Analytical and Clinical Performance of Amyloid-Beta Peptides Measurements in CSF of ADNIGO/2 Participants by an LC-MS/MS Reference Method.

BACKGROUND: Cerebrospinal fluid (CSF) amyloid-ß1-42 (Aß42) reliably detects brain amyloidosis based on its high concordance with plaque burden at autopsy and with amyloid positron emission tomography (PET) ligand retention observed in several studies. Low CSF Aß42 concentrations in normal aging and dementia are associated with the presence of fibrillary Aß across brain regions detected by amyloid PET imaging. METHODS: An LC-MS/MS reference method for Aß42, modified by adding Aß40 and Aß38 peptides to calibrators, was used to analyze 1445 CSF samples from ADNIGO/2 participants. Seventy runs were completed using 2 different lots of calibrators. For preparation of Aß42 calibrators and controls spiking solution, reference Aß42 standard with certified concentration was obtained from EC-JRC-IRMM (Belgium). Aß40 and Aß38 standards were purchased from rPeptide. Aß42 calibrators' accuracy was established using CSF-based Aß42 Certified Reference Materials (CRM). RESULTS: CRM-adjusted Aß42 calibrator concentrations were calculated using the regression equation Y (CRM-adjusted) = 0.89X (calibrators) + 32.6. Control samples and CSF pools yielded imprecision ranging from 6.5 to 10.2% (Aß42) and 2.2 to 7.0% (Aß40). None of the CSF pools showed statistically significant differences in Aß42 concentrations across 2 different calibrator lots. Comparison of Aß42 with Aß42/Aß40 showed that the ratio improved concordance with concurrent [18F]-florbetapir PET as a measure of fibrillar Aß (n = 766) from 81 to 88%. CONCLUSIONS: Long-term performance assessment substantiates our modified LC-MS/MS reference method for 3 Aß peptides. The improved diagnostic performance of the CSF ratio Aß42/Aß40 suggests that Aß42 and Aß40 should be measured together and supports the need for an Aß40 CRM.

The Alzheimer's Disease Neuroimaging Initiative 2 Biomarker Core: A review of progress and plans.

INTRODUCTION: We describe Alzheimer's Disease Neuroimaging Initiative (ADNI) Biomarker Core progress including: the Biobank; cerebrospinal fluid (CSF) amyloid beta (Aß1-42), t-tau, and p-tau181 analytical performance, definition of Alzheimer's disease (AD) profile for plaque, and tangle burden detection and increased risk for progression to AD; AD disease heterogeneity; progress in standardization; and new studies using ADNI biofluids. METHODS: Review publications authored or coauthored by ADNI Biomarker core faculty and selected non-ADNI studies to deepen the understanding and interpretation of CSF Aß1-42, t-tau, and p-tau181 data. RESULTS: CSF AD biomarker measurements with the qualified AlzBio3 immunoassay detects neuropathologic AD hallmarks in preclinical and prodromal disease stages, based on CSF studies in non-ADNI living subjects followed by the autopsy confirmation of AD. Collaboration across ADNI cores generated the temporal ordering model of AD biomarkers varying across individuals because of genetic/environmental factors that increase/decrease resilience to AD pathologies. DISCUSSION: Further studies will refine this model and enable the use of biomarkers studied in ADNI clinically and in disease-modifying therapeutic trials.

Improved protocol for measurement of plasma ß-amyloid in longitudinal evaluation of Alzheimer's Disease Neuroimaging Initiative study patients.

