Eur J Drug Metab Pharmacokinet DOI 10.1007/s13318-015-0252-y
SHORT COMMUNICATION
Metabolic behavior prediction of pazopanib by cytochrome P450 (CYP) 3A4 by molecular docking Xing-Jie Liu • Hua Lu • Ju-Xiang Sun Su-Rong Wang • Yan-Shuai Mo • Xing-Sheng Yang • Ben-Kang Shi
•
Received: 12 May 2014 / Accepted: 20 November 2014 Ó Springer International Publishing Switzerland 2015
Abstract Metabolism-mediated drug adverse effects (e.g., drug–drug interaction, bioactivation, etc.) strongly limit the utilization of clinical drugs. The present study aims to predict the metabolic capability of cytochrome P450 (CYP) 3A4 toward pazopanib which is an excellent drug exhibiting therapeutic role toward various cancers especially for ovarian cancer. Pazopanib can be well docked into the activity cavity of CYP3A4, and the interaction structure in pazopanib was methyl group located besides nitrogen in the five-membered ring. The distance between the hydrogen atom in methyl group and active ˚ . The interaction amino acid is Glu374. center is 3.64 A Furthermore, both pazopanib and ketoconazole were docked into the activity cavity of CYP3A4 to compare their binding potential. The distance between ketoconazole and ˚ ) is closer than the distance between activity center (2.10 A pazopanib and activity center of CYP3A4, indicating the easy influence of CYP3A4 inhibitor toward the metabolism of pazopanib. All these data were helpful for the clinical application of pazopanib, and R&D of other tinib drug candidates as new anti-tumor drugs. Keywords Pazopanib Cytochrome P450 (CYP) 3A4 Drug–drug interaction
X.-J. Liu H. Lu X.-S. Yang (&) B.-K. Shi Qilu Hospital of Shandong University, Jinan, Shandong, China e-mail: [email protected] X.-J. Liu H. Lu J.-X. Sun S.-R. Wang Y.-S. Mo Linyi People’s Hospital, Linyi, Shandong, China
1 Introduction Ovarian cancer is a cancerous growth arising from the ovary, and most ovarian cancers arise from the surface (epithelium) of the ovary. Ovarian cancer remains to be the 5th leading cause of cancer-related deaths among women, and the major management methods of ovarian cancer contain surgery, chemotherapy, radiation therapy, and immunotherapy (Llaurado et al. 2014). To date, the chemotherapy drugs approved by food and drug administration (FDA) to treat ovarian cancer contain cisplatin (Khrunin et al. 2014), cyclophosphamide (Barber et al. 2013), paclitaxel (Hahn et al. 2014), and topotecan (Stein et al. 2013). However, many disadvantages exist for the utilization of these drugs, including the drug resistance (Sharma et al. 2014; Kim et al. 2014). Searching an efficiently therapeutic drug is necessary and important for the treatment of ovarian cancer. Pazopanib, sold under the trade name Votrient, is a potent and selective inhibitor of multitargeted receptor tyrosine kinase (Davidson and Secord 2014). Pazopanib has been demonstrated to exhibit antiovarian cancer (Friedlander et al. 2010; Hashimoto et al. 2010). Metabolic behavior strongly limits the utilization and R&D of tinib drugs. For example, reactive metabolites formed through metabolism-mediated bioactivation have been strongly correlated with the adverse effects of this kind of drugs, including erlotinib and gefitinib (Li et al. 2009, 2010). In addition, some tinib drugs can inhibit the activity of some drug-metabolizing enzymes (e.g., cytochrome P450 1A2. etc.), which might induce the potential drug–drug interaction (Gu et al. 2014). The interaction between pazopanib and cytochrome P450 (CYP) remains unclear. CYP3A4 has been considered to be the most important drug-metabolizing enzyme
Eur J Drug Metab Pharmacokinet
involved in the metabolism of clinical drugs, and previous literature indicated that the inhibitor of CYP3A4 ketoconazole can strongly weaken the metabolic process of pazopanib (Tan et al. 2013). Molecular docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. Molecular docking is an important prediction tool for the metabolism of compounds, and metabolism-based drug–drug interaction. Therefore, the aim of the present study was to predict the interaction between pazopanib and CYP3A4.
