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Kharpan B, Chetia J, Pyngrope H, Nandi R, Pradhan AK, Paul PC, Kumar D. Investigation of antileishmanial, antioxidant activities, CT-DNA interaction and DFT study of novel cobalt(II) complexes derived from mesogenic aromatic amino acids based Schiff base ligands. Biometals 2024:10.1007/s10534-024-00627-9. [PMID: 39154301 DOI: 10.1007/s10534-024-00627-9] [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: 06/04/2024] [Accepted: 08/07/2024] [Indexed: 08/19/2024]
Abstract
In the present work, new Co(II) complexes were synthesized from mesogenic aromatic amino acids based Schiff base ligands, HL1 [Methyl 2-((2-hydroxy-4-(tetradecyloxy)benzylidene)amino)-3-phenylpropanoate] and HL2 [Methyl 2-((2-hydroxy-4-(tetradecyloxy)benzylidene)amino)-3-(1H-indol-2-yl)propanoate]. The compounds were thoroughly characterised using different elemental, thermogravimetric and spectroscopic studies. The in-vitro antileishmanial efficacy of the compounds against Leishmania donovani was evaluated by MTT assay and the antioxidant activity was performed by Mensor's method. The cell viability percentage and IC50 values for both the antileishmanial and antioxidant studies revealed that the cobalt(II) complexes are comparable to the standard, amphotericin B and ascorbic acid, respectively, signifying the potential applications of the biogenic compounds. The CT-DNA interaction experiments study using photophysical techniques indicated that the cobalt(II) complexes exhibited pronounced interactions as compared to the parent ligand. The parent ligands were found to possess mesogenicity as evidenced from the polarizing optical microscope (POM) and differential scanning calorimetry (DSC). The optical band gap of the compounds, as estimated from the Tauc plot of the UV-Vis spectra, lies within the domain of optoelectronic material properties, which was further supported through Density Functional Theory (DFT) study. Moreover, DFT methods have been used to explore the ground state geometry and DFT based reactivity descriptors of the two synthesised ligands, HL1 and HL2 along with their corresponding Co(II) complexes, Co(L1)2 and Co(L2)2. Reactivity descriptors obtained from Conceptual Density Functional Theory (CDFT) analysis reveal that Co(L1)2 is the most stable and Co(L2)2 is the most electrophilic.
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Affiliation(s)
| | - Jagritima Chetia
- Department of Chemistry, Assam University, Silchar, 788011, Assam, India
| | - Hunshisha Pyngrope
- Department of Chemistry, Assam University, Silchar, 788011, Assam, India
| | - Rajat Nandi
- Department of Microbiology, Assam University, Silchar, 788011, Assam, India
| | - Amit Kumar Pradhan
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, 110016, India
| | - Pradip C Paul
- Department of Chemistry, Assam University, Silchar, 788011, Assam, India.
| | - Diwakar Kumar
- Department of Microbiology, Assam University, Silchar, 788011, Assam, India
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Camargo PG, Dos Santos CR, Girão Albuquerque M, Rangel Rodrigues C, Lima CHDS. Py-CoMFA, docking, and molecular dynamics simulations of Leishmania (L.) amazonensis arginase inhibitors. Sci Rep 2024; 14:11575. [PMID: 38773273 PMCID: PMC11109165 DOI: 10.1038/s41598-024-62520-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 05/17/2024] [Indexed: 05/23/2024] Open
Abstract
Leishmaniasis is a disease caused by a protozoan of the genus Leishmania, affecting millions of people, mainly in tropical countries, due to poor social conditions and low economic development. First-line chemotherapeutic agents involve highly toxic pentavalent antimonials, while treatment failure is mainly due to the emergence of drug-resistant strains. Leishmania arginase (ARG) enzyme is vital in pathogenicity and contributes to a higher infection rate, thus representing a potential drug target. This study helps in designing ARG inhibitors for the treatment of leishmaniasis. Py-CoMFA (3D-QSAR) models were constructed using 34 inhibitors from different chemical classes against ARG from L. (L.) amazonensis (LaARG). The 3D-QSAR predictions showed an excellent correlation between experimental and calculated pIC50 values. The molecular docking study identified the favorable hydrophobicity contribution of phenyl and cyclohexyl groups as substituents in the enzyme allosteric site. Molecular dynamics simulations of selected protein-ligand complexes were conducted to understand derivatives' interaction modes and affinity in both active and allosteric sites. Two cinnamide compounds, 7g and 7k, were identified, with similar structures to the reference 4h allosteric site inhibitor. These compounds can guide the development of more effective arginase inhibitors as potential antileishmanial drugs.
