Recent advances and structure-activity relationship studies of DPP-4 inhibitors as anti-diabetic agents.

Diabetes mellitus (DM) is one of the largest public health problems worldwide and in the last decades various therapeutic targets have been investigated. For the treatment of type-2 DM (T2DM), dipeptidyl peptidase-4 (DPP-4) is one of the well reported target and has established safety in terms of cardiovascular complexicity. Preclinical and clinical studies using DPP-4 inhibitors have demonstrated its safety and effectiveness and have lesser risk of associated hypoglycaemic effect making it suitable for elderly patients. FDA has approved a number of structurally diverse DPP-4 inhibitors for clinical use. The present manuscript aims to focus on the well reported hybrid and non-hybrid analogues and their structural activity relationship (SAR) studies. It aims to provide structural insights for this class of compounds pertaining to favourable applicability of selective DPP-4 inhibitors in the treatment of T2DM.

Nanotechnology-Based Drug Delivery of Topical Antifungal Agents.

Among the various prominent fungal infections, superficial ones are widespread. A large number of antifungal agents and their formulations for topical use are commercially available. They have some pharmacokinetic limitations which cannot be retracted by conventional delivery systems. While nanoformulations composed of lipidic and polymeric nanoparticles have the potential to overcome the limitations of conventional systems. The broad spectrum category of antifungals i.e. azoles (ketoconazole, voriconazole, econazole, miconazole, etc.) nanoparticles have been designed, prepared and their pharmacokinetic and pharmacodynamic profile was established. This review briefly elaborates on the types of nano-based topical drug delivery systems and portrays their advantages for researchers in the related field to benefit the available antifungal therapeutics.

Preclinical to Clinical Profile of Curcuma longa as Antidiabetic Therapeutics.

Natural product substances have historically served as the most significant source of new leads for pharmaceutical development. Presently, drug discovery and development have adopted rational approaches to explore herbal resources for treating lifestyle-related diseases such as diabetes. For the treatment of diabetes, Curcumin longa has been extensively studied for evaluation of its antidiabetic potential using various in vivo and in vitro models. Literature resources such as PubMed and Google Scholar have been extensively searched to collect documented studies. Various parts of the plant and extracts have proven antidiabetic effects, namely, anti-hyperglycemic, antioxidant, and anti-inflammatory action, through different mechanisms. It is reported that the plant extract or its phytoconstituents regulate glucose and lipid metabolism. The reported study concluded the diversified antidiabetic role of C. longa and its phytoconstituents and, thus, its potential use as an antidiabetic agent.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Protein Informatics and Vaccine Development: Cancer Case Study.

Clinical translation is a challenging step in the development of cancer vaccines and is found to be related to the complex nature of cancer immunology. Vaccine-based therapeutic strategies for cancer have gained consideration with the advent of vaccine technology as well as an understanding of cancer immunology. Immunotherapy has been widely used in the treatment of cancer. Some promising candidates have been identified to engineer cancer vaccines like Glycoprotein, Mucin 1, MHC protein, etc. It has benefited from the availability of advanced techniques for rapid identification and selection of proteins for precision engineering. Simultaneously, nanovaccines have been focused on target delivery and artificial intelligence-based approaches for personalized vaccine development. The manuscript summarizes the advances in the development of structurebased cancer vaccines along with the status of clinical studies and applications.

Prospective mode of action of Ivermectin: SARS-CoV-2.

The well-known anti-helminthic drug ivermectin (IVM) has been established as an example of drug repurposing for the management of SARS-CoV-2 infection. Various study has been done to understand the inhibitory mechanism of IVM against SARS-CoV-2 targets. Broadly, IVM has been categorized as a host-directed agent and the proposed mechanism involves inhibition of the IMPα/ß1-mediated nuclear import of viral proteins. In addition, in vitro/in vivo and molecular docking/dynamic simulation studies suggested multitargets mechanism of IVM against SARS-CoV-2. Present manuscript attempts to provide an overview of the detailed mechanism of action based on experimental and computational studies. The knowledge of binding interaction of IVM and SARS-CoV-2 targets will give the direction to developed new and potential anti-COVID agents.

Non-cancer to anti-cancer: investigation of human ether-a-go-go-related gene potassium channel inhibitors as potential therapeutics.

