Literature DB >> 32797270

When Is an In Silico Representation a Digital Twin? A Biopharmaceutical Industry Approach to the Digital Twin Concept.

Rui M C Portela1, Christos Varsakelis2, Anne Richelle1, Nikolaos Giannelos2, Julia Pence2, Sandrine Dessoy2, Moritz von Stosch3,4.   

Abstract

Digital twins (DTs) are expected to render process development and life-cycle management much more cost-effective and time-efficient. A DT definition, a brief retrospect on their history and expectations for their deployment in today's business environment, and a detailed financial assessment of their attractive economic benefits are provided in this chapter. The argument that restrictive guidelines set forth by regulatory agencies would hinder the adoption of DTs in the (bio)pharmaceutical industry is revisited, concluding that those companies who collaborate with the agencies to further their technical capabilities will gain significant competitive advantage. The analyzed process development examples show high methodological readiness levels but low systematic adoption of technology. Given the technical feasibilities, financial opportunities, and regulatory encouragement, concerns regarding intellectual property and data sharing, though required to be taken into account, will at best delay an industry-wide adoption of DTs. In conclusion, it is expected that a strategic investment in DTs now will gain an advantage over competition that will be difficult to overcome by late adopters.

Entities:  

Keywords:  Cost benefit analysis; Digital twin; Quality by design; Upstream bioprocess modeling

Year:  2021        PMID: 32797270     DOI: 10.1007/10_2020_138

Source DB:  PubMed          Journal:  Adv Biochem Eng Biotechnol        ISSN: 0724-6145            Impact factor:   2.635


  21 in total

Review 1.  How to improve R&D productivity: the pharmaceutical industry's grand challenge.

Authors:  Steven M Paul; Daniel S Mytelka; Christopher T Dunwiddie; Charles C Persinger; Bernard H Munos; Stacy R Lindborg; Aaron L Schacht
Journal:  Nat Rev Drug Discov       Date:  2010-02-19       Impact factor: 84.694

Review 2.  Harnessing QbD, Programming Languages, and Automation for Reproducible Biology.

Authors:  Michael I Sadowski; Chris Grant; Tim S Fell
Journal:  Trends Biotechnol       Date:  2015-12-18       Impact factor: 19.536

3.  Automated disposable small scale reactor for high throughput bioprocess development: a proof of concept study.

Authors:  Rachel Bareither; Neil Bargh; Robert Oakeshott; Kathryn Watts; David Pollard
Journal:  Biotechnol Bioeng       Date:  2013-07-01       Impact factor: 4.530

4.  How much do clinical trials cost?

Authors:  Linda Martin; Melissa Hutchens; Conrad Hawkins; Alaina Radnov
Journal:  Nat Rev Drug Discov       Date:  2017-05-19       Impact factor: 84.694

5.  Estimated Costs of Pivotal Trials for Novel Therapeutic Agents Approved by the US Food and Drug Administration, 2015-2016.

Authors:  Thomas J Moore; Hanzhe Zhang; Gerard Anderson; G Caleb Alexander
Journal:  JAMA Intern Med       Date:  2018-11-01       Impact factor: 21.873

6.  Scale-down model qualification of ambr® 250 high-throughput mini-bioreactor system for two commercial-scale mAb processes.

Authors:  Matthew Manahan; Michael Nelson; Jonathan J Cacciatore; Jessica Weng; Sen Xu; Jennifer Pollard
Journal:  Biotechnol Prog       Date:  2019-07-09

Review 7.  Exploiting mAb structure characteristics for a directed QbD implementation in early process development.

Authors:  Micael Karlberg; Moritz von Stosch; Jarka Glassey
Journal:  Crit Rev Biotechnol       Date:  2018-03-07       Impact factor: 8.429

8.  Life-cycle and cost of goods assessment of fed-batch and perfusion-based manufacturing processes for mAbs.

Authors:  Phumthep Bunnak; Richard Allmendinger; Sri V Ramasamy; Paola Lettieri; Nigel J Titchener-Hooker
Journal:  Biotechnol Prog       Date:  2016-07-28

Review 9.  The complexity and cost of vaccine manufacturing - An overview.

Authors:  Stanley Plotkin; James M Robinson; Gerard Cunningham; Robyn Iqbal; Shannon Larsen
Journal:  Vaccine       Date:  2017-06-21       Impact factor: 3.641

Review 10.  The Future of Pharmaceutical Manufacturing Sciences.

Authors:  Jukka Rantanen; Johannes Khinast
Journal:  J Pharm Sci       Date:  2015-08-17       Impact factor: 3.534

View more
  1 in total

1.  Model-based intensification of CHO cell cultures: One-step strategy from fed-batch to perfusion.

Authors:  Anne Richelle; Brandon Corbett; Piyush Agarwal; Anton Vernersson; Johan Trygg; Chris McCready
Journal:  Front Bioeng Biotechnol       Date:  2022-08-22
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.