Drug Repositioning Strategies for the Identification of Novel Therapies for Rheumatic Autoimmune Inflammatory Diseases

Recent Advancements in Refashioning of NSAIDs and their Derivatives as Anticancer Candidates

Inflammation is critical to the formation and development of tumors and is closely associated with cancer. Therefore, addressing inflammation and the mediators that contribute to the inflammatory process may be a useful strategy for both cancer prevention and treatment. Tumor predisposition can be attributed to inflammation. It has been demonstrated that NSAIDs can modify the tumor microenvironment by enhancing apoptosis and chemosensitivity and reducing cell migration. There has been a recent rise in interest in drug repositioning or repurposing because the development of innovative medications is expensive, timeconsuming, and presents a considerable obstacle to drug discovery. Repurposing drugs is crucial for the quicker and less expensive development of anticancer medicines, according to an increasing amount of research. This review summarizes the antiproliferative activity of derivatives of NSAIDs such as Diclofenac, Etodolac, Celecoxib, Ibuprofen, Tolmetin, and Sulindac, published between 2017 and 2023. Their mechanism of action and structural activity relationships (SARs) were also discussed to set the path for potential future repositioning of NSAIDs for clinical deployment in the treatment of cancer.

Knowledge Mapping of Drug Repositioning's Theme and Development

Background

In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends.

Methodology

The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field.

Results

The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning.

Conclusion

The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.

Combination Therapy of Curcumin and Disulfiram Synergistically Inhibits the Growth of B16-F10 Melanoma Cells by Inducing Oxidative Stress

Oxidative stress plays a central role in the pathophysiology of melanoma. Curcumin (CUR) is a polyphenolic phytochemical that stimulates reactive oxygen species (ROS) production, while disulfiram (DSS) is a US FDA-approved drug for the treatment of alcoholism that can act by inhibiting the intracellular antioxidant system. Therefore, we hypothesized that they act synergistically against melanoma cells. Herein, we aimed to study the antitumor potential of the combination of CUR with DSS in B16-F10 melanoma cells using in vitro and in vivo models. The cytotoxic effects of different combination ratios of CUR and DSS were evaluated using the Alamar Blue method, allowing the production of isobolograms. Apoptosis detection, DNA fragmentation, cell cycle distribution, and mitochondrial superoxide levels were quantified by flow cytometry. Tumor development in vivo was evaluated using C57BL/6 mice bearing B16-F10 cells. The combinations ratios of 1:2, 1:3, and 2:3 showed synergic effects. B16-F10 cells treated with these combinations showed improved apoptotic cell death and DNA fragmentation. Enhanced mitochondrial superoxide levels were observed at combination ratios of 1:2 and 1:3, indicating increased oxidative stress. In vivo tumor growth inhibition for CUR (20 mg/kg), DSS (60 mg/kg), and their combination were 17.0%, 19.8%, and 28.8%, respectively. This study provided data on the potential cytotoxic activity of the combination of CUR with DSS and may provide a useful tool for the development of a therapeutic combination against melanoma.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.

Repurposing of Pirfenidone (Anti-Pulmonary Fibrosis Drug) for Treatment of Rheumatoid Arthritis

Clinical studies have shown that pirfenidone (PFD) effectively relieves joint pain in rheumatoid arthritis (RA) patients. However, the detailed mechanisms underlying the anti-RA effects of PFD have not been investigated. This study was undertaken to investigate the repurposing of PFD for the treatment of RA, and explore its anti-rheumatic mechanisms. A collagen-induced arthritis (CIA) rat model was used to observe joint pathological changes following PFD treatment. Based on bioinformatics to predict the mechanism of PFD anti-RA, using EA. hy926 and TNF-α-induced MH7A cells to establish in vitro model to explore its biological mechanism from the perspectives of synovial inflammation and angiogenesis. PFD significantly relieved pathological changes, including joint swelling, synovial hyperplasia, inflammatory cell infiltration and joint destruction. PFD was also associated with reduced expression of MMP-3 and VEGF in articular chondrocytes and synovial cells of CIA rats (p < 0.05). Using bioinformatic methods, we predicted that PFD inhibits cell inflammation and migration by interfering with the JAK2/STAT3 and Akt pathways. These results were verified using in vitro models. In particular, PFD effectively reduced the expression of pro-inflammatory, chondrogenic, and angiogenic cytokines, such as IL-1β, IL-6, IL-8, MMP-1/3/2/9 and VEGF (p < 0.05), in TNF-α-induced MH7A cells. In addition, PFD significantly reduced the production of MMP-2/9 and VEGF in EA. hy926 cells, thereby weakening migration and inhibiting angiogenesis (p < 0.05). These findings suggest that PFD may alleviate the pathological process in CIA rats, by inhibiting inflammation and angiogenesis through multiple pathways, and serve as a potential therapeutic drug for RA.

