Twelve-month frequency of drug-metabolizing enzyme and transporter-based drug-drug interaction potential in patients receiving oral enzyme-targeted kinase inhibitor antineoplastic agents

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

Clinical pharmacokinetics and drug-drug interactions of tyrosine-kinase inhibitors in chronic myeloid leukemia: A clinical perspective

In the past decade, numerous tyrosine kinase inhibitors (TKIs) have been introduced in the treatment of chronic myeloid leukemia. Given the significant interpatient variability in TKIs pharmacokinetics, potential drug-drug interactions (DDIs) can greatly impact patient therapy. This review aims to discuss the pharmacokinetic characteristics of TKIs, specifically focusing on their absorption, distribution, metabolism, and excretion profiles. Additionally, it provides a comprehensive overview of the utilization of TKIs in special populations such as the elderly, children, and patients with liver or kidney dysfunction. We also highlight known or suspected DDIs between TKIs and other drugs, highlighting various clinically relevant interactions. Moreover, specific recommendations are provided to guide haemato-oncologists, oncologists, and clinical pharmacists in managing DDIs during TKI treatment in daily clinical practice.

Drug-drug interactions associated with FLT3 inhibitors for acute myeloblastic leukemia: current landscape

INTRODUCTION

FLT3 inhibitors (FLT3i) are drugs in which there is limited experience and not yet enough information on the mechanisms of absorption, transport, and elimination; but especially on the potential drug-drug interactions (DDIs). There are therefore risks in the management of FLT3i DDIs (i.e. sorafenib, ponatinib, crenolanib, midostaurin, quizartinib, and gilteritinib) and ignoring them can compromise therapeutic success in acute myeloid leukemia (AML) treatment, in complex patients and secondary pathologies.

AREAS COVERED

This review summarizes the DDIs of FLT3i with P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporting (OAT), organic cationic transporting (OCT), cytochrome P450 (CYP) subunits, and other minor metabolic/transport pathways. EMBASE, PubMed, the Cochrane Central Register and the Web of Science were searched. The last literature search was performed on the 14 February 2022.

EXPERT OPINION

FLT3i will be combined with other therapeutic agents (supportive care, doublet, or triplet therapy) and in different clinical settings, which means a greater chance of controlling and even eradicating the disease effectively, but also an increased risk to patients due to potential DDIs. Healthcare professionals should be aware of the potential interactions that may occur and be vigilant in monitoring those patients who are receiving any potentially interacting drug.

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.

Association between clinically relevant toxicities of pazopanib and sunitinib and the use of weak CYP3A4 and P-gp inhibitors

PURPOSE

Sunitinib and pazopanib, two tyrosine kinase inhibitors (TKI), may be targets of potential pharmacokinetic drug-drug interactions (P-PK-DDIs). While strong cytochrome P4503A (CYP3A4) inhibitors or inducers should cause a clinically relevant modification in plasma TKI concentrations, the effect of weak inhibitors is unknown. The objective of this study was to evaluate the association between weak P-PK-DDI and clinically relevant toxicity in real life.

PATIENTS AND METHODS

This was a single-center retrospective study including patients treated with sunitinib or pazopanib for any malignancies, for whom a PK-DDI analysis was performed before starting TKI. The primary endpoint was the correlation between P-PK-DDIs and a dose decrease after 1 month of treatment. The secondary endpoint was the correlation between PK-DDIs and drug withdrawal due to toxicity.

RESULTS

Seventy-six patients were assessed. A P-PK-DDI with weak CYP3A4 or P-gp inhibition was found in 14 patients. In patients with P-PK-DDI or without, the dose was reduced during the first month in 57.1% and 17.7% (p = 0.003) and the drug withdrawn in 42.8% and 11.3% (p = 0.011), respectively. In multivariate analysis, a significant correlation was found between P-PK-DDI (CYP3A4 and P-gp inhibitors) and dose reduction, and between drug withdrawal and PK-DDI (CYP3A4 inhibitors).

CONCLUSION

P-PK-DDI was correlated with dose reduction and drug withdrawal due to toxicity. The causality of this relationship warrants to be assessed; therefore, therapeutic drug monitoring is necessary in patients treated with TKI.

