Contemporary Accounts in Drug Discovery and Development. Группа авторов

QSAR models. Comput. Nanotoxicol.: 437–493. https://doi.org/10.1201/9780429341373‐10.

      85 85 Hanser, T., Barber, C., Marchaland, J.F., and Werner, S. (2016). Applicability domain: towards a more formal definition. SAR QSAR Environ. Res. 27: 893–909.

      86 86 Leung, S.S.F., Sindhikara, D., and Jacobson, M.P. (2016). Simple predictive models of passive membrane permeability incorporating size‐dependent membrane‐water partition. J. Chem. Inf. Model. 56: 924–929.

      87 87 Leung, S.S.F., Mijalkovic, J., Borrelli, K., and Jacobson, M.P. (2012). Testing physical models of passive membrane permeation. J. Chem. Inf. Model. 52: 1621–1636.

      88 88 Hall, M.L., Jorgensen, W.L., and Whitehead, L. (2013). Automated ligand‐ and structure‐based protocol for in silico prediction of human serum albumin binding. J. Chem. Inf. Model. 53: 907–922.

      89 89 Lexa, K.W., Dolghih, E., and Jacobson, M.P. (2014). A structure‐based model for predicting serum albumin binding. PLoS One 9: e93323.

      90 90 Dickson, C.J., Velez‐Vega, C., and Duca, J.S. (2020). Revealing molecular determinants of hERG blocker and activator binding. J. Chem. Inf. Model. 60: 192–203.

      91 91 Mondal, S., Tresadern, G., Greenwood, J. et al. (2019). A Free Energy Perturbation Approach to Estimate the Intrinsic Solubilities of Drug‐like Small Molecules. ChemRxiv. doi:10.26434/chemrxiv.10263077

      92 92 Albanese, S.K., Chodera, J.D., Volkamer, A. et al. (2020). Is structure‐based drug design ready for selectivity optimization? J. Chem. Inf. Model. https://doi.org/10.1021/acs.jcim.0c00815.

      93 93 Harwood, H.J. Jr. (2005). Treating the metabolic syndrome: acetyl‐CoA carboxylase inhibition. Expert Opin. Ther. Targets 9: 267–281.

      94 94 Harwood, H.J. and James, H.H. (2012). The adipocyte as an endocrine organ in the regulation of metabolic homeostasis. Neuropharmacology: 57–75. https://doi.org/10.1016/j.neuropharm.2011.12.010.

      95 95 Lucas, C., Lucas, G., Lucas, N. et al. (2018). A systematic review of the present and future of non‐alcoholic fatty liver disease. Clin. Exp. Hepatol. 4: 165–174.

      96 96 Abu‐Elheiga, L., Matzuk, M.M., Abo‐Hashema, K.A., and Wakil, S.J. (2001). Continuous fatty acid oxidation and reduced fat storage in mice lacking acetyl‐CoA carboxylase 2. Science 291: 2613–2616.

      97 97 Abu‐Elheiga, L., Oh, W., Kordari, P., and Wakil, S.J. (2003). Acetyl‐CoA carboxylase 2 mutant mice are protected against obesity and diabetes induced by high‐fat/high‐carbohydrate diets. Proc. Natl. Acad. Sci. USA 100: 10207–10212.

      98 98 Savage, D.B., Choi, C.S., Samuel, V.T. et al. (2006). Reversal of diet‐induced hepatic steatosis and hepatic insulin resistance by antisense oligonucleotide inhibitors of acetyl‐CoA carboxylases 1 and 2. J. Clin. Invest. 116: 817–824.

      99 99 Qi, L., Heredia, J.E., Altarejos, J.Y. et al. (2006). TRB3 links the E3 ubiquitin ligase COP1 to lipid metabolism. Science 312: 1763–1766.

      100 100 Fullerton, M.D., Galic, S., Marcinko, K. et al. (2013). Single phosphorylation sites in Acc1 and Acc2 regulate lipid homeostasis and the insulin‐sensitizing effects of metformin. Nat. Med. 19: 1649–1654.

      101 101 Hoehn, K.L., Turner, N., Swarbrick, M.M. et al. (2010). Acute or chronic upregulation of mitochondrial fatty acid oxidation has no net effect on whole‐body energy expenditure or adiposity. Cell Metab.: 70–76. https://doi.org/10.1016/j.cmet.2009.11.008.

      102 102 Olson, D.P., Pulinilkunnil, T., Cline, G.W. et al. (2010). Gene knockout of Acc2 has little effect on body weight, fat mass, or food intake. Proc. Natl. Acad. Sci. USA: 7598–7603. https://doi.org/10.1073/pnas.0913492107.

