Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities.

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.

Genetic Correlations Among Corneal Biophysical Parameters and Anthropometric Traits.

Purpose: The genetic architecture of corneal dysfunction remains poorly understood. Epidemiological and clinical evidence suggests a relationship between corneal structural features and anthropometric measures. We used global and local genetic similarity analysis to identify genomic features that may underlie structural corneal dysfunction. Methods: We assembled genome-wide association study summary statistics for corneal features (central corneal thickness, corneal hysteresis [CH], corneal resistance factor [CRF], and the 3 mm index of keratometry) and anthropometric traits (body mass index, weight, and height) in Europeans. We calculated global genetic correlations (rg) between traits using linkage disequilibrium (LD) score regression and local genetic covariance using ρ-HESS, which partitions the genome and performs regression with LD regions. Finally, we identified genes located within regions of significant genetic covariance and analyzed patterns of tissue expression and pathway enrichment. Results: Global LD score regression revealed significant negative correlations between height and both CH (rg = -0.12; P = 2.0 × 10-7) and CRF (rg = -0.11; P = 6.9 × 10-7). Local analysis revealed 68 genomic regions exhibiting significant local genetic covariance between CRF and height, containing 2874 unique genes. Pathway analysis of genes in regions with significant local rg revealed enrichment among signaling pathways with known keratoconus associations, including cadherin and Wnt signaling, as well as enrichment of genes modulated by copper and zinc ions. Conclusions: Corneal biophysical parameters and height share a common genomic architecture, which may facilitate identification of disease-associated genes and therapies for corneal ectasias. Translational Relevance: Local genetic covariance analysis enables the identification of associated genes and therapeutic targets for corneal ectatic disease.

Integrative analysis of functional genomic screening and clinical data identifies a protective role for spironolactone in severe COVID-19.

We demonstrate that integrative analysis of CRISPR screening datasets enables network-based prioritization of prescription drugs modulating viral entry in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by developing a network-based approach called Rapid proXimity Guidance for Repurposing Investigational Drugs (RxGRID). We use our results to guide a propensity-score-matched, retrospective cohort study of 64,349 COVID-19 patients, showing that a top candidate drug, spironolactone, is associated with improved clinical prognosis, measured by intensive care unit (ICU) admission and mechanical ventilation rates. Finally, we show that spironolactone exerts a dose-dependent inhibitory effect on viral entry in human lung epithelial cells. Our RxGRID method presents a computational framework, implemented as an open-source software package, enabling genomics researchers to identify drugs likely to modulate a molecular phenotype of interest based on high-throughput screening data. Our results, derived from this method and supported by experimental and clinical analysis, add additional supporting evidence for a potential protective role of the potassium-sparing diuretic spironolactone in severe COVID-19.

Deep Learning for Localized Detection of Optic Disc Hemorrhages.

PURPOSE: To develop an automated deep learning system for detecting the presence and location of disc hemorrhages in optic disc photographs. DESIGN: Development and testing of a deep learning algorithm. METHODS: Optic disc photos (597 images with at least 1 disc hemorrhage and 1075 images without any disc hemorrhage from 1562 eyes) from 5 institutions were classified by expert graders based on the presence or absence of disc hemorrhage. The images were split into training (n = 1340), validation (n = 167), and test (n = 165) datasets. Two state-of-the-art deep learning algorithms based on either object-level detection or image-level classification were trained on the dataset. These models were compared to one another and against 2 independent glaucoma specialists. We evaluated model performance by the area under the receiver operating characteristic curve (AUC). AUCs were compared with the Hanley-McNeil method. RESULTS: The object detection model achieved an AUC of 0.936 (95% CI = 0.857-0.964) across all held-out images (n = 165 photographs), which was significantly superior to the image classification model (AUC = 0.845, 95% CI = 0.740-0.912; P = .006). At an operating point selected for high specificity, the model achieved a specificity of 94.3% and a sensitivity of 70.0%, which was statistically indistinguishable from an expert clinician (P = .7). At an operating point selected for high sensitivity, the model achieves a sensitivity of 96.7% and a specificity of 73.3%. CONCLUSIONS: An autonomous object detection model is superior to an image classification model for detecting disc hemorrhages, and performed comparably to 2 clinicians.

Association between spironolactone use and COVID-19 outcomes in population-scale claims data: a retrospective cohort study.

