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López Sacerio A, Tejeda Ramón MC, Morales Helguera A, Pérez Castillo Y, Cruz Rodríguez J, Guerra Rodríguez JF, Falanga A. Validation of venous thromboembolism predictive model in hematologic malignancies. Ann Hematol 2023; 102:3613-3620. [PMID: 37782372 DOI: 10.1007/s00277-023-05463-4] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/17/2023] [Indexed: 10/03/2023]
Abstract
Although several scores stratify venous thromboembolism (VTE) risk in solid tumors, hematologic malignancies (HM) are underrepresented. To develop an internal and external validation of a logistic regression model to predict VTE risk in hospitalized HM patients. Validation of the existing VTE predictive model was performed through a prospective case-control study in 496 hospitalized HM patients between December 2010 and 2020 at the Arnaldo Milián University Hospital, Cuba. The predictive model designed with data from 285 patients includes 5 predictive factors: hypercholesterolemia, tumoral activity, use of thrombogenic drugs, diabetes mellitus, and immobilization. The model was internally validated using bootstrap analysis. External validation was realized in a prospective cohort of 211 HM patients. The predictive model had a 76.4% negative predictive value (NPV) and an 81.7% positive predictive value (PPV) in the bootstrapping validation. The area under curve (AUC) in the bootstrapping set was 0.838. Accuracy was 80.1% and 82.9% in the internal and external validation, respectively. In the external validation, the model produced 89.7% of NPV, 67.7% of PPV, 74.6% of sensitivity, and 86.2% of specificity. The AUC in the external validation was 0.900. VTE predictive model is a reproducible and simple tool with good accuracy and discrimination.
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Affiliation(s)
| | | | | | - Yunierkis Pérez Castillo
- Bio-Chemoinformatics Group and School of Physical and Mathematical Sciences, University of Las Américas, Quito, Ecuador
| | | | | | - Anna Falanga
- Department of Transfusion Medicine and Hematology, Hospital Papa Giovanni XXIII, Bergamo, Italy
- University of Milan Bicocca, Monza, Italy
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Kamdem Tamo A, Doench I, Morales Helguera A, Hoenders D, Walther A, Madrazo AO. Biodegradation of Crystalline Cellulose Nanofibers by Means of Enzyme Immobilized-Alginate Beads and Microparticles. Polymers (Basel) 2020; 12:E1522. [PMID: 32660071 PMCID: PMC7407417 DOI: 10.3390/polym12071522] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 06/29/2020] [Accepted: 07/03/2020] [Indexed: 12/20/2022] Open
Abstract
Recent advances in nanocellulose technology have revealed the potential of crystalline cellulose nanofibers to reinforce materials which are useful for tissue engineering, among other functions. However, the low biodegradability of nanocellulose can possess some problems in biomedical applications. In this work, alginate particles with encapsulated enzyme cellulase extracted from Trichoderma reesei were prepared for the biodegradation of crystalline cellulose nanofibers, which carrier system could be incorporated in tissue engineering biomaterials to degrade the crystalline cellulose nanoreinforcement in situ and on-demand during tissue regeneration. Both alginate beads and microparticles were processed by extrusion-dropping and inkjet-based methods, respectively. Processing parameters like the alginate concentration, concentration of ionic crosslinker Ca2+, hardening time, and ionic strength of the medium were varied. The hydrolytic activity of the free and encapsulated enzyme was evaluated for unmodified (CNFs) and TEMPO-oxidized cellulose nanofibers (TOCNFs) in suspension (heterogeneous conditions); in comparison to solubilized cellulose derivatives (homogeneous conditions). The enzymatic activity was evaluated for temperatures between 25-75 °C, pH range from 3.5 to 8.0 and incubation times until 21 d. Encapsulated cellulase in general displayed higher activity compared to the free enzyme over wider temperature and pH ranges and for longer incubation times. A statistical design allowed optimizing the processing parameters for the preparation of enzyme-encapsulated alginate particles presenting the highest enzymatic activity and sphericity. The statistical analysis yielded the optimum particles characteristics and properties by using a formulation of 2% (w/v) alginate, a coagulation bath of 0.2 M CaCl2 and a hardening time of 1 h. In homogeneous conditions the highest catalytic activity was obtained at 55 °C and pH 4.8. These temperature and pH values were considered to study the biodegradation of the crystalline cellulose nanofibers in suspension. The encapsulated cellulase preserved its activity for several weeks over that of the free enzyme, which latter considerably decreased and practically showed deactivation after just 10 d. The alginate microparticles with their high surface area-to-volume ratio effectively allowed the controlled release of the encapsulated enzyme and thereby the sustained hydrolysis of the cellulose nanofibers. The relative activity of cellulase encapsulated in the microparticles leveled-off at around 60% after one day and practically remained at that value for three weeks.
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Affiliation(s)
- Arnaud Kamdem Tamo
- Institute of Microsystems Engineering IMTEK, Laboratory for Sensors, University of Freiburg, 79110 Freiburg, Germany; (A.K.T.); (I.D.)
- Freiburg Materials Research Center FMF, University of Freiburg, 79104 Freiburg, Germany; (D.H.); (A.W.)
- Freiburg Center for Interactive Materials and Bioinspired Technologies FIT, University of Freiburg, 79110 Freiburg, Germany
| | - Ingo Doench
- Institute of Microsystems Engineering IMTEK, Laboratory for Sensors, University of Freiburg, 79110 Freiburg, Germany; (A.K.T.); (I.D.)
