A Novel Enzymatic Hydrolysis Method for Urine Aldosterone Quantification: A Case for Reassessing Clinical Cut-Offs of Primary Aldosteronism.

BACKGROUND: Primary aldosteronism (PA) is a common endocrine cause of secondary hypertension. The aldosterone/renin ratio is an important tool for PA screening, and dynamic testing in serum or urine is used to confirm the diagnosis. While LC-MS/MS is considered the gold standard for testing, there is significant interlaboratory variability between the extraction procedures, which can impact diagnostic interpretation. To help overcome this, we present a simple and accurate LC-MS/MS method for the quantification of both serum and urine aldosterone using a novel enzymatic hydrolysis procedure. METHODS: Serum and urine aldosterone was extracted and measured by LC-MS/MS. Urine-conjugated aldosterone glucuronide was hydrolyzed using a genetically modified glucuronidase enzyme. The assay precision, accuracy, limit of quantification, recovery, and carryover were evaluated and the new assay cut-offs were proposed. RESULTS: The liquid chromatography method allowed for adequate separation of the aldosterone peak from closely eluting peaks. Significant in vitro aldosterone loss was observed during acid-catalyzed hydrolysis of urine, which was corrected with the addition of the internal standard to the urine before the hydrolysis step. Glucuronidase catalyzed hydrolysis of urine aldosterone glucuronide displays good correlation with the corrected acid-catalyzed hydrolysis. Serum aldosterone showed good agreement with reference values and the consensus range reported for external quality assessment specimens. CONCLUSION: A simple, fast, and highly accurate method for the detection of serum and urine aldosterone has been developed. The proposed novel enzymatic procedure allows for short hydrolysis time and compensates for urine aldosterone loss during the hydrolysis step.

The Present and Future of Lipid Testing in Cardiovascular Risk Assessment.

BACKGROUND: Lipids play a central role in the pathogenesis of cardiovascular disease (CVD), a leading cause of morbidity and mortality worldwide. Plasma lipids and lipoproteins are routinely measured to help identify individuals at high risk of developing CVD and to monitor patients' response to therapy. The landscape of lipid testing is rapidly changing, including new ways to estimate traditional lipid parameters (e.g., low-density lipoprotein-cholesterol [LDL-C] calculations) and new lipid parameters that show superiority for risk prediction (e.g., non-high-density lipoprotein-cholesterol [non-HDL-C], apolipoprotein B [apoB], and lipoprotein a [Lp(a)]). CONTENT: Various national guidelines for managing dyslipidemia to prevent CVD are available, which primarily focus on LDL-C for identifying those at high risk and setting thresholds for optimal response to therapy. However, LDL-C can be calculated and measured in various ways, each with advantages and disadvantages. Importantly, the recently established Sampson-NIH LDL-C equation appears to be superior to preceding calculations, as is clear from the literature and in guidelines. There is now a shift towards using lipid parameters other than LDL-C, such as non-HDL-C, apoB, and Lp(a), to identify high-risk patients and/or establish treatment targets. SUMMARY: The goal of this review is to discuss the present and future of lipid testing for CVD risk assessment through describing various national clinical guidelines, critically reviewing methods to calculate and measure LDL-C and discussing the clinical utility of additional lipid parameters.

Drug checking services as a surveillance tool for clinical laboratories: Examining trends in the unregulated fentanyl supply.

OBJECTIVES: Timely assessment and understanding of drug trends is essential for clinical laboratories to effectively respond to the overdose epidemic. In this proof-of-concept study, we sought to determine whether information obtained through Toronto's Drug Checking Services (DCS) and cross-provincial urine drug testing (UDT) data can be used as a surveillance tool for clinical laboratories and discuss the value of collaboration between the clinical laboratory, clinicians, and community partners to optimize patient care. DESIGN & METHODS: Mass spectrometry-based UDT data from LifeLabs Ontario (n = 127,529) and British Columbia (n = 14,848), and drug checking data from Toronto DCS (n = 3,308 drugs or used paraphernalia) was collected between August 2020 and October 2021. Fentanyl co-positivity with toxic adulterants such as benzodiazepine-related drugs and fentanyl analogues were examined. RESULTS: The percent co-positivity of fentanyl with etizolam, flualprazolam, flubromazolam, carfentanil, and acetylfentanyl in both Ontario UDT and DCS drugs/used paraphernalia showed similar trends. Regional differences in co-positivity with etizolam and fentanyl analogues were noted between Ontario and British Columbia UDT with patterns consistent over the entire 15-month collection period. CONCLUSIONS: Clinical laboratories should connect with their local DCS, if available, to understand and monitor unregulated drug trends. These data can be used as an important tool to help clinical laboratories tailor their UDT menus and thereby provide a community-focused service to improve patient care.

