Translational Bioinformatics Approaches to Drug Development

Identification of bio-active secondary metabolites from Actinobacteria as potential drug targets against Porphyromonas gingivalis in oral squamous cell carcinoma using molecular docking and dynamics study

Chronic periodontitis caused by the bacteria Porphyromonas gingivalis is thought to be a risk factor for the advancement of oral squamous cell carcinoma (OSCC). Virulence factors of P. gingivalis include gingipains, outer membrane surface lipoproteins, and fimbriae contribute to the activation of oncogenic pathways in OSCC by up-regulating different cytokines. Gingipains (Arg and Lys) proteases have an important role in the activation of proMMP-9, which promotes cellular invasion and metastatic ability of OSCC. Thus gingipains and MMP-9 were actively investigated as potential therapeutic targets in OSCC therapy. Various natural bioactive compounds from Actinobacteria have been explored for their anticancer potential in a variety of cancers, but very few studies have been reported in OSCC. Therefore, the current study is focused to identify potential actinobacterial compounds that can be considered as a therapeutic target against gingipains and inflammatory proteins in OSCC through high-throughput virtual screening, Molecular Docking (MD), and Molecular Dynamics Simulation (MDS) approaches. A total of 179 bioactive secondary metabolites of Actinobacteria were explored for their binding affinity against six virulence proteins of P. gingivalis. The Molecular Docking studies revealed that among 179 metabolites screened, Actinosporin G showed a highly acceptable binding affinity of -7.9 kcal/mol with RgpB (1CVR), and exhibited multi-protein targeting and drug-likeness property and passed level of toxicity. Comprehensive docking interaction of the best top-ranked Actinosporin G with OSCC-related protein targets illustrated high binding affinity towards MMP-9 and JAK-1 proteins among all targeted receptor proteins. The molecular dynamic (MD) simulation has been executed for the metabolite Actinosporin G for both bacterial gingipain (RgpB) and MMP-9 & JAK-1 showed stable intermolecular binding with both hydrogen and hydrophobic interactions. In conclusion, this work suggests that the bioactive secondary metabolite of Actinosporin G from Actinobacteria genera may serve as a promising therapy for P. gingivalis-induced OSCC.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00209-0.

Treatment of cancer with antipsychotic medications: Pushing the boundaries of schizophrenia and cancer

Over a century ago, the phenothiazine dye, methylene blue, was discovered to have both antipsychotic and anti-cancer effects. In the 20th-century, the first phenothiazine antipsychotic, chlorpromazine, was found to inhibit cancer. During the years of elucidating the pharmacology of the phenothiazines, reserpine, an antipsychotic with a long historical background, was likewise discovered to have anti-cancer properties. Research on the effects of antipsychotics on cancer continued slowly until the 21st century when efforts to repurpose antipsychotics for cancer treatment accelerated. This review examines the history of these developments, and identifies which antipsychotics might treat cancer, and which cancers might be treated by antipsychotics. The review also describes the molecular mechanisms through which antipsychotics may inhibit cancer. Although the overlap of molecular pathways between schizophrenia and cancer have been known or suspected for many years, no comprehensive review of the subject has appeared in the psychiatric literature to assess the significance of these similarities. This review fills that gap and discusses what, if any, significance the similarities have regarding the etiology of schizophrenia.

Bioinformatics Accelerates the Major Tetrad: A Real Boost for the Pharmaceutical Industry

With advanced technology and its development, bioinformatics is one of the avant-garde fields that has managed to make amazing progress in the pharmaceutical-medical field by modeling the infrastructural dimensions of healthcare and integrating computing tools in drug innovation, facilitating prevention, detection/more accurate diagnosis, and treatment of disorders, while saving time and money. By association, bioinformatics and pharmacovigilance promoted both sample analyzes and interpretation of drug side effects, also focusing on drug discovery and development (DDD), in which systems biology, a personalized approach, and drug repositioning were considered together with translational medicine. The role of bioinformatics has been highlighted in DDD, proteomics, genetics, modeling, miRNA discovery and assessment, and clinical genome sequencing. The authors have collated significant data from the most known online databases and publishers, also narrowing the diversified applications, in order to target four major areas (tetrad): DDD, anti-microbial research, genomic sequencing, and miRNA research and its significance in the management of current pandemic context. Our analysis aims to provide optimal data in the field by stratification of the information related to the published data in key sectors and to capture the attention of researchers interested in bioinformatics, a field that has succeeded in advancing the healthcare paradigm by introducing developing techniques and multiple database platforms, addressed in the manuscript.

