Kevin Yi-Lwern Yap

Drug interactions between chemotherapeutic regimens and antiepileptics

BACKGROUND Drug-drug interactions (DDIs) are commonly seen in daily clinical practice, particularly in the treatment of patients with cancer. Seizures are often seen in patients with brain tumors and brain metastases, in whom antiepileptic drugs (AEDs) are often indicated. The risk for DDIs between anticancer drugs and AEDs is high. OBJECTIVE This review aimed to investigate the types of interactions that are observed between the AEDs and the most commonly prescribed chemotherapeutic regimens. The risk for DDIs is discussed with regard to tumor type. METHODS Data on DDIs between anticancer drugs and AEDs were compiled from the British National Formulary, Drug Information Handbook, and Micromedex Healthcare Series version 5.1. Product information of the individual drugs, as well as literature on anticancer drug-AED interactions, was searched using PubMed (years: December 1970 to January 2008; search terms: anticancer, antiepileptic, chemotherapy regimen, drug interactions, and the generic names of the individual anticancer drugs and AEDs [acetazolamide, carbamazepine, ethosuximide, felbamate, gabapentin, lamotrigine, levetiracetam, oxcarbazepine, phenobarbital, phenytoin, primidone, tiagabine, topiramate, valproic acid, vigabatrin, and zonisamide]). RESULTS Our search identified clinically important DDIs observed with single-agent and combination regimens used for the treatment of breast cancers, colorectal cancers, lung cancers, lymphomas, and renal cell carcinomas. Carbamazepine, phenytoin, phenobarbital, and primidone were found to have prominent cytochrome P450 (CYP) enzyme-induction effects, while valproic acid had an inhibitory effect. The isozymes of major relevance to anticancer drug-AED interactions included CYP3A4, CYP2C9, and CYP2C19. Induction or inhibition of these isozymes by AEDs can cause a decrease or increase in anticancer drug concentrations. Similarly, enzyme inhibition or induction by anticancer drugs can lead to toxicity or loss of seizure control. CONCLUSIONS In this review of anticancer drug-AED DDIs, carbamazepine, phenytoin, phenobarbital, primidone, and valproic acid were found to interact the most frequently with anticancer drugs. Based on the results of this review, clinicians should be vigilant when AEDs are prescribed concurrently with anticancer drugs. DDIs can be avoided or minimized by selecting alternative AEDs that are less likely to interact. However, if potentially interacting drug combinations must be used for treatment, serum drug concentrations should be closely monitored to avoid toxicity in the patient, as well as to ensure adequate chemotherapeutic and antiepileptic coverage.

Clinically significant drug—drug interactions between oral anticancer agents and nonanticancer agents: A delphi survey of oncology pharmacists

BACKGROUND Drug-drug interactions (DDIs) can lead to adverse clinical outcomes, particularly in oncology, because of the narrow therapeutic index of chemotherapeutic agents and because patients with cancer are at a high risk due to polypharmacy and age-related organ dysfunction. In a previously published study, drug profiles were developed based on primary and tertiary literature reviews for a list of clinically significant DDIs involving 28 oral anticancer agents (OAAs). OBJECTIVE This study was based on a Delphi survey of oncology pharmacists; the survey results were used to develop a consensus list of clinically significant DDIs involving OAAs and nonanticancer agents. METHODS In this study, the DDI profiles previously developed were updated, and the DDI pairs that were listed both in the 2009 Drug Interaction Facts (DIF) and the Thomson Micromedex DrugDex System compendia and that also met the predetermined criteria for clinical significance were selected for further evaluation. In a 2-round, electronically administered Delphi survey of oncology pharmacists, a 5-point Likert scale (1-5, where 1 = strongly agree and 5 = strongly disagree) was used to evaluate the DDI pairs based on 8 clinical aspects (clinical importance; irreversible morbidity and mortality; quality of data; quantity of data; patients organ functions; comorbid conditions; awareness of interaction; and management burden). International pharmacists who specialized in oncology pharmacy practice and had > or =5 years of practice experience were eligible to participate. RESULTS Nine of the 23 surveyed pharmacists responded, giving a response rate of 39.1%. A total of 37 DDI pairs were selected from DIF and DrugDex and evaluated by the survey respondents, resulting in the identification, via consensus, of 12 clinically significant DDI pairs. The clinical aspects with the most DDIs that reached consensus of agreement were clinical importance (82.9%) and awareness of interaction (73.2%). CONCLUSION An expert panel identified 12 clinically significant DDIs involving OAAs.

