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Cañizares-Carmenate Y, Perera-Sardiña Y, Marrero-Ponce Y, Díaz-Amador R, Torrens F, Castillo-Garit JA. Ligand and structure-based discovery of phosphorus-containing compounds as potential metalloproteinase inhibitors. SAR QSAR Environ Res 2024; 35:219-240. [PMID: 38380444 DOI: 10.1080/1062936x.2024.2314103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/29/2024] [Indexed: 02/22/2024]
Abstract
In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD50, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.
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Affiliation(s)
- Y Cañizares-Carmenate
- Unit of Computer-Aided Molecular ''Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Departamento de Farmacia, Facultad de Química-Farmacia, Universidad Central ''Marta Abreu" de Las Villas, Santa Clara, Cuba
| | - Y Perera-Sardiña
- Departamento de Ciencias Básicas Biomédicas, Facultad de Ciencias de la Salud, Universidad de Talca, Talca, Chile
| | - Y Marrero-Ponce
- Grupo de Medicina Molecular Y Traslacional (MeM & T), Escuela de Medicina, Universidad San Francisco de Quito, Edificio de Especialidades Médicas, Quito, Ecuador
| | - R Díaz-Amador
- Laboratorio de Bioinformática y Química Computacional, Escuela de Química y Farmacia, Facultad de Medicina, Universidad Católica de Maule, Maule, Chile
| | - F Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, València, Spain
| | - J A Castillo-Garit
- Instituto Universitario de Investigación y Desarrollo Tecnológico (IDT), Universidad Tecnológica Metropolitana, Santiago, Chile
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2
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Martínez-López Y, Castillo-Garit JA, Casanola-Martin GM, Rasulev B, Rodríguez-Gonzalez AY, Martínez-Santiago O, Barigye SJ. Exploring proteasome inhibition using atomic weighted vector indices and machine learning approaches. Mol Divers 2023:10.1007/s11030-023-10638-2. [PMID: 37017875 DOI: 10.1007/s11030-023-10638-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 03/17/2023] [Indexed: 04/06/2023]
Abstract
Ubiquitin-proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. The UPS is involved in different biological activities, such as the regulation of gene transcription and cell cycle. Several researchers have applied cheminformatics and artificial intelligence methods to study the inhibition of proteasomes, including the prediction of UPP inhibitors. Following this idea, we applied a new tool for obtaining molecular descriptors (MDs) for modeling proteasome Inhibition in terms of EC50 (µmol/L), in which a set of new MDs called atomic weighted vectors (AWV) and several prediction algorithms were used in cheminformatics studies. In the manuscript, a set of descriptors based on AWV are presented as datasets for training different machine learning techniques, such as linear regression, multiple linear regression (MLR), random forest (RF), K-nearest neighbors (IBK), multi-layer perceptron, best-first search, and genetic algorithm. The results suggest that these atomic descriptors allow adequate modeling of proteasome inhibitors despite artificial intelligence techniques, as a variant to build efficient models for the prediction of inhibitory activity.
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Affiliation(s)
- Yoan Martínez-López
- Department of Computer Sciences, Faculty of Informatics, Camagüey University, 74650, Camagüey City, Cuba.
| | | | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58102, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, 58102, USA
| | - Ansel Y Rodríguez-Gonzalez
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE-UT3), Unidad de Transferencia Tecnológica de Tepic, Tepic, México
| | - Oscar Martínez-Santiago
- Alfa Vitamins Laboratories, Miami, FL, 33166, USA
- Laboratorio de Bioinformática y Química Computacional, Universidad Católica del Maule, Talca, Chile
| | - Stephen J Barigye
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), 28049, Madrid, Spain
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Castillo-Garit JA, Cañizares-Carmenate Y, Pham-The H, Pérez-Doñate V, Torrens F, Pérez-Giménez F. A Review of Computational Approaches Targeting SARS-CoV-2 Main Protease to the Discovery of New Potential Antiviral Compounds. Curr Top Med Chem 2023; 23:3-16. [PMID: 35473544 DOI: 10.2174/2667387816666220426133555] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/05/2022] [Accepted: 03/12/2022] [Indexed: 11/22/2022]
Abstract
The new pandemic caused by the coronavirus (SARS-CoV-2) has become the biggest challenge that the world is facing today. It has been creating a devastating global crisis, causing countless deaths and great panic. The search for an effective treatment remains a global challenge owing to controversies related to available vaccines. A great research effort (clinical, experimental, and computational) has emerged in response to this pandemic, and more than 125000 research reports have been published in relation to COVID-19. The majority of them focused on the discovery of novel drug candidates or repurposing of existing drugs through computational approaches that significantly speed up drug discovery. Among the different used targets, the SARS-CoV-2 main protease (Mpro), which plays an essential role in coronavirus replication, has become the preferred target for computational studies. In this review, we examine a representative set of computational studies that use the Mpro as a target for the discovery of small-molecule inhibitors of COVID-19. They will be divided into two main groups, structure-based and ligand-based methods, and each one will be subdivided according to the strategies used in the research. From our point of view, the use of combined strategies could enhance the possibilities of success in the future, permitting to development of more rigorous computational studies in future efforts to combat current and future pandemics.
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Affiliation(s)
- Juan A Castillo-Garit
- Department Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara, 50200, Villa Clara, Cuba.,Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Spain
| | - Yudith Cañizares-Carmenate
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química-Farmacia, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
| | - Hai Pham-The
- Department of Medicinal Chemistry, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi, Viet-nam
| | | | - Francisco Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P.O. Box 22085, E-46071, València, Spain
| | - Facundo Pérez-Giménez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Spain
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Resino-Ruiz D, Gonzalez-Madariaga Y, Nieto L, Linares YM, León JOG, Martín AV, Díaz AV, Torrens F, Castillo-Garit JA. Anti-inflammatory Activity: In silico and In vivo of Sapogenins Present in Agave brittoniana subsp. brachypus (Trel.). Antiinflamm Antiallergy Agents Med Chem 2023; 22:42-48. [PMID: 37114792 DOI: 10.2174/1871523022666230419103027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/08/2023] [Accepted: 02/27/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND Agave brittoniana subsp. brachypus is an endemic plant of Cuba, which contains different steroidal sapogenins with anti-inflammatory effects. This work aims to develop computational models which allow the identification of new chemical compounds with potential anti-inflammatory activity. METHODS The in vivo anti-inflammatory activity was evaluated in two rat models: carrageenaninduced paw edema and cotton pellet-induced granuloma. In each study, we used 30 Sprague Dawley male rats divided into five groups containing six animals. The products isolated and administrated were fraction rich in yuccagenin and sapogenins crude. RESULTS The obtained model, based on a classification tree, showed an accuracy value of 86.97% for the training set. Seven compounds (saponins and sapogenins) were identified as potential antiinflammatory agents in the virtual screening. According to in vivo studies, the yuccagenin-rich fraction was the greater inhibitor of the evaluated product from Agave. CONCLUSION The evaluated metabolites of the Agave brittoniana subsp. Brachypus showed an interesting anti-inflammatory effect.
