Abstract
Background/Aim
Hypertension occurs frequently in patients taking pazopanib. Therefore, this study aimed to clarify the predictive factors for pazopanib-induced hypertension.
Patients and Methods
In total, 47 patients who started pazopanib treatment for renal cell carcinoma or soft tissue sarcoma during hospitalization at Kurume University Hospital from November 2012 to February 2020 were included in the study. Patient background factors associated with pazopanib-induced hypertension were analyzed using a logistic regression model. Subsequently, a time-dependent receiver operating characteristic (ROC) analysis was performed to evaluate changes in the predictive performance of predictors of pazopanib-induced hypertension over time.
Results
Logistic regression analysis showed that total bilirubin (t-bil) and sex are predictors of pazopanib-induced hypertension, along with systolic blood pressure (SBP) before pazopanib introduction. Additionally, evaluation of area under the curve (AUC) changes over time during the first 20 days of pazopanib treatment using time-dependent ROC showed that the AUC tended to be higher in the first half for SBP and in the second half for t-bil. Moreover, models including these two factors (SBP+t-bil and SBP+t-bil+sex) maintained a higher AUC from the early to late stages of the treatment period.
Conclusion
Total bilirubin and sex can serve as predictors of pazopanib-induced hypertension. Total bilirubin may contribute to the prediction of the development of hypertension after day 5.
Keywords: Pazopanib, hypertension, soft tissue sarcoma, renal cell carcinoma
Pazopanib is an oral kinase inhibitor that targets multiple kinases involved in cell growth and angiogenesis and is frequently used to treat unresectable or metastatic renal cell carcinoma (RCC) and advanced soft tissue sarcoma (STS) (1,2). Among its targets, vascular endothelial growth factor receptor (VEGFR)-1 is found in monocytes, VEGFR-2 in vascular endothelial cells, VEGFR-3 in lymphatic vessels, platelet-derived growth factor receptor (PDGFR)-α and c-Kit in tumor cells, and PDGFR-β in pericytes. Inhibiting these kinase receptors suppresses vascular cell growth and “starves” cancer cells (3). Prior to the approval of pazopanib, the standard of care for advanced STS was a doxorubicin-containing regimen with no other treatment options; however, with the approval of pazopanib, drug treatment options have expanded (4). In RCC, pazopanib is recommended as a treatment option (5). Although combined immunotherapy, including immune checkpoint inhibitors, is currently the mainstay of treatment, pazopanib remains widely used in the treatment of advanced RCC. Pazopanib is a clinically useful drug; however, side effects, such as hepatic dysfunction, hypertension, cardiac dysfunction, thrombocytopenia, and leukopenia have been reported (4,6,7). Of these, hypertension is frequently observed. In phase III multinational studies involving patients with STS and those with RCC, hypertension was observed in approximately 40% of patients treated with pazopanib (4,7). The development of hypertension increases the risk of stroke, myocardial infarction, and other events that affect patient prognosis (8).
Three possible mechanisms for pazopanib-induced hypertension have been postulated (9). First, VEGF inhibition increases endothelin-1 (9,10). Activation of VEGFR tyrosine kinase is thought to suppress the production of endothelin-1, a known potent vasoconstrictor (10). Administration of sunitinib, a VEGFR inhibitor with the same effect, inhibits this pathway, resulting in increased blood pressure.
The second mechanism involves the rarefaction of the peripheral blood vessels (9). Previous studies have shown that VEGF plays an important role in vascular endothelial cell proliferation, maintenance of neovascularization, and survival of cardiomyocytes (11-16). Inhibition of VEGFR by pazopanib may lead to the regression of neovascular vessels, thereby increasing peripheral vascular resistance and afterload.
Finally, kidney damage leading to activation of the renin-angiotensin-aldosterone system has been proposed as a mechanism (9). In addition to peripheral microvessels, VEGF signaling has been shown to be essential for the proliferation of renal glomerular endothelial cells. Inhibition of the VEGF pathway may cause dilation of renal glomerular capillaries, leading to hypertension (17-20).
Moreover, hypertension following treatment with tyrosine kinase inhibitors in metastatic RCC has been reported as an event that predicts response to treatment (21). In the case of pazopanib, hypertension may also be an important side effect.
In a previous study, high systolic blood pressure (SBP) at baseline was reported as a predictor of hypertension with pazopanib. However, the study was limited to patients with RCC and did not include those with soft tissue tumors (22).
