Cite

1. S. A. Sheweita, Drug-metabolizing enzymes: mechanisms and functions, Curr. Drug Metab. 1 (2000) 107–132.10.2174/1389200003339117Search in Google Scholar

2. P. Baranczewski, A. Stańczak, K. Sundberg, R. Svensson, A. Wallin, J. Jansson, P. Garberg and H. Postlind, Introduction to in vitro estimation of metabolic stability and drug interactions of new chemical entities in drug discovery and development, Pharmacol. Rep. 58 (2006) 453–472.Search in Google Scholar

3. R. Laine, Metabolic stability: main enzymes involved and best tools to assess it, Curr. Drug Metab. 9 (2008) 921–927.Search in Google Scholar

4. V. Y. Martiny and M. A. Miteva, Advances in molecular modeling of human cytochrome P450 polymorphism, J. Mol. Biol. 425 (2013) 3978–3992; https://doi.org/10.1016/j.jmb.2013.07.01010.1016/j.jmb.2013.07.010Search in Google Scholar

5. U. M. Zanger and M. Schwab, Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation, Pharmacol. Ther. 138 (2013) 103–141; https://doi.org/10.1016/j.pharmthera.2012.12.00710.1016/j.pharmthera.2012.12.007Search in Google Scholar

6. I. Kola and C. Landis, Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 3 (2004) 711–715; https://doi.org/10.1038/nrd147010.1038/nrd1470Search in Google Scholar

7. O. Pelkonen, M. Turpeinen, J. Uusitalo, A. Rautio and H. Raunio, Prediction of drug metabolism and interactions on the basis of in vitro investigations, Basic Clin. Pharmacol. Toxicol. 96 (2005) 167–175.10.1111/j.1742-7843.2005.pto960305.xSearch in Google Scholar

8. W. A. Korfmacher, Advances in the integration of drug metabolism into the lead optimization paradigm, Mini-Rev. Med. Chem. 9 (2009) 703–716.10.2174/138955709788452694Search in Google Scholar

9. S. S. Singh, Preclinical pharmacokinetics: an approach towards safer and efficacious drugs, Curr. Drug Metab.7 (2006) 165–182.Search in Google Scholar

10. T. Iwatsubo, N. Hirota, T. Ooie, H. Suzuki, N. Shimada, K. Chiba, T. Ishizaki, C. E. Green, C. A. Tyson and Y. Sugiyama, Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data, Pharmacol. Ther. 73 (1997) 147–171.10.1016/S0163-7258(96)00184-2Search in Google Scholar

11. T. N. Thompson, Early ADME in support of drug discovery: the role of metabolic stability studies, Curr. Drug Metab. 1 (2000) 215–241.10.2174/138920000333901811465046Search in Google Scholar

12. C. M. Masimirembwa, U. Bredberg and T. B. Andersson, Metabolic stability for drug discovery an d development: pharmacokinetic and biochemical challenges, Clin. Pharmacokinet. 42 (2003) 515–528.10.2165/00003088-200342060-0000212793837Search in Google Scholar

13. J. B. Houston and A. Galetin, Methods for predicting in vivo pharmacokinetics using data from in vitro assays, Curr. Drug Metab. 9 (2008) 940–951.10.2174/13892000878648516418991591Search in Google Scholar

14. M. Chiba, Y. Ishii and Y. Sugiyama, Prediction of hepatic clearance in human from in vitro data for successful drug development, AAPS J. 11 (2009) 262–276; https://doi.org/10.1208/s12248-009-9103-610.1208/s12248-009-9103-6Search in Google Scholar

15. M. J. Gomóz-Lechón, J. V. Castell and M. T. Donato, Hepatocytes-the choice to investigate drug metabolism and toxicity in man: in vitro variability as a reflection of in vivo, Chem.-Biol. Interact. 168 (2007) 30–50.Search in Google Scholar

16. D. Zhang, G. Luo, X. Ding and C. Lu, Preclinical experimental models of drug metabolism and disposition in drug discovery and development, Acta Pharm. Sin. B2 (2012) 549–561; https://doi.org/10.1016/j.apsb.2012.10.00410.1016/j.apsb.2012.10.004Search in Google Scholar

17. A. P. Li, Preclinical in vitro screening assays for drug-like properties, Drug Discov. Today Technol. 2 (2005) 179–185; https://doi.org/10.1016/j.ddtec.2005.05.02410.1016/j.ddtec.2005.05.024Search in Google Scholar

18. R. J. Riley and K. Grime, Metabolic screening in vitro: metabolic stability, CYP inhibition and induction, Drug Discov. Today Technol. 1 (2004) 365–372; https://doi.org/10.1016/j.ddtec.2004.10.00810.1016/j.ddtec.2004.10.008Search in Google Scholar

