Toxicity Assessment and The Quantitative Structure -Acitivity Relationships of α,β-unsaturated Ketones based on the Effects on Pseudokirchneriella subcapitata / 以月芽藻類毒性試驗方法評估α,β-不飽和酮類之毒性與結構-活性關係之研究

碩士 / 國立交通大學 / 環境工程系所 / 99 / The objective of this study is to understand the toxic effect of ketones (that contains sixteen α,β-unsaturated ketones and two saturated ketones ) on Pseudokirchneriella subcapitata using a closed system test. The effects of ketones are evaluated by three kinds of response endpoints, i.e., final yield, growth rate, and the dissolved oxygen production. Median effective concentrations (EC50) are estimated using the Probit model with a test duration of 48hrs. Then, log (1/EC50) is utilized to make a regression with physical chemistry descriptors, electronic descriptors and reactive descriptors. Finally, we establish the QSAR models.
The results show that unsaturated ketones are the toxicest of these ketones. Alkynones are more toxic than alkenones, and no substitution ketones are more toxic than Methyl substitution ketones. Ketones become unstable owing to electron-withdrawing effect caused by the existence of carboxyl of ketones. In addition, unsaturated ketones contain ??bond that make electron easily transfer and interact with sulfhydryl group which is in organisms. Methyl group substitution cause steric hindrance, hense toxicity decrease a lot. The same results of toxicity are also discovered in Reactivity.
The order of the relative sensitivity is as follows:algae(Final yield)>algae(Do production) >algae(Growth rate)>Tetrahymena pyriformis.
The toxicant of this study is electrophile which results in poor relationship with 1-octanol/water partition coefficient. Toxicity is positive correlation with reactivity. The QSAR models are established well (R2=0.821~0.907) that based on electronic descriptors by stepwise analysis. The results of this study can help us to decrease the cost of toxicity test.

Identiferoai:union.ndltd.org:TW/099NCTU5515011
Date January 2010
CreatorsHuang, Sheng-Ran, 黃聖然
ContributorsChen, Chung-Yuan, 陳重元
Source SetsNational Digital Library of Theses and Dissertations in Taiwan
Languagezh-TW
Detected LanguageEnglish
Type學位論文 ; thesis
Format123

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