New Trends for Antimalarial Drugs: Synergism between Antineoplastics and Antimalarials on Breast Cancer Cells

Chemotherapy plays a key role in breast cancer therapy, but drug resistance and unwanted side effects make the treatment less effective. We propose a new combination model that combines antineoplastic drugs and antimalarials for breast cancer therapy. Cytotoxic effects of two antineoplastic agents alone and in combination with several antimalarials on MCF-7 tumor cell line was evaluated. Different concentrations in a fixed ratio were added to the cultured cells and incubated for 48 h. Cell viability was evaluated using MTT and SRB assays. Synergism was evaluated using the Chou-Talalay method. The results indicate doxorubicin (DOX) and paclitaxel (PTX) alone at concentrations of their IC50 and higher are cell growth inhibitors. Mefloquine, artesunate, and chloroquine at concentrations of their IC50 demonstrate anti-cancer activity. In combination, almost all antimalarials demonstrate higher ability than DOX and PTX alone to decrease cell viability at concentrations of IC50 and lower than their IC50. The combination of chloroquine, artesunate and mefloquine with DOX and PTX was synergic (CI < 1). The combination of DOX and mefloquine after 48 h incubation demonstrated the highest cytotoxicity against MCF-7 cells, and the combination of DOX and artesunate was the most synergic. These results suggest antimalarials could act synergistically with DOX/PTX for breast cancer therapy.

Repurposing old drugs to fight multidrug resistant cancers

Overcoming multidrug resistance represents a major challenge for cancer treatment. In the search for new chemotherapeutics to treat malignant diseases, drug repurposing gained a tremendous interest during the past years. Repositioning candidates have often emerged through several stages of clinical drug development, and may even be marketed, thus attracting the attention and interest of pharmaceutical companies as well as regulatory agencies. Typically, drug repositioning has been serendipitous, using undesired side effects of small molecule drugs to exploit new disease indications. As bioinformatics gain increasing popularity as an integral component of drug discovery, more rational approaches are needed. Herein, we show some practical examples of in silico approaches such as pharmacophore modelling, as well as pharmacophore- and docking-based virtual screening for a fast and cost-effective repurposing of small molecule drugs against multidrug resistant cancers. We provide a timely and comprehensive overview of compounds with considerable potential to be repositioned for cancer therapeutics. These drugs are from diverse chemotherapeutic classes. We emphasize the scope and limitations of anthelmintics, antibiotics, antifungals, antivirals, antimalarials, antihypertensives, psychopharmaceuticals and antidiabetics that have shown extensive immunomodulatory, antiproliferative, pro-apoptotic, and antimetastatic potential. These drugs, either used alone or in combination with existing anticancer chemotherapeutics, represent strong candidates to prevent or overcome drug resistance. We particularly focus on outcomes and future perspectives of drug repositioning for the treatment of multidrug resistant tumors and discuss current possibilities and limitations of preclinical and clinical investigations.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

The Use of Tick Salivary Proteins as Novel Therapeutics

The last three decades of research into tick salivary components have revealed several proteins with important pharmacological and immunological activities. Two primary interests have driven research into tick salivary secretions: the search for suitable pathogen transmission blocking or “anti-tick” vaccine candidates and the search for novel therapeutics derived from tick salivary components. Intensive basic research in the field of tick salivary gland transcriptomics and proteomics has identified several major protein families that play important roles in tick feeding and overcoming vertebrate anti-tick responses. Moreover, these families contain members with unrealized therapeutic potential. Here we review the major tick salivary protein families exploitable in medical applications such as immunomodulation, inhibition of hemostasis and inflammation. Moreover, we discuss the potential, opportunities, and challenges in searching for novel tick-derived drugs.

Drug databases and their contributions to drug repurposing

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.

The Utilization of Autoantibodies in Approaches to Precision Health

Precision health (PH) applied to autoimmune disease will need paradigm shifts in the use and application of autoantibodies and other biomarkers. For example, autoantibodies combined with other multi-analyte “omic” profiles will form the basis of disease prediction allowing for earlier intervention linked to disease prevention strategies, as well as earlier, effective and personalized interventions for established disease. As medical intervention moves to disease prediction and a model of “intent to PREVENT,” diagnostics will include an early symptom/risk-based, as opposed to a disease-based approach. Newer diagnostic platforms that utilize emerging megatrends such as deep learning and artificial intelligence and close the gaps in autoantibody diagnostics will benefit from paradigm shifts thereby facilitating the PH agenda.

Selective HDAC6 inhibition decreases early stage of lupus nephritis by down-regulating both innate and adaptive immune responses

We have demonstrated previously that histone deacetylase (HDAC6) expression is increased in animal models of systemic lupus erythematosus (SLE) and that inhibition of HDAC6 decreased disease. In our current studies, we tested if an orally active selective HDAC6 inhibitor would decrease disease pathogenesis in a lupus mouse model with established early disease. Additionally, we sought to delineate the cellular and molecular mechanism(s) of action of a selective HDAC6 inhibitor in SLE. We treated 20-week-old (early-disease) New Zealand Black (NZB)/White F₁ female mice with two different doses of the selective HDAC6 inhibitor (ACY-738) for 5 weeks. As the mice aged, we determined autoantibody production and cytokine levels by enzyme-linked immunosorbent assay (ELISA) and renal function by measuring proteinuria. At the termination of the study, we performed a comprehensive analysis on B cells, T cells and innate immune cells using flow cytometry and examined renal tissue for immune-mediated pathogenesis using immunohistochemistry and immunofluorescence. Our results showed a reduced germinal centre B cell response, decreased T follicular helper cells and diminished interferon (IFN)-γ production from T helper cells in splenic tissue. Additionally, we found the IFN-α-producing ability of plasmacytoid dendritic cells was decreased along with immunoglobulin isotype switching and the generation of pathogenic autoantibodies. Renal tissue showed decreased immunoglobulin deposition and reduced inflammation as judged by glomerular and interstitial inflammation. Taken together, these studies show selective HDAC6 inhibition decreased several parameters of disease pathogenesis in lupus-prone mice. The decrease was due in part to inhibition of B cell development and response.