In Silico Prediction of Amyloid-Related Image Abnormalities

Abstract

The dosage of the active ingredient in a treatment provided to or under consideration for providing to a subject is specified. The level of local amyloid beta is predicted based on the dosage of the active ingredient. T 2 The severity of amyloid-related imaging abnormalities (ARIA) (ARIA-E) that appear as high signal in fluid-attenuated inversion recovery (FLAIR) images is predicted based on the predicted amount of removal of local amyloid beta, and predicting the severity of ARIA-E includes predicting the degree of blood vessel wall damage. A result corresponding to the predicted ARIA-E severity is output.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit and priority of U.S. Provisional Application No. 63 / 337,992, filed on May 3, 2022, which is hereby incorporated by reference in its entirety for all purposes.

Background Art

[0002] Over the past several decades, amyloid - beta (Aβ) aggregates have been the most common target in clinical trials investigating disease - modifying therapies in Alzheimer's disease. Several therapies based on monoclonal antibodies against Aβ aggregates have reported amyloid - related imaging abnormalities (ARIA). Two types of ARIA have been identified by magnetic resonance imaging (MRI) of the brain. ARIA - E appears as a hyper - signal on T2 - weighted fluid - attenuated inversion recovery (FLAIR) images, suggesting vasogenic edema, sulcal exudates, and gyral swelling. ARIA - H appears as a hypo - signal on T2* - weighted gradient - echo sequences and is thought to represent hemosiderosis and microbleeds. ARIA - E is transient and typically resolves within a few months, while ARIA - H remains visible on subsequent MRIs. Most ARIA - E events are of mild to moderate severity and are usually asymptomatic, as evaluated by various radiological scales that show good correlation.

[0003] The risk of treatment-related ARIA is dose-dependent and appears to increase in APOEε4 carriers. To reduce the risk of ARIA, several anti-amyloid clinical trials implemented dose-escalation schemes relative to the target dose as well as routine brain MRI monitoring. The incidence of treatment-related ARIA events across various anti-amyloid clinical trials has been reported to be in the range of 10 - 42%, with the highest rates occurring in APOEε4 allele carriers. Spontaneous ARIA events were rarely seen in the placebo group (e.g., <3% ARIA-E) and outside the context of anti-amyloid clinical trials. The latter example is ARIA-like events that occur in cerebral amyloid angiopathy-related inflammation (CAA-ri), which is a rare autoimmune reaction against vascular Aβ aggregates. In the clinical setting, due to MRI monitoring requirements, ARIA can be a significant burden for Alzheimer's disease patients and the healthcare system. Furthermore, ARIA can limit a patient's ability to reach the target dose and benefit from amyloid-lowering disease-modifying therapies.

[0004] Therefore, it would be advantageous to be able to generate predictions of treatment-related ARIA events and their severity.

Brief Description of the Drawings

[0005] This disclosure is described in conjunction with the accompanying drawings.

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[0006] In some embodiments, a computer-implemented method is provided. The method includes an administered dose of an active ingredient in a treatment provided to, or contemplated for, a subject. The method further includes predicting a level of local amyloid-beta based on the administered dose of the active ingredient and T based on the predicted amount of removal of local amyloid-beta. 2Including predicting the severity of amyloid-related imaging abnormalities (ARIA) (ARIA-E) that appear as high signals in fluid-attenuated inversion recovery (FLAIR) images, and predicting the severity of ARIA-E includes predicting the degree of vascular wall damage. The method also includes outputting a result corresponding to the predicted ARIA-E severity.

[0007] The active ingredient may include an anti-amyloid monoclonal antibody.

[0008] The method may include using a pharmacokinetic model to predict the time course of the concentration of the active ingredient in plasma during treatment, and the prediction of local amyloid-beta is based on at least one predicted concentration of the active ingredient at the predicted time course.

[0009] Predicting the level of local amyloid-beta may include estimating the baseline level of local amyloid-beta, calculating the removal rate of local amyloid-beta based on the dose of the active ingredient and the baseline level of local amyloid-beta, and predicting the level of local amyloid-beta based on the calculated removal rate of local amyloid-beta.

[0010] Predicting the level of local amyloid-beta may include solving a pharmacodynamic differential equation assuming that the rate of change of amyloid-beta is proportional to the product of the concentration of the active ingredient and the local amyloid-beta level in the subject.

[0011] Predicting the level of vascular wall damage may include solving a differential equation including the level of local amyloid-beta.

[0012] Predicting the severity of ARIA-E may include solving an algebraic equation assuming a non-linear relationship between the level of vascular wall damage and the Barkhof Global Total Score (BGTS).

[0013] The method may include identifying a potential schedule for monitoring ARIA events based on the predicted severity of ARIA, and the result characterizes the potential schedule.

[0014] The method may include identifying a potential recommended dosage of an active ingredient for a subject based on the predicted severity of ARIA, and the result characterizes the potential recommended dosage.

[0015] In some embodiments, the system includes a non - transitory computer - readable storage medium that, when executed by one or more data processors, includes instructions for causing the one or more data processors to execute some or all of one or more of the methods and / or some or all of one or more of the processes disclosed herein. Some embodiments of the present disclosure include a computer program product tangibly embodied in a non - transitory machine - readable storage medium that includes instructions configured to cause one or more data processors to execute some or all of one or more of the methods and / or some or all of one or more of the processes disclosed herein.

[0016] The terms and expressions used are used as terms of description and not of limitation, and there is no intention, in their use, to exclude equivalents of the features shown and described or portions thereof. However, it is recognized that various modifications are possible within the scope of the claimed systems and methods. Accordingly, although the systems and methods are specifically disclosed by way of example and any features, modifications and variations of the concepts disclosed herein should be recognized by those skilled in the art and are considered to be within the scope of the systems and methods defined by the appended claims.

