A simple strategy for mitigating the effect of data variability on the identification of active chemotypes from high-throughput screening data.
Among the several goals of a high-throughput screening campaign is the identification of as many active chemotypes as possible for further evaluation. Often, however, the number of concentration response curves (e.g., IC(50)s or K(i)s) that can be collected following a primary screen is limited by practical constraints such as protein supply, screening workload, and so forth. One possible approach to this dilemma is to cluster the hits from the primary screen and sample only a few compounds from each cluster. This introduces the question as to how many compounds must be selected from a cluster to ensure that an active compound is identified, if it exists at all. This article seeks to address this question using a Monte Carlo simulation in which the dependence of the success of sampling is directly linked to screening data variability. Furthermore, the authors demonstrate that the use of replicated compounds in the screening collection can easily assess this variability and provide a priori guidance to the screener and chemist as to the extent of sampling required to maximize chemotype identification during the triage process. The individual steps of the Monte Carlo simulation provide insight into the correspondence between the percentage inhibition and eventual IC(50) curves.
Johnson
Stephen R
SR
Pharmaceutical Research Institute, Bristol-Myers Squibb, Princeton, NJ 08543-4000, USA. stephen.johnson@bms.com
Padmanabha
Ramesh
R
Vaccaro
Wayne
W
Hermsmeier
Mark
M
Cacace
Angela
A
Lawrence
Mike
M
Dickey
Joyce
J
Esposito
Kim
K
Pike
Kristen
K
Wong
Victoria
V
Poss
Michael
M
Loughney
Deborah
D
Tebben
Andrew
A
D000255:Adenosine Triphosphate / Q000378:metabolism; D001672:Biocompatible Materials / Q000737:chemistry; D019921:Biotinylation; D016000:Cluster Analysis; D003198:Computer Simulation; D003374:Coumarins / Q000378:metabolism; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D019793:Fluorescein; D031541:Fluorescence Resonance Energy Transfer; D005456:Fluorescent Dyes; D020128:Inhibitory Concentration 50; D009010:Monte Carlo Method; D011137:Polystyrenes / Q000737:chemistry; D011494:Protein Kinases / Q000032:analysis*; D020794:Receptor Protein-Tyrosine Kinases / Q000032:analysis* / Q000037:antagonists & inhibitors; D043562:Receptors, G-Protein-Coupled / Q000032:analysis* / Q000037:antagonists & inhibitors; D012494:Sampling Studies; D012588:Scintillation Counting / Q000379:methods; D012985:Software Design; D013053:Spectrophotometry; D014909:Wheat Germ Agglutinins / Q000737:chemistry
D001672:Biocompatible Materials; D003374:Coumarins; D005456:Fluorescent Dyes; D011137:Polystyrenes; D043562:Receptors, G-Protein-Coupled; D014909:Wheat Germ Agglutinins; D000255:Adenosine Triphosphate; C030123:coumarin; D011494:Protein Kinases; D020794:Receptor Protein-Tyrosine Kinases; D019793:Fluorescein