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Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

The National Clinical Cohort Collaborative (N3C) uses privacy-preserving record linkage (PPRL) to formulate longitudinal patient histories and for de-duplication across disparate data including electronic health records, administrative claims, and mortality data. The N3C employs a linkage honest broker implementation model which separates the entity holding de-identified PPRL tokens, from the entity holding the substantive data payloads. As a result of this data governance structure, linkage quality assessments are necessarily distributed into 2 distinct processes: 1) Linkage Honest Broker receipt and processing of linkages, 2) Data Aggregation entity assembling data based on received linkage crosswalks. Data is released on a weekly basis. This presentation will focus on Step 2 for the Mortality data linkages where the downstream data linkage pipelines must assess linkage quality across several dimensions of quality, focused on plausibility. There are 4 types of mortality data spanning the electronic health record, government Social Security Administration death master files, third-party obituary data, and administrative claims. Each of these sources has different levels of authoritativeness and data latency, compounding the challenges of reconciling fact of death including accurate death reporting dates. To address these challenges, we implemented an automated linkage quality pipeline which generates reports that assess linkage plausibility across the various data types, classifying concordance and discordance between data sources including age, dates of death, surfacing plausible and non-plausible activities post-death. Sharing our real-world implementation will aid conference attendees in understanding how to plan for such activities when considering specific implementation models for privacy-preserving linkages.

Data centralization is increasingly non-viable in the changing landscape of data privacy and data governance. New models of data sharing are needed, in particular when privacy-preserving record linkages (PPRL) are being used to connect data within restricted data enclaves and public open enclaves. We share a privacy-preserving data-sharing infrastructure that employs a linkage honest broker implementation of PPRL connecting structured electronic health record data between the National Center for Advancing Translational Science (NCATS) National Clinical Cohort Collaborative (N3C), a restricted data enclave, with the National Cancer Institute (NCI) Cancer Imaging Archive (TCIA), a public open enclave. In particular we will address: • Data governance models needed when linking data from restricted vs. public enclaves • Specific privacy-preserving consideration when linking to imaging data which contain specific structured data and imaging considerations • Privacy-preserving data aggregation when combining multi-modal data from restricted data enclaves with publicly discoverable imaging data

Abstract Combination of anti-cancer drugs is broadly seen as way to overcome the often-limited efficacy of single agents. The design and testing of combinations are however very challenging. Here we present a uniquely large dataset screening over 5000 targeted agent combinations across 81 non-small cell lung cancer cell lines. Our analysis reveals a profound heterogeneity of response across the tumor models. Notably, combinations very rarely result in a strong gain in efficacy over the range of response observable with single agents. Importantly, gain of activity over single agents is more often seen when co-targeting functionally proximal genes, offering a strategy for designing more efficient combinations. Because combinatorial effect is strongly context specific, tumor specificity should be achievable. The resource provided, together with an additional validation screen sheds light on major challenges and opportunities in building efficacious combinations against cancer and provides an opportunity for training computational models for synergy prediction.

Transepithelial electrical resistance (TEER) is a widely used technique for quantifying the permeability of epithelial and endothelial cell layers. However, traditional methods of measuring TEER are limited to single timepoint measurements and can subject cells to an altered environment during the measurement. Here, we assessed the validity of TEER measurements by the ECIS TEER96 device, which is designed to take continuous TEER measurements of a cell culture system in a standard laboratory incubator. We found that the instrument accurately measures TEER across TEER values ranging from 10 to 2050 Ω*cm2 and is more accurate than the manual epithelial voltohmmeter electrode at high TEER values. Furthermore, the high-resolution measurements provided by the device allowed for a unique insight into the mechanisms and kinetics of cells in vitro. To demonstrate the continuous measurement capability of the device, we tracked the formation of an MDCKI cell monolayer until TEER plateaued. Furthermore, we treated Caco-2 monolayers with different concentrations of DMSO and the antimicrobial and surfactant compound benzethonium chloride to measure disruptions to barrier integrity. Treatment of both compounds resulted in concentration-dependent loss of barrier integrity. Our results suggest that the ECIS TEER96 device is a reliable and convenient option for measuring TEER in cell cultures and can provide valuable insights into the behavior of cells in vitro. This technology will be especially useful for increasing throughput of drug permeability assays, inflammation studies, and gaining better understanding of disease states in a cell culture system.

