Replicates Number for Drug Stability Testing during Bioanalytical Method Validation-An Experimental and Retrospective Approach.

BACKGROUND: The stability of a drug or metabolites in biological matrices is an essential part of bioanalytical method validation, but the justification of its sample size (replicates number) is insufficient. The international guidelines differ in recommended sample size to study stability from no recommendation to at least three quality control samples. Testing of three samples may lead to results biased by a single outlier. We aimed to evaluate the optimal sample size for stability testing based on 90% confidence intervals. METHODS: We conducted the experimental, retrospective (264 confidence intervals for the stability of nine drugs during regulatory bioanalytical method validation), and theoretical (mathematical) studies. We generated experimental stability data (40 confidence intervals) for two analytes-tramadol and its major metabolite (O-desmethyl-tramadol)-in two concentrations, two storage conditions, and in five sample sizes (n = 3, 4, 5, 6, or 8). RESULTS: The 90% confidence intervals were wider for low than for high concentrations in 18 out of 20 cases. For n = 5 each stability test passed, and the width of the confidence intervals was below 20%. The results of the retrospective study and the theoretical analysis supported the experimental observations that five or six repetitions ensure that confidence intervals fall within 85-115% acceptance criteria. CONCLUSIONS: Five repetitions are optimal for the assessment of analyte stability. We hope to initiate discussion and stimulate further research on the sample size for stability testing.

An extension of biorelevant fed-state dissolution tests to clinical pharmacokinetics - A study on gastrointestinal factors influencing rivaroxaban exposure and efficacy in atrial fibrillation patients.

A direct oral anticoagulant rivaroxaban fails to prevent stroke and systemic embolism in one-to-several percent of patients with nonvalvular atrial fibrillation (NVAF), but the reasons are unknown. The study used semi-mechanistic in vitro-in vivo prediction (IVIVP) modeling to explore the reasons for ineffective thrombosis prevention in NVAF patients. Steady-state drug concentrations in plasma were measured at 0 h (Ctrough), 3 h (C3h), and 12 h post-dosing in thirty-four patients treated with 20 mg rivaroxaban daily. The clinical data were compared against "virtual twins" generated with a novel IVIVP model that combined drug dissolution modeling, mechanistic description of gastric drug transit, and population pharmacokinetics defining the variability of drug disposition. The nonresponders had significantly lower C3h and Ctrough than the responders (p < 0.001) and the covariates included in the population pharmacokinetic submodel did not fully explain this difference. Simulations involving varied gastrointestinal parameters in the "virtual twins" revealed that lower small intestinal effective permeability (Peff), rather than a slower stomach emptying rate, could explain low rivaroxaban exposure in the nonresponders. IVIVP modeling was effectively used for exploring pharmacotherapy failure. Low Peff, found as a major determinant of ineffective rivaroxaban treatment, encourages further research to find (pato)physiological factors influencing suboptimal absorption.

How does the order of sample analysis influence the matrix effect during LC-MS bioanalysis?

Mass spectrometry coupled with liquid chromatography is a valuable tool for drug development and personalised drug therapy. The matrix effect is caused by enhancing or suppressing the analyte signal intensity by the interfering compounds of biological fluids. The matrix effect may influence the reliability of the quantitative results. Thus, its evaluation is a critical part of bioanalytical method validation. Identified factors affecting the matrix effect are the physicochemical properties of the analyte, type of biological material, analytical conditions, the ion source construction and calculation method. The order of analysis of test samples (pure solutions and post-extraction spiked samples) is another factor possibly affecting quantifying the matrix effect variability between sources. Our primary goal was to find which experimental design - interleaved or set of blocks - is more sensitive to detect matrix effect variability. Additionally, to better understand the reason of variability, we evaluated the influence of chromatographic elution and the type of plasma (normal, lipemic or hemolyzed), co-elution, and carry-over of phospholipids. We used chemometric methods: Principal Component Analysis and Partial Least-Squares Discriminant Analysis. Although a comparable (but statistically different) matrix effect (%RSDMF) is observed using the interleaved and block schemes, for some compounds, the order of the samples strongly influences the results. The interleaved scheme was generally more sensitive in detecting the matrix effect than the block scheme. Thus, reporting the order of samples is needed to ensure the repeatability of experiments. Chemometrics suggests that lipemic samples analyzed in isocratic conditions are most prone to the matrix effect. Different compositions of matrix lots of the same type - especially lipemic - may influence method reliability. Thus, evaluating more than one source of lipemic and hemolyzed plasma is recommended.

Polytyramine Film-Coated Single-Walled Carbon Nanotube Electrochemical Chemosensor with Molecularly Imprinted Polymer Nanoparticles for Duloxetine-Selective Determination in Human Plasma.

