Predicting Biotransformation Potential from Molecular Structure.

Borodina, Yu. V.; Sadym, A.; Филимонов, Д. А.; Blinova, V. G.; Дмитриев, А. В.; Poroikov, Vladimir

doi:10.1002/chin.200351219

Cited by 8 publications

(7 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Angele, Moench, Oppermann, Staab, and Wenke [29] developed an ontology in which a reaction was described with respect to its participants (instances of a molecule class) and exist as part of a mixture. Borodina, Sadym, Filimonov, Blinova, Dmitriev, and Poroikov [30] suggested a representation for biomolecular transformation as a tuple of (X, reaction), with optional description of the enzyme. Hsu, Krishnamurthy, Rao, Zhao, Jagannathan, Caruthers, and Venkatasubramanian [22] modeled a reaction to have reactants, products and catalysts.…”

Section: Reaction Ontologymentioning

confidence: 99%

Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

Hailemariam

Venkatasubramanian

2010

J Pharm Innov

View full text Add to dashboard Cite

Introduction In pharmaceutical drug development and manufacturing, the amount and complexity of information of different types, ranging from raw experimental data to lab reports to complex mathematical models that needs to be stored, accessed, validated, manipulated, managed, and used for decision making is staggering. The information is often in different formats, used in different computer tools, making smooth interaction between these tools difficult. A common, explicit, and platform-independent vocabulary that is both machine accessible and human usable is needed to streamline the flow of information and knowledge generation. Methods The Purdue Ontology for Pharmaceutical Engineering (POPE) was developed to address this informatics challenge. POPE models information and knowledge and includes models of phases, material properties, molecular structures, experiments, reactions, and unit operations. Conclusion In Part 1, we describe the conceptual framework of POPE and in Part 2 its applications.

show abstract

Section: Reaction Ontologymentioning

confidence: 99%

Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

Hailemariam

Venkatasubramanian

2010

J Pharm Innov

View full text Add to dashboard Cite

show abstract

“…For this purpose we used computer program 'PASS' (Sergeiko et al 2008;Filimonov and Poroikov 2005;Borodina et al 2003;Stepanchikova et al 2003), which predicts about 2,500 pharmacological effects, mechanisms of action, mutagenicity, carcinogenicity, teratogenicity and embryotoxicity on the basis of structural formulae of compounds. PASS predictions are based on SAR (structure-activity relationships) analysis of the training set consisting of about 60,000 drugs, drug-candidates and lead compounds.…”

Section: Sar Activities Of Metabolites Isolated From Plant Speciesmentioning

confidence: 99%

Naturally occurring plant isoquinoline N-oxide alkaloids: Their pharmacological and SAR activities

Dembitsky¹,

Gloriozova

Poroikov

2015

Phytomedicine

View full text Add to dashboard Cite

“…There are several empirical methods for ranking the most likely pathway for metabolism. PASS-BioTransfo gives the likelihood of a particular class of reactions occurring [34]; SPORCalc [35] and MetaPrint 2D (http://www-metaprint2d.ch.cam.ac.uk/; accessed May 2017) ranks the most likely sites for metabolism in a molecule; TIMES (http://oasis-lmc.org/products/software/times.aspx; accessed May 2017) and Metadrug (https://lsresearch.thomsonreuters.com/pages/solutions/18/metadrug; accessed May 2017) give a probability for formation of a given metabolite; SyGMa (Systematic Generation of potential Metabolites) ranks predicted metabolites according to an empirically derived probability score [36]. Meteor Nexus (metabolite predictor software from Lhasa Ltd; https://www.lhasalimited.org/products/meteor-nexus.htm;…”

Section: The Likelihood Of a Given Reaction Occurringmentioning

confidence: 99%

In silico prediction of skin metabolism and its implication in toxicity assessment

Madden

Webb

Enoch

et al. 2017

Computational Toxicology

View full text Add to dashboard Cite

Skin, being the largest organ of the body, represents an important route of exposure, not only for the abundance of chemicals present in the environment, but also for products designed for topical application such as drugs and personal care products. Determining whether such incidental or intentional exposure poses a risk to human health requires consideration of temporal concentration, both externally and internally, in addition to assessing the chemical's intrinsic hazard. In order to elicit a toxic response in vivo the chemical must reach its site of action in sufficient concentration, as determined by its absorption, distribution, metabolism and elimination (ADME) profile. Whilst absorption and distribution into and through skin layers have been studied for decades, only more recently has skin metabolism become a subject of intense research, now recognised as playing a key role in both toxification and detoxification processes. The majority of information on metabolic processes, however, has generally been acquired via studies performed on the liver. This paper outlines strategies that may be used to leverage current knowledge, gained from liver metabolism studies, to inform predictions for skin metabolism through understanding the differences in the enzymatic landscapes between skin and liver. The strategies outlined demonstrate how an array of in silico tools may be used in concert to resolve a significant challenge in predicting toxicity following dermal exposure. The use of in vitro methods for determining skin metabolism, both to provide further experimental data for modelling and to verify predictions is also discussed. Herein, information on skin metabolism is placed within the context of toxicity prediction for risk assessment, which requires consideration of both exposure and hazard of parent chemicals and their metabolites.

show abstract

Predicting Biotransformation Potential from Molecular Structure.

Abstract: Computers in chemistryComputers in chemistry V 0380 Predicting Biotransformation Potential from Molecular Structure. -(BORODINA*, Y.; SADYM, A.; FILIMONOV, D.; BLINOVA, V.; DMITRIEV, A.; POROIKOV, V.; J. Chem. Inf.

Cited by 8 publications

References 1 publication

Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

Purdue Ontology for Pharmaceutical Engineering: Part I. Conceptual Framework

Naturally occurring plant isoquinoline N-oxide alkaloids: Their pharmacological and SAR activities

In silico prediction of skin metabolism and its implication in toxicity assessment

Contact Info

Product

Resources

About