High-throughput Screening Experiments Research Articles

Structure-based drug discovery facilitates future painkiller development.

Structure-based drug discovery facilitates future painkiller development.

A Multi-Objective Active Learning Platform and Web App for Reaction Optimization.

We report the development of an open-source experimental design via Bayesian optimization platform for multi-objective reaction optimization. Using high-throughput experimentation (HTE) and virtual screening data sets containing high-dimensional continuous and discrete variables, we optimized the performance of the platform by fine-tuning the algorithm components such as reaction encodings, surrogate model parameters, and initialization techniques. Having established the framework, we applied the optimizer to real-world test scenarios for the simultaneous optimization of the reaction yield and enantioselectivity in a Ni/photoredox-catalyzed enantioselective cross-electrophile coupling of styrene oxide with two different aryl iodide substrates. Starting with no previous experimental data, the Bayesian optimizer identified reaction conditions that surpassed the previously human-driven optimization campaigns within 15 and 24 experiments, for each substrate, among 1728 possible configurations available in each optimization. To make the platform more accessible to nonexperts, we developed a graphical user interface (GUI) that can be accessed online through a web-based application and incorporated features such as condition modification on the fly and data visualization. This web application does not require software installation, removing any programming barrier to use the platform, which enables chemists to integrate Bayesian optimization routines into their everyday laboratory practices.

150-OR: The Use of Luminescent Cell-Based Assays for Studying Insulin and Glucagon Secretion and Activity

The complex nature of metabolic diseases often relies on the use of animal models for drug development and testing. However, recent advances in disease-relevant cellular models and cell-based assays offer opportunity for more rapid and cost-effective studies. Here, we describe the development and implementation of luminescent cell-based assays to study insulin and glucagon secretion and measure their control of diverse metabolic pathways. To measure insulin and glucagon secretion, we developed no-wash immunoassays using luciferase complementation technology. Immunoassays have sensitive detection from 10pM to 8nM (insulin) and 0.5pM to 1nM (glucagon) . They have broad utility for measuring hormone secretion from cells and islets. Furthermore, they can be scaled to 384-well plates, which enables the rapid, cost-effective measurement of large numbers of samples, such as from perifusion or high-throughput screening experiments. To investigate hormone action on glucose and lipid metabolism, we developed a core luminescent technology that couples specific metabolite dehydrogenases to the production of NAD (P) H and the generation of light. Assays can be used in cellular models, reducing the need for animal studies. Luminescent glucose uptake assay was validated as insulin potency assay: insulin-stimulated glucose uptake in 3T3-L1 MBX adipocytes was measured within the expected pharmacology and a 10-fold assay window. The utility of a glucose assay for measuring insulin effect on gluconeogenesis was demonstrated using iPS-derived hepatocytes, and triglyceride assays provided rapid tool for lipogenesis studies in liver microtissues. All assays were scalable and amenable to automation, making them well-suited for high-throughput screening and new drug development. Disclosure K.Haupt: None. D.M.Leippe: None. J.Vidugiriene: None.

Simplified choice of suitable excipients within biologics formulation design using protein-protein interaction- and water activity-maps

Still today, high-concentration protein formulations are often developed based on high-throughput experimental screening approaches. Although likely delivering working formulations, these approaches do not lead to a deep/mechanistic understanding of the protein phase behavior in solution. Within this work, we thus optimized and enhanced a recent approach for an initial low effort selection of potential excipients and excipient mixtures to be used in high-concentration protein formulations. This approach considers both: molecular interactions and thermodynamic determinants to access the phase behavior of the proteins in solution, as well as pharmaceutical engineering boundaries (such as osmotic pressure and osmolality) to deliver on optimal formulation conditions. Water activity coefficient γW-calculations (used to describe the protein environment in solution), unfolding temperature (conformational stability) and protein-protein interactions (colloidal stability) are used as determinants. Amino acids (20 proteinogenic amino acids), selected amino acid mixtures, as well as mixtures of amino acids and trehalose (l-arginine-trehalose; l-histidine-trehalose) are considered as model excipients. The approach is extends by studying the long-term stability of the predicted formulation conditions for a γ-globulin from human blood and denosumab. The results reveal, that by combining protein-specific experiments as well as model-based studies for the selection of excipient mixtures in high concentration protein formulations, the effort as well as the resource requirements can be reduced significantly.

AI-Driven Cell Tracking to Enable High-Throughput Drug Screening Targeting Airway Epithelial Repair for Children with Asthma.

