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INTRODUCTION

As part of a sound pharmaceutical quality system (ICH Q10), an increasing trend of MS-based techniques in lab settings traditionally associated with optical-based assays has been observed in an effort to improve the body of knowledge surrounding a drug product. This has facilitated the introduction of LC/MS-based methods for semi-targeted monitoring of biotherapeutic protein attributes or “Multi-Attribute Monitoring” in an effort to reduce redundant assays and increase productivity. Challenges associated with these approaches include data-mining expansive data sets, determining screening parameters, and transferring methods.

In this study, we have generated a data set representative of a

process development setting to demonstrate how these

challenges can be addressed with a single CDS platform with

MS control and data processing capabilities.

SUPPORTING BIOPROCESS DEVELOPMENT AND METHOD TRANSFER USING MULTI ATTRIBUTE MONITORING METHODS

Robert Birdsall, Ximo Zhang, Ying Qing Yu, and Weibin Chen Waters Corporation Milford, MA

METHODS

RESULTS CONCLUSION

Empower/QDa Benefits

Affordable equipment

Simple Yes/No answer

Validated

Tech transferable

long-term use (>10 years)

Generalist operators

High throughput

Empowers support of the ACQUITY line of LC instruments and mass detectors such as the QDa provide a scalable Multi-Attribute Monitoring option that can support development and manufacturing processes through a drug products lifecycle with benefits such as:

Figure 1. The ACQUITY® QDa. The compact footprint of the ACQUITY

® QDa was chosen for this study for its

ability to easily integrate into existing LC platforms across industry to facilitate a peptide-based MAM workflow. The straightforward user interface combined with disposable source elements minimizes training and maintenance for daily operation and improved productivity in the lab.

Figure 5. Data Review and target monitoring. Using summary plots in combination with Empower’s reporting functionality allow users to generate screening reports as an efficient way to review data at a holistic level (%RSD plot) for immediate outlier identification as well as at a granular level with traditional tabular formats to identify more subtle nuances such as random errors that may not trigger a global % RSD error. When outliers are identified as in the case of the T21 oxidized species in this example, which has a % RSD significantly higher than the other attributes, the user can open up a preformatted outlier report to review data at an individual attribute level. In this example we have applied a sub-report based on the outlier results which shows us the individual results of the T21 oxidized peptide across the data set. Using the summary plot to visualize data the user can quickly identify which runs failed or are close to failing the criteria limit set within the Empower Limits tab as part of the system suitability license. Tabular data for the failed sequence is also provided as part of the report for additional insight as to why the particular run or process deviation occurred. Using this approach in a iterative fashion for MAM-based workflows allows users to easily visualize data for more informed decisions when making recommendations to modify process control parameters as well as migrate the MAM-based method to the manufacturing floor in a more efficient manner for targeted monitoring of CQAs. As an example, the report summary on the right has summary plots replaced by the actual chromatograms for visual review of assay results and tabular data is formatted with a PASS/FAIL criteria field based on the % Area (or % modification) thresholds that were refined during the process development stage.

Figure 4. Data management. Development of proper control strategies involve performing the same assay with high frequency on large samp le sets in a short amount of time (e.g. glycosylation profiles, cell line selection, stability testing, bio -reactor monitoring). Subtle differences across assays may be overlooked during manual curation of data, especially for MAM-based workflows that are monitoring multiple attributes in one assay. As shown in this example, %CV can be used as a holistic metric to identify potential process and instrument deviations such as random errors or systematic errors or a combination of both. As part of Empower’s system suitability license, users have access to summary plots such as these to help in data visualization and assist in making informed decisions more efficiently.

Figure 3. Increasing throughput. A benefit of MS-based detection in MAM-based workflows is the increased specificity afforded by the mass detector. In this example The SIR functionality of the QDa allows attributes of interest to be monitored using separate acquisition channels from non-critical species while shortening assay run times. For this method 50 minutes was found to offer sufficient resolution between critical pairs such as the oxidized form of T41 and the deamidated species of the T37 (PENNYK) peptide.