19. Z. E. Barter, M. K. Bayliss, P. H. Beaune, A. R. Boobis, D. J. Carlile, R. J. Edwards, J. B. Houston, B. G. Lake, J. C. Lipscomb, O. R. Pelkonen, G. T. Tucker and A. Rostami-Hodjegan, Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver, Curr. Drug Metab. 8 (2007) 33–45.Search in Google Scholar

20. C. A. McNaney, D. M. Drexler, S. Y. Hnatyshyn, T. A. Zvyaga, J. O. Knipe, J. V. Belcastro and M. Sanders, An automated liquid chromatography-mass spectrometry process to determine metabolic stability half-life and intrinsic clearance of drug candidates by substrate depletion, ASSAY Drug Dev. Technol. 6 (2008) 121–129; https://doi.org/10.1089/adt.2007.10310.1089/adt.2007.103Search in Google Scholar

21. P. Chao, A. S. Uss and K. C. Cheng, Use of intrinsic clearance for prediction of human hepatic clearance, Expert Opin. Drug Metab. Toxicol. 6 (2010) 189–198; https://doi.org/10.1517/1742525090340562210.1517/17425250903405622Search in Google Scholar

22. B. Davies and T. Morris, Physiological parameters in laboratory animals and humans, Pharm. Res.10 (1993) 1093–1095.10.1023/A:1018943613122Search in Google Scholar

23. J. B. Houston, Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance, Biochem. Pharmacol. 47 (1994) 1469–1479.10.1016/0006-2952(94)90520-7Search in Google Scholar

24. J. K. Singh, A. Solanki and V. S. Shirsath, Comparative in vitro intrinsic clearance of imipramine in multiple species liver micrososmes: human, rat, mouse and dog, J. Drug Metab. Toxicol. 3 (2012) 126; https://doi.org/10.4172/2157-7609.100012610.4172/2157-7609.1000126Search in Google Scholar

25. A. Basavapathruni, E. J. Olhava, S. R. Daigle, C. A. Therkelsen, L. Jin, P. A. Boriack-Sjodin, C. J. Allain, C. R. Klaus, A. Raimondi, M. P. Scott, A. Dovletoglou, V. M. Richon, R. M. Pollock, R. A. Copeland, M. P. Moyer, R. Chesworth, P. G. Pearson and N. J. Waters, Nonclinical pharmacokinetics and metabolism of EPZ-5676, a novel DOT1L histone methyltransferase inhibitor, Biopharm. Drug Dispos.35 (2014) 237–252; https://doi.org/10.1002/bdd.188910.1002/bdd.188924415392Search in Google Scholar

26. R. S. Obach, J. G. Baxter, T. E. Liston, B. M. Silber, B. C. Jones, F. Macintyre, D. J. Rance and P. Wastall, The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data, J. Pharmacol. Exp. Ther. 283 (1997) 46–58.Search in Google Scholar

27. J. H. Lin, M. Chiba, S. K. Balani, I. W. Chen, G. Y. Kwei, K. J. Vastag and J. A. Nishime, Species differences in the pharmacokinetics and metabolism of indinavir, a potent human immunodeficiency virus protease inhibitor, Drug Metab. Dispos. 24 (1996) 1111–1120.Search in Google Scholar

28. R. S. Obach, The importance of nonspecific binding in in vitro matrices, its impact on enzyme kinetic studies of drug metabolism reactions, and implications for in vitro-in vivo correlations, Drug Metab. Dispos. 24 (1996) 1047–1049.Search in Google Scholar

29. F. Liu, X. Zhuang, C. Yang, Z. Li, S. Xiong, Z. Zhang, J. Li, C. Lu and Z. Zhang, Characterization of preclinical in vitro and in vivo ADME properties and prediction of human PK using a physiologically based pharmacokinetic model for YQA-14, a new dopamine D3 receptor antagonist candidate for treatment of drug addiction, Biopharm. Drug Dispos. 35 (2014) 296–307; https://doi.org/10.1002/bdd.189710.1002/bdd.1897Search in Google Scholar

30. Z. Huang, H. Li, Q. Zhang, X. Tan, F. Lu, H. Liu and S. Li, Characterization of preclinical in vitro and in vivo pharmacokinetics properties for KBP-7018, a new tyrosine kinase inhibitor candidate for treatment of idiopathic pulmonary fibrosis, Drug Des. Devel. Ther. 9 (2015) 4319–4328; https://doi.org/10.2147/DDDT.S8305510.2147/DDDT.S83055Search in Google Scholar

31. L. Di, and R. S. Obach, Addressing the challenges of low clearance in drug research, AAPS J. 17 (2015) 352–357; https://doi.org/10.1208/s12248-014-9691-710.1208/s12248-014-9691-7Search in Google Scholar

