Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs

First-in-Class Selenium-Containing Potent Serotonin Receptor 5-HT6 Agents with a Beneficial Neuroprotective Profile against Alzheimer's Disease

Alzheimer's disease (AD) has a complex and not-fully-understood etiology. Recently, the serotonin receptor 5-HT6 emerged as a promising target for AD treatment; thus, here a new series of 5-HT6R ligands with a 1,3,5-triazine core and selenoether linkers was explored. Among them, the 2-naphthyl derivatives exhibited strong 5-HT6R affinity and selectivity over 5-HT1AR (13-15), 5-HT7R (14 and 15), and 5-HT2AR (13). Compound 15 displayed high selectivity for 5-HT6R over other central nervous system receptors and exhibited low risk of cardio-, hepato-, and nephrotoxicity and no mutagenicity, indicating its "drug-like" potential. Compound 15 also demonstrated neuroprotection against rotenone-induced neurotoxicity as well as antioxidant and glutathione peroxidase (GPx)-like activity and regulated antioxidant and pro-inflammatory genes and NRF2 nuclear translocation. In rats, 15 showed satisfying pharmacokinetics, penetrated the blood-brain barrier, reversed MK-801-induced memory impairment, and exhibited anxiolytic-like properties. 15's neuroprotective and procognitive-like effects, stronger than those of the approved drug donepezil, may pave the way for the use of selenotriazines to inhibit both causes and symptoms in AD therapy.

DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations

Background: Drug repurposing provides an effective method for high-speed, low-risk drug development. Clinical phenotype-based screening exceeded target-based approaches in discovering first-in-class small-molecule drugs. However, most of these approaches predict only binary phenotypic associations between drugs and diseases; the types of drug and diseases have not been well exploited. Principally, the clinical phenotypes of a known drug can be divided into indications (Is), side effects (SEs), and contraindications (CIs). Incorporating these different clinical phenotypes of drug–disease associations (DDAs) can improve the prediction accuracy of the DDAs.

Methods: We develop Drug Disease Interaction Type (DDIT), a user-friendly online predictor that supports drug repositioning by submitting known Is, SEs, and CIs for a target drug of interest. The dataset for Is, SEs, and CIs was extracted from PREDICT, SIDER, and MED-RT, respectively. To unify the names of the drugs and diseases, we mapped their names to the Unified Medical Language System (UMLS) ontology using Rest API. We then integrated multiple clinical phenotypes into a conditional restricted Boltzmann machine (RBM) enabling the identification of different phenotypes of drug–disease associations, including the prediction of as yet unknown DDAs in the input.

Results: By 10-fold cross-validation, we demonstrate that DDIT can effectively capture the latent features of the drug–disease association network and represents over 0.217 and over 0.072 improvement in AUC and AUPR, respectively, for predicting the clinical phenotypes of DDAs compared with the classic K-nearest neighbors method (KNN, including drug-based KNN and disease-based KNN), Random Forest, and XGBoost. By conducting leave-one-drug-class-out cross-validation, the AUC and AUPR of DDIT demonstrated an improvement of 0.135 in AUC and 0.075 in AUPR compared to any of the other four methods. Within the top 10 predicted indications, side effects, and contraindications, 7/10, 9/10, and 9/10 hit known drug–disease associations. Overall, DDIT is a useful tool for predicting multiple clinical phenotypic types of drug–disease associations.

Multifunctional Arylsulfone and Arylsulfonamide-Based Ligands with Prominent Mood-Modulating Activity and Benign Safety Profile, Targeting Neuropsychiatric Symptoms of Dementia

The current pharmaceutical market lacks therapeutic agents designed to modulate behavioral disturbances associated with dementia. To address this unmet medical need, we designed multifunctional ligands characterized by a nanomolar affinity for clinically relevant targets that are associated with the disease pathology, namely, the 5-HT_2A/6/7 and D₂ receptors. Compounds that exhibited favorable functional efficacy, water solubility, and metabolic stability were selected for more detailed study. Pharmacological profiling revealed that compound 11 exerted pronounced antidepressant activity (MED 0.1 mg/kg), outperforming commonly available antidepressant drugs, while compound 16 elicited a robust anxiolytic activity (MED 1 mg/kg), exceeding comparator anxiolytics. In contrast to the existing psychotropic agents tested, the novel chemotypes did not negatively impact cognition. At a chronic dose regimen (25 days), 11 did not induce significant metabolic or adverse blood pressure disturbances. These promising therapeutic-like activities and benign safety profiles make the novel chemotypes potential treatment options for dementia patients.

