An immune-protein score combining TRAIL, IP-10 and CRP for predicting severe COVID-19 disease.

COVID-19 patients are oftentimes over- or under-treated due to a deficit in predictive management tools. This study reports derivation of an algorithm that integrates the host levels of TRAIL, IP-10, and CRP into a single numeric score that is an early indicator of severe outcome for COVID-19 patients and can identify patients at-risk to deteriorate. 394 COVID-19 patients were eligible; 29% meeting a severe outcome (intensive care unit admission/non-invasive or invasive ventilation/death). The score's area under the receiver operating characteristic curve (AUC) was 0.86, superior to IL-6 (AUC 0.77; p = 0.033) and CRP (AUC 0.78; p < 0.001). Likelihood of severe outcome increased significantly (p < 0.001) with higher scores. The score differentiated severe patients who further deteriorated from those who improved (p = 0.004) and projected 14-day survival probabilities (p < 0.001). The score accurately predicted COVID-19 patients at-risk for severe outcome, and therefore has potential to facilitate timely care escalation and de-escalation and appropriate resource allocation.

A point-of-need platform for rapid measurement of a host-protein score that differentiates bacterial from viral infection: Analytical evaluation.

The objective was to evaluate the analytical performance of a new point-of-need platform for rapid and accurate measurement of a host-protein score that differentiates between bacterial and viral infection. The system comprises a dedicated test cartridge (MeMed BV®) and an analyzer (MeMed Key®). In each run, three host proteins (TRAIL, IP-10 and CRP) are measured quantitatively and a combinational score (0-100) computed that indicates the likelihood of Bacterial versus Viral infection (BV score). Serum samples collected from patients with acute infection representing viral (0 ≤ score < 35), equivocal (35 ≤ score ≤ 65), or bacterial (65 < score ≤ 100) scores based on pre-defined score cutoffs were employed for the analytical evaluation studies as well as samples from healthy individuals. To assess reproducibility, triplicate runs were conducted at 3 different sites, on 2 analyzers per site over 5 non-consecutive days. Lower limit of quantitation (LLoQ) and analytical measurement range were established utilizing recombinant proteins. Sample stability was evaluated using patient samples representative of BV score range (0-100). MeMed Key® and MeMed BV® passed the acceptance criteria for each study. In the reproducibility study, TRAIL, IP-10 and CRP measurements ranged with coefficient of variation from 9.7 to 12.7%, 4.6 to 6.2% and 5.0 to 11.6%, respectively. LLoQ concentrations were established as 15 pg/mL, 100 pg/mL and 1 mg/L for TRAIL, IP-10 and CRP, respectively. In summary, the analytical performance reported here, along with diagnostic accuracy established in the Apollo clinical validation study (NCT04690569), supports that MeMed BV® run on MeMed Key® can serve as a tool to assist clinicians in differentiating between bacterial and viral infection.

Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson's Disease.

Real-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease-modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson's disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N = 88,867) and IBM MarketScan Research Databases (N = 106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, beneficial against common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.

Framework for identifying drug repurposing candidates from observational healthcare data.

OBJECTIVE: Observational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of patients and their medication prescription history. We present an easy-to-customize framework that systematically analyzes such databases to identify new indications for on-market prescription drugs. MATERIALS AND METHODS: Our framework provides an interface for defining study design parameters and extracting patient cohorts, disease-related outcomes, and potential confounders in observational databases. It then applies causal inference methodology to emulate hundreds of randomized controlled trials (RCTs) for prescribed drugs, while adjusting for confounding and selection biases. After correcting for multiple testing, it outputs the estimated effects and their statistical significance in each database. RESULTS: We demonstrate the utility of the framework in a case study of Parkinson's disease (PD) and evaluate the effect of 259 drugs on various PD progression measures in two observational medical databases, covering more than 150 million patients. The results of these emulated trials reveal remarkable agreement between the two databases for the most promising candidates. DISCUSSION: Estimating drug effects from observational data is challenging due to data biases and noise. To tackle this challenge, we integrate causal inference methodology with domain knowledge and compare the estimated effects in two separate databases. CONCLUSION: Our framework enables systematic search for drug repurposing candidates by emulating RCTs using observational data. The high level of agreement between separate databases strongly supports the identified effects.

