Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

PIKfyve-specific inhibitors restrict replication of multiple coronaviruses in vitro but not in a murine model of COVID-19

The ongoing COVID-19 pandemic has claimed more than 6 million lives and continues to test the world economy and healthcare systems. To combat this pandemic, the biological research community has shifted efforts to the development of medical countermeasures, including vaccines and therapeutics. However, to date, the only small molecules approved for the treatment of COVID-19 in the United States are the nucleoside analogue Remdesivir and the protease inhibitor Paxlovid, though multiple compounds have received Emergency Use Authorization and many more are currently being tested in human efficacy trials. One such compound, Apilimod, is being considered as a COVID-19 therapeutic in a Phase II efficacy trial. However, at the time of writing, there are no published efficacy data in human trials or animal COVID-19 models. Here we show that, while Apilimod and other PIKfyve inhibitors have potent antiviral activity in various cell lines against multiple human coronaviruses, these compounds worsen disease in a COVID-19 murine model when given prophylactically or therapeutically.

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients

Importance

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit.

Objectives

To test if different statins differ in their ability to exert protective effects based on molecular computational predictions and electronic medical record analysis.

Main Outcomes and Measures

A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2, with a total of 2,436 drugs investigated. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus.

Results

Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells.

Conclusions and Relevance

Different statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Modeling pulmonary cystic fibrosis in a human lung airway-on-a-chip

BACKGROUND

Cystic fibrosis (CF) is a genetic disease caused by mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR), which results in impaired airway mucociliary clearance, inflammation, infection, and respiratory insufficiency. The development of new therapeutics for CF are limited by the lack of reliable preclinical models that recapitulate the structural, immunological, and bioelectrical features of human CF lungs.

METHODS

We leveraged organ-on-a-chip technology to develop a microfluidic device lined by primary human CF bronchial epithelial cells grown under an air-liquid interface and interfaced with pulmonary microvascular endothelial cells (CF Airway Chip) exposed to fluid flow. The responses of CF and healthy Airway Chips were analyzed in the presence or absence of polymorphonuclear leukocytes (PMNs) and the bacterial pathogen, Pseudomonas aeruginosa.

RESULTS

The CF Airway Chip faithfully recapitulated many features of the human CF airways, including enhanced mucus accumulation, increased cilia density, and a higher ciliary beating frequency compared to chips lined by healthy bronchial epithelial cells. The CF chips also secreted higher levels of IL-8, which was accompanied by enhanced PMN adhesion to the endothelium and transmigration into the airway compartment. In addition, CF Airway Chips provided a more favorable environment for Pseudomonas aeruginosa growth, which resulted in enhanced secretion of inflammatory cytokines and recruitment of PMNs to the airway.

CONCLUSIONS

The human CF Airway Chip may provide a valuable preclinical tool for pathophysiology studies as well as for drug testing and personalized medicine.

Modeling pulmonary cystic fibrosis in a human lung airway-on-a-chip: Cystic fibrosis airway chip

BACKGROUND

Cystic fibrosis (CF) is a genetic disease caused by mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR), which results in impaired airway mucociliary clearance, inflammation, infection, and respiratory insufficiency. The development of new therapeutics for CF are limited by the lack of reliable preclinical models that recapitulate the structural, immunological, and bioelectrical features of human CF lungs.

METHODS

We leveraged organ-on-a-chip technology to develop a microfluidic device lined by primary human CF bronchial epithelial cells grown under an air-liquid interface and interfaced with pulmonary microvascular endothelial cells (CF Airway Chip) exposed to fluid flow. The responses of CF and healthy Airway Chips were analyzed in the presence or absence of polymorphonuclear leukocytes (PMNs) and the bacterial pathogen, Pseudomonas aeruginosa.

RESULTS

The CF Airway Chip faithfully recapitulated many features of the human CF airways, including enhanced mucus accumulation, increased cilia density, and a higher ciliary beating frequency compared to chips lined by healthy bronchial epithelial cells. The CF chips also secreted higher levels of IL-8, which was accompanied by enhanced PMN adhesion to the endothelium and transmigration into the airway compartment. In addition, CF Airway Chips provided a more favorable environment for Pseudomonas aeruginosa growth, which resulted in enhanced secretion of inflammatory cytokines and recruitment of PMNs to the airway.

CONCLUSIONS

The human CF Airway Chip may provide a valuable preclinical tool for pathophysiology studies as well as for drug testing and personalized medicine.

A human-airway-on-a-chip for the rapid identification of candidate antiviral therapeutics and prophylactics

The rapid repurposing of antivirals is particularly pressing during pandemics. However, rapid assays for assessing candidate drugs typically involve in vitro screens and cell lines that do not recapitulate human physiology at the tissue and organ levels. Here we show that a microfluidic bronchial-airway-on-a-chip lined by highly differentiated human bronchial-airway epithelium and pulmonary endothelium can model viral infection, strain-dependent virulence, cytokine production and the recruitment of circulating immune cells. In airway chips infected with influenza A, the co-administration of nafamostat with oseltamivir doubled the treatment-time window for oseltamivir. In chips infected with pseudotyped severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), clinically relevant doses of the antimalarial drug amodiaquine inhibited infection but clinical doses of hydroxychloroquine and other antiviral drugs that inhibit the entry of pseudotyped SARS-CoV-2 in cell lines under static conditions did not. We also show that amodiaquine showed substantial prophylactic and therapeutic activities in hamsters challenged with native SARS-CoV-2. The human airway-on-a-chip may accelerate the identification of therapeutics and prophylactics with repurposing potential.

Interpretation of Post-operative Distal Humerus Radiographs After Internal Fixation: Prediction of Later Loss of Fixation

PURPOSE

Stable fixation of distal humerus fracture fragments is necessary for adequate healing and maintenance of reduction. The purpose of this study was to measure the reliability and accuracy of interpretation of postoperative radiographs to predict which implants will loosen or break after operative treatment of bicolumnar distal humerus fractures. We also addressed agreement among surgeons regarding which fracture fixation will loosen or break and the influence of years in independent practice, location of practice, and so forth.

METHODS

A total of 232 orthopedic residents and surgeons from around the world evaluated 24 anteroposterior and lateral radiographs of distal humerus fractures on a Web-based platform to predict which implants would loosen or break. Agreement among observers was measured using the multi-rater kappa measure.

RESULTS

The sensitivity of prediction of failure of fixation of distal humerus fracture on radiographs was 63%, specificity was 53%, positive predictive value was 36%, the negative predictive value was 78%, and accuracy was 56%. There was fair interobserver agreement (κ = 0.27) regarding predictions of failure of fixation of distal humerus fracture on radiographs. Interobserver variability did not change when assessed for the various subgroups.

CONCLUSIONS

When experienced and skilled surgeons perform fixation of type C distal humerus fracture, the immediate postoperative radiograph is not predictive of fixation failure. Reoperation based on the probability of failure might not be advisable.

TYPE OF STUDY/LEVEL OF EVIDENCE

Diagnostic III.