J Pharmacokinet Pharmacodyn [JOURNAL]
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200 papers
Cross-species translational modelling of targeted therapeutic oligonucleotides using physiologically based pharmacokinetics.
Derbalah A, Stader F, Liu C … +6 more , Zyla A, Abdulla T, Wu Q, Jamei M, Gardner I, Sepp A
J Pharmacokinet Pharmacodyn · 2025 Jun · PMID 40506605 · Full text
Oligonucleotide therapeutics hold promise for targeted gene silencing, yet achieving optimal tissue-specific delivery remains challenging. This study introduces a mechanistic whole-body physiologically based pharmacokine...
Oligonucleotide therapeutics hold promise for targeted gene silencing, yet achieving optimal tissue-specific delivery remains challenging. This study introduces a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to predict tissue uptake dynamics of both conjugated (targeted) and unconjugated oligonucleotides across species. The model incorporates two uptake pathways: a non-saturable nonspecific pathway for all oligonucleotides and receptor-mediated endocytosis (RME) specific to conjugated molecules. Parameters for nonspecific uptake were derived from plasma and tissue concentration data of unconjugated antisense oligonucleotides (ASOs) in rats, while RME parameters for N-acetylgalactosamine (GalNAc)-conjugated oligonucleotides targeting the asialoglycoprotein receptor (ASGPR) were obtained from literature. Model validation against experimental data for conjugated and unconjugated ASOs and small interfering RNAs (siRNAs) in rats and mice demonstrated good predictive performance, with median predicted-to-observed AUC ratios of 0.84 (Interquartile range [IQR] 0.434-1.22) in rats and 0.629 (IQR 0.3-1.6) in mice. Local sensitivity analyses identified key parameters and processes influencing organ uptake, including the unbound plasma fraction and receptor-mediated uptake efficiency. Simulations highlighted the potential of sustained-release formulations to improve targeting specificity by mitigating receptor saturation. This is the first whole-body PBPK model to describe oligonucleotide pharmacokinetics across species and modalities. The model provides critical mechanistic insights to optimize tissue-specific delivery, guide formulation strategies, and enhance therapeutic outcomes for targeted oligonucleotide therapeutics.
Leveraging large language models in pharmacometrics: evaluation of NONMEM output interpretation and simulation capabilities.
Cha HJ, Choe K, Shin E … +2 more , Ramanathan M, Han S
J Pharmacokinet Pharmacodyn · 2025 Jun · PMID 40464844 · Publisher ↗
Advancements in large language models (LLMs) have suggested their potential utility for diverse pharmacometrics tasks. This study investigated the performance of LLM for generating structure diagrams, publication-ready t...
Advancements in large language models (LLMs) have suggested their potential utility for diverse pharmacometrics tasks. This study investigated the performance of LLM for generating structure diagrams, publication-ready tables, analysis reports, and conducting simulations using output files from pharmacometrics models. Forty-four NONMEM output files were obtained from the GitHub software repository. The performance of Claude 3.5 Sonnet (Claude) and ChatGPT 4o was compared with two other candidate LLMs: Gemini 1.5 Pro and Llama 3.2. Prompt engineering was conducted for Claude for pharmacometrics tasks such as generating model structure diagrams, parameter tables, and analysis reports. Simulations were conducted using ChatGPT. Claude Artifacts was used to visualize model structure diagrams, parameter tables, and analysis reports. A web-based R Shiny application was implemented to provide an accessible interface for automating pharmacometric model structure diagrams, parameter tables, and analysis reports tasks. Claude was selected for investigation following performance comparisons with ChatGPT 4o, Gemini 1.5 Pro, and Llama on model structure diagram and parameter table generation tasks. Claude successfully generated the model structure diagrams for 40 (90.9%) of the 44 NONMEM output files with the initial prompts, and the remaining were resolved with an additional prompt. Claude consistently generated accurate parameter summary tables and succinct model analysis reports. Modest variability in model structure diagrams generated for replicate prompts was identified. ChatGPT demonstrated simulation capabilities but revealed limitations with complex PK/PD models. LLMs have the potential to enhance key pharmacometrics modeling tasks. However, expert review of the results generated is essential.
Interplay between pharmacokinetics and immunogenicity of therapeutic proteins: stepwise development of a bidirectional joint pharmacokinetics-anti-drug antibodies model.
van der Walt JS, Wilkins J, Khandelwal A … +3 more , Venkatakrishnan K, Gao W, Milenković-Grišić AM
J Pharmacokinet Pharmacodyn · 2025 May · PMID 40399699 · Full text
The aim of the analysis was to develop a phenomenological longitudinal population pharmacokinetics (PK)-anti-drug antibodies (ADA) model to enable an informed and quantitative framework for assessment of ADA influence. D...
