scholarly journals QSAR Modeling for Acute Toxicity Prediction in Rat by Common Painkiller Drugs

2016 ◽  
Vol 52 ◽  
pp. 9-18
Author(s):  
Jinia Sinha Roy ◽  
Kaushik Gupta ◽  
Soumendra Nath Talapatra
Keyword(s):  
Pain Therapy ◽  
Aquatic Toxicity ◽  
Lethal Dose ◽  
Software Tool ◽  
Quantitative Structure Activity Relationship ◽  
Oral Exposure ◽  
Toxicity Prediction ◽  
Qsar Modeling ◽  
Chemical Agents ◽  
Test Models

Painkiller drugs or analgesics are potent pain reliever chemical agents, which are commonly used in pain therapy. Mathematical modeling by QSAR (quantitative structure activity relationship) methods are well known practices to determine predictive toxicity in biota. Now-a-days, an easy screening of chemicals, QSAR can be done by using several recommended softwares. The present study was carried out by using software namely T.E.S.T. (Toxicity estimation software tool) for rat oral LD50 (median lethal dose) predictive toxicity for common painkiller drugs. These painkiller drugs were selected as 35 compounds and tabulated on the basis characteristics of one non-narcotic viz. acetaminophen, twenty non-steroidal anti-inflammatory such as bromofenac, diclofenac, diflunsial, etodolac, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketoprofen, ketorolac, maclofenamate sodium, mefenamic acid, meloxicam, nabumetone, naproxen, oxaprozin, phenylbutazone, piroxicam, sulindac and tolmetin as well as fourteen narcotic viz. buprenorphine, butorphanol, codeine, hydrocodone, hydromorphone, levorphanol, meperidine, methadone, morphine, nalbuphine, oxycodone, pentazocine, dextropropoxyphene and tapentadol. The data were tabulated on experimental (bioassay) from ChemIDPlus and predictive toxicity of 30 compounds out of 35 compounds by using T.E.S.T. The predictive data were found by T.E.S.T. that 20 and 10 compounds were very toxic and moderately toxic respectively but not extremely, super toxic and non-toxic in rat model after acute oral exposure. It is suggested to evaluate the predicted data further with other available recommended softwares with different test models like daphnia, fish etc. to know aquatic toxicity when these compounds may discharge into waterbodies.

Features of Bioaccumulation and Toxic Effects of Copper (II) Oxide Nanoparticles Under Repeated Oral Exposure in Rats

2021 ◽  
Vol 09 ◽  
Author(s):  
Mark Sergeevich Stepankov ◽  
Marina Aleksandrovna Zemlyanova ◽  
Nina Vladimirovna Zaitseva ◽  
Anna Mikhailovna Ignatova ◽  
Alena Evgenievna Nikolaeva
Keyword(s):  
Surface Area ◽  
Pore Volume ◽  
Mesangial Cells ◽  
Lethal Dose ◽  
Toxic Effects ◽  
Oral Exposure ◽  
Oxide Nanoparticles ◽  
Brown Pigment ◽  
Cuo Nps ◽  
Hematological Analysis