Inadequate response to imatinib treatment in chronic myeloid leukemia due to a drug interaction with phenytoin

Imatinib mesylate and the newer BCR-ABL tyrosine kinase inhibitors are the standard therapy for chronic myeloid leukemia. Although these are remarkably effective drugs, some mechanisms of resistance have been identified including drug-to-drug interactions. Here we present the case of a chronic myeloid leukemia patient with an inadequate response to imatinib due to concurrent phenytoin administration. Conspicuously low imatinib plasma trough levels were documented. Imatinib dose was increased from 400 to 800 mg with good response. In conclusion, drug-to-drug interactions should be ruled out in cases of resistance to tyrosine kinase inhibitor treatment. Potent inducers of cytochrome P450 isoenzyme CYP3A4, as phenytoin, could induce inadequate responses due to increased imatinib clearance and low imatinib trough plasma levels. Thus, this interaction should be avoided. When this is not possible, dose escalation of imatinib and measurement of plasma levels, if available, is recommended.

Sex-Divergent Clinical Outcomes and Precision Medicine: An Important New Role for Institutional Review Boards and Research Ethics Committees

The efforts toward individualized medicine have constantly increased in an attempt to improve treatment options. These efforts have led to the development of small molecules which target specific molecular pathways involved in cancer progression. We have reviewed preclinical studies of sunitinib that incorporate sex as a covariate to explore possible sex-based differences in pharmacokinetics and drug–drug interactions (DDI) to attempt a relationship with published clinical outputs. We observed that covariate sex is lacking in most clinical outcome reports and suggest a series of ethic-based proposals to improve research activities and identify relevant different sex outcomes. We propose a deeper integration of preclinical, clinical, and translational research addressing statistical and clinical significance jointly; to embed specific sex-divergent endpoints to evaluate possible gender differences objectively during all stages of research; to pay greater attention to sex-divergent outcomes in polypharmacy scenarios, DDI and bioequivalence studies; the clear reporting of preclinical and clinical findings regarding sex-divergent outcomes; as well as to encourage the active role of scientists and the pharmaceutical industry to foster a new scientific culture through their research programs, practice, and participation in editorial boards and Institutional Ethics Review Boards (IRBs) and Research Ethics Committees (RECs). We establish the IRB/REC as the centerpiece for the implementation of these proposals. We suggest the expansion of its competence to follow up clinical trials to ensure that sex differences are addressed and recognized; to engage in data monitoring committees to improve clinical research cooperation and ethically address those potential clinical outcome differences between male and female patients to analyze their social and clinical implications in research and healthcare policies.

Influence of OATP1B1 Function on the Disposition of Sorafenib-β-D-Glucuronide

The oral multikinase inhibitor sorafenib undergoes extensive UGT1A9-mediated formation of sorafenib-β-D-glucuronide (SG). Using transporter-deficient mouse models, it was previously established that SG can be extruded into bile by ABCC2 or follow a liver-to-blood shuttling loop via ABCC3-mediated efflux into the systemic circulation, and subsequent uptake in neighboring hepatocytes by OATP1B-type transporters. Here we evaluated the possibility that this unusual process, called hepatocyte hopping, is also operational in humans and can be modulated through pharmacological inhibition. We found that SG transport by OATP1B1 or murine Oatp1b2 was effectively inhibited by rifampin, and that this agent can significantly increase plasma levels of SG in wildtype mice, but not in Oatp1b2-deficient animals. In human subjects receiving sorafenib, rifampin acutely increased the systemic exposure to SG. Our study emphasizes the need to consider hepatic handling of xenobiotic glucuronides in the design of drug-drug interaction studies of agents that undergo extensive phase II conjugation.

Drug Interaction Database Sensitivity With Oral Antineoplastics: An Exploratory Analysis

Purpose:

Drug interactions are a concern in oncology with the shift toward oral antineoplastics (OAs). Using electronic databases to screen for drug interactions with OAs is a common practice. There is little literature to guide clinicians on the reliability of these systems with OAs. The primary objective of this study was to explore the sensitivity of commonly available drug interaction databases in detecting drug interactions with OAs.

Methods:

A list of 20 drug interactions with OAs was developed by two Board-certified oncology pharmacists. The list included multiple types of drug interactions. The sensitivity in detecting these interactions by MicroMedex, Facts & Comparisons, Lexi-Interact, and Epocrates were evaluated. These databases were chosen based on their local availability and widespread use in practice. Drugs.com was evaluated as a surrogate for a patient-accessible drug interaction database. The Cochran Q test was used to assess the sensitivity distribution across the five groups.

