发表机构
Intrepid Labs Inc.; Corbion N.V.（Intrepid实验室公司; Corbion公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合Corbion聚合物库与Intrepid的AI算法ANDROMEDA 1，通过多目标优化快速筛选出四种PLGA原位成型储库候选配方，实现释放与黏度平衡。
AI 中文摘要
开发长效可注射制剂需要同时优化载药量、释放动力学、黏度、可注射性、稳定性及其他目标。为驾驭这一多维空间，Corbion与Intrepid将Corbion多样化的PURASORB生物可吸收聚合物库与Intrepid Labs专有的AI算法（ANDROMEDA 1）相结合，为一种治疗性肽开发原位成型储库。在大约15周内，制备并表征了181种独特配方，涵盖6-12% w/w的载药量，通过广泛的配方空间映射和定向多目标优化进行。在6%、9%和12% w/w载药量下确定了四个领先候选配方。每个配方均满足预定的黏度和可注射性标准，同时提供不同的30天体外表征释放曲线。该研究评估了涵盖广泛分子量范围的聚合物，包括市售PURASORB等级和Corbion为扩展其聚合物工具箱而开发的新聚合物。ANDROMEDA 1识别出中等分子量的聚合物在持续释放和溶液黏度之间提供了有利的平衡。总之，这些发现证明了整合聚合物专业知识和AI驱动优化如何能够快速识别差异化的候选配方，聚焦开发空间，并为进一步优化和体内评估建立强大的数据驱动基础。
英文摘要
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.
Comments10 pages; 7 figures
