1 Introduction
Everolimus is an orally active inhibitor of the mammalian target of rapamycin (mTOR). Everolimus interacts with FK506 binding protein 1A, 12 kDa (FKBP-12), which results in an inhibitory complex that binds with high affinity to mTOR. Downstream signaling from mTOR occurs through an mTOR–Raptor complex, known as TORC1 [
1]. The primary downstream targets of mTOR include p70 ribosomal S6 kinase 1 (S6K1) and eukaryotic translation initiation factor 4E (eIF4E)-binding protein 1 (4EBP1) [
2]. The enzyme S6K1 functions in the G
1-phase of cell division through phosphorylation of the ribosomal protein S6 to increase the translation of messenger RNA (mRNA) that largely encode ribosomal proteins and other elements of the translational cascade [
3]. The phosphorylation of 4EBP1 leads to a reduction of the inhibitory binding to eIF4E. Inhibition of S6K1 in peripheral blood mononuclear cells and skin tissue has been proposed to be an adequate biomarker of mTOR inhibition by everolimus [
1].
Despite its proven efficacy, the use of everolimus is seriously hampered by its frequent and severe toxicity. Adverse events that are reported include stomatitis, rash, diarrhea, fatigue, anemia, hyperglycemia, hyperlipidemia, infections, and, less commonly but potentially life threatening, non-infectious pneumonitis [
4‐
6]. In the BOLERO-2 (Breast Cancer Trials of OraL EveROlimus-2) trial, in which breast cancer patients were randomized between everolimus and exemestane versus exemestane, 62 % of the patients treated with the combination required a dose interruption/reduction due to toxicity issues compared with 12 % of the patients treated with exemestane [
7]. In the phase III study in patients with metastatic renal cell carcinoma (RECORD-1 [REnal Cell cancer treatment with Oral RAD001 given Daily] study group), 7 % of the patients treated with everolimus required a dose reduction compared with 1 % of the patients treated with placebo, and 38 % needed a dose interruption compared with 11 % treated with placebo [
8]. In addition, in patients with advanced pancreatic neuroendocrine tumors (RADIANT-3 [RAD001 in Advanced Neuroendocrine Tumors, Third Trial] study group), 59 % of the patients treated with everolimus required a dose adjustment (reductions or temporary interruptions) compared with 28 % of the patients treated with placebo [
6]. This indicates that further research into dose individualization of everolimus is necessary.
Everolimus is rapidly absorbed after oral administration with only a very modest estimated oral bioavailability (5–11 %) and a terminal half-life of approximately 30 h [
2,
9]. Furthermore, everolimus is metabolized by cytochrome P450 (CYP) isoenzyme 3A4 (CYP3A4), is a substrate for the P-glycoprotein drug transporter, and accumulates in erythrocytes with a fixed erythrocyte to plasma accumulation ratio of 85:15 in the clinically relevant concentration range [
10]. As a consequence, hematocrit is a known confounder for whole-blood pharmacokinetics, as varying hematocrit will impact the disposition of drugs with a high affinity for red blood cells [
11]. This effect is likely to be important with everolimus, as everolimus use leads to anemia in ~16 % of patients [
4]. Furthermore, only the unbound plasma concentration of everolimus is able to act on its target. Consequently, for pharmacokinetic and pharmacodynamic analyses, a population pharmacokinetic model describing the everolimus plasma pharmacokinetics, accounting for the effect of hematocrit, is important. However, direct measurement of everolimus plasma concentrations is highly challenging because even minimal hemolysis of everolimus, which accumulates extensively in red blood cells, has a large effect on measured plasma concentrations and everolimus in plasma is not stable [
12,
13]. Therefore, whole-blood concentrations of everolimus are routinely measured in clinical pharmacological studies. Although several models have been published describing the pharmacokinetics of everolimus in whole blood of solid organ transplant patients [
14,
15], as it stands, no pharmacokinetic model for everolimus is available that accounts for the confounder hematocrit a priori to describe the plasma pharmacokinetics of everolimus in cancer patients. Moreover, unbound everolimus concentrations may be translated to antitumor activity by relating these concentrations to S6K1 inhibition [
16]. Our purpose was, therefore, to develop a population pharmacokinetic model in cancer patients treated with everolimus and to investigate the impact of varying hematocrit on the pharmacokinetics and in silico pharmacodynamics of everolimus.
4 Discussion and Conclusion
We have successfully developed a semi-physiological population pharmacokinetic model for everolimus in patients with cancer. The model reliably captured all observed whole-blood pharmacokinetic data and could be used to predict plasma concentrations as well as mTOR (S6K1) inhibition when the hematocrit level is known.
We observed two interesting findings in our study. Firstly, our analysis clearly demonstrates that hematocrit relevantly impacts whole-blood concentrations while plasma concentrations remain more or less the same. In patients with cancer, large variation in hematocrit is commonly observed [
23,
24]. This will result in variable whole-blood concentrations. Since only the unbound everolimus plasma concentration is responsible for the pharmacological effect of everolimus, measured whole-blood concentrations should always be corrected for hematocrit, to interpret the relationship between everolimus exposure and treatment outcome, which is currently not done in practice [
25,
26]. Secondly, we showed that despite high variability in systemic everolimus exposure among patients, throughout a dosing interval the model-predicted mTOR (S6K1) inhibition was nearly complete and that despite high variability in pharmacokinetics, variability in S6K1 inhibition was only modest. This indicated that at the current dosing regimen the mTOR inhibition may be at the top end of the concentration–effect curve and that dose reductions may not necessarily result in less mTOR inhibition. This encourages further prospective in vivo investigation to reduce everolimus toxicity without loss of efficacy.
