Quantitative systems pharmacology-based exploration of relevant anti-amyloid therapy challenges in clinical practice.

INTRODUCTION: Addressing practical challenges in clinical practice after the recent approvals of amyloid antibodies in Alzheimer's disease (AD) will benefit more patients. However, generating these answers using clinical trials or real-world evidence is not practical, nor feasible. METHODS: Here we use a Quantitative Systems Pharmacology (QSP) computational model of amyloid aggregation dynamics, well validated with clinical data on biomarkers and amyloid-related imaging abnormality-edema (ARIA-E) liability of six amyloid antibodies in clinical trials to explore various clinical practice challenges. RESULTS: Treatment duration to reach amyloid negativity ranges from 12 to 44, 16 to 40, and 6 to 20 months for lecanemab, aducanumab, and donanemab, respectively, for baseline central amyloid values between 50 and 200 Centiloids (CL). Changes in plasma cerebrospinal fluid Aß42 and the plasma Aß42/ Aß40 ratio-fluid biomarkers to detect central amyloid negativity-is greater for lecanemab than for aducanumab and donanemab, indicating that these fluid amyloid biomarkers are only suitable for lecanemab. After reaching amyloid negativity an optimal maintenance schedule consists of a 24-month, 48-month and 64-month interval for 10 mg/kg (mpk) lecanemab, 10 mpk aducanumab, and 20 mpk donanemab, respectively, to keep central amyloid negative for 10 years. Cumulative ARIA-E liability could be reduced to almost half by introducing a drug holiday in the first months. For patients experiencing ARIA-E, restarting treatment with a conservative titration strategy resulted in an additional delay ranging between 3 and 4 months (donanemab), 5 months (lecanemab), and up to 7 months (aducanumab) for reaching amyloid negativity, depending upon the timing of the incident. Clinical trial designs for Down syndrome patients suggested the same rank order for central amyloid reduction, but higher ARIA-E liability especially for donanemab, which can be significantly mitigated by adopting a longer titration period. DISCUSSION: This QSP platform could support clinical practice challenges to optimize real-world treatment paradigms for new and existing amyloid drugs.

Physiologically-based pharmacokinetic modeling to predict drug-drug interaction of enzalutamide with combined P-gp and CYP3A substrates.

Enzalutamide is known to strongly induce cytochrome P450 3A4 (CYP3A4). Furthermore, enzalutamide showed induction and inhibition of P-glycoprotein (P-gp) in in vitro studies. A clinical drug-drug interaction (DDI) study between enzalutamide and digoxin, a typical P-gp substrate, suggested enzalutamide has weak inhibitory effect on P-gp substrates. Direct oral anticoagulants (DOACs), such as apixaban and rivaroxaban, are dual substrates of CYP3A4 and P-gp, and hence it is recommended to avoid co-administration of these DOACs with combined P-gp and strong CYP3A inducers. Enzalutamide's net effect on P-gp and CYP3A for apixaban and rivaroxaban plasma exposures is of interest to physicians who treat patients for venous thromboembolism with prostate cancer. Accordingly, a physiologically-based pharmacokinetic (PBPK) analysis was performed to predict the magnitude of DDI on apixaban and rivaroxaban exposures in the presence of 160 mg once-daily dosing of enzalutamide. The PBPK models of enzalutamide and M2, a major metabolite of enzalutamide which also has potential to induce CYP3A and P-gp and inhibit P-gp, were developed and verified as perpetrators of CYP3A-and P-gp-mediated interaction. Simulation results predicted a 31% decrease in AUC and no change in Cmax for apixaban and a 45% decrease in AUC and a 25% decrease in Cmax for rivaroxaban when 160 mg multiple doses of enzalutamide were co-administered. In summary, enzalutamide is considered to decrease apixaban and rivaroxaban exposure through the combined effects of CYP3A induction and net P-gp inhibition. Concurrent use of these drugs warrants careful monitoring for efficacy and safety.

Learn-confirm in model-informed drug development: Assessing an immunogenicity quantitative systems pharmacology platform.

