Development and validation of a HPLC method for the simultaneous quantitation of Dapagliflozin and Pioglitazone in Tablet using Analytical Quality by Design
- 주제(키워드) Dapagliflozin , Pioglitazone , Simultaneous quantitation , Quality by Design , Box-Behnken
- 주제(DDC) 615.1
- 발행기관 아주대학교
- 지도교수 백승훈
- 발행년도 2022
- 학위수여년월 2022. 2
- 학위명 석사
- 학과 및 전공 일반대학원 약학과
- 실제URI http://www.dcollection.net/handler/ajou/000000031428
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
This study characterizes the development of an analytical method applying analytical quality by design of a high-performance liquid chromatography assay method capable of simultaneous analysis of dapagliflozin and pioglitazone. Design of experiment was applied for analytical quality by design. Risk assessment was performed to establish the analytical target profile of assay and to establish risk factors. Through evaluation of risk factors, ratio of solvent, pH of mobile phase, column temperature, injection volume, and flow rate were selected as parameters, and the effect of each factor was screened by applying a 1/2 partial factor design. Through screening, the parameters were reduced to ratio of solvent, pH of mobile phase, and flow rate, and the effect on Dapagliflozin and Pioglitazone peak retention time was designed by applying the box-Behnken design, and the analysis conditions were optimized. The final set analysis conditions were verified by performing method validation according to ICH Q2(R1). The analysis was performed under mobile phase pH 4.5 buffer:acetonitrile (52:48, v/v), Waters Symmetry C18, 3.9 μmx150 mm with 5 μm particle size column, at 30°C, flow rate 1.0 mL/min, injection volume 10 μL , UV-Vis detection at 235 nm conditions.
more목차
1. Introduction 1
2. Material and methods 4
2.1. Materials 4
2.2. Chromatographic instrumentation and conditions 4
2.3. Preparation of standard solution 5
2.4. Preparation of sample solution 5
2.5. Software and experimental design 5
3. Results and discussion 8
3.1. Analytical target profile 8
3.2. Risk Assessment 12
3.3. Screening Step 16
3.4. Optimization Step 23
3.5. Method validation 36
4. Conclusion 44
5. References 45