Drug Discovery Research Articles

Evaluation of in vitro antioxidant activities, total phenolic and elemental contents of common herbs and spices (Moringa oleifera leaves, Allium sativum (Garlic) and Momordica charantia (ejinrin) leaves) in South-West Nigeria

Abstract Natural remedies are being widely utilized in some disease’s conditions including cancer, diabetes, neurodegenerative diseases, atherosclerosis hypertension and other cardiovascular diseases. The therapeutic intervention of medicinal plants and spices cannot be overemphasized in the management and control of diseases but the exact modes of action of these herbs and plants have not been fully elucidated. This research work was designed to study the expression of selected elements, zinc (Zn), iron (Fe), copper (Cu), selenium (Se), lead (Pb) and cadmium (Cd); the phytochemical and the in vitro antioxidant properties of ethanolic extracts of Moringa oleifera leaves, Allium sativum (Garlic) and Momordica charantia (ejinrin) leaves for their potential involvement in the prevention and management of cardiovascular diseases and cancer through different chemical methods. In vitro antioxidant properties were assayed by investigating 2,2-diphenyl-2-picryl-hydrazyl (DPPH) free radicals scavenging potentials and Ferric Reducing Antioxidant Potential (FRAP); total phenolic content was determined by using Folin-Ciocalteu assay and the elemental contents of these extracts was investigated through the use of Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). All tests were run in triplicates and analysed. Our results revealed that the ethanolic extracts of M. oleifera leaves, A. sativum (Garlic) and M. charantia (ejinrin) leaves possess significant antioxidant activities and express important and beneficial elements (in marginal and trace amounts) and phytochemicals. These results suggest that the medicinal attributes of these plants may be linked to the radical scavenging abilities, beneficial elements and phytochemicals expression of their ethanolic extracts which may likely be a good direction in the area of drug discovery and development.

Innovative On-DNA Synthesis of Sulfides and Sulfoximines: Enriching the DEL Synthesis Toolbox.

DNA-encoded library (DEL) technologies enable the fast exploration of gigantic chemical space to identify ligands for the target protein of interest and have become a powerful hit finding tool for drug discovery projects. However, amenable DEL chemistry is restricted to a handful of reactions, limiting the creativity of drug hunters. Here, we describe a new on-DNA synthetic pathway to access sulfides and sulfoximines. These moieties, usually contemplated as challenging to achieve through alkylation and oxidation, can now be leveraged in routine DEL selection campaigns.

Animal PET scanner with a large field of view is suitable for high-throughput scanning of rodents.

In preclinical studies, high-throughput positron emission tomography (PET) imaging, known as simultaneous multiple animal scanning, can reduce the time spent on animal experiments, the cost of PET tracers, and the risk of synthesis of PET tracers. It is well known that the image quality acquired by high-throughput imaging depends on the PET system. Herein, we investigated the influence of large field of view (FOV) PET scanner on high-throughput imaging. We investigated the influence of scanning four objects using a small animal PET scanner with a large FOV. We compared the image quality acquired by four objects scanned with the one acquired by one object scanned using phantoms and animals. We assessed the image quality with uniformity, recovery coefficient (RC), and spillover ratio (SOR), which are indicators of image noise, spatial resolution, and quantitative precision, respectively. For the phantom study, we used the NEMA NU 4-2008 image quality phantom and evaluated uniformity, RC, and SOR, and for the animal study, we used Wistar rats and evaluated the spillover in the heart and kidney. In the phantom study, four phantoms had little effect on imaging quality, especially SOR compared with that for one phantom. In the animal study as well, four rats had little effect on spillover from the heart muscle and kidney cortex compared with that for one rat. This study demonstrated that an animal PET scanner with a large FOV was suitable for high-throughput imaging. Thus, the large FOV PET scanner can support drug discovery and bridging research through rapid pharmacological and pathological evaluation.

Comprehensive analysis of genomic variation, pan-genome and biosynthetic potential of Corynebacterium glutamicum strains.