32. P. Shah, E. Kerns, D. T. Nguyen, R. S. Obach, A. Q. Wang, A. Zakharov, J. McKew, A. Simeonov, C. E. Hop and X. Xu. An automated high-throughput metabolic stability assay using an integrated high-resolution accurate mass method and automated data analysis software, Drug Metab. Dispos. 44 (2016) 1653–1661; https://doi.org/10.1124/dmd.116.07201710.1124/dmd.116.072017Search in Google Scholar

33. A. L. Perryman, T. P. Stratton, S. Ekins and J. S. Freundlich. Predicting mouse liver microsomal stability with “pruned” machine learning models and public data, Pharm. Res. 33 (2016) 433–449; https://doi.org/10.1007/s11095-015-1800-510.1007/s11095-015-1800-5Search in Google Scholar

34. O. Pelkonen, A. Telonen, T. Rousu, L. Tursas, M. Turpeinen, J. Hokkanen, J. Uusitalo, M. Bouvier d’Yvoire and S. Coecke, Comparison of metabolic stability and metabolite identification of 55 ECVAM/ICCVAM validation compounds between human and rat liver homogenates and micro-somes – a preliminary analysis, ALTEX26 (2009) 214–222.10.14573/altex.2009.3.214Search in Google Scholar

35. A. E. Nassar, A. M. Kamel and C. Clarimont, Improving the decision-making process in the structural modification of drug candidates: enhancing metabolic stability, Drug Discov. Today9 (2004) 1020–1028.10.1016/S1359-6446(04)03280-5Search in Google Scholar

36. E. F. Brandon, C. D. Raap, I. Meijerman, J. H. Beijnen and J. H. Schellens, An update on in vitro test methods in human hepatic drug biotransformation research: pros and cons, Toxicol. Appl. Pharmacol. 189 (2003) 233–246.10.1016/S0041-008X(03)00128-5Search in Google Scholar

37. U. Fagerholm, Prediction of human pharmacokinetics-evaluation of methods for prediction of hepatic metabolic clearance, J. Pharm. Pharmacol.59 (2007) 803–828.10.1211/jpp.59.6.000717637173Search in Google Scholar

38. W. Richmond, M. Wogan, J. Isbell and W. P. Gordon, Interstrain differences of in vitro metabolic st ability and impact on early drug discovery, J. Pharm. Sci. 99 (2010) 4463–4468; https://doi.org/10.1002/jps.2217910.1002/jps.2217920845445Search in Google Scholar

39. T. S. Chan, H. Yu, A. Moore, S. R. Khetani and D. Tweedie, Meeting the challenge of predicting hepatic clearance of compounds slowly metabolized by cytochrome P450 using a novel hepatocyte model, HepatoPac, Drug Metab. Dispos.41 (2013) 2024–2032; https://doi.org/10.1124/dmd.113.05339710.1124/dmd.113.053397Search in Google Scholar

40. R. S. Obach, Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: an examination of in vitro half-life approach and nonspecific binding to microsomes, Drug Metab. Dispos.27 (1999) 1350–1359.Search in Google Scholar

41. G. N. Kumar and S. Surapaneni, Role of drug metabolism in drug discovery and development, Med. Res. Rev. 21 (2001) 397–411.10.1002/med.1016Search in Google Scholar

42. C. M. Masimirembwa, R. Thompson and T. B. Andersson, In vitro high throughput screening of compounds for favorable metabolic properties in drug discovery, Comb. Chem. High Throughput Screen. 4 (2001) 245–263.10.2174/1386207013331101Search in Google Scholar

43. H. Zhang, D. Zhang, W. Li, M. Yao, C. D’Arienzo, Y. X. Li, W. R. Ewing, Z. Gu, Y. Zhu, N. Murugesan, W. C. Shyu and W. G. Humphreys, Reduction of site-specific CYP3A-mediated metabolism for dual angiotensin and endothelin receptor antagonists in various in vitro systems and in cynomolgus monkeys, Drug Metab. Dispos.35 (2007) 795–805.10.1124/dmd.106.012781Search in Google Scholar

44. T. N. Thompson, Optimization of metabolic stability as a goal of modern drug design, Med. Res. Rev.21 (2001) 412–449.10.1002/med.1017Search in Google Scholar

45. T. Iwatsubo, H. Suzuki and Y. Sugiyama, Prediction of species differences (rats, dogs, humans) in the in vivo metabolic clearance of YM796 by the liver from in vitro data, J. Pharmacol.Exp. Ther. 283 (1997) 462–469.Search in Google Scholar

46. J. L. Bussiere, Species selection considerations for preclinical toxicology studies for biotherapeutics, Expert Opin. Drug Metab. Toxicol. 4 (2008) 871–877; https://doi.org/10.1517/17425255.4.7.87110.1517/17425255.4.7.871Search in Google Scholar

