Development of in-silico drug cardiac toxicity evaluation system with consideration of inter-individual variability.

Safety pharmacology examines the potential for new drugs to have unusual, rare side effects such as torsade de pointes (TdP). Recently, as a part of the Comprehensive in vitro Proarrhythmia Assay (CiPA) project, techniques for predicting the development of drug-induced TdP through computer simulations have been proposed and verified. However, CiPA assessment generally does not consider the effect of cardiac cell inter-individual variability, especially related to metabolic status. The study aimed to explore whether rare proarrhythmic effects may be linked to the inter-individual variability of cardiac cells and whether incorporating this variability into computational models could alter the prediction of drugs' TdP risks. This study evaluated the contribution of two biological characteristics to the proarrhythmic effects. The first was spermine concentration, which varies with metabolic status; the second was L-type calcium permeability that could occur due to mutations. Twenty-eight drugs were examined throughout this study, and qNet was analyzed as an essential feature. Even though there were some discrepancies of TdP risk predictions from the baseline model, we found that considering the inter-individual variability might change the TdP risk of drugs. Several drugs in the high-risk drugs group were predicted to affect as intermediate and low-risk drugs in some individuals and vice versa. Also, most intermediate-risk drugs were expected to act as low-risk drugs. When compared, the effects of inter-individual variability of L-type calcium were more significant than spermine in altering the TdP risk of compounds. These results emphasize the importance of considering inter-individual variability to assess drugs.

A standard to report biological variation data studies - based on an expert opinion.

There is a need for standards for generation and reporting of Biological Variation (BV) reference data. The absence of standards affects the quality and transportability of BV data, compromising important clinical applications. To address this issue, international expert groups under the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) have developed an online resource (https://tinyurl.com/bvmindmap) in the form of an interactive mind map that serves as a guideline for researchers planning, performing and reporting BV studies. The mind map addresses study design, data analysis, and reporting criteria, providing embedded links to relevant references and resources. It also incorporates a checklist approach, identifying a Minimum Data Set (MDS) to enable the transportability of BV data and incorporates the Biological Variation Data Critical Appraisal Checklist (BIVAC) to assess study quality. The mind map is open to access and is disseminated through the EFLM BV Database website, promoting accessibility and compliance to a reporting standard, thereby providing a tool to be used to ensure data quality, consistency, and comparability of BV data. Thus, comparable to the STARD initiative for diagnostic accuracy studies, the mind map introduces a Standard for Reporting Biological Variation Data Studies (STARBIV), which can enhance the reporting quality of BV studies, foster user confidence, provide better decision support, and be used as a tool for critical appraisal. Ongoing refinement is expected to adapt to emerging methodologies, ensuring a positive trajectory toward improving the validity and applicability of BV data in clinical practice.

Time-dependent characteristics of analytical measurands.

OBJECTIVES: Biological variation is a relevant component of diagnostic uncertainty. In addition to within-subject and between-subject variation, preanalytical variation also includes components that contribute to biological variability. Among these, daily recurring, i.e., diurnal physiological variation is of particular importance, as it contains both a random and a non-random component if the exact time of blood collection is not known. METHODS: We introduce four time-dependent characteristics (TDC) of diurnal variations for measurands to assess the relevance and extent of time dependence on the evaluation of laboratory results. RESULTS: TDC address (i) a threshold for considering diurnality, (ii) the expected relative changes per time unit, (iii) the permissible time interval between two blood collections at different daytimes within which the expected time dependence does not exceed a defined analytical uncertainty, and (iv) a rhythm-expanded reference change value. TDC and their importance will be exemplified by the measurands aspartate aminotransferase, creatine kinase, glucose, thyroid stimulating hormone, and total bilirubin. TDCs are calculated for four time slots that reflect known blood collection schedules, i.e., 07:00-09:00, 08:00-12:00, 06:00-18:00, and 00:00-24:00. The amplitude and the temporal location of the acrophase are major determinates impacting the diagnostic uncertainty and thus the medical interpretation, especially within the typical blood collection time from 07:00 to 09:00. CONCLUSIONS: We propose to check measurands for the existence of diurnal variations and, if applicable, to specify their time-dependent characteristics as outlined in our concept.