The aim of the analysis was to develop a phenomenological longitudinal population pharmacokinetics (PK)-anti-drug antibodies (ADA) model to enable an informed and quantitative framework for assessment of ADA influence. Data used were from seven clinical studies of avelumab across drug development phases in patients with several tumor types. ADA as covariate in a population PK model, and Markov models of ADA status (ADA+ or ADA-) were investigated. Finally, a joint PK-ADA model was developed. In the population PK models that evaluated ADA as a covariate, the clearance increase attributable to ADA+ status was 8.5% (time-varying ADA) to 19.9% (time-invariant ADA with inter-occasion variability in clearance). With a discrete-time Markov model (DTMM), tumor type was identified as a significant covariate on the probability of ADA- to ADA+ transition. When ADA time course predicted by the DTMM model was implemented as a covariate in the population PK model, an increase in avelumab clearance of 11-41% was estimated depending on tumor type. With a continuous-time Markov model (CTMM), in addition to tumor type, baseline ADA status was identified to significantly influence the ADA- to ADA+ transition rate constant. The joint PK-CTMM model estimated the maximal increase in CL due to ADA as 15% and a decrease in ADA- to ADA+ transition rate of up to 37% with increasing avelumab concentration, with 50% of the maximum decrease occurring at 349 µg/mL. The present work established a framework for the assessment of interactions between PK and immunogenicity for therapeutic proteins.
A comprehensive review of 20 years of progress in nonclinical QT evaluation and proarrhythmic assessment.
Delpy E, Bétat AM, Delaunois A … +3 more , Drieu la Rochelle C, Martel E, Valentin JP
J Pharmacokinet Pharmacodyn · 2025 May · PMID 40379846 · Publisher ↗
The assessment of drug-induced QT interval prolongation and associated proarrhythmic risks, such as Torsades de Pointes (TdP), has evolved significantly over the past decades. This review traces the development of noncli...
The assessment of drug-induced QT interval prolongation and associated proarrhythmic risks, such as Torsades de Pointes (TdP), has evolved significantly over the past decades. This review traces the development of nonclinical QT evaluation, highlighting key milestones and innovations that have shaped current practices in cardiac safety assessment. The emergence of regulatory guidelines, including International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) S7B, established a nonclinical framework for evaluating drug effects on cardiac repolarization, addressing concerns raised by drug withdrawals in the 1990s. Advances in in vitro, in vivo, and in silico models have enhanced the predictive accuracy of nonclinical studies, with the hERG assay and telemetry-based animal models becoming gold standards. Recent initiatives, such as the Comprehensive in vitro Proarrhythmia Assay (CiPA) and the Japan iPS Cardiac Safety Assessment (JiCSA), emphasize integrating mechanistic insights from human-derived cardiomyocyte models and computational approaches to refine risk predictions. The 2020s mark a shift toward integrated nonclinical-clinical risk assessments, as exemplified by the ICH E14/S7B Questions and Answers. These highlight the need of best practices for study design, data analysis, and interpretation to support regulatory decision-making. Furthermore, the adoption of New Approach Methodologies (NAMs) and reinforced adherence to 3Rs principles (Reduce, Refine, Replace) reflect a commitment to ethical and innovative safety science. This review underscores the importance of harmonized and translational approaches in cardiac safety evaluation, providing a foundation for advancing drug development while safeguarding patient safety. Future directions include further integration of advanced methodologies and regulatory harmonization to streamline nonclinical and clinical risk assessments.
Beyond the linear model in concentration-QT analysis.
Cellière G, Krause A, Bonnefois G … +1 more , Chauvin J
J Pharmacokinet Pharmacodyn · 2025 May · PMID 40347307 · Full text
The white-paper regression model is the standard method for assessing QT liability of drugs. The quantity of interest, placebo-corrected QTc change from baseline (ΔΔQTc) with corresponding confidence interval (CI), is de...
The white-paper regression model is the standard method for assessing QT liability of drugs. The quantity of interest, placebo-corrected QTc change from baseline (ΔΔQTc) with corresponding confidence interval (CI), is derived from the difference in model-estimated ΔQTc for active compound and placebo in a linear model. Model assumptions include linearity and no time delay between change in concentration and change in ΔQTc. Alternative models are commonly not considered unless there is a clear indication of inappropriateness of the assumptions. This work introduces several extensions for concentration-QT modeling in a pharmacometric context. The model is formulated as linear drug-effect model with treatment, nominal time, and centered baseline as covariates on the intercept. This approach enables straightforward use of other concentration-ΔQTc relationships, including loglinear, E, and indirect-effects models. In addition, the setup allows for the use of pharmacometric model assessments for ΔQTc and ΔΔQTc, including visual predictive checks and quantitative model comparison based on the Bayesian information criterion. The proposed approach is applied to several compounds from a previously published QTc study. The results suggest that a nonlinear mixed-effects model for ΔΔQTc and comparing a set of candidate models quantitatively can be a more powerful approach than fitting only the white-paper regression model. A semi-automated approach that compares nonlinear and hysteresis models to the linear model enables a reliable choice of the best model and determination of the degree of prolongation at the concentration of interest. Standard pharmacometric tools can assess the appropriateness of the models and the potential extent of hysteresis.