Clinically relevant drug interactions between anticancer drugs and psychotropic agents

Drug interactions are commonly seen in the treatment of cancer patients. Psychotropics are often indicated for these patients since they may also suffer from pre-existing psychological disorders or experience insomnia and anxiety associated with cancer therapy. Thus, the risk of anticancer drug (ACD)-psychotropic drug-drug interactions (DDIs) is high. Drug interactions were compiled from the British National Formulary (53rd edn), Lexi-Comps Drug Information Handbook (15th edn), Micromedex (v5.1), Hansten & Horns Drug Interactions (2000) and Drug Interaction Facts (2008 edn). Product information of the individual drugs, as well as documented literature on ACD-psychotropic interactions from PubMed and other databases was also incorporated. This paper identifies clinically important ACD-psychotropic DDIs that are frequently observed. Pharmacokinetic DDIs were observed for tyrosine kinase inhibitors, corticosteroids and antimicrotubule agents due to their inhibitory or inductive effects on cytochrome P450 isoenzymes. Pharmacodynamic DDIs were identified for thalidomide with central nervous system depressants, procarbazine with antidepressants, myelosuppressive ACDs with clozapine and anthracyclines with QT-prolonging psychotropics. Clinicians should be vigilant when psychotropics are prescribed concurrently with ACDs. Close monitoring of plasma drug levels should be carried out to avoid toxicity in the patient, as well as to ensure adequate chemotherapeutic and psychotropic coverage.

An onco-informatics database for anticancer drug interactions with complementary and alternative medicines used in cancer treatment and supportive care: an overview of the OncoRx project

PurposeCancer patients are at high risk of manifesting interactions from use of anticancer drugs (ACDs) and complementary and alternative medicines (CAMs). These interactions can result in sub-therapeutic effects or increased toxicities which may compromise the outcome of chemotherapy. It is important for practitioners to gain convenient access to ACD–CAM interaction information so as to make better-informed decisions in daily practice. This paper describes the creation of an oncology database (OncoRx) that documents ACD–CAM interactions, including traditional Chinese medicines (TCMs) that are commonly used for cancer treatment, prevention, and supportive care therapy.MethodsInformation regarding ACDs, CAMs, and drug interactions were collated from 14 sources, inclusive of hardcopy and online resources, and input into a modified web server with a database engine and a programming interface using a combination of software and programming scripts.ResultsOncoRx currently contains a total of 117 ACDs and 166 CAMs. Users are able to search for interactions based on various CAM uses: cancer treatment or prevention, immune-system-related, alopecia, nausea, and vomiting, peripheral neuropathy and pain, inflammation, fatigue, and non-cancer related. Pharmacokinetic data on ACDs and CAMs, characteristics of CAMs based on TCM principles, and drug interaction parameters such as effects, mechanisms, evidences, and proposed management plans, are shown in the search results.ConclusionOncoRx is an oncology database which detects ACD interactions. It is currently able to detect interactions with CAMs. It is hoped that OncoRx will serve as a useful resource to clinicians, educators, trainers, and students working in the oncology setting.