Management of Dementia-Related Psychosis, Agitation and Aggression: A Review of the Pharmacology and Clinical Effects of Potential Drug Candidates

Along with cognitive decline, 90% of patients with dementia experience behavioral and psychological symptoms of dementia, such as psychosis, aggression, agitation, and depression. Atypical antipsychotics are commonly prescribed off-label to manage certain symptoms, despite warnings from the regulatory agencies regarding the increased risk of mortality associated with their use in elderly patients. Moreover, these compounds display a limited clinical efficacy, mostly owing to the fact that they were developed to treat schizophrenia, a disease characterized by neurobiological deficits. Thus, to improve clinical efficacy, it has been suggested that patients with dementia should be treated with exclusively designed and developed drugs that interact with pharmacologically relevant targets. Within this context, numerous studies have suggested druggable targets that might achieve therapeutically acceptable pharmacological profiles. Based on this, several different drug candidates have been proposed that are being investigated in clinical trials for behavioral and psychological symptoms of dementia. We highlight the recent advances toward the development of therapeutic agents for dementia-related psychosis and agitation/aggression and discuss the relationship between the relevant biological targets and their etiology. In addition, we review the compounds that are in the early stage of development (discovery or preclinical phase) and those that are currently being investigated in clinical trials for dementia-related psychosis and agitation/aggression. We also discuss the mechanism of action of these compounds and their pharmacological utility in patients with dementia.

Drug repositioning using drug-disease vectors based on an integrated network

BACKGROUND

Diverse interactions occur between biomolecules, such as activation, inhibition, expression, or repression. However, previous network-based studies of drug repositioning have employed interaction on the binary protein-protein interaction (PPI) network without considering the characteristics of the interactions. Recently, some studies of drug repositioning using gene expression data found that associations between drug and disease genes are useful information for identifying novel drugs to treat diseases. However, the gene expression profiles for drugs and diseases are not always available. Although gene expression profiles of drugs and diseases are available, existing methods cannot use the drugs or diseases, when differentially expressed genes in the profiles are not included in their network.

RESULTS

We developed a novel method for identifying candidate indications of existing drugs considering types of interactions between biomolecules based on known drug-disease associations. To obtain associations between drug and disease genes, we constructed a directed network using protein interaction and gene regulation data obtained from various public databases providing diverse biological pathways. The network includes three types of edges depending on relationships between biomolecules. To quantify the association between a target gene and a disease gene, we explored the shortest paths from the target gene to the disease gene and calculated the types and weights of the shortest paths. For each drug-disease pair, we built a vector consisting of values for each disease gene influenced by the drug. Using the vectors and known drug-disease associations, we constructed classifiers to identify novel drugs for each disease.

CONCLUSION

We propose a method for exploring candidate drugs of diseases using associations between drugs and disease genes derived from a directed gene network instead of gene regulation data obtained from gene expression profiles. Compared to existing methods that require information on gene relationships and gene expression data, our method can be applied to a greater number of drugs and diseases. Furthermore, to validate our predictions, we compared the predictions with drug-disease pairs in clinical trials using the hypergeometric test, which showed significant results. Our method also showed better performance compared to existing methods for the area under the receiver operating characteristic curve (AUC).

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.

In vitro activities of kappa-carrageenan isolated from red marine alga Hypnea musciformis: Antimicrobial, anticancer and neuroprotective potential

This study assessed the antioxidant, antimicrobial, anticancer and neuroprotective activities of the kappa(k)-carrageenan isolated from the red alga Hypnea musciformis (Hm-SP). The chemical spectrum of the k-carrageenan from Hm-SP was confirmed by Fourier transform infrared (FT-IR) spectroscopy. Hm-SP revealed an antibacterial and antifungal action against Staphylococcus aureus and Candida albicans, respectively. Hm-SP did not promoted cytotoxic effects against Human breast cancer (MCF-7) and Human neuroblastoma (SH-SY5Y) cell-lines. However, it was observed a significant reduction of the cellular proliferation capacity in these cancer cells in presence of the Hm-SP. Furthermore, Hm-SP showed neuroprotective activity in 6-hydroxydopamine-induced neurotoxicity on SH-SY5Y cells by modulation of the mitochondria transmembrane potential and reducing Caspase 3 activity. In addition, Hm-SP demonstrates low antioxidant potential and did not induce significant cytotoxic effects or changes in the cell proliferation on Balb/c 3T3 mouse fibroblast cell-line. In summary, our data suggest that Hm-SP shows antimicrobial, anticancer and neuprotective activities.

Finding melanoma drugs through a probabilistic knowledge graph

Metastatic cutaneous melanoma is an aggressive skin cancer with some progression-slowing treatments but no known cure. The omics data explosion has created many possible drug candidates; however, filtering criteria remain challenging, and systems biology approaches have become fragmented with many disconnected databases. Using drug, protein and disease interactions, we built an evidence-weighted knowledge graph of integrated interactions. Our knowledge graph-based system, ReDrugS, can be used via an application programming interface or web interface, and has generated 25 high-quality melanoma drug candidates. We show that probabilistic analysis of systems biology graphs increases drug candidate quality compared to non-probabilistic methods. Four of the 25 candidates are novel therapies, three of which have been tested with other cancers. All other candidates have current or completed clinical trials, or have been studied in in vivo or in vitro. This approach can be used to identify candidate therapies for use in research or personalized medicine.