The North American opioid epidemic: opportunities and challenges for clinical laboratories.

Since 1999, the opioid epidemic in North America has resulted in over 1 million deaths, and it continues to escalate despite numerous efforts in various arenas to combat the upward trend. Clinical laboratories provide drug testing to support practices such as emergency medicine, substance use disorder treatment, and pain management; increasingly, these laboratories are collaborating in novel partnerships including drug-checking services (DCS) and multidisciplinary treatment teams. This review examines drug testing related to management of licit and illicit opioid use, new technologies and test strategies employed by clinical laboratories, barriers hindering laboratory response to the opioid epidemic, and areas for improvement and standardization within drug testing. Literature search terms included combinations of "opioid," "opiate," "fentanyl," "laboratory," "epidemic," "crisis," "mass spectrometry," "immunoassay," "drug screen," "drug test," "guidelines," plus review of PubMed "similar articles" and references within publications. While immunoassay (IA) and point-of-care (POC) test options for synthetic opioids are increasingly available, mass spectrometry (MS) platforms offer the greatest flexibility and sensitivity for detecting novel, potent opioids. Previously reserved as a second-tier application in most drug test algorithms, MS assays are gaining a larger role in initial screening for specific patients and DCS. However, there are substantial differences among laboratories in terms of updating test menus, algorithms, and technologies to meet changing clinical needs. While some clinical laboratories lack the resources and expertise to implement MS, many are also slow to adopt available IA and POC tests for newer opioids such as fentanyl. MS-based testing also presents challenges, including gaps in available guidance for assay validation and ongoing performance assessment that contribute to a dramatic lack of standardization among laboratories. We identify opportunities for improvement in laboratory operations, reporting, and interpretation of drug test results, including laboratorian and provider education and laboratory-focused guidelines. We also highlight the need for collaboration with providers, assay and instrument manufacturers, and national organizations to increase the effectiveness of clinical laboratory and provider efforts in preventing morbidity and mortality associated with opioid use and misuse.

Validating the NIH LDL-C equation in a specialized lipid cohort: Does it add up?

BACKGROUND: Guideline recommendations for the management of lipids in patients at risk for cardiovascular disease is largely based on low-density lipoprotein cholesterol (LDL-C) concentration. LDL-C is commonly calculated by the Friedewald equation, which has many limitations. The National Institutes of Health (NIH) equation better estimates LDL-C, particularly in patients with hypertriglyceridemia and/or low LDL-C. We validated the NIH LDL-C equation at the first Canadian clinical laboratory to implement this equation. METHODS: A total of 3161 lipid ultracentrifugation results from a specialized lipid cohort of 2836 patients were included. LDL-C was calculated using the NIH and Friedewald equations and compared to LDL-C measured by ultracentrifugation. We determined the accuracy of these equations at treatment thresholds and developed NIH equation restriction criteria to ensure only accurate results are reported. RESULTS: Ultracentrifugation LDL-C more strongly correlated with NIH-calculated LDL-C (r2 = 0.889) than Friedewald-calculated LDL-C (r2 = 0.807) and resulted in fewer non-sensical negative LDL-C values. The correlation for NIH-calculated LDL-C improved to r2 = 0.975 after applying our restriction criteria. The NIH equation showed equivalent or superior concordance with ultracentrifugation at treatment thresholds. The LDL-C mean absolute difference increased with increasing TG and decreasing LDL-C concentrations, although the NIH equation was more robust under both conditions. CONCLUSIONS: We validated the NIH equation against ultracentrifugation in a cohort with a wide lipid concentration range, which supported its superiority over the Friedewald equation. We recommend clinical implementing the NIH equation for all patients except those with type III hyperlipoproteinemia or TG > 9.04 mmol/L, with an LDL-C lower reporting limit of <0.50 mmol/L.

