De-risking drug formulation with AI predictive modelling

Through predictive modelling, AI supports target identification, prediction of physicochemical properties and formulation strategy selection. AI predictive modelling is helping to reduce trial-and-error in formulation development by combining molecular data with targeted experimental validation. We explore how this approach can prioritise high-probability candidates, cut iteration and de-risk, ultimately accelerating therapy delivery to patients. Bringing a new drug to market is one of the most time‑consuming and costly assignments within the pharma industry. From early research to launch, drug development typically takes 12 to 18 years, at an average cost of around US$2.6 billion [i] . Only about 10% of drug…

Source: Clinical Trials Arena