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Affiliation(s)
- Dayana Resino-Ruiz
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara, 50200, Villa Clara, Cuba
| | - Yisel Gonzalez-Madariaga
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara, 50200, Villa Clara, Cuba
| | - Leisy Nieto
- Departamento de Farmacia, Facultad de Química-Farmacia, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
| | - Yilka Mena Linares
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara, 50200, Villa Clara, Cuba
| | - Jose Orestes Guerra León
- Departamento de Química, Facultad de Química-Farmacia, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
| | - Arlena Vázquez Martín
- Departamento de Química, Facultad de Química-Farmacia, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
| | - Arianna Valido Díaz
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara, 50200, Villa Clara, Cuba
| | - Francisco Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P.O. Box 22085, E-46071, València, Spain
| | - Juan A Castillo-Garit
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara, 50200, Villa Clara, Cuba
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P.O. Box 22085, E-46071, València, Spain
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Cañizares-Carmenate Y, Nam NH, Díaz-Amador R, Thuan NT, Dung PTP, Torrens F, Pham-The H, Perez-Gimenez F, Castillo-Garit JA. Ligand-based discovery of new potential acetylcholinesterase inhibitors for Alzheimer's disease treatment. SAR QSAR Environ Res 2022; 33:49-61. [PMID: 35048766 DOI: 10.1080/1062936x.2022.2025615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/02/2022] [Indexed: 06/14/2023]
Abstract
The enzyme acetylcholinesterase (AChE) is currently a therapeutic target for the treatment of neurodegenerative diseases. These diseases have highly variable causes but irreversible evolutions. Although the treatments are palliative, they help relieve symptoms and allow a better quality of life, so the search for new therapeutic alternatives is the focus of many scientists worldwide. In this study, a QSAR-SVM classification model was developed by using the MATLAB numerical computation system and the molecular descriptors implemented in the Dragon software. The obtained parameters are adequate with accuracy of 88.63% for training set, 81.13% for cross-validation experiment and 81.15% for prediction set. In addition, its application domain was determined to guarantee the reliability of the predictions. Finally, the model was used to predict AChE inhibition by a group of quinazolinones and benzothiadiazine 1,1-dioxides obtained by chemical synthesis, resulting in 14 drug candidates with in silico activity comparable to acetylcholine.
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Affiliation(s)
- Y Cañizares-Carmenate
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química-Farmacia, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Cuba
| | - N-H Nam
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, Hanoi, Vietnam
| | - R Díaz-Amador
- Department of Computer Science, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Cuba
| | - N T Thuan
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, Hanoi, Vietnam
| | - P T P Dung
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, Hanoi, Vietnam
| | - F Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, València, Spain
| | - H Pham-The
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy, Hanoi, Vietnam
| | - F Perez-Gimenez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain
| | - J A Castillo-Garit
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Valencia, Spain
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara, Cuba
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Díaz-Amador R, Cañizares-Carmenate Y, Taboada-Crispi A, Castillo-Garit JA. Computational Modeling of Aldose Reductase Inhibitory Activity of Flavonoids Derivatives for Diabetic Complications Treatment. LETT DRUG DES DISCOV 2021. [DOI: 10.2174/1570180818666210604113206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Diabetes mellitus is a chronic metabolic disease that constitutes a risk factor
for patients infected by COVID-19. Aldose reductase (ALR2) is an enzyme that catalyzes the formation
of sorbitol in the metabolism of glucose via polyols in diabetic patients and leads to a group
of diabetic complications: cataracts, retinopathies, neuropathies, and nephropathies.
Introduction:
Inhibitors of this enzyme are therapeutic targets for the prophylaxis and treatment of
these conditions. The aim of this work was to identify flavonoids isolated from medicinal plants,
fruits, and vegetables as potential inhibitors of ALR2.
Methods:
In this study, using the MATLAB numerical computation system and the molecular descriptors
implemented in the DRAGON software, a regression tree was developed, with an R2 of
0.953 and adequate parameters for its fit.
Results:
he model was validated to take into account internal and external validation procedures.
Besides, the applicability domain was determined to guarantee the reliability of the predictions. Due
to its good predictive power (R2
ext = 0.949), the model was used to predict the inhibition of ALR2 by
flavonoids reported in dietary sources. The most promising flavonoids are Myricetin and Tricin
(pIC50predicted = 7.296), which are within the application domain and meet drug-like properties for
oral administration.
Conclusion:
Finally, we can conclude that the proposed tools are useful for the rapid and economical
identification of flavonoid-based potential drug candidates against ALR2 in diabetic complications.
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Affiliation(s)
- Roberto Díaz-Amador
- Department of Computer Science, Universidad Central “Marta Abreu” de Las Villas,Cuba
| | - Yudith Cañizares-Carmenate
- Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química-Farmacia, Universidad Central “Marta Abreu” de Las Villas, Santa Clara 54830, Villa Clara,Cuba
| | | | - Juan A. Castillo-Garit
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara 50200, Villa Clara,Cuba
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Cañizares-Carmenate Y, Mena-Ulecia K, MacLeod Carey D, Perera-Sardiña Y, Hernández-Rodríguez EW, Marrero-Ponce Y, Torrens F, Castillo-Garit JA. Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases. Mol Divers 2021; 26:1383-1397. [PMID: 34216326 DOI: 10.1007/s11030-021-10260-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 06/17/2021] [Indexed: 11/26/2022]
Abstract
With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases. To develop this study, we used a database of 191 thermolysin inhibitor compounds, which is the largest as far as we know. First, we use Dragon's molecular descriptors (0-3D) to develop classification models using Bayesian networks (Naive Bayes) and artificial neural networks (Multilayer Perceptron). The obtained models are used for virtual screening of small molecules in the international DrugBank database. Second, docking experiments are carried out for all three enzymes using the Autodock Vina program, to identify possible interactions with the active site of human metalloproteases. As a result, high-performance artificial intelligence QSAR models are obtained for training and prediction sets. These allowed the identification of 18 compounds with potential inhibitory activity and an adequate oral bioavailability profile, which were evaluated using docking. Four of them showed high binding energies for the three enzymes, and we propose them as potential dual ACE/NEP inhibitors for the control of blood pressure. In summary, the in silico strategies used here constitute an important tool for the early identification of new antihypertensive drug candidates, with substantial savings in time and money.
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Affiliation(s)
- Yudith Cañizares-Carmenate
- Unit of Computer-Aided Molecular ''Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química-Farmacia, Universidad Central ''Marta Abreu" de Las Villas, 54830, Santa Clara, Villa Clara, Cuba
| | - Karel Mena-Ulecia
- Departamento de Ciencias Biológicas Y Químicas, Facultad de Recursos Naturales, Universidad Católica de Temuco, Ave. Rudecindo Ortega, 02950, Temuco, Chile
- Núcleo de Investigación en Bioproductos Y Materiales Avanzados (BIOMA), Facultad de Ingeniería, Universidad Católica de Temuco, Ave. Rudecindo Ortega, 02950, Temuco, Chile
| | - Desmond MacLeod Carey
- Facultad de Ingeniería, Inorganic Chemistry and Molecular Materials Center, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, El Llano Subercaseaux, San Miguel, 2801, Santiago, Chile
| | - Yunier Perera-Sardiña
- Laboratorio de Bioinformática Y Química Computacional, Escuela de Química Y Farmacia, Facultad de Medicina, Universidad Católica de Maule, Talca, Chile
| | - Erix W Hernández-Rodríguez
- Laboratorio de Bioinformática Y Química Computacional, Escuela de Química Y Farmacia, Facultad de Medicina, Universidad Católica de Maule, Talca, Chile
| | - Yovani Marrero-Ponce
- Grupo de Medicina Molecular Y Traslacional (MeM & T), Escuela de Medicina, Universidad San Francisco de Quito, Edificio de Especialidades Médicas, Av. Interoceánica Km 12½, Quito, Ecuador
| | - Francisco Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici D'Instituts de Paterna, P.O. Box 22085, 46071, València, Spain
| | - Juan A Castillo-Garit
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Carretera a Acueducto Y Circunvalación, CP: 50200, Santa Clara, Villa Clara, Cuba.