      103 103 Harwood, H.J. Jr., Petras, S.F., Shelly, L.D. et al. (2003). Isozyme‐nonselective N‐substituted bipiperidylcarboxamide acetyl‐CoA carboxylase inhibitors reduce tissue malonyl‐CoA concentrations, inhibit fatty acid synthesis, and increase fatty acid oxidation in cultured cells and in experimental animals. J. Biol. Chem. 278: 37099–37111.

      104 104 Tong, L. and Harwood, H.J. Jr. (2006). Acetyl‐coenzyme a carboxylases: versatile targets for drug discovery. J. Cell. Biochem. 99: 1476–1488.

      105 105 Corbett, J.W., Freeman‐Cook, K.D., Elliott, R. et al. (2010). Discovery of small molecule isozyme non‐specific inhibitors of mammalian acetyl‐CoA carboxylase 1 and 2. Bioorg. Med. Chem. Lett. 20: 2383–2388.

      106 106 Bourbeau, M.P. and Bartberger, M.D. (2015). Recent advances in the development of acetyl‐CoA carboxylase (ACC) inhibitors for the treatment of metabolic disease. J. Med. Chem. 58: 525–536.

      107 107 Griffith, D.A., Kung, D.W., Esler, W.P. et al. (2014). Decreasing the rate of metabolic ketone reduction in the discovery of a clinical acetyl‐CoA carboxylase inhibitor for the treatment of diabetes. J. Med. Chem. 57: 10512–10526.

      108 108 Glund, S., Schoelch, C., Thomas, L. et al. (2012). Inhibition of acetyl‐CoA carboxylase 2 enhances skeletal muscle fatty acid oxidation and improves whole‐body glucose homeostasis in db/db mice. Diabetologia: 2044–2053. https://doi.org/10.1007/s00125‐012‐2554‐9.

      109 109 Tong, L. (2005). Acetyl‐coenzyme a carboxylase: crucial metabolic enzyme and attractive target for drug discovery. Cell. Mol. Life Sci. 62: 1784–1803.

      110 110 Zhang, H., Tweel, B., Li, J., and Tong, L. (2004). Crystal structure of the carboxyltransferase domain of acetyl‐coenzyme a carboxylase in complex with CP‐640186. Structure 12: 1683–1691.

      111 111 Harriman, G., Greenwood, J., Bhat, S. et al. (2016). Acetyl‐CoA carboxylase inhibition by ND‐630 reduces hepatic steatosis, improves insulin sensitivity, and modulates dyslipidemia in rats. Proc. Natl. Acad. Sci. USA 113: E1796–E1805.

      112 112 Abel, R., Mondal, S., Masse, C. et al. (2017). Accelerating drug discovery through tight integration of expert molecular design and predictive scoring. Curr. Opin. Struct. Biol. 43: 38–44.

      113 113 Tong, A. (2019). Gilead shores up hope for NASH cocktail with a glimpse at positive proof‐of‐concept data. https://endpts.com/gilead‐shores‐up‐hope‐for‐nash‐cocktail‐with‐a‐glimpse‐at‐proof‐of‐concept‐data (accessed 29 December 2020).

      114 114 Golan, D.E. (2008). Principles of Pharmacology: The Pathophysiologic Basis of Drug Therapy. Lippincott Williams & Wilkins.

      115 115 Mesa, R.A. (2010). Ruxolitinib, a selective JAK1 and JAK2 inhibitor for the treatment of myeloproliferative neoplasms and psoriasis. IDrugs 13: 394–403.

      116 116 Adis Editorial (2010). Tofacitinib. Drugs R. D. 10: 271–284.

      117 117 Tuttle, K.R., Brosius, F.C. 3rd, Adler, S.G. et al. (2018). JAK1/JAK2 inhibition by baricitinib in diabetic kidney disease: results from a phase 2 randomized controlled clinical trial. Nephrol. Dial. Transplant. 33: 1950–1959.

      118 118 Pardanani, A., Hood, J., Lasho, T. et al. (2007). TG101209, a small molecule JAK2‐selective kinase inhibitor potently inhibits myeloproliferative disorder‐associated JAK2V617F and MPLW515L/K mutations. Leukemia 21: 1658–1668.

      119 119 Ishizaki, M., Muromoto, R., Akimoto, T. et al. (2014). Tyk2 is a therapeutic target for psoriasis‐like skin inflammation. Int. Immunol. 26: 257–267.

      120 120 Diogo, D., Bastarache, L., Liao, K.P. et al. (2015). TYK2 protein‐coding variants protect against rheumatoid arthritis and autoimmunity, with no evidence of major pleiotropic effects on non‐autoimmune complex traits. PLoS One 10: e0122271.

      121 121 Gracey, E., Hromadová, D., Lim, M.