PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach

Transforming the Discovery of Targeted Protein Degraders: The Translational Power of Predictive PK/PD Modeling

Targeted protein degraders (TPDs), an emerging therapeutic modality, are attracting considerable interest with the promise to address disease-related proteins that are not druggable with conventional small molecule inhibitors. Despite their novel mechanism of action, the PK/PD relationship of degraders is still approached with a mindset deeply rooted in inhibitor drugs. Here, we establish how predictive mechanistic modeling specifically tailored to TPDs can significantly enhance the value of the available information during lead generation and optimization. By integrating the results from in vitro assays with routinely collected PK data, modeling accurately predicts degradation in vivo. These predictions transform the prioritization of compounds for in vivo studies as well as the selection of optimal dose schedules and most informative measurement time points with the least number of animals. Moreover, the comprehensive modeling framework (1) identifies the PK/PD driver of targeted protein degradation and subsequent downstream pharmacodynamic effects, and (2) uncovers the fundamental difference between degrader and inhibitor PK/PD relationships. The practical utility of our predictive modeling is demonstrated with relevant use cases. This framework will allow researchers to transition from current, mostly serendipity-based approaches to more sound model-informed decision making. Going forward, the presented predictive PK/PD modeling framework lays out a rational path to incorporate inter-species differences in the pharmacology and thus promises to help with getting the dose right in clinical trials.

EANM guidance document: dosimetry for first-in-human studies and early phase clinical trials

The numbers of diagnostic and therapeutic nuclear medicine agents under investigation are rapidly increasing. Both novel emitters and novel carrier molecules require careful selection of measurement procedures. This document provides guidance relevant to dosimetry for first-in human and early phase clinical trials of such novel agents. The guideline includes a short introduction to different emitters and carrier molecules, followed by recommendations on the methods for activity measurement, pharmacokinetic analyses, as well as absorbed dose calculations and uncertainty analyses. The optimal use of preclinical information and studies involving diagnostic analogues is discussed. Good practice reporting is emphasised, and relevant dosimetry parameters and method descriptions to be included are listed. Three examples of first-in-human dosimetry studies, both for diagnostic tracers and radionuclide therapies, are given.

Using molecularly dissolved drug concentrations in PBBMs improves the prediction of oral absorption from supersaturating formulations

Predicting the absorption of drugs from enabling formulations is still challenging due to the limited capabilities of standard physiologically based biopharmaceutics models (PBBMs) to capture complex absorption processes. Amongst others, it is often assumed that both, molecularly and apparently dissolved drug in the gastrointestinal lumen are prone to absorption. A recently introduced method for measuring concentrations of molecularly dissolved drug in a dynamic in vitro dissolution setup using microdialysis has opened new opportunities to test this hypothesis and refine mechanistic PBBM approaches. In the present study, we compared results of PBBMs that used either molecularly or apparently dissolved concentrations in the simulated gastrointestinal lumen as input parameters. The in vitro dissolution data from three supersaturating formulations of Posaconazole (PCZ) were used as model input. The modeling outcome was verified using PCZ concentration vs. time profiles measured in human intestinal aspirates and in the blood plasma. When using apparently dissolved drug concentrations (i.e., the sum of colloid-associated and molecularly dissolved drug) the simulated systemic plasma exposures were overpredicted, most pronouncedly with the ASD-based tablet. However, if the concentrations of molecularly dissolved drug were used as input values, the PBBM resulted in accurate prediction of systemic exposures for all three PCZ formulations. The present study impressively demonstrated the value of considering molecularly dissolved drug concentrations as input value for PBBMs of supersaturating drug formulations.

Building a Predictive PBPK Model for Human OATP Substrates: a Strategic Framework for Early Evaluation of Clinical Pharmacokinetic Variations Using Pitavastatin as an Example

To select a drug candidate for clinical development, accurately and promptly predicting human pharmacokinetic (PK) profiles, assessing drug-drug interactions (DDIs), and anticipating potential PK variations in disease populations are crucial steps in drug discovery. The complexity of predicting human PK significantly increases when hepatic transporters are involved in drug clearance (CL) and volume of distribution (Vss). A strategic framework is developed here, utilizing pitavastatin as an example. The framework includes the construction of a monkey physiologically-based PK (PBPK) model, model calibration to obtain scaling factors (SF) of in vitro-in vivo extrapolation (IVIVE) for various clearance parameters, human model development and validation, and assessment of DDIs and PK variations in disease populations. Through incorporating in vitro human parameters and calibrated SFs from the monkey model of 3.45, 0.14, and 1.17 for CLint,active, CLint,passive, and CLint,bile, respectively, and together with the relative fraction transported by individual transporters obtained from in vitro studies and the optimized Ki values for OATP inhibition, the model reasonably captured observed pitavastatin PK profiles, DDIs and PK variations in human subjects carrying genetic polymorphisms, i.e., AUC within 20%. Lastly, when applying the functional reduction based on measured OATP1B biomarkers, the model adequately predicted PK changes in the hepatic impairment population. The present study presents a strategic framework for early-stage drug development, enabling the prediction of PK profiles and assessment of PK variations in scenarios like DDIs, genetic polymorphism, and hepatic impairment-related disease states, specifically focusing on OATP substrates.

