Biopharmaceutical Informatics: Learning to Discover Developable Biotherapeutics

Despite the phenomenal clinical success of antibody-based biopharmaceuticals in recent years, discovery and development of these novel biomedicines remains a costly, time-consuming, and risky endeavor with low probability of success. To bring better biomedicines to patients faster, we have come up with a strategic vision of Biopharmaceutical Informatics which calls for syncretic use of computation and experiment at all stages of biologic drug discovery and pre-clinical development cycles to improve probability of successful clinical outcomes. Biopharmaceutical Informatics also encourages industry and academic scientists supporting various aspects of biotherapeutic drug discovery and development cycles to learn from our collective experiences of successes and, more importantly, failures. The insights gained from such learnings shall help us improve the rate of successful translation of drug discoveries into drug products available to clinicians and patients, reduce costs, and increase the speed of biologic drug discovery and development. Hopefully, the efficiencies gained from implementing such insights shall make novel biomedicines more affordable for patients.

This unique volume describes ways to invent and commercialize biomedicines more efficiently:

• Calls for digital transformation of biopharmaceutical industry by appropriately collecting, curating, and making available discovery and pre-clinical development project data using FAIR principles
• Describes applications of artificial intelligence and machine learning (AIML) in discovery of antibodies in silico (DAbI) starting with antigen design, constructing inherently developable antibody libraries, finding hits, identifying lead candidates, and optimizing them
• Details applications of AIML, physics-based computational design methods, and other bioinformatics tools in fields such as developability assessments, formulation and excipient design, analytical and bioprocess development, and pharmacology
• Presents pharmacokinetics/pharmacodynamics (PK/PD) and Quantitative Systems Pharmacology (QSP) models for biopharmaceuticals
• Describes uses of AIML in bispecific and multi-specific formats

Dr Sandeep Kumar has also edited a collection of articles dedicated to this topic which can be found in the Taylor and Francis journal mAbs.

Dr. Sandeep Kumar is a Distinguished Research Fellow (DRF) at the department of Biotherapeutics Discovery in Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA. Sandeep Kumar holds a Ph.D. in Computational Biophysics and has over 20 years of experience researching protein structure - Function relationships using the computational means. Sandeep Kumar has so far contributed towards approximately 100 research articles, reviews, book chapters, and edited a book. He has also contributed towards development of several antibody-body based biotherapeutics and a vaccine currently in the market. After joining Boehringer-Ingelheim in 2018, Sandeep has been contributing towards discovery of several monoclonal antibodies as well as multi-specific modalities that are currently in development. Based on the insights gained from these experiences, Sandeep has been advocating for Biopharmaceutical Informatics, a strategic vision dedicated to synergistic use of computation and experimentation towards a cost effective and more efficient discovery and development of Biotherapeutics.

Dr. Andrew Nixon is currently Vice President, Biotherapeutics Molecule Discovery at Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA. Andy earned his Ph.D. in Physical Biochemistry from the University of London for studies completed at the MRC’s National Institute for Medical Research. Andy has over 20 years of experience in biologic drug discovery and has contributed to over 100 antibody discovery programs resulting in numerous clinical candidates and approved biologics including TAKHZYRO, a fully human antibody inhibitor of plasma kallikrein.