Gonadotrophin-releasing hormone receptors (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

GnRH1 and GnRH2 receptors (provisonal nomenclature [35], also called Type I and Type II GnRH receptor, respectively [78]) have been cloned from numerous species, most of which express two or three types of GnRH receptor [78, 77, 107]. GnRH I (p-Glu-His-Trp-Ser-Tyr-Gly-Leu-Arg-Pro-Gly-NH2) is a hypothalamic decapeptide also known as luteinizing hormone-releasing hormone, gonadoliberin, luliberin, gonadorelin or simply as GnRH. It is a member of a family of similar peptides found in many species [78, 77, 107] including GnRH II (pGlu-His-Trp-Ser-His-Gly-Trp-Tyr-Pro-Gly-NH2 (which is also known as chicken GnRH-II). Receptors for three forms of GnRH exist in some species but only GnRH I and GnRH II and their cognate receptors have been found in mammals [78, 77, 107]. GnRH1 receptors are expressed by pituitary gonadotrophs, where they mediate the effects of GnRH on gonadotropin hormone synthesis and secretion that underpin central control of mammalian reproduction. GnRH analogues are used in assisted reproduction and to treat steroid hormone-dependent conditions [53]. Notably, agonists cause desensitization of GnRH-stimulated gonadotropin secretion and the consequent reduction in circulating sex steroids is exploited to treat hormone-dependent cancers of the breast, ovary and prostate [53]. GnRH1 receptors are selectively activated by GnRH I and all lack the COOH-terminal tails found in other GPCRs. GnRH2 receptors do have COOH-terminal tails and (where tested) are selective for GnRH II over GnRH I. GnRH2 receptors are expressed by some primates but not by humans [81]. Phylogenetic classifications divide GnRH receptors into three [78] or five groups [122] and highlight examples of gene loss through evolution, with humans retaining only one ancient gene

Gonadotrophin-releasing hormone receptors in GtoPdb v.2021.3

GnRH1 and GnRH2 receptors (provisonal nomenclature [39], also called Type I and Type II GnRH receptor, respectively [85]) have been cloned from numerous species, most of which express two or three types of GnRH receptor [85, 84, 114]. GnRH I (p-Glu-His-Trp-Ser-Tyr-Gly-Leu-Arg-Pro-Gly-NH2) is a hypothalamic decapeptide also known as luteinizing hormone-releasing hormone, gonadoliberin, luliberin, gonadorelin or simply as GnRH. It is a member of a family of similar peptides found in many species [85, 84, 114] including GnRH II (pGlu-His-Trp-Ser-His-Gly-Trp-Tyr-Pro-Gly-NH2 (which is also known as chicken GnRH-II). Receptors for three forms of GnRH exist in some species but only GnRH I and GnRH II and their cognate receptors have been found in mammals [85, 84, 114]. GnRH1 receptors are expressed by pituitary gonadotrophs, where they mediate the effects of GnRH on gonadotropin hormone synthesis and secretion that underpin central control of mammalian reproduction. GnRH analogues are used in assisted reproduction and to treat steroid hormone-dependent conditions [58]. Notably, agonists cause desensitization of GnRH-stimulated gonadotropin secretion and the consequent reduction in circulating sex steroids is exploited to treat hormone-dependent cancers of the breast, ovary and prostate [58]. GnRH1 receptors are selectively activated by GnRH I and all lack the COOH-terminal tails found in other GPCRs. GnRH2 receptors do have COOH-terminal tails and (where tested) are selective for GnRH II over GnRH I. GnRH2 receptors are expressed by some primates but not by humans [88]. Phylogenetic classifications divide GnRH receptors into three [85] or five groups [129] and highlight examples of gene loss through evolution, with humans retaining only one ancient gene. The structure of the GnRH1 receptor in complex with elagolix has been elucidated [132]

Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis In-house HTS Data.

