Optimizing antiviral therapy for COVID-19 with learned pathogenic model

Reparameterized multiobjective control of BCG immunotherapy

Bladder cancer is a cancerous disease that mainly affects elder men and women. The immunotherapy that uses Bacillus of Calmette and Guerin (BCG) effectively treats bladder cancer by stimulating the immune response of patients. The therapeutic performance of BCG relies on drug dosing, and the design of an optimal BCG regimen is an open question. In this study, we propose the reparameterized multiobjective control (RMC) approach for seeking an optimal drug dosing regimen and apply it to the design of BCG treatment. This approach utilizes constrained optimization based on a nonlinear bladder cancer model with impulsive drug instillation. We compare the performance of RMC with Koopman model predictive control (MPC) and validate the efficacy of optimal BCG dosing regimens through numerical simulations, demonstrating the efficient elimination of cancerous cells. The proposed control framework holds the potential for generalization to other model-based treatment designs.

Koopman-based Impulsive Model Predictive Control of BCG Immunotherapy

Bacillus of Calmette and Guerin (BCG) is the most effective immunologic treatment for non-muscle-invasive bladder cancer by stimulating the immune response of patients. The therapeutic performance of BCG treatment is limited by the dosing scheme, which is difficult to design due to nonlinear dynamics and constraints in the pharmacodynamic model. Here we present a computational method that combines linearization, impulsive control, and constrained optimization to design optimal drug dosing. We do so by first adopting Koopman theory to map the nonlinear pharmacodynamic model into linear space. Then we use model predictive control to design drug dosing schemes based on the transformed linear model with impulsive drug instillation, constrained by drug concentration. With this pipeline, we find that the Koopman-based linear system has almost identical dynamic behaviors to the original model based on numerical simulations. Also, the designed drug doses stay within the constraints and cancerous cell proliferation is effectively suppressed by driving the uninfected tumor cell population to a descending reference trajectory. Robustness tests are performed to show the proposed controller is robust to a certain level of model uncertainty. The method is expected to be generalized to the design of other model-based drug dosing schemes because of its optimality, impulsivity, and linearity.

Computational systems biology in disease modeling and control, review and perspectives

Omics-based approaches have become increasingly influential in identifying disease mechanisms and drug responses. Considering that diseases and drug responses are co-expressed and regulated in the relevant omics data interactions, the traditional way of grabbing omics data from single isolated layers cannot always obtain valuable inference. Also, drugs have adverse effects that may impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is applied to predict potential molecular interactions by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines' therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among gene, protein and drug molecules for modeling disease mechanisms and drug responses. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are also discussed for its clinical uses in this work.