Paradoxical Hypersusceptibility of Drug-resistant Mycobacteriumtuberculosis to ß-lactam Antibiotics.

Mycobacterium tuberculosis (M. tuberculosis) is considered innately resistant to ß-lactam antibiotics. However, there is evidence that susceptibility to ß-lactam antibiotics in combination with ß-lactamase inhibitors is variable among clinical isolates, and these may present therapeutic options for drug-resistant cases. Here we report our investigation of susceptibility to ß-lactam/ß-lactamase inhibitor combinations among clinical isolates of M. tuberculosis, and the use of comparative genomics to understand the observed heterogeneity in susceptibility. Eighty-nine South African clinical isolates of varying first and second-line drug susceptibility patterns and two reference strains of M. tuberculosis underwent minimum inhibitory concentration (MIC) determination to two ß-lactams: amoxicillin and meropenem, both alone and in combination with clavulanate, a ß-lactamase inhibitor. 41/91 (45%) of tested isolates were found to be hypersusceptible to amoxicillin/clavulanate relative to reference strains, including 14/24 (58%) of multiple drug-resistant (MDR) and 22/38 (58%) of extensively drug-resistant (XDR) isolates. Genome-wide polymorphisms identified using whole-genome sequencing were used in a phylogenetically-aware linear mixed model to identify polymorphisms associated with amoxicillin/clavulanate susceptibility. Susceptibility to amoxicillin/clavulanate was over-represented among isolates within a specific clade (LAM4), in particular among XDR strains. Twelve sets of polymorphisms were identified as putative markers of amoxicillin/clavulanate susceptibility, five of which were confined solely to LAM4. Within the LAM4 clade, 'paradoxical hypersusceptibility' to amoxicillin/clavulanate has evolved in parallel to first and second-line drug resistance. Given the high prevalence of LAM4 among XDR TB in South Africa, our data support an expanded role for ß-lactam/ß-lactamase inhibitor combinations for treatment of drug-resistant M. tuberculosis.

Flexible medical image management using service-oriented architecture.

Management of medical images increasingly involves the need for integration with a variety of information systems. To address this need, we developed Content Management Offering (CMO), a platform for medical image management supporting interoperability through compliance with standards. CMO is based on the principles of service-oriented architecture, implemented with emphasis on three areas: clarity of business process definition, consolidation of service configuration management, and system scalability. Owing to the flexibility of this platform, a small team is able to accommodate requirements of customers varying in scale and in business needs. We describe two deployments of CMO, highlighting the platform's value to customers. CMO represents a flexible approach to medical image management, which can be applied to a variety of information technology challenges in healthcare and life sciences organizations.

Spatially distributed successive approximation register (SDSAR) photonic ADCs based on phase-domain quantization.

We explore photonic ADC architectures based on encoding voltage-under-test into phase. The first step is to identify two basic optical building blocks: the optical phase comparator (1-bit ADC), based on interferometric comparison of phases in the well-known balanced photo-detection configuration, and the optical 1-bit DAC, namely electro-optic modulation with a bipolar electrical pulse. Equipped with these fundamental building blocks, we proceed to systematically port and adapt known ADC quantization architectures to photonic ADC, conceiving a hybrid between the Successive Approximation Register (SAR) and the Pipeline classic ADC architectures, referred to here as Spatially Distributed SAR (SDSAR). This novel photonic ADC, constructed out of B 1-bit ADCs and B-2 1-bit DACs, with B the number of bits, is not equivalent to any of the previous photonic ADCs in the literature, but appears superior to prior schemes in both optical power efficiency and electro-optic modulation complexity. We derive upper bounds on resolution, Effective Number of Bits (ENOB) performance as a function of average optical power for the new SDSAR device, developing analytic and numeric Monte-Carlo statistical models, comprising quantization, shot, thermal and DAC voltage noise sources. Our findings indicate that SDSAR is limited to ~11.5 ENOBs, assuming state-of-the-art mode-locked-lasers providing ~250 mW of average power (assuming ~7 dB excess losses). However, this upper bound is not tight, due to various physical impairments. In particular, the mode locked laser jitter is shown to have negligible impact on overall performance for RMS jitter < 20 fsec.

