Drug Discovery Strategies Research Articles

Drug Discovery Inspired from Nuclear Receptor Sensing of Microbial Signals

Host-microbiota interactions are vital for diverse pathophysiological events and may be targeted for innovative therapeutics. Nuclear receptors (NRs) are versatile host sensors of microbial signals that coordinate diverse environmental cues with local and remote adaptions. Harnessing NR-mediated sensory machinery could provide an alternative lynchpin for gut microbiota-oriented drug discovery strategy.

A Review of Target Identification Strategies for Drug Discovery: from Database to Machine-Based Methods

In recent years, target identification has become more efficient than before, and it helped to discover huge amounts of drugs for various diseases. The mystery buried behind was the methods that developed in recent years utilized in the target identification. The advances and research status of database, biological assay and machine-based method in recent years for target identification would be integrated by this review. The various databases help scientists to find information about target property, chemical property or on genome level. The biological assay, such as RNAi, RNA sequencing, DNA microarray, and Gal4/UAS system, is commonly used to identify the target in recent years. The machine-based strategies, such as random forest algorithm and Support Vector Machine (SVM) algorithm, could help scientists identify the target and find compound activity more efficiently. Among the three methods mentioned above, the machine-based methods could have higher efficiency and lower cost while maintaining higher accuracy. Despite the promising properties of machine-based methods, the combination use of biological assay would still be necessary. With wider application of more efficient strategies, target identification, as well as drug discovery process, would gain more rapid development.

Molecular Modeling Studies on 2,4-Disubstituted Imidazopyridines as Anti-Malarials: Atom-Based 3D-QSAR, Molecular Docking, Virtual Screening, In-Silico ADMET and Theoretical Analysis

Malarial parasites have been reported for moderate-high resistance towards classical antimalarial agents and henceforth development of newer novel chemical entities targeting multiple targets rather than targeting single target will be a highly promising strategy in antimalarial drug discovery. Herein, we carried out molecular modeling studies on 2,4-disubstituted imidazopyridines as anti-hemozoin formation inhibitors by using Schrödinger’s molecular modeling package (2020_4). We have developed statistically robust atom-based 3D-QSAR model (training set, [Formula: see text]; test set, [Formula: see text]; [Formula: see text] [Formula: see text]; root-mean-square error, [Formula: see text]; standard deviation, [Formula: see text]). Our molecular docking, in-silico ADMET analysis showed that dataset molecule 37, has highly promising results. Our ligand-based virtual screening resulted in top five ZINC hits, among them ZINC73737443 hit was observed with lesser energy gap, i.e. 7.85[Formula: see text]eV, higher softness value (0.127[Formula: see text]eV), and comparatively good docking score of [Formula: see text]10.2[Formula: see text]kcal/mol. Our in-silico analysis for a proposed hit, ZINC73737443 showed that this molecule has good ADMET, in-silico nonames toxic as well as noncarcinogenic profile. We believe that further experimental as well as the in-vitro investigation will throw more lights on the identification of ZINC73737443 as a potential antimalarial agent.

Targeting Lysosomal Degradation Pathways: New Strategies and Techniques for Drug Discovery.

A series of tools for targeted protein degradation are inspiring scientists to develop new drugs with advantages over traditional small-molecule drugs. Among these tools, proteolysis-targeting chimeras (PROTACs) are most representative of the technology based on proteasomes. However, the proteasome has little degradation effect on certain macromolecular proteins or aggregates, extracellular proteins, and organelles, which limits the application of PROTACs. Additionally, lysosomes play an important role in protein degradation. Therefore, lysosome-induced protein degradation drugs can directly regulate protein levels in vivo, achieve the goal of treating diseases, and provide new strategies for drug discovery. Lysosome-based degradation technology has the potential for clinical translation. In this review, strategies targeting lysosomal pathways and lysosome-based degradation techniques are summarized. In addition, lysosome-based degrading drugs are described, and the advantages and challenges are listed. Our efforts will certainly promote the design, discovery, and clinical application of drugs associated with this technology.

