of the target plasma free fatty acid concentration or the target ketone concentration may include using the digestion rate together with the PBB model. Predicting at least one of the target plasma free fatty acid concentration or the target ketone concentration may include predicting the bioavailability of the free fatty acid in the patient based on the digestion rate while using the PBB model. Furthermore, training the PBB model may include instructing the PBB model to consider the enzymatic conversion of free fatty acid to plasma ketone as a non-saturated first-order reaction process.

[0010]

[0010] In some cases, a method for predicting pharmacokinetic parameters after administration of a formulation includes generating training data representing one or more of gastric emptying parameters, digestion rate, absorption constant, or conversion rate using ketone concentration as a training target variable, training a PBB model using the training data with one or more computing devices executing computer-readable instructions, obtaining patient data corresponding to a patient receiving administration of the formulation with one or more computing devices, and / or predicting a target ketone concentration using the PBB model using the patient data.

[0011] In some examples, predicting the target ketone concentration includes predicting the patient's plasma free fatty acid concentration using a PBB model using patient data. The method may also include performing validation of the PBB model using clinical trial data quantifying free fatty acids and ketone bodies from the plasma of volunteers.

[0012]

[0012] In some cases, a method for predicting a pharmacokinetic parameter after administration of a formulation includes generating training data representing a gastric emptying parameter, digestion rate, absorption constant, or conversion rate using plasma free fatty acid concentration as a training target variable, training a PBB model with one or more computer devices executing computer readable instructions using the training data, obtaining patient data corresponding to a patient to be administered the formulation with one or more computer devices, and / or predicting a target plasma free fatty acid concentration using the PBB model using the patient data. In some examples, therapeutic measures may be taken, such as making adjustments to the dosage, concentration, or substance of the formulation, depending on the prediction of the target plasma free fatty acid concentration and / or the target ketone concentration.

[0013] In some examples, the method further includes predicting ketone concentrations using a PBB model using patient data, plasma free fatty acid concentrations, and an estimate of the conversion rate. Training the PBB model includes providing a gastric absorption emptying model having a signature algorithm with an amplitude variable, a periodicity variable, and a time to complete emptying variable. Additionally, the training data may include a first cluster including physiologically based parameters, a second cluster including population parameters, and a third cluster including in vitro parameters.

[0014]

[0014] Other embodiments are also described and recited herein. Moreover, while multiple embodiments are disclosed, still other embodiments of the presently disclosed technology will become apparent to those skilled in the art from the following detailed description, in which illustrative embodiments of the presently disclosed technology are shown and described. As will be recognized, the presently disclosed technology, in all its aspects, can be modified in various aspects without departing from the spirit and scope of the presently disclosed technology. Accordingly, the drawings and detailed description are illustrative in nature and not to be regarded as limiting. [Brief description of the drawings]

[0015] [Figure 1] FIG. 1 is a diagram illustrating an example of a biopharmaceutical modeling system that includes a physiologically-based biopharmaceutical (PBB) model. [Diagram 2]

[0016] FIG. 2 illustrates a schematic diagram of an example metabolic architecture for designing a PBB model, which may form at least a portion of the system shown in FIG. [Diagram 3]

[0017] FIG. 3 illustrates an example of dispersibility and lipolysis testing of an orally consumed formulation with respect to the PBB model (which may form at least a part of the system shown in FIG. 1). [Figure 4]

[0018] FIG. 4 illustrates an example of Phase I clinical trial data used in model validation. [Diagram 5]

[0019] FIG. 5 is a diagram illustrating an example graph of a gastric emptying model (which may form at least a part of the system shown in FIG. 1). [Figure 6]

[0020] FIG. 6 is a diagram illustrating a partial parameter sensitivity analysis for the PPB model (which may form at least a part of the system shown in FIG. 1). [Figure 7]

[0021] FIG. 7 illustrates a graph of simulated in vivo data for a PBB model including gastric emptying (which may form at least a part of the system shown in FIG. 1). [Figure 8]

[0022] FIG. 8 illustrates a graph of simulated in vivo data for a PBB model in which gastric emptying has been omitted (which may form at least a part of the system shown in FIG. 1). [Figure 9]

[0023] FIG. 9 illustrates a graph of simulated in vivo data for a PBB model (which may form at least a part of the system shown in FIG. 1) showing the effect of digestion rate on the PBB model. [Figure 10]

[0024] FIG. 10 illustrates a graph of simulated in vivo data for a PBB model (which may form at least a portion of the system shown in FIG. 1) showing the effect of bioavailability on the PBB model. [Figure 11]

[0025] FIG. 11 is a diagram illustrating a system including a computing device (which may be the same as or form at least a portion of the system shown in FIG. 1). [Figure 12]

[0026] FIG. 12 illustrates an example of a method for predicting pharmacokinetic parameters after administration of a formulation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016]

[0027] Aspects of the present disclosure include systems, methods, and devices for predicting pharmacokinetic parameters (e.g., plasma free fatty acid concentrations, such as octanoic acid concentrations, or ketone concentrations) following administration of a formulation comprising a medium chain triglyceride (MCT), such as tricaprylin. As used herein, "medium chain triglyceride" or "MCT" refers to a triglyceride in which all three fatty acid moieties are medium chain fatty acid moieties. Medium chain fatty acids (MCFAs) are fatty acids having 6 to 12 carbon atoms. In some aspects, MCT refers to a triglyceride that contains almost exclusively caproic acid (C6:0), octanoic acid (caprylic acid; C8:0), capric acid (C10:0), decanoic acid (capric acid; C10:0), or any combination thereof. In some aspects, the MCT is octanoic acid (caprylic acid; C8:0). In some method aspects, the MCT is tricaprylin. Tricaprylin is a semi-synthetic medium-chain triglyceride (MCT) consisting of 8-carbon fatty acids, with an aqueous solubility of 0.4 mg / L. Other MCTs can be used in the dietary management of AD and are within the scope of the present disclosure. Although MCT formulations containing tricaprylin are used as examples throughout this disclosure, one of skill in the art will appreciate that other MCT-containing formulations can be used in the methods disclosed herein. After oral administration, MCTs, such as tricaprylin, are digested by lipases in the gastrointestinal (GI) tract into free fatty acids (e.g., octanoic acid, also known as caprylic acid), which are then absorbed from the duodenum. The free fatty acids, such as octanoic acid, are then converted to ketone bodies by oxidation in the liver. Medium-chain fatty acids, such as octanoic acid, exhibit more favorable absorption and oxidation properties than long-chain fatty acids. Such ketones serve as an important energy source for the CNS. Thus, due to the highly ketogenic potential of free fatty acids such as octanoic acid, MCTs such as tricaprylin may offer advantages over previous techniques. Glucose enters the brain via an active transport mechanism, which may be saturated. A nonlinear relationship between brain and plasma glucose levels may be observed.Compared to transporter-mediated uptake of glucose into the CNS, there is a linear relationship between blood and brain concentrations of free fatty acids, such as octanoic acid, and therefore the increase in plasma ketone levels following administration of MCTs (e.g., tricaprylin) may contribute to a corresponding increase in brain ketone concentrations.

[0017]

[0028] Some pharmacokinetic studies are primarily concerned with direct quantification of plasma ketone levels following administration of MCTs (e.g., tricaprylin). However, the contributions of a series of individual processes related to digestion of MCTs (e.g., tricaprylin) and release of fatty acids (e.g., octanoic acid), absorption, and conversion of free fatty acids to ketone bodies are determined and used by the techniques disclosed herein. Machine learning modeling, such as physiologically based biopharmaceutical (PBB) modeling, may leverage knowledge of the physiological processes involved, but also together with selected in vitro and in vivo data sets, to generate predictions of the biopharmaceutical behavior of a drug. To encompass the complexities potentially present in the pharmacokinetics of MCTs (e.g., tricaprylin), a PBB model may be designed based on in vitro digestion studies and human clinical data. The model may be validated using the results of a Phase I clinical trial. For example, a Phase I clinical trial evaluating a single dose of tricaprylin formulation AC-SD-03 in healthy volunteers may be utilized.

[0018]

[0029] Additional advantages of the technology will become apparent from the disclosure herein.

[0030] FIG. 1 illustrates an example of a biopharmaceutical modeling system 100 including a PBB model 102. The PBB model 102 may be developed using MlxEditor 2020R1 (e.g., Lixoft, Antony, France) to describe the absorption, metabolism, and / or excretion of MCTs (e.g., tricaprylin) after oral consumption. Probability values ​​representing the effect of each physiological process on ketogenesis and the production of ketone bodies (e.g., β-hydroxybutyrate and acetoacetate) may be generated as model output 104 by the PBB model 102. Once the PBB model 102 has undergone training and / or validation, the PBB model 102 may obtain patient data 105 associated with a target patient. The PBB model 102 may analyze the patient data 105 using techniques discussed herein and may output predicted plasma free fatty acid concentrations 107, such as octanoic acid concentrations 107, and / or predicted ketone concentrations 109 based at least in part on the patient data 105. The design and operation of the PBB model 102 is discussed in further detail below.

