ArticleCPT: pharmacometrics & systems pharmacology2025
An integrated quantitative systems pharmacology virtual population approach for calibration with oncology efficacy endpoints.
Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- 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
- Quantitative Systems Pharmacology (QSP): Bridging Biology and Mechanism with Clinical Drug Development Decisions.Journal of clinical pharmacology · 2026Review
- Utilizing Virtual Clinical Trials to Inform Target Coverage That Drives RSV Antiviral Efficacy.CPT: pharmacometrics & systems pharmacology · 2026Article
- A Nonparametric Approach to Practical Identifiability of Nonlinear Mixed Effects Models.Bulletin of mathematical biology · 2026Article
- Trajectory-matching ABC-MCMC for simulating heterogeneous dynamics in mechanistic models.bioRxiv : the preprint server for biology · 2025Article
- Leveraging quantitative systems pharmacology modeling for elranatamab regimen optimization in relapsed or refractory multiple myeloma.NPJ systems biology and applications · 2025Article
- An integrated quantitative systems pharmacology virtual population approach for calibration with oncology efficacy endpoints.CPT: pharmacometrics & systems pharmacology · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In drug development, quantitative systems pharmacology (QSP) models are becoming an increasingly important mathematical tool for understanding response variability and for generating predictions to inform development decisions. Virtual populations are essential for sampling uncertainty and potential variability in QSP model predictions, but many clinical efficacy endpoints can be difficult to capture with QSP models that typically rely on mechanistic biomarkers. In oncology, challenges are particularly significant when connecting tumor size with time-to-event endpoints like progression-free survival while also accounting for censoring due to consent withdrawal, loss in follow-up, or safety criteria. Here, we expand on our prior work and propose an extended virtual population selection algorithm that can jointly match tumor burden dynamics and progression-free survival times in the presence of censoring. We illustrate the core components of our algorithm through simulation and calibration of a signaling pathway model that was fitted to clinical data for a small molecule targeted inhibitor. This methodology provides an approach that can be tailored to other virtual population simulations aiming to match survival endpoints for solid-tumor clinical datasets.
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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.