Drug-induced phospholipidosis is not correlated with the inhibition of SARS-CoV-2 - inhibition of SARS-CoV-2 is cell line-specific

Precision cut lung slices: an integrated ex vivo model for studying lung physiology, pharmacology, disease pathogenesis and drug discovery

Precision Cut Lung Slices (PCLS) have emerged as a sophisticated and physiologically relevant ex vivo model for studying the intricacies of lung diseases, including fibrosis, injury, repair, and host defense mechanisms. This innovative methodology presents a unique opportunity to bridge the gap between traditional in vitro cell cultures and in vivo animal models, offering researchers a more accurate representation of the intricate microenvironment of the lung. PCLS require the precise sectioning of lung tissue to maintain its structural and functional integrity. These thin slices serve as invaluable tools for various research endeavors, particularly in the realm of airway diseases. By providing a controlled microenvironment, precision-cut lung slices empower researchers to dissect and comprehend the multifaceted interactions and responses within lung tissue, thereby advancing our understanding of pulmonary pathophysiology.

Drug repurposing platform for deciphering the druggable SARS-CoV-2 interactome

The coronavirus disease 2019 (COVID-19) pandemic has heavily challenged the global healthcare system. Despite the vaccination programs, the new virus variants are circulating. Further research is required for understanding of the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and for discovery of therapeutic agents against the virus. Here, we took advantage of drug repurposing to identify if existing drugs could inhibit SARS-CoV-2 infection. We established an open high throughput platform for in vitro screening of drugs against SARS-CoV-2 infection. We screened ∼1000 drugs for their ability to inhibit SARS-CoV-2-induced cell death in the African green monkey kidney cell line (Vero-E6), analyzed how the hit compounds affect the viral N (nucleocapsid) protein expression in human cell lines using high-content microscopic imaging and analysis, determined the hit drug targets in silico, and assessed their ability to cause phospholipidosis, which can interfere with the viral replication. Duvelisib was found by in silico interaction assay as a potential drug targeting virus-host protein interactions. The predicted interaction between PARP1 and S protein, affected by Duvelisib, was further validated by immunoprecipitation. Our results represent a rapidly applicable platform for drug repurposing and evaluation of the new emerging viruses' responses to the drugs. Further in silico studies help us to discover the druggable host pathways involved in the infectious cycle of SARS-CoV-2.

Prolonged airway explant culture enables study of health, disease, and viral pathogenesis

In vitro models play a major role in studying airway physiology and disease. However, the native lung's complex tissue architecture and non-epithelial cell lineages are not preserved in these models. Ex vivo tissue models could overcome in vitro limitations, but methods for long-term maintenance of ex vivo tissue has not been established. We describe methods to culture human large airway explants, small airway explants, and precision-cut lung slices for at least 14 days. Human airway explants recapitulate genotype-specific electrophysiology, characteristic epithelial, endothelial, stromal and immune cell populations, and model viral infection after 14 days in culture. These methods also maintain mouse, rabbit, and pig tracheal explants. Notably, intact airway tissue can be cryopreserved, thawed, and used to generate explants with recovery of function 14 days post-thaw. These studies highlight the broad applications of airway tissue explants and their use as translational intermediates between in vitro and in vivo studies.

AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis

Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.