47. J. A. Sahi, A comprehensive evaluation of metabolic activity and intrinsic clearance in suspensions and monolayer cultures of cryopreserved primary human hepatocytes, J. Pharm. Sci. 101 (2012) 3989–4002; https://doi.org/10.1002/jps.2326210.1002/jps.23262Search in Google Scholar

48. L. Wang, C. W. Chiang, H. Liang, H. Wu, W. Feng, S. K. Quinney, J. Li and L. Li, How to choose in vitro systems to predict in vivo drug clearance: A system pharmacology perspective, BioMed. Res. Int. 2015 (2015) Article ID 857327 (9 pages); https://doi.org/10.1155/2015/85732710.1155/2015/857327Search in Google Scholar

49. K. M. L. Crommentuyn, J. H. M. Schellens, J. D. Van den Berg and J. H. Beijen, In vitro metabolism of anti-cancer drugs, methods and applications: Paclitaxel, docetaxel, tamoxifen and iosfamide, Cancer Treat. Rev.24 (1998) 345–366.10.1016/S0305-7372(98)90057-3Search in Google Scholar

50. M. T. Donato and J. V. Castel, Strategies and molecular probes to investigate the role of cytochrome P450 in drug metabolism, Clin. Pharmacokinet.42 (2003) 153–178.10.2165/00003088-200342020-0000412537515Search in Google Scholar

51. N. Plant, Strategies for using in vitro screens in drug metabolism, Drug Discov. Today9 (2004) 328–336.10.1016/S1359-6446(03)03019-8Search in Google Scholar

52. L. Jia and X. Liu, The conduct of drug metabolism studies considered good practice (II): in vitro experiments, Curr. Drug Metab. 8 (2007) 822–829.10.2174/138920007782798207275848018220563Search in Google Scholar

53. P. Poulin, J. R. Kenny, C. E. Hop and S. Haddad, In vitro-in vivo extrapolation of clearance: modeling hepatic metabolic clearance of highly bound drugs and comparative assessment with existing calculation methods, J. Pharm. Sci. 101 (2012) 838–851; https://doi.org/10.1002/jps.22792.10.1002/jps.2279222009717Search in Google Scholar

54. S. Ma, S. K. Chowdhury and K. B. Alton, Application of mass spectrometry for metabolite identification, Curr. Drug Metab.7 (2006) 503–523.10.2174/13892000677769789116787159Search in Google Scholar

55. C. Lu, P. Li, R. Gallegos, V. Uttamsingh, C. Q. Xia, G. T. Miwa, S. K. Balani and L. S. Gan, Comparis on of intrinsic clearance in liver microsomes and hepatocytes from rats and humans: evaluation of free fraction and uptake in hepatocytes, Drug Metab. Dispos. 34 (2006) 1600-1605.10.1124/dmd.106.01079316790553Search in Google Scholar

56. H. S. Brown, M. Griffin and J. B. Houston, Evaluation of cryopreserved human hepatocytes as an alternative in vitro system to microsomes for the prediction of metabolic clearance, Drug Metab. Dispos. 35 (2007) 293–301.10.1124/dmd.106.01156917132764Search in Google Scholar

57. J. Sahi, S. Grepper and C. Smith. Hepatocytes as a tool in drug metabolism, transport and safety evaluations in drug discovery, Curr. Drug Discov. Technol. 7 (2010) 188–198.10.2174/15701631079318057620843293Search in Google Scholar

58. M. J. Gómez-Lechón, M. T. Donato, J. V. Castell and R. Jover, Human hepatocytes in primary culture: the choice to investigate drug metabolism in man, Curr. Drug Metab.5 (2004) 443–462.Search in Google Scholar

59. A. P. Li, In vitro human hepatocyte-based experimental systems for the evaluation of human drug metabolism, drug-drug interactions, and drug toxicity in drug development, Curr. Top. Med. Chem.14 (2014) 1325–1338.10.2174/156802661466614050611441124805059Search in Google Scholar

60. K. M. Knights, D. M. Stresser J. O. Miners and C. L. Crespi, In vitro drug metabolism using liver microsomes, Curr. Protoc. Pharmacol.74 (2016) 7.8.1-7.8.24; https://doi.org/10.1002/cpph.910.1002/cpph.927636111Search in Google Scholar

61. H. Zhang, N. Gao, X. Tian, T. Liu, Y. Fang, J. Zhou, Q. Wen, B. Xu, B. Qi, J. Gao, H. Li, L. Jia and H. Qiao, Content and activity of human liver microsomal protein and prediction of individual hepatic clearance in vivo, Sci. Rep. 5 (2015) Article ID 17671 (12 pages); https://doi.org/10.1038/srep1767110.1038/srep17671466948826635233Search in Google Scholar