- Freiburg Materials Research Center FMF, University of Freiburg, 79104 Freiburg, Germany; (D.H.); (A.W.)
- Freiburg Center for Interactive Materials and Bioinspired Technologies FIT, University of Freiburg, 79110 Freiburg, Germany
| | - Aliuska Morales Helguera
- Chemical Bioactive Center CBQ, Molecular Simulation and Drug Design Group, Central University of Las Villas, Santa Clara 54830, Cuba;
| | - Daniel Hoenders
- Freiburg Materials Research Center FMF, University of Freiburg, 79104 Freiburg, Germany; (D.H.); (A.W.)
- Freiburg Center for Interactive Materials and Bioinspired Technologies FIT, University of Freiburg, 79110 Freiburg, Germany
- Institute for Macromolecular Chemistry, University of Freiburg, 79104 Freiburg, Germany
| | - Andreas Walther
- Freiburg Materials Research Center FMF, University of Freiburg, 79104 Freiburg, Germany; (D.H.); (A.W.)
- Freiburg Center for Interactive Materials and Bioinspired Technologies FIT, University of Freiburg, 79110 Freiburg, Germany
- Institute for Macromolecular Chemistry, University of Freiburg, 79104 Freiburg, Germany
| | - Anayancy Osorio Madrazo
- Institute of Microsystems Engineering IMTEK, Laboratory for Sensors, University of Freiburg, 79110 Freiburg, Germany; (A.K.T.); (I.D.)
- Freiburg Materials Research Center FMF, University of Freiburg, 79104 Freiburg, Germany; (D.H.); (A.W.)
- Freiburg Center for Interactive Materials and Bioinspired Technologies FIT, University of Freiburg, 79110 Freiburg, Germany
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Perez-Castillo Y, Helguera AM, Cordeiro MNDS, Tejera E, Paz-Y-Mino C, Sanchez-Rodriguez A, Borges F, Cruz-Monteagudo M. Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson's Disease Dual Target Ligands. Curr Neuropharmacol 2018; 15:1107-1116. [PMID: 28067172 PMCID: PMC5725543 DOI: 10.2174/1570159x15666170109143757] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2015] [Revised: 01/18/2016] [Accepted: 11/03/2016] [Indexed: 11/22/2022] Open
Affiliation(s)
- Yunierkis Perez-Castillo
- Seccion Fisico Quimica y Matematicas, Departamento de Quimica, Universidad Tecnica Particular de Loja, San Cayetano Alto S/N, EC1101608 Loja, Ecuador.,Molecular Simulation and Drug Design Group, Centro de Bioactivos Quimicos (CBQ), Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Cuba
| | - Aliuska Morales Helguera
- Molecular Simulation and Drug Design Group, Centro de Bioactivos Quimicos (CBQ), Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Cuba
| | - M Natalia D S Cordeiro
- REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Eduardo Tejera
- Instituto de Investigaciones Biomedicas (IIB), Universidad de Las Americas, 170513 Quito, Ecuador
| | - Cesar Paz-Y-Mino
- Instituto de Investigaciones Biomedicas (IIB), Universidad de Las Americas, 170513 Quito, Ecuador
| | - Aminael Sanchez-Rodriguez
- Departamento de Ciencias Naturales, Universidad Tecnica Particular de Loja, Calle Paris S/N, EC1101608 Loja, Ecuador
| | - Fernanda Borges
- CIQUP/Departamento de Quimica e Bioquimica, Faculdade de Ciencias, Universidade do Porto, Porto 4169-007, Portugal
| | - Maykel Cruz-Monteagudo
- Instituto de Investigaciones Biomedicas (IIB), Universidad de Las Americas, 170513 Quito, Ecuador.,CIQUP/Departamento de Quimica e Bioquimica, Faculdade de Ciencias, Universidade do Porto, Porto 4169-007, Portugal
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Cruz-Monteagudo M, Borges F, Cordeiro MNDS, Helguera AM, Tejera E, Paz-Y-Mino C, Sanchez-Rodriguez A, Perera-Sardina Y, Perez-Castillo Y. Chemoinformatics Profiling of the Chromone Nucleus as a MAO-B/A2AAR Dual Binding Scaffold. Curr Neuropharmacol 2018; 15:1117-1135. [PMID: 28093976 PMCID: PMC5725544 DOI: 10.2174/1570159x15666170116145316] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 03/14/2016] [Accepted: 11/03/2016] [Indexed: 11/22/2022] Open
Abstract
Background: In the context of the current drug discovery efforts to find disease modifying therapies for Parkinson´s disease (PD) the current single target strategy has proved inefficient. Consequently, the search for multi-potent agents is attracting more and more attention due to the multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the monoamine oxidase B (MAO-B), as well as adenosine A2A receptor (A2AAR) blockade, as a promising approach to prevent the neurodegeneration involved in PD. Currently, only two chemical scaffolds has been proposed as potential dual MAO-B inhibitors/A2AAR antagonists (caffeine derivatives and benzothiazinones). Methods: In this study, we conduct a series of chemoinformatics analysis in order to evaluate and advance the potential of the chromone nucleus as a MAO-B/A2AAR dual binding scaffold. Results: The information provided by SAR data mining analysis based on network similarity graphs and molecular docking studies support the suitability of the chromone nucleus as a potential MAO-B/A2AAR dual binding scaffold. Additionally, a virtual screening tool based on a group fusion similarity search approach was developed for the prioritization of potential MAO-B/A2AAR dual binder candidates. Among several data fusion schemes evaluated, the MEAN-SIM and MIN-RANK GFSS approaches demonstrated to be efficient virtual screening tools. Then, a combinatorial library potentially enriched with MAO-B/A2AAR dual binding chromone derivatives was assembled and sorted by using the MIN-RANK and then the MEAN-SIM GFSS VS approaches. Conclusion: The information and tools provided in this work represent valuable decision making elements in the search of novel chromone derivatives with a favorable dual binding profile as MAO-B inhibitors and A2AAR antagonists with the potential to act as a disease-modifying therapeutic for Parkinson´s disease.