Background: Currently, the range of copper (II) oxide nanoparticles’ (CuO NPs) applications is expanding and the global production of CuO NPs is increasing. In this regard, the risk of exposure of the population to this nanomaterial increases. Objective: The aim of the study is to investigate the patterns of bioaccumulation and toxic effects of CuO NPs after multiple oral exposures. Methods: The particle size was determined by scanning electron microscopy and dynamic laser light scattering. Specific surface area was measured by the method of Brunauer, Emmett, Teller. Total pore volume - by the method of Barrett, Joyner, Khalenda. Twenty-four hours after the final exposure, blood samples were taken for biochemical and hematological analysis, and internal organs were taken to determine their mass, copper concentration and histological analysis. The study was carried out in comparison with copper (II) oxide microparticles (CuO MPs). Results: In terms of size, surface area, and pore volume, the studied copper (II) oxide sample is a nanomaterial. The median lethal dose of CuO NPs was 13187.5 mg/kg of body weight. Bioaccumulation occurs in the stomach, blood, intestines, liver, lungs, kidneys and brain. Pathomorphological changes in the liver are manifested in the form of necrosis, degeneration, hepatitis; kidney - proliferation of mesangial cells, dystrophy; stomach - gastritis; small intestine - hyperplasia, enteritis; large intestine - colitis; lungs - hyperplasia, abscess, pneumonia, bronchitis, vasculitis. Clumps of brown pigment were detected in the kidneys, stomach and lungs. The mass of the stomach and intestines increased, the mass of the liver, kidneys and lungs decreased. Pathomorphological changes in organs are likely to cause an increase in the levels of activity of alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, lactate dehydrogenase, amylase, malondialdehyde concentration and a decrease in plasma antioxidant activity. The proportion of segmented neutrophils, the number of leukocytes are raised, the proportion of lymphocytes is reduced. Conclusion: The degree of bioaccumulation and toxicity of CuO NPs are more expressed in relation to CuO MPs.

scholarly journals QSAR Modeling for Relative Toxicity Prediction of (3-(2-chloroquinolin-3-yl)oxiran-2-yl)(phenyl) methanone Derivatives

2017 ◽  
Vol 7 (1) ◽  
pp. 16-24 ◽  
Author(s):  
Amit K Tiwari ◽  
Praveena Narasimhamurthy ◽  
Gopalpur Nagendrappa ◽  
Abhilash Thakur
Keyword(s):  
Relative Toxicity ◽  
Toxicity Prediction ◽  
Qsar Modeling

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

2015 ◽  
pp. 1-47 ◽  
Author(s):  
Maryam Hamzeh-Mivehroud ◽  
Babak Sokouti ◽  
Siavoush Dastmalchi
Keyword(s):  
Drug Design ◽  
Drug Development ◽  
Biological Activities ◽  
Model Development ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
New Drugs ◽  
Qsar Modeling ◽  
Qsar Studies ◽  
Modern Drug

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

Oncology ◽  
2017 ◽  
pp. 20-66
Author(s):  
Maryam Hamzeh-Mivehroud ◽  
Babak Sokouti ◽  
Siavoush Dastmalchi
Keyword(s):  
Drug Design ◽  
Drug Development ◽  
Biological Activities ◽  
Model Development ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
New Drugs ◽  
Qsar Modeling ◽  
Qsar Studies ◽  
Modern Drug

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

Risk Assessment of Cosmetic Preservatives Using QSAR

2020 ◽  
Vol 5 (1) ◽  
pp. 44-62
Author(s):  
Monika Bhardwaj ◽  
Neeraj Masand ◽  
Jagannath Sahoo ◽  
Vaishali M. Patil
Keyword(s):  
Microbial Contamination ◽  
Antimicrobial Agents ◽  
Quantitative Structure Activity Relationship ◽  
Toxicity Assessment ◽  
Cosmetic Products ◽  
Toxicity Prediction ◽  
Safety And Efficacy ◽  
Regulatory Aspects ◽  
Structure Activity ◽  
Consumer Safety

Cosmetic manufacturers need to demonstrate the safety and efficacy of the products against microbial contamination to assure consumer safety and to improve shelf-life. The preservation strategies include chemical, physical, or physiological strategies. The most common is the use of antimicrobial agents. The toxicity assessment of preservatives used in cosmetic products is essential. It can be done by computational methods such as quantitative structure-activity relationship (QSAR) using several software such as ADME-Tox, TOPKAT, Dragon, T.E.S.T., and ECOSAR. The present manuscript elaborates a detailed view on cosmetic preservatives, regulatory aspects and application of computational strategies for toxicity prediction.

