The Drug Design Data Resource aims to test and advance the state of the art in protein-ligand modeling by holding community-wide blinded, prediction challenges. Here, we report on our third major round, Grand Challenge 3 (GC3). Held 2017-2018, GC3 centered on the protein Cathepsin S and the kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1, and included both pose-prediction and affinity-ranking components. GC3 was structured much like the prior challenges GC2015 and GC2. First, Stage 1 tested pose prediction and affinity ranking methods; then all available crystal structures were released, and Stage 2 tested only affinity rankings, now in the context of the available structures. Unique to GC3 was the addition of a Stage 1b self-docking subchallenge, in which the protein coordinates from all of the cocrystal structures used in the cross-docking challenge were released, and participants were asked to predict the pose of CatS ligands using these newly released structures. We provide an overview of the outcomes and discuss insights into trends and best-practices.

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.

A series of phenoxyacetic acids as subtype selective and potent hPPARδ partial agonists is described. Many analogues were readily accessible via a single solution-phase synthetic route which resulted in the rapid identification of key structure-activity relationships (SAR), and the discovery of two potent exemplars which were further evaluated in vivo. Details of the SAR, optimization, and in vivo efficacy of this series are presented herein.

The peroxisome proliferator-activated receptor γ (PPARγ) is a ligand-activated nuclear receptor, thought to play a role in energy metabolism, glucose homeostasis and microglia-mediated neuroinflammation. A novel benzimidazole series of centrally penetrant PPARγ partial agonists has been identified. The optimization of PPARγ activity and in vivo pharmacokinetics leading to the identification of GSK1997132B a potent, metabolically stable and centrally penetrant PPARγ partial agonist, is described.

Repression of gene transcription by the nuclear receptor Rev-erb alpha plays an integral role in the core molecular circadian clock. We report the crystal structure of a nuclear receptor-co-repressor (N-CoR) interaction domain 1 (ID1) peptide bound to truncated human Rev-erb alpha ligand-binding domain (LBD). The ID1 peptide forms an unprecedented antiparallel beta-sheet with Rev-erb alpha, as well as an alpha-helix similar to that seen in nuclear receptor ID2 crystal structures but out of register by four residues. Comparison with the structure of Rev-erb beta bound to heme indicates that ID1 peptide and heme induce substantially different conformational changes in the LBD. Although heme is involved in Rev-erb repression, the structure suggests that Rev-erb alpha could also mediate repression via ID1 binding in the absence of heme. The previously uncharacterized secondary structure induced by ID1 peptide binding advances our understanding of nuclear receptor-co-repressor interactions.

The Peroxisome Proliferator-Activated Receptors-PPAR alpha, PPAR gamma, and PPAR delta--are members of the nuclear receptor gene family that have emerged as therapeutic targets for the development of drugs to treat human metabolic diseases. The discovery of high affinity, subtype-selective agonists for each of the three PPAR subtypes has allowed elucidation of the pharmacology of these receptors and development of first-generation therapeutic agents for the treatment of diabetes and dyslipidemia. However, despite proven therapeutic benefits of selective PPAR agonists, safety concerns and dose-limiting side effects have been observed, and a number of late-stage development failures have been reported. Scientists have continued to explore ligand-based activation of PPARs in hopes of developing safer and more effective drugs. This review highlights recent efforts on two newer approaches, the simultaneous activation of all three PPAR receptors with a single ligand (PPAR pan agonists) and the selective modulation of a single PPAR receptor in a cell or tissue specific manner (selective PPAR modulator or SPPARM) in order to induce a subset of target genes and affect a restricted number of metabolic pathways.

Anthranilic acid GW9371 was identified as a novel class of PPARdelta partial agonist through high-throughput screening. The design and synthesis of SAR analogues is described. GSK1115 and GSK7227 show potent partial agonism of the PPARdelta target genes CPT1a and PDK4 in skeletal muscle cells.

The peroxisome proliferator activated receptors PPARalpha, PPARgamma, and PPARdelta are ligand-activated transcription factors that play a key role in lipid homeostasis. The fibrates raise circulating levels of high-density lipoprotein cholesterol and lower levels of triglycerides in part through their activity as PPARalpha agonists; however, the low potency and restricted selectivity of the fibrates may limit their efficacy, and it would be desirable to develop more potent and selective PPARalpha agonists. Modification of the selective PPARdelta agonist 1 (GW501516) so as to incorporate the 2-aryl-2-methylpropionic acid group of the fibrates led to a marked shift in potency and selectivity toward PPARalpha agonism. Optimization of the series gave 25a, which shows EC50 = 4 nM on PPARalpha and at least 500-fold selectivity versus PPARdelta and PPARgamma. Compound 25a (GW590735) has been progressed to clinical trials for the treatment of diseases of lipid imbalance.

PPARgamma-activating thiazolidinediones and carboxylic acids such as farglitazar exert their anti-diabetic effects in part in PPARgamma rich adipose. Both pro- and anti-adipogenic PPARgamma ligands promote glucose and lipid lowering in animal models of diabetes. Herein, we disclose representatives of an array of 160 farglitazar analogues with atypical inverse agonism of PPARgamma in mature adipocytes.