The Use of Stem Cell-Derived Organoids in Disease Modeling: An Update

Organoids represent one of the most important advancements in the field of stem cells during the past decade. They are three-dimensional in vitro culturing models that originate from self-organizing stem cells and can mimic the in vivo structural and functional specificities of body organs. Organoids have been established from multiple adult tissues as well as pluripotent stem cells and have recently become a powerful tool for studying development and diseases in vitro, drug screening, and host–microbe interaction. The use of stem cells—that have self-renewal capacity to proliferate and differentiate into specialized cell types—for organoids culturing represents a major advancement in biomedical research. Indeed, this new technology has a great potential to be used in a multitude of fields, including cancer research, hereditary and infectious diseases. Nevertheless, organoid culturing is still rife with many challenges, not limited to being costly and time consuming, having variable rates of efficiency in generation and maintenance, genetic stability, and clinical applications. In this review, we aim to provide a synopsis of pluripotent stem cell-derived organoids and their use for disease modeling and other clinical applications.

Simple rapid in vitro screening method for SARS-CoV-2 anti-virals that identifies potential cytomorbidity-associated false positives

Background The international SARS-CoV-2 pandemic has resulted in an urgent need to identify new anti-viral drugs for treatment of COVID-19. The initial step to identifying potential candidates usually involves in vitro screening that includes standard cytotoxicity controls. Under-appreciated is that viable, but stressed or otherwise compromised cells, can also have a reduced capacity to replicate virus. A refinement proposed herein for in vitro drug screening thus includes a simple growth assay to identify drug concentrations that cause cellular stress or “cytomorbidity”, as distinct from cytotoxicity or loss of viability. Methods A simple rapid bioassay is presented for antiviral drug screening using Vero E6 cells and inhibition of SARS-CoV-2 induced cytopathic effects (CPE) measured using crystal violet staining. We use high cell density for cytotoxicity assays, and low cell density for cytomorbidity assays. Results The assay clearly illustrated the anti-viral activity of remdesivir, a drug known to inhibit SARS-CoV-2 replication. In contrast, nitazoxanide, oleuropein, cyclosporine A and ribavirin all showed no ability to inhibit SARS-CoV-2 CPE. Hydroxychloroquine, cyclohexamide, didemnin B, γ-mangostin and linoleic acid were all able to inhibit viral CPE at concentrations that did not induce cytotoxicity. However, these drugs inhibited CPE at concentrations that induced cytomorbidity, indicating non-specific anti-viral activity. Conclusions We describe the methodology for a simple in vitro drug screening assay that identifies potential anti-viral drugs via their ability to inhibit SARS-CoV-2-induced CPE. The additional growth assay illustrated how several drugs display anti-viral activity at concentrations that induce cytomorbidity. For instance, hydroxychloroquine showed anti-viral activity at concentrations that slow cell growth, arguing that its purported in vitro anti-viral activity arises from non-specific impairment of cellular activities. The cytomorbidity assay can therefore rapidly exclude potential false positives.

Targeting the Proteasome in Advanced Renal Cell Carcinoma: Complexity and Limitations of Patient-Individualized Preclinical Drug Discovery

Background: Systemic treatment options for metastatic renal cell carcinoma (RCC) have significantly expanded in recent years. However, patients refractory to tyrosine kinase and immune checkpoint inhibitors still have limited treatment options and patient-individualized approaches are largely missing. Patients and Methods: In vitro drug screening of tumor-derived short-term cultures obtained from seven patients with clear cell RCC was performed. For one patient, a patient-derived xenograft (PDX) mouse model was established for in vivo validation experiments. Drug effects were further investigated in established RCC cell lines. Results: The proteasome inhibitor carfilzomib was among the top hits identified in three of four patients in which an in vitro drug screening could be performed successfully. Carfilzomib also showed significant acute and long-term cytotoxicity in established RCC cell lines. The in vivo antitumoral activity of carfilzomib was confirmed in a same-patient PDX model. The cytotoxicity of carfilzomib was found to correlate with the level of accumulation of ubiquitinated proteins. Conclusions: In this proof-of-concept study, we show that patient-individualized in vitro drug screening and preclinical validation is feasible. However, the fact that carfilzomib failed to deliver a clinical benefit in RCC patients in a recent phase II trial unrelated to the present study underscores the complexities and limitations of this strategy.