Quantitative Prediction of OATP-Mediated Disposition and Biliary Clearance Using Human Liver Chimeric Mice

Drug biliary clearance (CLbile) in vivo is among the most difficult pharmacokinetic parameters to predict accurately and quantitatively because biliary excretion is influenced by metabolic enzymes, transporters, and passive diffusion across hepatocyte membranes. The purpose of this study is to demonstrate the use of Hu-FRG mice [Fah-/-/Rag2-/-/Il2rg-/- (FRG) mice transplanted with human-derived hepatocytes] to quantitatively predict human organic anion transporting polypeptide (OATP)-mediated drug disposition and CLbile To predict OATP-mediated disposition, six OATP substrates (atorvastatin, fexofenadine, glibenclamide, pitavastatin, pravastatin, and rosuvastatin) were administered intravenously to Hu-FRG and Mu-FRG mice (FRG mice transplanted with mouse hepatocytes) with or without rifampicin as an OATP inhibitor. We calculated the hepatic intrinsic clearance (CLh,int) and the change of hepatic clearance (CLh) caused by rifampicin (CLh ratio). We compared the CLh,int of humans with that of Hu-FRG mice and the CLh ratio of humans with that of Hu-FRG and Mu-FRG mice. For predicting CLbile, 20 compounds (two cassette doses of 10 compounds) were administered intravenously to gallbladder-cannulated Hu-FRG and Mu-FRG mice. We evaluated the CLbile and investigated the correlation of human CLbile with that of Hu-FRG and Mu-FRG mice. We found good correlations between humans and Hu-FRG mice in CLh,int (100% within threefold) and CLh ratio (R2 = 0.94). Moreover, we observed a much better relationship between humans and Hu-FRG mice in CLbile (75% within threefold). Our results suggest that OATP-mediated disposition and CLbile can be predicted using Hu-FRG mice, making them a useful in vivo drug discovery tool for quantitatively predicting human liver disposition. SIGNIFICANCE STATEMENT: OATP-mediated disposition and biliary clearance of drugs are likely quantitatively predictable using Hu-FRG mice. The findings can enable the selection of better drug candidates and the development of more effective strategies for managing OATP-mediated DDIs in clinical studies.

A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models

Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

Investigation of simplified physiologically-based pharmacokinetic models in rat and human

Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods

There is an ongoing aim to replace animal and in vitro laboratory models with in silico methods. Such replacement requires the successful validation and comparably good performance of the alternative methods. We have developed an in silico prediction system for human clinical pharmacokinetics, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, i.e. ANDROMEDA. The objectives of this study were: a) to evaluate how well ANDROMEDA predicts the human clinical pharmacokinetics of a previously proposed benchmarking data set comprising 24 physicochemically diverse drugs and 28 small drug molecules new to the market in 2021; b) to compare its predictive performance with that of laboratory methods; and c) to investigate and describe the pharmacokinetic characteristics of the modern drugs. Median and maximum prediction errors for the selected major parameters were ca 1.2 to 2.5-fold and 16-fold for both data sets, respectively. Prediction accuracy was on par with, or better than, the best laboratory-based prediction methods (superior performance for a vast majority of the comparisons), and the prediction range was considerably broader. The modern drugs have higher average molecular weight than those in the benchmarking set from 15 years earlier (ca 200 g/mol higher), and were predicted to (generally) have relatively complex pharmacokinetics, including permeability and dissolution limitations and significant renal, biliary and/or gut-wall elimination. In conclusion, the results were overall better than those obtained with laboratory methods, and thus serve to further validate the ANDROMEDA in silico system for the prediction of human clinical pharmacokinetics of modern and physicochemically diverse drugs.

Human pharmacokinetics prediction with an in vitro-in vivo correction factor approach and in vitro drug-drug interaction profile of bictegravir, a potent integrase-strand transfer inhibitor component in approved biktarvy® for the treatment of HIV-1 infection

Bictegravir (BIC) is a potent small-molecule integrase strand-transfer inhibitor (INSTI) and a component of Biktarvy^®, a single-tablet combination regimen that is currently approved for the treatment of human immunodeficiency virus type 1 (HIV-1) infection. The in vitro properties, pharmacokinetics (PK), and drug-drug interaction (DDI) profile of BIC were characterised in vitro and in vivo.BIC is a weakly acidic, ionisable, lipophilic, highly plasma protein-bound BCS class 2 molecule, which makes it difficult to predict human PK using standard methods. Its systemic plasma clearance is low, and the volume of distribution is approximately the volume of extracellular water in nonclinical species. BIC metabolism is predominantly mediated by cytochrome P450 enzyme (CYP) 3A and UDP-glucuronosyltransferase 1A1. BIC shows a low potential to perpetrate clinically meaningful DDIs via known drug metabolising enzymes or transporters.The human PK of BIC was predicted using a combination of bioavailability and volume of distribution scaled from nonclinical species and a modified in vitro-in vivo correlation (IVIVC) correction for clearance. Phase 1 studies in healthy subjects largely bore out the prediction and supported the methods used. The approach presented herein could be useful for other drug molecules where standard projections are not sufficiently accurate. .

Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom?

Modeling and simulation emerges as a fundamental asset of drug development. Mechanistic modeling builds upon its strength to integrate various data to represent a detailed structural knowledge of a physiological and biological system and is capable of informing numerous drug development and regulatory decisions via extrapolations outside clinically studied scenarios. Herein, physiologically based pharmacokinetic (PBPK) modeling is the fastest growing branch, and its use for particular applications is already expected or explicitly recommended by regulatory agencies. Therefore, appropriate applications of PBPK necessitates trust in the predictive capability of the tool, the underlying software platform, and related models. That has triggered a discussion on concepts of ensuring credibility of model-based derived conclusions. Questions like 'why', 'when', 'what', 'how' and 'by whom' remain open. We seek for harmonization of recent ideas, perceptions, and related terminology. First, we provide an overview on quality assurance of PBPK platforms with the two following concepts. Platform validation: ensuring software integrity, security, traceability, correctness of mathematical models and accuracy of algorithms. Platform qualification: demonstrating the predictive capability of a PBPK platform within a particular context of use. Second, we provide guidance on executing dedicated PBPK studies. A step-by-step framework focuses on the definition of the question of interest, the context of use, the assessment of impact and risk, the definition of the modeling strategy, the evaluation of the platform, performing model development including model building, evaluation and verification, the evaluation of applicability to address the question, and the model application under the umbrella of a qualified platform.