[0019]

[0031] The PBB model 102 can receive various model inputs 106, including MCTs, such as tricaprylin, experimental data 108, and / or human metabolic data 110. For example, individual patient profiles can be characterized by a bimodal phenomenon. The data can show that MCT formulations, such as tricaprylin formulations (AC-SD-03), can be administered orally at high doses. As a result, the effect of gastric emptying on in vivo performance can be hypothesized based on the gastric emptying model. For example, gastric emptying can be controlled by multiple factors, such as neural circuits and hormones, and gastric emptying occurs differently in fasting and fed states. As the data shows, in the fed state, gastric emptying of solid food can take several hours. Alternative routes, such as the gastric passage (Magenstrasse), can allow non-caloric liquids to leave the stomach in a much shorter time. Such an emptying pattern after ingestion of a high-fat homogenous meal can be observed by the PBB model 102. To mathematically interpret this complex physiological process, a pulsatile emptying pattern can be incorporated into the PBB model 102. For example, the pulsatile emptying pattern can include a signature algorithm that mimics an orderly transport pattern followed by a lag phase:

[0020]

number

[0021]

[0032] The amplitude (a) may represent the maximum transit rate, while the duration of the lag phase may be defined by the period (P). After a certain time (h), the gastric compartment may be completely emptied. Suitable simulation parameters can be identified, for example, using Stella® Architect (v1.5.1, isee systems, Lebanon, USA). A study measuring gastric volume after ingestion of a high-fat homogenous meal can be used to define one or more gastric emptying parameter ranges. To compare the model simulation with clinical data, the absolute mean fold error (AAFE) can be calculated using the following formula:

[0022]

number

[0023]

[0033] To estimate the digestion of MCTs (e.g., tricaprylin) after entering the small intestine, in vitro lipid digestion kinetics 114 can be used as model input 106. To model the digestion rate 116, the release profile 118 of fatty acids in the presence of lipase can be approximated assuming a first-order kinetic process. The digestion rate constant (k d ) can be obtained. Under conditions mimicking GI physiology, the digestion profile can reach a plateau of about 83%. Phase I clinical trials may also include quantification of MCTs, such as tricaprylin. Negligible uptake of undigested triglycerides (C max =1.05 μM) can be observed. It can be determined that the total digestion of MCTs (e.g., tricaprylin) in the GI tract is primarily related to the overall bioavailability (F) of free fatty acids (e.g., octanoic acid). Overall, the digestion of MCTs (e.g., tricaprylin) in the GI tract is primarily related to the overall bioavailability (F) of free fatty acids (e.g., octanoic acid).

[0024]

[0034] Further input parameters are shown in Table 1. Table 1 shows the in silico parameters 130 and parameter ranges that can be used as initial estimates for the in silico analysis of free fatty acids and ketones.

[0025] [Table 1]

[0026]

[0035] Reliable data sources can be identified for the purpose of generating a training data set describing the plasma pharmacokinetics of free fatty acids (e.g., octanoic acid, etc.) following intravenous administration of free fatty acids (e.g., octanoic acid, etc.) or ketones to estimate parameters involved in the distribution, metabolism, and excretion of free fatty acids (e.g., octanoic acid, etc.). Pharmacokinetic data 120 relating to MCTs (e.g., tricaprylin, etc.) and their metabolites in humans (which may be included in the training data 122 and / or used by the validation system 124 of the biopharmaceutical modeling system) are summarized in Table 2:

[0027] [Table 2]

[0028] In Table 2, DBHB is D-β-hydroxybutyrate; ET is essential tremor; SD is standard deviation.

[0036] In some examples, ketone plasma concentrations may be quantified after an intravenous bolus injection of sodium acetoacetate (n=12). Two major components of plasma ketones, acetoacetate and D-β-hydroxybutyrate (n=6), may also be quantified upon intravenous infusion of D-β-hydroxybutyrate at three different doses. Blood free fatty acid (e.g., octanoic acid, etc.) concentrations may be measured upon oral administration of free fatty acid (e.g., octanoic acid, etc.) at five different doses to a patient population (n=3 per dose). This pharmacokinetic data 120 may be analyzed using PKanalix 2020R1 (e.g., Lixoft, Antony, France). These injections, infusions, and / or dose consumption by the target patient may be provided to the PBB model 102 (e.g., as patient data 105).

[0029]

[0037] In some instances, a one-compartment model may adequately describe the pharmacokinetics of free fatty acids (e.g., octanoic acid, etc.) and ketones, which may then be introduced into the hybrid model. The resulting parameter ranges (minimum, maximum, and average values ​​across all studies) may be used to define initial guesses within the fixed ranges when modeling plasma concentration-time profiles obtained from Phase I clinical trials during model validation. In developing a pharmacokinetic model, several important assumptions may be made, including the model structure and differential equations used to simulate the individual processes. The pharmacokinetic studies provided to the PBB model 102 may include a wide dose range, where the scalability of rates between various dose levels and patient populations may be determined or assumed. This includes the enzymatic conversion of free fatty acids (e.g., octanoic acid, etc.) to plasma ketones, considered a non-saturable first-order process.

[0030]

[0038] In some scenarios, the PBB model 102 can be written in the MIXTRAN programming language and executed using Monolix 2020R1 (e.g., Antony, France). Initial parameters and fixed parameter ranges (e.g., such as those shown in Table 1 and / or model input 106) can be obtained (from experimental and / or literature data).

[0031]

[0039] In the following, population parameters can be obtained using the Stochastic Approximation Expectation Maximization (SAEM) algorithm for a target population treated with an MCT formulation (e.g., AC-SD-03). The population parameters can be considered to be log-normally distributed. In addition, empirical Bayesian estimation (EBE) and conditional distribution estimation tasks can be performed to calculate individual predictions and to provide individual parameters and data uncertainties for each individual, respectively. Individual clinical data 124 on free fatty acids (e.g., octanoic acid) and ketones obtained from pharmacokinetic studies can be used for model validation. For example, the accuracy of the predictions can be evaluated by calculating the relative mean absolute prediction error (MAPE, %) using the following formula:

[0032]

number

[0033]

[0040] In Equation 3, V O is the measured concentration, V P are the corresponding predicted concentrations. Predictions may be interpreted as excellent performance if the MAPE is <10% and good performance if it is between 10% and 20%. Additionally, when implemented by one or more validation / simulation system(s) 126, models may be evaluated by visual inspection of goodness-of-fit plots, plots of observed vs. individual predicted values, individually weighted residuals (IWRES) vs. time, and standardized prediction distribution error (NPDE) vs. time.

[0034]

[0041] In some cases, a partial parameter sensitivity analysis (PPSA) may be performed by investigating the effect of model input(s) 106 on the results of a simulation performed with the PBB model 102. The average PK profiles for both free fatty acids (e.g., octanoic acid) and ketones in humans may be used for the simulation. Three different combinations of input variables may be varied. Specifically, the gastric emptying parameters a, h, and P, and the in vivo parameter p metabo , k a , k conv , k e , V OA , and V ketone can be varied within a range of ±20%. d The release rate can be varied by up to 50% of the absolute value to visualize the effect of the individual in vivo parameters (p metabo , k a , k conv ), further PPSAs could be conducted to identify their effects.

[0035]

[0042] In some examples, simulations with the validation / simulation system(s) 126 can be performed using Simulx 2020R1 (Lixoft, Antony, France) from the PK model using individual estimates generated by Monolix software. One hundred hypothetical individuals can be simulated including plasma concentration-time profiles of free fatty acids (e.g., octanoic acid) and ketones considering a single dose of 42,500 μmol tricaprylin (formulated as AC-SD-03) during 0-16 hours. In some scenarios, statistical analysis can be performed using t-tests on GraphPad Prism8.

[0036]

[0043] Various neurodegenerative diseases cause brain hypometabolism due to a significant decrease in glucose utilization. Ketogenesis may provide an alternative energy source, but may also have beneficial effects on cognitive function in Alzheimer's disease patients. Medium-chain triglycerides, such as tricaprylin, are metabolized into ketone bodies and have the capacity to provide 80% of the energy required by the CNS. For other systems, determining the effect of these ketone bodies on bioavailability is a challenge. For that purpose, the PBB model 102 is a suitable in silico model developed to quantitatively describe the effects of various influences on ketone body production and physiological parameters on pharmacokinetics. In vitro digestion studies of MCTs (e.g., tricaprylin, etc.) in gastric and intestinal media can provide an insight into the expected degradation rate in the human intestine. Other in vivo parameters with potential effects on ketone body concentrations can be estimated based on information obtained from the literature. Phase I clinical trials evaluating MCT formulations (e.g., AC-SD-03) in healthy volunteers can provide the pharmacokinetic data necessary for model validation.

[0037]

[0044] Turning to FIG. 2, a schematic 202 of a metabolic engineering framework 204 for designing the PBB model 102 is shown.

[0045] As shown in Figure 2, the PBB model 102 can be used for in silico analysis and data simulation. The formulation can enter the stomach where MCTs (e.g., tricaprylin, etc.) are released and then move to the duodenum. The MCTs (e.g., tricaprylin, etc.) can be digested into free fatty acids (e.g., tricaprylin to octanoic acid), which can then be absorbed in the intestine and subsequently metabolized / converted. A portion of the absorbed free fatty acids (e.g., octanoic acid) can be converted into ketone bodies and then excreted from the body. A certain percentage can enter the metabolic pathway and disappear from the plasma.

