How AI/ML And Continuous Manufacturing Technologies Can Support Biologics Pipeline From Discovery To BLA

One of the major sectors that makes the best use of artificial intelligence and machine learning is manufacturing. Smart Factories, sometimes referred to as Smart Factories 4.0, significantly reduce unplanned downtime and improve product design in addition to enhancing productivity, reducing transition times, raising overall product quality, and enhancing worker safety. Industry 4.0 is powered by artificial intelligence, which increases productivity while preserving the environment.


The pharmaceutical industry has been working toward continuous manufacturing (CM) for a while now. Although this has been accomplished in many instances by small molecule drug substances and drug products, biologics are lagging behind in the global deployment of CM due to their more complex manufacturing processes.


Successful biologics must meet a variety of requirements, such as activity and specific physicochemical characteristics that are generally referred to as developability. In a huge design space of protein sequences and buffer compositions, these numerous features must be simultaneously tuned. The optimization of protein properties can be accelerated and improved, increasing their activity and safety while reducing their development time and manufacturing costs. Machine learning, in particular, has great potential to do this.


Drug discovery is being transformed by artificial intelligence (AI) and machine learning. The primary focus has been on small molecules over the last five to ten years, and we have noticed a significant increase in the number of compounds found through the use of AI technologies. Parallel to this, but less widely known, AI is starting to change other aspects of drug discovery, most notably biologics (protein and peptide-based medicines). There have been a lot of AI tools and algorithms that have been developed in the last ten years that help with biologics discovery and optimization.


The promise of AI/ML in biologics discovery includes the potential to: (i) shorten timelines and costs; (ii) enhance the quality and uniqueness of molecules discovered; and (iii) boost the likelihood that R&D programmes will be successful.


Leadvent is bringing together industry leaders and innovators to share ideas and information on how AI/ML and continuous manufacturing technologies can support biologics pipeline from discovery to BLA; on March 28–29, 2023 at the Steigenberger Airport Hotel, Berlin, Germany. Speakers will focus on protein engineering, early biophysical screening, and formulation as emerging applications of ML in biologics discovery and development. We'll talk about how machine learning can extract data from large datasets while requiring less experimental work to simultaneously meet many quality goals. Finally, we anticipate potential AI interventions in various stages of the biological landscape in the future.


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