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[email protected] Current Pharmaceutical Analysis, 2017, 13, 000-000
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RESEARCH ARTICLE
QbD-Enabled Development and Validation of a Liquid Chromatographic Method for Estimating Galantamine Hydrobromide in Biological Fluids Shikha Lohan2, Rajneet Kaur1, Shubham Bharti1, SK Mehta3 and Bhupinder Singh1,2,* 1
University Institute of Pharmaceutical Sciences, UGC Centre of Advanced Studies, Panjab University, Chandigarh, India 160 014; 2UGC-Centre of Excellence in Applications of Nanomaterials, Nanoparticles & Nanocomposites (Biomedical Sciences), Panjab University, Chandigarh, India 160 014; 3Department of Chemistry, Panjab University, Chandigarh, India 160 014 Abstract: Background: Analytical method development and validation remain the key prerequisites for the successful development of a product. Methods: An endeavour, therefore, was made for the development of an effortless, swift, sensitive and cost-effective method for estimation of galantamine hydrobromide using the established paradigms of quality by design (QbD).
ARTICLE HISTORY Received: May 04, 2017 Revised: July 27, 2017 Accepted: September 02, 2017 DOI: 10.2174/1573412913666170912111144
Results: Chromatographic separation was accomplished on a reverse phase C18 column employing an optimized mixture of methanol, acetonitrile and ammonium formate buffer, adjusted to pH 9, at a flow rate of 0.7 mL/min with UV detection at 212 nm. At the onset, risk assessment and factor screening studies using Taguchi design facilitated the understanding of the factors that are crucial in varying critical analytical attributes (CAAs). The mobile phase ratio and the flow rate through the column were identified as the critical method parameters (CMPs), and were subsequently employed for systematic method optimization using a face-centred cubic design (FCCD), employing four critical analytical attributes (CAAs) viz. retention time, peak area, peak tailing and theoretical plates. Statistical modeling was accomplished, followed by response surface analysis leading to complete analytical understanding and comprehending plausible interaction(s) among CMPs, if any. Numerical and graphical optimizations were employed in order to demarcate the design space and eventually search for an optimum solution. Conclusion: The studies successfully reveal the utility of analytical QbD (AQbD) paradigms for developing quite sensitive and precise liquid chromatographic method, for galantamine hydrobromide, with enhanced method performance and improved analytical understanding.
Keywords: Risk assessment, design of experiment, detection limit, method validation, quantification limit, response surface methodology. 1. INTRODUCTION Quality by Design (QbD) has been well established as a rational systematic approach aimed at product and process understanding governed by predefined objectives [1]. Of late, analytical quality by design (AQbD) paradigms have successfully evolved for systematic analytical method development, especially in the industrial milieu [2]. Particularly, the analytical methods, employing liquid chromatographic techniques like HPLC, UPLC or LC-MS/MS, encompass a multitude of independent variables, which make these techniques as more tedious, less robust and highly amenable to *Address correspondence to this author at the University Institute of Pharmaceutical Sciences, Coordinator, UGC Centre of Advanced Studies, Coordinator, UGC Center for Excellence in Nano Applications, Panjab University, Chandigarh 160 014, India; E-mails:
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1573-4129/17 $58.00+.00
variation in analytical milieu [3]. AQbD approach helps in identifying the potentially vital few independent variables or critical method variables (CMVs) actually affecting the critical quality attributes (CQAs), thus providing holistic understanding of the cause-and-effect relationship, and underlying plausible interactions among the factors affecting the CQAs [4, 5]. The AQbD approach as applied to chromatography, banks upon salient principles of prioritization using risk assessment and screening studies and optimization using design of experiments and sound science of chromatography [6, 7]. The experimental designs, in this context, are integral part of the AQbD exercise during factor screening studies for identification of CMVs and their subsequent response to surface optimization [8]. Besides, the AQbD approach also provides flexibility in the development of analytical method(s), along with demarcation of the analytical knowledge space, design space and control space. © 2017 Bentham Science Publishers
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Galantamine hydrobromide (GAL), an alkaloid isolated from the bulbs and flowers of Galanthus caucasicus, Galanthus woronowii and related genera, was initially developed as a drug for the treatment of myasthenia gravis. Chemically, GAL is (4aS,6R,8aS)-4a,5,9,10,11,12-hexahydro-3-methoxy11-methyl–6H–benzofuro [3a,3,2ef] [2] benzazepin– 6-ol hydrobromide (5,6,7,10,11) with its structure as depicted in Fig. (1). GAL is a parasympathomimetic agent, and specifically, a reversible cholinesterase inhibitor [9]. It has beenreported to undergo linear pharmacokinetics in man with the biological half-life of about 7 hours, and exhibits low brain bioavailability as well, ostensibly owing to its hydrophilic nature.
