Workshop A Tuesday, November 19
9:00 am - 12:00 pm
Implementing Machine Learning and AI Technologies
Research Fellow, BMS
Dana I. Filoti, Ph.D.,
Senior Scientist , AbbVie
- Gain an understanding of the current landscape of computational tools available
- Dive deep on data – what sources, what volume and what processes to train your algorithms and boost predictive power
- Learn to optimize candidate screening, selection and design
Stanley Krystek, Research Fellow, BMS
Dr. Krystek received his M.S. (1986) and PhD. in Biochemistry from Albany Medical College in 1989. Dr. Krystek joined Bristol-Myers Squibb (BMS) in 1989 and has remained at BMS throughout his 30 year career. Dr. Krystek focused his efforts developing computational methods for protein modeling and drug discovery. He applied computational methods to understanding protein-ligand interactions for important therapeutic targets such as GPCRs, proteases, and NHRs. Dr. Krystek has published over 65 papers in the area of protein structure and modeling including application of QSAR methods to drug discovery, structure-based drug design and GPCR modeling. He is the co-inventor on over 25 patents. Currently Dr. Krystek is a Research Fellow in the department of Molecular Structure and Design where he leads a team that is focused on design and engineering of protein therapeutics.
Dana I. Filoti, Ph.D., Senior Scientist , AbbVie
Dana Filoti is currently a member of the NBE Analytical R&D group at AbbVie, where she leads the analytical clinical development of ADC therapeutics along with preclinical candidate selection, drug delivery systems and subvisible particle characterization of protein therapeutics. She received her Ph.D. in Physics/Materials Science from University of New Hampshire. As a Post-Doctoral fellow, she worked with Dr. Thomas Laue at the Biomolecular Interaction Technologies Center (BITC) on the development of instrumentation and methods for examining macromolecular interactions via charge measurements to address colloidal stability issues associated with high concentration formulation development. She has about 10 years of experience in protein formulation, analytical development and biophysical characterization.
Workshop B Tuesday, November 19
1:00 pm - 4:00 pm
In-silico Immunogenicity Predictions (Hands-on Session)
Senior Scientist , Voyager Therapeutics
Computational immunogenicity predictions for antibodies, as well as pathogens, help in the rational design and re-engineering. This facilitates to minimize anti-drug antibodies (ADA) as well as better vaccine design. Latest in-silico tools can shorten the process from design to preclinical validations.
Topics to be covered:
• Computational prediction for T cell and B cell epitopes from primary sequences
• Antigen structure prediction via protein homology model
• Structure or homology model-based Vaccine/ antigen design
• Structural differences between MHC-I and MHC-II binding
• MHC class I and MHC class II predictions from peptide sequences
• Overview on the latest immunogenicity prediction programs
Vinodh Kurella, Senior Scientist , Voyager Therapeutics
Vinodh Kurella currently is a Senior Scientist at Voyager Therapeutics within the Antibody discovery and design group in the Vector Engineering department. He is a protein engineer with a focus on antibody and antigen designs via structure guided computational modeling-based approach. At Merrimack Pharmaceuticals, he was team lead for structure guided antibody engineering, humanization designs and validations. At Intrexon corporation, lead the antibody CAR-T cell designs and optimizations in the Immuno_Oncology division at Intrexon Corporation. He completed his post-doctoral training at Dana Farber Cancer Institute and Harvard Medical school in Dr. Wayne Marasco laboratory and received Ph.D. in Dr. David Worthylake lab from Dept. of Biochemistry at LSU Health Sciences Center in the field of protein X-ray crystallography.