Comparability of Mixed IC(50) Data – A Statistical Analysis

The biochemical half maximal inhibitory concentration (IC(50)) is the most commonly used metric for on-target activity in lead optimization. It is used to guide lead optimization, build large-scale chemogenomics analysis, off-target activity and toxicity models based on public data. However, the use of public biochemical IC(50) data is problematic, because they are assay specific and comparable only under certain conditions. For large scale analysis it is not feasible to check each data entry manually and it is very tempting to mix all available IC(50) values from public database even if assay information is not reported. As previously reported for K(i) database analysis, we first analyzed the types of errors, the redundancy and the variability that can be found in ChEMBL IC(50) database. For assessing the variability of IC(50) data independently measured in two different labs at least ten IC(50) data for identical protein-ligand systems against the same target were searched in ChEMBL. As a not sufficient number of cases of this type are available, the variability of IC(50) data was assessed by comparing all pairs of independent IC(50) measurements on identical protein-ligand systems. The standard deviation of IC(50) data is only 25% larger than the standard deviation of K(i) data, suggesting that mixing IC(50) data from different assays, even not knowing assay conditions details, only adds a moderate amount of noise to the overall data. The standard deviation of public ChEMBL IC(50) data, as expected, resulted greater than the standard deviation of in-house intra-laboratory/inter-day IC(50) data. Augmenting mixed public IC(50) data by public K(i) data does not deteriorate the quality of the mixed IC(50) data, if the K(i) is corrected by an offset. For a broad dataset such as ChEMBL database a K(i)- IC(50) conversion factor of 2 was found to be the most reasonable.