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Quotient Sciences, a UK‑based contract research organization, and Copenhagen‑based biotech Acesion Pharma have entered a collaboration to apply Quotient’s AI‑enhanced formulation technology to Acesion’s early‑stage atrial fibrillation . The partnership will integrate active machine‑learning and Bayesian optimisation algorithms into Quotient’s Translational Pharmaceutics platform, allowing the system to assess formulation data and suggest oral drug‑product options with higher probability of meeting development goals. The collaboration follows interim results from a recent clinical study in which Quotient’s AI successfully selected modified‑release formulations that met predefined pharmacokinetic targets within three dosing periods.
Quotient Sciences and Acesion Pharma announced a formal collaboration aimed at leveraging Quotient’s AI‑enhanced formulation technology for Acesion’s early‑stage atrial fibrillation drug candidates. The partnership will embed active machine‑learning and Bayesian optimisation methods into Quotient’s Translational Pharmaceutics platform, which already combines formulation, development, and clinical activities.
The AI system will ingest formulation data and generate recommendations for oral drug‑product options that are more likely to meet predefined development objectives, such as target pharmacokinetic profiles. This is intended to enable development teams to make formulation decisions earlier in the , reducing the time and resources spent on trial‑and‑error experimentation.
Quotient cited interim results from a clinical study conducted earlier in the month, where its AI selected modified‑release formulation compositions and doses that achieved the study’s pharmacokinetic targets within three dosing periods. The success of that study underpins confidence in the technology’s ability to guide formulation decisions in a real‑world development context.
Both companies emphasized that the collaboration could shorten timelines to clinical testing and increase the probability of achieving the intended product profile for Acesion’s small‑molecule compounds. No pricing, licensing terms, or broader access details were disclosed.
Forrás részletei: digitalhealthnews.com ↗
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The use of AI‑driven formulation tools could shorten the time required to identify viable drug‑product candidates, potentially accelerating the path to clinical testing for Acesion’s small‑molecule compounds. By reducing the number of experimental iterations, the approach may lower development costs and improve the likelihood of achieving desired product profiles, a critical factor for early‑stage cardiovascular therapies where timelines are tight. The partnership also showcases a concrete, industry‑focused application of active learning and Bayesian optimisation, signaling growing confidence in AI to streamline pharmaceutical R&D beyond early‑stage discovery.
Accelerating formulation decisions can have a direct impact on the overall drug‑development timeline, which is especially valuable for cardiovascular indications where market entry pressures are high.
By applying active learning and Bayesian optimisation, the partnership demonstrates a shift from heuristic, labor‑intensive formulation work toward data‑driven, predictive approaches, potentially setting a new standard for contract research organisations.
If successful, the AI‑enhanced workflow could reduce the number of failed formulation experiments, lowering R&D expenditures and freeing resources for other candidates.
The collaboration provides a concrete case study for the pharmaceutical industry on how AI can be integrated into existing translational platforms, offering a template for similar partnerships.
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Future updates on the collaboration’s impact on Acesion’s atrial‑fibrillation , including any acceleration of clinical‑trial milestones. Additional data on the performance of the AI algorithms in real‑world formulation decisions, especially any comparative studies against traditional methods. Potential expansion of the AI‑enhanced platform to other therapeutic areas or to other contract research organisations.
Publication of any quantitative results comparing the AI‑guided formulation process to traditional methods, which would help validate the claimed efficiency gains.
Announcements of expanded use of the AI platform beyond atrial fibrillation, such as applications to other therapeutic areas or to other contract research partners.
Regulatory feedback or guidance on the use of AI‑generated formulation data in IND submissions, which could influence broader industry adoption.
Potential commercial licensing or service offerings from Quotient Sciences that make the AI‑enhanced technology available to a wider set of biotech and pharma clients.