(5d) How Experiment-Driven Quantitative Modeling and Control Help Optimize Oncolytic Adenovirus Cancer Treatment | AIChE

(5d) How Experiment-Driven Quantitative Modeling and Control Help Optimize Oncolytic Adenovirus Cancer Treatment

Replication-selective adenoviruses replicate in cells containing certain mutations, motivating their use in targeted gene therapy. An example is ONYX-015, which aims to target and preferentially replicate in p53-defective tumor cells, potentially offering improved treatment options for certain cancers. Although recent studies demonstrate that ONYX-015 affects a variety of signaling pathways not necessarily involving p53, the adenovirus continues to be studied in a number of clinical trials.

The efficacy of adenovirus mediated therapy depends on three general processes: (i) cell entry via the coxsackievirus and adenovirus receptor, CAR, (ii) viral replication, and (iii) virus-mediated cell death. Experimental data suggests that CAR is down-regulated in highly malignant cells, hindering ONYX-015's ability to infect. Pharmaceutical intervention into the Raf-MEK-ERK pathway via the CI1040 MEK inhibitor has shown to counter this effect by up-regulating CAR expression. MEK inhibition, however, causes G1 cell cycle arrest thereby stunting viral replication and consequently virus-induced cancer cell death. Through the data-driven modeling of cancer cells subjected to CI1040 and ONYX-015, we aim to characterize and predict system dynamics, providing a means to optimize the efficacy of oncolytic adenovirus cancer treatment by manipulating the timing of drug treatment and infection. Validation of simulated results should provide a means to refine the current cellular level ODE model to include relevant sub-cellular phenomena that better characterizes both the mechanistic and dynamic behavior of adenovirus cancer treatment. Pending successful test of model predictions, our goal is to elucidate optimization strategies that could offer practical and effective means for minimizing cancer growth.

Engineering principles offer a variety of tools for the systematic investigation and prediction of complex behavior. I aim to refine control theoretic methodologies to address specific challenges in medicine and biology. One such challenge generally involves optimizing the combinatorial effect of therapeutic protocols to minimize cancer growth. As our pursuits in biology become increasingly complex, we create a greater demand for interdisciplinary research and maximally informative experiments/predictions. The refinement of tools provided by control theory and signal processing will be critical in shaping research strategies in medicine, experimental biology, and environmental stability.