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Affiliation(s)
- Maykel Cruz-Monteagudo
- CIQUP/Departamento de Quimica e Bioquimica, Faculdade de Ciencias, Universidade do Porto, Porto 4169-007, Portugal.,Instituto de Investigaciones Biomedicas (IIB), Universidad de Las Americas, 170513 Quito, Ecuador
| | - Fernanda Borges
- CIQUP/Departamento de Quimica e Bioquimica, Faculdade de Ciencias, Universidade do Porto, Porto 4169-007, Portugal
| | - M Natalia D S Cordeiro
- REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Aliuska Morales Helguera
- Molecular Simulation and Drug Design Group, Centro de Bioactivos Quimicos (CBQ), Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Cuba
| | - Eduardo Tejera
- Instituto de Investigaciones Biomedicas (IIB), Universidad de Las Americas, 170513 Quito, Ecuador
| | - Cesar Paz-Y-Mino
- Instituto de Investigaciones Biomedicas (IIB), Universidad de Las Americas, 170513 Quito, Ecuador
| | - Aminael Sanchez-Rodriguez
- Departamento de Ciencias Naturales, Universidad Tecnica Particular de Loja, Calle Paris S/N, EC1101608 Loja, Ecuador
| | - Yunier Perera-Sardina
- Departamento de Ciencias Quimicas, Facultad de Ciencias Exactas, Universidad Andres Bello, Santiago de Chile, Chile
| | - Yunierkis Perez-Castillo
- Molecular Simulation and Drug Design Group, Centro de Bioactivos Quimicos (CBQ), Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Cuba.,Seccion Fisico Quimica y Matematicas, Departamento de Quimica, Universidad Tecnica Particular de Loja, San Cayetano Alto S/N, EC1101608 Loja, Ecuador
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Morales Helguera A, Perez-Castillo Y, Natália D.S. Cordeiro M, Tejera E, Paz-y-Miño C, Sánchez-Rodríguez A, Teijeira M, Ancede-Gallardo E, Cagide F, Borges F, Cruz-Monteagudo M. Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A2A Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors. Curr Pharm Des 2016; 22:3082-96. [DOI: 10.2174/1381612822666160302103542] [Citation(s) in RCA: 10] [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] [Received: 01/06/2016] [Accepted: 03/01/2016] [Indexed: 11/22/2022]
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Bonet I, Franco-Montero P, Rivero V, Teijeira M, Borges F, Uriarte E, Morales Helguera A. Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists. J Chem Inf Model 2013; 53:3140-55. [PMID: 24289249 DOI: 10.1021/ci300516w] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.
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Affiliation(s)
- Isis Bonet
- Escuela de Ingeniería de Antioquia, Envigado, 055428 Antioquia, Colombia
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Helguera AM, Pérez-Garrido A, Gaspar A, Reis J, Cagide F, Vina D, Cordeiro MNDS, Borges F. Combining QSAR classification models for predictive modeling of human monoamine oxidase inhibitors. Eur J Med Chem 2012. [PMID: 23207409 DOI: 10.1016/j.ejmech.2012.10.035] [Citation(s) in RCA: 42] [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: 01/01/2023]
Abstract
Due to their role in the metabolism of monoamine neurotransmitters, MAO-A and MAO-B present a significant pharmacological interest. For instance the inhibitors of human MAO-B are considered useful tools for the treatment of Parkinson Disease. Therefore, the rational design and synthesis of new MAOs inhibitors is considered of great importance for the development of new and more effective treatments of Parkinson Disease. In this work, Quantitative Structure Activity Relationships (QSAR) has been developed to predict the human MAO inhibitory activity and selectivity. The first step was the selection of a suitable dataset of heterocyclic compounds that include chromones, coumarins, chalcones, thiazolylhydrazones, etc. These compounds were previously synthesized in one of our laboratories, or elsewhere, and their activities measured by the same assays and for the same laboratory staff. Applying linear discriminant analysis to data derived from a variety of molecular representations and feature selection algorithms, reliable QSAR models were built which could be used to predict for test compounds the inhibitory activity and selectivity toward human MAO. This work also showed how several QSAR models can be combined to make better predictions. The final models exhibit significant statistics, interpretability, as well as displaying predictive power on an external validation set made up of chromone derivatives with unknown activity (that are being reported here for first time) synthesized by our group, and coumarins recently reported in the literature.
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Affiliation(s)
- Aliuska Morales Helguera
- CIQ, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal.