Immunogenicity against therapeutic proteins frequently causes attrition owing to its potential impact on pharmacokinetics, pharmacodynamics, efficacy, and safety. Predicting immunogenicity is complex because of its multifactorial drivers, including compound properties, subject characteristics, and treatment parameters. To integrate these, the Immunogenicity Simulator was developed using published, predominantly late-stage trial data from 15 therapeutic proteins. This single-blinded evaluation with subject-level data from 10 further monoclonals assesses the Immunogenicity Simulator's credibility for application during the drug development process.

Application of physiologically based pharmacokinetic models for therapeutic proteins and other novel modalities.

The past two decades have seen diversification of drug development pipelines and approvals from traditional small molecule therapies to alternative modalities including monoclonal antibodies, engineered proteins, antibody drug conjugates (ADCs), oligonucleotides and gene therapies. At the same time, physiologically based pharmacokinetic (PBPK) models for small molecules have seen increased industry and regulatory acceptance.This review focusses on the current status of the application of PBPK models to these newer modalities and give a perspective on the successes, challenges and future directions of this field.There is greatest experience in the development of PBPK models for therapeutic proteins, and PBPK models for ADCs benefit from prior experience for both therapeutic proteins and small molecules. For other modalities, the application of PBPK models is in its infancy.Challenges are discussed and a common theme is lack of availability of physiological and experimental data to characterise systems and drug parameters to enable a priori prediction of pharmacokinetics. Furthermore, sufficient clinical data are required to build confidence in developed models.The PBPK modelling approach provides a quantitative framework for integrating knowledge and data from multiple sources and can be built on as more data becomes available.

The use of PBPK/PD to establish clinically relevant dissolution specifications for zolpidem immediate release tablets.

BACKGROUND: Zolpidem is a non-benzodiazepine hypnotic agent which has been shown to be effective in inducing and maintaining sleep in adults and is one of the most frequently prescribed hypnotics in the world. For drugs that are used to treat sleeping disorders, the time to reach the maximum concentration (Tmax) of the drug in plasma is important to achieving a fast onset of action and this must be maintained when switching from one product to another. OBJECTIVES: The main objective of the present work was to create a PBPK/PD model for zolpidem and establish a clinically relevant "safe space" for dissolution of zolpidem from the commercial immediate release (IR) formulation. A second objective was to analyze literature pharmacokinetic data to verify the negative food effect ascribed to zolpidem and consider its ramifications in terms of the "safe space" for dissolution. METHODS: Using dissolution, pharmacokinetic and pharmacodynamic data, an integrated PBPK/PD model for immediate release zolpidem tablets was constructed in Simcyp®. This model was used to identify the clinically relevant dissolution specifications necessary to ensure efficacy. RESULTS: According to the simulations, as long as 85% of the drug is released in 45 minutes or less, the impact on the PK and PD profiles of zolpidem would be minimal. According to the FDA, the drug has to dissolve from the test and reference products at a similar rate and to an extent of 85% in not more than 30 minutes to pass bioequivalence via the BCS-biowaiver test. Thus, the BCS-biowaiver specifications are somewhat more stringent than the "safe space" based on the PBPK/PD model. Published data from fasted and fed state pharmacokinetic studies suggest but do not prove a negative food effect of zolpidem. CONCLUSIONS: A PBPK/PD model indicates that current BCS-biowaiver criteria are more restrictive for immediate release zolpidem tablets than they need to be. In view of the close relationship between PK and PD, it remains advisable to avoid taking zolpidem tablets with or immediately after a meal, as indicated by the Stilnox® labeling.

Incorporation of the Time-Varying Postprandial Increase in Splanchnic Blood Flow into a PBPK Model to Predict the Effect of Food on the Pharmacokinetics of Orally Administered High-Extraction Drugs.