SAveRUNNER: an R-based tool for drug repurposing

BackgroundCurrently, no proven effective drugs for the novel coronavirus disease COVID-19 exist and despite widespread vaccination campaigns, we are far short from herd immunity. The number of people who are still vulnerable to the virus is too high to hamper new outbreaks, leading a compelling need to find new therapeutic options devoted to combat SARS-CoV-2 infection. Drug repurposing represents an effective drug discovery strategy from existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery.ResultsWe developed a network-based tool for drug repurposing provided as a freely available R-code, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), with the aim to offer a promising framework to efficiently detect putative novel indications for currently marketed drugs against diseases of interest. SAveRUNNER predicts drug–disease associations by quantifying the interplay between the drug targets and the disease-associated proteins in the human interactome through the computation of a novel network-based similarity measure, which prioritizes associations between drugs and diseases located in the same network neighborhoods.ConclusionsThe algorithm was successfully applied to predict off-label drugs to be repositioned against the new human coronavirus (2019-nCoV/SARS-CoV-2), and it achieved a high accuracy in the identification of well-known drug indications, thus revealing itself as a powerful tool to rapidly detect potential novel medical indications for various drugs that are worth of further investigation. SAveRUNNER source code is freely available at https://github.com/giuliafiscon/SAveRUNNER.git, along with a comprehensive user guide.

Drug Repositioning: A Unique Approach to Refurbish Drug Discovery.

For a decade, it has been observed that there is a remarkable decrease in the quantum of novel clinically approved drugs, in spite of modernization in the research and development process. We have highlighted repositioning of drugs as a methodology that has found new therapeutic implications for clinically approved drugs but with different indications. This can be considered as an upbringing strategy to deliver timely and cost-effective solutions, which still need exploration for getting over the shortage of novel drugs reaching the market. This review focuses on an activity-based drug repositioning approach, which is used to explore new uses of known drugs that are already approved for specific indications and are now being used for other indications on the basis that a single drug interacts with multiple targets. It also includes current research trends related to drug repositioning, which depends on strong knowledge of medicinal chemistry and involves elucidation of mechanisms of action and validation of novel targets. The review highlights the importance of computational tools and databases of various forms for drug repositioning purposes, which have enhanced the ability to pose reasonable and testable hypotheses. The critical nature of this aspect is obvious in cases where data gathered from in vitro, or animal models do not confirm in subsequent clinical trials. Hence, considering the positive outcomes of drug repositioning, it can be surmised that this approach can serve as a promising one that can develop into a robust drug discovery strategy.

Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery.

The optimal pharmacokinetic (PK) required for a drug candidate to elicit efficacy is highly dependent on the targeted pharmacology, a relationship that is often not well characterized during early phases of drug discovery. Generic assumptions around PK and potency risk misguiding screening and compound design toward nonoptimal absorption, distribution, metabolism, and excretion (ADME) or molecular properties and ultimately may increase attrition as well as hit-to-lead and lead optimization timelines. The present work introduces model-based target pharmacology assessment (mTPA), a computational approach combining physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling, sensitivity analysis, and machine learning (ML) to elucidate the optimal combination of PK, potency, and ADME specific for the targeted pharmacology. Examples using frequently encountered PK/PD relationships are presented to illustrate its application, and the utility and benefits of deploying such an approach to guide early discovery efforts are discussed.

Phage Display Technique as a Tool for Diagnosis and Antibody Selection for Coronaviruses.

