The concept of quality by design

Transcription

1 B i o P r o c e s s TECHNICAL Implementing Quality By Design in Analytical Development A Case Study on the Development of an Anion-Exchange HPLC Method Quan Yuan, Weijun Li, and Lisa Regan The concept of quality by design (QbD) initially was outlined in ICH Q guidance for drugproduct development and later in Q11 for drug-substance development (1, 2). Since then, the QbD concept was further expanded to the development of analytical methods. FDA issued a 215 guidance on analytical procedures and method validation for drugs and biologics (3). Although the agency did not explicitly state the requirement for implementation of QbD in analytical method development, the concept is embedded in its section on analytical method development, including these two quotes: Early in the development of a new analytical procedure, the choice of analytical instrumentation and methodology should be selected based on the intended purpose and scope of the analytical method.... To fully understand the effect of changes in method parameters on Product Focus: Recombinant proteins Process Focus: Downstream processing Who Should Read: QA/QC, process development, analytical Keywords: Analytical method development, release assays, design of experiments, product-related impurities Level: Advanced Figure 1: Chromatograms of Protein F sample tested on a Tosoh Q-STAT column without (top) and with (bottom) 5 mm EDTA added to the sample matrix; injection volume varied from 5 to 1 and 2 µl Minimum protein elution at void volume an analytical procedure, you should adopt a systematic approach for a method robustness study (e.g., a design of experiments with method parameters). You should begin with an initial risk assessment and follow with multivariate experiments. Such approaches allow you to understand factorial parameter effects on method performance. Proteins eluted at void volume (zoom-in) with no separation Those expectations are in line with the concept of QbD in ICH Q as a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, No addition of EDTA Blue: 5 µl Red: 1 µl Green: 2 µl Separated protein peaks No addition of EDTA Blue: 5 µl Red: 1 µl Green: 2 µl based on sound science and quality risk management. And the US Pharmacopeial Convention s Chemical Analysis Expert Committee has published a stimulus paper on developing a new general chapter <122> covering the analytical procedure lifecycle. It has begun to adopt the QbD concept for analytical procedure design and development (4). Here, we present a seven-step workflow for analytical method development using a QbD approach, considering that fewer case studies are available for biologics than for small molecules (5 7). We used one proprietary recombinant protein REPRINT WITH PERMISSION ONLY 34 BioProcess International 15(5) May 217

2 Figure 2: Representative chromatogram of Protein F sample eluted on a Tosoh Q-STAT column using a mm salt gradient at ph.5 before DoE optimization; charge variants are shown as Peaks 1 4, and degraded product is referred to as the Impurity Peak Peak 1 Peak product (herein designated Protein F ) as a model protein for the case study. One of its domains contains multiple carboxylate residues (negatively charged). The intended purpose of our method is to separate charge variants differentiated by the numbers of carboxylate residues in that domain (presumably up to 1 per molecule). Materials and Equipment Instrument: We developed a highperformance liquid chromatography (HPLC) method using Agilent 11 and 126 HPLC systems with a thermostatted column compartment and a UV detector. Method Parameters: Our optimized method condition includes the use of a Tosoh Bioscience TSKgel Q-STAT HPLC column (catalog #216). Mobile phase A is 5 mm Tris at ph.5, and mobile phase B is 5 mm Tris with 1. M NaCl at ph.5. The flow rate is 1. ml/min. Column temperature is C. The injection volume is 1 µl. UV detection occurs at 2 nm wavelength. The elution gradient (linear change) goes from 15% B to 3% B at 2 minutes, followed by eight minutes of column wash with 1% B and five minutes of equilibration at 15% B. Sample Preparation: Addition of 5 mm ethylenediaminetetraacetic acid (EDTA) to the samples is used to deplete calcium ions. Statistical Analysis: We used JMP software (SAS, Inc.) for statistical analysis including design of experiments (DoE). Method Development Results Step 1 Method Development Planning: An analytical target profile (ATP) normally is defined during the planning of method development, and Peak 3 Peak 4 Impurity Peak it may evolve throughout development. ATPs include the intended use and validation requirements of an analytical method. In addition, a preliminary risk assessment should be performed to identify potential analytical challenges based on molecular properties, a literature search, prior experience/knowledge, and available technical capabilities. In this case study, we intended the assay for separating Protein F charge variants based on their different carboxylate levels. Anion-exchange (AEX) HPLC method is the ideal choice for this purpose because