High-throughput Processing Research Articles

Scalable, solution-based dye-sensitized photovoltaic fibers with an electrolytic solid-state ion conductor

We have created a textile with solid-state dye-sensitized photovoltaic fibers (DSPFs) fabricated using a unique combination of inexpensive, commercial-off-the-shelf materials and solution-based processing deposition techniques. DSPFs were made possible by macroscopic support of liquid state components in both the electrolyte and photoanode of the DSPFs. A plastic crystal system of succinonitrile was doped with iodide salts to achieve a solid-state ion-conducting electrolyte. Similarly, utilization of a mesoporous TiO2 photoanode created an adsorptive scaffold for photoactive dyes in a liquid solution. An inexpensive pairing of coated carbon and stainless steel, replacing the traditional and expensive platinum-based counter electrode, was used as a catalytic counter electrode. A solid-state DSPF including both electrodes aligned in comparative orientations provided peak power of up to 0.4 µW/mm with open-circuit voltage (VOC) of 0.7 V. A module of solid-state DSPFs was then incorporated into a simple weave in a mini-loom to demonstrate textile capability. Solid-state DSPFs woven and connected in series in the loom produced peak power of over 50 µW and VOC of 4.5 V. Creation of the flexible DSPFs was achieved through a series of simple and scalable solution-based coating processes, and to the best of our knowledge, has not been previously reported in literature to produce fully encapsulated solid-state dye-sensitized photovoltaic cells with a fiber form factor. Low cost, high throughput processing of this technology can potentially support the manufactural transition for the next generation of commercial energy harvesting wearable electronics.

A comprehensive assessment of the applicability of RoboColumn as a chromatography scale-down model for use in biopharmaceutical process validation

High-throughput process development has become a standard practice in the biopharmaceutical industry to enable time, cost, and material savings. In downstream biopharmaceutical process development, miniaturized, parallelized chromatography columns, known as RoboColumn, have become the standard for process development, as RoboColumn have shown generally comparable performance to bench and manufacturing scale columns. However, RoboColumn have yet to be widely implemented in process validation and characterization, where many multifactor experiments are typically executed, and there is a strong value proposition for performing high-throughput experiments. The hesitancy to utilize RoboColumn in process validation arises from scale differences that result in exacerbated peak broadening at RoboColumn scale relative to traditional bench or manufacturing scales. Thus, to support reliable application of RoboColumn in process validation, the present study provides a comprehensive investigation to understand how scale differences affect chromatographic performance by comparing RoboColumn, bench, and manufacturing scales using seven different production processes covering three different antibody formats, five different resin types, and three chromatographic modes of operation. RoboColumn chromatographic performance was compared at target and off-target conditions to emulate scale-down model qualification and multifactor studies, respectively. RoboColumn demonstrated good comparability at both target and off-target process conditions. To further demonstrate an understanding of comparability, a study was performed to show a rare case in which product quality offsets may occur as a result RoboColumn scale differences. By showing scale comparability and an understanding of potential offsets, this work demonstrates that RoboColumn can be used in any stage of process development, including process validation and characterization.

Accelerated Screening of Ternary Chalcogenides for Potential Photovoltaic Applications.

Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent contacts, and thin-film transistors. In comparison to oxide counterparts, chalcogenides have demonstrated higher mobility and p-type dopability, owing to larger orbital overlaps between metal-X covalent chemical bondings and higher-energy valence bands derived by p-orbitals. Despite the potential of chalcogenides, the number of successfully synthesized compounds remains relatively low compared to that of oxides, suggesting the presence of numerous unexplored chalcogenides with fascinating physical characteristics. In this study, we implemented a systematic high-throughput screening process combined with first-principles calculations on ternary chalcogenides using 34 crystal structure prototypes. We generated a computational material database containing over 400,000 compounds by exploiting the ion-substitution approach at different atomic sites with elements in the periodic table. The thermodynamic stabilities of the candidates were validated using the chalcogenides included in the Open Quantum Materials Database. Moreover, we trained a model based on crystal graph convolutional neural networks to predict the thermodynamic stability of novel materials. Furthermore, we theoretically evaluated the electronic structures of the stable candidates using accurate hybrid functionals. A series of in-depth characteristics, including the carrier effective masses, electronic configuration, and photovoltaic conversion efficiency, was also investigated. Our work provides useful guidance for further experimental research in the synthesis and characterization of such chalcogenides as promising candidates, as well as charting the stability and optoelectronic performance of ternary chalcogenides.

