A PLS study on the psychotropic activity for a series of cannabinoid compounds.

INTRODUCTION: The use of the Cannabis sativa plant by man has been common for centuries due to its numerous therapeutic properties resulting from the compounds present in it, called cannabinoids. However, the use of these compounds as drugs is still limited due to the psychotropic effects caused by them. The proteins that act as receptors of cannabinoid compounds were identified and characterized, being called CB1 and CB2 receptors. There is a series of 50 cannabinoid compounds that was studied through quantum and chemometric methods in order to obtain a mathematical model that could relate the structure of these compounds to their psychotropic activity. That model proved to be effective by predicting the psychoactivity of the 50 compounds from the series and elucidating relevant characteristics that imply in psychoactivity. However, most of these 50 compounds do not have experimental data of biological activity with CB1 and CB2 receptors. OBJECTIVES: This study aims to generate QSAR models in order to predict the biological activity of the 50 cannabinoid compounds and then relate the predicted biological activity values to the already known psychoactivity. METHODS: Another series of cannabinoid compounds was selected to generate and validate QSAR models, aiming to predict the biological activity of the 50 cannabinoid compounds with both CB1 and CB2 receptors. RESULTS: The PLS-CB1 and PLS-CB2 QSAR models were generated and validated in this work, proving to be highly predictive, and the biological activities (pK ) of the 50 cannabinoid compounds were predicted by them. It is important to highlight compounds Ic14, Ic18, and Ic19 (psychotropic inactive) which presented higher predicted pK values than the main cannabinoid compounds (Δ9-THC and Δ8-THC). Also, compound Ic21 stood out as the highest value of the predicted biological activities in the interaction with the CB2 receptor. CONCLUSION: The generated PLS models and the predicted pKi values of the 50 cannabinoid compounds can provide valuable information in the drug design of new cannabinoid compounds that can interact with CB1 and CB2 receptors in a therapeutic way with no psychotropic effects.

A partial least squares and artificial neural network study for a series of arylpiperazines as antidepressant agents.

Depression affects more than 300 million people around the world and can lead to suicide. About 30% of patients on treatment for depression drop out of therapy due to side effects or to latency time associated to therapeutic effects. 5-HT receptor, known as serotonin, is considered the key in depression treatment. Arylpiperazine compounds are responsible for several pharmacological effects and are considered as ligands in serotonin receptors, such as the subtype 5-HT2a. Here, in silico studies were developed using partial least squares (PLSs) and artificial neural networks (ANNs) to design new arylpiperazine compounds that could interact with the 5-HT2a receptor. First, molecular and electronic descriptors were calculated and posteriorly selected from correlation matrixes and genetic algorithm (GA). Then, the selected descriptors were used to construct PLS and ANN models that showed to be robust and predictive. Lastly, new arylpiperazine compounds were designed and their biological activity values were predicted by both PLS and ANN models. It is worth to highlight compounds G5 and G7 (predicted by the PLS model) and G3 and G15 (predicted by the ANN model), whose predicted pIC50 values were as high as the three highest values from the arylpiperazine original set studied here. Therefore, it can be asserted that the two models (PLS and ANN) proposed in this work are promising for the prediction of the biological activity of new arylpiperazine compounds and may significantly contribute to the design of new drugs for the treatment of depression.

Drug design of new 5-HT6R antagonists aided by artificial neural networks.

Alzheimer's Disease (AD) is the most frequent illness and cause of death amongst the age related-neurodegenerative disorders. The Alzheimer's Disease International (ADI) reported in 2019 that over 50 million people were living with dementia in the world and this number could potentially be around 152 million by 2050.5-hydroxtryptamine subtype 6 receptor (5-HT6R) has been identified as a potential anti-amnesic drug target and therefore, the administration of 5-HT6R antagonists can likely mitigate the memory loss and intellectual deterioration associated with AD. Herein, computational tools were applied to design new 5-HT6 antagonists and their biological activity values were predicted by our QSAR model obtained from Artificial Neural Networks (ANN). The proposed compounds here from the QSAR-ANN model presented significant biological activity values and some of them have achieved pKi above 9.00. Furthermore, our results suggest that the presence of halogen atoms (especially bromine) linked to the aromatic ring at para-position (HYD) contribute considerably to the increase of the biological activity values while bulky groups in the PI position do not culminate with the increase antagonist activity of compounds here analyzed. Finally, the ADME/Tox profile as well as the synthetic accessibility of new proposed compounds qualify them to go on further with experimental procedures and thenceforward their antagonist effects can be confirmed.