Drug Discovery Research Articles

Synthesis, antidiabetic activity and in silico studies of benzo[b]thiophene based small molecule α-amylase inhibitors

Benzo[b]thiophene has been implicated as molecular framework in the drug discovery against broad spectrum of biological targets. In the antidiabetic drug regime, benzo[b]thiophene based SGLT2 and ALR2 inhibitors have been recently developed but their potential towards α-amylase inhibition remained unexplored to date. In this context, a series of novel small molecule benzo[b]thiophene-2-carboxylic acid derivatives (3a-p) was synthesized, characterized, and evaluated for antidiabetic activity as α-amylase inhibitors. We found that, all benzo[b]thiophene derivatives exhibited significant α-amylase inhibition with IC50 value ranging from 5.37 ± 0.25 μM to 29.89 ± 0.68 μM. The SAR studies showed benzo[b]thiophene carboxylate bearing bis(2-hydroxyethyl)amino group (3b) was most potent with IC50 5.37 ± 0.25 μM compared to standard drug Acarbose (IC50 = 6.40 ± 0.14 μM). Further, the enzyme inhibition mechanism study regarded 3b as competitive inhibitor of α-amylase with Ki value of 1.76 μM. A detailed in silico study was also performed in order to estimate binding properties, drug likeness and predict toxicity profile of these agents. It was demonstrated that novel small molecule benzo[b]thiophene derivative (3b) can effectively bind through H-bonding, hydrophobic and π-stacking interactions within α-amylase active site. Moreover, drug likeness and toxicity prediction studies suggested compound 3b as potential & safter α-amylase inhibitor. Overall, our present study disclosed a novel class of benzo[b]thiophene based α-amylase inhibitors and opened a template for further lead optimization and development.

A decentralized solid compound storage facility managed by a centralized electronic platform at a growing drug discovery company

Within a growing drug discovery company, scientists acquire (either through in house synthesis or purchase) then store, retrieve, and ship solid compound samples daily between multiple locations. The efficient management and tracking of this entire process to support drug discovery is a significant challenge. This article describes a decentralized and cost-effective inventory facility that simplifies the solid compound storage and retrieval process. Standardized storage cabinets from the market are utilized, providing a cost-effective physical infrastructure. The cabinets can be distributed across storage rooms at multiple sites and arranged into spaces with a variety of dimensions, allowing the system to be retrofitted into existing facilities and scaled up easily. We can provide storage close to work areas at each location, minimizing both unnecessary movement of staff and transportation of substances. We have applied a systematic barcoding method to the compound batch identifier that correlates with its compound location. This simplifies the compound registration process as well as the process of finding and returning compounds. Additionally, a centralized electronic platform has been employed to store, update and track solid compound information, such as properties, location and quantity. Compound shipment may be initiated from different sites, and a centralized electronic platform assists the information retrieval process, ensuring each location possesses up-to-date information. The electronic platform we present streamlines the management of compound registration, location tracking, weight updates and shipment information, facilitating seamless record sharing among all stakeholders. Every step of the process can be tracked in real time by the project team. The platform can be flexibly configured to adapt to an evolving set of storage locations, with all information and processes being audited.

A parameterised model for link prediction using node centrality and similarity measure based on graph embedding

Link prediction is a crucial aspect of graph machine learning, with applications as diverse as disease prediction, social network recommendations, and drug discovery. It involves the prediction of potential new links between nodes within a network. Despite its importance, current models for link prediction exhibit notable limitations. Graph Convolutional Networks have shown high efficiency in link prediction across various datasets. However, they face significant challenges when applied to short-path networks and ego networks, resulting in poor performance. This issue represents a critical area of concern that our work seeks to address. This paper introduces the Node Centrality and Similarity Based Parameterised Model (NCSM), a novel method for link prediction tasks. NCSM uniquely integrates node centrality and similarity measures as edge features in a customised Graph Neural Network (GNN) layer, effectively leveraging the topological information of large networks. This model represents the first parameterised GNN-based link prediction model that considers topological information. The proposed model was evaluated on five benchmark graph datasets, each comprising thousands of nodes and edges. Experimental results highlight NCSM's superiority over existing state-of-the-art models like Graph Convolutional Networks and Variational Graph Autoencoder, as it outperforms them across various metrics and datasets. This exceptional performance can be attributed to NCSM's innovative integration of node centrality, similarity measures, and its efficient use of topological information.