QSAR modeling for reaction rate constants of eaq− with diverse organic compounds in water

2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei
Keyword(s):  
Organic Compounds ◽  
Reaction Rate ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Reaction Rate Constants ◽  
Structure Activity ◽  
Qsar Models ◽  
The Impact

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.

Ensemble-Based Modeling of Chemical Compounds with Antimalarial Activity

2019 ◽  
Vol 19 (11) ◽  
pp. 957-969 ◽  
Author(s):  
Ana Yisel Caballero-Alfonso ◽  
Maykel Cruz-Monteagudo ◽  
Eduardo Tejera ◽  
Emilio Benfenati ◽  
Fernanda Borges ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Computational Models ◽  
Antimalarial Activity ◽  
Majority Vote ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Qsar Modeling ◽  
Design Work ◽  
Data Set ◽  
Selection Of

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows. Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds. Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance. Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds. Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method.

Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method

2021 ◽  
Author(s):  
Zhenxing Wu ◽  
Dejun Jiang ◽  
Chang-Yu Hsieh ◽  
Guangyong Chen ◽  
Ben Liao ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Predictive Power ◽  
Molecular Descriptors ◽  
Quantitative Structure Activity Relationship ◽  
Biological Properties ◽  
Modeling Method ◽  
Qsar Modeling ◽  
Highly Efficient ◽  
Structure Activity ◽  
The Impact

Abstract Accurate predictions of druggability and bioactivities of compounds are desirable to reduce the high cost and time of drug discovery. After more than five decades of continuing developments, quantitative structure–activity relationship (QSAR) methods have been established as indispensable tools that facilitate fast, reliable and affordable assessments of physicochemical and biological properties of compounds in drug-discovery programs. Currently, there are mainly two types of QSAR methods, descriptor-based methods and graph-based methods. The former is developed based on predefined molecular descriptors, whereas the latter is developed based on simple atomic and bond information. In this study, we presented a simple but highly efficient modeling method by combining molecular graphs and molecular descriptors as the input of a modified graph neural network, called hyperbolic relational graph convolution network plus (HRGCN+). The evaluation results show that HRGCN+ achieves state-of-the-art performance on 11 drug-discovery-related datasets. We also explored the impact of the addition of traditional molecular descriptors on the predictions of graph-based methods, and found that the addition of molecular descriptors can indeed boost the predictive power of graph-based methods. The results also highlight the strong anti-noise capability of our method. In addition, our method provides a way to interpret models at both the atom and descriptor levels, which can help medicinal chemists extract hidden information from complex datasets. We also offer an HRGCN+'s online prediction service at https://quantum.tencent.com/hrgcn/.

scholarly journals Rodent Carcinogenicity Dataset

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Natalja Fjodorova ◽  
Marjana Novič
Keyword(s):  
Molecular Structure ◽  
Quantitative Structure Activity Relationship ◽  
Future Research ◽  
Qsar Modeling ◽  
Carcinogenic Potency ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
Funded Project ◽  
Rodent Carcinogenicity

The rodent carcinogenicity dataset was compiled from the Carcinogenic Potency Database (CPDBAS) and was applied for the classification of quantitative structure-activity relationship (QSAR) models for the prediction of carcinogenicity based on the counter-propagation artificial neural network (CP ANN) algorithm. The models were developed within EU-funded project CAESAR for regulatory use. The dataset contains the following information: common information about chemicals (ID, chemical name, and their CASRN), molecular structure information (SDF files and SMILES), and carcinogenic (toxicological) properties information: carcinogenic potency (TD50_Rat_mg; carcinogen/noncarcinogen) and structural alert (SA) for carcinogenicity based on mechanistic data. Molecular structure information was used to get chemometrics information to calculate molecular descriptors (254 MDL and 784 Dragon descriptors), which were further used in predictive QSAR modeling. The dataset presented in the paper can be used in future research in oncology, ecology, or chemicals' risk assessment.

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