Development of a Continuous Flow Baldwin Rearrangement Process and Its Comparison to Traditional Batch Mode.

A new and highly efficient continuous flow process is presented for the synthesis of aziridines via the thermal Baldwin rearrangement. The process was initially explored using traditional batch synthesis techniques but suffered from moderate yields, long reaction times, and moderate diastereoselectivities. Here we demonstrate that the process can be greatly improved upon its transfer to continuous flow, which afforded the aziridine targets in high yields, short reaction times, and consistently high diastereoselectivities, with the high-throughput process rendering multigram quantities of product in short periods of time. In addition, flow processing extended the substrate scope including several examples that had failed in batch mode, thus demonstrating the value of this overlooked entry into valuable aziridine species.

P13.04.A REPRODUCIBLE CRISPR-SEQ PIPELINE FOR NGS FUNCTIONAL GENOMICS SCREENINGS FOR GLIOMA CELL LINES

Abstract BACKGROUND The CRISPR/Cas9 genome editing technology has revolutionized the field of functional genomics by enabling precise editing of DNA. Over the past few years, CRISPR technology has been widely employed to identify genetic dependencies in a broad spectrum of applications paving the way for the discovery and validation of relevant molecular targets, for example potential drug targetsGlioblastoma are incurable primary tumors of the central nervous system. Functional genomics could enable the discovery of novel therapeutic targets or even combination therapies. MATERIAL AND METHODS Here, we present a bioinformatics pipeline that combines functional screens and target editing analysis to identify potential vulnerabilities and drug targets in glioblastoma or other diseases. The pipeline consists of two workflows, one performing functional screening and one performing base editing analysis. This developed CRISPR-seq pipeline is reproducible and containerized, allowing high-throughput processing. All processes are modularized in Nextflow DSL2 (Domain Specific Language) and versioned which facilitates maintainability and customization, as well as the addition of new tools. A quality control report is also automatically generated, allowing the user an overview of their ran experiments. The pipeline also helps us to keep the data FAIR (Findable, Accessible, Interoperable and Reusable) and optimized, making it easier to share with other researchers and collaborate on future studies. RESULTS This pipeline was applied to functional screens using CRISPR/Cas9 genome-wide knockout or activation libraries to detect genetic vulnerabilities that might enhance drug treatment efficacy, e.g. for ataxia telangiectasia and Rad3 related (ATR) inhibition in experimental glioma.With this pipeline, we were able to identify several genes that are involved in drug resistance in glioblastoma, by starting from raw data (fastq files) to genes presenting vulnerabilities.The results obtained through our pipeline provided important information on the disease's molecular mechanisms, which were subsequently confirmed through wet lab validation. CONCLUSION In conclusion, the crisprseq software described in this abstract is a powerful tool for identifying potential genetic dependencies. The pipeline aims at analyzing this output data in a reproducible and containerized way using Nextflow and respecting nf-core community standards. By combining functional screens and base editing analysis with bioinformatics tools, this pipeline allows for the identification of essential genes and pathways, as well as the characterization of specific mutations that contribute to the development and progression of glioblastoma.

Parametric configuration and programming of flexible robotic cells producing biotechnology experiments

