CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Bayesian Workflow for Minimal PBPK Models: Case Study of Dapagliflozin","authors":"Anna Mikhailova,&nbsp;Kirill Peskov,&nbsp;Gabriel Helmlinger,&nbsp;Victor Sokolov","doi":"10.1002/psp4.70307","DOIUrl":"10.1002/psp4.70307","url":null,"abstract":"<p>Numerous clinical trials (CTs) and population pharmacokinetic (PK) models have been published on dapagliflozin, an approved SGLT2 inhibitor used to treat type 2 diabetes, heart failure, and chronic kidney disease. This study proposes a Bayesian workflow for the development of minimal physiologically-based PK models (mPBPK) that integrates all available PK information, to support uncertainty quantification and informed drug development. First, a systematic collection of published dapagliflozin PK data and models was performed. A mPBPK model was then developed, and posterior parameter distributions were obtained based on data from parallel-design CTs. Three Bayesian modeling packages: <i>NIMBLE</i> (v1.1.0), <i>MCSim</i> (v6.2.0), and <i>Torsten</i> (v0.89.0), and three alternative prior specifications were evaluated. Model validation was performed using PK data from crossover CTs and urinary recovery data, followed by sensitivity analysis. 18 studies reporting PK data and 10 reporting PK models were identified. Posterior distributions were comparable across programs: 95% credible intervals overlapped. <i>Torsten</i> showed superior sampling efficiency, as compared to <i>NIMBLE</i> and <i>MCSim</i> (5.7 and 10.0 times higher in tails and central posterior regions, respectively); however, the average effective sample size per hour was comparable for <i>Torsten</i> and <i>MCSim</i>. The predicted urinary recovery was 2.2%–4.4% (mean 3.2%), while the observed values were in the 0.8%–4.0% range (mean 2.0%). Glomerular filtration rate and fraction unbound were the main contributors to inter-trial variability in urinary recovery, while volume of distribution and clearance had the highest influence on maximum concentration and area-under-the-concentration curve, respectively. The proposed Bayesian workflow is flexible and transferable to other mechanistic model types.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13429805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148663575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Review of Published Physiologically Based Pharmacokinetic Models for Drug Excretion Into Human Breastmilk: Knowledge Gaps and Opportunities to Optimize Reporting and Modeling Practices.","authors":"Joshua Kiptoo, Davies Otieno, Kenta Yoshida, Neel Deferm, Aida Nakayiwa Kawuma, Francis Williams Ojara, Adeniyi Olagunju, Jackson K Mukonzo, Catriona Waitt","doi":"10.1002/psp4.70321","DOIUrl":"10.1002/psp4.70321","url":null,"abstract":"<p><p>Physiologically based pharmacokinetic (PBPK) modeling is increasingly used to predict infant exposure to medications via breast milk. Existing guidance on reporting of PBPK modeling analyses is unstructured and nonbinding to researchers. We evaluated reporting practices and modeling approaches in published PBPK modeling studies. A systematic search was conducted across three online databases. Studies published through March 2025 that leveraged PBPK to evaluate milk drug excretion and infant exposure were included. We developed a 22-item structured data abstraction tool-Lactation PBPK Model Reporting Checklist (LAC-PBPK). Study findings were reported using descriptive and narrative analyses. Twenty-six published studies modeling 54 medications met the inclusion criteria. Using the data abstraction tool, only 4 (18%) of reporting domains were reported in all studies. These include the drug modeled, choice of mammary distribution model, context of PBPK modeling (model purpose), and the modeling platform. Most studies (88.4%) implemented whole-body PBPK model structure, with one-half (53%) assuming perfusion-rate-limited milk excretion. Assumptions reflect modeling limitations, primarily due to inadequate reporting in clinical lactation studies and knowledge gaps in infant and mammary physiology. PBPK models can support mechanistic assessment of drug excretion into breast milk, but confidence in model outputs depends on intended use, data availability, and transparent reporting. This highlights the need for a prioritized, fit-for-purpose lactation PBPK reporting framework. From a regulatory perspective, transparent reporting and cautious selection of approaches for estimating infant milk intake, model performance evaluation, physiological extrapolation, and parameter uncertainty analyses are essential to ensure reproducibility and confidence in PBPK-informed infant exposure assessment.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":"e70321"},"PeriodicalIF":2.