Discovery Applications Research Articles

Biological Properties of Mentha viridis L. Essential Oil and Its Main Monoterpene Constituents.

This research aimed to evaluate the antidiabetic, dermatoprotective, and antibacterial activities of Mentha viridis L. essential oil (MVEO) collected in the province of Ouezzane (Northwest Morocco). Gas chromatography-mass spectrometry (GC-MS) analysis revealed that the main constituents of MVEO were carvone (37.26 %), 1,8-cineole (11.82 %), limonene (5.27 %), α-terpineol (4.16 %), and β-caryophyllene (4.04 %). MVEO showed strong inhibitory effects on α-amylase and α-glucosidase activities, exceeding those of acarbose, but weak anti-elastase activity. The main compounds, β-caryophyllene (IC50=79.91±2.24 and 62.08±2.78 μg/mL) and limonene (IC50=90.73±3.47 and 68.98±1, 60 μg/mL), demonstrated the strongest inhibitory effects on both digestive enzymes (α-glucosidase and α-amylase, respectively). In silico investigations, using molecular docking, also showed the inhibitory potential of these bioactive compounds against the enzymes tested. In conclusion, MVEO, due to its main components such as limonene, 1,8-cineole, β-caryophyllene, carvone, and α-terpineol, shows promising prospects for drug discovery and natural therapeutic applications.

A multiplexed sensing system with multimodal readout electronics and thread-based electrochemical sensors for high throughput screening

High throughput screening (HTS) based on microwell (or bioreactor) arrays is a well-established approach for rapid screening and identification of lead molecules or target analytes for drug discovery and other applications in biotechnology. However, it has been challenging to scale up these platforms due to the lack of integrated sensors to monitor the microenvironment of each microwell in the array needed. In this work, we present a practical solution for a miniaturized multiplexed sensing system with integrated circuitry capable of performing continuous electrochemical potentiometric and amperometric measurements. The platform uses lightweight multiplexed electrochemical sensors implemented on thin textile threads, all of which share a single operational amplifier (op-amp) and a switch network for both voltage and current measurements. The resulting platform forgoes the need for large-area sensors or bulky multichannel potentiostat with a simple readout circuitry capable of multimodal measurements. The design and validation of the multiplexed sensing system have been reported in the context of measuring pH and oxygen (O2), which are crucial biomarkers for any HTS application. The thread-based pH and O2 sensors possess sensitivities of ∼65.87 mV/pH for the pH sensor (n = 3) and 0.6621 nA/mg L-1 for the O2 sensor (n = 3). This architecture is modular such that it can be easily extended to monitoring multiple biomarkers employing thread-based sensors and sharing the same readout circuitry, making it a practical and versatile tool for biotechnology research.

An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.

Innovations in Molecular Docking: A Detailed Analysis of Methodological Developments and Their Applications in Drug Discovery

Innovations in Molecular Docking: A Detailed Analysis of Methodological Developments and Their Applications in Drug Discovery

A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies.

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes. A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent. In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level. The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

CSP Data: A Data Discovery Web Application of Commercial CSP Plants

CSP Data is a free web application for data discovery of all commercial Concentrating Solar Power (CSP) plants. CSP data is fed with CSP.guru data, an open source dataset of all Concentrating Solar Power (CSP) plants. This paper introduces and explains the tools and information provided by CSP Data to potential users so they can use it efficiently together with the ongoing work being performed to release new features. It also presents the CSP.guru dataset, how the original idea was developed, its key features and how this dataset is used by industry and academia.

Benzene‐1,3‐disulfonyl fluoride and Benzene‐1,3,5‐trisulfonyl fluoride: Low‐Cost, Stable, and Selective Reagents for SuFEx‐Driven Deoxyazidation

The development of synthetic methods for the synthesis of organic azides is highly important, given their critical role in advancing click chemistry over the last twenty years. We report a reagent‐economical, reliable, and scalable synthesis of alkyl azides from primary and secondary alcohols. This robust click method capitalizes on the synergistic interaction between Sulfur Fluoride Exchange (SuFEx) reagents—specifically, benzene‐1,3‐disulfonyl fluoride (BDSF) or benzene‐1,3,5‐trisulfonyl fluoride (BTSF)—and trimethylsilyl azide (TMSN3). The method offers procedural ease, accommodates a wide array of substrates, and enables late‐stage functionalization. Additionally, we demonstrate the protocol's adaptability by validating a straightforward one‐pot deoxyazidation‐CuAAC sequence for drug discovery applications.