[0038]

[0046] As previously shown, the formulation may reach the intestine after medium chain fatty acids (MCTs) (e.g., tricaprylin) have passed through the stomach. Dissolution and lipolysis of the MCT formulation (e.g., AC-SD-03) is believed to cause the release of free fatty acids (e.g., octanoic acid), which are then absorbed into the blood circulation. Conversion of the free fatty acids (e.g., octanoic acid) to ketone bodies then occurs. This conversion may involve some loss to other metabolic pathways, including the Krebs cycle. Ketone bodies may appear in plasma and serve as an energy source for the brain and other tissues. Clinical trials used to validate the model may involve quantification of free fatty acids (e.g., octanoic acid, etc.) and ketone bodies obtained from the plasma of healthy volunteers. In the following, the in vitro and in vivo data sources used to design and validate the model are discussed in more detail.

[0039]

[0047] In some examples, an initial dispersibility test can be performed to evaluate the physical stability of the MCT formulation (e.g., AC-SD-03, etc.) in gastric media (pH 1.2, 37°C). This test provides more information regarding the physical state of the gastric dispersion that may affect gastric emptying. Considering that multiple peaks may be observed in the first 4 hours after administration, this period may be investigated. Other periods may also be investigated based on the peaks observed for the MCT formulation being tested.

[0040]

[0048] 3 shows a dispersibility and lipolysis study of the AC-SD-03 formulation carried out in 50 mM phosphate buffer (pH 1.2) at 37° C. Image 302 is a photograph showing the sedimentation of the AC-SD-03 formulation within 4 hours. Any of the information obtained from this study can be used as model input 106 and / or to validate the PBB model 102.

[0041]

[0049] As shown in Figure 3, graph 304 shows the percentage of tricaprylin in the supernatant of the AC-SD-03 suspension within 4 hours (n=3). Graph 306 shows the digestion profile of AC-SD-03 expressed as the percentage digestibility per hour. The solid line represents an approximation of first order kinetics. Data is expressed as the mean ± standard deviation (SD) (n=3).

[0042]

[0050] In some instances, settling of the MCT formulation (e.g., AC-SD-03, etc.) may occur quickly, as shown in image 302 and graph 304 in FIG. 3. Although creaming or phase separation of oil from the aqueous phase is not observable, significant amounts may settle along with the insoluble excipients. The experiment may be performed without stirring, so settling may be overestimated. Nevertheless, adsorption of the MCT (e.g., tricaprylin) to the solid silica particles may lead to significant gastric retention. The initial dose may be given in a total volume of 300 mL, resulting in a low viscosity. Thus, most of the MCT (e.g., tricaprylin) may have traveled from the cardia of the stomach through the mid-pylorus into the duodenum. Repeated intake of water after 1 hour likely caused further "washout" of the drug. In this example, therefore, a more stable dispersion may increase the amount of tricaprylin delivered via this route.

[0043]

[0051] As a next step, the in vitro digestion of the MCT formulation (e.g., AC-SD-03, etc.) may be measured. The formulation may be exposed to gastric media, followed by lipolysis in a medium simulating intestinal conditions. Lipolysis may result in the release of fatty acids, expressed as a percentage of free fatty acids (e.g., tricaprylin) digested, as shown in graph 306. For every mole of tricaprylin, 3 moles of titratable fatty acids may be released. In the first 5 minutes, 40±3% of tricaprylin may be converted to octanoic acid, after which the digestion rate gradually decreases. After 15 minutes, the digestion profile plateaued at 83±4%. Assuming rapid absorption from the intestine, the digestion rate (kd) and the extent of lipolysis (F) are primarily related to the bioavailability of octanoic acid. In the simulations performed by the validation / simulation system(s) 126, this percentage (83%) may be used as a baseline value in simulating different bioavailabilities. Incidentally, there is a certain probability that undigested tricaprylin may also be absorbed into the blood circulation. However, one estimated rate for this process may be 30-52 gmol / h, which corresponds to less than 0.5% of the total dose in the first 4 hours. Also, negligible amounts of tricaprylin were detectable in the blood circulation during the Phase I clinical trial, which also indicates the low importance of this pathway. Metabolic processes such as intrahepatic conversion of tricaprylin generally lead to the formation of octanoic acid, which is then converted into ketone bodies.

[0044]

[0052] As outlined above, the digestion parameter k d and F have a strong effect on the absorption of free fatty acids (e.g., octanoic acid), which can be determined and used by the digestion parameter model of the PBB model 102. The in vitro lipolysis profile was fitted to a first-order kinetic model (as shown in graph 306 of FIG. 3), resulting in a digestion rate of 4.1 h -1However, some evidence suggests that in vitro systems do not resemble physiological processes and that even higher digestion rates can be expected in vivo. Prehydrolysis of medium-chain triglycerides by gastric lipase may also increase digestion. Considering the potential differences between in vitro and in vivo lipolysis, a 1–5 h period was used to determine the rate of digestion. -1 Digestive parameters within the range of 0.1 to 0.5% can be determined. Thus, the PBB model 102 may approximate individual profiles with approximately 20% higher digestion rates. Based on in vitro observations, it may also be estimated that 83±4% of the initial dose undergoes lipolysis. This is subject to in vitro uncertainties. Thus, higher and lower bioavailabilities may be defined in hypothetical clinical trials, as discussed below.

[0045]

[0053] In some examples, a gastric emptying model may be generated and used by the PBB model 102. As a non-limiting example, MCT formulation AC-SD-03 is a powder formulation with a high tricaprylin content. The high dose combined with the low density of the drug enhanced the effect of gastric emptying on the pharmacokinetics. A non-uniform distribution of partially bound tricaprylin in the gastric contents may occur unexpectedly. Therefore, an algorithm may be implemented to simulate such an emptying pattern 128. Training data 122 representing measurements of gastric volume changes after ingestion of a high-fat homogenous meal in human subjects may be used. This data may be assumed to resemble a standard breakfast and simultaneous ingestion of tricaprylin by the PBB model 102. A signature algorithm may be used to simulate both a slow emptying pattern without additional water intake, as well as repeated "washouts" of tricaprylin. Repeated "washouts" of tricaprylin are the result of a clinical protocol that allows free access to water one hour after administration. Suitable parameter ranges may be identified by approximation to literature data. An AAFE of 1.11 is believed to represent the best representation of the profile. Parameters used by the algorithm may include the amplitude (a) of the sine curve, the period (P), and the time to complete discharge (h). These are 1.65h -1 , 2.03h, and 0.62h. The variation of the parameters can be estimated within a range of ±50% of the initial value (Table 1).

[0046]

[0054] In some examples, the PBB model 102 may generate and / or use a model of absorption and metabolism of free fatty acids (e.g., octanoic acid, etc.). The training data 122 includes data from 0.23 to 3.16 h -1The pharmacokinetics of free fatty acids (e.g., octanoic acid, etc.) after oral administration at various dose levels may be included, with absorption rate constants in the range of 0.01 to 0.01. If absorption is poor, the effect of gastric emptying on pharmacokinetics and bioavailability may be reduced. However, in the case of free fatty acids (e.g., octanoic acid, etc.), the absorption rate is considered to be high enough to ensure complete absorption within the intestinal transit time. Still, the absorption rate is considered to be one of the influential parameters and can be further evaluated in the PPSA.

[0047]

[0055] In some examples, the PBB model 102 may include a ketone body model for determining ketone body production from free fatty acids (e.g., octanoic acid, etc.) and the pharmacokinetics of ketone bodies. The conversion rate constant (k conv ) can be measured in rats and / or humans. Human clinical trials can be used as training data 122 to estimate this parameter. Plasma ketone concentrations observed after infusion of sodium caprylate in male Sprague-Dawley rats can be considered as confirmation of first-order reaction kinetics without using this rate for further analysis (e.g., if used by the validation / simulation system 126). The conversion rate (k conv ) can be estimated based on the excretion rate of free fatty acids (e.g., octanoic acid in the case of tricaprylin, etc.). Here, ketogenesis can be considered as the main metabolic pathway. Constant losses to other biochemical pathways can be determined. The Krebs cycle utilizes the available chemical energy of acetyl coenzyme A (an intermediate of octanoic acid) to convert it into nicotinamide adenine dinucleotide (NADH), which serves as an energy source. Also, β-hydroxy-3-methylglutaryl coenzyme A can be used in cholesterol synthesis. The conversion rate k conv or the metabolized fraction p metaboIf the model is highly sensitive to , its predictive power may be reduced. Therefore, for the PBB model 102, we systematically analyzed their sensitivity in a partial parameter sensitivity analysis (PPSA).

[0048]

[0056] In some scenarios, the PBB model 102 may perform a simulation of human pharmacokinetics, for example using the validation / simulation system 126. The PBB model 102 may fill the knowledge gap between administration of MCTs (e.g., tricaprylin, etc.) and the appearance of ketone bodies in plasma. Breaking this process into separate steps followed by partial validation may be one of the aims of the PBB model 102. The final structure may include a simulation of gastric emptying in the fed state with and without additional water intake. Dispersibility testing may confirm the formation of a stable low-viscosity emulsion and that more than 80% of tricaprylin may be rapidly digested to octanoic acid, for example, during an in vitro lipolysis experiment. Absorption and conversion rate constants of free fatty acids (e.g., octanoic acid, etc.) and volume of distribution (VOA) may be obtained by the PBB model 102 by performing a pharmacokinetic analysis of clinical data.

