Identification of any structure-specific hepatotoxic potential of different pyrrolizidine alkaloids using random forests and artificial Neural Networks

Abstract

Pyrrolizidine alkaloids (PAs) are characteristicmetabolites of some plant families and forma powerful defensemechanism 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. Themain target organs for PAmetabolismand 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. Thesemodels were trained with a recently established dataset specific for acute hepatotoxicity in humans. Using this dataset, a set ofmolecular predictors was identified to predict the hepatotoxic potential of each compound in validated QSAR models. Based on thesemodels, the hepatotoxic potential of the 602 PAs was predicted and the following hepatotoxic rank order in 3main categories defined (1) for necine base: otonecine > retronecine > platynecine; (2) for necine basemodification: 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.