🧪 With AI and machine learning, it is possible to design any genetic sequence and synbio allows for testing in biological systems.
💊 The pharmaceutical industry has shifted from small molecule drugs to biologics, such as monoclonal antibodies and mRNA.
🚀 Innovations like large language models (LLMs) and jumping genes are being leveraged to improve antibody stability and yield.
🔬 The goal is to engineer biology in a faster, more efficient, and predictable way.
🌐 These new technologies are expected to accelerate the production of biologics and improve drug development processes.
Introduction:
The pharmaceutical industry has shifted its focus from manufacturing small molecules to biologics in recent years. This transition has led to the adoption of new technologies to improve biologics manufacturing. Claes Gustafsson, co-founder and CCO of ATUM, has highlighted the combination of artificial intelligence (AI) and synthetic biology as a promising approach to accelerate the production of monoclonal antibodies. Gustafsson will discuss these innovations at the Bioprocessing Summit Europe in March 2024.
- ATUM and the pharmaceutical industry are utilizing artificial intelligence and synthetic biology to enhance the manufacturing of biologics.
- The use of large language models can analyze antibody databases to identify patterns of “genetic language” and improve stability and yield of antibodies.
- Historical genetic sequences can be explored to create new design spaces for antibodies.
- Synthetic biology enables the rapid development and testing of genetic elements to improve protein expression during cell-line development.
- The integration of AI, machine learning, and physical implementation in biological systems is essential for efficient commercial manufacturing.
Conclusion:
The pharmaceutical industry is leveraging new technologies, such as AI and synthetic biology, to enhance the manufacturing of biologics. These approaches offer the potential to optimize antibody production by improving stability, yield, and binding to drug targets. The integration of AI and machine learning with physical implementation in biological systems is crucial for accelerating the development and commercialization of biologic drugs. These innovations represent the industry’s commitment to advancing technology to meet the demands of biologic manufacturing.