[0017] This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used alone to determine the scope of the claimed subject matter. These exemplary examples are not meant to limit or define the present disclosure, but are referred to in order to provide examples to assist in its understanding. Further embodiments and examples are discussed in the detailed description, where additional explanation is provided. The subject matter should be understood by reference to the appropriate portions of the entire specification of the present disclosure, any or all of the drawings, and each claim.

[0018] The above will become more apparent when reference is made to the following specification, claims, and attached drawings, together with other features and embodiments.

Mode for Carrying Out the Invention

[0019] The exact pathophysiological mechanisms of ARIA are not fully elucidated. Pharmacologically mediated removal of Aβ aggregates has been proposed to increase the permeability of the cerebral vascular wall to the entry of fluids and blood preparations into the brain, resulting in ARIA-E and ARIA-H, respectively. In the Alzheimer's brain, Aβ aggregates are found in the brain parenchyma as well as in the cerebral vascular wall in the form of cerebral amyloid angiopathy (CAA). Anti-amyloid antibodies associated with ARIA bind to various forms of Aβ aggregates (e.g., oligomers, fibrils, and plaques) and remove them by degradation and / or effector cell-mediated phagocytosis.

[0020] Figure 1 shows the major pathophysiological pathways thought to link pharmacologically mediated removal of Aβ aggregates to ARIA. Overall, all of the hypothesized mechanisms of ARIA appear to involve some degree of disruption to the cerebral vascular wall. It remains unclear to what extent the degree of amyloid burden, or the location and rate of amyloid clearance, affects the onset, course, and severity of ARIA. Obtaining a better understanding of the etiology of ARIA has the potential to clarify additional risk factors that could accelerate the path to patient-centered strategies for dosing and monitoring in individuals with Alzheimer's disease.

[0021] Some embodiments of the present invention relate to predicting the temporal dynamics and dose-dependence of ARIA-E severity by using a semi-mechanistic in-silico model of ARIA-E called the vascular wall damage (VWD) model. For example, the model may receive input data including dose information indicating the amount of an anti-amyloid monoclonal antibody (e.g., gantenerumab) administered in a simulation according to a given schedule (e.g., such that the model simulates the administration of a dose every four weeks), and the model may generate results corresponding to the predicted occurrence and / or severity of ARIA (or ARIA-E) at each of a set of time points relative to a reference point of the schedule (e.g., the administration of the first dose). The predicted severity can be used to generate potential monitoring schedules, dosing schedules, and / or dose adjustment criteria. For example, based on an initial dosing plan, a schedule for monitoring and safety data collection may be proposed, and dose adjustment criteria may be defined to propose reducing the dose by a specified amount only if MRI shows at least a defined degree of ARIA-E.

[0022] The framework of the VWD model is consistent with the ARIA-E pathophysiological mechanism proposed in Figure 1 and allows for different biological interpretations of the modeled mechanism. The VWD model links several pharmacological and biological factors to the observed pharmacokinetics and magnitude of ARIA-E. One biological factor considered in the model is local amyloid, either vascular or parenchymal, and its drug-mediated clearance can trigger a cascade of events that result in ARIA-E in one or more regions of the brain. Another biological variable of the model is the level of VWD, which is a hypothetical measure of susceptibility to disruption of vascular integrity and / or leakage of fluid into the brain due to amyloid-drug interactions and / or perivascular inflammation. The VWD model formulation allows for the assessment of potential interactions between drug-mediated clearance of local amyloid and the endogenous vascular repair process, which can determine or influence the level of VWD, which in turn further affects the magnitude of ARIA-E. ARIA-E Prediction Network

[0023] Figure 2 shows an ARIA-E prediction network that includes an ARIA-E prediction system 205 and a user device 210. The ARIA-E prediction system 205 controls a VWD model that includes both a pharmacokinetic model component (controlled by a pharmacokinetic model controller 215) and a pharmacodynamic model component ( Pharmacodynamics controlled by a model controller 220). The VWD model (as shown on the left side of Figure 3) can be a semi-mechanistic pharmacokinetic-pharmacodynamic model that integrates known and / or estimated processes and / or (e.g., simultaneously) accounts for inter-individual variability in model parameters.

[0024] Each of the pharmacokinetic model controller 215 and the pharmacodynamic model controller 220 may be configured to specify the equations used in the model, retrieve stored data (e.g., parameter values) from memory, and execute the respective model components. The aggregate model controller 225 may call the pharmacokinetic model controller 215 and / or the pharmacodynamic model controller 220, transmit input data, and / or receive outputs. Thus, the aggregate model controller 225 may be configured to control the definition and execution of the VWD model by interacting with the pharmacokinetic model controller 215 and / or the pharmacodynamic model controller 220. The aggregate model controller 225 may further perform preprocessing and / or postprocessing. For example, such preprocessing and / or postprocessing may adjust subject-specific characteristics such as age, APOE e4 genotype, baseline ARIA-H load, and vascular risk factors (alternatively or additionally, factors that may be used by the pharmacokinetic model and / or the pharmacodynamic model to generate predictions).

[0025] The pharmacokinetic model components are configured to predict the time course of the concentration of the active ingredient in the plasma of a subject based on the administration history and long-term observation of the plasma concentration. Exemplary models may include the pharmacokinetic model, which may be disclosed by Retout S et al., ‘‘Disease Modeling and Model-Based Meta-Analyses to Define a New Direction for a Phase III Program of Gantenerumab in Alzheimer’s Disease.’’ Clin Pharmacol Ther. 2022 Apr;111(4):857-866, which is hereby incorporated by reference in its entirety for all purposes.