62. P. Krüger, R. Daneshfar, G. P. Eckert, J. Klein, D. A. Volmer, U. Bahr, W. E. Müller, M. Karas, M. Schubert-Zsilavecz and M. Abdel-Tawab, Metabolism of boswellic acids in vitro and in vivo, Drug Metab. Dispos. 36 (2008) 1135–1142; https://doi.org/10.1124/dmd.107.01842410.1124/dmd.107.01842418356270Search in Google Scholar

63. R. Mukkavilli, J. Pinjari, B. Patel, S. Sengottuvelan, S. Mondal, A. Gadekar, M. Verma, J. Patel, L. Pothuri, G. Chandrashekar, P. Koiram, T. Harisudhan, A. Moinuddin, D. Launay, N. Vachharajani, V. Ramanathan and D. Martin, In vitro metabolism, disposition, preclinical pharmacokinetics and prediction of human pharmacokinetics of DNDI-VL-2098, a potential oral treatment for Visceral L eishmaniasis, Eur. J. Pharm. Sci. 65 (2014) 147–155; https://doi.org/10.1016/j.ejps.2014.09.00610.1016/j.ejps.2014.09.00625261338Search in Google Scholar

64. K. N. Ellefsen, A. Wohlfarth, M. J. Swortwood, X. Diao, M. Concheiro and M. A. Huestis, 4-Methoxy-α-PVP: in silico prediction, metabolic stability, and metabolite identification by human hepatocyte incubation and high-resolution mass spectrometry, Forensic Toxicol. 34 (2016) 61–75; https://doi.org/10.1007/s11419-015-0287-410.1007/s11419-015-0287-4470513626793277Search in Google Scholar

65. A. S. Gandhi, A. Wohlfarth, M. Zhu, S. Pang, M. Castaneto, K. B. Scheidweiler and M. A. Huestis, High-resolution mass spectrometric metabolite profiling of a novel synthetic designer drug, N-(adamantan-1-yl)-(adamantan-1-yl)-1-(5-fluoropentyl)-1H-indole-3-carboxamide (STS-135), using cryopreserved human hepatocytes and assessment of metabolic stability with human liver micro-somes, Drug Test. Anal. 7 (2015) 197–198; https://doi.org/10.1002/dta.166210.1002/dta.1662423248724827428Search in Google Scholar

66. X. Zhang, J. Zhang, W. Li, L. Liu, B. Sun, Z. Guo, C. Shi and Y. Zhao, In vitro metabolism of 20(R)-25-methoxyl-dammarane-3, 12, 20 triol from Panax notoginseng in human, monkey, dog, rat, and mouse liver microsomes, PLoS One9 (2014)e94962; https://doi.org/10.1371/journal.pone.009496210.1371/journal.pone.0094962398808524736630Search in Google Scholar

67. B. D. Palmer, A. M. Thompson, H. S. Sutherlan, A. Blaser, I. Kmentova, S. G. Franzblau, B. Wan, Y. Wang, Z. Ma and W. A. Denny, Synthesis and structure-activity studies of biphenyl analogues of the tuberculosis drug (6S)-2-nitro-6-{[4-(trifluoromethoxy)benzyl]oxy}-6,7-dihydro-5H-imidazo[2,1-b][1,3]oxazine (PA-824), J. Med. Chem. 53 (2010) 282–294; https://doi.org/10.1021/jm901207n10.1021/jm901207n19928920Search in Google Scholar

68. L. Quintieri, M. Fantin, P. Palatini, S. De Martin, A. Rosato, M. Caruso, C. Geroni and M. Floreani, In vitro hepatic conversion of the anticancer agent nemorubicin to its active metabolite PNU-159682 in mice, rats and dogs: a comparison with human liver microsomes, Biochem. Pharmacol. 76 (2008) 784–795; https://doi.org/10.1016/j.bcp.2008.07.00310.1016/j.bcp.2008.07.00318671948Search in Google Scholar

69. C. M. Li, Y. Lu, R. Narayanan, D. D. Miller and J. T. Dalton, Drug metabolism and pharmacokinetics of 4-substituted methoxybenzoyl-aryl-thiazoles, Drug Metab. Dispos. 38 (2010) 2032–2039; https://doi.org/10.1124/dmd.110.03434810.1124/dmd.110.03434820675405Search in Google Scholar

70. L. Di, E. H. Kerns, Y. Hong, T. A. Kleintop, O. J. McConnell and D. M. Huryn, Optimization of a higher throughput microsomal stability screening assay for profiling drug discovery candidates, J. Biomol. Screen. 8 (2003) 453–462.10.1177/108705710325598814567798Search in Google Scholar