The biological variation of insulin resistance markers: data from the European Biological Variation Study (EuBIVAS).

OBJECTIVES: An insulin resistant state is characteristic of patients with type 2 diabetes, polycystic ovary syndrome, and metabolic syndrome. Identification of insulin resistance (IR) is most readily achievable using formulae combining plasma insulin and glucose results. In this study, we have used data from the European Biological Variation Study (EuBIVAS) to examine the biological variability (BV) of IR using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and the Quantitative Insulin sensitivity Check Index (QUICKI). METHODS: Ninety EuBIVAS non-diabetic subjects (52F, 38M) from five countries had fasting HOMA-IR and QUICKI calculated from plasma glucose and insulin samples collected concurrently on 10 weekly occasions. The within-subject (CVI) and between-subject (CVG) BV estimates with 95 % CIs were obtained by CV-ANOVA after analysis of trends, variance homogeneity and outlier removal. RESULTS: The CVI of HOMA-IR was 26.7 % (95 % CI 25.5-28.3), driven largely by variability in plasma insulin and the CVI for QUICKI was 4.1 % (95 % CI 3.9-4.3), reflecting this formula's logarithmic transformation of glucose and insulin values. No differences in values or BV components were observed between subgroups of men or women below and above 50 years. CONCLUSIONS: The EuBIVAS, by utilising a rigorous experimental protocol, has produced robust BV estimates for two of the most commonly used markers of insulin resistance in non-diabetic subjects. This has shown that HOMA-IR, in particular, is highly variable in the same individual which limits the value of single measurements.

Early detection of myocardial infarction with reference to baseline levels during health: impact on biological variation of high-sensitivity cardiac troponin.

A 78-year-old male was seen in the emergency department (ED) with chest pain. Fifteen months earlier, he had presented to the ED with shoulder and elbow pain. High-sensitivity cardiac troponin I (hs-cTnI) testing was conducted at that time, which produced normal results of 10 and 13 ng/L (cutoff <48 ng/L). During the current admission, his electrocardiogram was unremarkable, with a borderline prolonged PR interval noted. The patient's hs-cTnI results were 25, 47, and 254 ng/L at 0, 1, and 7 hours, respectively. He was diagnosed with demand ischemia and admitted to the hospital. The detection of acute myocardial infarction in this case was made during the first sample collection (t = 0), despite the fact that this result was well within the normal range.

Within- and between-subject biological variation estimates for the enumeration of lymphocyte deep immunophenotyping and monocyte subsets.

OBJECTIVES: This study aimed to deliver biological variation (BV) estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping (memory T/B cells, regulatory T cells, etc.) and classical, intermediate, and nonclassical monocyte subsets based on the full spectrum flow cytometry (FS-FCM) and a Biological Variation Data Critical Appraisal Checklist (BIVAC) design. METHODS: Samples were collected biweekly from 60 healthy Chinese adults over 10 consecutive two-week periods. Each sample was measured in duplicate within a single run for lymphocyte deep immunophenotyping and monocyte subset determination using FS-FCM, including the percentage (%) and absolute count (cells/µL). After trend adjustment, a Bayesian model was applied to deliver the within-subject BV (CVI) and between-subject BV (CVG) estimates with 95 % credibility intervals. RESULTS: Enumeration (% and cells/µL) for 25 types of lymphocyte deep immunophenotyping and three types of monocyte subset percentages showed considerable variability in terms of CVI and CVG. CVI ranged from 4.23 to 47.47 %. Additionally, CVG ranged between 10.32 and 101.30 %, except for CD4+ effector memory T cells re-expressing CD45RA. No significant differences were found between males and females for CVI and CVG estimates. Nevertheless, the CVGs of PD-1+ T cells (%) may be higher in females than males. Based on the desired analytical performance specification, the maximum allowable imprecision immune parameter was the CD8+PD-1+ T cell (cells/µL), with 23.7 %. CONCLUSIONS: This is the first study delivering BV estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping, along with classical, intermediate, and nonclassical monocyte subsets, using FS-FCM and adhering to the BIVAC design.

