Estimation of the acute toxicity and prediction of the metabolism site for organic molecules using GALAS methodology /

The dissertation presents GALAS models for the estimation of the acute toxicity towards two rodent species following different administration routes as well as for the prediction of CYP3A4 and CYP2D6 regioselectivity in the main metabolic reactions mediated by these enzymes (13 individual models in total). All these models feature the ability of the quantitative model Applicability Domain (AD) evaluation via the estimated prediction Reliability Indices (RI). I.e., the obtained models conform to one of the main requirements for the QSAR model acceptance as an alternative research method by the EU regulatory institutions. Evident correlation between prediction reliability and its accuracy allowed classifying each model result into one of several qualitative classes according to RI values. One possible way of utilizing such information, discussed in this study, is compound prioritization before experimental testing potentially resulting in reduction of the number of necessary measurements. As demonstrated the AD of the obtained GALAS models can be easily expanded to cover specific compound classes of researcher interest using ‘in-house’ databases of experimental data. This feature significantly improves the possibilities for the practical application of these models, based on public data, in industry. Especially given the fact that the described improvements in predictions following the addition of similar compounds was instant and required no rebuilding of the baseline models.

Grantor dc:publisher

Institutional Repository of Vilnius University

Year dc:date

2010

Author dc:creator

• Sazonovas, Andrius,

Contributors dc:contributor

• Butkus, Eugenijus

• GALAS modeling method ; acute toxicity ; CYP450 regioselectivity ; model applicability domain ; reliability index

Statement dc:rights

• info:eu-repo/semantics/openAccess

Language dc:language

eng

Repository record dc:identifier

https://repository.vu.lt/VU:ELABAETD1936663&prefLang=en_US

OAI identifier oai:identifier

oai:vu.lt:elaba:1936663