2 Materials and methods 2.1 Structure selection and preparation for CYP3A4 and pazopanib To better understand the interaction between pazopanib and CYP3A4, suitable CYP3A4 crystal structure was needed. In the present study, the crystal structure of CYP3A4 binded with ketoconazole (PDB code 2V0M) was selected from protein data bank (http://www.rcsb.org/pdb). The structure was processed using the protein preparation wizard in the Schro¨dinger suite of programs, and the missing residues in the middle of the chain were added, and hydrogen atoms were assigned. Chemdraw software was used to draw the two-dimensional structure of linifanib with standard bond lengths and angles. 2.2 Molecular docking of pazopanib into the activity cavity of CYP3A4 At this stage, the structure of pazopanib was docked into the processed crystal structure of CYP3A4. The structure of ketoconazole was firstly extracted from the cavity of CYP3A4. The molecular energy was minimized using Tripos force field, and the most stable conformation was searched using the Powell conjugate gradient algorithm with a convergence criterion of 0.001 kcal/mol. 0.05 k˚ was employed as the limit of energy gradient. cal/mol. A Gasteiger–Huckel method in SYBYL-X program (Tripos Inc.) was used to calculate the compounds’ partial charges. The top 20 ranking poses were saved, and significantly different poses were preferred during this screening procedure to capture more diversity of pazopanib–CYP3A4 complex. The structure of linifanib was docked into the activity cavity using Surflex-Dock (within Sybyl-X1.2, Tripos International). The protoMol was generated with the automated method, a threshold of 0.50 and a bloat value of 2. The Surflex-Dock mode was employed, and the other parameters were at default values. To ensure that reasonable docked poses were obtained, the docked
conformations of ligands in the 3A4 protein were compared to the conformation of the inhibitor seen in the crystal structure.
3 Results The docking of pazopanib into the activity cavity was shown in Fig. 1. Pazopanib can be well docked into the activity cavity of CYP3A4, and the interaction structure in pazopanib was methyl group located besides nitrogen in the five-membered ring. The distance between the hydro˚ . The gen atom in methyl group and active center is 3.64 A interaction amino acid is Glu374. Furthermore, both pazopanib and ketoconazole were docked into the activity cavity of CYP3A4 to compare their binding potential. As shown in Fig. 2, the distance between ketoconazole and ˚ ) is closer than the distance between activity center (2.10 A pazopanib and activity center of CYP3A4, indicating the easy influence of CYP3A4’s strong inhibitor toward the metabolism of pazopanib.
4 Discussion Drug metabolism-mediated adverse effects strongly limit the clinical application of drugs and R&D of new chemical entities (NCEs), including drug metabolism-mediated drug–drug interaction and bioactivation (Fang et al. 2010a). Sager et al. demonstrated the complex drug–drug interaction related with fluoxetine and its circulating metabolite norfluoxetine caused by the inhibition toward the activity of multiple cytochrome P450 isoforms, including CYP3A4, CYP2D6, and CYP2C19 (Sager et al.
Fig. 1 Molecular docking of pazopanib into the activity cavity of CYP3A4. The crystal structure of CYP3A4 binded with ketoconazole (PDB code 2V0M) was selected from protein data bank (http://www. rcsb.org/pdb). The structure of ketoconazole was firstly extracted from the cavity of CYP3A4 before the docking of pazopanib into the activity cavity
Eur J Drug Metab Pharmacokinet
Fig. 2 Molecular docking of both pazopanib and ketoconazole into the activity cavity of CYP3A4. The green color represents the structure of ketoconazole and the red color represents the structure of pazopanib
2014). The inhibition of noscapine toward the activity of CYP3A4 and CYP2C9 has been an important reason for clinical noscapine–warfarin interaction (Fang et al. 2010b). For the metabolism-catalyzed bioactivation, many structural alerts have been reported (Kalgutkar and Didiuk 2009). For example, the bioactivation of acetaminophen (APAP) into N-acetyl-p-benzoquinone imine (NAPQI) has been widely regarded as the major reactive metabolites to result in the APAP-induced liver toxicity when overdose of APAP (Ukairo et al. 2013). Molecular docking can be used to predict the metabolism-mediated adverse effects of drugs. For example, molecular docking method was used to predict the metabolic behavior of aryl hydrocarbon receptor antagonist GNF-351 and the potential drug–drug interaction with CYP3A4 inhibitor ketoconazole (Liu et al. 2014). The molecular docking method has also been used to explain the structure-UDP-glucuronosyltransferase (UGT) 2B7 activity inhibition relationship (Ma et al. 2014). In the present study, we used molecular docking to predict the metabolism of pazopanib by CYP3A4, and potential drug– drug interaction between pazopanib and ketoconazole. The results indicated the high binding capability of pazopanib toward the activity cavity of CYP3A4, and the stronger binding ability of ketoconazole than pazopanib, which might explain why the drug–drug interaction occurs when ketoconazole was co-administered with pazopanib (Tan et al. 2013).
