Feedback-Based Quantum Control for Safe and Synergistic Drug Combination Design

TL;DR

This paper introduces a quantum-control framework utilizing feedback-based algorithms to optimize drug combinations by encoding interactions into Ising models, aiming to improve safety and efficacy in clinical treatments.

Contribution

It presents a novel quantum optimization method for drug combination design that accounts for drug-drug interactions, leveraging Ising Hamiltonians and the FALQON algorithm.

Findings

Efficient convergence to high-quality solutions in simulated clinical scenarios

Effective handling of safety and synergy constraints in drug combinations

Successful case studies including COVID-19 drug sets

Abstract

Drug-drug interactions (DDIs) strongly affect the safety and efficacy of combination therapies. Despite the availability of large DDI databases, selecting optimal multi-drug combinations that balance safety, therapeutic benefit, and regimen size remains a challenging combinatorial optimization problem. Here, we present a quantum-control-based framework for DDI-aware drug combination optimization, in which known harmful and synergistic interactions are encoded into Ising Hamiltonians as penalties and rewards, respectively. The optimization is performed using the feedback-based quantum algorithm FALQON, a gradient-free variational approach. We study two clinically motivated tasks: the Maximum Safe Subset problem and the Synergy-Constrained Optimization problem. Numerical simulations using interaction data from Drugs.com and SYNERGxDB demonstrate efficient convergence and high-quality solutions for clinically relevant drug sets, including COVID-19 case studies.