The airway epithelium of children with asthma is characterized by aberrant repair that may be therapeutically modifiable. The development of epithelial-targeting therapeutics that enhance airway repair could provide a novel treatment avenue for childhood asthma. Drug discovery efforts utilizing high-throughput live cell imaging of patient-derived airway epithelial culture-based wound repair assays can be used to identify compounds that modulate airway repair in childhood asthma. Manual cell tracking has been used to determine cell trajectories and wound closure rates, but is time consuming, subject to bias, and infeasible for high-throughput experiments. We therefore developed software, EPIC, that automatically tracks low-resolution low-framerate cells using artificial intelligence, analyzes high-throughput drug screening experiments and produces multiple wound repair metrics and publication-ready figures. Additionally, unlike available cell trackers that perform cell segmentation, EPIC tracks cells using bounding boxes and thus has simpler and faster training data generation requirements for researchers working with other cell types. EPIC outperformed publicly available software in our wound repair datasets by achieving human-level cell tracking accuracy in a fraction of the time. We also showed that EPIC is not limited to airway epithelial repair for children with asthma but can be applied in other cellular contexts by outperforming the same software in the Cell Tracking with Mitosis Detection Challenge (CTMC) dataset. The CTMC is the only established cell tracking benchmark dataset that is designed for cell trackers utilizing bounding boxes. We expect our open-source and easy-to-use software to enable high-throughput drug screening targeting airway epithelial repair for children with asthma.

High Throughput Calculation and Characteristic Structure Selection of Cu-Cr-Sn Copper Alloy Based on Matclould Platform

In this paper, high-throughput crystal structure modeling and high-throughput screening optimization algorithm are used to optimize the performance parameters such as characteristic microstructure type, generation conditions, strength and conductivity. The theoretical criterion of multi parameter collaborative optimization and control of key properties of matrix and second phase is established, based on which the optimal composition range of matrix and second phase is determined, which guides high-throughput experimental verification and material screening, and provides basic data for material composition design and structure control. Realize 102 secondary high-throughput parallel computing and screening based on matcloud platform, and calculate and screen the number of candidate material samples ≧106 and other related technologies. Through the functions of online generation, submission and job monitoring of first principle high-throughput jobs, all computing processes are realized automatically, and the computing results are extracted, standardized processed and stored in the database according to certain rules. In this project, the prediction model and process design platform of material composition structure process performance relationship are established according to the dynamic selection parameters of copper alloy structure, pseudopotential recommendation, k-point file generated according to calculation tasks and structure, and the correlation of some other main parameters.

Reconstruction of human protein-coding gene functional association network based on machine learning.

Networks consisting of molecular interactions are intrinsically dynamical systems of an organism. These interactions curated in molecular interaction databases are still not complete and contain false positives introduced by high-throughput screening experiments. In this study, we propose a framework to integrate interactions of functional associated protein-coding genes from 31 data sources to reconstruct a network with high coverage and quality. For each interaction, 369 features were constructed including properties of both the interaction and the involved genes. The training and validation sets were built on the pathway interactions as positives and the potential negative instances resulting from our proposed semi-supervised strategy. Random forest classification method was then applied to train and predict multiple times to give a score for each interaction. After setting a threshold estimated by a Binomial distribution, a Human protein-coding Gene Functional Association Network (HuGFAN) was reconstructed with 20 383 genes and 1185 429 high confidence interactions. Then, HuGFAN was compared with other networks from data sources with respect to network properties, suggesting that HuGFAN is more function and pathway related. Finally, HuGFAN was applied to identify cancer driver through two famous network-based methods (DriverNet and HotNet2) to show its outstanding performance compared with other networks. HuGFAN and other supplementary files are freely available at https://github.com/xthuang226/HuGFAN.