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Castillo-Garit JA, Barigye SJ, Pham-The H, Pérez-Doñate V, Torrens F, Pérez-Giménez F. Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree. SAR QSAR Environ Res 2021; 32:71-83. [PMID: 33455460 DOI: 10.1080/1062936x.2020.1863857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 12/10/2020] [Indexed: 06/12/2023]
Abstract
Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation procedure and through a test set, achieving accuracy values over 90.5% and 92.2%, correspondingly. The values of sensitivity and specificity were around 90% in all series; also the false alarm rate values were under 10.5% for all sets. In addition, a simulated ligand-based virtual screening for several compounds recently reported as promising anti-chagasic agents was carried out, yielding good agreement between predictions and experimental results. Finally, the present work constitutes an example of how this rational computer-based method can help reduce the cost and increase the rate in which novel compounds are developed against Chagas disease.
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Affiliation(s)
- J A Castillo-Garit
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara , Villa Clara, Cuba
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València , Valencia, Spain
| | - S J Barigye
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM) , Madrid, Spain
| | - H Pham-The
- Department of Pharmaceutical Chemistry, Hanoi University of Pharmacy , Hanoi, Viet-nam
| | - V Pérez-Doñate
- Departamento de Microbiología, Hospital Universitario de la Ribera , Valencia, Spain
| | - F Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna , València, Spain
| | - F Pérez-Giménez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València , Valencia, Spain
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Cañizares-Carmenate Y, Campos Delgado LE, Torrens F, Castillo-Garit JA. Thorough evaluation of OECD principles in modelling of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine derivatives using QSARINS. SAR QSAR Environ Res 2020; 31:741-759. [PMID: 32892643 DOI: 10.1080/1062936x.2020.1810116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 08/11/2020] [Indexed: 06/11/2023]
Abstract
The human immunodeficiency virus is a lethal pathology considered as a worldwide problem. The search for new strategies for the treatment of this disease continues to be a great challenge in the scientific community. In this study, a series of 107 derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine, previously evaluated experimentally against HIV-I reverse transcriptase, was used to model antiretroviral activity. A model of linear regression, implemented in the QSARINS software, was developed with a genetic algorithm for variable selection. The fit of its parameters was good and exhaustive validation, according to the OECD regulatory principles, was performed. Also, the applicability domain was established. In addition, its robustness (r 2 = 0.84), stability (Q 2 LOO = 0.81; Q 2 LMO = 0.80) and good predictive power (r 2 EXT = 0.85) is proved. So, it was used to predict the antiretroviral activity of eight compounds obtained by rational drug design. Finally, it can be affirmed that the proposed tools allow the rapid and economic identification of potential antiretroviral drugs.
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Affiliation(s)
- Y Cañizares-Carmenate
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química-Farmacia, Universidad Central "Marta Abreu" de Las Villas , Santa Clara, Cuba
| | - L E Campos Delgado
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Facultad de Química-Farmacia, Universidad Central "Marta Abreu" de Las Villas , Santa Clara, Cuba
| | - F Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna , València, Spain
| | - J A Castillo-Garit
- Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara , Santa Clara, Cuba
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Cañizares-Carmenate Y, Alcántara Cárdenas A, Roche Llerena V, Torrens F, Castillo-Garit JA. Computational approach to the discovery of potential neprilysin inhibitors compounds for cardiovascular diseases treatment. Med Chem Res 2020. [DOI: 10.1007/s00044-020-02529-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Pham-The H, Cabrera-Pérez MÁ, Nam NH, Castillo-Garit JA, Rasulev B, Le-Thi-Thu H, Casañola-Martin GM. In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling. Curr Top Med Chem 2019; 18:2209-2229. [PMID: 30499410 DOI: 10.2174/1568026619666181130140350] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 10/16/2018] [Accepted: 11/19/2018] [Indexed: 11/22/2022]
Abstract
One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.
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Affiliation(s)
- Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Miguel Á Cabrera-Pérez
- Unit of Modeling and Experimental Biopharmaceutics, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.,Department of Engineering, Area of Pharmacy and Pharmaceutical Technology, Miguel Hernández University, 03550 Sant Juan d'Alacant, Alicante, Spain
| | - Nguyen-Hai Nam
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Juan A Castillo-Garit
- Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas "Dr. Serafín Ruiz de Zarate Ruiz" de Villa Clara, Santa Clara, 50200, Villa Clara, Cuba
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymer Materials, North Dakota State University, Fargo, ND, 58102, United States
| | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, 144 Xuan Thuy, Hanoi, Vietnam
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymer Materials, North Dakota State University, Fargo, ND, 58102, United States
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12
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Casañola-Martin GM, Pham-The H, Castillo-Garit JA, Le-Thi-Thu H. Atom based linear index descriptors in QSAR-machine learning classifiers for the prediction of ubiquitin-proteasome pathway activity. Med Chem Res 2018. [DOI: 10.1007/s00044-017-2091-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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13
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Barigye SJ, Freitas MP, Ausina P, Zancan P, Sola-Penna M, Castillo-Garit JA. Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity. ACS Comb Sci 2018; 20:75-81. [PMID: 29297675 DOI: 10.1021/acscombsci.7b00155] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.
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Affiliation(s)
- Stephen J. Barigye
- Department
of Chemistry, McGill University, 801 Sherbrooke Street West, Montréal, QC H3A 0B8, Canada
| | - Matheus P. Freitas
- Department
of Chemistry, Federal University of Lavras, P.O. Box 3037, 37200-000 Lavras-MG Brazil
| | - Priscila Ausina
- Laboratório
de Enzimologia e Controle do Metabolismo (LabECoM), Departamento de
Biotecnologia Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, 21941-902 Rio de
Janeiro-RJ, Brazil
| | - Patricia Zancan
- Laboratório
de Oncobiologia Molecular (LabOMol), Departamento de Biotecnologia
Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, 21941-902 Rio de Janeiro-RJ, Brazil
| | - Mauro Sola-Penna
- Laboratório
de Enzimologia e Controle do Metabolismo (LabECoM), Departamento de
Biotecnologia Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, 21941-902 Rio de
Janeiro-RJ, Brazil
| | - Juan A. Castillo-Garit
- Unidad
de Toxicología Experimental, Universidad de Ciencias Médicas “Serafín Ruiz de Zárate Ruiz”, Santa Clara, 50200 Villa Clara, Cuba
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14
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Pham-The H, Nam NH, Nga DV, Hai DT, Dieguez-Santana K, Marrero-Poncee Y, Castillo-Garit JA, Casanola-Martin GM, Le-Thi-Thu H. Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry. Curr Top Med Chem 2018; 17:3269-3288. [DOI: 10.2174/1568026618666171212111018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 11/14/2017] [Accepted: 11/22/2017] [Indexed: 11/22/2022]
Affiliation(s)
- Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Nguyen-Hai Nam
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hanoi, Vietnam
| | - Doan-Viet Nga
- School of Medicine and Pharmacy, Vietnam National University (VNU), 144 Xuan Thuy, Hanoi, Vietnam
| | - Dang Thanh Hai
- University of Engineering and Technology, Vietnam National University, 144 Xuan Thuy, Hanoi, Vietnam
| | | | - Yovani Marrero-Poncee
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA),Escuela de Medicina, Edificio de Especialidades Medicas, Quito, Ecuador
| | - Juan A. Castillo-Garit
- Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas, Dr. Serafín Ruiz de Zarate Ruiz, de Villa Clara, Cuba
| | | | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University (VNU), 144 Xuan Thuy, Hanoi, Vietnam
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15
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Martínez-López Y, Barigye SJ, Martínez-Santiago O, Marrero-Ponce Y, Green J, Castillo-Garit JA. Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors. Environ Toxicol Pharmacol 2017; 56:314-321. [PMID: 29091819 DOI: 10.1016/j.etap.2017.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 10/09/2017] [Accepted: 10/11/2017] [Indexed: 06/07/2023]
Abstract
Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50)-1). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure-toxicity relationships; the best model shows values of Q2=0.830 and R2=0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives.
