To our knowledge, this paper is the first to present stability data on various serum and urine biomarkers measured with ELISA, while also showing that control of assay performance variability is essential for correct assessment of the results.
Both within urine and serum three biomarkers passed the stability and acceptance criteria under all conditions, whereas the other biomarkers varied in bias compared to the ref -80⁰C measured concentration. With respect to the freeze-thaw stability of various biomarkers, data indicated that within both serum and urine various biomarkers remained stable after 10 FT cycles whereas others lost stability after 5 cycles. Both the differences in stability and FT of some biomarkers indicate possible assay performance variability and decrease the reliability of generated results for those biomarkers with varying stability patterns.
Due to the nature of Ligand Binding Assays (LBA) like ELISA, a basic starting point for every biomarker study should be the assurance of confident and reproducible results. Within our biomarker study we used of the shelve commercial ELISA kits, which are ideal for early studies focused on biomarker discovery. Vendors of commercial ELISA kits provide basic data information on cross reactivity, limit of quantification, calibration range and assay precision. The last two are often re-assessed before widespread use. To determine suitability for the study in question, these assay parameters can be supplemented with data on frozen storage stability and freeze/thaw stability as we have done in our biomarker stability experiment. Along the way of biomarker assay development, additional assay parameters may become of interest for better assurance of assay performance and reproducibility of results. Agencies such as the American Food and Drug Administration (FDA) as well as the European Medicines Agency (EMA), provide guidelines on assay development and validation directed towards the assurance of accurate and reproducible results [25–29]. Biomarker assay development however, requires a more unconventional approach due to the biological nature of the analytes of interest. The development and validation of a biomarker assay should follow the context of use principle and be developed as fit for purpose. Structural pillars for this development include assay parameters such as parallelism, selectivity, sensitivity and stability [30, 31]. Based on data obtained for these parameters assay acceptance criteria can be set which can be different between various biomarker assays [30]. These factors do not need to be determined within an early phase but are considered imperative along the way of biomarker assay development to assure that the method is fit for purpose.
Following the context of use and fit for purpose principle, early phase biomarker assay development could benefit from implementing assay performance parameters such as acceptance criteria for calibrators (bias and CV) and the utilization of quality control samples. Acceptance criteria for calibrators assure that the used calibration curve is of sufficient quality and also sets up the possibility to track assay performance over time. The use of QC samples in every analytical run can be used for run acceptance control and also provides a way of tracking plate shifts and assay drifts over time [32, 33]. QC samples should be representative for the study samples used (same matrix preferably) and the concentrations of the analytes should also be representative for the study samples used. Preparation of QC samples for biomarker assays are complicated due to the endogenous nature of the analytes but various options are available. The concentration of QC samples subsequently needs to be determined over several runs after which a nominal value can be set on which acceptance criteria can be determined based on the precision of the measurements over several runs [33].
Within the psychiatric biomarker field, several studies have identified potential biomarkers which could be utilized within a diagnostic setting but results varied and the road to actual clinical application is still long [6, 9, 12, 13, 34–36]. The complex underlying biological background of psychiatric disorders may however not be the only explanation for the huge variations in biomarkers studies. A fast majority of these biomarker studies utilize ligand binding assays which are often of the shelve research kits and no resources are spent at implementing basic assay performance criteria which also includes analyte/body fluid stability. For example, cytokines are often an interesting target for psychiatric biomarker research [37–39]. Within these studies, blood samples are often analyzed from cohorts from which samples sometimes already have been stored for up to five years prior to analysis. A study from 2009 [40], showed that long term storage of cytokines are prone to degradation which in combination with increased variations in ELISA antibody binding capacity leads to increased variations in measured concentrations and reduction of reliability. This not only indicates that stability data on biomarkers are essential for assessing the suitability of samples but also shows that implementation of basic assay performance parameters may be of high value in improving the reliability of study results. Suitability of a biomarker assay might be further improved by incorporating also clinical acceptance criteria related to the clinical concentration range of a certain biomarker. Early preliminary data indicates that when applying clinical acceptance criteria based on variability in QC samples (assay performance parameter) and clinical samples, this will improve the suitability of a specific biomarker assay.
Within our study we set assay performance acceptance criteria but we did not incorporate additional assay performance parameters which could additionally have increased the reliability of our generated results, possibly leading to a decrease of inconclusive results which we have seen now for several biomarkers. Incorporation of duplicate analysis in combination with a low CV acceptance criteria for example could further increase the validity of the measured biomarker concentration. Addition of low and high QC samples would have provided a level of assay performance measurement to assure that the assay performed as intended and could have been used as assay acceptance criteria. The large amount of inconclusive results in the literature may also be the result of pipetting errors which could have been missed due to measuring samples in singlicate instead of duplicate. Pipetting errors for example, can occur when using relatively small amounts of sample volume (<= 10,0 µl) in some biomarker assays. Errors in dilution steps may also have contributed to the inconclusive results. Alpha 1 antitrypsin for example was in our assay at least 1000x diluted by following several dilution steps before adding samples to the ELISA plate. Due to the limited amount of information available with respect to the used assay, one cannot rule out possible dilution effects which can impact the assay performance. To asses assay performance with high diluted samples an dilution linearity experiment could have been performed [41].
In conclusion, basic assay performance and analyte stability parameters should be considered as the starting point of every biomarker assay study, using either commercial and/or in-house developed assays. Depending on the status of an assay, different requirements for an assay may be needed. In an early phase, a short investigation on reproducibility may be sufficient. The more weight is ascribed to an assay, the more time should be invested to verify that the assay performs adequately and is fit for purpose.