High-throughput screening: new technology for the 21st century

Coupling Droplet Microfluidics with Mass Spectrometry for Ultrahigh-Throughput Analysis of Complex Mixtures up to and above 30 Hz

High- and ultrahigh-throughput label-free sample analysis is required by many applications, extending from environmental monitoring to drug discovery and industrial biotechnology. HTS methods predominantly are based on a targeted workflow, which can limit their scope. Mass spectrometry readily provides chemical identity and abundance for complex mixtures, and here, we use microdroplet generation microfluidics to supply picoliter aliquots for analysis at rates up to and including 33 Hz. This is demonstrated for small molecules, peptides, and proteins up to 66 kDa on three commercially available mass spectrometers from salty solutions to mimic cellular environments. Designs for chip-based interfaces that permit this coupling are presented, and the merits and challenges of these interfaces are discussed. On an Orbitrap platform droplet infusion rates of 6 Hz are used for analysis of cytochrome c, on a DTIMS Q-TOF similar rates were obtained, and on a TWIMS Q-TOF utilizing IM-MS software rates up to 33 Hz are demonstrated. The potential of this approach is demonstrated with proof of concept experiments on crude mixtures including egg white, unpurified recombinant protein, and a biotransformation supernatant.

Phenotypic Screening in C. elegans as a Tool for the Discovery of New Geroprotective Drugs

Population aging is one of the largest challenges of the 21st century. As more people live to advanced ages, the prevalence of age-related diseases and disabilities will increase placing an ever larger burden on our healthcare system. A potential solution to this conundrum is to develop treatments that prevent, delay or reduce the severity of age-related diseases by decreasing the rate of the aging process. This ambition has been accomplished in model organisms through dietary, genetic and pharmacological interventions. The pharmacological approaches hold the greatest opportunity for successful translation to the clinic. The discovery of such pharmacological interventions in aging requires high-throughput screening strategies. However, the majority of screens performed for geroprotective drugs in C. elegans so far are rather low throughput. Therefore, the development of high-throughput screening strategies is of utmost importance.

Competing Roles of Two Kinds of Ligand during Nonclassical Crystallization of Pillared-Layer Metal-Organic Frameworks Elucidated Using Microfluidic Systems

To diversify metal-organic frameworks (MOFs), multi-component MOFs constructed from more than two kinds of bridging ligand have been actively investigated due to the high degree of design freedom afforded by the combination of multiple ligands. Predicting the synthesis conditions for such MOFs requires an understanding of the crystallization mechanism, which has so far remained elusive. In this context, microflow systems are efficient tools for capturing non-equilibrium states as they facilitate precise and efficient mixing with reaction times that correspond to the distance from the mixing point, thus enabling reliable control of non-equilibrium crystallization processes. Herein, we prepared coordination polymers with pillared-layer structures and observed the intermediates in the syntheses with an in-situ measurement system that combines microflow reaction with UV/Vis and X-ray absorption fine-structure spectroscopies, thereby enabling their rapid nucleation to be monitored. Based on the results, a three-step nonclassical nucleation mechanism involving two kinds of intermediate is proposed.

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping

Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such datasets are either application-specific or technically complex and error-prone. Here, we introduce DNA-based phenotypic recording as a widely applicable, practicable approach to generate large-scale sequence-function datasets. We use a site-specific recombinase to directly record a GRE's effect in DNA, enabling readout of both sequence and quantitative function for extremely large GRE-sets via next-generation sequencing. We record translation kinetics of over 300,000 bacterial ribosome binding sites (RBSs) in >2.7 million sequence-function pairs in a single experiment. Further, we introduce a deep learning approach employing ensembling and uncertainty modelling that predicts RBS function with high accuracy, outperforming state-of-the-art methods. DNA-based phenotypic recording combined with deep learning represents a major advance in our ability to predict function from genetic sequence.

Bioentity2vec: Attribute- and behavior-driven representation for predicting multi-type relationships between bioentities

BACKGROUND

The explosive growth of genomic, chemical, and pathological data provides new opportunities and challenges for humans to thoroughly understand life activities in cells. However, there exist few computational models that aggregate various bioentities to comprehensively reveal the physical and functional landscape of biological systems.

RESULTS

We constructed a molecular association network, which contains 18 edges (relationships) between 8 nodes (bioentities). Based on this, we propose Bioentity2vec, a new method for representing bioentities, which integrates information about the attributes and behaviors of a bioentity. Applying the random forest classifier, we achieved promising performance on 18 relationships, with an area under the curve of 0.9608 and an area under the precision-recall curve of 0.9572.

CONCLUSIONS

Our study shows that constructing a network with rich topological and biological information is important for systematic understanding of the biological landscape at the molecular level. Our results show that Bioentity2vec can effectively represent biological entities and provides easily distinguishable information about classification tasks. Our method is also able to simultaneously predict relationships between single types and multiple types, which will accelerate progress in biological experimental research and industrial product development.

Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery

BACKGROUND

In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases is crucial. To the best of our knowledge, this is the first systematic review on this issue.

OBJECTIVE

The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review.

RESULTS

Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced.

CONCLUSION

In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure.

Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization

High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures.

Weighted Gene Co-expression Network Analysis (WGCNA) Reveals the Hub Role of Protein Ubiquitination in the Acquisition of Desiccation Tolerance in Boea hygrometrica

Boea hygrometrica can survive extreme drought conditions and has been used as a model to study desiccation tolerance. A genome-wide transcriptome analysis of B. hygrometrica showed that the plant can survive rapid air-drying after experiencing a slow soil-drying acclimation phase. In addition, a weighted gene co-expression network analysis was used to study the transcriptomic datasets. A network comprising 22 modules was constructed, and seven modules were found to be significantly related to desiccation response using an enrichment analysis. Protein ubiquitination was observed to be a common process linked to hub genes in all the seven modules. Ubiquitin-modified proteins with diversified functions were identified using immunoprecipitation coupled with mass spectrometry. The lowest level of ubiquitination was noted at the full soil drying priming stage, which coincided the accumulation of dehydration-responsive gene BhLEA2. The highly conserved RY motif (CATGCA) was identified from the promoters of ubiquitin-related genes that were downregulated in the desiccated samples. An in silico gene expression analysis showed that the negative regulation of ubiquitin-related genes is potentially mediated via a B3 domain-containing transcription repressor VAL1. This study suggests that priming may involve the transcriptional regulation of several major processes, and the transcriptional regulation of genes in protein ubiquitination may play a hub role to deliver acclimation signals to posttranslational level in the acquisition of desiccation tolerance in B. hygrometrica.

Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research

In recent years, the development of high-throughput screening (HTS) technologies and their establishment in an industrialized environment have given scientists the possibility to test millions of molecules and profile them against a multitude of biological targets in a short period of time, generating data in a much faster pace and with a higher quality than before. Besides the structure activity data from traditional bioassays, more complex assays such as transcriptomics profiling or imaging have also been established as routine profiling experiments thanks to the advancement of Next Generation Sequencing or automated microscopy technologies. In industrial pharmaceutical research, these technologies are typically established in conjunction with automated platforms in order to enable efficient handling of screening collections of thousands to millions of compounds. To exploit the ever-growing amount of data that are generated by these approaches, computational techniques are constantly evolving. In this regard, artificial intelligence technologies such as deep learning and machine learning methods play a key role in cheminformatics and bio-image analytics fields to address activity prediction, scaffold hopping, de novo molecule design, reaction/retrosynthesis predictions, or high content screening analysis. Herein we summarize the current state of analyzing large-scale compound data in industrial pharmaceutical research and describe the impact it has had on the drug discovery process over the last two decades, with a specific focus on deep-learning technologies.

Fluorescence anisotropy imaging in drug discovery

Non-invasive measurement of drug-target engagement can provide critical insights in the molecular pharmacology of small molecule drugs. Fluorescence polarization/fluorescence anisotropy measurements are commonly employed in protein/cell screening assays. However, the expansion of such measurements to the in vivo setting has proven difficult until recently. With the advent of high-resolution fluorescence anisotropy microscopy it is now possible to perform kinetic measurements of intracellular drug distribution and target engagement in commonly used mouse models. In this review we discuss the background, current advances and future perspectives in intravital fluorescence anisotropy measurements to derive pharmacokinetic and pharmacodynamic measurements in single cells and whole organs.

Parameter Optimization in High-Throughput Testing for Structural Materials

High-throughput screenings are established evaluation methods in the development of functional materials and pharmaceutical active ingredients. The transfer of this approach to the development of structural materials requires extensive adaptations. In addition to the investigation of new test procedures for the determination of material properties and the treatment of metallic materials, the design of experiments is a research focus. Based on given descriptor target values, the statistical design of experiments determines investigations and treatments for the investigation of these materials. In this context, process parameters also have to be determined, as these have a major influence on the later material properties, especially during the treatment of samples. In this article, a method is presented which determines the process parameters iteratively. The validation of the calculated process parameters takes place based on differential scanning calorimetry used as the furnace for the heat treatment of small batches and particle-oriented peening as the characterization method.

Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies

A significant challenge in high-throughput screening (HTS) campaigns is the identification of assay technology interference compounds. A Compound Interfering with an Assay Technology (CIAT) gives false readouts in many assays. CIATs are often considered viable hits and investigated in follow-up studies, thus impeding research and wasting resources. In this study, we developed a machine-learning (ML) model to predict CIATs for three assay technologies. The model was trained on known CIATs and non-CIATs (NCIATs) identified in artefact assays and described by their 2D structural descriptors. Usual methods identifying CIATs are based on statistical analysis of historical primary screening data and do not consider experimental assays identifying CIATs. Our results show successful prediction of CIATs for existing and novel compounds and provide a complementary and wider set of predicted CIATs compared to BSF, a published structure-independent model, and to the PAINS substructural filters. Our analysis is an example of how well-curated datasets can provide powerful predictive models despite their relatively small size.

