Literature DB >> 28155992

Biopharmaceutical Informatics: supporting biologic drug development via molecular modelling and informatics.

Sandeep Kumar1, Nikolay V Plotnikov1, Jason C Rouse2, Satish K Singh1.   

Abstract

OBJECTIVES: The purpose of this article is to introduce an emerging field called 'Biopharmaceutical Informatics'. It describes how tools from Information technology and Molecular Biophysics can be adapted, developed and gainfully employed in discovery and development of biologic drugs. KEY
FINDINGS: The findings described here are based on literature surveys and the authors' collective experiences in the field of biologic drug product development. A strategic framework to forecast early the hurdles faced during drug product development is weaved together and elucidated using chemical degradation as an example. Efficiency of translating biologic drug discoveries into drug products can be significantly improved by combining learnings from experimental biophysical and analytical data on the drug candidates with molecular properties computed from their sequences and structures via molecular modeling and simulations.
SUMMARY: Biopharmaceutical Informatics seeks to promote applications of computational tools towards discovery and development of biologic drugs. When fully implemented, industry-wide, it will enable rapid materials-free developability assessments of biologic drug candidates at early stages as well as streamline drug product development activities such as commercial scale production, purification, formulation, analytical characterization, safety and in vivo performance.
© 2017 Royal Pharmaceutical Society.

Keywords:  Biopharmaceutical Informatics; antibody; chemical degradation; curation; molecular modelling

Mesh:

Substances:

Year:  2017        PMID: 28155992     DOI: 10.1111/jphp.12700

Source DB:  PubMed          Journal:  J Pharm Pharmacol        ISSN: 0022-3573            Impact factor:   3.765


  9 in total

1.  In Silico Prediction of Diffusion Interaction Parameter (kD), a Key Indicator of Antibody Solution Behaviors.

Authors:  Dheeraj S Tomar; Satish K Singh; Li Li; Matthew P Broulidakis; Sandeep Kumar
Journal:  Pharm Res       Date:  2018-08-20       Impact factor: 4.200

Review 2.  Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.

Authors:  Rahul Khetan; Robin Curtis; Charlotte M Deane; Johannes Thorling Hadsund; Uddipan Kar; Konrad Krawczyk; Daisuke Kuroda; Sarah A Robinson; Pietro Sormanni; Kouhei Tsumoto; Jim Warwicker; Andrew C R Martin
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

Review 3.  How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data.

Authors:  Aleksandr Kovaltsuk; Konrad Krawczyk; Jacob D Galson; Dominic F Kelly; Charlotte M Deane; Johannes Trück
Journal:  Front Immunol       Date:  2017-12-08       Impact factor: 7.561

4.  Long-Term Stability Prediction for Developability Assessment of Biopharmaceutics Using Advanced Kinetic Modeling.

Authors:  Andreas Evers; Didier Clénet; Stefania Pfeiffer-Marek
Journal:  Pharmaceutics       Date:  2022-02-08       Impact factor: 6.321

Review 5.  Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.

Authors:  Wiktoria Wilman; Sonia Wróbel; Weronika Bielska; Piotr Deszynski; Paweł Dudzic; Igor Jaszczyszyn; Jędrzej Kaniewski; Jakub Młokosiewicz; Anahita Rouyan; Tadeusz Satława; Sandeep Kumar; Victor Greiff; Konrad Krawczyk
Journal:  Brief Bioinform       Date:  2022-07-18       Impact factor: 13.994

6.  Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties along the entire pharmaceutical process chain.

Authors:  Joschka Bauer; Sven Mathias; Sebastian Kube; Kerstin Otte; Patrick Garidel; Martin Gamer; Michaela Blech; Simon Fischer; Anne R Karow-Zwick
Journal:  MAbs       Date:  2020 Jan-Dec       Impact factor: 5.857

7.  Computational approaches to therapeutic antibody design: established methods and emerging trends.

Authors:  Richard A Norman; Francesco Ambrosetti; Alexandre M J J Bonvin; Lucy J Colwell; Sebastian Kelm; Sandeep Kumar; Konrad Krawczyk
Journal:  Brief Bioinform       Date:  2020-09-25       Impact factor: 11.622

Review 8.  In silico prediction of post-translational modifications in therapeutic antibodies.

Authors:  Shabdita Vatsa
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

9.  Intrinsic physicochemical profile of marketed antibody-based biotherapeutics.

Authors:  Lucky Ahmed; Priyanka Gupta; Kyle P Martin; Justin M Scheer; Andrew E Nixon; Sandeep Kumar
Journal:  Proc Natl Acad Sci U S A       Date:  2021-09-14       Impact factor: 11.205

  9 in total

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