You get what you screen for: Standards for experimental design and data fitting in drug discovery.

Computer simulation Dose–response curve Global data fitting Hill equation Ligand binding kinetics Mechanism-based data fitting Nonlinear regression Transient kinetics

Journal

Methods in enzymology
ISSN: 1557-7988
Titre abrégé: Methods Enzymol
Pays: United States
ID NLM: 0212271

Informations de publication

Date de publication:
2023
Historique:
medline: 23 10 2023
pubmed: 20 10 2023
entrez: 20 10 2023
Statut: ppublish

Résumé

A common mantra in drug discovery is that "You get what you screen for." This is not a promise that you will always get an effective drug candidate, but rather a warning that inaccuracies in your protocol for screening will more likely produce a compound that fails to be an effective candidate because it matches the properties of your screen, not the desired features of an ideal lead compound. It is with this in mind that we examine the current protocols for evaluating drug candidates and highlight some deficiencies while pointing the way to better methods. Many of the errors in data fitting can be rectified by abandoning the traditional equation-based data fitting methods and adopting the more rigorous mechanism-based fitting afforded by computer simulation based on numerical integration of rate equations. Using these methods bypasses the errors in judgement in choosing the appropriate equation for data fitting and the approximations required to derive those equations. In this chapter we outline the limitations and systematic errors in conventional methods of data fitting and illustrate the advantages of computer simulation and introduce the methods of analysis.

Identifiants

pubmed: 37858528
pii: S0076-6879(23)00254-9
doi: 10.1016/bs.mie.2023.08.003
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

131-157

Subventions

Organisme : NIAID NIH HHS
ID : R01 AI163336
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM114223
Pays : United States

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

Auteurs

Kenneth A Johnson (KA)

Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, United States. Electronic address: kajohnson@utexas.edu.

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Classifications MeSH