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Full Description
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.
Contents
Foreword
Preface
About the editors
List of contributors
1. Biopharmaceutical Informatics: An Introduction
2. Digital transformation in the biopharmaceutical industry: rebuilding the way we discover complex therapeutics
3. Computational protein design strategies for optimization of antigen generation to drive antibody discovery
4. Bioinformatic Analyses of Antibody Repertoires and Their Roles in Modern Antibody Drug Discovery
5. Applications of Artificial Intelligence and Machine Learning Towards Antibody Discovery and Development
6. From Deep Generative Models to Structure-Based Simulations: Computational Approaches for Antibody Design
7. Computational biophysical analyses of antibody structure-function relationships with emphasis on therapeutic antibody-based biologics
8. Use of molecular simulations to understand structural dynamics of antibodies
9. Considerations of developability during the early stages of antibody drug discovery and design
10. In Silico Approaches to Deliver Better Antibodies by Design - The Past, the Present and the Future
11. Use of systems biology approaches towards target discovery, validation, and drug development
12. Recent advances in PK/PD and Quantitative Systems Pharmacology (QSP) models for biopharmaceuticals
13. The Artificial Intelligence Revolution: Transforming the Design and Optimization of Multispecific Antibodies
Index.