A plasma signature of human mitochondrial disease revealed through metabolic profiling of spent media from cultured muscle cells.

Mutations in either the mitochondrial or nuclear genomes can give rise to respiratory chain disease (RCD), a large class of devastating metabolic disorders. Their clinical management is challenging, in part because we lack facile and accurate biomarkers to aid in diagnosis and in the monitoring of disease progression. Here we introduce a sequential strategy that combines biochemical analysis of spent media from cell culture with analysis of patient plasma to identify disease biomarkers. First, we applied global metabolic profiling to spotlight 32 metabolites whose uptake or secretion kinetics were altered by chemical inhibition of the respiratory chain in cultured muscle . These metabolites span a wide range of pathways and include lactate and alanine, which are used clinically as biomarkers of RCD. We next measured the cell culture-defined metabolites in human plasma to discover that creatine is reproducibly elevated in two independent cohorts of RCD patients, exceeding lactate and alanine in magnitude of elevation and statistical significance. In cell culture extracellular creatine was inversely related to the intracellular phosphocreatine:creatine ratio suggesting that the elevation of plasma creatine in RCD patients signals a low energetic state of tissues using the phosphocreatine shuttle. Our study identifies plasma creatine as a potential biomarker of human mitochondrial dysfunction that could be clinically useful. More generally, we illustrate how spent media from cellular models of disease may provide a window into the biochemical derangements in human plasma, an approach that could, in principle, be extended to a range of complex diseases.

Metabolite profiling of blood from individuals undergoing planned myocardial infarction reveals early markers of myocardial injury.

Emerging metabolomic tools have created the opportunity to establish metabolic signatures of myocardial injury. We applied a mass spectrometry-based metabolite profiling platform to 36 patients undergoing alcohol septal ablation treatment for hypertrophic obstructive cardiomyopathy, a human model of planned myocardial infarction (PMI). Serial blood samples were obtained before and at various intervals after PMI, with patients undergoing elective diagnostic coronary angiography and patients with spontaneous myocardial infarction (SMI) serving as negative and positive controls, respectively. We identified changes in circulating levels of metabolites participating in pyrimidine metabolism, the tricarboxylic acid cycle and its upstream contributors, and the pentose phosphate pathway. Alterations in levels of multiple metabolites were detected as early as 10 minutes after PMI in an initial derivation group and were validated in a second, independent group of PMI patients. A PMI-derived metabolic signature consisting of aconitic acid, hypoxanthine, trimethylamine N-oxide, and threonine differentiated patients with SMI from those undergoing diagnostic coronary angiography with high accuracy, and coronary sinus sampling distinguished cardiac-derived from peripheral metabolic changes. Our results identify a role for metabolic profiling in the early detection of myocardial injury and suggest that similar approaches may be used for detection or prediction of other disease states.

Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity.

Glucose ingestion after an overnight fast triggers an insulin-dependent, homeostatic program that is altered in diabetes. The full spectrum of biochemical changes associated with this transition is currently unknown. We have developed a mass spectrometry-based strategy to simultaneously measure 191 metabolites following glucose ingestion. In two groups of healthy individuals (n=22 and 25), 18 plasma metabolites changed reproducibly, including bile acids, urea cycle intermediates, and purine degradation products, none of which were previously linked to glucose homeostasis. The metabolite dynamics also revealed insulin's known actions along four key axes--proteolysis, lipolysis, ketogenesis, and glycolysis--reflecting a switch from catabolism to anabolism. In pre-diabetics (n=25), we observed a blunted response in all four axes that correlated with insulin resistance. Multivariate analysis revealed that declines in glycerol and leucine/isoleucine (markers of lipolysis and proteolysis, respectively) jointly provide the strongest predictor of insulin sensitivity. This observation indicates that some humans are selectively resistant to insulin's suppression of proteolysis, whereas others, to insulin's suppression of lipolysis. Our findings lay the groundwork for using metabolic profiling to define an individual's 'insulin response profile', which could have value in predicting diabetes, its complications, and in guiding therapy.