[0049]

[0057] In some examples, as shown in FIG. 4, the training data 122 includes phase I clinical trial data 400, which provides plasma concentrations of free fatty acids (octanoic acid in this example) and ketone bodies over time in 20 healthy volunteers. Pharmacokinetic profiles of up to 20 subjects may be used in model validation. Individual in silico simulations of octanoic acid (blue dots) and ketones (red dots) are shown in purple and black lines, respectively, while comparing with in vivo pharmacokinetic data of octanoic acid (blue dots) and ketones (red dots). The model structure still provides a variety of possible situations for this simulation, but with fixed parameter ranges, the estimation can be improved by weighting each influence. As expected, certain subjects still yielded high prediction errors. However, the overall performance of the PBB model 102 for the phase I clinical trial is acceptable. Individual parameters may be narrowly distributed, with the strongest variation observed in the volume of distribution of free fatty acids (e.g., octanoic acid). The prediction errors are summarized in Table 3. Table 3 shows the mean absolute prediction error (MAPE) of the PBB model 102 for simulations of human clinical data across all subjects.

[0050] [Table 3]

[0051]

[0058] The absence of systematic bias can be confirmed by visualizing the individual weighted residuals (IWRES) and the standardized prediction error (NPDE) versus time after administration. The mean values ​​may not be significantly different from zero (p=0.06 for IWRES and p=0.09 for NPDE, respectively). Each analysis may show accurate predictions.

[0052]

[0059] In some cases, gastric emptying can have a strong effect on pharmacokinetics and can be difficult to simulate. For example, initial administration of AC-SD-03 with 300 mL of water can lead to rapid emptying along the gastric pathway. The corresponding emptying patterns in 20 healthy volunteers are shown in Figure 5.

[0053]

[0060] Figure 5 illustrates the validation technique of the PBB model 102. Graph 500 represents the predicted amount of tricaprylin in the stomach of 20 subjects within 3 hours after AC-SD-03 administration. The thick curves (green, blue, red) show typical cases where subjects had different gastric emptying patterns when they consumed different amounts of water during the trial. Schematic diagram 502 shows how AC-SD-03 suspension is emptied from the stomach in the fed state along the gastric pathway (created using biorender.com).

[0054]

[0061] In some instances, initial gastric emptying may affect 50-100% of the total dose. As fluid intake is not permitted until 1 hour after tricaprylin administration, additional fluid intake may be expected in some patients at later time points. Thus, a model algorithm may be used that has the flexibility to simulate various gastric emptying patterns. Evacuation of caloric content occurs at a fairly slow rate and may plateau (e.g., graph 500 in FIG. 5). The observed emptying pattern 1 hour after tricaprylin ingestion may be due to uneven distribution of tricaprylin in the stomach or additional fluid intake.

[0055]

[0062] Considering that in some scenarios all population parameters fall within the initial range and prediction errors can remain at low levels, the initial values ​​(Table 1) can provide estimates for physiological and formulation-related effects. This leads to the following conclusion: Tricaprylin at 1.7 h -1 Fatty acid absorption may be the rate-limiting step when fatty acids reach the small intestine at a rate of 4.6 h. -1 ) and ketogenesis (2.6h -1), there may be a more significant variation in absorption rates between different individuals. The conversion of octanoic acid in the liver may play a less important role in the availability of the active form of tricaprylin. One of the main effects may be related to the formulation of the drug, which may cause tricaprylin to be retained in the stomach for a period of time in many patients. These observations may be systematically evaluated in the PPSA and / or provided to the PBB model 102 as model input 106 or as part of the validation process.

[0056]

[0063] In some instances, partial parameter sensitivity analysis (PPSA) may be performed. Model parameters may have significant errors associated with them. However, if the simulation is based on in vitro data, it is expected to have the greatest uncertainty. PPSA identifies parameters that have a strong influence on the simulation. If high uncertainty coincides with high model sensitivity in one or more parameters, the predictive ability may be reduced.

[0057]

[0064] For example, the input parameters may be initially divided into three clusters. To identify combinations with major effects, physiologically based parameters (e.g. gastric emptying) and population parameters (e.g. excretion rate) may be varied over a 20% range. In vitro parameters (e.g. digestion rate) may be varied over a larger range.

[0058]

[0065] FIG. 6 depicts a partial parameter sensitivity analysis 600 of an in silico model in which (A and D) gastric emptying parameters (a, h, P), (B and E) in vitro parameters kd and F, and (C and F) in vivo parameters (p metabo , k a , k conv , k e , VOA, and V ketone) are applied. These parameters can be varied in light of the simulated average (A, B, and C) octanoic acid, and (D, E, and F) simulated average ketone plasma profiles. The observed plasma concentrations can be plotted as symbols with error bars (n=20) representing the standard deviation.

[0059]

[0066] In some examples, the effect of systematically varying parameters on the simulated pharmacokinetic profiles of octanoic acid and ketones is shown in Figure 6. The corresponding pharmacokinetic parameters (C max , T max , A.U. Call ), but also gastric emptying parameters (a, h, and P), in vitro parameters (k d and F), and in vivo parameters (p metabo , k a , k conv , k e , VOA, and V ketone ) may make the model sensitive to changes in both.

[0060]

[0067] In some cases, gastric emptying is C max While it may have the strongest effect on AU, in vitro and in vivo parameters Call By varying the individual parameters, the absorption rate (k a ) can be further elucidated. conv The enzymatic conversion rate (k conv ) or metabolic ratio (p metabo) may have little or no effect on pharmacokinetics. These parameters may be even more important for some patient populations, for example due to impaired liver function. If the absorption rate is lower than the estimated digestion and conversion rates, they may act as gatekeepers for ketone concentrations.

[0061]

[0068] In some instances, the next step may be to conduct virtual clinical trials in a larger population to simulate expected pharmacokinetics, where the digestion rate (k d ), the extent of intestinal digestion (F), and the effect of gastric emptying can be determined separately.

[0062]

[0069] In some cases, in silico predictions of pharmacokinetics can be made by the PBB model 102. For example, a Phase I clinical trial can be used to validate the model, but it includes only a small number of subjects (n=20). Therefore, additional simulations of the predicted performance of AC-SD-03 can be performed on a larger population (n=100).

[0063]

[0070] 7 shows a first graph 700 showing simulated in vivo data for octanoic acid and a second graph 702 showing simulated in vivo data for ketones. The blue bands show percentiles of the simulated data for all individuals and all replicates computed. The solid black curve indicates the median.

[0064]

[0071] As shown in FIG. 7, the biopharmaceutical modeling system 100 can provide predicted plasma concentration-time profiles of octanoic acid and ketones in 100 virtual subjects. max , T max , and AU CallThe medians for octanoic acid and ketones could be 124.7 μM, 1 h, and 388.2 μM h, respectively. The powder formulations could lead to higher fluctuations in plasma octanoic acid concentrations, whereas plasma ketone levels could remain in a narrower range, suggesting that the effect of conversion rate is minor.

[0065]

[0072] 8 shows a first graph 800 of simulated in vivo octanoic acid and a second graph 802 of simulated ketone concentrations using a physiologically based biopharmaceutical model that does not include gastric emptying. The blue bands show percentiles of the simulated data for all individuals and all iterations computed. The solid black curve indicates the median.

[0066]

[0073] In some instances, several formulation parameters may be predicted to have a profound effect on plasma ketone levels. This may include the rate and extent of gastric emptying and lipid digestion. The effect of gastric emptying on the in vivo fate of AC-SD-03 may be investigated by completely removing / omitting the stomach compartment from the PBB model 102. The results are shown in FIG. 8. The simulations demonstrate that gastric emptying has a profound effect on plasma concentrations, particularly C max This suggests that the PBB model 102 significantly reduces T (70% and 26% increase for octanoic acid and ketones, respectively). A comparison of predictions using the PBB model 102 with and without gastric emptying is shown in FIG. 8. As expected, T max The AU of octanoic acid and ketones was reduced from 2 hours to 1 hour. Call remained almost the same. max can be a strong indicator of an adequate supply of ketone bodies to the brain, and thus maximizing peak concentrations would most likely contribute to favorable effects in treating AD.

[0067]

[0074] FIG. 9 shows the in vivo digestion rate (k d9A-9C depict a first graph 900 and a second graph 902 illustrating the effect of k on ketone levels, as well as a third graph 904 and a fourth graph 906 illustrating the effect of k on ketone levels. d 50% increase in k d The blue bands represent percentiles of the simulated data for all individuals and all replicates computed. The solid black curve indicates the median.

[0068]

[0075] In some cases, further simulations have been performed to determine the relationship between plasma ketone concentration and digestive rate (k d ) can be determined. The digestion rate can be increased by 50% and 100%. As shown in FIG. d A significant increase in plasma ketone levels can be observed when the median C is increased by 50%. max and AU Call can be calculated to be 129.5 μM and 311.5 μM h (octanoic acid), and 488.7 μM and 1126.5 μM h (ketone), respectively.

[0069]

[0076] In some scenarios, taking into account the results of the dispersibility test, an increase in the digestion rate may be achieved, for example, by developing a more stable liquid emulsion system with optimized dispersibility and physical stability: the greater the surface area, the greater the proportion of tricaprylin that is rapidly digested.

