High-throughput Biological Assays Research Articles

Abstract 1975: Discovery of I-1000233, a potent, selective and brain permeable SHP2 allosteric inhibitor

Abstract Background: SHP2 (Src homology region 2-containing protein tyrosine phosphatase 2) is a target of interest for cancer therapy due to its key role in the regulation of RAS/MAPK signal transduction downstream of Receptor Tyrosine Kinases (RTKs). We report here the identification of I-1000233, a novel, highly potent, orally available and brain permeable SHP2 allosteric inhibitor, with potential for the treatment of tumors with dysregulated RTK/RAS/ERK signaling pathways. Material and Methods: High-throughput biological, biochemical and pharmacodynamic (PD) assays were used to inform Structure Activity Relationship studies which led to the identification of a chemical series of potent SHP2 inhibitors. Iterative optimization of physicochemical and pharmacological properties resulted in the identification of I-1000233, an orally available SHP2 inhibitor that showed excellent drug-like properties in preclinical studies. Results: I-1000233 is a potent inhibitor of the SHP2 enzyme in vitro (IC50 = 2 nM) with an exceptionally long residence time on the target. When tested in cell lines, it exhibits significant anti-proliferation activity against brain tumor cells and multiple RAS or EGFR mutant models. It strongly inhibits pERK, a downstream marker of MAPK pathway activity, in a dose dependent manner. Further, in vivo preclinical data confirmed that I-1000233 penetrates the BBB and shows anti-tumor activity, supporting its potential in the treatment of CNS and brain-metastasized tumors. Conclusions: I-1000233 is a potent, selective and orally available SHP2 inhibitor with potential for once-a-day dosing and its brain permeability in humans makes it a promising therapeutic agent against SHP2i responsive brain tumors and metastases. Citation Format: Francesca Puca, Danilo Fabbrini, Alina Ciammaichella, Antonino Missineo, Monica Bisbocci, Martina Nibbio, Annalise Di Marco, Claudia Apicella, Fabrizio Colaceci, Romina Sasso, Daniela Natale, Francesco Scalabrì, Cristina Alli, Vincenzo Pucci, Christian Montalbetti, Alessia Petrocchi, Alessandro Carugo, Carlo Toniatti. Discovery of I-1000233, a potent, selective and brain permeable SHP2 allosteric inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1975.

Dipodal Silanes Greatly Stabilize Glass Surface Functionalization for DNA Microarray Synthesis and High-Throughput Biological Assays.

Glass is by far the most common substrate for biomolecular arrays, including high-throughput sequencing flow cells and microarrays. The native glass hydroxyl surface is modified by using silane chemistry to provide appropriate functional groups and reactivities for either in situ synthesis or surface immobilization of biologically or chemically synthesized biomolecules. These arrays, typically of oligonucleotides or peptides, are then subjected to long incubation times in warm aqueous buffers prior to fluorescence readout. Under these conditions, the siloxy bonds to the glass are susceptible to hydrolysis, resulting in significant loss of biomolecules and concomitant loss of signal from the assay. Here, we demonstrate that functionalization of glass surfaces with dipodal silanes results in greatly improved stability compared to equivalent functionalization with standard monopodal silanes. Using photolithographic in situ synthesis of DNA, we show that dipodal silanes are compatible with phosphoramidite chemistry and that hybridization performed on the resulting arrays provides greatly improved signal and signal-to-noise ratios compared with surfaces functionalized with monopodal silanes.

Statistical energy minimization theory for systems of drop-carrier particles.

Drop-carrier particles (DCPs) are solid microparticles designed to capture uniform microscale drops of a target solution without using costly microfluidic equipment and techniques. DCPs are useful for automated and high-throughput biological assays and reactions, as well as single-cell analyses. Surface energy minimization provides a theoretical prediction for the volume distribution in pairwise droplet splitting, showing good agreement with macroscale experiments. We develop a probabilistic pairwise interaction model for a system of such DCPs exchanging fluid volume to minimize surface energy. This leads to a theory for the number of pairwise interactions of DCPs needed to reach a uniform volume distribution. Heterogeneous mixtures of DCPs with different sized particles require fewer interactions to reach a minimum energy distribution for the system. We optimize the DCP geometry for minimal required target solution and uniformity in droplet volume.