Streamlined Electrochemical Dihalogenation (F, Cl, and Br) of gem‐Difluoroalkenes Using Hydrogen Halides as Reagents

AbstractWe established a straightforward electrochemical dihalogenation (F, Cl, and Br) process for gem‐difluoroalkenes. This reaction exhibits a broad functional group tolerance and has been effectively utilized in the construction of complex molecules. The subsequent synthetic transformations of the products have highlighted the adaptable nature of the dihalogenated compounds, demonstrating their potential application in drug discovery and the investigation of innovative materials. The effective utilization of the dihalogenated product for 18F‐labeling represents a novel approach in generating highly potent 18F‐labeled tracers.

Library‐Friendly Synthesis of Bicyclo[1.1.1]pentane‐Containing Sulfonamides via a Transition Metal‐Free Three‐Component Reaction

AbstractHerein we report the synthesis of bicyclo[1.1.1]pentane (BCP)‐containing sulfonamides via a transition metal‐free process of a three‐component reaction among BCP‐containing N‐tosyl hydrazones, 1,4‐diazabicyclo[2.2.2]octane bis(sulfur dioxide) adduct (DABSO), and amines. Different from existing methods to synthesize BCP‐containing sulfonamides, our method avoids the usage of sulfonyl halides, which could be challenging due to chemical instabilities. We have also demonstrated that this methodology is suitable for library synthesis of such sulfonamides. As a result, we can expect future application in drug discovery.

Revolutionizing Healthcare and Drug Discovery: The Impact of Artificial Intelligence on Pharmaceutical Development

Artificial intelligence (AI) is reshaping drug discovery and delivery in the pharmaceutical industry, fundamentally transforming traditional methods. In drug discovery, AI algorithms rapidly analyze vast biological and chemical datasets to identify potential drug candidates with unprecedented accuracy. Machine learning models predict compound efficacy and safety, accelerating earlystage drug development. AI also facilitates drug repurposing, uncovering new therapeutic uses for existing medications. At the drug delivery front, AI optimizes formulations and systems, enabling targeted and personalized approaches. Intelligent algorithms enhance the understanding of pharmacokinetics and pharmacodynamics, guiding the development of precision medicine strategies. This integration of AI not only expedites innovative drug discovery but also refines delivery mechanisms, promising more effective and tailored treatments with the potential to revolutionize patient care. The data-processing capabilities of AI drive digitalization and widespread utilization. Applications in drug discovery, development, repurposing, and clinical trials aim to alleviate human workload, expedite objectives, and foster innovation. Despite promising prospects, concerns about job displacement and stringent regulations accompany AI implementation. Emphasizing the intent to augment human labor rather than replace it entirely, the industry anticipates that AI will become a pivotal resource, propelling efficiency, innovation, and advancements in healthcare. This review emphasizes the role of AI in transforming drug discovery and delivery

Designer cellular spheroids with DNA origami for drug screening.

Current in vitro models struggle to balance the complexity of human diseases with suitability for large-scale drug tests. While 3D cultures simulate human tissues, they lack cellular intricacy, and integrating these models with high-throughput drug screening remains a challenge. Here, we introduce a method that uses self-assembling nucleic acid nanostructures decorated living cells, termed NACs, to create spheroids with a customizable 3D layout. To demonstrate its uniqueness, our method effectively creates designer 3D spheroids by combining parenchymal cells, stromal cells, and immune cells, leading to heightened physiological relevance and detailed modeling of complex chronic diseases and immune-stromal interactions. Our approach achieves a high level of biological fidelity while being standardized and straightforward to construct with the potential for large-scale drug discovery applications. By merging the precision of DNA nanotechnology with advanced cell culture techniques, we are streamlining human-centric models, striking a balance between complexity and standardization, to boost drug screening efficiency.

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery.

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Microfluidic Chip Coupled with MALDI-TOF MS for Multitarget Detection of Allergens in Crucian Carp (Carassius auratus).