In the last decades there has been an ever-increasing trend of accelerated scientific research in the biotechnology sector and the COVID-19 pandemic has further emphasized the need for high-throughput yet flexible processes. Even though automation has gained significant popularity to deal with this problem, the focus has been placed at laboratory level and for specific type of processes only. The aim of these automation applications is to offset the increasing costs of clinical trials, automate tedious laboratory work, run experiments in parallel and make scientific testing efficient and programmable. This paper investigates the application of robotic systems at a larger scale that will allow the automated execution of even custom defined biotechnology experiments so that these can be offered as a service to any interested stakeholder. A digital model of the proposed robotic biotechnology workcell is developed and the necessary methodology is investigated to showcase the feasibility of this approach. The goal is to use the digital model for evaluating different design considerations, such as equipment layout and robot types, as well as for planning and simulating the robot operation under various offline programming strategies. To achieve this goal, the Damped Least Squares Method is used for inverse kinematics control and a robotic arm trajectory planner is developed parametrically using characteristic intermediate points so as to create a motion plan that can be used for any robotic manipulator and any experiment within a family of facility setups. Two experiments are simulated using a SCARA robot and an articulated arm robot, each with an alternative cell layout to show the flexibility and robustness of the proposed approach. The obtained results show that the trajectory planner can consistently generate appropriate motion plans.

Embedding the de.NBI Cloud in the National Research Data Infrastructure Activities

In recent years, modern life sciences research underwent a rapid development driven mainly by the technical improvements in analytical areas leading to miniaturization, parallelization, and high throughput processing of biological samples. This has led to the generation of huge amounts of experimental data. To meet these rising demands, the German Network for Bioinformatics Infrastructure (de.NBI) was established in 2015 as a national bioinformatics consortium aiming to provide high quality bioinformatics services, comprehensive training, powerful computing capacities (de.NBI Cloud) as well as connections to the European Life Science Infrastructure ELIXIR, with the goal to assist researchers in exploring and exploiting data more effectively.
 Since its foundation, de.NBI Cloud has formed the scientific and collaborative backbone for new major German initiatives like NFDI or EOSC-Life in the European sector of computational biosciences. Above all, the cooperation with various NFDI consortia such as NFDI4Biodiversity, DataPLANT, GHGA, FAIRagro or NFDI4Microbiota showcases the power, range and flexibility of the de.NBI Cloud, especially for the national life science community.
 In conclusion, the de.NBI Cloud provides the ability to unlock the full potential of research data and enables easier collaboration across different ecosystems and research areas, which in turn enables scientists to innovate and scale-up their data-driven research, not only in the life and computational biosciences, but across the different science domains addressed by the NFDI.

Single Update Sketch with Variable Counter Structure

Per-flow size measurement is key to many streaming applications and management systems, particularly in high-speed networks. Performing such measurement on the data plane of a network device at the line rate requires on-chip memory and computing resources that are shared by other key network functions. It leads to the need for very compact and fast data structures, called sketches, which trade off space for accuracy. Such a need also arises in other application context for extremely large data sets. The goal of sketch design is two-fold: to measure flow size as accurately as possible and to do so as efficiently as possible (for low overhead and thus high processing throughput). The existing sketches can be broadly categorized to multi-update sketches and single update sketches. The former are more accurate but carry larger overhead. The latter incur small overhead but their accuracy is poor. This paper proposes a Single update Sketch with a Variable counter Structure (SSVS), a new sketch design which is several times faster than the existing multi-update sketches with comparable accuracy, and is several times more accurate than the existing single update sketches with comparable overhead. The new sketch design embodies several technical contributions that integrate the enabling properties from both multi-update sketches and single update sketches in a novel structure that effectively controls the measurement error with minimum processing overhead.

A semi-rational mutagenesis approach for improved substrate activity of microbial transglutaminase.

A higher specific activity of microbial transglutaminase (mTGase) is desirable for a broad range of applications ranging from food industry to biotechnology. Three-dimensional docking simulation of mTGase revealed that residues V65, W69, and Y75 were critical for substrate recognition. A semi-rational mutagenesis approach was applied to each residue to generate three separate mini mutant libraries. A high-throughput screening process identified five mutants that demonstrated improved specific activities than the wild type (WT) mTGase were isolated from the Y75 mini mutant library. Mutant Y75L showed approximately 60% increment in specific activity and improved substrate specificity. Conjugation of two heterologous single-chain fragment variable clones to generate a diabody with mutant Y75L was successfully performed and validated. This work demonstrates the successful application of semi-rational mutagenesis coupled with a high-throughput screening approach to identify mTGase mutants with improved specific activities and specificities which are beneficial for protein-protein conjugation.

