ArticleCPT: pharmacometrics & systems pharmacology2016
Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models.
Article in CPT: pharmacometrics & systems pharmacology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 1 of them a synthesis that pooled it.
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Who cites it
107 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Population Pharmacodynamic Dose-Response Analysis of Serum Potassium Following Dosing with Sodium Zirconium Cyclosilicate.Clinical pharmacokinetics · 2024Trial
- Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model.Journal for immunotherapy of cancer · 2022Trial
- Innovative Randomized Phase I Study and Dosing Regimen Selection to Accelerate and Inform Pivotal COVID-19 Trial of Nirmatrelvir.Clinical pharmacology and therapeutics · 2022Trial
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- 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
- Bayesian Workflow for Minimal PBPK Models: Case Study of Dapagliflozin.CPT: pharmacometrics & systems pharmacology · 2026Article
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- Advancing IBD Management: A Literature Review on the Role of Non-Invasive Blood-Based Biomarkers in Predicting and Assessing Pharmacodynamic Response to Treatment.Clinical and translational science · 2026Review
- The Role of Estradiol in Oxidative Stress in Health and Cystathionine ß-Synthase Deficiency: A Mechanistic Model.CPT: pharmacometrics & systems pharmacology · 2026Article
- Utilization of Model-Informed Drug Development in Early Clinical Development: Case Study of a Hb Modulator for Treatment of Sickle Cell Disease.CPT: pharmacometrics & systems pharmacology · 2026Article
- A virtual cohort framework with applications to adoptive cell therapy in bladder cancer.bioRxiv : the preprint server for biology · 2026Article
- A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment.Frontiers in oncology · 2026Review
- Identification of predictive biomarkers and dose optimization for camrelizumab combined with apatinib in the treatment of advanced hepatocellular carcinoma: a quantitative systems pharmacology approach.Frontiers in immunology · 2026Article
- Identification and characterization of virtual sub-populations through phenotype-guided filtering. The challenging case of nonidentifiable models in the context of therapeutic evaluation.Journal of pharmacokinetics and pharmacodynamics · 2025Article
- A Step-by-Step Workflow for Performing In Silico Clinical Trials With Nonlinear Mixed Effects Models.CPT: pharmacometrics & systems pharmacology · 2025Article
- Trajectory-matching ABC-MCMC for simulating heterogeneous dynamics in mechanistic models.bioRxiv : the preprint server for biology · 2025Article
47 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed "virtual patients." In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations.
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