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Affiliation(s)
- Yoan Martínez-López
- Department of Computer Sciences, Faculty of Informatics, Camaguey University, Camaguey City, 74650, Camaguey, Cuba; Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy. Universidad Central "Martha Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
| | - Stephen J Barigye
- Departamento de Química, Universidade Federal de Lavras, CP 3037, 37200-000, Lavras, MG, Brazil
| | - Oscar Martínez-Santiago
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy. Universidad Central "Martha Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Av. Interoceánica Km 12 ½, Cumbayá, Ecuador
| | - James Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada
| | - Juan A Castillo-Garit
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy. Universidad Central "Martha Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba; Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada; Unidad de Toxicologia Experimental, Universidad de Ciencias Médicas de Villa Clara Santa Clara, 50200, Villa Clara, Cuba.
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16
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Martinez-Lopez Y, Caballero Y, Barigye SJ, Marrero-Ponce Y, Millan-Cabrera R, Madera J, Torrens F, Castillo-Garit JA. State of the Art Review and Report of New Tool for Drug Discovery. Curr Top Med Chem 2017; 17:2957-2976. [PMID: 28828995 DOI: 10.2174/1568026617666170821123856] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2017] [Revised: 05/30/2017] [Accepted: 05/30/2017] [Indexed: 11/22/2022]
Abstract
BACKGROUND There are a great number of tools that can be used in QSAR/QSPR studies; they are implemented in several programs that are reviewed in this report. The usefulness of new tools can be proved through comparison, with previously published approaches. In order to perform the comparison, the most usual is the use of several benchmark datasets such as DRAGON and Sutherland's datasets. METHODS Here, an exploratory study of Atomic Weighted Vectors (AWVs), a new tool useful for drug discovery using different datasets, is presented. In order to evaluate the performance of the new tool, several statistics and QSAR/QSPR experiments are performed. Variability analyses are used to quantify the information content of the AWVs obtained from the tool, by means of an information theory-based algorithm. RESULTS Principal components analysis is used to analyze the orthogonality of these descriptors, for which the new MDs from AWVs provide different information from those codified by DRAGON descriptors (0-2D). The QSAR models are obtained for every Sutherland's dataset, according to the original division into training/test sets, by means of the multiple linear regression with genetic algorithm (MLR-GA). These models have been validated and compared favorably to several previously published approaches, using the same benchmark datasets. CONCLUSION The obtained results show that this tool should be a useful strategy for the QSAR/QSPR studies, despite its simplicity.
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Affiliation(s)
- Yoan Martinez-Lopez
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy. Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara. Cuba
| | - Yaile Caballero
- Department of Computer Sciences, Faculty of Computer Sciences, Camaguey University, Camaguey city, 74650, Camaguey. Cuba
| | - Stephen J Barigye
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy. Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara. Cuba
| | - Yovani Marrero-Ponce
- Grupo de Medicina Molecular y Traslacional (MEM&T), Universidad San Francisco de Quito. Ecuador
| | - Reisel Millan-Cabrera
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy. Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara. Cuba
| | - Julio Madera
- Department of Computer Sciences, Faculty of Computer Sciences, Camaguey University, Camaguey city, 74650, Camaguey. Cuba
| | - Francisco Torrens
- Institut Universitari de Ciencia Molecular, Universitat de Valencia, E-46071, Valencia. Spain
| | - Juan A Castillo-Garit
- Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas de Villa Clara, Carretera a acueducto y Circunvalacion, Santa Clara, Villa Clara, CP: 50200. Cuba
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17
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Castillo-Garit JA, Casanola-Martin GM, Le-Thi-Thu H, Pham-The H, Barigye SJ. A Simple Method to Predict Blood-Brain Barrier Permeability of Drug- Like Compounds Using Classification Trees. Med Chem 2017; 13:664-669. [DOI: 10.2174/1573406413666170209124302] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 12/15/2016] [Accepted: 02/03/2017] [Indexed: 11/22/2022]
Affiliation(s)
- Juan A. Castillo-Garit
- Unidad de Toxicologia Experimental, Universidad de Ciencias Medicas de Villa Clara, Carretera a acueducto y circunvalacion, Santa Clara, Villa Clara, Cuba
| | - Gerardo M. Casanola-Martin
- Unidad de Investigacion de Diseno de Farmacos y Conectividad Molecular, Departamento de /Quimica Fisica, Facultad de Farmacia, Universitat de Valencia, Spain
| | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam
| | - Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi, Vietnam
| | - Stephen J. Barigye
- Department of Chemistry, McGill University, 801 Sherbrooke St. W., Montreal, Quebec H3A, Canada
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18
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Castillo-Garit JA, Casañola-Martin GM, Barigye SJ, Pham-The H, Torrens F, Torreblanca A. Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis. SAR QSAR Environ Res 2017; 28:735-747. [PMID: 29022372 DOI: 10.1080/1062936x.2017.1376705] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Accepted: 09/01/2017] [Indexed: 06/07/2023]
Abstract
The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector machine, classification trees, and artificial neural networks, have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. They showed global accuracy values between 95.9% and 97.7% and area under Receiver Operator Curve values between 0.978 and 0.998; additionally, false alarm rate values were below 8.2% for training set. In order to validate our models, cross-validation (10-folds-out) and external test-set were performed with good behaviour in all cases. These models, obtained with ML techniques, were compared with others previously reported by other researchers, and the improvement was significant.