BACKGROUND: The interassay variability and inconsistency of plasma ß-amyloid (Aß) measurements among centers are major factors precluding the interpretation of results and a substantial obstacle in the meta-analysis across studies of this biomarker. The goal of this investigation was to address these problems by improving the performance of the bioanalytical method. METHODS: We used the Luminex immunoassay platform with a multiplex microsphere-based reagent kit from Innogenetics. A robotic pipetting system was used to perform crucial steps of the procedure. The performance of this method was evaluated using two kit control samples and two quality control plasma samples from volunteer donors, and by retesting previously assayed patient samples in each run. This setup was applied to process 2454 patient plasma samples from the Alzheimer's Disease Neuroimaging Initiative study biofluid repository. We have additionally evaluated the correlations between our results and cerebrospinal fluid (CSF) biomarker data using mixed-effects modeling. RESULTS: The average precision values of the kit controls were 8.3% for Aß(1-40) and 4.0% for Aß(1-42), whereas the values for the plasma quality controls were 6.4% for Aß(1-40) and 4.8% for Aß(1-42). From the test-retest evaluation, the average precision was 7.2% for Aß(1-40) and 4.5% for Aß(1-42). The range of final plasma results for Alzheimer's Disease Neuroimaging Initiative patients was 13 to 372 pg/mL (median: 164 pg/mL) for Aß(1-40) and 3.5 to 103 pg/mL (median: 39.3 pg/mL) for Aß(1-42). We found that sample collection parameters (blood volume and time to freeze) have a small, but significant, influence on the result. No significant difference was found between plasma Aß levels for patients with Alzheimer's disease and healthy control subjects. We have determined multiple significant correlations of plasma Aß(1-42) levels with CSF biomarkers. The relatively strongest, although modest, correlation was found between plasma Aß(1-42) levels and CSF p-tau(181)/Aß(1-42) ratio in patients with mild cognitive impairment. Plasma Aß(1-40) correlations with CSF biomarkers were weaker and diminished completely when we used longitudinal data. No significant correlations were found for the plasma Aß(1-42)/Aß(1-40) ratio. CONCLUSIONS: The precision of our robotized method represents a substantial improvement over results reported in the literature. Multiple significant correlations between plasma and CSF biomarkers were found. Although these correlations are not strong enough to support the use of plasma Aß measurement as a diagnostic screening test, plasma Aß(1-42) levels are well suited for use as a pharmacodynamic marker.

High-performance liquid chromatography-mass spectroscopy/mass spectroscopy method for simultaneous quantification of total or free fraction of mycophenolic acid and its glucuronide metabolites.

Measurement of unbound fractions of mycophenolic acid and its metabolites may prove useful in explaining the complicated pharmacokinetic and pharmacodynamic behavior of this drug as well as in therapeutic drug monitoring. We developed a reliable, accurate, and sensitive liquid chromatography-tandem mass spectrometric method for the simultaneous quantification of mycophenolic acid (MPA), MPA glucuronide (MPAG), and MPA acyl-glucuronide (AcMPAG), total or unbound, in plasma, urine, and tissue extract. This method uses a single internal standard, carboxy-butoxy ether of mycophenolic acid (MPAC), and involves a simple sample preparation step. Aliquots of plasma, urine, or dissolved tissue extract (100 microL) or plasma ultrafiltrate for free analytes (50 microL) are treated with acetonitrile/formic acid mixture (99.5/0.5 v/v) followed by centrifugation and dilution with water. The prepared samples are then injected onto an extraction column (Eclipse XDB-C18 12.5 x 4.1 mm; Agilent Technologies, Palo Alto, CA) and washed with mobile phase composed of acetonitrile/water/formic acid (10/89.5/0.5 v/v/v) at a flow rate of 2.8 mL/min. A switching valve is activated 1 minute after sample injection. The analytes are eluted onto the analytical column (Eclipse XDB-C18 150 x 4.1 mm; Agilent Technologies) with a gradient of 0.5% aqueous formic acid, methanol, acetonitrile, and water. We used a tandem mass spectrometer with electrospray ion source, in which the tandem mass spectroscopy transitions were (m/z): 338-->207 for MPA, 438-->303 for MPAC, and 514-->303 for MPAG and AcMPAG. The dynamic ranges (lower limit of quantitation and upper limit of quantitation) were as follows: 0.05 to 30 mg/L for total MPA and 1 to 300 microg/L for free MPA; 0.5 to 300 mg/L of total MPAG and 0.2 to 60 mg/L for free MPAG; and 0.025 to 15 mg/L of total AcMPAG and 1 to 60 microg/L for free AcMPAG. The precision at lower limit of quantitation was in the range of 8.0% to 11.9% for all three total analytes and 13.8 to 18.7% for the free analytes. Accuracy at lower limit of quantitation was in the range of 100% to 105% for total and 97% to 99% for free analytes. Between-day precision of quality control samples was 4.0% to 6.3% for human plasma spiked with total analytes and 4.5% to 14.4% for spiked plasma ultrafiltrate for free analytes. Mean absolute recovery ranged from 98.5% to 101.7% for MPA (both total and free), from 78.1% to 103.4% for MPAG and from 91.5% to 110.4% for AcMPAG. No significant ion suppression was found under these conditions for any of the analytes. Carryover effect was found to be at a maximum level of 0.02%. This method was successfully applied to analyze over 11,000 samples for total analytes, and over 8000 samples for free analytes in plasma, and has been in operation for nearly 3 years without loss of performance.

