Population Modeling Integrating Pharmacokinetics, Pharmacodynamics, Pharmacogenetics, and Clinical Outcome in Patients With Sunitinib-Treated Cancer

A Randomized, Double-Blind, Placebo-Controlled Trial Evaluating the Effect of Topical Urea for Secondary Prophylaxis of Hand Foot Skin Reaction in Renal Cell Cancer Patients on Sunitinib Therapy

INTRODUCTION

Hand foot skin reaction (HFSR) is a common dose-limiting adverse effect of multi kinase inhibitors (MKI) whose mechanism is not fully understood, and the prophylaxis is inadequate.

OBJECTIVE

In this pilot study, a double-blind, randomized placebo-controlled trial was conducted to evaluate the effect of topical urea in secondary prevention of sunitinib-induced HFSR in renal cell cancer patients.

METHODS

Out of 55 screened patients, 14 were randomized to receive topical urea or placebo for four weeks. The association of HFSR with drug levels of sunitinib and its metabolite (n-desethyl sunitinib), genetic polymorphism of VEGFR2 gene, quality of life (QOL) and biochemical markers was also assessed.

RESULTS

The results showed that urea-based cream was not superior to placebo (P = .075). There was no change in the QOL in both the groups. Single nucleotide polymorphism was checked for two nucleotides rs1870377 and rs2305948 located in VEGFR2 gene on chromosome 4. SNP (variant T > A) at rs1870377 was associated with appearance of new HFSR as compared to the wild type, although the association was not statistically significant (OR 0.714). There was no statistically significant difference between mean plasma levels of sunitinib and N-desethyl sunitinib in urea arm as compared to placebo arm as compared to placebo. The best fit population pharmacokinetic model for sunitinib was one compartment model with first order absorption and linear elimination. The median (IQR) of population parameters calculated from the population pharmacokinetics model for Ka, V and Cl was 0.22 (0.21-0.24) h-1, 4.4 (4.09-4.47) L, 0.049 (0.042-0.12) L/hr, respectively.

CONCLUSION

The study suggested that the urea-based cream was not superior to placebo in decreasing the appearance of new HFSR in renal cancer patients receiving 4:2 regimen of sunitinib.

Therapeutic Drug Monitoring for Tyrosine Kinase Inhibitors in Metastatic Renal Cell Carcinoma

Inter-individual variability in drug response pose significant challenges to treatment with tyrosine kinase inhibitors (TKIs) in patients with metastatic renal cell carcinoma (mRCC). TKIs meet traditional criteria for using therapeutic drug monitoring (TDM), but research is still limited. Understanding the role of TDM in individualizing treatment strategies could help optimize treatment. Here we review the state of knowledge of TDM for TKIs in mRCC treatment. A comprehensive literature review of original research studies focusing on TDM of TKIs in mRCC treatment, clinical in vivo studies reporting on pharmacokinetics-pharmacodynamics, therapeutic ranges, drug concentrations, dose adjustments, clinical outcomes, or other relevant aspects related to TDM. We reviewed studies involving human subjects published in peer-reviewed journals. A narrative synthesis approach was employed to summarize the findings. Key themes and trends related to TDM of TKIs in mRCC treatment were identified and synthesized to provide a comprehensive overview of the current state of knowledge. Our search yielded 25 articles. Most were observational. The most consistently reported association between plasma concentration and effect was pazopanib Ctrough >20 µg/mL, but this concentration was not significant across all studies. We found inconsistent evidence for sunitinib and cabozantinib. For axitinib, we found a clear exposure-response relationship, but research was too diverse to conclude on a therapeutic window to use for TDM. We found much heterogeneity between recommended time of measurement (minimum plasma concentration [Cmin], maximal plasma concentration [Cmax], area under the curve [AUC]) and large variation in plasma concentration associated with clinical outcomes, which makes it difficult to recommend specific concentration intervals based on 1 or more of these measurements. Results were more consistent with TKIs continuously administered. Further research is needed to elucidate the long-term impact of TDM to possibly establish standardized therapeutic intervals. Prospective studies are suggested. The application of TDM in TKI-combination therapy is warranted in future research.

