Hesperidin: Enrichment, forced degradation, and structural elucidation of potential degradation products using spectral techniques.

RATIONALE: Hesperidin (HES) is a well-known citrus bioflavonoid phyto-nutraceutical agent with polypharmacological properties. After 2019, HES was widely used for prophylaxis and COVID-19 treatment. Moreover, it is commonly prescribed for treating varicose veins and other diseases in routine clinical practice. Pharmaceutical impurities and degradation products (DP) impact the drug's quality and safety and thus its effectiveness. Therefore, forced degradation studies help study drug stability, degradation mechanisms, and their DPs. This study was performed because stress stability studies using detailed structural characterization of hesperidin are currently unavailable in the literature. METHODS: In the HES enrichment method crude HES was converted to its pure form (98% purity) using column chromatography and then subjected to forced degradation under acid, base, and neutral hydrolyses followed by oxidative, reductive, photolytic, and thermal stress testing (International Conference on Harmonization guidelines). The stability-indicating analytical method (SIAM) was developed to determine DPs using reversed-phase high-performance liquid chromatography (C18 column with methanol and 0.1% v/v acetic acid in deionized water [70:30, v/v] at 284 nm). Further, structural characterization of DPs was performed using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) and nuclear magnetic resonance (NMR) spectroscopy. In addition, in silico toxicity predictions were performed using pKCSM and DataWarior freeware. RESULTS: HES was found to be susceptible to acidic and basic hydrolytic conditions and yielded three DPs in each, which were detected using designed SIAM. Of six DPs, three were pseudo-DPs (short lived), and the remaining were characterized using LC-MS/MS and NMR spectroscopy. The tentative mechanism of the formation of proposed DPs was explained. The proposed DPs were found inactive from in silico toxicity predictions. CONCLUSIONS: Hesperidin was labile under acidic and basic stress conditions. The potential DPs were characterized using LC-ESI-MS/MS and NMR spectral techniques. The proposed mechanism of formation was hypothesized. In addition, to identify and characterize the DPs, a SIAM, which has broad biomedical applications, was successfully developed.

Design and synthesis of N-acyl and dimeric N-Arylpiperazine derivatives as potential antileishmanial agents.

The current regime for leishmaniasis is associated with several adverse effects, expensive, parenteral treatment for longer periods and the emergence of drug resistance. To develop affordable and potent antileishmanial agents, a series of N-acyl and homodimeric aryl piperazines were synthesized with high purity, predicted druggable properties by in silico methods and investigated their antileishmanial activity. The in vitro biological activity of synthesized compounds against clinically validated intracellular amastigote and extracellular promastigote form of Leishmania donovani parasite showed eight compounds inhibited 50% amastigotes growth below 25 µM. The half maximal inhibitory concentration (IC50) and cytotoxicity assessment of eight active compounds, 4a, 4d and 4e demonstrated activity with an IC50 2.0 - 9.1 µM and selectivity index 10 - 42. Compound 4d (IC50 2.0 µM, SI = 42) found to be the best among them with four-folds more potent and eight-folds less toxic than the control drug miltefosine. Overall, results demonstrated that compound 4d is a promising lead candidate for further development as antileishmanial drug.

Targeting non-structural proteins of Hepatitis C virus for predicting repurposed drugs using QSAR and machine learning approaches.

Hepatitis C virus (HCV) infection causes viral hepatitis leading to hepatocellular carcinoma. Despite the clinical use of direct-acting antivirals (DAAs) still there is treatment failure in 5-10% cases. Therefore, it is crucial to develop new antivirals against HCV. In this endeavor, we developed the "Anti-HCV" platform using machine learning and quantitative structure-activity relationship (QSAR) approaches to predict repurposed drugs targeting HCV non-structural (NS) proteins. We retrieved experimentally validated small molecules from the ChEMBL database with bioactivity (IC50/EC50) against HCV NS3 (454), NS3/4A (495), NS5A (494) and NS5B (1671) proteins. These unique compounds were divided into training/testing and independent validation datasets. Relevant molecular descriptors and fingerprints were selected using a recursive feature elimination algorithm. Different machine learning techniques viz. support vector machine, k-nearest neighbour, artificial neural network, and random forest were used to develop the predictive models. We achieved Pearson's correlation coefficients from 0.80 to 0.92 during 10-fold cross validation and similar performance on independent datasets using the best developed models. The robustness and reliability of developed predictive models were also supported by applicability domain, chemical diversity and decoy datasets analyses. The "Anti-HCV" predictive models were used to identify potential repurposing drugs. Representative candidates were further validated by molecular docking which displayed high binding affinities. Hence, this study identified promising repurposed drugs viz. naftifine, butalbital (NS3), vinorelbine, epicriptine (NS3/4A), pipecuronium, trimethaphan (NS5A), olodaterol and vemurafenib (NS5B) etc. targeting HCV NS proteins. These potential repurposed drugs may prove useful in antiviral drug development against HCV.

Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning.

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.