Therefore, this study aimed to examine the predictors of pazopanib-induced blood pressure elevation in patients with RCC and patients with STS from multiple perspectives and to promote the appropriate use of pazopanib in the future.
Patients and Methods
Patients. Patients with advanced STS or RCC who received pazopanib at Kurume University Hospital from November 2012 to February 2020 were included in the study. Among these patients, those on pazopanib in inpatient care, where blood pressure trends and the addition of antihypertensive drugs could be followed in detail, were included, and those who were on pazopanib in outpatient care were excluded. Pediatric patients under 18 years of age were excluded because their physiological functions were different from those of the adults. The remaining patients were included in this study. The study was approved by the Ethics Committee of Kurume University (Study No. 21014) and was conducted in accordance with the guidelines of the Declaration of Helsinki. This study was a retrospective study, therefore, the informed consent of the individual participants who were included in this study was waived.
Extraction of variables. Patient background factors investigated in this study included Body Mass Index (BMI), dose per body surface area, sex, age, cancer type, prior chemotherapy, initial dose, hemoglobin, alanine aminotransferase, lactate dehydrogenase, alkaline phosphatase, t-bil, estimated glomerular filtration rate, albumin, neutrophils, platelets, baseline SBP, baseline diastolic blood pressure (DBP), diabetes, dyslipidemia, smoking, prior hypertension, and various medications [calcium antagonists, Renin-Angiotensin System (RAS) inhibitors, antihypertensive drugs, and 3-Hydroxy-3-methylglutaryl coenzyme-A (HMG-CoA) inhibitors]. Proton pump inhibitors and histamine 2-receptor antagonists were excluded from the analysis because these drugs should not be used with pazopanib due to their potential to decrease pazopanib efficacy and were discontinued in almost all patients when pazopanib was initiated. The survey items were extracted from hospital electronic medical records.
Definition of variables and outcomes. Patients with a diagnosis of hypertension in the medical record or patients taking antihypertensive medications were considered to have a history of hypertension in their background.
Baseline SBP and DBP were calculated as the mean values measured on the day before pazopanib administration. For patients who started pazopanib on the day of admission, the mean blood pressure on the day of admission up to the point immediately before pazopanib administration was used as the baseline. For patients without blood pressure measurements on the day before pazopanib induction, the mean pre-induction blood pressure on the day pazopanib was started was treated as the baseline SBP.
Pazopanib-induced hypertension was defined as that required addition, change, or increase in the dose of antihypertensive drugs during the treatment period after the introduction of pazopanib. This definition is based on the Common Terminology Criteria for Adverse Events Version 5.0 (23), which specifies Grade 2 or higher as hypertension “requiring a change in medical therapy”.
Statistical analysis. For descriptive statistics, continuous variables are presented as medians (25 percentile, 75 percentile) and discrete variables as n (%). A logistic regression analysis was performed using the occurrence of hypertension after the introduction of pazopanib as the objective variable and various patient background factors as explanatory variables. Variables included in the multivariable logistic regression models had a p-value of 0.1 or less in the univariate logistic regression analysis. To evaluate the performance of the obtained predictors in predicting the occurrence of hypertension, we plotted a ROC curve using the presence or absence of pazopanib-induced hypertension as the objective variable and each obtained predictor and its combination as explanatory variables. Comparisons of covariates between sexes was performed using t-test, Fisher’s exact test or logistic regression analysis.
For models that included multiple predictors, categorical net reclassification improvement (NRI), continuous NRI, and integrated discrimination improvement (IDI) were calculated to assess the usefulness of new predictors. The NRI and IDI measure the extent to which adding another event predictor to an existing event predictor improves its ability to predict whether an event will occur (24). Categorical NRI was calculated using logistic regression with a predicted probability of 0.5 or greater as a prediction of hypertension and a predicted probability of less than 0.5 as a prediction of no hypertension. The 95% confidence intervals (CIs) for the NRI and IDI were calculated using the bootstrap method.
Subsequently, stratified 5-fold cross-validation was performed to verify the validity of the prediction factors. The dataset was randomly divided into five layers (four for training data and one for test data) while maintaining the event occurrence ratios, and the test data was predicted from the training data. This sequence was repeated five times, with each layer being the test data once, and we calculated the sensitivity, specificity, and ROC-area under the curve (AUC) for predicting pazopanib-induced hypertension. For prediction from the training data, a prediction probability of 0.5 or greater obtained applying logistic regression was defined as the occurrence of hypertension, and a prediction probability of less than 0.5 was defined as the absence of hypertension.