Results:

Lexi-Interact and Drugs.com had a sensitivity of 95% for the 20 tested drug interaction pairs. Epocrates had a sensitivity of 90%, and both Micromedex and Facts & Comparisons had a sensitivity of 70%. There was a statistically significant difference ( P = .016) in the distribution across the databases in detecting clinically significant drug interactions.

Conclusion:

Commonly used databases for identifying drug interactions with oral antineoplastics vary significantly in their sensitivity. Clinicians should not rely on a single database and should consider using multiple resources as well as sound clinical judgment. Further work is needed in this area.

Severe liver toxicity in a chronic myeloid leukemia patient probably induced by a drug interaction between imatinib and sertraline

Our objective is to describe a chronic myeloid leukemia patient with a severe liver toxicity likely due to a drug-drug interaction between imatinib and sertraline. The patient started treatment with sertraline three months after starting imatinib. From the beginning of sertraline treatment, the patient developed vomiting, and five weeks later she developed a severe hepatic failure and was admitted to the hospital. The Naranjo nomogram showed a probable correlation between this adverse effect and the interaction between imatinib and sertraline. This interaction is extremely rare and the mechanism of action is not clear; it could be a mix of pharmacokinetic and pharmacodynamic processes. To our knowledge, this is the first case in medical literature of a severe liver toxicity due to an interaction between imatinib and sertraline. This interaction is also not described in the main secondary data sources, such as Lexicomp® and Micromedex®. However, due to the severity of this event, the hepatic function should be carefully monitored in patients treated with imatinib and sertraline.

Pharmacovigilance during ibrutinib therapy for chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL) in routine clinical practice

Due to Cytochrome P450 3A (CYP3A) metabolism, clinical trials of ibrutinib-treated chronic lymphocytic leukemia (CLL) patients prohibited concurrent medications metabolized by CYP3A. We evaluated concomitant medication use in 118 ibrutinib-treated CLL patients outside the context of clinical trials. Seventy-five (64%) patients were on medications that could increase ibrutinib toxicity and 4 (3%) were on drugs that could decrease ibrutinib efficacy. Nineteen (16%) patients were on concomitant CYP3A inhibitors (11 moderate, 8 strong), and 4 (3%) were on CYP3A inducers (two patients were on both CYP3A inhibitors and inducers). Although the ibrutinib starting dose was changed in 18 patients on CYP3A interacting medications, no difference in 18-month progression-free survival or rate of ibrutinib discontinuation was observed in patients who were not. In routine clinical practice, 2 of 3 CLL patients commencing ibrutinib are on a concomitant medication with potential to influence ibrutinib metabolism. Formal medication review by a pharmacist should be considered in all patients initiating ibrutinib.

Drug-drug interactions with imatinib: An observational study

Many patients treated with imatinib, used in cancer treatment, are using several other drugs that could interact with imatinib. Our aim was to study all the drug-drug interactions (DDIs) observed in patients treated with imatinib.We performed 2 observational studies, between the 1st January 2012 and the 31st August 2015 in the Midi-Pyrénées area (South Western France), using the French health insurance reimbursement database and then the French Pharmacovigilance Database (FPVD).A total of 544 patients received at least 1 reimbursement for imatinib. Among them, 486 (89.3%) had at least 1 drug that could potentially interact with imatinib. Paracetamol was the most frequent drug involved (77.4%). Proton pump inhibitors, dexamethasone and levothyroxine, were found in >10% of patients. In the FPVD, among a total of 25 reports of ADRs with imatinib recorded in the Midi-Pyrénées area, 10 (40%) had potential DDIs with imatinib. Imatinib was most frequently prescribed by hospital physicians and drugs interacting with imatinib, by general practitioners.Our study showed that at least 40% of the patients treated with imatinib were at risk of DDIs and that all prescribers must be cautious with DDIs in patients treated with imatinib. During imatinib treatment, we particularly recommend to limit the dose of paracetamol at 1300 mg per day, to avoid the use of dexamethasone, and to double the dose of levothyroxine.

Sunitinib DDI with paracetamol, diclofenac, mefenamic acid and ibuprofen shows sex-divergent effects on the tissue uptake and distribution pattern of sunitinib in mice

PURPOSE

Pharmacokinetic interaction of sunitinib with diclofenac, paracetamol, mefenamic acid and ibuprofen was evaluated due to their P450 mediated metabolism and OATP1B1, OATP1B3, ABCB1, ABCG2 transporters overlapping features.