Pharmacogenetics of Drugs Used in the Treatment of Cancers

Pharmacogenomics is based on the understanding of the individual differences in drug use, the response to drug therapy (efficacy and toxicity), and the mechanisms underlying variable drug responses. The identification of DNA variants which markedly contribute to inter-individual variations in drug responses would improve the efficacy of treatments and decrease the rate of the adverse side effects of drugs. This review focuses only on the impact of polymorphisms within drug-metabolizing enzymes on drug responses. Anticancer drugs usually have a very narrow therapeutic index; therefore, it is very important to use appropriate doses in order to achieve the maximum benefits without putting the patient at risk of life-threatening toxicities. However, the adjustment of the appropriate dose is not so easy, due to the inheritance of specific polymorphisms in the genes encoding the target proteins and drug-metabolizing enzymes. This review presents just a few examples of such polymorphisms and their impact on the response to therapy.

Developing a Nationwide Infrastructure for Therapeutic Drug Monitoring of Targeted Oral Anticancer Drugs: The ON-TARGET Study Protocol

Simple Summary

Relationships between drug concentrations in blood and efficacy and/or toxicity have been reported for up to 80% of oral anticancer drugs (OADs). Most OADs exhibit highly variable drug concentrations at the approved dose. This may result in a significant proportion of patients with suboptimal drug concentrations. Therapeutic Drug Monitoring (TDM), which is dose optimization based on measured drug concentrations, can be used to personalize drug dosing with the overall goal to improve the benefit-risk ratio of anticancer drug treatment. The ON-TARGET study aims to investigate the feasibility of TDM in patients receiving either axitinib or cabozantinib for the treatment of renal-cell carcinoma with the main objective to improve severe tyrosine kinase inhibitor associated toxicity. Additionally, the feasibility of volumetric absorptive microsampling (VAMS), a novel minimally invasive and easy to handle blood sampling technique, for TDM sample collection is investigated.

Abstract

Exposure-efficacy and/or exposure-toxicity relationships have been identified for up to 80% of oral anticancer drugs (OADs). Usually, OADs are administered at fixed doses despite their high interindividual pharmacokinetic variability resulting in large differences in drug exposure. Consequently, a substantial proportion of patients receive a suboptimal dose. Therapeutic Drug Monitoring (TDM), i.e., dosing based on measured drug concentrations, may be used to improve treatment outcomes. The prospective, multicenter, non-interventional ON-TARGET study (DRKS00025325) aims to investigate the potential of routine TDM to reduce adverse drug reactions in renal cell carcinoma patients receiving axitinib or cabozantinib. Furthermore, the feasibility of using volumetric absorptive microsampling (VAMS), a minimally invasive and easy to handle blood sampling technique, for sample collection is examined. During routine visits, blood samples are collected and sent to bioanalytical laboratories. Venous and VAMS blood samples are collected in the first study phase to facilitate home-based capillary blood sampling in the second study phase. Within one week, the drug plasma concentrations are measured, interpreted, and reported back to the physician. Patients report their drug intake and toxicity using PRO-CTCAE-based questionnaires in dedicated diaries. Ultimately, the ON-TARGET study aims to develop a nationwide infrastructure for TDM for oral anticancer drugs.

The PHARMACOM-EPI Framework for Integrating Pharmacometric Modelling Into Pharmacoepidemiological Research Using Real-World Data: Application to Assess Death Associated With Valproate