Informatics for toxicokinetics.
Padilla Mercado G, Cook C, Adkins N … +25 more , Albrecht L, Cary G, Edwards B, Haggard DE, Hanley NM, Hughes MF, Jarnagin A, Kodavanti TD, Korol-Bexell E, Kreutz A, Ngo M, Patullo C, Rowan EG, Huse LM, Correa VA, Kesic B, Casey W, Aboabdo J, Wolf K, Sayre R, Sharma B, Wall JT, Yamazaki H, Wambaugh JF, Ring CL
J Pharmacokinet Pharmacodyn · 2025 May · PMID 40325299 · Publisher ↗
Toxicokinetic and pharmacokinetic (PK) summary parameters, such as C (peak concentration), AUC (time-integrated area under the plasma concentration curve), and t (elimination half-life from the body), are important infor...
Toxicokinetic and pharmacokinetic (PK) summary parameters, such as C (peak concentration), AUC (time-integrated area under the plasma concentration curve), and t (elimination half-life from the body), are important information for understanding chemical safety in both pharmaceuticals and commercial industry. Although standardized tools exist for PK analysis of individual chemicals, new workflows can enhance chemoinformatic trend analysis. The Concentration versus Time Database (CvTdb) is a public repository of PK data at the U.S. Environmental Protection Agency (EPA). The CvTdb contains manually curated, standardized toxicokinetic data from hundreds of publications. Experimental time-course data of chemical concentrations in body fluids and tissues are extracted along with descriptive metadata. The advantage of standardized data is that it can be analyzed systematically. For example, we observe that 88.6% of replicate measurements of blood or plasma concentrations of chemicals after intravenous or oral dosing are within two-fold of the mean concentration. Although most experimental data have final timepoints within three days, some experiments extend up to a year, usually for long-lived chemicals. Here we have estimated PK parameters of CvTdb data using a custom R package, invivoPKfit. Standardized 1- and 2- compartmental PK model parameters were estimated using all data associated with a particular compound, including data that spans multiple references. We used invivoPKfit to estimate PK parameters such as volume of distribution (V) and t. The parameter values estimated with invivoPKfit are distributed similar to estimates made in the literature by a variety of methods. Overall, CvTdb serves as a standardized set of open data and for calibrating and evaluating PK models, while invivoPKfit allows for batch processing of this data type in a transparent and scalable manner. In addition to scientific insights, chemical risk assessment may be better informed by transparent, reproducible, and open-source workflows for PK informatics.
Leveraging large language models to compare perspectives on integrating QSP and AI/ML.
Androulakis IP, Cheng L, Cho CR … +1 more , Zhang T
J Pharmacokinet Pharmacodyn · 2025 May · PMID 40325298 · Publisher ↗
Two recent papers offer contrasting perspectives on integrating Quantitative Systems Pharmacology (QSP) and Artificial Intelligence/Machine Learning (AI/ML): one views QSP as the primary driver using AI/ML to enhance com...
Two recent papers offer contrasting perspectives on integrating Quantitative Systems Pharmacology (QSP) and Artificial Intelligence/Machine Learning (AI/ML): one views QSP as the primary driver using AI/ML to enhance computational tasks, while the other argues that AI/ML should provide an alternative mechanistic framework. Rather than perpetuate this tension, we used Large Language Models (LLMs) to examine both papers in two tests-one comparing their core arguments and another probing which methodology LLM should take precedence. Repeating each test multiple times with an identical and neutral prompt, the LLM revealed that each perspective suits specific stages of the drug development pipeline. QSP offers mechanistic rigor and regulatory clarity, and AI/ML excels in high-dimensional data analysis and exploratory modeling. A hybrid approach might best serve researchers and decision-makers, especially when harmonizing data-driven insights with mechanistic integrity. This exercise also highlights the potential of LLMs as promising tools for synthesizing complex information, offering an arguably less biased viewpoint that can trigger deeper discussion from the broader community seeking to align QSP and AI/ML in model-informed drug development (MIDD). By combining our human expertise with AI-driven analyses, we hope to further discuss with the scientific community how QSP and AI/ML-and the synergy between them-can drive innovation in therapeutic discovery and optimization.
Stochastic pharmacodynamics of a heterogeneous tumour-cell population.
Truong VT, Vicini P, Yates J … +2 more , Dubois V, Lythe G
J Pharmacokinet Pharmacodyn · 2025 May · PMID 40325283 · Full text
Standard pharmacodynamic models are ordinary differential equations without the features of stochasticity and heterogeneity. We develop and analyse a stochastic model of a heterogeneous tumour-cell population treated wit...
