Abstract: Analytical techniques are in high demand for the determination of organic capping agents on surfaces of metallic nanoparticles (NPs) such as gold (Au) and silver (Ag). In this study, the potential of laser desorption ionisation time-of-flight mass spectrometry (LDI-ToF-MS) as a technique fit for this purpose is demonstrated. First, a collection of reference spectra of most commonly used organic capping agents, including small molecules and polymers was established. Second, the robustness of the method was tested towards parameters like NP core material and NP size. In a third step, the quantitative capabilities of LDI-ToF-MS were determined. Finally, the potential to detect chemical alterations of the organic capping agent was evaluated. LDI-ToF-MS is able to detect capping agents ranging from small molecules (citric acid, tannic acid, lipoic acid) to large polymers (polyvinylpyrrolidone, branched polyethylenimine and methoxy polyethylene glycol sulfhydryl) on Au and Ag NPs based on characteristic signals for each capping agent. Small molecules showed characteristic fragment ions with low intensities, whereas polymers showed intense signals of the monomeric subunit. The NP concentration range comprises about two orders of magnitude with lowest detection limits of 5 mg/L or a capping agent concentration in the lower nM range. Changes in capping agent composition are detectable at NP concentrations in the g/L range. Thus, LDI-ToF-MS is particularly suitable for characterisation of polymer-capped NPs with high NP concentrations. This may be the case for quality control as part of the material synthesis and testing. Graphical abstract.

Abstract: IMPACT STATEMENT: Although connexins are known to be the major genetic factors associated with HI, only a few studies have investigated GJB4 and GJC3 variants among hearing-impaired patients. This study is the first to report GJB4 and GJC3 variants from an African HI cohort. We have demonstrated that GJB4 and GJC3 genes may not contribute significantly to HI in Ghana, hence these genes should not be considered for routine clinical screening in Ghana. However, it is important to study a larger population to determine the association of GJB4 and GJC3 variants with HI.

Abstract: Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.

Abstract: In a short time, the COVID-19 pandemic has left the world with over 25 million cases and staggering death tolls that are still rising. Treatments for SARS-CoV-2 infection are desperately needed as there are currently no approved drug therapies. With limited knowledge of viral mechanisms, a network controllability method of prioritizing existing drugs for repurposing efforts is optimal for quickly moving through the drug approval pipeline using limited, available, virus-specific data. Based on network topology and controllability, 16 proteins involved in translation, cellular transport, cellular stress, and host immune response are predicted as regulators of the SARS-CoV-2 infected cell. Of the 16, eight are prioritized as possible drug targets where two, PVR and SCARB1, are previously unexplored. Known compounds targeting these genes are suggested for viral inhibition study. Prioritized proteins in agreement with previous analysis and viral inhibition studies verify the ability of network controllability to predict biologically relevant candidates.

Abstract: Decades of research and experimental studies have investigated Huntington's disease (HD), a rare neurodegenerative disease. Similarly, several studies have investigated whether high/moderate adherence to the Mediterranean Diet and specific macro and micronutrients can decrease cognitive loss and provide a neuroprotective function to neurons. This review systematically identifies and examines studies that have investigated Mediterranean Diet adherence, micro- and macronutrients, supplementation and caloric intake in people with HD, in order to identify if dietary exposures resulted in improvement of disease symptoms, a delay in age of onset or if they contributed to an earlier age of onset in people with HD. A systematic search of PubMed, Directory of open access journal and HubMed was performed independently by two reviewers using specific search terms criteria for studies. The identified abstracts were screened and the studies were included in the review if they satisfied predetermined inclusion criteria. Reference screening of included studies was also performed. A total of 18 studies were included in the review. A few studies found that patients who had high/moderate adherence to Mediterranean Diet showed a slight improvement in their Unified Huntington's Disease Rating Scale and Total Functional Capacity. In addition, people with HD who had high Mediterranean Diet adherence showed an improvement in both cognitive and motor scores and had a better quality of life compared to patients who had low Mediterranean Diet adherence. Furthermore, a few studies showed that supplementation with specific nutrients, such as triheaptanoin, L-acetyl-carnitine and creatine, had no beneficial effect on the patients' Unified Huntington's Disease Rating Scale score. A few studies suggest that the Mediterranean Diet may confer a motor and cognitive benefit to people with HD. Unfortunately, there was little consistency among study findings. It is important for more research to be conducted to have a better understanding of which dietary exposures are beneficial and may result delaying age of onset or disease progression in people with HD.

Abstract: BACKGROUND: Several experimental models of polyglutamine (polyQ) diseases have been previously developed that are useful for studying disease progression in the primarily affected central nervous system. However, there is a missing link between cellular and animal models that would indicate the molecular defects occurring in neurons and are responsible for the disease phenotype in vivo. METHODS: Here, we used a computational approach to identify dysregulated pathways shared by an in vitro and an in vivo model of ATXN1(Q82) protein aggregation, the mutant protein that causes the neurodegenerative polyQ disease spinocerebellar ataxia type-1 (SCA1). RESULTS: A set of common dysregulated pathways were identified, which were utilized to construct cerebellum-specific protein-protein interaction (PPI) networks at various time-points of protein aggregation. Analysis of a SCA1 network indicated important nodes which regulate its function and might represent potential pharmacological targets. Furthermore, a set of drugs interacting with these nodes and predicted to enter the blood-brain barrier (BBB) was identified. CONCLUSIONS: Our study points to molecular mechanisms of SCA1 linked from both cellular and animal models and suggests drugs that could be tested to determine whether they affect the aggregation of pathogenic ATXN1 and SCA1 disease progression.