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Affiliation(s)
- Priscila Goes Camargo
- Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Carine Ribeiro Dos Santos
- Laboratório de Modelagem Molecular (LabMMol), Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Magaly Girão Albuquerque
- Laboratório de Modelagem Molecular (LabMMol), Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Carlos Rangel Rodrigues
- Faculdade de Farmácia, Departamento de Fármacos e Medicamentos, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
| | - Camilo Henrique da Silva Lima
- Laboratório de Modelagem Molecular (LabMMol), Instituto de Química, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
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Kumar S, Jayan J, Manoharan A, Benny F, Abdelgawad MA, Ghoneim MM, El-Sherbiny M, Thazhathuveedu Sudevan S, Aneesh TP, Mathew B. Discerning of isatin-based monoamine oxidase (MAO) inhibitors for neurodegenerative disorders by exploiting 2D, 3D-QSAR modelling and molecular dynamics simulation. J Biomol Struct Dyn 2024; 42:2328-2340. [PMID: 37261844 DOI: 10.1080/07391102.2023.2214216] [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: 01/23/2023] [Accepted: 04/13/2023] [Indexed: 06/02/2023]
Abstract
Almost a billion people worldwide suffer from neurological disorders, which pose public health challenges. An important enzyme that is well-known for many neurodegenerative illnesses is monoamine oxidase (MAO). Although several promising drugs for the treatment of MAO inhibition have recently been examined, it is still necessary to identify the precise structural requirements for robust efficacy. Atom-based, field-based, and GA-MLR (genetic algorithm multiple linear regression) models were created for this investigation. All of the models have strong statistical (R2 and Q2) foundations because of both internal and external validation. Our dataset's molecule has a higher docking score than safinamide, a well-known and co-crystallized MAO-B inhibitor, as we also noticed. Using the SwissSimilarity platform, we further inquired which of our docked molecules would be the best for screening. We chose ZINC000016952895 as the screen molecule with the best binding docking score (XP score = -13.3613). Finally, the 100 ns for the ZINC000016952895-MAO-B complex in our MD investigations is stable. For compounds that we hit, also anticipate ADME properties. Our research revealed that the successful compound ZINC000016952895 might pave the way for the future development of MAO inhibitors for the treatment of neurological disease.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sunil Kumar
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Jayalakshmi Jayan
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Amritha Manoharan
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Feba Benny
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Mohamed A Abdelgawad
- Department of pharmaceutical chemistry, College of pharmacy, Jouf university, Sakaka, Saudi Arabia
- Department of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Beni-Suef University, Beni-Suef, Egypt
| | - Mohammed M Ghoneim
- Department of Pharmacy Practice, College of Pharmacy, AlMaarefa University, Saudi Arabia
| | - Mohamed El-Sherbiny
- Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, Riyadh, Saudi Arabia
| | - Sachithra Thazhathuveedu Sudevan
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - T P Aneesh
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
| | - Bijo Mathew
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences Campus, Kochi, India
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Bernal FA, Schmidt TJ. A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †. Molecules 2023; 28:molecules28083399. [PMID: 37110632 PMCID: PMC10144340 DOI: 10.3390/molecules28083399] [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: 03/08/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.
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Affiliation(s)
- Freddy A Bernal
- University of Münster, Institute of Pharmaceutical Biology and Phytochemistry (IPBP), PharmaCampus-Corrensstraße 48, 48149 Münster, Germany
| | - Thomas J Schmidt
- University of Münster, Institute of Pharmaceutical Biology and Phytochemistry (IPBP), PharmaCampus-Corrensstraße 48, 48149 Münster, Germany
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McNair D. Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond. Annu Rev Pharmacol Toxicol 2023; 63:77-97. [PMID: 35679624 DOI: 10.1146/annurev-pharmtox-051921-023255] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research and development has to date focused on research: target identification; docking-, fragment-, and motif-based generation of compound libraries; modeling of synthesis feasibility; rank-ordering likely hits according to structural and chemometric similarity to compounds having known activity and affinity to the target(s); optimizing a smaller library for synthesis and high-throughput screening; and combining evidence from screening to support hit-to-lead decisions. Applying AI/ML methods to lead optimization and lead-to-candidate (L2C) decision-making has shown slower progress, especially regarding predicting absorption, distribution, metabolism, excretion, and toxicology properties. The present review surveys reasons why this is so, reports progress that has occurred in recent years, and summarizes some of the issues that remain. Effective AI/ML tools to derisk L2C and later phases of development are important to accelerate the pharmaceutical development process, ameliorate escalating development costs, and achieve greater success rates.
