Charged aerosol detection in early and late-stage pharmaceutical development: selection of regressionmodels at optimum power function value.

In recent years, the use of quantitative liquid chromatography (LC) coupled charged aerosol detection (CAD) for poor UV absorbing analytes in multicomponent mixtures has grown exponentially across academic and industrial sectors. The ballpark of previous LC-CAD reports is focused on practical applications, as well as optimization of critical parameters such as: response dependencies on temperature, nebulization process, analyte volatility, and mobile-phase composition. However, straightforward approaches to deal with the characteristic nonlinear response of CAD still scarce. A highly overlooked parameter is the power function value (PFV), whose optimization enables a detection signal that is more linear with higher signal-to-noise ratio (S/N) and lower relative standard deviation (RSD) of area counts. Herein, a systematic investigation of different regression models (log-log, first-and second-degree polynomial) by both interpolation and extrapolation process in conjunction with PFV optimization throughout the development of LC-CAD assays is reported. The accuracy of the results via interpolation is always good (< 5%) when operating in the vicinity of the optimum PFV regardless the regression model choice. On the contrary, extrapolation process only worked when applying log-log regression at the optimum PFV (accuracy <5%). This outcome indicates that a first-order regression via interpolation can be a safe and simple choice for quantitative LC-CAD in highly regulated laboratories (GLP, GMP, etc.). Whereas a straightforward extrapolation combined with log-log regression can enable the deployment of high-throughput LC-CAD assays, especially but not limited to laboratories where the synthetic process route is undergoing rapid change and optimization (medicinal chemistry, discovery, biocatalysis, process chemistry, etc.). This approach is crucial in developing quantitative LC-CAD assays for poor UV absorbing pharmaceuticals that are sensitive, precise, accurate and robust across early and late-stage pharmaceutical development.

Revealing the inner workings of the power function algorithm in Charged Aerosol Detection: A simple and effective approach to optimizing power function value for quantitative analysis.

In recent years, charged aerosol detection (CAD) has become a valuable tool for fast and efficient quantitative chromatographic analysis of drug substances with weak UV absorption. In analytical method development using CAD, the power function settings available in the instrument software are key for linearization of the signal response with respect to analyte concentration. However, the relatively poor understanding of the power function algorithm has limited a more widespread use of CAD for quantitative assays, especially in the late stage of method validation and GMP laboratories. Herein, we present an approach to understand the inner workings of the power function value (PFV), the PFV optimization algorithm, as well as a method to determine the optimum PFV based on the signals acquired at PFV = 1 (default CAD settings). The exponent and the constant in the PFV equation used for modeling follow a trend as a function of PFV. The CAD signal at any PFV was modeled based on the signal acquired at PFV = 1, the modelling was successful for two analytes at different concentration levels on two different CAD detectors of the same model. This method reveals the functionality of the PFV which substantially simplifies the workflow needed to optimize the detector signal. The accuracy between the experimental and theoretical results showed high correlation and always resulted in the same optimum PFV determined by both ways. The approach described in this investigation simplifies the selection of the optimum PFV at which the signal is more linear, the signal-to-noise is higher, and the area reproducibility is better. The power function algorithm elucidated herein enables determination of optimum PFV from minimal experimental output and excellent overall accuracy. This paper provides an approach that includes no data transformation outside the vendor software, a very important requirement to easily validate and report results in a GMP environment.

Kinetics and Degradation Mechanism of Adrenaline Derivative CpQ in Diluted Aqueous Solutions.

The degradation kinetics of an adrenaline (epinephrine) derivative, CpQ, was studied in solution in the pH range of 1-12 at 40-80 °C by high-performance liquid chromatography and ultraviolet-visible spectroscopy. The pH-rate profile exhibits a bell-shaped curve with two sigmoidal regions in the specific acid-catalyzed and specific base-catalyzed regions. The pH range of maximum stability was 2.5-4.5 with the main degradation pathway being the oxidative N-dealkylation of the aliphatic amino moiety followed by fast interconversion of the resulting fragments to stable degradation products. The autoxidation reaction was slower than the reaction of the oxygen reactive species. The chiral center underwent R to S racemization by a polar reaction mechanism under acidic conditions with a rate minimum at pH 4. The rates of degradation of the R and S enantiomers were similar across all pHs. CpQ degradation in the presence of hydrogen peroxide at 40 °C was significantly faster, and the extent of increases with pH. Metal ions bind to CpQ and catalyze its hydrolysis in the order Fe3+ > Fe2+ > Mg2+ > Mn2+ > Ti3+ > Sr2+ > Zn2+, with a rate enhancement of ≤1 order of magnitude at the studied pH values of 1 and 5. There was no buffer catalysis observed in the hydrolysis of the studied compound for maleate and phosphate but significant buffer catalysis in the case of citrate and malate.

A four parameter optimization and troubleshooting of a RPLC - charged aerosol detection stability indicating method for determination of S-lysophosphatidylcholines in a phospholipid formulation.

A four parameter optimization of a stability indicating method for non-chromophoric degradation products of 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC), 1-stearoyl-sn-glycero-3-phosphocholine and 2-stearoyl-sn-glycero-3-phosphocholine was achieved using a reverse phase liquid chromatography-charged aerosol detection (RPLC-CAD) technique. Using the hydrophobic subtraction model of selectivity, a core-shell, polar embedded RPLC column was selected followed by gradient-temperature optimization, resulting in ideal relative peak placements for a robust, stability indicating separation. The CAD instrument parameters, power function value (PFV) and evaporator temperature were optimized for lysophosphatidylcholines to give UV absorbance detector-like linearity performance within a defined concentration range. The two lysophosphatidylcholines gave the same response factor in the selected conditions. System specific power function values needed to be set for the two RPLC-CAD instruments used. A custom flow-divert profile, sending only a portion of the column effluent to the detector, was necessary to mitigate detector response drifting effects. The importance of the PFV optimization for each instrument of identical build and how to overcome recovery issues brought on by the matrix effects from the lipid-RP stationary phase interaction is reported.

