Atom- and Bond-Based 2D TOMOCOMD-CARDD Approach and Ligand-Based Virtual Screening for the Drug Discovery of New Tyrosinase Inhibitors

Casañola-Martín, Gerardo M.; Marrero-Ponce, Yovani; Khan, Mahmud Tareq Hassan; Torrens, Francisco; Pérez-Giménez, Facundo; Rescigno, Antonio

doi:10.1177/1087057108326078

Cited by 30 publications

(16 citation statements)

References 34 publications

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“…This linear equation presented good results both in training and external validation series with overall Accuracy in training series above 90%. The values of accuracy higher than 75% are acceptable for LDA models; according to previous reports (21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The reader should be aware that N here is not number of compounds but number of statistical cases.…”

Section: Results and Discussion 21 Linear Multi-target Model Of Drumentioning

confidence: 66%

New Predictor Model for Classification Anti-breast cancer Compounds According to Multiple Parameters

Aguirre-Crespo¹,

Arguello-Chan²,

Dominguez³

et al. 2015

Proceedings of the 19th International Electronic Conference on Synthetic Organic Chemistry

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According to Global Health in 2013, it was estimated that there were 508 000 women deaths in the world in the year 2011 caused by breast cancer. Even though cancer can be treated with different treatments for example: immunotherapy, radiotherapy and chemotherapy surgical operation, this disease continues being a severe medical problem. For that reason it has to be found another methods for cancer treatment. The discovery of new drugs with better activity and less toxicity for the treatment of Breast Cancer is a goal of the major importance. In this sense, theoretical models as QSAR can be useful to discover new anti-breast cancer drugs. For this reason, we developed a new multi-parameter-QSAR (mp-QSAR) model to discover new drugs. However, almost all the computational models known focus in only one target or receptor. In this work, Breast cancer type 1 susceptibility protein, ATP-binding cassette sub-family G member 2, Human breast cancer cell lines, Peroxisome proliferator-activated receptor gamma/nuclear receptor coactivator 3, nuclear receptor coactivator 3 and STE20-related kinase adapter protein alpha were used as receptor inputs in the model. A linear technique like Linear Discriminant Analysis (LDA) is our statistical analysis, and we compared with others models to seek alternative multi-target models for inhibitors of some of these receptors. In so doing, we used as input Topological Indices, in specific Wiener, Barabasi and Harary indices calculated by Dragon software. These operators quantify the deviations of the structure of one drug from the expected values for all drugs assayed in different boundary conditions or parameters (type of receptor, type of assay, type of target, target mapping). The best model correctly classifies as active compounds 84.00 % and non-active compounds (99.06 %) in the training series. Overall training performance was 95.91%. Validation of the model was carried out by means of external predicting series. Overall predictability performance was 95.52%. By the first time, the present work reports the attempts to calculate within unified framework probabilities of new anti-Breast cancer drugs.

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Section: Results and Discussion 21 Linear Multi-target Model Of Drumentioning

confidence: 66%

New Predictor Model for Classification Anti-breast cancer Compounds According to Multiple Parameters

Aguirre-Crespo¹,

Arguello-Chan²,

Dominguez³

et al. 2015

Proceedings of the 19th International Electronic Conference on Synthetic Organic Chemistry

View full text Add to dashboard Cite

show abstract

“…This discriminant function presented good results both in training and external validation series with overall Accuracy higher than 90%. According to previous reports in the QSAR literature (Patankar and Jurs 2003, Garcia-Garcia et al 2004, Marrero-Ponce et al 2005a, Marrero-Ponce et al 2005b, Casanola-Martin et al 2007, Casanola-Martin et al 2008, Casanola-Martin et al 2010 values Accuracy higher than 75% are acceptable. All the statistical data of this model are resumed in Table 1.…”

Section: Model Training and Validationmentioning

confidence: 65%

Computational model for multiplex assay of drug immunotoxicity in macrophage - study of the anti-microbial G1 using flow cytometry

Borroto

González-Dı́az

Prado

2013

Proceedings of the 17th International Electronic Conference on Synthetic Organic Chemistry