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Affiliation(s)
- J A Castillo-Garit
- a Unidad de Toxicología Experimental , Universidad de Ciencias Médicas de Villa Clara , Santa Clara , Villa Clara , Cuba
- b Departament de Biología Funcional i Antropología Física , Universitat de València , Burjassot , Spain
| | - G M Casañola-Martin
- c Departamento de Química Física, Facultad de FarmaciaUnidad de Investigación de Diseño de Fármacos y Conectividad Molecular , Universitat de València , Spain
| | - S J Barigye
- d Department of Chemistry , McGill University , Montréal , Québec , Canada
| | - H Pham-The
- e Hanoi University of Pharmacy , Hoan Kiem, Hanoi , Vietnam
| | - F Torrens
- f Institut Universitari de Ciència Molecular , Universitat de València, Edifici d'Instituts de Paterna , Valencia , Spain
| | - A Torreblanca
- b Departament de Biología Funcional i Antropología Física , Universitat de València , Burjassot , Spain
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19
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Cañizares-Carmenate Y, Hernandez-Morfa M, Torrens F, Castellano G, Castillo-Garit JA. Larvicidal activity prediction against Aedes aegypti mosquito using computational tools. J Vector Borne Dis 2017; 54:164-171. [PMID: 28748838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023] Open
Abstract
BACKGROUND & OBJECTIVES Aedes aegypti is an important vector for transmission of dengue, yellow fever, chikun- gunya, arthritis, and Zika fever. According to the World Health Organization, it is estimated that Ae. aegypti causes 50 million infections and 25,000 deaths per year. Use of larvicidal agents is one of the recommendations of health organizations to control mosquito populations and limit their distribution. The aim of present study was to deduce a mathematical model to predict the larvicidal action of chemical compounds, based on their structure. METHODS A series of different compounds with experimental evidence of larvicidal activity were selected to develop a predictive model, using multiple linear regression and a genetic algorithm for the selection of variables, implemented in the QSARINS software. The model was assessed and validated using the OECDs principles. RESULTS The best model showed good value for the determination coefficient (R2 = 0.752), and others parameters were appropriate for fitting (s = 0.278 and RMSEtr = 0.261). The validation results confirmed that the model hasgood robustness (Q2LOO = 0.682) and stability (R2-Q2LOO = 0.070) with low correlation between the descriptors (KXX = 0.241), an excellent predictive power (R2 ext = 0.834) and was product of a non-random correlation R2 Y-scr = 0.100). INTERPRETATION & CONCLUSION The present model shows better parameters than the models reported earlier in the literature, using the same dataset, indicating that the proposed computational tools are more efficient in identifying novel larvicidal compounds against Ae. aegypti.
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Affiliation(s)
- Yudith Cañizares-Carmenate
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR) Unit, Facultad de Química-Farmacia, Universidad Central "Marta Abreu"
| | - Mirelys Hernandez-Morfa
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR) Unit, Facultad de Química-Farmacia, Universidad Central "Marta Abreu"
| | - Francisco Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, València, Spain
| | - Gloria Castellano
- Departamento de Ciencias Experimentales y Matemáticas, Facultad de Veterinaria y Ciencias Experimentales, Universidad Católica de Valencia "San Vicente Mártir", Guillem de Castro
| | - Juan A Castillo-Garit
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR) Unit, Facultad de Química-Farmacia, Universidad Central "Marta Abreu"; Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara, Villa Clara, Cuba
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20
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Marrero-Ponce Y, Castañeda YG, Vivas-Reyes R, Vergara FM, Arán VJ, Castillo-Garit JA, Pérez-Giménez F, Torrens F, Le-Thi-Thu H, Pham-The H, Montenegro YV, Ibarra-Velarde F. Dry selection and wet evaluation for the rational discovery of new anthelmintics. Mol Phys 2017. [DOI: 10.1080/00268976.2017.1296194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, Quito, Ecuador
- Universidad San Francisco de Quito (USFQ), Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito, Ecuador
- Computer-Aided Molecular “Biosilico” Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Quito, Ecuador
- GIA (Grupo de Investigación Ambiental), Fundación Universitaria Tecnológico de Comfenalco, Facultad de Ingenierías, Programa de Ingeniería de Procesos, Cartagena de Indias, Bolívar, Colombia
| | - Yeniel González Castañeda
- Computer-Aided Molecular “Biosilico” Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Quito, Ecuador
| | - Ricardo Vivas-Reyes
- Grupo de Química Cuántica y Teórica, Facultad de Ciencias, Universidad de Cartagena, Cartagena de Indias, Bolívar, Colombia
- Grupo CipTec, Fundación Universitaria Tecnológico de Comfenalco, Facultad de Ingenierías, Programa de Ingeniería Industrial, Cartagena de Indias, Bolívar, Colombia
| | - Fredy Máximo Vergara
- Grupo de Química Cuántica y Teórica, Facultad de Ciencias, Universidad de Cartagena, Cartagena de Indias, Bolívar, Colombia
- Grupo CipTec, Fundación Universitaria Tecnológico de Comfenalco, Facultad de Ingenierías, Programa de Ingeniería Industrial, Cartagena de Indias, Bolívar, Colombia
| | | | - Juan A. Castillo-Garit
- Computer-Aided Molecular “Biosilico” Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Quito, Ecuador
- Unidad de Toxicología Experimental, Universidad de Ciencias Medicas de Villas Clara, Santa Clara, 50200, Cuba
| | - Facundo Pérez-Giménez
- Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, València, Spain
| | - Francisco Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, València, Spain
| | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Hai Pham-The
- Pharmacy Department, Hanoi University of Pharmacy , 13-15 Le Thonh Tong, Hoan Kiem, Hanoi, Vietnam
| | - Yolanda Vera Montenegro
- Department of Parasitology, Faculty of Veterinarian Medicinal and Zootecnic, UNAM, Mexico, Mexico
| | - Froylán Ibarra-Velarde
- Department of Parasitology, Faculty of Veterinarian Medicinal and Zootecnic, UNAM, Mexico, Mexico
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21
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Dieguez-Santana K, Pham-The H, Villegas-Aguilar PJ, Le-Thi-Thu H, Castillo-Garit JA, Casañola-Martin GM. Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database. Chemosphere 2016; 165:434-441. [PMID: 27668720 DOI: 10.1016/j.chemosphere.2016.09.041] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Revised: 09/10/2016] [Accepted: 09/12/2016] [Indexed: 06/06/2023]
Abstract
In this article, the modeling of inhibitory grown activity against Tetrahymena pyriformis is described. The 0-2D Dragon descriptors based on structural aspects to gain some knowledge of factors influencing aquatic toxicity are mainly used. Besides, it is done by some enlarged data of phenol derivatives described for the first time and composed of 358 chemicals. It overcomes the previous datasets with about one hundred compounds. Moreover, the results of the model evaluation by the parameters in the training, prediction and validation give adequate results comparable with those of the previous works. The more influential descriptors included in the model are: X3A, MWC02, MWC10 and piPC03 with positive contributions to the dependent variable; and MWC09, piPC02 and TPC with negative contributions. In a next step, a median-size database of nearly 8000 phenolic compounds extracted from ChEMBL was evaluated with the quantitative-structure toxicity relationship (QSTR) model developed providing some clues (SARs) for identification of ecotoxicological compounds. The outcome of this report is very useful to screen chemical databases for finding the compounds responsible of aquatic contamination in the biomarker used in the current work.
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Affiliation(s)
- Karel Dieguez-Santana
- Universidad Estatal Amazónica, Facultad de Ingeniería Ambiental, Paso Lateral Km 21/2 Via Napo, Puyo, Ecuador.
| | - Hai Pham-The
- Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi, Viet Nam
| | | | - Huong Le-Thi-Thu
- School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Viet Nam
| | - Juan A Castillo-Garit
- Unidad de Toxicologia Experimental, Universidad de Ciencias Médicas Dr. Serafin Ruiz de Zárate Ruiz Santa Clara, 50200, Villa Clara, Cuba
| | - Gerardo M Casañola-Martin
- Universidad Estatal Amazónica, Facultad de Ingeniería Ambiental, Paso Lateral Km 21/2 Via Napo, Puyo, Ecuador; Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi, Viet Nam; Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Spain.
