Raport de cercetare - Lorentz JÄNTSCHI

Published in: Xenobiotica, Volume 29, Issue 1 January 1999 , pages 27 - 42
Subjects: Pharmacology; Toxicology;
Abstract
1. Quantitative relationships between molecular physico-chemical properties of 22 substituted
benzoic acids and the extent of excretion of their metabolites in rat urine have been investigated
using computational chemistry and multivariate statistics. 2. A data set of 34 theoretically derived
physico-chemical descriptors calculated was used to classify the benzoic acids according to their
predominant urinary metabolic fate. 3. Quantitative structure-metabolism relationships were
obtained by linear regression using combinations of physico-chemical descriptors allowing the
prediction of % urinary excretion of glycine (r=0.73) and glucuronide conjugates (r=0.82) and %
urinary excretion of the parent compound (r=0.91).
Nr Articol
11 Modelling mutagenicity using properties calculated by computational chemistry
Authors: D. J. Livingstone; R. Greenwood a; R. Rees b; M. D. Smith b
Affiliations: a School of Biological Sciences, University of Portsmouth, Portsmouth, Hants, PO1
2DY, UK.
b Department of Genetic Toxicology, SmithKline Beecham Pharmaceuticals, The Frythe, Welwyn,
Hertfordshire, UK.
DOI: 10.1080/10629360290002064
Publication Frequency: 8 issues per year
Published in: SAR and QSAR in Environmental Research, Volume 13, Issue 1 2002 , pages 21 - 33
Subjects: Applied & Industrial Chemistry; Chemistry; Environmental & Ecological Toxicology;
Environmental Sciences; History & Philosophy of Mathematics;
Abstract
The recent advances in combinatorial chemistry and high throughput screening technologies have
led to an explosion in the numbers of possible therapeutic candidates being produced at the early
stages of drug discovery. This rapid increase in the number of chemicals to be classified results in a
greater need for alternative methods for the prediction of toxicity. Most QSAR models for
mutagenicity have been constructed for congeneric series. The prediction requirements of the
pharmaceutical industry, however, cover quite diverse chemical structures. This paper reports a
study of mutagenicity data for a diverse set of 90 compounds. Good discriminant models have been
built for this data set using properties calculated by the techniques of computational chemistry.
Jack-knifed (leave one out) predictions for these models are of the order of 85%.
Keywords: Discriminant Analysis; Eva Descriptor; Variable Selection; Ames Test; Jack-knife
Predictions; Qsar
Nr Articol
12 Computational modelling of low-energy electron-induced DNA damage by early physical and
chemical events
Authors: H. NIKJOO; P. O'NEILL; D. T. GOODHEAD; M. TERRISSOL
DOI: 10.1080/095530097143798
Publication Frequency: 12 issues per year
Published in: International Journal of Radiation Biology, Volume 71, Issue 5 May 1997 , pages 467
- 483
Subjects: Nuclear Medicine; Radiation Oncology;
Abstract
Modelling and calculations are presented as a first step towards mechanistic interpretation and
prediction of radiation effects based on the spectrum of initial DNA damage produced by low
energy electrons (100eV-4.5keV) that can be compared with experimental information. Relative
yields of single and clustered strand breaks are presented in terms of complexity and source of
damage, either by direct energy deposition or by reaction of OH radicals, and dependence on the
activation probability of OH radicals and the amount of energy required to give a single strand
break (ssb). Data show that the majority of interactions in DNA do not lead to damage in the form
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