aI (μM) represents the Cmax of patients with solid tumours, which were cited from (Di Gion et al., 2011; Gschwind et al., 2005; Sparano et al., 2009) for imatinib, (Di Gion et al., 2011) for sunitinib and (Scheffler, Di Gion, Doroshyenko, Wolf & Fuhr, 2011) for gefitinib.
b The AUC ratio was calculated based on Equations (2)–(4).
Discussion
There is extensive clinical application of rivaroxaban in combination with TKIs, and cancer patients are one of the main groups that receive rivaroxaban treatment. Due to the hypercoagulability of cancer patients and the thrombogenicity of anti-cancer agents, anticoagulant therapy has become an essential treatment for cancer patients. Increasing evidence has indicated that DOACs are safer and more convenient than classical anticoagulation medications, which is reflected in increasing clinical guidelines recommending that DOACs replace classical anticoagulation medications for cancer patient VTE therapy. In our previous study, CYP2J2, but not CYP3A4, was found to be dominant in the P450 metabolism of rivaroxaban with a contribution of 41.1%. However, few published reports have targeted CYP2J2 to explore the safety of rivaroxaban combinations, and even fewer of these have looked at rivaroxaban in combination with TKIs. Therefore, the estimation of DDI risk between TKIs and rivaroxaban based on CYP2J2 and CYP3A4 is necessary and meaningful for clinical practice. This was the focus of the present study.
All three TKIs were found to have a remarkable inhibitory effect on CYP3A4-mediated rivaroxaban metabolism. Imatinib showed the strongest reversible inhibitory effect towards CYP3A4 with a Ki value of 1.92 μM. The inhibition of CYP3A4 by sunitinib or gefitinib was also potent, with Kivalues of 13.24 and 4.91 μM, respectively. In addition to reversible inhibition, the three TKIs also effected time-dependent inactivation of CYP3A4, especially sunitinib, which greatly increased the DDI risk in combination with rivaroxaban. Compared with reversible inhibition, mechanism-dependent inactivation is always more frequently related to unfavourable DDIs in clinical practice (Kalgutkar, Obach & Maurer, 2007). More importantly, the expression of CYP3A4—which is abundantly expressed P450 isoform in the liver in most individuals—showed more than 100-fold population variability (Zanger & Schwab, 2013). And meanwhile, it has been reported that sunitinib has a 10-fold higher concentration in the liver than in blood (Lau et al., 2015). Thus, being cautious about the CYP3A4 enzyme expression in patients who take both rivaroxaban and TKIs is necessary.
The present study found that CYP2J2, which dominates the metabolism of rivaroxaban, was inhibited by the TKIs. Notably, the three TKIs showed different inhibitory activity: imatinib and gefitinib were potent inhibitors of CYP2J2 with Ki values of 3.53 and 2.99 μM, respectively, but sunitinib had almost no inhibitory effect on CYP2J2. Additionally, there was no irreversible inhibition of CYP2J2 by the three TKIs, with the results showing IC50 shifts of less than 1.5-fold. The basic principle of mechanism-based inactivation is the bioactivation of inhibitors, which is achieved by their metabolism by the inhibited enzymes (Kalgutkar, Obach & Maurer, 2007). Therefore, the results might suggest that the three TKIs were not or were rarely metabolised by CYP2J2.
Although the three TKIs did not irreversibly inactivate CYP2J2-mediated rivaroxaban metabolism, the DDI risk produced by inhibiting CYP2J2 cannot be ignored. The distribution of CYP2J2 in vivo is a potential factor that may increase the DDI risk of rivaroxaban in combination with TKIs. CYP2J2 was first detected in the liver but was since identified as an enzyme that is mainly distributed in the heart (Das, Weigle, Arnold, Kim, Carnevale & Huff, 2020); indeed, the mRNA levels of CYP2J2 in the cardiovascular system exceed those of other detected isozymes by 3 million to 62 times (Michaud, Frappier, Dumas & Turgeon, 2010; Wu, Moomaw, Tomer, Falck & Zeldin, 1996). Although CYP2J2 is not usually considered to be a DDI target due to its lower content in the liver, if the heart is set as the organ for potential DDIs, there may be an extremely high risk of DDI between rivaroxaban and TKIs.
In addition to its physiological distribution in the cardiovascular system, CYP2J2 has also recently been found to be highly expressed in various tumour tissues (Das, Weigle, Arnold, Kim, Carnevale & Huff, 2020; Jiang et al., 2005; Karkhanis, Hong & Chan, 2017). Due to the overexpression of receptors in tumours (such as PDGFR and EGFR), TKIs would accumulate in the tumour tissue. Therefore, the DDI risks of rivaroxaban in combination with TKIs for cancer patients may be higher than in our predictions, and CYP2J2 may need to become a target for DDI assessments involving rivaroxaban.
Imatinib was predicted to have moderate DDI risk when combined with rivaroxaban. Based on the inhibitory constants of imatinib on CYP2J2 and CYP3A4, and the metabolic contributions of two isoforms, imatinib was predicted to yield at most a 2.44-fold rivaroxaban AUC increase. Therefore, according to the FDA’s guidelines for the relative risk of DDIs, a moderate DDI risk may exist for the combination of rivaroxaban and imatinib (Table 6). This result was predicted from the pharmacokinetic data of patients with solid tumours who were received TKIs. Notably, cancer patients have poorer metabolic function than normal patients, which may directly lead to higher plasma concentrations of TKIs for cancer patients. Therefore, using pharmacokinetic data from cancer patients may make our prediction more accurate. However, metabolic enzyme activity in the normal population is subject to individual variability, so factors such as hepatic blood flow and genetic polymorphism cannot be neglected (Zanger & Schwab, 2013). Factors that influence the enzyme activity of cancer patients may be more difficult to determine due to the more complicated in vivo environment. All these factors would affect the accuracy of our prediction in clinical practice. Additionally, the plasma concentrations of anti-cancer drugs in patients with solid tumours that are refractory to standard therapy are much higher than those of normal patients, which may result from large doses of anti-cancer drugs and metabolic function disorders (Scheffler, Di Gion, Doroshyenko, Wolf & Fuhr, 2011). Furthermore, it has been reported that lower plasma concentrations of TKIs (< 1100 ng·ml–1) are closely related to the much faster development of progressive disease and lower objective response rates for GIST patients (von Mehren & Widmer, 2011). All these factors would produce great diversity in the pharmacokinetic data of TKIs in cancer patients. Thus, a rough in vitro prediction may neglect to indicate the risk of severe DDI in some individuals. Therefore, an individual physiologically-based pharmacokinetic model for cancer patients would be encouraged in the prediction of the pharmacokinetic behaviour of rivaroxaban in combination with TKIs.
In summary, all three TKIs (imatinib, sunitinib and gefitinib) showed inhibitory effects on CYP2J2- and CYP3A4-mediated rivaroxaban metabolism. Imatinib and gefitinib exerted significant reversible inhibition of CYP2J2 and CYP3A4, while sunitinib only showed reversible inhibition of CYP3A4. The three TKIs also demonstrated time-dependent inactivation of CYP3A4, with this effect being slight on CYP2J2. Furthermore, the combination of rivaroxaban with imatinib was predicted to constitute a moderate DDI risk. Our results provide data for the clinical safety assessment of the combination of rivaroxaban with imatinib, sunitinib and gefitinib in cancer patients, and also give new insights for DDI assessment involving rivaroxaban.
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