Data-Driven Modeling and Decision Support for Precision Oncology

Precision oncology shows where cancer therapy is heading: treatment plans tailored to a patient’s own genetic profile, targeting the specific alterations driving their disease.

We analyze patient-specific genetic patterns and match them against a large knowledge base of therapeutic and clinical trial data. We also look at how treatment regimens evolve over time as patient health markers and treatment responses change.

Research Scope

Our research examines advanced pharmacokinetic models and the integration of Reinforcement Learning (RL) techniques. We focus on two main areas: developing a data-driven Physiology-Based PharmacoKinetic (PBPK) model for individualized therapy predictions and exploring the application of RL for dynamic treatment optimization. This approach allows us to precisely model and forecast the pharmacokinetics of theranostics agents, leading to more effective, patient-specific treatment strategies.

Research Hypothesis

We hypothesize that pairing data-driven PBPK modeling with RL-based decision support improves the precision of cancer treatment. Personalizing therapy regimens and optimizing dosing should improve treatment outcomes and reduce toxicities. We test this with data analysis, model development, and clinical simulations.

Team Members

• Binesh Kumar
• Advisor: Dr. Vahid Behzadan

Publications

Sadanandan, B., Behzadan, V. (2025). “Promise of Data-Driven Modeling and Decision Support for Precision Oncology and Theranostics.” arXiv preprint arXiv:2505.09899.