Drug Repurposing Approaches: Existing Leads For Novel Threats And Drug Targets

Novel drug design and repurposing: An opportunity to improve translational research in cardiovascular diseases?

Drug repurposing is defined as the use of approved therapeutic drugs for indications different from those for which they were originally designed. Repositioning diminishes both the time and cost for drug development by omitting the discovery stage, the analysis of absorption, distribution, metabolism, and excretion routes, as well as the studies of the biochemical and physiological effects of a new compound. Besides, drug repurposing takes advantage of the increased bioinformatics knowledge and availability of big data biology. There are many examples of drugs with repurposed indications evaluated in in vitro studies, and in pharmacological, preclinical, or retrospective clinical analyses. Here, we briefly review some of the experimental strategies and technical advances that may improve translational research in cardiovascular diseases. We also describe exhaustive research from basic science to clinical studies that culminated in the final approval of new drugs and provide examples of successful drug repurposing in the field of cardiology.

COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments

BACKGROUND

With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination.

METHODS

We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments.

RESULT AND CONCLUSIONS

Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.

The spike-ACE2 binding assay: An in vitro platform for evaluating vaccination efficacy and for screening SARS-CoV-2 inhibitors and neutralizing antibodies

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 has become a worldwide pandemic, and there is a pressing need for the rapid development of novel therapeutic strategies. SARS-CoV-2 viral entry is mediated by interaction between the receptor binding domain (RBD) of the SARS-CoV-2 Spike protein and host cellular receptor, human angiotensin converting enzyme 2 (ACE2). The lack of a high throughput screening (HTS) platform for candidate drug screening means that no targeted COVID-19 treatments have been developed to date. To overcome this limitation, we developed a novel, rapid, simple, and HTS binding assay platform to screen potential inhibitors of the RBD-ACE2 complex. Three “neutralizing” mouse monoclonal antibodies capable of blocking the RBD-ACE2 interaction were identified using our binding assay and pseudovirus neutralization assay followed by further validation with the Focus Reduction Neutralization Test (FRNT), which analyzes the neutralization capacity of samples in the presence of live SARS-CoV-2. Furthermore, the consistency of our binding assay and FRNT results (R² = 0.68) was demonstrated by patients' serum, of which were COVID-19 positive (n = 34) and COVID-19 negative (n = 76). Several small molecules selected for their potential to inhibit the Spike-ACE2 complex in silico were also confirmed with the binding assay. In addition, we have evaluated vaccine efficacy using binding assay platform and validated through pseudovirus neutralization assay. The correlation between binding assay & psuedovirus assay of the post vaccinated serum showed well correlated (R² = 0.09) Moreover, our binding assay platform successfully validated different Spike RBD mutants. These results indicate that our binding assay can be used as a platform for in vitro screening of small molecules and monoclonal antibodies, and high-throughput assessment of antibody levels after vaccination. When conducting drug screening, computer virtual screening lacks actual basis, construction of pseudoviruses is relatively complicated, and even FRNT requires a P3 laboratory. There are few methods to determine the competitiveness of the target drug and SRBD or ACE2. Our binding assay can fill this gap and accelerate the process and efficiency of COVID-19 drug screening.

Databases, DrugBank, and virtual screening platforms for therapeutic development

The upsurge of the severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has turned into a global health disaster. Many remodeled medications were suggested for treatment in the early stages of this pandemic, but these dosages afterward came across with distinct offshoots. Thus, these consequences compelled the scientists to develop new drugs using various antiviral, antiinflammatory, antibacterial, and phytochemical compounds. A handful of drugs have been scrutinized in silico, in vitro, plus through human trials such as anti-SARS-CoV-2 agents and made available as various databases by various scientific communities. The SARS-CoV-2 pandemic databases are designed to allay difficulties associated with this scenario. Some of the popular databases are GESS (global evaluation of SARS-CoV-2/HCoV-19 sequences) which gives a thorough study of data based on tenfold of thousands of complete coverage and quality of SARS-CoV-2 genomes, CORona Drug InTERactions (CORDITE) database for SARS-CoV-2 which profoundly combines the understanding of potential drugs and make it available for scientists and medicos. SARSCOVIDB set one’s sights to merge all differential gene expression data, at mRNA and protein levels, helping to accelerate analysis and research on the molecular impact of covid-19. This chapter aims to provide a piece of complete information about the SARS-CoV-2 virus databases, potentially available drugs, and virtual screening methods. And also provides a different webserver to reach out for information related to the COVID-19 pandemic and its future.

Machine Learning and Deep Learning based AI Tools for Development of Diagnostic Tools

Artificial intelligence (AI) systems exhibit human-like intelligence. Human intelligence is converted to machines or computer technologies using AI algorithms. Machine learning (ML) is a subset of AI that can learn from extracted data and models to perform a task whereas deep learning (DL) is a subset of ML that imitates the human brain functioning to solve real-world problems in almost all fields. AI caused a paradigm shift in healthcare that can be employed for decision support and forecasting. Medical diagnostic tools developed using AI, perform disease diagnosis based on the symbolic models of disease and provide therapy recommendations. The key AI applications employed with medical diagnosis are characterized as learning systems and expert systems. Diagnostic tools, developed using Expert systems utilize facts, implications, and knowledge processing techniques for disease diagnosis, whereas a learning system utilizes statistical pattern recognition, ML, and neural networks. In March 2020, an infectious disease caused by the severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) virus, Coronavirus disease-2019 (COVID-19) was declared a pandemic by the World Health Organization. Recent research studies have shown that AI, ML, and DL can be leveraged to combat COVID-19 having objectives of disease diagnosis, to forecast epidemic and sustainable development, and so on. DL algorithms are implemented on image data, more specifically on chest X-rays and computed tomography scans, for developing diagnostic tools. In this chapter, various ML and DL-based AI tools for the development of diagnostic tools have been discussed.

Targeted therapy for LIMD1-deficient non-small cell lung cancer subtypes

An early event in lung oncogenesis is loss of the tumour suppressor gene LIMD1 (LIM domains containing 1); this encodes a scaffold protein, which suppresses tumorigenesis via a number of different mechanisms. Approximately 45% of non-small cell lung cancers (NSCLC) are deficient in LIMD1, yet this subtype of NSCLC has been overlooked in preclinical and clinical investigations. Defining therapeutic targets in these LIMD1 loss-of-function patients is difficult due to a lack of 'druggable' targets, thus alternative approaches are required. To this end, we performed the first drug repurposing screen to identify compounds that confer synthetic lethality with LIMD1 loss in NSCLC cells. PF-477736 was shown to selectively target LIMD1-deficient cells in vitro through inhibition of multiple kinases, inducing cell death via apoptosis. Furthermore, PF-477736 was effective in treating LIMD1-/- tumours in subcutaneous xenograft models, with no significant effect in LIMD1+/+ cells. We have identified a novel drug tool with significant preclinical characterisation that serves as an excellent candidate to explore and define LIMD1-deficient cancers as a new therapeutic subgroup of critical unmet need.