In pharmacoepidemiology, it is usually expected that the observed association should be directly or indirectly related to the pharmacological effects of the drug/s under investigation. Pharmacological effects are, in turn, strongly connected to the pharmacokinetic and pharmacodynamic properties of a drug, which can be characterized and investigated using pharmacometric models. Recently, the use of pharmacometrics has been proposed to provide pharmacological substantiation of pharmacoepidemiological findings derived from real-world data. However, validated frameworks suggesting how to combine these two disciplines for the aforementioned purpose are missing. Therefore, we propose PHARMACOM-EPI, a framework that provides a structured approach on how to identify, characterize, and apply pharmacometric models with practical details on how to choose software, format dataset, handle missing covariates/dosing data, how to perform the external evaluation of pharmacometric models in real-world data, and how to provide pharmacological substantiation of pharmacoepidemiological findings. PHARMACOM-EPI was tested in a proof-of-concept study to pharmacologically substantiate death associated with valproate use in the Danish population aged ≥ 65 years. Pharmacological substantiation of death during a follow-up period of 1 year showed that in all individuals who died (n = 169) individual predictions were within the subtherapeutic range compared with 52.8% of those who did not die (n = 1,084). Of individuals who died, 66.3% (n = 112) had a cause of death possibly related to valproate and 33.7% (n = 57) with well-defined cause of death unlikely related to valproate. This proof-of-concept study showed that PHARMACOM-EPI was able to provide pharmacological substantiation for death associated with valproate use in the study population.

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

Different target exposures with sunitinib have been proposed in metastatic renal cell carcinoma (mRCC) patients, such as trough concentrations or AUCs. However, most of the time, rather than therapeutic drug monitoring (TDM), clinical evidence is preferred to tailor dosing, i.e., by reducing the dose when treatment-related toxicities show, or increasing dosing if no signs of efficacy are observed. Here, we compared such empirical dose adjustment of sunitinib in mRCC patients, with the parallel dosing proposals of a PK/PD model with TDM support. In 31 evaluable patients treated with sunitinib, 53.8% had an empirical change in dosing after treatment started (i.e., 46.2% decrease in dosing, 7.6% increase in dosing). Clinical benefit was observed in 54.1% patients, including 8.3% with complete response. Overall, 58.1% of patients experienced treatment discontinuation eventually, either because of toxicities or progressive disease. When choosing 50-100 ng/mL trough concentrations as a target exposure (i.e., sunitinib + active metabolite N-desethyl sunitinib), 45% patients were adequately exposed. When considering 1200-2150 ng/mL.h as a target AUC (i.e., sunitinib + active metabolite N-desethyl sunitinib), only 26% patients were in the desired therapeutic window. TDM with retrospective PK/PD modeling would have suggested decreasing sunitinib dosing in a much larger number of patients as compared with empirical dose adjustment. Indeed, when using target trough concentrations, the model proposed reducing dosing for 61% patients, and up to 84% patients based upon target AUC. Conversely, the model proposed increasing dosing in 9.7% of patients when using target trough concentrations and in 6.5% patients when using target AUC. Overall, TDM with adaptive dosing would have led to tailoring sunitinib dosing in a larger number of patients (i.e., 53.8% vs. 71-91%, depending on the chosen metrics for target exposure) than a clinical-based decision. Interestingly, sunitinib dosing was empirically reduced in 41% patients who displayed early-onset severe toxicities, whereas model-based recommendations would have immediately proposed to reduce dosing in more than 80% of those patients. This observation suggests that early treatment-related toxicities could have been partly avoided using prospective PK/PD modeling with adaptive dosing. Conversely, the possible impact of model-based adapted dosing on efficacy could not be fully evaluated because no clear relationship was found between baseline exposure levels and sunitinib efficacy measured at 3 months.

Therapeutic Drug Monitoring of Targeted Anticancer Protein Kinase Inhibitors in Routine Clinical Use: A Critical Review

BACKGROUND

Therapeutic response to oral targeted anticancer protein kinase inhibitors (PKIs) varies widely between patients, with insufficient efficacy of some of them and unacceptable adverse reactions of others. There are several possible causes for this heterogeneity, such as pharmacokinetic (PK) variability affecting blood concentrations, fluctuating medication adherence, and constitutional or acquired drug resistance of cancer cells. The appropriate management of oncology patients with PKI treatments thus requires concerted efforts to optimize the utilization of these drug agents, which have probably not yet revealed their full potential.

METHODS

An extensive literature review was performed on MEDLINE on the PK, pharmacodynamics, and therapeutic drug monitoring (TDM) of PKIs (up to April 2019).