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Pérez-Garrido A, Helguera AM, Morillas Ruiz JM, Zafrilla Rentero P. Topological sub-structural molecular design approach: Radical scavenging activity. Eur J Med Chem 2012; 49:86-94. [DOI: 10.1016/j.ejmech.2011.12.030] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2011] [Revised: 12/14/2011] [Accepted: 12/20/2011] [Indexed: 12/01/2022]
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Pérez-Garrido A, Helguera AM, Borges F, Cordeiro MNDS, Rivero V, Escudero AG. Two new parameters based on distances in a receiver operating characteristic chart for the selection of classification models. J Chem Inf Model 2011; 51:2746-59. [PMID: 21923162 DOI: 10.1021/ci2003076] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There are several indices that provide an indication of different types on the performance of QSAR classification models, being the area under a Receiver Operating Characteristic (ROC) curve still the most powerful test to overall assess such performance. All ROC related parameters can be calculated for both the training and test sets, but, nevertheless, neither of them constitutes an absolute indicator of the classification performance by themselves. Moreover, one of the biggest drawbacks is the computing time needed to obtain the area under the ROC curve, which naturally slows down any calculation algorithm. The present study proposes two new parameters based on distances in a ROC curve for the selection of classification models with an appropriate balance in both training and test sets, namely the following: the ROC graph Euclidean distance (ROCED) and the ROC graph Euclidean distance corrected with Fitness Function (FIT(λ)) (ROCFIT). The behavior of these indices was observed through the study on the mutagenicity for four genotoxicity end points of a number of nonaromatic halogenated derivatives. It was found that the ROCED parameter gets a better balance between sensitivity and specificity for both the training and prediction sets than other indices such as the Matthews correlation coefficient, the Wilk's lambda, or parameters like the area under the ROC curve. However, when the ROCED parameter was used, the follow-on linear discriminant models showed the lower statistical significance. But the other parameter, ROCFIT, maintains the ROCED capabilities while improving the significance of the models due to the inclusion of FIT(λ).
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Affiliation(s)
- Alfonso Pérez-Garrido
- Cátedra de Ingeniería y Toxicología Ambiental, Universidad Cátolica San Antonio, Guadalupe, Murcia, Spain
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Saíz-Urra L, Cabrera Pérez MÁ, Helguera AM, Froeyen M. Combining molecular docking and QSAR studies for modelling the antigyrase activity of cyclothialidine derivatives. Eur J Med Chem 2011; 46:2736-47. [PMID: 21530019 DOI: 10.1016/j.ejmech.2011.03.061] [Citation(s) in RCA: 10] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2010] [Revised: 03/21/2011] [Accepted: 03/29/2011] [Indexed: 11/20/2022]
Abstract
DNA gyrase is a well-established antibacterial target consisting of two subunits, GyrA and GyrB, in a heterodimer A(2)B(2), where GyrB catalyzes the hydrolysis of ATP. Cyclothialidine (Ro 09-1437) has been considered as a promising inhibitor whose modifications might lead to more potent compounds against the enzyme. We report here for the first time, QSAR studies regarding to ATPase inhibitors of DNA Gyrase. 1D, 2D and 3D descriptors from DRAGON software were used on a set of 42 cyclothialidine derivatives. Based on the core of the cyclothialidine GR122222X, different conformations were created by using OMEGA. FRED was used to dock these conformers in the cavity of the GyrB subunit to select the best conformations, paying special attention to the 12-membered ring. Three QSAR models were developed considering the dimension of the descriptors. The models were robust, predictive and good in statistical significance, over 70% of the experimental variance was explained. Interpretability of the models was possible by extracting the SAR(s) encoded by these predictive models. Analyzing the compound-enzyme interactions of the complexes obtained by docking allowed us to increase the reliability of the information obtained for the QSAR models.
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Affiliation(s)
- Liane Saíz-Urra
- Centro de Bioactivos Quimicos, Universidad Central "Marta Abreu" de las Villas, Carretera a Camajuani Km 5.5, Santa Clara (54830), Villa Clara, Cuba.
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Helguera AM, Pérez-Machado G, Cordeiro MNDS, Combes RD. Quantitative structure-activity relationship modelling of the carcinogenic risk of nitroso compounds using regression analysis and the TOPS-MODE approach. SAR QSAR Environ Res 2010; 21:277-304. [PMID: 20544552 DOI: 10.1080/10629361003773930] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Worldwide, legislative and governmental efforts are focusing on establishing simple screening tools for identifying those chemicals most likely to cause adverse effects without experimentally testing all chemicals of regulatory concern. This is because even the most basic biological testing of compounds of concern, apart from requiring a huge number of test animals, would be neither resource nor time effective. Thus, alternative approaches such as the one proposed here, quantitative structure-activity relationship (QSAR) modelling, are increasingly being used for identifying the potential health hazards and subsequent regulation of new industrial chemicals. This paper follows up on our earlier work that demonstrated the use of the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach to QSAR modelling for predictions of the carcinogenic potency of nitroso compounds. The data set comprises 56 nitroso compounds which have been bio-assayed in female rats and administered by the oral water route. The QSAR model was able to account for about 81% of the variance in the experimental activity and exhibited good cross-validation statistics. A reasonable interpretation of the TOPS-MODE descriptors was achieved by means of bond contributions, which in turn afforded the recognition of structural alerts (SAs) regarding carcinogenicity. A comparison of the SAs obtained from different data sets showed that experimental factors, such as the sex and the oral administration route, exert a major influence on the carcinogenicity of nitroso compounds. The present and previous QSAR models combined together provide a reliable tool for estimating the carcinogenic potency of yet untested nitroso compounds and they should allow the identification of SAs, which can be used as the basis of prediction systems for the rodent carcinogenicity of these compounds.
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Affiliation(s)
- A M Helguera
- Department of Chemistry, Central University of Las Villas, Santa Clara, Villa Clara, Cuba.