ADME and DMPK considerations for the discovery and development of antibody drug conjugates (ADCs)

 The therapeutic concept of antibody drug conjugates (ADCs) is to selectively target tumour cells with small molecule cytotoxic drugs to maximise cell kill benefit and minimise healthy tissue toxicity.An ADC generally consists of an antibody that targets a protein on the surface of tumour cells chemically linked to a warhead small molecule cytotoxic drug.To deliver the warhead to the tumour cell, the antibody must bind to the target protein and in general be internalised into the cell. Following internalisation, the cytotoxic agent can be released in the endosomal or lysosomal compartment (via different mechanisms). Diffusion or transport out of the endosome or lysosome allows the cytotoxic drug to express its cell-killing pharmacology. Alternatively, some ADCs (e.g. EDB-ADCs) rely on extracellular cleavage releasing membrane permeable warheads.One potentially important aspect of the ADC mechanism is the 'bystander effect' whereby the cytotoxic drug released in the targeted cell can diffuse out of that cell and into other (non-target expressing) tumour cells to exert its cytotoxic effect. This is important as solid tumours tend to be heterogeneous and not all cells in a tumour will express the targeted protein.The combination of large and small molecule aspects in an ADC poses significant challenges to the disposition scientist in describing the ADME properties of the entire molecule.This article will review the ADC landscape and the ADME properties of successful ADCs, with the aim of outlining best practice and providing a perspective of how the field can further facilitate the discovery and development of these important therapeutic modalities.

In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology

Pharmacokinetic/toxicokinetic (PK/TK) information for chemicals in humans is generally lacking. Here we applied machine learning, conformal prediction and a new physiologically-based PK/TK model for prediction of the human PK/TK of 65 chemicals from different classes, including carcinogens, food constituents and preservatives, vitamins, sweeteners, dyes and colours, pesticides, alternative medicines, flame retardants, psychoactive drugs, dioxins, poisons, UV-absorbents, surfactants, solvents and cosmetics.About 80% of the main human PK/TK (fraction absorbed, oral bioavailability, half-life, unbound fraction in plasma, clearance, volume of distribution, fraction excreted) for the selected chemicals was missing in the literature. This information was now added (from in silico predictions). Median and mean prediction errors for these parameters were 1.3- to 2.7-fold and 1.4- to 4.8-fold, respectively. In total, 59 and 86% of predictions had errors <2- and <5-fold, respectively. Predicted and observed PK/TK for the chemicals was generally within the range for pharmaceutical drugs.The results validated the new integrated system for prediction of the human PK/TK for different chemicals and added important missing information. No general difference in PK/TK-characteristics was found between the selected chemicals and pharmaceutical drugs.

Characterization of Preclinical Pharmacokinetic Properties and Prediction of Human PK Using a Physiologically Based Pharmacokinetic Model for a Novel Anti-Arrhythmic Agent Sulcardine Sulfate

PURPOSE

Sulcardine sulfate (Sul) is a novel antiarrhythmic agent with promising pharmacological properties, which is currently being evaluated in several clinical trials as an oral formulation. To meet the medication needs of patients with acute conditions, the injection formulation of Sul has been developed. The objective of this study was to systemically investigate the pharmacokinetic profiles of Sul after intravenous infusion.

METHODS

This research included the plasma protein binding and metabolic stability studies in vitro, plasma pharmacokinetics, biodistribution, excretion studies in animals, and the prediction of the clinical PK of Sul injection using a physiologically based pharmacokinetics (PBPK) model.

RESULTS

The metabolic stability was similarly in dogs and humans but lower in rats. The plasma protein binding rates showed a concentration-dependent manner and species differences. The pharmacokinetic behavior after intravenous administration was linear in rats within the dose range of 30-90 mg/kg, but nonlinear in dogs within 30-60 mg/kg. Sul could be rapidly and widely distributed in multiple tissues after intravenous administration. About 12% of the parent compound were excreted via the urine and only a small fraction via bile and feces,and eight metabolites were found and identified in the rat excretion. The PBPK models were developed and simulated the observed PK date well in both rats and dogs. The PBPK model refined with human data predicted the PK characteristics of the first intravenous infusion of Sul in human.

CONCLUSIONS

Our study systematically explored the pharmacokinetic characteristics of Sul and successfully developed the PBPK model to predict of its clinical PK.

Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma

Variability of the unbound fraction in plasma (f_u) between labs, methods and conditions is known to exist. Variability and uncertainty of this parameter influence predictions of the overall pharmacokinetics of drug candidates and might jeopardise safety in early clinical trials. Objectives of this study were to evaluate the variability of human in vitro f_u-estimates between labs for a range of different drugs, and to develop and validate an in silico f_u-prediction method and compare the results to the lab variability.A new in silico method with prediction accuracy (Q²) of 0.69 for log f_u was developed. The median and maximum prediction errors were 1.9- and 92-fold, respectively. Corresponding estimates for lab variability (ratio between max and min f_u for each compound) were 2.0- and 185-fold, respectively. Greater than 10-fold lab variability was found for 14 of 117 selected compounds.Comparisons demonstrate that in silico predictions were about as reliable as lab estimates when these have been generated during different conditions. Results propose that the new validated in silico prediction method is valuable not only for predictions at the drug design stage, but also for reducing uncertainties of f_u-estimations and improving safety of drug candidates entering the clinical phase.