BACKGROUND: The expression of hERG K+ channels is observed in various cancer cells including epithelial, neuronal, leukemic, and connective tissue. The role of hERG potassium channels in regulating the growth and death of cancer cells include cell proliferation, survival, secretion of proangiogenic factors, invasiveness, and metastasis. METHODS: In the reported study, an attempt has been made to investigate some non-cancer hERG blockers as potential cancer therapeutics using a computational drug repurposing strategy. Preliminary investigation for hERG blockers/non-blockers has identified 26 potential clinically approved compounds for further studies using molecular modeling. RESULTS: The interactions at the binding pockets have been investigated along with the prioritization based on the binding score. Some of the identified potential hERG inhibitors, i.e., Bromocriptine, Darglitazone, and Troglitazone, have been investigated to derive the mechanism of cancer inhibition. CONCLUSIONS: The proposed mechanism for anti-cancer properties via hERG blocking for some of the potential compounds is required to be explored using other experimental methodologies. The drug repurposing approach applied to investigate anti-cancer therapeutics may direct to provide a therapeutic solution to late-stage cancer and benefit a significant population of patients.

QSAR Studies to Predict Activity of HSP90 Inhibitors.

Heat shock protein 90 (HSP90) is a multichaperone complex that mediates the maturation and stability of a variety of oncogenic signaling proteins. HSP90 has emerged as a promising target for the development of anticancer agents. Heterocyclic chemical moieties with HSP90 inhibitory activity were studied continuously during the last decades, and resulting data were applied by medicinal chemists to design and develop new drugs. Their structure-activity relationship (SAR) studies and QSAR models have been derived to assist the current drug development process. The QSAR models are obtained via multiple linear regression (MLR) and non-linear approaches. Interpretation of the reported model highlights the core template required to design novel, potent HSP90 inhibitors to be used as anticancer agents.

Chromones: Privileged scaffold in anticancer drug discovery.

In the design and discovery of anticancer drugs, various natural heterocyclic scaffolds have attracted considerable interest as privileged structures. For rational drug design, some of the natural scaffolds such as chromones have exhibited wide acceptability due to their drug-like properties. Among the approved anticancer drugs, the scaffolds with high selectivity for a small group of closely related targets are of importance. In the development of selective anticancer agents, the natural, as well as synthetic, can generate highly selective compounds toward cancer targets. The present manuscript includes more particularly the development of cancer inhibitors incorporating the chromone scaffold, with a strong emphasis on their molecular interactions in the anticancer mechanism. It also includes the structure-activity relationship studies and related examples of lead optimization.

Safe and Effective Kinase Inhibitors for the Treatment of Gynecological Cancers: In Silico Approach.

AIMS: This study aimed at studying various types of gynecological cancers and the available therapeutics to investigate safe and effective drugs. BACKGROUND: Cancer is the most common cause of mortality throughout the world. When the statistics are being considered for gynecological cancers, ovarian, cervical, and uterine cancers are among the most prevalent types. They have the worst prognosis and the highest mortality rate and by the year 2040 significant increase in mortality rate is predicted. OBJECTIVE: The major limitation with the development of anti-cancer therapeutics for gynecological cancers is the safety of the therapeutics for the developing fetus as well as the mother. Various medicinal classes of natural to synthetic therapeutics have been reported including kinase inhibitors as the most promising category of anti-cancer drugs. METHODS: A dataset of kinase inhibitors clinically approved as anticancer agents was derived through a literature review. A QSAR based approach i.e. VEGAQSAR has been applied to evaluate the reproductive and developmental toxicity for the selected class of kinase inhibitors. RESULT: In the present work, the promising category of anticancer kinase inhibitors has been investigated for its toxicity potential with the help of in silico approach. The anti-cancer kinase inhibitors were categorized based on the found non-toxic or toxic properties towards reproductive and developmental toxicity. CONCLUSION: Early prediction of the available or proposed anti-cancer therapeutics for their contribution towards developmental and reproductive toxicity is an important criterion for their use in pregnancy-associated cancers. The investigation of the toxicity profile of available anti-cancer kinase therapeutics will be helpful to design and develop novel and safe anti-cancer drugs in the near future. The study outcomes will benefit the current anticancer drug development efforts.

Natural Product Databases and Tools for Anti-cancer Drug Discovery.

Throughout the ages, compounds collected and/or isolated from natural sources have been the basic source of medicinal agents against a variety of diseases. The data generated from the extensive experimentation conducted in research labs from all over the world has diversity and complexity, and the challenging task for database creators is to store, represent, and exchange this data. The natural product database is required to be easily accessible for supporting drug discovery efforts. A possible solution for this is provided by chemical databases based on bio- and chem-informatic approaches. Some of the anti-cancer natural product databases, along with the tools required for creating and accessing the information, are discussed here.