Diurnal and day-to-day biological variation of salivary cortisol and cortisone.

OBJECTIVES: There is a growing interest in the relevance of salivary cortisol and cortisone concentrations in stress-related research. To correctly attribute the magnitude of salivary cortisol and cortisone variation as an effect of a stressful event, a coherent understanding of the day-to-day intra-individual and inter-individual variability across the diurnal cycle of the two steroids is required. However, such information is currently lacking. METHODS: This study aimed to overcome these existing limitations by performing an investigation of the biological variation (BV) of salivary cortisol and cortisone within one day and between five days using an LC-MS/MS method. Saliva samples were collected from 20 healthy volunteers immediately after waking up, at 8:00, 12:00, 15:00, 19:00 and 23:00 on each day over five days. All samples were analyzed in duplicate in one run. Nested ANOVA was used to calculate the sums of squares for analytical and biological components of variation. RESULTS: The within-subject BV of salivary cortisol and cortisone (CVI) ranged from a minimum of 29.3 and 19.0 % to a maximum of 56.5 and 49.1 %, respectively, while the between-subject biological variation (CVG) ranged from 29.7 and 29.0 % to 51.6 and 43.6 %. The reference change values (RCVs) ranged from 96 to 245 % for cortisol and from 55 to 194 % for cortisone. A medium index of individuality was observed for both compounds at all time points. CONCLUSIONS: This study provides updated BV estimates and RCVs for different times of day that can be used to assess the magnitude of change in biomarkers in future stress-related research.

Applying the Milan models to setting analytical performance specifications - considering all the information.

Analytical performance specifications (APS) are used for decisions about the required analytical quality of pathology tests to meet clinical needs. The Milan models, based on clinical outcome, biological variation, or state of the art, were developed to provide a framework for setting APS. An approach has been proposed to assign each measurand to one of the models based on a defined clinical use, physiological control, or an absence of quality information about these factors. In this paper we propose that in addition to such assignment, available information from all models should be considered using a risk-based approach that considers the purpose and role of the actual test in a clinical pathway and its impact on medical decisions and clinical outcomes in addition to biological variation and the state-of-the-art. Consideration of APS already in use and the use of results in calculations may also need to be considered to determine the most appropriate APS for use in a specific setting.

Single nucleotide polymorphisms in the cannabinoid CB2 receptor: Molecular pharmacology and disease associations.

Preclinical evidence implicating cannabinoid receptor 2 (CB2) in various diseases has led researchers to question whether CB2 genetics influence aetiology or progression. Associations between conditions and genetic loci are often studied via single nucleotide polymorphism (SNP) prevalence in case versus control populations. In the CNR2 coding exon, ~36 SNPs have high overall population prevalence (minor allele frequencies [MAF] ~37%), including non-synonymous SNP (ns-SNP) rs2501432 encoding CB2 63Q/R. Interspersed are ~27 lower frequency SNPs, four being ns-SNPs. CNR2 introns also harbour numerous SNPs. This review summarises CB2 ns-SNP molecular pharmacology and evaluates evidence from ~70 studies investigating CB2 genetic variants with proposed linkage to disease. Although CNR2 genetic variation has been associated with a wide variety of conditions, including osteoporosis, immune-related disorders, and mental illnesses, further work is required to robustly validate CNR2 disease links and clarify specific mechanisms linking CNR2 genetic variation to disease pathophysiology and potential drug responses.

Preanalytical Errors in Clinical Laboratory Testing at a Glance: Source and Control Measures.

