Repurposing of FDA-Approved NSAIDs for DPP-4 Inhibition as an Alternative for Diabetes Mellitus Treatment: Computational and in Vitro Study

Predicting responsiveness to GLP-1 pathway drugs using real-world data

BACKGROUND

Medications targeting the glucagon-like peptide-1 (GLP-1) pathway are an important therapeutic class currently used for the treatment of Type 2 diabetes (T2D). However, there is not enough known about which subgroups of patients would receive the most benefit from these medications.

OBJECTIVE

The goal of this study was to develop a predictive model for patient responsiveness to medications, here collectively called GLP-1 M, that include GLP-1 receptor agonists and dipeptidyl peptidase-4 (DPP4) inhibitors (that normally degrade endogenously-produced GLP-1). Such a model could guide clinicians to consider certain patient characteristics when prescribing second line medications for T2D.

METHODS

We analyzed de-identified electronic health records of 7856 subjects with T2D treated with GLP-1 M drugs at Vanderbilt University Medical Center from 2003-2019. Using common clinical features (including commonly ordered lab tests, demographic information, other T2D medications, and diabetes-associated complications), we compared four different models: logistic regression, LightGBM, artificial neural network (ANN), and support vector classifier (SVC).

RESULTS

Our analysis revealed that the traditional logistic regression model outperforms the other machine learning models, with an area under the Receiver Operating Characteristic curve (auROC) of 0.77.Our model showed that higher pre-treatment HbA1C is a dominant feature for predicting better response to GLP-1 M, while features such as use of thiazolidinediones or sulfonylureas is correlated with poorer response to GLP-1 M, as assessed by lowering of hemoglobin A1C (HbA1C), a standard marker of glycated hemoglobin used for assessing glycemic control in individuals with diabetes. Among female subjects under 40 taking GLP-1 M, the simultaneous use of non-steroidal anti-inflammatory drugs (NSAIDs) was associated with a greater reduction in HbA1C (0.82 ± 1.72% vs 0.28 ± 1.70%, p = 0.008).

CONCLUSION

These findings indicate a thorough analysis of real-world electronic health records could reveal new information to improve treatment decisions for the treatment of T2D. The predictive model developed in this study highlights the importance of considering individual patient characteristics and medication interactions when prescribing GLP-1 M drugs.

Induction of mitochondrial toxicity by non-steroidal anti-inflammatory drugs (NSAIDs): The ultimate trade-off governing the therapeutic merits and demerits of these wonder drugs

Non-steroidal anti-inflammatory drugs (NSAIDs) are most extensively used over-the-counter FDA-approved analgesic medicines for treating inflammation, musculoskeletal pain, arthritis, pyrexia and menstrual cramps. Moreover, aspirin is widely used against cardiovascular complications. Owing to their non-addictive nature, NSAIDs are also commissioned as safer opioid-sparing alternatives in acute trauma and post-surgical treatments. In fact, therapeutic spectrum of NSAIDs is expanding. These "wonder-drugs" are now repurposed against lung diseases, diabetes, neurodegenerative disorders, fungal infections and most notably cancer, due to their efficacy against chemoresistance, radio-resistance and cancer stem cells. However, prolonged NSAID treatment accompany several adverse effects. Mechanistically, apart from cyclooxygenase inhibition, NSAIDs directly target mitochondria to induce cell death. Interestingly, there are also incidences of dose-dependent effects where NSAIDs are found to improve mitochondrial health thereby suggesting plausible mitohormesis. While mitochondria-targeted effects of NSAIDs are discretely studied, a comprehensive account emphasizing the multiple dimensions in which NSAIDs affect mitochondrial structure-function integrity, leading to cell death, is lacking. This review discusses the current understanding of NSAID-mitochondria interactions in the pathophysiological background. This is essential for assessing the risk-benefit trade-offs of NSAIDs for judiciously strategizing NSAID-based approaches to manage pain and inflammation as well as formulating effective anti-cancer strategies. We also discuss recent developments constituting selective mitochondria-targeted NSAIDs including theranostics, mitocans, chimeric small molecules, prodrugs and nanomedicines that rationally optimize safer application of NSAIDs. Thus, we present a comprehensive understanding of therapeutic merits and demerits of NSAIDs with mitochondria at its cross roads. This would help in NSAID-based disease management research and drug development.

