Literature DB >> 28684136

Open innovation: Towards sharing of data, models and workflows.

Daniela J Conrado1, Mats O Karlsson2, Klaus Romero1, Céline Sarr3, Justin J Wilkins4.   

Abstract

Sharing of resources across organisations to support open innovation is an old idea, but which is being taken up by the scientific community at increasing speed, concerning public sharing in particular. The ability to address new questions or provide more precise answers to old questions through merged information is among the attractive features of sharing. Increased efficiency through reuse, and increased reliability of scientific findings through enhanced transparency, are expected outcomes from sharing. In the field of pharmacometrics, efforts to publicly share data, models and workflow have recently started. Sharing of individual-level longitudinal data for modelling requires solving legal, ethical and proprietary issues similar to many other fields, but there are also pharmacometric-specific aspects regarding data formats, exchange standards, and database properties. Several organisations (CDISC, C-Path, IMI, ISoP) are working to solve these issues and propose standards. There are also a number of initiatives aimed at collecting disease-specific databases - Alzheimer's Disease (ADNI, CAMD), malaria (WWARN), oncology (PDS), Parkinson's Disease (PPMI), tuberculosis (CPTR, TB-PACTS, ReSeqTB) - suitable for drug-disease modelling. Organized sharing of pharmacometric executable model code and associated information has in the past been sparse, but a model repository (DDMoRe Model Repository) intended for the purpose has recently been launched. In addition several other services can facilitate model sharing more generally. Pharmacometric workflows have matured over the last decades and initiatives to more fully capture those applied to analyses are ongoing. In order to maximize both the impact of pharmacometrics and the knowledge extracted from clinical data, the scientific community needs to take ownership of and create opportunities for open innovation.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Data sharing; Drug development; Modelling workflows; Open innovation; Pharmacometric models

Mesh:

Year:  2017        PMID: 28684136     DOI: 10.1016/j.ejps.2017.06.035

Source DB:  PubMed          Journal:  Eur J Pharm Sci        ISSN: 0928-0987            Impact factor:   4.384


  11 in total

Review 1.  Reproducible pharmacokinetics.

Authors:  John P A Ioannidis
Journal:  J Pharmacokinet Pharmacodyn       Date:  2019-04-19       Impact factor: 2.745

2.  Data standards for model-informed drug development: an ISoP initiative.

Authors:  Andrijana Radivojevic; Brian Corrigan; Nicholas Downie; Robert Fox; Jill Fiedler-Kelly; Huan Liu; Murad Melhem; David Radke; Peter Schaefer; Jing Su; Maciej J Swat; Nathan S Teuscher; Neelima Thanneer; Alice Zong; Justin J Wilkins
Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-07-25       Impact factor: 2.745

3.  Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy.

Authors:  Daniela J Conrado; Jane Larkindale; Alexander Berg; Micki Hill; Jackson Burton; Keith R Abrams; Richard T Abresch; Abby Bronson; Douglass Chapman; Michael Crowther; Tina Duong; Heather Gordish-Dressman; Lutz Harnisch; Erik Henricson; Sarah Kim; Craig M McDonald; Stephan Schmidt; Camille Vong; Xiaoxing Wang; Brenda L Wong; Florence Yong; Klaus Romero
Journal:  J Pharmacokinet Pharmacodyn       Date:  2019-05-24       Impact factor: 2.745

Review 4.  Open Science Practices in Gambling Research Publications (2016-2019): A Scoping Review.

Authors:  Eric R Louderback; Sally M Gainsbury; Robert M Heirene; Karen Amichia; Alessandra Grossman; Bo J Bernhard; Debi A LaPlante
Journal:  J Gambl Stud       Date:  2022-06-09

Review 5.  Open Data Revolution in Clinical Research: Opportunities and Challenges.

Authors:  Mohamed H Shahin; Sanchita Bhattacharya; Diego Silva; Sarah Kim; Jackson Burton; Jagdeep Podichetty; Klaus Romero; Daniela J Conrado
Journal:  Clin Transl Sci       Date:  2020-03-10       Impact factor: 4.689

6.  Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages.

Authors:  Matthew Fidler; Justin J Wilkins; Richard Hooijmaijers; Teun M Post; Rik Schoemaker; Mirjam N Trame; Yuan Xiong; Wenping Wang
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2019-07-16

Review 7.  Next-Generation DILI Biomarkers: Prioritization of Biomarkers for Qualification and Best Practices for Biospecimen Collection in Drug Development.

Authors:  Sharin E Roth; Mark I Avigan; David Bourdet; David Brott; Rachel Church; Ajit Dash; Douglas Keller; Philip Sherratt; Paul B Watkins; Lucas Westcott-Baker; Silvia Lentini; Michael Merz; Lila Ramaiah; Shashi K Ramaiah; Ann Marie Stanley; John Marcinak
Journal:  Clin Pharmacol Ther       Date:  2019-09-14       Impact factor: 6.875

8.  Standardized Data Structures in Rare Diseases: CDISC User Guides for Duchenne Muscular Dystrophy and Huntington's Disease.

Authors:  Ariana P Mullin; Diane Corey; Emily C Turner; Richard Liwski; Daniel Olson; Jackson Burton; Sudhir Sivakumaran; Lynn D Hudson; Klaus Romero; Diane T Stephenson; Jane Larkindale
Journal:  Clin Transl Sci       Date:  2020-08-25       Impact factor: 4.689

Review 9.  Opportunities and Challenges for Machine Learning in Rare Diseases.

Authors:  Sergio Decherchi; Elena Pedrini; Marina Mordenti; Andrea Cavalli; Luca Sangiorgi
Journal:  Front Med (Lausanne)       Date:  2021-10-05

10.  Machine Learning in Drug Discovery and Development Part 1: A Primer.

Authors:  Alan Talevi; Juan Francisco Morales; Gregory Hather; Jagdeep T Podichetty; Sarah Kim; Peter C Bloomingdale; Samuel Kim; Jackson Burton; Joshua D Brown; Almut G Winterstein; Stephan Schmidt; Jensen Kael White; Daniela J Conrado
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-03-11
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