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Affiliation(s)
- Douglas McNair
- Global Health, Integrated Development, Bill & Melinda Gates Foundation, Seattle, Washington, USA;
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Kumar S, Manoharan A, J J, Abdelgawad MA, Mahdi WA, Alshehri S, Ghoneim MM, Pappachen LK, Zachariah SM, Aneesh TP, Mathew B. Exploiting butyrylcholinesterase inhibitors through a combined 3-D pharmacophore modeling, QSAR, molecular docking, and molecular dynamics investigation †. RSC Adv 2023; 13:9513-9529. [PMID: 36968055 PMCID: PMC10035067 DOI: 10.1039/d3ra00526g] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 03/14/2023] [Indexed: 03/25/2023] Open
Abstract
Alzheimer's disease (AD), a neurodegenerative condition associated with ageing, can occur. AD gradually impairs memory and cognitive function, which leads to abnormal behavior, incapacity, and reliance. By 2050, there will likely be 100 million cases of AD in the world's population. Acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibition are significant components of AD treatment. This work developed models using the genetic method multiple linear regression, atom-based, field-based, and 3-D pharmacophore modelling. Due to internal and external validation, all of the models have solid statistical (R2 > 0.81 and Q2 > 0.77) underpinnings. From a pre-plated CNS library (6055), we discovered a hit compound using virtual screening on a QSAR model. Through molecular docking, additional hit compounds were investigated (XP mode). Finally, a molecular dynamics simulation revealed that the Molecule5093-4BDS complex was stable (100 ns). Finally, the expected ADME properties for the hit compounds (Molecule5093, Molecule1076, Molecule4412, Molecule1053, and Molecule3344) were found. According to the results of our investigation and the prospective hit compounds, BuChE inhibitors may be used as a treatment for AD. Alzheimer's disease (AD), a neurodegenerative condition associated with ageing, can occur.![]()
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Affiliation(s)
- Sunil Kumar
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences CampusKochi682 041India
| | - Amritha Manoharan
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences CampusKochi682 041India
| | - Jayalakshmi J
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences CampusKochi682 041India
| | - Mohamed A. Abdelgawad
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jouf UniversitySakaka72341Saudi Arabia
- Department of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Beni-Suef UniversityBeni-SuefEgypt
| | - Wael A. Mahdi
- Department of Pharmaceutics, College of Pharmacy, King Saud UniversityRiyadh11451Saudi Arabia
| | - Sultan Alshehri
- Department of Pharmaceutics, College of Pharmacy, King Saud UniversityRiyadh11451Saudi Arabia
| | - Mohammed M. Ghoneim
- Department of Pharmacy Practice, College of Pharmacy, AlMaarefa UniversityAd Diriyah13713Saudi Arabia
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy, Al-Azhar UniversityCairo11884Egypt
| | - Leena K. Pappachen
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences CampusKochi682 041India
| | - Subin Mary Zachariah
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences CampusKochi682 041India
| | - T. P. Aneesh
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences CampusKochi682 041India
| | - Bijo Mathew
- Department of Pharmaceutical Chemistry, Amrita School of Pharmacy, Amrita Vishwa Vidyapeetham, AIMS Health Sciences CampusKochi682 041India
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Soni M, Pratap JV. Development of Novel Anti-Leishmanials: The Case for Structure-Based Approaches. Pathogens 2022; 11:pathogens11080950. [PMID: 36015070 PMCID: PMC9414883 DOI: 10.3390/pathogens11080950] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022] Open
Abstract
The neglected tropical disease (NTD) leishmaniasis is the collective name given to a diverse group of illnesses caused by ~20 species belonging to the genus Leishmania, a majority of which are vector borne and associated with complex life cycles that cause immense health, social, and economic burdens locally, but individually are not a major global health priority. Therapeutic approaches against leishmaniasis have various inadequacies including drug resistance and a lack of effective control and eradication of the disease spread. Therefore, the development of a rationale-driven, target based approaches towards novel therapeutics against leishmaniasis is an emergent need. The utilization of Artificial Intelligence/Machine Learning methods, which have made significant advances in drug discovery applications, would benefit the discovery process. In this review, following a summary of the disease epidemiology and available therapies, we consider three important leishmanial metabolic pathways that can be attractive targets for a structure-based drug discovery approach towards the development of novel anti-leishmanials. The folate biosynthesis pathway is critical, as Leishmania is auxotrophic for folates that are essential in many metabolic pathways. Leishmania can not synthesize purines de novo, and salvage them from the host, making the purine salvage pathway an attractive target for novel therapeutics. Leishmania also possesses an organelle glycosome, evolutionarily related to peroxisomes of higher eukaryotes, which is essential for the survival of the parasite. Research towards therapeutics is underway against enzymes from the first two pathways, while the third is as yet unexplored.