[0026] The uppermost graph of the central panel in FIG. 3 shows an exemplary time course of the concentration of the active ingredient (left axis) in the plasma of a subject, as estimated using a pharmacokinetic model, when the active ingredient is administered at the time indicated by the horizontal position of the plus symbol at the dose indicated by the vertical position of the plus symbol on the right axis.

[0027] The pharmacodynamic model component is configured to predict ARIA-E findings based on the concentration of the active ingredient, based on intermediate calculations that predict changes in the levels of local amyloid-β and vascular wall damage caused by the active ingredient. The intermediate calculations may include solving one or more differential equations and / or performing one or more algebraic calculations. Exemplary equations that may be used in the pharmacodynamic model component follow.

[0028] The first exemplary differential equation is based on the assumption that the rate of drug-mediated removal of local Aβ depends on the product of the drug concentration in the plasma and the level of local Aβ(t) [arbitrary units] via the parameter

Number

Number

Number

[0029] . 除去It is shown as, and the proportionality coefficient called the removal constant can be interpreted as the rate constant of the biochemical reaction between the anti-amyloid antibody and the amyloid aggregate. Such a reaction corresponds to the drug-mediated amyloid removal that occurs, and its magnitude can vary between individuals. The baseline level of local amyloid (referred to herein as amyloid 0 as shown) affects the initial rate of amyloid removal and can also vary between individuals. Other processes that can change local amyloid levels, such as physiological amyloid production and endogenous amyloid clearance, are not included in the model.

[0030] The second graph from the top in the central panel of Figure 3 shows the estimated time course of local Aβ estimated using Equation (1). In this particular example, local amyloid is estimated to decrease over time.

[0031] The second exemplary differential equation assumes that the removal of local Aβ drives the accumulation of VWD(t) [arbitrary units], which is offset by a primary vascular repair process characterized by a repair rate k 修復 [per day -1 : [Equation] where the initial value of VWD at the start of the OLE treatment (i.e., VWD(t = 0)) is zero.

[0032] The parameter amyloid d 0 , α 除去 and k 修復 are allowed to vary between subjects, as shown in Figure 7, and their statistical distributions (typical values and widths) are estimated. One, several, or all of the remaining model parameters may be constant between individuals.

[0033] The third graph from the top in the central panel of Figure 3 shows the estimated time course of VWD estimated using Equation (2). In this particular case, VWD is estimated to first increase and then generally decrease.

[0034] The "VWD" variable corresponds to a hypothetical concept that describes the susceptibility to fluid leakage into the brain due to treatment-induced deficits in the blood-brain barrier and in the blood vessels and perivascular structures of the brain such as the impaired perivascular drainage pathway of the wall arteries. In the absence of interference, VWD is equal to 0. The accumulation of VWD is promoted by drug-mediated amyloid removal and offset by the presumed endogenous blood vessel repair process. Regarding Equation (2), blood vessel repair is assumed to be a first-order process with a rate constant (k 修復 ) and a half-life independent of the magnitude of VWD. The magnitude of VWD may be related to the predicted ARIA-E score (BGTS) by, for example, a sigmoid function. This relationship can ensure a zero or small BGTS at low levels of VWD, a sharp rise in the ARIA-E score at intermediate levels, and an upper limit on the magnitude of ARIA-E (BGTS = 60) at high levels of VWD.

[0035] Two additional exemplary algebraic equations relate VWD to the observed magnitude of ARIA-E. Using two equations for this relationship addresses the complexity that there are a large number of zero BGTS values in the ARIA-E score dataset. The first equation predicts the probability of an ARIA-E event (i.e., non-zero BGTS) for each BGTS observed value, and the second equation quantifies the magnitude of ARIA-E when the event is predicted to occur with a high probability.

[0036] The first exemplary algebraic equation addresses the probability p(t) of positive (non-zero) BGTS under the assumption that the logarithm of the odds ratio is linearly related to VWD(t) as follows.

Equation

Equation

[0037] The graph below the central panel of FIG. 3 shows the estimated time course of BGTS for a typical case related to the other graphs in the central panel. The estimated time course of BGTS is determined using equations (3) and (4). In this typical case, BGTS increases immediately after the start of the treatment regimen and then generally decreases.

[0038] The aggregate model controller 225 can facilitate the determination of the proposed dosing schedule and / or the proposed monitoring schedule for an individual who has experienced ARIA-E using the predicted BGTS. For example, the aggregate model controller can adjust the generation of the predicted time course of BGTS for various prescribed schedules for administration, various prescribed schedules for monitoring, and / or various different BGTS cutoff values (thresholds of ARIA-E severity that result in treatment interruption in the model). Thus, for each prescribed dosing schedule, monitoring schedule, and / or BGTS cutoff value, a predicted progression of ARIA-E severity (time course of BGTS) can be generated, which can then be output (e.g., via the interface controller 235) to assist the user's (e.g., clinician's) decision regarding which dosing schedule, monitoring schedule, and / or BGTS cutoff value to use in clinical trials and / or real-world use cases.

[0039] Therefore, the VWD model can interconnect the following factors: (i) the dose of gantenerumab, (ii) the drug concentration in plasma, (iii) the drug-induced removal of local amyloid, (iv) VWD, which is presumed to result in fluid leakage into the brain, (v) the endogenous blood vessel repair process, and (vi) BGTS, which quantifies the severity of ARIA-E. While individual-specific information regarding a given dose and administration time can be provided as input, parameters regarding some or all of the other factors can be estimated.

