Artificial intelligence (AI) has increased in various sectors, including the pharmaceutical industry. Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate how humans learn, gradually improving its accuracy. Thus, Artificial Intelligence (AI) and Machine Learning (ML) is a branch of computer science, statistics, and engineering that uses algorithms or models to perform tasks and exhibit behaviors such as learning, making decisions, and making predictions. ML is considered a subset of AI that allows models to be developed by training algorithms through the analysis of data without models being explicitly programmed.


AI’s Impact on Pharmaceutical Innovation: From Drug Design to Clinical Trials

Artificial intelligence can aid in drug designing, target detection, drug/target interaction, decision making; personalizing medicines; and management of the clinical data for future clinical drug development. One of the most used of AI is predicting lead compounds that might pass clinical trials. The US Food and Drug Administration (USFDA) has seen a significant increase in drug and biologic application submissions using AI/ML components over the past few years, with more than 100 submissions reported in 2021.


USFDA Explores AI/ML in Drug Development: Opportunities and Challenges

AI/ML creates opportunities and new and unique challenges. USFDA has accelerated its efforts to create an agile regulatory ecosystem to facilitate innovation while safeguarding public health. So the US Food and Drug Administration (USFDA) issued a discussion paper, “Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products.” The discussion paper aims to communicate artificial intelligence and machine learning (AI/ML) with stakeholders, including industry and academia, to promote mutual learning and discussion.

FDA is soliciting feedback on the opportunities and challenges of utilizing AI/ML in drug development and medical device development for drugs. USFDA expects this discussion paper to complement and help prepare future guidance on AI/ML in drug development. In this discussion paper, three main topics are discussed:

  1. The landscape of current and potential uses of AI/ML: Section II provides examples of the use of AI/ML to highlight the potential impact of AI/ML across the drug development 60 process and includes a brief description of the FDA’s experience with AI/ML in drug development.
  2. Considerations for the use of AI/ML: Section III briefly describes several key efforts to develop general principles, standards, and practices for the use of AI/ML across diverse applications and then explores the principles and considerations that may be particularly applicable when using AI/ML for drug development activities.
  3. Next steps and stakeholder engagement: USFDA welcomes feedback on this discussion paper and any AI/ML-related issues pertaining to drug development. Section III includes several key questions to which interested parties can provide perspectives, and Section IV outlines opportunities for future engagement.

USFDA solicit feedback on three key areas in the context of AI/ML in drug development. These areas are:

  1. human-led governance, accountability, and transparency
  2. quality, reliability and representativeness of data
  3. model development, performance, monitoring, and validation

Thus, it shall be prudent to see suggestions and guidance documents on AI/ML by USFDA in the coming months.

As we all wait for the next steps in forming the AI/ML guidance from the FDA, ProductLife Group reminds you to check out our Pre-Clinical and Clinical Services.

  • Pre-Clinical and Clinical Study Protocol and Report Preparation
  • Safety & Toxicology Study Assessment
  • Drafting of Biowaiver and Justification Documents

Feel free to contact us at [email protected] to learn more about these services and the many others we support our global clients with.



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Introducing Artificial Intelligence and Machine Learning in the Pharmaceutical Industry