Abstract: BACKGROUND: Inflammatory bowel disease (IBD) is a severe digestive system condition, characterized by chronic and relapsing inflammation of the gastrointestinal tract. Scutellaria baicalensis Georgi (Huangqin, HQ) and Paeonia lactiflora Pall (Baishao, BS) from a typical herbal synergic pair in traditional Chinese medicine (TCM) for IBD treatments. However, the mechanisms of action for the synergy are still unclear. Therefore, this paper aimed to predict the anti-IBD targets and the main active ingredients of the HQ-BS herbal pair. METHODS: A systems pharmacology approach was used to identify the bioactive compounds and to delineate the molecular targets and potential pathways of HQ-BS herbal pair. Then, the characteristics of the candidates were analyzed according to their oral bioavailability and drug-likeness indices. Finally, gene enrichment analysis with DAVID Bioinformatics Resources was performed to identify the potential pathways associated with the candidate targets. RESULTS: The results showed that, a total of 38 active compounds were obtained from HQ-BS herbal pair, and 54 targets associated with IBD were identified. Gene Ontology and pathway enrichment analysis yielded the top 20 significant results with 54 targets. Furthermore, the integrated IBD pathway revealed that the HQ-BS herbal pair probably acted in patients with IBD through multiple mechanisms of regulation of the nitric oxide biosynthetic process and anti-inflammatory effects. In addition, cell experiments were carried out to verify that the HQ-BS herbal pair and their Q-markers could attenuate the levels of nitric oxide (NO), prostaglandin E2 (PGE2), inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) in lipopolysaccharide (LPS)-stimulated THP-1-derived macrophage inflammation. In particular, the crude materials exerted a much better anti-inflammatory effect than their Q-markers, which might be due to their synergistic effect. CONCLUSION: This study provides novel insight into the molecular pathways involved in the mechanisms of the HQ-BS herbal pair acting on IBD.

Abstract: Cilia are dynamic microtubule-based organelles present on the surface of many eukaryotic cell types and can be motile or non-motile primary cilia. Cilia defects underlie a growing list of human disorders, collectively called ciliopathies, with overlapping phenotypes such as developmental delays and cognitive and memory deficits. Consistent with this, cilia play an important role in brain development, particularly in neurogenesis and neuronal migration. These findings suggest that a deeper systems-level understanding of how ciliary proteins function together may provide new mechanistic insights into the molecular etiologies of nervous system defects. Towards this end, we performed a protein-protein interaction (PPI) network analysis of known intraflagellar transport, BBSome, transition zone, ciliary membrane and motile cilia proteins. Known PPIs of ciliary proteins were assembled from online databases. Novel PPIs were predicted for each ciliary protein using a computational method we developed, called High-precision PPI Prediction (HiPPIP) model. The resulting cilia "interactome" consists of 165 ciliary proteins, 1,011 known PPIs, and 765 novel PPIs. The cilia interactome revealed interconnections between ciliary proteins, and their relation to several pathways related to neuropsychiatric processes, and to drug targets. Approximately 184 genes in the cilia interactome are targeted by 548 currently approved drugs, of which 103 are used to treat various diseases of nervous system origin. Taken together, the cilia interactome presented here provides novel insights into the relationship between ciliary protein dysfunction and neuropsychiatric disorders, for e.g. interconnections of Alzheimer's disease, aging and cilia genes. These results provide the framework for the rational design of new therapeutic agents for treatment of ciliopathies and neuropsychiatric disorders.

Abstract: Since 1977, the World Health Organization publishes a list of essential medicines, i.e., those that satisfy the priority health care needs of the population and are selected with regard to disease prevalence and public health relevance, evidence of clinical efficacy, and safety, as well as comparative costs and cost-effectiveness. The Essential Medicines List (EML) is an invaluable tool for all countries to select those medicines that have an excellent risk/benefit ratio and that are reputed to be of pivotal importance to health. In the present perspective, we describe the chemical composition and the main features of the small molecules that are included in the EML, spanning from their origin, to their stereochemistry and measure of drug-likeness. Most and foremost, we wish to disseminate the importance of the EML, which can be both a helpful teaching tool in an ever-expanding world of medicines and an inspiration for those involved in pharmaceutical R&D.

Abstract: Metabolic processes in the human body can alter the structure of a drug affecting its efficacy and safety. As a result, the investigation of the metabolic fate of a candidate drug is an essential part of drug design studies. Computational approaches have been developed for the prediction of possible drug metabolites in an effort to assist the traditional and resource-demanding experimental route. Current methodologies are based upon metabolic transformation rules, which are tied to specific enzyme families and therefore lack generalization, and additionally may involve manual work from experts limiting scalability. We present a rule-free, end-to-end learning-based method for predicting possible human metabolites of small molecules including drugs. The metabolite prediction task is approached as a sequence translation problem with chemical compounds represented using the SMILES notation. We perform transfer learning on a deep learning transformer model for sequence translation, originally trained on chemical reaction data, to predict the outcome of human metabolic reactions. We further build an ensemble model to account for multiple and diverse metabolites. Extensive evaluation reveals that the proposed method generalizes well to different enzyme families, as it can correctly predict metabolites through phase I and phase II drug metabolism as well as other enzymes. Compared to existing rule-based approaches, our method has equivalent performance on the major enzyme families while it additionally finds metabolites through less common enzymes. Our results indicate that the proposed approach can provide a comprehensive study of drug metabolism that does not restrict to the major enzyme families and does not require the extraction of transformation rules.