References Barber EL, Zsiros E, Lurain JR, Rademaker A, Schink JC, Neubauer NL (2013) The combination of intravenous bevacizumab and
metronomic oral cyclophosphamide is an effective regimen for platinum-resistant recurrent ovarian cancer. J Gynecol Oncol 24(3):258–264 Davidson BA, Secord AA (2014) Profile of pazopanib and its potential in the treatment of epithelial ovarian cancer. Int J Womens Health 6:289–300 Fang ZZ, Zhang YY, Wang XL, Cao YF, Huo H, Yang L (2010a) Bioactivation of herbal constituents: simple alerts in the complex system. Expert Opin Drug MetabToxicol 7(8):989–1007 Fang ZZ, Zhang YY, Ge GB, Huo H, Liang SC, Yang L (2010b) Time-dependent inhibition (TDI) of CYP3A4 and CYP2C9 by noscapine potentially explains clinical noscapine–warfarin interaction. Br J Clin Pharmacol 69(2):193–199 Friedlander M, Hancock KC, Rischin D, Messing MJ, Stringer CA, Matthys GM, Ma B, Hodge JP, Lager JJ (2010) A Phase II, openlabel study evaluating pazopanib in patients with recurrent ovarian cancer. Gynecol Oncol 119(1):32–37 Gu R, Hibbs DE, Ong JA, Edwards RJ, Murray M (2014) The multikinase inhibitor axitinib is a potent inhibitor of human CYP1A2. Biochem Pharmacol 88(2):245–252 Hahn HS, Lee KH, Lee IH, Lee JH, Whang CS, Jo YW, Kim TJ (2014) Metronomic oral paclitaxel shows anti-tumor effects in an orthotopic mouse model of ovarian cancer. J Gynecol Oncol 25(2):130–135 Hashimoto K, Man S, Xu P, Cruz-Munoz W, Tang T, Kumar R, Kerbel RS (2010) Potent preclinical impact of metronomic lowdose oral topotecan combined with the antiangiogenic drug pazopanib for the treatment of ovarian cancer. Mol Cancer Ther 9(4):996–1006 Kalgutkar AS, Didiuk MT (2009) Structural alerts, reactive metabolites, and protein covalent binding: how reliable are these attributes as predictors of drug toxicity? Chem Biodivers 6(11):2115–2137 Khrunin AV, Khokhrin DV, Moisseev AA, Gorbunova VA, Limborska SA (2014) Pharmacogenomic assessment of cisplatinbased chemotherapy outcomes in ovarian cancer. Pharmacogenomics 15(3):329–337 Kim YW, Kim EY, Jeon D, Liu JL, Kim HS, Choi JW, Ahn WS (2014) Differential microRNA expression signatures and cell type-specific association with Taxol resistance in ovarian cancer cells. Drug Des Devel Ther 8:293–314 Li X, Kamenecka TM, Cameron MD (2009) Bioactivation of the epidermal growth factor receptor inhibitor gefitinib: implications for pulmonary and hepatic toxicities. Chem Res Toxicol 22(10):1736–1742 Li X, Kamenecka TM, Cameron MD (2010) Cytochrome P450mediated bioactivation of the epidermal growth factor receptor inhibitor erlotinib to a reactive electrophile. Drug Metab Dispos 38(7):1238–1245 Liu T, Qian G, Wang W, Zhang Y (2014) Molecular docking to understand the metabolic behavior of GNF-351 by CYP3A4 and its potential drug–drug interaction with ketoconazole. Eur J Drug Metab Pharmacokinet. doi:10.1007/s13318-014-0201-1 Llaurado M, Majem B, Altadill T, Lanau L, Castellvi J, SanchezIglesias JL, Cabrera S, de la Torre J, Dı´az-Feijoo B, Pe´rezBenavente A, Colas E, Olivan M, Doll A, Alameda F, MatiasGuiu X, Moreno-Bueno G, Carey M, Del Campo JM, GilMoreno A, Reventos J, Rigau M (2014) MicroRNAs as prognostic markers in ovarian cancer. Mol Cell Endocrinol. doi:10.1016/j.mce.2014.03.006 Ma HY, Sun DX, Cao YF, Ai CZ, Qu YQ, Hu CM, Jiang C, Dong PP, Sun XY, Hong M, Tanaka N, Gonzalez FJ, Ma XC, Fang ZZ (2014) Herb–drug interaction prediction based on the high specific inhibition of andrographolide derivatives towards UDPglucuronosyltransferase (UGT) 2B7. Toxicol Appl Pharmacol 277(1):86–94
