Comparability of Mixed IC50 Data – A Statistical Analysis

Abstract

The biochemical half maximal inhibitory concentration (IC50) 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 IC50 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 IC50 values from public database even if assay information is not reported. As previously reported for Ki database analysis, we first analyzed the types of errors, the redundancy and the variability that can be found in ChEMBL IC50 database. For assessing the variability of IC50 data independently measured in two different labs at least ten IC50 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 IC50 data was assessed by comparing all pairs of independent IC50 measurements on identical protein-ligand systems. The standard deviation of IC50 data is only 25% larger than the standard deviation of Ki data, suggesting that mixing IC50 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 IC50 data, as expected, resulted greater than the standard deviation of in-house intra-laboratory/inter-day IC50 data. Augmenting mixed public IC50 data by public Ki data does not deteriorate the quality of the mixed IC50 data, if the Ki is corrected by an offset. For a broad dataset such as ChEMBL database a Ki- IC50 conversion factor of 2 was found to be the most reasonable.

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling

“…Bioactivity data from ChEMBL was limited to four different constants: K D , K i , IC 50 , and EC 50 . It has been shown previously that K i and IC 50 can be combined for modeling (55). In order to check if these constants could be used interchangeably, a statistical analysis of their pChEMBL values was performed.…”

Quality Issues with Public Domain Chemogenomics Data

“…[45] Currently it is almost impossible to check if two IC 50 values from different publications are comparable and thus using mixed IC 50 data introduces more error to chemogenomic modelling than using K i data only. [46] 3.5 Improperly Archived Original Data A smaller, but nevertheless important issue is the lack of standards for proper scientific data archiving. A publication might make use of a third party data source that is no longer available, rendering the study irreproducible.…”

“…Such value pairs can be identified by looking at the values from different publications which differ only by a rounding margin (which will always be less than 0.17 log units). [31,46] An example in ChEMBL is the K i measurement for compound ID CHEMBL282021 against the homo sapiens carbonic anhydrase II (Target ID CHEMBL205). The value of 3.1 nM measured from Ilies et al [54] is cited and rounded to 3 nM by Vullo et al [55] It is important to remember that even if measurements have been replicated with very similar values in a different lab, the result might still be an experimental artefact, for example caused by aggregation.…”

Data quality in drug discovery: the role of analytical performance in ligand binding assays

“…For instance, binding constants from different laboratories showed an average deviation of approximately 0.5 log units, which translates to a factor of about 3 by which the measurements differ [1][2][3]. Surprisingly, this is just the average.…”

“…Data sets extracted from public databases also critically depend on the initial data quality [1][2][3]. The data sets can be curated to some extent by removing implausible data records (i.e.…”