[0070]

[0077] FIG. 10 shows a first graph 1000 and a second graph 1002 showing the effect of bioavailability (e.g., tricaprylin digestion (F)) on in vivo octanoic acid, and a third graph 1004 and a fourth graph 1006 showing the same effect on ketone concentrations. The first graph 1000 and the third graph 1004 are when F is set to 50%, and the second graph 1002 and the third graph 1004 are when F is set to 100%. The blue bands indicate percentiles of simulated data for all individuals and all iterations computed. The solid black curve indicates the median.

[0071]

[0078] In some instances, the extent of lipolysis on bioavailability can be evaluated in silico. Degradation of MCTs (e.g. tricaprylin) may be primarily related to the fraction of the initial dose available in plasma. For AC-SD-03, using the digestibility found in vitro (83%) as a reference value, the increase in bioavailability can be simulated and an increase in bioavailability of up to 17% can be observed (corresponding to a bioavailability of 100%). With the further assumption that the estimation of absolute bioavailability may be inaccurate, even higher values ​​may be obtained, as shown in Figure 10. However, considering all aspects, the limited dispersibility and incomplete lipolysis of AC-SD-03 may remain the most important formulation parameters limiting bioavailability.

[0072]

[0079] Therefore, additional approaches may focus on formulation strategies that enhance the rate and extent of digestion and reduce the effects of gastric emptying.

[0080] In some examples, a study / analysis using the biopharmaceutical modeling system 100 describes and explains the ketone body generation after tricaprylin ingestion while using the PBB model 102. The simulation can accurately predict the in vivo fate of the spray-dried powder formulation in human subjects. Estimating physiological and formulation-related influences on pharmacokinetics, the extent of tricaprylin digestion can have a strong effect on bioavailability. Not only can incomplete lipolysis be observed in vitro, but dispersibility studies can also provide further evidence that a certain percentage of tricaprylin is adsorbed to the insoluble excipients of AC-SD-03. In addition, tricaprylin is distributed and adsorbed non-uniformly to the gastric contents, which is thought to be mainly related to the strong influence of gastric emptying on the pharmacokinetics. A significant absorption delay can be observed along with a decrease in the peak plasma concentration of ketone bodies. When octanoic acid reaches the small intestine, fatty acid absorption rather than enzymatic conversion is more important for the C of ketones. max It may have a certain gatekeeper effect on the plasma concentration. Even at high doses, the enzyme capacity may circumvent plasma concentration limitations. When formulating tricaprylin, a medium chain triglyceride, formulation strategies aimed at shortening gastric transit time as well as improving dispersibility (to enhance the digestion process) are possible. Spray-dried powder formulations can be at least partially processed as low density solids to facilitate the effect of gastric emptying and reduce the extent of its digestion.

[0073]

[0081] In some examples, the biopharmaceutical modeling system 100 can use various materials to implement the techniques disclosed herein. For example, the spray-dried tricaprylin powder formulation (AC-SD-03) is composed of 40% active pharmaceutical ingredient tricaprylin and 60% inactive ingredients, including Kolliphor RH40 (BASF, Germany), glyceryl monooleate (Abitec Corp., USA), soy phosphatidylcholine (Lipoid LLC, USA), polyvinylpyrrolidone (JRS Pharma GmbH & Co. KG, Germany), Cabosil® fumed silica (Koninklijke DSM NV, Netherlands), sucralose, acesulfame potassium, and flavorings. This formulation can be manufactured by Lonza (Oregon, USA) with a license from Cerecin Inc. (Singapore). Octanoic acid (>99% purity) can be purchased from Nu-Chek Prep Inc. (Minnesota, USA). Anhydrous sodium dihydrogen orthophosphate (>98% purity) can be purchased from Chem Supply (SA, Australia). Methanol (LiChrosolv) can be purchased from Merck Millipore (Bayswater, Australia). Sodium hydroxide pellets (>97% purity) can be purchased from Ajax Finechem (NSW, Australia). Trifluoroacetic acid (HPLC, >99% purity) and 4-bromophenylboronic acid (4-BPBA, >95% purity) can be purchased from Sigma-Aldrich (St. Louis, USA). FaSSIF (Fasted Simulated Intestinal Fluid) can be purchased from Biorelevant.com Ltd (London, UK). USP grade porcine pancreatin extract can be purchased from Southern Biologicals (VIC, Australia). A colorimetric kit for quantifying triglycerides can be purchased from Sigma-Aldrich Pte. Ltd. (MO, USA).Distilled and deionized water (18.2 Mil / cm at 25° C.) can be obtained from a Millipore water purification system (Billerica, USA). Those skilled in the art will appreciate that other MCT formulations can also be used in the biopharmaceutical modeling systems and methods disclosed herein, and that AC-SD-03 is only one example of such a formulation.

[0074]

[0082] In some cases, MCT formulations (e.g., AC-SD-03) can be characterized for in vitro dispersibility and digestion behavior.

[0083] In some cases, dispersibility testing may include methods to assess the dispersibility and lipolysis of medium chain triglycerides (MCTs), which determine the gastric physical stability and gastric emptying of formulations containing MCTs using a simulated gastric medium. The method includes dispersing the formulation in a gastric medium, incubating the formulation at 36° C. to 38° C. for 3.5 to 4.5 hours without stirring, obtaining samples of supernatant from the formulation at one or more of the following intervals: 0 minutes, 5 minutes, 10 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 1.25 hours, 1.5 hours, 1.75 hours, 2 hours, 2.25 hours, 2.5 hours, 2.75 hours, 3 hours, 3.25 hours, 3.5 hours, 3.75 hours, 4 hours, 4.25 hours, and 4.5 hours, quantifying the amount of MCT in each sample, and analyzing the quantified amount of MCT to determine the time point at which sedimentation of MCT in the formulation occurs, the time point of sedimentation being indicative of the extent of gastric retention, MCT bioavailability, and / or digestion.

[0075]

[0084] In some cases, any gastric medium known in the art may be used in the method for determining the physical stability of the formulation. The gastric medium may include a phosphate buffer at pH 1.2. The phosphate buffer may include a 50 mM phosphate buffer. The gastric medium may be prepared from a mixture of sodium chloride (2.0 g), dilute hydrochloric acid (24 ml), and water (approximately 1000 ml). In some aspects, the gastric medium was prepared by dissolving pepsin (1 g), gastric mucin (1.5 g), and NaCl (8.775 g) in 1 L of distilled water and adjusting the pH to 1.3 using 6N HCl. The gastric medium may be a commercially available artificial gastric fluid or a simulated gastric fluid. The gastric medium may include a solution that simulates the composition and pH of gastric digestive fluid. Non-limiting examples of components in the gastric medium include sodium chloride, dilute acid, pepsin, mucin, etc.

[0076]

[0085] The method may include dispersing the MCT-containing formulation in a gastric medium. Dispersing the formulation may include mixing a powder of the MCT-containing formulation in a phosphate buffer or an artificial gastric medium. Dispersing the formulation may result in a suspension of the MCT-containing formulation in the phosphate buffer or the artificial gastric medium. Further incubation may be performed with stirring. The suspension may then be incubated at 35°C to 39°C for 3 hours to 5 hours without stirring.

[0077]

[0086] The incubation may be carried out for about 3 hours, about 3.5 hours, about 4 hours, about 4.5 hours, or about 5 hours. In some cases, the incubation may be carried out for about 4 hours.

[0078]

[0087] The suspension may be incubated at a temperature of about 35° C. to about 39° C. In some aspects, the suspension is incubated at a temperature of about 35° C., about 36° C., about 37° C., about 38° C., or about 39° C. In some aspects, the suspension is incubated at a temperature of about 37° C.

[0079]

[0088] The method may further comprise obtaining a sample of the supernatant from the formulation after incubation. Samples may be obtained at several intervals. Samples may be obtained at the start of incubation, at 0 minutes, and at one or more intervals. The one or more intervals may include obtaining samples at 0 minutes, 5 minutes, 10 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 1.25 hours, 1.5 hours, 1.75 hours, 2 hours, 2.25 hours, 2.5 hours, 2.75 hours, 3 hours, 3.25 hours, 3.5 hours, 3.75 hours, 4 hours, 4.25 hours, and / or 4.5 hours.

[0080]

[0089] The method may further include quantifying the amount of MCT in the samples obtained at several intervals. Quantifying the MCT in the sample may include quantifying using a colorimetric assay. Any known quantitative colorimetric or fluorescent assay kit available on the market may be used to quantify MCT in the disclosed method (e.g., Sigma-Aldrich MAK266-1KT). During the assay period, triglycerides may be converted to free fatty acids and glycerol through chemical hydrolysis, enzymatic hydrolysis, or any combination thereof. In some cases, chemical hydrolysis may be performed by adding an alcoholic solution of an alkali metal hydroxide. In some cases, enzymatic hydrolysis may be performed using enzymes, non-limiting examples of which include lipase, cholesterol esterase, carboxylesterase, protease, or any combination thereof. Glycerol is then oxidized to produce products that react with the probe to produce color and fluorescence, which are detected using a spectrophotometer. Glycerol is ... ++and enzymatically converting glycerol to glycerol-1-phosphate using oxidized nicotinamide adenine dinucleotide (NAD) to dihydroxyacetone phosphate, which reduces NAD to reduced NADH, and determining the amount of NADH formed. NADH can be determined by reacting it with a colorless tetrazolium dye in the presence of an electron carrier, the dye being designed to form a colored product in such a reaction, and photometrically determining the amount of the resulting colored tetrazolium product.