Cleaning verification: A five parameter study of a Total Organic Carbon method development and validation for the cleaning assessment of residual detergents in manufacturing equipment.

A Total Organic Carbon (TOC) based analytical method to quantitate trace residues of clean-in-place (CIP) detergents CIP100® and CIP200® on the surfaces of pharmaceutical manufacturing equipment was developed and validated. Five factors affecting the development and validation of the method were identified: diluent composition, diluent volume, extraction method, location for TOC sample preparation, and oxidant flow rate. Key experimental parameters were optimized to minimize contamination and to improve the sensitivity, recovery, and reliability of the method. The optimized concentration of the phosphoric acid in the swabbing solution was 0.05M, and the optimal volume of the sample solution was 30mL. The swab extraction method was 1min sonication. The use of a clean room, as compared to an isolated lab environment, was not required for method validation. The method was demonstrated to be linear with a correlation coefficient (R) of 0.9999. The average recoveries from stainless steel surfaces at multiple spike levels were >90%. The repeatability and intermediate precision results were ≤5% across the 2.2-6.6ppm range (50-150% of the target maximum carry over, MACO, limit). The method was also shown to be sensitive with a detection limit (DL) of 38ppb and a quantitation limit (QL) of 114ppb. The method validation demonstrated that the developed method is suitable for its intended use. The methodology developed in this study is generally applicable to the cleaning verification of any organic detergents used for the cleaning of pharmaceutical manufacturing equipment made of electropolished stainless steel material.

Failure of Cleaning Verification in Pharmaceutical Industry Due to Uncleanliness of Stainless Steel Surface.

The aim of this work is to identify the parameters that affect the recovery of pharmaceutical residues from the surface of stainless steel coupons. A series of factors were assessed, including drug product spike levels, spiking procedure, drug-excipient ratios, analyst-to-analyst variability, intraday variability, and cleaning procedure of the coupons. The lack of a well-defined procedure that consistently cleaned the coupon surface was identified as the major contributor to low and variable recoveries. Assessment of cleaning the surface of the coupons with clean-in-place solutions (CIP) gave high recovery (>90%) and reproducible results (Srel≤4%) regardless of the conditions that were assessed previously. The approach was successfully applied for cleaning verification of small molecules (MW <1,000 Da) as well as large biomolecules (MW up to 50,000 Da).

A simplified guide for charged aerosol detection of non-chromophoric compounds-Analytical method development and validation for the HPLC assay of aerosol particle size distribution for amikacin.

Amikacin, an aminoglycoside antibiotic lacking a UV chromophore, was developed into a drug product for delivery by inhalation. A robust method for amikacin assay analysis and aerosol particle size distribution (aPSD) determination, with comparable performance to the conventional UV detector was developed using a charged aerosol detector (CAD). The CAD approach involved more parameters for optimization than UV detection due to its sensitivity to trace impurities, non-linear response and narrow dynamic range of signal versus concentration. Through careful selection of the power transformation function value and evaporation temperature, a wider linear dynamic range, improved signal-to-noise ratio and high repeatability were obtained. The influences of mobile phase grade and glassware binding of amikacin during sample preparation were addressed. A weighed (1/X2) least square regression was used for the calibration curve. The limit of quantitation (LOQ) and limit of detection (LOD) for this method were determined to be 5µg/mL and 2µg/mL, respectively. The method was validated over a concentration range of 0.05-2mg/mL. The correlation coefficient for the peak area versus concentration was 1.00 and the y-intercept was 0.2%. The recovery accuracies of triplicate preparations at 0.05, 1.0, and 2.0mg/mL were in the range of 100-101%. The relative standard deviation (Srel) of six replicates at 1.0mg/mL was 1%, and Srel of five injections at the limit of quantitation was 4%. A robust HPLC-CAD method was developed and validated for the determination of the aPSD for amikacin. The CAD method development produced a simplified procedure with minimal variability in results during: routine operation, transfer from one instrument to another, and between different analysts.

Cleaning verification: Exploring the effect of the cleanliness of stainless steel surface on sample recovery.

The parameters affecting the recovery of pharmaceutical residues from the surface of stainless steel coupons for quantitative cleaning verification method development have been studied, including active pharmaceutical ingredient (API) level, spiking procedure, API/excipient ratio, analyst-to-analyst variability, inter-day variability, and cleaning procedure of the coupons. The lack of a well-defined procedure that consistently cleaned coupon surface was identified as the major contributor to low and variable recoveries. Assessment of acid, base, and oxidant washes, as well as the order of treatment, showed that a base-water-acid-water-oxidizer-water wash procedure resulted in consistent, accurate spiked recovery (>90%) and reproducible results (Srel≤4%). By applying this cleaning procedure to the previously used coupons that failed the cleaning acceptance criteria, multiple analysts were able to obtain consistent recoveries from day-to-day for different APIs, and API/excipient ratios at various spike levels. We successfully applied our approach for cleaning verification of small molecules (MW<1000Da) as well as large biomolecules (MW up to 50,000Da). Method robustness was greatly influenced by the sample preparation procedure, especially for analyses using total organic carbon (TOC) determination.