ArticlePharmaceutical research2025
Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks.
Article in Pharmaceutical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Practical Tutorial on Physics-Informed Networks for Pharmacometrics and Quantitative Systems Pharmacology.CPT: pharmacometrics & systems pharmacology · 2026Article
- The architecture of computational antiviralism: a multi-scale framework from molecular targeting to viral ecosystem engineering.Molecular diversity · 2026Review
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- The future of mathematical oncology in the age of AI.NPJ systems biology and applications · 2026Review
- Learning Model Parameter Dynamics in a Combination Therapy for Bladder Cancer from Sparse Biological Data.ArXiv · 2025Article
- Modeling Adoptive Cell Therapy in Bladder Cancer from Sparse Biological Data using PINNs.ArXiv · 2025Article
- Article
- Drug and therapeutic intravaginal delivery targeting diseases in the female reproductive tract: A mathematical modeling perspective.Journal of controlled release : official journal of the Controlled Release Society · 2025Review
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- Estimating treatment sensitivity in synthetic and in vitro tumors using a random differential equation model.NPJ systems biology and applications · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveQuantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However, significant manual distillation of the literature is needed in order to construct a QSP model. Parameters may need to be fit, and simplifying assumptions of the model need to be made. In this work, we apply Universal Physics-Informed Neural Networks (UPINNs) to learn unknown components of various differential equations that model chemotherapy pharmacodynamics.
methodsUsing UPINNs, we learn three commonly employed chemotherapeutic drug actions (log-kill, Norton-Simon, and
resultsWe show that the UPINN can successfully learn the hidden terms and unknown parameters in a variety of differential equations (with differing time and variable scales) that model the effect of chemotherapeutics on cancer cells.
conclusionsAs the examples we study are only toy examples, we highlight the usefulness of UPINNs in learning unknown terms in pharmacodynamic and pharmacokinetic models. UPINNs can be used to find these terms and analyze them further to understand new chemotherapeutics and biological mechanisms that interact with them.
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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.