RESULTS

This review provides the criteria for determining PKIs suitable candidates for TDM (eg, availability of analytical methods, observational PK studies, PK-pharmacodynamics relationship analysis, and randomized controlled studies). It reviews the major characteristics and limitations of PKIs, the expected benefits of TDM for cancer patients receiving them, and the prerequisites for the appropriate utilization of TDM. Finally, it discusses various important practical aspects and pitfalls of TDM for supporting better implementation in the field of cancer treatment.

CONCLUSIONS

Adaptation of PKIs dosage regimens at the individual patient level, through a rational TDM approach, could prevent oncology patients from being exposed to ineffective or unnecessarily toxic drug concentrations in the era of personalized medicine.

Therapeutic drug monitoring of oral targeted antineoplastic drugs

PURPOSE

This review provides an overview of the current challenges in oral targeted antineoplastic drug (OAD) dosing and outlines the unexploited value of therapeutic drug monitoring (TDM). Factors influencing the pharmacokinetic exposure in OAD therapy are depicted together with an overview of different TDM approaches. Finally, current evidence for TDM for all approved OADs is reviewed.

METHODS

A comprehensive literature search (covering literature published until April 2020), including primary and secondary scientific literature on pharmacokinetics and dose individualisation strategies for OADs, together with US FDA Clinical Pharmacology and Biopharmaceutics Reviews and the Committee for Medicinal Products for Human Use European Public Assessment Reports was conducted.

RESULTS

OADs are highly potent drugs, which have substantially changed treatment options for cancer patients. Nevertheless, high pharmacokinetic variability and low treatment adherence are risk factors for treatment failure. TDM is a powerful tool to individualise drug dosing, ensure drug concentrations within the therapeutic window and increase treatment success rates. After reviewing the literature for 71 approved OADs, we show that exposure-response and/or exposure-toxicity relationships have been established for the majority. Moreover, TDM has been proven to be feasible for individualised dosing of abiraterone, everolimus, imatinib, pazopanib, sunitinib and tamoxifen in prospective studies. There is a lack of experience in how to best implement TDM as part of clinical routine in OAD cancer therapy.

CONCLUSION

Sub-therapeutic concentrations and severe adverse events are current challenges in OAD treatment, which can both be addressed by the application of TDM-guided dosing, ensuring concentrations within the therapeutic window.

A Novel Integrated Pharmacokinetic-Pharmacodynamic Model to Evaluate Combination Therapy and Determine In Vivo Synergism

Understanding pharmacokinetic (PK)-pharmacodynamic (PD) relationships is essential in translational research. Existing PK-PD models for combination therapy lack consideration of quantitative contributions from individual drugs, whereas interaction factor is always assigned arbitrarily to one drug and overstretched for the determination of in vivo pharmacologic synergism. Herein, we report a novel generic PK-PD model for combination therapy by considering apparent contributions from individual drugs coadministered. Doxorubicin (Dox) and sorafenib (Sor) were used as model drugs whose PK data were obtained in mice and fit to two-compartment model. Xenograft tumor growth was biphasic in mice, and PD responses were described by three-compartment transit models. This PK-PD model revealed that Sor (contribution factor = 1.62) had much greater influence on overall tumor-growth inhibition than coadministered Dox (contribution factor = 0.644), which explains the mysterious clinical findings on remarkable benefits for patients with cancer when adding Sor to Dox treatment, whereas there were none when adding Dox to Sor therapy. Furthermore, the combination index method was integrated into this predictive PK-PD model for critical determination of in vivo pharmacologic synergism that cannot be correctly defined by the interaction factor in conventional models. In addition, this new PK-PD model was able to identify optimal dosage combination (e.g., doubling experimental Sor dose and reducing Dox dose by 50%) toward much greater degree of tumor-growth inhibition (>90%), which was consistent with stronger synergy (combination index = 0.298). These findings demonstrated the utilities of this new PK-PD model and reiterated the use of valid method for the assessment of in vivo synergism. SIGNIFICANCE STATEMENT: A novel pharmacokinetic (PK)-pharmacodynamic (PD) model was developed for the assessment of combination treatment by considering contributions from individual drugs, and combination index method was incorporated to critically define in vivo synergism. A greater contribution from sorafenib to tumor-growth inhibition than that of coadministered doxorubicin was identified, offering explanation for previously inexplicable clinical observations. This PK-PD model and strategy shall have broad applications to translational research on identifying optimal dosage combinations with stronger synergy toward improved therapeutic outcomes.