Phage display is one of the important and effective molecular biology techniques and has remained indispensable for research community since its discovery in the year 1985. As a large number of nucleotide fragments may be cloned into the phage genome, a phage library may harbour millions or sometimes billions of unique and distinctive displayed peptide ligands. The ligand–receptor interactions forming the basis of phage display have been well utilized in epitope mapping and antigen presentation on the surface of bacteriophages for screening novel vaccine candidates by using affinity selection-based strategy called biopanning. This versatile technique has been modified tremendously over last three decades, leading to generation of different platforms for combinatorial peptide display. The translation of new diagnostic tools thus developed has been used in situations arising due to pathogenic microbes, including bacteria and deadly viruses, such as Zika, Ebola, Hendra, Nipah, Hanta, MERS and SARS. In the current situation of pandemic of Coronavirus disease (COVID-19), a search for neutralizing antibodies is motivating the researchers to find therapeutic candidates against novel SARS-CoV-2. As phage display is an important technique for antibody selection, this review presents a concise summary of the very recent applications of phage display technique with a special reference to progress in diagnostics and therapeutics for coronavirus diseases. Hopefully, this technique can complement studies on host–pathogen interactions and assist novel strategies of drug discovery for coronaviruses.

In Vitro Methods to Model Cardiac Mechanobiology in Health and Disease.

In vitro cardiac modeling has taken great strides in the past decade. While most cell and engineered tissue models have focused on cell and tissue contractile function as readouts, mechanobiological cues from the cell environment that affect this function, such as matrix stiffness or organization, are less well explored. In this study, we review two-dimensional (2D) and three-dimensional (3D) models of cardiac function that allow for systematic manipulation or precise control of mechanobiological cues under simulated (patho)physiological conditions while acquiring multiple readouts of cell and tissue function. We summarize the cell types used in these models and highlight the importance of linking 2D and 3D models to address the multiscale organization and mechanical behavior. Finally, we provide directions on how to advance in vitro modeling for cardiac mechanobiology using next generation hydrogels that mimic mechanical and structural environmental features at different length scales and diseased cell types, along with the development of new tissue fabrication and readout techniques.Impact statementUnderstanding the impact of mechanobiology in cardiac (patho)physiology is essential for developing effective tissue regeneration and drug discovery strategies and requires detailed cause–effect studies. The development of three-dimensional in vitro models allows for such studies with high experimental control, while integrating knowledge from complementary cell culture models and in vivo studies for this purpose. Complemented by the use of human-induced pluripotent stem cells, with or without predisposed genetic diseases, these in vitro models will offer promising outlooks to delineate the impact of mechanobiological cues on human cardiac (patho)physiology in a dish.

From high content screening to target deconvolution: New insights for phenotypic approaches

The advent of the molecular biology and the completion of the human genome sequencing prompted the pharmaceutical industry to progressively implement target-centric drug discovery strategies. However, concerns regarding the research and development productivity during the last ten years, combined with technological developments in high-content screening, automation, image analysis and artificial intelligence triggered a renewed interest for the phenotypic drug discovery approaches. Target-centric and phenotypic approaches are more and more considered complementary, hence, positioning the target deconvolution on the critical path. This review analyzes the evolution of the target-centric and phenotypic approaches, focusing more specifically on the high-content screening and the target deconvolution technologies currently available.

Targeting SARS-CoV-2 Nsp3 macrodomain structure with insights from human poly(ADP-ribose) glycohydrolase (PARG) structures with inhibitors