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

High-Throughput Approaches in Carbohydrate-Active Enzymology: Glycosidase and Glycosyl Transferase Inhibitors, Evolution, and Discovery

Carbohydrates are attached and removed in living systems through the action of carbohydrate-active enzymes such as glycosyl transferases and glycoside hydrolases. The molecules resulting from these enzymes have many important roles in organisms, such as cellular communication, structural support, and energy metabolism. In general, each carbohydrate transformation requires a separate catalyst, and so these enzyme families are extremely diverse. To make this diversity manageable, high-throughput approaches look at many enzymes at once. Similarly, high-throughput approaches can be a powerful way of finding inhibitors that can be used to tune the reactivity of these enzymes, either in an industrial, a laboratory, or a medicinal setting. In this review, we provide an overview of how these enzymes and inhibitors can be sought using techniques such as high-throughput natural product and combinatorial library screening, phage and mRNA display of (glyco)peptides, fluorescence-activated cell sorting, and metagenomics.

A novel D(-)-lactic acid-inducible promoter regulated by the GntR-family protein D-LldR of Pseudomonas fluorescens

Lactic acid has two stereoisomers of D(-)- and L(+)-forms, both of which are important monomers of biodegradable plastic, poly-lactic acid. In this study, a novel d-lactate inducible system was identified in Pseudomonas fluorescens A506, partially characterized and tested as biosensor. The d-lactate catabolic operon (lldP-dld-II) was negatively regulated through the inversely transcribed D-lldR (encoding a GntR-type regulator), where the repression is relieved by addition of d-lactate. The derepression was specific to d-lactate and marginally affected by l-lactate. The D-LldR-responsive operator, showing dyad symmetry and separated by one base, was located between +11 and + 27 from the transcription start site of the lldP-dld-II operon. By site-directed mutagenesis, a motif with a dyad symmetry (AATTGGTAtTACCAATT), present in the upstream region of lldP, was identified as essential for the binding of LldR. d-lactate biosensors were developed by connecting the upregulation by d-lactate to a green fluorescent readout. About ~6.0-fold induction by 100 mM d-lactate was observed compared to l-lactate.

X-ray Nanocrystal Scintillator-Based Aptasensor for Autofluorescence-Free Detection

Optical biosensors that enable highly sensitive detection of biomolecules are useful for applications in early disease diagnosis. However, the presence of UV-vis-induced background fluorescence in biological samples is still challenging. Thanks to the weak scattering and nearly no absorption of biological chromophores under X-ray excitation, we describe the development of an X-ray nanocrystal scintillator-based aptasensor that is able to achieve sensitive and homogeneous detection of target biomolecules. In this work, aptamer-labeled lanthanide-doped nanocrystal scintillators was designed to rapidly and sensitively detect lysozyme via fluorescence resonance energy transfer (FRET) in human serum samples. Benefiting from the use of low-dose X-ray as an excitation source and high-efficiency luminescence of heavy atoms-contained nanocrystals, the proposed X-ray nanocrystal scintillator-based aptasensor can readily detect lysozyme with a high sensitivity up to 0.94 nM, as well as an excellent specificity and sample recoveries. Thus, our technique suggests that the X-ray scintillating aptasensor can create a new generation of autofluorescence-free high-sensitivity strategy for biomarker sensing in biomedical applications.

Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control

During process development, the experimental search space is defined by the number of experiments that can be performed in specific time frames but also by its sophistication (e.g., inputs, sensors, sampling frequency, analytics). High-throughput liquid-handling stations can perform a large number of automated experiments in parallel. Nevertheless, the experimental data sets that are obtained are not always relevant for development of industrial bioprocesses, leading to a high rate of failure during scale-up. We present an automated mini bioreactor platform that enables parallel cultivations in the milliliter scale with online monitoring and control, well-controlled conditions, and advanced feeding strategies similar to industrial processes. The combination of two liquid handlers allows both automated mini bioreactor operation and at-line analysis in parallel. A central database enables end-to-end data exchange and fully integrated device and process control. A model-based operation algorithm allows for the accurate performance of complex cultivations for scale-down studies and strain characterization via optimal experimental redesign, significantly increasing the reliability and transferability of data throughout process development. The platform meets the tradeoff between experimental throughput and process control and monitoring comparable to laboratory-scale bioreactors.

Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions

Linking in vitro bioactivity and in vivo toxicity on a dose basis enables the use of high-throughput in vitro assays as an alternative to traditional animal studies. In this study, we evaluated assumptions in the use of a high-throughput, physiologically based toxicokinetic (PBTK) model to relate in vitro bioactivity and rat in vivo toxicity data. The fraction unbound in plasma (f_up) and intrinsic hepatic clearance (Cl_int) were measured for rats (for 67 and 77 chemicals, respectively), combined with f_up and Cl_int literature data for 97 chemicals, and incorporated in the PBTK model. Of these chemicals, 84 had corresponding in vitro ToxCast bioactivity data and in vivo toxicity data. For each possible comparison of in vitro and in vivo endpoint, the concordance between the in vivo and in vitro data was evaluated by a regression analysis. For a base set of assumptions, the PBTK results were more frequently better associated than either the results from a “random” model parameterization or direct comparison of the “untransformed” values of AC₅₀ and dose (performed best in 51%, 28%, and 21% of cases, respectively). We also investigated several assumptions in the application of PBTK for IVIVE, including clearance and internal dose selection. One of the better assumptions sets–restrictive clearance and comparing free in vivo venous plasma concentration with free in vitro concentration–outperformed the random and untransformed results in 71% of the in vitro-in vivo endpoint comparisons. These results demonstrate that applying PBTK improves our ability to observe the association between in vitro bioactivity and in vivo toxicity data in general. This suggests that potency values from in vitro screening should be transformed using in vitro-in vivo extrapolation (IVIVE) to build potentially better machine learning and other statistical models for predicting in vivo toxicity in humans.

High throughput screening of complex biological samples with mass spectrometry – from bulk measurements to single cell analysis

We review the state of the art in HTS using mass spectrometry with minimal sample preparation from complex biological matrices. We focus on industrial and biotechnological applications.

Catalyst discovery through megalibraries of nanomaterials

The nanomaterial landscape is so vast that a high-throughput combinatorial approach is required to understand structure-function relationships. To address this challenge, an approach for the synthesis and screening of megalibraries of unique nanoscale features (>10,000,000) with tailorable location, size, and composition has been developed. Polymer pen lithography, a parallel lithographic technique, is combined with an ink spray-coating method to create pen arrays, where each pen has a different but deliberately chosen quantity and composition of ink. With this technique, gradients of Au-Cu bimetallic nanoparticles have been synthesized and then screened for activity by in situ Raman spectroscopy with respect to single-walled carbon nanotube (SWNT) growth. Au₃Cu, a composition not previously known to catalyze SWNT growth, has been identified as the most active composition.

Screening the Marker Components in Psoralea corylifolia L. with the Aids of Spectrum-Effect Relationship and Component Knock-Out by UPLC-MS²

Psoralea corylifolia L., (P. corylifolia), which is used for treating vitiligo in clinic, shows inhibitory and activating effects on tyrosinase, a rate-limiting enzyme of melanogenesis. This study aimed to determine the active ingredients in the ethenal extracts of P. corylifolia on tyrosinase activity. The spectrum-effect relationship and knock-out method were established to predict the active compounds. Their structures were then identified with the high resolution mass spectra. A high performance liquid chromatography method was established to obtain the specific chromatograms. Tyrosinase activity in vitro was assayed by the method of oxidation rate of levodopa. Partial least squares method was used to test the spectrum-effect relationships. Chromatographic peaks P2, P4, P9, P10, P11, P13, P21, P26, P28, and P30 were positively related to the activating effects on tyrosinase activity in PE, whereas chromatographic peaks P1, P3, P6, P14, P16, P19, P22, and P29 were negatively related to the activating effects on tyrosinase in the P. corylifolia (PEs). When the sample concentration was 0.5 g·mL⁻¹, equal to the amount of raw medicinal herbs, the target components were daidzein (P2), psoralen (P5), neobavaisoflavone (P13), and psoralidin (P20), which were consistent with the results of spectrum-effect relationships.

Microfluidics within a well: an injection-molded plastic array 3D culture platform

Polydimethylsiloxane (PDMS) has been widely used in fabricating microfluidic devices for prototyping and proof-of-concept experiments. Due to several material limitations, PDMS has not been widely adopted for commercial applications that require large-scale production. This paper describes a novel injection-molded plastic array 3D culture (IMPACT) platform that incorporates a microfluidic design to integrate patterned 3D cell cultures within a single 96-well (diameter = 9 mm) plate. Cell containing gels can be sequentially patterned by capillary-guided flow along the corner and narrow gaps designed within the 96-well form factor. Compared to PDMS-based hydrophobic burst valve designs, this work utilizes hydrophilic liquid guides to obtain rapid and reproducible patterned gels for co-cultures. When a liquid droplet (i.e. cell containing fibrin or collagen gel) is placed on a corner, spontaneous patterning is achieved within 1 second. Optimal dimensionless parameters required for successful capillary loading have been determined. To demonstrate the utility of the platform for 3D co-culture, angiogenesis experiments were performed by patterning HUVEC (human umbilical endothelial cells) and LF (lung fibroblasts) embedded in 3D fibrin gels. The angiogenic sprouts (with open lumen tip cells expressing junctional proteins) are comparable to those observed in PDMS based devices. The IMPACT device has the potential to provide a robust high-throughput experimental platform for vascularized microphysiological systems.