METHODS

Male and female mice were administered 6 sunitinib doses (60 mg/kg) PO every 12 h and 30 min before the last dose were administered vehicle (control groups), 250 mg/kg paracetamol, 30 mg/kg diclofenac, 50 mg/kg mefenamic acid or 30 mg/kg ibuprofen (study groups), euthanized 6 h post last administration and sunitinib plasma, liver, kidney, brain concentrations analyzed.

RESULTS

Ibuprofen halved sunitinib plasma concentration in female mice (p < 0.01) and showed 59 % lower concentration than male mice (p < 0.05). Diclofenac and paracetamol female mice showed 45 and 25 % higher plasma concentrations than male mice which were 27 % lower in mefenamic acid female mice. Paracetamol increased 2.2 (p < 0.05) liver and 1.4-fold (p < 0.05) kidney sunitinib concentrations in male mice that were lower in female mice (p < 0.01, p < 0.001, respectively). Ibuprofen increased 2.9-fold (p < 0.01) liver concentration in male mice that were higher than in female mice (p < 0.001). Female control mice had 35 % higher sunitinib brain concentration than male mice but the concentration decreased 37, 33, 10 and 57 % in the diclofenac, paracetamol, mefenamic acid and ibuprofen (p < 0.001), respectively. Tissue-plasma concentrations correlations were nonsignificant in control, paracetamol, mefenamic acid and ibuprofen groups but was significant in the diclofenac group in male mice (liver, brain) and female mice (liver, kidney).

CONCLUSIONS

These results portray gender-based sunitinib pharmacokinetic differences and NSAIDs selective effects on male or female mice, with potential clinical translatability.

Overcoming resistance in chronic myelogenous leukemia

Resistance in chronic myelogenous leukemia is an issue that has developed in parallel to the availability of rationally designed small molecule tyrosine kinase inhibitors to treat the disease. A significant fraction of patients with clinical resistance are recognized to harbor point mutations/substitutions in the Abl kinase domain, which limit or preclude drug binding and activity. Recent data suggest that compound mutations may develop as well. Proper identification of clinical resistance and prudent screening for all causes of resistance, ranging from adherence to therapy to Abl kinase mutations, is crucial to success with kinase inhibitor therapy. There is currently an array of Abl kinase inhibitors with unique toxicity and activity profiles available, allowing for individualizing therapy beginning with initial choice at diagnosis and as well informed choice of subsequent therapy in the face of toxicity or resistance, with or without Abl kinase domain mutations. Recent studies continue to highlight the merits of increasingly aggressive initial therapy to subvert resistance and importance of early response to identify need for change in therapy. Proper knowledge and navigation amongst novel therapy options and consideration of drug toxicities, individual patient characteristics, disease response, and vigilance for development of resistance are necessary elements of optimized care for the patient with chronic myelogenous leukemia.

Metabolism-related pharmacokinetic drug-drug interactions with tyrosine kinase inhibitors: current understanding, challenges and recommendations

Drug-drug interactions (DDIs) occur when a patient's response to the drug is modified by administration or co-exposure to another drug. The main cytochrome P450 (CYP) enzyme, CYP3A4, is implicated in the metabolism of almost all of the tyrosine kinase inhibitors (TKIs). Therefore, there is a substantial potential for interaction between TKIs and other drugs that modulate the activity of this metabolic pathway. Cancer patients are susceptible to DDIs as they receive many medications, either for supportive care or for treatment of toxicity. Differences in DDI outcomes are generally negligible because of the wide therapeutic window of common drugs. However for anticancer agents, serious clinical consequences may occur from small changes in drug metabolism and pharmacokinetics. Therefore, the objective of this review is to highlight the current understanding of DDIs among TKIs, with a focus on metabolism, as well as to identify challenges in the prediction of DDIs and provide recommendations.