A systematic review on use of aminoquinolines for the therapeutic management of COVID-19: Efficacy, safety and clinical trials.

Recent global outbreak of the pandemic caused by coronavirus (COVID-19) emphasizes the urgent need for novel antiviral therapeutics. It can be supplemented by utilization of efficient and validated drug discovery approaches such as drug repurposing/repositioning. The well reported and clinically used anti-malarial aminoquinoline drugs (chloroquine and hydroxychloroquine) have shown potential to be repurposed to control the present pandemic by inhibition of COVID-19. The review elaborates the mechanism of action, safety (side effects, adverse effects, toxicity) and details of clinical trials for chloroquine and hydroxychloroquine to benefit the clinicians, medicinal chemist, pharmacologist actively involved in controlling the pandemic and to provide therapeutics for the treatment of COVID-19 infection.

Cinnamaldehyde Analogs: Docking Based Optimization, COX-2 Inhibitory In Vivo and In Vitro Studies.

BACKGROUND: In the past decade CADD has emerged as a rational approach in drug development so with the help molecular docking approach we planned to perform virtual screening of the designed data set of Schiff bases of cinnamaldehyde. The research work will be helpful to put some light on the drug receptor interactions required for anti-inflammatory activity. METHODS: For carrying out virtual screening of the developed cinnamaldehyde Schiff base data set, AutoDock 4.0 was used. The active hits identified through in silico screening were synthesized. Anti-inflammatory evaluation was carried out using Carrageenan-induced paw oedema method. RESULTS: Compounds V2A44, V2A55, V2A76, V2A82, V2A119, V2A141 and V2A142 has shown highest binding energy (-4.84, -4.76, -4.59, -4.78, -4.74, -4.85 and -4.72 kcal/mol, respectively) and the binding interactions with amino acids namely, Phe478, Glu479, Lys492, Ala493, Asp497 and Ile498. Some of the analogs have shown significant activity and were comparable to Indomethacin (standard drug). CONCLUSION: Five new compounds have shown significant activity and the results obtained from in silico studies are parallel to those of in vivo studies.

N-Substituted Aryl Sulphonamides as Potential Anti-Alzheimer's Agents: Design, Synthesis and Biological Evaluation.

INTRODUCTION: A novel series of multifunctional anti-Alzheimer's agents based on Nsubstituted aryl sulphonamides were designed and synthesized. During in vivo moderate to good anti- Alzheimer's Disease (AD) activity was observed as correlated by the modulation of some selected biochemical markers of AD as well as during behavioral assessment. METHODS: Among the series, some compounds have shown multi-functional potency by inhibition of Acetylcholinesterase (AChE), Scopolamine induced oxidative stress and were found comparable to the standard drug. Successful modulation of biochemical markers of oxidative stress in AD, displays neuroprotective properties and did not exert any significant toxicity. RESULTS AND CONCLUSION: Thus, the present study has evidently shown that these series of compounds have potential to be optimized as anti-AD agents with multi-functional properties. The aryl sulphonamide nucleus might serve as a promising lead candidate for developing novel anti-AD drug.

Designing of Selective γ-Secretase Inhibitory Benzenesulfonamides through Comparative In Vitro and In Silico Analysis.

BACKGROUND: In Alzheimer's disease (AD), the gene mutations have been identified in the amyloid precursor protein (APP), the presenilin-1 (PS1) and -2 (PS2) genes. APP is a transmembrane protein which gets cleaved by α- and ß- secretase enzymes and releases Aß peptides which forms senile plaques in brain tissue. It contributes for local inflammatory response, subsequent oxidative stress, biochemical changes and neuronal death. Targeting the development of Aß aggregates in the senile plaques is an important strategy in the treatment of AD. To facilitate the normal processing of APP, some of the reported approaches are stimulation of α- secretase activity or the modulation/inhibition of the ß- and γ-secretase complex. METHODS: The mechanism of γ-secretase inhibition is targeted based on the QSAR and molecular docking methods. The series based on 3-chloro-2-hydroxymethylbenzenesulfonamide was selected for in silico ligand-based modeling. Significant correlations, between their γ-Secretase inhibitory profile and 2D-descriptors, were obtained through multiple linear regression (MLR) computational procedure. RESULTS: During QSAR nalysis, calculated molar refractivity (CMR) and surface tension (ST) were found to be contributing parameters along with halogen substituent at a particular position. Applicability analysis revealed that the suggested models have acceptable predictability (rpred2 = 0.827). CONCLUSION: The inferences drawn from MLR were utilized to prepare a data set of fourteen substituted benzenesulfonamides (N1-N14). The in silico studies provides strong impetus towards systematic application of such methods during lead identification and optimization.