Evolution and current status of mhealth research: a systematic review

This systematic review provides a chronological overview of how mhealth research has evolved with changes in mobile technologies. The review involved a PubMed search complemented by manual searching of all issues of the Journal of Medical Internet Research and Telemedicine Journal and eHealth, from inception to January 2015. Articles reporting the evaluation of mhealth interventions in any patient group for any health-related outcomes were analysed without restrictions on the study design. A total of 3476 publications were obtained from the PubMed search and manual searching of eHealth journals. Analysis was based on an abstract review of 515 (14.8%) original research articles, which fulfilled preset inclusion criteria. Three distinct time periods were identified on the basis of mobile devices used in mhealth research. Personal digital assistants (PDAs) dominated mhealth research in the years before 2007 (17 of 33 articles, 51.5%). Basic and feature phones were the main methods of mhealth intervention from 2007 to 2012 (95 of 193 articles, 49.2%). After 2012, smart devices (smartphones, tablet PCs and iPod touches) were highly used in mhealth research (173 of 289 articles, 59.9%). Despite a growing focus on infectious diseases and maternal and child health in the most recent years, non-communicable conditions continued to overshadow the trend of mhealth research. Overall, mHealth research has evolved over the past decade in terms of the mobile devices employed, health conditions addressed and its purpose. While chronic medical conditions have clearly been the focus of mhealth research, a shift in trends is expected as the application of mhealth interventions spreads to other under-studied areas. Future research should continue to leverage on the advancements and ubiquitous nature of mobile devices to make healthcare accessible to all.

Computational prediction of state anxiety in Asian patients with cancer susceptible to chemotherapy-induced nausea and vomiting.

Abstract State anxiety, a risk factor for chemotherapy-induced nausea and vomiting (CINV), is a subjective symptom and difficult to quantify. Clinicians need appropriate anxiety measures to assess patients’ risks of CINV. This study aimed to determine the anxiety characteristics that can predict CINV based on computational analysis of an objective assessment tool. A single-center, prospective, observational study was carried out between January 2007 and July 2010. Patients with breast, head and neck, and gastrointestinal cancers were recruited and treated with a variety of chemotherapy protocols and appropriate antiemetics. Chemotherapy-induced nausea and vomiting characteristics and antiemetic use were recorded using a standardized diary, whereas patients’ anxiety characteristics were evaluated using the Beck Anxiety Inventory. Principal component (PC) analysis was performed to analyze the anxiety characteristics. A subset known as principal variables, which had the highest PC weightings, was identified for patients with and without complete response, complete protection, and complete control. Chemotherapy-induced nausea and vomiting events and anxiety characteristics of 710 patients were collated; 51%, 30%, and 20% were on anthracycline-, oxaliplatin-, and cisplatin-based therapies, respectively. Most patients suffered from delayed CINV, with decreasing proportions achieving complete response (58%), complete protection (42%), and complete control (27%). Seven symptoms (fear of dying, fear of the worst, unable to relax, hot/cold sweats, nervousness, faintness, numbness) were identified as potential CINV predictors. This study demonstrates the usefulness of PC analysis, an unsupervised machine learning technique, to identify 7 anxiety characteristics that are useful as clinical CINV predictors. Clinicians should be aware of these characteristics when assessing CINV in patients on emetogenic chemotherapies.

Improving pharmaceutical care in oncology by pharmacoinformatics: the evolving role of informatics and the internet for drug therapy

Health-care has rapidly evolved with the informatics revolution. The rapid growth of the world-wide web as a tool for global connectivity has affected the way in which health-related information is distributed and accessed over the internet. Many informatics and internet applications are now available for use by both oncology health-care professionals and patients with cancer, with many people using the internet to search for drug-related and other health-related information. The practice of pharmaceutical care aims to ensure optimum medication-related therapeutic outcomes in patients, and involves identifying, solving, and preventing potential or actual drug-related problems (DRPs) with regards to a patients drug therapy. Pharmacoinformatics involves the use of informatics, the internet, and interactive technologies to solve DRPs, with a focus on providing optimum pharmaceutical care and improved patient safety. This paper highlights the different pharmacoinformatics channels that have been used in the provision of pharmaceutical care, which are relevant to both oncology health-care professionals and patients with cancer. We will discuss several issues that have arisen as a result of cybermedicine, which can potentially affect the quality of pharmaceutical care in patients with cancer, and also provide insights into how pharmacoinformatics can potentially affect the future of healthcare. The opportunity of integrating pharmacoinformatics in the practice of clinical oncology as an aid to solve DRPs is indeed appealing. Oncology practitioners should not only focus on the acquistion of new treatment strategies, but also continue to embrace and harness new information and communication technologies, so as to increase their efficiency and improve on the pharmaceutical care of patients with cancer.