Assessing site-specific PEGylation of TEM-1 β-lactamase with cell-free protein synthesis and coarse-grained simulation

PEGylation is a broadly used strategy to enhance the pharmacokinetic properties of therapeutic proteins. It is well established that the location and extent of PEGylation have a significant impact on protein properties. However, conventional PEGylation techniques have limited control over PEGylation sites. Emerging site-specific PEGylation technology provides control of PEG placement by conjugating PEG polymers via click chemistry reaction to genetically encoded non-canonical amino acids. Unfortunately, a method to rapidly determine the optimal PEGylation location has yet to be established. Here we seek to address this challenge. In this work, coarse-grained molecular dynamic simulations are paired with high-throughput experimental screening utilizing cell-free protein synthesis to investigate the effect of site-specific PEGylation on the two-state folder protein TEM-1 β-lactamase. Specifically, the conjugation efficiency, thermal stability, and enzymatic activity are studied for the enzyme PEGylated at several different locations. The results of this analysis confirm that the physical properties of the PEGylated protein vary considerably with PEGylation site and that traditional design recommendations are insufficient to predict favorable PEGylation sites. In this study, the best predictor of the most favorable conjugation site is coarse-grained simulation. Thus, we propose a dual combinatorial screening approach in which coarse-grained molecular simulation informs site selection for high-throughput experimental verification.

A Review on Parallel Virtual Screening Softwares for High-Performance Computers.

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein–ligand complexes (on the order of 10 to 10), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

Derin Sinir Ağları ve Klasik Makine Öğrenimi Modelleri Kullanılarak Küçük İlaç Moleküllerinin Aktivite Sınıflandırması

Objective: The main goal in the early phase of drug discovery studies is to detect small drug molecules that show activity against a specific receptor. For this purpose, small drug molecules are classified as actives or inactives by performing high-throughput screening (HTS) experiments. The datasets obtained from these experiments are uploaded to the PubChem database. This database contains more than one million bioassays that are obtained through HTS experiments. Alternatively, classification models can be developed using datasets in the PubChem database. Material and Methods: In this study, we obtained 5 datasets with different degrees of imbalance structure from the PubChem database. We trained these datasets using deep neural networks (DNN) for the classification of small drug molecules as actives or inactives. The test set performances of DNN models were compared with the support vector machines (SVM) and random forest (RF) algorithms. Results: The DNN achieved better balanced accuracy (minimum-maximum: 0.764-0.865), recall (minimum-maximum: 0.630-0.823), F1-score (minimum-maximum: 0.496-0.843) and Matthews correlation coefficient (minimum-maximum: 0.439- 0.721) compared to the SVM and RF. Conclusion: Our results showed that the DNN is a well-performed machine learning algorithm that can be in the early phase of drug discovery studies since it performs better than traditional machine learning algorithms in the case of imbalanced class structures.

Electrostatics Drive Oligomerization and Aggregation of Human Interferon Alpha-2a.

Aggregation is a common phenomenon in the field of protein therapeutics and can lead to function loss or immunogenic patient responses. Two strategies are currently used to reduce aggregation: (1) finding a suitable formulation, which is labor-intensive and requires large protein quantities, or (2) engineering the protein, which requires extensive knowledge about the protein aggregation pathway. We present a biophysical characterization of the oligomerization and aggregation processes by Interferon alpha-2a (IFNα-2a), a protein drug with antiviral and immunomodulatory properties. This study combines experimental high throughput screening with detailed investigations by small-angle X-ray scattering and analytical ultracentrifugation. Metropolis Monte Carlo simulations are used to gain insight into the underlying intermolecular interactions. IFNα-2a forms soluble oligomers that are controlled by a fast pH and concentration-dependent equilibrium. Close to the isoelectric point of 6, IFNα-2a forms insoluble aggregates which can be prevented by adding salt. We show that monomer attraction is driven mainly by molecular anisotropic dipole–dipole interactions that increase with increasing pH. Repulsion is due to monopole–monopole interactions and depends on the charge of IFNα-2a. The study highlights how combining multiple methods helps to systematically dissect the molecular mechanisms driving oligomer formation and to design ultimately efficient strategies for preventing detrimental protein aggregation.

Virtual and experimental high throughput screening of substituted hydrazones on β-Tubulin polymerization

Microtubule targeting agents that disrupt the dynamic functioning of the mitotic spindle are some of the best chemotherapeutic agents. Interruption of microtubule dynamics through polymerization or depolymerization causes cell arrest leading to apoptosis. We report a novel class of aroylhydrazones with anticancer properties. Tubulin inhibition studies were performed using both computational and biological methods. Docking and pharmacophore mapping showed efficient binding between the ligands and the protein. Tubulin inhibition assay showed the aroylhydrazones to be inhibitors of tubulin polymerization. DFT studies explains the geometrical and electronic properties of the compounds. Furthermore, anticancer studies using lung and liver cancer cell lines gave low IC50 values with the methyl substituted hydrazone MH-2 being the most potent. (IC50 of 0.0896 and 0.1040 µM respectively). The methyl group is responsible for the effective binding to the protein. Thus, a new class of tubulin binding agents have been identified as potential agents in cancer therapy.