Standard pharmacodynamic models are ordinary differential equations without the features of stochasticity and heterogeneity. We develop and analyse a stochastic model of a heterogeneous tumour-cell population treated with a drug, where each cell has a different value of an attribute linked to survival. Once the drug reduces a cell's value below a threshold, the cell is susceptible to death. The elimination of the last cell in the population is a natural endpoint that is not available in deterministic models. We find formulae for the probability density of this extinction time in a collection of tumour cells, each with a different regulator value, under the influence of a drug. There is a logarithmic relationship between tumour population size and mean time to extinction. We also analyse the population under repeated drug doses and subsequent recoveries. Stochastic cell death and division events (and the relevant mechanistic parameters) determine the ultimate fate of the cell population. We identify the critical division rate separating long-term tumour population growth from successful multiple-dose treatment.
A translational physiologically-based pharmacokinetic model for MMAE-based antibody-drug conjugates.
Chang HP, Shah DK
J Pharmacokinet Pharmacodyn · 2025 May · PMID 40325253 · Full text
The objective of this work was to develop a translational physiologically-based pharmacokinetic (PBPK) model for antibody-drug conjugates (ADCs), using monomethyl auristatin E (MMAE)-based ADCs. A previously established...
The objective of this work was to develop a translational physiologically-based pharmacokinetic (PBPK) model for antibody-drug conjugates (ADCs), using monomethyl auristatin E (MMAE)-based ADCs. A previously established dual-structured whole-body PBPK model for MMAE-based ADCs in mice was scaled to higher species (i.e., rats and monkeys) and humans. Species-specific physiological and drug-related parameters for the payload and antibody backbone of ADCs were obtained from literature. Parameters associated with payload release, including the deconjugation rate, were optimized using an allometric scaling approach, and antibody degradation rate was adjusted to account for the enhanced clearance of ADCs due to conjugation across different species. The translational PBPK model predicted the PK profiles for various ADC analytes in rats, monkeys, and humans reasonably well. The optimized PBPK model suggested decreased rate of deconjugation for ADCs in higher species, whereas the effects of payload conjugation on ADC clearance were more pronounced in higher species and humans. The translational PBPK model presented here may enable prediction of different ADC analyte PK at the site-of-action, offering valuable insights for the development of exposure-response relationships for ADCs. The modeling framework presented here can also serve as a platform for the development of PBPK model for other ADCs.
Diffusion dimensionality modeling of subcutaneous/intramuscular absorption of antibodies and long-acting injectables.
Ramanathan M, Krzyzanski W
J Pharmacokinet Pharmacodyn · 2025 Apr · PMID 40263185 · Publisher ↗
To evaluate the role of diffusion process dimensionality in drug absorption following subcutaneous or intramuscular administration. The diffusion dimensionality model is based on analytical solutions of the 1-, 2- or 3-d...
To evaluate the role of diffusion process dimensionality in drug absorption following subcutaneous or intramuscular administration. The diffusion dimensionality model is based on analytical solutions of the 1-, 2- or 3-dimensional diffusion equations for a bolus input linked to a central compartment with first-order elimination. The model equations were reparameterized to contain three parameters for the time needed for the drug diffusion from the administration site, drug absorption into the central compartment, and the elimination rate constant. The diffusion dimensionality models were challenged with previously published subcutaneous absorption data for 13 antibody drugs and insulin lispro, and the long-acting injectable antipsychotic drugs: subcutaneous Perseris™, intramuscular Invega Sustenna®, Risperdal Consta®, and olanzapine. The Bayesian information criterion was used for model selection. The solution to the diffusion equation for a bolus dose administration is strongly dependent on the number of dimensions. The maximal concentration is lowest for the 3-dimensional diffusion equation. The pharmacokinetic profiles of all 13 antibodies were satisfactorily approximated by a diffusion dimensionality model. Three antibodies (CNTO5825, ACE910 and ustekinumab) were best described by the 2-dimensional diffusion equation. The 2- and 3-dimensional diffusion equations were equivalent for ABT981, guselkumab, adalimumab, nemolizumab, omalizumab, and secukinumab. Golimumab, DX2930, AMG139, and mepolizumab were best described by the 3-dimensional diffusion equation. All the long-acting antipsychotic dosage forms except Risperdal Consta were modeled satisfactorily. Diffusion dimensionality models are a parsimonious and effective approach for modeling drug absorption profiles of subcutaneously and intramuscularly administered small molecule and protein drugs and their dosage forms.
Machine learning approaches for assessing medication transfer to human breast milk.