Ethylthio-1H-tetrazole (ETT) as coupling additive for the solid-phase synthesis (SPS) of hindered amino acid-containing peptides

Peptides are finding broad applications in drug discovery as Active Pharmaceutical Ingredients (APIs) and delivery systems, and also in the field of new materials. In this context, the increasing relevance of these molecules has fueled the development of more efficient synthetic strategies at both research and industrial scales. The new generation of peptides in the market contains hindered amino acids, examples include the antidiabetic and antiobesity drugs semaglutide and tirzepatide, which contain α,α-dimethylglycine (Aib), and trofinetide, which contains α-methylproline. Given that peptide synthesis involves the proper combination of protecting groups and coupling reagents, it is important to develop coupling strategies for these hindered amino acid-containing peptides. In this first communication, we report that ethylthio-1H-tetrazole (ETT), a reagent widely used in oligonucleotide synthesis but unknown in peptide synthesis, is well suited to be used in combination with N.N’-diisopropylcarbodiimide (DIC) for the preparation of hindered amino acid-containing peptides. ETT is more acidic than 1-hydroxybenzotriazole (HOBt) and ethyl 2-hydroxyimino-2-cyanoacetate (OxymaPure), and, as such, the active species formed will have an excellent leaving group, thereby facilitating the coupling reaction. To demonstrate this concept, we synthesized analogs of Leu-enkephalin amide, where the two consecutive Gly amino acids were substituted by Aib, NMeGly, and NMeAla. The syntheses carried out with ETT were superior to those done with OxymaPure, HOBt, and 1-hydroxy-7-azabenzotriazole. (HOAt). However, in terms of racemization, ETT showed poorer performance than the other coupling additives, especially in the case of His(Trt). In conclusion, ETT emerges as an important contributor to the toolbox of coupling reagents and additives used for peptide and amide formation.

A review of recent advances in the use of complex metal nanostructures for biomedical applications from diagnosis to treatment.

Complex metal nanostructures represent an exceptional category of materials characterized by distinct morphologies and physicochemical properties. Nanostructures with shape anisotropies, such as nanorods, nanostars, nanocages, and nanoprisms, are particularly appealing due to their tunable surface plasmon resonances, controllable surface chemistries, and effective targeting capabilities. These complex nanostructures can absorb light in the near-infrared, enabling noteworthy applications in nanomedicine, molecular imaging, and biology. The engineering of targeting abilities through surface modifications involving ligands, antibodies, peptides, and other agents potentiates their effects. Recent years have witnessed the development of innovative structures with diverse compositions, expanding their applications in biomedicine. These applications encompass targeted imaging, surface-enhanced Raman spectroscopy, near-infrared II imaging, catalytic therapy, photothermal therapy, and cancer treatment. This review seeks to provide the nanomedicine community with a thorough and informative overview of the evolving landscape of complex metal nanoparticle research, with a specific emphasis on their roles in imaging, cancer therapy, infectious diseases, and biofilm treatment. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Diagnostic Tools > Diagnostic Nanodevices.

Drug Repositioning for Ovarian Cancer Treatment: An Update.

Ovarian cancer (OC) is one of the most prevalent malignancies in female reproductive organs, and its 5-year survival is below 45%. Despite the advances in surgical and chemotherapeutic options, OC treatment is still a challenge, and new anticancer agents are urgently needed. Drug repositioning has gained significant attention in drug discovery, representing a smart way to identify new clinical applications for drugs whose human safety and pharmacokinetics have already been established, with great time and cost savings in pharmaceutical development endeavors. This review offers an update on the most promising drugs repurposable for OC treatment and/or prevention.

Engineered shapes using electrohydrodynamic atomization for an improved drug delivery.

The shapes of micro- and nano-products have profound influences on their functional performances, which has not received sufficient attention during the past several decades. Electrohydrodynamic atomization (EHDA) techniques, mainly include electrospinning and electrospraying, are facile in manipulate their products' shapes. In this review, the shapes generated using EHDA for modifying drug release profiles are reviewed. These shapes include linear nanofibers, round micro-/nano-particles, and beads-on-a-string hybrids. They can be further divided into different kinds of sub-shapes, and can be explored for providing the desired pulsatile release, sustained release, biphasic release, delayed release, and pH-sensitive release. Additionally, the shapes resulted from the organizations of electrospun nanofibers are discussed for drug delivery, and the shapes and inner structures can be considered together for developing novel drug delivery systems. In future, the shapes and the related shape-performance relationships at nanoscale, besides the size, inner structure and the related structure-performance relationships, would further play their important roles in promoting the further developments of drug delivery field. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies.

Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure.