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Le-Thi-Thu H, Canizares-Carmenate Y, Marrero-Ponce Y, Torrens F, A. Castillo-Garit J. Prediction of Caco-2 Cell Permeability Using Bilinear Indices and Multiple Linear Regression. LETT DRUG DES DISCOV 2015. [DOI: 10.2174/1570180812666150630183511] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Casanola-Martin GM, Le-Thi-Thu H, Marrero-Ponce Y, Castillo-Garit JA, Torrens F, Rescigno A, Abad C, Khan MTH. Tyrosinase enzyme: 1. An overview on a pharmacological target. Curr Top Med Chem 2015; 14:1494-501. [PMID: 24853560 DOI: 10.2174/1568026614666140523121427] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Revised: 02/10/2014] [Accepted: 02/11/2014] [Indexed: 11/22/2022]
Abstract
The tyrosinase enzyme (EC 1.14.18.1) is an oxidoreductase inside the general enzyme classification and is involved in the oxidation and reduction process in the epidermis. These chemical reactions that the enzyme catalyzes are of principal importance in the melanogenesis process. This process of melanogenesis is related to the melanin formation, a heteropolymer of indolic nature that provides the different tonalities in the skin and helps to the protection from the ultraviolet radiation. However, a pigment overproduction, come up by the action of the tyrosinase, can cause different disorders in the skin related to the hyperpigmentation. Several studies mainly focused on the characteristics of the enzyme have been reported. In this work, an approximation to general aspects related to this enzyme is made. Besides, it is treated the researches that have been published in the part of the biochemical anatomy dealing with diseases associated with this protein (melanogenesis), its active place and its physiological states, the molecular mechanism, the methods carried out to detect the inhibitory activity, and the used substrates.
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Affiliation(s)
| | | | | | | | | | | | | | - Mahmud Tareq Hassan Khan
- Departament de Bioquímica i Biologia Molecular, Universitat de València, E-46100 Burjassot, Spain.
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Castillo-Garit JA, Marrero-Ponce Y, Barigye SJ, Medina-Marrero R, Bernal MG, Vega JMGDL, Torrens F, Arán VJ, Pérez-Giménez F, García-Domenech R, Acevedo-Barrios R. In silicoAntibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach. J BRAZIL CHEM SOC 2015. [DOI: 10.5935/0103-5053.20150087] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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25
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Brito-Sánchez Y, Castillo-Garit JA, Le-Thi-Thu H, González-Madariaga Y, Torrens F, Marrero-Ponce Y, Rodríguez-Borges JE. Comparative study to predict toxic modes of action of phenols from molecular structures. SAR QSAR Environ Res 2013; 24:235-251. [PMID: 23437773 DOI: 10.1080/1062936x.2013.766260] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.
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Affiliation(s)
- Y Brito-Sánchez
- Unit of Computer-Aided Molecular Biosilico Discovery and Bioinformatic Research, Faculty of Chemistry-Pharmacy, Universidad Central Marta Abreu de Las Villas, Santa Clara, Cuba
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Castillo-Garit JA, del Toro-Cortés O, Kouznetsov VV, Puentes CO, Romero Bohórquez AR, Vega MC, Rolón M, Escario JA, Gómez-Barrio A, Marrero-Ponce Y, Torrens F, Abad C. Identification In Silico and In Vitro of Novel Trypanosomicidal Drug-Like Compounds. Chem Biol Drug Des 2012; 80:38-45. [DOI: 10.1111/j.1747-0285.2012.01378.x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Marrero-Ponce Y, Martínez-Albelo ER, Casañola-Martín GM, Castillo-Garit JA, Echevería-Díaz Y, Zaldivar VR, Tygat J, Borges JER, García-Domenech R, Torrens F, Pérez-Giménez F. Bond-based linear indices of the non-stochastic and stochastic edge-adjacency matrix. 1. Theory and modeling of ChemPhys properties of organic molecules. Mol Divers 2010; 14:731-53. [DOI: 10.1007/s11030-009-9201-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2009] [Accepted: 10/19/2009] [Indexed: 10/20/2022]
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Castillo-Garit JA, Marrero-Ponce Y, Torrens F, García-Domenech R, Romero-Zaldivar V. Bond-based 3D-chiral linear indices: Theory and QSAR applications to central chirality codification. J Comput Chem 2008; 29:2500-12. [DOI: 10.1002/jcc.20964] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Castillo-Garit JA, Martinez-Santiago O, Marrero-Ponce Y, Casañola-Martín GM, Torrens F. Atom-based non-stochastic and stochastic bilinear indices: Application to QSPR/QSAR studies of organic compounds. Chem Phys Lett 2008. [DOI: 10.1016/j.cplett.2008.08.094] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Castillo-Garit JA, Marrero-Ponce Y, Escobar J, Torrens F, Rotondo R. A novel approach to predict aquatic toxicity from molecular structure. Chemosphere 2008; 73:415-427. [PMID: 18597811 DOI: 10.1016/j.chemosphere.2008.05.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2008] [Revised: 04/29/2008] [Accepted: 05/07/2008] [Indexed: 05/26/2023]
Abstract
The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity using atom-based non-stochastic and stochastic linear indices. The used dataset consist of 392 benzene derivatives, separated into training and test sets, for which toxicity data to the ciliate Tetrahymena pyriformis were available. Using multiple linear regression, two statistically significant QSAR models were obtained with non-stochastic (R2=0.791 and s=0.344) and stochastic (R2=0.799 and s=0.343) linear indices. A leave-one-out (LOO) cross-validation procedure was carried out achieving values of q2=0.781 (scv=0.348) and q2=0.786 (scv=0.350), respectively. In addition, a validation through an external test set was performed, which yields significant values of Rpred2 of 0.762 and 0.797. A brief study of the influence of the statistical outliers in QSAR's model development was also carried out. Finally, our method was compared with other approaches implemented in the Dragon software achieving better results. The non-stochastic and stochastic linear indices appear to provide an interesting alternative to costly and time-consuming experiments for determining toxicity.
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Affiliation(s)
- Juan A Castillo-Garit
- Applied Chemistry Research Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
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Castillo-Garit JA, Marrero-Ponce Y, Torrens F, García-Domenech R. Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-stochastic Linear Indices. J Pharm Sci 2008; 97:1946-76. [PMID: 17724669 DOI: 10.1002/jps.21122] [Citation(s) in RCA: 56] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The in vitro determination of the permeability through cultured Caco-2 cells is the most often-used in vitro model for drug absorption. In this report, we use the largest data set of measured P(Caco-2), consisting of 157 structurally diverse compounds. Linear discriminant analysis (LDA) was used to obtain quantitative models that discriminate higher absorption compounds from those with moderate-poorer absorption. The best LDA model has an accuracy of 90.58% and 84.21% for training and test set. The percentage of good correlation, in the virtual screening of 241 drugs with the reported values of the percentage of human intestinal absorption (HIA), was greater than 81%. In addition, multiple linear regression models were developed to predict Caco-2 permeability with determination coefficients of 0.71 and 0.72. Our method compares favorably with other approaches implemented in the Dragon software, as well as other methods from the international literature. These results suggest that the proposed method is a good tool for studying the oral absorption of drug candidates.
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Affiliation(s)
- Juan A Castillo-Garit
- Applied Chemistry Research Center, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba.
