PolyModels Hub’s Post

⚙️🔍 Enhancing Protein A Chromatography Resin Screening with Computer-Aided Design Space Identification 🧬🔧 The increasing demand for monoclonal antibodies (mAbs) in the biopharmaceutical sector highlights the importance of refining production processes, such as Protein A affinity chromatography, crucial for mAb purification. Traditionally, the screening of Protein A resins hasn't fully considered process flexibility, vital for accommodating variations in feed streams. 📊This interesting study by Steven Sachio , Blaž Likozar , Cleo Kontoravdi  and Maria Papathanasiou introduces a model-based approach combined with machine learning for the identification of design spaces, focusing on the performance and flexibility of processes for Protein A chromatography resin screening. Key Features: 🧪 Targeted Resin Screening: Assesses five significant Protein A resins, factoring in process adaptability. 💻 Machine Learning Integration: Offers a systematic approach to design space identification, enhancing process evaluation. ⏳ Efficient Design Exploration: Facilitates rapid discovery of extensive design spaces, streamlining the screening process. 🔄 Focus on Flexibility: Aims to design processes resilient to feed stream variations, improving production stability. 📚 Link to Publication: https://lnkd.in/dE52VSC6 #ProteinAChromatography #MachineLearning #DesignSpaceIdentification #Biopharmaceuticals #ProcessOptimization #PolyModelsHub

Computer-aided design space identification for screening of protein A affinity chromatography resins

Computer-aided design space identification for screening of protein A affinity chromatography resins

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