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Dobrachinski L, Ferreira LL, Cirino ME, Andrade-de-Siqueira AI, Mafud AC, Mascarenhas YP, Andricopulo AD, Moraes JD. The 3D pharmacophore modeling to explore new antischistosomal agents among US FDA approved drugs. Future Med Chem 2024:1-9. [PMID: 39072451 DOI: 10.1080/17568919.2024.2379231] [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: 04/10/2024] [Accepted: 06/14/2024] [Indexed: 07/30/2024] Open
Abstract
Aim: To identify potential antischistosomal agents through 3D pharmacophore-based virtual screening of US FDA approved drugs. Materials & methods: A comprehensive virtual screening was conducted on a dataset of 10,000 FDA approved drugs, employing praziquantel as a template. Promising candidates were selected and assessed for their impact on Schistosoma mansoni viability in vitro and in vivo using S. mansoni infected mice. Results & conclusion: Among the selected drugs, betamethasone and doxazosin demonstrated in vitro efficacy, with effective concentration 50% (EC50) values ranging from 35 to 60 μM. In vivo studies revealed significant (>50%) reductions in worm burden for both drugs. These findings suggest that betamethasone and doxazosin hold promise for repurposing in treating schistosomiasis. Additionally, the study showcases a useful approach for identifying new antischistosomal drugs.
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Affiliation(s)
- Leandro Dobrachinski
- Núcleo de Pesquisa em Doenças Negligenciadas, Universidade Guarulhos, Guarulhos, SP, Brazil
| | - Leonardo Lg Ferreira
- Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, SP, Brazil
| | - Maria E Cirino
- Núcleo de Pesquisa em Doenças Negligenciadas, Universidade Guarulhos, Guarulhos, SP, Brazil
| | | | - Ana C Mafud
- Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, SP, Brazil
| | - Yvonne P Mascarenhas
- Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, SP, Brazil
| | - Adriano D Andricopulo
- Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, SP, Brazil
| | - Josué de Moraes
- Núcleo de Pesquisa em Doenças Negligenciadas, Universidade Guarulhos, Guarulhos, SP, Brazil
- Núcleo de Pesquisa em Doenças Negligenciadas, Universidade Brasil, São Paulo, SP, Brazil
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Liu Y, Tong JB, Gao P, Fan XL, Xiao XC, Xing YC. Combining QSAR techniques, molecular docking, and molecular dynamics simulations to explore anti-tumor inhibitors targeting Focal Adhesion Kinase. J Biomol Struct Dyn 2024:1-17. [PMID: 38173145 DOI: 10.1080/07391102.2023.2301055] [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: 09/13/2023] [Accepted: 12/15/2023] [Indexed: 01/05/2024]
Abstract
Focal Adhesion Kinase (FAK) is an important target for tumor therapy and is closely related to tumor cell genesis and progression. In this paper, we selected 46 FAK inhibitors with anticancer activity in the pyrrolo pyrimidine backbone to establish 3D/2D-QSAR models to explore the relationship between inhibitory activity and molecular structure. We have established two ideal models, namely, the Topomer CoMFA model (q 2 = 0.715, r 2 = 0.984) and the Holographic Quantitative Structure-Activity Relationship (HQSAR) model (q 2 = 0.707, r 2 = 0.899). Both models demonstrate excellent external prediction capabilities.Based on the QSAR results, we designed 20 structurally modified novel compounds, which were subjected to molecular docking and molecular dynamics studies, and the results showed that the new compounds formed many robust interactions with residues within the active pocket and could maintain stable binding to the receptor proteins. This study not only provides a powerful screening tool for designing novel FAK inhibitors, but also presents a series of novel FAK inhibitors with high micromolar activity that can be used for further characterization. It provides a reference for addressing the shortcomings of drug metabolism and drug resistance of traditional FAK inhibitors, as well as the development of novel clinically applicable FAK inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yuan Liu
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an, China
| | - Jian-Bo Tong
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an, China
| | - Peng Gao
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an, China
| | - Xuan-Lu Fan
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an, China
| | - Xue-Chun Xiao
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an, China
| | - Yi-Chaung Xing
- College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology, Xi'an, China
- Shaanxi Key Laboratory of Chemical Additives for Industry, Shaanxi University of Science and Technology, Xi'an, China
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Halder AK, Dias Soeiro Cordeiro MN. Advanced in Silico Methods for the Development of Anti- Leishmaniasis and Anti-Trypanosomiasis Agents. Curr Med Chem 2020; 27:697-718. [DOI: 10.2174/0929867325666181031093702] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 07/24/2018] [Accepted: 09/19/2018] [Indexed: 11/22/2022]
