Impact Drug Discovery Research Articles

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs.

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

Synthesis of highly stable nitrogen doped carbon encapsulated nano Cu2O pom discs (NCEN[sbnd]Cu2O pom discs) and its click chemistry

Click chemistry has growing impact in drug discovery. It is an azide alkyne cycloaddition in presence of Cu(I) catalyst. Click reaction gives high yields with a variety of starting materials and easy to perform and insensitive to oxygen or water. However vast applications of Click reactions were studied with homogeneous catalyst. Main disadvantages of homogeneous catalysts are processing of the product and regeneration of the catalyst. These disadvantages can overcome by the use of heterogeneous catalysts. Thus heterogeneous catalysts are highly economic in industrial applications. This article reports the synthesis of highly stable and reusable heterogeneous nitrogen doped carbon encapsulated nano Cu2O (NCEN Cu2O) pom discs. These pom discs were synthesized under normal temperature and pressure conditions by using copper nitrate as precursor and triethanolamine as stabilizing agent, hydrazine hydrate as reducing agent. Water and propanol were used as solvents. Thus obtained reddish brown powder was washed with ethanol and filtered using membrane and analyzed using powder XRD, FT-IR, and TGA, SEM, SEM–EDX, TEM, TEM-EDX, TEM-SAED, HR-TEM analyses. And it showed high activity and reproducibility towards Click chemistry in synthesis of 1,2,3 triazoles.

Metabolomics to investigate the effect of preconditioned mesenchymal stem cells with crocin on pulmonary epithelial cells exposed to 2-chloroethyl ethyl sulfide

Metabolomics significantly impacts drug discovery and precise disease management. This study meticulously assesses the metabolite profiles of cells treated with Crocin, Dexamethasone, and mesenchymal stem cells (MSCs) under oxidative stress induced by 2-chloroethyl ethyl sulfide (CEES). Gas chromatography/mass spectrometry (GC/MS) analysis unequivocally identified substantial changes in 37 metabolites across the treated groups. Notably, pronounced alterations were observed in pathways associated with aminoacyl-tRNA biosynthesis and the metabolism of aspartate, serine, proline, and glutamate. These findings demonstrate the potent capacity of the analyzed treatments to effectively reduce inflammation, mitigate reactive oxygen species production, and enhance cell survival rates. Significance•Crocin, Dexamethasone, and the metabolites of the conditioned media of mesenchymal stem cells to decline the injury caused by CEES.•Metabolites can acquaint treatment groups in diminishing inflammation and ROS production and expanding the percentage of cell survival.•Aminoacyl-tRNA biosynthesis, nitrogen metabolism, glyoxylate and dicarboxylate metabolism, and propanoate metabolism were significant pathways involved among groups.

Artificial Intelligence Applications in Neonatal Critical Care: A Scoping Review

Background: The development of artificial intelligence (AI) approaches impacted drug discovery, medical imaging, customized diagnostics, and therapeutics. Medicine will be transformed by AI. One such area of medicine where AI is significantly improving care is neonatology. Objective: The objective of this scoping review is to explore the applications of AI in neonatal critical care and its outcome. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a scoping review was conducted utilizing the Web of Science, MEDLINE (PubMed), and Scopus databases. The search was limited to full-text publications on AI applications in neonatal critical care that were published between January 1, 2019, and December 31, 2023. Articles specifically addressing the application of AI in neonatal care have been considered within the scope of this review. At least three reviewers had independently executed the screening, data abstraction, and exploration. Results: Database searches yielded 631 articles, of which 11 met the inclusion criteria. The research encompassed extensive AI applications in neonatal critical care, employed for prognosis, diagnosis, and therapy strategizing. Artificial neural networks, machine learning, deep learning, and shallow hybrid neural networks were the commonly utilized AI techniques (neonatal critical care). These methods were applied to screen for inborn metabolic abnormalities, predict various outcomes, including death and sepsis, identify diseases such as sepsis, and assess neurodevelopmental outcomes in preterm newborns, helping plan several medical treatments. The included research demonstrated encouraging outcomes when using AI in neonatal critical care. Conclusions: AI-driven electronic arrangements upgrade neonatal basic care by improving risk forecast, promising critical commitments to future health care. Be that as it may, careful appraisal, evidence-based considers, and determination of safety, ethics, and information straightforwardness issues are essential before implementation. Acceptance by administrative bodies and the therapeutic community pivots on tending to these concerns.