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Castillo-Garit JA, Marrero-Ponce Y, Torrens F, Rotondo R. Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification. J Mol Graph Model 2007; 26:32-47. [PMID: 17110145 DOI: 10.1016/j.jmgm.2006.09.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2006] [Revised: 09/08/2006] [Accepted: 09/20/2006] [Indexed: 11/16/2022]
Abstract
Non-stochastic and stochastic 2D bilinear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. In order to evaluate the effectiveness of this novel approach in drug design we have modeled the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models had shown an accuracy of 95.65% for the training set and 100% for the external prediction set. Next the prediction of the sigma-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by multiple linear regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R(2)=0.953 and s=0.238) and stochastic (R(2)=0.961 and s=0.219) 3D-chiral bilinear indices were used. These models showed adequate predictive power (assessed by the leave-one-out cross-validation experiment) yielding values of q(2)=0.935 (s(cv)=0.259) and q(2)=0.946 (s(cv)=0.235), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The obtained results are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide a very interesting alternative to other more common 3D-QSAR descriptors.
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Affiliation(s)
- Juan A Castillo-Garit
- Applied Chemistry Research Center, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba.
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Marrero-Ponce Y, Meneses-Marcel A, Castillo-Garit JA, Machado-Tugores Y, Escario JA, Barrio AG, Pereira DM, Nogal-Ruiz JJ, Arán VJ, Martínez-Fernández AR, Torrens F, Rotondo R, Ibarra-Velarde F, Alvarado YJ. Predicting antitrichomonal activity: A computational screening using atom-based bilinear indices and experimental proofs. Bioorg Med Chem 2006; 14:6502-24. [PMID: 16875830 DOI: 10.1016/j.bmc.2006.06.016] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2006] [Revised: 06/06/2006] [Accepted: 06/08/2006] [Indexed: 11/30/2022]
Abstract
Existing Trichomonas vaginalis therapies are out of reach for most trichomoniasis people in developing countries and, where available, they are limited by their toxicity (mainly in pregnant women) and their cost. New antitrichomonal agents are needed to combat emerging metronidazole-resistant trichomoniasis and reduce the side effects associated with currently available drugs. Toward this end, atom-based bilinear indices, a new TOMOCOMD-CARDD molecular descriptor, and linear discriminant analysis (LDA) were used to discover novel, potent, and non-toxic lead trichomonacidal chemicals. Two discriminant functions were obtained with the use of non-stochastic and stochastic atom-type bilinear indices for heteroatoms and H-bonding of heteroatoms. These atomic-level molecular descriptors were calculated using a weighting scheme that includes four atomic labels, namely atomic masses, van der Waals volumes, atomic polarizabilities, and atomic electronegativities in Pauling scale. The obtained LDA-based QSAR models, using non-stochastic and stochastic indices, were able to classify correctly 94.51% (90.63%) and 93.41% (93.75%) of the chemicals in training (test) sets, respectively. They showed large Matthews' correlation coefficients (C); 0.89 (0.79) and 0.87 (0.85), for the training (test) sets, correspondingly. The result of predictions on the 15% full-out cross-validation test also evidenced the robustness and predictive power of the obtained models. In addition, canonical regression analyses corroborated the statistical quality of these models (R(can) of 0.749 and of 0.845, correspondingly); they were also used to compute biological activity canonical scores for each compound. On the other hand, a close inspection of the molecular descriptors included in both equations showed that several of these molecular fingerprints are strongly interrelated with each other. Therefore, these models were orthogonalized using the Randić orthogonalization procedure. These classification functions were then applied to find new lead antitrichomonal agents and six compounds were selected as possible active compounds by computational screening. The designed compounds were synthesized and tested for in vitro activity against T. vaginalis. Out of the six compounds that were designed, and synthesized, three molecules (chemicals VA5-5a, VA5-5c, and VA5-12b) showed high to moderate cytocidal activity at the concentration of 10 microg/ml, other two compounds (VA5-8pre and VA5-8) showed high cytocidal and cytostatic activity at the concentration of 100 microg/ml and 10 microg/ml, correspondingly, and the remaining chemical (compound VA5-5e) was inactive at these assayed concentrations. Nonetheless, these compounds possess structural features not seen in known trichomonacidal compounds and thus can serve as excellent leads for further optimization of antitrichomonal activity. The LDA-based QSAR models presented here can be considered as a computer-assisted system that could potentially significantly reduce the number of synthesized and tested compounds and increase the chance of finding new chemical entities with antitrichomonal activity.
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Affiliation(s)
- Yovani Marrero-Ponce
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, Poligon la Coma s/n (detras de Canal Nou), PO Box 22085, E-46071 Valencia, Spain.
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Castillo-Garit JA, Marrero-Ponce Y, Torrens F. Atom-based 3D-chiral quadratic indices. Part 2: Prediction of the corticosteroid-binding globulinbinding affinity of the 31 benchmark steroids data set. Bioorg Med Chem 2006; 14:2398-408. [PMID: 16325409 DOI: 10.1016/j.bmc.2005.11.024] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2005] [Revised: 11/09/2005] [Accepted: 11/09/2005] [Indexed: 10/25/2022]
Abstract
A quantitative structure-activity relationship (QSAR) study to predict the relative affinities of the steroid 'benchmark' data set to the corticosteroid-binding globulin (CBG) is described. It is shown that the 3D-chiral quadratic indices closely correlate with the measured CBG affinity values for the 31 steroids. The calculated descriptors were correlated with biological data through multiple linear regressions. Two statistically significant models were obtained when non-stochastic (R = 0.924 and s = 0.46) as well as stochastic (R = 0.929 and s = 0.46) 3D-chiral quadratic indices were used. A leave-one-out (LOO) approach to model validation is used here; the best results obtained in the cross-validation procedure with non-stochastic (q2 = 0.781) and stochastic (q2 = 0.735) 3D-chiral quadratic indices are better or similar to most of the 3D-QSAR approaches reported so far. These results support the idea that the 3D-chiral quadratic indices may be helpful in prediction of the corticosteroid-binding affinity for new compounds.
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Affiliation(s)
- Juan A Castillo-Garit
- Applied Chemistry Research Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
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Marrero-Ponce Y, Castillo-Garit JA. 3D-chiral Atom, Atom-type, and Total Non-stochastic and Stochastic Molecular Linear Indices and their Applications to Central Chirality Codification. J Comput Aided Mol Des 2005; 19:369-83. [PMID: 16231198 DOI: 10.1007/s10822-005-7575-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2005] [Accepted: 05/18/2005] [Indexed: 10/25/2022]
Abstract
Non-stochastic and stochastic 2D linear indices have been generalized to codify chemical structure information for chiral drugs, making use of a trigonometric 3D-chirality correction factor. These descriptors circumvent the inability of conventional 2D non-stochastic [Y. Marrero-Ponce. J. Chem. Inf. Comp., Sci. l 44 (2004) 2010] and stochastic [Y. Marrero-Ponce, et al. Bioorg. Med. Chem., 13 (2005) 1293] linear indices to distinguish sigma-stereoisomers. In order to test the potential of this novel approach in drug design we have modelled the angiotensin-converting enzyme inhibitory activity of perindoprilate's sigma-stereoisomers combinatorial library. Two linear discriminant analysis models, using non-stochastic and stochastic linear indices, were obtained. The models showed an accuracy of 100% and 96.65% for the training set; and 88.88% and 100% in the external test set, respectively. Canonical regression analysis corroborated the statistical quality of these models (R(can) of 0.78 and of 0.77) and was also used to compute biology activity canonical scores for each compound. After that, the prediction of the sigma-receptor antagonists of chiral 3-(3-hydroxyphenyl)piperidines by linear multiple regression analysis was carried out. Two statistically significant QSAR models were obtained when non-stochastic (R2 = 0.982 and s = 0.157) and stochastic (R2 = 0.941 and s = 0.267) 3D-chiral linear indices were used. The predictive power was assessed by the leave-one-out cross-validation experiment, yielding values of q2 = 0.982 (s(cv) = 0.186) and q2 = 0.90 (s(cv) = 0.319), respectively. Finally, the prediction of the corticosteroid-binding globulin binding affinity of steroids set was performed. The best results obtained in the cross-validation procedure with non-stochastic (q2 = 0.904) and stochastic (q2 = 0.88) 3D-chiral linear indices are rather similar to most of the 3D-QSAR approaches reported so far. The validation of this method was achieved by comparison with previous reports applied to the same data set. The non-stochastic and stochastic 3D-chiral linear indices appear to provide an interesting alternative to other more common 3D-QSAR descriptors.
