Separating Pharmacokinetic and Pharmacodynamic Components in Empirical Adjustment Factor Distributions

For a particular chemical, one can treat the chemical-by-chemical variation in relative doses for equal toxicity in experimental animals and humans as a characterization of the likelihoods of extrapolation factors of different magnitudes. An emerging approach to noncancer risk assessment is to use such empirical distributions in place of fixed Uncertainty Factors. This paper discusses dividing the overall variation into two sub-distributions representing pharmacokinetic (PK) and pharmacodynamic (PD) contributions to the variation among chemicals in the animal-to-human toxicologically equivalent dose. If a physiologically based pharmacokinetic model (PBPK model) is used to derive a compound specific adjustment factor (CSAF) for the pharmacokinetic component, the deconvolution of the PK and PD components allows one to remove the PK component (to be replaced with the CSAF), while retaining the uncertainty in pharmacodynamics that PBPK models do not address. One must then add back the uncertainty in the PBPK determination of the CSAF (which may be considerable). A candidate criterion for whether one can use an uncertain PBPK model is whether the generic uncertainty about cross-species pharmacokinetics (reflected in the PK component of the overall empirical distribution) is larger than the chemical-specific uncertainty in the determination of kinetically equivalent doses in experimental animals and humans.     

PK/DB: database for pharmacokinetic properties and predictive in silico ADME models

The study of pharmacokinetic properties (PK) is of great importance in drug discovery and development. In the present work, PK/DB (a new freely available database for PK) was designed with the aim of creating robust databases for pharmacokinetic studies and in silico absorption, distribution, metabolism and excretion (ADME) prediction. Comprehensive, web-based and easy to access, PK/DB manages 1203 compounds which represent 2973 pharmacokinetic measurements, including five models for in silico ADME prediction (human intestinal absorption, human oral bioavailability, plasma protein binding, blood-brain barrier and water solubility). AVAILABILITY: http://www.pkdb.ifsc.usp.br     

Applications of high-throughput ADME in drug discovery

Assessment of physicochemical and pharmacological properties is now conducted at very early stages of drug discovery for the purpose of accelerating the conversion of hits and leads into qualified development candidates. In particular, in vitro absorption, distribution, metabolism and elimination (ADME) assays and in vivo drug metabolism pharmacokinetic (DMPK) studies are being conducted throughout the discovery process, from hit generation through to lead optimization, with the goal of reducing the attrition rate of these potential drug candidates as they progress through development. Because the continuing trend in drug discovery has been to access ADME information earlier and earlier in the discovery process, the need has arisen within the analytical community to introduce faster and better analytical methods to enhance the 'developability' of drug leads. Strategies for streamlined ADME assessment of drug candidates in discovery and pre-clinical development are presented within.     

Mechanistic prediction of food effects for Compound A tablet using PBPK model

Physiologically based pharmacokinetic (PBPK) modeling has been extensively used to study the factors of effect drug absorption, distribution, metabolize and extraction progress in human. In this study, Compound A(CPD A) is a BCS Class II drug, which has been extensive applied in clinical as lipid-lowering drug, administered orally after food, they displayed positive food effects in human, A PBPK model was built to mechanistic investigate the food effect of CPD A tablet in our study. By using gastroplus™ software, the PBPK models accurately predicted the results of food effects and predicted data were within 2-fold error of the observed results. The PBPK model mechanistic illuminated the changes of pharmacokinetic values for the positive food effects of the compound in human. Here in, the PBPK modeling which were combined with ACAT absorption models in it, successfully simulated the food effect in human of the drug. The simulation results were proved that PBPK model can be able to serve as a potential tool to predict the food effect on certain oral drugs.     