QDa Frag Modification m/z

1 T25 Glycopeptides FA2-879.2, FA2G1-933.2, FA2G2-987.3, A2-830.5, M5-803.1, FA1G1-865.5, FA1G1Gc1-726.2

2 T1 Pyro-Glu (native) 616.7, 308.9

3 T21 Oxidized 852.0, 426.5

4 T42 C-term Lysine (native) 788.9, 395.0

5 T21 Native 836.0, 418.5

6 T1 Pyro-Glu (mod) 599.7, 300.4

7 T42 C-term Lysine clip 660.7, 330.9

8 T41 Oxidized 705.5, 564.6

9 T41 Native 701.5, 561.4

11 T37 PENNYK (deamidated) 849.6, 637.4

12 T37 PENNYK (Native) 849.2, 637.2

13 T26 Deamidated 905.5, 604.0

14 T26 Native 905.1, 603.7

15 T15 Deamidated 1120.6, 960.6, 840.7, 747.4, 672.7

16 T15 Native 1120.4, 960.5, 840.6, 747.3, 672.6

Empower instrument control panel: QDa function table Charge states to be monitored QDa function = 1 2 3 5 4 6 7 8

TIC

Pyro glycosylation C-term clipping oxidation deamidation deamidation

SIR

T1

T25

T42

T21*

T42* T1*

T41*

T21 T37 (PENNYK)

T41

T26

T37* T26*

T15

T15*

Figure 2. Monitoring (P)CQAs. Programming the ACQUITY QDa can be done in a straightforward manner through the instrument control panel. Using the HRMS characterization data (data not shown), a list of PCQA’s was generated and programmed through Empowers instrument control panel to track individual charge states associated with each PCQA’s native and modified form. In this data set selected ion recording (SIR) is used in a staggered acquisition fashion to maximize dwell time spent on acquiring signal for increased sensitivity.

Time (min) Flow (mL/

min) %A %B Curve

Initial 0.200 99.0 1.0 Initial

2.00 0.200 99.0 1.0 6

52.00 0.200 65.0 35.0 6

58.00 0.200 15.0 85.0 6

62.00 0.200 15.0 85.0 6

67.00 0.200 99.0 1.0 6

80.00 0.200 99.0 1.0 6

Table 1. Gradient Table. A 50 minute separation gradient was deployed to demonstrate MAM-based workflows can be deployed in high-throughput environments with robust results.

AS-FTN - Sample temp 10 °C

Binary Pump - Mixer volume = 380 µL - MP A = H2O, 0.1% FA

- MP B = MeCN, 0.1% FA

CM-A (column heater)

- Column temp 60 C - Column CSH - 2.1 x 100mm, 1.7 µm

TUV - 10 mm analytical flow cell - Sampling rate 10 Hz

- = 214 nm

QDa - SIR acquisition - 5 Hz sampling rate

Upstream Process Downstream Process Manufacturing QC Release

Process Development & Control Product Monitoring & Lot Release

Screening report Outlier report Lot release report

Iterative review

process

method transfer

Faulted data

Holistic review

Granular review

Mu

ltiple

Inje

ctio

ns

Process Dev. Screening

Results Data

visualization

Reference material

Random error

Instrument deviation

Systematic error / process deviation

% RSD = 3.25

% RSD = 13.01

% RSD = 8.76

% RSD = 14.23

Collectively, this user case demonstrates how Empower’s integrated features can be used to streamline MAM-based workflows through visualization and data management and help reduce some of the burden associated with the deployment and migration of methods from process development to manufacturing and QC lot release through: increased productivity by monitoring multiple attributes in a

single acquisition Facilitation of fast decision making during process

development Ease of deployment in non-regulated and regulated

environments Greater accessibility and easy adoption for global QC

deployment Straightforward method transfer and lifecycle management

outlier

+ 4

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