Theoretical and experimental relationships between percent inhibition and IC50 data observed in high-throughput screening

Journal of Biomolecular Screening

Volumn 18, Issue 1, 2013, Pages 1-13

  Gubler, Hanspeter ^a   Schopfer, Ulrich ^a   Jacoby, Edgar ^a  

^a NOVARTIS PHARMA AG   (Switzerland)

Author keywords

correlations; high throughput screening; percent inhibition and IC50 result comparison; relationship of primary and dose response screening results; sensitivity analysis; simulation studies

Indexed keywords

AUTOTAXIN; HISTONE DEACETYLASE 4;

ARTICLE; BIOTRANSFORMATION; CONCENTRATION RESPONSE; CONCEPTUAL FRAMEWORK; CORRELATION ANALYSIS; EXPERIMENTAL STUDY; HIGH THROUGHPUT SCREENING; IC 50; INHIBITION KINETICS; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; STATISTICAL ANALYSIS;

ALGORITHMS; COMPUTER SIMULATION; DRUG EVALUATION, PRECLINICAL; ENZYME ASSAYS; HIGH-THROUGHPUT SCREENING ASSAYS; HISTONE DEACETYLASE INHIBITORS; HISTONE DEACETYLASES; HUMANS; MODELS, STATISTICAL; MONTE CARLO METHOD; PHOSPHODIESTERASE INHIBITORS; PHOSPHORIC DIESTER HYDROLASES; REPRESSOR PROTEINS; STATISTICS, NONPARAMETRIC;

EID: 84871604633     PISSN: 10870571     EISSN: 1552454X     Source Type: Journal    
DOI: 10.1177/1087057112455219     Document Type: Article

References (27)

1. Hüser J., Lohmann E., Kalthof B., Burkhardt N., Brueggemeier U. High-throughput Screening for Lead Discovery. Hüser J., ed. Weinheim, Germany: Wiley-VCH ; 2006: 15-36.
2. Malo N., Hanley J. A., Cerquozzi S., Pelletier J., Nadon R.. Statistical Practice in High-Throughput Screening Data Analysis. Nat. Biotechnol. 2006 ; 24: 167-175
3. Coma I., Herranz J., Martin J. High-Throughput Screening, Methods and Protocols;. Janzen W. P. Bernasconi P., ed. Totowa, NJ: Humana Press ; 2009: 69-106.
4. De Lean A., Munson P. J., Rodbard D.. Simultaneous Analysis of Families of Sigmoidal Curves: Applications to Bioassay, Radioligand Assay, and Physiological Dose Response Curves. Am. J. Physiol. 1978 ; 235: E97 - E102
5. Fomenko I., Durst M., Balaban D.. Robust Regression for High Throughput Drug Screening. Comput. Methods Programs Biomed. 2006 ; 82: 31-37
6. Gottschalk P. G., Dunn J. R.. The Five-Parameter Logistic: A Characterization and Comparison with the Four-Parameter Logistic. Anal. Biochem. 2005 ; 343: 54-65
7. McFadyen I., Walker G., Alvarez J. Chemoinformatics in Drug Discovery;. Oprea T. I., ed. Weinheim, Germany: Wiley-VCH ; 2004: 143-173.
8. Spencer R. W.. High-throughput Screening of Historic Collections: Observations on File Size, Biological Targets, and File Diversity. Biotechnol. Bioeng. (Comb. Chem.). 1998 ; 61: 61-67
9. Inglese J., Auld D. S., Jadhav A., Johnson R. L., Simeonov A., Yasgar A., Zheng W., Austin C. P.. Quantitative High-Throughput Screening: A Titration-Based Approach That Efficiently Identifies Biological Activities in Large Chemical Libraries. Proc. Natl. Acad. Sci. U. S. A. 2006 ; 103: 11473-11478
10. RitzC.StreibigJ. C.Bioassay Analysis Using R. J. Stat. Software2005, 12. http://www.jstatsoft.org/
11. R Development Core Team. R: A Language and Environment for Statistical Computing;R Foundation for Statistical Computing: Vienna, Austria, 2008. http://www.R-project.org
12. Rocke D. M., Lorenzato S.. A Two-Component Model for Measurement Error in Analytical Chemistry. Technometrics. 1995 ; 37: 176-184
13. Durbin B., Rocke D. M.. Estimation of Transformation Parameters for Microarray Data. Bioinformatics. 2003 ; 19: 1360-1367
14. KellyM. A.Sample Integrity: The Missing Link in Quality HTS. Paper presented at IBC Life Sciences 2nd Annual Conference on Optimisation of High Quality Libraries; February 21-24, 2006; Amsterdam, Netherlands and at AAPS Meeting on Critical Issues in Discovering Quality Clinical Candidates; April24-26, 2006; Philadelphia, PA. http://www.aapspharmaceutica.com/meetings/ files/61/MicheleKellysPresentation.pdf
15. NelsenR. B.Pearson Product-Moment Correlation Coefficient. In Encyclopaedia of Mathematics;HazewinkelM., Ed.; Springer: Berlin, Germany, 2002. http://eom.springer.de/
16. Sebaugh J. L., McCray P. D.. Defining the Linear Portion of a Sigmoid-Shaped Curve: Bend Points. Pharm. Stat. 2003 ; 2: 167-174
17. Swamidass S. J., Bittker J. A., Bodycombe N. E., Ryder S. P., Clemons P. A.. An Economic Framework to Prioritize Confirmatory Tests after a High-Throughput Screen. J. Biomol. Screen. 2010 ; 15: 680-686
18. ProkhorovA. V.Spearman Coefficient of Rank Correlation. In Encyclopaedia of Mathematics;HazewinkelM., Ed.; Springer: Berlin, Germany, 2002. http://eom.springer.de/
19. NelsenR. B.Kendall Tau Metric. In Encyclopaedia of Mathematics; HazewinkelM., Ed.; Springer: Berlin, Germany, 2002. http://eom.springer.de/
20. Joe H.. Relative Entropy Measures of Multivariate Dependence. J. Am. Stat. Assoc. 1989 ; 84: 157-164
21. Steuer R., Kurths J., Daub C. O., Weise J., Selbig J.. The Mutual Information: Detecting and Evaluating Dependencies between Variables. Bioinformatics. 2002 ; 18: S231 - S240
22. GentlemanR. C.CareyV. J.BatesD. M.BolstadB.DettlingM.DudoitS.EllisB. GautierL.GeY.GentryJ.. Bioconductor: Open Software Development for Computational Biology and Bioinformatics. Genome Biol. 2005, 5, R80. http://genomebiology. com/2004/5/10/R80
23. Loader C. Local Regression and Likelihood;. New York: Springer ; 1999 :
24. LoaderC.Locfit: An Introduction. Stat. Comput. Graphics Newsletter1997, 8, 11-17. http://stat-computing.org/newsletter/
25. Gubler H. High-throughput Screening for Lead Discovery;. Hüser J., ed. Weinheim, Germany: Wiley-VCH ; 2006: 151-205.
26. Ferguson C. G., Bigman C. S., Richardson R. D., Van Meeteren L. A., Moolenaar W. H., Prestwich G. D.. Fluorogenic Phospholipid Substrate to Detect Lysophospholipase D/Autotaxin Activity. Org. Lett. 2006 ; 8: 2023-2026
27. Hoeglund A. B., Howard A. L., Wanjala I. W., Pham T. C. T., Parrill A. L., Baker D. L.. Characterization of non-lipid autotaxin inhibitors. Bioorg. Med. Chem. 2010 ; 18: 769-776