Eur J Drug Metab Pharmacokinet Sager JE, Lutz JD, Foti RS, Davis C, Kunze KL, Isoherranen N (2014) Fluoxetine- and Norfluoxetine-Mediated Complex Drug– Drug Interactions: In Vitro to In Vivo Correlation of Effects on CYP2D6, CYP2C19, and CYP3A4. Clin Pharmacol Ther. doi:10.1038/clpt.2014.50 Sharma S, Santiskulvong C, Rao J, Gimzewski JK, Dorigo O (2014) The role of Rho GTPase in cell stiffness and cisplatin resistance in ovarian cancer cells. Integr Biol (Camb). doi:10.1039/ C3IB40246K Stein SM, Tiersten A, Hochster HS, Blank SV, Pothuri B, Curtin J, Shapira I, Levinson B, Ivy P, Joseph B, Guddati AK, Muggia F (2013) A phase 2 study of oxaliplatin combined with continuous
infusion topotecan for patients with previously treated ovarian cancer. Int J Gynecol Cancer 23(9):1577–1582 Tan AR, Gibbon DG, Stein MN, Lindquist D, Edenfield JW, Martin JC, Gregory C, Suttle AB, Tada H, Botbyl J, Stephenson JJ (2013) Effects of ketoconazole and esomeprazole on the pharmacokinetics of pazopanib in patients with solid tumors. Cancer Chemother Pharmacol 71(6):1635–1643 Ukairo O, McVay M, Krzyzewski S, Aoyama S, Rose K, Andersen ME, Khetani SR, Lecluyse EL (2013) Bioactivation and toxicity of acetaminophen in a rat hepatocyte micropatterned coculture system. J Biochem Mol Toxicol 27(10):471–478
SHORT COMMUNICATION
Metabolic behavior prediction of pazopanib by cytochrome P450 (CYP) 3A4 by molecular docking Xing-Jie Liu • Hua Lu • Ju-Xiang Sun Su-Rong Wang • Yan-Shuai Mo • Xing-Sheng Yang • Ben-Kang Shi
•
Received: 12 May 2014 / Accepted: 20 November 2014 Ó Springer International Publishing Switzerland 2015
Abstract Metabolism-mediated drug adverse effects (e.g., drug–drug interaction, bioactivation, etc.) strongly limit the utilization of clinical drugs. The present study aims to predict the metabolic capability of cytochrome P450 (CYP) 3A4 toward pazopanib which is an excellent drug exhibiting therapeutic role toward various cancers especially for ovarian cancer. Pazopanib can be well docked into the activity cavity of CYP3A4, and the interaction structure in pazopanib was methyl group located besides nitrogen in the five-membered ring. The distance between the hydrogen atom in methyl group and active ˚ . The interaction amino acid is Glu374. center is 3.64 A Furthermore, both pazopanib and ketoconazole were docked into the activity cavity of CYP3A4 to compare their binding potential. The distance between ketoconazole and ˚ ) is closer than the distance between activity center (2.10 A pazopanib and activity center of CYP3A4, indicating the easy influence of CYP3A4 inhibitor toward the metabolism of pazopanib. All these data were helpful for the clinical application of pazopanib, and R&D of other tinib drug candidates as new anti-tumor drugs. Keywords Pazopanib Cytochrome P450 (CYP) 3A4 Drug–drug interaction
X.-J. Liu H. Lu X.-S. Yang (&) B.-K. Shi Qilu Hospital of Shandong University, Jinan, Shandong, China e-mail: [email protected] X.-J. Liu H. Lu J.-X. Sun S.-R. Wang Y.-S. Mo Linyi People’s Hospital, Linyi, Shandong, China
1 Introduction Ovarian cancer is a cancerous growth arising from the ovary, and most ovarian cancers arise from the surface (epithelium) of the ovary. Ovarian cancer remains to be the 5th leading cause of cancer-related deaths among women, and the major management methods of ovarian cancer contain surgery, chemotherapy, radiation therapy, and immunotherapy (Llaurado et al. 2014). To date, the chemotherapy drugs approved by food and drug administration (FDA) to treat ovarian cancer contain cisplatin (Khrunin et al. 2014), cyclophosphamide (Barber et al. 2013), paclitaxel (Hahn et al. 2014), and topotecan (Stein et al. 2013). However, many disadvantages exist for the utilization of these drugs, including the drug resistance (Sharma et al. 2014; Kim et al. 2014). Searching an efficiently therapeutic drug is necessary and important for the treatment of ovarian cancer. Pazopanib, sold under the trade name Votrient, is a potent and selective inhibitor of multitargeted receptor tyrosine kinase (Davidson and Secord 2014). Pazopanib has been demonstrated to exhibit antiovarian cancer (Friedlander et al. 2010; Hashimoto et al. 2010). Metabolic behavior