Corynebacterium glutamicum is a non-pathogenic species of the Corynebacteriaceae family. It has been broadly used in industrial biotechnology for the production of valuable products. Though it is widely accepted at the industrial level, knowledge about the genomic diversity of the strains is limited. Here, we investigated the comparative genomic features of the strains and pan-genomic characteristics. We also observed phylogenetic relationships among the strains based on average nucleotide identity (ANI). We found diversity between strains at the genomic and pan-genomic levels. Less than one-third of the C. glutamicum pan-genome consists of core genes and soft-core genes. Whereas, a large number of strain-specific genes covered about half of the total pan-genome. Besides, C. glutamicum pan-genome is open and expanding, which indicates the possible addition of new gene families to the pan-genome. We also investigated the distribution of biosynthetic gene clusters (BGCs) among the strains. We discovered slight variations of BGCs at the strain level. Several BGCs with the potential to express novel bioactive secondary metabolites have been identified. Therefore, by utilizing the characteristic advantages of C. glutamicum, different strains can be potential applicants for natural drug discovery.

Identification of novel hit molecules targeting M. tuberculosis polyketide synthase 13 by combining generative AI and physics-based methods

In this work we investigated the Pks13-TE domain, which plays a critical role in the viability of the mycobacteria. In this report, we have used a series of AI and Physics-based tools to identify Pks13-TE inhibitors. The Reinvent 4, pKCSM, KDeep, and SwissADME are AI-ML-based tools. AutoDock Vina, PLANTS, MDS, and MM-GBSA are physics-based methods. A combination of these methods yields powerful support in the drug discovery cycle. Known inhibitors of Pks13-TE were collected, curated, and used as input for the AI-based tools, and Mol2Mol molecular optimisation methods generated novel inhibitors. These ligands were filtered based on physics-based methods like molecular docking and molecular dynamics using multiple tools for consensus generation. Rigorous analysis was performed on the selected compounds to reduce the chemical space while retaining the most promising compounds. The molecule interactions, stability of the protein-ligand complexes and the comparable binding energies with the native ligand were essential factors for narrowing the ligands set. The filtered ligands from docking, MDS, and binding energy colocations were further tested for their ADMET properties since they are among the essential criteria for this series of molecules. It was found that ligands Mt1 to Mt6 have excellent predicted pharmacokinetic, pharmacodynamic and toxicity profiles and good synthesisability.

Enantioselective construction of stereogenic-at-sulfur(IV) centres via catalytic acyl transfer sulfinylation.

Chiral sulfur pharmacophores are crucial for drug discovery in bioscience and medicinal chemistry. While the catalytic asymmetric synthesis of sulfoxides and sulfinate esters with stereogenic-at-sulfur(IV) centres is well developed, the synthesis of chiral sulfinamides remains challenging, which has primarily been attributed to the high nucleophilicity and competing reactions of amines. In this study, we have developed an efficient methodology for the catalytic asymmetric synthesis of chiral sulfinamides and sulfinate esters by the sulfinylation of diverse nucleophiles, including aromatic amines and alcohols, using our bifunctional chiral 4-arylpyridine N-oxides as catalysts. The remarkable results are a testament to the efficiency, versatility and broad applicability of the developed synthetic approach, serving as a valuable tool for the synthesis of sulfur pharmacophores. Mechanistic experiments and density functional theory calculations revealed that the initiation and stereocontrol of this reaction are induced by an acyl transfer catalyst. Our research provides an efficient approach for the construction of optically pure sulfur(IV) centres.

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity.

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.

The Edifice of Vasculature-On-Chips: A Focused Review on the Key Elements and Assembly of Angiogenesis Models.

The conception of vascularized organ-on-a-chip models provides researchers with the ability to supply controlled biological and physical cues that simulate the in vivo dynamic microphysiological environment of native blood vessels. The intention of this niche research area is to improve our understanding of the role of the vasculature in health or disease progression in vitro by allowing researchers to monitor angiogenic responses and cell-cell or cell-matrix interactions in real time. This review offers a comprehensive overview of the essential elements, including cells, biomaterials, microenvironmental factors, microfluidic chip design, and standard validation procedures that currently govern angiogenesis-on-a-chip assemblies. In addition, we emphasize the importance of incorporating a microvasculature component into organ-on-chip devices in critical biomedical research areas, such as tissue engineering, drug discovery, and disease modeling. Ultimately, advances in this area of research could provide innovative solutions and a personalized approach to ongoing medical challenges.