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13453128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systems Pharmacology Model of Aging Identifies Optimal Combination Therapies With Secondary Benefits on Weight Loss and Metabolic Health.","authors":"Igor Goryanin, Bob Damms, Irina Goryanin","doi":"10.1002/psp4.70322","DOIUrl":"10.1002/psp4.70322","url":null,"abstract":"<p><p>Aging is a systems-level process linking metabolic dysfunction, inflammation, impaired repair, frailty, and multimorbidity, whereas existing pharmacological strategies usually optimize disease-specific endpoints such as weight loss or HbA1c rather than aging-related trajectories. We developed an SBML-compliant quantitative systems pharmacology (QSP) model in which aging is represented as a dynamic, pharmacologically modifiable endpoint. The model integrates four coupled layers: metabolic/pharmacodynamic responses to GLP-1 receptor agonism, SGLT2 inhibition, metformin and rapamycin; adverse-event dynamics; aging states including damage accumulation, repair capacity, frailty and biological age gap; and biomarker outputs including GDF15, cystatin C, leptin, adiponectin and estimated glucose disposal rate. The semaglutide submodel was calibrated against published STEP trial endpoints, and Bayesian hierarchical meta-analysis, global sensitivity analysis, practical identifiability analysis and internal consistency checks were used to assess model behavior. The calibrated model reproduced semaglutide-associated weight loss, HbA1c reduction and transient nausea within pre-specified error benchmarks. Bayesian meta-analysis confirmed strong metabolic effects for semaglutide, moderate glycaemic effects for SGLT2 inhibitors and metformin, and a near-zero HbA1c effect for rapamycin. Sensitivity analysis revealed largely orthogonal metabolic and aging parameter spaces. Combination simulations identified two mechanistically distinct optima: GLP-1 receptor agonist plus SGLT2 inhibitor plus metformin for metabolic improvement, and GLP-1 receptor agonist plus SGLT2 inhibitor plus rapamycin for aging-related benefit. Metabolic optimisation and aging optimisation are therefore mechanistically distinct objectives that do not converge on the same drug combination. These predictions are hypothesis-generating and require external validation against independent longitudinal datasets and clinical safety evaluation before translation to treatment recommendations.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":"e70322"},"PeriodicalIF":2.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13454951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148700461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Treatment Response in Tuberculosis Early Bactericidal Activity Trials.","authors":"Mairi C W McClean, Simon E Koele, Julia Dreisbach, Susanne Mirold-Mei, Fred Njeleka, Daniel Mapamba, Bariki Mtafya, Patrick P J Phillips, Veronique R De Jager, Rodney Dawson, Kim Narunsky, Andreas H Diacon, Elin M Svensson, Norbert Heinrich, Francesco Paolo Casale, Michael Hoelscher","doi":"10.1002/psp4.70311","DOIUrl":"10.1002/psp4.70311","url":null,"abstract":"<p><p>Culture-based monitoring of bacterial load is slow and susceptible to missing data, contributing to the length and cost of TB clinical trials. Non-culture-based alternatives, like the Tuberculosis Molecular Load Bacterial Assay (TB-MBLA), could represent a solution. Our objectives were to evaluate TB-MBLA as a biomarker in early bactericidal activity (EBA) studies and explore whether combining biomarkers with joint modeling could provide insight into underlying biological processes. We generated TB-MBLA (LifeArc) data from sputum samples from all 78 patients from the PanACEA BTZ-043 Phase Ib/IIa trial and derived a summary measure of the joint distribution of the three TB-MBLA, colony forming units (CFU), and time-to-positivity (TTP) biomarkers as the first principal component derived from a probabilistic principal component analysis (pPCA). With TB-MBLA marker and the principal component 1 (PC1) values, we reevaluated the original stage IIa dose-response and stages Ib/IIa pharmacokinetics-pharmacodynamics (PK-PD) exposure-response analyses, applying linear and non-linear mixed models, respectively. For TB-MBLA, we could not detect an exposure-response effect in the PK-PD analysis, in contrast with CFU and TTP. When combining biomarkers, we observed a significant but less pronounced E_max exposure-response between days 0-3 compared with CFU and TTP alone. We also successfully applied pPCA as a modeling framework and show evidence that combining CFU and TTP in a joint latent component can improve detection of treatment effects compared with either biomarker alone. In this study, we present novel EBA data for the first-in-class antimycobacterial compound BTZ-043 and contextualize the value of emerging bacteriological markers within the EBA trial framework. Trial Registration: ClinicalTrials.gov identifier: NCT04044001.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":"e70311"},"PeriodicalIF":2.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148688529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration","authors":"Joel Eliason,&nbsp;Aleksander S. Popel","doi":"10.1002/psp4.70306","DOIUrl":"10.1002/psp4.70306","url":null,"abstract":"<p>Quantitative systems pharmacology (QSP) models require calibration data from literature, yet manual curation is inconsistently documented and large language model (LLM) extraction can hallucinate values and fabricate citations. We present MAPLE (Model-Aware Parameterization from Literature Evidence), which uses structured validation schemas as a collaboration interface between LLMs and modelers. Two schemas span two scales: the SubmodelTarget schema for isolated experiments constraining individual parameters, and the CalibrationTarget schema for clinical and in vivo endpoints constraining the full model. Both