Pharmacokinetic monitoring of mycophenolic acid in heart transplant patients: correlation the side-effects and rejections with pharmacokinetic parameters.

BACKGROUND: Mycophenolate mofetil is a commonly used immunosuppressant in heart transplantation but pharmacokinetic monitoring is not routinely done. We performed a prospective pilot multi-center trial in de-novo heart transplant recipients to evaluate the pharmacokinetics (PK) of mycophenolic acid (MPA) at multiple time points in the first year following transplant.
MATERIAL/METHODS: MPA trough and estimated area-under-the-curve (AUC) values were obtained at multiple visits from 21 enrolled patients. We attempted to correlate the side-effects and rejections with PK parameters.
RESULTS: MPA AUC and trough levels increased modestly over 12 months with substantial inter and intra patient variability. Cardiac rejection was associated with low MPA AUC values with a threshold of <36.2 mg×h/L during the first two post-transplant weeks. A threshold of 2-weeks average MPA trough level of 1.43 mg/L provided a sensitivity 82% and a specificity of 60%.
CONCLUSIONS: Adequate MPA levels are associated with decreased risk of allograft rejection. For patients with Cyclosporine co-immunosuppression, we propose an MPA trough of 1.4 mg/L and an MPA AUC of 36 mg × h/L as threshold values for dose adjustments. We recommend monitoring MPA levels at 1, 2 and 4 weeks, 6 months, 1 year and whenever an unexplained side-effect or allograft rejection occurs. Additional MPA AUC measurements are recommended when trough levels do not explain the clinical picture.

Development of a predictive limited sampling strategy for estimation of mycophenolic acid area under the concentration time curve in patients receiving concomitant sirolimus or cyclosporine.

Limited sampling strategies for estimation of the area under the concentration time curve (AUC) for mycophenolic acid (MPA) co-administered with sirolimus (SRL) have not been previously evaluated. The authors developed and validated 68 regression models for estimation of MPA AUC for two groups of patients, one with concomitant SRL (n = 24) and the second with concomitant cyclosporine (n=14), using various combinations of time points between 0 and 4 hours after drug administration. To provide as robust a model as possible, a dataset-splitting method similar to a bootstrap was used. In this method, the dataset was randomly split in half 100 times. Each time, one half of the data was used to estimate the equation coefficients, and the other half was used to test and validate the models. Final models were obtained by calculating the median values of the coefficients. Substantial differences were found in the pharmacokinetics of MPA between these groups. The mean MPA AUC as well as the standard deviation was much greater in the SRL group, 56.4 +/- 23.5 mg.h/L, compared with 30.4 +/- 11.0 mg.h/L in the cyclosporine group (P < 0.001). Mean maximum concentration was also greater in the SRL group: 16.4 +/- 7.7 mg/L versus 11.7 +/- 7.1mg/L (P < 0.005). The second absorption peak in the pharmacokinetic profile, presumed to result from enterohepatic recycling of glucuronide MPA, was observed in 70% of the profiles in the SRL group and in 35% of profiles from the cyclosporine group. Substantial differences in the predictive performance of the regression models, based on the same time points, were observed between the two groups. The best model for the SRL group was based on 0 (trough) and 40 minutes and 4 hour time points with R2, root mean squared error, and predictive performance values of 0.82, 10.0, and 78%, respectively. In the cyclosporine group, the best model was 0 and 40 minutes and 2 hours, with R2, RMSE, and predictive performance values of 0.86, 4.1, and 83%, respectively. The model with 2 hours as the last time point is also recommended for the SRL group for practical reasons, with the above parameters of 0.77, 11.3, and 69%, respectively.