Personalization of Optimal Chemotherapy Dosing Based on Estimation of Uncertain Model Parameters Using Artificial Neural Network

Background/Objectives: The effectiveness of chemotherapy in cancer treatment is often compromised by inter-patient variability, leading to suboptimal outcomes. Traditional dosing protocols rely on population-based models that do not account for individual patient responses and the cancer phenotype. This study aims to develop a personalized chemotherapy dosing strategy by estimating uncertain model parameters using artificial neural networks, ensuring an optimal and individualized treatment approach. Methods: A dynamical model of tumor growth, immune response, and chemotherapy effects is used as the foundation for personalization. A training dataset is generated by simulating state responses across a diverse population of virtual patients, capturing inter-subject variability. The state responses are parameterized (approximated) using the sum of exponential functions to reduce dimensionality, and a multilayer perceptron artificial neural network is trained to estimate patient-specific model parameters based on response data from a single chemotherapy dose. Results: The proposed method effectively estimates patient-specific model parameters, significantly reducing uncertainty compared to conventional population-based models or the nonlinear least squares method. Numerical experiments demonstrate that personalized chemotherapy dosing, optimized using the estimated model parameters, achieves fast tumor remission while minimizing the total drug amount. Conclusions: By integrating the artificial neural network as the parameter estimator with model-based optimization, this study presents a novel approach to personalized chemotherapy dosing.