Development of Physiologically Based Pharmacokinetic/Pharmacodynamic Model for Indomethacin Disposition in Pregnancy

Findings of a recent clinical study showed indomethacin has lower plasma levels and higher steady-state apparent clearance in pregnant subjects when compared to those in non-pregnant subjects reported in separate studies. Thus, in the current work we developed a pregnancy physiological based pharmacokinetic/pharmacodynamic (PBPK/PD) model for indomethacin to explain the differences in indomethacin pharmacokinetics between pregnancy and non-pregnancy. A whole-body PBPK model with key pregnancy-related physiological changes was developed to characterize indomethacin PK in pregnant women and compare these parameters to those in non-pregnant subjects. Data related to maternal physiological and biological changes were obtained from literature and incorporated into the structural PBPK model that describes non-pregnant PK data. Changes in indomethacin area under the curve (AUC), maximum concentration (C_max) and average steady-state concentration (C_ave) in pregnant women were predicted. Model-simulated PK profiles were in agreement with observed data. The predicted mean ratio (non-pregnant:second trimester (T₂)) of indomethacin C_ave was 1.6 compared to the observed value of 1.59. In addition, the predicted steady-state apparent clearance (CL/F_ss) ratio was almost similar to the observed value (0.46 vs. 0.42). Sensitivity analysis suggested changes in CYP2C9 activity, and to a lesser extent UGT2B7, as the primary factor contributing to differences in indomethacin disposition between pregnancy and non-pregnancy. The developed PBPK model which integrates prior physiological knowledge, in vitro and in vivo data, allowed the successful prediction of indomethacin disposition during T₂. Our PBPK/PD model suggested a higher indomethacin dosing requirement during pregnancy.     

Rapid hit to lead evaluation of pyrazolo[3,4-d]pyrimidin-4-one as selective and orally bioavailable mGluR1 antagonists

Our HTS effort yielded a preferential mGluR1 pyrimidinone antagonist 1 with lead-like characteristics. Rapid hit to lead (HTL) study identified compounds with improved functional activity and selectivity such as 1b with little improvements in ADME properties. Addition of an aminosulfonyl group on the N-1 aromatic ring led to 2f, a compound with similar in vitro biochemical profiles as those of 1b but drastically improved in vitro ADME properties. These improvements were paralleled by rat PK study characterized by low clearance and quantitative bioavailability. Compound 2f represented a true lead-like molecule that is amenable for further lead optimization (LO) evaluation.     

Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations

Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.     

Lead identification and characterization of hTrkA type 2 inhibitors

Virtual in silico structure-guided modeling, followed by in vitro biochemical screening of a subset of commercially purchasable compound collection resulted in the identification of several human tropomyosin receptor kinase A (hTrkA) inhibitors that bind the orthosteric ATP site and exhibit binding preference for the inactive kinase conformation. The type 2 binding mode with the DFG-out and αC-helix out hTrkA kinase domain conformation was confirmed from X-ray crystallographic solution of a representative inhibitor analog, 1b. Additional hTrkA and hTrkB (selectivity) assays in recombinant cells, neurite outgrowth inhibition using rat PC12 cells, early ADME profiling, and preliminary pharmacokinetic evaluation in rodents guided the lead inhibitor progression in the discovery screening funnel.     

Toward in vitro-to-in vivo translation of monoclonal antibody pharmacokinetics: Application of a neonatal Fc receptor-mediated transcytosis assay to understand the interplaying clearance mechanisms