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Pérez-Garrido A, Helguera AM, Rodríguez FG, Cordeiro MNDS. QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds. Dent Mater 2010; 26:397-415. [PMID: 20122717 DOI: 10.1016/j.dental.2009.11.158] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2009] [Revised: 09/08/2009] [Accepted: 11/26/2009] [Indexed: 11/17/2022]
Abstract
OBJECTIVE The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals. METHODS Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software. RESULTS For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers. SIGNIFICANCE These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk.
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Affiliation(s)
- Alfonso Pérez-Garrido
- Enviromental Engineering and Toxicology Dpt., Catholic University of San Antonio, Guadalupe, Murcia, Spain.
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Pérez-Garrido A, Helguera AM, López GC, Cordeiro MNDS, Escudero AG. A topological substructural molecular design approach for predicting mutagenesis end-points of alpha, beta-unsaturated carbonyl compounds. Toxicology 2009; 268:64-77. [PMID: 20004227 DOI: 10.1016/j.tox.2009.11.023] [Citation(s) in RCA: 19] [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: 10/20/2009] [Revised: 11/29/2009] [Accepted: 11/30/2009] [Indexed: 11/18/2022]
Abstract
Chemically reactive, alpha, beta-unsaturated carbonyl compounds are common environmental pollutants able to produce a wide range of adverse effects, including, e.g. mutagenicity. This toxic property can often be related to chemical structure, in particular to specific molecular substructures or fragments (alerts), which can then be used in specialized software or expert systems for predictive purposes. In the past, there have been many attempts to predict the mutagenicity of alpha, beta-unsaturated carbonyl compounds through quantitative structure activity relationships (QSAR) but considering only one exclusive endpoint: the Ames test. Besides, even though those studies give a comprehensive understanding of the phenomenon, they do not provide substructural information that could be useful forward improving expert systems based on structural alerts (SAs). This work reports an evaluation of classification models to probe the mutagenic activity of alpha, beta-unsaturated carbonyl compounds over two endpoints--the Ames and mammalian cell gene mutation tests--based on linear discriminant analysis along with the topological Substructure molecular design (TOPS-MODE) approach. The obtained results showed the better ability of the TOPS-MODE approach in flagging structural alerts for the mutagenicity of these compounds compared to the expert system TOXTREE. Thus, the application of the present QSAR models can aid toxicologists in risk assessment and in prioritizing testing, as well as in the improvement of expert systems, such as the TOXTREE software, where SAs are implemented.
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Affiliation(s)
- Alfonso Pérez-Garrido
- Enviromental Engineering and Toxicology Dpt., Catholic University of San Antonio, Guadalupe, Murcia, C.P. 30107, Spain.
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Pérez-Garrido A, Helguera AM, Cordeiro MND, Escudero AG. QSPR modelling with the topological substructural molecular design approach: β-cyclodextrin complexation. J Pharm Sci 2009; 98:4557-76. [DOI: 10.1002/jps.21747] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Castillo-González D, Cabrera-Pérez MA, Pérez-González M, Morales Helguera A, Durán-Martínez A. Prediction of telomerase inhibitory activity for acridinic derivatives based on chemical structure. Eur J Med Chem 2009; 44:4826-40. [PMID: 19726112 DOI: 10.1016/j.ejmech.2009.07.029] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2009] [Revised: 06/02/2009] [Accepted: 07/23/2009] [Indexed: 01/22/2023]
Abstract
Telomerase is a reverse transcriptase enzyme that activates in more than 85% of cancer cells and it is associated with the acquisition of a malignant phenotype. Some experimental strategies have been suggested in order to avoid the enzyme effect on unstopped telomere elongation. One of them, the stabilization of the G-quartet structure, has been widely studied. Nevertheless, no QSAR studies to predict this activity have been developed. In the present study a classification model was carried out to identify, through molecular descriptors with structural fragments and groups information, those acridinic derivatives with better inhibitory concentration on telomerase enzyme. A linear discriminant model was developed to classify a data set of 90 acridinic derivatives (48 more potent derivatives with IC(50) < 1 microM and 42 less potent with IC(50) > or = 1 microM). The final model fit the data with sensitivity of 87.50% and specificity of 82.85%, for a final accuracy of 85.33%. The predictive ability of the model was assessed by a prediction set (15 compounds of 90% and 82.29% of prediction accuracy); a tenfold full cross-validation procedure (removing 15 compounds in each cycle, 84.80% of good prediction) and the prediction of inhibitory concentration on telomerase enzyme for external data of 10 novel acridines (90% of good prediction). The results of this study suggest that the established model has a strong predictive ability and can be prospectively used in the molecular design and action mechanism analysis of this kind of compounds with anticancer activity.
