Prevalence of the coprescription of clinically important interacting drug combinations involving oral anticancer agents in Singapore: a retrospective database study

Potential drug-drug interactions among geriatric oncology patients: a retrospective study in Saudi Arabia

BACKGROUND

Drug-drug interactions (DDIs) are significant causes of adverse drug reactions among patients with cancer. We aimed to identify the prevalence, severity, and predictors of potential DDIs among geriatric oncology patients.

METHODS

A cross-sectional, retrospective study was conducted at two tertiary medical centers. Geriatric patients (≥ 65 years) who were diagnosed with solid tumors and received outpatient prescriptions with a minimum of two drugs between January 2018 and December 2022 were included in the study. Patients' medications were screened for DDIs using Lexi-Interact. Univariate and multivariable logistic regression models were used to explore factors associated with DDIs.

RESULTS

The study included 247 geriatric patients with a mean age of 74.0 ± 7.3 years, and 48.6% of the patients were female. The most common type of cancer was gastrointestinal cancer (35.6%), followed by genitourinary cancer (20.6%), and 50.6% of the patients had metastasized tumors. Approximately one-half of the patients (49.0%) received anticancer therapy, and hormonal therapy (21.9%) or chemotherapy (16.6%) was the most common therapy. The mean number of medications used per patient was 6.9 ± 3.5. The majority of patients (79.4%) had at least one DDI, with a mean of 5.6 ± 5.3 DDIs per patient. Most of the interactions were classified as moderate (58.9%), and only 19.3% were classified as major. Multiple logistic regression revealed that females were more vulnerable to DDIs than their male counterparts were (adjusted odds ratio (AOR) = 37.4; 95% CI 4.13-338.3). The number of medications used was significantly associated with the risk of DDIs (AOR = 4.07; 95% CI 2.53-6.54). Compared with patients with gastrointestinal cancers, patients with breast or gynecologic cancers had lower odds of experiencing DDI (AOR = 0.02; 95% CI < 0.01-0.24 and AOR = 0.04; 95% CI < 0.01-0.29, respectively).

CONCLUSION

This study revealed a high prevalence of DDIs among geriatric oncology patients, with most interactions classified as moderate. Female patients and patients taking multiple medications had a greater risk of experiencing DDIs. Routine screening for potential DDIs is essential for this vulnerable population, and the factors identified in this study should be carefully considered.

Anti-Angiogenic Tyrosine Kinase Inhibitor-Related Toxicities Among Cancer Patients: A Systematic Review and Meta-Analysis

BACKGROUND

Targeting of angiogenesis has become a major therapeutic approach for the treatment of various advanced cancers. There are many unresolved questions on the toxicity of anti-angiogenic tyrosine kinase inhibitors (TKIs).

OBJECTIVE

We performed a meta-analysis to assess the toxicity prevalence of the different anti-angiogenic TKIs among cancer patients and in subpopulations of interest including patients with renal cell carcinoma.

PATIENTS AND METHODS

We searched the MEDLINE and Cochrane Library databases to November 2023. Clinical trials were eligible if they set out to report the grade ≥3 toxicities related to one of the seven currently approved anti-angiogenic TKIs as monotherapies. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was applied with PROSPERO (CRD42023411946).

RESULTS

The 421 eligible studies included a total of 56,895 cancer patients treated with anti-angiogenic TKI monotherapy. Twenty-four different cancer types were identified, mainly renal cell carcinoma (41.9% of the patients). The anti-angiogenic TKI was sorafenib (34.5% of the patients), sunitinib (30.5%), regorafenib (10.7%), pazopanib (9.4%), cabozantinib (7.7%), axitinib (4.3%), and lenvatinib (2.9%). The pooled prevalence of grade 3 and 4 toxicities was 56.1% (95% confidence interval 53.5-58.6), with marked between-study heterogeneity (I2 = 96.8%). Toxicity profiles varied considerably depending on the type of TKI, the cancer type, and the specific patient characteristics. In particular, Asian patients and elderly people had higher prevalences of severe toxicities, with pazopanib being the best-tolerated drug. For patients treated with sunitinib, particularly those with metastatic RCC, there was no significant difference in terms of toxicity according to the regimen schedule.

