QSAR Analysis of the Structure—Toxicity Relationship of Aconitum and Delphinium Diterpene Alkaloids

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.

Neuropharmacological Potential of Diterpenoid Alkaloids

This study provides a narrative review of diterpenoid alkaloids (DAs), a family of extremely important natural products found predominantly in some species of Aconitum and Delphinium (Ranunculaceae). DAs have long been a focus of research attention due to their numerous intricate structures and diverse biological activities, especially in the central nervous system (CNS). These alkaloids originate through the amination reaction of tetra or pentacyclic diterpenoids, which are classified into three categories and 46 types based on the number of carbon atoms in the backbone structure and structural differences. The main chemical characteristics of DAs are their heterocyclic systems containing β-aminoethanol, methylamine, or ethylamine functionality. Although the role of tertiary nitrogen in ring A and the polycyclic complex structure are of great importance in drug-receptor affinity, in silico studies have emphasized the role of certain sidechains in C13, C14, and C8. DAs showed antiepileptic effects in preclinical studies mostly through Na⁺ channels. Aconitine (1) and 3-acetyl aconitine (2) can desensitize Na⁺ channels after persistent activation. Lappaconitine (3), N-deacetyllapaconitine (4), 6-benzoylheteratisine (5), and 1-benzoylnapelline (6) deactivate these channels. Methyllycaconitine (16), mainly found in Delphinium species, possesses an extreme affinity for the binding sites of α7 nicotinic acetylcholine receptors (nAChR) and contributes to a wide range of neurologic functions and the release of neurotransmitters. Several DAs such as bulleyaconitine A (17), (3), and mesaconitine (8) from Aconitum species have a drastic analgesic effect. Among them, compound 17 has been used in China for decades. Their effect is explained by increasing the release of dynorphin A, activating the inhibitory noradrenergic neurons in the β-adrenergic system, and preventing the transmission of pain messages by inactivating the Na⁺ channels that have been stressed. Acetylcholinesterase inhibitory, neuroprotective, antidepressant, and anxiolytic activities are other CNS effects that have been investigated for certain DAs. However, despite various CNS effects, recent advances in developing new drugs from DAs were insignificant due to their neurotoxicity.

Design, synthesis and insecticidal activity and mechanism research of Chasmanthinine derivatives

Unrestricted reproduction and spread of pest had caused great damage to the quality and yield of crops in recent years. Besides the use of traditional chemical pesticides, natural products also make a huge contribution against pests. Chasmanthinine, a diterpenoid alkaloid isolated from Aconitum franchetii var. villosulum, shown extremely antifeedant activity against Spodoptera exigua. Therefore, a series of novel Chasmanthinine derivatives were synthesized and their biological activity was studied in this work. Compound 33 showed the strongest antifeedant activity (EC₅₀ = 0.10 mg/cm²) among all the test compounds. The mechanism research of 33 revealed that its antifeedant effect was related to the inhibition of carboxylesterase (CES), and proved the thiophene acyl group could form a strong binding effect with CES by molecular docking. Moreover, compound 10 exhibited the strongest cytotoxicity (IC₅₀ = 12.87 μM) against Sf9 cell line and moderate contact toxicity. The mechanism research indicated that compound 10 could induce Sf9 cells apoptosis. In summary, the results lay a foundation for the application of diterpene alkaloids in plant protection.

Aconitum and delphinium diterpenoid alkaloids of local anesthetic activity: comparative QSAR analysis based on GA-MLRA/PLS and optimal descriptors approach

The duration of anesthesia (related to protein binding of a drug) and the onset time (determined by the pKa) are important characteristics in assessment of local anesthetic agents. They are known to be affected by a number of factors. Early studies of antiarrhythmic diterpenoid alkaloids from plants Aconitum and Delphinium suggested that they possess local anesthetic activity due to their ability to suppress sodium currents of excited membranes. In this study we utilized toxicity, duration, and onset of action as endpoints to construct Quantitative Structure-Activity Relationship (QSAR) models for the series of 34 diterpenoid alkaloids characterized by local anesthetic activity using genetic algorithm-based multiple linear regression analysis/partial least squares and simplified molecular input line entry system (SMILES)-based optimal descriptors approach. The developed QSAR models correctly reflected factors that determine three endpoints of interest. Toxicity correlates with descriptors describing partition and reactivity of compounds. The duration of anesthesia was encoded by the parameters defining the ability of a compound to bind at the receptor site. The size and number of H-bond acceptor atoms were found not to favor the speed of onset, while topographic electronic descriptor demonstrated strong positive effect on it. SMILES-based optimal descriptors approach resulted in overall improvement of models. This approach was shown to be more sensitive to structural peculiarities of molecules than regression methods. The results clearly indicate that obtained QSARs are able to provide distinct rationales for compounds optimization with respect to particular endpoint.