Following a meal, a transient increase in splanchnic blood flow occurs that can result in increased exposure to orally administered high-extraction drugs. Typically, physiologically based pharmacokinetic (PBPK) models have incorporated this increase in blood flow as a time-invariant fed/fasted ratio, but this approach is unable to explain the extent of increased drug exposure. A model for the time-varying increase in splanchnic blood flow following a moderate- to high-calorie meal (TV-Q Splanch) was developed to describe the observed data for healthy individuals. This was integrated within a PBPK model and used to predict the contribution of increased splanchnic blood flow to the observed food effect for two orally administered high-extraction drugs, propranolol and ibrutinib. The model predicted geometric mean fed/fasted AUC and C max ratios of 1.24 and 1.29 for propranolol, which were within the range of published values (within 1.0-1.8-fold of values from eight clinical studies). For ibrutinib, the predicted geometric mean fed/fasted AUC and C max ratios were 2.0 and 1.84, respectively, which was within 1.1-fold of the reported fed/fasted AUC ratio but underestimated the reported C max ratio by up to 1.9-fold. For both drugs, the interindividual variability in fed/fasted AUC and C max ratios was underpredicted. This suggests that the postprandial change in splanchnic blood flow is a major mechanism of the food effect for propranolol and ibrutinib but is insufficient to fully explain the observations. The proposed model is anticipated to improve the prediction of food effect for high-extraction drugs, but should be considered with other mechanisms.

Potential Sources of Inter-Subject Variability in Monoclonal Antibody Pharmacokinetics.

Understanding inter-subject variability in drug pharmacokinetics and pharmacodynamics is important to ensure that all patients attain suitable drug exposure to achieve efficacy and avoid toxicity. Inter-subject variability in the pharmacokinetics of therapeutic monoclonal antibodies (mAbs) is generally moderate to high; however, the factors responsible for the high inter-subject variability have not been comprehensively reviewed. In this review, the extent of inter-subject variability for mAb pharmacokinetics is presented and potential factors contributing to this variability are explored and summarised. Disease status, age, sex, ethnicity, body size, genetic polymorphisms, concomitant medication, co-morbidities, immune status and multiple other patient-specific details have been considered. The inter-subject variability for mAb pharmacokinetics most likely depends on the complex interplay of multiple factors. However, studies aimed at investigating the reasons for the inter-subject variability are sparse. Population pharmacokinetic models and physiologically based pharmacokinetic models are useful tools to identify important covariates, aiding in the understanding of factors contributing to inter-subject variability. Further understanding of inter-subject variability in pharmacokinetics should aid in development of dosing regimens that are more appropriate.

Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

Pharmacokinetics, pharmacodynamics and physiologically-based pharmacokinetic modelling of monoclonal antibodies.

Development of monoclonal antibodies (mAbs) and their functional derivatives represents a growing segment of the development pipeline in the pharmaceutical industry. More than 25 mAbs and derivatives have been approved for a variety of therapeutic applications. In addition, around 500 mAbs and derivatives are currently in different stages of development. mAbs are considered to be large molecule therapeutics (in general, they are 2-3 orders of magnitude larger than small chemical molecule therapeutics), but they are not just big chemicals. These compounds demonstrate much more complex pharmacokinetic and pharmacodynamic behaviour than small molecules. Because of their large size and relatively poor membrane permeability and instability in the conditions of the gastrointestinal tract, parenteral administration is the most usual route of administration. The rate and extent of mAb distribution is very slow and depends on extravasation in tissue, distribution within the particular tissue, and degradation. Elimination primarily happens via catabolism to peptides and amino acids. Although not definitive, work has been published to define the human tissues mainly involved in the elimination of mAbs, and it seems that many cells throughout the body are involved. mAbs can be targeted against many soluble or membrane-bound targets, thus these compounds may act by a variety of mechanisms to achieve their pharmacological effect. mAbs targeting soluble antigen generally exhibit linear elimination, whereas those targeting membrane-bound antigen often exhibit non-linear elimination, mainly due to target-mediated drug disposition (TMDD). The high-affinity interaction of mAbs and their derivatives with the pharmacological target can often result in non-linear pharmacokinetics. Because of species differences (particularly due to differences in target affinity and abundance) in the pharmacokinetics and pharmacodynamics of mAbs, pharmacokinetic/pharmacodynamic modelling of mAbs has been used routinely to expedite the development of mAbs and their derivatives and has been utilized to help in the selection of appropriate dose regimens. Although modelling approaches have helped to explain variability in both pharmacokinetic and pharmacodynamic properties of these drugs, there is a clear need for more complex models to improve understanding of pharmacokinetic processes and pharmacodynamic interactions of mAbs with the immune system. There are different approaches applied to physiologically based pharmacokinetic (PBPK) modelling of mAbs and important differences between the models developed. Some key additional features that need to be accounted for in PBPK models of mAbs are neonatal Fc receptor (FcRn; an important salvage mechanism for antibodies) binding, TMDD and lymph flow. Several models have been described incorporating some or all of these features and the use of PBPK models are expected to expand over the next few years.