71. J. Huang, L. Si, Z. Fan, L. Hu, J. Qiu and G. Li, In vitro metabolic stability and metabolite profiling of TJ0711 hydrochloride, a newly developed vasodilatory β-blocker, using a liquid chromatography-tandem mass spectrometry method, J. Chromatogr. B879 (2011) 3386–3392; https://doi/org/10.1016/j.jchromb.2011.09.01010.1016/j.jchromb.2011.09.01021963275Search in Google Scholar

72. C. Sakai, S. Iwano, Y. Yamazaki, A. Ando, F. Nakane, M. Kouno, H. Yamazaki and Y. Miyamoto, Species differences in the pharmacokinetic parameters of cytochrome P450 probe substrates between experimental animals, such as mice, rats, dogs, monkeys, and microminipigs, and humans, J. Drug Metab. Toxicol. 5 (2014) Article ID 1000173 (12 pages); https://doi.org/10.4172/2157-7609.100017310.4172/2157-7609.1000173Search in Google Scholar

73. K. Słoczyńska, K. Pańczyk, A. M. Waszkielewicz, H. Marona and E. Pękala, In vitro mutagenic, antimutagenic, and antioxidant activities evaluation and biotransformation of some bioactive 4-substituted 1-(2-methoxyphenyl)piperazine derivatives, J. Biochem. Mol. Toxicol. 30 (2016) 593–601; https://doi.org/10.1002/jbt.2182610.1002/jbt.2182627450225Search in Google Scholar

74. A. Gunia-Krzyżak, D. Żelaszczyk, A. Rapacz, E. Żesławska, A. M. Waszkielewicz, K. Pańczyk, K. Słoczyńska, E. Pękala, W. Nitek, B. Filipek and H. Marona, Structure-anticonvulsant activity studies in the group of (E)-N-cinnamoyl aminoalkanols derivatives monosubstituted in phenyl ring with 4-Cl, 4-CH3 or 2-CH3, Bioorg. Med. Chem. 25 (2017) 471–482; https://doi.org/10.1016/j.bmc.2016.11.01410.1016/j.bmc.2016.11.01427876250Search in Google Scholar

75. A. M. Waszkielewicz, K. Słoczyńska, E. Pękala, P. Żmudzki, A. Siwek, A. Gryboś and H. Marona, Design, synthesis, and anticonvulsant activity of some derivatives of xanthone with aminoalkanol moieties, Chem. Biol. Drug Des. 89 (2017) 339–352; https://doi.org/10.1111/cbdd.1284210.1111/cbdd.1284227543433Search in Google Scholar

76. M. Marcinkowska, M. Kołaczkowski, K. Kamiński, A. Bucki, M. Pawłowski, A. Siwek, T. Karcz, G. Starowicz, K. Słoczyńska, E. Pękala, A.Wesołowska, J. Samochowiec, P. Mierzejewski and P. Bienkowski, 3-Aminomethyl derivatives of 2-phenylimidazo[1,2-a]-pyridine as positive allosteric modulators of GABAA receptor with potential antipsychotic activity, ACS Chem. Neurosci. 8 (2017) 1291–1298; https://doi.org/10.1021/acschemneuro.6b0043210.1021/acschemneuro.6b0043228211669Search in Google Scholar

77. R. Stringer, P. L. Nicklin and J. B. Houston, Reliability of human cryopreserved hepatocytes and liver microsomes as in vitro systems to predict metabolic clearance, Xenobiotica38 (2008) 1313–1329; https://doi.org/10.1080/0049825080244628610.1080/0049825080244628618853387Search in Google Scholar

78. R. A. Stringer, C. Strain-Damerell, P. Nicklin and J. B. Houston, Evaluation of recombinant cytochrome p450 enzymes as an in vitro system for metabolic clearance predictions, Drug Metab. Dispos.37 (2009) 1025–1034; https://doi.org/10.1124/dmd.108.02481010.1124/dmd.108.02481019196847Search in Google Scholar

79. K. Bachmann, J. Byers and R. Ghosh, Prediction of in vivo hepatic clearance from in vitro data using cryopreserved human hepatocytes, Xenobiotica33 (2003) 475–483.10.1080/004982503100007617712746104Search in Google Scholar

80. U. Zanelli, N. P. Caradonna, D. Hallifax, E. Turlizzi and J. B. Houston, Comparison of cryopreserved HepaRG cells with cryopreserved human hepatocytes for prediction of clearance for 26 drugs, Drug Metab. Dispos. 40 (2016) 104–110; https://doi.org/10.1124/dmd.111.04230910.1124/dmd.111.04230921998403Search in Google Scholar

81. D. F. McGinnity, M. G. Soars, R. A. Urbanowicz and R. J. Riley. Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance, Drug Metab. Dispos.32 (2004) 1247–1253.10.1124/dmd.104.00002615286053Search in Google Scholar

