An In Silico Approach to Exploring the Antinociceptive Biological Activities of Linalool and its Metabolites.

Pain is characterized by the unpleasant sensory and emotional sensation associated with actual or potential tissue damage, whereas nociception refers to the mechanism by which noxious stimuli are transmitted from the periphery to the CNS. The main drugs used to treat pain are nonsteroidal anti-inflammatory drugs (NSAIDs) and opioid analgesics, which have side effects that limit their use. Therefore, in the search for new drugs with potential antinociceptive effects, essential oils have been studied, whose constituents (monoterpenes) are emerging as a new therapeutic possibility. Among them, linalool and its metabolites stand out. The present study aims to investigate the antinociceptive potential of linalool and its metabolites through a screening using an in silico approach. Molecular docking was used to evaluate possible interactions with important targets involved in antinociceptive activity, such as α2-adrenergic, GABAergic, muscarinic, opioid, adenosinergic, transient potential, and glutamatergic receptors. The compounds in the investigated series obtained negative energies for all enzymes, representing satisfactory interactions with the targets and highlighting the multi-target potential of the L4 metabolite. Linalool and its metabolites have a high likelihood of modulatory activity against the targets involved in nociception and are potential candidates for future drugs.

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.

Promising Schiff bases in antiviral drug design and discovery.

Emerging and re-emerging illnesses will probably present a new hazard of infectious diseases and have fostered the urge to research new antiviral agents. Most of the antiviral agents are analogs of nucleosides and only a few are non-nucleoside antiviral agents. There is quite a less percentage of marketed/clinically approved non-nucleoside antiviral medications. Schiff bases are organic compounds that possess a well-demonstrated profile against cancer, viruses, fungus, and bacteria, as well as in the management of diabetes, chemotherapy-resistant cases, and malarial infections. Schiff bases resemble aldehydes or ketones with an imine/azomethine group instead of a carbonyl ring. Schiff bases have a broad application profile not only in therapeutics/medicine but also in industrial applications. Researchers have synthesized and screened various Schiff base analogs for their antiviral potential. Some of the important heterocyclic compounds like istatin, thiosemicarbazide, quinazoline, quinoyl acetohydrazide, etc. have been used to derive novel Schiff base analogs. Keeping in view the outbreak of viral pandemics and epidemics, this manuscript compiles a review of Schiff base analogs concerning their antiviral properties and structural-activity relationship analysis.

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.

Mutation informatics: SARS-CoV-2 receptor-binding domain of the spike protein.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) undergoes mutations at a high rate and with frequent genetic reassortment (antigenic drift/shift), leading to variability in targets. The receptor-binding domain (RBD) of the spike (S) protein has a major role in the binding of SARS-CoV-2 with human angiotensin-converting enzyme 2 (ACE2). Mutations at the RBD influence the binding interaction at the SARS-CoV-2 S-ACE2 interface and impact viral pathogenicity. Here, we discuss different reported mutations of concern in RBD, physicochemical characteristic changes resulting from mutated amino acids and their effect on binding between the RBD and ACE2. Along with mutation informatics, we highlight recently developed small-molecule inhibitors of RBD and the ACE2 interface. This information provides a rational basis for the design of inhibitors against the multivariant strains of SARS-CoV-2.

Molecular Insights into Coumarin Analogues as Antimicrobial Agents: Recent Developments in Drug Discovery.

A major global health risk has been witnessed with the development of drug-resistant bacteria and multidrug-resistant pathogens linked to significant mortality. Coumarins are heterocyclic compounds belonging to the benzophenone class enriched in different plants. Coumarins and their derivatives have a wide range of biological activity, including antibacterial, anticoagulant, antioxidant, anti-inflammatory, antiviral, antitumour, and enzyme inhibitory effects. In the past few years, attempts have been reported towards the optimization, synthesis, and evaluation of novel coumarin analogues as antimicrobial agents. Several coumarin-based antibiotic hybrids have been developed, and the majority of them were reported to exhibit potential antibacterial effects. In the present work, studies reported from 2016 to 2020 about antimicrobial coumarin analogues are the focus. The diverse biological spectrum of coumarins can be attributed to their free radical scavenging abilities. In addition to various synthetic strategies developed, some of the structural features include a heterocyclic ring with electron-withdrawing/donating groups conjugated with the coumarin nucleus. The suggested structure-activity relationship (SAR) can provide insight into how coumarin hybrids can be rationally improved against multidrug-resistant bacteria. The present work demonstrates molecular insights for coumarin derivatives having antimicrobial properties from the recent past. The detailed SAR outcomes will benefit towards leading optimization during the discovery and development of novel antimicrobial therapeutics.

Therapeutic Outcomes of Isatin and Its Derivatives against Multiple Diseases: Recent Developments in Drug Discovery.