With increased automation and larger compound collections, the development of high-throughput screening (HTS) started replacing previous approaches in drug discovery from around the 1980s onward. However, even today it is not always appropriate, or even feasible, to screen large collections of compounds in a particular assay. Here, we present an efficient method for iterative screening of small subsets of compound libraries. With this method, the retrieval of active compounds is optimized using their structural information and biological activity fingerprints. We validated this approach retrospectively on 34 Novartis in-house HTS assays covering a wide range of assay biology, including cell proliferation, antibacterial activity, gene expression, and phosphorylation. This method was employed to retrieve subsets of compounds for screening, where selected hits from any given round of screening were used as starting points to select chemically and biologically similar compounds for the next iteration. By only screening ∼1% of the full screening collection (∼15 000 compounds), the method consistently retrieves diverse compounds belonging to the top 0.5% of the most active compounds for the HTS campaign. For most of the assays, over half of the compounds selected by the method were found to be among the 5% most active compounds of the corresponding full-deck HTS. In addition, the stringency of the iterative method can be modified depending on the number of compounds one can afford to screen, making it a flexible tool to discover active compounds efficiently.S. Paricharak thanks the Netherlands Organisation for Scientific Research (NWO, grant number NWO-017.009-065), Novartis Institutes for BioMedical Research (NIBR) and the Prins Bernhard Cultuurfonds for funding and C. Parker, M. Frederiksen, G. Landrum and N. Fechner for insightful discussions.This is the author accepted manuscript. The final version is available from ACS Publications via http://dx.doi.org/10.1021/acschembio.6b0002

Hydroxycarboxylic acid receptors (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

The hydroxycarboxylic acid family of receptors (ENSFM00500000271913, nomenclature as agreed by the NC-IUPHAR Subcommittee on Hydroxycarboxylic acid receptors [32, 10]) respond to organic acids, including the endogenous hydroxy carboxylic acids 3-hydroxy butyric acid and L-lactic acid, as well as the lipid lowering agents nicotinic acid (niacin), acipimox and acifran [47, 54, 57]. These receptors were provisionally described as nicotinic acid receptors, although nicotinic acid shows submicromolar potency at HCA2 receptors only and is unlikely to be the natural ligand [54, 57]

Hydroxycarboxylic acid receptors in GtoPdb v.2023.1

The hydroxycarboxylic acid family of receptors (ENSFM00500000271913, nomenclature as agreed by the NC-IUPHAR Subcommittee on Hydroxycarboxylic acid receptors [36, 12]) respond to organic acids, including the endogenous hydroxy carboxylic acids 3-hydroxy butyric acid and L-lactic acid, as well as the lipid lowering agents nicotinic acid (niacin), acipimox and acifran [53, 60, 65]. These receptors were provisionally described as nicotinic acid receptors, although nicotinic acid shows submicromolar potency at HCA2 receptors only and is unlikely to be the natural ligand [60, 65]

Application of portfolio optimization to drug discovery

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this work, a problem of selecting a subset of molecules, which are potential lead candidates for drug discovery, is considered. Such molecule subset selection problem is formulated as a portfolio optimization, well known and studied in financial management. The financial return, more precisely the return rate, is interpreted as return rate from a potential lead and calculated as a product of gain and probability of success (probability that a selected molecule becomes a lead), which is related to performance of the molecule, in particular, its (bio-)activity. The risk is associated with not finding active molecules and is related to the level of diversity of the molecules selected in portfolio. It is due to potential of some molecules to contribute to the diversity of the set of molecules selected in portfolio and hence decreasing risk of portfolio as a whole. Even though such molecules considered in isolation look inefficient, they are located in sparsely sampled regions of chemical space and are different from more promising molecules. One way of computing diversity of a set is associated with a covariance matrix, and here it is represented by the Solow-Polasky measure. Several formulations of molecule portfolio optimization are considered taking into account the limited budget provided for buying molecules and the fixed size of the portfolio. The proposed approach is tested in experimental settings for three molecules datasets using exact and/or evolutionary approaches. The results obtained for these datasets look promising and encouraging for application of the proposed portfolio-based approach for molecule subset selection in real settings

Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development

In quite a few diseases, drug resistance due to target variability poses a serious problem in pharmacotherapy. This is certainly true for HIV, and hence, it is often unknown which drug is best to use or to develop against an individual HIV strain. In this work we applied ‘proteochemometric’ modeling of HIV Non-Nucleoside Reverse Transcriptase (NNRTI) inhibitors to support preclinical development by predicting compound performance on multiple mutants in the lead selection stage. Proteochemometric models are based on both small molecule and target properties and can thus capture multi-target activity relationships simultaneously, the targets in this case being a set of 14 HIV Reverse Transcriptase (RT) mutants. We validated our model by experimentally confirming model predictions for 317 untested compound – mutant pairs, with a prediction error comparable with assay variability (RMSE 0.62). Furthermore, dependent on the similarity of a new mutant to the training set, we could predict with high accuracy which compound will be most effective on a sequence with a previously unknown genotype. Hence, our models allow the evaluation of compound performance on untested sequences and the selection of the most promising leads for further preclinical research. The modeling concept is likely to be applicable also to other target families with genetic variability like other viruses or bacteria, or with similar orthologs like GPCRs

A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization.

BACKGROUND: G protein-coupled receptors (GPCRs) represent a family of well-characterized drug targets with significant therapeutic value. Phylogenetic classifications may help to understand the characteristics of individual GPCRs and their subtypes. Previous phylogenetic classifications were all based on the sequences of receptors, adding only minor information about the ligand binding properties of the receptors. In this work, we compare a sequence-based classification of receptors to a ligand-based classification of the same group of receptors, and evaluate the potential to use sequence relatedness as a predictor for ligand interactions thus aiding the quest for ligands of orphan receptors. RESULTS: We present a classification of GPCRs that is purely based on their ligands, complementing sequence-based phylogenetic classifications of these receptors. Targets were hierarchically classified into phylogenetic trees, for both sequence space and ligand (substructure) space. The overall organization of the sequence-based tree and substructure-based tree was similar; in particular, the adenosine receptors cluster together as well as most peptide receptor subtypes (e.g. opioid, somatostatin) and adrenoceptor subtypes. In ligand space, the prostanoid and cannabinoid receptors are more distant from the other targets, whereas the tachykinin receptors, the oxytocin receptor, and serotonin receptors are closer to the other targets, which is indicative for ligand promiscuity. In 93% of the receptors studied, de-orphanization of a simulated orphan receptor using the ligands of related receptors performed better than random (AUC > 0.5) and for 35% of receptors de-orphanization performance was good (AUC > 0.7). CONCLUSIONS: We constructed a phylogenetic classification of GPCRs that is solely based on the ligands of these receptors. The similarities and differences with traditional sequence-based classifications were investigated: our ligand-based classification uncovers relationships among GPCRs that are not apparent from the sequence-based classification. This will shed light on potential cross-reactivity of GPCR ligands and will aid the design of new ligands with the desired activity profiles. In addition, we linked the ligand-based classification with a ligand-focused sequence-based classification described in literature and proved the potential of this method for de-orphanization of GPCRs.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Adenosine receptors (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

Adenosine receptors (nomenclature as agreed by the NC-IUPHAR Subcommittee on Adenosine Receptors [103]) are activated by the endogenous ligand adenosine (potentially inosine also at A3 receptors). Crystal structures for the antagonist-bound [146, 305, 213, 55], agonist-bound [362, 196, 198] and G protein-bound A2A adenosine receptors [43] have been described. The structures of an antagonist-bound A1 receptor [123] and an adenosine-bound A1 receptor-Gi complex [80] have been resolved by cryo-electronmicroscopy. Another structure of an antagonist-bound A1 receptor obtained with X-ray crystallography has also been reported [51]