scholarly journals HUBUNGAN KUANTITATIF STRUKTUR TOKSISITAS : REVIEW

2020 ◽  
Vol 3 (2) ◽  
pp. 107-126
Author(s):  
Purwaniati Purwaniati
Keyword(s):  
In Silico ◽  
Toxicity Data ◽  
Quantitative Structure ◽  
Training Set ◽  
Drug Discovery And Development ◽  
Independent Variables ◽  
Drug Molecules ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship ◽  
In Silico Methods

AbstrakProses penemuan dan pengembangan obat merupakan proses panjang yang memerlukan banyak waktu dan biaya. Ada banyak calon molekul obat yang gagal mencapai pasaran karena alasan toksisitasnya yang tinggi, sehingga harus dapat diidentifikasi sedini mungkin. Hubungan kuantitatif struktur toksisitas (HKST) merupakan salah satu metode in silico yang cukup tangguh untuk memprediksi toksisitas. HKST merupakan persamaan matematis yang dibentuk dari variabel data endpoint toksisitas seperti LD50 sebagai variabel terikat dan sejumlah deskriptor sebagai variable bebas yang dihitung dari senyawa-senyawa dalam training set. Persamaan HKST kemudian digunakan untuk memprediksi toksisitas senyawa baru.Kata kunci : toksisitas, hubungan kuantitatif struktur toksisitas (HKST)AbstractThe process of drug discovery and development is a long process that requires a lot of time and costly. There are many prospective drug molecules that fail to reach the market due to high toxicity reasons, so they must be identified as early as possible. The quantitative structure toxicity relationship  (QSTR) is one of the in silico methods that is strong enough to predict toxicity. QSTR is a mathematical equation formed from endpoint toxicity data variables such as LD50 as a bound variable and a number of descriptors as independent variables calculated from the compounds in the training set. The QSTR equation is then used to predict the toxicity of new compounds.Keywords: toxicity, quantitative structure toxicity relationship (QSTR)

Predictive Quantitative Structure Toxicity Relationship Study on Avian Toxicity of Some Diverse Agrochemical Pesticides by Monte Carlo Method

2017 ◽  
Vol 2 (1) ◽  
pp. 19-34 ◽  
Author(s):  
Amit Kumar Halder ◽  
Achintya Saha ◽  
Tarun Jha
Keyword(s):  
Monte Carlo ◽  
Toxicity Data ◽  
Quantitative Structure ◽  
Target Species ◽  
Monte Carlo Optimization ◽  
Relationship Study ◽  
Structure Toxicity Relationship ◽  
External Test ◽  
Toxicity Relationship ◽  
Chemical Pesticides

Application of pesticides may have serious adverse consequences in environment. Birds are one of the most important non-target species that are harmed by agricultural chemical pesticides. In the current study, Monte Carlo optimization based Quantitative Structure Toxicity Relationship (QSTR) analyses were performed on a dataset containing diverse chemical pesticides with toxicity data determined on Bobwhite quail. Hybrid models containing SMILES and graph based descriptors were developed on three different training and test set combinations. The best model was selected based on validation statistics on internal training (n = 96) and external test (n = 31) as well as validation (n=25) sets. The best model was thoroughly analyzed to understand structural requirements of the chemical pesticides for higher avian toxicity. The models developed in the current analyses may be useful to design novel less toxic pesticides.

Can Toxicity for Different Species Be Correlated?

2016 ◽  
pp. 339-371
Author(s):  
Supratik Kar ◽  
Rudra Narayan Das ◽  
Kunal Roy ◽  
Jerzy Leszczynski
Keyword(s):  
Experimental Evaluation ◽  
In Silico ◽  
Scientific Community ◽  
Toxicity Tests ◽  
Quantitative Structure ◽  
Predictive Toxicology ◽  
Data Gaps ◽  
Structure Toxicity Relationship ◽  
The Cost ◽  
Toxicity Relationship

Experimental evaluation of the toxicity of a compound is an expensive practice, and it requires sacrifice of a large number of animals. As a consequence, in silico techniques for predictive toxicology are taking the central stage of attention of the scientific community. Interspecies quantitative structure-toxicity relationship (i-QSTR) modeling provides a tool for estimation of contaminant's sensitivity with known levels of uncertainty for a diverse pool of species. It is capable of extrapolating data for one toxicity endpoint to another toxicity endpoint when the data for the second species are unavailable. The emerging i-QSTR approach can overcome the cost of multiple toxicity tests, improve the understanding of the mechanism of toxic action (MOA) of chemicals for different organisms and endpoints and is very useful in order to fill the data gaps where toxicity value for a particular compound is absent for a specific endpoint.

Can Toxicity for Different Species be Correlated?

2016 ◽  
Vol 1 (2) ◽  
pp. 23-51 ◽  
Author(s):  
Supratik Kar ◽  
Rudra Narayan Das ◽  
Kunal Roy ◽  
Jerzy Leszczynski
Keyword(s):  
Experimental Evaluation ◽  
In Silico ◽  
Scientific Community ◽  
Toxicity Tests ◽  
Quantitative Structure ◽  
Predictive Toxicology ◽  
Data Gaps ◽  
Structure Toxicity Relationship ◽  
The Cost ◽  
Toxicity Relationship

Experimental evaluation of the toxicity of a compound is an expensive practice, and it requires sacrifice of a large number of animals. As a consequence, in silico techniques for predictive toxicology are taking the central stage of attention of the scientific community. Interspecies quantitative structure-toxicity relationship (i-QSTR) modeling provides a tool for estimation of contaminant's sensitivity with known levels of uncertainty for a diverse pool of species. It is capable of extrapolating data for one toxicity endpoint to another toxicity endpoint when the data for the second species are unavailable. The emerging i-QSTR approach can overcome the cost of multiple toxicity tests, improve the understanding of the mechanism of toxic action (MOA) of chemicals for different organisms and endpoints and is very useful in order to fill the data gaps where toxicity value for a particular compound is absent for a specific endpoint.

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