Enzymes in removal of pharmaceuticals from wastewater: A critical review of challenges, applications and screening methods for their selection

At present, the removal of trace organic chemicals such as pharmaceuticals in wastewater treatment plants is often incomplete resulting in a continuous discharge into the aqueous environment. To overcome this issue, bioremediation approaches gained significant importance in recent times, since they might have a lower carbon footprint than chemical or physical treatment methods. In this context, enzyme-based technologies represent a promising alternative since they are able to specifically target certain chemicals. For this purpose, versatile monitoring of enzymatic reactions is of great importance in order to understand underlying transformation mechanisms and estimate the suitability of various enzymes exhibiting different specificities for bioremediation purposes. This study provides a comprehensive review, summarizing research on enzymatic transformation of pharmaceuticals in water treatment applications using traditional and state-of-the-art enzyme screening approaches with a special focus on mass spectrometry (MS)-based and high-throughput tools. MS-based enzyme screening represents an approach that allows a comprehensive mechanistic understanding of enzymatic reactions and, in particular, the identification of transformation products. A critical discussion of these approaches for implementation in wastewater treatment processes is also presented. So far, there are still major gaps between laboratory- and field-scale research that need to be overcome in order to assess the viability for real applications.

Accelerating High-Throughput Screening for Structural Materials with Production Management Methods

High-throughput screenings are widely accepted for pharmaceutical developments for new substances and the development of new drugs with required characteristics by evolutionary studies. Current research projects transfer this principle of high-throughput testing to the development of metallic materials. In addition to new generating and testing methods, these types of high-throughput systems need a logistical control and handling method to reduce throughput time to get test results faster. Instead of the direct material flow found in classical high-throughput screenings, these systems have a very complex structure of material flow. The result is a highly dynamic system that includes short-term changes such as rerun stations, partial tests, and temporarily paced sequences between working systems. This paper presents a framework that divides the actions for system acceleration into three main sections. First, methods for special applications in high-throughput systems are designed or adapted to speed up the generation, treatment, and testing processes. Second, methods are needed to process trial plans and to control test orders, which can efficiently reduce waiting times. The third part of the framework describes procedures for handling samples. This reduces non-productive times and reduces order processing in individual lots.

Discovery of Novel Small-Molecule Inhibitors of NF-κB Signaling with Antiinflammatory and Anticancer Properties

Excessive NF-κB activation contributes to the pathogenesis of numerous diseases. Small-molecule inhibitors of NF-κB signaling have significant therapeutic potential especially in treating inflammatory diseases and cancers. In this study, we performed a cell-based high-throughput screening to discover novel agents capable of inhibiting NF-κB signaling. On the basis of two hit scaffolds from the screening, we synthesized 69 derivatives to optimize the potency for inhibition of NF-κB activation, leading to successful discovery of the most potent compound Z9j with over 170-fold enhancement of inhibitory activity. Preliminary mechanistic studies revealed that Z9j inhibited NF-κB signaling via suppression of Src/Syk, PI3K/Akt, and IKK/IκB pathways. This novel compound also demonstrated antiinflammatory and anticancer activities, warranting its further development as a potential multifunctional agent to treat inflammatory diseases and cancers.

Complementary, Semiautomated Methods for Creating Multidimensional PEG-Based Biomaterials

Tunable biomaterials that mimic selected features of the extracellular matrix (ECM) such as its stiffness, protein composition, and dimensionality are increasingly popular for studying how cells sense and respond to ECM cues. In the field, there exists a significant trade-off for how complex and how well these biomaterials represent the in vivo microenvironment versus how easy they are to make and how adaptable they are to automated fabrication techniques. To address this need to integrate more complex biomaterials design with high-throughput screening approaches, we present several methods to fabricate synthetic biomaterials in 96-well plates and demonstrate that they can be adapted to semiautomated liquid handling robotics. These platforms include (1) glass bottom plates with covalently attached ECM proteins and (2) hydrogels with tunable stiffness and protein composition with either cells seeded on the surface or (3) laden within the three-dimensional hydrogel matrix. This study includes proof-of-concept results demonstrating control over breast cancer cell line phenotypes via these ECM cues in a semiautomated fashion. We foresee the use of these methods as a mechanism to bridge the gap between high-throughput cell-matrix screening and engineered ECM-mimicking biomaterials.

Program-Guided Design of High-Throughput Enzyme Screening Experiments and Automated Data Analysis/Evaluation

The open source LARA software suite is designed for guiding manual or automated high-throughput screening experiments. Process planning, data reading, analysis, and visualization are herein explained in a step-by-step guide using exemplary dataset.