Accurate prediction of a new compound's pharmacokinetic (PK) profile is pivotal for the success of drug discovery programs. An initial assessment of PK in preclinical species and humans is typically performed through allometric scaling and mathematical modeling. These methods use parameters estimated from invitro or invivo experiments, which although helpful for an initial estimation, require extensive animal experiments. Furthermore, mathematical models are limited by the mechanistic underpinning of the drugs' absorption, distribution, metabolism, and elimination (ADME) which are largely unknown in the early stages of drug discovery. In this work, we propose a novel methodology in which concentration versus time profile of small molecules in rats is directly predicted by machine learning (ML) using structure-driven molecular properties as input and thus mitigating the need for animal experimentation. The proposed framework initially predicts ADME properties based on molecular structure and then uses them as input to a ML model to predict the PK profile. For the compounds tested, our results demonstrate that PK profiles can be adequately predicted using the proposed algorithm, especially for compounds with Tanimoto score greater than 0.5, the average mean absolute percentage error between predicted PK profile and observed PK profile data was found to be less than 150%. The suggested framework aims to facilitate PK predictions and thus support molecular screening and design earlier in the drug discovery process.

Federated learning for predicting compound mechanism of action based on image-data from cell painting

Having access to sufficient data is essential in order to train accurate machine learning models, but much data is not publicly available. In drug discovery this is particularly evident, as much data is withheld at pharmaceutical companies for various reasons. Federated Learning (FL) aims at training a joint model between multiple parties but without disclosing data between the parties. In this work, we leverage Federated Learning to predict compound Mechanism of Action (MoA) using fluorescence image data from cell painting. Our study evaluates the effectiveness and efficiency of FL, comparing to non-collaborative and data-sharing collaborative learning in diverse scenarios. Specifically, we investigate the impact of data heterogeneity across participants on MoA prediction, an essential concern in real-life applications of FL, and demonstrate the benefits for all involved parties. This work highlights the potential of federated learning in multi-institutional collaborative machine learning for drug discovery and assessment of chemicals, offering a promising avenue to overcome data-sharing constraints.

A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Antiobesity Drug Discovery Research: in vitro Models for Shortening the Drug Discovery Pipeline.

Obesity is a growing global health problem, leading to various chronic diseases. Despite standard treatment options, the prevalence of obesity continues to rise, emphasizing the need for new drugs. in vitro methods of drug discovery research provide a time and cost-saving platform to identify new antiobesity drugs. The review covers various aspects of obesity and drug discovery research using in vitro models. Besides discussing causes, diagnosis, prevention, and treatment, the review focuses on the advantages and limitations of in vitro studies and exhaustively covers models based on enzymes and cell lines from different animal species and humans. In contrast to conventional in vivo animal investigations, in vitro preclinical tests using enzyme- and cell line-based assays provide several advantages in development of antiobesity drugs. These methods are quick, affordable, and provide high-throughput screening. They can also yield insightful information about drug-target interactions, modes of action, and toxicity profiles. By shedding light on the factors that lead to obesity, in vitro tests can also present a chance for personalized therapy. Technology will continue to evolve, leading to the creation of more precise and trustworthy in vitro assays, which will become more and more crucial in the search for novel antiobesity medications.

Recent Advances in Molecular Imprinting for Proteins on Magnetic Microspheres.

The separation of proteins in biological samples plays an essential role in the development of disease detection, drug discovery, and biological analysis. Protein imprinted polymers (PIPs) serve as a tool to capture target proteins specifically and selectively from complex media for separation purposes. Whereas conventional molecularly imprinted polymer is time-consuming in terms of incubation studies and solvent removal, magnetic particles are introduced using their magnetic properties for sedimentation and separation, resulting in saving extraction and centrifugation steps. Magnetic protein imprinted polymers (MPIPs), which combine molecularly imprinting materials with magnetic properties, have emerged as a new area of research hotspot. This review provides an overview of MPIPs for proteins, including synthesis, preparation strategies, and applications. Moreover, it also looks forward to the future directions for research in this emerging field.

Text summarization with ChatGPT for drug labeling documents

Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety.