Heterogeneous Off-Target Effects of Ultra-Low Dose Dimethyl Sulfoxide (DMSO) on Targetable Signaling Events in Lung Cancer In Vitro Models

Targetable alterations in cancer offer novel opportunities to the drug discovery process. However, pre-clinical testing often requires solubilization of these drugs in cosolvents like dimethyl sulfoxide (DMSO). Using a panel of cell lines commonly used for in vitro drug screening and pre-clinical testing, we explored the DMSO off-target effects on functional signaling networks, drug targets, and downstream substrates. Eight Non-Small Cell Lung Cancer (NSCLC) cell lines were incubated with three concentrations of DMSO (0.0008%, 0.002%, and 0.004% v/v) over time. Expression and activation levels of 187 proteins, of which 137 were kinases and downstream substrates, were captured using the Reverse Phase Protein Array (RPPA). The DMSO effect was heterogeneous across cell lines and varied based on concentration, exposure time, and cell line. Of the 187 proteins measured, all were statistically different in at least one comparison at the highest DMSO concentration, followed by 99.5% and 98.9% at lower concentrations. Only 46% of the proteins were found to be statistically different in more than 5 cell lines, indicating heterogeneous response across models. These cell line specific alterations modulate response to in vitro drug screening. Ultra-low DMSO concentrations have broad and heterogeneous effects on targetable signaling proteins. Off-target effects need to be carefully evaluated in pre-clinical drug screening and testing.

Targeting fibrosis in the Duchenne Muscular Dystrophy mice model: an uphill battle

AimFibrosis is the most common complication from chronic diseases, and yet no therapy capable of mitigating its effects is available. Our goal is to unveil specific signallings regulating the fibrogenic process and to identify potential small molecule candidates that block fibrogenic differentiation of fibro/adipogenic progenitors.MethodWe performed a large-scale drug screen using muscle-resident fibro/adipogenic progenitors from a mouse model expressing EGFP under the Collagen1a1 promotor. We first confirmed that the EGFP was expressed in response to TGFβ1 stimulation in vitro. Then we treated cells with TGFβ1 alone or with drugs from two libraries of known compounds. The drugs ability to block the fibrogenic differentiation was quantified by imaging and flow cytometry. From a two-rounds screening, positive hits were tested in vivo in the mice model for the Duchenne muscular dystrophy (mdx mice). The histopathology of the muscles was assessed with picrosirius red (fibrosis) and laminin staining (myofiber size).Key findingsFrom the in vitro drug screening, we identified 21 drugs and tested 3 in vivo on the mdx mice. None of the three drugs significantly improved muscle histopathology.SignificanceThe in vitro drug screen identified various efficient compounds, none of them strongly inhibited fibrosis in skeletal muscle of mdx mice. To explain these observations, we hypothesize that in Duchenne Muscular Dystrophy, in which fibrosis is a secondary event due to chronic degeneration and inflammation, the drugs tested could have adverse effect on regeneration or inflammation, balancing off any positive effects and leading to the absence of significant results.

In vitro models of intestinal epithelium: Toward bioengineered systems

The intestinal epithelium, the fastest renewing tissue in human, is a complex tissue hosting multiple cell types with a dynamic and multiparametric microenvironment, making it particularly challenging to recreate in vitro. Convergence of recent advances in cellular biology and microfabrication technologies have led to the development of various bioengineered systems to model and study the intestinal epithelium. Theses microfabricated in vitro models may constitute an alternative to current approaches for studying the fundamental mechanisms governing intestinal homeostasis and pathologies, as well as for in vitro drug screening and testing. Herein, we review the recent advances in bioengineered in vitro intestinal models.

A novel decatenation assay for DNA topoisomerases using a singly-linked catenated substrate

Decatenation is a crucial in vivo reaction of DNA topoisomerases in DNA replication and is frequently used in in vitro drug screening. Usually this reaction is monitored using kinetoplast DNA as a substrate, although this assay has several limitations. Here we have engineered a substrate for Tn 3 resolvase that generates a singly-linked catenane that can readily be purified from the DNA substrate after restriction enzyme digestion and centrifugation. We show that this catenated substrate can be used with high sensitivity in topoisomerase assays and drug-inhibition assays.

Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.