What Makes a Potent Nitrosamine? Statistical Validation of Expert-Derived Structure–Activity Relationships

The discovery of
carcinogenic nitrosamine impurities
above the
safe limits in pharmaceuticals has led to an urgent need to develop
methods for extending structure–activity relationship (SAR)
analyses from relatively limited datasets, while the level of confidence
required in that SAR indicates that there is significant value in
investigating the effect of individual substructural features in a
statistically robust manner. This is a challenging exercise to perform
on a small dataset, since in practice, compounds contain a mixture
of different features, which may confound both expert SAR and statistical
quantitative structure–activity relationship (QSAR) methods.
Isolating the effects of a single structural feature is made difficult
due to the confounding effects of other functionality as well as issues
relating to determining statistical significance in cases of concurrent
statistical tests of a large number of potential variables with a
small dataset; a naïve QSAR model does not predict any features
to be significant after correction for multiple testing. We propose
a variation on Bayesian multiple linear regression to estimate the
effects of each feature simultaneously yet independently, taking into
account the combinations of features present in the dataset and reducing
the impact of multiple testing, showing that some features have a
statistically significant impact. This method can be used to provide
statistically robust validation of expert SAR approaches to the differences
in potency between different structural groupings of nitrosamines.
Structural features that lead to the highest and lowest carcinogenic
potency can be isolated using this method, and novel nitrosamine compounds
can be assigned into potency categories with high accuracy.