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

TL;DR

Clinical trials incur high failure rates due to design and recruitment challenges. The paper proposes leveraging quantum computing to enhance clinical trial design through three pillars: simulations, site selection, and cohort identification. It reviews classical methods and introduces quantum optimization, QML, and quantum data-encoding approaches as a framework for improved efficiency and generalizability. The work highlights potential near-term benefits with variational algorithms and discusses future prospects toward fault-tolerant quantum computing for scalable, equitable, and patient-centric trials.

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.

Figures (5)

• Figure 1: Protocol design is a crucial step for successfully optimizing the logistics of the trial. Using quantum differential solvers can enhance our understanding of mechanistic properties of the drug. Once protocols are set, this will inform the logistical optimization steps of the trial about how to select sites and how to identify the correct cohort. We propose portfolio-based quantum optimization to determine the best set of trial sites and quantum generative models to improve cohort identification.
• Figure 2: Table lists some of the problem classes within optimization and machine learning problems, along with some commonly used examples of quantum algorithms for optimization and machine learning.
• Figure 3: Schematic Representation of the PBPK Model Development and Estimation Workflow for Clinical Trials: The process begins with the construction of the model, incorporating drug properties and biological system attributes. It proceeds with data preprocessing, including feature selection, dimensionality reduction and topological data analysis. The final phase involves model estimation, leveraging quantum computational techniques such as quantum differential equation solvers, QSVMs, QNNs, and QGNNs, leading to the formulation of the model.
• Figure 4: After the site-related data is collected and processed, we propose two approaches that utilize quantum computing. Portfolio-based optimization maps the problem with site-specific parameters and trial-specific constraints to select the best portfolio of sites. If the historical site performance data is limited, we propose in parallel, one can set a QSVC to classify which sites are feasible.
• Figure 5: Overview of the cohort identification process with multi-modal data, and applications of quantum machine learning for classification and generative model in the cohort identification process.