The accuracy of diagnostic results in clinical laboratory testing is paramount for informed healthcare decisions and effective patient care. While the focus has traditionally been on the analytical phase, attention has shifted towards optimizing the preanalytical phase due to its significant contribution to total laboratory errors. This review highlights preanalytical errors, their sources, and control measures to improve the quality of laboratory testing. Blood sample quality is a critical concern, with factors such as hemolysis, lipemia, and icterus leading to erroneous results. Sources of preanalytical errors encompass inappropriate test requests, patient preparation lapses, and errors during sample collection, handling, and transportation. Mitigating these errors includes harmonization efforts, education and training programs, automated methods for sample quality assessment, and quality monitoring. Collaboration between laboratory personnel and healthcare professionals is crucial for implementing and sustaining these measures to enhance the accuracy and reliability of diagnostic results, ultimately improving patient care.

Biological variation of PIVKA-II in blood serum of healthy subjects measured by automated electrochemiluminescent assay.

Background: Prothrombin/Protein Induced by Vitamin K Absence-II (PIVKA-II) is a candidate biomarker of hepatocellular cancer, recommended both for diagnostics and monitoring. The aim was to evaluate biological variation (BV) of serum PIVKA-II. Methods: Within-subject (CVI) and between-subject (CVG) BV estimates were assessed in 14 healthy volunteers in a 6-week protocol. Serum concentrations of PIVKA-II were measured by a Roche Elecsys PIVKA-II diagnostic kit (cobas e8000). Precision (CVA) was assessed from duplicate measurements of all volunteers' samples. Two methods were used for the estimation of CVI: SD-ANOVA and CV-ANOVA method. We calculated the index of individuality (II) and reference change value. The experiment was fully compliant with EFLM database checklist. Results: The CVI of PIVKA-II in healthy persons, as calculated by two statistical methods, were 8.2% (SD-ANOVA with CVA of 3.2%) and 9.4% (CV-ANOVA) with CVA of 2.7%). The CVG was 19.5% (SD-ANOVA), and respective II and RCV were 0.42 and 24.4%. Conclusions: CVI and CVG of PIVKA-II were 8.2% and 19.5%, respectively, with CVA below 4%. The low II and RCV below 25% enable the use of this biomarker both for diagnostics and monitoring. More data are needed before the introduction of PIVKA-II into clinical practice.

mRNA expression levels of cytochrome P450 CYP1A2, CYP3A4, and CYP3A5 in the epidermis: a focus on individual differences among Japanese individuals.

Various cytochrome P450 enzymes (CYPs) that contribute to drug metabolism are expressed in the skin. However, variation among individuals in CYP expression profiles is not well-understood.To investigate CYPs related to the metabolism of transdermal preparations in Japan, multiple skin tissue specimens of individuals of Japanese descent were prepared, and the mRNA expression levels of CYP1A2, CYP3A4, and CYP3A5 were measured. Associations between the expression patterns of these CYPs and body mass index (BMI) were also investigated.There were considerable individual differences in epidermal CYP1A2 mRNA expression levels, and CYP1A2 showed a weak positive correlation with CYP3A4 mRNA expression levels. In contrast to previous results for other organs, epidermal CYP3A4 mRNA expression levels showed a weak positive correlation with BMI.CYP3A4 in the epidermis may have been locally enhanced as a defence mechanism against xenobiotics in response to impaired barrier function. These differences in mRNA expression in the skin may affect the transdermal absorption of drugs, such as lidocaine and fentanyl, which are metabolised by multiple overlapping CYPs.Our study provides new insights into drug metabolism in the skin. These results are valuable for predicting drug effects and transdermal drug transfer rates in Japanese patients.

Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues.