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

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.

Master Regulators Connectivity Map: A Transcription Factors-Centered Approach to Drug Repositioning

Drug discovery is a very expensive and time-consuming endeavor. Fortunately, recent omics technologies and Systems Biology approaches introduced interesting new tools to achieve this task, facilitating the repurposing of already known drugs to new therapeutic assignments using gene expression data and bioinformatics. The inherent role of transcription factors in gene expression modulation makes them strong candidates for master regulators of phenotypic transitions. However, transcription factors expression itself usually does not reflect its activity changes due to post-transcriptional modifications and other complications. In this aspect, the use of high-throughput transcriptomic data may be employed to infer transcription factors-targets interactions and assess their activity through co-expression networks, which can be further used to search for drugs capable of reverting the gene expression profile of pathological phenotypes employing the connectivity maps paradigm. Following this idea, we argue that a module-oriented connectivity map approach using transcription factors-centered networks would aid the query for new repositioning candidates. Through a brief case study, we explored this idea in bipolar disorder, retrieving known drugs used in the usual clinical scenario as well as new candidates with potential therapeutic application in this disease. Indeed, the results of the case study indicate just how promising our approach may be to drug repositioning.

Big Data in Drug Discovery

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

MeSHDD: Literature-based drug-drug similarity for drug repositioning

OBJECTIVE

Drug repositioning is a promising methodology for reducing the cost and duration of the drug discovery pipeline. We sought to develop a computational repositioning method leveraging annotations in the literature, such as Medical Subject Heading (MeSH) terms.

METHODS

We developed software to determine significantly co-occurring drug-MeSH term pairs and a method to estimate pair-wise literature-derived distances between drugs.

RESULTS

We found that literature-based drug-drug similarities predicted the number of shared indications across drug-drug pairs. Clustering drugs based on their similarity revealed both known and novel drug indications. We demonstrate the utility of our approach by generating repositioning hypotheses for the commonly used diabetes drug metformin.

CONCLUSION

Our study demonstrates that literature-derived similarity is useful for identifying potential repositioning opportunities. We provided open-source code and deployed a free-to-use, interactive application to explore our database of similarity-based drug clusters (available at http://apps.chiragjpgroup.org/MeSHDD/ ).

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.

ksRepo: a generalized platform for computational drug repositioning

Background

Repositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in repositioning, however, the majority of available methodologies require highly specific data inputs that preclude the use of many datasets and databases. There is a clear unmet need for a generalized methodology that enables the integration of multiple types of both gene expression data and database schema.

Results

ksRepo eliminates the need for a single microarray platform as input and allows for the use of a variety of drug and chemical exposure databases. We tested ksRepo’s performance on a set of five prostate cancer datasets using the Comparative Toxicogenomics Database (CTD) as our database of gene-compound interactions. ksRepo successfully predicted significance for five frontline prostate cancer therapies, representing a significant enrichment from over 7000 CTD compounds, and achieved specificity similar to other repositioning methods.

Conclusions

We present ksRepo, which enables investigators to use any data inputs for computational drug repositioning. ksRepo is implemented in a series of four functions in the R statistical environment under a BSD3 license. Source code is freely available at http://github.com/adam-sam-brown/ksRepo. A vignette is provided to aid users in performing ksRepo analysis.

Novel insight into drug repositioning: Methylthiouracil as a case in point

Drug repositioning refers to the development of existing drugs for new indications. These drugs may have (I) failed to show efficacy in late stage clinical trials without safety issues; (II) stalled in the development for commercial reasons; (III) passed the point of patent expiry; or (IV) are being explored in new geographic markets. Over the past decade, pressure on the pharmaceutical industry caused by the 'innovation gap' owing to rising development costs and stagnant product output have become major reasons for the growing interest in drug repositioning. Companies that offer a variety of broad platforms for identifying new indications have emerged; some have been successful in building their own pipelines of candidates with reduced risks and timelines associated with further clinical development. The business models and platforms offered by these companies will be validated if they are able to generate positive proof-of-concept clinical data for their repositioned compounds. This review describes the strategy of biomarker-guided repositioning of chemotherapeutic drugs for inflammation therapy, considering the repositioning of methylthiouracil (MTU), an antithyroid drug, as a potential anti-inflammatory reagent.

Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction

Background

Addictions to alcohol and tobacco, known risk factors for cancer, are complex heritable disorders. Addictive behaviors have a bidirectional relationship with pain. We hypothesize that the associations between alcohol, smoking, and opioid addiction observed in cancer patients have a genetic basis. Therefore, using bioinformatics tools, we explored the underlying genetic basis and identified new candidate genes and common biological pathways for smoking, alcohol, and opioid addiction.

Results

Literature search showed 56 genes associated with alcohol, smoking and opioid addiction. Using Core Analysis function in Ingenuity Pathway Analysis software, we found that ERK1/2 was strongly interconnected across all three addiction networks. Genes involved in immune signaling pathways were shown across all three networks. Connect function from IPA My Pathway toolbox showed that DRD2 is the gene common to both the list of genetic variations associated with all three addiction phenotypes and the components of the brain neuronal signaling network involved in substance addiction. The top canonical pathways associated with the 56 genes were: 1) calcium signaling, 2) GPCR signaling, 3) cAMP-mediated signaling, 4) GABA receptor signaling, and 5) G-alpha i signaling.

Conlusions

Cancer patients are often prescribed opioids for cancer pain thus increasing their risk for opioid abuse and addiction. Our findings provide candidate genes and biological pathways underlying addiction phenotypes, which may be future targets for treatment of addiction. Further study of the variations of the candidate genes could allow physicians to make more informed decisions when treating cancer pain with opioid analgesics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0167-x) contains supplementary material, which is available to authorized users.

Potential therapeutic targets and the role of technology in developing novel antileishmanial drugs

Leishmaniasis is the most prevalent pathogenic disease in many countries around the world, but there are few drugs available to treat it. Most antileishmanial drugs available are highly toxic, have resistance issues or require hospitalization for their use; therefore, they are not suitable for use in most of the affected countries. Over the past decade, the completion of the genomes of many human pathogens, including that of Leishmania spp., has opened new doors for target identification and validation. Here, we focus on the potential drug targets that can be used for the treatment of leishmaniasis and bring to light how recent technological advances, such as structure-based drug design, structural genomics, and molecular dynamics (MD), can be used to our advantage to develop potent and affordable antileishmanial drugs.

Identification of Nitazoxanide as a Group I Metabotropic Glutamate Receptor Negative Modulator for the Treatment of Neuropathic Pain: An In Silico Drug Repositioning Study

PURPOSE

Drug repositioning strategies were employed to explore new therapeutic indications for existing drugs that may exhibit dual negative mGluR1/5 modulating activities as potential treatments for neuropathic pain.

METHOD

A customized in silico-in vitro-in vivo drug repositioning scheme was assembled and implemented to search available drug libraries for compounds with dual mGluR1/5 antagonistic activities, that were then evaluated using in vitro functional assays and, for validated hits, in an established animal model for neuropathic pain.

RESULTS

Tizoxanide, the primary active metabolite of the FDA approved drug nitazoxanide, fit in silico pharmacophore models constructed for both mGluR1 and mGluR5. Subsequent calcium (Ca++) mobilization functional assays confirmed that tizoxanide exhibited appreciable antagonist activity for both mGluR1 and mGluR5 (IC50 = 1.8 μM and 1.2 μM, respectively). The in vivo efficacy of nitazoxanide administered by intraperitoneal injection was demonstrated in a rat model for neuropathic pain.

CONCLUSION

The major aim of the present study was to demonstrate the utility of an in silico-in vitro-in vivo drug repositioning protocol to facilitate the repurposing of approved drugs for new therapeutic indications. As an example, this particular investigation successfully identified nitazoxanide and its metabolite tizoxanide as dual mGluR1/5 negative modulators. A key finding is the vital importance for drug screening libraries to include the structures of drug active metabolites, such as those emanating from prodrugs which are estimated to represent 5-7% of marketed drugs.