carboxylate residues are negatively charged. Ideally, the domain with multiple carboxylates should bind to an AEX column, with elution depending on the charge strength of that domain. At basic ph, however, the other two domains of the molecule also could be negatively charged and bind to the AEX resin, affecting selectivity of the separation. Therefore, buffer ph should be evaluated carefully to allow for specific binding of the AEX resin with the carboxylate domain but minimum binding with other domains. Specific challenges of Protein F analysis were identified as follows: It was known from literature that Ca 2+ in the sample matrix (although essential) could induce a conformational change to the carboxylate domain. That change can fold carboxylate residues into the domain core and cause loss of binding to the AEX resin. Protein F is prone to autoactivated degradation, becoming an impurity under certain conditions, including interaction with a positively charged surface. Considering that AEX resin is positively charged, the potential for autoactivation should be accounted for. Step 2 Early Assay Scouting: The chosen chromatographic column can determine the performance of an HPLC method. During early scouting studies, we evaluated five types of AEX columns from different vendors. We conducted preliminary scouting with 5 mm Tris at ph as a buffer system with a broad saltgradient elution from to mm NaCl. Agilent s Buffer Advisor software helped us generate different ph and salt gradients for dynamically mixing four components (mobile phases) on a quaternary-pump HPLC system: (A) water, (B) 2 M NaCl, (C) 2 mm Tris HCl, and (D) 2 mm Tris base. The software dramatically reduced the time and workload needed for buffer preparation. We found that the Q-STAT column from Tosoh Bioscience exhibited the best resolution and fastest elution among the five candidate columns evaluated, so we selected that for further optimization. In early scouting, we had confirmed that the presence of Ca 2+ in the sample matrix could reduce substantially binding of Protein F to the AEX column and decrease protein recovery, which is in line with our Step 1 assessment above. As Figure 1 (top) shows, direct injection of the sample resulted in significant protein loss (protein elution at the column void volume without separation). This issue was more dramatic with higher sample injection volumes. To disrupt the Protein F Ca 2+ interaction, we added EDTA into the sample to deplete Ca 2+. As Figure 1 (bottom) shows, most Protein F remained on the column through charge interaction at low salt concentration and eluted later at high salt concentration. Well-separated charge-variant profiles were consistent at different injection volumes (5, 1, and 2 µl). However, it s interesting to note that adding EDTA directly to the mobile phases could not resolve the protein recovery issue. Competitive binding of EDTA to the positively 36 BioProcess International 15(5) May 217

3 Table 1: Fractional factorial DoE for initial optimization of assay conditions Method Parameters Salt Gradient Condition Temperature ( C) ph (Start) ph (End) ( mm NaCl) Replicates Simple Complex Complex Complex Complex Complex Simple Complex Complex Complex Complex Complex 3 Figure 3: Screening plot (half-normal plot) for identification of CMPs in the DoE study Absolute Contrast ph (Start) Temp ( C) Temp ( C), ph (Start) 2 1 Gradient ph (Start) 2 ph (Start) * ph (End) Temp ( C), ph (End) Null 16 Temp ( C), ph (Start), ph (End) Half-Normal Quantile charged functional groups on the column resin may be the problem. We discovered that adding EDTA to the samples still could affect column performance over time. That performance has to be monitored carefully to ensure consistent data. The column could be regenerated by a vender-recommended cleaning procedure (.1 M NaOH wash). Step 3 Initial Optimization Using DoE: Our early assay-scouting results proved that AEX-HPLC is an effective method to separate charge variants. We observed four chargevariant peaks (Figure 2). As discussed above, Protein F is prone to autoactivation (degradation). In Figure 2, the peak coming after the chargevariant peaks represents the degradation product (impurity) before DoE optimization. The impurity could be a mixture of actual productrelated impurity generated from the manufacturing process and assayinduced impurity from the AEX- HPLC column. The column has a positively charged surface that could induce such degradation. Therefore, one main goal in our DoE study was Figure 4: Demonstrating the effect of CMPs on a given CMA using DoE modeling; interaction profiles plot showing effects of each CMP and their combined effects on the CMA (TPPA as an example). TPPA TPPA TPPA TPPA Temperature ( C) Simple Complex ph (Start) Simple Complex 4 Gradient 4 Simple Complex ph (End) Complex Simple Summary of Fit: R 2.222, R 2 adjusted.65, root mean square error , mean of response 4.1, and observations (sum weights) 36 were used to evaluate model-fitting quality. 