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Affiliation(s)
- Mohini Soni
- Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Sector-10, Jankipuram Extension, Sitapur Road, Lucknow 226031, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - J. Venkatesh Pratap
- Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Sector-10, Jankipuram Extension, Sitapur Road, Lucknow 226031, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
- Correspondence:
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Veit-Acosta M, de Azevedo Junior WF. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery. Curr Med Chem 2021; 29:2438-2455. [PMID: 34365938 DOI: 10.2174/0929867328666210806105810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. OBJECTIVE This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. METHOD We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. RESULTS Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. CONCLUSION All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.
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Affiliation(s)
- Martina Veit-Acosta
- Western Michigan University, 1903 Western, Michigan Ave, Kalamazoo, MI 49008. United States
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Winkler DA. Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases. Front Chem 2021; 9:614073. [PMID: 33791277 PMCID: PMC8005575 DOI: 10.3389/fchem.2021.614073] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/18/2021] [Indexed: 12/11/2022] Open
Abstract
Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.
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Affiliation(s)
- David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia.,Latrobe Institute for Molecular Science, La Trobe University, Bundoora, VIC, Australia.,School of Pharmacy, University of Nottingham, Nottingham, United Kingdom.,CSIRO Data61, Pullenvale, QLD, Australia
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Sánchez-Suárez J, Bernal FA, Coy-Barrera E. Colombian Contributions Fighting Leishmaniasis: A Systematic Review on Antileishmanials Combined with Chemoinformatics Analysis. Molecules 2020; 25:E5704. [PMID: 33287235 PMCID: PMC7730898 DOI: 10.3390/molecules25235704] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 11/30/2020] [Accepted: 11/30/2020] [Indexed: 01/15/2023] Open
Abstract
Leishmaniasis is a parasitic morbid/fatal disease caused by Leishmania protozoa. Twelve million people worldwide are appraised to be currently infected, including ca. two million infections each year, and 350 million people in 88 countries are at risk of becoming infected. In Colombia, cutaneous leishmaniasis (CL) is a public health problem in some tropical areas. Therapeutics is based on traditional antileishmanial drugs, but this practice has several drawbacks for patients. Thus, the search for new antileishmanial agents is a serious need, but the lack of adequately funded research programs on drug discovery has hampered its progress. Some Colombian researchers have conducted different research projects focused on the assessment of the antileishmanial activity of naturally occurring and synthetic compounds against promastigotes and/or amastigotes. Results of such studies have separately demonstrated important hits and reasonable potential, but a holistic view of them is lacking. Hence, we present the outcome from a systematic review of the literature (under PRISMA guidelines) on those Colombian studies investigating antileishmanials during the last thirty-two years. In order to combine the general efforts aiming at finding a lead against Leishmania panamensis (one of the most studied and incident parasites in Colombia causing CL) and to recognize structural features of representative compounds, fingerprint-based analyses using conventional machine learning algorithms and clustering methods are shown. Abstraction from such a meta-description led to describe some function-determining molecular features and simplify the clustering of plausible isofunctional hits. This systematic review indicated that the Colombian efforts for the antileishmanials discovery are increasingly intensified, though improvements in the followed pathways must be definitively pursued. In this context, a brief discussion about scope, strengths and limitations of such advances and relationships is addressed.
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Affiliation(s)
- Jeysson Sánchez-Suárez
- Bioprospecting Research Group, School of Engineering, Universidad de La Sabana, Chía 250001, Colombia;
| | - Freddy A. Bernal
- Bioorganic Chemistry Laboratory, Universidad Militar Nueva Granada, Cajicá 250247, Colombia;
| | - Ericsson Coy-Barrera
- Bioorganic Chemistry Laboratory, Universidad Militar Nueva Granada, Cajicá 250247, Colombia;
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Affiliation(s)
- Luciana Scotti
- Federal University of Paraíba, Health Sci. Center, 50670-910, Joao Pessoa PB, Brazil
| | - Marcus T. Scotti
- Federal University of Paraíba, Health Sci. Center, 50670-910, Joao Pessoa PB, Brazil
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