CONCLUSIONS

This meta-analysis highlights the toxicity profiles of anti-angiogenic TKI monotherapies, and thus enables high-level recommendations for the choice of anti-angiogenic TKIs on the basis of the patient's age, ethnicity, comorbidities, and comedications, for personalized treatment.

[Contribution of the pharmaceutical record in the analysis of drug interactions during retrocession in a centre for cancer research]

OBJECTIVES

To implement the pharmaceutical record in retrocession, to evaluate its contribution to the analysis of drug interactions and to estimate the sustainability of this approach.

METHODS

This prospective, descriptive, monocentric study was conducted over five months. All patients presenting at the retrocession were eligible. After having offered them the pharmaceutical record and having completed it, drug interactions were sought. If the impact was considered significant, a pharmaceutical intervention was transmitted to the referring physician of the institution and recorded in the computerized patient record.

RESULTS

The pharmaceutical record was offered to 497 patients, i.e., 87 % of eligible patients. At the first meeting, 7 % of patients (n=34) were aware of it and 72 % had one open. In total, 395 pharmaceutical records were filled in at least once, 41 of which we created. Only 25 patients (5 %) refused the process and 90 % of the existing records were filled by the pharmacy. In total, 419 prescriptions were analysed for 330 patients: the pharmaceutical record was therefore a useful tool for 66 % of patients. For 17 % (n=57) of them, or 11 % of included patients, 99 drug interactions with a high risk of clinical impact were detected with the retroceded drug. On average, the presentation, creation and feeding of the drug record took one minute each and the analysis of interactions 14minutes.

CONCLUSIONS

Easy to implement, the pharmaceutical record is a useful tool to search for drug interactions with retroceded drugs. It helps to optimize patient follow-up, despite the limited information available.

A comprehensive evaluation of potentially significant drug-drug, drug-herb, and drug-food interactions among cancer patients receiving anticancer drugs

INTRODUCTION

During the cancer treatment path, cancer patients use numerous drugs, including anticancer, supportive, and other prescribed medications, along with herbs and certain products. This puts them at risk of significant drug interactions (DIs). This study describes DIs in cancer patients and their prevalence and predictors.

METHODS

A cross-sectional study design was used to achieve the study objectives. The study was carried out in two centers in the northern West Bank, Palestine. The Lexicomp® Drug Interactions tool (Lexi-Comp, Hudson OH, USA) was applied to check the potential DIs. In addition, the Statistical Package for the Social Sciences (SPSS) was used to show the results and find the associations.

RESULTS

The final analysis included 327 patients. Most of the participants were older than 50 years (61.2%), female (68.5%), and had a solid tumor (74.6%). The total number of potential DIs was 1753, including 1510 drug-drug interactions (DDIs), 24 drug-herb interactions, and 219 drug-food interactions. Importantly, the prevalence of DDIs was 88.1%. In multivariate analysis, the number of potential DDIs significantly decreased with the duration of treatment (p = 0.007), while it increased with the number of comorbidities (p < 0.001) and the number of drugs used (p < 0.001).

CONCLUSIONS

We found a high prevalence of DIs among cancer patients. This required health care providers to develop a comprehensive protocol to monitor and evaluate DIs by improving doctor-pharmacist communication and supporting the role of clinical pharmacists.

The optimization of combinatorial drug therapies: Strategies and laboratorial platforms

Designing optimal combinatorial drug therapies is challenging, because the drug interactions depend not only on the drugs involved, but also on their doses. With recent advances, combinatorial drug therapy is closer than ever to clinical application. Herein, we summarize approaches and advances over the past decade for identifying and optimizing drug combination therapies, with innovations across research fields, covering physical laboratory platforms for combination screening to computational models and algorithms designed for synergism prediction and optimization. By comparing different types of approach, we detail a three-step workflow that could maximize the overall optimization efficiency, thus enabling the application of personalized optimization of combinatorial drug therapy.

