DISCOVER, DESIGN, DEVELOP and DELIVER
The current COVID-19 pandemic has revealed the critical need for novel drugs and therapies that pass clinical development at a faster pace than the spread of disease. The immediate necessity in the current situation is to opt for cost- and time-efficient processes that drive high outcome research and development. Computational drug design and development allows for shorter screening times, while also decreasing the number of in vitro and in vivo experiments in early lead discovery.
To address these challenges, Dassault Systèmes offers flexible scientific consulting and contract research engagements that leverage advanced modeling/simulation, machine learning and computational science techniques.
Designing an effective vaccine against viral infections requires several in vivo experiments to test the ability of viral epitopes to induce immune response. In a previous Contract Research project, BIOVIA scientists successfully identified the key amino acid interactions of the viral peptides with the MHC complexes that was required to elicit adequate immune response. This crucial DISCOVERY enabled the DESIGN of a potent vaccine with high immunogenicity.
Traditional drug discovery typically uses high-throughput screening assays to assess a large number of molecules. This can be time consuming, and often the outcome is very few hits that develop into usable drug leads. In several other collaborative projects with our customers, we harnessed the predictive power of machine learning to DISCOVER new drug leads, while also shortening the time required to identify and optimize drug leads.
Harnessing antibodies in diagnostics and cancer therapies created a necessity for the development of efficacious biotherapeutics. However, the successful development of biotherapeutic leads involves resource-intensive steps such as the development of a cell line, a manufacturing process or a formulation.1 We employed a set of fast predictive computational modeling, screening and formulation tools to DEVELOP a clinical therapeutic candidate with promising properties.
References
- Jarasch, A., Koll, H., Regula, J. T., Bader, M., Papadimitriou, A., & Kettenberger, H. (2015). Developability assessment during the selection of novel therapeutic antibodies. Journal of pharmaceutical sciences, 104(6), 1885-1898.
Dr. Lalitha Subramanian is the Global Head of Outcome-Based Contract Research at Dassault Systèmes and a Science Fellow of its BIOVIA brand. She has a Ph.D. in chemistry and did post-doctoral work with Nobel Laureate Roald Hoffman at Cornell University. Her specialties include materials research for sustainability, energy transformation (including battery and fuel cells), as well as drug development and delivery to improve human health.