Additionally, to examine changes over time in the discriminant performance of the hypertension prediction model based on baseline values, we evaluated longitudinal AUC changes using time-dependent ROC curves (25).
Statistical analysis was performed using EZR on R commander version 1.54 (Saitama medical center, Jichi medical University, Saitama, Japan) (26), R version 4.1.0 (R foundation for Statical Computing, Vienna, Austria) and R studio version 1.4.1106 (Posit PBC, Boston, MA, United States). The package “rsample” was used for data partitioning in cross-validation, the package “survivalROC” was used to calculate the AUC over time for time-dependent ROC, and the functions of the package “survivalROC” were used with Kaplan-Meier as the method. Unless otherwise mentioned, statistical tests were two-tailed, with p<0.05 considered statistically significant.
Results
Participants. Pazopanib was initiated in 84 patients with STS or RCC at Kurume University Hospital between November 2012 and February 2020. Of these, 47 patients were included in the study, excluding 31 patients who were started as outpatients and two patients who were under 18 years of age.
Patient characteristics. Table I presents the distribution of patient background characteristics. Of the 47 patients included in the study, 27 were male and 20 were female, with an overall mean age of 65 years. Among them, there were 35 patients with STS and 12 with RCC.
Table I. Clinical characteristics of patients (n=47).
All data are summarized by median (25 percentile, 75 percentile) or n (%). BMI: Body mass index; RCC: renal cell carcinoma; STS: soft tissue sarcoma; ALT: alanine aminotransferase; LDH: Lactate dehydrogenase; ALP: Alkaline phosphatase; t-bil: total bilirubin; eGFR: estimated glomerular filtration rate; SBP: systolic blood pressure; DBP: diastolic blood pressure.
Incidence of pazopanib-induced hypertension. Figure 1 shows a box-and-whisker diagram of SBP before pazopanib initiation up to 14 days after initiation. The mean SBP increased on the day after pazopanib initiation, with a median SBP increase of +15 mmHg on the fourth day after initiation. Of the 47 patients, 29 were administered additional or increased doses of antihypertensive drugs, confirming the development of pazopanib-induced hypertension. In most cases, calcium antagonists or renin angiotensin system (RAS) inhibitors were used to treat pazopanib-induced hypertension.
Figure 1. Systolic blood pressure during the first 14 days of treatment with pazopanib.
Predictive factor of pazopanib-induced hypertension. Table II presents the odds ratios, 95%Cis, and p-values obtained from the logistic regression analysis, with each patient background factor as an explanatory variable and the occurrence of pazopanib-induced hypertension as the objective variable. In the logistic regression analysis of single explanatory variables, sex (female), BMI, t-bil, neutrophils, and baseline SBP, dose per surface area were significant at p<0.1, suggesting that they are predictors of hypertension.
Table II. Univariate and multivariate logistic regression analysis using pazopanib-induced hypertension as the objective variable.
In univariate analysis, logistic regression analysis was performed with pazopanib-induced hypertension as the objective variable and each patient background parameter as the explanatory variable. In the multivariate analysis, the variables that were p<0.1 in the univariate analysis were used as the objective variables in the variable reduction method. OR: Odds ratio; CI: confidence interval; BMI: body mass index; RCC: renal cell carcinoma; STS: soft tissue sarcoma; ALT: alanine aminotransferase; LDH: Lactate dehydrogenase; ALP: Alkaline phosphatase; t-bil: total bilirubin; eGFR: estimated glomerular filtration rate; SBP: systolic blood pressure; DBP: diastolic blood pressure.
Although many patients with STS had previously received regimens containing doxorubicin with cardiotoxicity, this univariate logistic regression analysis was not significant for STS patients.
When these five factors were used as explanatory variables and variables were selected using the backward stepwise method, sex (female), t-bil, and SBP remained statistically significant variables in the model (p<0.05 for all three), defining them as predictors of increased blood pressure with pazopanib. The baseline SBP was significantly higher in females than in males (Table III). There was no difference by sex in the number of antihypertensive medications taken.
Table III. Comparison of patient backgrounds in men and women.
SBP: Systolic blood pressure.