Potential drug interactions between targeted oral antineoplastic agents and concomitant medication in clinical practice

OBJECTIVES

Our objective was to analyze potential drug interactions (PDIs) between targeted OAAs and concomitant therapy in clinical practice.

METHODS

A cross-sectional observational study was performed in cancer outpatients who started treatment with a targeted OAA between 1 December 2015 and 31 May 2019. PDIs were analyzed using the Lexicomp® and the database About Herbs®. PDIs were classified according to severity, risk, and reliability ratings and their underlying mechanism. Univariate and multivariate analysis were performed to identify risk factors associated with PDIs.

RESULTS

A total of 881 patients were included, of whom 50.9% had at least 1 PDI between the OAA and the concomitant medication. The factors associated with a higher risk of PDIs were polypharmacy (≥5 concomitant medicines) (OR = 3.64 (2.54-5.20), p < 0.001), type of tumor (prostate cancer [OR = not available, p < 0.001], chronic myelogenous leukemia [OR = 5.10 (1.08-24.05), p = 0.040], sarcoma [OR = 4.97 (1.05-23.55), p = 0.043]), and treatment with hormone therapies (OR = not available, p < 0.001).

CONCLUSION

A search of PDIs should be prioritized, especially in patients receiving targeted OAAs with risk factors, such as polymedication, prostate cancer, chronic myelogenous leukemia, sarcoma, and treatment with hormone therapies.

Prevalence and significance of potential drug-drug interactions among cancer patients receiving chemotherapy

Background

Cancer patients often receive multiple drugs to maximize their therapeutic benefit, treat co-morbidities and counter the adverse effects of chemotherapy. Concomitant administration of multiple drugs increases the risk of drug interactions leading to compromised therapeutic efficacy or safety of therapy. The purpose of this study was to identify the prevalence, levels and predictors of potential drug-drug interactions (pDDIs) among cancer patients.

Methods

Six hundred and 78 patients receiving chemotherapy from two tertiary care hospitals were included in this cross-sectional study. Patient medication profiles were screened for pDDIs using the Micromedex® database. Logistic regression analysis was performed to identify the predictors of pDDIs.

Results

The overall prevalence of pDDIs was 78%, majority of patients had 1–2 pDDIs (39.2%). A total of 1843 pDDIs were detected. Major-pDDIs were most frequent (67.3%) whereas, a significant association of pDDIs was found between > 7 all prescribed drugs (p < 0.001) and ≥ 3 anti-cancer drugs (p < 0.001). Potential adverse outcomes of these interactions include reduced therapeutic effectiveness, QT interval prolongation, tendon rupture, bone marrow suppression and neurotoxicity.

Conclusions

Major finding of this study is the high prevalence of pDDIs signifying the need of strict patient monitoring for pDDIs among cancer patients. Patients at higher risk to pDDIs include those prescribed with > 7 any types of drugs or ≥ 3 anticancer drugs. Moreover, list of most frequently identified major and moderate interactions will aid health care professional in timely identification and prevention of pDDIs.

Nationwide prevalence of potential drug-drug interactions associated with non-anticancer agents in patients on oral anticancer agents in South Korea

PURPOSE

We analyzed the prevalence and severity of potential drug-drug interactions (PDDIs) in Korean patients receiving oral anticancer agents (OAAs) during two different periods.

METHODS

A cross-sectional study was conducted using the national insurance reimbursement database. The subjects were adult outpatients diagnosed with cancer and prescribed OAAs at least once in 2010 or 2014. PDDIs were identified using a database and the PDDI severity was categorized as category X (contraindications) or D (consideration of therapy modification). The associated factors for the occurrence of PDDIs were also analyzed.