[0040] Parameter collector 230 first identifies the respective parameters of the pharmacokinetic model component and the pharmacodynamic model component. In some examples, parameter collector 230 identifies the value for each of one or more parameters by querying an external source for the parameter. In some examples, the parameter Collector230 learns the value of each of the one or more parameters by fitting a model component or a VWD model using one or more training data sets. For example, the training data set may include results from a study in which a set of subjects (e.g., each diagnosed with Alzheimer's disease) were administered a given active ingredient (or an active ingredient corresponding to a particular class of active ingredients), such as gantenerumab, at one or more specific dosages according to one or more dosing schedules. The dosing schedule may indicate the relative times at which various dosages of the active ingredient are administered relative to (e.g.) an initial dosage, a most recent dosage, or another baseline. In some examples, the dosage of the active ingredient administered at each specified dosing time of the schedule is the same for each other dosing time. In some examples, the dosage of the active ingredient administered at each of at least two dosing times is different. For example, the dosage may increase over at least two of the dosages. This study may be conducted such that MRIs are performed regularly and evaluated to monitor ARIA-E, and samples (e.g., blood samples) are collected regularly and evaluated to determine the concentration of the active ingredient. The evaluation may include assigning a severity to any detected ARIA-E using a scoring technique such as the Barkhof total score.

[0041] The research data from the data source may be further filtered to selectively include data corresponding to a subset of the subjects such that the training data set includes the selected data. The subset of subjects may include (e.g.) subjects in whom the occurrence of ARIA-E (e.g., corresponding to a severity greater than a given threshold, such as a severity greater than zero) was observed during the trial or within one year of the first administration of the trial.

[0042] In some examples, the parameter collector 230 identifies the parameters of a single model component in the isolation of the other model components. For example, the parameters of a pharmacokinetic model component may be defined by performing a Bayesian analysis on the research data.

[0043] In some examples, the parameter collector 230 fixes some parameters while adapting other parameters. For example, the parameter collector 230 may fix the parameters of the pharmacokinetic model components while adapting the pharmacodynamic model components. The parameter collector 230 may estimate parameter values using a non-linear mixed effects modeling technique.

[0044] The parameter collector 230 can use assumptions regarding the distribution of variables (e.g., the shape of the distribution), the maximum value, whether various variables are dependent on each other, etc. For example, the parameter Collector 230 can define the assumption that the parameter amyloid 0 , α 除去 and k 修復 have a log-normal inter-individual variability, estimate the fixed effects of their typical values, and estimate the width of the distribution (random effect). As another example, the parameter collector 230 can define the estimated values of the parameters β 1 , VWD 50 and pow without inter-individual variability (i.e., as only fixed effects). It can be shown that VWD(t) is proportional to amyloid A 0 , meaning that the parameters amyloid 0 and EG 50 appear effectively only as the ratio in the formula of BGTS(t). To ensure structural identifiability, the parameter collector 230 can set EG 50 to 1 without loss of generality. The parameter collector 230 can fix the maximum value of BGTS to 60 along the limits of the 60-point BGTS severity scale. The residual (unexplained) variability that describes the difference between the observed BGTS and the predicted BGTS can be assumed to be independent of the prediction and normally distributed.

[0045] The ARIA-E prediction system 205 includes an interface controller 235 that facilitates communication between the ARIA-E prediction system 205 and the user device 210. The interface controller 235 processes incoming communications from the user device 210 to detect a request for a prediction generated by, for example, an aggregate model controller (e.g., based on outputs from the pharmacokinetic model controller 215 and / or the pharmacodynamic model controller 220), and / or to detect information (e.g., identifying the dosage amount and / or dosing schedule used to treat a given subject or being considered for the treatment of a given subject) that is used as an input to the prediction.

[0046] The interface controller 235 may further or alternatively be configured to generate code that can be read by a software application and that initiates the generation of a presentation or interface (e.g., a web page) that can include or represent one or more predictions generated by the ARIA-E prediction system 205. For example, the interface controller 235 may generate web page code configured to present, when executed by a browser, a web page (on a website) that identifies one or more predictions. The website and / or the web page may initially operate to collect inputs on the user device 210 used to execute the model.

[0047] When the user device 210 receives information and / or code from the ARIA-E prediction system 205, the user device 210 may generate and present a presentation that includes information and / or data that specifies how the code is to be presented. Thus, the user device 210 may present data corresponding to a prediction regarding whether and to what extent a given treatment regimen may result in ARIA-E. ARIA-E Prediction Process

[0048] Figure 4 shows a flowchart of an exemplary process 400 for predicting ARIA-E according to some embodiments of the present invention. In block 402, the aggregate model controller 225 identifies the actual or potential dosage of the active ingredient. The identification may include identifying the actual or potential dosage administered or to be administered at a given time within the treatment regimen (e.g., 0 hours, 8 weeks after the start of the regimen, when a certain dosage is reached, etc.). The dosage can be, for example, the dosage indicated in a communication received from the user device 210 (e.g., as identified by the interface controller 235), a default dosage (e.g., when identified and / or retrieved by the aggregate model controller 225), one or more possible dosages (e.g., when identified and / or retrieved by the aggregation model controller 225), and the like.

[0049] In block 404, the pharmacodynamics model controller 220 predicts the level of local amyloid. More specifically, the pharmacokinetics model controller 215 can predict the time course of the active ingredient concentration based on the dosage of the active ingredient, and the pharmacodynamics model controller 220 predicts the time course of the local amyloid level based on the predicted active ingredient concentration. The predicted level of local amyloid is predicted simultaneously with the administration of the dosage, predicted for the date at a specific time point compared to when the first (or given) dosage is administered, and can be predicted for each of a plurality of time points compared to when the first (or given) dosage is administered, and so on.