Monoclonal antibodies (mAbs) are a rapidly growing drug class for which great efforts have been made to optimize certain molecular features to achieve the desired pharmacokinetic (PK) properties. One approach is to engineer the interactions of the mAb with the neonatal Fc receptor (FcRn) by introducing specific amino acid sequence mutations, and to assess their effect on the PK profile with in vivo studies. Indeed, FcRn protects mAbs from intracellular degradation, thereby prolongs antibody circulation time in plasma and modulates its systemic clearance. To allow more efficient and focused mAb optimization, in vitro input that helps to identify and quantitatively predict the contribution of different processes driving non-target mediated mAb clearance in vivo and supporting translational PK modeling activities is essential. With this aim, we evaluated the applicability and in vivo-relevance of an in vitro cellular FcRn-mediated transcytosis assay to explain the PK behavior of 25 mAbs in rat or monkey. The assay was able to capture species-specific differences in IgG-FcRn interactions and overall correctly ranked Fc mutants according to their in vivo clearance. However, it could not explain the PK behavior of all tested IgGs, indicating that mAb disposition in vivo is a complex interplay of additional processes besides the FcRn interaction. Overall, the transcytosis assay was considered suitable to rank mAb candidates for their FcRn-mediated clearance component before extensive in vivo testing, and represents a first step toward a multi-factorial in vivo clearance prediction approach based on in vitro data.     

Biomeasures and mechanistic modeling highlight PK/PD risks for a monoclonal antibody targeting Fn14 in kidney disease

Discovery of the upregulation of fibroblast growth factor-inducible-14 (Fn14) receptor following tissue injury has prompted investigation into biotherapeutic targeting of the Fn14 receptor for the treatment of conditions such as chronic kidney diseases. In the development of monoclonal antibody (mAb) therapeutics, there is an increasing trend to use biomeasures combined with mechanistic pharmacokinetic/pharmacodynamic (PK/PD) modeling to enable decision making in early discovery. With the aim of guiding preclinical efforts on designing an antibody with optimized properties, we developed a mechanistic site-of-action (SoA) PK/PD model for human application. This model incorporates experimental biomeasures, including concentration of soluble Fn14 (sFn14) in human plasma and membrane Fn14 (mFn14) in human kidney tissue, and turnover rate of human sFn14. Pulse-chase studies using stable isotope-labeled amino acids and mass spectrometry indicated the sFn14 half-life to be approximately 5 hours in healthy volunteers. The biomeasures (concentration, turnover) of sFn14 in plasma reveals a significant hurdle in designing an antibody against Fn14 with desired characteristics. The projected dose (>1 mg/kg/wk for 90% target coverage) derived from the human PK/PD model revealed potential high and frequent dosing requirements under certain conditions. The PK/PD model suggested a unique bell-shaped relationship between target coverage and antibody affinity for anti-Fn14 mAb, which could be applied to direct the antibody engineering towards an optimized affinity. This investigation highlighted potential applications, including assessment of PK/PD risks during early target validation, human dose prediction and drug candidate optimization.     

Tissue expression profile of human neonatal Fc receptor (FcRn) in Tg32 transgenic mice

The neonatal Fc receptor (FcRn) is a homeostatic receptor responsible for prolonging immunoglobulin G (IgG) half-life by protecting it from lysosomal degradation and recycling it to systemic circulation. Tissue-specific FcRn expression is a critical parameter in physiologically-based pharmacokinetic (PBPK) modeling for translational pharmacokinetics of Fc-containing biotherapeutics. Using online peptide immuno-affinity chromatography coupled with high resolution mass spectrometry, we established a quantitative FcRn tissue protein expression profile in human FcRn (hFcRn) transgenic mice, Tg32 homozygous and hemizygous strains. The concentration of hFcRn across 14 tissues ranged from 3.5 to 111.2 pmole per gram of tissue. Our hFcRn quantification data from Tg32 mice will enable a more refined PBPK model to improve the accuracy of human PK predictions for Fc-containing biotherapeutics.     

A strategy for risk mitigation of antibodies with fast clearance

A majority of human therapeutic antibody candidates show pharmacokinetic properties suitable for clinical use, but an unexpectedly fast antibody clearance is sometimes observed that may limit the clinical utility. Pharmacokinetic data in cynomolgus monkeys collected for a panel of 52 antibodies showed broad distribution of target-independent clearance values (2.4–61.3 mL/day/kg), with 15 (29%) having clearance > 10 mL/day/kg. Alteration in the interaction with the recycling FcRn receptor did not account for the faster than expected clearance observed for the antibodies; off-target binding was presumed to account for the fast clearance. We developed an assay based on ELISA detection of non-specific binding to baculovirus particles that can identify antibodies having increased risk for fast clearance. This assay can be used during lead generation or optimization to identify antibodies with increased risk of having fast clearance in both humans and cynomolgus monkeys, and thus increase the likelihood of obtaining a suitable drug candidate.     