Examination of Urinary Excretion of Unchanged Drug in Humans and Preclinical Animal Models: Increasing the Predictability of Poor Metabolism in Humans

PURPOSE

A dataset of fraction excreted unchanged in the urine (fe) values was developed and used to evaluate the ability of preclinical animal species to predict high urinary excretion, and corresponding poor metabolism, in humans.

METHODS

A literature review of fe values in rats, dogs, and monkeys was conducted for all Biopharmaceutics Drug Disposition Classification System (BDDCS) Class 3 and 4 drugs (n=352) and a set of Class 1 and 2 drugs (n=80). The final dataset consisted of 202 total fe values for 135 unique drugs. Human and animal data were compared through correlations, two-fold analysis, and binary classifications of high (fe ≥30%) versus low (<30%) urinary excretion in humans. Receiver Operating Characteristic curves were plotted to optimize animal fe thresholds.

RESULTS

Significant correlations were found between fe values for each animal species and human fe (p<0.05). Sixty-five percent of all fe values were within two-fold of human fe with animals more likely to underpredict human urinary excretion as opposed to overpredict. Dogs were the most reliable predictors of human fe of the three animal species examined with 72% of fe values within two-fold of human fe and the greatest accuracy in predicting human fe ≥30%. ROC determined thresholds of ≥25% in rats, ≥19% in dogs, and ≥10% in monkeys had improved accuracies in predicting human fe of ≥30%.

CONCLUSIONS

Drugs with high urinary excretion in animals are likely to have high urinary excretion in humans. Animal models tend to underpredict the urinary excretion of unchanged drug in humans.

Application of physiologically based pharmacokinetic models to promote the development of veterinary drugs with high efficacy and safety

Physiologically based pharmacokinetic (PBPK) models have become important tools for the development of novel human drugs. Food-producing animals and pets comprise an important part of human life, and the development of veterinary drugs (VDs) has greatly impacted human health. Owing to increased affordability of and demand for drug development, VD manufacturing companies should have more PBPK models required to reduce drug production costs. So far, little attention has been paid on applying PBPK models for the development of VDs. This review begins with the development processes of VDs; then summarizes case studies of PBPK models in human or VD development; and analyzes the application, potential, and advantages of PBPK in VD development, including candidate screening, formulation optimization, food effects, target-species safety, and dosing optimization. Then, the challenges of applying the PBPK model to VD development are discussed. Finally, future opportunities of PBPK models in designing dosing regimens for intracellular pathogenic infections and for efficient oral absorption of VDs are further forecasted. This review will be relevant to readers who are interested in using a PBPK model to develop new VDs.

Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology

Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We describe a new methodology where F in humans is predicted directly from chemical structure using an integrated strategy combining 9 machine learning models, 3 sets of structural alerts, and 2 physiologically-based pharmacokinetic models. We evaluate the model on a benchmark dataset consisting of 184 compounds, obtaining a predictive accuracy (Q²) of 0.50, which is successful according to a pharmaceutical industry proposal. Twenty-seven compounds were found (beforehand) to be outside the main applicability domain for the model. We compare our results with interspecies correlations (rat, mouse and dog vs. human) using the same dataset, where animal vs. human-correlations (R²) were found to be 0.21 to 0.40 and maximum prediction errors were smaller than maximum interspecies differences. We conclude that our method has sufficient predictive accuracy to be practically useful with applications in human exposure and dose predictions, compound optimization and decision making, with potential to rationalize drug discovery and development and decrease failures and overexposures in early clinical trials with candidate drugs.

Overcoming the shortcomings of the extended-clearance concept: a framework for developing a physiologically-based pharmacokinetic (PBPK) model to select drug candidates involving transporter-mediated clearance

Introduction:Human pharmacokinetic (PK) prediction can be a significant challenge to drug candidates undergoing transporter-mediated clearance, when only animal data and in vitro human parameters are available in the drug discovery stage.Areas covered:The extended clearance concept (ECC) that incorporates the processes of hepatic uptake, passive diffusion, metabolism and biliary secretion has been adapted to determine the rate-determining process of hepatic clearance and drug-drug interactions (DDIs). However, since the ECC is derived from the well-stirred model and does not consider the liver as a drug distribution organ to reflect the time-dependent variation of drug concentrations between the liver and plasma, it can be misused for compound selection in drug discovery.Expert opinion:The PBPK model consists of a set of differential equations of drug mass balance, and can overcome the shortcomings of the ECC in predicting human PK. The predictability, relevance and reliability of the model and the scaling factors for IVIVE must be validated using either the measured liver concentrations or DDI data with known transporter inhibitors, or both, in monkeys. A human PBPK model that incorporates in vitro human data and SFs obtained from the validated monkey PBPK model can be used for compound selection in the drug discovery phase.

How Many Urine Samples Are Needed to Accurately Assess Exposure to Non-Persistent Chemicals? The Biomarker Reliability Assessment Tool (BRAT) for Scientists, Research Sponsors, and Risk Managers

In epidemiologic and exposure research, biomonitoring is often used as the basis for assessing human exposure to environmental chemicals. Studies frequently rely on a single urinary measurement per participant to assess exposure to non-persistent chemicals. However, there is a growing consensus that single urine samples may be insufficient for adequately estimating exposure. The question then arises: how many samples would be needed for optimal characterization of exposure? To help researchers answer this question, we developed a tool called the Biomarker Reliability Assessment Tool (BRAT). The BRAT is based on pharmacokinetic modeling simulations, is freely available, and is designed to help researchers determine the approximate number of urine samples needed to optimize exposure assessment. The BRAT performs Monte Carlo simulations of exposure to estimate internal levels and resulting urinary concentrations in individuals from a population based on user-specified inputs (e.g., biological half-life, within- and between-person variability in exposure). The BRAT evaluates—through linear regression and quantile classification—the precision/accuracy of the estimation of internal levels depending on the number of urine samples. This tool should guide researchers towards more robust biomonitoring and improved exposure classification in epidemiologic and exposure research, which should in turn improve the translation of that research into decision-making.

