Prediction of potential drug interactions between repurposed COVID-19 and antitubercular drugs: an integrational approach of drug information software and computational techniques data - Levin Thomas; Sumit Raosaheb Birangal; Rajdeep Ray; Sonal Sekhar Miraj; Murali Munisamy; Muralidhar Varma; Chidananda Sanju S.V; Mithu Banerjee; Gautham G. Shenoy; Mahadev Rao (SAGE Publishing, 2021)
Introduction: Tuberculosis is a major respiratory disease globally with a higher prevalence in Asian and African countries than rest of the world. With a larger population of tuberculosis patients anticipated to be co-infected with COVID-19 infection, an ongoing pandemic, identifying, preventing and managing drug-drug interactions is inevitable for maximizing patient benefits for the current repurposed COVID-19 and antitubercular drugs. Methods: We assessed the potential drug-drug interactions between repurposed COVID-19 drugs and antitubercular drugs using the drug interaction checker of IBM Micromedex®. Extensive computational studies were performed at a molecular level to validate and understand the drug-drug interactions found from the Micromedex drug interaction checker database at a molecular level. The integrated knowledge derived from Micromedex and computational data was collated and curated for predicting potential drug-drug interactions between repurposed COVID-19 and antitubercular drugs. Results: A total of 91 potential drug-drug interactions along with their severity and level of documentation were identified from Micromedex between repurposed COVID-19 drugs and antitubercular drugs. We identified 47 pharmacodynamic, 42 pharmacokinetic and 2 unknown DDIs. The majority of our molecular modelling results were in line with drug-drug interaction data obtained from the drug information software. QT prolongation was identified as the most common type of pharmacodynamic drug-drug interaction, whereas drug-drug interactions associated with cytochrome P450 3A4 (CYP3A4) and P-glycoprotein (P-gp) inhibition and induction were identified as the frequent pharmacokinetic drug-drug interactions. The results suggest antitubercular drugs, particularly rifampin and second-line agents, warrant high alert and monitoring while prescribing with the repurposed COVID-19 drugs. Conclusion: Predicting these potential drug-drug interactions, particularly related to CYP3A4, P-gp and the human Ether-à-go-go-Related Gene proteins, could be used in clinical settings for screening and management of drug-drug interactions for delivering safer chemotherapeutic tuberculosis and COVID-19 care. The current study provides an initial propulsion for further well-designed pharmacokinetic-pharmacodynamic-based drug-drug interaction studies. Plain Language Summary Introduction: Tuberculosis is a major respiratory disease globally with a higher prevalence in Asian and African countries than rest of the world. With a larger population of tuberculosis patients predicted to be infected with COVID-19 during this period, there is a higher risk for the occurrence of medication interactions between the medicines used for COVID-19 and tuberculosis. Hence, identifying and managing these interactions is vital to ensure the safety of patients undergoing COVID-19 and tuberculosis treatment simultaneously. Methods: We studied the major medication interactions that could likely happen between the various medicines that are currently given for COVID-19 and tuberculosis treatment using the medication interaction checker of a drug information software (Micromedex®). In addition, thorough molecular modelling was done to confirm and understand the interactions found from the medication interaction checker database using specific docking software. Molecular docking is a method that predicts the preferred orientation of one medicine molecule to a second molecule, when bound to each other to form a stable complex. Knowledge of the preferred orientation may be used to determine the strength of association or binding affinity between two medicines using scoring functions to determine the extent of the interactions between medicines. The combined knowledge from Micromedex and molecular modelling data was used to properly predict the potential medicine interactions between currently used COVID-19 and antitubercular medicines. Results: We found a total of 91 medication interactions from Micromedex. Majority of our molecular modelling findings matched with the interaction information obtained from the drug information software. QT prolongation, an abnormal heartbeat, was identified as one of the most common interactions. Our findings suggest that antitubercular medicines, mainly rifampin and second-line agents, suggest high alert and scrutiny while prescribing with the repurposed COVID-19 medicines. Conclusion: Our current study highlights the need for further well-designed studies confirming the current information for recommending safe prescribing in patients with both infections.