Identification of New Activators of Mitochondrial Fusion Reveals a Link between Mitochondrial Morphology and Pyrimidine Metabolism

Mitochondria are dynamic organelles that produce most of the cellular ATP, and are involved in many other cellular functions such as Ca²⁺ signaling, differentiation, apoptosis, cell cycle, and cell growth. One key process of mitochondrial dynamics is mitochondrial fusion, which is catalyzed by mitofusins (MFN1 and MFN2) and OPA1. The outer mitochondrial membrane protein MFN2 plays a relevant role in the maintenance of mitochondrial metabolism, insulin signaling, and mutations that cause neurodegenerative disorders. Therefore, modulation of proteins involved in mitochondrial dynamics has emerged as a potential pharmacological strategy. Here, we report the identification of small molecules by high-throughput screen that promote mitochondrial elongation in an MFN1/MFN2-dependent manner. Detailed analysis of their mode of action reveals a previously unknown connection between pyrimidine metabolism and mitochondrial dynamics. Our data indicate a link between pyrimidine biosynthesis and mitochondrial dynamics, which maintains cell survival under stress conditions characterized by loss of pyrimidine synthesis.

Spectrum Effect Relationship and Component Knock-Out in Angelica Dahurica Radix by High Performance Liquid Chromatography-Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer

Different extracts of Angelica dahuricae were available for whitening or treating vitiligo clinically. They showed inhibitory or activating effects on tyrosinase, a rate-limiting enzyme of melanogenesis. This study aimed to identify active compounds on tyrosinase in water extract of Angelica dahurica Radix. We applied spectrum-effect relationship and component knock-out methods to make it clear. HPLC was used to obtain the specific chromatograms. The effects on tyrosinase activity were examined by measuring the oxidation rate of levodopa in vitro. Partial least squares method was used to examine the spectrum-effect relationships. The knocked-out samples were prepared by HPLC method, and the identification of knocked-out compounds was conducted by the high performance liquid chromatography-four stage rod-electrostatic field orbit trap high resolution mass spectrometry. Results showed that S6, S14, S18, S21, S35, S36, S37, S40, and S41 were positively correlated to inhibitory activity of Angelica dahuricae on tyrosinase whereas S9, S11, S8, S12, S22, and S30 were negatively correlated. When the concentration of each sample was 1 g·mL^-1, equal to the amount of raw medicinal herbs, oxypeucedanin hydrate, imperatorin, cnidilin, and isoimperatorin had inhibitory effects on tyrosinase activity whereas byakangelicin and bergapten had activating effects.

Droplet control technologies for microfluidic high throughput screening (μHTS)

The transition from micro well plate and robotics based high throughput screening (HTS) to chip based screening has already started. This transition promises reduced droplet volumes thereby decreasing the amount of fluids used in these studies. Moreover, it significantly boosts throughput allowing screening to keep pace with the overwhelming number of molecular targets being discovered. In this review, we analyse state-of-the-art droplet control technologies that exhibit potential to be used in this new generation of screening devices. Since these systems are enclosed and usually planar, even some of the straightforward methods used in traditional HTS such as pipetting and reading can prove challenging to replicate in microfluidic high throughput screening (μHTS). We critically review the technologies developed for this purpose in depth, describing the underlying physics and discussing the future outlooks.

Isoprenyl carboxyl methyltransferase inhibitors: a brief review including recent patents

Among the enzymes involved in the post-translational modification of Ras, isoprenyl carboxyl methyltransferase (ICMT) has been explored by a number of researchers as a significant enzyme controlling the activation of Ras. Indeed, inhibition of ICMT exhibited promising anti-cancer activity against various cancer cell lines. This paper reviews patents and research articles published between 2009 and 2016 that reported inhibitors of ICMT as potential chemotherapeutic agents targeting Ras-induced growth factor signaling. Since ICMT inhibitors can modulate Ras signaling pathway, it might be possible to develop a new class of anti-cancer drugs targeting Ras-related cancers. Researchers have discovered indole-based small-molecular ICMT inhibitors through high-throughput screening. Researchers at Duke University identified a prototypical inhibitor, cysmethynil. At Singapore University, Ramanujulu and his colleagues patented more potent compounds by optimizing cysmethynil. In addition, Rodriguez and Stevenson at Universidad Complutense De Madrid and Cancer Therapeutics CRC PTY Ltd., respectively, have developed inhibitors based on formulas other than the indole base. However, further optimization of chemicals targeted to functional groups is needed to improve the characteristics of ICMT inhibitors related to their application as drugs, such as solubility, effectiveness, and safety, to facilitate clinical use.