P35 Adaptation of a phenotypic screening assay to incorporate 384-well plates: opening the door to high-throughput screening

Abstract Introduction and aims High-throughput screening has significantly impacted drug discovery by increasing speed of experiments and reducing reagent and sample costs. We have previously developed several screening assays to measure myofibroblast inhibition via quantification of α-smooth muscle actin (α-SMA) expression, in a 96-well format. Currently, there are no drugs for the treatment of hypertrophic scarring, a major side-effect of burn injuries that occurs by myofibroblast transformation, leaving a huge gap in this sector. The aim for this project was to adapt our current assay for use in 384-well microplates. Methods Human primary dermal fibroblasts were transformed to myofibroblasts using 10 ng mL−1 transforming growth factor (TGF)-β1. α-SMA expression was measured using the In-Cell enzyme-linked immunosorbent assay method, with cell viability measured using DRAQ5 nuclear staining. Assay accuracy and reliability was determined by calculating the robust z-factor, with guidelines set to an average score over 0.5, with a minimum score per plate above 0.4. To further validate the assay, concentration-response curves (CRCs) for the drug vehicle [dimethyl sulfoxide (DMSO)] and our tool compound (SB-505124) were constructed at varying concentrations, in coincubation with TGF-β1. Results Optimization of well metrics was performed, producing an average robust z-factor of 0.52, confirming the assay Results are reliable and reproducible. The DMSO CRC produced no significant drop in cell viability at concentrations that are expected to be used in the screening of compounds. Our tool compound (SB-505124) produced a concentration-dependant curve for the inhibition of myofibroblast transformation. Conclusions A 384-well assay has been designed, tested and validated for the determination of myofibroblast transformation in human primary dermal fibroblasts. The assay has been shown to deliver an average robust z-factor of above 0.5, it contains a positive control and is compatible with compounds stored in up to 100% DMSO.

Revolutionizing Drug Discovery: Harnessing Machine Learning Algorithms

Drug discovery is a crucial element of biomedical research, with the goal of finding and creating new medical treatments for a variety of illnesses. Yet, the conventional process of finding new drugs is frequently impeded by its intrinsic difficulties, such as expensive expenses, long durations, and poor success rates in trials with patients. Recently, the incorporation of machine learning (ML) algorithms has become a revolutionary method to streamline and improve different phases of drug discovery. This summary offers a glimpse into the rapidly growing area of drug discovery using machine learning algorithms, emphasizing its potential to transform the process of developing treatments. The usual process of discovering drugs involves various stages such as identifying the target, finding lead compounds, conducting preclinical tests, undergoing clinical trials, and obtaining regulatory approval. All these phases require a lot of labor, time, and resources, leading to high attrition rates and limited success in turning potential compounds into approved therapies. Nevertheless, researchers can enhance and speed up crucial parts of the drug discovery process by using ML algorithms. ML algorithms use data to aid in drug discovery by utilizing computational models to examine large quantities of biological, chemical, and clinical data. These algorithms can learn from various types of data, such as genomic data, chemical structures, protein interactions, and clinical outcomes, to discover hidden patterns, find new targets for drugs, and forecast the effectiveness and safety of potential treatments. Moreover, machine learning algorithms allow for the investigation of intricate connections between molecular structures and biological effects, making it easier to create improved drug candidates with better effectiveness and specificity. Important uses of machine learning in pharmaceutical research involve finding and confirming targets, screening compounds and improving leads, repurposing drugs, and tailoring treatments for individuals. Commonly used for classification and regression tasks, supervised learning algorithms like support vector machines and random forests predict compound activity, toxicity, and pharmacokinetic properties. Clustering and dimensionality reduction techniques utilized in unsupervised learning algorithms help analyze vast datasets and discover new drug-target interactions. Advanced abilities for analyzing molecular structures, virtual screening, and designing new drugs are provided by deep learning models like convolutional neural networks and recurrent neural networks. Multiple case studies demonstrate how ML algorithms can significantly impact drug discovery. Collaboration among academia, industry, and research institutions has resulted in the creation of new ML-based methods for drug development, identifying targets, and categorizing patients. Nevertheless, there are challenges accompanying the widespread use of ML in drug discovery. In healthcare, it is crucial to address ethical considerations, regulatory hurdles, and data privacy concerns to ensure the responsible and ethical use of ML algorithms. The potential for transforming drug discovery and therapeutic development is immense with the incorporation of machine learning algorithms. Through utilizing data-driven methods, researchers can speed up the discovery of new potential drugs, enhance the effectiveness of treatments, and ultimately enhance the results for patients. Ongoing innovation, teamwork, and cross-disciplinary studies are crucial to fully leverage the potential of ML in revolutionizing drug discovery and precision medicine.