[0050] In block 406, the pharmacodynamics model controller 220 predicts the level of vascular wall damage based on the predicted level of local amyloid and the predicted vascular repair. The predicted level of vascular wall damage can be generated by solving a differential equation such as equation (2). The predicted level of vascular wall damage may further depend, for example, on the predicted repair rate.

[0051] In block 408, the pharmacodynamic model controller 220 predicts the ARIA-E severity one or more times (e.g., over time of ARIA-E severity). Block 408 may include predicting the level of local amyloid beta at one or more time points (e.g., over time of the level), and / or using the predicted level. Block 406 may include predicting the predicted level of vascular wall damage at one or more time points (e.g., over time of vascular wall damage), and / or using the predicted level. The local amyloid beta level may be predicted based on the estimated active ingredient concentration level using one or more differential equations. The degree of vascular wall damage may be predicted based on the predicted local amyloid beta level (e.g., and / or using a technique as shown with reference to block 406) using one or more differential equations.

[0052] In block 410, a result corresponding to the predicted ARIA-E severity level can be output. For example, the ARIA-E prediction system 205 can send the result to the user device 210. The result may include one or more predicted ARIA levels (e.g., over time of ARIA-E level), a potential schedule for MRI-based monitoring of ARIA-E (e.g., by BGTS scoring), or a potential revised dosing schedule (e.g., indicating one or more proposed doses of the active ingredient and / or the relative timing between one or more consecutive dose administrations). Examples Dynamic behavior of exemplary cases

[0053] The data used for model development consisted of long-term measurements of plasma drug concentrations and the magnitude of ARIA-E from a subset of individuals (N = 112) who developed ARIA-E during the open-label extension (OLE) of the Scarlet Road (SR; NCT01224106) and Marguerite Road (MR; NCT02051608) studies of gantenerumab, including participants in the studies of prodromal Alzheimer's disease and mild Alzheimer's disease, respectively. Briefly, SR / MR trial participants who received double-blind treatment and had at least one follow-up visit were eligible for non-blind continuation (OLE) participation. During the SR / MR OLE trial, all participants received subcutaneous gantenerumab every 4 weeks while escalating towards a target dose of 1200 mg. Each participant was assigned to one of five dosing regimens based on their APOE4 carrier status and the last dose during double-blind treatment (Table 1). Protocol-defined routine MRI monitoring for ARIA-E detection was performed at regular intervals. If ARIA-E was detected, dosing was adjusted based on its radiographic severity and / or the presence of symptoms. The magnitude of ARIA-E on MRI scans was evaluated using the Barkhof Global Total Score (BGTS), a 60-point severity scale where higher scores indicate higher severity, which accounted for the number and size of parenchymal hyperintensities, sulcal hyperintensities, and gyral swelling present on FLAIR images. The reported BGTS described the number and size of parenchymal hyperintensities, sulcal hyperintensities, and gyral swelling present on FLAIR images.

Table 1

[0054] The four ARIA-E cases shown in Figures 3 and 7 were selected to highlight various ARIA-E features (size, dissipation, recurrence) that are mechanistically insightful but do not reflect the typical ARIA-E profile of gantenerumab. Each trial participant was assigned to one of the five dosing regimens described in Table 1. Since the model accounted for inter-individual variability, the modeled time profiles for each ARIA-E case were generated based on empirical Bayes estimates, i.e., the most likely values of the individual parameters.

[0055] The VWD parameters were estimated by fitting the whole VWD model to a set of long-term BGTS observations while keeping the individual pharmacokinetic parameters fixed. Boolean data from the first part (e.g., yes / no for ARIA-E occurrence) and continuous observations from the second part (e.g., BGTS values within the range of ARIA-E magnitude) were implemented in the data file as two different types of observations and modeled simultaneously. Parameter estimation was performed using the non-linear mixed effects method of Monolix (version 2020R1).

[0056] The data shown in Figure 5 indicate that the previously constructed population pharmacokinetic model was able to describe the individual PK data from the SR / MR OLE trial, and the residuals were uniformly distributed around zero with most values within -2 and +2 standard deviations, thereby indicating no large systematic bias.

[0057] Parameter amyloid 0 , α 除去 and k 修復 were assumed to have log-normal inter-individual variability, and their typical values (fixed effects) and the width of the distribution (random effects) were estimated. Parameters β 1 , VWD 50 and pow were estimated without inter-individual variability (i.e., as fixed effects only). It could be shown that VWD(t) is proportional to amyloid A 0 , meaning that parameters amyloid 0 and EG 50 appear effectively only as the ratio in the equation of BGTS(t). Therefore, to ensure structural identifiability, without loss of generality, EG 50 was set to 1. Furthermore, BGTS 最大The value was fixed at 60 along the limit of the 60-point BGTS severity scale. The residual (unexplained) variability describing the difference between the observed and predicted BGTS was assumed to be independent of the prediction and to be normally distributed. The estimated population parameters (representative values and inter-individual variability) are shown in Table 2. The diagnostic plots from Figure 6 demonstrate the ability of the model to predict the probability of ARIA-E events at the population level, as well as the magnitude of ARIA-E, particularly at the individual level. The residuals over time are evenly concentrated around zero, with no large systematic bias, and most values are within -2 and +2 standard deviations.

Table 2

[0058] The estimated time course of the active ingredient concentration was calculated using the pharmacokinetic model disclosed by Retout S et al., "Disease Modeling and Model-Based Meta-Analyses to Define a New Direction for a Phase III Program of Gantenerumab in Alzheimer’s Disease." Clin Pharmacol Ther. 2022 Apr;111(4):857-866. The estimated time course of local amyloid-beta was calculated using Equation (1). The estimated time course of VWD was calculated using Equation (2). The estimated time course of BGTS was calculated using Equations (3) and (4).