4 Temperature ( C) ph (Start) Gradient ph (End) to minimize assay-induced impurities during further assay development. Here we designate the charge variants as Peak 1, Peak 2, Peak 3, and Peak 4, referring to the degraded product as Impurity Peak. We use sums of the peak areas (PAs) for the corresponding charge variants to evaluate protein recovery, which serves as an opposite indicator for the extent of assay-induced degradation on the AEX column. PAs are defined as follows: For total Protein F peak area, TPFPA = PAPeak 1 + PAPeak 2 + PAPeak 3 + PAPeak 4. For total protein peak area, TPPA = TPFPA + PAImpurity Peak. For relative peak area (RPA) of the impurity peak, RPAImpurity Peak (%Impurity) = PAImpurity Peak TPPA 1%. We created a fractional factorial design to evaluate four method parameters: column temperature ( or 4 C), beginning and ending ph, and salt-gradient complexity (simple and complex gradients, mm NaCl). The latter concerns a minor drift in ph caused by changing ionic strength in the salt gradient. Our simple salt gradient is a typical linear salt gradient without compensating for that ph drift. We created our complex salt gradient using Agilent s Buffer Advisor software by including several additional ph target points along the gradient to minimize the drift. We tested each condition of the DoE study in triplicate (Table 1) and considered the four method 3 BioProcess International 15(5) May 217

4 Figure 5: Representative overlaid chromatograms from a DoE study to optimize the salt gradient (mm); 15 mm is the final gradient Figure 6a: Interaction profile (6a) and contour profiles (6b) plots show the salt-gradient design space and its effect on resolution (between Peaks 1 and 2) Peak Resolution (2 to 1) NaCl Concentration (mm) NaCl End (mm) NaCl (mm) Start End Table 2: Full factorial experiment design for optimization of salt gradient on a Tosoh Bioscience Q-STAT column Start Salt Concentration (mm) End Salt Concentration (mm) parameters to be potential critical method parameters (CMPs). Based on prior experience, we did not consider other assay parameters (e.g., injection volume and flow rate) to be critical. On the other hand, we identified four chromatographic characteristics as critical method attributes (CMAs) for assessing assay performance: TPPA, TPFPA, resolution of Peaks 1 and 2, and resolutions of Peaks 2 and 3. TPPA and TPFPA are indicators of protein recovery from column elution, which should be maximized to ensure assay accuracy. The peak resolutions are standard CMAs for HPLC assays. Step 4 CMP Identification: To determine the criticality of potential method parameters, we used the Screening Plot (Half-Normal Plot) function in JMP based on the DoE data in Table 1. For example, we showed the method parameters effects on TPPA (Figure 3). In the Half- Normal Plot function, if the effect of one method parameter on CMA is insignificant, then its absolutecontrast value would be random noise close to the trending line. A value that falls far from the trending line represents a significant effect. In other words, the farther away from the Figure 6b: Interaction profile (6a) and contour profile (b) plots show the salt-gradient design space and its effect on resolution (between Peaks 1 and 2). NaCl End (mm) Horiz Vert. NaCl start (mm) NaCl end (mm) trending line a method parameter falls, the stronger its effect is on the given CMA (TPPA in this case). Thus we concluded that column temperature is a CMP because of its significant effect on TPPA (protein recovery). More quantitative criteria could be developed using the contrast value for each parameter of the analysis (data not shown). Step 5 DoE Modeling of the CMP CMA Relationship: After identifying all CMPs for all the CMAs, we generated a statistical DoE model to simulate the relationships among them by fitting the same set of DoE data (Table 1). To evaluate the effects of all CMPs and their interactions (combinational effect) on a given CMA, we used Current X 75 Response Contour Current Y Low Limit High Limit Peak Resolution (2 to 1) Resolution of Peaks 2 to 1 NaCl at End (mm) Peak Resolution (2 to 1) JMP s Interaction Profiles Plot function. Figure 4 is an example correlation for the TPPA CMA (TPPA). In this plot, the overlapping lines (such as complex and simple gradients) indicate parameters with insignificant effects on the method attribute, whereas parallel lines (such as for temperature) indicate those with significant effects. Lines with differing trends indicate combinational effects (such as temperature and starting ph). We evaluated other CMAs such as resolution in the same way (data not shown). JMP s Summary of Fit function (Figure 4) demonstrated good fitting of our DoE models for each CMA, with an R 2 of.. From the DoE modeling, we concluded the following: May (5) BioProcess International 3

