Accelerating the Development of PLGA In Situ Forming Depots Through AI-Driven Multi-Objective Optimization
Developing long-acting injectable formulations requires the simultaneous optimization of drug loading, release kinetics, viscosity, injectability, stability and other objectives.
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Key points
- 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.
- Each met the predefined viscosity and injectability criteria while providing distinct 30-day in vitro release profiles.
- 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.
Sources (1)
- [1]Accelerating the Development of PLGA In Situ Forming Depots Through AI-Driven Multi-Objective OptimizationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 01:54 PM
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.
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