ArticleCPT: pharmacometrics & systems pharmacology2023
Using machine learning surrogate modeling for faster QSP VP cohort generation.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.Computer methods in applied mechanics and engineering · 2026Article
- A Practical Tutorial on Physics-Informed Networks for Pharmacometrics and Quantitative Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2026Article
- Digital twins to accelerate target identification and drug development for immune-mediated disorders.FEBS open bio · 2026Review
- Tackling High Dimensionality in QSP: Guiding Model Order Reduction With Index Analysis.CPT: pharmacometrics & systems pharmacology · 2026Article
- 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
- Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.Journal of mathematical biology · 2025Review
- A Proxy-guided Workflow for Virtual Population Development.The AAPS journal · 2025Article
- The dawn of a new era: can machine learning and large language models reshape QSP modeling?Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model.CPT: pharmacometrics & systems pharmacology · 2025Article
- Virtual patients inspired by multiomics predict the efficacy of an anti-IFNα mAb in cutaneous lupus.iScience · 2025Article
- Is Ockham's razor losing its edge? New perspectives on the principle of model parsimony.Proceedings of the National Academy of Sciences of the United States of America · 2025Review
- Future Directions for Quantitative Systems Pharmacology.Handbook of experimental pharmacology · 2025Review
- The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases.Pharmaceutics · 2024Review
- Quantitative systems pharmacology in the age of artificial intelligence.CPT: pharmacometrics & systems pharmacology · 2023Article
- Using machine learning surrogate modeling for faster QSP VP cohort generation.CPT: pharmacometrics & systems pharmacology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Virtual patients (VPs) are widely used within quantitative systems pharmacology (QSP) modeling to explore the impact of variability and uncertainty on clinical responses. In one method of generating VPs, parameters are sampled randomly from a distribution, and possible VPs are accepted or rejected based on constraints on model output behavior. This approach works but can be inefficient (i.e., the vast majority of model runs typically do not result in valid VPs). Machine learning surrogate models offer an opportunity to improve the efficiency of VP creation significantly. In this approach, surrogate models are trained using the full QSP model and subsequently used to rapidly pre-screen for parameter combinations that result in feasible VPs. The overwhelming majority of parameter combinations pre-vetted using the surrogate models result in valid VPs when tested in the original QSP model. This tutorial presents this novel workflow and demonstrates how a surrogate model software application can be used to select and optimize the surrogate models in a case study. We then discuss the relative efficiency of the methods and scalability of the proposed method.
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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 Socratic graph.