A strategy for risk mitigation of antibodies with fast clearance

A majority of human therapeutic antibody candidates show pharmacokinetic properties suitable for clinical use, but an unexpectedly fast antibody clearance is sometimes observed that may limit the clinical utility. Pharmacokinetic data in cynomolgus monkeys collected for a panel of 52 antibodies showed broad distribution of target-independent clearance values (2.4–61.3 mL/day/kg), with 15 (29%) having clearance > 10 mL/day/kg. Alteration in the interaction with the recycling FcRn receptor did not account for the faster than expected clearance observed for the antibodies; off-target binding was presumed to account for the fast clearance. We developed an assay based on ELISA detection of non-specific binding to baculovirus particles that can identify antibodies having increased risk for fast clearance. This assay can be used during lead generation or optimization to identify antibodies with increased risk of having fast clearance in both humans and cynomolgus monkeys, and thus increase the likelihood of obtaining a suitable drug candidate.     

Animal models in the pharmacokinetic/pharmacodynamic evaluation of antimicrobial agents

Animal infection models in the pharmacokinetic/pharmacodynamic (PK/PD) evaluation of antimicrobial therapy serve an important role in preclinical assessments of new antibiotics, dosing optimization for those that are clinically approved, and setting or confirming susceptibility breakpoints. The goal of animal model studies is to mimic the infectious diseases seen in humans to allow for robust PK/PD studies to find the optimal drug exposures that lead to therapeutic success. The PK/PD index and target drug exposures obtained in validated animal infection models are critical components in optimizing dosing regimen design in order to maximize efficacy while minimize the cost and duration of clinical trials. This review outlines the key components in animal infection models which have been used extensively in antibiotic discovery and development including PK/PD analyses.     

Integration of a physiologically-based pharmacokinetic model with a whole-body,organ-resolved genome-scale model for characterization of ethanol and acetaldehyde metabolism

Ethanol is one of the most widely used recreational substances in the world and due to its ubiquitous use, ethanol abuse has been the cause of over 3.3 million deaths each year. In addition to its effects, ethanol’s primary metabolite, acetaldehyde, is a carcinogen that can cause symptoms of facial flushing, headaches, and nausea. How strongly ethanol or acetaldehyde affects an individual depends highly on the genetic polymorphisms of certain genes. In particular, the genetic polymorphisms of mitochondrial aldehyde dehydrogenase, ALDH2, play a large role in the metabolism of acetaldehyde. Thus, it is important to characterize how genetic variations can lead to different exposures and responses to ethanol and acetaldehyde. While the pharmacokinetics of ethanol metabolism through alcohol dehydrogenase have been thoroughly explored in previous studies, in this paper, we combined a base physiologically-based pharmacokinetic (PBPK) model with a whole-body genome-scale model (WBM) to gain further insight into the effect of other less explored processes and genetic variations on ethanol metabolism. This combined model was fit to clinical data and used to show the effect of alcohol concentrations, organ damage, ALDH2 enzyme polymorphisms, and ALDH2-inhibiting drug disulfiram on ethanol and acetaldehyde exposure. Through estimating the reaction rates of auxiliary processes with dynamic Flux Balance Analysis, The PBPK-WBM was able to navigate around a lack of kinetic constants traditionally associated with PK modelling and demonstrate the compensatory effects of the body in response to decreased liver enzyme expression. Additionally, the model demonstrated that acetaldehyde exposure increased with higher dosages of disulfiram and decreased ALDH2 efficiency, and that moderate consumption rates of ethanol could lead to unexpected accumulations in acetaldehyde. This modelling framework combines the comprehensive steady-state analyses from genome-scale models with the dynamics of traditional PK models to create a highly personalized form of PBPK modelling that can push the boundaries of precision medicine.     