5 Figure 7a: Using statistical tools to fine-tune an analytical method; (7a) Prediction Profiler plot predicts the impurity peak level in relative peak area (RPA) with changing start and end salt concentrations; (7b) actual resolutions (between Peaks 1 and 2, between Peaks 2 and 3) with starting salt concentrations; (7c) actual RPA of the impurity peak against starting salt concentration Impurity Peak RPA NaCl End (mm) Higher column temperature significantly reduced TPPA, the indicator for protein recovery. At 4 C, the reduction was more severe with a higher starting ph than with a lower one. At C, TPPA was not affected significantly by the starting ph, which could help improve the robustness of this analytical method. However, higher column temperature slightly improved its resolution (Peak 1 and Peak 2), especially with a higher starting ph. Taking into account all this information, we decided that a C column temperature should be used for further assay development because protein recovery is the primary consideration. Higher starting and ending ph values (ph ) slightly improved peak resolution, but ending ph did not affect TPPA significantly. We saw peak fronting at ph. compared with ph.5 and., probably because of incomplete deprotonation of the carboxylic acid residues in the domain at lower ph. Considering all this information, we decided that using the ph gradient offered no significant benefit. Therefore, we used a Tris buffer at fixed ph (.5) and a salt gradient in further assay development. Step 6 Method Fine-Tuning and Design Space: To explore a wide range of CMPs in a screening DoE study, we used the fractional factorial design. From this study, we concluded that the ph gradient would not be needed. The next step was to finetune the salt gradient. For that purpose, we created another fullfactorial design (Table 2) that would test each condition in six replicates. 15 Figure 5 shows a representative overlaid chromatogram. Similar to our statistical approaches in step 5, we used the new set of DoE data to show the effect of CMAs (starting and ending salt concentrations) on peak resolution (e.g., resolution between Peaks 1 and 2 in Figure 2). A higher start salt concentration (15 mm) significantly improved the peak resolution. TPFPA, the indicator of the Protein F recovery, was significantly better at higher start salt concentrations (data not shown). A lower-end salt concentration ( mm) also improved peak resolution but had minor effects on TPFPA (data not shown). The unshaded area at the right bottom corner of Figure 6b shows the salt-gradient design space in terms of its effect on resolution, based on a contour profiler plot. This design space covers a starting salt concentration around 15 mm and an ending salt concentration around mm. Within these defined parameters, the gradient would ensure that both resolutions (between Peaks 1 and 2 and Peaks 2 and 3) are no lower than.7. Statistical tools also can be used to predict the results of further optimization efforts. Considering that the design space falls into the corner of the graph (Figure 6b), we wondered whether we could further increase the starting salt concentration and/or lower the ending salt concentration beyond the range of the DoE study (Table 2) to make the gradient shallower for even better resolution and recovery of Protein F. That seemed to be a Figure 7b: Using statistical tools to fine-tune an analytical method; (7a) Prediction Profiler plot predicts the impurity peak level in relative peak area (RPA) with changing start and end salt concentrations; (7b) actual resolutions (between Peaks 1 and 2, between Peaks 2 and 3) with starting salt concentrations; (7c) actual RPA of the impurity peak against starting salt concentration Resolution (Peak 2/Peak 1) Resolution (Peak 3/Peak 2) Bivariate Fit Bivariate Fit reasonable expectation when we used JMP s Prediction Profiler function to project the effects of starting and ending salt concentrations on formation of the assay-induced impurity peak. As shown in the modeling (Figure 7a), the RPA of the impurity peak could decrease further along the increase of the starting salt concentration beyond 15 mm, whereas the RPA already reached a plateau at the ending salt concentration of mm. Thus, we collected additional DoE data at a starting-concentration range of 14 2 mm, with a fixed ending concentration of mm. It turned out that the chargevariant peaks eluted too fast with insufficient separation when we used a 2 mm starting salt concentration (data not shown). Nevertheless, as we plotted the resolutions and RPA of the impurity peak against the starting salt concentration ( 1 mm), both resolutions (between Peaks 1 and 2 and between Peaks 2 and 3) reached their highest levels with a starting concentration of ~15 mm (Figure 4 BioProcess International 15(5) May 217

