Journal of Pharmacokinetics and Pharmacodynamics最新文献

{"title":"The dawn of a new era: can machine learning and large language models reshape QSP modeling?","authors":"Ioannis P Androulakis, Lourdes Cucurull-Sanchez, Anna Kondic, Krina Mehta, Cesar Pichardo, Meghan Pryor, Marissa Renardy","doi":"10.1007/s10928-025-09984-5","DOIUrl":"10.1007/s10928-025-09984-5","url":null,"abstract":"<p><p>Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies, enhance decision-making, and optimize therapeutic strategies. By modeling biological systems and drug interactions, QSP enables predictions of outcomes, optimization of dosing regimens, and personalized medicine applications. Recent advancements in artificial intelligence (AI) and machine learning (ML) hold the potential to significantly transform QSP by enabling enhanced data extraction, fostering the development of hybrid mechanistic ML models, and supporting the introduction of surrogate models and digital twins. This manuscript explores the transformative role of AI and ML in reshaping QSP modeling workflows. AI/ML tools now enable automated literature mining, the generation of dynamic models from data, and the creation of hybrid frameworks that blend mechanistic insights with data-driven approaches. Large Language Models (LLMs) further revolutionize the field by transitioning AI/ML from merely a tool to becoming an active partner in QSP modeling. By facilitating interdisciplinary collaboration, lowering barriers to entry, and democratizing QSP workflows, LLMs empower researchers without deep coding expertise to engage in complex modeling tasks. Additionally, the integration of Artificial General Intelligence (AGI) holds the potential to autonomously propose, refine, and validate models, further accelerating innovation across multiscale biological processes. Key challenges remain in integrating AI/ML into QSP workflows, particularly in ensuring rigorous validation pipelines, addressing ethical considerations, and establishing robust regulatory frameworks to address the reliability and reproducibility of AI-assisted models. Moreover, the complexity of multiscale biological integration, effective data management, and fostering interdisciplinary collaboration present ongoing hurdles. Despite these challenges, the potential of AI/ML to enhance hybrid model development, improve model interpretability, and democratize QSP modeling offers an exciting opportunity to revolutionize drug development and therapeutic innovation. This work highlights a pathway toward a transformative era for QSP, leveraging advancements in AI and ML to address these challenges and drive innovation in the field.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 4","pages":"36"},"PeriodicalIF":2.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12170689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-species translational modelling of targeted therapeutic oligonucleotides using physiologically based pharmacokinetics.","authors":"Abdallah Derbalah, Felix Stader, Cong Liu, Adriana Zyla, Tariq Abdulla, Qier Wu, Masoud Jamei, Ian Gardner, Armin Sepp","doi":"10.1007/s10928-025-09980-9","DOIUrl":"10.1007/s10928-025-09980-9","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 4","pages":"35"},"PeriodicalIF":2.2,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144285072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging large language models in pharmacometrics: evaluation of NONMEM output interpretation and simulation capabilities.","authors":"Hwa Jun Cha, Kyuyeon Choe, Euibeom Shin, Murali Ramanathan, Sungpil Han","doi":"10.1007/s10928-025-09982-7","DOIUrl":"https://doi.org/10.1007/s10928-025-09982-7","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 3","pages":"34"},"PeriodicalIF":2.2,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144216165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interplay between pharmacokinetics and immunogenicity of therapeutic proteins: stepwise development of a bidirectional joint pharmacokinetics-anti-drug antibodies model.","authors":"Jan-Stefan van der Walt, Justin Wilkins, Akash Khandelwal, Karthik Venkatakrishnan, Wei Gao, Ana-Marija Milenković-Grišić","doi":"10.1007/s10928-025-09971-w","DOIUrl":"10.1007/s10928-025-09971-w","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 3","pages":"33"},"PeriodicalIF":2.2,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12095442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of 20 years of progress in nonclinical QT evaluation and proarrhythmic assessment.","authors":"Eric Delpy, Anne-Marie Bétat, Annie Delaunois, Christophe Drieu la Rochelle, Eric Martel, Jean-Pierre Valentin","doi":"10.1007/s10928-025-09979-2","DOIUrl":"https://doi.org/10.1007/s10928-025-09979-2","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 3","pages":"32"},"PeriodicalIF":2.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144086429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the linear model in concentration-QT analysis.","authors":"Géraldine Cellière, Andreas Krause, Guillaume Bonnefois, Jonathan Chauvin","doi":"10.1007/s10928-025-09975-6","DOIUrl":"https://doi.org/10.1007/s10928-025-09975-6","url":null,"abstract":"<p><p>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_max, 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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 3","pages":"31"},"PeriodicalIF":2.2,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065759/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Informatics for toxicokinetics.","authors":"Gilberto Padilla Mercado, Christopher Cook, Norman Adkins, Lucas Albrecht, Grace Cary, Brenda Edwards, Derik E Haggard, Nancy M Hanley, Michael F Hughes, Anna Jarnagin, Tirumala D Kodavanti, Evgenia Korol-Bexell, Anna Kreutz, Mayla Ngo, Caitlyn Patullo, Evelyn G Rowan, L McKenna Huse, Veronica A Correa, Branislav Kesic, Will Casey, Jennat Aboabdo, Kaitlyn Wolf, Risa Sayre, Bhaskar Sharma, Jonathan T Wall, Hiroshi Yamazaki, John F Wambaugh, Caroline L Ring","doi":"10.1007/s10928-025-09977-4","DOIUrl":"https://doi.org/10.1007/s10928-025-09977-4","url":null,"abstract":"<p><p>Toxicokinetic and pharmacokinetic (PK) summary parameters, such as C_max (peak concentration), AUC (time-integrated area under the plasma concentration curve), and t_1/2 (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_d) and t_1/2. 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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 3","pages":"30"},"PeriodicalIF":2.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143998533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging large language models to compare perspectives on integrating QSP and AI/ML.","authors":"Ioannis P Androulakis, Limei Cheng, Carolyn R Cho, Tongli Zhang","doi":"10.1007/s10928-025-09976-5","DOIUrl":"https://doi.org/10.1007/s10928-025-09976-5","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 3","pages":"29"},"PeriodicalIF":2.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic pharmacodynamics of a heterogeneous tumour-cell population.","authors":"Van Thuy Truong, Paolo Vicini, James Yates, Vincent Dubois, Grant Lythe","doi":"10.1007/s10928-025-09974-7","DOIUrl":"https://doi.org/10.1007/s10928-025-09974-7","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 3","pages":"28"},"PeriodicalIF":2.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A translational physiologically-based pharmacokinetic model for MMAE-based antibody-drug conjugates.","authors":"Hsuan-Ping Chang, Dhaval K Shah","doi":"10.1007/s10928-025-09978-3","DOIUrl":"https://doi.org/10.1007/s10928-025-09978-3","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 3","pages":"27"},"PeriodicalIF":2.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144026453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}