Harnessing the Power of C–H Functionalization Chemistry to Accelerate Drug Discovery

AbstractThe field of C–H functionalization chemistry has experienced rapid growth in the past twenty years, with increasingly powerful applications in organic synthesis. Recognizing the potential of this emerging field to impact drug discovery, a dedicated effort was established in our laboratories more than ten years ago, with the goal of facilitating the application of C–H functionalization chemistries to active medicinal-chemistry programs. Our approach centered around the strategy of late-stage functionalization (LSF) wherein C–H functionalization chemistry is employed in a systematic and targeted manner to generate high-value analogues from advanced drug leads. To successfully realize this approach, we developed broadly useful LSF chemistry platforms and workflows that increased the success rates of the C–H functionalization chemistries and accelerated access to new derivatives. The LSF strategy, when properly applied, enabled a rapid synthesis of molecules designed to address specific medicinal-chemistry issues. Several case studies are presented, along with descriptions of the group’s platforms and workflows.1 Introduction2 Building an LSF Chemistry Toolbox2.1 C–H Borylation2.2 Minisci Platforms2.3 Automated Direct-Metalation Platform3 Building an LSF Workflow4 LSF Application Case Studies4.1 BTK Inhibitor Program4.2 GPR40 Agonist Program5 Conclusions

Unlocking the Potential of In-silico Approaches: Drug Development and Vaccine Design

Unmatched in its field, bioinformatics combines several academic fields such as statistics, computer science, mathematics, and biology to create state-of-the-art techniques for biological data retrieval, storage, and analysis that lead to a thorough understanding of the biological world. Countless options currently accessible in field of living sciences by the expansion of in-silico biology. Paradigm of life sciences has changed as a result of in-silico technologies, which offer researchers a valuable and affordable way to focus on in-silico techniques like homology modeling, epitope prediction, and molecular docking, which have impacted drug discovery and vaccine design. These techniques also provide previously unheard-of predictions and insights.

Topological deep learning based deep mutational scanning

High-throughput deep mutational scanning (DMS) experiments have significantly impacted protein engineering, drug discovery, immunology, cancer biology, and evolutionary biology by enabling the systematic understanding of protein functions. However, the mutational space associated with proteins is astronomically large, making it overwhelming for current experimental capabilities. Therefore, alternative methods for DMS are imperative. We propose a topological deep learning (TDL) paradigm to facilitate in silico DMS. We utilize a new topological data analysis (TDA) technique based on the persistent spectral theory, also known as persistent Laplacian, to capture both topological invariants and the homotopic shape evolution of data. To validate our TDL-DMS model, we use SARS-CoV-2 datasets and show excellent accuracy and reliability for binding interface mutations. This finding is significant for SARS-CoV-2 variant forecasting and designing effective antibodies and vaccines. Our proposed model is expected to have a significant impact on drug discovery, vaccine design, precision medicine, and protein engineering.

Deep learning in image-based phenotypic drug discovery.

Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).

Demystifying racemic natural products in the homochiral world.

Natural products possess structural complexity, diversity and chirality with attractive functions and biological activities that have significantly impacted drug discovery initiatives. Chiral natural products are abundant in nature but rarely occur as racemates. The occurrence of natural products as racemates is very intriguing from a biosynthetic point of view; as enzymes are chiral molecules, enzymatic reactions generating natural products should be stereospecific and lead to single-enantiomer products. Despite several reports in the literature describing racemic mixtures of stereoisomers isolated from natural sources, there has not been a comprehensive review of these intriguing racemic natural products. The discovery of many more natural racemates and their potential enzymatic sources in recent years allows us to describe the distribution and chemical diversity of this ‘class of natural products’ to enrich discussions on biosynthesis. In this Review, we describe the chemical classes, occurrence and distribution of pairs of enantiomers in nature and provide insights about recent advances in analytical methods used for their characterization. Special emphasis is on the biosynthesis, including plausible enzymatic and non-enzymatic formation of natural racemates, and their pharmacological significance.

DRUG-seq Provides Unbiased Biological Activity Readouts for Neuroscience Drug Discovery.

