Clinical-Based vs. Model-Based Adaptive Dosing Strategy: Retrospective Comparison in Real-World mRCC Patients Treated with Sunitinib

Real-World Treatment Patterns and Clinical Outcomes Among Patients With Advanced Renal Cell Carcinoma

BACKGROUND

Nearly 30% of new renal cell carcinoma (RCC) cases are diagnosed at an advanced or metastatic stage. Recent approvals of immunotherapies (IO) have significantly impacted patient care, but real-world outcomes of these treatments have not been widely evaluated.

METHODS

Eligible physicians abstracted demographic and clinical data from patient medical records for patients with advanced clear and non-clear cell RCC (aRCC) who initiated treatment between January 1, 2018, and December 31, 2020. Overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan-Meier method. A multivariate Cox regression model was developed to assess the impact of treatment category on clinical outcomes while controlling for International Metastatic RCC Database Consortium (IMDC) risk category, histology, and other patient characteristics.

RESULTS

A total of 498 patients were included (201 from US, 62 from Canada, 58 from UK, 59 from France, 58 from Germany, 60 from Spain). Of these, 250 received tyrosine kinase inhibitor (TKI) monotherapy, 197 received immunotherapy (IO) combination (119 IO+TKI, 78 IO+IO), and 32 received IO monotherapy as first-line treatment for aRCC; 19 patients received various other regimens. 16% of patients had a favorable IMDC risk score. Based on results of multivariable Cox regression, PFS (hazard ratio [HR] [95% confidence interval (CI)]: 0.50 [0.36-0.72]) (P < .001) and time to next treatment (TTNT) were significantly longer (HR [95% CI]: 0.54 [0.39-0.73]) (P < .001) for patients treated with IO combination versus TKI monotherapy. IO combination had a numerically reduced, but statistically insignificant, risk of death versus TKI monotherapy (HR: 0.66; P = .114). IO+TKI combination was associated with significantly longer PFS and reduced risk of progression (HR: 0.52; P = .04) versus IO+IO combination; similar results were observed for TTNT (HR: 0.57; P = .03).

CONCLUSION

Our evaluation of real-world treatment outcomes in aRCC revealed that IO + TKI combination is associated with improved PFS and prolonged TTNT compared with TKI monotherapy and IO+IO combination.

Therapeutic Drug Monitoring of Tyrosine Kinase Inhibitors in the Treatment of Advanced Renal Cancer

Seven tyrosine kinase inhibitor compounds with anti-angiogenic properties remain key drugs to treat advanced renal cell carcinoma. There is a strong rationale to develop therapeutic drug monitoring for these drugs. General considerations of such monitoring of the several groups of anticancer drugs are given, with a focus on oral therapy. Pharmacokinetics and the factors of inter- and intraindividual variabilities of these tyrosine kinase inhibitors are described together with an exhaustive presentation of their pharmacokinetic/pharmacodynamic relationships. The latter was observed in studies where every patient was treated with the same dose, and the results of several prospective studies based on dose individualization support the practice of increasing individual dosage in case of low observed plasma drug concentrations. Finally, the benefits and limits of therapeutic drug monitoring as a routine practice are discussed.

Interindividual Variability in Cytochrome P450 3A and 1A Activity Influences Sunitinib Metabolism and Bioactivation

Sunitinib is an orally administered tyrosine kinase inhibitor associated with idiosyncratic hepatotoxicity; however, the mechanisms of this toxicity remain unclear. We have previously shown that cytochromes P450 1A2 and 3A4 catalyze sunitinib metabolic activation via oxidative defluorination leading to a chemically reactive, potentially toxic quinoneimine, trapped as a glutathione (GSH) conjugate (M5). The goals of this study were to determine the impact of interindividual variability in P450 1A and 3A activity on sunitinib bioactivation to the reactive quinoneimine and sunitinib N-dealkylation to the primary active metabolite N-desethylsunitinib (M1). Experiments were conducted in vitro using single-donor human liver microsomes and human hepatocytes. Relative sunitinib metabolite levels were measured by liquid chromatography-tandem mass spectrometry. In human liver microsomes, the P450 3A inhibitor ketoconazole significantly reduced M1 formation compared to the control. The P450 1A2 inhibitor furafylline significantly reduced defluorosunitinib (M3) and M5 formation compared to the control but had minimal effect on M1. In CYP3A5-genotyped human liver microsomes from 12 individual donors, M1 formation was highly correlated with P450 3A activity measured by midazolam 1'-hydroxylation, and M3 and M5 formation was correlated with P450 1A2 activity estimated by phenacetin O-deethylation. M3 and M5 formation was also associated with P450 3A5-selective activity. In sandwich-cultured human hepatocytes, the P450 3A inducer rifampicin significantly increased M1 levels. P450 1A induction by omeprazole markedly increased M3 formation and the generation of a quinoneimine-cysteine conjugate (M6) identified as a downstream metabolite of M5. The nonselective P450 inhibitor 1-aminobenzotriazole reduced each of these metabolites (M1, M3, and M6). Collectively, these findings indicate that P450 3A activity is a key determinant of sunitinib N-dealkylation to the active metabolite M1, and P450 1A (and potentially 3A5) activity influences sunitinib bioactivation to the reactive quinoneimine metabolite. Accordingly, modulation of P450 activity due to genetic and/or nongenetic factors may impact the risk of sunitinib-associated toxicities.

Adaptive dosing of sunitinib in a metastatic renal cell carcinoma patient: when in silico modeling helps to go quicker to the point

PURPOSE

Adaptive dosing strategy with oral targeted therapies in oncology is mostly based upon clinical signs. Using pharmacokinetics (PK) models to customize dosing could help saving time, i.e., by predicting clinical outcome through early monitoring of drug levels.

CASE REPORT

We present the case of a metastatic renal cell carcinoma patient treated with standard Sunitinib dosing (i.e., 50 mg QD). Clinical signs suggested lack of efficacy. Therapeutic Drug Monitoring (TDM) confirmed that exposure was below the expected target exposure. PK modeling suggested that dosing could be increased safely to 75 mg QD. Sunitinib dosing was instead changed empirically to 62.5 mg only, increasing drug exposure to the lower part of the therapeutic window. Resolution of bone pains plus Stable Disease were observed. Even though further modeling suggested to increase Sunitinib dosing to 75 mg again, the intermediate dosing was maintained for the subsequent cycles to preserve the safety. Unfortunately, severe pains plus degradation of the general state were reported and imaging showed Progressive Disease. The patient was finally switched to alternative therapy, without being treated at the 75 mg level of Suntitinib.

CONCLUSIONS AND DISCUSSION

This case suggests that model-based adaptive dosing could have allowed to reach quicker the best dosing with Sunitinib, thus possibly ensuring a better management of this patient. Model-informed dosing should be used instead of empirical search for the most appropriate dosing to ensure a good benefit/risk ratio with Sunitinib, especially in the context of such aggressive disease.