Comparative Analyses of Three Point-of-Care Urine Drug Test Devices' Performance Characteristics for Use in Ambulatory Clinic Settings.

BACKGROUND: Urine drug testing (UDT) is a standard practice used for monitoring controlled and illicit substances in ambulatory care patients. Point-of-care (POC) UDTs are useful tools that allow for drug identification within minutes, providing rapid and objective diagnostic assistance for clinicians. The objective of this study was to evaluate the performance characteristics of 3 different POC UDT devices compared to reference methods. METHODS: A total of 106 residual urine specimens were collected to evaluate the 3 POC UDT devices: the Profile®-V MEDTOX Scan® drugs of abuse test, Quidel Triage® TOX Drug screen, and Quidel Triage Rapid OXY-BUP-MDMA panel. Device performance was assessed by their ability to identify drug classes/compounds compared to manufacturer and reference method (mass spectrometry) cutoffs. RESULTS: The results from quantitative mass spectrometry showed that 77% (84/106) of the samples were positive for one or more drugs. Each device had variable performance across each drug class. Overall, the specificity of the Profile-V MEDTOX Scan test was 90.1%, while the Quidel Triage TOX Drug Screen and Rapid OXY-BUP-MDMA devices had specificities of 89.0% and 50.0% using their respective manufacturer-stated cutoffs. Overall sensitivity was determined to be 98.6%, 97.0%, and 100% for the Profile-V MEDTOX Scan, Quidel Triage TOX Drug Screen, and Rapid OXY-BUP-MDMA, respectively. CONCLUSIONS: Of the 3 POC UDT devices evaluated, the Profile-V MEDTOX Scan demonstrated the best overall sensitivity and specificity compared to reference methods. False positive and negative results are possible with UDTs, ultimately the best device may depend on patient population and drugs of interest.

A High-Level Overview of the Regulations Surrounding a Clinical Laboratory and Upcoming Regulatory Challenges for Laboratory Developed Tests.

OBJECTIVE: Regulations for clinical laboratories in the United States are complex. The goal of this review is to improve the clarity of laboratory-developed test (LDT) regulation to facilitate innovation. METHODS: A literature and regulation review of current legislation for compliance by U.S. clinical laboratories was performed, and examples of the steps to implement LDTs within compliance with the regulatory environment are shared. RESULTS: Many federal and state jurisdictions are critical to the functionality of a laboratory in addition to upcoming potential promulgation of the Verifying Accurate Leading-Edge IVCT Development Act. Increased regulation, although imperative to maintain consistent, high-standard clinical care, could mean additional costs for developers and healthcare while also hindering innovation. CONCLUSION: An extensive discussion of proposed regulations for LDTs needs to occur. Laboratory testing requires the sustained use of innovative methods at a cost that will permit continued, timely, uninterrupted high-quality service.

Assessment of mass spectrometry-based pain management testing in clinical laboratories across North America.

BACKGROUND: Urine drug testing (UDT) is a useful tool in monitoring compliance to prescribed medication and can also help identify behaviors of drug misuse, abuse, and diversion. Mass spectrometry (MS)-based screening is recommended as the first-line of UDT for pain management patients; however, this testing comes with an inherent lack of standardization in methodologies and various analytical challenges. The objective of this study was to assess the current state of UDT for pain management in a cross-section of clinical laboratories in North America. MATERIALS AND METHODS: A total of 10 blinded urine samples were sent to 6 laboratories across the United States and Canada. Urine samples containing drugs and/or metabolites of interest were included to represent different clinical scenarios commonly seen in pain management settings. Assessment was based on the ability of the laboratories to correctly identify drugs and provide a meaningful interpretation of the findings (when offered by the performing laboratory). RESULTS: Across the laboratories involved in the study, 85% of tests correctly identified and appropriately reported the drugs present in the urine samples. Similarly, 84% of samples were considered to have an accurate interpretation included in the UDT report. Out of the total number of drugs included in the samples, 11% were not offered on every test menu. CONCLUSIONS: This study revealed the lack of standardization in pain management UDT performed in a limited cross-section of clinical laboratories across North America.

A New Equation for Calculation of Low-Density Lipoprotein Cholesterol in Patients With Normolipidemia and/or Hypertriglyceridemia.

