Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Antineoplastics for treating Alzheimer's disease and dementia: Evidence from preclinical and observational studies

As the world population ages, there will be an increasing need for effective therapies for aging-associated neurodegenerative disorders, which remain untreatable. Dementia due to Alzheimer's disease (AD) is one of the leading neurological diseases in the aging population. Current therapeutic approaches to treat this disorder are solely symptomatic, making the need for new molecular entities acting on the causes of the disease extremely urgent. One of the potential solutions is to use compounds that are already in the market. The structures have known pharmacokinetics, pharmacodynamics, toxicity profiles, and patient data available in several countries. Several drugs have been used successfully to treat diseases different from their original purposes, such as autoimmunity and peripheral inflammation. Herein, we divulge the repurposing of drugs in the area of neurodegenerative diseases, focusing on the therapeutic potential of antineoplastics to treat dementia due to AD and dementia. We briefly touch upon the shared pathological mechanism between AD and cancer and drug repurposing strategies, with a focus on artificial intelligence. Next, we bring out the current status of research on the development of drugs, provide supporting evidence from retrospective, clinical, and preclinical studies on antineoplastic use, and bring in new areas, such as repurposing drugs for the prion-like spreading of pathologies in treating AD.

Recent Advances in the Discovery of SIRT1/2 Inhibitors via Computational Methods: A Perspective

Sirtuins (SIRTs) are classified as class III histone deacetylases (HDACs), a family of enzymes that catalyze the removal of acetyl groups from the ε-N-acetyl lysine residues of histone proteins, thus counteracting the activity performed by histone acetyltransferares (HATs). Based on their involvement in different biological pathways, ranging from transcription to metabolism and genome stability, SIRT dysregulation was investigated in many diseases, such as cancer, neurodegenerative disorders, diabetes, and cardiovascular and autoimmune diseases. The elucidation of a consistent number of SIRT-ligand complexes helped to steer the identification of novel and more selective modulators. Due to the high diversity and quantity of the structural data thus far available, we reviewed some of the different ligands and structure-based methods that have recently been used to identify new promising SIRT1/2 modulators. The present review is structured into two sections: the first includes a comprehensive perspective of the successful computational approaches related to the discovery of SIRT1/2 inhibitors (SIRTIs); the second section deals with the most interesting SIRTIs that have recently appeared in the literature (from 2017). The data reported here are collected from different databases (SciFinder, Web of Science, Scopus, Google Scholar, and PubMed) using "SIRT", "sirtuin", and "sirtuin inhibitors" as keywords.

Discovery of a Novel Chemo-Type for TAAR1 Agonism via Molecular Modeling

The search for novel effective TAAR1 ligands continues to draw great attention due to the wide range of pharmacological applications related to TAAR1 targeting. Herein, molecular docking studies of known TAAR1 ligands, characterized by an oxazoline core, have been performed in order to identify novel promising chemo-types for the discovery of more active TAAR1 agonists. In particular, the oxazoline-based compound S18616 has been taken as a reference compound for the computational study, leading to the development of quite flat and conformationally locked ligands. The choice of a "Y-shape" conformation was suggested for the design of TAAR1 ligands, interacting with the protein cavity delimited by ASP103 and aromatic residues such as PHE186, PHE195, PHE268, and PHE267. The obtained results allowed us to preliminary in silico screen an in-house series of pyrimidinone-benzimidazoles (1a-10a) as a novel scaffold to target TAAR1. Combined ligand-based (LBCM) and structure based (SBCM) computational methods suggested the biological evaluation of compounds 1a-10a, leading to the identification of derivatives 1a-3a (hTAAR1 EC50 = 526.3-657.4 nM) as promising novel TAAR1 agonists.

The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials

Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.

Fluoxetine exerts anti-inflammatory effects on human epidermal keratinocytes and suppresses their endothelin release

Fluoxetine is a safe antidepressant with remarkable anti-inflammatory actions; therefore, we aimed to investigate its effects on immortalized (HaCaT) as well as primary human epidermal keratinocytes in a polyinosinic-polycytidylic acid (p(I:C))-induced inflammatory model. We found that a non-cytotoxic concentration (MTT-assay, CyQUANT-assay) of fluoxetine significantly suppressed p(I:C)-induced expression and release of several pro-inflammatory cytokines (Q-PCR, cytokine array, ELISA), and it decreased the release of the itch mediator endothelins (ELISA). These effects were not mediated by the inhibition of the NF-κB or p38 MAPK pathways (western blot), or by the suppression of the p(I:C)-induced elevation of mitochondrial ROS production (MitoSOX Red labeling). Instead, unbiased activity profiling revealed that they were most likely mediated via the inhibition of the phosphoinositide 3-kinase (PI3K) pathway. Importantly, the PI3K-inhibitor GDC0941 fully mimicked the effects of fluoxetine (Q-PCR, ELISA). Although fluoxetine was able to occupy the binding site of GDC0941 (in silico molecular docking), and exerted direct inhibitory effect on PI3K (cell-free PI3K activity assay), it exhibited much lower potency and efficacy as compared to GDC0941. Finally, RNA-Seq analysis revealed that fluoxetine deeply influenced the transcriptional alterations induced by p(I:C)-treatment, and exerted an overall anti-inflammatory activity. Collectively, our findings demonstrate that fluoxetine exerts potent anti-inflammatory effects, and suppresses the release of the endogenous itch mediator endothelins in human keratinocytes, most likely via interfering with the PI3K pathway. Thus, clinical studies are encouraged to explore whether the currently reported beneficial effects translate in vivo following its topical administration in inflammatory and pruritic dermatoses.