Isatin (1H indole 2, 3-dione) is a heterocyclic, endogenous lead molecule recognized in humans and different plants. The isatin nucleus and its derivatives are owed the attention of researchers due to their diverse pharmacological activities such as anticancer, anti-TB, antifungal, antimicrobial, antioxidant, anti-inflammatory, anticonvulsant, anti-HIV, and so on. Many research chemists take advantage of the gentle structure of isatins, such as NH at position 1 and carbonyl functions at positions 2 and 3, for designing biologically active analogues via different approaches. Literature surveys based on reported preclinical, clinical, and patented details confirm the multitarget profile of isatin analogues and thus their importance in the field of medicinal chemistry as a potent chemotherapeutic agent. This review represents the recent development of isatin analogues possessing potential pharmacological action in the years 2016-2020. The structure-activity relationship is also discussed to provide a pharmacophoric pattern that may contribute in the future to the design and synthesis of potent and less toxic therapeutics.

Exploring the Prominent and Concealed Inhibitory Features for Cytoplasmic Isoforms of Hsp90 Using QSAR Analysis.

Cancer is a major life-threatening disease with a high mortality rate in many countries. Even though different therapies and options are available, patients generally prefer chemotherapy. However, serious side effects of anti-cancer drugs compel us to search for a safer drug. To achieve this target, Hsp90 (heat shock protein 90), which is responsible for stabilization of many oncoproteins in cancer cells, is a promising target for developing an anti-cancer drug. The QSAR (Quantitative Structure-Activity Relationship) could be useful to identify crucial pharmacophoric features to develop a Hsp90 inhibitor. Therefore, in the present work, a larger dataset encompassing 1141 diverse compounds was used to develop a multi-linear QSAR model with a balance of acceptable predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The new developed six-parameter model satisfies the recommended values for a good number of validation parameters such as R2tr = 0.78, Q2LMO = 0.77, R2ex = 0.78, and CCCex = 0.88. The present analysis reveals that the Hsp90 inhibitory activity is correlated with different types of nitrogen atoms and other hidden structural features such as the presence of hydrophobic ring/aromatic carbon atoms within a specific distance from the center of mass of the molecule, etc. Thus, the model successfully identified a variety of reported as well as novel pharmacophoric features. The results of QSAR analysis are further vindicated by reported crystal structures of compounds with Hsp90.

Drug Repurposing Strategies for Non-cancer to Cancer Therapeutics.

Global efforts invested in the prevention and treatment of cancer need to be repositioned to develop safe, effective, and economic anticancer therapeutics by adopting rational approaches of drug discovery. Drug repurposing is one of the established approaches to reposition old, clinically approved off-patent noncancer drugs with known targets into newer indications. The literature review suggests a key role of drug repurposing in the development of drugs intended for cancer as well as noncancer therapeutics. A wide category of noncancer drugs such as, drugs acting on CNS, anthelmintics, cardiovascular drugs, antimalarial drugs, anti-inflammatory drugs, have come out with interesting outcomes during preclinical and clinical phases. In the present article, a comprehensive overview of the current scenario of drug repurposing for the treatment of cancer has been focused. The details of some successful studies along with examples have been included followed by associated challenges.

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.

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop.

We report the major conclusions of the online open-access workshop "Computational Applications in Secondary Metabolite Discovery (CAiSMD)" that took place from 08 to 10 March 2021. Invited speakers from academia and industry and about 200 registered participants from five continents (Africa, Asia, Europe, South America, and North America) took part in the workshop. The workshop highlighted the potential applications of computational methodologies in the search for secondary metabolites (SMs) or natural products (NPs) as potential drugs and drug leads. During 3 days, the participants of this online workshop received an overview of modern computer-based approaches for exploring NP discovery in the "omics" age. The invited experts gave keynote lectures, trained participants in hands-on sessions, and held round table discussions. This was followed by oral presentations with much interaction between the speakers and the audience. Selected applicants (early-career scientists) were offered the opportunity to give oral presentations (15 min) and present posters in the form of flash presentations (5 min) upon submission of an abstract. The final program available on the workshop website ( https://caismd.indiayouth.info/ ) comprised of 4 keynote lectures (KLs), 12 oral presentations (OPs), 2 round table discussions (RTDs), and 5 hands-on sessions (HSs). This meeting report also references internet resources for computational biology in the area of secondary metabolites that are of use outside of the workshop areas and will constitute a long-term valuable source for the community. The workshop concluded with an online survey form to be completed by speakers and participants for the goal of improving any subsequent editions.

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.

Molecular Processes Exploited as Drug Targets for Cancer Chemotherapy.

Cancer is an uncontrolled malignant tumor growth taking place in any tissue of the body and attains complex diversity which makes it difficult for oncologists to choose therapeutics. The changes leading to formation of cancerous cells occur due to a series of molecular events. Now scientists are trying to understand the various molecular processes that are involved in the growth of cancers. This article presents a brief account of epigenetics with reference to DNA methylation and histone modification as an important contributor to the formation of cancer cells. Drug targeting the epigenetic regulators has been considered for various types of cancer. The enzymes in DNA methylation and histone modification, FDA approved clinical drugs along with the challenges associated with the development of anti-cancer target based therapeutics are summarized.