The PNPLA3 I148M variant is associated with transaminase elevations in type 2 diabetes patients treated with basal insulin peglispro.

Basal insulin peglispro (BIL) is a novel insulin with hepato-preferential action. In phase 3 trials, BIL showed significantly improved glycemic control but higher levels of transaminases (alanine aminotransferase (ALT) and aspartate aminotransferase (AST)), triglycerides (TGs) and liver fat content (LFC) compared with insulin glargine (GL). As variants in PNPLA3 (I148M) and TM6SF2 (E167K) are associated with nonalcoholic fatty liver disease, we assessed these variants in type 2 diabetes (T2D) patients randomized to receive BIL (n=1822) or GL (n=1270) in three phase 3 trials. Magnetic resonance imaging assessments of LFC were conducted in a subset of patients (n=296). Analyses showed α-corrected significant increases in change from baseline in AST (P=0.0004) and nominal increases in ALT (P=0.019), and LFC (P=0.035) for PNPLA3 (148M/M) genotypes in the BIL arm at 26 weeks but no significant associations in GL. PNPLA3 (148M/M) was also associated with increases in total cholesterol (P=0.014) and low-density lipoprotein cholesterol (P=0.005) but not with hemoglobin A1c or TG. T2D patients with the PNPLA3 (148M/M) genotype treated with BIL may be more susceptible to increased liver fat deposition. The current data provide further insights into the biological role of PNPLA3 in lipid metabolism.

Analysis of a large structure/biological activity data set using recursive partitioning.

Combinatorial chemistry and high-throughput screening are revolutionizing the process of lead discovery in the pharmaceutical industry. Large numbers of structures and vast quantities of biological assay data are quickly being accumulated, overwhelming traditional structure/activity relationship (SAR) analysis technologies. Recursive partitioning is a method for statistically determining rules that classify objects into similar categories or, in this case, structures into groups of molecules with similar potencies. SCAM is a computer program implemented to make extremely efficient use of this methodology. Depending on the size of the data set, rules explaining biological data can be determined interactively. An example data set of 1650 monoamine oxidase inhibitors exemplifies the method, yielding substructural rules and leading to general classifications of these inhibitors. The method scales linearly with the number of descriptors, so hundreds of thousands of structures can be analyzed utilizing thousands to millions of molecular descriptors. There are currently no methods to deal with statistical analysis problems of this size. An important aspect of this analysis is the ability to deal with mixtures, i.e., identify SAR rules for classes of compounds in the same data set that might be binding in different ways. Most current quantitative structure/activity relationship methods require that the compounds follow a single mechanism. Advantages and limitations of this methodology are presented.