Quotient Sciences and Acesion Pharma have formed a new collaboration to apply AI‑enhanced formulation development within Quotient’s Translational Pharmaceutics platform.
The partnership will support Acesion’s early‑stage pipeline of small molecules targeting atrial fibrillation, the most common sustained cardiac arrhythmia. The companies said the approach will help compress early clinical decision‑making.
Quotient’s AI‑enhanced solution uses active machine learning with Bayesian optimisation to identify and prioritise drug products most likely to succeed in the clinic. The aim is to enable faster, better‑informed decisions earlier in development. Atrial fibrillation affects an estimated 50–60 million people globally and is associated with a five‑fold increased risk of stroke.
The Translational Pharmaceutics platform integrates formulation and clinical activities to optimise early drug development. It has been used for more than 20 years by biotech and pharmaceutical companies seeking to streamline progression through early clinical phases. Quotient said the addition of AI allows teams to analyse data and predict formulation performance more precisely.
Dr Andrew Lewis, Chief Scientific Officer at Quotient Sciences, said: “This collaboration with Acesion Pharma reflects exactly what our AI-enhanced formulation development solution, combined with Translational Pharmaceutics, is designed to deliver: helping our partners make better-informed, faster decisions about their drug product formulations.”
He added: “We’re proud to be working with Acesion on a shared commitment to innovation, and to support their pipeline in the field of atrial fibrillation.”
Dr Elisabeth V Carstensen, Vice President CMC at Acesion Pharma, explained: “We are excited to, together with Quotient Sciences, explore how their AI-enhanced algorithms can help guide and accelerate oral formulation development for our small-molecule compounds.”
She continued: “By helping to guide and accelerate development decisions, this approach has the potential to shorten timelines for clinical testing and increase the likelihood of achieving the desired product profile.”