Importance: Low-density lipoprotein cholesterol (LDL-C), a key cardiovascular disease marker, is often estimated by the Friedewald or Martin equation, but calculating LDL-C is less accurate in patients with a low LDL-C level or hypertriglyceridemia (triglyceride [TG] levels ≥400 mg/dL). Objective: To design a more accurate LDL-C equation for patients with a low LDL-C level and/or hypertriglyceridemia. Design, Setting, and Participants: Data on LDL-C levels and other lipid measures from 8656 patients seen at the National Institutes of Health Clinical Center between January 1, 1976, and June 2, 1999, were analyzed by the ß-quantification reference method (18 715 LDL-C test results) and were randomly divided into equally sized training and validation data sets. Using TG and non-high-density lipoprotein cholesterol as independent variables, multiple least squares regression was used to develop an equation for very low-density lipoprotein cholesterol, which was then used in a second equation for LDL-C. Equations were tested against the internal validation data set and multiple external data sets of either ß-quantification LDL-C results (n = 28 891) or direct LDL-C test results (n = 252 888). Statistical analysis was performed from August 7, 2018, to July 18, 2019. Main Outcomes and Measures: Concordance between calculated and measured LDL-C levels by ß-quantification, as assessed by various measures of test accuracy (correlation coefficient [R2], root mean square error [RMSE], mean absolute difference [MAD]), and percentage of patients misclassified at LDL-C treatment thresholds of 70, 100, and 190 mg/dL. Results: Compared with ß-quantification, the new equation was more accurate than other LDL-C equations (slope, 0.964; RMSE = 15.2 mg/dL; R2 = 0.9648; vs Friedewald equation: slope, 1.056; RMSE = 32 mg/dL; R2 = 0.8808; vs Martin equation: slope, 0.945; RMSE = 25.7 mg/dL; R2 = 0.9022), particularly for patients with hypertriglyceridemia (MAD = 24.9 mg/dL; vs Friedewald equation: MAD = 56.4 mg/dL; vs Martin equation: MAD = 44.8 mg/dL). The new equation calculates the LDL-C level in patients with TG levels up to 800 mg/dL as accurately as the Friedewald equation does for TG levels less than 400 mg/dL and was associated with 35% fewer misclassifications when patients with hypertriglyceridemia (TG levels, 400-800 mg/dL) were categorized into different LDL-C treatment groups. Conclusions and Relevance: The new equation can be readily implemented by clinical laboratories with no additional costs compared with the standard lipid panel. It will allow for more accurate calculation of LDL-C level in patients with low LDL-C levels and/or hypertriglyceridemia (TG levels, ≤800 mg/dL) and thus should improve the use of LDL-C level in cardiovascular disease risk management.

Predicting Escitalopram Exposure to Breastfeeding Infants: Integrating Analytical and In Silico Techniques.

BACKGROUND: Escitalopram is used for post-partum depression; however, there are limited pharmacokinetic data of escitalopram in milk and plasma of infants breastfed by women taking the drug. OBJECTIVE: The objective of this study was to apply physiologically-based pharmacokinetic (PBPK) modelling to predict infant drug exposure (plasma area under the curve from time zero to infinity [AUC∞]) based on drug monitoring data of escitalopram in breast milk. METHODS: Using a newly developed liquid chromatography-tandem mass spectrometry (LC-MS/MS) method, we quantified escitalopram concentrations in milk samples of 18 breastfeeding women with escitalopram therapy at steady state, collected at three to five time points. The escitalopram concentrations in breast milk were used with infant feeding parameters from the literature to simulate infant daily dose. We used PK-Sim® to develop an adult PBPK model for escitalopram and extrapolated it to a population of 1600 infants up to 12 months of age. An integration of the simulated infant daily dose and the virtual infants with variable physiological-pharmacological parameters was used to predict drug exposure (plasma AUC∞) distribution in the population of infants breastfed by women receiving escitalopram 20 mg/day. RESULTS: Escitalopram concentrations in milk were 50 ± 17 ng/mL (mean ± standard deviation). The simulated infant plasma AUC∞ following escitalopram exposure through breast milk was low, with a median of 1.7% (range 0.5-5.9%) of the corresponding maternal plasma AUC∞, indicating no substantial exposure. CONCLUSIONS: Infant exposure levels to escitalopram in breast milk are low. A PBPK modeling approach can be used to translate data on drug monitoring in milk into a population distribution of infant plasma levels for drug safety assessment.