Repurposing drugs to inhibit innate immune responses associated with TLR4, IL1, and NLRP3 signaling in joint cells

Osteoarthritis (OA) affects more than 300 million people worldwide and it is about to become the first disabling disease. OA is characterized by the progressive degradation of the articular cartilage but is a disease of the whole joint. Articular innate immune responses (IIR) associated with tissue degradation contribute to its progression. However, no treatment is available to block these IIRs. Through data text mining and computational pharmacology, we identified two clinical available drugs, naloxone, and thalidomide, with potential inhibitory properties on toll-like receptor 4 (TLR4), a major activator of these IIR. Proteome analysis confirmed that activation of this receptor or the IL1 receptor generated OA-like and gout-like proteomic changes in human primary chondrocytes. Both compounds were found to block TLR4 complex and inhibit TLR4 and IL1R-mediated IIR in OA chondrocytes, osteoblasts, and synoviocytes. Furthermore, naloxone and thalidomide inhibitory effects involved the downregulation of the NLRP3 inflammasome pathway, which is downstream of TLR4/IL1R signaling. We demonstrated that these compounds, within a therapeutic range of concentrations, exhibited anti-inflammatory and anti-catabolic properties in joint primary OA cells without any toxic effect. This data underpins naloxone & thalidomide repurpose to treat OA-associated inflammatory responses.

Network pharmacology approach to decipher signaling pathways associated with target proteins of NSAIDs against COVID-19

Non-steroidal anti-inflammatory drugs (NSAIDs) showed promising clinical efficacy toward COVID-19 (Coronavirus disease 2019) patients as potent painkillers and anti-inflammatory agents. However, the prospective anti-COVID-19 mechanisms of NSAIDs are not evidently exposed. Therefore, we intended to decipher the most influential NSAIDs candidate(s) and its novel mechanism(s) against COVID-19 by network pharmacology. FDA (U.S. Food & Drug Administration) approved NSAIDs (19 active drugs and one prodrug) were used for this study. Target proteins related to selected NSAIDs and COVID-19 related target proteins were identified by the Similarity Ensemble Approach, Swiss Target Prediction, and PubChem databases, respectively. Venn diagram identified overlapping target proteins between NSAIDs and COVID-19 related target proteins. The interactive networking between NSAIDs and overlapping target proteins was analyzed by STRING. RStudio plotted the bubble chart of the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis of overlapping target proteins. Finally, the binding affinity of NSAIDs against target proteins was determined through molecular docking test (MDT). Geneset enrichment analysis exhibited 26 signaling pathways against COVID-19. Inhibition of proinflammatory stimuli of tissues and/or cells by inactivating the RAS signaling pathway was identified as the key anti-COVID-19 mechanism of NSAIDs. Besides, MAPK8, MAPK10, and BAD target proteins were explored as the associated target proteins of the RAS. Among twenty NSAIDs, 6MNA, Rofecoxib, and Indomethacin revealed promising binding affinity with the highest docking score against three identified target proteins, respectively. Overall, our proposed three NSAIDs (6MNA, Rofecoxib, and Indomethacin) might block the RAS by inactivating its associated target proteins, thus may alleviate excessive inflammation induced by SARS-CoV-2.

Field-Template, QSAR, Ensemble Molecular Docking, and 3D-RISM Solvation Studies Expose Potential of FDA-Approved Marine Drugs as SARS-CoVID-2 Main Protease Inhibitors