Electronic database to detect drug–drug interactions between antidepressants and oral anticancer drugs from a cancer center in Singapore: implications to clinicians

Electronic drug interaction databases are often utilized in clinical practice to detect for possible drug–drug interactions between drug pairs. It is uncertain, however, whether most of these detections interactions are clinically important in practice. To demonstrate these issues, this study utilized a comprehensive drug–drug interaction (DDI) electronic database to elucidate the prevalence of DDIs at a cancer centre between antidepressants and oral anticancer drugs (ACDs).

Clinically-relevant chemotherapy interactions with complementary and alternative medicines in patients with cancer.

Complementary and alternative medicines (CAMs), in particular herbal medicines, are commonly used by cancer patients in conjunction with chemotherapy treatment for their anticancer properties and supportive care. However, the effects of many of these herbs are not well-documented due to limited studies done on them. Severe herb-drug interactions (HDIs) have been recorded in some cases, and failure to recognize these harmful HDIs can lead to dire consequences in cancer patients. This study discusses clinically-relevant interactions between anticancer drugs (ACDs) and herbs classified into 7 categories: cancer treatment and prevention, immune-system-related, alopecia, nausea and vomiting, peripheral neuropathy and pain, inflammation, and fatigue. Some promising patents which contain these herbs and thus may manifest these interactions are also presented in this article. Pharmacokinetic interactions involved mainly induction or inhibition of the cytochrome P450 isozymes and p-glycoprotein, while pharmacodynamic interactions were related to increased risks of central nervous system-related effects, hepatotoxicity and bleeding, among others. Clinicians should be vigilant when treating cancer patients who take CAMs with concurrent chemotherapy since they face a high risk of HDIs. These HDIs can be minimized or avoided by selecting herb-drug pairs which are less likely to interact. Furthermore, close monitoring of pharmacological effects and plasma drug levels should be carried out to avoid toxicity and ensure adequate chemotherapeutic coverage in patients with cancer.

Visualizing clinical predictors of febrile neutropenia in Asian cancer patients receiving myelosuppressive chemotherapy.

Purpose: Febrile neutropenia is a serious complication among cancer patients receiving myelosuppressive chemotherapy. Patient-specific risk factors, chemotherapy-related and disease-related characteristics can affect the clinical outcome and management of febrile neutropenia. Although many factors have been identified, they vary among different patient populations. We identified clinically-relevant febrile neutropenia predictors in Asian cancer patients through visualization of these factors. Methods: A single-centered, retrospective study was conducted from May to July 2011 at a local cancer center. Demographics and risk factor data were collated from electronic health records and four cancer registries. Data were summarized using descriptive statistics. Additionally, potential febrile neutropenia predictors were identified using categorical principal component and multiple correspondence analyses. Results: A total of 583 patients were analyzed. Majority was females (79%), Chinese (75%) and diagnosed with breast cancers (60%). Six risk factors were identified as potential predictors: types of cancer (16.9–19.8% of variance), chemotherapy regimen (anthracycline-based 11.8–12.9%, taxane-based 8.1%), liver function tests (alanine transaminase 8.6%, alkaline phosphatase 4.0%), renal function tests (serum creatinine 3.1%), prior granulocyte colony stimulating factor use (5.6%) and diabetes mellitus (6.6–6.9%). In terms of cancer types, lymphomas were more predictive than breast cancers. Conclusion: From our knowledge, this is the first study that has identified clinically-relevant febrile neutropenia predictors in Asian cancer patients through visualization of their risk factors. The use of these predictors to identify patients at risk for adverse reactions, such as FN, can allow clinicians to optimize prophylactic granulocyte colony stimulating factor usage in these patients.