Arrival of the novel SARS-CoV-2 has launched a worldwide effort to identify both pre-approved and novel therapeutics targeting the viral proteome, highlighting the urgent need for efficient drug discovery strategies. Even with effective vaccines, infection is possible, and at-risk populations would benefit from effective drug compounds that reduce the lethality and lasting damage of COVID-19 infection. The CoV-2 MacroD-like macrodomain (Mac1) is implicated in viral pathogenicity by disrupting host innate immunity through its mono(ADP-ribosyl) hydrolase activity, making it a prime target for antiviral therapy. We therefore solved the structure of CoV-2 Mac1 from non-structural protein 3 (Nsp3) and applied structural and sequence-based genetic tracing, including newly determined A. pompejana MacroD2 and GDAP2 amino acid sequences, to compare and contrast CoV-2 Mac1 with the functionally related human DNA-damage signaling factor poly(ADP-ribose) glycohydrolase (PARG). Previously, identified targetable features of the PARG active site allowed us to develop a pharmacologically useful PARG inhibitor (PARGi). Here, we developed a focused chemical library and determined 6 novel PARGi X-ray crystal structures for comparative analysis. We applied this knowledge to discovery of CoV-2 Mac1 inhibitors by combining computation and structural analysis to identify PARGi fragments with potential to bind the distal ribose and adenosyl pockets of the CoV-2 Mac1 active site. Scaffold development of these PARGi fragments has yielded two novel compounds, PARG-345 and PARG-329, that crystallize within the Mac1 active site, providing critical structure-activity data and a pathway for inhibitor optimization. The reported structural findings demonstrate ways to harness our PARGi synthesis and characterization pipeline to develop CoV-2 Mac1 inhibitors targeting the ADP-ribose active site. Together, these structural and computational analyses reveal a path for accelerating development of antiviral therapeutics from pre-existing drug optimization pipelines.

DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation.

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

New drug discovery strategies for targeting drug-resistant bacteria

The rise of multidrug-resistant human pathogenic bacteria is calling for alternative approaches to design antibacterial drugs. Here, we review new approaches based on antisense oligonucleotides as antibacterial agents, fecal microbiota transplantation, and antimicrobial peptides and cell-penetrating peptides with antibacterial activity.

Recent achievements on the agricultural applications of thioether derivatives: A 2010–2020 decade in review

AbstractThioether derivatives, an important synthetic strategy in drug discovery, are acquiring more importance in plant protection field in recent years because of their broad spectrum of agricultural activities, such as antifungal, antibacterial, insecticidal, acaricidal, nematocidal, antiviral, herbicidal, and plant growth‐regulating activity. Thioethers and their analogues have been an area of great interest in recent years. This review summarizes the structure and agricultural activities of the thioether derivatives as reported in the literature that mainly covers the past 10 years from 2010 to 2020 as well as may contribute to a better understanding of thioether derivatives as efficient agricultural agents and provide some ideas on the development of novel pesticides.

Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury

Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes to functional behavioral deficits. Effective therapeutic strategies for CNS injury are therefore likely to manifest multi-factorial effects across a broad range of biological and functional outcome measures. Thus, multivariate analytic approaches are needed to capture the linkage between biological and neurobehavioral outcomes. Injury-induced neuroinflammation (NI) presents a particularly challenging therapeutic target, since NI is involved in both degeneration and repair. Here, we used big-data integration and large-scale analytics to examine a large dataset of preclinical efficacy tests combining five different blinded, fully counter-balanced treatment trials for different acute anti-inflammatory treatments for cervical spinal cord injury in rats. Multi-dimensional discovery, using topological data analysis (TDA) and principal components analysis (PCA) revealed that only one showed consistent multidimensional syndromic benefit: intrathecal application of recombinant soluble TNFα receptor 1 (sTNFR1), which showed an inverse-U dose response efficacy. Using the optimal acute dose, we showed that clinically-relevant 90 min delayed treatment profoundly affected multiple biological indices of NI in the first 48 h after injury, including reduction in pro-inflammatory cytokines and gene expression of a coherent complex of acute inflammatory mediators and receptors. Further, a 90 min delayed bolus dose of sTNFR1 reduced the expression of NI markers in the chronic perilesional spinal cord, and consistently improved neurological function over 6 weeks post SCI. These results provide validation of a novel strategy for precision preclinical drug discovery that is likely to improve translation in the difficult landscape of CNS trauma, and confirm the importance of TNFα signaling as a therapeutic target.