Background: Spironolactone has been proposed as a potential modulator of SARS-CoV-2 cellular entry. We aimed to measure the effect of spironolactone use on the risk of adverse outcomes following COVID-19 hospitalization. Methods: We performed a retrospective cohort study of COVID-19 outcomes for patients with or without exposure to spironolactone, using population-scale claims data from the Komodo Healthcare Map. We identified all patients with a hospital admission for COVID-19 in the study window, defining treatment status based on spironolactone prescription orders. The primary outcomes were progression to respiratory ventilation or mortality during the hospitalization. Odds ratios (OR) were estimated following either 1:1 propensity score matching (PSM) or multivariable regression. Subgroup analysis was performed based on age, gender, body mass index (BMI), and dominant SARS-CoV-2 variant. Findings: Among 898,303 eligible patients with a COVID-19-related hospitalization, 16,324 patients (1.8%) had a spironolactone prescription prior to hospitalization. 59,937 patients (6.7%) met the ventilation endpoint, and 26,515 patients (3.0%) met the mortality endpoint. Spironolactone use was associated with a significant reduction in odds of both ventilation (OR 0.82; 95% CI: 0.75-0.88; p < 0.001) and mortality (OR 0.88; 95% CI: 0.78-0.99; p = 0.033) in the PSM analysis, supported by the regression analysis. Spironolactone use was associated with significantly reduced odds of ventilation for all age groups, men, women, and non-obese patients, with the greatest protective effects in younger patients, men, and non-obese patients. Interpretation: Spironolactone use was associated with a protective effect against ventilation and mortality following COVID-19 infection, amounting to up to 64% of the protective effect of vaccination against ventilation and consistent with an androgen-dependent mechanism. The findings warrant initiation of large-scale randomized controlled trials to establish a potential therapeutic role for spironolactone in COVID-19 patients.

Assessment of Team Dynamics and Operative Efficiency in Hip and Knee Arthroplasty.

Importance: Surgical team communication is a critical component of operative efficiency. The factors underlying optimal communication, including team turnover, role composition, and mutual familiarity, remain underinvestigated in the operating room. Objective: To assess staff turnover, trainee involvement, and surgeon staff preferences in terms of intraoperative efficiency. Design, Setting, and Participants: Retrospective analysis of staff characteristics and operating times for all total joint arthroplasties was performed at a tertiary academic medical center by 5 surgeons from January 1 to December 31, 2018. Data were analyzed from May 1, 2021, to February 18, 2022. The study included cases with primary total hip arthroplasties (THAs) and primary total knee arthroplasties (TKAs) comprising all primary total joint arthroplasties performed over the 1-year study interval. Exposures: Intraoperative turnover among nonsurgical staff, presence of trainees, and presence of surgeon-preferred staff. Main Outcomes and Measures: Incision time, procedure time, and room time for each surgery. Multivariable regression analyses between operative duration, presence of surgeon-preferred staff, and turnover among nonsurgical personnel were conducted. Results: A total of 641 cases, including 279 THAs (51% female; median age, 64 [IQR, 56.3-71.5] years) and 362 TKAs (66% [238] female; median age, 68 [IQR, 61.1-74.1] years) were considered. Turnover among circulating nurses was associated with a significant increase in operative duration in both THAs and TKAs, with estimated differences of 19.6 minutes (SE, 3.5; P < .001) of room time in THAs and 14.0 minutes (SE, 3.1; P < .001) of room time in TKAs. The presence of a preferred anesthesiologist or surgical technician was associated with significant decreases of 26.5 minutes (SE, 8.8; P = .003) of procedure time and 12.6 minutes (SE, 4.0; P = .002) of room time, respectively, in TKAs. The presence of a surgeon-preferred vendor was associated with a significant increase in operative duration in both THAs (26.3 minutes; SE, 7.3; P < .001) and TKAs (29.6 minutes; SE, 9.6; P = .002). Conclusions and Relevance: This study found that turnover among operative staff is associated with procedural inefficiency. In contrast, the presence of surgeon-preferred staff may facilitate intraoperative efficiency. Administrative or technologic support of perioperative communication and team continuity may help improve operative efficiency.

Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19.

MOTIVATION: Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. RESULTS: We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19. AVAILABILITY AND IMPLEMENTATION: GSPA is available for download as a command-line Python package at https://github.com/henrycousins/gspa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Influence of team composition on turnover and efficiency of total hip and knee arthroplasty.