(Invited) accelerating Polymer Discovery for Multifunctional Polymer Thin Films: High-Throughput Characterization

Identifying new formulations of polymers that have multiple highly tuned properties is a grand challenge of materials development. This is especially important in polymer thin film devices where several distinct transport properties must be controlled. For instance, solid-state battery electrolyte materials must be conductive to ions, but remain electronically insulating. In order to achieve the materials advances needed to transform the use of such polymer thin films, new paradigms of accelerated materials development are needed.In this talk, we discuss our recent efforts to accelerate the development of advanced polymer thin film materials for energy applications. A crucial feature of accelerating the discovery of high performing formulations and processing conditions is a rapid means to assess the properties of polymer films with spatial resolution commensurate with nanoscopic defects and throughput that can accommodate macroscopic devices. We initially focus on the challenge of verifying that films are electrically insulating. Maintaining electrical insulation across large films is a particularly insidious challenge as small “pin-hole” defects can result in an entire device failing. We explore an optical method to detect and characterize conductive defects based upon the electrochemiluminescence of luminol. In particular, when a voltage is applied to a conductive surface that is protected by a nominally insulating film, regions where the film is not present or is electrically conductive will result in the local generation of light. A major virtue of this approach is that the minimum detectable feature is not directly determined by the wavelength of light or the optical magnification in use, but rather the intensity of light generated per unit area on the surface. To explore this approach, we conduct an extensive series of optimization experiments to maximize the intensity of light generated by electrochemiluminescence by adjusting the timing, voltage, and solution properties. The result of this optimization is that lines narrower than 100 nm are readily detectable using this method. Finally, we present efforts to use this approach to screen centimeter-square areas to find and characterize nanoscale defects.Looking beyond the important task of characterizing films at high throughput, we conclude the talk with a discussion of how this approach fits in a broader materials discovery pipeline. In particular, recent years have seen a number of examples of autonomous experimentation systems that combine automation to perform experiments and machine learning to select experiments to vastly increased the speed at which knowledge is gained. The reported high-throughput analytical process is anticipated to form an important link in this process by providing a path to rapidly and effectively screening films for electrical properties.

(Invited) Rapid, Large Area Preparation of Few-Layer Graphene Films Using Xenon Flash Lamp Pyrolysis for High-Performance Structural Micro-Supercapacitors

Graphene offers excellent electrical conductivity and very high surface area, which holds great promise for energy storage applications including supercapacitors. Current preparation methods for graphene require relatively long processing times, extremely high temperatures within controlled atmospheres, and/or involve multi-step reactions that present challenges for high throughput fabrication of graphene-based energy storage devices. We report a novel photothermal route to large-scale production of graphene within milliseconds using a commercially available polymers (polybenzoxazine, polyacrylonitrile and polyaniline) and a high intensity xenon flash lamp on various substrates, including carbon fibers, at ambient conditions. The xenon flash lamp provides large-area illumination and a wide emission band (300 nm –1100 nm) that was used to convert the polymeric material directly into few layer graphene upon millisecond exposures. The precursor material is heated to extremely high temperatures in a fraction of a second – a duration that is much shorter than the timescale for thermal equilibrium. This enabled the conversion of the polymeric material into few layer graphene in air and at room temperature, and without thermally damaging the substrate. Conversion to few-layer graphene was strongly dependent on pulse energy and the local temperature which was controlled via pulse power modulation. The obtained graphene composites at optimized conditions exhibited excellent conductivity (0.1 ohm-cm) with ID/IG ratio of 0.3 (Figure 1a). Particularly, graphene derived from polyaniline resulted in the formation of macroporous network (Figure 1b) with good adhesion to the carbon fiber, all of which facilitate the transport of ions through the material. The fabricated supercapacitor devices exhibited a very high areal capacitance of 200 mF/cm2 at 10 mV/s with retention of more than 70% of their capacitance, even at scan rates up to 100 mV/s. Moreover, mechanically, and structurally stable graphene derived from polyaniline on carbon fiber was utilized for the preparation of structural energy storage devices. We assembled symmetric structural supercapacitor comprised of carbonaceous material (carbon fiber and few-layer graphene) and polymer gel electrolyte. This resulted in the preparation of large area device (40 cm2) as shown in the Figure 1c with the capacitance exceeding 2000 mF and almost complete retention of capacitance after 5000 cycles.The preparation of high-quality graphene via photothermal pyrolysis of polymer is amenable to high throughput processing, enabling large-scale production of high performance structural electrochemical energy storage devices. Figure 1