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Pérez-Garrido A, Helguera AM, Guillén AA, Cordeiro MND, Escudero AG. Convenient QSAR model for predicting the complexation of structurally diverse compounds with β-cyclodextrins. Bioorg Med Chem 2009; 17:896-904. [DOI: 10.1016/j.bmc.2008.11.040] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2008] [Revised: 11/04/2008] [Accepted: 11/12/2008] [Indexed: 10/21/2022]
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Morales Helguera A, Pérez González M, Dias Soeiro Cordeiro MN, Cabrera Pérez MÁ. Quantitative Structure−Carcinogenicity Relationship for Detecting Structural Alerts in Nitroso Compounds: Species, Rat; Sex, Female; Route of Administration, Gavage. Chem Res Toxicol 2008; 21:633-42. [DOI: 10.1021/tx700336n] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Aliuska Morales Helguera
- Department of Chemistry and Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba, and REQUIMTE, Chemistry Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Maykel Pérez González
- Department of Chemistry and Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba, and REQUIMTE, Chemistry Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Maria Natália Dias Soeiro Cordeiro
- Department of Chemistry and Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba, and REQUIMTE, Chemistry Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Miguel Ángel Cabrera Pérez
- Department of Chemistry and Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba, and REQUIMTE, Chemistry Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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Helguera AM, González MP, D S Cordeiro MN, Pérez MAC. Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds. Toxicol Appl Pharmacol 2007; 221:189-202. [PMID: 17477948 DOI: 10.1016/j.taap.2007.02.021] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.3] [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: 01/05/2007] [Revised: 02/16/2007] [Accepted: 02/21/2007] [Indexed: 02/01/2023]
Abstract
Prevention of environmentally induced cancers is a major health problem of which solutions depend on the rapid and accurate screening of potential chemical hazards. Lately, theoretical approaches such as the one proposed here - Quantitative Structure-Activity Relationship (QSAR) - are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the Topological Substructural Molecular Design (TOPS-MODE) approach, aiming at predicting the rodent carcinogenicity of a set of nitroso-compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises nitrosoureas (14 chemicals), N-nitrosamines (18 chemicals) C-nitroso-compounds (1 chemical), nitrosourethane (1 chemical) and nitrosoguanidine (1 chemical), which have been bioassayed in male rat using gavage as the route of administration. Here we are especially concerned in gathering the role of both parameters on the carcinogenic activity of this family of compounds. First, the regression model was derived, upon removal of one identified nitrosamine outlier, and was able to account for more than 84% of the variance in the experimental activity. Second, the TOPS-MODE approach afforded the bond contributions -- expressed as fragment contributions to the carcinogenic activity -- that can be interpreted and provide tools for better understanding the mechanisms of carcinogenesis. Finally, and most importantly, we demonstrate the potentialities of this approach towards the recognition of structural alerts for carcinogenicity predictions.
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Affiliation(s)
- Aliuska Morales Helguera
- Department of Chemistry, Faculty of Chemistry and Pharmacy, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba
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Helguera AM, Rodríguez-Borges JE, García-Mera X, Fernández F, Cordeiro MNDS. Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set. J Med Chem 2007; 50:1537-45. [PMID: 17341060 DOI: 10.1021/jm061445m] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [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: 11/28/2022]
Abstract
The cancer research community has begun to address the in silico modeling approaches, such as quantitative structure-activity relationships (QSAR), as an important alternative tool for screening potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Here, we report a systematic evaluation of classification models to probe anticancer activity, based on linear discriminant analysis along with 2D-molecular descriptors. This strategy afforded a final QSAR model with very good overall accuracy and predictability on external data. Finally, we search for similarities between the natural nucleosides, present in RNA/DNA, and the active nucleosides well-predicted by the model. The structural information then gathered and the QSAR model per se shall aid in the future design of novel potent anticancer nucleosides.
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Affiliation(s)
- Aliuska Morales Helguera
- REQUIMTE and CIQ, Department of Chemistry, University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal
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González MP, Terán C, Teijeira M, Helguera AM. QSAR Studies Using Radial Distribution Function for Predicting A1 Adenosine Receptors Agonists. Bull Math Biol 2006; 69:347-59. [PMID: 17061056 DOI: 10.1007/s11538-006-9127-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [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] [Accepted: 03/29/2006] [Indexed: 10/24/2022]
Abstract
The radial distribution function (RDF) approach has been applied to the study of the A(1) adenosine receptors agonist effect of 32 adenosine analogues. A model able to describe more than 79% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the three different approaches, including the use of 2D autocorrelations, BCUT and 3D-MORSE descriptors were able to explain more than 72% of the variance in the mentioned property with the same number of variables in the equation. In addition, we established a comparison with other models reported by us for this receptor subtype using this data set, and the RDF descriptors continue getting the best results.
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Affiliation(s)
- Maykel Pérez González
- Service Unit, Experimental Sugar Cane Station Villa Clara-Cienfuegos, Ranchuelo, C.P. 53100 Villa Clara, Cuba.
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González MP, Terán C, Teijeira M, Helguera AM. Quantitative Structure Activity Relationships as Useful Tools for the Design of New Adenosine Receptor Ligands. 1. Agonist. Curr Med Chem 2006; 13:2253-66. [PMID: 16918353 DOI: 10.2174/092986706777935195] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [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: 11/22/2022]
Abstract
In order to minimize expensive drug failures it is essential to determine the potential biological activity of new candidates as early as possible. In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of a drugs biological activity is advisable even before synthesis and this can be achieved using predictive biological activity methods. In this sense, computer aided rational drug design strategies like Quantitative Structure Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of traditional QSAR applications in the development of new agonist molecules with affinity toward adenosine receptors is scarce. This review attempts to summarize the current level of knowledge concerning computational affinity predictions for adenosine receptors using QSAR models based on knowledge of the agonist ligands. Several computational protocols and different 2D and 3D descriptors have been described in the literature for these targets, but more effort is still required in this area.