82. P. J. Bungay, S. Tweedy, D. C. Howe, K. R. Gibson, H. M. Jones and N. M. Mount, Preclinical and clinical pharmacokinetics of PF-02413873, a nonsteroidal progestrone receptor antagonist, Drug Metab. Dispos. 39 (2011) 1396–1405; https://doi.org/10.1124/dmd.110.03723410.1124/dmd.110.03723421543556Search in Google Scholar

83. S. Klieber, C. Arabeyre-Fabre, P. Moliner, E. Marti, M. Mandray, R. Ngo, C. Ollier, P. Brun and G. Fabre, Identification of metabolic pathways and enzyme systems involved in the in vitro human hepatic metabolism of dronedarone, a potent new oral antiarrythmic drug, Pharmacol. Res. Perspect.2 (2014) e00044; https://doi.org/10.1002/prp2.4410.1002/prp2.44418641325505590Search in Google Scholar

84. M. D. Green, X. Yang, M. Cramer and C. D. King, In vitro metabolic studies on the selective metabotropic glutamate receptor sub-type 5 (mGluR5) antagonist 3-[(2-methyl-1,3-thiazol-4-yl) ethynyl]-pyridine (MTEP), Neurosci. Lett. 391 (2006) 91–95.10.1016/j.neulet.2005.08.03216153770Search in Google Scholar

85. A. V. Rudraraju, M. F. Hossain, A. Shrestha, P. N. A. Amoyaw, B. L. Tekwani and M. O. F. Khan, In vitro metabolic stability study of new cyclen based antimalarial drug leads using RP-HPLC and LC-MS/MS, Mod. Chem. Appl. 2 (2014) Article ID 1000129 (8 pages); https://doi.org/10.4172/2329-6798.100012910.4172/2329-6798.1000129Search in Google Scholar

86. W. Klopf and P. Worboys, Scaling in vivo pharmacokinetics from in vitro metabolic stability data in drug discovery, Comb. Chem. High Throughput Screen.13 (2010) 159–169.10.2174/13862071079059679020053167Search in Google Scholar

87. O. Pelkonen and H. Raunio, In vitro screening of drug metabolism during drug development: can we trust the predictions?, Expert Opin. Drug Metab. Toxicol.1 (2005) 49–59.10.1517/17425255.1.1.4916922652Search in Google Scholar

88. K. Ito and J. B. Houston, Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes, Pharm. Res. (NY) 21 (2004) 785–792.10.1023/B:PHAM.0000026429.12114.7dSearch in Google Scholar

89. R. J. Riley, D. F. McGinnity and R. P. Austin, A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes, Drug Metab. Dispos. 33 (2005) 1304–1311.10.1124/dmd.105.00425915932954Search in Google Scholar

90. B. J. Ring, J. Y. Chien, K. K. Adkison, H. M. Jones, M. Rowland, R. D. Jones, J. W. Yates, M. S. Ku, C. R. Gibson, H. He, R. Vuppugalla, P. Marathe, V. Fischer, S. Dutta, V. K. Sinha, T. Björnsson, T. Lavé and P. Poulin, PhRMA CPCDC initiative on predictive models of human pharmacokinetics; Part 3: comparative assessement of prediction methods of human clearance, J. Pharm. Sci. 100 (2011) 4090–4110; https://doi.org/10.1002/jps.2255210.1002/jps.2255221541938Search in Google Scholar

91. Z. E. Barter, G. T. Tucker and K. Rowland-Yeo, Differences in cytochrome p450-mediated pharmacokinetics between Chinese and Caucasian populations predicted by mechanistic physiologically based pharmacokinetic modelling, Clin. Pharmacokinet. 52 (2013) 1085–1100; https://doi.org/10.1007/s40262-013-0089-y10.1007/s40262-013-0089-y23818090Search in Google Scholar

92. H. Wan, P. Bold, L. O. Larsson, J. Ulander, S. Peters, B. Löfberg, A. L. Ungell, M. Någård and A. Llinàs, Impact of input parameters on the prediction of hepatic plasma clearance using the well-stirred model, Curr. Drug Metab.11 (2010) 583–594; https://doi.org/10.2174/13892001079292733410.2174/13892001079292733420629632Search in Google Scholar

93. J. C. Kalvass, D.A. Tess, C. Giragossian, M. C. Linhares, and T. S. Maurer, Influence of microsomal concentration on apparent intrinsic clearance: implications for scaling in vitro data, Drug Metab. Dispos. 29 (2001) 1332–1336.Search in Google Scholar

94. P. R. Venkatesh, E. Goh, P. Zheng, L. S. New, L. Xin, M. K. Pasha, K. Sangthongpitag, P. Yeo and E. Kantharaj, In vitro phase I cytochrome P450 metabolism, permeability and pharmacokinetics of SB639, a novel histone deacetylase inhibitor in preclinical species, Biol. Pharm. Bull. 30 (2007) 1021–1024.10.1248/bpb.30.102117473456Search in Google Scholar