RESULTS

Among the 118,258 patients prescribed OAAs in 2014, approximately 59% were middle-aged, and approximately half were diagnosed with breast cancer. The number of comorbidities increased over time, and majority were diagnosed with gastrointestinal disorders, hyperlipidemia, and psychonervous disorders. The PDDIs due to protein kinase inhibitors (PKIs) with gastrointestinal/metabolic and neurological drugs increased 3.1- and 4.9-fold, respectively, over the 5 years, and 24.0% of the PDDIs fell into category X. Tamoxifen, the most commonly prescribed OAAs, caused the PDDIs with antidepressants through QTc prolongation or pharmacokinetic interaction. The PKIs prescription, cancer type like breast or hematologic cancer, and number of comorbidities or co-prescribing drugs were independently associated with the occurrence of PDDIs.

CONCLUSIONS

The risk of PDDIs in patients receiving OAAs increases, particularly with the concomitant use of PKIs with gastrointestinal or psychiatric drugs and endocrine agents with antidepressants. Considering the potential risk of chronic concomitant use of these drug classes in outpatients, healthcare professionals should be made aware of the potential interactions.

Real-world prevalence of potential drug-drug interactions involving oral antineoplastic agents: a population-based study

PURPOSE

We aimed to gain insight into the real-world prevalence of potentially significant drug-drug interactions (DDIs) involving oral antineoplastic agents using nationwide data in Korea.

METHODS

The data from the 2016 and 2017 Health Insurance Review and Assessment Service-National Patients Sample (HIRA-NPS) of South Korea were used. The drugs prescribed concomitantly with oral anticancer drugs were screened for the potential DDIs by using two international DDI databases: Lexicomp^TM and Micromedex®. Potentially significant DDIs were defined as DDIs with a severity rating of "major" or higher from at least one reference. The DDIs were classified into category 1 if the severity ratings were major or higher using both references.

RESULTS

Overall 5657 cases of DDIs in 2925 patients (26.4%) and 1640 cases of category 1 DDIs in 997 patients (9.0%) were identified among 11,076 patients receiving oral anticancer drugs. The prevalence was highest among the targeted agents (63.2%) followed by traditional (21.2%) and endocrine agents (19.3%). The common potential clinical consequences were increased risk of corrected QT interval prolongation (36.7%), reduced efficacy of antineoplastic agents (30.4%), and increased toxicities of antineoplastic agents (8.0%). Polypharmacy and the duration of oral cancer treatment increased the likelihood of potential DDIs in addition to individual antineoplastic agents.

CONCLUSIONS

This study showed that potentially significant DDIs with oral antineoplastic agents were prevalent in real-world practice. Recognizing the high prevalence of DDIs among patients taking oral antineoplastic agents is a necessary step toward improving the clinical outcome.

Proton Pump Inhibitors and Survival Outcomes in Patients With Metastatic Renal Cell Carcinoma

INTRODUCTION

Proton pump inhibitors (PPIs) are potent inhibitors of gastric acid secretion and can affect the optimal absorption of concomitant oral medications, such as vascular endothelial growth factor (VEGF) tyrosine kinase inhibitors (TKIs). The purpose of this study was to investigate the effect of PPI use on survival in metastatic renal cell carcinoma (mRCC) patients treated in the targeted therapy era.

MATERIALS AND METHODS

We conducted a pooled analysis of mRCC patients treated in phase II and III clinical trials. Statistical analyses were performed using Cox regression adjusted for several risk factors and the Kaplan-Meier method.

