ArticleCPT: pharmacometrics & systems pharmacology2023
Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA).
Article in CPT: pharmacometrics & systems pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 5 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Model-based meta-analysis of individual patient data for the characterization of intravenous 5-fluorouracil population pharmacokinetics.Cancer chemotherapy and pharmacology · 2026Pooled it
- A Meta-Analysis of International Flunixin Pharmacokinetics in Horses: Toward Regulatory Harmonization and Individualized Detection Times Using Bayesian Paradigm.Drug testing and analysis · 2026Pooled it
- IPDMA - RADICAL - NSCLC: individual participant data meta-analysis and systematic review of radical local therapy for oligometastatic NSCLC.BMJ open respiratory research · 2025Pooled it
- Pooled it
- Penetration of Antibiotics into Subcutaneous and Intramuscular Interstitial Fluid: A Meta-Analysis of Microdialysis Studies in Adults.Clinical pharmacokinetics · 2024Pooled it
- Construction of a curated human pharmacokinetics database for molecular fragment analysis and machine learning applications.Drug metabolism and disposition: the biological fate of chemicals · 2026Article
- From review to synthesis: A step-by-step methodological guide to systematic reviews and multilevel meta-analyses.Behavior research methods · 2026Article
- PKRxiv: A Best Practice Model for Advancing Pharmacoequity Through Open Pharmacokinetic Data Sharing.Clinical pharmacology and therapeutics · 2026Article
- A fluconazole population pharmacokinetics study to improve target attainment in critically ill patients.Infection · 2026Article
- Pharmacometric-Based Evaluation of Salmeterol and Its Metabolite α-Hydroxysalmeterol in Plasma and Urine: Practical Implications for Doping Control.CPT: pharmacometrics & systems pharmacology · 2026Article
- Deriving Dosages for Levofloxacin Tuberculosis Preventive Treatment for Young People Exposed to Rifampicin-Resistant Tuberculosis.The Journal of infectious diseases · 2025Article
- Why the racing industry and equestrian disciplines need to implement population pharmacokinetics: To learn, explain, summarize, harmonize, and individualize.Drug testing and analysis · 2025Review
- Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA).CPT: pharmacometrics & systems pharmacology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.