We devised, fabricated, and tested differential pulse voltammetry (DPV) and impedance spectroscopy (EIS) chemosensors for duloxetine (DUL) antidepressant determination in human plasma. Polyacrylic nanoparticles were synthesized by precipitation polymerization and were molecularly imprinted with DUL (DUL-nanoMIPs). Then, together with the single-walled carbon nanotube (SWCNT) scaffolds, they were uniformly embedded in polytyramine films, i.e., nanoMIPs-SWCNT@(polytyramine film) surface constructs, deposited on gold electrodes by potentiodynamic electropolymerization. These constructs constituted recognition units of the chemosensors. The molecular dynamics (MD) designing of DUL-nanoMIPs helped select the most appropriate functional and cross-linking monomers and determine the selectivity of the chemosensor. Three different DUL-nanoMIPs and non-imprinted polymer (nanoNIPs) were prepared with these monomers. DUL-nanoMIPs, synthesized from respective methacrylic acid and ethylene glycol dimethyl acrylate as the functional and cross-linking monomers, revealed the highest affinity to the DUL analyte. The linear dynamic concentration range, extending from 10 pM to 676 nM DUL, and the limit of detection (LOD), equaling 1.6 pM, in the plasma were determined by the DPV chemosensor, outperforming the EIS chemosensor. HPLC-UV measurements confirmed the results of DUL electrochemical chemosensing.

LC-MS/MS determination of dutasteride and its major metabolites in human plasma.

Dutasteride is a specific and selective inhibitor of both 5α-reductase isoforms used mainly in benign prostatic hyperplasia and lower urinary tract symptoms. Although the drug is extensively metabolized in humans, data on the concentrations of its main metabolites are lacking. There is also a lack of data on dutasteride stability in frozen plasma samples. Our method was used to determine dutasteride and its active metabolites: 4'-hydroxydutasteride, 6ß-hydroxydutasteride, and 1,2-dihydrodutasteride in plasma after a single administration of 0.5 mg of dutasteride. We also assessed the long-term stability (two years in the freezer) of dutasteride in clinical samples. The developed method covered the range of 0.1-3.5 ng/mL for dutasteride and 0.08-1.2 ng/mL for 1,2-dihydrodutasteride, 4'-hydroxydutasteride, 6ß-hydroxydutasteride. It was proved to be reliable as it met all validation criteria required by the European Medicine Agency for bioanalytical methods. 4'-hydroxydutasteride and 1,2-dihydrodutasteride concentrations in plasma were higher than 6ß-hydroxydutasteride. Dutasteride was stable in the freezer for up to 2 years in clinical samples. Thus within 1014 days of storage (below - 65 °C), samples can be reanalyzed without the risk of unreliable results.

Determination of Antidepressants in Human Plasma by Modified Cloud-Point Extraction Coupled with Mass Spectrometry.

Cloud-point extraction (CPE) is rarely combined with liquid chromatography coupled to mass spectrometry (LC-MS) in drug determination due to the matrix effect (ME). However, we have recently shown that ME is not a limiting factor in CPE. Low extraction efficiency may be improved by salt addition, but none of the salts used in CPE are suitable for LC-MS. It is the first time that the influences of a volatile salt-ammonium acetate (AA)-on the CPE extraction efficiency and ME have been studied. Our modification of CPE included also the use of ethanol instead of acetonitrile to reduce the sample viscosity and make the method more environmentally friendly. We developed and validated CPE-LC-MS for the simultaneous determination of 21 antidepressants in plasma that can be useful for clinical and forensic toxicology. The selected parameters included Triton X-114 concentration (1.5 and 6%, w/v), concentration of AA (0, 10, 20 and 30%, w/v), and pH (3.5, 6.8 and 10.2). The addition of 10% of AA increased recovery twice. For 20 and 30% (w/v) of AA, three phases were formed that prolonged the extraction process. The developed CPE method (6% Triton X-114, 10% AA, pH 10.2) was successfully validated through LC-MS/MS simultaneous determination of 21 antidepressants in human plasma. The linearity was in the range of 10-750 ng/mL (r2 > 0.990).

Quantitative evaluation of the matrix effect in bioanalytical methods based on LC-MS: A comparison of two approaches.

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful tool for studying pharmacokinetics and toxicokinetics. Reliable bioanalysis requires the characterization of the matrix effect, i.e. influence of the endogenous or exogenous compounds on the analyte signal intensity. We have compared two methods for the quantitation of matrix effect. The CVs(%) of internal standard normalized matrix factors recommended by the European Medicines Agency were evaluated against internal standard normalized relative matrix effects derived from Matuszewski et al. (2003). Both methods use post-extraction spiked samples, but matrix factors require also neat solutions. We have tested both approaches using analytes of diverse chemical structures. The study did not reveal relevant differences in the results obtained with both calculation methods. After normalization with the internal standard, the CV(%) of the matrix factor was on average 0.5% higher than the corresponding relative matrix effect. The method adopted by the European Medicines Agency seems to be slightly more conservative in the analyzed datasets. Nine analytes of different structures enabled a general overview of the problem, still, further studies are encouraged to confirm our observations.