Receiver operating characteristic (ROC) curves. Next, ROC curves were drawn with pazopanib-induced hypertension as the objective variable and SBP alone, t-bil alone, and combinations of predictors (SBP+t-bil, SBP+sex, t-bil+sex, and SBP+t-bil+sex) as explanatory variables, and their areas under the curve (AUCs) were calculated (Figure 2). The ROC-AUC was 0.77 (95%CI=0.63-0.91) for SBP alone and 0.69 (0.53-0.85) for t-bil alone. When both were combined, the ROC-AUC values were greatly increased [SBP+t-bil: 0.84 (0.72-0.96)]. When sex was added as a predictor variable, the ROC-AUC for SBP and t-bil was greater than that for SBP alone and t-bil alone [SBP+sex: 0.85 (0.74-0.96); t-bil+sex: 0.87 (0.77-0.97)]. The ROC-AUC was highest when these three predictors were evaluated together [SBP+t-bil+sex: 0.91 (0.82-0.99)].
Figure 2. Receiver operating characteristic curves with pazopanib-induced hypertension as the objective variable and various background factors as explanatory variables. SBP: Systolic blood pressure; t-bil: total bilirubin.
Net reclassification improvement (NRI) and integrated discrimination improvement (IDI). SBP is a known predictor that has been examined in previous studies. Therefore, we examined the extent to which incorporating t-bil and sex, newly suggested predictors of hypertension, in addition to the known SBP, improves the predictive ability of hypertension development. To evaluate this, we calculated categorical NRI, continuous NRI, and IDI. The results, presented in Table IV, demonstrate that adding t-bil or sex to SBP increased all three; adding both t-bil and sex to SBP further increased categorical NRI, continuous NRI, and IDI.
Table IV. Categorial net reclassification improvement (NRI), continuous NRI, and integrated discrimination improvement (IDI) using the systolic blood pressure (SBP) alone model as reference.
CI: Confidence interval; t-bil: total bilirubin. Categorical NRIs are calculated by defining hypertension as having a predicted probability of 0.5 or greater by logistic regression and no hypertension as having a predicted probability of less than 0.5.
5-fold cross validation. A stratified 5-fold cross-validation was then performed to examine the internal validity of the prediction factors. The sensitivity, specificity, discriminant predictive value, and ROC-AUC values are shown in Table V. The SBP+t-bil+sex model showed the highest discriminant predictive value and ROC-AUC, indicating the internal validity of the predictive factors.
Table V. Sensitivity, specificity, area under the receiver-operating characteristic curve (AUC-ROC), and their standard deviations calculated by 5-fold cross-validation.
SBP: Systolic blood pressure; t-bil: total bilirubin.
Time-dependent ROC. Further, time-dependent ROC analysis was performed to examine the predictive ability of baseline pazopanib-induced hypertension predictors for time-dependent event occurrence (Figure 3). The AUC over time for the first 20 days showed that for SBP alone, the values were higher in the first half of the study but decreased in the second half. Conversely, for t-bil, the AUC was low in the first half but increased in the second half, whereas in the models including the two (SBP+t-bil and SBP+t-bil+sex), the AUC remained high (0.7-0.8) throughout the entire observation period.
Figure 3. Time-dependent receiver operating characteristic-area under the curve (ROC-AUC) for the development of hypertension in the first 20 days after initiating pazopanib. SBP: Systolic blood pressure; t-bil: total bilirubin.
Discussion
The present study indicates that, in addition to SBP prior to pazopanib initiation as noted in previous studies, t-bil and sex may influence the increase in blood pressure after pazopanib initiation.
Particularly, our findings suggest that t-bil may contribute to pazopanib-induced hypertension in a time-dependent manner. Phase I studies have indicated that the primary route of excretion of pazopanib is in the feces, primarily as an unchanged drug, suggesting biliary excretion. Organic cation Transporter-1 (OCT-1) has been reported as a liver uptake transporter for pazopanib (27). And in vitro studies have demonstrated that the expression of OCT1 is reduced by bilirubin (28). The fact that t-bil was found to be a predictor of pazopanib-induced hypertension in this study suggests that bilirubin may delay the elimination of pazopanib and increase its concentration in the blood by down-regulating OCT1 expression. Regarding the organic anion transporter peptide 1B1 (OATP1B1), another liver uptake transporter, there have been studies both confirming and denying its involvement in pazopanib liver uptake (29,30). If OATP1B1 is involved, bilirubin concentration represents the activity of OATP1B1 and indirectly indicates the hepatic uptake capacity of pazopanib. Time-dependent ROC analysis showed that t-bil exhibited strong discriminatory ability after five days of pazopanib administration. This is consistent with previous reports of pazopanib accumulation due to decreased hepatic uptake transporter function (27,29).
