Towards quantum computing for clinical trial design and optimization: A perspective on new opportunities and challenges

TL;DR

This paper explores how quantum computing can revolutionize clinical trial design by improving simulation, site selection, and cohort identification, addressing current challenges and offering innovative solutions.

Contribution

It introduces quantum algorithms tailored for clinical trial optimization, providing a novel framework that enhances efficiency and effectiveness over classical methods.

Findings

Quantum algorithms show potential for faster clinical trial simulations.

Improved site selection and cohort identification using quantum approaches.

Framework offers a new direction for integrating quantum computing in clinical research.

Abstract

Clinical trials are pivotal in the drug discovery process to determine the safety and efficacy of a drug candidate. The high failure rates of these trials are attributed to deficiencies in clinical model development and protocol design. Improvements in the clinical drug design process could therefore yield significant benefits for all stakeholders involved. This paper examines the current challenges faced in clinical trial design and optimization, reviews established classical computational approaches, and introduces quantum algorithms aimed at enhancing these processes. Specifically, the focus is on three critical aspects: clinical trial simulations, site selection, and cohort identification. This study aims to provide a comprehensive framework that leverages quantum computing to innovate and refine the efficiency and effectiveness of clinical trials.