Abstract: (1) Background: Clozapine is the most effective antipsychotic. It is, however, associated with many adverse drug reactions. Nose-to-brain (N2B) delivery offers a promising approach. This study aims to develop clozapine-encapsulated thermosensitive sol-gels for N2B delivery. (2) Methods: Poloxamer 407 and hydroxypropyl methylcellulose were mixed and hydrated with water. Glycerin and carbopol solutions were added to the mixture and stirred overnight at 2-8 degrees C. Clozapine 0.1% w/w was stirred with polysorbate 20 (PS20) or polysorbate 80 (PS80) at RT (25 degrees C) before being added to the polymer solution. The final formulation was made to 10 g with water, stirred overnight at 2-8 degrees C and then adjusted to pH 5.5. (3) Results: Formulations F3 (3% PS20) and F4 (3% PS80) were selected for further evaluation, as their gelation temperatures were near 28 degrees C. The hydrodynamic particle diameter of clozapine was 18.7 +/- 0.2 nm in F3 and 20.0 +/- 0.4 nm in F4. The results show a crystallinity change in clozapine to amorphous. Drug release studies showed a 59.1 +/- 3.0% (F3) and 53.1 +/- 2.7% (F4) clozapine release after 72 h. Clozapine permeated after 8 h was 20.8 +/- 3.0% (F3) and 17.8 +/- 3.1% (F4). The drug deposition was higher with F4 (144.8 +/- 1.4 microg/g) than F3 (110.7 +/- 2.7 microg/g). Both sol-gels showed no phase separation after 3 months. (4) Conclusions: Binary PS80-P407 mixed micelles were more thermodynamically stable and rigid due to the higher synergism of both surfactants. However, binary mixed PS20-P407 micelles showed better drug permeation across the nasal mucosa tissue and may be a preferable carrier system for the intranasal administration of clozapine.

Abstract: Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

Abstract: BACKGROUND: Drug-disease associations (DDAs) can provide important information for exploring the potential efficacy of drugs. However, up to now, there are still few DDAs verified by experiments. Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. How to integrate different biological data sources and identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms is still a challenging problem. RESULTS: In this paper, we proposed a novel computation model for DDA predictions based on graph representation learning over multi-biomolecular network (GRLMN). More specifically, we firstly constructed a large-scale molecular association network (MAN) by integrating the associations among drugs, diseases, proteins, miRNAs, and lncRNAs. Then, a graph embedding model was used to learn vector representations for all drugs and diseases in MAN. Finally, the combined features were fed to a random forest (RF) model to predict new DDAs. The proposed model was evaluated on the SCMFDD-S data set using five-fold cross-validation. Experiment results showed that GRLMN model was very accurate with the area under the ROC curve (AUC) of 87.9%, which outperformed all previous works in terms of both accuracy and AUC in benchmark dataset. To further verify the high performance of GRLMN, we carried out two case studies for two common diseases. As a result, in the ranking of drugs that were predicted to be related to certain diseases (such as kidney disease and fever), 15 of the top 20 drugs have been experimentally confirmed. CONCLUSIONS: The experimental results show that our model has good performance in the prediction of DDA. GRLMN is an effective prioritization tool for screening the reliable DDAs for follow-up studies concerning their participation in drug reposition.

Abstract: BACKGROUND: Detecting pathogenic proteins is the origin way to understand the mechanism and resist the invasion of diseases, making pathogenic protein prediction develop into an urgent problem to be solved. Prediction for genome-wide proteins may be not necessarily conducive to rapidly cure diseases as developing new drugs specifically for the predicted pathogenic protein always need major expenditures on time and cost. In order to facilitate disease treatment, computational method to predict pathogenic proteins which are targeted by existing drugs should be exploited. RESULTS: In this study, we proposed a novel computational model to predict drug-targeted pathogenic proteins, named as M2PP. Three types of features were presented on our constructed heterogeneous network (including target proteins, diseases and drugs), which were based on the neighborhood similarity information, drug-inferred information and path information. Then, a random forest regression model was trained to score unconfirmed target-disease pairs. Five-fold cross-validation experiment was implemented to evaluate model's prediction performance, where M2PP achieved advantageous results compared with other state-of-the-art methods. In addition, M2PP accurately predicted high ranked pathogenic proteins for common diseases with public biomedical literature as supporting evidence, indicating its excellent ability. CONCLUSIONS: M2PP is an effective and accurate model to predict drug-targeted pathogenic proteins, which could provide convenience for the future biological researches.

Abstract: Religiously integrated interventions for treating mental illnesses have proved effective. However, many studies have yet to adequately address the effects of Islamic religious-based rituals on mental health among Muslims. The present study investigated the impact of a purposefully designed Islamic religion-based intervention on reducing depression and anxiety disorders among Muslim patients using a randomised controlled trial design. A total of 62 Muslim patients (30 women and 32 men) were divided by gender into two groups, with each group assigned randomly to either treatment or control groups. The participants who received the Islamic-based intervention were compared to participants who received the control intervention. Taylor's (cite date) manifest anxiety scale and Steer and Beck's (cite the date) depression scale were used to examine the effects on depression and anxiety levels. ANCOVA results revealed that the Islamic intervention significantly reduced anxiety levels in women (d = 0.75) and depression levels in men (d = 0.80) compared to the typical care control groups.

Abstract: Objective: To summarize the currently available phase I and II clinical trials of the effects of nonoxynol-9 (N-9) on human sperm structure and functions. Methods: A systematic review and meta-analysis aiming to evaluate the spermicidal activity of N-9 on motility, was conducted in PubMed, EMBASE, and Cochrane databases by 10 March 2021. The counted numbers of progressive motile (PR) sperm in cervical mucus and the vanguard sperm penetration distances were analyzed. Other effects on sperm structures and physiological activities were reviewed as well. Results: In the pooled results, percentages or counted numbers of PR sperm decreased after the treatment of N-9. Vanguard sperm penetration distance was shortened in treated groups. N-9 has been confirmed to damage the structures of sperm, as well as other organelles like acrosome and mitochondria. The physiological activities such as generation of reactive oxygen species, superoxide dismutase activity, acrosin activity, and hemizona binding were all inhibited in the reviewed studies. Conclusions: N-9 has several impacts on sperm owing to its potency in reducing sperm motility and cervical mucus penetration, as well as other functional competencies. Lay summary: Nonoxynol-9 (N-9) has been used worldwide as a spermicide to kill sperm for more than 60 years but can cause side effects including vaginal irritation and can increase the rate of contraceptive failure. A detailed analysis of published literature aiming to evaluate the spermicidal activity of N-9 on sperm was carried out. In the pooled results, N-9 reduced the number of active sperm and the distance they traveled. It also caused damage to the structures of sperm and to the way the sperm acted and interacted with the egg. In conclusion, N-9 impacts on sperm in a number of ways that lead to sperm death and dysfunction.

Abstract: Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios.

Abstract: Exploring drug-target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug-target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug-target interaction prediction research. In this review, details of the specific applications of machine learning in drug-target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models.

Abstract: Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.

Abstract: The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.