RESULTS

We identified 2188 patients treated with sunitinib (n = 952), axitinib (n = 626) or sorafenib (n = 610), of whom 120 were PPI users. Overall, PPI users showed similar overall survival compared with non-PPI users (hazard ratio [HR], 1.051; 95% confidence interval [CI], 0.769-1.438; P = .754; median, 24.1 vs. 21.3 months). Similarly, progression-free survival (HR, 1.016; 95% CI, 0.793-1.301; P = .902; median, 5.5 vs. 8.0 months) and objective response rates (23.3% vs. 27.4%; P = .344) were not different between PPI users and nonusers. These findings were consistent across International mRCC Database Consortium risk groups and according to line of therapy. Adverse events were similar between PPI users and nonusers.

CONCLUSION

We showed that PPI use does not appear to negatively affect the efficacy and safety of select VEGF-TKIs in patients with mRCC. Documentation of concomitant medications and patient education on potential drug interactions are critical for optimizing the use of oral cancer-targeting therapy.

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.

Drug–drug interactions in patients receiving tyrosine kinase inhibitors

Rationale

Tyrosine kinase inhibitors are increasingly used in the treatment of cancer. Drug interactions involving tyrosine kinase inhibitors are commonly encountered in clinical practice. The objective of this study was to describe the frequency of tyrosine kinase inhibitor-associated drug interactions among a cohort of oncology patients.

Methods

Adult patients were included who presented to either of two outpatient oncology practices and were prescribed a tyrosine kinase inhibitor during 2 January 2013 to 1 January 2015. Demographic and medication data were abstracted from electronic medical records. Lexicomp®, Micromedex Solutions®, and medication labeling were utilized to identify potential interactions between tyrosine kinase inhibitors and concomitant medications. Interactions were then assessed by the investigators for clinical significance. The primary outcome was the frequency of significant drug interactions involving tyrosine kinase inhibitors and concomitant medications. Secondary outcomes included describing the nature and clinical impact of interactions, and describing interactions by medication class.

Results

A total of 356 patients were identified for analysis, in whom 244 potential interactions were identified, and 109 (44.7%) of which were considered severe. Decreased tyrosine kinase inhibitor absorption due to acid suppressive therapy and CYP3A4 interactions were the most frequent mechanisms of potential subtherapeutic and supratherapeutic concentrations, respectively. Potential clinical consequences included QTc prolongation ( n = 53, 48.6%), decreased tyrosine kinase inhibitor concentration ( n = 53, 48.6%), and increased tyrosine kinase inhibitor concentration ( n = 3, 2.8%).

Conclusions

Safer alternative therapy and/or more frequent clinical monitoring should be considered if an interaction poses a significant risk of increased tyrosine kinase inhibitor toxicity or decreased tyrosine kinase inhibitor efficacy. Oncology pharmacists can play a role in screening for tyrosine kinase inhibitor-associated interactions, recommending alternative therapies or dosing strategies, and monitoring tyrosine kinase inhibitor efficacy and toxicity.

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.

Drug-drug interaction software in clinical practice: a systematic review

PURPOSE

Several electronic databases which report the prevalence of drug-drug interactions (DDIs) are used as a tool for evaluation of potentially harmful DDIs. The aim of our review was to evaluate the usability and appropriateness of commercially available electronic databases which assess the prevalence of potential DDIs.

METHODS

The systematic electronic literature search was conducted with the following search terms: "database" AND "software," and "drug-drug interactions" AND "database," and the inclusion and exclusion criteria were applied in order to identify the publications of interest.

RESULTS

A total of 3766 papers were identified by systematic search. After applying inclusion and exclusion criteria, 38 publications were included in the analysis. The most commonly used software in the included studies was Micromedex® Drug-Reax, for which some authors argue to be the most reliable due to highest sensitivity. It gives information about clinical consequences of DDIs, classifies underlying mechanism and onset of the adverse outcome (either rapid, or delayed) as well as severity (such as minor, moderate, or major), and provides the level of evidence which supports this information. This data is also provided by Drug Interaction Facts®, Lexi-Interact®, and Pharmavista®. A small number of studies which compared assessment of DDIs with electronic database and the clinician's assessment showed large discrepancy in number and relevance of detected DDIs. The overlap was in some cases as low as 11 %.