Room-temperature-processed transparent hemispherical optoelectronic array for electronic eyes

Bio-inspired electronic eyes mimicking the geometry and function of natural light-sensing organs are highly attractive in next-generation miniaturized imaging systems. The processing of electronic eyes usually adopts complex and equipment-intensive techniques, involving vacuum-based materials growth, doping, deposition and patterning. Here, we report an electronic eye system with a fully transparent artificial retina by a simple solution-based method at room-temperature. The artificial retina contains a flexible 16 × 16 photodetector array, which composes of a ZnO nanoparticle-MoS2 nanosheet composite film as the light sensing unit and the patterned silver nanowires as conductive paths within 1 cm2. It shows a reliable ultra-violet response and high transparence across the visible and near-infrared range, and hence allows the artificial retina to perceive light from all directions without weakening the photo-response. Demonstrations of two kinds of electronic eye prototypes (concave and convex hemispheres) in a single device configuration show the possibility for double-sided imaging. This work provides a simple, rapid and high-throughput processing route for bio-inspired electronic eye that could be used for integration of various functional materials with different properties, which opens a new avenue for the multifunctional electronic eye devices.

High-throughput process & analytical platforms to accelerate gene therapy scale-up

High-throughput process & analytical platforms to accelerate gene therapy scale-up

Comparison of four DNA extraction methods for 16s rRNA microbiota profiling of human faecal samples

ObjectiveGrowth in large population-based studies assessing contributions of the gut microbiota to health and disease requires high-throughput sample processing and analysis methods. This study assessed the impact that modifications to a commercially available magnetic bead based, semi-automated DNA extraction kit had on determination of microbial composition, relative to an established in-house method involving a combination of mechanical and chemical lysis. DNA was extracted from faecal samples from healthy adults (n = 12; 34–69 years), microbial composition was determined by V3-V4 16s rRNA sequencing and compared between extraction methods.ResultsDiversity metrics did not differ between extraction methods. Differences in the relative abundance of key phyla, including a significantly lower abundance of the Firmicutes (p = 0.004) and higher relative abundance of the Bacteroidetes (p = 0.005) and Proteobacteria (p = 0.008) phyla were noted where the DNA extraction did not include additional chemical and mechanical lysis. Principal coordinate analysis of family and genera level data also suggested a potential for sample pre-processing to impact microbial composition. Observations of the potential for skewed microbial composition profiles from samples prepared using a semi-automated DNA extraction kit without additional sample pre-processing highlights a need for consideration of standardisation of methodological approaches to increase the comparability of microbial compositional data.

Construction of a QSAR Model Based on Flavonoids and Screening of Natural Pancreatic Lipase Inhibitors.

Pancreatic lipase (PL) is a key hydrolase in lipid metabolism. Inhibition of PL activity can intervene in obesity, a global sub-health disease. The natural product is considered a good alternative to chemically synthesized drugs due to its advantages, such as low side effects. However, traditional experimental screening methods are labor-intensive and cost-consuming, and there is an urgent need to develop high-throughput screening methods for the discovery of anti-PL natural products. In this study, a high-throughput virtual screening process for anti-PL natural products is provided. Firstly, a predictable anti-PL natural product QSAR model (R2train = 0.9444, R2test = 0.8962) were developed using the artificial intelligence drug design software MolAIcal based on genetic algorithms and their conformational relationships. 1068 highly similar (FS > 0.8) natural products were rapidly enriched based on the structure-activity similarity principle, combined with the QSAR model and the ADMET model, for rapid prediction of a total of five potentially efficient anti-PL natural products (IC50pre < 2 μM). Subsequently, molecular docking, molecular dynamics simulation, and MMGBSA free energy calculation were performed to not only reveal the interaction of candidate novel natural products with the amino acid residues of PL but also to validate the stability of these novel natural compounds bound to PL. In conclusion, this study greatly simplifies the screening and discovery of anti-PL natural products and accelerates the development of novel anti-obesity functional foods.

