Machine Learning-Based Analysis Reveals Triterpene Saponins and Their Aglycones in Cimicifuga racemosa as Critical Mediators of AMPK Activation.

Cimicifuga racemosa (CR) extracts contain diverse constituents such as saponins. These saponins, which act as a defense against herbivores and pathogens also show promise in treating human conditions such as heart failure, pain, hypercholesterolemia, cancer, and inflammation. Some of these effects are mediated by activating AMP-dependent protein kinase (AMPK). Therefore, comprehensive screening for activating constituents in a CR extract is highly desirable. Employing machine learning (ML) techniques such as Deep Neural Networks (DNN), Logistic Regression Classification (LRC), and Random Forest Classification (RFC) with molecular fingerprint MACCS descriptors, 95 CR constituents were classified. Calibration involved 50 randomly chosen positive and negative controls. LRC achieved the highest overall test accuracy (90.2%), but DNN and RFC surpassed it in precision, sensitivity, specificity, and ROC AUC. All CR constituents were predicted as activators, except for three non-triterpene compounds. The validity of these classifications was supported by good calibration, with misclassifications ranging from 3% to 17% across the various models. High sensitivity (84.5-87.2%) and specificity (84.1-91.4%) suggest suitability for screening. The results demonstrate the potential of triterpene saponins and aglycones in activating AMP-dependent protein kinase (AMPK), providing the rationale for further clinical exploration of CR extracts in metabolic pathway-related conditions.

Modeling Structure-Activity Relationship of AMPK Activation.

The adenosine monophosphate activated protein kinase (AMPK) is critical in the regulation of important cellular functions such as lipid, glucose, and protein metabolism; mitochondrial biogenesis and autophagy; and cellular growth. In many diseases-such as metabolic syndrome, obesity, diabetes, and also cancer-activation of AMPK is beneficial. Therefore, there is growing interest in AMPK activators that act either by direct action on the enzyme itself or by indirect activation of upstream regulators. Many natural compounds have been described that activate AMPK indirectly. These compounds are usually contained in mixtures with a variety of structurally different other compounds, which in turn can also alter the activity of AMPK via one or more pathways. For these compounds, experiments are complicated, since the required pure substances are often not yet isolated and/or therefore not sufficiently available. Therefore, our goal was to develop a screening tool that could handle the profound heterogeneity in activation pathways of the AMPK. Since machine learning algorithms can model complex (unknown) relationships and patterns, some of these methods (random forest, support vector machines, stochastic gradient boosting, logistic regression, and deep neural network) were applied and validated using a database, comprising of 904 activating and 799 neutral or inhibiting compounds identified by extensive PubMed literature search and PubChem Bioassay database. All models showed unexpectedly high classification accuracy in training, but more importantly in predicting the unseen test data. These models are therefore suitable tools for rapid in silico screening of established substances or multicomponent mixtures and can be used to identify compounds of interest for further testing.

Development of an in vitro screening method of acute cytotoxicity of the pyrrolizidine alkaloid lasiocarpine in human and rodent hepatic cell lines by increasing susceptibility.

ETHNOPHARMACOLOGICAL RELEVANCE: Pyrrolizidine alkaloids (PAs) are secondary plant ingredients formed in many plant species to protect against predators. PAs are generally considered acutely hepatotoxic, genotoxic and carcinogenic. Up to now, only few in vitro and in vivo investigations were performed to evaluate their relative toxic potential. AIM OF THE STUDY: The aim was to develop an in vitro screening method of their cytotoxicity. MATERIALS AND METHODS: Human and rodent hepatocyte cell lines (HepG2 and H-4-II-E) were used to assess cytotoxicity of the PA lasiocarpine. At concentrations of 25 µM up to even 2400 µM, no toxic effects in neither cell line was observed with standard cell culture media. Therefore, different approaches were investigated to enhance the susceptibility of cells to PA toxicity (using high-glucose or galactose-based media, induction of toxifying cytochromes, inhibition of metabolic carboxylesterases, and inhibition of glutathione-mediated detoxification). RESULTS: Galactose-based culture medium (11.1 mM) increased cell susceptibility in both cell-lines. Cytochrome P450-induction by rifampicin showed no effect. Inhibition of carboxylesterase-mediated PA detoxification by specific carboxylesterase 2 inhibitor loperamide (2.5 µM) enhanced lasiocarpine toxicity, whereas the unspecific carboxylesterase inhibitor bis(4-nitrophenyl)phosphate (BNPP, 100 µM)) had a weaker effect. Finally, the inhibition of glutathione-mediated detoxification by buthionine sulphoximine (BSO, 100 µM) strongly enhanced lasiocarpine toxicity in H-4-II-E cells in low and medium, but not in high concentrations. CONCLUSIONS: If no toxicity is observed under standard conditions, susceptibility enhancement by using galactose-based media, loperamide, and BSO may be useful to assess relative acute cytotoxicity of PAs in different cell lines.

Editor's Highlight: Identification of Any Structure-Specific Hepatotoxic Potential of Different Pyrrolizidine Alkaloids Using Random Forests and Artificial Neural Networks.

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.