Computational Drug Design Strategies for Fighting the COVID-19 Pandemic

The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.

LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening

Background

Drug-target interaction (DTI) is a vital drug design strategy that plays a significant role in many processes of complex diseases and cellular events. In the face of challenges such as extensive protein data and experimental costs, it is suggested to apply bioinformatics approaches to exploit potential interactions to design new targeted medications. Different data and interaction types bring difficulties to study involving incompatible and heterology formats. The analysis of drug-target interactions in a comprehensive and unified model is a significant challenge.

Method

Here, we propose a general method for predicting interactions between small-molecule drugs and protein targets, Large-scale Drug target Screening Convolutional Neural Network (LDS-CNN), which used unified encoding to achieve the calculation of the different data formats in an integrated model to realize feature abstraction and potential object prediction.

Result

On 898,412 interaction data involving 1683 small-molecule compounds and 14,350 human proteins from 8.8 billion records, the proposed method achieved an area under the curve (AUC) of 0.96, an area under the precision-recall curve (AUPRC) of 0.95, and an accuracy of 90.13%. The experimental results illustrated that the proposed method attained high accuracy on the test set, indicating its high predictive ability in drug-target interaction prediction. LDS-CNN is effective for the prediction of large-scale datasets and datasets composed of data with different formats.

Conclusion

In this study, we propose a DTI prediction method to solve the problems of unified encoding of large-scale data in multiple formats. It provides a feasible way to efficiently abstract the features among different types of drug-related data, thus reducing experimental costs and time consumption. The proposed method can be used to identify potential drug targets and candidates for the treatment of complex diseases. This work provides a reference for DTI to process large-scale data and different formats with deep learning methods and provides certain suggestions for future research.

Novel Thiazole-Based SIRT2 Inhibitors Discovered via Molecular Modelling Studies and Enzymatic Assays

Recently, the development of sirtuin small molecule inhibitors (SIRTIs) has been gaining attention for the treatment of different cancer types, but also to contrast neurodegenerative disease, diabetes, and autoimmune syndromes. In the search for SIRT2 modulators, the availability of several X-crystallographic data regarding SIRT2-ligand complexes has allowed for setting up a structure-based study, which is herein presented. A set of 116 SIRT2 inhibitors featuring different chemical structures has been collected from the literature and used for molecular docking studies involving 4RMG and 5MAT PDB codes. The information found highlights key contacts with the SIRT2 binding pocket such as Van der Waals and π-π stacking with Tyr104, Phe119, Phe234, and Phe235 in order to achieve high inhibitory ability values. Following the preliminary virtual screening studies, a small in-house library of compounds (1a-7a), previously investigated as putative HSP70 inhibitors, was described to guide the search for dual-acting HSP70/SIRT2 inhibitors. Biological and enzymatic assays validated the whole procedure. Compounds 2a and 7a were found to be the most promising derivatives herein proposed.

Molecular docking analysis of natural compounds as TNF-α inhibitors for Crohn's disease management

Crohn's disease (CD) is a type of inflammatory bowel disease that is immune-mediated and affects the gastrointestinal tract. The chronic and severe nature of this condition leads to diminished health-related life quality, and frequent hospitalization. While medications such as sulfasalazine, corticosteroids, and immuno-suppressants are used to manage the condition, there are no definite treatments for pain and inflammation associated with CD. TNF-α is a prominent target, and medicines such as infliximab and adalimumab have pharmacological efficacy; however, they also have significant toxicity. Here, the natural compound library (2706 compounds) was screened against TNF-α to find natural TNF-α inhibitors to combat CD. The compounds namely ZINC5223934, ZINC6482465, ZINC4098633, ZINC1702729, and ZINC4649679 had higher binding affinity and interaction with the TNF-α protein than the positive control. Furthermore, these compounds had promising drug-like properties, indicating their potential for future exploration and optimization as TNF-α inhibitors for the treatment of CD.