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
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
131-157Subventions
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.