Magnetic sensing platform technologies for biomedical applications

Detection and quantification of a variety of micro- and nanoscale entities, e.g. molecules, cells, and particles, are crucial components of modern biomedical research, in which biosensing platform technologies play a vital role. Confronted with the drastic global demographic changes, future biomedical research entails continuous development of new-generation biosensing platforms targeting even lower costs, more compactness, and higher throughput, sensitivity and selectivity. Among a wide choice of fundamental biosensing principles, magnetic sensing technologies enabled by magnetic field sensors and magnetic particles offer attractive advantages. The key features of a magnetic sensing format include the use of commercially available magnetic field sensing elements, e.g. magnetoresistive sensors which bear huge potential for compact integration, a magnetic field sensing mechanism which is free from interference by complex biomedical samples, and an additional degree of freedom for the on-chip handling of biochemical species rendered by magnetic labels. In this review, we highlight the historical basis, routes, recent advances and applications of magnetic biosensing platform technologies based on magnetoresistive sensors.

Fluorescent Neuraminidase Assay Based on Supramolecular Dye Capture After Enzymatic Cleavage

A conceptually new type of enzymatic cleavage assay is reported that utilizes in situ supramolecular capture of the fluorescent product. A squaraine-derived substrate with large blocking groups at each end of its structure cannot be threaded by a tetralactam macrocycle until the blocking groups are removed by enzyme cleavage. A prototype design responds to viral neuraminidase, an indicator of influenza infection, and also measures susceptibility of the sample to neuraminidase inhibitor drugs. The substrate structure incorporates three key features: (a) a bis(4-amino-3-hydroxyphenyl)squaraine core with bright deep-red fluorescence and excellent photostability, (b) an N-methyl group at each end of the squaraine core that ensures fast macrocycle threading kinetics, and (c) sialic acid blocking groups that prevent macrocycle threading until they are removed by viral neuraminidase. The enzyme assay can be conducted in aqueous solution where dramatic colorimetric and fluorescence changes are easily observed by the naked eye. Alternatively, affinity capture beads coated with macrocycle can be used to immobilize the liberated squaraine and enable a range of heterogeneous analysis options. With further optimization, this new type of neuraminidase assay may be useful in a point of care clinic to rapidly diagnose influenza infection and also determine which of the approved antiviral inhibitor drugs is likely to be the most effective treatment for an individual patient. The assay design is generalizable and can be readily modified to monitor virtually any type of enzyme-catalyzed cleavage reaction.

High content analysis in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating disease characterized by the progressive loss of motor neurons. Neurons, astrocytes, oligodendrocytes and microglial cells all undergo pathological modifications in the onset and progression of ALS. A number of genes involved in the etiopathology of the disease have been identified, but a complete understanding of the molecular mechanisms of ALS has yet to be determined. Currently, people affected by ALS have a life expectancy of only two to five years from diagnosis. The search for a treatment has been slow and mostly unsuccessful, leaving patients in desperate need of better therapies. Until recently, most pre-clinical studies utilized the available ALS animal models. In the past years, the development of new protocols for isolation of patient cells and differentiation into relevant cell types has provided new tools to model ALS, potentially more relevant to the disease itself as they directly come from patients. The use of stem cells is showing promise to facilitate ALS research by expanding our understanding of the disease and help to identify potential new therapeutic targets and therapies to help patients. Advancements in high content analysis (HCA) have the power to contribute to move ALS research forward by combining automated image acquisition along with digital image analysis. With modern HCA machines it is possible, in a period of just a few hours, to observe changes in morphology and survival of cells, under the stimulation of hundreds, if not thousands of drugs and compounds. In this article, we will summarize the major molecular and cellular hallmarks of ALS, describe the advancements provided by the in vitro models developed in the last few years, and review the studies that have applied HCA to the ALS field to date.

Discovery of a new Mycobacterium tuberculosis thymidylate synthase X inhibitor with a unique inhibition profile

Tuberculosis (TB), mainly caused by Mycobacterium tuberculosis (Mtb), is an infection that is responsible for roughly 1.5 million deaths per year. The situation is further complicated by the wide-spread resistance to the existing first- and second-line drugs. As a result of this, it is urgent to develop new drugs to combat the resistant bacteria as well as have lower side effects, which can promote adherence to the treatment regimens. Targeting the de novo synthesis of thymidylate (dTMP) is an important pathway to develop drugs for TB. Although Mtb carries genes for two families of thymidylate synthases (TS), ThyA and ThyX, only ThyX is essential for its normal growth. Both enzymes catalyze the conversion of uridylate (dUMP) to dTMP but employ a different catalytic approach and have different structures. Also, ThyA is the only TS found in humans. This is the rationale for identifying selective inhibitors against ThyX. We exploited the NADPH oxidation to NADP⁺ step, catalyzed by ThyX, to develop a spectrophotometric biochemical assay. Success of the assay was demonstrated by its effectiveness (average Z'=0.77) and identification of selective ThyX inhibitors. The most potent compound is a tight-binding inhibitor with an IC₅₀ of 710nM. Its mechanism of inhibition is analyzed in relation to the latest findings of ThyX mechanism and substrate and cofactor binding order.