A general strategy for high-throughput experimental screening of promising bulk thermoelectric materials

High-throughput (HTP) experiments play key roles in accelerating the discovery of advanced materials, but the HTP preparation and characterization, especially for bulk samples, are extremely difficult. In this work, we developed a novel and general strategy for HTP screening of high-performance bulk thermoelectric materials. The performed full-chain HTP experiments cover rapid synthesis of the bulk sample with quasi-continuous composition, microarea phase identification and structure analysis, and measurement of the spatial distribution of the sample composition, electrical and thermal transport properties. According to our experiments, bulk Bi2− x Sb x Te3 ( x = 1–2) and Bi2Te3− x Se x ( x = 0–1.5) samples with quasi-continuous compositions have been rapidly fabricated by this HTP method. The target thermoelectric materials with the best Sb/Bi and Te/Se ratios are successfully screened out based on subsequent HTP characterization results, demonstrating that this HTP technique is effective in speeding up the exploration of novel high-performance thermoelectric materials.

Vertical ferroelectricity in two-dimensional mixed-valence tin sulfide system: Unprecedented piezoelectricity, efficient nanogenerator and facile control of morphotoropic phase transformations

Abstract For the recently emerging two-dimensional van der Waals ferroelectrics, their promising prospects in nanoelectronic applications are hindered by their low vertical polarizations. Despite recent experimental breakthroughs and theoretical high throughput screening of material database, the obtained vertical polarizations of two-dimensional ferroelectrics are still limited. Here we propose a strategy of constructing two-dimensional mixed-valence compounds. We show first-principles evidence that Sn2S3 monolayer can be synthesized via epitaxial growth of SnS on SnS2 monolayer with degenerate mixed-valence bi-states. Such a system possesses a room-temperature robust vertical polarization higher than 10 pC/m, which can be switched via interchange of oxidation states between two layers crossing an energy-saving low barrier, either by applying an electric field or mechanical bending. If such Sn2S3 monolayer is utilized as a nanogenerator, an unprecedented alternating voltage of ~130 V can be generated by applying an oscillating driving force that repeatedly reverses its polarization. Moreover, it possesses multiple metastable phases with distinct electronic properties. The transformations between them are not only strain-tunable, but can also be controlled via external electric field or low frequency linearly polarized light. Due to the small energy difference between polar and nonpolar states with distinct thicknesses, akin to morphotoropic phases, an ultra-high piezoelectric coefficient (> 2700 pm/V) can be obtained in the phase transformation under pressure, which can be further greatly enhanced via critical doping.

Combinatorial screening of the crystal structure in Ba–Sr–Mn–Ce perovskite oxides with ABO3stoichiometry

High throughput experimental screening using combinatorial pulsed laser deposition identifies BaCexMn1−xO3–Sr1−xCexMnO3pseudo-binary oxide alloys with tetragonal perovskite structure and broad composition–temperature range of stability.

Predicting the photocurrent–composition dependence in organic solar cells

High-throughput experimental screening and machine-learning algorithms are implemented in a synergic workflow to predict the photocurrent phase space of organic photovoltaic blends. We identify accurate models employing only the materials band gaps.

Accelerating organic solar cell material's discovery: high-throughput screening and big data.

The discovery of novel high-performing materials such as non-fullerene acceptors and low band gap donor polymers underlines the steady increase of record efficiencies in organic solar cells witnessed during the past years. Nowadays, the resulting catalogue of organic photovoltaic materials is becoming unaffordably vast to be evaluated following classical experimentation methodologies: their requirements in terms of human workforce time and resources are prohibitively high, which slows momentum to the evolution of the organic photovoltaic technology. As a result, high-throughput experimental and computational methodologies are fostered to leverage their inherently high exploratory paces and accelerate novel materials discovery. In this review, we present some of the computational (pre)screening approaches performed prior to experimentation to select the most promising molecular candidates from the available materials libraries or, alternatively, generate molecules beyond human intuition. Then, we outline the main high-throuhgput experimental screening and characterization approaches with application in organic solar cells, namely those based on lateral parametric gradients (measuring-intensive) and on automated device prototyping (fabrication-intensive). In both cases, experimental datasets are generated at unbeatable paces, which notably enhance big data readiness. Herein, machine-learning algorithms find a rewarding application niche to retrieve quantitative structure–activity relationships and extract molecular design rationale, which are expected to keep the material's discovery pace up in organic photovoltaics.

