Background: The increasing rate of appearance of multidrug-resistant strains
of Mycobacterium tuberculosis (MDR-TB) is a serious problem at the present time.
MDR-TB forms do not respond to the standard treatment with the commonly used drugs
and can take some years or more to treat with drugs that are less potent, more toxic and
much more expensive.
Objective: The goal of this work is to identify the novel effective drug candidates active
against MDR-TB strains through the use of methods of cheminformatics and computeraided
Methods: This paper describes Quantitative Structure-Activity Relationships
(QSAR) studies using Artificial Neural Networks, synthesis and in vitro
antitubercular activity of several potent compounds against H37Rv and resistant
Mycobacterium tuberculosis (Mtb) strains.
Results: Eight QSAR models were built using various types of descriptors with four publicly
available structurally diverse datasets, including recent data from PubChem and
ChEMBL. The predictive power of the obtained QSAR models was evaluated with a
cross-validation procedure, giving a q2=0.74-0.78 for regression models and overall
accuracy 78.9-94.4% for classification models. The external test sets were predicted with
accuracies in the range of 84.1-95.0% (for the active/inactive classifications) and q2=0.80-
0.83 for regressions. The 15 synthesized compounds showed inhibitory activity against
H37Rv strain whereas the compounds 1-7 were also active against resistant Mtb strain
(resistant to isoniazid and rifampicin).
Conclusion: The results indicated that compounds 1-7 could serve as promising leads for
further optimization as novel antibacterial inhibitors, in particular, for the treatment of
drug resistance of Mtb forms.