Tissue gene expression studies are impacted by biological and technical sources of variation, which can be broadly classified into wanted and unwanted variation. The latter, if not addressed, results in misleading biological conclusions. Methods have been proposed to reduce unwanted variation, such as normalization and batch correction. A more accurate understanding of all causes of variation could significantly improve the ability of these methods to remove unwanted variation while retaining variation corresponding to the biological question of interest. We used 17,282 samples from 49 human tissues in the Genotype-Tissue Expression data set (v8) to investigate patterns and causes of expression variation. Transcript expression was transformed to z-scores, and only the most variable 2% of transcripts were evaluated and clustered based on coexpression patterns. Clustered gene sets were assigned to different biological or technical causes based on histologic appearances and metadata elements. We identified 522 variable transcript clusters (median: 11 per tissue) among the samples. Of these, 63% were confidently explained, 16% were likely explained, 7% were low confidence explanations, and 14% had no clear cause. Histologic analysis annotated 46 clusters. Other common causes of variability included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), disease status, and age. Technical causes included blood draw timing and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens data set of single-cell expression. This is among the largest explorations of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression and demonstrated the utility of matched histologic specimens. It further demonstrated the value of acquiring meaningful tissue harvesting metadata elements to use for improved normalization, batch correction, and analysis of both bulk and single-cell RNA-seq data.

The impact of physiological variations on personalized reference intervals and decision limits: an in-depth analysis.

The interpretation of laboratory data is a comparative procedure. Physicians typically need reference values to compare patients' laboratory data for clinical decisions. Therefore, establishing reliable reference data is essential for accurate diagnosis and patient monitoring. Human metabolism is a dynamic process. Various types of systematic and random fluctuations in the concentration/activity of biomolecules are observed in response to internal and external factors. In the human body, several biomolecules are under the influence of physiological rhythms and are therefore subject to ultradian, circadian and infradian fluctuations. In addition, most biomolecules are also characterized by random biological variations, which are referred to as biological fluctuations between subjects and within subjects/individuals. In routine practice, reference intervals based on population data are used, which by nature are not designed to capture physiological rhythms and random biological variations. To ensure safe and appropriate interpretation of patient laboratory data, reference intervals should be personalized and estimated using individual data in accordance with systematic and random variations. In this opinion paper, we outline (i) the main variations that contribute to the generation of personalized reference intervals (prRIs), (ii) the theoretical background of prRIs and (iii) propose new methods on how to harmonize prRIs with the systematic and random variations observed in metabolic activity, based on individuals' demography.

Analytical performance specifications based on biological variation data - considerations, strengths and limitations.

Analytical performance specifications (APS) are typically established through one of three models: (i) outcome studies, (ii) biological variation (BV), or (iii) state-of-the-art. Presently, The APS can, for most measurands that have a stable concentration, be based on BV. BV based APS, defined for imprecision, bias, total allowable error and allowable measurement uncertainty, are applied to many different processes in the laboratory. When calculating APS, it is important to consider the different APS formulae, for what setting they are to be applied and if they are suitable for the intended purpose. In this opinion paper, we elucidate the background, limitations, strengths, and potential intended applications of the different BV based APS formulas. When using BV data to set APS, it is important to consider that all formulae are contingent on accurate and relevant BV estimates. During the last decade, efficient procedures have been established to obtain reliable BV estimates that are presented in the EFLM biological variation database. The database publishes detailed BV data for numerous measurands, global BV estimates derived from meta-analysis of quality-assured studies of similar study design and automatic calculation of BV based APS.

Reliability, stability during long-term storage, and intra-individual variation of circulating levels of osteopontin, osteoprotegerin, vascular endothelial growth factor-A, and interleukin-17A.

