Recent progress in machine learning approaches for predicting carcinogenicity in drug development

ABSTRACT

Introduction

This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance.

Areas covered

The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency.

Expert opinion

Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.

KEYWORDS:

• Artificial intelligence
• carcinogenicity prediction
• drug development
• machine learning
• predictive modeling
• safety assessment
• toxicogenomics
• computational toxicology

Article highlights

• Examines the historical development and impact of machine learning in carcinogenicity prediction for drug development.

• Highlights the role of diverse machine learning methodologies, including deep learning, in enhancing drug safety assessments.

• Shows the application of machine learning from early compound screening to clinical trial optimization.

• Illustrates how machine learning improves predictive accuracy and efficiency in safety assessments over traditional methods.

• Addresses the limitations of in vivo and in vitro assays, advocating for machine learning as a more efficient alternative.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was partly funded by TUEBA, TNU-level project [ID: ĐH2023-TN08-05].