Feasible mechanisms and therapeutic potential of food probiotics to mitigate diabetes‐associated cancers: A comprehensive review and in silico validation

AbstractPeople with diabetes mellitus (DM) and hyperglycemia are linked with cancer risk. Diabetes and cancer have been corroborated by high morbidity and mortality rates. Studies revealed that elevated levels of insulin secretions trigger insulin‐like growth factor 1 (IGF‐1) production. Moreover, IGF‐1 is a key regulator involved in promoting cancer cell progression and is linked with DM. Cancer drug resistance and ototoxic effects can adversely affect the health and lifespan of an individual. However, naturally derived bioactive compounds are gaining attention for their nontoxic properties and specific behavior. Likewise, probiotics have also been regarded as safe and successful alternatives to treat DM‐linked cancers. The present review aims to highlight the therapeutic potential and feasible functions of probiotics to mitigate or inhibit DM‐associated cancers. Meanwhile, the intracellular signaling cascades involved in promoting DM‐linked cancer are enumerated for future prospective research. However, metabolomics interactions and protein–protein interactions are to be discussed for deeper insights into affirmative principles in diabetic‐linked cancers. Drug discovery and innovative preclinical evaluation need further adjuvant and immune‐enhancement therapies. Furthermore, the results of the in silico assessment could provide scientific excellence of IGF‐1 in diabetes and cancer. Overall, this review summarizes the mechanistic insights and therapeutic targets for diabetes‐associated cancer.

Degree Descriptors and Graph Entropy Quantities of Zeolite ACO.

Cheminformatics is a fascinating emerging subfield of chemical graph theory that studies quantitative structure-activity and property relationships of molecules and, in turn, uses these to predict the physical and chemical properties, which are extremely useful in drug discovery and optimization. Knowledge discovery can be put to use in pharmaceutical data matching to help in finding promising lead compounds. Topological descriptors are numerical quantities corresponding to the chemical structures that are used in the study of these phenomena. This paper is concerned with developing the generalized analytical expression of topological descriptors for zeolite ACO structures with underlying degree and degree-sum parameters. To demonstrate improved discrimination power between the topological descriptors, we have further modified Shannon's entropy approach and used it to calculate the entropy measures of zeolite ACO structures.

Molecular glues for protein-protein interactions: Progressing toward a new dream.

The modulation of protein-protein interactions with small molecules is one of the most rapidly developing areas in drug discovery. In this review, we discuss advances over the past decade (2014-2023) focusing on molecular glues (MGs)-monovalent small molecules that induce proximity, either by stabilizing native interactions or by inducing neomorphic interactions. We include both serendipitous and rational discoveries and describe the different approaches that were used to identify them. We classify the compounds in three main categories: degradative MGs, non-degradative MGs or PPI stabilizers, and MGs that induce self-association. Diverse, illustrative examples with structural data are described in detail, emphasizing the elements of molecular recognition and cooperative binding at the interface that are fundamental for a MG mechanism of action.

Artemisinin derivatives as potential drug candidates against Mycobacterium tuberculosis: insights from molecular docking, MD simulations, PCA, MM/GBSA and ADMET analysis

ABSTRACT Tuberculosis, one of the most ancient and formidable infectious diseases, primarily arises from the pathogenic bacterium Mycobacterium tuberculosis (Mtb). Phosphoenolpyruvate carboxykinase (Pck), a potential drug target, is essential for the growth of Mtb that involves in the pathway of gluconeogenesis at the centre of phosphoenolpyruvate-pyruvate-oxaloacetate node. This study aims to understand drug-like properties of the compounds derived from artemisinin, and to investigate their inhibitory roles against Mtb. Molecular docking was performed on a set of 56 artemisinin compounds to identify their binding efficacy to the Mtb protein target. Subsequently, the selected top three complexes were subjected to molecular dynamics simulations. By the trajectory analysis, root mean square deviation and radius of gyration indicated the compactness of the systems without much fluctuation. The principal component analysis revealed that the complexes were less dynamic and energetically more favourable, and Gibbs energy landscape revealed the favourable energetic transitions between conformations. The artemisinin dimer primary alcohol holding good bioavailability scores appears highly stable in complex with Pck (MM/GBSA of −37.67 kcal/mol) that stands as a potential inhibitor of the Mtb target. However, further preclinical experiments and investigations are necessary to evaluate the intrinsic properties and to confirm effectiveness of the drug candidate.

Overcoming Therapeutic Challenges of Antibiotic Delivery with Cubosome Lipid Nanocarriers.

Low discovery rates for new antibiotics, commercial disincentives to invest, and inappropriate use of existing drugs have created a perfect storm of antimicrobial resistance (AMR). This "silent pandemic" of AMR looms as an immense, global threat to human health. In tandem, many potential novel drug candidates are not progressed due to elevated hydrophobicity, which may result in poor intracellular internalization and undesirable serum protein binding. With a reducing arsenal of effective antibiotics, enabling technology platforms that improve the outcome of treatments, such as repurposing existing bioactive agents, is a prospective option. Nanocarrier (NC) mediated drug delivery is one avenue for amplifying the therapeutic outcome. Here, the performance of several antibiotic classes encapsulated within the lipid-based cubosomes is examined. The findings demonstrate that encapsulation affords significant improvements in drug concentration:inhibition outcomes and assists in other therapeutic challenges associated with internalization, enzyme degradation, and protein binding. We emphasize that a currently sidelined compound, novobiocin, became active and revealed a significant increase in inhibition against the pathogenic Gram-negative strain, Pseudomonas aeruginosa. Encapsulation affords co-delivery of multiple bioactives as a strategy for mitigating failure of monotherapies and tackling resistance. The rationale in optimized drug selection and nanocarrier choice is examined by transport modeling which agrees with experimental inhibition results. The results demonstrate that lipid nanocarrier encapsulation may alleviate a range of challenges faced by antibiotic therapies and increase the range of antibiotics available to treat bacterial infections.