Molecular Communication for Equilibrium State Estimation in Biochemical Processes on a Lab-on-a-Chip.

A basic problem in molecular biology is to estimate equilibrium states of biochemical processes. To this end, advanced spectroscopy methods have been developed in order to estimate chemical concentrations in situ or in vivo. However, such spectroscopy methods can require special conditions that do not allow direct observation of the biochemical process. A natural means of resolving this problem is to transmit chemical signals to another location within a lab-on-a-chip device; that is, employing molecular communication in order to perform spectroscopy in a different location. In this paper, we develop such a signaling strategy and estimation algorithms for equilibrium states of a biochemical process. In two biologically-inspired models, we then study via simulation the tradeoff between the rate of obtaining spectroscopy measurements and the estimation error, providing insights into requirements of spectroscopy devices for high-throughput biological assays.

Pregnancy cohorts and biobanking in sub-Saharan Africa: a systematic review

BackgroundTechnological advances and high throughput biological assays can facilitate discovery science in biobanks from population cohorts, including pregnant women. Biological pathways associated with health outcomes differ depending on geography, and...

Lag penalized weighted correlation for time series clustering

BackgroundThe similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets generated with high-throughput biological assays, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure.ResultsWe propose a clustering similarity measure called Lag Penalized Weighted Correlation (LPWC) to group pairs of time series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns time series profiles to identify common temporal patterns. It down-weights aligned profiles based on the length of the temporal lags that are introduced. We demonstrate the advantages of LPWC versus existing time series and general clustering algorithms. In a simulated dataset based on the biologically-motivated impulse model, LPWC is the only method to recover the true clusters for almost all simulated genes. LPWC also identifies clusters with distinct temporal patterns in our yeast osmotic stress response and axolotl limb regeneration case studies.ConclusionsLPWC achieves both of its time series clustering goals. It groups time series with correlated changes over time, even if those patterns occur earlier or later in some of the time series. In addition, it refrains from introducing large shifts in time when searching for temporal patterns by applying a lag penalty. The LPWC R package is available at https://github.com/gitter-lab/LPWC and CRAN under a MIT license.

Microfluidic Systems with Embedded Cell Culture Chambers for High-Throughput Biological Assays.

The ability to generate chemical and mechanical gradients on chips is important for either creating biomimetic designs or enabling high-throughput assays. However, there is still a significant knowledge gap in the generation of mechanical and chemical gradients in a single device. In this study, we developed gradient-generating microfluidic circuits with integrated microchambers to allow cell culture and to introduce chemical and mechanical gradients to cultured cells. A chemical gradient is generated across the microchambers, exposing cells to a uniform concentration of drugs. The embedded microchamber also produces a mechanical gradient in the form of varied shear stresses induced upon cells among different chambers as well as within the same chamber. Cells seeded within the chambers remain viable and show a normal morphology throughout the culture time. To validate the effect of different drug concentrations and shear stresses, doxorubicin is flowed into chambers seeded with skin cancer cells at different flow rates (from 0 to 0.2 μL/min). The experimental results show that increasing doxorubicin concentration (from 0 to 30 μg/mL) within chambers not only prohibits cell growth but also induces cell death. In addition, the increased shear stress (0.005 Pa) at high flow rates poses a synergistic effect on cell viability by inducing cell damage and detachment. Moreover, the ability of the device to seed cells in a 3D microenvironment was also examined and confirmed. Collectively, the study demonstrates the potential of microchamber-embedded microfluidic gradient generators in 3D cell culture and high-throughput drug screening.

Stochastic DNA Walkers in Droplets for Super-Multiplexed Bacterial Phenotype Detection.

