Blog

Let’s explore how AI-driven approaches to drug development can be used to decode DDIs, why it is important to detect them, as well as how to integrate AI into your strategy of detecting potential DDIs.

There are many possible roadblocks on the path to IND approval using traditional methods of drug discovery and development alone. Adverse drug-drug interactions (DDIs), ineffective drug formulation, and poorly understood patient stratification are among the top. Hurdles like these can represent “blind alleys” invisible when a candidate is initially discovered, that make carrying forward translational research and clinical trials extremely risky. Luckily, the integration of artificial intelligence (AI) into drug development can help in these three core aspects of de-risking R&D decisions and successfully getting a new drug to market.

Hardly a week passes without the press spotlighting the growing attention governments are paying to artificial intelligence (AI). For the pharmaceutical and biotech industries, this has created some confusion. Should the progress made by scientists to integrate AI technology as another method to advance exploration and discovery be reconsidered? Are regulators now more concerned about research supported by AI and ML? Will my investigational new drug application be slowed or rejected if submitted with AI-based research? Read the blog to learn more.l

Recently the FDA released a publication, “Using Artificial Intelligence & Machine Learning in the Development of Drug and Biological Products,” and requested comments on the framework outlined within it from subject matter experts in the industry. VeriSIM Life, which provides a computational drug development platform that uses AI and ML to streamline the drug development process, was eager to contribute our thoughts and feedback to the conversation.