The goal of the present study was to establish a rapid, simple method for simultaneous allergy testing of sera from multiple fish-allergic patients. Sera from fish-allergic patients were pooled and used for capturing allergens in fish muscle of crucian carp (Carassius auratus), which was studied as a fish model. Sarcoplasmic proteins of crucian carp (Carassius auratus) were extracted for the analysis of allergens. Anti-human IgE antibody-functionalized magnetic beads were utilized to collect IgE antibodies from human pooled sera. The isolation of allergenic proteins was immunomagnetically performed in microfluidic channels, and the elution of the captured allergenic proteins was done with 5% (v/v) acetic acid aqueous solution. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and peptide mass fingerprinting were used for the analysis of tryptic digests of eluted proteins. Ten potential allergenic proteins were identified from crucian carp (Carassius auratus). The present protocol provides a rapid, efficient, and simple method for simultaneous detection of multiple allergens, based on multitargeted antibodies from pooled sera of allergic patients. The constructed multiple antibody-modified MBs can be applied for the deallergenicity of food matrices. The efficiency of allergen detection can be greatly improved, with promising application in allergen discovery and filtration for other muscle-based foods.

STORMM: Structure and topology replica molecular mechanics for chemical simulations.

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle-mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license.

A Carrier-Based Quantitative Proteomics Method Applied to Biomarker Discovery in Pericardial Fluid

Data-dependent liquid chromatography tandem mass spectrometry is challenged by the large concentration range of proteins in plasma and related fluids. We adapted the SCoPE method from single-cell proteomics to pericardial fluid, where a myocardial tissue carrier was used to aid protein quantification. The carrier proteome and patient samples were labeled with distinct isobaric labels, which allowed separate quantification. Undepleted pericardial fluid from patients with type 2 diabetes mellitus and/or heart failure undergoing heart surgery was analyzed with either a traditional liquid chromatography tandem mass spectrometry method or with the carrier proteome. In total, 1398 proteins were quantified with a carrier, compared to 265 without, and a higher proportion of these proteins were of myocardial origin. The number of differentially expressed proteins also increased nearly four-fold. For patients with both heart failure and type 2 diabetes mellitus, pathway analysis of upregulated proteins demonstrated the enrichment of immune activation, blood coagulation, and stress pathways. Overall, our work demonstrates the applicability of a carrier for enhanced protein quantification in challenging biological matrices such as pericardial fluid, with potential applications for biomarker discovery. Mass spectrometry data are available via ProteomeXchange with identifier PXD053450.

Untargeted metabolomic profiling of Prinsepia utilis Royle leaves by Ultra-Performance Liquid Chromatography-Quadrupole-Time of Flight-Mass Spectrometry.

Prinsepia utilis Royle, native to the Himalayas, is esteemed in Chinese and Indian folk medicine for its diverse medicinal benefits, targeting arthritis, pain relief, bone disorders, and joint discomfort. This study examined the 25% aqueous methanol extract of P.utilis leaves using UPLC-Q-TOF-MS/MS, identifying 78 metabolites, 76 of which were reported for the first time in P.utilis. These included 64 phenolics represented by 56 flavonoids, 5 phenolic acids, 3 phenolic glycosides, 4 terpenoids, 2 lignan glycosides, and 8 other compounds, expanding the knowledge of its chemical composition. These findings lay a foundation for further research, providing insights into potential bioactive compounds and opening avenues for applications in natural product drug discovery, traditional medicine, and nutraceutical development, leveraging the plant's established traditional uses.

Overcoming Challenges in O-Nitration: Selective Alcohol Nitration Deploying N,6-Dinitrosaccharin and Lewis Acid Catalysis.

Nitrate esters hold pivotal roles in pharmaceuticals, energetic materials, and atmospheric processes, motivating the development of efficient synthesis routes. Here, we present a novel catalytic method for the synthesis of nitrates via the direct O-nitration of alcohols, addressing limitations of current traditional methods. Leveraging bench-stable and recoverable N,6-dinitrosaccharin reagent, our catalytic strategy employs magnesium triflate to achieve mild and selective O-nitration of alcohols, offering broad substrate scope and unprecedentedly large functional group tolerance (e.g. alkenes, alkynes, carbonyls). DFT mechanistic studies reveal a dual role of the magnesium catalyst in the activation of both the nitrating reagent and the alcohol substrate. They also unveil a barrierless proton transfer upon formation of a widely-accepted - yet elusive in solution - nitrooxonium ion intermediate. Overall, our work contributes to the development of mild, selective, and sustainable approaches to nitrates synthesis, with potential applications in drug discovery, materials science, and environmental chemistry.