Lohan et al.
from whole blood, was added to different diluted working solutions in a tube to make up the volume up to 0.5 mL with acetonitrile. The samples were centrifuged at 10,000 g for 10 minutes to separate the plasma proteins. The fraction of the supernatant containing the drug solution, i.e., GAL was separated and subjected to evaporation to obtain a dried residue. The residue was reconstituted with 1 mL of the mobile phase, filtered through 0.22 membrane filter and subjected to further analysis. 2.3. Instrumentation and Chromatographic Conditions The HPLC system consisted of a Shimadzu UV-VIS detector LC-2010C HT version 3.01 system (M/s Shimadzu Inc., Tokyo, Japan) containing quaternary pump of varying capacity, mobile phase degasser, column thermostat, UV-Vis detector and a SCL 10AVP system controller with an auto sampler. A reverse phase C18 (4.6 x 250, 4m) chromatographic column (M/s Thermo Fisher Scientific, USA) was employed. The mobile phase consisted of a rational mixture of methanol, acetonitrile and ammonium formate buffer, adjusted to pH 9. An aliquot of 10 L was injected at 0.7 mL.min-1 and the detection of GAL was performed at max of 212 nm at 22° C. The concentration of GAL was calculated using the area under the peak employing Shimadzu LC solution ver. 1.23 software.
Fig. (1). Chemical Structure of Galantamine.
2.4. Defining the Quality Target Method Parameter and Critical Analytical Attributes Various literature studies report the establishment of validated methods for estimation of GAL and related substances in oral and bulk dosage forms [10-13]. However, none of the methods described in literature are holistic in providing significant outcomes in terms of units of detection and quantification. Attempts, therefore, were undertaken to develop a quick, robust, sensitive and cost-effective RP-LC method employing an AQbD approach for estimation of GAL in biological fluids, as active drug and its prepared formulation systems. 2. EXPERIMENTAL 2.1. Materials GAL was generously provided ex-gratis by M/s Sun Pharmaceuticals Industries Ltd., Vadodara, India, as the working standard. Whole blood was obtained from Rotary Blood Bank, Chandigarh, India. Ammonium formate (AR grade) was procured from M/s SD Fine Chemicals, Mumbai, India. HPLC-grade solvents, i.e., acetonitrile, methanol and triple distilled water purchased from M/s Merck Ltd., Mumbai, India, were employed for the protocol. All other chemicals and reagents (analytical grade) were employed as obtained. 2.2. Preparation of Standard Stock Solution and Bioanalytical Samples in Plasma A standard stock solution (100 g.mL-1 GAL) was prepared by dissolving 10 mg GAL in 100 mL of methanol. The stock solution, thus prepared, was diluted further with methanol for spiking in plasma to obtain calibration curve standards containing GAL in concentration range of 5 to 40 g.mL-1. An aliquot of 100 L of the blank plasma, isolated
Initially, the quality target method parameter (QTMP) was defined, encompassing a summary of the quality characteristics of the analytical method as mentioned in the previous reports by our group [7]. Various critical analytical attributes (CAAs) were identified to meet the desired QTMP, i.e., the peak area, retention time, number of theoretical plates, and peak tailing factor. 2.5. Risk Assessment Studies In order to identify the CMPs, which possess high risk to affect the CAAs, risk assessment studies were performed. Besides, risk assessment also indicates the possibility of any interaction(s) amongst the CMPs and CPPs, estimating the chances of subsequent failure(s), if any. The first step during the risk assessment plan involves the construction of an Ishikawa fish-bone diagram employing Minitab® 17 software (Provided ex gratis by M/s Minitab Inc., Philadelphia, USA). This cause and effect display helps to structure the risk operation plan for determining all the potential factors affecting the CAAs of the method employed [14, 15]. Subsequently, for selecting the CMPs/ CPPs associated with high risk, prioritization studies were carried out employing a risk estimation matrix (REM). This matrix qualitatively analyses the risk involved in the process to be employed by assigning low, medium and high risk(s) levels to each of the method parameters (MPs) and/or process parameter (PPs) [16, 17]. 2.6. Factor Screening Studies To categorize those CMPs/CPPs which tend to affect the method CAAs (i.e., theoretical plates and peak tailing) critically, a Taguchi design (seven-factor eight-run) was
QbD-Enabled Development and Validation of a Liquid Chromatographic Method
employed for factor screening studies [5]. The design matrix with the studied factors and the decoded translation of their respective low and high levels is shown in Table 1. Experimental runs (n=8) were conducted in accordance with the design and analyzed for determining the influence of the studied factors on the CAAs. Model fitting exercise was performed for selecting the linear polynomial model by obviating the interaction term(s). Subsequently, the Pareto charts were employed for quantitatively identifying the effect of each factor on the selected CAAs [15, 18]. 2.7. Method Development Based on the Experimental Design Having accomplished preliminary risk assessment studies and screening studies, the next step involved the selection of the CMPs actually affecting the method performance. A face-centered cubic design (FCCD; = 1) was used for optimizing the chosen CMPs, viz. mobile phase ratio (X1) and flow rate (X2), at different equidistant levels, i.e., low (-1), intermediate (0) and high (+1) as depicted in Table 2. A standard concentration of 10 g.mL-1 was employed for all the runs and analyzed for CAAs viz. peak area, retention time, theoretical plates and peak tailing. Table 1.
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2.8. QbD-based Optimization Data Analysis and Validation Minitab® 17 software (M/s Minitab Inc., Philadelphia, USA) was employed to perform the optimization data analysis using a multiple linear regression analysis (MLRA) to correlate the studied responses with the examined variables by fitting the data into a quadratic polynomial model (second-order) along with added interaction terms. Only the coefficients with p
