{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:29:19Z","timestamp":1780104559953,"version":"3.54.0"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Research Foundation of Korea (NRF) grant funded by the Korean government","award":["NRF-2019M3E5D4065860 and 2020R1C1C1003218"],"award-info":[{"award-number":["NRF-2019M3E5D4065860 and 2020R1C1C1003218"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Melanin-concentrating hormone receptor 1 (MCHR1) has been a target for appetite suppressants, which are helpful in treating obesity. However, it is challenging to develop an MCHR1 antagonist because its binding site is similar to that of the human Ether-\u00e0-go-go-Related Gene (hERG) channel, whose inhibition may cause cardiotoxicity. Most drugs developed as MCHR1 antagonists have failed in clinical development due to cardiotoxicity caused by hERG inhibition. Machine learning-based prediction models can overcome these difficulties and provide new opportunities for drug discovery. In this study, we identified KRX-104130 with potent MCHR1 antagonistic activity and no cardiotoxicity through virtual screening using two MCHR1 binding affinity prediction models and an hERG-induced cardiotoxicity prediction model. In addition, we explored other possibilities for expanding the new indications for KRX-104130 using a transcriptome-based drug repositioning approach. KRX-104130 increased the expression of low-density lipoprotein receptor (LDLR), which induced cholesterol reduction in the gene expression analysis. This was confirmed by comparison with gene expression in a nonalcoholic steatohepatitis (NASH) patient group. In a NASH mouse model, the administration of KRX-104130 showed a protective effect by reducing hepatic lipid accumulation, liver injury, and histopathological changes, indicating a promising prospect for the therapeutic effect of NASH as a new indication for MCHR1 antagonists.<\/jats:p>","DOI":"10.3390\/ijms23073807","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T21:22:14Z","timestamp":1648675334000},"page":"3807","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Identification and New Indication of Melanin-Concentrating Hormone Receptor 1 (MCHR1) Antagonist Derived from Machine Learning and Transcriptome-Based Drug Repositioning Approaches"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8253-7631","authenticated-orcid":false,"given":"Gyutae","family":"Lim","sequence":"first","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ka Young","family":"You","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Korea"},{"name":"Graduate School of New Drug Discovery and Development, Chungnam National University, Daejeon 34134, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0504-0119","authenticated-orcid":false,"given":"Jeong Hyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Korea"},{"name":"Graduate School of New Drug Discovery and Development, Chungnam National University, Daejeon 34134, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moon Kook","family":"Jeon","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0717-0948","authenticated-orcid":false,"given":"Byung Ho","family":"Lee","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Yong","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Duksung Women\u2019s University, 33 Samyang-ro 144-gil, Dobong-gu, Seoul 01369, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4242-0818","authenticated-orcid":false,"given":"Kwang-Seok","family":"Oh","sequence":"additional","affiliation":[{"name":"Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Korea"},{"name":"Department of Medicinal and Pharmaceutical Chemistry, University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/0361-9230(85)90213-8","article-title":"Immunohistochemical localization of a melanin concentrating hormone-like peptide in the rat brain","volume":"15","author":"Skofitsch","year":"1985","journal-title":"Brain Res. Bull."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1002\/cne.903190204","article-title":"The melanin-concentrating hormone system of the rat brain: An immuno- and hybridization histochemical characterization","volume":"319","author":"Bittencourt","year":"1992","journal-title":"J. Comp. Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1038\/380243a0","article-title":"Maratos-Flier, E. A role for melanin-concentrating hormone in the central regulation of feeding behaviour","volume":"380","author":"Qu","year":"1996","journal-title":"Nature"},{"key":"ref_4","first-page":"E627","article-title":"Melanin-concentrating hormone: A functional melanocortin antagonist in the hypothalamus","volume":"274","author":"Ludwig","year":"1998","journal-title":"Am. J. Physiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1172\/JCI10660","article-title":"Melanin-concentrating hormone overexpression in transgenic mice leads to obesity and insulin resistance","volume":"107","author":"Ludwig","year":"2001","journal-title":"J. Clin. Investig."