Co-prescription trends in a large cohort of subjects predict substantial drug-drug interactions

Pharmaceutical prescribing and drug-drug interaction data underlie recommendations on drug combinations that should be avoided or closely monitored by prescribers. Because the number of patients taking multiple medications is increasing, a comprehensive view of prescribing patterns in patients is important to better assess real world pharmaceutical response and evaluate the potential for multi-drug interactions. We obtained self-reported prescription data from NHANES surveys between 1999 and 2010, and confirm the previously reported finding of increasing drug use in the elderly. We studied co-prescription drug trends by focusing on the 2009-2010 survey, which contains prescription data on 690 drugs used by 10,537 subjects. We found that medication profiles were unique for individuals aged 65 years or more, with ≥98 unique drug regimens encountered per 100 subjects taking 3 or more medications. When drugs were viewed by therapeutic class, it was found that the most commonly prescribed drugs were not the most commonly co-prescribed drugs for any of the 16 drug classes investigated. We cross-referenced these medication lists with drug interaction data from Drugs.com to evaluate the potential for drug interactions. The number of drug alerts rose proportionally with the number of co-prescribed medications, rising from 3.3 alerts for individuals prescribed 5 medications to 11.7 alerts for individuals prescribed 10 medications. We found 22% of elderly subjects taking both a substrate and inhibitor of a given cytochrome P450 enzyme, and 4% taking multiple inhibitors of the same enzyme simultaneously. By examining drug pairs prescribed in 0.1% of the population or more, we found low agreement between co-prescription rate and co-discussion in the literature. These data show that prescribing trends in treatment could drive a large extent of individual variability in drug response, and that current pairwise approaches to assessing drug-drug interactions may be inadequate for predicting real world outcomes.

Sunitinib tissue distribution changes after coadministration with ketoconazole in mice

Sunitinib is a multitargeted tyrosine kinase inhibitor approved for gastrointestinal stromal tumor (GIST), advanced renal cell carcinoma (RCC) and pancreatic neuroendocrine tumors. It is metabolized via CYP3A4 and has low brain penetration due to efflux transporters ABCB1B and ABCG2. We studied the interaction with ketoconazole (50 mg/kg), antifungal drug which shares metabolic pathways and efflux transporters, in ICR female mice after oral coadministration (30 min apart) of 60 mg/kg sunitinib (study group) versus sunitinib alone (control group). Plasma, liver, kidney and brain sunitinib concentrations were measured by HPLC at 2, 5, 10, 20, 40 min, 1, 2, 4, 6, 12 h post-sunitinib administration, and non-compartmental pharmacokinetic parameters estimated. In plasma, ketoconazole coadministration increased plasma maximum concentration (C MAX) 60 %, delayed time to C MAX (T MAX); 1.6-fold greater area under the curve AUC0→∞ (p < 0.001); lower apparent steady-state volume of distribution (V SS/F) and oral clearance (Cl/F) 40 and 61 %, respectively; and shorter elimination half-life (t 1/2). Sunitinib exhibited extensive tissue distribution which increased after ketoconazole coadministration: total area under the curve (AUC0→∞) increased 1.8-, 2.8- and 1.2-fold in kidney, liver and brain, respectively (all p < 0.001). Sunitinib presented high tissue-to-plasma AUC0→∞ ratio in liver (17.8 ± 1.2), kidney (14.6 ± 1.52) and brain (2.25 ± 0.18) which was modified after coadministration: AUC0→∞ ratio increased in liver (31.4 ± 4.7; p < 0.001), kidney (17.1 ± 2.2; p > 0.05) and decreased in brain (1.70 ± 0.23, p > 0.05). The results showed a significant ketoconazole-sunitinib interaction that affected plasma, tissue pharmacokinetics and tissue uptake mechanisms. The study portrays the risk to increase toxicity and potential clinical translatability to treat tumors in tissues.

QTc interval prolongation with vascular endothelial growth factor receptor tyrosine kinase inhibitors

Background:

Multi-targeted vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKIs) are known to cause cardiac toxicity, but the relative risk (RR) of QTc interval prolongation and serious arrhythmias associated with them are not reported.

Methods:

We conducted a trial-level meta-analysis of randomised phase II and III trials comparing arms with and without a US Food and Drug Administration-approved VEGFR TKI (sunitinib, sorafenib, pazopanib, axitinib, vandetanib, cabozantinib, ponatinib and regorafenib). A total of 6548 patients from 18 trials were selected. Statistical analyses were conducted to calculate the summary incidence, RR and 95% CIs.