1233. Identification of Novel Inhibitors of Clostridioides difficile Enoyl-Reductase II (FabK) by High-Throughput Virtual and Experimental Screening

Background Clostridioides difficile is one of only three bacteria categorized as an ‘urgent’ drug-resistant threat by the CDC. This pathogen is responsible for nearly 500,000 hospital-acquired infections and 29,000 deaths per year at a cost of nearly $4.8 billion. There is a critical need for the identification of novel, anti-difficile agents with narrow-spectrum activity that can spare the human microbiome. We previously reported the essentiality of the FAS-II enzyme, enoyl-ACP reductase (FabK) in C. difficile, and the narrow-spectrum activity of a series of phenylimidazole inhibitors. We present here experimental and virtual compound screening studies that identified novel FabK inhibitors with sub-micromolar activity and follow-up SAR and structural studies.MethodsUsing a novel luminescence assay, 20K diverse compounds from the St. Jude drug-like and lead-like libraries were screened. In parallel, a ligand-based virtual screen was performed against 2.4 million lead- and drug-like compounds from commercial libraries. Hit compounds were confirmed using an orthogonal fluorescence-intensity assay and SAR studies were performed by testing commercially available hit analogs. The most potent inhibitor was advanced into co-crystallography structural studies.ResultsThe compound screening campaigns resulted in the identification of several confirmed hit compounds with low to sub-micromolar activity and novel scaffolds relative to the known phenylimidazole inhibitors. Importantly, the first confirmed nanomolar inhibitor of C. difficile FabK was identified and validated with an IC50 of 0.35 μM. SAR studies of the hits along with a high-resolution co-crystal structure have allowed new insights into the key binding determinants and structural requirements for activity as well as the structural requirements for species specificity.ConclusionThe C. difficile FabK enzyme offers a promising narrow-spectrum drug target that can potentially spare the human microbiome. The known activity of the phenylimidazole inhibitor series supports the druggability of this target. The newly discovered inhibitor class presented here further demonstrates the potential of FabK inhibition and will facilitate the development of clinically relevant, narrow-spectrum anti-difficile agents.Disclosures All Authors: No reported disclosures

A Validated "Pool and Split" Approach to Screening and Optimization of Copper-Catalyzed C-N Cross-Coupling Reactions.

A general method for the quick identification of effective catalytic systems for copper-catalyzed C-N cross-couplings is described. This is based on evaluating mixtures of copper sources, ancillary ligands, and bases in different solvents followed by two deconvolution procedures, which aim at identifying the most proper reagent combination in only three distinct steps. Despite being a high-throughput approach in nature, the proposed method utilizes only frugal technological platforms such as 24-well microplates while offering a screening efficiency-the number of executed experiments vs the total number of possible experiments-higher than 95%. To facilitate visualization and mining of the high-throughput experimentation (HTE) data, Visual Basic scripts have been developed, which allow streamlining the extraction of raw HPLC data into TIBCO Spotfire for the graphical display in the form of pie charts. The unique capabilities of this "pool and split" approach have been demonstrated by applying it to literature known cross-coupling reactions. In every case, the described experimental setup was validated by retrieving the original literature conditions in addition to exposing several additional solutions with a minimum number of parallel experiments. Finally, examples are provided for the successful application of this HTE screening workflow to internal projects.

The HMOX1 Pathway as a Promising Target for the Treatment and Prevention of SARS-CoV-2 of 2019 (COVID-19).

The coronavirus disease of 2019 (COVID-19) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global pandemic with increasing incidence and mortality rates. Recent evidence based on the cytokine profiles of severe COVID-19 cases suggests an overstimulation of macrophages and monocytes associated with reduced T-cell abundance (lymphopenia) in patients infected with SARS-CoV-2. The SARS-CoV-2 open reading frame 3 a (ORF3a) protein was found to bind to the human HMOX1 protein at a high confidence through high-throughput screening experiments. The HMOX1 pathway can inhibit platelet aggregation, and can have anti-thrombotic and anti-inflammatory properties, amongst others, all of which are critical medical conditions observed in COVID-19 patients. Here, we review the potential of modulating the HMOX1-ORF3a nexus to regulate the innate immune response for therapeutic benefits in COVID-19 patients. We also review other potential treatment strategies and suggest novel synthetic and natural compounds that may have the potential for future development in clinic.