Combined siRNA and Small-Molecule Phenotypic Screening Identifies Targets Regulating Rhinovirus Replication in Primary Human Bronchial Epithelial Cells

Human rhinovirus (RV) is the most common cause of acute upper respiratory tract infections and has recently been shown to play a significant role in exacerbations of asthma and chronic obstructive pulmonary disease (COPD). There is a significant unmet medical need for agents for the prevention and/or treatment of exacerbations triggered by human RV infection. Phenotypic drug discovery programs using different perturbation modalities, for example, siRNA, small-molecule compounds, and CRISPR, hold significant value for identifying novel drug targets. We have previously reported the identification of lanosterol synthase as a novel regulator of RV2 replication through a phenotypic screen of a library of siRNAs against druggable genes in normal human bronchial epithelial (NHBE) cells. Here, we describe a follow-up phenotypic screen of small-molecule compounds that are annotated to be pharmacological regulators of target genes that were identified to significantly affect RV2 replication in the siRNA primary screen of 10,500 druggable genes. Two hundred seventy small-molecule compounds selected for interacting with 122 target gene hits were screened in the primary RV2 assay in NHBE cells by quantifying viral replication via in situ hybridization followed by secondary quantitative PCR-based assays for RV2, RV14, and RV16. The described follow-up phenotypic screening allowed us to identify Fms-related tyrosine kinase 4 (FLT4) as a novel target regulating RV replication. We demonstrate that a combination of siRNA and small-molecule compound screening models is a useful phenotypic drug discovery approach for the identification of novel drug targets.

An HPLC method for simultaneous quantification of sunitinib and its active metabolite, SU12662, using hydrophilic interaction chromatography principle

Aim: To develop a sensitive HPLC method for the quantitation of sunitinib (SU) and its active metabolite N-desethyl-sunitinib (SU12662) in human plasma. Materials & methods: The analytes were extracted from 500 μl of plasma using liquid–liquid extraction followed by protein precipitation. Chromatographic separation of two analytes and internal standard, vandetenib, was achieved on a hydrophilic interaction liquid chromatography analytical column using a gradient program. Calibration curves were linear over the range of 10–250 ng/ml for both SU and SU12662. The method was validated according to the US FDA guidelines for bioanalytical methods. Accuracy of the method at 10 ng/ml for SU and SU12662 was 8.7 and 6.7%, respectively, and precision was 10.18% and 17.3%, respectively. Conclusion: This method allows a specific, sensitive and reliable determination of SU and SU12662 in human plasma in a single analytical run which makes it useful for therapeutic drug monitoring.

Limitations to the Therapeutic Potential of Tyrosine Kinase Inhibitors and Alternative Therapies for Kidney Cancer

Background: Renal cell carcinomas (RCCs) are the most common primary renal tumor. RCCs have a high rate of metastasis and have the highest mortality rate of all genitourinary cancers. They are often diagnosed late when metastases have developed, and these metastases are difficult to treat successfully. Since 2006, the standard first-line treatment for patients with metastatic RCC has been multitargeted tyrosine kinase inhibitors (TKIs) that include mammalian target of rapamycin (mTOR) inhibitors. RCCs are highly vascularized tumors, and their angiogenesis is controlled by tyrosine kinases that play a vital role in growth factor signaling to stimulate this process. TKI therapy was introduced for direct targeting of angiogenesis in RCC. TKIs have been moderately successful in the treatment of metastatic RCC and initially increased cancer-specific survival times. However, RCC rapidly becomes resistant to TKIs, and no current drug has produced a cure for advanced RCC. Methods: We provide an overview of RCC, explain some reasons for therapy resistance in RCC, and describe some therapies that may overcome resistance to TKIs. The key pathways that determine therapy resistance are illustrated. Results: Factors involved in the development and progression of RCC include genetic mutations, activation of hypoxia-inducible factor and related proteins, cellular metabolism, the tumor microenvironment, and growth factors and their receptors. Resistance to the therapeutic potential of TKIs can be acquired or intrinsic. Alternative therapies include other small molecule drugs and immunotherapy based on immune checkpoint blockade. Conclusion: The treatment of RCC is undergoing a paradigm shift from sole use of small molecule antiangiogenesis TKIs as first-line therapy to include newly approved agents for second-line and third-line therapy that now involve the mTOR pathway and immune checkpoint blockade drugs for patients with advanced RCC.