Zhao Z, Zou P, Fang Y … +4 more , Si T, Li Y, Yi B, Zhang T
J Pharmacokinet Pharmacodyn · 2025 Apr · PMID 40240653 · Full text
The human milk/plasma (M/P) drug concentration ratio is crucial in pharmacology, especially for breastfeeding mothers undergoing treatment. It determines the extent to which drugs ingested by the mother pass into breast...
The human milk/plasma (M/P) drug concentration ratio is crucial in pharmacology, especially for breastfeeding mothers undergoing treatment. It determines the extent to which drugs ingested by the mother pass into breast milk, potentially affecting the infant. This study conducted a comprehensive evaluation of multiple machine learning algorithms to assess their effectiveness in predicting the M/P ratio. The dataset consists of 162 drugs and 11 predictor variables. M/P ratios were categorized into two groups of (0, 1) and (≥ 1), and a refined three-category system: (0, < 0.5), (0.5, < 1), and (≥ 1). The ML techniques utilized include K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM), and Neural Networks. We implied the five-fold cross-validation to ensure the model's robustness and Principal Component Analysis (PCA) was applied for data visualization. Bayesian Information Criterion (BIC) was used in the KNN model selection to balance complexity and explanatory power. In our study, KNN achieved average accuracies of 79% for the two-category system and 60% for the three-category. Random Forest models show 77 and 64% average accuracy, respectively. SVM achieved similar results with 78 and 67%, while Neural Networks have the overall best result among the other models with average accuracies of 82 and 76% accuracy. The study highlights the potential of machine learning (ML) techniques in predicting M/P ratios, offering valuable insights for risk assessment during drug development. These predictive models can serve as a valuable tool for estimating drug transfer into breast milk, helping to bridge knowledge gaps in drug safety for lactating individuals. Further validation and refinement by incorporating larger datasets can enhance their reliability and applicability. Advancing these techniques can support safer medication use and informed clinical decision-making for lactating individuals.
Predicting tumor dynamics in treated patients from patient-derived-xenograft mouse models: a translational model-based approach.
Ronchi D, Tosca EM, Magni P
J Pharmacokinet Pharmacodyn · 2025 Apr · PMID 40240647 · Full text
This study presents a translational modeling framework designed to predict tumor size dynamics in cancer patients undergoing anticancer treatment, using data from patient-derived xenograft (PDX) mice. In the first step,...
This study presents a translational modeling framework designed to predict tumor size dynamics in cancer patients undergoing anticancer treatment, using data from patient-derived xenograft (PDX) mice. In the first step, a population tumor growth inhibition (TGI) model to estimate the distribution of exponential tumor growth rates and anticancer drug potency in PDX mice was built. Then, model parameters were allometrically scaled from mice to humans to inform a TGI model predicting tumor size dynamics in cancer patients. Longitudinal tumor dynamics predicted by the PDX-informed TGI model were expressed in terms of tumor progression events to allow validation against literature time-to-progression (TTP) data. The proposed approach was tested on two case studies: gemcitabine treatment for pancreatic cancer and sorafenib treatment for hepatocellular cancer. The framework successfully predicted median tumor size dynamics, closely aligned with clinical TTP curves for gemcitabine-pancreatic cancer case study. While predictions for extreme tumor size percentiles highlighted potential avenues for refinement, such as incorporating resistance mechanisms, the overall accuracy underscored the goodness of the approach. For the sorafenib-hepatocellular cancer case study, the framework provided plausible tumor size predictions, with TTP curves closely aligned with clinical observations, despite the limited availability of clinical data prevented a full validation. Overall, the translational modeling framework showed potential for predicting tumor dynamics in cancer patients, with results suggesting its applicability as a valid tool to support early decision-making in oncology.
Interoccasion variability in population pharmacokinetic models: identifiability, influence, interdependencies and derived study design recommendations.
Behrens E, Wicha SG
J Pharmacokinet Pharmacodyn · 2025 Apr · PMID 40216605 · Full text
Modeling interoccasion variability (IOV) of pharmacokinetic parameters is challenging in sparse study designs. We conducted a simulation study with stochastic simulation and estimation (SSE) to evaluate the influence of...
Modeling interoccasion variability (IOV) of pharmacokinetic parameters is challenging in sparse study designs. We conducted a simulation study with stochastic simulation and estimation (SSE) to evaluate the influence of IOV (25, 75%CV) from numerous perspectives (power, type I error, accuracy and precision of parameter estimates, consequences of neglecting an IOV, capability to detect the 'correct' IOV). To expand the scope from modeling-related aspects to clinical trial practice, we investigated the minimal sample size for IOV detection and calculated areas under the concentration-time curve (AUC) derived from models containing IOV and mis-specified models. The power to correctly detect an IOV increased from one to three occasions (OCC) and the type I error rate to falsely include an IOV was not elevated. Two sampling schemes were compared (with/without trough sample) and including a trough sample resulted in better performance throughout the different evaluations in this simulation study. Parameters were estimated more precisely when more OCCs were included and IOV was of high effect size. Neglecting an IOV that was truly present had a high impact on bias and imprecision of the parameter estimates, mostly on interindividual variabilities and residual error. To reach a power of ≥ 95% in all scenarios when sampling in three OCCs between 10 and 50 patients were required in the investigated setting. AUC calculations with mis-specified models revealed a distorted AUC distribution as IOV was not considered.
