Search

Developing highly efficient non-viral gene delivery reagents is still difficult for many hard-to-transfect cell types and, to date, has mostly been conducted via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development of devices or therapeutics by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a dataset of synthetic biodegradable polymers, poly(beta-amino ester)s (PBAEs), which have shown exciting promise for therapeutic gene delivery in vitro and in vivo. The data set includes polymer properties as inputs as well as polymeric nanoparticle transfection performance and nanoparticle toxicity in a range of cells as outputs. This data was used to train and evaluate several state-of-the-art machine learning algorithms for their ability to predict transfection and understand structure-function relationships. By developing an encoding scheme for vectorizing the structure of a PBAE polymer in a machine-readable format, we demonstrate that a random forest model can satisfactorily predict DNA transfection in vitro based on the chemical structure of the constituent PBAE polymer in a cell line dependent manner. Based on the model, we synthesized PBAE polymers and used them to form polymeric gene delivery nanoparticles that were predicted in silico to be successful. We validated the computational predictions in two cell lines in vitro, RAW 264.7 macrophages and Hep3B liver cancer cells, and found that the Spearman's R correlation between predicted and experimental transfection was 0.57 and 0.66 respectively. Thus, a computational approach that encoded chemical descriptors of polymers was able to demonstrate that in silico computational screening of polymeric nanomedicine compositions had utility in predicting de novo biological experiments. STATEMENT OF SIGNIFICANCE: Developing highly efficient non-viral gene delivery reagents is difficult for many hard-to-transfect cell types and, to date, has mostly been explored via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development for therapeutic or biomanufacturing purposes by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a large compiled PBAE DNA gene delivery nanoparticle dataset across many cell types to develop predictive models for transfection and nanoparticle cytotoxicity. We develop a novel computational pipeline to encode PBAE nanoparticles with chemical descriptors and demonstrate utility in a de novo experimental context., Competing Interests: Declaration of Competing Interest JJG is a co-inventor on patents filed by Johns Hopkins University related to technologies discussed in the manuscript. JJG is a co-founder, manager and CTO of Dome Therapeutics, co-founder, board member and CSO of Cove Therapeutics, co-founder and manager of OncoSwitch Therapeutics, and co-founder of WyveRNA Therapeutics. All other authors declare they have no competing interests. Any potential conflicts of interest are managed by the Johns Hopkins University Committee on Outside Interests., (Copyright © 2022. Published by Elsevier Ltd.)

Objective: Higher mortality in COVID-19 in men compared to women is recognized, but sex differences in cardiovascular events are less well established. We aimed to determine the independent contribution of sex to stroke, myocardial infarction and death in the setting of COVID-19 infection., Methods: We performed a retrospective cohort study of hospitalized COVID-19 patients in a racially/ethnically diverse population. Clinical features, laboratory markers and clinical events were initially abstracted from medical records, with subsequent clinician adjudication., Results: Of 2060 patients, myocardial injury (32% vs 23%, p = 0.019), acute myocardial infarction (2.7% vs 1.6%, p = 0.114), and ischemic stroke (1.8% vs 0.7%, p = 0.007) were more common in men vs women. In-hospital death occurred in 160 men (15%) vs 117 women (12%, p = 0.091). Men had higher odds of myocardial injury (odds ratio (OR) 2.04 [95% CI 1.43-2.91], p < 0.001), myocardial infarction (1.72 [95% CI 0.93-3.20], p = 0.085) and ischemic stroke (2.76 [95% CI 1.29-5.92], p = 0.009). Despite adjustment for demographics and cardiovascular risk factors, male sex predicted mortality (HR 1.33; 95% CI:1.01-1.74; p = 0.041). While men had significantly higher markers of inflammation, in sex-stratified analyses, increase in interleukin-6, C-reactive protein, ferritin and d-dimer were predictive of mortality and myocardial injury similarly in both sexes., Conclusions: Adjusted odds of myocardial injury, ischemic stroke and all-cause mortality, but not myocardial infarction, are significantly higher in men compared to women with COVID-19. Higher inflammatory markers are present in men but associated similarly with risk in both men and women. These data suggest that adverse cardiovascular outcomes in men vs. women are independent of cardiovascular comorbidities., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Adults with repaired tetralogy of Fallot (rTOF) are at increased risk for ventricular tachycardia (VT) due to fibrotic remodeling of the myocardium. However, the current clinical guidelines for VT risk stratification and subsequent implantable cardioverter-defibrillator deployment for primary prevention of sudden cardiac death in rTOF remain inadequate., Objective: The purpose of this study was to determine the feasibility of using an rTOF-specific virtual-heart approach to identify patients stratified incorrectly as being at low VT risk by current clinical criteria., Methods: This multicenter retrospective pilot study included 7 adult rTOF patients who were considered low risk for VT based on clinical criteria. Patient-specific computational heart models were generated from late gadolinium enhanced magnetic resonance imaging (LGE-MRI), incorporating the individual distribution of rTOF fibrotic remodeling in both ventricles. Simulations of rapid pacing determined VT inducibility. Model creation and simulations were performed by operators blinded to clinical outcome., Results: Two patients in the study experienced clinical VT. The virtual hearts constructed from LGE-MRI scans of 7 rTOF patients correctly predicted reentrant VT in the models from VT-positive patients and no arrhythmia in those from VT-negative patients. There were no statistically significant differences in clinical criteria commonly used to assess VT risk, including QRS duration and age, between patients who did and those who did not experience clinical VT., Conclusion: This study demonstrates the feasibility of image-based virtual-heart modeling in patients with congenital heart disease and structurally abnormal hearts. It highlights the potential of the methodology to improve VT risk stratification in patients with rTOF., (Copyright © 2019 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.)