title: Adopting a practical statistical approach for evaluating assay agreement in drug discovery.
abstract: The authors assess the equivalence of 2 assays and put forward a general approach for assay agreement analysis that can be applied during drug discovery. Data sets generated by different assays are routinely compared to each other during the process of drug discovery. For a given target, the assays used for high-throughput screening and structure-activity relationship studies will most likely differ in their assay reagents, assay conditions, and/or detection technology, which makes the interpretation of data between assays difficult, particularly as most assays are used to measure quantitative changes in compound potency against the target. To better quantify the relationship of data sets from different assays for the same target, the authors evaluated the agreement between results generated by 2 different assays that measure the activity of compounds against the same protein, ALK5. The authors show that the agreement between data sets can be quantified using correlation and Bland-Altman plots, and the precision of the assays can be used to define the expectations of agreement between 2 assays. They propose a scheme for addressing issues of assay data equivalence, which can be applied to address questions of how data sets compare during the lead identification and lead optimization processes in which assays are frequently added and changed.
authors.affiliation: Biogen Idec Inc., Cambridge, MA 02142, USA.
authors.forename: Dongyu
authors.initials: D
authors.lastname: Sun
authors.forename: Adrian
authors.initials: A
authors.lastname: Whitty
authors.forename: James
authors.initials: J
authors.lastname: Papadatos
authors.forename: Miki
authors.initials: M
authors.lastname: Newman
authors.forename: Jason
authors.initials: J
authors.lastname: Donnelly
authors.forename: Scott
authors.initials: S
authors.lastname: Bowes
authors.forename: Serene
authors.initials: S
authors.lastname: Josiah
mesh_terms: D030201:Activin Receptors, Type I / Q000737:chemistry*; D000255:Adenosine Triphosphate / Q000737:chemistry; D003627:Data Interpretation, Statistical; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D006801:Humans; D020128:Inhibitory Concentration 50; D007700:Kinetics; D008024:Ligands; D015233:Models, Statistical; D004364:Pharmaceutical Preparations; D010766:Phosphorylation; D011485:Protein Binding; D017346:Protein Serine-Threonine Kinases; D000077293:Receptor, Transforming Growth Factor-beta Type I; D018125:Receptors, Transforming Growth Factor beta / Q000737:chemistry*; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship; D013678:Technology, Pharmaceutical / Q000379:methods*; D013810:Therapeutic Equivalency
chemical_list: D008024:Ligands; D004364:Pharmaceutical Preparations; D018125:Receptors, Transforming Growth Factor beta; D000255:Adenosine Triphosphate; D017346:Protein Serine-Threonine Kinases; D030201:Activin Receptors, Type I; D000077293:Receptor, Transforming Growth Factor-beta Type I; C000625841:TGFBR1 protein, human