3D-QSAR design of new escitalopram derivatives for the treatment of major depressive disorders

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.

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

The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.

Current Treatment Strategies and Nanoparticle-Mediated Drug Delivery Systems for Pulmonary Arterial Hypertension

There are three critical pathways for the pathogenesis and progression of pulmonary arterial hypertension (PAH): the prostacyclin (prostaglandin I₂) (PGI₂), nitric oxide (NO), and endothelin pathways. The current approved drugs targeting these three pathways, including prostacyclin (PGI₂), phosphodiesterase type-5 (PDE5) inhibitors, and endothelin receptor antagonists (ERAs), have been shown to be effective, however, PAH remains a severe clinical condition and the long-term survival of patients with PAH is still suboptimal. The full therapeutic abilities of available drugs are reduced by medication, patient non-compliance, and side effects. Nanoparticles are expected to address these problems by providing a novel drug delivery approach for the treatment of PAH. Drug-loaded nanoparticles for local delivery can optimize the efficacy and minimize the adverse effects of drugs. Prostacyclin (PGI₂) analogue, PDE5 inhibitors, ERA, pitavastatin, imatinib, rapamycin, fasudil, and oligonucleotides-loaded nanoparticles have been reported to be effective in animal PAH models and in vitro studies. However, the efficacy and safety of nanoparticle mediated-drug delivery systems for PAH treatment in humans are unknown and further clinical studies are required to clarify these points.

Preparation and Characterization of Whey Protein Isolate-DIM Nanoparticles

3,3’-Diindolylmethane (DIM) is a bioactive compound found in Cruciferous vegetables that possesses health benefits such as antioxidant, anticancer, and anti-inflammatory effects. However, hydrophobicity and photolabile limit its pharmaceutical applications. This study aims to prepare and characterize DIM-encapsulated whey protein isolate (WPI) nanoparticles mixed at different ratios of WPI and DIM using the combined heating–ultrasound method. Results showed that all the samples showed adequate physicochemical characteristics: The mean particle size of the nanoparticles could be controlled down to 96–157 nm depending on the DIM to WPI ratio used in the preparation with a low polydispersity index (<0.5), higher negative values of zeta potential (>−40 mV) as well as with greater encapsulation efficiency (>82%). Flow behavior indices showed the shear-thinning Non-Newtonian or pseudoplastic (n < 1) behavior of the nanoparticles. The thermal properties were characterized by differential scanning calorimetry (DSC), which showed that DIM was successfully entrapped in WPI nanoparticles. The secondary structure of WPI was changed after DIM incorporation; electrostatic interaction and hydrogen bonding were major facilitating forces for nanoparticles formation, confirmed by Fourier Transform Infrared Spectroscopy (FT-IR). Transmission electron microscopy (TEM) micrographs showed that all the samples had a smooth surface and spherical structure. The wall material (WPI) and encapsulation method provide effective protection to DIM against UV light and a broad range of physiologically relevant pH’s (2.5, 3.5, 4.5, 5.5, and 7). In conclusion, whey protein isolate (WPI)-based nanoparticles are a promising approach to encapsulate DIM and overcome its physicochemical limitations with improved stability.

Prevention of Deficit in Neuropsychiatric Disorders through Monitoring of Arsenic and Its Derivatives as Well as Through Bioinformatics and Cheminformatics

Neuropsychiatric disorders are induced by various risk factors, including direct exposure to environmental chemicals. Arsenic exposure induces neurodegeneration and severe psychiatric disorders, but the molecular mechanisms by which brain damage is induced are not yet elucidated. Our aim is to better understand the molecular mechanisms of arsenic toxicity in the brain and to elucidate possible ways to prevent arsenic neurotoxicity, by reviewing significant experimental, bioinformatics, and cheminformatics studies. Brain damage induced by arsenic exposure is discussed taking in account: the correlation between neuropsychiatric disorders and the presence of arsenic and its derivatives in the brain; possible molecular mechanisms by which arsenic induces disturbances of cognitive and behavioral human functions; and arsenic influence during psychiatric treatments. Additionally, we present bioinformatics and cheminformatics tools used for studying brain toxicity of arsenic and its derivatives, new nanoparticles used as arsenic delivery systems into the human body, and experimental ways to prevent arsenic contamination by its removal from water. The main aim of the present paper is to correlate bioinformatics, cheminformatics, and experimental information on the molecular mechanism of cerebral damage induced by exposure to arsenic, and to elucidate more efficient methods used to reduce its toxicity in real groundwater.

3D-QSAR study indicates an enhancing effect of membrane ions on psychiatric drugs targeting serotonin receptor 5-HT1A

Antidepressants and antipsychotics are psychiatric agents used for the treatment of various types of psychiatric diseases. Although currently among the most commonly prescribed drugs, their effectiveness and adverse effects are the topic of many studies and controversial claims. Here we generate QSAR models based on compounds series including 20 drugs recommended for two critical psychiatric diseases: depression and schizophrenia and we use these QSAR models to predict the biological activity of these 20 antidepressants and antipsychotics. We establish the membrane ions' contributions (sodium, potassium, calcium and iron) mediated by water to the antagonism of these drugs at the 5-HT1A receptor. The reliability of our QSAR models in predicting compounds activity is indicated by significant values for cross-validated correlation q² (0.60-0.76) and fitted correlation r² (0.96-0.98) coefficients. Our results indicate that potassium, calcium and iron play a key role for the antagonistic activity of drugs at the 5-HT1A receptor. Moreover, based on the established QSAR equations, we analysed 24 new escitalopram derivatives as possibly improved antidepressants targeting the 5-HT1A receptor. We identified that the presence of methyl groups and hydrogen atoms improves antidepressant activity while the simultaneous presence of ethyl, propyl or halogens decreased drastically antidepressant activity at the 5-HT1A site.