Phase III Trial of Adjuvant Sunitinib in Patients with High-Risk Renal Cell Carcinoma: Exploratory Pharmacogenomic Analysis

PURPOSE

In the S-TRAC trial, adjuvant sunitinib prolonged disease-free survival (DFS) versus placebo in patients with loco-regional renal cell carcinoma at high risk of recurrence after nephrectomy. An exploratory analysis evaluated associations between SNPs in several angiogenesis- or hypoxia-related genes and clinical outcomes in S-TRAC.

PATIENTS AND METHODS

Blood samples were genotyped for 10 SNPs and one insertion/deletion mutation using TaqMan assays. DFS was compared using log-rank tests for each genotype in sunitinib versus placebo groups and between genotypes within each of three (sunitinib, placebo, and combined sunitinib plus placebo) treatment groups. P values were unadjusted.

RESULTS

In all, 286 patients (sunitinib, n = 142; placebo, n = 144) were genotyped. Longer DFS [HR; 95% confidence interval (CI)] was observed with sunitinib versus placebo for VEGFR1 rs9554320 C/C (HR 0.44; 95% CI, 0.21-0.91; P = 0.023), VEGFR2 rs2071559 T/T (HR 0.46; 95% CI, 0.23-0.90; P = 0.020), and eNOS rs2070744 T/T (HR 0.53; 95% CI, 0.30-0.94; P = 0.028). Shorter DFS was observed for VEGFR1 rs9582036 C/A versus C/C with sunitinib, placebo, and combined therapies (P ≤ 0.05), and A/A versus C/C with sunitinib (P = 0.022). VEGFR1 rs9554320 A/C versus A/A was associated with shorter DFS in the placebo (P = 0.038) and combined (P = 0.006) groups.

CONCLUSIONS

Correlations between VEGFR1 and VEGFR2 SNPs and longer DFS with sunitinib suggest germline SNPs are predictive of improved outcomes with adjuvant sunitinib in patients with renal cell carcinoma. Independent validation studies are needed to confirm these findings.

Single nucleotide polymorphisms as prognostic and predictive biomarkers in renal cell carcinoma

Despite major advances in the knowledge of the molecular basis of renal cell carcinoma, prognosis is still defined using clinical and pathological parameters. Moreover, no valid predictive biomarkers exist to help us selecting the best treatment for each patient. With these premises, we aimed to analyse the expression and to determine the prognostic and predictive value of 64 key single nucleotide polymorphisms in 18 genes related with angiogenesis or metabolism of antiangiogenics in two cohorts of patients with localized and advanced renal cell cancer treated at our institution. The presence of the selected single nucleotide polymorphisms was correlated with clinical features, disease free survival, overall survival and response rate. In patients with localized renal cell cancer, 5 of these polymorphisms in 3 genes involved in angiogenesis predicted for worse disease free survival (VEGFR2: rs10013228; PDGFRA: rs2228230) or shorter overall survival (VEGFR2: rs10013228; VEGFR3: rs6877011, rs307826) (p < 0.05). Rs2071559 in VEGFR2 showed a protective effect (p = 0.01). In the advanced setting, 5 SNPs determined inferior overall survival (IL8: rs2227543, PRKAR1B: rs9800958, PDGFRB: rs2302273; p = 0.05) or worse response rate (VEGFA: rs699947, rs3025010 p ≤ 0.01)). Additionally 1 single nucleotide polymorphism in VEGFB predicted for better response rate rs594942 (p = 0.03). Genetic analysis of renal cell carcinoma patients might provide valuable prognostic/predictive information. A set of SNPs in genes critical to angiogenesis and metabolism of antiangiogenics drugs seem to determine post-surgical outcomes and treatment response in our series.