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The development of a drug is a complex and lengthy process, requiring precise decisions about dosing to ensure both the efficacy and safety of a drug. Artificial intelligence (AI) can be used for dose selection in drug development—helping to save time, money, and increase accuracy. Before embarking upon this journey, it is important to grasp not only the basics of dose selection and why it’s important in drug development, but also how exactly AI improves the dose selection process.

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