Abstract
Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account
for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of
chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand
urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last
decades, different in silico methods have been successfully implemented for supporting the lengthy and
expensive drug discovery process. In the current review, we discuss recent advances pertaining to in
silico analyses towards lead identification, lead modification and target identification of antileishmaniasis
and anti-trypanosomiasis agents. We describe recent applications of some important in
silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so
forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic
anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse
methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c)
recent applications and advances in the last five years; (d) experimental validations of in silico predictions;
(e) virtual screening tools; and (f) rationale or justification for the selection of these in silico
methods.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@ REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, Porto 4169-007, Portugal
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Two- and three-dimensional QSAR studies on hURAT1 inhibitors with flexible linkers: topomer CoMFA and HQSAR. Mol Divers 2019; 24:141-154. [PMID: 30868332 DOI: 10.1007/s11030-019-09936-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 03/01/2019] [Indexed: 12/20/2022]
Abstract
hURAT1 (human urate transporter 1) is a successful target for hyperuricemia. Recently, the modification work on hURAT1 inhibitors showed that the flexible linkers would benefit biological activity. The study aimed to investigate the contribution of the linkers and give modification strategies on this kind of structures based on QSAR models (HQSAR and topomer CoMFA). The most effective HQSAR and topomer CoMFA models were generated by applying the training set containing 63 compounds, with the cross-validated q2 values of 0.869/0.818 and the non-cross-validated correlation coefficients r2 of 0.951/0.978, respectively. The Y-randomization test was applied to ensure the robustness of the models. The external predictive correlation coefficient (rpred2) grounded on the external test set (21 compounds) of two models was 0.910 and 0.907, respectively. In addition, the models were validated by Golbraikh-Tropsha and Roy methods, as well as other statistical metrics. The results showed that both models were reliable. Topomer CoMFA steric/electrostatic contours and HQSAR atomic contribution maps illustrated the structural features which governed their inhibitory potency. The dependable results could provide important insights to guide the designing of more potential hURAT1 inhibitors.
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Kryshchyshyn A, Devinyak O, Kaminskyy D, Grellier P, Lesyk R. Development of Predictive QSAR Models of 4-Thiazolidinones Antitrypanosomal Activity Using Modern Machine Learning Algorithms. Mol Inform 2017; 37:e1700078. [PMID: 29134756 DOI: 10.1002/minf.201700078] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 10/30/2017] [Indexed: 01/24/2023]
Abstract
This paper presents novel QSAR models for the prediction of antitrypanosomal activity among thiazolidines and related heterocycles. The performance of four machine learning algorithms: Random Forest regression, Stochastic gradient boosting, Multivariate adaptive regression splines and Gaussian processes regression have been studied in order to reach better levels of predictivity. The results for Random Forest and Gaussian processes regression are comparable and outperform other studied methods. The preliminary descriptor selection with Boruta method improved the outcome of machine learning methods. The two novel QSAR-models developed with Random Forest and Gaussian processes regression algorithms have good predictive ability, which was proved by the external evaluation of the test set with corresponding Q2ext =0.812 and Q2ext =0.830. The obtained models can be used further for in silico screening of virtual libraries in the same chemical domain in order to find new antitrypanosomal agents. Thorough analysis of descriptors influence in the QSAR models and interpretation of their chemical meaning allows to highlight a number of structure-activity relationships. The presence of phenyl rings with electron-withdrawing atoms or groups in para-position, increased number of aromatic rings, high branching but short chains, high HOMO energy, and the introduction of 1-substituted 2-indolyl fragment into the molecular structure have been recognized as trypanocidal activity prerequisites.