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Affiliation(s)
- Yovani Marrero-Ponce
- Department of Pharmacy, Faculty of Chemical-Pharmacy, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
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Marrero-Ponce Y, Medina-Marrero R, Castillo-Garit JA, Romero-Zaldivar V, Torrens F, Castro EA. Protein linear indices of the ‘macromolecular pseudograph α-carbon atom adjacency matrix’ in bioinformatics. Part 1: Prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor. Bioorg Med Chem 2005; 13:3003-15. [PMID: 15781410 DOI: 10.1016/j.bmc.2005.01.062] [Citation(s) in RCA: 48] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2004] [Revised: 01/28/2005] [Accepted: 01/31/2005] [Indexed: 10/25/2022]
Abstract
A novel approach to bio-macromolecular design from a linear algebra point of view is introduced. A protein's total (whole protein) and local (one or more amino acid) linear indices are a new set of bio-macromolecular descriptors of relevance to protein QSAR/QSPR studies. These amino-acid level biochemical descriptors are based on the calculation of linear maps on Rn[f k(xmi):Rn-->Rn] in canonical basis. These bio-macromolecular indices are calculated from the kth power of the macromolecular pseudograph alpha-carbon atom adjacency matrix. Total linear indices are linear functional on Rn. That is, the kth total linear indices are linear maps from Rn to the scalar R[f k(xm):Rn-->R]. Thus, the kth total linear indices are calculated by summing the amino-acid linear indices of all amino acids in the protein molecule. A study of the protein stability effects for a complete set of alanine substitutions in the Arc repressor illustrates this approach. A quantitative model that discriminates near wild-type stability alanine mutants from the reduced-stability ones in a training series was obtained. This model permitted the correct classification of 97.56% (40/41) and 91.67% (11/12) of proteins in the training and test set, respectively. It shows a high Matthews correlation coefficient (MCC=0.952) for the training set and an MCC=0.837 for the external prediction set. Additionally, canonical regression analysis corroborated the statistical quality of the classification model (Rcanc=0.824). This analysis was also used to compute biological stability canonical scores for each Arc alanine mutant. On the other hand, the linear piecewise regression model compared favorably with respect to the linear regression one on predicting the melting temperature (tm) of the Arc alanine mutants. The linear model explains almost 81% of the variance of the experimental tm (R=0.90 and s=4.29) and the LOO press statistics evidenced its predictive ability (q2=0.72 and scv=4.79). Moreover, the TOMOCOMD-CAMPS method produced a linear piecewise regression (R=0.97) between protein backbone descriptors and tm values for alanine mutants of the Arc repressor. A break-point value of 51.87 degrees C characterized two mutant clusters and coincided perfectly with the experimental scale. For this reason, we can use the linear discriminant analysis and piecewise models in combination to classify and predict the stability of the mutant Arc homodimers. These models also permitted the interpretation of the driving forces of such folding process, indicating that topologic/topographic protein backbone interactions control the stability profile of wild-type Arc and its alanine mutants.
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Affiliation(s)
- Yovani Marrero-Ponce
- Department of Pharmacy, Faculty of Chemical-Pharmacy, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba.
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Marrero-Ponce Y, Castillo-Garit JA, Olazabal E, Serrano HS, Morales A, Castañedo N, Ibarra-Velarde F, Huesca-Guillen A, Jorge E, del Valle A, Torrens F, Castro EA. Tomocomd-Cardd, a novel approach for computer-aided ? rational? drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds. J Comput Aided Mol Des 2005; 18:615-34. [PMID: 15849993 DOI: 10.1007/s10822-004-5171-y] [Citation(s) in RCA: 53] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.37% of compounds in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. The QSAR model correctly classified 88.18% of compounds in this external prediction set. A second model was performed to outline some conclusions about the possible modes of action of anthelmintic drugs. This model permits the correct classification of 94.52% of compounds in the training set, and 80.00% of good global classification in the external prediction set. After that, the developed model was used in virtual in silico screening and several compounds from the Merck Index, Negwer's handbook and Goodman and Gilman were identified by models as anthelmintic. Finally, the experimental assay of one organic chemical (G-1) by an in vivo test coincides fairly well (100%) with model predictions. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.
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Affiliation(s)
- Yovani Marrero-Ponce
- Department of Pharmacy, Faculty of Chemical-Pharmacy, Central University of Las Villas, Santa Clara 54830, Villa Clara, Cuba.
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Marrero-Ponce Y, Castillo-Garit JA, Olazabal E, Serrano HS, Morales A, Castañedo N, Ibarra-Velarde F, Huesca-Guillen A, Sánchez AM, Torrens F, Castro EA. Atom, atom-type and total molecular linear indices as a promising approach for bioorganic and medicinal chemistry: theoretical and experimental assessment of a novel method for virtual screening and rational design of new lead anthelmintic. Bioorg Med Chem 2005; 13:1005-20. [PMID: 15670908 DOI: 10.1016/j.bmc.2004.11.040] [Citation(s) in RCA: 73] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2004] [Revised: 11/17/2004] [Accepted: 11/22/2004] [Indexed: 11/21/2022]
Abstract
Helminth infections are a medical problem in the world nowadays. In this paper a novel atom-level chemical descriptor has been applied to estimate the anthelmintic activity. Total and local linear indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The discriminant model has an accuracy of 90.11% in the training set, with a high Matthews' correlation coefficient (MCC=0.80). To assess the robustness and predictive power of the obtained model, internal (leave-n-out) and external validation process was performed. The QSAR model correctly classified 88.55% of compounds in this external prediction set, yielding a MCC of 0.77. Another LDA model was carried out to outline some conclusions about the possible modes of action of anthelmintic drugs. It has an accuracy of 93.50% in the training set, and 80.00% in the external prediction set. After that, the developed model was used in the virtual--in silico--screening and several compounds from the Merck Index, Negwer's Handbook and Goodman and Gilman were identified by the model as anthelmintic. Finally, the experimental assay of an organic chemical (a furylethylene derivative) by an in vivo test permits us to carry out an assessment of the model. An accuracy of 100% with the theoretical predictions was observed. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.
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Affiliation(s)
- Yovani Marrero-Ponce
- Department of Pharmacy, Faculty of Chemical-Pharmacy, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba.
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