strongly limits the utilization and R&D of tinib drugs. For example, reactive metabolites formed through metabolism-mediated bioactivation have been strongly correlated with the adverse effects of this kind of drugs, including erlotinib and gefitinib (Li et al. 2009, 2010). In addition, some tinib drugs can inhibit the activity of some drug-metabolizing enzymes (e.g., cytochrome P450 1A2. etc.), which might induce the potential drug–drug interaction (Gu et al. 2014). The interaction between pazopanib and cytochrome P450 (CYP) remains unclear. CYP3A4 has been considered to be the most important drug-metabolizing enzyme
Eur J Drug Metab Pharmacokinet
involved in the metabolism of clinical drugs, and previous literature indicated that the inhibitor of CYP3A4 ketoconazole can strongly weaken the metabolic process of pazopanib (Tan et al. 2013). Molecular docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. Molecular docking is an important prediction tool for the metabolism of compounds, and metabolism-based drug–drug interaction. Therefore, the aim of the present study was to predict the interaction between pazopanib and CYP3A4.
2 Materials and methods 2.1 Structure selection and preparation for CYP3A4 and pazopanib To better understand the interaction between pazopanib and CYP3A4, suitable CYP3A4 crystal structure was needed. In the present study, the crystal structure of CYP3A4 binded with ketoconazole (PDB code 2V0M) was selected from protein data bank (http://www.rcsb.org/pdb). The structure was processed using the protein preparation wizard in the Schro¨dinger suite of programs, and the missing residues in the middle of the chain were added, and hydrogen atoms were assigned. Chemdraw software was used to draw the two-dimensional structure of linifanib with standard bond lengths and angles. 2.2 Molecular docking of pazopanib into the activity cavity of CYP3A4 At this stage, the structure of pazopanib was docked into the processed crystal structure of CYP3A4. The structure of ketoconazole was firstly extracted from the cavity of CYP3A4. The molecular energy was minimized using Tripos force field, and the most stable conformation was searched using the Powell conjugate gradient algorithm with a convergence criterion of 0.001 kcal/mol. 0.05 k˚ was employed as the limit of energy gradient. cal/mol. A Gasteiger–Huckel method in SYBYL-X program (Tripos Inc.) was used to calculate the compounds’ partial charges. The top 20 ranking poses were saved, and significantly different poses were preferred during this screening procedure to capture more diversity of pazopanib–CYP3A4 complex. The structure of linifanib was docked into the activity cavity using Surflex-Dock (within Sybyl-X1.2, Tripos International). The protoMol was generated with the automated method, a threshold of 0.50 and a bloat value of 2. The Surflex-Dock mode was employed, and the other parameters were at default values. To ensure that reasonable docked poses were obtained, the docked
conformations of ligands in the 3A4 protein were compared to the conformation of the inhibitor seen in the crystal structure.
3 Results The docking of pazopanib into the activity cavity was shown in Fig. 1. Pazopanib can be well docked into the activity cavity of CYP3A4, and the interaction structure in pazopanib was methyl group located besides nitrogen in the five-membered ring. The distance between the hydro˚ . The gen atom in methyl group and active center is 3.64 A interaction amino acid is Glu374. Furthermore, both pazopanib and ketoconazole were docked into the activity cavity of CYP3A4 to compare their binding potential. As shown in Fig. 2, the distance between ketoconazole and ˚ ) is closer than the distance between activity center (2.10 A pazopanib and activity center of CYP3A4, indicating the easy influence of CYP3A4’s strong inhibitor toward the metabolism of pazopanib.