Pathogen detection is growing in importance in the global health arena because of the high morbidity and mortality associated with bacterial blood stream infections. In this work, we present stochastic DNA walkers in droplets (SDwalker-Drop), a one-step, rapid, and super-multiplex method for ultrahigh-throughput bacterial detection. The SDwalkers, by exploiting cascade signal amplification, endow our analytical platform with fast analysis times and single-cell analysis ability. The autonomous and multiple-step walking behavior of the SDwalkers provides a super-multiplex droplet-encoding strategy by embedding intensity coded barcodes into a sequence of color-multiplexed barcodes. We realized a theoretical coding capacity of 83 -1=511 and achieved 20 distinct patterns for bacterial phenotype detection and identification. Moreover, our SDwalker-Drop platform could be readily integrated with a flow cytometer to afford a general approach for super-multiplexed, high-throughput biological assays and screening.

In vitro assessment of biological activity and stability of the ALVAC-HIV vaccine

The first evidence in humans that a safe and effective preventive vaccine for HIV is possible came from the phase III HIV clinical trial RV144 in Thailand. This trial was based on a prime/boost combination of a recombinant canarypox vaccine and two glycoprotein 120 proteins (ALVAC-HIV and AIDSVAX B/E). A pivotal phase IIb/III trial has recently commenced in the Republic of South Africa, for which the infectious titer assay was applied as the quantitative release test for the ALVAC-HIV vaccine. The infectious titer assay measures the ability of the vaccine vector to infect target permissive cells, but does not indicate if the vaccine transgenes are expressed. We have developed a high-throughput biological activity assay that provides results in agreement with the infectious titer assay. This assay uses flow cytometry to quantify expression of ALVAC-HIV encoded proteins gp120 and p24 in human cells. This transgene expression is detected by two cross-clade-reactive, biologically functional human anti-gp120 monoclonal antibodies isolated from clinical trial participants and a commercial mouse anti-p24 monoclonal antibody. The relative biological activity of the vaccine test sample is calculated by comparison of the test sample dose-response curve against that of a reference standard. We show that the novel biological activity assay is specific, accurate, precise, stability-indicating, and robust. The assay is being used for characterization of ALVAC-HIV (vCP2438) product, the efficacy of which is being evaluated in the pivotal phase IIb/III clinical trial HVTN702. The biological activity assay has the potential to indicate vaccine consistency and quality as a complement to the infectious titer assay.

Controller for microfluidic large-scale integration.

Microfluidic devices with integrated valves provide precise, programmable fluid handling platforms for high-throughput biological or chemical assays. However, setting up the infrastructure to control such platforms often requires specific engineering expertise or expensive commercial solutions. To address these obstacles, we present a Kit for Arduino-based Transistor Array Actuation (KATARA), an open-source and low-cost Arduino-based controller that can drive 70 solenoid valves to pneumatically actuate integrated microfluidic valves. We include a python package with a GUI to control the KATARA from a personal computer. No programming experience is required.

Elasto-Inertial Focusing of Mammalian Cells and Bacteria Using Low Molecular, Low Viscosity PEO Solutions.

The ability to manipulate biological cells is critical in a diversity of biomedical and industrial applications. Microfluidic-based cell manipulations provide unique opportunities for sophisticated and high-throughput biological assays such as cell sorting, rare cell detection, and imaging flow cytometry. In this respect, cell focusing is an extremely useful functional operation preceding downstream biological analysis, since it allows the accurate lateral and axial positioning of cells moving through microfluidic channels, and thus enables sophisticated cell manipulations in a passive manner. Herein, we explore the utility of viscoelastic carrier fluids for enhanced elasto-inertial focusing of biological species within straight, rectangular cross section microfluidic channels. Since the investigated polymer solutions possess viscosities close to that of water and exhibit negligible shear thinning, focusing occurs over a wide range of elasticity numbers and a large range of Reynolds numbers. With a view to applications in the robust focusing of cells and bacteria, we assess and characterize the influence of accessible focusing parameters, including blockage ratio, volumetric flow rate, cell concentration, and polymer chain length.