Evaluation of forage quality in a pea breeding program using a hyperspectral sensing system

Forage quality evaluation is essential in breeding and selecting forage crops to improve livestock health and reduce greenhouse gas emissions. A non-destructive and high-throughput process to assess forage quality can advance cultivar development research and commercial feed analysis. The overall objective of this study was to estimate 12 biomass quality traits of field peas in the 2019 and 2020 field seasons using in-field hyperspectral spectroscopy (350–2500 nm) at the leaf level. Six machine-learning models were utilized to develop the relationships between each quality trait and four feature extraction datasets (first derivative reflectance data, vegetation indices, normalized difference spectral indices, and ratio spectral indices) of leaf reflectance spectra from the hyperspectral data. The high and consistent performance of all quality traits was produced from only ridge regression, elastic net regression, and random forest regression models in first derivative reflectance and vegetation indices datasets. In addition, higher prediction performance (0.81 < R2 < 0. 93; 0.05 < root mean square error (%) < 1.80; 0.03 < mean absolute error (%) < 1.32) was found in the random forest model using normalized difference spectral indices and ratio spectral indices datasets after utilizing a feature selection technique (leave one feature out). The results suggest that the proposed evaluation approach of spectroscopy may be applied to predict field pea quality traits in a timely fashion to assist breeders and farmers in their decision-making.

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy.

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.

Hexanary blends: a strategy towards thermally stable organic photovoltaics

Non-fullerene based organic solar cells display a high initial power conversion efficiency but continue to suffer from poor thermal stability, especially in case of devices with thick active layers. Mixing of five structurally similar acceptors with similar electron affinities, and blending with a donor polymer is explored, yielding devices with a power conversion efficiency of up to 17.6%. The hexanary device performance is unaffected by thermal annealing of the bulk-heterojunction active layer for at least 23 days at 130 °C in the dark and an inert atmosphere. Moreover, hexanary blends offer a high degree of thermal stability for an active layer thickness of up to 390 nm, which is advantageous for high-throughput processing of organic solar cells. Here, a generic strategy based on multi-component acceptor mixtures is presented that permits to considerably improve the thermal stability of non-fullerene based devices and thus paves the way for large-area organic solar cells.

Generating colours through a novel approach based on spatial ALD and laser processing

This work studies the combination of direct femtosecond laser structuring of metal surfaces and Spatial Atomic Layer Deposition (SALD) of metal oxides as a novel approach to generate colours on different types of day-to-day metallic objects. In particular, a stainless-steel knife and an outdated 25 ct Dutch florin coin have been selected for the study. Our results show that it is possible both, to preserve the iridescence properties produced by laser processing and to tune the final metal surface colour by controlling the thickness of the ZnO coating. At the same time, this oxide coating could act as a protecting layer for the original material. We thus explore two different strategies to generate colour, namely, iridescence and interference, which can be even developed selectively. This novel methodology to colour metallic surfaces is a promising route to achieve cheap, scalable, and high-throughput processing methods and opens up a new avenue of possibilities and applications related to colour.

Safety pharmacology 2023 and implementation of the ICH E14/S7B Q&A guidance document

This editorial prefaces the annual themed issue on safety pharmacology (SP) methods published since 2004 in the Journal of Pharmacological and Toxicological Methods (JPTM). We highlight here the content derived from the recent 2022 Safety Pharmacology Society (SPS) and Canadian Society of Pharmacology and Therapeutics (CSPT) joint meeting held in Montreal, Quebec, Canada. The meeting also generated 179 abstracts (reproduced in the current volume of JPTM). As in previous years the manuscripts reflect various areas of innovation in SP including a comparison of the sensitivity of cross-over and parallel study designs for QTc assessment, use of human-induced pluripotent stem cell (hi-PSC) neuronal cell preparations for use in neuropharmacological safety screening, and hiPSC derived cardiac myocytes in assessing inotropic adversity. With respect to the latter, we anticipate the emergence of a large data set of positive and negative controls that will test whether the imperative to miniaturize, humanize and create a high throughput process is offset by any loss of precision and accuracy.