Exploring the therapeutic potential of Cynanchum tunicatum (Retz.) Alston- assessment of phytochemicals and biological activities

Cynanchum tunicatum (Retz.) Alston is native to the Asian region and is distributed in the tropical areas of India and Sri Lanka. The aim of this study is to explore the phytochemical composition, antioxidant potential, and anti-inflammatory properties of C. tunicatum. Metabolic profiling was carried out using phytochemical screening to detect and quantify the secondary metabolites. To evaluate the potential secondary metabolites using the standard methods, Fourier transform infrared (FTIR), High-Performance Thin Layer Chromatography (HPTLC) and Gas Chromatography–Mass Spectrometry (GC–MS) from organic extracts of C. tunicatum and its biological activities. FTIR investigated peaks that represent alkane and aromatic compounds. GC–MS revealed the presence of 22 constituents such as 1-Hexacosene (0.145 %), l-(+)-Ascorbic acid 2,6-dihexadecanoate (8.129 %), Campesterol (5.243 %), Beta −Amyrin (10.614 %), Lupeol (13.061 %), Octadecanoic acid (0.751 %) are the major active compounds present. HPTLC fingerprinting confirms the bioactive compounds such as colchicine, strychnine, coumarin etc. which are represented with corresponding Rf values. Among all the extracts of C. tunicatum methanolic extract showed highest antioxidant activities. In 2,2-diphenylpicrylhydrazyl method exhibit the IC50 of (38.91 µg/mL), Ferric reducing antioxidant power assay (1.6 µg/mL), total antioxidant assay (IC50 = 32.91 µg/mL) and IC50 of anti-inflammatory activity (42.31 µg/mL) respectively. These findings enrich the knowledge of the species Cynanchum tunicatum for the possible application as a source of bioactive compounds in drug discovery.

AUTOMATION IN ANALYTICAL CHEMISTRY: THE ROLE OF AI IN CHROMATOGRAPHY

Artificial Intelligence (AI) has facilitated significant breakthroughs in drug discovery, the design of materials, and organic synthesis. The advancements in the latter group are especially remarkable due to the abilities of the latest computational methods (molecular design algorithms) that enable the exploration of extensive chemical spaces and enhance research in fields such as predicting molecule properties, designing molecules, retrosynthesis, predicting reaction conditions, and predicting reaction outcomes. A literary review was conducted following PRISMA guidelines. This study aimed to review existing data on the application of AI in separation chromatography. The evolution and utilization of AI in the pharmaceutical industry and its future aspects were articulated in this study. The utilization of AI can completely transform the field of chromatography analysis by facilitating expedited, more precise, and more effective data processing. By automating chromatography analysis, AI can enhance efficiency and minimize the potential for human mistakes. This advancement enables scientists to dedicate their efforts towards addressing intricate and demanding analytical issues. With the evolution of technology and the increasing adoption, we can anticipate more progress in chromatography analysis and analytical chemistry.

Synthesis of novel coumarin-triazole hybrids and first evaluation of the 4-phenyl substituted hybrid loaded PLGA nanoparticles delivery system to the anticancer activity

