mQC: A Heuristic Quality-Control Metric for High-Throughput Drug Combination Screening

Reproducibility of drug-induced effects on the contractility of an engineered heart tissue derived from human pluripotent stem cells

Introduction: Engineered heart tissues (EHTs) are three-dimensional culture platforms with cardiomyocytes differentiated from human pluripotent stem cells (hPSCs) and were designed for assaying cardiac contractility. For drug development applications, EHTs must have a stable function and provide reproducible results. We investigated these properties with EHTs made with different tissue casting batches and lines of differentiated hPSC-cardiomyocytes and analyzed them at different times after being fabricated. Methods: A video-optical assay was used for measuring EHT contractile outputs, and these results were compared with results from motion traction analysis of beating hPSC-cardiomyocytes cultured as monolayers in two-dimensional cultures. The reproducibility of induced contractile variations was tested using compounds with known mechanistic cardiac effects (isoproterenol, EMD-57033, omecamtiv mecarbil, verapamil, ranolazine, and mavacamten), or known to be clinically cardiotoxic (doxorubicin, sunitinib). These drug-induced variations were characterized at different electrical pacing rates and variations in intracellular calcium transients were also assessed in EHTs. Results: To ensure reproducibility in experiments, we established EHT quality control criteria based on excitation-contraction coupling and contractile sensitivity to extracellular calcium concentration. In summary, a baseline contractile force of 0.2 mN and excitation-contraction coupling of EHTs were used as quality control criteria to select suitable EHTs for analysis. Overall, drug-induced contractile responses were similar between monolayers and EHTs, where a close relationship was observed between contractile output and calcium kinetics. Contractile variations at multiple time points after adding cardiotoxic compounds were also detectable in EHTs. Discussion: Reproducibility of drug-induced effects in EHTs between experiments and relative to published work on these cellular models was generally observed. Future applications for EHTs may require additional mechanistic criteria related to drug effects and cardiac functional outputs to be measured in regard to specific contexts of use.

The synergy of BET inhibitors with aurora A kinase inhibitors in MYCN-amplified neuroblastoma is heightened with functional TP53

Amplification of MYCN is a poor prognostic feature in neuroblastoma (NBL) indicating aggressive disease. We and others have shown BET bromodomain inhibitors (BETi) target MYCN indirectly by downregulating its transcription. Here we sought to identify agents that synergize with BETi and to identify biomarkers of resistance. We previously performed a viability screen of ∼1,900 oncology-focused compounds combined with BET bromodomain inhibitors against MYCN-amplified NBL cell lines. Reanalysis of our screening results prominently identified inhibitors of aurora kinase A (AURKAi) to be highly synergistic with BETi. We confirmed the anti-proliferative effects of several BETi+AURKAi combinations in MYCN-amplified NBL cell lines. Compared to single agents, these combinations cooperated to decrease levels of N-myc. We treated both TP53-wild type and mutant, MYCN-amplified cell lines with the BETi JQ1 and the AURKAi Alisertib. The combination had improved efficacy in the TP53-WT context, notably driving apoptosis in both genetic backgrounds. JQ1+Alisertib combination treatment of a MYCN-amplified, TP53-null or TP53-restored genetically engineered mouse model of NBL prolonged survival better than either single agent. This was most profound with TP53 restored, with marked tumor shrinkage and apoptosis induction in response to combination JQ1+Alisertib. BETi+AURKAi in MYCN-amplified NBL, particularly in the context of functional TP53, provided anti-tumor benefits in preclinical models. This combination should be studied more closely in a pediatric clinical trial.

Issues Of Z-factor and an approach to avoid them for quality control in high-throughput screening studies

MOTIVATION

High throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that z-factor has major issues.

RESULTS

In this article, the following four major issues are explored and demonstrated so that researchers may use the z-factor appropriately. First, the z-factor violates the Pythagorean Theorem of Statistics. Second, there is no adjustment of sampling error in the application of the z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based z-factor does not exist. Fourth, the thresholds in the z-factor based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies.

AVAILABILITY

The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Harnessing the Neural Stem Cell Secretome for Regenerative Neuroimmunology

Increasing evidence foresees the secretome of neural stem cells (NSCs) to confer superimposable beneficial properties as exogenous NSC transplants in experimental treatments of traumas and diseases of the central nervous system (CNS). Naturally produced secretome biologics include membrane-free signaling molecules and extracellular membrane vesicles (EVs) capable of regulating broad functional responses. The development of high-throughput screening pipelines for the identification and validation of NSC secretome targets is still in early development. Encouraging results from pre-clinical animal models of disease have highlighted secretome-based (acellular) therapeutics as providing significant improvements in biochemical and behavioral measurements. Most of these responses are being hypothesized to be the result of modulating and promoting the restoration of key inflammatory and regenerative programs in the CNS. Here, we will review the most recent findings regarding the identification of NSC-secreted factors capable of modulating the immune response to promote the regeneration of the CNS in animal models of CNS trauma and inflammatory disease and discuss the increased interest to refine the pro-regenerative features of the NSC secretome into a clinically available therapy in the emerging field of Regenerative Neuroimmunology.

Breeze: an integrated quality control and data analysis application for high-throughput drug screening

SUMMARY

High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose-response curve fitting and quantification of the drug responses along with interactive visualization of the results.

AVAILABILITY AND IMPLEMENTATION

The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0.

CONTACT

swapnil.potdar@helsinki.fi.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Optimization of cell viability assays to improve replicability and reproducibility of cancer drug sensitivity screens

Cancer drug development has been riddled with high attrition rates, in part, due to poor reproducibility of preclinical models for drug discovery. Poor experimental design and lack of scientific transparency may cause experimental biases that in turn affect data quality, robustness and reproducibility. Here, we pinpoint sources of experimental variability in conventional 2D cell-based cancer drug screens to determine the effect of confounders on cell viability for MCF7 and HCC38 breast cancer cell lines treated with platinum agents (cisplatin and carboplatin) and a proteasome inhibitor (bortezomib). Variance component analysis demonstrated that variations in cell viability were primarily associated with the choice of pharmaceutical drug and cell line, and less likely to be due to the type of growth medium or assay incubation time. Furthermore, careful consideration should be given to different methods of storing diluted pharmaceutical drugs and use of DMSO controls due to the potential risk of evaporation and the subsequent effect on dose-response curves. Optimization of experimental parameters not only improved data quality substantially but also resulted in reproducible results for bortezomib- and cisplatin-treated HCC38, MCF7, MCF-10A, and MDA-MB-436 cells. Taken together, these findings indicate that replicability (the same analyst re-performs the same experiment multiple times) and reproducibility (different analysts perform the same experiment using different experimental conditions) for cell-based drug screens can be improved by identifying potential confounders and subsequent optimization of experimental parameters for each cell line.

High-Throughput Screening for Drug Combinations

The identification of drug combinations as alternatives to single-agent therapeutics has traditionally been a slow, largely manual process. In the last 10 years, high-throughput screening platforms have been developed that enable routine screening of thousands of drug pairs in an in vitro setting. In this chapter, we describe the workflow involved in screening a single agent versus a library of mechanistically annotated, investigation, and approved drugs using a full dose-response matrix scheme using viability as the readout. We provide details of the automation required to run the screen and the informatics required to process data from screening robot and subsequent analysis and visualization of the datasets.