GENIUS In Silico Screening Technology for HCV Drug Discovery.

The various reported in silico screening protocols such as molecular docking are associated with various drawbacks as well as benefits. In molecular docking, on interaction with ligand, the protein or receptor molecule gets activated by adopting conformational changes. These conformational changes cannot be utilized to predict the 3D structure of a protein-ligand complex from unbound protein conformations rigid docking, which necessitates the demand for understanding protein flexibility. Therefore, efficiency and accuracy of docking should be achieved and various available/developed protocols may be adopted. One such protocol is GENIUS induced-fit docking and it is used effectively for the development of anti-HCV NS3-4A serine protease inhibitors. The present review elaborates the GENIUS docking protocol along with its benefits and drawbacks.

HCV Inhibitors: Role of Compounds from Botanical Sources.

The developing number of hepatitis C virus infected cases worldwide has threatened people's health. The available therapeutic options have low specificity, side effects and high rate of drug resistance and thus potentiate the need for novel effective anti-HCV drugs. Agents obtained from natural sources offer an enormous scope of structural diversity and broad therapeutic range of coverage. This review summarizes the research and development of anti-HCV agents (plant extracts/isolated components) obtained from various natural sources along with the associated mechanism of HCV inhibition. Some of the reported examples include triterpenes, naringenin, Proanthocyanidin, curcumin, Epigallocatechin-3-gallate, quercetin and abrogates having diverse anti-HCV properties. The compiled knowledge regarding anti-HCV agents from natural sources will provide considerable information for developing novel safe and effective anti-HCV drugs.

Heterocyclic Secretase Inhibitors for the Treatment of Alzheimer's Disease: An Overview.

Alzheimer's disease (AD), the most common neurodegenerative disorder and demands to find a way for prevention and delayed onset. The development of therapeutics for AD is based on the amyloid cascade hypothesis (vaccines, ß- and γ-Secretase inhibitors), or targeting tau and neurofibrillary tangle formation, neuroinflammation, etc. Cholinesterase, BACE-1, amyloid-ß 1-42, γ and ß-Secretase, Phosphodiesterase type IV (PDE4) inhibitors are the reported treatment strategies. Among these, the γ- and ß-Secretase inhibitors can be clustered in several heterocyclic classes (imidazoles, thiazoles, indoles, benzaldehydes, pyrimidine, etc), with subsequent description of the structure-activity relationships, and extended to the pharmacological profile in order to evaluate their drug-likeness, with special attention to toxicity and bioavailability. This article discusses the approaches proposed by several research groups working on the synthesis of enzyme inhibitors, based on modelling studies and the way these findings were used to obtain new drugs for the treatment of AD.

3D-QSAR and docking studies on a series of benzothiadiazine derivatives as genotype 1 HCV polymerase inhibitors.

The Benzothiadiazine derivatives have been regarded as a novel class of HCV genotype 1 polymerase inhibitors. To explore the relationship between the structures of substituted Benzothiadiazine derivatives and their inhibitory activities against HCV, 3D-QSAR and molecular docking studies were performed on a dataset of ninty-eight compounds. The 3D-QSAR models resulted from seventy-eight molecules in the training set gave q(2) value of 0.81 and a test set of twenty compounds, gave predictive r(2) value of 0.94. 3D-QSAR model generated from kNN-MFA along with the docking binding structures provided enough information about the structural requirements for better activity. The results can serve as a useful guideline to design novel HCV genotype 1 inhibitors with better potencies.

Synthesis, in vitro and in silico NS5B polymerase inhibitory activity of benzimidazole derivatives.

Hepatitis C virus (HCV) NS5B polymerase is the key replicating protein of the virus and thus an attractive target for drug development. Here we report on the synthesis and biological evaluation of a new series of benzimidazole derivatives as HCV NS5B inhibitors. This yielded compound 6b and 6d bearing 2-(2-benzyloxy)phenyl and 2-(4-methylbenzyloxy)phenyl moieties, respectively, as promising leads. Binding mode of compound 6d in allosteric pocket (AP)-1 of NS5B will form the basis for future structure-activity relationship optimization.