Unbiased transcriptomic RNA-seq data has provided deep insights into biological processes. However, its impact in drug discovery has been narrow given high costs and low throughput. Proof-of-concept studies with Digital RNA with pertUrbation of Genes (DRUG)-seq demonstrated the potential to address this gap. We extended the DRUG-seq platform by subjecting it to rigorous testing and by adding an open-source analysis pipeline. The results demonstrate high reproducibility and ability to resolve the mechanism(s) of action for a diverse set of compounds. Furthermore, we demonstrate how this data can be incorporated into a drug discovery project aiming to develop therapeutics for schizophrenia using human stem cell-derived neurons. We identified both an on-target activation signature, induced by a set of chemically distinct positive allosteric modulators of the N-methyl-d-aspartate (NMDA) receptor, and independent off-target effects. Overall, the protocol and open-source analysis pipeline are a step toward industrializing RNA-seq for high-complexity transcriptomics studies performed at a saturating scale.

From traditional to data-driven medicinal chemistry: A case study

Artificial intelligence (AI) and data science are beginning to impact drug discovery. It usually takes considerable time and efforts until new scientific concepts or technologies make a transition from conceptual stages to practical applicability and experience values are gathered. Especially for computational approaches, demonstrating measurable impact on drug discovery projects is not a trivial task. A pilot study at Daiichi Sankyo Company has attempted to integrate data science into practical medicinal chemistry and quantify the impact, as reported herein. Although characteristic features and focal points of early-phase drug discovery naturally vary at different pharmaceutical companies, the results of this pilot study indicate significant potential of data-driven medicinal chemistry and suggest new models for internal training of next-generation medicinal chemists.

Has Artificial Intelligence Impacted Drug Discovery?

Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle. The main focus of this chapter is the application in molecular generation with the aid of deep neural networks (DNN). We present a historical overview of the main advances in the field. We analyze the concepts of distribution and goal-directed learning and then highlight some of the recent applications of generative models in drug design with a focus into research work from the biopharmaceutical industry. We present in some more detail REINVENT which is an open-source software developed within our group in AstraZeneca and the main platform for AI molecular design support for a number of medicinal chemistry projects in the company and we also demonstrate some of our work in library design. Finally, we present some of the main challenges in the application of AI in Drug Discovery and different approaches to respond to these challenges which define areas for current and future work.

E3 Ligase Ligands in Successful PROTACs: An Overview of Syntheses and Linker Attachment Points.

Proteolysis-targeting chimeras (PROTACs) have received tremendous attention as a new and exciting class of therapeutic agents that promise to significantly impact drug discovery. These bifunctional molecules consist of a target binding unit, a linker, and an E3 ligase binding moiety. The chemically-induced formation of ternary complexes leads to ubiquitination and proteasomal degradation of target proteins. Among the plethora of E3 ligases, only a few have been utilized for the novel PROTAC technology. However, extensive knowledge on the preparation of E3 ligands and their utilization for PROTACs has already been acquired. This review provides an in-depth analysis of synthetic entries to functionalized ligands for the most relevant E3 ligase ligands, i.e. CRBN, VHL, IAP, and MDM2. Less commonly used E3 ligase and their ligands are also presented. We compare different preparative routes to E3 ligands with respect to feasibility and productivity. A particular focus was set on the chemistry of the linker attachment by discussing the synthetic opportunities to connect the E3 ligand at an appropriate exit vector with a linker to assemble the final PROTAC. This comprehensive review includes many facets involved in the synthesis of such complex molecules and is expected to serve as a compendium to support future synthetic attempts towards PROTACs.

Development of Diverse Range of Biologically Relevant Carbohydrate-Containing Molecules: Twenty Years of Our Journey*.

There is an increasing demand for significant amount of carbohydrate-containing molecules owing to their complete chemical, biological, and pharmacological investigations to better understand their role in many important biological events. Clinical studies of a wide range of simple carbohydrates or their derivatives, glycohybrids, glycoconjugates, and neoglycoconjugates have been conducted worldwide for the successful treatment of various frontline diseases. Herein, a brief perspective of carbohydrate-based molecular scaffolding and my experience during the last 20 years in the area of synthetic carbohydrate chemistry, mainly for their impact in drug discovery & development, is presented.

Imaging microphysiological systems: a review.