[0081]

[0090] The method may further comprise the step of measuring the time for settling of the MCTs in the formulation to occur. The time for settling of the MCTs in the formulation to occur may be measured qualitatively by visual means.

[0091] The amount of MCT quantified and the time it takes for sedimentation of the MCT in the formulation to occur can be used to determine the gastric retention, bioavailability, and / or extent of digestion of the MCT.

[0082]

[0092] In some cases, the dispersibility test of AC-SD-03 can be carried out in 50 mM phosphate buffer at pH 1.2. The powder (0.5 g) can be dispersed in 8 mL of phosphate buffer and incubated at 37° C. for 4 hours without further stirring. To evaluate the stability, an aliquot (0.1 mL) of the supernatant can be taken at 0 min, 5 min, 10 min, 30 min, 1 h, 2 h, 3 h, and 4 h, respectively. The amount of tricaprylin can be measured using a colorimetric kit for triglyceride quantification (n=3).

[0083]

[0093] In some cases, the disclosure provides a method for determining the digestibility of a formulation comprising MCTs. The disclosed method may include the steps of incubating a formulation comprising MCT in gastric medium at 35° C.-39° C. for about 10 minutes to about 60 minutes with constant agitation; taking samples of the formulation in the gastric medium at regular intervals over a 30 minute period, adding a lipase inhibitor to each sample taken at the time of collection, and adjusting the pH of the formulation in the gastric medium to 6.80 after the 30 minute incubation; adding FaSSIF solution to the pH adjusted formulation; initiating lipolysis in the formulation by adding lipase to the formulation; incubating the formulation for 60 minutes while maintaining the pH at 6.8; taking samples of the formulation at regular intervals prior to incubation and then during the 60 minute incubation, and adding a lipase inhibitor to each sample taken at the time of collection; quantifying the amount of digested MCT in each sample, optionally using HPLC; and analyzing the quantitative information to determine the digestion rate, extent, and / or profile of the formulation comprising MCT.

[0084]

[0094] Any gastric medium known in the art may be used in the method for determining digestion of a formulation. The gastric medium may include a phosphate buffer at pH 1.2. The phosphate buffer may include a 50 mM phosphate buffer. The gastric medium may be prepared from a mixture of sodium chloride (2.0 g), dilute hydrochloric acid (24 ml), and water (approximately 1000 ml). The gastric medium may be prepared by dissolving pepsin (1 g), gastric mucin (1.5 g), and NaCl (8.775 g) in 1 L of distilled water and adjusting the pH to 1.3 using 6N HCl. The gastric medium may be a commercially available artificial gastric fluid or a simulated gastric fluid. The gastric medium may include a solution that simulates the composition and pH of gastric digestive fluid. Non-limiting examples of components in the gastric medium include sodium chloride, dilute acid, pepsin, mucin, etc.

[0085]

[0095] The formulation may be incubated at 36°C to 38°C for about 10 minutes to about 60 minutes with constant agitation. The formulation may be incubated for about 10 minutes, about 15 minutes, about 20 minutes, about 25 minutes, about 30 minutes, about 35 minutes, about 40 minutes, about 45 minutes, about 50 minutes, about 55 minutes, or about 60 minutes. The formulation may be incubated for about 30 minutes. The formulation may be incubated at a temperature of about 35°C to about 39°C. The formulation may be incubated at a temperature of about 35°C, about 36°C, about 37°C, about 38°C, or about 39°C. In some cases, the formulation may be incubated at about 37°C.

[0086]

[0096] As disclosed herein, the method may further include taking samples of the formulation in the gastric medium at regular intervals over a 30 minute period and adding a lipase inhibitor. Samples may be taken at about 0 minutes, about 5 minutes, about 10 minutes, about 15 minutes, about 20 minutes, about 25 minutes, and / or about 30 minutes. Samples may be taken at about 0 minutes, about 15 minutes, and about 30 minutes. The lipase inhibitor may include Orlistat, 4-BPBA, lipstatin, panclicin, hesperidin, ebelactone, estellastin, valilactone, benzoxazinone, or derivatives thereof. The lipase inhibitor may include 4-BPBA. In some cases, about 1 μL to about 5 μL of 0.5 M lipase inhibitor may be added. In some cases, about 1 μL, about 1.5 μL, about 2 μL, about 2.5 μL, about 3 μL, about 3.5 μL, about 4 μL, about 4.5 μL, or about 5 μL of lipase inhibitor is added. In some cases, a lipase inhibitor (2.5 μL) containing 0.5 M 4-BPBA in methanol may be added.

[0087]

[0097] The method of the present disclosure may further comprise adjusting the pH of the formulation in the gastric medium to about 6.80. For example, a 5M NaOH solution may be used to adjust the pH to about 6.80.

[0088]

[0098] According to a particular embodiment of the method, a fasted state simulated intestinal fluid (FaSSIF) solution is used to simulate intestinal conditions. The FaSSIF solution may be prepared by mixing 3 mM sodium taurocholate, 0.75 mM lecithin, 0.174 g NaOH (pellets), 1.977 g NaH2PO4·H2O or 1.719 g anhydrous NaH2PO4, and 3.093 g NaCl in 500 mL of purified water. The FaSSIF solution may have a pH of about 6.5 and an osmolality of about 270 mOsmol / kg. Commercially available FaSSIF (e.g., Biorelevant V2FAS) may be used in the disclosed method. About 1-4 mL of 10× concentrated FaSSIF solution may be added. About 1 mL, about 2 mL, about 3 mL, or about 4 mL of 10× concentrated FaSSIF solution may be added. Approximately 2 mL of 10× concentrated FaSSIF solution is added.

[0089]

[0099] In some cases, the method may include initiating lipolysis of the formulation by adding lipase to the formulation and incubating the formulation for 60 minutes while maintaining the pH at 6.8. Examples of lipases suitable for the present invention include, but are not limited to, animal lipases (e.g., porcine lipase, pancreatin lipase), bacterial lipases (e.g., Pseudomonas lipase and / or Burkholderia lipase), fungal lipase, plant lipase, recombinant lipases (e.g., recombinant lipases produced via recombinant DNA technology by a suitable host cell selected from a bacterial, yeast, fungal, plant, insect, or mammalian host cell in culture, or recombinant lipases comprising an amino acid sequence that is homologous or substantially identical to a naturally occurring sequence, lipases encoded by a nucleic acid that is homologous or substantially identical to a naturally occurring lipase-encoding nucleic acid, etc.), synthetic lipases, chemically modified lipases, and mixtures thereof. In some cases, the lipase is a pancreatin lipase suspension. The lipase suspension may be prepared using any method known in the art. For example, the lipase may be dispersed in an aqueous solvent (e.g., water, acetone, ethanol). In some cases, about 1-4 mL of lipase may be added to the formulation. In some cases, about 1 mL, about 2 mL, about 3 mL, or about 4 mL of lipase may be added to the formulation. In some cases, about 2 mL of lipase may be added to the formulation.

[0090]

[0100] During or after the start of lipolysis, the method may further include obtaining samples. Samples may be obtained at 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60 minutes. The samples may be further processed. In some cases, the samples may be centrifuged and the supernatant may be used for further analysis.

[0091]

[0101] The method may further include analysis of the free fatty acids released by digestion. Any method known in the art may be used to quantify the octanoic acid digested formulation, including high performance liquid chromatography (HPLC), gas chromatography (GC), or liquid chromatography-mass spectrometry (LC-MS). The free fatty acids may be quantified using HPLC. In short, chromatographic separation of the free fatty acids (e.g., octanoic acid, etc.) may be performed using the Phenomenex Security Guard Cartridge C. 18 Waters Symmetry C with 18 The run is carried out on a column (3.5 μm, 100 Å, 4.6×75 mm) at 40° C. The free fatty acids are then assayed using an isocratic elution with a flow rate of 1 mL / min. The mobile phase may include a mixture of water and methanol (40:60) with 0.1% trifluoroacetic acid. The free fatty acids (e.g., octanoic acid) are detected at about 220 nm. The injection volume that may be used may be about 10 μL, the run time may be about 10 minutes, and the retention time of the free fatty acids (e.g., octanoic acid) may be about 5.9 minutes. A free fatty acid stock solution (e.g., octanoic acid stock solution dissolved in water:methanol (40:60)) may be used to prepare a standard reference range of 0.01 to 2.00 mg / mL in the same solvent. Calculation of the free fatty acid concentration from the peak area may be performed based on a calibration curve.

[0092]

[0102] In some aspects, quantification of free fatty acids (e.g., octanoic acid) can be used to understand the digestion rate and / or digestion profile of the MCT formulations of the present disclosure.