separate data extraction from modeling decisions, recording every value with full provenance. Targeted validators catch characteristic LLM errors by matching values to source snippets, resolving DOIs, and executing code. For a pancreatic ductal adenocarcinoma QSP model, we used MAPLE to extract and curate 37 SubmodelTargets and 45 CalibrationTargets. Before any human review, the validators triggered 50 automated retries; every value carries a direct quote from its source and a verified citation; and 11 of 19 parameters are supported by more than one independent source. The LLM drafted usable forward models and code from context, while the modeler supplied the context and scientific judgment it cannot infer, revising forward-model choices in 65% of SubmodelTargets, priors in 46%, and source relevance in all files. This evaluation covers one model in one disease area, by a single group, so it characterizes the framework rather than establishing how broadly it generalizes. MAPLE records the modeler's reasoning in a form that can be re-run and independently checked, so it is not lost when the modeling team changes.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148629746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Enables Rapid Prediction of Acid-Reducing Agent Drug Interactions: A Streamlined Complement to PBPK Modeling","authors":"Yuanfang Qin,&nbsp;Lehua Yu,&nbsp;Tao Chen,&nbsp;Yonghui Zuo,&nbsp;Hongyun Wang,&nbsp;Guoping Yang,&nbsp;Qi Pei","doi":"10.1002/psp4.70314","DOIUrl":"https://doi.org/10.1002/psp4.70314","url":null,"abstract":"<p>pH-dependent drug–drug interactions (DDIs) commonly occur when acid-reducing agents (ARAs) are co-administered with weakly basic drugs. Although physiologically based pharmacokinetic (PBPK) modeling effectively evaluates such DDIs, its use is limited by reliance on costly commercial software. This study developed a PBPK-informed machine learning model to support early assessment of pH-dependent DDI risk in drug development. PBPK models were built for 14 representative weakly basic drugs using literature-derived parameters to identify eight key determinants (e.g., solubility and pKa). Based on these distributions, virtual drugs were generated and simulated under varying gastric pH conditions; compounds with DDI AUC ratios &lt; 0.1 were excluded, yielding 4339 virtual drugs. An extreme gradient boosting (XGBoost) algorithm was used to develop the machine learning model, and an external validation set comprising clinically observed data from an additional eight drugs was employed. The XGBoost model showed excellent internal performance (training: <i>R</i>² = 1.00, MAPE = 0.99; test: <i>R</i>² = 0.98, MAPE = 2.64). When evaluated using an external validation set comprising clinically observed data from eight drugs, 100% of the predicted values fell within the 0.5–2.0-fold range of the observed clinical values. For DDI AUC risk classification, the XGBoost model achieved an accuracy of 87.5% (7/8). This PBPK-informed ML framework enables efficient screening of pH-dependent DDI risk for weakly basic drugs co-administered with ARAs. The freely accessible web tool (https://ddi-antacid.xy3yx.com/), integrating structure-based ADMETlab3.0 estimation, offers a practical complement to conventional PBPK modeling for early drug development.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148647688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing Virtual Clinical Trials to Inform Target Coverage That Drives RSV Antiviral Efficacy","authors":"J. Cody Herron,&nbsp;Kathryn G. Link,&nbsp;Natalie Meacham,&nbsp;Negar Niki Alami,&nbsp;Daniel W. Brookes,&nbsp;Elaine Thomas,&nbsp;Jan Adams,&nbsp;Sima S. Toussi,&nbsp;Ryan Franke,&nbsp;Cynthia J. Musante,&nbsp;Isabel Najera,&nbsp;Richard Allen,&nbsp;Britton Boras","doi":"10.1002/psp4.70308","DOIUrl":"https://doi.org/10.1002/psp4.70308","url":null,"abstract":"<p>Respiratory syncytial virus (RSV) infection can result in a range of disease severity from mild upper respiratory tract symptoms to lower respiratory tract illness, with clinical manifestations dependent on age, co-morbidities, and immune status. At present, there is no antiviral approved for adults with RSV disease, and only one treatment option with limited utilization is available for pediatric patients. While several RSV antiviral candidates have demonstrated virological efficacy in healthy adult challenge studies, to date they have subsequently failed to show efficacy in patients. Mechanistically understanding the translation of efficacy from preclinical to challenge to patient studies is essential in addressing this unmet medical need in children and adults with RSV disease. This work uses a quantitative systems pharmacology (QSP) approach to mathematically represent the underlying pathophysiology of RSV infection to simulate and predict the effects of investigational direct acting RSV antiviral therapeutics. We developed virtual populations that capture viral load dynamics in viral challenge studies, where the participants are healthy adult volunteers, upon treatment with the F-protein inhibitor sisunatovir and the N-protein inhibitor zelicapavir. Due to underlying differences in dynamics between key populations, we also developed a pediatric patient virtual population to match the results from the zelicapavir pediatric Phase 2 study. This work projects that for a minimum coverage &gt; 1× free EC₉₀ at C_min, a treatment window of 5 to 7 days post-symptom onset is feasible for observing virological efficacy in pediatric patients and may lead to potential success in a phase 3 pivotal RSV antiviral drug trial.