4 Discussion Drug metabolism-mediated adverse effects strongly limit the clinical application of drugs and R&D of new chemical entities (NCEs), including drug metabolism-mediated drug–drug interaction and bioactivation (Fang et al. 2010a). Sager et al. demonstrated the complex drug–drug interaction related with fluoxetine and its circulating metabolite norfluoxetine caused by the inhibition toward the activity of multiple cytochrome P450 isoforms, including CYP3A4, CYP2D6, and CYP2C19 (Sager et al.
Fig. 1 Molecular docking of pazopanib into the activity cavity of CYP3A4. The crystal structure of CYP3A4 binded with ketoconazole (PDB code 2V0M) was selected from protein data bank (http://www. rcsb.org/pdb). The structure of ketoconazole was firstly extracted from the cavity of CYP3A4 before the docking of pazopanib into the activity cavity
Eur J Drug Metab Pharmacokinet
Fig. 2 Molecular docking of both pazopanib and ketoconazole into the activity cavity of CYP3A4. The green color represents the structure of ketoconazole and the red color represents the structure of pazopanib
2014). The inhibition of noscapine toward the activity of CYP3A4 and CYP2C9 has been an important reason for clinical noscapine–warfarin interaction (Fang et al. 2010b). For the metabolism-catalyzed bioactivation, many structural alerts have been reported (Kalgutkar and Didiuk 2009). For example, the bioactivation of acetaminophen (APAP) into N-acetyl-p-benzoquinone imine (NAPQI) has been widely regarded as the major reactive metabolites to result in the APAP-induced liver toxicity when overdose of APAP (Ukairo et al. 2013). Molecular docking can be used to predict the metabolism-mediated adverse effects of drugs. For example, molecular docking method was used to predict the metabolic behavior of aryl hydrocarbon receptor antagonist GNF-351 and the potential drug–drug interaction with CYP3A4 inhibitor ketoconazole (Liu et al. 2014). The molecular docking method has also been used to explain the structure-UDP-glucuronosyltransferase (UGT) 2B7 activity inhibition relationship (Ma et al. 2014). In the present study, we used molecular docking to predict the metabolism of pazopanib by CYP3A4, and potential drug– drug interaction between pazopanib and ketoconazole. The results indicated the high binding capability of pazopanib toward the activity cavity of CYP3A4, and the stronger binding ability of ketoconazole than pazopanib, which might explain why the drug–drug interaction occurs when ketoconazole was co-administered with pazopanib (Tan et al. 2013).
References Barber EL, Zsiros E, Lurain JR, Rademaker A, Schink JC, Neubauer NL (2013) The combination of intravenous bevacizumab and
metronomic oral cyclophosphamide is an effective regimen for platinum-resistant recurrent ovarian cancer. J Gynecol Oncol 24(3):258–264 Davidson BA, Secord AA (2014) Profile of pazopanib and its potential in the treatment of epithelial ovarian cancer. Int J Womens Health 6:289–300 Fang ZZ, Zhang YY, Wang XL, Cao YF, Huo H, Yang L (2010a) Bioactivation of herbal constituents: simple alerts in the complex system. Expert Opin Drug MetabToxicol 7(8):989–1007 Fang ZZ, Zhang YY, Ge GB, Huo H, Liang SC, Yang L (2010b) Time-dependent inhibition (TDI) of CYP3A4 and CYP2C9 by noscapine potentially explains clinical noscapine–warfarin interaction. Br J Clin Pharmacol 69(2):193–199 Friedlander M, Hancock KC, Rischin D, Messing MJ, Stringer CA, Matthys GM, Ma B, Hodge JP, Lager JJ (2010) A Phase II, openlabel study evaluating pazopanib in patients with recurrent ovarian cancer. Gynecol Oncol 119(1):32–37 Gu R, Hibbs DE, Ong JA, Edwards RJ, Murray M (2014) The multikinase inhibitor axitinib is a potent inhibitor