Abstract Background Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. Methods Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. Results We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. Conclusions The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

Effective small molecule therapies to combat the SARS-CoV-2 infection are still lacking as the COVID-19 pandemic continues globally. High throughput screening assays are needed for lead discovery and optimization of small molecule SARS-CoV-2 inhibitors. In this work, we have applied viral pseudotyping to establish a cell-based SARS-CoV-2 entry assay. Here, the pseudotyped particles (PP) contain SARS-CoV-2 spike in a membrane enveloping both the murine leukemia virus (MLV) gag-pol polyprotein and luciferase reporter RNA. Upon addition of PP to HEK293-ACE2 cells, the SARS-CoV-2 spike protein binds to the ACE2 receptor on the cell surface, resulting in priming by host proteases to trigger endocytosis of these particles, and membrane fusion between the particle envelope and the cell membrane. The internalized luciferase reporter gene is then expressed in cells, resulting in a luminescent readout as a surrogate for spike-mediated entry into cells. This SARS-CoV-2 PP entry assay can be executed in a biosafety level 2 containment lab for high throughput screening. From a collection of 5,158 approved drugs and drug candidates, our screening efforts identified 7 active compounds that inhibited the SARS-CoV-2-S PP entry. Of these seven, six compounds were active against live replicating SARS-CoV-2 virus in a cytopathic effect assay. Our results demonstrated the utility of this assay in the discovery and development of SARS-CoV-2 entry inhibitors as well as the mechanistic study of anti-SARS-CoV-2 compounds. Additionally, particles pseudotyped with spike proteins from SARS-CoV-2 B.1.1.7 and B.1.351 variants were prepared and used to evaluate the therapeutic effects of viral entry inhibitors.

Understanding the infection parameters and host responses against SARS-CoV-2 require data from large cohorts using standardized methods. Here, the authors optimize a serum ELISA protocol that has minimal cross-reactivity and flexible sample collection workflow in an attempt to standardize data generation and help inform on COVID-19 pandemic and immunity.

In the fall of 2014, an international news agency reported that patients suffering from Ebola virus disease (EVD) in Liberia were treated successfully with lamivudine, an antiviral drug used to treat human immunodeficiency virus-1 and hepatitis B virus infections. According to the report, 13 out of 15 patients treated with lamivudine survived and were declared free from Ebola virus disease. In this study, the anti-Ebola virus (EBOV) activity of lamivudine and another antiretroviral, zidovudine, were evaluated in a diverse set of cell lines against two variants of wild-type EBOV. Variable assay parameters were assessed to include different multiplicities of infection, lengths of inoculation times, and durations of dosing. At a multiplicity of infection of 1, lamivudine and zidovudine had no effect on EBOV propagation in Vero E6, Hep G2, or HeLa cells, or in primary human monocyte-derived macrophages. At a multiplicity of infection of 0.1, zidovudine demonstrated limited anti-EBOV activity in Huh 7 cells. Under certain conditions, lamivudine had low anti-EBOV activity at the maximum concentration tested (320 μM). However, lamivudine never achieved greater than 30% viral inhibition, and the activity was not consistently reproducible. Combination of lamivudine and zidovudine showed no synergistic antiviral activity. Independently, a set of in vitro experiments testing lamivudine and zidovudine for antiviral activity against an Ebola-enhanced green fluorescent protein reporter virus was performed at the Centers for Disease Control and Prevention. No antiviral activity was observed for either compound. A study evaluating the efficacy of lamivudine in a guinea pig model of EVD found no survival benefit. This lack of benefit was observed despite plasma lamivudine concentrations in guinea pig of about 4 μg/ml obtained in a separately conducted pharmacokinetics study. These studies found no evidence to support the therapeutic use of lamivudine for the treatment of EVD.

Phosphoinositide kinases regulate diverse cellular functions and are important targets for therapeutic development for diseases, such as diabetes and cancer. Preparation of the lipid substrate is crucial for the development of a robust and miniaturizable lipid kinase assay. Enzymatic assays for phosphoinositide kinases often use lipid substrates prepared from lyophilized lipid preparations by sonication, which result in variability in the liposome size from preparation to preparation. Herein, we report a homogeneous 1536-well luciferase-coupled bioluminescence assay for PI5P4Kα. The substrate preparation is novel and allows the rapid production of a DMSO-containing substrate solution without the need for lengthy liposome preparation protocols, thus enabling the scale-up of this traditionally difficult type of assay. The Z'-factor value was greater than 0.7 for the PI5P4Kα assay, indicating its suitability for high-throughput screening applications. Tyrphostin AG-82 had been identified as an inhibitor of PI5P4Kα by assessing the degree of phospho transfer of γ-(32)P-ATP to PI5P; its inhibitory activity against PI5P4Kα was confirmed in the present miniaturized assay. From a pilot screen of a library of bioactive compounds, another tyrphostin, I-OMe tyrphostin AG-538 (I-OMe-AG-538), was identified as an ATP-competitive inhibitor of PI5P4Kα with an IC(50) of 1 µM, affirming the suitability of the assay for inhibitor discovery campaigns. This homogeneous assay may apply to other lipid kinases and should help in the identification of leads for this class of enzymes by enabling high-throughput screening efforts.