Abstract
Mycobacterium tuberculosis (Mtb) is an endemic bacterium worldwide that causes tuberculosis (TB) and involves long-term treatment that is not always effective. In this context, several studies are trying to develop and evaluate new substances active against Mtb. In silico techniques are often used to predict the effects on some known target. We used a systematic approach to find and evaluate manuscripts that applied an in silico technique to find antimycobacterial molecules and tried to prove its predictive potential by testing them in vitro or in vivo. After searching three different databases and applying exclusion criteria, we were able to retrieve 46 documents. We found that they all follow a similar screening procedure, but few studies exploited equal targets, exploring the interaction of multiple ligands to 29 distinct enzymes. The following in vitro/vivo analysis showed that, although the virtual assays were able to decrease the number of molecules tested, saving time and money, virtual screening procedures still need to develop the correlation to more favorable in vitro outcomes. We find that the in silico approach has a good predictive power for in vitro results, but call for more studies to evaluate its clinical predictive possibilities.
Highlights
According to the latest World Health Organization (WHO) report, tuberculosis (TB) is still one of the top 10 causes of death and the leading cause from a single infectious agent
Developing a new drug traditionally requires at least 10 years of extensive research and funding; the employment of an initial computer-aided drug design (CADD) or in silico approach can decrease that [3]
Four manuscripts did not aim for enzymes: two analyzed an iron-dependent regulator (IdeR) gene [21,22], one analyzed a Filamenting temperature-sensitive mutant (FtsZ) protein [23], and one made a pharmacophore-based quantitative structure-activity relationship (QSAR), comparing isoniazid with derivatives [24]
Summary
According to the latest World Health Organization (WHO) report, tuberculosis (TB) is still one of the top 10 causes of death and the leading cause from a single infectious agent (above HIV/AIDS). In 2017 alone, TB caused approximately 1.3 million deaths among HIV-negative people and additional 300,000 deaths among HIV-positive patients [1]. Added to this framework, multidrug-resistant TB (MDR/TB) and extensively drug-resistant TB (XDR/TB) have been increasing over the years as a result of spontaneous mutations in the genome of M. tuberculosis and the emergence of those mutants as the dominant strain, resulting in a loss of effect of first and second lines of anti-TB drugs, like Rifampicin and Isoniazid [2]. Developing a new drug traditionally requires at least 10 years of extensive research and funding; the employment of an initial computer-aided drug design (CADD) or in silico approach can decrease that [3]. As a virtual shortcut, is used to assist researchers in choosing between millions of molecules from different databases and studying their affinity to known targets, helping them to exclude molecules that would have no biological action and optimize their research [4]
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