Despite the discovery of many chemotherapeutic drugs that prevent uncontrolled cell division processes in the last century, many studies are still being carried out to develop drugs with higher anticancer efficacy and lower level of side effects. Herein, we designed, synthesized, and characterized six novel coumarin-triazole hybrids, and evaluated for anticancer activity of the one with the highest potential against the breast cancer cell line, MCF-7 and human cervical cancer cell line, human cervical adenocarcinoma (HeLa). Compound 21 which was the coumarin derivative including phenyl substituent with the lowest IC50 value displayed the highest cytotoxicity against the studied cancer cell line. Furthermore, the potential use of poly (lactic-co-glycolic acid) nanoparticles (PLGA NPs) prepared by the emulsifying solvent evaporation method as a platform for a drug delivery system was studied on a selected coumarin derivative 21. This coumarin derivative-loaded PLGA NPs were produced with an average size of 225.90 ± 2.96 nm, −16.90 ± 0.85 mV zeta potential, and 4.12 ± 0.90% drug loading capacity. The obtained 21-loaded PLGA nanoparticles were analyzed spectroscopically and microscopically with FT-IR, UV–vis, and scanning electron microscopy as well as thermogravimetric analysis, Raman, and x-ray diffraction. The in vitro release of 21 from the nanoparticles exhibited a controlled release profile just over one month following a burst release in the initial six hours and in addition to this a total release ratio of %50 and %85 were obtained at pH 7.4 and 5.5, respectively. 21-loaded PLGA nanoparticles displayed remarkably effective anticancer activity than 21. The IC50 values were determined as IC50 (21-loaded PLGA nanoparticles): 0.42 ± 0.01 mg ml−1 and IC50 (free 21 molecule): 5.74 ± 3.82 mg ml−1 against MCF-7 cells, and as IC50 (21-loaded PLGA nanoparticles): 0.77 ± 0.12 mg ml−1 and IC50 (free 21 molecule): 1.32 ± 0.31 mg ml−1 against HeLa cells after the incubation period of 24 h. Our findings indicated that triazole-substituted coumarins may be used as an anticancer agent by integrating them into a polymeric drug delivery system providing improved drug loading and effective controlled drug release.

DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.

Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. Datasets and source codes are available at https://github.com/Biohang/DiSMVC. Supplementary data are available at Bioinformatics online.

PARTITIONING BASED PHYSICOCHEMICAL MODELS FOR ASSESSING INTESTINAL PERMEABILITY AND ABSORPTION OF DRUGS

Oral administration of drugs is highly preferred for almost all human beings than any other route of drug delivery except during some health challenges. Therefore, permeability assessment of drugs across intestinal membrane is essential in the early stages of drug discovery for time and cost reasons. Animals, including humans, have been used for decades as in vivo models for determining intestinal drug permeability and absorption. However, in vivo models are very invasive, time-consuming, and not cost-effective methods. Numerous in vitro models have been used to screen drug permeability and absorption through intestinal membranes. In this article partitioning based physicochemical models that can predict a compound/drug permeability potential across intestinal membrane will be elaborated upon.

Pharmacological inhibition of α-synuclein aggregation within liquid condensates

Aggregated forms of α-synuclein constitute the major component of Lewy bodies, the proteinaceous aggregates characteristic of Parkinson’s disease. Emerging evidence suggests that α-synuclein aggregation may occur within liquid condensates formed through phase separation. This mechanism of aggregation creates new challenges and opportunities for drug discovery for Parkinson’s disease, which is otherwise still incurable. Here we show that the condensation-driven aggregation pathway of α-synuclein can be inhibited using small molecules. We report that the aminosterol claramine stabilizes α-synuclein condensates and inhibits α-synuclein aggregation within the condensates both in vitro and in a Caenorhabditis elegans model of Parkinson’s disease. By using a chemical kinetics approach, we show that the mechanism of action of claramine is to inhibit primary nucleation within the condensates. These results illustrate a possible therapeutic route based on the inhibition of protein aggregation within condensates, a phenomenon likely to be relevant in other neurodegenerative disorders.

Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics.

Proteomics is making important contributions to drug discovery, from target deconvolution to mechanism of action (MoA) elucidation and the identification of biomarkers of drug response. Here we introduce decryptE, a proteome-wide approach that measures the full dose-response characteristics of drug-induced protein expression changes that informs cellular drug MoA. Assaying 144 clinical drugs and research compounds against 8,000 proteins resulted in more than 1 million dose-response curves that can be interactively explored online in ProteomicsDB and a custom-built Shiny App. Analysis of the collective data provided molecular explanations for known phenotypic drug effects and uncovered new aspects of the MoA of human medicines. We found that histone deacetylase inhibitors potently and strongly down-regulated the T cell receptor complex resulting in impaired human T cell activation in vitro and ex vivo. This offers a rational explanation for the efficacy of histone deacetylase inhibitors in certain lymphomas and autoimmune diseases and explains their poor performance in treating solid tumors.

Computational Design of Cyclic Peptide Inhibitors of a Bacterial Membrane Lipoprotein Peptidase.

