The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).
Renal ischemic-reperfusion injury decreases the chances
of long-term
kidney graft survival and may lead to the loss of a transplanted kidney.
During organ excision, the cycle of warm ischemia from the donor and
cold ischemia is due to storage in a cold medium after revascularization
following organ transplantation. The reperfusion of the kidney graft
activates several pathways that generate reactive oxygen species,
forming a hypoxic-reperfusion injury. Animal models are generally
used to model and investigate renal hypoxic-reperfusion injury. However,
these models face ethical concerns and present a lack of robustness
and intraspecies genetic variations, among other limitations. We introduce
a microfluidics-based renal hypoxic-reperfusion (RHR) injury-on-chip
model to overcome current limitations. Primary human renal proximal
tubular epithelial cells and primary human endothelial cells were
cultured on the apical and basal sides of a porous membrane. Hypoxic
and normoxic cell culture media were used to create the RHR injury-on-chip
model. The disease model was validated by estimating various specific
hypoxic biomarkers of RHR. Furthermore, retinol, ascorbic acid, and
combinational doses were tested to devise a therapeutic solution for
RHR. We found that combinational vitamin therapy can decrease the
chances of RHR injury. The proposed RHR injury-on-chip model can serve
as an alternative to animal testing for injury investigation and the
identification of new therapies.
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