posted on 2021-06-25, 17:34authored bySelvaraman Nagamani, G. Narahari Sastry
The drug-resistant
strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this
indicates the urgent need for the development of novel antitubercular
drugs. However, genetic mutations, complex cell wall system of M.tb, and influx–efflux transporter systems are the
major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are
ineffective to arrest the M.tb cell growth, even
though they are effective at the cellular level. To address the permeability
issue, different machine learning models that effectively distinguish
permeable and impermeable compounds were developed. The enzyme-based
(IC50) and cell-based (minimal inhibitory concentration)
data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds
that have high activity in both enzyme-based and cell-based assays
possess the required M.tb cell wall permeability.
The XGBoost model was outperformed when compared to the other models
generated from different algorithms such as random forest, support
vector machine, and naïve Bayes. The XGBoost model was further
validated using the validation data set (21 permeable and 19 impermeable
compounds). The obtained machine learning models suggested that various
descriptors such as molecular weight, atom type, electrotopological
state, hydrogen bond donor/acceptor counts, and extended topochemical
atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore,
potential antimycobacterial drugs were identified using computational
drug repurposing. All the approved drugs from DrugBank were collected
and screened using the developed permeability model. The screened
compounds were given as input in the PASS server for the identification
of possible antimycobacterial compounds. The drugs that were retained
after two filters were docked to the active site of 10 different potential
antimycobacterial drug targets. The results obtained from this study
may improve the understanding of M.tb permeability
and activity that may aid in the development of novel antimycobacterial
drugs.