Variations observed for insulin concentrations in an interlaboratory quality control program may be due to

Raúl Bonnin Rigo1, Mariona García Panyella, Luis Roncero Bartolomé

  • 1Hormone and Genetic Section (Biochemistry Department), IDIBELL-Hospital Universitario de Bellvitge, Feixa Llarga s/n, 08097, L'Hospitalet de Llobregat, Barcelona, Spain.

The present study was carried out to observe the behaviour of insulin concentrations in an interlaboratory quality control program from BioRad Laboratories (Irving, CA) applied to Immulite 2000 (Diagnostics Product Corporation, Los Angeles, CA) for three control materials of Lyphocheck Immunoassay Plus Control. Insulin was measured for 261 consecutive working days in a year using a solid-phase immunometric chemiluminescent assay; likewise insulin was measured for 55 days during a period of 4 months in a pool of sera obtained from patients with insulin concentrations within the normal range of our laboratory. The results from each control material were classified in three groups according to the closeness among concentrations and time; mean concentrations were significantly different between consecutive groups for the three control materials (p<0.0001). However, no differences were observed in samples from pool sera. The variations observed in insulin concentrations of the control materials may be due to some interferences or matrix effect on the control material caused by the reagents to quantify insulin in the Immulite 2000.

Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
Pharmaceutical Alternatives: Excipients and Impurities-Related Therapeutic Nonequivalence01:19

Pharmaceutical Alternatives: Excipients and Impurities-Related Therapeutic Nonequivalence

Pharmaceutical products contain more than just the active drug; they also contain various excipients such as binders, solubilizers, stabilizers, preservatives, and other elements. In some cases, impurities or contaminants might be present. Traditionally, quality control in pharmaceuticals has primarily focused on the analysis of the active drug, often overlooking the impact of these additional components. The recent issue with heparin contamination by over-sulfated chondroitin sulfate, a...
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences01:20

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences

Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and refractory oxide ion...
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...