Innovative target mining stratagems to navigate drug repurposing endeavours.

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Bioinformatics-guided disproportionality analysis of sevoflurane-induced nephrogenic diabetes insipidus using the FDA Adverse Event Reporting System database.

AIMS: Sevoflurane is an ether-based inhalational anaesthetic that induces and maintains general anaesthesia. Our study aimed to detect sevoflurane-induced nephrogenic diabetes insipidus using data mining algorithms (DMAs) and molecular docking. The FAERS database was analysed using OpenVigil 2.1 for disproportionality analysis. METHODS: We analysed FAERS data from 2004 to 2022 to determine the incidence of nephrogenic diabetes insipidus associated with sevoflurane. Reporting odds ratios (RORs) and proportional reporting ratios (PRRs) with 95% confidence intervals were calculated. We also used molecular docking with AutoDock Vina to examine sevoflurane's binding affinity to relevant receptors. RESULTS: A total of 554 nephrogenic diabetes insipidus cases were reported in FAERS, of which 2.5% (14 cases) were associated with sevoflurane. Positive signals were observed for sevoflurane with ROR of 76.012 (95% CI: 44.67-129.35) and PRR of 75.72 (χ2 : 934.688). Of the 14 cases, 50% required hospitalization, 14% resulted in death, and the remaining cases were categorized as other outcomes. Molecular docking analysis showed that sevoflurane exhibited high binding affinity towards AQP2 (4NEF) and AVPR2 (6U1N) with docking scores of -4.9 and -5.3, respectively. CONCLUSIONS: Sevoflurane use is significantly associated with the incidence of nephrogenic diabetes insipidus. Healthcare professionals should be cautious when using this medication and report any adverse events to regulatory agencies. Further research is needed to validate these findings and identify risk factors while performing statistical adjustments to prevent false-positives. Clinical monitoring is crucial to validate potential adverse effects of sevoflurane.

Purine antimetabolites associated Pneumocystis jirovecii pneumonia.

PURPOSE: To detect the possible safety signal of purine antimetabolites associated with Pneumocystis jirovecii pneumonia through disproportionality analysis in the FDA Adverse Event Reporting System (FAERS) Database. METHODS: A case/non-case retrospective disproportionality analysis was performed in the publicly available FAERS database using AERSmine (2004Q1-2021Q3). Four models were developed to explore the signal strength of PAs among different populations with possible confounding factors. Reporting odds ratio (ROR) and Proportional reporting ratio (PRR) was used as the data mining algorithm for the analysis. A value of ROR-1.96SE > 1 and PRR ≥ 2 with an associated X2 value of 4 or more was considered the threshold for a signal. RESULTS: A total of 7073 reports associated with Pneumocystis jirovecii pneumonia were present in the database, of which 899 reports were associated with purine antimetabolites. A crude signal strength of ROR 15.76(14.70-16.91) was obtained for purine antimetabolites associated PJP, with the highest signal strength reported with fludarabine and thioguanine [ROR 19.63(17.42-22.13); 19.45(13.21-28.63)]. Stratifying the cases based on autoimmune disorders and the cancer population revealed an ROR of 3.33(2.46-4.50) and 2.93(2.26-3.79) respectively. The highest risk of PJP with use of PAs was observed amongst children with a higher risk of nearly 2 times than the adult population [ROR 11.57(9.16-14.62)]. CONCLUSIONS: Our study provided evidence on the occurrence of PJP with the use of purine antimetabolites among the autoimmune and cancer population. We identified signals for PJP with azathioprine, mercaptopurine, thioguanine, cladribine, fludarabine, and clofarabine. More research with a superior epidemiological study design of a defined population is required to validate these findings.

Drugs-associated with red man syndrome: An integrative approach using disproportionality analysis and Pharmip.