6 7a c). On the other hand, RPA of the impurity peak decreased and reached a plateau in the range of 14 1 mm for a starting salt concentration. The RPA plateau indicates that the assayinduced impurity has been minimized to its lowest level. Because RPA is the reportable value for that impurity, the RPA plateau ~15 mm starting salt concentration is a good sign of method robustness. Our additional study (Figure 7a c) further confirmed that a gradient from 15 mm (start) to mm (end) maximizes assay performance while minimizing assay-induced artificial impurities. That was in line with the Figure 6 design space. Therefore, we locked down this salt-gradient condition for the final procedure. Step 7 Additional Verification Studies to Finalize Procedure: In the steps above, we had optimized a salt gradient as one final method condition. CMAs used in that optimization are indicators of chromatographic performance but not necessarily reportable values of the method. Thus, it was important to investigate method robustness further using reportable values with adequate replicates in the defined design space. In this case, the reportable results (the RPA of each charge variant peak) are consistent with a starting salt concentration of 15 ± 1 mm (data not shown). Column lot-to-lot variation should be checked as part of HPLC method development. We recommend using at least three column gel lots to do so. Information regarding column qualification, maintenance, and conditioning should be captured in a given method s procedure and/or development report. Discussion We used a seven-step QbD workflow to develop an AEX-HPLC method that minimizes an assay-induced impurity and provides optimal separation of charge-variant peaks for Protein F. The QbD approach for analytical development normally starts with defining a method s ATP, which then sets requirements for the method to deliver. Besides that, it is important in the planning stage to assess Figure 7c: Using statistical tools to fine-tune an analytical method; (7a) Prediction Profiler plot predicts the impurity peak level in relative peak area (RPA) with changing start and end salt concentrations; (7b) actual resolutions (between Peaks 1 and 2, between Peaks 2 and 3) with starting salt concentrations; (7c) actual RPA of the impurity peak against starting salt concentration Impurity Peak RPA Bivariate Fit analytical challenges and analytespecific information that affect method performance. Prior and platform experience with similar molecules helps us narrow down initial conditions for method scouting. In the real world, it may not be practical to evaluate every single method attribute and parameter in a laboratory, considering resource availability and instrument capacity. So science- and experience-based risk assessment is vital to prioritizing and narrowing down method parameters to a subset that can be evaluated experimentally. DoE studies are the core of a QbD approach. When understanding of a method is limited in early development, it is practical to initiate a DoE screening study (e.g., a fractional factorial design or two-level Plackett-Burman design) of a broader range of parameters. That can identify CMPs and their relationships with CMAs. It can be followed by a full-factorial design with a narrowed range and more replicates to fine-tune a design space and method conditions. That can be an iterative process as you gain more experience with a molecule and method until an optimized method condition is achieved. In practice, we started our initial DoE using a fractional factorial design to establish initial conditions, such as whether a ph gradient or ph salt combined gradient would be needed. In a second DoE (fullfactorial design), the focus is to finetune parameters (e.g., salt gradient, in our case) based on what is already concluded from the initial DoE study. Every analyte is different. This is especially true for biologic products. So this DoE approach should be customized for each specific need in analytical development for every method and molecule. Acknowledgments We thank Paramjeet Bains for execution of experiments and Susan Chen for supporting our early scouting study. References 1 ICH Q (R2). Pharmaceutical Development. US Fed. Reg. 71() 2; www. ich.org/fileadmin/public_web_site/ich_ Products/Guidelines/Quality/Q_R1/Step4/ Q_R2_Guideline.pdf. 2 ICH Q11. Development and Manufacture of Drug Substances. US Fed. Reg. 77(224) 212: ; fileadmin/public_web_site/ich_products/ Guidelines/Quality/Q11/Q11_Step_4.pdf. 3 CBER/CDER. Analytical Procedures and Methods Validation for Drugs and Biologics: Guidance for Industry. US Food and Drug Administration: Rockville, MD, July 215; pdf/ pdf. 4 Chemical Analysis Expert Committee. <122> Stimuli to the Revision Process: Proposed New USP General Chapter The Analytical Procedure Lifecycle. US Pharmacopeial Convention: Rockville, MD, Phil N, et al. QBD for Better Method Validation and Transfer. Pharma. Manufact. 21: Karmarkar S, et al. Quality By Design- Based Development of a Stability-Indicating HPLC Method for Drug and Impurities. J. Chromatog. Sci. 4, 211: Monks K, et al. Quality By Design: Multi-Dimensional Exploration of the Design Space in High-Performance Liquid Chromatography Method Development for Better Robustness Before Validation. J. Chromatog. A 1232, 212: Corresponding author Weijun Li, PhD, is senior manager of analytical transfers and special projects; Quan Yuan, PhD, is staff development scientist; and Lisa Regan, PhD, is vice president of analytical development and validation at Bayer Pharmaceuticals, Dwight Way, Berkeley, CA 471; ; weijun.li@bayer.com. To share this in PDF or professionally printed format, contact Rhonda Brown: rhondab@ fosterprinting. com, x14. May (5) BioProcess International 41