AIMS: Surgical costs are a major component of healthcare expenditures in the USA. Intraoperative communication is a key factor contributing to patient outcomes. However, the effectiveness of communication is only partially determined by the surgeon, and understanding how non-surgeon personnel affect intraoperative communication is critical for the development of safe and cost-effective staffing guidelines. Operative efficiency is also dependent on high-functioning teams and can offer a proxy for effective communication in highly standardized procedures like primary total hip and knee arthroplasty. We aimed to evaluate how the composition and dynamics of surgical teams impact operative efficiency during arthroplasty. METHODS: We performed a retrospective review of staff characteristics and operating times for 112 surgeries (70 primary total hip arthroplasties (THAs) and 42 primary total knee arthroplasties (TKAs)) conducted by a single surgeon over a one-year period. Each surgery was evaluated in terms of operative duration, presence of surgeon-preferred staff, and turnover of trainees, nurses, and other non-surgical personnel, controlling cases for body mass index, presence of osteoarthritis, and American Society of Anesthesiologists (ASA) score. RESULTS: Turnover among specific types of operating room staff, including the anaesthesiologist (p = 0.011), circulating nurse (p = 0.027), and scrub nurse (p = 0.006), was significantly associated with increased operative duration. Furthermore, the presence of medical students and nursing students were associated with improved intraoperative efficiency in TKA (p = 0.048) and THA (p = 0.015), respectively. The presence of surgical fellows (p > 0.05), vendor representatives (p > 0.05), and physician assistants (p > 0.05) had no effect on intraoperative efficiency. Finally, the presence of the surgeon's 'preferred' staff did not significantly shorten operative duration, except in the case of residents (p = 0.043). CONCLUSION: Our findings suggest that active management of surgical team turnover and composition may provide a means of improving intraoperative efficiency during THA and TKA. Cite this article: Bone Joint J 2021;103-B(2):347-352.

Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns.

BACKGROUND: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. OBJECTIVE: We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. METHODS: We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. RESULTS: Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. CONCLUSIONS: Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.

Nailfold Capillary Morphology in Alzheimer's Disease Dementia.

BACKGROUND: Cerebrovascular disease (CVD) is highly comorbid with Alzheimer's disease (AD), yet its role is not entirely understood. Nailfold video capillaroscopy (NVC) is a noninvasive method of live imaging the capillaries near the fingernail's cuticle and may help to describe further vascular contributions to AD. OBJECTIVE: To examine finger nailfold capillary morphology using NVC in subjects with AD dementia, mild cognitive impairment (MCI), and normal cognition (NC). METHODS: We evaluated nailfold capillary hemorrhages, avascular zones ≥100 microns, and degree of tortuosity in 28 NC, 15 MCI, and 18 AD dementia subjects using NVC. Tortuosity was measured with a semi-quantitative rating scale. To assess the relation between nailfold capillary morphological features and diagnostic grouping, univariate and multivariable logistic regression models were fit to the data. RESULTS: 56% of subjects with AD dementia compared to 14% with NC and 13% with MCI displayed moderate to severe tortuosity. Greater severity of tortuosity was associated with 10.6-fold (95% confidence interval [CI]: 2.4, 46.2; p = 0.0018) and 7.4-fold (95% CI: 1.3, 41.3; p = 0.023) increased odds of AD dementia relative to NC and MCI, respectively, after adjusting for multiple covariates. CONCLUSION: Greater nailfold capillary tortuosity was found in participants with AD dementia compared to those with MCI or NC. These data provide preliminary evidence of a systemic microvasculopathy in AD that may be noninvasively and inexpensively evaluated through NVC.

Toward Precision Medicine for Neurological and Neuropsychiatric Disorders.

The genetic complexity, clinical variability, and inaccessibility of affected tissue in neurodegenerative and neuropsychiatric disorders have largely prevented the development of effective disease-modifying therapeutics. A precision medicine approach that integrates genomics, deep clinical phenotyping, and patient stem cell models may facilitate identification of underlying biological drivers and targeted drug development.

Role for Wnt Signaling in Retinal Neuropil Development: Analysis via RNA-Seq and In Vivo Somatic CRISPR Mutagenesis.

Screens for genes that orchestrate neural circuit formation in mammals have been hindered by practical constraints of germline mutagenesis. To overcome these limitations, we combined RNA-seq with somatic CRISPR mutagenesis to study synapse development in the mouse retina. Here synapses occur between cellular layers, forming two multilayered neuropils. The outer neuropil, the outer plexiform layer (OPL), contains synapses made by rod and cone photoreceptor axons on rod and cone bipolar dendrites, respectively. We used RNA-seq to identify selectively expressed genes encoding cell surface and secreted proteins and CRISPR-Cas9 electroporation with cell-specific promoters to assess their roles in OPL development. Among the genes identified in this way are Wnt5a and Wnt5b. They are produced by rod bipolars and activate a non-canonical signaling pathway in rods to regulate early OPL patterning. The approach we use here can be applied to other parts of the brain.