Therefore, we considered why bilirubin was listed as a predictor of pazopanib-induced hypertension. However, information on the pharmacokinetics of pazopanib and the metabolism and excretion of bilirubin is limited. We were unable to examine bilirubin in this study because of the small number of patients who had direct bilirubin measurements; however, a more comprehensive understanding of the mechanisms contributing to hypertension development could be obtained if we could examine each fraction of bilirubin.
Additionally, high SBP at baseline, which has been previously reported (22), was noted as a predictor in the current study. It was found to have a good discriminatory ability for hypertension immediately after pazopanib initiation based on the results of the time-dependent ROC analysis. Conversely, t-bil demonstrated a high discriminatory ability for the development of hypertension several days after pazopanib initiation. The combination of these two factors may be able to predict hypertension after pazopanib initiation over time. This would provide valuable information when following up on side effects after pazopanib initiation.
Further, sex (female) was identified as a predictor in this study, which differs from a previous study (22). As shown in Table III, there were no significant differences in the number of antihypertensive medications taken at baseline or the initiated dose of pazopanib in males and females. However, as seen in Table III, the baseline SBP tended to be greater in females than in males in the current dataset. This difference in baseline SBP may explain why female sex was included as a predictor. Additionally, pazopanib is administered at a fixed dose of 800 mg, with a large difference in dose per body weight or body surface area. We believe that these factors likely contributed to the higher incidence of hypertension in females. Hence, we consider the association between sex and pazopanib-induced hypertension a pseudo-correlation.
It has been reported that hypertension with pazopanib led to prolonged OS and PFS in patients. It is possible that the predictors obtained in this study may also be predictive of patient prognosis.
Study limitations. First, it was conducted at a single institution, and external validity could not be determined. Additionally, this is an observational study and may include unknown confounding factors. Moreover, the number of cases may be too small to perform a logistic regression analysis using three explanatory variables. Assuming a 60% incidence of hypertension, a minimum of 75 cases is desirable. A reanalysis will be considered in the future after the number of cases has accumulated.
Since there were a few cases in which bilirubin was measured directly, we were unable to examine each fraction of bilirubin. A more detailed examination of the reasons why bilirubin was identified as a predictor may be possible by examining the data based on bilirubin fractions.
The GLI1 gene has been associated with the efficacy of pazopanib in treating soft-tissue sarcomas (31), suggesting that a specific gene may also be involved in blood pressure. However, genetic factors were not examined in the present study because only the information readily available in the medical record database was used. Genetic information may be important as a predictor of the pharmacodynamic aspects of pazopanib-induced hypertension; if the hypothesis that elevated t-bil indicates elevated blood pazopanib concentrations is correct, the interaction of t-bil with unknown pharmacodynamic predictors may be critical in predicting pazopanib-induced hypertension. Analyzing this interaction is also an important area for future studies.
Finally, the present study did not compare overall survival and progression-free survival by the presence or absence of hypertension. It is not clear whether the predictors of pazopanib-induced hypertension obtained in this study have an impact on patient outcomes.
Conclusion
This study suggests that a high t-bil is a predictor of pazopanib-induced hypertension, in addition to the existing high baseline SBP noted in previous reports. A high t-bil may indicate the accumulation of pazopanib in the body due to delayed elimination, potentially contributing to the development of hypertension. In addition to SBP before pazopanib initiation, t-bil could be considered to predict pazopanib-induced hypertension long after initiation.
Funding
The Authors received no financial support for the research, authorship, and publication of this article.
Conflicts of Interest
The Authors declare that there are no conflicts of interest in relation to this study.
Authors’ Contributions
All Authors contributed to the conception and design of the study. Data collection and analysis were performed by Masayuki Kunitake and Aya Goto. The first draft of the manuscript was written by Masayuki Kunitake, and all Authors commented on the previous versions of the manuscript. All Authors read and approved the final manuscript.
Acknowledgements
The Authors would like to thank Editage (www.editage.com) for English language editing.
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