There remains a critical need for new antibiotics against multi-drug-resistant Gram-negative bacteria, a major global threat that continues to impact mortality rates. Lipoprotein signal peptidase II is an essential enzyme in the lipoprotein biosynthetic pathway of Gram-negative bacteria, making it an attractive target for antibacterial drug discovery. Although natural inhibitors of LspA have been identified, such as the cyclic depsipeptide globomycin, poor stability and production difficulties limit their use in a clinical setting. We harness computational design to generate stable de novo cyclic peptide analogues of globomycin. Only 12 peptides needed to be synthesized and tested to yield potent inhibitors, avoiding costly preparation of large libraries and screening campaigns. The most potent analogues showed comparable or better antimicrobial activity than globomycin in microdilution assays against ESKAPE-E pathogens. This work highlights computational design as a general strategy to combat antibiotic resistance.

Development of Inhibitors, Probes, and PROTAC Provides a Complete Toolbox to Study PARK7 in the Living Cell.

The integration of diverse chemical tools like small-molecule inhibitors, activity-based probes (ABPs), and proteolysis targeting chimeras (PROTACs) advances clinical drug discovery and facilitates the exploration of various biological facets of targeted proteins. Here, we report the development of such a chemical toolbox for the human Parkinson disease protein 7 (PARK7/DJ-1) implicated in Parkinson's disease and cancers. By combining structure-guided design, miniaturized library synthesis, and high-throughput screening, we identified two potent compounds, JYQ-164 and JYQ-173, inhibiting PARK7 in vitro and in cells by covalently and selectively targeting its critical residue, Cys106. Leveraging JYQ-173, we further developed a cell-permeable Bodipy probe, JYQ-196, for covalent labeling of PARK7 in living cells and a first-in-class PARK7 degrader JYQ-194 that selectively induces its proteasomal degradation in human cells. Our study provides a valuable toolbox to enhance the understanding of PARK7 biology in cellular contexts and opens new opportunities for therapeutic interventions.

Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors.

Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.

CRISPR-dCas13d-based deep screening of proximal and distal splicing-regulatory elements

Pre-mRNA splicing, a key process in gene expression, can be therapeutically modulated using various drug modalities, including antisense oligonucleotides (ASOs). However, determining promising targets is hampered by the challenge of systematically mapping splicing-regulatory elements (SREs) in their native sequence context. Here, we use the catalytically inactive CRISPR-RfxCas13d RNA-targeting system (dCas13d/gRNA) as a programmable platform to bind SREs and modulate splicing by competing against endogenous splicing factors. SpliceRUSH, a high-throughput screening method, was developed to map SREs in any gene of interest using a lentivirus gRNA library that tiles the genetic region, including distal intronic sequences. When applied to SMN2, a therapeutic target for spinal muscular atrophy, SpliceRUSH robustly identifies not only known SREs but also a previously unknown distal intronic SRE, which can be targeted to alter exon 7 splicing using either dCas13d/gRNA or ASOs. This technology enables a deeper understanding of splicing regulation with applications for RNA-based drug discovery.

An activity-based probe for antimicrobial target DXP synthase, a thiamin diphosphate-dependent enzyme

This work reports an alkyl acetylphosphonate (alkylAP) activity-based probe (ABP) for 1-deoxy-d-xylulose 5-phosphate synthase DXPS, a promising antimicrobial target. This essential thiamin diphosphate (ThDP)-dependent enzyme operates at a branchpoint in bacterial central metabolism and is believed to play key roles in pathogen adaptation during infection. How different bacterial pathogens harness DXPS activity to adapt and survive within host environments remains incompletely understood, and tools for probing DXPS function in different contexts of infection are lacking. Here, we have developed alkylAP-based ABP 1, designed to react with the ThDP cofactor on active DXPS to form a stable C2α-phosphonolactylThDP adduct which subsequently crosslinks to the DXPS active site upon photoactivation. ABP 1 displays low micromolar potency against DXPS and dose-dependent labeling of DXPS that is blocked by alkylAP-based inhibitors. The probe displays selectivity for DXPS over ThDP-dependent enzymes and is capable of detecting active DXPS in a complex proteome. These studies represent an important advance toward development of tools to probe DXPS function in different contexts of bacterial infection, and for drug discovery efforts on this target.