ABSTRACT
Tuberculosis (TB) is an air-borne infectious disease and is the leading cause of death among all infectious diseases globally. The current treatment regimen for TB is overtly long and patient non-compliance often leads to drug resistant TB resulting in a need to develop new drugs that will act via novel mechanisms. In this research work, we selected Mycobacterium membrane protein large (MmpL3) as the drug target and indole-2-carboximide as our molecule of interest for further designing new molecules. A homology model was prepared for the Mycobacterium tuberculosis MmpL3 from the crystal structure of Mycobacterium smegmatis MmpL3. A series of indoles which are known to be MmpL3 inhibitors were docked in the prepared protein and the binding site properties were identified. Based on that, 10 molecules were designed and synthesized and their antitubercular activities evaluated. We identified four hits among which the highest potency candidate possessed a minimum inhibitory concentration (MIC) of 1.56 µM at 2-weeks. Finally, molecular dynamics simulation studies were done with 3b and a previously reported MmpL3 inhibitor to understand the intricacies of their binding in real time and to correlate the experimental findings with the simulation data.
ABSTRACT
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.
ABSTRACT
BACKGROUND: Tuberculosis is one of the leading causes of deaths due to infectious disease worldwide. There is an urgent need for developing new drugs due to the rising incidents of drug resistance. Previously, triazole molecules showing antitubercular activity, were reported. Various computational tools pave the way for a rational approach to understanding the structural importance of these compounds in inhibiting the growth of Mycobacterium Tuberculosis. OBJECTIVE: The aim of this study is to develop and compare two different QSAR models based on a set of previously reported triazole molecules and use the best one for gaining structural insights into those molecules. METHODS: In this current study, two separate models were made with CoMFA and CoMSIA descriptors based on a dataset of triazole molecules showing antitubercular activity. Several one dimensional (1D) descriptors were added to each of the models and the validation results and contour data generated from them were compared. The best model was analysed to give a detailed understanding of the triazole molecules and their role in the antitubercular activity. RESULTS: The r2, q2, predicted r2 and SEP (Standard error of prediction) for the CoMFA model were 0.866, 0.573, 0.119 and 0.736 respectively and for the CoMSIA model, the r2, q2, predicted r2 and SEP were calculated to be 0.998, 0.634, 0.013 and 0.869 respectively. Although both the QSAR models produced acceptable internal and external validation scores, but the CoMSIA results were significantly better. The CoMSIA contours also provided a better match than CoMFA with most of the features of the active compound 30b. Hence, the CoMSIA model was chosen and its contours were explored for gaining structural insights into the triazole molecules. CONCLUSION: The CoMSIA contours helped us understand the role of several atoms and groups of the triazole molecules in their biological activity. The possibilities for substitution in the triazole compounds that would enhance the activity were also analyzed. Thus, this study paves the way for designing new antitubercular drugs in future.