of human CYP1A2. Biochem Pharmacol 88(2):245–252 Hahn HS, Lee KH, Lee IH, Lee JH, Whang CS, Jo YW, Kim TJ (2014) Metronomic oral paclitaxel shows anti-tumor effects in an orthotopic mouse model of ovarian cancer. J Gynecol Oncol 25(2):130–135 Hashimoto K, Man S, Xu P, Cruz-Munoz W, Tang T, Kumar R, Kerbel RS (2010) Potent preclinical impact of metronomic lowdose oral topotecan combined with the antiangiogenic drug pazopanib for the treatment of ovarian cancer. Mol Cancer Ther 9(4):996–1006 Kalgutkar AS, Didiuk MT (2009) Structural alerts, reactive metabolites, and protein covalent binding: how reliable are these attributes as predictors of drug toxicity? Chem Biodivers 6(11):2115–2137 Khrunin AV, Khokhrin DV, Moisseev AA, Gorbunova VA, Limborska SA (2014) Pharmacogenomic assessment of cisplatinbased chemotherapy outcomes in ovarian cancer. Pharmacogenomics 15(3):329–337 Kim YW, Kim EY, Jeon D, Liu JL, Kim HS, Choi JW, Ahn WS (2014) Differential microRNA expression signatures and cell type-specific association with Taxol resistance in ovarian cancer cells. Drug Des Devel Ther 8:293–314 Li X, Kamenecka TM, Cameron MD (2009) Bioactivation of the epidermal growth factor receptor inhibitor gefitinib: implications for pulmonary and hepatic toxicities. Chem Res Toxicol 22(10):1736–1742 Li X, Kamenecka TM, Cameron MD (2010) Cytochrome P450mediated bioactivation of the epidermal growth factor receptor inhibitor erlotinib to a reactive electrophile. Drug Metab Dispos 38(7):1238–1245 Liu T, Qian G, Wang W, Zhang Y (2014) Molecular docking to understand the metabolic behavior of GNF-351 by CYP3A4 and its potential drug–drug interaction with ketoconazole. Eur J Drug Metab Pharmacokinet. doi:10.1007/s13318-014-0201-1 Llaurado M, Majem B, Altadill T, Lanau L, Castellvi J, SanchezIglesias JL, Cabrera S, de la Torre J, Dı´az-Feijoo B, Pe´rezBenavente A, Colas E, Olivan M, Doll A, Alameda F, MatiasGuiu X, Moreno-Bueno G, Carey M, Del Campo JM, GilMoreno A, Reventos J, Rigau M (2014) MicroRNAs as prognostic markers in ovarian cancer. Mol Cell Endocrinol. doi:10.1016/j.mce.2014.03.006 Ma HY, Sun DX, Cao YF, Ai CZ, Qu YQ, Hu CM, Jiang C, Dong PP, Sun XY, Hong M, Tanaka N, Gonzalez FJ, Ma XC, Fang ZZ (2014) Herb–drug interaction prediction based on the high specific inhibition of andrographolide derivatives towards UDPglucuronosyltransferase (UGT) 2B7. Toxicol Appl Pharmacol 277(1):86–94
Eur J Drug Metab Pharmacokinet Sager JE, Lutz JD, Foti RS, Davis C, Kunze KL, Isoherranen N (2014) Fluoxetine- and Norfluoxetine-Mediated Complex Drug– Drug Interactions: In Vitro to In Vivo Correlation of Effects on CYP2D6, CYP2C19, and CYP3A4. Clin Pharmacol Ther. doi:10.1038/clpt.2014.50 Sharma S, Santiskulvong C, Rao J, Gimzewski JK, Dorigo O (2014) The role of Rho GTPase in cell stiffness and cisplatin resistance in ovarian cancer cells. Integr Biol (Camb). doi:10.1039/ C3IB40246K Stein SM, Tiersten A, Hochster HS, Blank SV, Pothuri B, Curtin J, Shapira I, Levinson B, Ivy P, Joseph B, Guddati AK, Muggia F (2013) A phase 2 study of oxaliplatin combined with continuous
infusion topotecan for patients with previously treated ovarian cancer. Int J Gynecol Cancer 23(9):1577–1582 Tan AR, Gibbon DG, Stein MN, Lindquist D, Edenfield JW, Martin JC, Gregory C, Suttle AB, Tada H, Botbyl J, Stephenson JJ (2013) Effects of ketoconazole and esomeprazole on the pharmacokinetics of pazopanib in patients with solid tumors. Cancer Chemother Pharmacol 71(6):1635–1643 Ukairo O, McVay M, Krzyzewski S, Aoyama S, Rose K, Andersen ME, Khetani SR, Lecluyse EL (2013) Bioactivation and toxicity of acetaminophen in a rat hepatocyte micropatterned coculture system. J Biochem Mol Toxicol 27(10):471–478
