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[2509.12666] PBPK-iPINNs: Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models
Summary
The paper presents PBPK-iPINNs, a method combining inverse physics-informed neural networks with physiologically based pharmacokinetic modeling to enhance drug delivery predictions in the brain.
Why It Matters
This research is significant as it addresses the challenges of accurately estimating drug parameters in human brains, which is crucial for developing effective therapies for conditions like brain cancer. By integrating machine learning with pharmacokinetic modeling, it opens new avenues for personalized medicine.
Key Takeaways
- PBPK-iPINNs improve parameter estimation for drug delivery models.
- The method requires careful tuning of hyperparameters for optimal performance.
- Accurate drug concentration profiles can enhance therapy design for brain cancer.
Statistics > Machine Learning arXiv:2509.12666 (stat) [Submitted on 16 Sep 2025 (v1), last revised 22 Feb 2026 (this version, v3)] Title:PBPK-iPINNs: Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models Authors:Charuka D. Wickramasinghe, Krishanthi C. Weerasinghe, Pradeep K. Ranaweera, Nelum S.S.M. Hapuhinna View a PDF of the paper titled PBPK-iPINNs: Inverse Physics-Informed Neural Networks for Physiologically Based Pharmacokinetic Brain Models, by Charuka D. Wickramasinghe and 3 other authors View PDF HTML (experimental) Abstract:Physics-Informed Neural Networks (PINNs) integrate machine learning with differential equations to solve forward and inverse problems while ensuring that predictions adhere to physical laws. Physiologically based pharmacokinetic (PBPK) modeling advances beyond classical compartmental approaches by employing a mechanistic, physiology-focused framework. Such models involve many unknown parameters that are difficult to measure directly in humans due to ethical and practical constraints. PBPK models are constructed as systems of ordinary differential equations (ODEs) and these parametric ODEs are often stiff, and traditional numerical and statistical methods frequently fail to converge. In this study, we consider a permeability-limited, four-compartment PBPK brain model that mimics human brain functionality in drug delivery. We introduce PBPK-iPINN, a method for estimating drug-specific or patient-specific ...
