Identification Of Novel Drug Candidates Research Articles

A Mild Metal‐Free Approach for the Direct Synthesis of Carbamoyl Azides from Carboxylic Acid Redox Active Esters

AbstractCarbamoyl azide is a pivotal compound used in the transformation into urea and amines, as well as in nitrene insertion reactions. In this study, we present a pioneering method for synthesizing carbamoyl azide directly from acid redox active esters without the use of metals or oxidants. This approach demonstrates tolerance for various functional groups, enhancing its applicability. Furthermore, this technique facilitates late‐stage modifications of natural products and pharmaceuticals, thereby accelerating the development and identification of novel drug candidates.

Transfer of Exercise-Microbiome Is Suffcient to Induce a Shift in the Skeletal Muscle Metabolomic Profile without an Exercise Stimulus

Introduction: Skeletal muscle is a tissue that dynamically responds to disuse, which can induce rapid loss of mass and function. Identification of novel drug candidates will facilitate the development of supportive therapies for extended periods of decreased mobility, both planned and unanticipated. Recent findings from our lab as well as others indicate the important role of the gut microbiome in mediating the beneficial effects of exercise. Hypothesis: Transfer of microbiome from excise-trained mice into exercise-naïve mice will induce metabolic adaptations in the recipient skeletal muscle. Method: Sedentary and exercise-trained mice were used as donors for microbiome transfer. Exercise training consisted of 8-weeks of processive weighted wheel running. Exercise-naïve recipient mice were bowel cleansed with polyethylene glycol and transplanted with either sedentary (SED) or exercise-trained (EXER) donor microbiome. Recipient cecal content, serum, and gastrocnemius muscle were collected for untargeted metabolomics using liquid chromatography mass spectrometry. Results: Partial Least Squares Discriminant Analysis indicated significant metabolic discrimination between EXER and SED in all samples (cecal, serum, and muscle). Variable Importance in Projection scores identified several metabolites as significant discriminating metabolic features between EXER and SED, including ones involved in the TCA cycle, lysine and fatty acid degradation pathways. Conclusion: Transfer of microbial content from exercise-trained mice is suffcient to induce changes in muscle metabolite profile reminiscent of exercise in the absence of such a stimulus. Funding: This work is supported by the National Institute on Aging (AG071888). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Exploring the synthesis, molecular structure and biological activities of novel Bis-Schiff base derivatives: A combined theoretical and experimental approach

A library of six novel bis-Schiff base derivatives (2a-f) were synthesized, characterized through modern spectroscopic techniques and screened for their α-glucosidase and α-amylase inhibitory activities (in vitro). In the series, compound 2a (IC50 = 5.64 ± 2.22 µM) and 2f (IC50 = 22.78 ± 2.37 µM) were the most potent α-amylase inhibitors while the remaining four compounds showed significant to less activity. In case of α-glucosidase inhibition, four compounds 2e (IC50 = 2.83 ± 0.18 µM), 2c (IC50 = 7.03 ± 0.15 µM), 2f (IC50 = 9.99 ± 0.20 µM), and 2d (IC50 = 14.68 ± 0.21 µM) displayed excellent inhibitory activity while two compounds showed good inhibitory activity, comparing with the standard acarbose drug. The DFT assay was used to measure the FMO of the synthesized molecules, which indicated their stability, bioactivity and charge transfer. The MEP analysis revealed the distribution of electrostatic potential on the molecular surface of 2a-f, which is helpful for understanding reactivity and interactions. The AIM study showed low hydrogen bond energy and non-covalent interactions. This implies that these molecules may have weak hydrogen bonding and non-covalent interactions, which could affect their chemical behavior. The molecular docking study has provided valuable insights into the interactions between the synthesized derivatives of 2,4-dihydroxyacetophenone with α-glucosidase and α-amylase proteins. The results not only shed light on the binding affinity and key interaction sites but also offer potential avenues for the design of novel drug candidates to control postprandial glucose level in diabetic patients.

Advancement of Computational Design Drug Delivery System in COVID-19: Current Updates and Future Crosstalk- A Critical update.