WHAT IS KNOWN AND OBJECTIVE: Red man syndrome (RMS) is a non-IgE-mediated anaphylactoid adverse event frequently witnessed after a rapid infusion of vancomycin. This study aims to unravel drugs and associated off-label targets that induce RMS by exploiting FDA Adverse Event Reporting System (FAERS) and Pharmacovigilance/Pharmacogenomics Insilico Pipeline (PHARMIP). METHODS: The case/non-case retrospective observational study was conducted in the FAERS database. Reporting odds ratio (ROR) and proportional reporting ratio (PRR) data mining algorithms were used to evaluate the strength of the signal. The off-label targets of the drugs with potential signals were obtained using online servers by applying a similarity ensemble approach and a reverse pharmacophore database, which was further validated by molecular docking studies. RESULTS AND DISCUSSION: Oritavancin exhibited a strong positive signal (PRR:1185.20 and ROR:1256), which suggests a higher risk for causing RMS. The literature search revealed the involvement of the MRGPRX2 gene in the development of RMS. PHARMIP study unearthed Carbonic anhydrase II (CA2) as the common off-label target among the drugs causing RMS. The results obtained from molecular docking studies reinforced the findings as mentioned earlier, wherein the highest docking score was disinterred for oritavancin (-9.4 for MRGPRX2 and - 8.7 for CA2). WHAT IS NEW AND CONCLUSION: Many antibiotics and other classes of medications have been discovered in the quest for drugs that may induce RMS, although a causal relationship could not be established. The implication of MRGPX2 and CA2 in the initial stages of pathogenesis necessitates the development of inhibitors that could be used as potential therapeutic agents against RMS.

Application of comprehensive bioinformatics approaches to reconnoiter crucial genes and pathways underpinning hepatocellular carcinoma: a drug repurposing endeavor.

Hepatocellular carcinoma (HCC) is the fifth most common neoplasm in the world. Chronic inflammation of liver and associated wound healing processes collectively contribute to the development of cirrhosis which further progresses to dysplastic nodule and then to HCC. Etiological mediators and ongoing manipulations at cellular level in HCC are well established; however, key protein interactions and genetic alterations involved in stepwise hepatocarcinogenic pathways are seldom explored. This study aims to unravel novel targets of HCC and repurpose the FDA-approved drugs against the same. Genetic data pertinent to different stages of HCC were retrieved from GSE6764 dataset and analyzed via GEO2R. Subsequently, protein-protein interaction network analysis of differentially expressed genes was performed to identify the hub genes with significant interaction. Hub genes displaying higher interactions were considered as potential HCC targets and were validated thorough UALCAN and GEPIA databases. These targets were screened against FDA-approved drugs through molecular docking and dynamics simulation studies to capture the drugs with potential activity against HCC. Finally, cytotoxicity of the shortlisted drug was confirmed in vitro by MTT assay. CDC20 was identified as potential druggable target. Docking, binding energy calculations, and dynamic studies revealed significant interaction exhibited by Labetalol with CDC20. Further, in MTT assay, Labetalol demonstrated an IC50 of 200.29 µg/ml in inhibiting the cell growth of HepG2 cell line. In conclusion, this study discloses a series of key genetic underpinnings of HCC and recommends the pertinence of labetalol as a potential repurposable drug against HCC.

Repression of Polyol Pathway Activity by Hemidesmus indicus var. pubescens R.Br. Linn Root Extract, an Aldose Reductase Inhibitor: An In Silico and Ex Vivo Study.

Development of diabetic cataract is mainly associated with the accumulation of sorbitol via the polyol pathway through the action of Aldose reductase (AR). Hence, AR inhibitors are considered as potential agents in the management of diabetic cataract. This study explored the AR inhibition potential of Hemidesmus indicus var. pubescens root extract by in silico and ex vivo methods. Molecular docking studies (Auto Dock tool) between ß-sitosterol, hemidesminine, hemidesmin-1, hemidesmin-2, and AR showed that ß-sitosterol (- 10.2 kcal/mol) and hemidesmin-2 (- 8.07 kcal/mol) had the strongest affinity to AR enzyme. Ex vivo studies were performed by incubating isolated goat lenses in artificial aqueous humor using galactose (55 mM) as cataract inducing agent at room temperature (pH 7.8) for 72 h. After treatment with Vitamin E acetate - 100 µg/mL (standard) and test extract (500 and 1000 µg/mL) separately, the estimation of biochemical markers showed inhibition of lens AR activity and decreased sorbitol levels. Additionally, extract also normalized the levels of antioxidant markers like SOD, CAT, GSH. Our results showed evidence that H. indicus var. pubescens root was able to prevent cataract by prevention of opacification and formation of polyols that underlines its potential as a possible therapeutic agent against diabetic complications.