Machine Learning Approaches Toward Building Predictive Models for Small Molecule Modulators of miRNA and Its Utility in Virtual Screening of Molecular Databases.

The ubiquitous role of microRNAs (miRNAs) in a number of pathological processes has suggested that they could act as potential drug targets. RNA-binding small molecules offer an attractive means for modulating miRNA function. The availability of bioassay data sets for a variety of biological assays and molecules in public domain provides a new opportunity toward utilizing them to create models and further utilize them for in silico virtual screening approaches to prioritize or assign potential functions for small molecules. Here, we describe a computational strategy based on machine learning for creation of predictive models from high-throughput biological screens for virtual screening of small molecules with the potential to inhibit microRNAs. Such models could be potentially used for computational prioritization of small molecules before performing high-throughput biological assay.

Life Sciences Discovery and Technology Highlights

Life Sciences Discovery and Technology Highlights

Recent developments in scale-up of microfluidic emulsion generation via parallelization

Microfluidics affords precise control over the flow of multiphasic fluids in micron-scale channels. By manipulating the viscous and surface tension forces present in multiphasic flows in microfluidic channels, it is possible to produce highly uniform emulsion droplets one at a time. Monodisperse droplets generated based on microfluidics are useful templates for producing uniform microcapsules and microparticles for encapsulation and delivery of active ingredients as well as living cells. Also, droplet microfluidics have been extensively exploited as a means to enable highthroughput biological screening and assays. Despite the promise droplet-based microfluidics hold for a wide range of applications, low production rate ( >100) and networks of fluid channels that distribute fluid to each of these generators onto a single chip. To parallelize droplet generation and, at the same time, maintain high uniformity of emulsion droplets, several considerations have to be made including the design of channel geometries to ensure even distribution of fluids to each droplet generator, methods for large-scale and uniform fabrication of microchannels, device materials for mechanically robust operation to withstand high-pressure injection, and development of commercially feasible fabrication techniques for three-dimensional microfluidic devices. We highlight some of the recent advances in the mass production of highly uniform microfluidics droplets via parallelization and discuss outstanding issues.

Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008-2014.

SummaryThe European Chemicals Agency (ECHA) warehouses the largest public dataset of in vivo and in vitro toxicity tests. In December 2014 this data was converted into a structured, machine readable and searchable database using natural language processing. It contains data for 9,801 unique substances, 3,609 unique study descriptions and 816,048 study documents. This allows exploring toxicological data on a scale far larger than previously possible.Substance similarity analysis was used to determine clustering of substances for hazards by mapping to PubChem. Similarity was measured using PubChem 2D conformational substructure fingerprints, which were compared via the Tanimoto metric. Following K-Core filtration, the Blondel et al. (2008) module recognition algorithm was used to identify chemical modules showing clusters of substances in use within the chemical universe.The Global Harmonized System of Classification and Labelling provides a valuable information source for hazard analysis. The most prevalent hazards are H317 “May cause an allergic skin reaction” with 20% and H318 “Causes serious eye damage” with 17% positive substances. Such prevalences obtained for all hazards here are key for the design of integrated testing strategies. The data allowed estimation of animal use.The database covers about 20% of substances in the high-throughput biological assay database Tox21 (1,737 substances) and has a 917 substance overlap with the Comparative Toxicogenomics Database (~7% of CTD). The biological data available in these datasets combined with ECHA in vivo endpoints have enormous modeling potential. A case is made that REACH should systematically open regulatory data for research purposes.

Cell Microarrays for Biomedical Applications.

In this chapter the state of the art of live cell microarrays for high-throughput biological assays are reviewed. The fabrication of novel microarrays with respect to material science and cell patterning methods is included. A main focus of the chapter is on various aspects of the application of cell microarrays by providing selected examples in research fields such as biomaterials, stem cell biology and neuroscience. Additionally, the importance of microfluidic technologies for high-throughput on-chip live-cell microarrays is highlighted for single-cell and multi-cell assays as well as for 3D tissue constructs.

Kilo-scale droplet generation in three-dimensional monolithic elastomer device (3D MED).

