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Predictive toxinology: An initial foray using calculated molecular descriptors to describe toxicity using saxitoxins as a model

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dc.contributor Australian Institute Of Marine Science
dc.contributor Australian Inst Marine Sci
dc.contributor Australian Institute Of Marine Science (aims) en
dc.contributor.author LLEWELLYN, LYNDON E.
dc.date.accessioned 2017-03-21T00:57:52Z
dc.date.accessioned 2013-02-28T06:42:36Z
dc.date.accessioned 2013-02-28T06:42:36Z
dc.date.accessioned 2019-05-09T01:20:34Z
dc.date.available 2017-03-21T00:57:52Z
dc.date.available 2017-03-21T00:57:52Z
dc.date.available 2013-02-28T06:42:36Z
dc.date.available 2019-05-09T01:20:34Z
dc.date.issued 2007-12-01
dc.identifier 7497 en
dc.identifier.citation Llewellyn LE (2007) Predictive toxinology: an initial foray using calculated molecular descriptors to describe toxicity using saxitoxins as a model. Toxicon. 50:901-913. en
dc.identifier.issn 0041-0101
dc.identifier.uri http://epubs.aims.gov.au/11068/7497
dc.description Link to abstract/full text - http://dx.doi.org/10.1016/j.toxicon.2007.06.015 en
dc.description.abstract Molecular descriptors and their mathematical combination have been used for predictive toxicology and risk assessments of environmental pollutants and pharmaceutical leads. However, this approach has not yet been used for natural toxins and may contribute to health and environmental risk assessments of newly discovered toxins without having to undertake whole animal toxicology. To explore this approach, over 3000 descriptors were calculated for each of the 30 saxitoxins for which mouse toxicities have been reported. This dataset was reduced to only 87 descriptors by firstly eliminating descriptors that were the same for all toxins or could not be calculated for all 30 toxins, and then removing those descriptors that did not have a statistically significant linear relationship with toxicity values. From the remaining 87 descriptors, a subset of seven descriptors was identified upon which various mathematical approaches were assessed for their ability to fit the dataset both with and without leave-one-out cross-validation. K-nearest neighbours and support vector machine regression along with various combinations of these seven descriptors fit the toxicity data almost perfectly and also achieved high predictability as measured by leave-one-out cross-validation. Of these seven descriptors, five incorporated weighting by estimates of atomic polarizability and electronic states. Predicted toxicities of several saxitoxins of unknown toxicity bore similarities to the pattern of known potencies of these toxins on various isoforms of the voltage-gated sodium channel. Some of these predicted toxicity values however would not be expected if there was a direct relationship between mammalian sodium channel affinity of the saxitoxins and whole animal toxicity. Crown Copyright (c) 2007 Published by Elsevier Ltd. All rights reserved.
dc.description.uri http://dx.doi.org/10.1016/j.toxicon.2007.06.015 en
dc.language English
dc.language en en
dc.relation.ispartof Toxicon - pages: 50:901-913 en
dc.relation.ispartof Null
dc.relation.uri http://data.aims.gov.au/metadataviewer/uuid/52e8f4a0-4000-4650-9372-fd97de9e7725 en
dc.subject Neosaxitoxin
dc.subject Drug Design
dc.subject Risk Assessments
dc.subject Sodium-channel
dc.subject Toxins
dc.subject Dinoflagellate Gymnodinium-catenatum
dc.subject Tropical Dinoflagellate
dc.subject Toxicity
dc.subject Saxitoxin
dc.subject Structure-information
dc.subject Pharmacology & Pharmacy
dc.subject Molecular Descriptors
dc.subject Toxicology
dc.subject Stereochemistry
dc.subject Shellfish
dc.title Predictive toxinology: An initial foray using calculated molecular descriptors to describe toxicity using saxitoxins as a model
dc.type journal article en
dc.identifier.doi 10.1016/j.toxicon.2007.06.015
dc.identifier.wos WOS:000251476400003


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