New Investigations with Lupane Type A-Ring Azepane Triterpenoids for Antimycobacterial Drug Candidate Design

Abstract

Twenty lupane type A-ring azepano-triterpenoids were synthesized from betulin and its related derivatives and their antitubercular activity against Mycobacterium tuberculosis, mono-resistant MTB strains, and nontuberculous strains Mycobacterium abscessus and Mycobacterium avium were investigated in the framework of AToMIc (Anti-mycobacterial Target or Mechanism Identification Contract) realized by the Division of Microbiology and Infectious Diseases, NIAID, National Institute of Health. Of all the tested triterpenoids, 17 compounds showed antitubercular activity and 6 compounds were highly active on the H37Rv wild strain (with MIC 0.5 µM for compound 7), out of which 4 derivatives also emerged as highly active compounds on the three mono-resistant MTB strains. Molecular docking corroborated with a machine learning drug-drug similarity algorithm revealed that azepano-triterpenoids have a rifampicin-like antitubercular activity, with compound 7 scoring the highest as a potential M. tuberculosis RNAP potential inhibitor. FIC testing demonstrated an additive effect of compound 7 when combined with rifampin, isoniazid and ethambutol. Most compounds were highly active against M. avium with compound 14 recording the same MIC value as the control rifampicin (0.0625 µM). The antitubercular ex vivo effectiveness of the tested compounds on THP-1 infected macrophages is correlated with their increased cell permeability. The tested triterpenoids also exhibit low cytotoxicity and do not induce antibacterial resistance in MTB strains.

Understanding the bacteria in Mycobacterium avium complex (MAC) from a bioinformatic perspective – a review

“…A blend of molecular docking, machine learning, and experimental techniques revealed the antimycobacterial activity of seventeen azepano-triterpenoids against M. avium. Furthermore, the compound 14 displayed similar MIC as rifampicin (Kazakova et al 2021) (Fig. 3).…”

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…Several tools have been developed for identification of lead compounds against Mycobacterium tuberculosis, for prediction of the potential toxicity of a compound of interest, and for classifying drug resistance in isolates and linking specific mutations with resistance [165]. Machine learning techniques have been utilised to predict the antitubercular effects of drugs [166]. Researchers have developed a computational model (termed iAMAP-SCM) that can identify and characterise peptides with antimalarial activity using just sequence information [167].…”

Pentacyclic Triterpenoid Phytochemicals with Anticancer Activity: Updated Studies on Mechanisms and Targeted Delivery

“…BA was found to possess antinociceptive, antihelmintic, and anti-HSV-1 activities [ 3 ], while semisynthetic BA and B derivatives have antileishmanial activities [ 54 ]. New investigations with Azepane, which has a Lupane Type A-Ring, showed its antimycobacterial activity and were being recommended as a drug candidate [ 55 ]. Antibacterial TTs from Dillenia papuana and their structure–activity relationships were also reported [ 56 ].…”