Time-Resolved Fluorescence Spectroscopy and Fluorescence Lifetime Imaging Microscopy for Characterization of Dendritic Polymer Nanoparticles and Applications in Nanomedicine

The emerging field of nanomedicine provides new approaches for the diagnosis and treatment of diseases, for symptom relief and for monitoring of disease progression. One route of realizing this approach is through carefully constructed nanoparticles. Due to the small size inherent to the nanoparticles a proper characterization is not trivial. This review highlights the application of time-resolved fluorescence spectroscopy and fluorescence lifetime imaging microscopy (FLIM) for the analysis of nanoparticles, covering aspects ranging from molecular properties to particle detection in tissue samples. The latter technique is particularly important as FLIM allows for distinguishing of target molecules from the autofluorescent background and, due to the environmental sensitivity of the fluorescence lifetime, also offers insights into the local environment of the nanoparticle or its interactions with other biomolecules. Thus, these techniques offer highly suitable tools in the fields of particle development, such as organic chemistry, and in the fields of particle application, such as in experimental dermatology or pharmaceutical research.

Method for Identifying Small Molecule Inhibitors of the Protein-protein Interaction Between HCN1 and TRIP8b

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the excitability of neurons. The subcellular distribution of these channels in pyramidal neurons of hippocampal area CA1 is regulated by tetratricopeptide repeat-containing Rab8b interacting protein (TRIP8b), an auxiliary subunit. Genetic knockout of HCN pore forming subunits or TRIP8b, both lead to an increase in antidepressant-like behavior, suggesting that limiting the function of HCN channels may be useful as a treatment for Major Depressive Disorder (MDD). Despite significant therapeutic interest, HCN channels are also expressed in the heart, where they regulate rhythmicity. To circumvent off-target issues associated with blocking cardiac HCN channels, our lab has recently proposed targeting the protein-protein interaction between HCN and TRIP8b in order to specifically disrupt HCN channel function in the brain. TRIP8b binds to HCN pore forming subunits at two distinct interaction sites, although here the focus is on the interaction between the tetratricopeptide repeat (TPR) domains of TRIP8b and the C terminal tail of HCN1. In this protocol, an expanded description of a method for purifying TRIP8b and executing a high throughput screen to identify small molecule inhibitors of the interaction between HCN and TRIP8b, is described. The method for high throughput screening utilizes a Fluorescence Polarization (FP) -based assay to monitor the binding of a large TRIP8b fragment to a fluorophore-tagged eleven amino acid peptide corresponding to the HCN1 C terminal tail. This method allows 'hit' compounds to be identified based on the change in the polarization of emitted light. Validation assays are then performed to ensure that 'hit' compounds are not artifactual.

Synthesis, biological evaluation and molecular modeling studies of imidazo[1,2-a]pyridines derivatives as protein kinase inhibitors

We report here the synthesis, the biological evaluation and the molecular modeling studies of new imidazo[1,2-a]pyridines derivatives designed as potent kinase inhibitors. This collection was obtained from 2-aminopyridines and 2-bromoacetophenone which afforded final compound in only one step. The bioactivity of this family of new compounds was tested using protein kinase and ATP competition assays. The structure-activity relationship (SAR) revealed that six compounds inhibit DYRK1A and CLK1 at a micromolar range. Docking studies provided possible explanations that correlate with the SAR data. The most active compound 4c inhibits CLK1 (IC50 of 0.7 μM) and DYRK1A (IC50 of 2.6 μM).

High-throughput downstream process development for cell-based products using aqueous two-phase systems

As the clinical development of cell-based therapeutics has evolved immensely within the past years, downstream processing strategies become more relevant than ever. Aqueous two-phase systems (ATPS) enable the label-free, scalable, and cost-effective separation of cells, making them a promising tool for downstream processing of cell-based therapeutics. Here, we report the development of an automated robotic screening that enables high-throughput cell partitioning analysis in ATPS. We demonstrate that this setup enables fast and systematic investigation of factors influencing cell partitioning. Moreover, we examined and optimized separation conditions for the differentiable promyelocytic cell line HL-60 and used a counter-current distribution-model to investigate optimal separation conditions for a multi-stage purification process. Finally, we show that the separation of CD11b-positive and CD11b-negative HL-60 cells is possible after partial DMSO-mediated differentiation towards the granulocytic lineage. The modeling data indicate that complete peak separation is possible with 30 transfers, and >93% of CD11b-positive HL-60 cells can be recovered with >99% purity. The here described screening platform facilitates faster, cheaper, and more directed downstream process development for cell-based therapeutics and presents a powerful tool for translational research.