Category: Formulation and Quality
Purpose: Reversed Phase Liquid Chromatography (RP-LC) is the widest analytical tecnhology used in pharmaceutical industry and its development is critical and challenging. Often, using a trial-and-error approach, this process is less streamlined and automated leading to a significant time consuming procedure and it is less likely to obtain robust methods. In order to agilize the tasks of chromatographic method development, the combination of a systematic and scientific based approach, based on Analytical Quality by Design (AQbD) principles, with the right instrumentation, automation and software enables a fast, modernized and efficient method development process. The present work aims to demonstrate the advantages of combining AQbD with the high-throughput toolkit, based on automated instrumentation, software and in silico tools, to screen the most critical method parameters during the development of a RP-LC method of a specific API and its 10 related substances.
Methods: An API at target concentration (0.2 mg/mL), spiked with its 10 related substance/impurities at 0.5% w/w level was used. Percepta® module from ACD/Labs® software was used to predict physico-chemical properties. Four stationary phases were screened: Acquity UPLC BEH C18 (100 × 2.1 mm, 1.7 mm); Acquity UPLC BEH Phenyl (100 x 2.1 mm , 1.7 um), Acquity UPLC BEH Shield RP18, (100 x 2.1 mm, 1.7 um) and Luna Omega C18 (2.1 x 100 mm, 1.6 um). Acetonitrile (ACN) and Methanol (MeOH) were screened and a mixture of 50:50 (%v/v) of both was selected based on optimization using LC simulator ®module from ACD/Labs® software. A Design of Experiment (DoE) was performed to screen pH (3.5, 4.5, 5.6 and 6) and temperature (30-55ºC), using Fusion QbD® software to plan and perform data analysis. Gradient used started with 5% of eluent B (organic solvents) for 1.5 min as initial hold time, and then it was linearly increased to 95% B during 12.7 min; the isocratic 95% B conditions were maintained for 3 min. The injection volume was 1 mL and flow rate 0.2 mL min-1. The chromatographic analysis was carried out by an Acquity UPLC ® H-class system (Waters®) equipped with a Solvent Selection Valve and Column Manager. Detection was done with a Photo-Diode Array (PDA) and single-stage mass detector (Acquity® QDaTM). All chromatographic data were processed using Empower®3. The final working operating conditions were the following: 50:50 (%v/v) MeOH:ACN as organic solvent in eluent B, pH 4.0 + 0.1, flow rate 0.2 mL min−1, temperature 35 + 3ºC, maintaining constant the remaining operating conditions.
Results: The main goal of this work was to obtain sufficient method selectivity for assay of one specific API and determination of its 10 related substances (Figure 1). Prior to any development activities, prior knowledge was gathered: analytes physicochemical properties prediction that are likely to be important during RP-LC method development. The % microspecies distribution and Log D vs pH are the most relevant (Figure 2). This information supported the selection of the optimum pH working range (3-6) and the selection of the stationary phase chemistries.
Using this information and through a risk assessment exercise, an experimental strategy was defined. Firstly, different stationary phases and organic solvents were screened. Through an automated system and using MS orthogonal detection, accurate peak tracking was rapidly achieved. As an outcome, a BEH C18 column with a mixture of ACN:MeOH (50:50 %v/v) presented best selectivity. pH and temperature were then screened, through a DoE. The critical resolutions and retention times were selected as the response attributes. A D-optimal design was selected, and 11 runs and 3 center point were performed. This plan was automatically designed by Fusion QbD® and automatically transferred to Empower 3. Statistically significant models were obtained for each attributed studied, leading to better method understanding. Based on the regression models, Method Operable Design Region (MODR) and Normal Operable Range (NOR) conditions (Figure 3) were defined. A prediction of the final chromatogram profile was simulated (Figure 3) and a good agreement between the experimental data was achieved.
Finally, an optimization of organic mixture was performed. For that, three different organic mixture composition (ACN: MeOH 50:50; 70:30 and 30:70, %v/v:) were tested and included into a simulation software. From the in silico results, the best resolution was predicted to be achieved with the organic mixture, ACN:MeOH 50:50 (%v/v). Final operating conditions were set, including an effective analytical control strategy, based on the enhanced method understanding obtained.
Conclusion: The advantages of using a high-throughput toolkit combined with a structured and scientific based approach (AQbD) was highlighted in this work. The use of innovative technology, automation in instrumentation, databases, predictive tools and screening approaches, allowed a fast method development. Moreover, with this approach the likelihood of achieving method understanding and control of eventual sources of variability throughout method lifecycle is increased.