Determining Optimal Combination Regimens for Patients with Multiple Myeloma

TL;DR

This paper develops a mathematical model to optimize combination therapy regimens for multiple myeloma, aiming to maximize patient lifespan and improve treatment scheduling.

Contribution

It introduces an optimized control approach to determine effective drug combinations and schedules, enhancing prior disease-immune dynamic models.

Findings

Optimal control with approximation outperforms other methods.

The approach produces clinically feasible, near-optimal regimens.

Implications for dose optimization and drug scheduling.

Abstract

While many novel therapies have been approved in recent years for treating patients with multiple myeloma, there is still no established curative regimen, especially for patients with high risk disease. In this work, we use a mathematical modeling approach to determine combination therapy regimens that maximize healthy lifespan for patients with multiple myeloma. We start with a model of ordinary differential equations for the underlying disease and immune dynamics, which was presented and analyzed previously. We add the effects of three therapies to the model: pomalidomide, dexamethasone, and elotuzumab. We consider multiple approaches to optimizing combinations of these therapies. We find that optimal control combined with approximation outperforms other methods, in that they can quickly produce a combination regimen that is clinically-feasible and near-optimal. Implications of this work can be used to optimize doses and advance the scheduling of drugs.