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148647736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refinement of Operating Characteristics for Model-Based TGI Metrics Decision Support to Ungate a Pivotal Trial in Oncology","authors":"Mathilde Marchand,&nbsp;Kenta Yoshida,&nbsp;Antonio Gonçalves,&nbsp;Rene Bruno,&nbsp;Chunze Li,&nbsp;Jin Y. Jin,&nbsp;Pascal Chanu","doi":"10.1002/psp4.70304","DOIUrl":"https://doi.org/10.1002/psp4.70304","url":null,"abstract":"<p>The oncology market is evolving, with an increasing number of therapeutic options making the inference of treatment benefit from early development critical. The “Go” versus “No Go” decisions from such trials usually rely on probabilities of achieving meaningful improvements (effect sizes) in endpoints like objective response rate, tailored to products' profile, supporting faster transitions to confirmatory trials. Operating characteristics are sensitive to methodological details, important for establishing an early decision framework for ungating a pivotal clinical trial.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex-Related Differences in Physiologically-Based Biopharmaceutics Modeling","authors":"Marianela Chavarría-Rojas,&nbsp;Mub Murshed,&nbsp;Marianela Lorier,&nbsp;Nikoletta Fotaki,&nbsp;Manuel Ibarra","doi":"10.1002/psp4.70310","DOIUrl":"https://doi.org/10.1002/psp4.70310","url":null,"abstract":"<p>Physiologically based pharmacokinetic (PBPK) and physiologically-based biopharmaceutics (PBBM) modeling are valuable tools in drug development, allowing mechanistic predictions of drug absorption and disposition. However, sex-related differences in gastrointestinal physiology are often underrepresented in virtual populations, potentially limiting prediction accuracy. This study aimed to evaluate how sex-related physiological differences are incorporated into commonly used PBPK platforms and to illustrate their impact on pharmacokinetic predictions using ketoprofen as a case study. Three PBPK platforms were systematically reviewed to assess predefined sex-specific gastrointestinal parameters. All three platforms incorporated sex-related differences in general anatomy and physiology but overlooked sex-specific variability in gastrointestinal tract parameters. In addition, three PBBM models of ketoprofen were developed and verified in males and subsequently extrapolated to females using default and refined sex-specific parameters. Under default female settings, the models overpredicted Cmax and underestimated Tmax, resulting in concentration–time profiles that were nearly indistinguishable from those of males. Refining gastrointestinal tract parameters for females population improved prediction performance and better reflected observed sex differences. These findings indicate that current PBPK platforms may require user-defined adjustments to adequately represent sex-specific gastrointestinal physiology and that incorporating such parameters could lead to more representative PBBM applications.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-Marketing Requirements for Anticancer Drugs Approved in Japan, 2001–2024: A Cross-Sectional Analysis","authors":"Aina Tsuno,&nbsp;Hiroe Kitagaki,&nbsp;Hideki Maeda","doi":"10.1002/psp4.70309","DOIUrl":"10.1002/psp4.70309","url":null,"abstract":"<p>The accelerated development of innovative anticancer therapies has led to the early approval of an increasing number of drugs. However, clinical evidence available at the time of approval is often limited, necessitating additional post-marketing studies to supplement safety and efficacy data. Therefore, post-marketing requirements (PMRs) have gained greater regulatory importance. Nevertheless, the current landscape of PMRs for anticancer drugs in Japan has not been clearly characterized. In this study, we aimed to characterize the status, content, and trends of PMRs for anticancer drugs approved in Japan. We reviewed publicly available regulatory documents for anticancer drugs approved in Japan between 2001 and 2024 and extracted data on background characteristics and PMRs. Analysis of 471 anticancer agents identified 328 (69.6%) drugs requiring at least one PMR. The temporal trend analysis showed a consistent increase in the number of drugs requiring PMRs after 2011. Among 453 PMR assignments, the development and implementation of a risk management plan (RMP) was the most frequent PMR type (<i>n</i> = 254, 56.1%), followed by post-marketing all-case surveillance (<i>n</i> = 147, 32.5%). Twelve drugs (2.6%) required post-marketing clinical trials, among which one (0.2%) involved dose optimization. In conclusion, our findings show that PMRs for anticancer drugs in Japan are predominantly focused on safety monitoring, and that only one PMR was related to dose optimization. These findings suggest that further consideration may be needed in Japan to strengthen post-approval evidence generation for dose setting and to support more evidence-based dose selection.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148599737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}