Positive strides have been achieved in developing vaccines to combat the coronavirus-2019 infection (COVID-19) pandemic. Still, the outline of variations, particularly the most current delta divergent, has posed significant health encounters for people. Therefore, developing strong treatment strategies, such as an anti-COVID-19 medicine plan, may help deal with the pandemic more effectively. During the COVID-19 pandemic, some drug design techniques were effectively used to develop and substantiate relevant critical medications. Extensive research, both experimental and computational, has been dedicated to comprehending and characterizing the devastating COVID-19 disease. The urgency of the situation has led to the publication of over 130,000 COVID-19-related research papers in peer-reviewed journals and preprint servers. A significant focus of these efforts has been the identification of novel drug candidates and the repurposing of existing drugs to combat the virus. Many projects have utilized computational or computer-aided approaches to facilitate their studies. In this overview, we will explore the key computational methods and their applications in the discovery of small-molecule therapeutics for COVID-19, as reported in the research literature. We believe that the true effectiveness of computational tools lies in their ability to provide actionable and experimentally testable hypotheses, which in turn facilitate the discovery of new drugs and combinations thereof. Additionally, we recognize that open science and the rapid sharing of research findings are vital in expediting the development of much-needed therapeutics for COVID-19.

Roles of anoikis in colorectal cancer therapy and the assessment of anoikis-regulatory molecules as therapeutic targets.

Colorectal cancer (CRC) is a deadly malignancy and therapeutic approaches for CRC are evolving every day. Anoikis is a key mechanism for programmed cell death of cancer cells that undergo anchorage-independent growth at a different matrix than the one which is expected. Yet, anoikis is a less studied mechanism of cell death in comparison to other mechanisms such as apoptosis. Relating to this, resistance to anoikis among cancer cells remains critical for improved metastasis and survival in a new environment evading anoikis. Since CRC cells have the ability to metastasize from proximal sites to secondary organs such as liver and promote cancer in those distant sites, a clear knowledge of the mechanisms essential for anchorage-independent growth and subsequent metastasis is necessary to counteract CRC progression and spread. Therefore, the identification of novel drug candidates and studying the roles of anoikis in assisting CRC therapy using such drugs can prevent anchorage-independent cancer cell growth. Additionally, the identification of novel biomarkers or therapeutic targets seems essential for implementing superior therapy, impeding relapse among malignant cells and improving the survival rate of clinical patients. As there are no reviews published on this topic till date, anoikis as a mechanism of cell death and its therapeutic roles in CRC are discussed in this review. In addition, several molecules were identified as therapeutic targets for CRC.

Selecting the right therapeutic target for kidney disease

Kidney disease is a complex disease with several different etiologies and underlying associated pathophysiology. This is reflected by the lack of effective treatment therapies in chronic kidney disease (CKD) that stop disease progression. However, novel strategies, recent scientific breakthroughs, and technological advances have revealed new possibilities for finding novel disease drivers in CKD. This review describes some of the latest advances in the field and brings them together in a more holistic framework as applied to identification and validation of disease drivers in CKD. It uses high-resolution 'patient-centric' omics data sets, advanced in silico tools (systems biology, connectivity mapping, and machine learning) and 'state-of-the-art' experimental systems (complex 3D systems in vitro, CRISPR gene editing, and various model biological systems in vivo). Application of such a framework is expected to increase the likelihood of successful identification of novel drug candidates based on strong human target validation and a better scientific understanding of underlying mechanisms.

The impact of simulation time in predicting binding free energies using end-point approaches.

The profound impact of <i>in silico</i> studies for a fast-paced drug discovery pipeline is undeniable for pharmaceutical community. The rational design of novel drug candidates necessitates considering optimization of their different aspects prior to synthesis and biological evaluations. The affinity prediction of small ligands to target of interest for rank-ordering the potential ligands is one of the most routinely used steps in the context of virtual screening. So, the end-point methods were employed for binding free energy estimation focusing on evaluating simulation time effect. Then, a set of human aldose reductase inhibitors were selected for molecular dynamics (MD)-based binding free energy calculations. A total of 100[Formula: see text]ns MD simulation time was conducted for the ligand-receptor complexes followed by prediction of binding free energies using MM/PB(GB)SA and LIE approaches under different simulation time. The results revealed that a maximum of 30[Formula: see text]ns simulation time is sufficient for determination of binding affinities inferred from steady trend of squared correlation values (R²) between experimental and predicted [Formula: see text]G as a function of MD simulation time. In conclusion, the MM/PB(GB)SA algorithms performed well in terms of binding affinity prediction compared to LIE approach. The results provide new insights for large-scale applications of such predictions in an affordable computational cost.

Identification of Novel Drug Candidates for the Inhibition of Catalytic Cleavage Activity of Coronavirus 3CL-like Protease Enzyme.