[0103] In some cases, to evaluate the release of octanoic acid and simulate gastric conditions, formulation AC-SD-03 (2.5 g) can be added to 50 mM phosphate buffer (20 mL) at pH 1.2 and thoroughly vortexed. The formulation can be incubated in a 70 mL thermostatted glass vessel (37° C.) for 30 minutes with constant magnetic stirring. Samples (100 μL) can be collected in Eppendorf tubes containing 2.5 μL of lipase inhibitor (methanol containing 0.5 M 4-BPBA) and 147.7 μL of methanol and vortexed for 30 seconds at 0, 15, and 30 minutes. The pH of the mixture can then be adjusted to 6.800±0.003 with 5 M NaOH solution. To simulate intestinal conditions, 2 mL of 10× concentrated FaSSIF solution can be added to the vessel. Lipolysis can be initiated by the addition of pancreatin lipase suspension (2 mL). Pancreatin lipase suspension can be prepared from pancreatin extract using a previously described method

[26] . The pH of the sample in the digestion vessel can be maintained at 6.8 during the digestion period using a 2 M NaOH solution titrated into the digestion medium by a pH stat control module (Metrohm AG, Herisau, Switzerland). Before the start of digestion (0 min), a sample (100 μL) can be collected in an Eppendorf tube containing lipase inhibitor (methanol with 0.5 M 4-BPBA) (2.5 μL) and methanol (147.7 μL) and vortexed for 30 s, after which samples can be collected at 5 min intervals for 60 min. The sample can then be centrifuged at 16142 g for 30 min. The resulting supernatant can be transferred to HPLC vials for analysis of octanoic acid released by digestion. Experiments may be performed in triplicate.

[0093]

[0104] In some cases, the biopharmaceutical modeling system 100 can perform quantification of free fatty acids from digested samples. In some cases, the free fatty acid is octanoic acid. The digested formulations can be analyzed on a Shimadzu (Shimadzu Corporation, Kyoto, Japan) UPLC system (consisting of a CBM-20A system controller, an LC-30AD solvent delivery module, a SIL-30AC autosampler, and a CTO-20AC column oven coupled to an SPD-M30A diode array). Chromatographic separation of octanoic acid can be performed at 40° C. on a Waters Symmetry C18 column (3.5 μm, 100 Å, 4.6×75 mm) with a Phenomenex Security Guard Cartridge C18. Octanoic acid can be assayed using an isocratic elution with a flow rate of 1 mL / min. The mobile phase consisted of a mixture of water and methanol (40:60) with 0.1% trifluoroacetic acid. Octanoic acid can be detected at 220 nm. The injection volume may be about 10 μL, the run time may be about 10 minutes, and the retention time of octanoic acid may be about 5.9 minutes. A stock solution of octanoic acid dissolved in water:methanol (40:60) may be used to prepare a standard reference range of 0.01-2.00 mg / mL in the same solvent. The detection limit and quantification limit of the HPLC method may be 0.002 mg / mL and 0.006 mg / mL, respectively. Calculation of octanoic acid concentration from peak area may be performed based on a calibration curve with R2>0.999. One skilled in the art will appreciate that other free fatty acids may also be used in the biopharmaceutical modeling systems and methods disclosed herein, and that octanoic acid is only one example.

[0094]

[0105] In some scenarios, the biopharmaceutical modeling system 100 can perform literature data extraction and analysis to generate training data 122 for the PBB model 102. Selected plasma concentration-time profiles from the literature can be extracted using WebPlotDigitizer (v4.3, Ankit Rohatgi, USA). For pharmacokinetic analysis, model design, and data simulation, MonolixSuiteD4 2020R1 (Lixoft, Antony, France) is used, as shown in FIG. 2.

[0095]

[0106] Clinical pharmacokinetic studies can be performed with MCT formulations (e.g., to generate model inputs 106). For example, a clinical pharmacokinetic study approved by the local Institutional Review Board and conducted in compliance with the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH-GCP) guidelines was conducted on tricaprylin product AC-SD-03. The study was an open-label, randomized, two-way crossover study. Twenty healthy male (Caucasian and Chinese) volunteers within the age range of 18-50 years were enrolled. All subjects maintained a supervised overnight fast of at least 8 hours until 1 hour prior to the scheduled morning dose. A breakfast consisting of two slices of buttered toast, two slices of bacon, two scrambled eggs, 113 g hash browns, and 240 mL of whole milk was standardized for all subjects. AC-SD-03 (50 g, equivalent to 42,500 μmol tricaprylin) dispersed in 240 mL of water was administered to each subject 30 minutes after the completion of a standard breakfast. An additional 60 mL of water was used to wash the dosing cup and administer it to the subject. Water was restricted to 1 hour before and 1 hour after dosing, but was allowed ad libitum at other times. Food and other liquid intake was restricted throughout the entire restraint period. Upon single oral administration of the formulation, blood concentrations of tricaprylin, octanoic acid, and ketone bodies were quantified. For tricaprylin, blood samples were taken at 0, 1.5, 2.0, 2.5, and 8 hours. For octanoic acid and ketones (β-hydroxybutyric acid and acetoacetate), blood samples were taken at 0, 0.5, 1.0, 1.5, 2.0, 2.5, 4.0, and 8 hours after dosing, respectively. Those skilled in the art will appreciate that other MCT formulations may also be used in the biopharmaceutical modeling systems and methods disclosed herein, and that AC-SD-03 is merely one example of such a formulation.

[0096]

[0107] 11 illustrates an example system 1100 for implementing the biopharmaceutical modeling system 100 with the PBB model 102. The system 1100 can include one or more computing device(s) 1102 (capable of implementing the systems 100-1000 and / or performing the methods discussed herein). In one embodiment, the one or more computing device(s) 1102 include a clinic device of the biopharmaceutical modeling system 100 or any other device discussed throughout this disclosure.

[0097]

[0108] In some instances, the computing device(s) 1102 include a computer, a personal computer, a desktop computer, a laptop computer, a terminal, a workstation, a cell phone or mobile telephone, a mobile device, a smart mobile device, a tablet, a wearable device (e.g., a smart watch, smart glasses, smart skin devices, etc.), a multimedia console, a television, an Internet of Things (IoT) device, a smart home device, a medical device, a virtual reality (VR) or augmented reality (AR) device, a vehicle (e.g., a smart bicycle, an in-vehicle computer, etc.), combinations thereof, etc. It is understood that a particular implementation of such a device may be an implementation of different possible specific computing architectures, not all of which are specifically discussed herein, but which would be understood by one of ordinary skill in the art.

[0098]

[0109] The computing device 1102 may be a computing system capable of executing a computer program product to perform computer processing. The PBB model 102 may be stored and executed (e.g., as one or more software components, algorithmic modules, etc.) on the computing device(s) 1102. Data and program files may be input to the computing device 1102, which reads the files and executes the programs contained therein to provide the various components of the PBB model 102 and generate the predictions discussed herein. Some of the elements of the computing device 1102 include one or more hardware processors 1104, one or more memory devices 1106, and / or one or more ports, such as input / output (IO) port(s) 1108 and / or communication port(s) 1110. The various elements of the computing device 1102 may communicate with each other via the communication port(s) 1110 and / or one or more communication buses, point-to-point communication paths, or other communication means.

[0099]

[0110] The processor 1104 may include, for example, a central processing unit (CPU), a microprocessor, a microcontroller, a digital signal processor (DSP), and / or one or more internal cache levels. There may be one or more processors 1104 such that the processor 1104 comprises a single central processing unit, or there may be multiple processing units capable of executing instructions and performing tasks in parallel with each other, commonly referred to as a parallel processing environment.

[0100]

[0111] The computing device 1102 may be a conventional computer, a distributed computer, or any other type of computer, such as one or more external computers available via a cloud computing architecture. The techniques described herein may be optionally implemented in software stored on a data storage device(s), such as memory device(s) 1106, and / or communicated via one or more of ports 1108 and 1110, thereby transforming the computing device 1102 shown in FIG. 11 into a special purpose machine (for the purpose of performing the work of the PBB model 102 providing a computer-generated 3D space).

[0101]

[0112] The one or more memory device(s) 1106 may include any non-volatile data storage device capable of storing data generated or employed within the computing device 1102 (e.g., computer-executable instructions for implementing computer processes, which may include both application program instructions and operating system (OS) instructions that manage the various components of the computing device 1102). The memory device(s) 1106 may include, but are not limited to, magnetic disk drives, optical disk drives, solid-state drives (SSDs), flash drives, and the like. The memory device(s) 1106 may include removable data storage media, non-removable data storage media, and / or external storage devices available via wired or wireless network architectures along with computer program products, including one or more database management products, web server products, application server products, and / or other additional software components. Examples of removable data storage media include compact disc read only memory (CD-ROM), digital versatile disc read only memory (DVD-ROM), magnetic optical disks, flash drives, and the like. Examples of non-removable data storage media include internal magnetic hard disks, SSDs, etc. The one or more memory device(s) 1106 may include volatile memory (e.g., dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and / or non-volatile memory (e.g., read only memory (ROM), flash memory, etc.).

[0102]

[0113] A computer program product including mechanisms for enabling the systems and methods according to the techniques described herein may reside in memory device(s) 1106 (sometimes referred to as a machine-readable medium). It will be appreciated that a machine-readable medium may include any tangible, non-transitory medium capable of storing or encoding instructions, or data structures and / or modules utilized by or associated with such instructions, for performing any one or more of the operations of the present disclosure intended for execution by a machine. A machine-readable medium may include a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) that stores one or more executable instructions or data structures.

[0103]

[0114] In some embodiments, computing device 1102 may include one or more ports, such as I / O port 1108 and communication port 1110 for communicating with other computing devices, network devices, or in-vehicle devices. It will be understood that I / O port 1108 and communication port 1110 may be combined or separate and that more or fewer ports may be included in computing device 1102.

[0104]

[0115] I / O ports 1108 may connect to I / O devices or other devices by which information is input to or output from computing device 1102. Such I / O devices may include, but are not limited to, one or more input devices, output devices, and / or environmental transducer devices.

