Lignans and Neolignans Anti-tuberculosis Identified by QSAR and Molecular Modeling

Abstract

Background: Tuberculosis is a disease with high incidence and high mortality rate, especially in Brazil. Although there are several medications available for treatment, in cases of resistance, there is a need to use more than one medication. Objective: Therefore, cases of toxicity increase and reports of resistance have been worrying the population. In addition, some medications have a short period of effectiveness. To achieve the goal, ligand-based and structure-based approaches were used. Method: Thus, in an attempt to discover potent inhibitors against Mycobacterium tuberculosis enzymes, we sought to identify natural products with high therapeutic potential for the treatment of Tuberculosis through QSAR, Molecular Modeling and ADMET studies. Results: The results showed that the models generated from two sets of molecules with known activity against M. tuberculosis enzymes InhA and PS were able to select 11 and 8 compounds respectively, between Lignans and Neolignans with 50 to 60% activity probability. In addition, molecular docking contributed to confirm the mechanism of action of compounds and increase the accuracy of methodologies. All molecules showed higher binding energy values for the drug Isoniazid. We conclude that compounds 33, 34, 110, 114, 133 are promising for InhA target and compounds 07, 08, 19, 21, 42, 48, 75 and 141 for target PS. In addition, most molecules did not show any toxicity according to the evaluated parameters. Conclusions: Therefore, Lignans and Neolignans may be an alternative for the treatment of Tuberculosis.

Antimicrobial Potential of Betulinic Acid and Investigation of the Mechanism of Action against Nuclear and Metabolic Enzymes with Molecular Modeling

“…External cross-validation was performed to estimate the predictive power of the developed models. In addition, the performance of external models was evaluated by ROC analysis and Matthews’s coefficient (MCC) [ 28 ] to evaluate the model globally.…”

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

“…Artificial Intelligence has made important progress toward the acceleration of research and development of novel bioactive natural compounds with industrial applications. This approach has been widely applied in different steps related to the virtual screening strategies, for example to predict some pharmacokinetic properties (Wei et al, 2017 ; Qiang et al, 2018 ) [e.g., penetration of compounds into the blood–brain barrier (Zhang et al, 2017 ; Dai et al, 2021 ) and cell membrane (Wei et al, 2017 ; Wolfe et al, 2018 )], compounds' side effects (Dimitri and Lió, 2017 ), their toxicity (Mayr et al, 2016 ; Pu et al, 2019 ; Zheng et al, 2020 ), molecular targets (Wang et al, 2013 ; Jeon et al, 2014 ), and their bioactivity (Li and Huang, 2012 ; Schaduangrat et al, 2019 ; Shoombuatong et al, 2019 ) [e.g., anti-tuberculosis (Gomes et al, 2017 ; Maia S. M. et al, 2020 ), anticancer (Charoenkwan et al, 2021 ), and insecticidal activities (Soares Rodrigues et al, 2021 )] as well as to identify the pan-assay interference compounds (PAINS), i.e., highly reactive and promiscuous molecules that are often false positives in high-throughput screening assays (Jasial et al, 2018 ). In some cases, the ML algorithms have been reported with superior efficiency and, thus, are more suitable to predict hit compounds from chemical libraries than are the traditional QSAR methods (Tsou et al, 2020 ).…”

Identification of New Targets and the Virtual Screening of Lignans against Alzheimer’s Disease

“…The Knime 3.5.3 software (KNIME 3.5.3, Konstanz Information Miner Copyright, 2018, https://www.knime.org ) was used to perform the analyses and to generate the in silico models. Given the success of our previous studies [ 110 , 111 ], we opted to perform a 3D QSAR analysis for each bank of enzymes. All studied compounds with a solved chemical structure were saved in special data file (SDF) format and imported into the Dragon 7.0 software [ 112 ], to generate descriptors.…”