Currently, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has infected people among all countries and is a pandemic as declared by the World Health Organization (WHO). SARS-CoVID-2 main protease is one of the therapeutic drug targets that has been shown to reduce virus replication, and its high-resolution 3D structures in complex with inhibitors have been solved. Previously, we had demonstrated the potential of natural compounds such as serine protease inhibitors eventually leading us to hypothesize that FDA-approved marine drugs have the potential to inhibit the biological activity of SARS-CoV-2 main protease. Initially, field-template and structure–activity atlas models were constructed to understand and explain the molecular features responsible for SARS-CoVID-2 main protease inhibitors, which revealed that Eribulin Mesylate, Plitidepsin, and Trabectedin possess similar characteristics related to SARS-CoVID-2 main protease inhibitors. Later, protein–ligand interactions are studied using ensemble molecular-docking simulations that revealed that marine drugs bind at the active site of the main protease. The three-dimensional reference interaction site model (3D-RISM) studies show that marine drugs displace water molecules at the active site, and interactions observed are favorable. These computational studies eventually paved an interest in further in vitro studies. Finally, these findings are new and indeed provide insights into the role of FDA-approved marine drugs, which are already in clinical use for cancer treatment as a potential alternative to prevent and treat infected people with SARS-CoV-2.

Non-steroidal anti-inflammatory drugs (NSAIDs) and organ damage: A current perspective

Owing to the efficacy in reducing pain and inflammation, non-steroidal anti-inflammatory drugs (NSAIDs) are amongst the most popularly used medicines confirming their position in the WHO's Model List of Essential Medicines. With escalating musculoskeletal complications, as evident from 2016 Global Burden of Disease data, NSAID usage is evidently unavoidable. Apart from analgesic, anti-inflammatory and antipyretic efficacies, NSAIDs are further documented to offer protection against diverse critical disorders including cancer and heart attacks. However, data from multiple placebo-controlled trials and meta-analyses studies alarmingly signify the adverse effects of NSAIDs in gastrointestinal, cardiovascular, hepatic, renal, cerebral and pulmonary complications. Although extensive research has elucidated the mechanisms underlying the clinical hazards of NSAIDs, no review has extensively collated the outcomes on various multiorgan toxicities of these drugs together. In this regard, the present review provides a comprehensive insight of the existing knowledge and recent developments on NSAID-induced organ damage. It precisely encompasses the current understanding of structure, classification and mode of action of NSAIDs while reiterating on the emerging instances of NSAID drug repurposing along with pharmacophore modification aimed at safer usage of NSAIDs where toxic effects are tamed without compromising the clinical benefits. The review does not intend to vilify these 'wonder drugs'; rather provides a careful understanding of their side-effects which would be beneficial in evaluating the risk-benefit threshold while rationally using NSAIDs at safer dose and duration.

Uncovering New Drug Properties in Target-Based Drug-Drug Similarity Networks

Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.

Phytochemicals in Garlic Extract Inhibit Therapeutic Enzyme DPP-4 and Induce Skeletal Muscle Cell Proliferation: A Possible Mechanism of Action to Benefit the Treatment of Diabetes Mellitus

Diabetes mellitus is a severe health problem in Mexico, and its prevalence is increasing exponentially every year. Recently, DPP-4 (dipeptidyl peptidase-4) inhibitors have become attractive oral anti-hyperglycemic agents to reduce the pathology of diabetes. Gliptin’s family, such as sitagliptin, vildagliptin, and alogliptin, are in clinical use to treat diabetes mellitus but possess side effects. Therefore, there is a specific need to look for new therapeutic scaffolds (biomolecules). Garlic bulb is widely used as a traditional remedy for the treatment of diabetes. The garlic extracts are scientifically proven to control glucose levels in patients with diabetes, despite the unknown mechanism of action. The aim of the study is to investigate the antidiabetic effects of ultrasonication assisted garlic bulb extract. To achieve this, in-vitro assays such as DPP-4 inhibitory and antioxidant activities were investigated. Further, functional group analysis using FTIR and identification of phytochemicals using mass spectrometry analysis was performed. The results showed that 70.9 µg/mL of garlic bulb extract inhibited 50% DPP-4 activity. On top of that, the garlic extract exhibited a 20% scavenging activity, equivalent to 10 µg/mL of ascorbic acid. Molecular docking simulations on identified phytochemicals using mass spectrometry revealed their potential binding at the DPP-4 druggable region, and therefore the possible DPP-4 inhibition mechanism. These results suggest that prepared garlic extract contains phytochemicals that inhibit DPP-4 and have antioxidant activity. Also, the prepared extract induces skeletal muscle cell proliferation that demonstrates the antidiabetic effect and its possible mechanism of action.