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The development of in vitro cytotoxicity assays has been driven by the need to rapidly evaluation of potential toxicity of large numbers of compounds, to reduce animal experimentation, and to save time and material resources. The large number of experimental results reported by different groups worldwide has lead to the accumulation of huge amounts of ontology-like data in large public databases as in ChEMBL. Conversely, many drugs have been assayed only for some selected tests. In this context, High-throughput multi-target Quantitative Structure-Activity (High-throughput mt-QSAR) techniques may become an important tool to rationalize drug discovery process. In this work, we train and validate by the first time mt-QSAR model using TOPS-MODE approach to calculate drug molecular descriptors and the software STATISTICA to seek a Linear Discriminant Analysis (LDA) function. This model correctly classifies 8,258 out of 9,000 (Accuracy = 91.76%) multiplexing assay endpoints of 7903 drugs (including both train and validation series). Each endpoint correspond to one out of 1418 assays, 36 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). After that, we determined experimentally, by the first time, the values of EC50 = 21.58 μg/mL and Cytotoxicity = 23.6 % for the antimicrobial / anti-parasite drug G1 over Balb/C mouse peritoneal macrophages using flow cytometry. In addition, the model predicts for G1 only 7 positive endpoints out 1,251 cytotoxicity assays (0.56% of probability of cytotoxicity in multiple assays). Both experimental and theoretical results point to a low macrophage cytotoxicity of G1. The results obtained are very important because they complement the toxicological studies of this important drug. This work opens a new door for the "in silico" multiplexing screening of large libraries of compounds. 2 grouping of cells that are derived from monocytes. They have a multitude of functions depending on their final differentiated state. These functions range from phagocytosis to antigen presentation to bone destruction, to name a few. Their importance in both the innate and acquired immune functions is undeniable. Xenobiotics that degrade their functional status can have grave consequences. Many published reports on the effect of xenobiotics on macrophage function make comparisons between treated versus untreated macrophages isolated in an identical manner to control for this problem. A commonly used source of mouse and rat macrophages is the peritoneal cavity. Two types of macrophages from the peritoneal cavity are used, resident and elicited (Barnett J. B. and Brundage Kathleen M. 2010). Often in the cytotoxicity assay to increase the number of macrophages, a sterile irritant, such as thioglycollate, is injected several days prior to harvesting the cells. The resulting peritoneal cells are referred to as elicited macrophages. The process of cytotoxicity is the result of a sequence of stages and complex biological interactions that can be influenced by severa...

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“…Table 1 comes about here This linear equation presented good results both in training and external validation series with overall Accuracy in training series above 90% (see Table 2). According to previous reports [35][36][37][38][39][40][41][42][43] values accuracy higher than 75% are acceptable for LDA-QSAR models. The reader should be aware that N here is not number of compounds but number of statistical cases.…”

Section: Multi-target Dragon Model Of Drug-neuroenzyme Interactionmentioning

confidence: 66%

Prediction of Neurological Enzyme Targets for Known and New Compounds with a Model using Galvez's Topological Indices

Prado

González-Dı́az

Sánchez

et al. 2014

Proceedings of the 18th International Electronic Conference on Synthetic Organic Chemistry

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Alzheimer's Disease (AD), Parkinson, and other neurodegenerative diseases are a major health problem nowadays. However, in many cases, current therapies are merely palliative and only temporarily slow cognitive decline. In this sense, the discovery of new drugs for the treatment of neurodegenerative diseases is a goal of the major importance. Public databases, like ChEMBL, contain a large amount of data about multiplexing assays of inhibitors of a group of enzymes with special relevance in central nervous system. Mono Amino Oxidases (MAOs), Acetyl Cholinesterase (AChE), Glycogen Synthase Kinase-3 (GSK-3), AChE (AChE), and 5α-reductases (5αRs). This data conform an important information source for the application of multi-target computational models. However, almost all the computational models known focus in only one target. In this work, we developed linear multi-target QSAR models (mt-QSAR) for inhibitors of 8 different enzymes promising in the treatment of different neurodegenerative diseases. In so doing, we combined by the first time the software DRAGON with Moving Average parameters with this objective. The best DRAGON model found predict with very high accuracy, specificity, and sensitivity >90% a very large data set >10000 cases in training and validation series.

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Atom- and Bond-Based 2D TOMOCOMD-CARDD Approach and Ligand-Based Virtual Screening for the Drug Discovery of New Tyrosinase Inhibitors

Cited by 30 publications

References 34 publications

New Predictor Model for Classification Anti-breast cancer Compounds According to Multiple Parameters

New Predictor Model for Classification Anti-breast cancer Compounds According to Multiple Parameters

Computational model for multiplex assay of drug immunotoxicity in macrophage - study of the anti-microbial G1 using flow cytometry

Prediction of Neurological Enzyme Targets for Known and New Compounds with a Model using Galvez's Topological Indices

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