Predictive Value of Microdose Pharmacokinetics

Phase 0 microdose trials are exploratory studies to early assess human pharmacokinetics of new chemical entities, while limiting drug exposure and risks for participants. The microdose concept is based on the assumption that microdose pharmacokinetics can be extrapolated to pharmacokinetics of a therapeutic dose. However, it is unknown whether microdose pharmacokinetics are actually indicative of the pharmacokinetics at therapeutic dose. The aim of this review is to investigate the predictive value of microdose pharmacokinetics and to identify drug characteristics that may influence the scalability of these parameters. The predictive value of microdose pharmacokinetics was determined for 46 compounds and showed adequate predictability for 28 of 41 orally administered drugs (68%) and 15 of 16 intravenously administered drugs (94%). Microdose pharmacokinetics were considered predictive if the mean observed values of the microdose and the therapeutic dose were within twofold. Nonlinearity may be caused by saturation of enzyme and transporter systems, such as intestinal and hepatic efflux and uptake transporters. The high degree of success regarding linear pharmacokinetics shows that phase 0 microdose trials can be used as an early human model for determination of drug pharmacokinetics.

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

Ethambutol disposition in humans: Challenges and limitations of whole-body physiologically-based pharmacokinetic modelling in early drug development

Whole-body physiologically based pharmacokinetic (WB-PBPK) models have become an important tool in drug development, as they enable characterization of pharmacokinetic profiles across different organs based on physiological (systems-specific) and physicochemical (drug-specific) properties. However, it remains unclear which data are needed for accurate predictions when applying the approach to novel candidate molecules progressing into the clinic. In this work, as case study, we investigated the predictive performance of WB-PBPK models both for prospective and retrospective evaluation of the pharmacokinetics of ethambutol, considering scenarios that reflect different stages of development, including settings in which the data are limited to in vitro experiments, in vivo preclinical data, and when some clinical data are available. Overall, the accuracy of PBPK model-predicted systemic and tissue exposure was heavily dependant on prior knowledge about the eliminating organs. Whilst these findings may be specific to ethambutol, the challenges and potential limitations identified here may be relevant to a variety of drugs, raising questions about (1) the minimum requirements for prospective use of WB-PBPK models during the characterization of drug disposition and (2) implication of uncertainty for dose selection in humans.

Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades

During drug discovery and prior to the first human dose of a novel candidate drug, the pharmacokinetic (PK) behavior of the drug in humans is predicted from preclinical data. This helps to inform the likelihood of achieving therapeutic exposures in early clinical development. Once clinical data are available, the observed human PK are compared with predictions, providing an opportunity to assess and refine prediction methods. Application of best practice in experimental data generation and predictive methodologies, and a focus on robust mechanistic understanding of the candidate drug disposition properties before nomination to clinical development, have led to maximizing the probability of successful PK predictions so that 83% of AstraZeneca drug development projects progress in the clinic with no PK issues; and 71% of key PK parameter predictions [64% of area under the curve (AUC) predictions; 78% of maximum concentration (C_max) predictions; and 70% of half-life predictions] are accurate to within twofold. Here, we discuss methods to predict human PK used by AstraZeneca, how these predictions are assessed and what can be learned from evaluating the predictions for 116 candidate drugs.

The Novel In Vitro Method to Calculate Tissue-to-Plasma Partition Coefficient in Humans for Predicting Pharmacokinetic Profiles by Physiologically-Based Pharmacokinetic Model With High Predictability

Proper prediction of human pharmacokinetic (PK) profiles can accelerate the compound selection in drug discovery. Recently, we reported a robust bottom-up physiologically-based pharmacokinetic (PBPK) approach (J Pharm Sci. 2019 Aug; 108(8):2718-2727), which uses the in vivo rat distribution volume at the steady state (V_ss) to determine human tissue-to-plasma partition coefficients (Kp_tissue). Here, we report on a bottom-up PBPK approach that can simulate the PK profile with both high-throughput and high-predictive accuracy only using in vitro data. In this study, as an alternative parameter of in vivo rat V_ss which was used for the correction of human Kp_tissue, V_{ss, in vitro} was obtained from protein binding data in rats, and the values of V_{ss, in vitro} for 31 reference compounds showed good correlation with the observed rat V_ss (R² = 0.859). Next, rat and human PK profiles of reference compounds were predicted by the bottom-up PBPK approach using Kp_tissue corrected by rat V_{ss, in vitro}. As a result, the absolute average fold errors for pharmacokinetic parameters were almost less than 2, showing that these PK profiles could be accurately predicted using in vitro data. This method enables the screening of promising compounds with good PK profiles in humans at an early stage of drug discovery.

Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fu_p) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Synthesis and structure-activity relationship study of pyrrolidine-oxadiazoles as anthelmintics against Haemonchus contortus

Parasitic roundworms (nematodes) are significant pathogens of humans and animals and cause substantive socioeconomic losses due to the diseases that they cause. The control of nematodes in livestock animals relies heavily on the use of anthelmintic drugs. However, their extensive use has led to a widespread problem of drug resistance in these worms. Thus, the discovery and development of novel chemical entities for the treatment of parasitic worms of humans and animals is needed. Herein, we describe our medicinal chemistry optimization efforts of a phenotypic hit against Haemonchus contortus based on a pyrrolidine-oxadiazole scaffold. This led to the identification of compounds with potent inhibitory activities (IC₅₀ = 0.78-22.4 μM) on the motility and development of parasitic stages of H. contortus, and which were found to be highly selective in a mammalian cell counter-screen. These compounds could be used as suitable chemical tools for drug target identification or as lead compounds for further optimization.

An in vitro toolbox to accelerate anti-malarial drug discovery and development

Background

Modelling and simulation are being increasingly utilized to support the discovery and development of new anti-malarial drugs. These approaches require reliable in vitro data for physicochemical properties, permeability, binding, intrinsic clearance and cytochrome P450 inhibition. This work was conducted to generate an in vitro data toolbox using standardized methods for a set of 45 anti-malarial drugs and to assess changes in physicochemical properties in relation to changing target product and candidate profiles.

Methods

Ionization constants were determined by potentiometric titration and partition coefficients were measured using a shake-flask method. Solubility was assessed in biorelevant media and permeability coefficients and efflux ratios were determined using Caco-2 cell monolayers. Binding to plasma and media proteins was measured using either ultracentrifugation or rapid equilibrium dialysis. Metabolic stability and cytochrome P450 inhibition were assessed using human liver microsomes. Sample analysis was conducted by LC–MS/MS.

Results

Both solubility and fraction unbound decreased, and permeability and unbound intrinsic clearance increased, with increasing Log D_7.4. In general, development compounds were somewhat more lipophilic than legacy drugs. For many compounds, permeability and protein binding were challenging to assess and both required the use of experimental conditions that minimized the impact of non-specific binding. Intrinsic clearance in human liver microsomes was varied across the data set and several compounds exhibited no measurable substrate loss under the conditions used. Inhibition of cytochrome P450 enzymes was minimal for most compounds.

Conclusions

This is the first data set to describe in vitro properties for 45 legacy and development anti-malarial drugs. The studies identified several practical methodological issues common to many of the more lipophilic compounds and highlighted areas which require more work to customize experimental conditions for compounds being designed to meet the new target product profiles. The dataset will be a valuable tool for malaria researchers aiming to develop PBPK models for the prediction of human PK properties and/or drug–drug interactions. Furthermore, generation of this comprehensive data set within a single laboratory allows direct comparison of properties across a large dataset and evaluation of changing property trends that have occurred over time with changing target product and candidate profiles.

Phase 0, including microdosing approaches: Applying the Three Rs and increasing the efficiency of human drug development

Phase 0 approaches, including microdosing, involve the use of sub-therapeutic exposures to the tested drugs, thus enabling safer, more-relevant, quicker and cheaper first-in-human (FIH) testing. These approaches also have considerable potential to limit the use of animals in human drug development. Recent years have witnessed progress in applications, methodology, operations, and drug development culture. Advances in applications saw an expansion in therapeutic areas, developmental scenarios and scientific objectives, in, for example, protein drug development and paediatric drug development. In the operational area, the increased sensitivity of Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS), expansion of the utility of Positron Emission Tomography (PET) imaging, and the introduction of Cavity Ring-Down Spectroscopy (CRDS), have led to the increased accessibility and utility of Phase 0 approaches, while reducing costs and exposure to radioactivity. PET has extended the application of microdosing, from its use as a predominant tool to record pharmacokinetics, to a method for recording target expression and target engagement, as well as cellular and tissue responses. Advances in methodology include adaptive Phase 0/Phase 1 designs, cassette and cocktail microdosing, and Intra-Target Microdosing (ITM), as well as novel modelling opportunities and simulations. Importantly, these methodologies increase the predictive power of extrapolation from microdose to therapeutic level exposures. However, possibly the most challenging domain in which progress has been made, is the culture of drug development. One of the main potential values of Phase 0 approaches is the opportunity to terminate development early, thus not only applying the principle of 'kill-early-kill-cheap' to enhance the efficiency of drug development, but also obviating the need for the full package of animal testing required for therapeutic level Phase 1 studies. Finally, we list developmental scenarios that utilised Phase 0 approaches in novel drug development.

Quantitative Analysis of Tozadenant Using Liquid Chromatography-Mass Spectrometric Method in Rat Plasma and Its Human Pharmacokinetics Prediction Using Physiologically Based Pharmacokinetic Modeling

Tozadenant is one of the selective adenosine A2a receptor antagonists with a potential to be a new Parkinson's disease (PD) therapeutic drug. In this study, a liquid chromatography-mass spectrometry based bioanalytical method was qualified and applied for the quantitative analysis of tozadenant in rat plasma. A good calibration curve was observed in the range from 1.01 to 2200 ng/mL for tozadenant using a quadratic regression. In vitro and preclinical in vivo pharmacokinetic (PK) properties of tozadenant were studied through the developed bioanalytical methods, and human PK profiles were predicted using physiologically based pharmacokinetic (PBPK) modeling based on these values. The PBPK model was initially optimized using in vitro and in vivo PK data obtained by intravenous administration at a dose of 1 mg/kg in rats. Other in vivo PK data in rats were used to validate the PBPK model. The human PK of tozadenant after oral administration at a dose of 240 mg was simulated by using an optimized and validated PBPK model. The predicted human PK parameters and profiles were similar to the observed clinical data. As a result, optimized PBPK model could reasonably predict the PK in human.