Advancing inclusive healthcare through PBPK modelling: predicting the impact of CYP genotypes and enzyme ontogenies on infant exposures of venlafaxine and its active metabolite O-desmethylvenlafaxine in lactation.
Pan X, Rowland Yeo K
J Pharmacokinet Pharmacodyn · 2025 Apr · PMID 40185984 · Full text
About 15-20% of women experience postnatal depression and may seek advice about medication use whilst breastfeeding. Venlafaxine is a potent and selective neuronal serotonin-norepinephrine reuptake inhibitor indicated fo...
About 15-20% of women experience postnatal depression and may seek advice about medication use whilst breastfeeding. Venlafaxine is a potent and selective neuronal serotonin-norepinephrine reuptake inhibitor indicated for treating major depressive disorders. The drug is mainly metabolised by cytochrome P450 2D6 (CYP2D6) to its active metabolite O-desmethylvenlafaxine (ODV), with small contributions from CYP2C9 and CYP2C19. Subsequently, the formed ODV undergoes CYP3A4- and UGT-mediated metabolism and renal excretion. A physiologically based pharmacokinetic (PBPK) model describing the disposition of both venlafaxine and ODV was developed. Consistent with observed data, simulations showed that exposure of the combined active moieties (venlafaxine plus ODV) was similar for both CYP2D6 extensive (EM) and poor metaboliser (PM) subjects. Clinical lactation data for venlafaxine were available from several studies but CYP genotypes were not recorded. Interestingly, based on simulated exposures in breast milk, the estimated average relative infant daily dose (RIDD) ranged from 3.8% for all EMs to 7.6% for all PMs of CYP2D6, CYP2C9 and CYP2C19. Furthermore, simulations in breastfed infants indicated that both CYP polymorphisms and enzyme ontogenies contribute to the significant variability that is observed clinically but the combined exposures of venlafaxine and ODV remain below the thresholds that have been reported for adverse events in adults and children. The data generated here add to the existing knowledge base and can help clinicians and their patients make a more informed decision on the use of venlafaxine during breastfeeding.
Information-theoretic evaluation of covariate distributions models.
Hartung N, Khatova A
J Pharmacokinet Pharmacodyn · 2025 Mar · PMID 40148687 · Full text
Statistical modelling of covariate distributions allows to generate virtual populations or to impute missing values in a covariate dataset. Covariate distributions typically have non-Gaussian margins and show nonlinear c...
Statistical modelling of covariate distributions allows to generate virtual populations or to impute missing values in a covariate dataset. Covariate distributions typically have non-Gaussian margins and show nonlinear correlation structures, which simple descriptions like multivariate Gaussian distributions fail to represent. Prominent non-Gaussian frameworks for covariate distribution modelling are copula-based models and models based on multiple imputation by chained equations (MICE). While both frameworks have already found applications in the life sciences, a systematic investigation of their goodness-of-fit to the theoretical underlying distribution, indicating strengths and weaknesses under different conditions, is still lacking. To bridge this gap, we thoroughly evaluated covariate distribution models in terms of Kullback-Leibler (KL) divergence, a scale-invariant information-theoretic goodness-of-fit criterion for distributions. Methodologically, we proposed a new approach to construct confidence intervals for KL divergence by combining nearest neighbour-based KL divergence estimators with subsampling-based uncertainty quantification. In relevant data sets of different sizes and dimensionalities with both continuous and discrete covariates, non-Gaussian models showed consistent improvements in KL divergence, compared to simpler Gaussian or scale transform approximations. KL divergence estimates were also robust to the inclusion of latent variables and large fractions of missing values. While good generalization behaviour to new data could be seen in copula-based models, MICE shows a trend for overfitting and its performance should always be evaluated on separate test data. Parametric copula models and MICE were found to scale much better with the dimension of the dataset than nonparametric copula models. These findings corroborate the potential of non-Gaussian models for modelling realistic life science covariate distributions.
Imputation of missing clock times - application to procalcitonin concentration time course after birth.
Bokor AJ, Holford N, Hannam JA
J Pharmacokinet Pharmacodyn · 2025 Mar · PMID 40102294 · Full text
The time course of biomarkers (e.g., acute phase proteins) are typically described using days relative to events of interest, such as surgery or birth, without specifying the sample time. This limits their use as they ma...