Chondrocyte proliferation, viability and differentiation is declined following administration of methylphenidate utilized for the treatment of attention-deficit/hyperactivity disorder

PURPOSE

Methylphenidate (MPH) derivative drugs are used because of psychostimulants effects on attention-deficit hyperactivity disorder in children and adults. As far as we know, toxic or anti-proliferative effects of MPH against cartilage tissue were not studied in the literature. The present study was carried out to investigate the possible effects of MPH on the proliferation, viability and differentiation of primary human chondrocytes, in vitro.

METHODS

Monolayer primary chondrocyte cultures were prepared using osteochondral tissue obtained from patients who underwent a total knee prosthesis operation. Stock solution of MPH was prepared and aliquots having 1-1000 µM concentrations of the drug was composed. These solutions were applied to the wells containing cultured chondrocyte samples within the well plates. Control groups were composed of pure chondrocyte culture and no solution was added into them. All groups were evaluated at 24, 48 and 72 h in order to determine the possible negative effects of the drug on the chondrocytes. The data were evaluated by Tukey's honestly significantly different test following analysis of variance.

RESULTS

In the group where MPH was applied, it was found that viability, proliferation and stage-specific embryonic antigen-1 protein expression were decreased in comparison to the control group.

CONCLUSIONS

It was emphasized that clinicians should not disregard the fact that this drug might suppress chondrocyte cell proliferation and chondrogenic differentiation.

A Novel Drug-Mouse Phenotypic Similarity Method Detects Molecular Determinants of Drug Effects

The molecular mechanisms that translate drug treatment into beneficial and unwanted effects are largely unknown. We present here a novel approach to detect gene-drug and gene-side effect associations based on the phenotypic similarity of drugs and single gene perturbations in mice that account for the polypharmacological property of drugs. We scored the phenotypic similarity of human side effect profiles of 1,667 small molecules and biologicals to profiles of phenotypic traits of 5,384 mouse genes. The benchmarking with known relationships revealed a strong enrichment of physical and indirect drug-target connections, causative drug target-side effect links as well as gene-drug links involved in pharmacogenetic associations among phenotypically similar gene-drug pairs. The validation by in vitro assays and the experimental verification of an unknown connection between oxandrolone and prokineticin receptor 2 reinforces the ability of this method to provide new molecular insights underlying drug treatment. Thus, this approach may aid in the proposal of novel and personalized treatments.

In order to avoid unwanted effects of current drug interventions, it is necessary to expand the knowledge of the molecular mechanisms related to drug action. Side effects offer insight into drug action, as for example similar side effects of unrelated drugs can be caused by their common off-targets. Moreover, the phenotypes of systematic single gene perturbation screenings in mice strongly contribute to the comprehension of gene function. Here, we present a novel approach that detects molecular interactions of drugs based on the phenotypic similarity of drugs and mouse models. The method is benchmarked with diverse data sets including drug-target interactions as well as gene-drug links of pharmacogenetic associations and validated by in vitro assays.

STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data

Interactions between proteins and small molecules are an integral part of biological processes in living organisms. Information on these interactions is dispersed over many databases, texts and prediction methods, which makes it difficult to get a comprehensive overview of the available evidence. To address this, we have developed STITCH (‘Search Tool for Interacting Chemicals’) that integrates these disparate data sources for 430 000 chemicals into a single, easy-to-use resource. In addition to the increased scope of the database, we have implemented a new network view that gives the user the ability to view binding affinities of chemicals in the interaction network. This enables the user to get a quick overview of the potential effects of the chemical on its interaction partners. For each organism, STITCH provides a global network; however, not all proteins have the same pattern of spatial expression. Therefore, only a certain subset of interactions can occur simultaneously. In the new, fifth release of STITCH, we have implemented functionality to filter out the proteins and chemicals not associated with a given tissue. The STITCH database can be downloaded in full, accessed programmatically via an extensive API, or searched via a redesigned web interface at http://stitch.embl.de.

Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases

Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.

Organ system heterogeneity DB: a database for the visualization of phenotypes at the organ system level

Perturbations of mammalian organisms including diseases, drug treatments and gene perturbations in mice affect organ systems differently. Some perturbations impair relatively few organ systems while others lead to highly heterogeneous or systemic effects. Organ System Heterogeneity DB (http://mips.helmholtz-muenchen.de/Organ_System_Heterogeneity/) provides information on the phenotypic effects of 4865 human diseases, 1667 drugs and 5361 genetically modified mouse models on 26 different organ systems. Disease symptoms, drug side effects and mouse phenotypes are mapped to the System Organ Class (SOC) level of the Medical Dictionary of Regulatory Activities (MedDRA). Then, the organ system heterogeneity value, a measurement of the systemic impact of a perturbation, is calculated from the relative frequency of phenotypic features across all SOCs. For perturbations of interest, the database displays the distribution of phenotypic effects across organ systems along with the heterogeneity value and the distance between organ system distributions. In this way, it allows, in an easy and comprehensible fashion, the comparison of the phenotypic organ system distributions of diseases, drugs and their corresponding genetically modified mouse models of associated disease genes and drug targets. The Organ System Heterogeneity DB is thus a platform for the visualization and comparison of organ system level phenotypic effects of drugs, diseases and genes.