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Canales CSC, Pavan AR, Dos Santos JL, Pavan FR. In silico drug design strategies for discovering novel tuberculosis therapeutics. Expert Opin Drug Discov 2024; 19:471-491. [PMID: 38374606 DOI: 10.1080/17460441.2024.2319042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/12/2024] [Indexed: 02/21/2024]
Abstract
INTRODUCTION Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments. AREAS COVERED In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis. EXPERT OPINION These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.
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Affiliation(s)
- Christian S Carnero Canales
- School of Pharmaceutical Science, São Paulo State University (UNESP), Araraquara, Brazil
- School of Pharmacy, biochemistry and biotechnology, Santa Maria Catholic University, Arequipa, Perú
| | - Aline Renata Pavan
- School of Pharmaceutical Science, São Paulo State University (UNESP), Araraquara, Brazil
| | | | - Fernando Rogério Pavan
- School of Pharmaceutical Science, São Paulo State University (UNESP), Araraquara, Brazil
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Naidu A, Nayak SS, Lulu S S, Sundararajan V. Advances in computational frameworks in the fight against TB: The way forward. Front Pharmacol 2023; 14:1152915. [PMID: 37077815 PMCID: PMC10106641 DOI: 10.3389/fphar.2023.1152915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.
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Affiliation(s)
| | | | | | - Vino Sundararajan
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, India
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Alzain AA, Makki AA, Ibraheem W. Insights into the Inhibition of Mycolic Acid Synthesis by Cytosporone E Derivatives for Tuberculosis Treatment Via an In Silico Multi-target Approach. CHEMISTRY AFRICA 2023. [DOI: 10.1007/s42250-023-00605-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Arumuganainar D, Yadalam PK, Alzahrani KJ, Alsharif KF, Alzahrani FM, Alshammeri S, Ahmed SSSJ, Vinothkumar TS, Baeshen HA, Patil S. Inhibitory effect of lupeol, quercetin, and solasodine on Rhizopus oryzae: A molecular docking and dynamic simulation study. J Infect Public Health 2022; 16:117-124. [PMID: 36512968 DOI: 10.1016/j.jiph.2022.12.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 11/30/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Mucormycosis is an infection caused by fungi belonging to the order Mucorales. Rhizopus oryzae is one of the most prevalent organisms identified in mucormycosis patients. Because it spreads quickly through the blood vessels, this opportunistic illness has an exceptionally high fatality rate, even when vigorous treatment is administered. Nonetheless, it has a high tolerance to antifungal medicines, limiting treatment options. As a result, improved methods for preventing and treating mucormycosis are desperately needed. Hence, this study was aimed at assessing the effect of lupeol, quercetin, and solasodine against mucormycosis based on computational approaches. METHODS The Rhizopus oryzae RNA-dependent RNA polymerase (RdRp) was the target for the design of drugs against the deadly mucormycosis. The three-dimensional structure of the RdRp was modelled with a Swiss model and validated using PROCHECK, VERIFY 3D, and QMEAN. Using the Schrodinger maestro module, a molecular docking study was performed between RdRp and the antimicrobial phytochemicals lupeol, quercetin, and solasodine. A molecular dynamics (MD) simulation study was used to assess the stability and interaction of the RdRp with these phytochemicals. RESULTS The RdRp protein binds strongly to lupeol (-7.2 kcal/mol), quercetin (-9.1 kcal/mol), and solasodine (-9.6 kcal/mol), according to molecular docking assessment based on the lowest binding energy, confirmation, and bond interaction. Simulations suggest that lupeol, quercetin, and solasodine complexes with RdRp and showed stable confirmation with minimal fluctuation throughout the 200 nanoseconds based on the RMSD and RMSF trajectory assessments. CONCLUSION The molecular docking and MD simulation investigation improved our understanding of phytochemical-RdRp interactions. Due to its high affinity for RdRp, solasodine may be a better treatment option for mucormycosis.
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Affiliation(s)
- Deepavalli Arumuganainar
- Department of Periodontics, Ragas Dental College and Hospital, 2/102, East Coast Road, Uthandi, Chennai 600119, India.
| | - Pradeep Kumar Yadalam
- Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India.
| | - Khalid J Alzahrani
- Department of Clinical Laboratory Sciences, College of Applied medical sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
| | - Khalaf F Alsharif
- Department of Clinical Laboratory Sciences, College of Applied medical sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
| | - Fuad M Alzahrani
- Department of Clinical Laboratory Sciences, College of Applied medical sciences, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
| | - Saleh Alshammeri
- Department of Optometry, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia.
| | - Sheik S S J Ahmed
- Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam 603103, Tamil Nadu, India.
| | - Thilla Sekar Vinothkumar
- Department of Restorative Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; Department of Conservative Dentistry and Endodontics, Saveetha Dental College & Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India.
| | - Hosam Ali Baeshen
- Department of Orthodontics, College of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
| | - Shankargouda Patil
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan UTAH - 84095, USA; Centre of Molecular Medicine and Diagnostics (COMManD), Saveetha Dental College & Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India.
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Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach. Int J Mol Sci 2021; 22:ijms222413259. [PMID: 34948055 PMCID: PMC8703488 DOI: 10.3390/ijms222413259] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/09/2021] [Accepted: 11/14/2021] [Indexed: 12/12/2022] Open
Abstract
Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.
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