By: IPP Bureau
Last updated : May 28, 2026 8:44 pm
Company launches clinical study of AI-designed oral formulation following MHRA approval, marking a major milestone in AI-driven drug development.
Quotient Sciences has announced the successful initiation of a Phase I clinical study evaluating a drug product developed using artificial intelligence (AI), marking a significant milestone in the application of AI for pharmaceutical formulation design and drug development.
The clinical study, launched at the company’s UK facility after receiving approval from the Medicines and Healthcare products Regulatory Agency, will evaluate the safety and pharmacokinetics of an oral solid dose formulation in healthy volunteers.
According to the company, the study aims to validate the use of AI in drug product development and demonstrate how AI-driven approaches can accelerate formulation design and support more informed decision-making during the early stages of drug development. Quotient Sciences believes this is the first reported instance of an AI-designed formulation entering clinical evaluation.
The company’s AI-enabled formulation development programme integrates advanced machine learning with real-time clinical testing through its Translational Pharmaceutics® platform, enabling pharmaceutical and biotech companies to optimise formulation strategies earlier in development. The approach is designed to improve clinical predictability and enhance the probability of successful outcomes.
Andy Lewis, Chief Scientific Officer at Quotient Sciences, said, “This program marks a significant advancement in harnessing the power of advanced machine learning alongside deep scientific expertise to streamline drug development. This approach, coupled with our Translational Pharmaceutics® platform, enables our customers to make earlier, more informed decisions with greater confidence, ultimately increasing the likelihood of clinical success.”
The study utilised advanced machine learning algorithms developed by Intrepid Labs to support formulation design and optimisation. The AI-driven platform enabled rapid evaluation of formulation options and data-driven optimisation aimed at improving the expected clinical performance of the drug product.