Optimization of Novel 1-Methyl-1 H-Pyrazole-5-carboxamides Leads to High Potency Larval Development Inhibitors of the Barber's Pole Worm

A phenotypic screen of a diverse library of small molecules for inhibition of the development of larvae of the parasitic nematode Haemonchus contortus led to the identification of a 1-methyl-1 H-pyrazole-5-carboxamide derivative with an IC₅₀ of 0.29 μM. Medicinal chemistry optimization targeted modifications on the left-hand side (LHS), middle section, and right-hand side (RHS) of the scaffold in order to elucidate the structure-activity relationship (SAR). Strong SAR allowed for the iterative and directed assembly of a focus set of 64 analogues, from which compound 60 was identified as the most potent compound, inhibiting the development of the fourth larval (L4) stage with an IC₅₀ of 0.01 μM. In contrast, only 18% inhibition of the mammary epithelial cell line MCF10A viability was observed, even at concentrations as high as 50 μM.

Utility of physiologically based pharmacokinetic (PBPK) modeling in oncology drug development and its accuracy: a systematic review

PURPOSE

Physiologically based pharmacokinetic (PBPK) modeling, a mathematical modeling approach which uses a pharmacokinetic model to mimick human physiology to predict drug concentration-time profiles, has been used for the discover and development of drugs in various fields, including oncology, since 2000. There have been a few general review articles on the utilization of PBPK in the development of oncology drugs, but these do not include an evaluation of model prediction accuracy. We therefore conducted a systematic review to define the accuracy of PBPK model prediction and its utility throughout all the developmental phases of oncology drugs.

METHODS

A systematic search was performed in the PubMed, PubMed Central and Cochrane Library databases from 1980 to February 2017 for articles (1) written in English, (2) focused on oncology or antineoplastic or anticancer drugs, tumor or cancer or anticancer drugs listed in the U.S. National Institutes of Health and (3) involving a PBPK model. The absolute-average-folding-errors (AAFEs) of the area under the curve (AUC) between predicted and observed values in each article were calculated to assess model prediction accuracy.

RESULTS

Of the 2341 articles initially identified by our search of the databases, 40 were included in the review analysis. These articles reported on six types of studies, i.e. in vivo (n = 4), first-in-human (n = 5), phase II/III clinical trials (n = 9), organ impairment (n = 3), pediatrics (n = 4) and drug-drug interactions (n = 15). AAFEs of the predicted AUC for all groups of studies were within 1.3-fold of each other despite variations in experimental methodologies.

CONCLUSION

PBPK modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development. The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. The limited number of publications of unsuccessful model application to date may contribute to bias toward the usefulness of modeling.

Development of a Physiologically Based Pharmacokinetic Model of Ethionamide in the Pediatric Population by Integrating Flavin-Containing Monooxygenase 3 Maturational Changes Over Time

Currently, ethionamide is the most frequently prescribed second-line antituberculosis drug in children. After extensive metabolism by flavin-containing monooxygenase (FMO) isoform 3 in the liver, the drug may exert cytotoxic effects. The comparison of children in different age groups revealed a significant age-related increase in ethionamide elimination in vivo. However, to date, the exact mechanism underlying this dynamic increase in ethionamide elimination has not been elucidated. We hypothesized that the age-dependent changes in ethionamide elimination were predominantly a result of the progressive increases in the expression and metabolic capacity of FMO3 during childhood. To test this hypothesis, a full physiologically based pharmacokinetic (PBPK) model of ethionamide was established and validated in adults through incorporation of comprehensive metabolism and transporter profiles, then expanded to the pediatric population through integration of FMO3 maturational changes over time. Thus, a good prediction PBPK model was validated successfully both in adults and children and applied to demonstrate the critical contribution of FMO3 in the mechanistic elimination pathway of ethionamide. In addition, a significant correlation between clearance and age was observed in children by accounting for ethionamide maturation, which could offer a mechanistic understanding of the age-associated changes in ethionamide elimination. In conclusion, this study underlines the benefits of in vitro-in vivo extrapolation and a quantitative PBPK approach for the investigation of transporter-enzyme interplay in ethionamide disposition and the demonstration of FMO3 ontogeny in children.

Development and validation of a physiology-based model for the prediction of pharmacokinetics/toxicokinetics in rabbits

The environmental fates of pharmaceuticals and the effects of crop protection products on non-target species are subjects that are undergoing intense review. Since measuring the concentrations and effects of xenobiotics on all affected species under all conceivable scenarios is not feasible, standard laboratory animals such as rabbits are tested, and the observed adverse effects are translated to focal species for environmental risk assessments. In that respect, mathematical modelling is becoming increasingly important for evaluating the consequences of pesticides in untested scenarios. In particular, physiologically based pharmacokinetic/toxicokinetic (PBPK/TK) modelling is a well-established methodology used to predict tissue concentrations based on the absorption, distribution, metabolism and excretion of drugs and toxicants. In the present work, a rabbit PBPK/TK model is developed and evaluated with data available from the literature. The model predictions include scenarios of both intravenous (i.v.) and oral (p.o.) administration of small and large compounds. The presented rabbit PBPK/TK model predicts the pharmacokinetics (Cmax, AUC) of the tested compounds with an average 1.7-fold error. This result indicates a good predictive capacity of the model, which enables its use for risk assessment modelling and simulations.

