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Physiologically based pharmacokinetic (PBPK) models have been proposed as a tool for more accurate individual pharmacokinetic (PK) predictions and model‐informed precision dosing, but their application in clinical practice is still rare. This study systematically assesses the benefit of using individual patient information to improve PK predictions. A PBPK model of caffeine was stepwise personalized by using individual data on (1) demography, (2) physiology, and (3) cytochrome P450 (CYP) 1A2 phenotype of 48 healthy volunteers participating in a single‐dose clinical study. Model performance was benchmarked against a caffeine base model simulated with parameters of an average individual. In the first step, virtual twins were generated based on the study subjects' demography (height, weight, age, sex), which implicated the rescaling of average organ volumes and blood flows. The accuracy of PK simulations improved compared with the base model. The percentage of predictions within 0.8‐fold to 1.25‐fold of the observed values increased from 45.8% (base model) to 57.8% (Step 1). However, setting physiological parameters (liver blood flow determined by magnetic resonance imaging, glomerular filtration rate, hematocrit) to measured values in the second step did not further improve the simulation result (59.1% in the 1.25‐fold range). In the third step, virtual twins matching individual demography, physiology, and CYP1A2 activity considerably improved the simulation results. The percentage of data within the 1.25‐fold range was 66.15%. This case study shows that individual PK profiles can be predicted more accurately by considering individual attributes and that personalized PBPK models could be a valuable tool for model‐informed precision dosing approaches in the future.

Liver international 42(3), 640-650 (2022). doi:10.1111/liv.15161, Published by Wiley-Blackwell, Oxford

Development and guidance of dosing schemes in children have been supported by physiology‐based pharmacokinetic (PBPK) modeling for many years. PBPK models are built on a generic basis, where compound‐ and system‐specific parameters are separated and can be exchanged, allowing the translation of these models from adults to children by accounting for physiological differences. Owing to these features, PBPK modeling is a valuable approach to support clinical decision making for dosing in children. In this analysis, we evaluate pediatric PBPK models for 10 small‐molecule compounds that were applied to support clinical decision processes at Bayer for their predictive power in different age groups. Ratios of PBPK‐predicted to observed PK parameters for the evaluated drugs in different pediatric age groups were estimated. Predictive performance was analyzed on the basis of a 2‐fold error range and the bioequivalence range (ie, 0.8 ≤ predicted/observed ≤ 1.25). For all 10 compounds, all predicted‐to‐observed PK ratios were within a 2‐fold error range (n = 27), with two‐thirds of the ratios within the bioequivalence range (n = 18). The findings demonstrate that the pharmacokinetics of these compounds was successfully and adequately predicted in different pediatric age groups. This illustrates the applicability of PBPK for guiding dosing schemes in the pediatric population.

Background Because of the vulnerability and frailty of elderly adults, clinical drug development has traditionally been biased towards young and middle-aged adults. Recent efforts have begun to incorporate data from paediatric investigations. Nevertheless, the elderly often remain underrepresented in clinical trials, even though persons aged 65 years and older receive the majority of drug prescriptions. Consequently, a knowledge gap exists with regard to pharmacokinetic (PK) and pharmacodynamic (PD) responses in elderly subjects, leaving the safety and efficacy of medicines for this population unclear. Objectives The goal of this study was to extend a physiologically based pharmacokinetic (PBPK) model for adults to encompass the full course of healthy aging through to the age of 100 years, to support dose selection and improve pharmacotherapy for the elderly age group. Methods For parameterization of the PBPK model for healthy aging individuals, the literature was scanned for anthropometric and physiological data, which were consolidated and incorporated into the PBPK software PK-Sim®. Age-related changes that occur from 65 to 100 years of age were the main focus of this work. For a sound and continuous description of an aging human, data on anatomical and physiological changes ranging from early adulthood to old age were included. The capability of the PBPK approach to predict distribution and elimination of drugs was verified using the test compounds morphine and furosemide, administered intravenously. Both are cleared by a single elimination pathway. PK parameters for the two compounds in younger adults and elderly individuals were obtained from the literature. Matching virtual populations—with regard to age, sex, anthropometric measures and dosage—were generated. Profiles of plasma drug concentrations over time, volume of distribution at steady state (V ss) values and elimination half-life (t ½) values from the literature were compared with those predicted by PBPK simulations for both younger adults and the elderly. Results For most organs, the age-dependent information gathered in the extensive literature analysis was dense. In contrast, with respect to blood flow, the literature study produced only sparse data for several tissues, and in these cases, linear regression was required to capture the entire elderly age range. On the basis of age-informed physiology, the predicted PK profiles described age-associated trends well. The root mean squared prediction error for the prediction of plasma concentrations of furosemide and morphine in the elderly were improved by 32 and 49 %, respectively, by use of age-informed physiology. The majority of the individual V ss and t ½ values for the two model compounds, furosemide and morphine, were well predicted in the elderly population, except for long furosemide half-lifes. Conclusion The results of this study support the feasibility of using a knowledge-driven PBPK aging model that includes the elderly to predict PK alterations throughout the entire course of aging, and thus to optimize drug therapy in elderly individuals. These results indicate that pharmacotherapy and safety-related control of geriatric drug therapy regimens may be greatly facilitated by the information gained from PBPK predictions. Electronic supplementary material The online version of this article (doi:10.1007/s40262-016-0422-3) contains supplementary material, which is available to authorized users.