CONCLUSION

The deficiency of clinical relevance of detected DDIs should be addressed in the upcoming research as it would provide more relevant information to the prescribers' in clinical practice.

The prognostic importance of polypharmacy in older adults treated for acute myelogenous leukemia (AML)

We retrospectively evaluated the prognostic significance of polypharmacy and inappropriate medication use among 150 patients >60 years of age receiving induction chemotherapy for acute myelogenous leukemia (AML). After adjustment for age and comorbidity, increased number of medications at diagnosis (≥ 4 versus ≤ 1) was associated with increased 30-day mortality (OR=9.98, 95% CI=1.18-84.13), lower odds of complete remission status (OR=0.20, 95% CI=0.06-0.65), and higher overall mortality (HR=2.13, 95% CI=1.15-3.92). Inappropriate medication use (classified according to Beers criteria) was not significantly associated with clinical outcomes. Polypharmacy warrants further study as a modifiable marker of vulnerability among older adults with AML.

Characteristics of unplanned hospital admissions due to drug-related problems in cancer patients

BACKGROUND

Studies have examined drug-related problems (DRPs) that have led to hospital admissions in the general population. However, there is a lack of information on the characteristics of DRPs in the cancer population.

OBJECTIVE

The objective of this study was to investigate the type, causality, severity, and preventability of DRPs that result in unplanned hospitalizations among cancer patients.

METHODS

This was a prospective, cohort study conducted in two oncology wards between July and December 2012. All patients who were diagnosed with solid tumor or lymphoma and required unplanned hospitalization were included. The incidence of DRPs among hospital admissions was captured, and the nature of the DRPs (causality, severity, and preventability) was characterized.

RESULTS

There were 1,299 admissions and 1,275 were eligible for analysis. Among the 1,275 eligible admissions, 158 (12.4%) were considered to be associated with a DRP. In the 158 admissions associated with DRPs, 164 DRPs were found. The majority of the DRP-related admissions were adverse drug reactions (ADRs) (n=155/164, 94.5%), probable (n=98, 59.8%), moderately severe (n=155, 94.5%), and probably or definitely preventable (n=86, 52.4%). Most patients with DRPs recovered and were discharged. Febrile neutropenia was the most common adverse drug reaction, and drug combinations involving antihypertensives and long-term corticosteroids raised the risks for potential drug-drug interactions among patients with cancer.

CONCLUSION

The incidence of DRP-induced unplanned hospital admissions was 12.4% among cancer patients. Approximately half of these were preventable events.

Prevalence of potential drug-drug interactions in cancer patients treated with oral anticancer drugs

BACKGROUND

Potential drug-drug interactions (PDDIs) in patients with cancer are common, but have not previously been quantified for oral anticancer treatment. We assessed the prevalence and seriousness of potential PDDIs among ambulatory cancer patients on oral anticancer treatment.

METHODS

A search was conducted in a computer-based medication prescription system for dispensing oral anticancer drugs to outpatients in three Dutch centres. Potential drug-drug interactions were identified using electronic (Drug Interaction Fact software) and manual screening methods (peer-reviewed reports).

RESULTS

In the 898 patients included in the study, 1359 PDDIs were identified in 426 patients (46%, 95% confidence interval (CI)=42-50%). In 143 patients (16%), a major PDDI was identified. The drug classes most frequently involved in a major PDDI were coumarins and opioids. The majority of cases concerned central nervous system interactions, PDDIs that can cause gastrointestinal toxicity and prolongation of QT intervals. In multivariate analysis, concomitant use of more drugs (odds ratio (OR)=1.66, 95% CI=1.54-1.78, P<0001) and genito-urinary cancer (OR=0.25, 95% CI=0.12-0.52, P<0001) were risk factors.

CONCLUSION

Potential drug-drug interactions are very common among cancer patients on oral cancer therapy. Physicians and pharmacists should be more aware of these potential interactions.