Droplet-based microfluidics has led to transformational new approaches in diverse areas including materials synthesis and high-throughput biological assays. However, the translation of droplet microfluidics technology into commercial applications requires scale-up of droplet generation from the laboratory (<10 mL h(-1)) to the industrial (>1 L h(-1)) scale. To address this challenge, we develop a three-dimensional monolithic elastomer device (3D MED) for mass production of monodisperse emulsion droplets. Using double-sided imprinting, 3D microchannels are formed in a single elastomer piece that has 1000 parallel flow focusing generators (k-FFGs). Compared to previous work that parallelizes droplet generation, the 3D MED eliminates the needs for alignment and bonding of multiple pieces and thus makes it possible to achieve the high flow rates and pressure necessary for the kilo-scale generation of droplets. Using this approach, we demonstrate mass production of water-in-oil (W/O) emulsion droplets at production rates as high as 1.5 L h(-1) (>30 billion 45 μm diameter droplets per hour), with a coefficient of variation of droplet diameter of only 6.6%. Because of the simplicity, robustness, and manufacturability of our 3D MED architecture, it is well suited to bridge the gap between the continuously growing library of promising microfluidic technologies to generate microparticles that have been demonstrated in laboratory settings and their successful application in industry.

Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays.

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.

Quasi-spherical microwells on superhydrophobic substrates for long term culture of multicellular spheroids and high throughput assays

Multicellular tumour spheroids closely recapitulate the physiological environment of tumour tissues. However, their implementation in drug screening assays remains limited due to the technological challenges of forming large numbers of high quality spheroids in platforms compatible with high throughput screening. A simple bench-top microfabrication strategy is demonstrated here based on the principle of ice lithography carried out on superhydrophobic substrates to fabricate quasi-spherical microwells (spheriwells). The microwells shapes and dimensions are directly controlled by the hydrophobicity of the substrate and the volume of the water droplets. The prepared concave microwells enable the formation of dense and homogeneous multicellular tumour spheroids. Spheroids formed within spheriwells are trapped within the microwells, which eliminate loss during media manipulation and facilitate long-term on-chip culture. Morphological and phenotypical changes associated with the growth of MCF-7 adenocarcinoma cells in spheriwells were characterised using imaging flow cytometry and revealed the appearance of heterogeneous populations with loss of E-Cadherin expression. The compatibility of the spheriwells with an on-chip MTT assay is demonstrated. The very unusual shape of the spheriwells, prepared using materials and methods routinely used in most research laboratories, provides a straightforward and scalable platform to prepare high quality multicellular tumour spheroids compatible with high throughput biological screening assays.

APPLICATIONS OF SUPPORT VECTOR MACHINES AS A ROBUST TOOL IN HIGH THROUGHPUT VIRTUAL SCREENING

Chemical space is enormously huge but not all of it is pertinent for the drug designing. Virtual screening methods act as knowledge-based filters to discover the coveted novel lead molecules possessing desired pharmacological properties. Support Vector Machines (SVM) is a reliable virtual screening tool for prioritizing molecules with the required biological activity and minimum toxicity. It has to its credit inherent advantages such as support for noisy data mainly coming from varied high-throughput biological assays, high sensitivity, specificity, prediction accuracy and reduction in false positives. SVM-based classification methods can efficiently discriminate inhibitors from non-inhibitors, actives from inactives, toxic from non-toxic and promiscuous from non-promiscuous molecules. As the principles of drug design are also applicable for agrochemicals, SVM methods are being applied for virtual screening for pesticides too. The current review discusses the basic kernels and models used for binary discrimination and also features used for developing SVM-based scoring functions, which will enhance our understanding of molecular interactions. SVM modeling has also been compared by many researchers with other statistical methods such as Artificial Neural Networks, k-nearest neighbour (kNN), decision trees, partial least squares, etc. Such studies have also been discussed in this review. Moreover, a case study involving the use of SVM method for screening molecules for cancer therapy has been carried out and the preliminary results presented here indicate that the SVM is an excellent classifier for screening the molecules.