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Affiliation(s)
- Anna Kryshchyshyn
- Department of Pharmaceutical, Organic and Bioorganic Chemistry, Danylo Halytsky Lviv National Medical University, Pekarska str. 69, 79010, Lviv, Ukraine
| | - Oleg Devinyak
- Department of Pharmaceutical Disciplines, Uzhgorod National University, Narodna sq. 1, 88000, Uzhgorod, Ukraine
| | - Danylo Kaminskyy
- Department of Pharmaceutical, Organic and Bioorganic Chemistry, Danylo Halytsky Lviv National Medical University, Pekarska str. 69, 79010, Lviv, Ukraine
| | - Philippe Grellier
- National Museum of Natural History, UMR 7245 CNRS MCAM, Sorbonne Universités, CP 52, 57 Rue Cuvier, Paris, 75005, France
| | - Roman Lesyk
- Department of Pharmaceutical, Organic and Bioorganic Chemistry, Danylo Halytsky Lviv National Medical University, Pekarska str. 69, 79010, Lviv, Ukraine
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Combined HQSAR, topomer CoMFA, homology modeling and docking studies on triazole derivatives as SGLT2 inhibitors. Future Med Chem 2017. [DOI: 10.4155/fmc-2017-0002] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Aim: Sodium–glucose cotransporter 2 (SGLT2) is a promising target for diabetes therapy. We aimed to develop computational approaches to identify structural features for more potential SGLT2 inhibitors. Materials & methods: In this work, 46 triazole derivatives as SGLT2 inhibitors were studied using a combination of several approaches, including hologram quantitative structure–activity relationships (HQSAR), topomer comparative molecular field analysis (CoMFA), homology modeling, and molecular docking. HQSAR and topomer CoMFA were used to construct models. Molecular docking was conducted to investigate the interaction of triazole derivatives and homology modeling of SGLT2, as well as to validate the results of the HQSAR and topomer CoMFA models. Results: The most effective HQSAR and topomer CoMFA models exhibited noncross-validated correlation coefficients of 0.928 and 0.891 for the training set, respectively. External predictions were made successfully on a test set and then compared with previously reported models. The graphical results of HQSAR and topomer CoMFA were proven to be consistent with the binding mode of the inhibitors and SGLT2 from molecular docking. Conclusion: The models and docking provided important insights into the design of potent inhibitors for SGLT2.
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de Souza AS, Ferreira LG, Andricopulo AD. 2D and 3D QSAR Studies on a Series of Antichagasic Fenarimol Derivatives. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Chagas disease is one of the most important neglected tropical diseases. Endemic in Latin America, the disease is a global public health problem, affecting several countries in North America, Europe, Asia and Oceania. The disease affects around 8-10 million people worldwide and the limited treatments available present low efficacy and severe side effects, highlighting the urgent need for new therapeutic options. In this work, the authors developed QSAR models for a series of fenarimol derivatives exhibiting anti-T. cruzi activity. The models were constructed using the Hologram QSAR (HQSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. The QSAR models presented substantial predictive ability for a series of test set compounds (HQSAR, r2pred = 0.66; CoMFA, r2pred = 0.82; and CoMSIA, r2pred = 0.76), and were valuable to identify key structural features related to the observed trypanocidal activity. The results reported herein are useful for the design of novel derivatives having improved antichagasic properties.
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Affiliation(s)
- Anacleto S. de Souza
- Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil
| | - Leonardo G. Ferreira
- Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, Brazil
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