[0059] The estimated time courses of concentration, local amyloid-beta, VWD, and BGTS are shown in the central panel of Figure 3 for Case 1 and on the left side of Figure 7 for Cases 2-4.

[0060] Cases 1 and 2 were assigned to the same rapid escalation regimen (Regimen 4), but nevertheless developed ARIA-E at significantly different times. (See the central panel of Figure 3 and the uppermost graph set of Figure 7). Case 1 reached the target dose by week 16 and experienced the first ARIA-E event at week 20, while Case 2 developed ARIA-E at week 12 before reaching the target dose. These two cases appear to experience different rates of amyloid clearance around week 12 (as can be seen from the different slopes of the local amyloid curves). This is explained by the model with different estimated values of the clearance constant (see the α 除去 value in the upper right plot of Figure 7). This difference occurs despite similar drug concentration levels during the first 12 weeks and comparable estimated values of baseline local amyloid levels.

[0061] Case 3, assigned to the slowest escalation regimen (Regimen 1), experienced two treatment interruptions due to ARIA-E (see the plus symbols in the uppermost graph of the central plot set in Figure 7). The onset of ARIA-E during the slow escalation may be due to the relatively high baseline levels of local amyloid estimated for this subject (see the amyloid 0 value in the central right histogram of Figure 7). The model results suggest a recurrence of ARIA-E upon re-administration and increasing-dose settings due to the combination of persistent high levels of local amyloid and relatively high drug concentrations, which drives another increase in VWD.

[0062] Case 4, assigned to the second slowest escalation regimen (Regimen 2), developed an initial ARIA-E that was much larger than any of the ARIA-E events experienced by the other three cases (see the lowermost graph in the lowermost plot set of Figure 7). The model did not estimate significantly different levels of baseline local amyloid or drug concentration that could explain the observed differences in the magnitude of ARIA-E. However, both the estimated clearance constant and repair constant for Case 4 were significantly different from the constants estimated for the other three cases. Specifically, a significantly larger value of the clearance constant was associated with a small repair rate constant (α in Figure 7 除去and k 修復 Drives local amyloid removal at a high rate that cannot match the slow rate of vascular repair determined by (wanting to refer to the value). This imbalance results in a large VWD level and ultimately a high BGTS.

[0063] The model predictions for the four ARIA-E cases share multiple features. This model predicted the steepest decrease in local amyloid levels near the time of observed ARIA-E events and the subsequent increase in the magnitude of VWD and BGTS. This pattern is prominent at week 20 in case 1, week 12 in case 2, weeks 28 and 60 in case 3, and weeks 12 and 52 in case 4 (Figures 3 and 7). During the entire period of treatment interruption, the model predicted a plateau in local amyloid levels and a gradual decrease in VWD. The latter also reflects the repair process and transitions to a gradual decrease in the magnitude of ARIA-E that closely follows the observed dissipation of ARIA-E, as can be seen between weeks 20 - 36 for case 1, weeks 12 - 24 for case 2, weeks 28 - 44 for case 3, and weeks 12 - 40 for case 4 (Figures 3 and 7). Exploration of the influence of major model parameters on the dynamics of ARIA-E

[0064] Considering that both the baseline level of local amyloid and the removal constant affect the rate of local amyloid removal, their roles in the dynamics of ARIA-E were investigated separately in a series of simulations shown in Figures 8A - 8B. The control plot is the one shown in the central panel of Figure 3 and corresponds to case 1. The other plots are simulations with a single variable modified (removal constant in Figure 8A, baseline local amyloid beta in Figure 8B, and repair rate constant in Figure 8C).

[0065] The greater the clearance constant, the earlier both VWD and ARIA-E increased. On the other hand, the smaller the clearance constant, the more delayed the appearance of ARIA-E, but the clearance of local amyloid was also delayed. The data points at 12 and 24 weeks provide a comparison. For higher baseline levels of local amyloid, the model predicted early damage in the vessel wall and recurrent ARIA-E events due to persistent high levels of local amyloid. In contrast, when the baseline level of local amyloid was smaller, the model predicted continuous clearance of local amyloid without significant ARIA-E events. Figure 8C shows the evolution of ARIA-E when the estimated vascular repair process is decreased or enhanced by variations in the repair rate constant. When the repair rate constant of the endogenous vascular repair process is smaller, the clearance of local amyloid results in repeated damage to the vessel wall and ultimately ARIA-E recurrence. Conversely, in this model, a vascular repair process with a larger rate constant can counteract the predicted amyloid clearance rate at the end of the increment and prevent significant ARIA-E. Interpretation

[0066] This semi-mechanistic in-silico model addressed the ambiguity and complexity of the ARIA-E pathophysiology in the context of long-term observations of the size of ARIA-E from anti-amyloid clinical trials. Previous ARIA-E models developed using data from bapineuzumab and gantenerumab are event hazard models that can predict the probability of ARIA-E occurrence for various dosing regimens. The mathematical framework used in this example allows for different biological interpretations of the modeled mechanisms based on the following key factors: local amyloid load, drug exposure (dose and drug concentration), amyloid clearance, and vascular wall injury and repair. For the various ARIA-E scenarios shown in Figure 1, the same mathematical relationship describes the drug-mediated clearance of local amyloid, whether the substance or vascular Aβ load is being cleared via direct degradation or cellular-mediated phagocytosis. Although the exact biological details underlying ARIA-E are not elucidated, a common feature across the different mechanistic pathways that can cause an ARIA-E event appears to be the damaged vascular wall. A model that links the drug-mediated clearance of local amyloid to the level of VWD and ultimately to the severity of ARIA-E (as measured by BGTS) performed well in capturing the variability between subjects. Parameters were estimated using non-linear mixed effects methods by fitting the model to the BGTS dataset from the SR / MR OLE trial of gantenerumab. The resulting VWD model provides a good subject-level explanation of ARIA-E reported in the SR / MR OLE trial of gantenerumab, thereby confirming the validity of the mechanisms implemented.

