Ligand‐based computer‐aided pesticide design. A review of applications of the CoMFA and CoMSIA methodologies

Bordás, Barna; Kőmíves, Tamás; Lopata, Antal

doi:10.1002/ps.614

Cited by 48 publications

(30 citation statements)

References 44 publications

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“…However, any QSAR model, classical or multidimensional, is useful and instructive insofar as it satisfactorily explains the available data, provides physicochemical insights, or proves to be a reliable tool for prediction. The use of CoMFA and CoMSIA in pesticide science has been reviewed [46].…”

Section: Discussionmentioning

confidence: 99%

Multidimensional Quantitative Structure–Activity Relationships of Diacylhydrazine Toxicity to Lepidopteran and Coleopteran Insect Pests

2008

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Insecticidal activity indices, i.e., median lethal dose causing mortality in 50% of insects treated (pLD 50 ) values, of 77 Diacylhydrazines (DAHs) were determined against larvae of the Beet Armyworm (Spodoptera exigua), the rice stem borer (Chilo suppressalis), and the Colorado Potato Beetle (Leptinotarsa decemlineata). A series of multidimensional Comparative Molecular Field Analysis (CoMFA) Quantitative Structure -Activity Relationship (QSAR) models was constructed for each type of insect toxicity, and these models were evaluated by q 2 of 67-member training sets and r 2 of ten-member test sets. Favored models yielded r 2 values in the range of 0.77 -0.88, q 2 values in the range of 0.54 -0.62 after consideration of outliers, test set r 2 values in the range of 0.54 -0.72, and external q 2 values in the range of 0.31 -0.58. The structure -activity relationships for each type of insect toxicity can be partially, but not entirely, rationalized in view of Ecdysone Receptor (EcR) LBD sequence alignments and putative contact residues relative to the known crystallized Heliothis virescens EcR LBD/BYI06830 holoreceptor. Alternate ligand poses or LBD remodeling may pertain across members of the DAH chemotype.

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Section: Discussionmentioning

confidence: 99%

Multidimensional Quantitative Structure–Activity Relationships of Diacylhydrazine Toxicity to Lepidopteran and Coleopteran Insect Pests

2008

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“…[3][4][5] An increase in the need for bioinformatics methods to cope with omics-related data sets has transferred to the QSAR field, with a number of advances in chemoinformatic techniques. 5,6) (Q)SARs are used extensively in the design of novel biologically active compounds, either pharmaceutical or agrochemical, 7) and can subsequently be useful in industrial research settings. However, these methods are also increasingly being used by regulatory agencies to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects.…”

Section: Overview Of Structure-activity Relationshipsmentioning

confidence: 99%

Structure-activity relationships and pathway analysis of biological degradation processes

et al. 2006

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Quantitative (and qualitative) structure-activity relationships, (Q)SARs, have been extensively developed for use in biochemistry and toxicology. Numerous applications exist in other fields, such as the physical sciences and ecological health; however they are beyond the scope of this review. In molecular biology, knowledge about genomes, proteomes, chemical structure and relationships between these molecules have been collected and stored in public databases. 1) These two methods are utilized in different ways to understand biological systems. For example, in the development of drugs, a biological database is used to identify a drug target using data acquired via high-throughput approaches (i.e. microarray data) and then (Q)SAR analysis is used to identify small molecules that bind the drug target. However binding to a target is insufficient for full evaluation of a compound's biological activity and eventual utility as a pesticide or pharmaceutical. It is also vital to understand the compound's adsorption, distribution, metabolism, excretion and toxicity (ADME-Tox) properties. This means that (Q)SAR analysis is insufficient to understand the pharmacokinetics of compounds in a biological system. One solution to this problem is to consider drug metabolites on an individual basis, integrating the enzymes that are responsible for the degradation/metabolism of a given compound as well as its metabolites. This analysis can reveal the dynamics of xenobiotics in biological systems. For example, Ekins et al. integrated QSAR coupled to a gene network dataset to examine drug metabolism and toxicity.2) However methods to incorporate integrated (Q)SAR analysis with that of a biological database are not sufficiently advanced for wide-scale application. In this review, we give a brief overview of (Q)SAR and a publicly available biological database. We then show an example of a combined chemical and genomics analysis by using SAR and pathway analysis of the biological degradation processes of endocrine disrupting chemicals (EDCs). Structure-activity relationships Overview of structure-activity relationships(Q)SARs are mathematical relationships that link chemical structure to biological (ecological, toxicological or pharmacological) activity for a series of compounds. There are a multitude of methods available that can be used in (Q)SAR analy- (Q)SARs estimate biological activity; however these models are insufficient to fully understand and predict the ADME-Tox processes of small molecules in biological systems. By integrating (Q)SARs with biological databases, the predictive capability of these models can be significantly improved. However, the techniques and methods for integrated analysis have not yet been sufficiently developed for these combined systems. In this review, we discuss standard (Q)SAR methods and biological database construction as well as provide an example of how SAR and metabolic pathway analysis can be combined to examine the biological degradation processes of endocrine disrupting chemicals.

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“…The pharmacodynamic aspects, as well as the hardware and software tools employed in computer aided pesticide design are practically identical with those used in drug design, except for the pharmacokinetic considerations in the later 37 . The number of known experimental structures of pharmacological targets in the field of pesticide chemistry is significantly smaller than in medicinal chemistry 38 . Hence, pesticide chemists have to rely more upon indirect ligand based pesticide design methodologies.…”

Section: In-silico Tools In Pesticide Design and Developmentmentioning

confidence: 99%

Computer Aided Pesticide Design: A Rational Tool for Supplementing DDT Lacunae

2014

Chem Sci Trans

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Abstract:The reliance of mankind on synthetic pesticide, particularly dichloro diphenyl-trichloro ethane (DDT) reached its zenith during 1950s-60s, since the first application during second world War. But very soon it faced a serious scientific opposition due to its bio-magnifying nature and was banned in 1970s in most part of world with certain exceptional usage in health control programmes. Although, the pesticidal activity range and economic viability of DDT has never been questioned and still there are several voices to bring back DDT. To answer this controversy is the search for new DDT like pesticide with similar pesticidal efficacy and lower ecological accumulative effects. Recent advances in computer aided molecular design has provided us very robust, economic and time and labour saving tool to design and develop new chemical entities with pesticidal activity similar to DDT and has good commercial prospects as well. The present paper reviews the brief history of DDT and the developments in computational and molecular design methods to rejuvenate the era of new pesticide research.

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Ligand‐based computer‐aided pesticide design. A review of applications of the CoMFA and CoMSIA methodologies

Cited by 48 publications

References 44 publications

Multidimensional Quantitative Structure–Activity Relationships of Diacylhydrazine Toxicity to Lepidopteran and Coleopteran Insect Pests

Multidimensional Quantitative Structure–Activity Relationships of Diacylhydrazine Toxicity to Lepidopteran and Coleopteran Insect Pests

Structure-activity relationships and pathway analysis of biological degradation processes

Computer Aided Pesticide Design: A Rational Tool for Supplementing DDT Lacunae

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