The design, synthesis, and biological evaluation of potent receptor tyrosine kinase inhibitors

Variously substituted indolin-2-ones were synthesized and evaluated for activity against KDR, Flt-1, FGFR-1 and PDGFR. Extension at the 5-position of the oxindole ring with ethyl piperidine (compound 7i) proved to be the most beneficial for attaining both biochemical and cellular potencies. Further optimization of 7i to balance biochemical and cellular potencies with favorable ADME/ PK properties led to the identification of 8h, a compound with a clean CYP profile, acceptable pharmacokinetic and toxicity profiles, and robust efficacy in multiple xenograft tumor models.     

A method for representing and developing process models

Scientists investigate the dynamics of complex systems with quantitative models, employing them to synthesize knowledge, to explain observations, and to forecast future system behavior. Complete specification of systems is impossible, so models must be simplified abstractions. Thus, the art of modeling involves deciding which system elements to include and determining how they should be represented. We view modeling as search through a space of candidate models that is guided by model objectives, theoretical knowledge, and empirical data. In this contribution, we introduce a method for representing process-based models that facilitates the discovery of structures that explain observed behavior. This representation casts dynamic systems as interacting sets of processes that act on entities. Using this approach, a modeler first encodes relevant ecological knowledge into a library of generic entities and processes, then instantiates these theoretical components, and finally assembles candidate models from these elements. We illustrate this methodology with a model of the Ross Sea ecosystem.     

Pharmacokinetic de-risking tools for selection of monoclonal antibody lead candidates

Pharmacokinetic studies play an important role in all stages of drug discovery and development. Recent advancements in the tools for discovery and optimization of therapeutic proteins have created an abundance of candidates that may fulfill target product profile criteria. Implementing a set of in silico, small scale in vitro and in vivo tools can help to identify a clinical lead molecule with promising properties at the early stages of drug discovery, thus reducing the labor and cost in advancing multiple candidates toward clinical development. In this review, we describe tools that should be considered during drug discovery, and discuss approaches that could be included in the pharmacokinetic screening part of the lead candidate generation process to de-risk unexpected pharmacokinetic behaviors of Fc-based therapeutic proteins, with an emphasis on monoclonal antibodies.     

Key factors influencing ADME properties of therapeutic proteins: A need for ADME characterization in drug discovery and development

Protein therapeutics represent a diverse array of biologics including antibodies, fusion proteins, and therapeutic replacement enzymes. Since their inception, they have revolutionized the treatment of a wide range of diseases including respiratory, vascular, autoimmune, inflammatory, infectious, and neurodegenerative diseases, as well as cancer. While in vivo pharmacokinetic, pharmacodynamic, and efficacy studies are routinely carried out for protein therapeutics, studies that identify key factors governing their absorption, distribution, metabolism, and excretion (ADME) properties have not been fully investigated. Thorough characterization and in-depth study of their ADME properties are critical in order to support drug discovery and development processes for the production of safer and more effective biotherapeutics. In this review, we discuss the main factors affecting the ADME characteristics of these large macromolecular therapies. We also give an overview of the current tools, technologies, and approaches available to investigate key factors that influence the ADME of recombinant biotherapeutic drugs, and demonstrate how ADME studies will facilitate their future development.     

PKQuest: volatile solutes – application to enflurane, nitrous oxide, halothane, methoxyflurane and toluene pharmacokinetics

Background  

The application of physiologically based pharmacokinetic models (PBPK) to human studies has been limited by the lack of the detailed organ information that is required for this analysis. PKQuest is a new generic PBPK that is designed to avoid this problem by using a set of "standard human" default parameters that are applicable to most solutes.