Results:

The RR for all-grade and high-grade QTc prolongation for the TKI vs no TKI arms was 8.66 (95% CI 4.92–15.2, P<0.001) and 2.69 (95% CI 1.33–5.44, P=0.006), respectively, with most of the events being asymptomatic QTc prolongation. Respectively, 4.4% and 0.83% of patients exposed to VEGFR TKI had all-grade and high-grade QTc prolongation. On subgroup analysis, only sunitinib and vandetanib were associated with a statistically significant risk of QTc prolongation, with higher doses of vandetanib associated with a greater risk. The rate of serious arrhythmias including torsades de pointes did not seem to be higher with high-grade QTc prolongation. The risk of QTc prolongation was independent of the duration of therapy.

Conclusions:

In the largest study to date, we show that VEGFR TKI can be associated with QTc prolongation. Although most cases were of low clinical significance, it is unclear whether the same applies to patients treated off clinical trials.

The role of age on dose-limiting toxicities in phase I dose-escalation trials

PURPOSE

Elderly oncology patients are not enrolled in early-phase trials in proportion to the numbers of geriatric patients with cancer. There may be concern that elderly patients will not tolerate investigational agents as well as younger patients, resulting in a disproportionate number of dose-limiting toxicities (DLT). Recent single-institution studies provide conflicting data on the relationship between age and DLT.

EXPERIMENTAL DESIGN

We retrospectively reviewed data about patients treated on single-agent, dose-escalation, phase I clinical trials sponsored by the Cancer Therapy Evaluation Program (CTEP) of the National Cancer Institute. Patients' dose levels were described as a percentage of maximum tolerated dose, the highest dose level at which <33% of patients had a DLT, or recommended phase II dose (RP2D). Mixed-effect logistic regression models were used to analyze relationships between the probability of a DLT and age and other explanatory variables.

RESULTS

Increasing dose, increasing age, and worsening performance status (PS) were significantly related to an increased probability of a DLT in this model (P < 0.05). There was no association between dose level administered and age (P = 0.57).

CONCLUSIONS

This analysis of phase I dose-escalation trials, involving more than 500 patients older than 70 years of age, is the largest reported. As age and dose level increased and PS worsened, the probability of a DLT increased. Although increasing age was associated with occurrence of DLT, this risk remained within accepted thresholds of risk for phase I trials. There was no evidence of age bias on enrollment of patients on low or high dose levels.

Acquired resistance to TKIs in solid tumours: learning from lung cancer

The use of advanced molecular profiling to direct the use of targeted therapy, such as tyrosine kinase inhibitors (TKIs) for patients with advanced-stage non-small-cell lung cancer (NSCLC), has revolutionized the treatment of this disease. However, acquired resistance, defined as progression after initial benefit, to targeted therapies inevitably occurs. This Review explores breakthroughs in the understanding and treatment of acquired resistance in NSCLC, focusing on EGFR mutant and ALK rearrangement-positive disease, which may be relevant across multiple different solid malignancies with oncogene-addicted subtypes. Mechanisms of acquired resistance may be pharmacological (that is, failure of delivery of the drug to its target) or biological, resulting from evolutionary selection on molecularly diverse tumours. A number of clinical approaches can maintain control of the disease in the acquired resistance setting, including the use of radiation to treat isolated areas of progression and adding or switching to cytotoxic chemotherapy. Furthermore, novel approaches that have already proven successful include the development of second-generation and third-generation inhibitors and the combination of some of these inhibitors with antibodies directed against the same target. With our increased understanding of the spectrum of acquired resistance, major changes in how we conduct clinical research in this setting are now underway.

Comorbidities and polypharmacy impact on complete cytogenetic response in chronic myeloid leukaemia elderly patients

BACKGROUND

In older patients comorbidity and polypharmacy can significantly influence the success of the treatment, as well as the cognitive and psycho-social aspects. A significant proportion of chronic myeloid leukaemia (CML) patients are "elderly": in the past the aim of therapy in this subset of patients was only to contain the leukaemic mass, but nowadays, with the advent of the protein-tyrosine kinase inhibitors, also elderly patients can access these treatments. We want to assess if even old CML patients, with a correct geriatric evaluation, can be successfully treated with protein-tyrosine kinase inhibitors.

METHODS

A complete geriatric evaluation in 16 old CML patients aged >65years treated with TKI was performed in order to assess the comorbidity, the polypharmacy and the cognitive, physical and psychological states. The Charlson comorbity index (CCI) and the polypharmacy were correlated to the obtained cytogenetic response. Seven scales of geriatric evaluation were used to assess the autonomy of patients before they were included into the study.

