Wai Keung Chui

Drug interactions between chemotherapeutic regimens and antiepileptics

BACKGROUNDnDrug-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.nnnOBJECTIVEnThis 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.nnnMETHODSnData 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]).nnnRESULTSnOur 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.nnnCONCLUSIONSnIn 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 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.

Neuro-cognitive impairment in breast cancer patients: Pharmacological considerations

Post-chemotherapy cognitive impairment has been an issue of concern in cancer survivors. While most reviews are focused on patient-related factors, it is proposed that drug-related factors may also be determinants. The objective of this review is to study the relationship between the types and dose intensities of chemotherapy regimens on cognitive impairment in breast cancer patients through a systematic literature search. Eighteen prospective studies were selected. The types, dose intensities and durations of chemotherapy regimens received by subjects were compared against prevalence results obtained in individual studies. It was observed that the duration of impairment varied across different generations of chemotherapy regimens. Concurrent administration of multiple cytotoxic agents can lead to a synergistic decline on cognition. Current clinical evidence is insufficient to evaluate the relationship between the types, dose intensities of chemotherapy regimens and cognitive impairment. More investigation is needed to examine the role of pharmacological factors in chemotherapy-associated cognitive changes.

Effects of Chemotherapy and Psychosocial Distress on Perceived Cognitive Disturbances in Asian Breast Cancer Patients

Background: There is conflicting evidence on the effect of chemotherapy and psychosocial distress on perceived cognitive changes in cancer patients. Objective: To compare the severity of perceived cognitive disturbance in Asian breast cancer patients receiving chemotherapy and those not receiving chemotherapy, and identify clinical characteristics associated with perceived cognitive disturbances. Methods: A cross-sectional, observational study was conducted at the largest cancer center in Singapore. Breast cancer patients receiving chemotherapy and not receiving chemotherapy completed the Functional Assessment of Cancer Therapy–Cognitive Function (FACT-Cog), European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (QLQ-C30), and Beck Anxiety Inventory to assess their perceived cognitive functioning, health-related quality of life, and anxiety, respectively. Multiple regression was conducted to delineate the factors associated with perceived cognitive disturbances. Results: A total of 85 breast cancer patients receiving chemotherapy and 81 not receiving chemotherapy were recruited. Chemotherapy patients experienced more fatigue (QLQ-C30 fatigue scores: 33.3 vs 22.2 points; p = 0.005) and moderate-to-severe anxiety (21.9% vs 8.6%; p = 0.002) compared to non-chemotherapy patients. Non-chemotherapy patients reported better perceived cognitive functioning than those who received chemotherapy (FACT-Cog scores: 124 vs 110 points, respectively; p < 0.001). Chemotherapy and endocrine therapy were strongly associated with perceived cognitive disturbances (p < 0.001 and 0.021, respectively). The interacting effect between anxiety and fatigue was moderately associated with perceived cognitive disturbances (β = -0.29; p = 0.037). Conclusions: Chemotherapy and endocrine treatment were associated with significant cognitive disturbances among Asian breast cancer patients. Psychosocial factors could be used to identify cancer patients who are more susceptible to cognitive disturbances in the clinical setting.

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.

Harnessing the internet cloud for managing drug interactions with chemotherapy regimens in patients with cancer suffering from depression

Abstract Concomitant use of anticancer drugs (ACDs) and antidepressants (ADs) in the treatment of depression in patients with cancer may result in potentially harmful drug-drug interactions (DDIs). It is crucial that clinicians make timely, accurate, safe and effective decisions regarding drug therapies in patients. The ubiquitous nature of the internet or “cloud” has enabled easy dissemination of DDI information, but there is currently no database dedicated to allow searching of ACD interactions by chemotherapy regimens. We describe the implementation of an AD interaction module to a previously published oncology-specific DDI database for clinicians which focuses on ACDs, single-agent and multiple-agent chemotherapy regimens. Methods. Drug- and DDI-related information were collated from drug information handbooks, databases, package inserts, and published literature from PubMed, Scopus and Science Direct. Web documents were constructed using Adobe software and programming scripts, and mounted on a domain served from the internet cloud. Results. OncoRx is an oncology-specific DDI database whose structure is designed around all the major classes of ACDs and their frequently prescribed chemotherapy regimens. There are 117 ACDs and 256 regimens in OncoRx, and it can detect over 1 500 interactions with 21 ADs. Clinicians are provided with the pharmacokinetic parameters of the drugs, information on the regimens and details of the detected DDIs during an interaction search. Conclusion. OncoRx is the first database of its kind which allows detection of ACD and chemotherapy regimen interactions with ADs. This tool will assist clinicians in improving clinical response and reducing adverse effects based on the therapeutic and toxicity profiles of the drugs.

Drug-drug interactions between oral antiepileptics and oral anticancer drugs: implications to clinicians.

Background: Existing research has suggested that there can be potential drug-drug interaction (DDI) between antiepileptic drugs (AED) and anticancer drugs (ACD). However, information on the prevalence of patients on concurrent oral AED and oral ACD is limited. Methods: A retrospective study was conducted at the National Cancer Centre Singapore. Prevalence was calculated by identifying prescriptions with both oral AED and oral ACD from the outpatient prescription database over three years. Prevalence and physicians’ prescribing patterns were evaluated. Co-prescription was defined as medications that were prescribed by the same physician on the same day. Potentially interacting combinations were further detected using an existing database, OncoRx (www.onco-informatics.com). Results: 42,810 prescriptions that contained at least one oral ACD were identified from the database. The number and prevalence of prescriptions that had a combination of oral ACD and AED were 274 and 0.64%, respectively, with the majority (82.8%) of the AED-oral ACD pairs being co-prescribed. Per patient, the average number of exposure days to the AED-oral ACD pair was 19.5 days annually. Fifty-one (18.6%) prescriptions were identified as containing potentially interacting AED-oral ACD pairs. Discussion: There is a relatively low prevalence of AED-oral ACD combined exposure in the population we sampled; however, the combined exposure is long enough to produce clinically important DDI effects.

Cancer informatics for the clinician: an interaction database for chemotherapy regimens and antiepileptic drugs

BACKGROUNDnCancer patients are at high risks of manifesting drug-drug interactions (DDIs) which can potentiate serious negative outcomes. It is important for clinicians to make accurate, timely, safe and effective decisions with regards to drug use in the patient.nnnOBJECTIVEnTo provide clinicians with an oncology-specific drug interaction database that is relevant to their daily practice. This database focuses on DDIs with anticancer drugs (ACDs), single- and multiple-agent chemotherapy regimens.nnnMETHODSnDrug-related and interaction-related information between anticancer and antiepileptic drugs were compiled from drug information handbooks and databases, package inserts, and published literature from PubMed. Web documents were served from a modified web server with a database engine and programming interface constructed using Adobe software and various programming scripts.nnnRESULTSnOncoRx is an oncology-specific database whose structure is designed around chemotherapy regimens and generic drug names used in anticancer treatment. OncoRx currently comprises of 117 ACDs and 256 single-agent and combination chemotherapy regimens. It covers all the major classes of ACDs and their frequently prescribed chemotherapy regimens, and can detect up to over 2000 interactions with 24 antiepileptic drugs. Data provided to clinicians include pharmacokinetic parameters of the drugs, information regarding the chemotherapy regimens and the detected DDIs.nnnCONCLUSIONnOncoRx is able to identify DDIs between ACDs and adjuvant drug therapy. This is the first database of its kind to be able to detect interactions for combination chemotherapy regimens. This tool will assist clinicians in improving clinical response and reducing adverse effects based on the therapeutic and toxicity profiles of the drugs.