There has been tremendous pressure on healthcare facilities globally due to the recent emergence of novel coronavirus infection known as COVID-19 and its rapid spread across the continents. The lack of effective therapeutics for the management of the pandemic calls for the discovery of new drugs and vaccines. In the present study, a chemical library was screened for molecules against three coronavirus 3CL-like protease enzymes (SARS-CoV-2 3CLpro, SARS-CoV 3CLpro and MERS-CoV 3CLpro), which are a key player in the viral replication cycle. Extensive computational methods such as virtual screening and molecular docking were employed in this study. Two lead molecules, ZINC08825480 (4-bromo-N'-{(E)-[1-phenyl-3-(pyridin-3-yl)-1H-pyrazol- 4-yl]methylidene}benzene-1-sulfonohydrazide) and ZINC72009942 (N-[[2-[[(3S)-3-methyl-1-piperidyl] methyl]phenyl]methyl]-6-oxo-1-(p-tolyl)-4,5-dihydro-1,2,4-triazine-3-carboxamide), were identified with better affinity with the three target enzymes as compared to the approved antiviral drugs. Both the lead molecules possessed favorable drug-like properties, fit well into the active site pocket close to His- Cys dyad and showed a good number of hydrogen bonds with the backbone as well as side chains of key amino acid residues. Thus, the present study offers two novel chemical entities against coronavirus infections which can be validated through various biological assays.

CRISPR Screening: Molecular Tools for Studying Virus-Host Interactions.

CRISPR/Cas is a powerful tool for studying the role of genes in viral infections. The invention of CRISPR screening technologies has made it possible to untangle complex interactions between the host and viral agents. Moreover, whole-genome and pathway-specific CRISPR screens have facilitated identification of novel drug candidates for treating viral infections. In this review, we highlight recent developments in the fields of CRISPR/Cas with a focus on the use of CRISPR screens for studying viral infections and identifying new candidate genes to aid development of antivirals.

Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.

Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance and high toxicity of currently used tubulin-binding agents have necessitated the pursuit of novel drug candidates with increased therapeutic potency. The design of novel drug candidates can be achieved using efficient computational techniques to support existing efforts. Proteochemometric (PCM) modeling is a computational technique that can be employed to elucidate the bioactivity relations between related targets and multiple ligands. We have developed a PCM-based Support Vector Machine (SVM) approach for predicting the bioactivity between tubulin receptors and small, drug-like molecules. The bioactivity datasets used for training the SVM algorithm were obtained from the Binding DB database. The SVM-based PCM model yielded a good overall predictive performance with an area under the curve (AUC) of 87%, Matthews correlation coefficient (MCC) of 72%, overall accuracy of 93%, and a classification error of 7%. The algorithm allows the prediction of the likelihood of new interactions based on confidence scores between the query datasets, comprising ligands in SMILES format and protein sequences of tubulin targets. The algorithm has been implemented as a web server known as TubPred, accessible via http://35.167.90.225:5000/ .

Binding of SEP-363856 within TAAR1 and the 5HT1A receptor: implications for the design of novel antipsychotic drugs.

Current medications for schizophrenia typically modulate dopaminergic neurotransmission. While affecting positive symptoms, antipsychotic drugs have little clinical effect on negative symptoms and cognitive impairment. Moreover, newer 'atypical' antipsychotic drugs also have significant metabolic adverse-effects. The recent positive clinical trial of the novel drug candidate SEP-363856, which targets non-dopamine receptors (trace amine-associated receptor and the 5HT1A receptor), is a potentially promising development for the management of schizophrenia. In this perspective, we briefly overview the role of TAAR1 and the 5HT1A receptor in schizophrenia and explore the specific binding characteristics of SEP-363856 at these receptors. Molecular dynamics simulations (MDS) indicate that SEP-363856 interacts with a small, common set of conserved residues within the TAAR1 and 5HT1A ligand-binding domain. The primary interaction of SEP-363856 involves binding to the negatively charged aspartate residue (Asp1033.32, TAAR1; Asp1163.32, 5HT1A). In general, the binding of SEP-363856 within TAAR1 involves a greater number of aromatic contacts compared to 5HT1A. MDS provides important insights into the molecular basis of binding site interactions of SEP-363856 with TAAR1 and the 5HT1A receptor, which will be beneficial for understanding the pharmacological uniqueness of SEP-363856 and for the design of novel drug candidates for these newly targeted receptors in the treatment of schizophrenia and related disorders.

Indole/isatin-containing hybrids as potential antibacterial agents.

The emergence and worldwide spread of drug-resistant bacteria have already posed a serious threat to human life, creating the urgent need to develop potent and novel antibacterial drug candidates with high efficacy. Indole and isatin (indole-2,3-dione) present a wide structural and mechanistic diversity, so their derivatives possess various pharmacological properties and occupy a salient place in the development of new drugs. Indole/isatin-containing hybrids, which demonstrate a promising activity against a panel of clinically important Gram-positive and Gram-negative bacteria, are privileged scaffolds for the discovery of novel antibacterial candidates. This review, covering articles published between January 2015 and May 2020, focuses on the development and structure-activity relationship (SAR) of indole/isatin-containing hybrids with potential application for fighting bacterial infections, to facilitate further rational design of novel drug candidates.