Anti-Austerity Activity of Thai Medicinal Plants: Chemical Constituents and Anti-Pancreatic Cancer Activities of Kaempferia parviflora

Human pancreatic tumor cells have an intrinsic ability to tolerate nutrition starvation and survive in the hypovascular tumor microenvironment, the phenomenon termed as “austerity”. Searching for an agent that inhibits such tolerance to nutrient starvation and kills the pancreatic cancer cells preferentially in nutrient-starvation is a unique anti-austerity strategy in anti-cancer drug discovery. In this strategy, plant extracts and compounds are tested against PANC-1 human pancreatic cancer cell line under the conditions of nutrient-deprived medium (NDM) and nutrient-rich medium (DMEM), to discover the compounds that show selective cytotoxicity in NDM. Screening of twenty-five Thai indigenous medicinal plant extracts for their anti-austerity activity against the PANC-1 human pancreatic cancer cell line in nutrient deprived medium (NDM) resulted in the identification of four active plants, Derris scandens, Boesenbergia pandurata, Citrus hystrix, and Kaempferia parviflora, with PC50 values 0.5–8.9 µg/mL. K. parviflora extract also inhibited PANC-1 cancer cell colony formation. Phytochemical investigation of K. parviflora extract led to the isolation of fourteen compounds, including two polyoxygenated cyclohexanes (1 and 2), eleven flavonoids (3–13), and β-sitosterol (14). Stereochemical assignment of compound 1 was confirmed through X-ray analysis. All isolated compounds were tested for their preferential cytotoxicity against PANC-1 cells. Among them, 5-hydroxy-7-methoxyflavone (3) displayed the most potent activity with a PC50 value of 0.8 µM. Mechanistically, it was found to induce apoptosis in PANC-1 cell death in NDM as evident by caspase cleavage. It was also found to inhibit PANC-1 cancer cell colony formation in DMEM. Therefore, compound 3 can be considered as a potential lead compound for the anticancer drug development based on the anti-austerity strategy.

A Bibliographic Assessment Using the Degrees of Publication Method: Medicinal Plants from the Rural Greater Mpigi Region (Uganda).

In ethnopharmacological research, many field assessment tools exist. Yet, these miss that critical point of how to really determine which species merit the costly lab studies, e.g., evaluation of traditional use via pharmacological assays and isolation of bioactive secondary metabolites. This gap can be filled with the introduction of a new tool for literature assessment: the Degrees of Publication (DoPs). In this study, its application is illustrated through an extensive bibliographic assessment of 16 medicinal plant species that were recently identified in the Greater Mpigi region of Uganda as being frequently used by local traditional healers in the treatment of medical disorders (namely, Albizia coriaria, Cassine buchananii, Combretum molle, Erythrina abyssinica, Ficus saussureana, Harungana madagascariensis, Leucas calostachys, Microgramma lycopodioides, Morella kandtiana, Plectranthus hadiensis, Securidaca longipedunculata, Sesamum calycinum subsp. angustifolium, Solanum aculeastrum, Toddalia asiatica, Warburgia ugandensis, and Zanthoxylum chalybeum). These species are suspected to be understudied, and a thorough bibliographic assessment has not been previously performed. Thus, the objectives of our study were to undertake a comparative assessment of the degree to which each of these plant species has been studied in the past, including evaluation of the quality of the journals where results were published in. The determination of the DoPs enabled successful assessment of the degrees to which each individual plant species has been studied so far, while also taking into account the methodological “research chain of ethnopharmacology” from ethnobotanical studies (“traditional use”) to pharmacological assays (“bioactivity”) and finally to pharmacognostic research (“structure elucidation”). The significance of a research paper was assessed by determining whether its journal and publishing house were members of the Committee on Publication Ethics (COPE). In total, 634 peer-reviewed publications were reviewed covering the period of 1960–2019, 53.3% of which were published in journals and by publishing houses affiliated with COPE (338 publications). The literature assessment resulted in the identification of understudied plants among the selected species. The majority of plants reviewed have not been sufficiently studied; six species were classified as being highly understudied and three more as being understudied: C. buchananii, F. saussureana, L. calostachys, M. lycopodioides, M. kandtiana, and S. calycinum subsp. angustifolium and A. coriaria, P. hadiensis, and S. aculeastrum, respectively. The newly introduced DoPs are a useful tool for the selection of traditionally used species for future laboratory studies, especially for pharmacological bioassays, isolation procedures, and drug discovery strategies.