Although recently several population pharmacokinetic models for everolimus have been described, we believe our model adds to the knowledge currently available because the previously described models studied a different patient population (solid organ transplant patients) and used very different dose levels with an empirical pharmacokinetic model instead of a semi-physiologically well-stirred liver model without accounting for hematocrit and erythrocyte accumulation [
14,
27,
28]. Furthermore, in contrast with previous work, our model enables prediction of the plasma concentration and this allows everolimus plasma pharmacokinetics to be linked with its pharmacodynamics.
Our pharmacokinetic model relied on assumptions of erythrocyte and plasma protein binding of everolimus, based on the best available data, which could not be verified in vivo. All results of the simulation study should be interpreted with this in mind. One may argue that the absence of measured (unbound) plasma concentrations is a shortcoming of our study. As stated in the Introduction, quantification of everolimus plasma concentrations may be challenging, and the protein binding is known to be concentration independent with very limited variability [
10,
12,
13]. Although we accounted for the limited variability in plasma protein binding in our simulation study, we could not account for variability in erythrocyte binding, as this is unknown. Thus, the variability in plasma concentrations and S6K1 inhibition in our simulation study, as presented in Table
3, may be under-predicted. It should also be noted that saturation of everolimus accumulation may occur at concentrations higher than usually observed in routine clinical practice, and that the fixed accumulation ratio might not be applicable in this situation. Prediction of everolimus pharmacokinetics with our model at substantially higher concentrations than usually observed when using 10 mg once daily should, therefore, be performed with caution. Future studies should be directed towards establishing everolimus erythrocyte-binding constants and its associated variability in order to account for this variability and possible saturated binding at higher whole-blood concentrations. Furthermore, red blood cell binding of a drug is, in rare cases, known to be influenced by other drugs [
29]. This may cause a clinically relevant pharmacokinetic interaction for drugs that extensively accumulate in erythrocytes, such as everolimus. As it stands, there are no data supporting displacement of everolimus from erythrocytes, but since this may relevantly change its plasma pharmacokinetics, this warrants further research.
The pharmacodynamic model describing the relationship between unbound plasma concentrations and S6K1 inhibition used may not representative of the human situation, as it was initially developed in rodents. However, there has been extensive research on this subject and this showed that differences in pharmacokinetics in rodents and humans were the only determinants for observed differences in S6K1 inhibition and that there was only a limited difference between tumor-bearing rats and cancer patients regarding the concentration effect of everolimus and its effect on signal transduction proteins such as S6K1 [
22]. Therefore, this pharmacokinetic/pharmacodynamic model was also used to select the everolimus doses of 5 and 10 mg for the clinical phase II and III trials for treatment of solid malignancies [
16]. Consequently, we think the model used is adequate to predict S6K1 inhibition, but it encourages prospective in vivo evaluation.
In addition, the use of strong inhibitors or inducers of CYP3A, the main enzyme involved in everolimus metabolism [
10], and impairment of gastrointestinal function or gastrointestinal disease that may significantly alter the absorption of study drugs were exclusion criteria in our study. Therefore, extrapolation from our pharmacokinetic model to these situations may be limited. Finally, extrapolation of our model to different dosing regimens should be performed with caution since dose non-proportional pharmacokinetics cannot be ruled out over the large dosing range used within oncology. Currently, there are no data that support the assumption of non-linear pharmacokinetics; however, this should be investigated before broader use of this model.
For our simulation study, we implemented a previously developed pharmacokinetic/pharmacodynamic model that describes the relationship between unbound plasma concentrations of everolimus and tumor S6K1 inhibition and this model was used to rationally guide clinical development of everolimus dosing schedules. The variability in predicted tumor S6K1 inhibition in our study may be under-estimated, as this previously developed model does not account for variability in the pharmacodynamics parameters. Also, this pharmacodynamic model only accounted for tumor S6K1 inhibition. As it stands, no pharmacokinetic/pharmacodynamic model is available to describe the relationship between plasma concentrations and inhibition of peIF-4G, another downstream effector of mTOR. It is known, however, that a higher drug exposure is necessary for complete inhibition of other downstream mTOR pathways than S6K1. The clinical relevance of this difference remains unknown and should be further investigated. However, as S6K1 inhibition is considered a good biomarker for monitoring mTOR inhibition [
16,
22,
30], it would be interesting and relevant to investigate the effect of variable plasma everolimus concentrations on S6K1 inhibition in patients treated with everolimus and correlate the S6K1 inhibition potential to treatment outcome. This will help to better understand the mechanism underlying everolimus-induced efficacy and toxicity and the involvement of everolimus pharmacokinetics herein [
7].
The current analysis clearly demonstrates that hematocrit relevantly influences whole-blood concentrations while plasma concentrations remain unaffected. Since the unbound everolimus concentration is available to interact with the target, we believe that plasma concentrations should be used to investigate exposure–treatment outcome relationships. Our semi-physiological model can be used for this purpose.
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