ReviewThe AAPS journal2025
Physiologically Based Pharmacokinetic Modeling and Simulation in Regulatory Review: US FDA CBER Experience and Perspectives.
Review in The AAPS journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- From Prediction to Decision Making: PBPK and QSP as Regulatory-Grade NAMs.Clinical pharmacology and therapeutics · 2026Review
- New approach methodologies (NAMs) for preclinical and translational evaluation of mRNA-lipid nanoparticle (LNP) therapeutics.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Decoding Nonlinearities in AAV-Based Gene Therapy Using PBPK Modelling.The AAPS journal · 2026Article
- Industry Perspective on Translational and Clinical Pharmacology Aspects of Viral-based Gene Therapies and Vaccines-Key Considerations and Learnings From Approved Products.The AAPS journal · 2026Review
- From Small Data to Big Decisions: How Clinical Pharmacology Shapes Rare Disease Development.Journal of clinical pharmacology · 2026Review
- Best Practices in Physiologically Based Pharmacokinetic (PBPK) Modeling.CPT: pharmacometrics & systems pharmacology · 2026Review
- Physiologically based pharmacokinetic modeling of granisetron for optimizing antiemetic therapy in patients with cancer: multi-route pharmacokinetic characterization and target-site exposure prediction.Cancer chemotherapy and pharmacology · 2026Article
- Introduction to Single-cell Physiologically-Based Pharmacokinetic (scPBPK) Models.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Physiologically based pharmacokinetic (PBPK) modeling has emerged as a valuable tool in model-informed drug development (MIDD). This approach enables the integration of diverse experimental data to predict pharmacokinetics (PK) and dosing regimens and facilitates understanding of mechanism of action (MoA) and pharmacodynamics (PD). In this article we provide a landscape analysis of PBPK submissions at the U.S. Food and Drug Administration, Center for Biologics Evaluation and Research (CBER). We summarize CBER's experience on PBPK modeling and simulation (M&S) for therapeutic proteins, cell and gene therapy products. We discuss specific case studies that illustrate the use of PBPK for dose selection of therapeutic proteins, highlight recent progress and provide our perspectives on potential application of PBPK for adeno-associated virus (AAV)-based gene therapies and messenger RNA (mRNA) therapeutics. For cell and gene therapy products, PBPK M&S is emerging as MIDD approaches to support clinical trial design, dose selection, predicting PK/PD, and facilitate quantitative understanding of safety and efficacy. As the field continues to evolve, PBPK modeling is well positioned to provide supportive evidence to facilitate the development of safe and effective biological products.
Indexed as
Identifiers
What OpenQuestion holds
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.