Calanolides E1 and E2, two related coumarins from Calophyllum brasiliense Cambess. (Clusiaceae), displayed in vitro activity against amastigote forms of Trypanosoma cruzi and Leishmania infantum

In the present work, the MeOH extract from stem barks of Calophyllum brasiliense Cambess. (Clusiaceae) displayed activity against amastigote forms of Trypanosoma cruzi and Leishmania infantum and was subjected to a bioactivity-guided fractionation to give two related coumarins - calanolides E1 (1) and E2 (2). Compounds 1 and 2 were actives to T. cruzi with EC50 values of 12.1 and 8.2 μM, respectively. When tested against L. infantum, the EC50 values were 37.1 and 29.1 μM, respectively. Compound 2, corresponding to anti isomer, showed the best selectivity index (SI) with values >24.4 to T. cruzi and >6.9 to L. infantum in comparison to the syn isomer 1. Furthermore, using an in silico multi-parametric prediction, both compounds did not contain any PAINS sub-structures. Therefore, these data suggest that coumarins 1 and 2 may contribute as scaffolds for the design of novel drug candidates for leishmaniasis and Chagas disease.

Biased Agonism as an Emerging Strategy in the Search for Better Opioid Analgesics.

Morphine and related drugs that act through activating opioid receptors are the most effective analgesics for the relief of severe pain. They have been used for decades, despite the range of unwanted side effects that they produce, as no alternative has been found so far. The major goal of opioid research is to understand the mechanism of action of opioid receptor agonists and to improve the therapeutic utility of opioid drugs. In the search for safer and more potent analgesics, analogs with mixed opioid receptor profile gained a lot of interest. However, recently the concept of biased agonism, that highlights the fact that some ligands are able to differentially activate receptor downstream pathways, became a new approach in the design of novel drug candidates for clinical application. In this review, we summarize current knowledge on the development of opioid ligands of peptide and nonpeptide structure, showing how much opioid pharmacology evolved in recent years.

Label-Free Bioelectrochemical Methods for Evaluation of Anticancer Drug Effects at a Molecular Level.

Cancer is a multifactorial family of diseases that is still a leading cause of death worldwide. More than 100 different types of cancer affecting over 60 human organs are known. Chemotherapy plays a central role for treating cancer. The development of new anticancer drugs or new uses for existing drugs is an exciting and increasing research area. This is particularly important since drug resistance and side effects can limit the efficacy of the chemotherapy. Thus, there is a need for multiplexed, cost-effective, rapid, and novel screening methods that can help to elucidate the mechanism of the action of anticancer drugs and the identification of novel drug candidates. This review focuses on different label-free bioelectrochemical approaches, in particular, impedance-based methods, the solid supported membranes technique, and the DNA-based electrochemical sensor, that can be used to evaluate the effects of anticancer drugs on nucleic acids, membrane transporters, and living cells. Some relevant examples of anticancer drug interactions are presented which demonstrate the usefulness of such methods for the characterization of the mechanism of action of anticancer drugs that are targeted against various biomolecules.

Homology Modeling and Virtual Screening Studies of Antigen MLAA-42 Protein: Identification of Novel Drug Candidates against Leukemia-An In Silico Approach.

Monocytic leukemia-associated antigen-42 (MLAA-42) is associated with excessive cell division and progression of leukemia. Thus, human MLAA-42 is considered as a promising target for designing of new lead molecules for leukemia treatment. Herein, the 3D model of the target was generated by homology modeling technique. The model was then evaluated using various cheminformatics servers. Moreover, the virtual screening studies were performed to explore the possible binding patterns of ligand molecules to MLAA's active site pocket. Thirteen ligand molecules from the ChemBank™ database were identified as they showed good binding affinities, scaffold diversity, and preferential ADME properties which may act as potent drug candidates against leukemia. The study provides the way to identify novel therapeutics with optimal efficacy, targeting MLAA-42.

Drug Repurposing Screen Identifies Novel Classes of Drugs with Anticancer Activity in Mantle Cell Lymphoma.

