TY - JOUR
T1 - Optimization of HPLC–CAD method for simultaneous analysis of different lipids in lipid nanoparticles with analytical QbD
AU - Kim, Ki Hyun
AU - Lee, Ji Eun
AU - Lee, Jae Chul
AU - Maharjan, Ravi
AU - Oh, Hyunsuk
AU - Lee, Kyeong
AU - Kim, Nam Ah
AU - Jeong, Seong Hoon
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/10/25
Y1 - 2023/10/25
N2 - Since lipid nanoparticles (LNP) have emerged as a potent drug delivery system, the objective of this study was to develop and optimize a robust high-performance liquid chromatography with charged aerosol detectors (HPLC–CAD) method to simultaneously quantify different lipids in LNPs using the analytical quality by design (AQbD) approach. After defining analytical target profile (ATP), critical method attributes (CMAs) were established as a resolution between the closely eluting lipid peaks and the total analysis time. Thus, potential high-risk method parameters were identified through the initial risk assessment. These parameters were screened using Plackett–Burman design, and three critical method parameters (CMPs)—MeOH ratio, flow rate, and column temperature—were selected for further optimization. Box–Behnken design was employed to develop the quadratic models that explain the relationship between the CMPs and CMAs and to determine the optimal operating conditions. Moreover, to ensure the robustness of the developed method, a method operable design region (MODR) was established using the Monte Carlo simulation. The MODR was identified within the probability map, where the risk of failure to achieve the desired CMAs was less than 1%. The optimized method was validated according to the ICH guidelines (linearity: R2 > 0.995, accuracy: 97.15–100.48% recovery, precision: RSD < 5%) and successfully applied for the analysis of the lipid in the LNP samples. The development of the analytical method to quantify the lipids is essential for the formulation development and quality control of LNP-based drugs since the potency of LNPs is significantly dependent on the compositions and contents of the lipids in the formation.
AB - Since lipid nanoparticles (LNP) have emerged as a potent drug delivery system, the objective of this study was to develop and optimize a robust high-performance liquid chromatography with charged aerosol detectors (HPLC–CAD) method to simultaneously quantify different lipids in LNPs using the analytical quality by design (AQbD) approach. After defining analytical target profile (ATP), critical method attributes (CMAs) were established as a resolution between the closely eluting lipid peaks and the total analysis time. Thus, potential high-risk method parameters were identified through the initial risk assessment. These parameters were screened using Plackett–Burman design, and three critical method parameters (CMPs)—MeOH ratio, flow rate, and column temperature—were selected for further optimization. Box–Behnken design was employed to develop the quadratic models that explain the relationship between the CMPs and CMAs and to determine the optimal operating conditions. Moreover, to ensure the robustness of the developed method, a method operable design region (MODR) was established using the Monte Carlo simulation. The MODR was identified within the probability map, where the risk of failure to achieve the desired CMAs was less than 1%. The optimized method was validated according to the ICH guidelines (linearity: R2 > 0.995, accuracy: 97.15–100.48% recovery, precision: RSD < 5%) and successfully applied for the analysis of the lipid in the LNP samples. The development of the analytical method to quantify the lipids is essential for the formulation development and quality control of LNP-based drugs since the potency of LNPs is significantly dependent on the compositions and contents of the lipids in the formation.
KW - Analytical quality by design
KW - Charged aerosol detector
KW - Design of experiment
KW - Lipid nanoparticles
UR - http://www.scopus.com/inward/record.url?scp=85171453044&partnerID=8YFLogxK
U2 - 10.1016/j.chroma.2023.464375
DO - 10.1016/j.chroma.2023.464375
M3 - Article
C2 - 37734240
AN - SCOPUS:85171453044
SN - 0021-9673
VL - 1709
JO - Journal of Chromatography A
JF - Journal of Chromatography A
M1 - 464375
ER -