Data from: Identification of any structure-specific hepatotoxic potential of different pyrrolizidine alkaloids using Random Forest and artificial Neural Network

Pyrrolizidine alkaloids (PAs) are characteristic metabolites of some plant
families and form a powerful defence 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 characterised 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 three main categories defined: (i) for
necine base: otonecine > retronecine > platynecine; (ii) for
necine base modification: dehydropyrrolizidine >> tertiary
PA = N-oxide and (iii) 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.