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González MP, Helguera AM, Collado IG. A topological substructural molecular design to predict soil sorption coefficients for pesticides. Mol Divers 2006; 10:109-18. [PMID: 16710808 DOI: 10.1007/s11030-005-9004-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [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: 08/10/2005] [Accepted: 10/19/2005] [Indexed: 11/25/2022]
Abstract
A TOPological Sub-structural MOlecular DEsign (TOPS-MODE) approach was used to predict the soil sorption coefficients for a set of pesticide compounds. The obtained model accounted for more than 85% of the data variance and demonstrated the importance of the dipole moment, the standard distance, the polarizability, and the hydrophobicity in describing the property under study. In addition, we compared this new model to a previous one using different descriptors such as WHIM and molecular connectivity indices. Finally, the TOPS-MODE was used to calculate the contribution of different fragments to the soil sorption coefficient of the compounds studied. The present approximation proved to be a good method for studying the soil sorption coefficient for pesticides, but it could also be extended to other series of chemicals.
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Affiliation(s)
- Maykel Pérez González
- Unit of Services, Experimental Sugar Cane Station "Villa Clara-Cienfuegos", Ranchuelo, 53100, Villa Clara, Cuba.
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González MP, Caballero J, Helguera AM, Garriga M, González G, Fernández M. 2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors. Bull Math Biol 2006; 68:735-51. [PMID: 16802081 DOI: 10.1007/s11538-005-9006-3] [Citation(s) in RCA: 21] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2005] [Accepted: 03/03/2005] [Indexed: 01/13/2023]
Abstract
The inhibitory activity towards p34(cdc2)/cyclin b kinase (CBK) enzyme of 30 cytokinin-derived compounds has been successfully modelled using 2D spatial autocorrelation vectors. Predictive linear and non-linear models were obtained by forward stepwise multi-linear regression analysis (MRA) and artificial neural network (ANN) approaches respectively. A variable selection routine that selected relevant non-linear information from the data set was employed prior to networks training. The best ANN with three input variables was able to explain about 87% data variance in comparison with 80% by the linear equation using the same number of descriptors. Similarly, the neural network had higher predictive power. The MRA model showed a linear dependence between the inhibitory activities and the spatial distributions of masses, electronegativities and van der Waals volumes on the inhibitors molecules. Meanwhile, ANN model evidenced the occurrence of non-linear relationships between the inhibitory activity and the mass distribution at different topological distance on the cytokinin-derived compounds. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (SOM) built using the input variables of the best neural network.
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Affiliation(s)
- Maykel Pérez González
- Unit of Service, Drug Design Department, Experimental Sugar Cane Station Villa Clara-Cienfuegos, Ranchuelo, Villa Clara, CP 53100, Cuba
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González MP, Caballero J, Tundidor-Camba A, Helguera AM, Fernández M. Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches. Bioorg Med Chem 2006; 14:200-13. [PMID: 16185882 DOI: 10.1016/j.bmc.2005.08.009] [Citation(s) in RCA: 48] [Impact Index Per Article: 2.7] [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: 05/31/2005] [Revised: 08/01/2005] [Accepted: 08/02/2005] [Indexed: 11/24/2022]
Abstract
Inhibition of farnesyltransferase (FT) enzyme by a set of 78 thiol and non-thiol peptidomimetic inhibitors was successfully modeled by a genetic neural network (GNN) approach, using radial distribution function descriptors. A linear model was unable to successfully fit the whole data set; however, the optimum Bayesian regularized neural network model described about 87% inhibitory activity variance with a relevant predictive power measured by q2 values of leave-one-out and leave-group-out cross-validations of about 0.7. According to their activity levels, thiol and non-thiol inhibitors were well-distributed in a topological map, built with the inputs of the optimum non-linear predictor. Furthermore, descriptors in the GNN model suggested the occurrence of a strong dependence of FT inhibition on the molecular shape and size rather than on electronegativity or polarizability characteristics of the studied compounds.
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Affiliation(s)
- Maykel Pérez González
- Unit of Service, Drug Design Department, Experimental Sugar Cane Station Villa Clara-Cienfuegos, Ranchuelo, Villa Clara, C.P. 53100, Cuba
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González MP, Terán C, Teijeira M, Helguera AM. Radial distribution function descriptors: an alternative for predicting A2 A adenosine receptors agonists. Eur J Med Chem 2006; 41:56-62. [PMID: 16253394 DOI: 10.1016/j.ejmech.2005.08.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [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: 07/29/2005] [Revised: 08/30/2005] [Accepted: 08/31/2005] [Indexed: 10/25/2022]
Abstract
The Radial Distribution Function approach has been applied to the study of the A2 A adenosine receptors agonist effect of 29 adenosine analogues: N6- arylcarbamoyl, 2-arylalkynyl-N6 -arylcarbamoyl, and N6 -carboxamido derivatives. A model able to describe around 85% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, no one of nine different approaches, including the use of Galvez Topological Charges indexes, BCUT, Geometrical, 2D autocorrelations, Topological, Randić Molecular profile, WHIM, 3D-MORSE and GETAWAY descriptors were able to explain more than 78% of the variance in the mentioned property with the same number of variables in the equation. Finally, the model support that the bulkiness and stereoselectivity play an important role in the affinity for this receptor in this kind of compounds.
