Systematic drug repurposing to enable precision medicine: A case study in breast cancer

Repurposing integrase inhibitors against human T-lymphotropic virus type-1: a computational approach

Adult T-cell Lymphoma (ATL) is caused by the delta retrovirus family member known as Human T-cell Leukaemia Type I (HTLV-1). Due to the unavailability of any cure, the study gained motivation to identify some repurposed drugs against the virus. A quick and accurate method of screening licensed medications for finding a treatment for HTLV-1 is by cheminformatics drug repurposing in order to analyze a dataset of FDA approved integrase antivirals against HTLV-1 infection. To determine how the antiviral medications interacted with the important residues in the HTLV-1 integrase active regions, molecular docking modeling was used. The steady behavior of the ligands inside the active region was then confirmed by molecular dynamics for the probable receptor-drug complexes. Cabotegravir, Raltegravir and Elvitegravir had the best docking scores with the target, indicating that they can tightly bind to the HTLV-1 integrase. Moreover, MD simulation revealed that the Cabotegravir-HTLV-1, Raltegravir-HTLV-1 and Elvitegravir-HTLV-1 interactions were stable. It is obvious that more testing of these medicines in both clinical trials and experimental tests is necessary to demonstrate their efficacy against HTLV-1 infection.Communicated by Ramaswamy H. Sarma.

New Perspectives about Drug Candidates Targeting HTLV-1 and Related Diseases

Among the human T-lymphotropic virus (HTLV) types, HTLV-1 is the most prevalent, and it has been linked to a spectrum of diseases, including HAM/TSP, ATLL, and hyperinfection syndrome or disseminated strongyloidiasis. There is currently no globally standard first-line treatment for HTLV-1 infection and its related diseases. To address this, a comprehensive review was conducted, analyzing 30 recent papers from databases PubMed, CAPES journals, and the Virtual Health Library (VHL). The studies encompassed a wide range of therapeutic approaches, including antiretrovirals, immunomodulators, antineoplastics, amino acids, antiparasitics, and even natural products and plant extracts. Notably, the category with the highest number of articles was related to drugs for the treatment of ATLL. Studies employing mogamulizumab as a new perspective for ATLL received greater attention in the last 5 years, demonstrating efficacy, safe use in the elderly, significant antitumor activity, and increased survival time for refractory patients. Concerning HAM/TSP, despite corticosteroid being recommended, a more randomized clinical trial is needed to support treatment other than corticoids. The study also included a comprehensive review of the drugs used to treat disseminated strongyloidiasis in co-infection with HTLV-1, including their administration form, in order to emphasize gaps and facilitate the development of other studies aiming at better-directed methodologies. Additionally, docking molecules and computer simulations show promise in identifying novel therapeutic targets and repurposing existing drugs. These advances are crucial in developing more effective and targeted treatments against HTLV-1 and its related diseases.

Genetic risk factors for ME/CFS identified using combinatorial analysis

BACKGROUND

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to help to alleviate some of these issues for patients.

METHODS

We applied both GWAS and the PrecisionLife combinatorial analytics platform to analyze ME/CFS cohorts from UK Biobank, including the Pain Questionnaire cohort, in a case-control design with 1000 cycles of fully random permutation. Results from this study were supported by a series of replication and cohort comparison experiments, including use of disjoint Verbal Interview CFS, post-viral fatigue syndrome and fibromyalgia cohorts also derived from UK Biobank, and compared results for overlap and reproducibility.

RESULTS

Combinatorial analysis revealed 199 SNPs mapping to 14 genes that were significantly associated with 91% of the cases in the ME/CFS population. These SNPs were found to stratify by shared cases into 15 clusters (communities) made up of 84 high-order combinations of between 3 and 5 SNPs. p-values for these communities range from 2.3 × 10-10 to 1.6 × 10-72. Many of the genes identified are linked to the key cellular mechanisms hypothesized to underpin ME/CFS, including vulnerabilities to stress and/or infection, mitochondrial dysfunction, sleep disturbance and autoimmune development. We identified 3 of the critical SNPs replicated in the post-viral fatigue syndrome cohort and 2 SNPs replicated in the fibromyalgia cohort. We also noted similarities with genes associated with multiple sclerosis and long COVID, which share some symptoms and potentially a viral infection trigger with ME/CFS.

CONCLUSIONS

This study provides the first detailed genetic insights into the pathophysiological mechanisms underpinning ME/CFS and offers new approaches for better diagnosis and treatment of patients.

Systematic indication extension for drugs using patient stratification insights generated by combinatorial analytics

Indication extension or repositioning of drugs can, if done well, provide a faster, cheaper, and derisked route to the approval of new therapies, creating new options to address pockets of unmet medical need for patients and offering the potential for significant commercial and clinical benefits. We look at the promises and challenges of different repositioning strategies and the disease insights and scalability that new high-resolution patient stratification methodologies can bring. This is exemplified by a systematic analysis of all development candidates and on-market drugs, which identified 477 indication extension opportunities across 30 chronic disease areas, each supported by patient stratification biomarkers. This illustrates the potential that new artificial intelligence (AI) and combinatorial analytics methods have to enhance the rate and cost of innovation across the drug discovery industry.

Repurposing antiviral drugs against HTLV-1 protease by molecular docking and molecular dynamics simulation

Human T-cell leukemia virus type I (HTLV-1) belongs to the delta retrovirus family and the etiological agent of adult T-cell leukemia (ATL(. While the current HTLV-1 therapy, relies on using Zidovudine plus IFN-γ, there is no FDA approved drugs against it. In silico drug repurposing is a fast and accurate way for screening US-FDA approved drugs to find a therapeutic option for the HTLV-1 infection. So that, this research aims to analyze a dataset of approved antiviral drugs as a potential prospect for an anti-viral drug against HTLV-1 infection. Molecular docking simulation was performed to identify interactions of the antiviral drugs with the key residues in the HTLV-1 protease binding site. Then, molecular dynamics simulation was also performed for the potential protein-ligand complexes to confirm the stable behavior of the ligands inside the binding pocket. The best docking scores with the target was found to be Simeprevir, Atazanavir, and Saquinavir compounds which indicate that these drugs can firmly bind to the HTLV-1 protease. The MD simulation confirmed the stability of Simeprevir-protease, Atazanavir-Protease, and Saquinavir-Protease interactions. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against HTLV-1 infection.Communicated by Ramaswamy H. Sarma.

Artificial Intelligence Effecting a Paradigm Shift in Drug Development

The inverse relationship between the cost of drug development and the successful integration of drugs into the market has resulted in the need for innovative solutions to overcome this burgeoning problem. This problem could be attributed to several factors, including the premature termination of clinical trials, regulatory factors, or decisions made in the earlier drug development processes. The introduction of artificial intelligence (AI) to accelerate and assist drug development has resulted in cheaper and more efficient processes, ultimately improving the success rates of clinical trials. This review aims to showcase and compare the different applications of AI technology that aid automation and improve success in drug development, particularly in novel drug target identification and design, drug repositioning, biomarker identification, and effective patient stratification, through exploration of different disease landscapes. In addition, it will also highlight how these technologies are translated into the clinic. This paradigm shift will lead to even greater advancements in the integration of AI in automating processes within drug development and discovery, enabling the probability and reality of attaining future precision and personalized medicine.