Mantle Cell Lymphoma (MCL) is typically an aggressive and rare disease with poor prognosis, therefore new effective therapeutics are urgently needed. Drug repurposing for cancer treatment is becoming increasingly more attractive as an alternative approach to discover clinically approved drugs that demonstrate antineoplastic effect. The objective of this study was to screen an approved drug library and identify candidate compounds with an antineoplastic effect in MCL cells using High-Throughput Screening (HTS) technique. Using the HTS technique, nearly 3,800 clinically approved drugs and drug candidates were screened in Jeko and Mino MCL cell lines. We also demonstrated the selectivity window of the candidate compounds in six normal cell lines. Further validations were performed in caspase-3/7 apoptosis assay and three-dimensional (3D) multicellular aggregates model using Z138 cell line. We identified 98 compounds showing >50% inhibition in either MCL cell line screened, they were distributed across eight unique therapeutic categories and have different mechanisms of action (MOA). We selected alisertib, carfilzomib, pracinostat and YM155 for further validation based on their antiproliferative activity in two MCL cell lines, selectivity to normal cell lines, and drug developing stages in terms of clinical research. Alisertib and carfilzomib showed antiproliferative effect on MCL cell with EC50 = 6 nM and >100-fold selectivity to normal cell lines, especially for alisertib which demonstrated >1000-fold selectivity to 5 out of 6 normal cell lines. Pracinostat and YM155 had potency of 11 and 12 nM in MCL cell with >20-fold selectivity to normal cell lines. All four compounds had been tested in caspase-dependent apoptosis assay. We further validated and demonstrated their anti-MCL effect on cell proliferation and (3D) multicellular aggregates model using Z138 cell line. This is the first study to examine such a large library of clinically approved compounds for the identification of novel drug candidates for MCL treatment, the results could be rapidly translated into clinical practice in patients with MCL.

Identification of competitive inhibitors of the human taurine transporter TauT in a human kidney cell line

The osmolyte and antioxidant taurine plays an important role in regulation of cellular volume, oxidative status and Ca2+-homeostasis. Taurine uptake in human cells is regulated by the Na+- and Cl--dependent taurine transporter TauT. In order to gain deeper structural insights about the substrate binding pocket of TauT, a HEK293 cell line producing a GFP-TauT fusion protein was generated. Transport activity was validated using cell-based [3H]-taurine transport assays. We determined the Km and IC50 values of taurine, β-alanine and γ-aminobutyrate. Additionally we were able to identify structurally similar compounds as potential new substrates or inhibitors of the TauT transporter. Substrate induced cytotoxicity was analyzed using a cell viability assay. In this study we show competitive effects of the 3-pyridinesulfonate, 2-aminoethylhydrogen sulfate, 5-aminovalerate, β-aminobutyrate, piperidine-4-sulfonate, 2-aminoethylphosphate and homotaurine. We demonstrate that taurine uptake can be inhibited by a phosphate. Furthermore our studies revealed that piperidine-4-sulfonate interacts with TauT with a higher affinity than γ-aminobutyrate and imidazole-4-acetate. We propose that piperidine-4-sulfonate may serve as a potential lead structure for the design of novel drug candidates required for specific modulation of the TauT transporter in therapy of neurodegenerative diseases.

Moleculer dynamics simulaiton revealed reciever domain of Acinetobacter baumannii BfmR enzyme as the hot spot for future antibiotics designing

Acinetobacter baumannii is an alarming nosocomial pathogen that is resistant to multiple drugs. The pathogen is forefront of scientific attention because of high mortality and morbidity found for its complications in the past decade. As a consequence, identification of novel drug candidates and subsequent designing of novel chemical scaffolds is an imperative need of time. In the present study, we used a recently reported structure of BfmR enzyme and performed structure based virtual screening, MD simulation and binding free energies calculations. MD simulation revealed a profound movement of the best-characterized inhibitor towards the α4-β5-α5 face of the enzyme receiver domain, thus indicating its high affinity for this site compared to phosphorylation. Furthermore, it was observed that the enzyme and enzyme-inhibitor complex have high structure stability with mean RMSD of 1.2 and 1.1 Å, respectively. Binding free energy calculations for the complex unraveled high stability with MMGBSA score of −26.21 kcal/mol and MMPBSA score of −1.47 kcal/mol. Van der Waal energy was found highly favorable with value of −30.25 kcal/mol and dominated significantly the overall binding energy. Furthermore, a novel WaterSwap assay was used to circumvent the limitations of MMGB/PBSA that complements the inhibitor affinity for enzyme active pocket as depicted by the low convergence of Bennett, TI and FEP algorithms. Results yielded from this study will not only give insight into the phenomena of inhibitor movement towards the enzyme receiver domain, but will also provide a useful baseline for designing derivatives with improved biological and pharmacokinetics profiles.Communicated by Ramaswamy H. Sarma

Homology modeling in drug discovery: Overview, current applications, and future perspectives.

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.