Data standards in drug discovery: A long way to go

Jens M Kelm; Marc Ferrer; Martin-Immanuel Bittner; Madhu Lal-Nag

doi:10.1016/j.drudis.2024.103879

Data standards in drug discovery: A long way to go

Drug Discov Today. 2024 Feb;29(2):103879. doi: 10.1016/j.drudis.2024.103879. Epub 2024 Jan 11.

Authors

Jens M Kelm¹, Marc Ferrer², Martin-Immanuel Bittner³, Madhu Lal-Nag⁴

Affiliations

¹ Precomb Therapeutics AG, Hombrechtikon, Switzerland. Electronic address: jens.kelm@precomb.com.
² Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD, USA.
³ Arctoris Ltd, Oxford, UK; Young Academy of the German National Academy of Sciences, Berlin, Germany.
⁴ InSphero AG, Schlieren, Switzerland.

PMID: 38216119
DOI: 10.1016/j.drudis.2024.103879

Abstract

Each year, millions to trillions of data points are generated to evaluate the response of chemicals and biologicals to human cells in vitro and in vivo using various technologies and endpoints. Despite the vast amount of data available, the development process has not become significantly more efficient in recent years. Given the increasing use of more complex physiological models, which are time-consuming and significantly more expensive, it is crucial to maximize the value of these valuable data through improved standardization.

Keywords: 3D cell culture; alternatives for animal tests; artificial intelligence; cell-based assays; data banking; drug discovery; micro-physiological systems; screening.

MeSH terms

Drug Discovery* / standards