[0105]

[0116] In one embodiment, the input device converts human-generated signals, such as human voice, body movements, physical touch or pressure, into electrical signals as input data that is input to the computing device 1102 via the I / O port 1108. Similarly, the output device may convert electrical signals received from the computing device 1102 via the I / O port 1108 into signals that can be perceived as output by a human, such as sound, light, or touch. The input device may be an alphanumeric input device, including alphanumeric and other keys for communicating information and / or command selections to the processor 1104 via the I / O port 1108. The input device may be another type of user input device, including, but not limited to, a direction and selection control device, such as a mouse, a trackball, cursor direction keys, a joystick, and / or a wheel; one or more sensors, such as a camera, a microphone, a position sensor, an orientation sensor, an inertial sensor, and / or an accelerometer; and / or a touch-sensitive display screen (touch screen). Output devices may include, but are not limited to, displays, touch screens, speakers, tactile and / or haptic output devices, etc. In some embodiments, the input device and the output device may be the same device, such as in the case of a touch screen.

[0106]

[0117] In one embodiment, the communication port 1110 can be connected to a network and the computing device 1102 can receive network data useful in performing the methods and systems described herein and in transmitting information and network configuration changes determined thereby. In other words, the communication port 1110 can connect the computing device 1102 to one or more communication interface devices configured to transmit and / or receive information between the computing device 1102 and other devices over one or more wired or wireless communication networks or connections. Examples of such networks or connections include, but are not limited to, Universal Serial Bus (USB), Ethernet, Wi-Fi, Bluetooth, Near Field Communication (NFC), etc. One or more such communication interface devices may be utilized via communication port 1110 to communicate with one or more other machines directly, via a point-to-point communication path, via a wide area network (WAN) (e.g., the Internet), via a local area network (LAN), via a cellular network (e.g., third generation (3G), fourth generation (4G), Long-Term Evolution (LTE), fifth generation (5G), etc.), or via other communication means. Additionally, communication port 1110 may be in communication with an antenna or other link for transmitting and / or receiving electromagnetic signals.

[0107]

[0118] In one example embodiment, the PBB model 102 may be embodied by instructions stored on the memory device 1106 and executed by the processor 1104.

[0108]

[0119] The system 1100 shown in Figure 11 is just one possible example of a computer system that may be employed or configured in accordance with aspects of the present disclosure. It will be understood that other non-transitory tangible computer readable storage media that store computer executable instructions for implementing the techniques of the present disclosure on a computing system may also be utilized. In the present disclosure, the disclosed methods may be implemented as a set of instructions or software readable by a computing device 1102.

[0109]

[0120] Diagram 1200 illustrates an example of a method for predicting pharmacokinetic parameters following administration of a formulation, which may be implemented by any of systems 100-1100 disclosed herein.

[0110]

[0121] In some examples, at operation 1202, the method 1200 may generate training data indicative of a human metabolic outcome of the formulation, including at least one of a gastric emptying parameter, a digestion rate, an absorption constant, or a conversion rate, where the training target variable is a plasma free fatty acid (e.g., octanoic acid, etc.) concentration or a ketone concentration. At operation 1204, the method 1200 may train a PBB model using the training data by one or more computer devices executing computer readable instructions. At operation 1206, the method 1200 may vary input parameters of the training data in light of a simulated average plasma free fatty acid (e.g., octanoic acid) profile or a simulated average ketone plasma profile as part of a parameter sensitivity analysis of the PBB model. At operation 1208, the method 1200 may obtain patient data corresponding to a patient receiving oral administration of the formulation by one or more computer devices. In operation 1210, the method 1200 can predict at least one of a target plasma free fatty acid (eg, octanoic acid, etc.) concentration or a target ketone concentration using the PBB model using the patient data.

[0111]

[0122] It is understood that the specific order or hierarchy of steps included in method 1200 shown in Figure 12 or throughout this disclosure are examples of example approaches and can be rearranged while remaining within the scope of the disclosed subject matter. For example, any of the operations shown in Figure 12 or throughout this disclosure can be omitted, repeated, performed in parallel, performed in a different order, and / or combined with any of the other operations shown in Figure 12 or throughout this disclosure.

[0112]

[0123] Although the present disclosure has been described with reference to various embodiments, it will be understood that such embodiments are illustrative and that the scope of the disclosure is not limited thereto. Many variations, modifications, additions, and improvements are possible. More generally, embodiments based on the present disclosure are described in the context of specific embodiments. Features may be separated or integrated in different ways or described using different terms in various embodiments of the present disclosure. These and other variations, modifications, additions, and improvements may be included within the scope of the present disclosure, as defined in the following claims.

Claims

1. 1. A method for predicting pharmacokinetic parameters following administration of a formulation comprising medium chain triglycerides (MCT), comprising: generating training data indicative of a human metabolic outcome of said formulation; training a physiologically-based biopharmaceutics (PBB) model using the training data with one or more computing devices executing computer-readable instructions; Varying input parameters of the training data in light of simulated mean plasma free fatty acid profiles or simulated mean ketone plasma profiles as part of a parameter sensitivity analysis of the PBB model; obtaining, by said one or more computing devices, patient data corresponding to a patient receiving oral administration of said formulation; predicting at least one of a target plasma free fatty acid concentration or a target ketone concentration using a PBB model using the patient data; The method includes:

2. 2. The method of claim 1, wherein the formulation comprises medium chain triglycerides (MCT) composed of 8 carbon fatty acids having a water solubility of 0.4 mg / L, and the target plasma free fatty acid concentration is a target plasma octanoic acid concentration.

3. The method of claim 1 , wherein the training data comprises a dataset based on a gastric emptying model.

4. The method of claim 3 , wherein the gastric emptying model comprises a signature algorithm corresponding to a rhythmic emptying pattern.

5. The method of claim 3 , wherein the gastric emptying model comprises a first pathway for solid foods and a second pathway for liquid foods.

6. The method of claim 3 , wherein the gastric emptying model comprises a gastric amplitude parameter, a gastric complete emptying parameter, and a gastric periodicity parameter.

7. 2. The method of claim 1, wherein the step of predicting at least one of the target plasma free fatty acid concentration or the target ketone concentration comprises determining a digestion rate constant obtained by approximating the release profile of the formulation in the presence of lipase, assuming a first-order kinetic process.

8. The method of claim 1 , wherein the training data includes digestion rate or bioavailability digestibility as an in vitro parameter.

9. 2. The method of claim 1, wherein the training data includes at least one of the following in vivo parameters: metabolized fraction, absorption constant, conversion rate, excretion rate, volume distribution of free fatty acids, or volume distribution of ketone bodies.

10. 2. The method of claim 1, further comprising the step of performing a dispersibility test on the MCT to determine the physical stability of the MCT, and wherein predicting at least one of the target plasma free fatty acid concentration or the target ketone concentration is based on the dispersibility test.

11. modeling the digestive rate using the release profile of the formulation; obtaining a digestion rate constant based on modeling the digestion rate; 2. The method of claim 1, further comprising: predicting at least one of the target plasma free fatty acid concentration or the target ketone concentration comprises using the digestion rate in conjunction with the PBB model.

12. 12. The method of claim 11, wherein predicting at least one of the target plasma free fatty acid concentration or the target ketone concentration comprises predicting the bioavailability of free fatty acids for the patient based on the digestion rate using the PBB model.

13. 2. The method of claim 1, wherein training the PBB model comprises instructing the PBB model to consider the enzymatic conversion of free fatty acids to plasma ketones as a non-saturable first-order reaction process.

14. 1. A method for predicting pharmacokinetic parameters following administration of a formulation, comprising: generating training data representing one or more of a gastric emptying parameter, a digestion rate, an absorption constant, or a conversion rate while using ketone concentration as a training target variable; training a physiologically-based biopharmaceutics (PBB) model using the training data with one or more computing devices executing computer-readable instructions; obtaining, by said one or more computing devices, patient data corresponding to a patient to be administered said formulation; predicting a target ketone concentration using the PBB model using the patient data; The method includes:

15. 15. The method of claim 14, wherein predicting the target ketone concentration comprises predicting the patient's plasma free fatty acid concentration using the PBB model using the patient data.

16. 15. The method of claim 14, further comprising a step of performing validation of the PBB model using clinical trial data quantifying free fatty acids and ketone bodies from plasma of volunteers.

17. 1. A method for predicting pharmacokinetic parameters following administration of a formulation comprising medium chain triglycerides (MCT), comprising: generating training data representing a gastric emptying parameter, digestion rate, absorption constant, or conversion rate using plasma free fatty acid concentration as a training target variable; training a physiologically-based biopharmaceutics (PBB) model using the training data with one or more computing devices executing computer-readable instructions; obtaining, by said one or more computing devices, patient data corresponding to a patient to be administered said formulation; predicting a target plasma free fatty acid concentration using the PBB model using the patient data; The method includes:

18. 20. The method of claim 17, further comprising predicting ketone concentrations using the PBB model using the patient data, the target plasma free fatty acid concentration, and an estimate of conversion rate.

19. 20. The method of claim 17, wherein the step of training the PBB model comprises providing a gastric absorption emptying model having a signature algorithm having an amplitude variable, a periodicity variable, and a time to complete emptying variable.

20. 20. The method of claim 17, wherein the training data comprises a first cluster comprising physiologically based parameters, a second cluster comprising population parameters, and a third cluster comprising in vitro parameters.

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