[0067] In addition to fitting the individual time courses of ARIA-E events, the VWD model provides several insights. According to this model, (i) high exposure (dose and drug concentration), (ii) high local amyloid, and / or (iii) high efficiency in local amyloid clearance (here, the clearance constant α 除去Due to (captured by), high rates of local amyloid removal can promote the development of ARIA-E. In the case of anti-amyloid antibodies such as aducanumab, donanemab, and gantenerumab, which rely heavily on Fc receptor-mediated phagocytosis of amyloid aggregates by immune cells, higher estimated values of the clearance constant may reflect a greater, perhaps excessive, response of immune cells activated by the amyloid-drug complex. Image-based analysis of a small number of ARIA-E cases from the gantenerumab study revealed that regions with FLAIR hyperintensity often show a marked decrease in amyloid PET signal, thereby supporting the idea of more efficient cell-mediated phagocytosis of Aβ aggregates at ARIA-E sites. Notably, significant amyloid reduction was also observed in regions without FLAIR abnormalities. Based on the VWD model, local amyloid reduction can occur without ARIA-E as long as the local rate of amyloid removal is offset by the rate of the endogenous vascular repair process. Furthermore, according to this model, ARIA-E can completely dissipate before the VWD level returns to zero (BGTS = 0). This behavior is a result of the presumed non-linear VWD-BGTS relationship that ensures, as shown in Figure 3, that small disruptions in the vessel wall do not lead to detectable ARIA-E events. Finally, the VWD model suggests that recurrence of ARIA-E can be the combined effect of high drug concentration and persistence of high levels of local amyloid, and that amyloid depletion ultimately reduces the risk of ARIA-E over time, even at high drug concentrations.

[0068] When comparing the model results to amyloid reduction findings in anti-amyloid clinical trials reporting ARIA, it is useful to distinguish between the overall and local properties of the modeled / measured amyloid. For example, studies using comprehensive PET evaluations of baseline amyloid in the bapineuzumab and gantenerumab studies showed no significant difference between the ARIA-E group and the non-ARIA-E group. Instead, when the amyloid PET signal was partially evaluated, a significantly higher baseline amyloid burden from the occipital lobe region was found in the ARIA-E group than in the non-ARIA-E group. Considering the predominance of CAA in the occipital vasculature, the higher baseline levels of local amyloid estimated by the VWD model may be related to a higher local burden of vascular amyloid.

[0069] Based solely on amyloid PET imaging, it is not possible to definitively say whether the reduction in amyloid PET signal is due to the removal of vascular and / or parenchymal amyloid. The relative contributions of the two forms of Aβ aggregates to the amyloid PET signal have been a subject of debate. Some studies have concluded that amyloid PET can detect vascular Aβ in CAA, while other recent studies have reported that the amyloid PET signal is mainly driven by parenchymal amyloid plaques and does not appear to be significantly confounded by CAA. The latter finding is limited by the fact that only half of the subjects met the clinical diagnosis of probable CAA with a median number of two cerebral microbleeds, despite pathological confirmation of CAA in all subjects. Notably, 10 years ago, the Alzheimer’s Association Research Roundtable Workgroup recommended that individuals with more than four cerebral microbleeds at baseline should not be included in anti-amyloid clinical trials. Thus, the contribution of vascular Aβ aggregates to the PET signal in clinically more severe cases of CAA, particularly those enrolled in more recent anti-amyloid studies, remains undetermined. To further clarify the local burden of vascular amyloid and the relative risk of ARIA-E associated with its removal, more analyses are needed that integrate long-term PET and MRI data from anti-amyloid trials and quantify the local distribution of amyloid signal, FLAIR hyperintensities, and CAA imaging markers. The VWD model could be improved using future biomarkers that can better distinguish between vascular and parenchymal amyloid burdens, thereby predicting the evolution of ARIA-E in patients with various degrees of AD pathology (based on parenchymal amyloid burden) and CAA (based on vascular amyloid burden).

[0070] The VWD model of the semi-mechanistic provides a mathematical and semi-mechanistic description of the time patterns observed in the BGTS data of gantenerumab studies. The VWD model enables an in-silico individual-level investigation of biologically plausible variables and processes that affect the onset and severity of ARIA-E. Upon its validation, the VWD model will be useful for generating hypotheses that can be tested in clinical studies for the management of ARIA. Using the VWD model, it is possible to simulate the effects of the rates of local amyloid removal and vascular repair on the time course of ARIA-E in individuals with an ARIA-E history. Such model-based predictions can assist in decisions regarding the continuation or reintroduction of treatment to minimize the risk of ARIA-E progression.

[0071] While the subject matter has been described in detail with respect to its specific embodiments, it will be understood by those skilled in the art that upon achieving the above understanding, changes, modifications, and equivalents to such embodiments can be readily generated. Accordingly, it should be understood that the present disclosure is presented for purposes of illustration and not limitation, and does not exclude such changes, modifications, and / or additional inclusion to the subject matter that would be readily apparent to those skilled in the art. Indeed, the methods and systems described herein may be embodied in various other forms. Furthermore, various omissions, substitutions, and changes may be made in the form of the methods and systems described herein without departing from the spirit of the present disclosure. The appended claims and their equivalents are intended to cover such forms or modifications as fall within the scope and spirit of the present disclosure.