Control Compounds for Preclinical Drug-Induced Liver Injury Assessment: Consensus-driven systematic review by the ProEuroDILI Network

Background & AimsIdiosyncratic drug-induced liver injury (DILI) is a complex and unpredictable event caused by drugs, herbal or dietary supplements. Early identification of human hepatotoxicity at preclinical stages remains a major challenge, in which the selection of validated in vitro systems and test drugs has a significant impact. This systematic review analyzed the compounds used in hepatotoxicity assays and established a list of DILI positive and negative control drugs for validation of in vitro models of DILI, supported by literature and clinical evidence and endorsed by an expert committee from COST Action ProEuroDILI Network (CA17112). MethodsFollowing 2020 PRISMA guidelines, original research articles focusing on DILI which used in vitro human models and performed at least one hepatotoxicity assay with positive and negative control compounds, were included. Bias of the studies was assessed by a modified ‘Toxicological Data Reliability Assessment Tool’. Results51 studies (out of 2,936) met the inclusion criteria, with 30 categorized as reliable without restrictions. Although there was a broad consensus on positive compounds, the selection of negative compounds lacked clarity. 2D monoculture, short exposure times and cytotoxicity endpoints were the most tested, although there was no consensus on the drug concentrations. ConclusionsThe extensive analysis highlighted the lack of agreement on control compounds for in vitro DILI assessment. Following comprehensive in vitro and clinical data analysis together with input from the expert committee, an evidence-based consensus-driven list of 10 positive and negative drugs is proposed for validating in vitro models for improving preclinical drug safety testing regimes. IMPACT AND IMPLICATIONSPrediction of human toxicity early in the drug development process remains a major challenge. For this, human in vitro models are becoming increasingly important, however, the development of more physiologically relevant liver models and careful selection of control DILI+ and DILI- drugs are requisites to better predict DILI liability of new drug candidates. Thus, this systematic study holds critical implications for standardizing validation of new in vitro models for studying drug-induced liver injury (DILI). By establishing a consensus-driven list of positive and negative control drugs, the study provides a scientifically justified framework for enhancing the consistency of preclinical testing, thereby addressing a significant challenge in early hepatotoxicity identification. The results are of paramount importance to all the actors involved in the drug development process, offering a standardized approach to assess hepatotoxic risks. Practically, these findings can guide researchers in evaluating safety profiles of new drugs, refining in vitro models, and informing regulatory agencies on potential improvements to regulatory guidelines, ensuring a more systematic and efficient approach to drug safety assessment.

In silico analysis of soybean phytocompounds against Plasmodium falciparum

IntroductionNatural products have been indispensable since immemorial and still act as a supportive healer. Parasitic diseases are a significant hurdle for humans and are responsible for global morbidity and mass mortality. Due to the building up of resistance in parasites over existing drugs and the severe side effects of the drugs, there is an urgent need to explore various plant extracts to discover novel antiparasitic agents. This article deals with the pharmacological aspects of soybeans against Plasmodium falciparum, which causes many of the deadliest and most popular diseases, such as malaria. MethodsThe molecular basis for selective inhibition of soy phytoconstituents like beta-sitosterol, soya-saponin I, soya-saponin II, soya-saponin II methyl ester, dehydrosoyasaponin I, phytic acid, and tryptophyl leucine were evaluated using in silico molecular docking approaches and molecular dynamic simulations against the falcipain-2 (FP2) and phosphoethanolamine methyltransferase (PMT) of P. falciparum. ResultsThe results indicated that tryptophyl leucine exhibited potential inhibitory action against both enzymes based on pharmacophore, molecular docking, simulations, RMSD, RMSF, ADME and toxicity analyses. ConclusionMedicinal plants represent a treasure for bioactive lead compounds and eventually provide scope for further identification and investigation of novel drug candidates which may be important in the design of selective PfPMT/PfFP-2 inhibitors as potential antimalarials.