BACKGROUND: Studies in many populations have reported associations between circulating cytokine levels and various physiological or pathological conditions. However, the reliability of cytokine measurements in population studies, which measure cytokines in multiple assays over a prolonged period, has not been adequately examined; nor has stability during sample storage or intra-individual variation been assessed. METHODS: We assessed (1) analytical reliability in short- and long-term repeated measurements; (2) stability and analytical reliability during long-term sample storage, and (3) variability within individuals over seasons, of four cytokines-osteopontin (OPN), osteoprotegerin (OPG), vascular endothelial growth factor-A (VEGF-A), and interleukin-17A (IL-17A). Measurements in plasma or serum samples were made with commercial kits according to standard procedures. Estimation was performed by fitting a random or mixed effects linear model on the log scale. RESULTS: In repeated assays over a short period, OPN, OPG, and VEGF-A had acceptable reliability, with intra- and inter-assay coefficients of variation (CV) less than 0.11. Reliability of IL-17A was poor, with inter- and intra-assay CV 0.85 and 0.43, respectively. During long-term storage, OPG significantly decayed (- 33% per year; 95% confidence interval [- 54, - 3.7]), but not OPN or VEGF-A (- 0.3% or - 6.3% per year, respectively). Intra- and inter-assay CV over a long period were comparable to that in a short period except for a slight increase in inter-assay CV of VEGF-A. Within-individual variation was small for OPN and VEGF-A, with intra-class correlations (ICC) 0.68 and 0.83, respectively, but large for OPG (ICC 0.11). CONCLUSIONS: We conclude that OPN and VEGF-A can be reliably measured in a large population, that IL-17A is suitable only for small experiments, and that OPG should be assessed with caution due to degradation during storage and intra-individual variation. The overall results of our study illustrate the need for validation under relevant conditions when measuring circulating cytokines in population studies.

Analytical performance of feline plasma on current Heska and IDEXX point-of-care biochemistry analyzers compared with a commercial laboratory analyzer.

BACKGROUND: Point-of-care (POC) biochemistry analyzers are widely used in small animal clinical practice but infrequently independently assessed for performance. OBJECTIVE: To assess the performance of two current model point-of-care biochemistry analyzers (Heska Element DC and IDEXX Catalyst) compared with a commercial laboratory analyzer (Cobas 8000). METHODS: One hundred twenty-one cats from a feline hospital population were sampled with plasma results from a single lithium heparin tube assessed on all three analyzers. Plasma biochemistry results from each POC analyzer were compared with the commercial laboratory analyzer using Bland-Altman difference plots and by determining whether the limits of agreement (LOAs) (95% of differences) fell within various quality goals after correcting for inherent bias. RESULTS: Only 7 of 14 analytes on the Heska analyzer and 2 analytes on the IDEXX analyzer attained the most stringent LOA quality goal, which was being within desirable total error based on biologic variation (TEdes ). The number of analytes achieving quality goals increased with less stringent standards such as American Society of Veterinary Clinical Pathologists allowable total error (ASVCP TEA ) guidelines or if <95% of clinical comparisons reaching these quality goals is considered acceptable. Widespread bias was found between both POC analyzers and the commercial laboratory analyzer. CONCLUSIONS: The performance of both POC biochemistry analyzers was variable compared with a commercial laboratory analyzer. Performance goals were only able to be attained after the bias for each analyzer was accounted for by offsetting the LOAs and quality goals set by the mean bias for each analyte on each analyzer.

Serum free light chains among twin siblings: is the kappa/lambda ratio genetically determined?

BACKGROUND: Serum kappa, lambda, the K/λ light chain concentrations are used for screening, diagnosis, and monitoring of patients with multiple myeloma and other plasma cell disorders. Biological variation studies conducted on healthy subjects showed that free light chains have a low within and high between-individual variation. We determined if this variation were genetically linked. METHODS: We obtained a single serum sample from 16 pairs of identical twins, 8 neonate twins, and 19 presumed directly-related siblings children, measured Κ and λ light chains and computed the Κ/λ ratio. RESULTS: As expected, Κ/λ results from each twin neonate were near identical (reflecting maternal/placental transfer). For older children and adult twins, the Κ/λ ratio form a cluster of results that were a subset of the reference range. There was one outlier, a female with a high, different from her twin sister. She likely had a monoclonal gammopathy (no followup was possible). Excluding this pair, results from neonate twins (14.4% ±10.3%) and non-neonate twins (18.0 ± 15.3%) were not significantly different. Results between non-twin siblings were more scattered (53.2%±53.4%) and different from neonate and non-neonate twin adult and children. CONCLUSION: We suggest that the Κ/λ free light chains may be genetically linked.