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1016\/j.bmcl.2011.02.099","article-title":"Synthesis and SAR investigations of novel 2-arylbenzimidazole derivatives as melanin-concentrating hormone receptor 1 (MCH-R1) antagonists","volume":"21","author":"Lim","year":"2011","journal-title":"Bioorganic Med. Chem. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.bmcl.2011.10.111","article-title":"4-Arylphthalazin-1(2H)-one derivatives as potent antagonists of the melanin concentrating hormone receptor 1 (MCH-R1)","volume":"22","author":"Lim","year":"2012","journal-title":"Bioorganic Med. Chem. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1016\/j.bmcl.2013.01.053","article-title":"Synthesis and SAR study of pyrrolo[3,4-b]pyridin-7(6H)-one derivatives as melanin concentrating hormone receptor 1 (MCH-R1) antagonists","volume":"23","author":"Lim","year":"2013","journal-title":"Bioorganic Med. Chem. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1016\/j.bmcl.2012.08.025","article-title":"Melanin concentrating hormone receptor 1 (MCHR1) antagonists-Still a viable approach for obesity treatment?","volume":"22","author":"Hogberg","year":"2012","journal-title":"Bioorg. Med. Chem. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"118257","DOI":"10.1016\/j.apcatb.2019.118257","article-title":"A machine learning framework for the analysis and prediction of catalytic activity from experimental data","volume":"263","author":"Smith","year":"2020","journal-title":"Appl. Catal. B Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/S0097-8485(01)00094-8","article-title":"Drug design by machine learning: Support vector machines for pharmaceutical data analysis","volume":"26","author":"Burbidge","year":"2001","journal-title":"Comput. Chem."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2006","DOI":"10.1021\/jm8015365","article-title":"Identification of novel antibacterial peptides by chemoinformatics and machine learning","volume":"52","author":"Fjell","year":"2009","journal-title":"J. Med. Chem."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.1093\/bioinformatics\/btaa075","article-title":"DeepHIT: A deep learning framework for prediction of hERG-induced cardiotoxicity","volume":"36","author":"Ryu","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1021\/acs.jcim.8b00769","article-title":"Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity","volume":"59","author":"Cai","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"17138","DOI":"10.1038\/s41598-021-96612-0","article-title":"Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach","volume":"11","author":"Lim","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s41544-020-00054-1","article-title":"Identification of small non-coding RNAs from Rhizobium etli by integrated genome-wide and transcriptome-based methods","volume":"2","author":"Rajendran","year":"2020","journal-title":"ExRNA"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xie, L., He, S., Song, X., Bo, X., and Zhang, Z. (2018). Deep learning-based transcriptome data classification for drug-target interaction prediction. BMC Genom., 19.","DOI":"10.1186\/s12864-018-5031-0"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium","volume":"25","author":"Ashburner","year":"2000","journal-title":"Nat. Genet."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/nar\/28.1.27","article-title":"KEGG: Kyoto encyclopedia of genes and genomes","volume":"28","author":"Kanehisa","year":"2000","journal-title":"Nucleic Acids Res."},{"key":"ref_20","first-page":"A68","article-title":"The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge","volume":"19","author":"Tomczak","year":"2015","journal-title":"Contemp. Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1016\/j.cell.2017.10.049","article-title":"A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles","volume":"171","author":"Subramanian","year":"2017","journal-title":"Cell"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"14969","DOI":"10.1038\/s41598-018-33039-0","article-title":"LINCS L1000 dataset-based repositioning of CGP-60474 as a highly potent anti-endotoxemic agent","volume":"8","author":"Han","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104789","DOI":"10.1016\/j.antiviral.2020.104789","article-title":"LINCS dataset-based repositioning of rosiglitazone as a potential anti-human adenovirus drug","volume":"179","author":"Wang","year":"2020","journal-title":"Antivir. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.ygeno.2014.06.003","article-title":"Differential gene expression analysis in CCLE lesions provides new insights regarding the genetics basis of skin vs. systemic disease","volume":"104","author":"Smith","year":"2014","journal-title":"Genomics"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, Y., McGee, J., Chen, X., Doman, T.N., Gong, X., Zhang, Y., Hamm, N., Ma, X., Higgs, R.E., and Bhagwat, S.V. (2014). Identification of druggable cancer driver genes amplified across TCGA datasets. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0098293"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1146\/annurev.biochem.74.082803.133354","article-title":"Structure and physiologic function of the low-density lipoprotein receptor","volume":"74","author":"Jeon","year":"2005","journal-title":"Annu. Rev. Biochem."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9252","DOI":"10.1073\/pnas.89.19.9252","article-title":"Rabbit very low density lipoprotein receptor: A low density lipoprotein receptor-like protein with distinct ligand specificity","volume":"89","author":"Takahashi","year":"1992","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.nbd.2018.08.012","article-title":"Melanin-concentrating hormone neurons contribute to dysregulation of rapid eye movement sleep in narcolepsy","volume":"120","author":"Naganuma","year":"2018","journal-title":"Neurobiol. Dis."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e141513","DOI":"10.1172\/JCI141513","article-title":"Neutrophil-to-hepatocyte communication via LDLR-dependent miR-223-enriched extracellular vesicle transfer ameliorates nonalcoholic steatohepatitis","volume":"131","author":"He","year":"2021","journal-title":"J. Clin. Investig."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1093\/bioinformatics\/btab547","article-title":"PredMS: A random Forest model for predicting metabolic stability of drug candidates in human liver microsomes","volume":"38","author":"Ryu","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.tem.2015.11.008","article-title":"The Role of Cholesterol in the Pathogenesis of NASH","volume":"27","author":"Ioannou","year":"2016","journal-title":"Trends Endocrinol. Metab."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1002\/hep.26088","article-title":"Cholesterol and nonalcoholic fatty liver disease: Renewed focus on an old villain","volume":"56","author":"Kerr","year":"2012","journal-title":"Hepatology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.cmet.2012.04.004","article-title":"Increased Hepatic Synthesis and Dysregulation of Cholesterol Metabolism Is Associated with the Severity of Nonalcoholic Fatty Liver Disease","volume":"15","author":"Min","year":"2012","journal-title":"Cell Metab."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1111\/liv.14743","article-title":"Serum aromatic and branched-chain amino acids associated with NASH demonstrate divergent associations with serum lipids","volume":"41","author":"Sehgal","year":"2021","journal-title":"Liver Int."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/j.jhep.2012.11.014","article-title":"Non-alcoholic steatohepatitis: The role of oxidized low-density lipoproteins","volume":"58","author":"Walenbergh","year":"2013","journal-title":"J. Hepatol"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13321-018-0258-y","article-title":"Mordred: A molecular descriptor calculator","volume":"10","author":"Moriwaki","year":"2018","journal-title":"J. Cheminform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s13321-018-0270-2","article-title":"PyBioMed: A python library for various molecular representations of chemicals, proteins and DNAs and their interactions","volume":"10","author":"Dong","year":"2018","journal-title":"J. Cheminform."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem. Inf. Model."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_41","first-page":"2960","article-title":"Practical Bayesian optimization of machine learning algorithms","volume":"25","author":"Pereira","year":"2012","journal-title":"Neural Inf. Processing Syst."},{"key":"ref_42","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nprot.2008.211","article-title":"Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources","volume":"4","author":"Huang","year":"2009","journal-title":"Nat. Protoc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/nar\/gkn923","article-title":"Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists","volume":"37","author":"Sherman","year":"2009","journal-title":"Nucleic Acids Res."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/23\/7\/3807\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:46:40Z","timestamp":1760136400000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/23\/7\/3807"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,30]]},"references-count":44,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["ijms23073807"],"URL":"https:\/\/doi.org\/10.3390\/ijms23073807","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,30]]}}}