Editor's Highlight: Identification of Any Structure-Specific Hepatotoxic Potential of Different Pyrrolizidine Alkaloids Using Random Forests and Artificial Neural Networks. (9th September 2017)
- Record Type:
- Journal Article
- Title:
- Editor's Highlight: Identification of Any Structure-Specific Hepatotoxic Potential of Different Pyrrolizidine Alkaloids Using Random Forests and Artificial Neural Networks. (9th September 2017)
- Main Title:
- Editor's Highlight: Identification of Any Structure-Specific Hepatotoxic Potential of Different Pyrrolizidine Alkaloids Using Random Forests and Artificial Neural Networks
- Authors:
- Schöning, Verena
Hammann, Felix
Peinl, Mark
Drewe, Jürgen - Abstract:
- Abstract: Pyrrolizidine alkaloids (PAs) are characteristic metabolites of some plant families and form a powerful defense mechanism against herbivores. More than 600 different PAs are known. PAs are ester alkaloids composed of a necine base and a necic acid, which can be used to divide PAs in different structural subcategories. The main target organs for PA metabolism and toxicity are liver and lungs. Additionally, PAs are potentially genotoxic, carcinogenic and exhibit developmental toxicity. Only for very few PAs, in vitro and in vivo investigations have characterized their toxic potential. However, these investigations suggest that structural differences have an influence on the toxicity of single PAs. To investigate this structural relationship for a large number of PAs, a quantitative structural-activity relationship (QSAR) analysis for hepatotoxicity of over 600 different PAs was performed, using Random Forest- and artificial Neural Networks-algorithms. These models were trained with a recently established dataset specific for acute hepatotoxicity in humans. Using this dataset, a set of molecular predictors was identified to predict the hepatotoxic potential of each compound in validated QSAR models. Based on these models, the hepatotoxic potential of the 602 PAs was predicted and the following hepatotoxic rank order in 3 main categories defined (1) for necine base: otonecine > retronecine > platynecine; (2) for necine base modification: dehydropyrrolizidine ≫ tertiaryAbstract: Pyrrolizidine alkaloids (PAs) are characteristic metabolites of some plant families and form a powerful defense mechanism against herbivores. More than 600 different PAs are known. PAs are ester alkaloids composed of a necine base and a necic acid, which can be used to divide PAs in different structural subcategories. The main target organs for PA metabolism and toxicity are liver and lungs. Additionally, PAs are potentially genotoxic, carcinogenic and exhibit developmental toxicity. Only for very few PAs, in vitro and in vivo investigations have characterized their toxic potential. However, these investigations suggest that structural differences have an influence on the toxicity of single PAs. To investigate this structural relationship for a large number of PAs, a quantitative structural-activity relationship (QSAR) analysis for hepatotoxicity of over 600 different PAs was performed, using Random Forest- and artificial Neural Networks-algorithms. These models were trained with a recently established dataset specific for acute hepatotoxicity in humans. Using this dataset, a set of molecular predictors was identified to predict the hepatotoxic potential of each compound in validated QSAR models. Based on these models, the hepatotoxic potential of the 602 PAs was predicted and the following hepatotoxic rank order in 3 main categories defined (1) for necine base: otonecine > retronecine > platynecine; (2) for necine base modification: dehydropyrrolizidine ≫ tertiary PA = N -oxide; and (3) for necic acid: macrocyclic diester ≥ open-ring diester > monoester. A further analysis with combined structural features revealed that necic acid has a higher influence on the acute hepatotoxicity than the necine base. … (more)
- Is Part Of:
- Toxicological sciences. Volume 160:Number 2(2017)
- Journal:
- Toxicological sciences
- Issue:
- Volume 160:Number 2(2017)
- Issue Display:
- Volume 160, Issue 2 (2017)
- Year:
- 2017
- Volume:
- 160
- Issue:
- 2
- Issue Sort Value:
- 2017-0160-0002-0000
- Page Start:
- 361
- Page End:
- 370
- Publication Date:
- 2017-09-09
- Subjects:
- pyrrolizidine alkaloids -- QSAR -- Random Forest -- artificial Neural Networks -- hepatotoxicity
Toxicology -- Periodicals
Toxicology -- Periodicals
Toxicology
Periodicals
615.9 - Journal URLs:
- http://www.sciencedirect.com/science/journal/10966080 ↗
http://toxsci.oxfordjournals.org/ ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/toxsci/kfx187 ↗
- Languages:
- English
- ISSNs:
- 1096-6080
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8873.031900
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14234.xml