arXiv：cs.LG· Pauric Bannigan, Siddarth Chandrasekaran, Brigitte A. G. Lamers, Inge Hermsen, Gary Tom, Riley J. Hickman, Bahar Yeniad, Morgan Fox, Christine Allen·· 4 小时前AI 评分32
Corbion 与 Intrepid 用 ANDROMEDA 1 加速 PLGA 原位成型缓释制剂开发
Accelerating the Development of PLGA In Situ Forming Depots Through AI-Driven Multi-Objective Optimization
AI 导读
Corbion 与 Intrepid 将 PURASORB 可生物吸收聚合物库与 Intrepid Labs 的 AI 算法 ANDROMEDA 1 结合，约 15 周内制备并表征了 181 种药物载量 6-12% w/w 的独特配方，用于治疗性多肽的原位成型缓释制剂。
正文
Abstract:Developing long-acting injectable formulations requires the simultaneous optimization of drug loading, release kinetics, viscosity, injectability, stability and other objectives. To navigate this multidimensional space, Corbion and Intrepid combined Corbion's diverse PURASORB bioresorbable polymer library with Intrepid Labs' proprietary AI algorithm (ANDROMEDA 1) to develop in situ forming depots for a therapeutic peptide. Over approximately 15 weeks, 181 unique formulations spanning drug loadings of 6-12% w/w were prepared and characterized through broad design-space mapping and targeted multi-objective optimization. Four lead candidate formulations were identified at 6%, 9%, and 12% w/w drug loading. Each met the predefined viscosity and injectability criteria while providing distinct 30-day in vitro release profiles. The study evaluated polymers spanning a broad range of molecular weights, including commercially available PURASORB grades and new polymers under development by Corbion to expand its polymer toolbox. ANDROMEDA 1 identified that polymers with intermediate molecular weights provided a favorable balance between sustained release and solution viscosity. Together, these findings demonstrate how integrated polymer expertise and AI-driven optimization can rapidly identify differentiated formulation candidates, focus the development space, and establish a strong data-driven foundation for further optimization and in vivo evaluation.
| Comments: | 10 pages; 7 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08368 [cs.LG] |
| (or arXiv:2610.08368v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08368 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pauric Bannigan [view email]
[v1]
Tue, 6 Oct 2026 13:54:38 UTC (3,518 KB)
来源：arXiv：cs.LG · arxiv.org