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Helguera AM, Cabrera Pérez MA, González MP, Ruiz RM, González Díaz H. A topological substructural approach applied to the computational prediction of rodent carcinogenicity. Bioorg Med Chem 2005; 13:2477-88. [PMID: 15755650 DOI: 10.1016/j.bmc.2005.01.035] [Citation(s) in RCA: 49] [Impact Index Per Article: 2.6] [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: 11/29/2004] [Revised: 01/20/2005] [Accepted: 01/21/2005] [Indexed: 11/27/2022]
Abstract
The carcinogenic activity has been investigated by using a topological substructural molecular design approach (TOPS-MODE). A discriminant model was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 189 compounds. The percentage of correct classification was 76.32%. The predictive power of the model was validated by three test: an external test set (compounds not used in the develop of the model, with a 72.97% of good classification), a leave-group-out cross-validation procedure (4-fold full cross-validation, removing 20% of compounds in each cycle, with a good prediction of 76.31%) and two external prediction sets (the first and second exercises of the National Toxicology Program). This methodology evidenced that the hydrophobicity increase the carcinogenic activity and the dipole moment of the molecule decrease it; suggesting the capacity of the TOPS-MODE descriptors to estimate this property for new drug candidates. Finally, the positive and negative fragment contributions to the carcinogenic activity were identified (structural alerts) and their potentialities in the lead generation process and in the design of 'safer' chemicals were evaluated.
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Affiliation(s)
- Aliuska Morales Helguera
- Department of Chemistry, Faculty of Chemistry and Pharmacy, Central University of Las Villas, Santa Clara, 54830 Villa Clara, Cuba
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González MP, Helguera AM, Cabrera MA. Quantitative structure-activity relationship to predict toxicological properties of benzene derivative compounds. Bioorg Med Chem 2005; 13:1775-81. [PMID: 15698794 DOI: 10.1016/j.bmc.2004.11.059] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.1] [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: 11/05/2004] [Revised: 11/30/2004] [Accepted: 11/30/2004] [Indexed: 10/26/2022]
Abstract
TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was used to assess acute aquatic toxicity of a series of 69 benzene derivatives. The obtained model was able to explain more than 88% of data variance, stressing the importance of molecule hydrophobicity and its dipolar moment, as well as the distance between their bonds to describe the property under study. On the other hand, this model was better than those obtained with Dragon software (Constitutional, Galvez topological charges indices and BCUT) using the same number of variables. This approach proved to be a very good method to assess acute aquatic toxicity of these king of compounds, which could be applied to other series of substances.
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Affiliation(s)
- Maykel Pérez González
- Drug Design Department, Experimental Sugar Cane Station 'Villa Clara-Cienfuegos', Ranchuelo, Villa Clara 53100, Cuba.
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Fernández M, Caballero J, Helguera AM, Castro EA, González MP. Quantitative structure–activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds. Bioorg Med Chem 2005; 13:3269-77. [PMID: 15809162 DOI: 10.1016/j.bmc.2005.02.038] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.2] [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: 12/14/2004] [Revised: 02/11/2005] [Accepted: 02/14/2005] [Indexed: 11/29/2022]
Abstract
Inhibitory activity against aldose reductase enzyme of flavonoid derivatives were modelled using 11 kinds of molecular descriptors from Dragon software. Model with four Galvez Charge Indices described 67% of data variance and overtaken other models using the same number of variables. Galvez indices showed to contain important information on the relationship between the inhibitor structures and its activity by describing the molecular topology and charge transfer through the molecule. In addition, artificial neural networks were trained using charge indices from the linear models but the obtaining networks overfitted the data having low predictive power.
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Affiliation(s)
- Michael Fernández
- Molecular Modeling Group, Center for Biotechnological Studies, Faculty of Agronomy, University of Matanzas, 44740 Matanzas, Cuba
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Pérez González M, Dias LC, Helguera AM, Rodríguez YM, de Oliveira LG, Gomez LT, Diaz HG. TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds. Bioorg Med Chem 2004; 12:4467-75. [PMID: 15265497 DOI: 10.1016/j.bmc.2004.05.035] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.4] [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: 04/01/2004] [Revised: 05/22/2004] [Accepted: 05/26/2004] [Indexed: 11/30/2022]
Abstract
A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in anti-inflammatory compounds using computer-aided molecular design. Two series of compounds, one containing anti-inflammatory and the other containing nonanti-inflammatory compounds were processed by a k-means cluster analysis in order to design the training and prediction sets. A linear classification function to discriminate the anti-inflammatory from the inactive compounds was developed. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441). While in the prediction set, they showed an overall predictability of 88% and 84% for active and inactive compounds, being the global percentage of good classification of 85%. Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments towards anti-inflammatory property, also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general 'in silico' technique to experimentation in anti-inflammatory discovery.
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Affiliation(s)
- Maykel Pérez González
- Unit of Service, Drug Design Department, Experimental Sugar Cane Station Villa Clara-Cienfuegos, Villa Clara, Ranchuelo 53100, Cuba.
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González MP, Helguera AM, Ruiz RM, Garcı́a Fárdales JR. A topological sub-structural approach of the mutagenic activity in dental monomers. 1. Aromatic epoxides. POLYMER 2004. [DOI: 10.1016/j.polymer.2004.02.047] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Abstract
The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach has been applied to the study of the permeability coefficient of various compounds through low-density polyethylene at 0 degrees C. A model able to describe more than 92% of the variance in the experimental permeability of 38 organic compounds was developed with the use of the mentioned approach. In contrast, none of eight different approaches, including the use of constitutional, topological, BCUT, 2D autocorrelations, geometrical, RDF, 3D Morse, and GETAWAY descriptors was able to explain more than 75% of the variance in the mentioned property with the same number of descriptors. In addition, the TOPS-MODE approach permitted to find the contribution of different fragments to the permeability coefficients, giving to the model a straightforward structural interpretability.
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Affiliation(s)
- Maykel Pérez González
- Unit of Services, Experimental Sugar Cane Station Villa Clara-Cienfuegos, Ranchuelo, Cuba.
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