Explore the importance of robustness in analytical method validation with practical guidance, a case study, and FAQs to help you perform the test effectively and efficiently
Robustness in Analytical Method Validation: Practical Insights, Case Study, and FAQs
Robustness in Analytical Method Validation is one of the most critical parameters, assessing the method’s ability to remain unaffected by small, deliberate variations in analytical conditions. While performing a robustness test requires both technical knowledge and significant time investment, understanding the right approach can simplify the process. In this article, I will share practical, skill-based knowledge supported by a real-life case study and address frequently asked questions to help you perform robustness testing more efficiently and confidently.
The following are the 7- steps strategies to perform robustness:
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Acceptance criteria of robustness test is decided on system suitability test of the method.
Inject the system suitability test (SST) solution into each robustness (change) condition and record the system suitability test result. The robustness condition must meet the SST acceptance criteria.
Let us consider a drug substance D has the following specifications, and we have to perform precision test:
Sample concentration is 1.0 mg/ml. SST acceptance criteria is resolution (R) between main analyte peak D and impurity peak A should be ≥ 2.0 and following are the method chromatographic conditions:
Column: C18, (150 x 4.6)mm, 5μm. Buffer 0.02 M KH2PO4, pH 2.0, with 10 Phosphoric acid solution. Mobile phase is the mixture of buffer and acetonitrile in a ratio of 60:40. The flow rate of the mobile phase is 1.0 ml/minute. Column temperature is 30 °C, Injection volume is 20μl and detector wavelength is 254 nm.
Let us define the critical analytical variables and perform the robustness test.
For example, pH may be changed 2.5 and 3.0 from 2.5 (±0,2). Flow rate may be changed 0.9 and 1.0 from 1.0 ml/minute and similarly other analytical variables can also be defined as in table-1
Table-1
| S.N | Robustness parameters | Nominal | Level (-1) | Level(+1) |
| 1 | pH | 2.7 | 2.5 | 3.0 |
| 2 | Flow rate (ml/minute) | 1 | 0.9 | 1.0 |
| 3 | Column temp | 30oC | 25oC | 35oC |
| 4 | KH2PO4 concentration | 0.02M | 0.01M | 0.03M |
| 5 | Mobile phase composition (Buffer : ACN) | 60:40 | 57:43 | 63:37 |
| 6 | Column | Make X | Make Y | Make Z |
Table-2
The following are the SST- Resolution between Main Analyte peak D and Impurity peak A in different change conditions:
| S.N | Robustness Parameters | Resolution (R) Nominal | Resolution (R) Level (-1) | Resolution (R) Level(+1) |
| 1 | pH | 3.1 | 3.5 | 5 |
| 2 | Flow rate (ml/minute) | 3.2 | 3.6 | 3.5 |
| 3 | Column temp | 3.4 | 3.6 | 5 |
| 4 | Buffer concentration | 3.6 | 4 | 4 |
| 5 | Mobile phase composition (Buffer : ACN | 2.8 | 2.5 | 2.9 |
| 5 | Column, make X, Y and Z | 4.2 | 3.7 | 4.1 |
Resolution R ≥ 2.0, at each lower level and a higher level of the above analytical change variables and hence, analysis can be performed in that range.
Robustness testing is a critical step, but very important in any AMV test. It assesses the analytical method’s capability to produce consistent and reliable results under extreme conditions. Now I hope this article has cleared all your doubts, and you can independently perform robustness testing during method development and method validation. For any opinions or suggestions related to this article, you can write in the comment section. For any further assistance, you can contact me using the contact form.
You may also want to check out other articles on my blog, such as:
References
Abbreviations
Identify critical analytical variables of the method ( e.g. pH, buffer concentration, solvent compositions, column-temperature, sample preparation procedures, etc. ) that are likely to influence the result. define the two extreme variable range. Inject the system suitability solution. Method must meet SST acceptance criteria in change condition. If fails then re-optimize the condition and perform the analysis in the modified condition.
Robustness in method validation assessing the method’s ability to remain unaffected by small, deliberate variations in analytical conditions.
Disclaimer: The numerical data used in the tables or calculations are not actual data. It is designed to explain the topic.
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