3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

Therapeutic Potentials of Aconite-like Alkaloids: Bioinformatics and Experimental Approaches

Compounds from plants that are used in traditional medicine may have medicinal properties. It is well known that plants belonging to the genus Aconitum are highly poisonous. Utilizing substances derived from Aconitum sp. has been linked to negative effects. In addition to their toxicity, the natural substances derived from Aconitum species may have a range of biological effects on humans, such as analgesic, anti-inflammatory, and anti-cancer characteristics. Multiple in silico, in vitro, and in vivo studies have demonstrated the effectiveness of their therapeutic effects. In this review, the clinical effects of natural compounds extracted from Aconitum sp., focusing on aconitelike alkaloids, are investigated particularly by bioinformatics tools, such as the quantitative structure- activity relationship method, molecular docking, and predicted pharmacokinetic and pharmacodynamic profiles. The experimental and bioinformatics aspects of aconitine's pharmacogenomic profile are discussed. Our review could help shed light on the molecular mechanisms of Aconitum sp. compounds. The effects of several aconite-like alkaloids, such as aconitine, methyllycacintine, or hypaconitine, on specific molecular targets, including voltage-gated sodium channels, CAMK2A and CAMK2G during anesthesia, or BCL2, BCL-XP, and PARP-1 receptors during cancer therapy, are evaluated. According to the reviewed literature, aconite and aconite derivatives have a high affinity for the PARP-1 receptor. The toxicity estimations for aconitine indicate hepatotoxicity and hERG II inhibitor activity; however, this compound is not predicted to be AMES toxic or an hERG I inhibitor. The efficacy of aconitine and its derivatives in treating many illnesses has been proven experimentally. Toxicity occurs as a result of the high ingested dose; however, the usage of this drug in future research is based on the small quantity of an active compound that fulfills a therapeutic role.

Machine learning methods applied for the prediction of biological activities of triple reuptake inhibitors

Major depressive disorder (MDD) is characterized by a series of disabling symptoms like anhedonia, depressed mood, lack of motivation for daily tasks and self-extermination thoughts. The monoamine deficiency hypothesis states that depression is mainly caused by a deficiency of monoamine at the synaptic cleft. Thus, major efforts have been made to develop drugs that inhibit serotonin (SERT), norepinephrine (NET) and dopamine (DAT) transporters and increase the availability of these monoamines. Current gold standard treatment of MDD uses drugs that target one or more monoamine transporters. Triple reuptake inhibitors (TRIs) can target SERT, NET, and DAT simultaneously, and are believed to have the potential to be early onset antidepressants. Quantitative structure-activity relationship models were developed using machine learning algorithms in order to predict biological activities of a series of triple reuptake inhibitor compounds that showed in vitro inhibitory activity against multiple targets. The results, using mostly interpretable descriptors, showed that the internal and external predictive ability of the models are adequate, particularly of the DAT and NET by Random Forest and Support Vector Machine models. The current work shows that models developed from relatively simple, chemically interpretable descriptors can predict the activity of TRIs with similar structure in the applicability domain using ML methods.Communicated by Ramaswamy H. Sarma.

Effects of linalyl acetate on oxidative stress, inflammation and endothelial dysfunction: can linalyl acetate prevent mild cognitive impairment?

Mild cognitive impairment (MCI) is a major public health challenge with an increasing prevalence. Although the mechanisms underlying the development of MCI remain unclear, MCI has been reported to be associated with oxidative stress, inflammatory responses, and endothelial dysfunction, suggesting that agents that reduce these factors may be key to preventing MCI. Currently, no agents have been approved for the treatment of MCI, with the efficacy of commonly prescribed cholinesterase inhibitors remaining unclear. Relatively safe natural products that can prevent the development of MCI are of great interest. Linalyl acetate (LA), the major component of clary sage and lavender essential oils, has been shown to have a variety of pharmacological effects, including anti-hypertensive, anti-diabetic, neuroprotective, anti-inflammatory, and antioxidant properties, which may have the potential for the prevention of MCI. The present review briefly summarizes the pathogenesis of MCI related to oxidative stress, inflammatory responses, and endothelial dysfunction as well as the benefits of LA against these MCI-associated factors. The PubMed and Google Scholar databases were used to search the relevant literature. Further clinical research may lead to the development of new strategies for preventing MCI, particularly in high-risk populations with oxidative stress, inflammatory responses, and endothelial dysfunction (e.g., patients with hypertension and/or diabetes mellitus).

Bioinformatics Tools for the Analysis of Active Compounds Identified in Ranunculaceae Species

The chemical compounds from extracts of three Ranunculaceae species, Aconitum toxicum Rchb., Anemone nemorosa L. and Helleborus odorus Waldst. & Kit. ex Willd., respectively, were isolated using the HPLC purification technique and analyzed from a bioinformatics point of view. The classes of compounds identified based on the proportion in the rhizomes/leaves/flowers used for microwave-assisted extraction and ultrasound-assisted extraction were alkaloids and phenols. Here, the quantifying of pharmacokinetics, pharmacogenomics and pharmacodynamics helps us to identify the actual biologically active compounds. Our results showed that (i) pharmacokinetically, the compounds show good absorption at the intestinal level and high permeability at the level of the central nervous system for alkaloids; (ii) regarding pharmacogenomics, alkaloids can influence tumor sensitivity and the effectiveness of some treatments; (iii) and pharmacodynamically, the compounds of these Ranunculaceae species bind to carbonic anhydrase and aldose reductase. The results obtained showed a high affinity of the compounds in the binding solution at the level of carbonic anhydrases. Carbonic anhydrase inhibitors extracted from natural sources can represent the path to new drugs useful both in the treatment of glaucoma, but also of some renal, neurological and even neoplastic diseases. The identification of natural compounds with the role of inhibitors can have a role in different types of pathologies, both associated with studied and known receptors such as carbonic anhydrase and aldose reductase, as well as new pathologies not yet addressed.

Advanced Bioinformatics Tools in the Pharmacokinetic Profiles of Natural and Synthetic Compounds with Anti-Diabetic Activity

Diabetes represents a major health problem, involving a severe imbalance of blood sugar levels, which can disturb the nerves, eyes, kidneys, and other organs. Diabes management involves several synthetic drugs focused on improving insulin sensitivity, increasing insulin production, and decreasing blood glucose levels, but with unclear molecular mechanisms and severe side effects. Natural chemicals extracted from several plants such as Gymnema sylvestre, Momordica charantia or Ophiopogon planiscapus Niger have aroused great interest for their anti-diabetes activity, but also their hypolipidemic and anti-obesity activity. Here, we focused on the anti-diabetic activity of a few natural and synthetic compounds, in correlation with their pharmacokinetic/pharmacodynamic profiles, especially with their blood-brain barrier (BBB) permeability. We reviewed studies that used bioinformatics methods such as predicted BBB, molecular docking, molecular dynamics and quantitative structure-activity relationship (QSAR) to elucidate the proper action mechanisms of antidiabetic compounds. Currently, it is evident that BBB damage plays a significant role in diabetes disorders, but the molecular mechanisms are not clear. Here, we presented the efficacy of natural (gymnemic acids, quercetin, resveratrol) and synthetic (TAK-242, propofol, or APX3330) compounds in reducing diabetes symptoms and improving BBB dysfunctions. Bioinformatics tools can be helpful in the quest for chemical compounds with effective anti-diabetic activity that can enhance the druggability of molecular targets and provide a deeper understanding of diabetes mechanisms.