Both relaxin-3 and its receptor (GPCR135) are expressed predominantly in brain regions known to play important roles in processing sensory signals. Recent studies have shown that relaxin-3 is involved in the regulation of stress and feeding behaviors. The mechanisms underlying the involvement of relaxin-3/GPCR135 in the regulation of stress, feeding, and other potential functions remain to be studied. Because relaxin-3 also activates the relaxin receptor (LGR7), which is also expressed in the brain, selective GPCR135 agonists and antagonists are crucial to the study of the physiological functions of relaxin-3 and GPCR135 in vivo. Previously, we reported the creation of a selective GPCR135 agonist (a chimeric relaxin-3/ INSL5 peptide designated R3/I5). In this report, we describe the creation of a high affinity antagonist for GPCR135 and GPCR142 over LGR7. This GPCR135 antagonist, R3(B⌬23-27)R/I5, consists of the relaxin-3 B-chain with a replacement of Gly 23 to Arg, a truncation at the C terminus (Gly 24 -Trp 27 deleted), and the A-chain of INSL5. In vitro pharmacological studies showed that R3(B⌬23-27)R/I5 binds to human GPCR135 (IC 50 ‫؍‬ 0.67 nM) and GPCR142 (IC 50 ‫؍‬ 2.29 nM) with high affinity and is a potent functional GPCR135 antagonist (pA2 ‫؍‬ 9.15) but is not a human LGR7 ligand. Furthermore, R3(B⌬23-27)R/I5 had a similar binding profile at the rat GPCR135 receptor (IC 50 ‫؍‬ 0.25 nM, pA2 ‫؍‬ 9.6) and lacked affinity for the rat LGR7 receptor. When administered to rats intracerebroventricularly, R3(B⌬23-27)R/I5 blocked food intake induced by the GPCR135 selective agonist R3/I5. Thus, R3(B⌬23-27)R/I5 should prove a useful tool for the further delineation of the functions of the relaxin-3/GPCR135 system.Relaxin-3 (R3) 2 (1) is the most recently identified member of the insulin-relaxin peptide family. Both relaxin-3 and its receptor, GPCR135 (2), are predominantly expressed in the brain (2, 3). GPCR135, an inhibitory receptor, is expressed in many regions of the rodent brain such as the superior colliculus, sensory cortex, olfactory bulb, amygdale, and paraventricular nucleus (4 -6), suggesting potential physiological involvement in neuroendocrine and sensory processing. Recent in vivo studies have further shown that relaxin-3 and GPCR135 are involved in the stress response and in regulation of feeding. More specifically, water restraint stress or intracerebroventricular corticotrophin-releasing factor (CRF) infusion induces relaxin-3 expression in cells of the nucleus incertus, a region where CRF receptor-1 is also expressed (7), and central administration of relaxin-3 induces feeding in rat (8, 9). These findings suggest that GPCR135 and relaxin-3 may be involved in multiple physiological processes, some of which might be as yet unknown.In vitro relaxin-3 activates GPCR135 (2), GPCR142 (10), and LGR7 (11) receptors. The predominant brain expression of both relaxin-3 and GPCR135, coupled with their high affinity interaction, strongly suggests that relaxin-3 is the endogenous ligand for GPCR135 (2). Phar...

No abstract

No abstract

Over the past decade, the pharmaceutical industry has begun to address an addition to ADME/Tox profiling--the ability of a compound to bind to and inhibit the human ether-a-go-go-related gene (hERG)-encoded cardiac potassium channel. With the compilation of a large and diverse set of compounds measured in a single, consistent hERG channel inhibition assay, we recognized a unique opportunity to attempt to construct predictive QSAR models. Early efforts with classification models built from this training set were very encouraging. Here, we report a systematic evaluation of regression models based on neural network ensembles in conjunction with a variety of structure representations and feature selection algorithms. The combination of these modeling techniques (neural networks to capture non-linear relationships in the data, feature selection to prevent over-fitting, and aggregation to minimize model instability) was found to produce models with very good internal cross-validation statistics and good predictivity on external data.

We present a novel approach for enhancing the diversity of a chemical library rooted on the theory of the wisdom of crowds. Our approach was motivated by a desire to tap into the collective experience of our global medicinal chemistry community and involved four basic steps: (1) Candidate compounds for acquisition were screened using various structural and property filters in order to eliminate clearly nondrug-like matter. (2) The remaining compounds were clustered together with our in-house collection using a novel fingerprint-based clustering algorithm that emphasizes common substructures and works with millions of molecules. (3) Clusters populated exclusively by external compounds were identified as "diversity holes," and representative members of these clusters were presented to our global medicinal chemistry community, who were asked to specify which ones they liked, disliked, or were indifferent to using a simple point-and-click interface. (4) The resulting votes were used to rank the clusters from most to least desirable, and to prioritize which ones should be targeted for acquisition. Analysis of the voting results reveals interesting voter behaviors and distinct preferences for certain molecular property ranges that are fully consistent with lead-like profiles established through systematic analysis of large historical databases.