Feeding deterrents from Aconitum episcopale roots against the red flour beetle, Tribolium castaneum

The screening for insecticidal principles from several Chinese medicinal herbs showed that the ethanol extract of Aconitum episcopale roots possessed significant feeding deterrence against the red flour beetle, Tribolium castaneum . From the ethanol extract, six feeding deterrents were isolated by bioassay-guided fractionation. The compounds were identified as chasmanine, crassicauline A, karacoline, sachaconitine, talatisamine, and yunaconitine from their spectroscopic data. Chasmanine, talatisamine, karacoline, and sachaconitine exhibited feeding deterrent activity against T. castaneum adults, with EC(50) values of 297.0, 342.8, 395.3, and 427.8 ppm, respectively. Yunaconitine and crassicauline A also possessed feeding deterrent activity against T. castaneum adults, with EC(50) values of 653.4 and 1134.5 ppm, respectively.

Aconitum and Delphinium alkaloids "Drug-likeness" descriptors related to toxic mode of action

Large series of Aconitum and Delphinium alkaloids have been investigated by means of QSAR analysis. Descriptors related to "drug-likeness" of molecules were selected to discriminate between "drugs" and "non-drugs" amongst diterpenoid alkaloids of interest. A usefulness of such approach has been assessed and it proved to give reliable results on whether a particular diterpenoid alkaloid is likely to be either poison or drug. A number of QSAR models with "drug-likeness" descriptors have also been obtained and discussed in terms of their relativity to the mode of toxic action exhibited by the alkaloids. The QSAR models were obtained with r value in the range 0.69-0.94. The q(2) (cross validation of r(2)) values also confirm the statistical significance of our models.

Aconitum and Delphinium sp. alkaloids as antagonist modulators of voltage-gated Na+ channels. AM1/DFT electronic structure investigations and QSAR studies

Early pharmacological studies of Aconitum and Delphinium sp. alkaloids suggested that these neurotoxins act at site 2 of voltage-gated Na(+) channel and allosterically modulate its function. Understanding structural requirements for these compounds to exhibit binding activity at voltage-gated Na(+) channel has been important in various fields. This paper reports quantum-chemical studies and quantitative structure-activity relationships (QSARs) based on a total of 65 natural alkaloids from two plant species, which includes both blockers and openers of sodium ion channel. A series of 18 antagonist alkaloids (9 blockers and 9 openers) have been studied using AM1 and DFT computational methods in order to reveal their structure-activity (structure-toxicity) relationship at electronic level. An examination of frontier orbitals obtained for ground and protonated forms of the compounds revealed that HOMOs and LUMOs were mainly represented by nitrogen atom and benzyl/benzoylester orbitals with -OH and -OCOCH(3) contributions. The results obtained from this research have confirmed the experimental findings suggesting that neurotoxins acting at type 2 receptor site of voltage-dependent sodium channel are activators and blockers with common structural features and differ only in efficacy. The energetic tendency of HOMO-LUMO energy gap can probably distinguish activators and blockers that have been observed. Genetic Algorithm with Multiple Linear Regression Analysis (GA-MLRA) technique was also applied for the generation of three-descriptor QSAR models for the set of 65 blockers. Additionally to the computational studies, the HOMO-LUMO gap descriptor in each obtained QSAR model has confirmed the crucial role of charge transfer in receptor-ligand interactions. A number of other descriptors such as logP, I(BEG), nNH2, nHDon, nCO have been selected as complementary ones to LUMO and their role in activity alteration has also been discussed.