Activation of PKC supports the anticancer activity of tigilanol tiglate and related epoxytiglianes

The long-standing perception of Protein Kinase C (PKC) as a family of oncoproteins has increasingly been challenged by evidence that some PKC isoforms may act as tumor suppressors. To explore the hypothesis that activation, rather than inhibition, of these isoforms is critical for anticancer activity, we isolated and characterized a family of 16 novel phorboids closely-related to tigilanol tiglate (EBC-46), a PKC-activating epoxytigliane showing promising clinical safety and efficacy for intratumoral treatment of cancers. While alkyl branching features of the C12-ester influenced potency, the 6,7-epoxide structural motif and position was critical to PKC activation in vitro. A subset of the 6,7-epoxytiglianes were efficacious against established tumors in mice; which generally correlated with in vitro activation of PKC. Importantly, epoxytiglianes without evidence of PKC activation showed limited antitumor efficacy. Taken together, these findings provide a strong rationale to reassess the role of PKC isoforms in cancer, and suggest in some situations their activation can be a promising strategy for anticancer drug discovery.

Structure-based peptide design targeting intrinsically disordered proteins: Novel histone H4 and H2A peptidic inhibitors

A growing body of research has demonstrated that targeting intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs) is feasible and represents a new trending strategy in drug discovery. However, the number of inhibitors targeting IDPs/IDPRs is increasing slowly due to limitations of the methods that can be used to accelerate the discovery process. We have applied structure-based methods to successfully develop the first peptidic inhibitor (HIPe - Histone Inhibitory Peptide) that targets histone H4 that are released from NETs (Neutrophil Extracellular Traps). HIPe binds stably to the disordered N-terminal tail of histone H4, thereby preventing histone H4-induced cell death. Recently, by utilisation of the same state-of-the-art approaches, we have developed a novel peptidic inhibitor (CHIP - Cyclical Histone H2A Interference Peptide) that binds to NET-resident histone H2A, which results in a blockade of monocyte adhesion and consequently reduction in atheroprogression. Here, we present comprehensive details on the computational methods utilised to design and develop HIPe and CHIP. We have exploited protein–protein complexes as starting structures for rational peptide design and then applied binding free energy methods to predict and prioritise binding strength of the designed peptides with histone H4 and H2A. By doing this way, we have modelled only around 20 peptides and from these were able to select 4–5 peptides, from a total of more than a trillion candidate peptides, for functional characterisation in different experiments. The developed computational protocols are generic and can be widely used to design and develop novel inhibitors for other disordered proteins.

PRODUCTOS NATURALES: DE LA MEDICINA TRADICIONAL A CABEZAS DE SERIE PARA EL DESARROLLO DE NUEVOS FÁRMACOS DEL SIGLO XXI

Natural products have historically contributed to drug discovery as a source of bioactive molecules, due to their great diversity and structural complexity. They have provided “lead” molecules for the development of drugs in different therapeutic areas, with a very prominent representation in the treatment of pain and inflammation, coagulation disorders, metabolic disorders, as well as in the treatment of cancer and infectious diseases. In recent decades there has been a paradigm shift in drug discovery strategies that has allowed the identification of new active natural products in therapeutic targets. Combinatorial Chemistry and biological tests (High Throughput Screening), together with the development of computational techniques, have contributed decisively to the design and optimization of libraries of natural product derivatives based on their biological activity. In parallel, technological advances in the field of Omics sciences and in data processing lead to a multidimensional approach in the drug discovery process. These powerful tools will allow the analysis of the pharmacological potential of natural products and their derivatives for the conversion of these molecules to active products with low toxicity. In the Precision Medicine era, natural products continue to be molecules with great potential in pharmaceutical development, since, unlike other therapeutic strategies, they have a favorable cost-benefit ratio, which will allow their future use in this discipline.