Microphysiological systems (MPS), often referred to as "organ-on-chips," are microfluidic-based in vitro models that aim to recapitulate the dynamic chemical and mechanical microenvironment of living organs. MPS promise to bridge the gap between in vitro and in vivo models and ultimately improve the translation from preclinical animal studies to clinical trials. However, despite the explosion of interest in this area in recent years, and the obvious rewards for such models that could improve R&D efficiency and reduce drug attrition in the clinic, the pharmaceutical industry has been slow to fully adopt this technology. The ability to extract robust, quantitative information from MPS at scale is a key requirement if these models are to impact drug discovery and the subsequent drug development process. Microscopy imaging remains a core technology that enables the capture of information at the single-cell level and with subcellular resolution. Furthermore, such imaging techniques can be automated, increasing throughput and enabling compound screening. In this review, we discuss a range of imaging techniques that have been applied to MPS of varying focus, such as organoids and organ-chip-type models. We outline the opportunities these technologies can bring in terms of understanding mechanistic biology, but also how they could be used in higher-throughput screens, widening the scope of their impact in drug discovery. We discuss the associated challenges of imaging these complex models and the steps required to enable full exploitation. Finally, we discuss the requirements for MPS, if they are to be applied at a scale necessary to support drug discovery projects.

Cryo-EM: The Resolution Revolution and Drug Discovery.

Single-particle cryogenic electron microscopy (cryo-EM) has been elevated to the mainstream of structural biology propelled by technological advancements in numerous fronts, including imaging analysis and the development of direct electron detectors. The drug discovery field has watched with (initial) skepticism and wonder at the progression of the technique and how it revolutionized the molecular understanding of previously intractable targets. This article critically assesses how cryo-EM has impacted drug discovery in diverse therapeutic areas. Targets that have been brought into the realm of structure-based drug design by cryo-EM and are thus reviewed here include membrane proteins like the GABAA receptor, several TRP channels, and G protein-coupled receptors, and multiprotein complexes like the ribosomes, the proteasome, and eIF2B. We will describe these studies highlighting the achievements, challenges, and caveats.

A Computational Pipeline to Predict Cardiotoxicity: From the Atom to the Rhythm.

Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery. To predict the impact of a drug from the drug chemistry on the cardiac rhythm. In a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction. We present a multiscale model framework to predict electrotoxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue-level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.

Droplet-based Microfluidic Platform for Intracellular Ion Channel Drug Discovery - EPSRC

New technology is widening the chance of developing new pharmacological compounds and has the potential to create new jobs and have economic and societal impact on healthcare. Economic impact: The average expenditure to develop and bring to market a new drug is estimated to be approximately $2 billion, with target identification, discovery and the development of a new chemical compound before clinical trials accounting for 30% of the total cost. Commercialization of the developed platform has potential to add considerable information over the gold standard obtained by current electrophysiological tests with automated patch-clamp technology and impact the pre-screening stages of compounds, most of which is currently provided as a service by CROs and/or CMOs. Therefore, outcomes from this project have potential to carve a unique sector in the market of drug screening instrumentation. To assess this impact and available opportunities, we have secured the participation of members from academia, healthcare technology and industry to act as an advisory board for consultancy regarding knowledge transfer and commercialisation. Industrial impact: The developed system will be tested in collaboration with project partner Apconix for the development of pharmacological assays based on combinatorial chemistry approaches. Outside the immediate benefit in identifying new active compounds for the CLIC4 channel, the potential for translating the developed procedures for other pharmacologically relevant eukaryotic ion channels present cost-saving arguments against current live-cell based, large-screening assay in the Pharmaceutical industry. In addition, the collaboration with project partner Smartox will explore a further application of the technology for use with limited resources of venom samples only available in extremely small quantities that currently elude industrial automated screening. The proposed technology has the potential to greatly impact drug discovery by identifying new efficient venom-derived drugs. Together, this double partnership will enable us to address different screening needs for both SME and the Pharmaceutical Industry. Societal impact: Identification of new drug candidates from the developed technology will expand the panel of drugs with implications in cancer, mitochondrial dysfunction and neurodegenerative diseases. Improving the efficacy of identified drugs at preclinical level is expected to improve outcomes during clinical trials. Therefore, wide societal benefits would be in the form of new medicine and their improved efficacy. Furthermore, the applicability of the proposed technology to the other identified applications (see academic beneficiaries) and healthcare related fields could impact the screening of vaccines and environmental toxins, all of which will produce massive societal benefits.