Digital twins for accelerating drug discovery and development: opportunities and challenges

Area of Science:

• Pharmacology and Pharmaceutical Sciences
• Computational Biology and Bioinformatics
• Healthcare Systems and Management

Background:

• Digital twins are emerging as a powerful tool with the potential to revolutionize pharmaceutical research and patient care.
• The integration of digital twins promises to accelerate drug development timelines and enhance the precision of personalized medicine.

Purpose of the Study:

• To provide a comprehensive review of digital twin applications across the entire pharmaceutical value chain.
• To identify and discuss the key challenges hindering the widespread adoption and effectiveness of digital twins in healthcare.

Main Methods:

• Systematic literature review of digital twin applications in drug discovery, preclinical research, clinical trials, manufacturing, and post-market surveillance.
• Analysis of current challenges including data integration, model validation, regulatory hurdles, and privacy concerns.

Main Results:

• Digital twins can significantly reduce costs and failure rates in drug development.
• Applications span from early-stage target identification to post-market patient management, improving therapy safety and efficacy.
• Significant barriers remain, including data interoperability, model trustworthiness, regulatory frameworks, and patient data security.

Conclusions:

• Overcoming challenges in data integration, model reliability, regulatory acceptance, and privacy is essential for unlocking the full potential of digital twins.
• Collaborative efforts and technological innovation are required to implement digital twins effectively across the healthcare ecosystem.
• Digital twins hold immense promise for enhancing patient outcomes and optimizing the pharmaceutical industry.