Predicting Oral Drug Absorption: Mini Review on Physiologically-Based Pharmacokinetic Models

Most marketed drugs are administered orally, despite the complex process of oral absorption that is difficult to predict. Oral bioavailability is dependent on the interplay between many processes that are dependent on both compound and physiological properties. Because of this complexity, computational oral physiologically-based pharmacokinetic (PBPK) models have emerged as a tool to integrate these factors in an attempt to mechanistically capture the process of oral absorption. These models use inputs from in vitro assays to predict the pharmacokinetic behavior of drugs in the human body. The most common oral PBPK models are compartmental approaches, in which the gastrointestinal tract is characterized as a series of compartments through which the drug transits. The focus of this review is on the development of oral absorption PBPK models, followed by a brief discussion of the major applications of oral PBPK models in the pharmaceutical industry.

Cell cultures in drug discovery and development: The need of reliable in vitro-in vivo extrapolation for pharmacodynamics and pharmacokinetics assessment

For ethical and cost-related reasons, use of animals for the assessment of mode of action, metabolism and/or toxicity of new drug candidates has been increasingly scrutinized in research and industrial applications. Implementation of the 3 "Rs"¹; rule (Reduction, Replacement, Refinement) through development of in silico or in vitro assays has become an essential element of risk assessment. Physiologically based pharmacokinetic (PBPK²) modeling is the most potent in silico tool used for extrapolation of pharmacokinetic parameters to animal or human models from results obtained in vitro. Although, many types of in vitro assays are conducted during drug development, use of cell cultures is the most reliable one. Two-dimensional (2D) cell cultures have been a part of drug development for many years. Nowadays, their role is decreasing in favor of three-dimensional (3D) cell cultures and co-cultures. 3D cultures exhibit protein expression patterns and intercellular junctions that are closer to in vivo states in comparison to classical monolayer cultures. Co-cultures allow for examinations of the mutual influence of different cell lines. However, the complexity and high costs of co-cultures and 3D equipment exclude such methods from high-throughput screening (HTS).³In vitro absorption, distribution, metabolism, and excretion assessment, as well as drug-drug interaction (DDI), are usually performed with the use of various cell culture based assays. Progress in in silico and in vitro methods can lead to better in vitro-in vivo extrapolation (IVIVE⁴) outcomes and have a potential to contribute towards a significant reduction in the number of laboratory animals needed for drug research. As such, concentrated efforts need to be spent towards the development of an HTS in vitro platform with satisfactory IVIVE features.

Translational Modeling of Drug-Induced Myelosuppression and Effect of Pretreatment Myelosuppression for AZD5153, a Selective BRD4 Inhibitor

In this work, we evaluate the potential risk of thrombocytopenia in man for a BRD4 inhibitor, AZD5153, based on the platelet count decreases from a Han Wistar rat study. The effects in rat were modeled and used to make clinical predictions for human populations with healthy baseline blood counts. At doses >10 mg, a dose-dependent effect on circulating platelets is expected, with similar predicted changes for both q.d. and b.i.d. dose schedules. These results suggest that at predicted efficacious doses, AZD5153 is likely to have some reductions in the clinical platelet counts, but within the normal range at projected efficacious doses. The model was then extended to incorporate preexisting myelosuppression where bone marrow function is inhibited by acute myeloid leukemia. Under these conditions, duration of platelet count recovery has the potential to be prolonged due to drug-induced myelosuppression.

Prediction of renal transporter-mediated drug-drug interactions for a drug which is an OAT substrate and inhibitor using PBPK modelling

A PBPK modelling approach was used to predict organic anion transporter (OAT) mediated drug-drug interactions involving S44121, a substrate and an inhibitor of OAT1 and OAT3. Model predictions were then compared to the results of a clinical DDI study which was carried out to investigate the interaction of S44121 with probenecid, tenofovir and ciprofloxacin. PBPK models were developed and qualified using existing clinical data, and inhibition constants were determined in vitro. The model predictions for S44121 as an OAT inhibitor were similar to the results obtained from the clinical DDI study, with no interaction observed for tenofovir or ciprofloxacin in the presence of S44121. An observed AUC ratio of 2.2 was obtained for S44121 in the presence of probenecid, which was slightly higher than the model predicted AUC ratio of 1.6. A DDI study in the monkey was also carried out for the interaction between S44121 and probenecid, since the monkey has previously been reported to be a good preclinical model for OAT-mediated DDI. However, this study highlighted a species difference in the major route of S44121 elimination between monkey (mainly hepatic metabolism) and human (mainly renal excretion of unchanged drug), rendering a comparison between the two DDI studies difficult. Overall, for S44121 the PBPK modelling approach gave a better prediction of the extent of DDI than the static predictions based on inhibitor C_max and IC₅₀, therefore this can be considered a potentially valuable tool within drug development.

Oral drug absorption in pediatrics: the intestinal wall, its developmental changes and current tools for predictions

The dissolution, intestinal absorption and presystemic metabolism of a drug depend on its physicochemical characteristics but also on numerous physiological (e.g. gastrointestinal pH, volume, transit time, morphology) and biochemical factors (e.g. luminal enzymes and flora, intestinal wall enzymes and transporters). Over the past decade, evidence has accumulated indicating that these factors may differ in children and adults resulting in age-related changes in drug exposure and drug response. Thus, drug dosage may require adjustment for the pediatric population to ensure the desired therapeutic outcome and to avoid side-effects. Although tremendous progress has been made in understanding the effects of age on intestinal physiology and function, significant knowledge gaps remain. Studying and predicting pharmacokinetics in pediatric patients remains challenging due to ethical concerns associated with clinical trials in this vulnerable population, and because of the paucity of predictive in vitro and in vivo animal assays. This review details the current knowledge related to developmental changes determining intestinal drug absorption and pre-systemic metabolism. Supporting experimental approaches as well as physiologically based pharmacokinetic modeling are also discussed together with their limitations and challenges. Copyright © 2016 John Wiley & Sons, Ltd.