Pharmacotherapy in the elderly is challenging due to age-related physiological changes, high interindividual variability, and increasing frequency of multimorbidity. The resulting polypharmacy increases the risk of drug-drug interactions and requires an individual risk assessment. Some drug-drug interactions are documented to be associated with harm in older adults including intoxication, gastrointestinal bleeding, or falls. Therefore, they are considered to be of special importance in the elderly. Moreover, frequent risk factors and continuous physiological alterations in the elderly should be taken into account during risk assessment. This review exemplifies clinically relevant drug-drug interactions and risk factors in the elderly. In addition, assessment tools as well as prevention and management strategies for clinical practice are presented.

Background Physiologically-based pharmacokinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics by continuous knowledge integration. Objective The objective of this study was to build a ciprofloxacin PBPK model for intravenous and oral dosing based on a comprehensive literature review, and evaluate the predictive performance towards pediatric and geriatric patients. Methods The aim of this report was to establish confidence in simulations of the ciprofloxacin PBPK model along the development process to facilitate reliable predictions outside of the tested adult age range towards the extremes of ages. Therefore, mean data of 69 published clinical trials were identified and integrated into the model building, simulation and verification process. The predictive performance on both ends of the age scale was assessed using individual data of 258 subjects observed in own clinical trials. Results Ciprofloxacin model verification demonstrated no concentration-related bias and accurate simulations for the adult age range, with only 4.8% of the mean observed data points for intravenous administration and 12.1% for oral administration being outside the simulated twofold range. Predictions towards the extremes of ages for the area under the plasma concentration–time curve (AUC) and the maximum plasma concentration (Cmax) over the entire span of life revealed a reliable estimation, with only two pediatric AUC observations outside the 90% prediction interval. Conclusion Overall, this ciprofloxacin PBPK modeling approach demonstrated the predictive power of a thoroughly informed middle-out approach towards age groups of interest to potentially support the decision-making process. Electronic supplementary material The online version of this article (10.1007/s40262-018-0661-6) contains supplementary material, which is available to authorized users.

The aim of this tutorial is to introduce the fundamental concepts of physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling with a special focus on their practical implementation in a typical PBPK model building workflow. To illustrate basic steps in PBPK model building, a PBPK model for ciprofloxacin will be constructed and coupled to a pharmacodynamic model to simulate the antibacterial activity of ciprofloxacin treatment.

Physiologically based pharmacokinetic (PBPK) models describe adsorption, distribution, metabolisation and excretion (ADME) of drugs in the body of an organism based on a large amount of prior anatomical and physiological knowledge. In contrast to compartmental pharmacokinetic modeling which uses rather empirical model structures, PBPK models aim for a detailed mechanistic representation of physiological processes underlying drug pharmacokinetics within the body. That means that the relevant organs or tissues of an organism are explicitly represented in a PBPK model. Organs in PBPK models are usually divided in subcompartments such as plasma, interstitial space, intracellular space and red blood cells. Mass transfer between the different compartments which ultimately governs intracellular drug delivery is quantified either by so-called distribution models for the calculation of organ-plasma partition coefficients or by permeability-surface area products quantifying passive diffusion, respectively. Notably, both types of calculation methods are parameterized based upon the physicochemical properties of the investigated drug, respectively. These properties include amongst others lipophilicity and the molecular weight of the compound. Additional physiological information ranging from the whole body level (e.g. organ volumes, blood flow rates, tissue composition) to relative tissue-specific protein abundance is explicitly provided in the model. PBPK models are nowadays routinely used to analyze pharmacokinetics in drug development Due to the large amount of mechanistic information which is implicitly provided in the structural equations, PBPK models are in particular well-suited for both in-depth analyses of ADME processes underlying drug pharmacokinetics as well as for extrapolation to novel indications, patient populations or treatment designs. In this review we will present and discuss calculation methods used in PBPK model to describe and to quantify intracellular drug delivery.