Overcoming Challenges in O‐Nitration: Selective Alcohol Nitration Deploying N,6‐Dinitrosaccharin and Lewis Acid Catalysis

Nitrate esters hold pivotal roles in pharmaceuticals, energetic materials, and atmospheric processes, motivating the development of efficient synthesis routes. Here, we present a novel catalytic method for the synthesis of nitrates via the direct O‐nitration of alcohols, addressing limitations of current traditional methods. Leveraging bench‐stable and recoverable N,6‐dinitrosaccharin reagent, our catalytic strategy employs magnesium triflate to achieve mild and selective O‐nitration of alcohols, offering broad substrate scope and unprecedented large functional group tolerance (e.g. alkenes, alkynes, carbonyls). DFT mechanistic studies reveal a dual role of the magnesium catalyst in the activation of both the nitrating reagent and the alcohol substrate. They also unveil a barrierless proton transfer upon formation of a widely‐accepted — yet elusive in solution — nitrooxonium ion intermediate. Overall, our work contributes to the development of mild, selective, and sustainable approaches to nitrates synthesis, with potential applications in drug discovery, materials science, and environmental chemistry.

Predicting blood–brain barrier permeability of molecules with a large language model and machine learning

Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a novel artificial intelligence platform is necessary for drug discovery. Machine learning and a large language model on artificial intelligence (AI) tools improve the accuracy and shorten the time for new drug development. The primary goal of this research is to develop artificial intelligence (AI) computing models and novel deep learning architectures capable of predicting whether molecules can permeate the human blood–brain barrier (BBB). The in silico (computational) and in vitro (experimental) results were validated by the Natural Products Research Laboratories (NPRL) at China Medical University Hospital (CMUH). The transformer-based MegaMolBART was used as the simplified molecular input line entry system (SMILES) encoder with an XGBoost classifier as an in silico method to check if a molecule could cross through the BBB. We used Morgan or Circular fingerprints to apply the Morgan algorithm to a set of atomic invariants as a baseline encoder also with an XGBoost classifier to compare the results. BBB permeability was assessed in vitro using three-dimensional (3D) human BBB spheroids (human brain microvascular endothelial cells, brain vascular pericytes, and astrocytes). Using multiple BBB databases, the results of the final in silico transformer and XGBoost model achieved an area under the receiver operating characteristic curve of 0.88 on the held-out test dataset. Temozolomide (TMZ) and 21 randomly selected BBB permeable compounds (Pred scores = 1, indicating BBB-permeable) from the NPRL penetrated human BBB spheroid cells. No evidence suggests that ferulic acid or five BBB-impermeable compounds (Pred scores < 1.29423E−05, which designate compounds that pass through the human BBB) can pass through the spheroid cells of the BBB. Our validation of in vitro experiments indicated that the in silico prediction of small-molecule permeation in the BBB model is accurate. Transformer-based models like MegaMolBART, leveraging the SMILES representations of molecules, show great promise for applications in new drug discovery. These models have the potential to accelerate the development of novel targeted treatments for disorders of the central nervous system.

Deciphering the geochemical influences on bacterial diversity and communities among two Algerian hot springs.

Northeastern Algeria boasts numerous hot springs, yet these hydrothermal sites remain largely unexplored for their microbial ecology. The present study explores the bacterial abundance and diversity within two distinct Algerian hot springs (Hammam Saïda and Hammam Debagh) and investigates the link between the prevailing bacteria with geochemical parameters. High-throughput 16S rRNA gene sequencing of water and sediment samples revealed a bacterial dominance of 99.85-91.16% compared to Archaea (0.14-0.66%) in both springs. Interestingly, Saïda hot spring, characterized by higher temperatures and sodium content, harbored a community dominated by Pseudomonadota (51.13%), whereas Debagh, a Ca-Cl-SO4 type spring, was primarily populated by Bacillota with 55.33%. Bacteroidota displayed even distribution across both sites. Additional phyla, including Chloroflexota, Deinococcota, Cyanobacteriota, and Chlorobiota, were also present. Environmental factors, particularly temperature, sodium, potassium, and alkalinity, significantly influenced bacterial diversity and composition. These findings shed light on the interplay between distinct microbial communities and their associated geochemical properties, providing valuable insights for future research on biogeochemical processes in these unique ecosystems driven by distinct environmental conditions, including potential applications in bioremediation and enzyme discovery.