The time course of biomarkers (e.g., acute phase proteins) are typically described using days relative to events of interest, such as surgery or birth, without specifying the sample time. This limits their use as they may change rapidly during a single day. We investigated strategies to impute missing clock times, using procalcitonin for population modelling as the motivating example. 1275 procalcitonin concentrations from 282 neonates were available with dates but not sample times (Scenario 0). Missing clock times were imputed using a random uniform distribution under three scenarios: (1) minimum sampling intervals (8-12 h); (2) procalcitonin concentrations increase for postnatal days 0-1 then decrease; (3) standard sampling practice at the study hospital. Unique datasets (n = 100) were created with scenario-specific imputed clock times. Procalcitonin was modelled for each scenario using the same non-linear mixed effects model using NONMEM. Scenarios were evaluated by the NONMEM objective function value compared to Scenario 0 (∆OFV) and with visual predictive checks. Scenario 3, based on standard sampling practice at the study hospital, was the best imputation procedure with an improved objective function value compared to Scenario 0 (∆OFV: -62.6). Scenario 3 showed a shorter lag time between the birth event and the procalcitonin concentration increase (average: 12.0 h, 95% interval: 9.7 to 14.3 h) compared to other scenarios (averages: 15.3 to 18.7 h). A methodology for selecting imputation strategies for clock times was developed. This may be applied to other problems where clock times are missing.
Sampling from covariate distribution may not always be necessary in PK/PD simulations: illustrative examples with antibiotics.
Liu F, Cheng Z, Li S … +1 more , Xie F
J Pharmacokinet Pharmacodyn · 2025 Mar · PMID 40038131 · Publisher ↗
Pharmacokinetics (PK)/pharmacodynamics (PD) modeling and simulation is crucial for optimizing antimicrobial dosing. This study assessed covariate impact on PK variability and identified scenarios where fixing the covaria...
Pharmacokinetics (PK)/pharmacodynamics (PD) modeling and simulation is crucial for optimizing antimicrobial dosing. This study assessed covariate impact on PK variability and identified scenarios where fixing the covariate with median value proves effective PK/PD simulations for antibiotics with population PK (popPK) model including only one covariate effect. Three published popPK models were employed, with creatinine clearance (CRCL) identified as a covariate on clearance (CL) for meropenem and tobramycin, and total body weight (WT) associated with the volume of distributions for daptomycin. Given a fixed dose for Meropenem (1000 mg), and a WT based dose for tobramycin (7 mg/kg) and daptomycin (6 mg/kg), PK/PD simulation outcomes (e.g., percentage of PK/PD target attainment (PTA) and toxicity risk) were compared between fixed covariate-based and covariate distribution-based approaches. Covariate impact on PK was assessed through deterministic simulation using outer bounds of covariate versus simulation using median covariate value, with an overlap ratio calculated the percentage of overlapped area under concentration-time curve (AUC) between these two simulation approaches. Meropenem and tobramycin simulations showed a broader PK profiles and distinct PTA distribution with sampled covariate distribution, while daptomycin simulations exhibited consistency in PK/PD characteristics. CRCL had a relative strong impact on meropenem and tobramycin PK, while a weak impact of WT on daptomycin PK was observed from extensive overlap in simulated PK curves, with an overlap ratio of 99.5%. Regarding a weak covariate impact on PK with high overlap ratio, sampling from covariate distribution may not significantly enhance simulation performance, fixing covariate with median value could be efficient for antibiotic PK/PD simulations.
The impact of misspecified covariate models on inclusion and omission bias when using fixed effects and full random effects models.
Nyberg J, Jonsson EN
J Pharmacokinet Pharmacodyn · 2025 Feb · PMID 39985638 · Full text
Identification of covariates that can explain sources of variability among individuals in pharmacometric models is key, as it can lead to patient-subgrouping or patient-specific dosing strategies. Common recommendations...
Identification of covariates that can explain sources of variability among individuals in pharmacometric models is key, as it can lead to patient-subgrouping or patient-specific dosing strategies. Common recommendations propose to limit the covariate-parameters relationships to be tested to those that are scientifically plausible, a process called covariate "scope reduction". We investigated the possible impact of scope reduction on model parameters estimated with misspecified models in terms of omission bias (when a relevant covariate is not included in a model) and inclusion bias (when a non-relevant covariate is included). One-hundred datasets were simulated with a rich-sampling design using 8 variations of a one-compartment model with first-order absorption, having clearance (CL), volume of distribution (V), and absorption rate constant (Ka) as parameters, and body weight (WT) as covariate. Parameters were estimated using 14 models that included the covariate using fixed-effects (FEM) and 2 full random-effects models (FREM), with combinations of covariate-parameter relationships and IIV correlations. Estimated parameters were compared to the parameter values used for simulations in terms of accuracy (bias) and precision. Results showed that, in misspecified FEMs, covariate coefficients and IIV parameters were sensitive to omission bias. Conversely, misspecified covariate models did not introduce inclusion bias since the impact of a non-relevant covariate was estimated, as expected, to values close to zero, and in these cases FREM performed better than FEM. In conclusion, while inclusion bias does not seem to be an issue in misspecified models, the risk of introducing omission bias in parameter estimates should be kept in mind when considering covariate scope reduction when covariate models are implemented using fixed effects.
