Calibration Curve enables Proper Quantitation and Suitable Analytical Assay Sensitivity
We set-up and validate robust analytical methods for quantitation of your drugs and biomarkers, considering distinct assay factors such as analytical specificity, selectivity, and sensitivity. Analytical specificity represents the assay’s ability to measure a specific analyte within a sample with matrix and ingredients. Analytical selectivity defines the capability to separate and detect the analyte within a complex matrix without any interference from other components. Analytical sensitivity asserts the smallest amount of an analyte that can be measured in an assay correctly.
Likewise, our team utilizes a suitable calibration curve to determine the concentration of your analyte in an unknown sample. This analysis requires a comparison of unknown samples to a set of standard samples of known concentration and quality controls processed within the same matrix. Sometimes, these assays become very challenging – especially if the unknown sample is endogenous, environmental, or biologically complex. That said, our veteran scientists at NorthEast BioLab are quite familiar with these method and analysis hurdles and capably resolve such issues if/when they arise.
Our team of scientists works diligently to base our research and studies as per the boundaries outlined within your analytical assay. We tenaciously pursue precision and accuracy for our methods to ensure reliable and reproducible results. We rely extensively on the triple quadrupole mass spectrometry and ligand binding detection techniques as they usually deliver superior sample analysis results.
Calibration Curve controls Assay Sensitivity and Data Quality
At NorthEast BioLab, we understand the overall value and proven significance of the calibration curve towards analytical assay sensitivity and robustness. For example, multiple studies have shown that the linearity of a calibration curve plays an instrumental role in the overall ruggedness of the bioanalytical method. Thus, this linear curve lays the foundation for minimal error in the calibration model. Ideally, the calibration model reflects that all calculated measurements are accurate, precise, and reproducible for analytes of interest in test samples from biological and chemical matrices.
In general, calibration curves are used in various scientific fields – including pharmaceutical chemistry and biochemistry. We use these curves with bioanalytical data to measure the analyte concentration in a wide variety of samples. For example, we regularly assess the levels of active pharmaceutical ingredients and their metabolites in biological matrices using calibration curves for preclinical toxicology and clinical safety determinations. Furthermore, we follow FDA and ICH guidelines regarding analytical method validation and sample analysis, including the direction on calibration curves. Our compliance with these international guidelines benefits clients as they receive and further report consistent data for drug approval.