95. S. Ahn, J. D. Kearbey, C. M. Li, C. B. Duke, 3rd, D. D. Miller and J. T. Dalton, Biotransformation of a novel antimitotic agent, I-387, by mouse, rat, dog, monkey, and human liver microsomes and in vivo pharmacokinetics in mice, Drug Metab. Dispos.39 (2011) 636–643; https://doi.org/10.1124/dmd.110.03667310.1124/dmd.110.03667321233217Search in Google Scholar

96. V. Kumar, E. L. Schuck, R. D. Pelletier, N. Farah, K. B. Condon, M. Ye, C. Rowbottom, B. M. King, Z. Y. Zhang, P. L. Saxton and Y. N. Wong, Pharmacokinetic characterization of a natural product-inspired novel MEK1 inhibitor E6201 in preclinical species, Cancer Chemother. Pharmacol. 69 (2012) 229–237; https://doi.org/10.1007/s00280-011-1687-810.1007/s00280-011-1687-821698359Search in Google Scholar

97. J. O. Enoru, B. Yang, S. Krishnamachari, E. Villanueva, W. DeMaio, A. Watanyar, R. Chinnasamy, J. B. Arterburn and R. G. Perez, Preclinical metabolism, pharmacokinetics and in vivo analysis of new blood-brain-barrier penetrant fingolimod analogues: FTY720-C2 and FTY720-Mitoxy, PLoS One11 (2016) e0162162; https://doi.org/10.1371/journal.pone.016216210.1371/journal.pone.0162162501774927611691Search in Google Scholar

98. M. Zainuddin, A. B. Vinod, S. D. Gurav, A. Police, A. Kumar, C. Mithra, P. Dewang, R. R. Kethiri and R. Mullangi, Preclinical assessment of Orteronel(®), a CYP17A1 enzyme inhibitor in rats, Eur. J. Drug Metab. Pharmacokinet.41 (2016) 1–7; https://doi.org/10.1007/s13318-014-0229-210.1007/s13318-014-0229-225297456Search in Google Scholar

99. K. Tabata, N. Hamakawa, S. Sanoh, S. Terashita and T. Teramura, Exploratory population pharmacokinetics (e-PPK) analysis for predicting human PK using exploratory ADME data during early drug discovery research, Eur. J. Drug Metab. Pharmacokinet. 34 (2009) 117–128.10.1007/BF0319116019645221Search in Google Scholar

100. S. A. Wring, R. Randolph, S. Park, G. Abruzzo, Q. Chien, A. Flattery, G. Garrett, M. Peel, R. Outcalt, K. Powell, M. Truckis, D. Angulo and K. Borroto-Esoda, Preclinical pharmacokinetics and pharacodynamic target of SCY-078, a first-in-class orally active antifungal glucan synthesis inhibitor, in murine models of disseminated candidiasis, Antimicrob. Agents Chemother. 61 (2017) e02068-16 (15 pages); https://doi.org/10.1128/AAC.02068-1610.1128/AAC.02068-16536564528137806Search in Google Scholar

101. W. Z. Zhong, B. Lalovic and J. Zhan, Characterization of in vitro and in vivo metabolism of AG-024322, a novel cyclin-dependent kinase (CDK) inhibitor, Health1 (2009) 249–262; https://doi.org/10.4236/health.2009.1404110.4236/health.2009.14041Search in Google Scholar

102. A. Saxena, G. R. Valicherla, G. K. Jain, R. S. Bhatta, A. K. Saxena and J. R. Gayen, Metabolic profiling of a novel antithrombotic compound, S002-333, and its enantiomers: metabolic stability, species comparison and in vitro-in vivo extrapolation, Biopharm. Drug Dispos. 37 (2016) 185–199; https://doi.org/10.1002/bdd.199510.1002/bdd.199526477787Search in Google Scholar

103. J. Bylund and T. Bueters, Presystemic metabolism of AZ’0908, a novel mPGES-1 inhibitor: an in vitro and in vivo cross-species comparison, J. Pharm. Sci. 102 (2013) 1106–1115; https://doi.org/10.1002/jps.2344310.1002/jps.2344323316000Search in Google Scholar

104. J. H. Lin, Applications and limitations of interspecies scaling and in vitro extrapolation in pharmacokinetics, Drug Metab. Dispos. 26 (1998) 1202–1212.Search in Google Scholar

105. H. Bun, B. Disdier, C. Aubert and J. Catalin, Interspecies variability and drug interactions of clozapine metabolism by microsomes, Fundam. Clin. Pharmacol. 13 (1999) 577–581.10.1111/j.1472-8206.1999.tb00364.x10520731Search in Google Scholar

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