Drug–drug interactions in patients undergoing chemoradiotherapy and the impact of an expert team intervention

Background Several studies have examined the drug–drug interaction patterns in different patient populations and treatment settings; however, there is a need, particularly in the field of oncology and radiotherapy, for evaluating methods targeted towards preventing potential drug–drug interactions. One of the measures proposed is identifying potential interactions using computer programs and their evaluation by pharmacologists or clinical pharmacists, thereby providing clinically relevant information to the treating physician regarding the required prescription changes. Objective To determine the prevalence of potential drug–drug interactions in patients receiving chemoradiotherapy and assess the usefulness of expert team recommendations in minimizing interactions. Setting Patients admitted to the radiotherapy and oncology ward of a tertiary care teaching hospital in Karnataka, India. Method We conducted a prospective, cross-sectional study of prescriptions written for patients receiving chemoradiotherapy. Prescriptions containing two or more drugs, at least one of the drugs being an anticancer drug, were analyzed. They were screened for potential drug–drug interactions using the Lexicomp® drug interaction software. The interactions were classified as X, drug combination to be avoided; D, modification of therapy to be considered; and C, therapy to be monitored, as per the Lexicomp criteria. Main outcome measure The number of drug–drug interactions detected that were accepted by the treating radio-oncologist as requiring prescription change before and after the prescription review by an expert team. Results Two hundred twenty-three prescriptions were screened for the presence of drug–drug interactions; 106 prescriptions (47.53%) containing 620 drugs and 211 drug–drug interactions were identified. Of the 211 interactions identified, 6.64% (14/211), 18.48% (39/211), and 74.88% (158/211) drug–drug interactions belonged to category X, D, and C, respectively. Twenty-seven (50.94%) of the 53 category X and D interactions identified were accepted the oncologist as requiring a change in the prescription; an additional 13 (24.53%) interactions were identified as significant by the expert team, and 11 (84.62%) of these were accepted by the oncologist. Conclusion A system of alerting the treating physician to a potential drug–drug interaction leads to avoidance of prescription of the interacting drug combination, and the assistance by an expert team adds significantly to avoidance of clinically relevant drug interactions.