In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints

Matched molecular pair analysis (MMPA) has emerged as a powerful approach to mine and extract tacit knowledge from measured databases of small molecules. Extracted knowledge from past experimentation can assist future lead optimization as an idea generation tool and, hence, reduce the number of design-synthesis-test cycles. While attractive and intuitive, MMPA still presents several limitations. Analyses of internal absorption, distribution, metabolism, and excretion (ADME) databases of measured compounds show that chemical transformations with 10 pairs or more represent less than 1% of the total transforms identified by MMPA. A great wealth of design ideas remains effectively untapped and underutilized as the lack of measured data hinders extraction of robust trends. In this study we report the use of a quantitative structure-activity relationship (QSAR) model augmented MMPA approach (MMPA-by-QSAR) to infer the overall effect of chemical transformations on two essential ADME endpoints-lipophilicity and metabolic clearance. First, QSAR models are employed to predict compound activities, and subsequently, MMPA is used to identify and to extract virtual trends. Results obtained from retrospective analyses showed the ability to predict magnitudes of change close to experimental ones for the majority of transforms from each ADME data set. In the case of the lipophilicity endpoint (SFLogD) 73.7%, 87.85%, and 99% of transforms were predicted within 0.1, 0.15, and 0.3 units of the actual change. In the case of the clearance endpoint (HLM) 67.2%, 82.3%, and 99.5% of transforms were predicted within 0.08, 0.11, and 0.3 log units, respectively. Prospective application of MMPA-by-QSAR on untested compounds identified several novel transforms not observed in our measured data sets. When MMPs from these transforms were screened in our internal assays, it was found that the correct directionality of change was predicted for all but one of the tested transforms, and the predicted magnitudes of change have varying errors between predicted and measured mean changes ranging from 0.01 to 0.24 units for SFLogD and from 0.0 to 0.38 log units for HLM. This proposed MMPA-by-QSAR modeling approach has the potential to allow exploration of infrequent transforms or even identify completely novel transforms where no measured MMP is available.