RESULTS

In our cohort of elderly patients treated with imatinib, comorbidities and polypharmaco-therapy demonstrated an influence on TKI therapeutic success. In fact, the majority of complete cytogenetic response was obtained by patients who presented a low score of CCI and did not take any other drugs other than TKI.

CONCLUSION

Also old chronic myeloid leukaemia patients can benefit from TKI treatment if a good cooperation between the haematologist and the geriatrician is established.

Drug interactions and the pharmacist: focus on everolimus

OBJECTIVE

To evaluate everolimus drug-drug and drug-food interactions, with an emphasis on patients with cancer.

DATA SOURCES

Literature was accessed through PubMed (1990-March 2013) using Boolean combinations of the terms drug interactions, herb-drug interactions, food-drug interactions, everolimus, antineoplastic agents, hormonal, and breast neoplasms. In addition, reference citations from publications and the prescribing information for everolimus were reviewed.

STUDY SELECTION AND DATA EXTRACTION

All articles published in English, including human, animal, and in vitro studies, identified from the data sources were included.

DATA SYNTHESIS

Patients with cancer are at increased risk for drug interactions because of the multiple medications they are prescribed to treat their disease and comorbid conditions. Everolimus, an oral mammalian target of rapamycin (mTOR) inhibitor, is indicated for the treatment in adults with progressive neuroendocrine tumors of pancreatic origin that are unresectable, locally advanced, or metastatic; adults with advanced renal cell carcinoma after failure of treatment with sunitinib or sorafenib; and, recently, postmenopausal women with advanced hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer in combination with exemestane after failure of treatment with letrozole or anastrozole. As its use increases among patients with cancer, clinicians must be knowledgeable about potential drug and/or food/nutrient interactions and the mechanisms by which these interactions occur, to mitigate and prevent unwanted reactions and ensure patient safety.

CONCLUSIONS

Everolimus is a widely used oral mTOR inhibitor that has the potential for drug interactions that may affect therapeutic outcomes, produce toxicities, or both. This article provides a review of evidence-based literature, along with the prescribing information, to educate clinicians on the significance of these drug interactions and their impact on management with everolimus.

Potential protective effect of sunitinib after administration of diclofenac: biochemical and histopathological drug-drug interaction assessment in a mouse model

Sunitinib is a tyrosine kinase inhibitor for GIST and advanced renal cell carcinoma. Diclofenac is used in cancer pain management. Coadministration may mediate P450 toxicity. We evaluate their interaction, assessing biomarkers ALT, AST, BUN, creatinine, and histopathological changes in the liver, kidney, heart, brain, and spleen. ICR mice (male, n = 6 per group/dose) were administered saline (group A) or 30 mg/kg diclofenac ip (group B), or sunitinib po at 25, 50, 80, 100, 140 mg/kg (group C) or combination of diclofenac (30 mg/kg, ip) and sunitinib (25, 50, 80, 100, 140 mg/kg po). Diclofenac was administered 15 min before sunitinib, mice were euthanized 4 h post-sunitinib dose, and biomarkers and tissue histopathology were assessed. AST was 92.2 ± 8.0 U/L in group A and 159.7 ± 14.6 U/L in group B (p < 0.05); in group C, it the range was 105.1-152.6 U/L, and in group D, it was 156.0-209.5 U/L (p < 0.05). ALT was 48.9 ± 1.6 U/L (group A), 95.1 ± 4.5 U/L (p < 0.05) in group B, and 50.5-77.5 U/L in group C and 82.3-115.6 U/L after coadministration (p < 0.05). Renal function biomarker BUN was 16.3 ± 0.6 mg/dl (group A) and increased to 29.9 ± 2.6 mg/dl in group B (p < 0.05) and it the range was 19.1-33.3 mg/dl (p < 0.05) and 26.9-40.8 mg/dl in groups C and D, respectively. Creatinine was 5.9 pmol/ml in group A; 6.2 pmol/ml in group B (p < 0.01), and the range was 6.0-6.2 and 6.2-6.4 pmol/ml in groups C and D, respectively (p < 0.05 for D). Histopathological assessment (vascular and inflammation damages) showed toxicity in group B (p < 0.05) and mild toxicity in group C. Damage was significantly lesser in group D than group B (p < 0.05). Spleen only showed toxicity after coadministration. These results suggest vascular and inflammation protective effects of sunitinib, not shown after biomarker analysis.