A novel fluorescent histamine H(1) receptor antagonist demonstrates the advantage of using fluorescence correlation spectroscopy to study the binding of lipophilic ligands.

BACKGROUND AND PURPOSE: Fluorescent ligands facilitate the study of ligand-receptor interactions at the level of single cells and individual receptors. Here, we describe a novel fluorescent histamine H(1) receptor antagonist (mepyramine-BODIPY630-650) and use it to monitor the membrane diffusion of the histamine H(1) receptor. EXPERIMENTAL APPROACH: The human histamine H(1) receptor fused to yellow fluorescent protein (YFP) was transiently expressed in CHO-K1 cells. The time course of binding of mepyramine-BODIPY630-650 to the H(1) receptor was determined by confocal microscopy. Additionally, fluorescence correlation spectroscopy (FCS) was used to characterize the diffusion coefficient of the H(1) receptor in cell membranes both directly (YFP fluorescence) and in its antagonist-bound state (with mepyramine-BODIPY630-650). KEY RESULTS: Mepyramine-BODIPY630-650 was a high-affinity antagonist at the histamine H(1) receptor. Specific membrane binding, in addition to significant intracellular uptake of the fluorescent ligand, was detected by confocal microscopy. However, FCS was able to quantify the receptor-specific binding in the membrane, as well as the diffusion coefficient of the antagonist-H(1) receptor-YFP complexes, which was significantly slower than when determined directly using YFP. FCS also detected specific binding of mepyramine-BODIPY630-650 to the endogenous H(1) receptor in HeLa cells. CONCLUSIONS AND IMPLICATIONS: Mepyramine-BODIPY630-650 is a useful tool for localizing the H(1) receptor using confocal microscopy. However, its use in conjunction with FCS allows quantification of ligand binding at the membrane, as well as determining receptor diffusion in the absence of significant bleaching effects. Finally, these methods can be successfully extended to endogenously expressed untagged receptors in HeLa cells.

Bimolecular fluorescence complementation: lighting up seven transmembrane domain receptor signalling networks.

There is increasing complexity in the organization of seven transmembrane domain (7TM) receptor signalling pathways, and in the ability of their ligands to modulate and direct this signalling. Underlying these events is a network of protein interactions between the 7TM receptors themselves and associated effectors, such as G proteins and beta-arrestins. Bimolecular fluorescence complementation, or BiFC, is a technique capable of detecting these protein-protein events essential for 7TM receptor function. Fluorescent proteins, such as those from Aequorea victoria, are split into two non-fluorescent halves, which then tag the proteins under study. On association, these fragments refold and regenerate a mature fluorescent protein, producing a BiFC signal indicative of complex formation. Here, we review the experimental criteria for successful application of BiFC, considered in the context of 7TM receptor signalling events such as receptor dimerization, G protein and beta-arrestin signalling. The advantages and limitations of BiFC imaging are compared with alternative resonance energy transfer techniques. We show that the essential simplicity of the fluorescent BiFC measurement allows high-content and advanced imaging applications, and that it can probe more complex multi-protein interactions alone or in combination with resonance energy transfer. These capabilities suggest that BiFC techniques will become ever more useful in the analysis of ligand and 7TM receptor pharmacology at the molecular level of protein-protein interactions.