Defining preclinical efficacy with the DNAPK inhibitor AZD7648 in combination with olaparib: a minimal systems pharmacokinetic-pharmacodynamic model.
DeJongh J, Cadogan E, Davies M … +4 more , Ramos-Montoya A, Smith A, van Steeg T, Richards R
J Pharmacokinet Pharmacodyn · 2025 Feb · PMID 39961902 · Full text
AZD7648 is a potent inhibitor of DNA-dependent protein kinase (DNA-PK), which is part of the non-homologous end-joining DNA repair pathway. When combined with the PARP inhibitor olaparib, AZD7648 shows robust combination...
AZD7648 is a potent inhibitor of DNA-dependent protein kinase (DNA-PK), which is part of the non-homologous end-joining DNA repair pathway. When combined with the PARP inhibitor olaparib, AZD7648 shows robust combination activity in pre-clinical ATM-knockout mouse xenograft models. To understand the combination activity of AZD7648 and olaparib, we developed a semi-mechanistic pharmacokinetic/pharmacodynamic (PK-PD) model that incorporates the mechanism of action for each drug which links to proliferating, quiescent, and dying cell states with an additional Allee effect-like term to account for the non-linear growth and regression observed at low cell densities. Model parameters were fitted to training data sets that contained continuous treatment of either monotherapy or the combination. The observed interaction of AZD7648 on olaparib PK was incorporated in the PK-PD model by an effect function specific for each of the drug's MoA and was found essential to quantify drug effects at high dose levels of combination treatments. The model was able to adequately describe the observed efficacy for both monotherapy and sustained regressions in combination groups, mainly driven by maintaining a > 2:1 AUC ratio of apoptotic:proliferating cell fractions. We found that this model was suitable for forecasting intermittent dosing schedules a priori and resulted in accurate predictions when compared to xenograft efficacy data, without the need for extra, descriptive terms to describe supra-additive effects under combined dose regimes. This model provides quantitative understanding on the combination effect of AZD7648 and olaparib and allows for the exploration of the full exposure landscape without the need to experimentally test all scenarios. Furthermore, the model can be utilized to assess what exposures would be necessary in the clinic by linking it to observed or predicted human PK exposures. The model suggests 64.9 uM olaparib is sufficient to achieve tumor stasis in the absence of AZD7648, while the combination of AZD7648 and olaparib only requires plasma concentrations of 20.2 uM AZD7648 and 19.9 uM olaparib at steady-state to achieve the same effect.
Reliability of in vitro data for the mechanistic prediction of brain extracellular fluid pharmacokinetics of P-glycoprotein substrates in vivo; are we scaling correctly?
van Valkengoed DW, Hirasawa M, Rottschäfer V … +1 more , de Lange ECM
J Pharmacokinet Pharmacodyn · 2025 Feb · PMID 39921770 · Full text
Plasma pharmacokinetic (PK) profiles often do not resemble the PK within the central nervous system (CNS) because of blood-brain-border (BBB) processes, like active efflux by P-glycoprotein (P-gp). Methods to predict CNS...
Plasma pharmacokinetic (PK) profiles often do not resemble the PK within the central nervous system (CNS) because of blood-brain-border (BBB) processes, like active efflux by P-glycoprotein (P-gp). Methods to predict CNS-PK are therefore desired. Here we investigate whether in vitro apparent permeability (P) and corrected efflux ratio (ER) extracted from literature can be repurposed as input for the LeiCNS-PK3.4 physiologically-based PK model to confidently predict rat brain extracellular fluid (ECF) PK of P-gp substrates. Literature values of in vitro Caco-2, LLC-PK1-mdr1a/MDR1, and MDCKII-MDR1 cell line transport data were used to calculate P-gp efflux clearance (CL). Subsequently, CL was scaled from in vitro to in vivo through a relative expression factor (REF) based on P-gp expression differences. BrainECF PK was predicted well (within twofold error of the observed data) for 2 out of 4 P-gp substrates after short infusions and 3 out of 4 P-gp substrates after continuous infusions. Variability of in vitro parameters impacted both predicted rate and extent of drug distribution, reducing model applicability. Notably, use of transport data and in vitro P-gp expression obtained from a single study did not guarantee an accurate prediction; it often resulted in worse predictions than when using in vitro expression values reported by other labs. Overall, LeiCNS-PK3.4 shows promise in predicting brainECF PK, but this study highlights that the in vitro to in vivo translation is not yet robust. We conclude that more information is needed about context and drug dependency of in vitro data for robust brainECF PK predictions.
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