[0072] Unless otherwise specified, throughout this specification, discussions using terms such as "processing", "computing", "calculating", "determining", and "identifying" refer to the operations or processes of one or more computers or similar electronic computing devices, such as a computing device that manipulates or transforms data represented as physical electronic or magnetic quantities within the memory, registers, or other information storage devices, transmission devices, or display devices of a computing platform.

[0073] One or more systems described herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provides a result conditioned on one or more inputs. Suitable computing devices include general-purpose computing devices, multi-purpose microprocessor-based computing systems that access stored software to program or configure a computing system, to dedicated computing devices that implement one or more embodiments of the subject matter. Any suitable programming, scripting, or other type of language or combination of languages can be used to implement the teachings herein in the software used to program or configure a computing device.

[0074] Embodiments of the methods disclosed herein can be executed in the operation of such computing devices. The order of the blocks shown in the above examples can be changed, e.g., the blocks can be rearranged, combined, and / or divided into sub-blocks. Certain blocks or processes can be executed in parallel.

[0075] Conditional language used in this specification, such as, among others, "can", "could", "might", "may", "for example", etc., generally conveys that a particular example includes a particular feature, element, and / or step, but other examples do not, unless otherwise specified or understood in a different sense within the context in which it is used. Thus, such conditional language is not generally intended to mean that a feature, element, and / or step is required in any way in one or more examples, or that one or more examples necessarily include logic for determining whether these features, elements, and / or steps are included in, or performed by, any particular example, regardless of the presence or absence of author input or prompts.

[0076] Terms such as "comprising," "including," "having," etc. are synonyms and are used in an inclusive, open-ended manner, and do not exclude additional elements, features, acts, operations, etc. Also, the term "or" is used in an inclusive sense (not an exclusive sense), e.g., when used to connect a list of elements, the term "or" means one, some, or all of the elements in the list. The use of "adapted to" or "configured to" in this specification means open and inclusive language that does not exclude devices adapted or configured to perform additional tasks or steps. Further, the use of "based on" means that a process, step, calculation, or other operation "based on" one or more recited conditions or values may in fact be based on additional conditions or values beyond those recited. Similarly, the use of "based at least in part on" means that a process, step, calculation, or other operation "based at least in part on" one or more recited conditions or values may in fact be based on additional conditions or values beyond those recited. Headings, lists, and numbering included in this specification are for ease of explanation only and are not meant to be limiting.

[0077] The various features and processes described above may be used independently of each other or may be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of the present disclosure. Further, in some implementations, certain methods or process blocks may be omitted. The methods and processes described herein are also not limited to any particular sequence, and the associated blocks or states may be executed in other suitable sequences. For example, the described blocks or states may be executed in an order other than the specifically disclosed order, or multiple blocks or states may be combined into a single block or state. Exemplary blocks or states may be executed serially, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed examples. Similarly, the exemplary systems and components described herein may be configured differently than those described. For example, elements may be added, removed, or rearranged compared to the disclosed examples.

Claims

Claim 1 identifying a dosage of an active ingredient in a treatment that is being provided to or considered for a subject; predicting a level of local amyloid-beta based on the dosage of the active ingredient; Based on the predicted removal amount of local amyloid beta, T ２ Predicting the severity of amyloid-related imaging abnormalities (ARIA) (ARIA-E) that appear as high signals in T ２ weighted fluid-attenuated inversion recovery (FLAIR) images, wherein predicting the severity of ARIA-E includes predicting the severity of ARIA, which includes predicting the degree of vascular wall damage. outputting a result corresponding to a predicted ARIA-E severity A computer-implemented method comprising the steps above. Claim 2 The method according to claim 1, wherein the active ingredient comprises an anti-amyloid monoclonal antibody. Claim 3 The method according to claim 1, further comprising using a pharmacokinetic model to predict the time course of the concentration of the active ingredient in plasma during the treatment, wherein the prediction of the local amyloid-beta is based on at least one predicted concentration of the active ingredient at a predicted time course. Claim 4 Predicting the level of local amyloid-beta comprises: estimating a baseline level of local amyloid-beta; calculating a clearance rate of the local amyloid-beta based on the dosage of the active ingredient and the baseline level of local amyloid-beta; predicting the level of local amyloid-beta based on the calculated clearance rate of the local amyloid-beta The method according to claim 1, comprising the steps above. Claim 5 Predicting the level of local amyloid-beta comprises solving a pharmacodynamic differential equation assuming that the rate of change of amyloid-beta is proportional to the product of the concentration of the active ingredient and the local amyloid-beta level in the subject. The method according to claim 1, comprising the steps above. Claim 6 Predicting the level of vascular wall damage comprises solving a differential equation that includes the level of local amyloid-beta. The method according to claim 1, comprising the steps above. Claim 7 Predicting the severity of ARIA-E comprises solving an algebraic equation assuming a non-linear relationship between the level of vascular wall damage and the Barkhof Global Total Score (BGTS). The method according to claim 1, comprising the steps above. Claim 8 Identifying a potential schedule for monitoring ARIA events based on the predicted severity of ARIA, wherein the result characterizes the potential schedule. The method according to claim 1, comprising the steps above. Claim 9 Identifying a potential recommended dosage of the active ingredient for the subject based on the predicted severity of ARIA, the result characterizing the potential recommended dosage, the method of claim 1 including identifying.

10. One or more data processors, A non-transitory computer-readable storage medium including instructions that, when executed by the one or more data processors, cause the one or more data processors to execute some or all of one or more methods disclosed herein A system comprising.

11. A computer program product tangibly embodied in a non-transitory machine-readable storage medium including instructions configured to cause one or more data processors to execute some or all of one or more methods disclosed herein.

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