Introduction Analytical Quality by Design
(AQbD) Implementation of AQbD-
Practical aspects Case study Conclusion References
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Introduction
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AQbD- Key components
Role of analytical methods in drug Role of analytical methods in drug development processdevelopment process
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AQbD- Drug Development Process
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AQbD- Benefits
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Traditional versus AQbD
Steps Synthetic development (QbD)
Analytical development (AQbD)
1 QTPP identification ATP (Analytical TargetProfile) identification
2 CQA/CMA identification,Risk Assessment
CQA identification, InitialRisk Assessment
3 Define product design space with DoE
Method Optimization anddevelopment with DOE
4 Refine product design space
MODR (Method OperableDesign Region)
5 Control Strategy with RiskAssessment
Control Strategy with RiskAssessment
6 Process validation AQbD Method Validation7 Continuous process
MonitoringContinuous process Monitoring
QbD tools for synthetic development and analytical development.
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Traditional versus AQbD
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AQbD Workflow
Analytical Target Profile (ATP) Analytical Method Performance
CharacteristicsS. No. Method performance characteristics
Defined by ICH and USP
1 Accuracy, specificity, and linearity
Systematic variability
2 Precision, detection limit, and quantification
limit
Inherent random variability
3 Range and robustness Not applicable
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AQbD Practical Aspects
Selection of Analytical Techniques Risk Assessment
Design of Experiments (DoE) › Screening› Optimization› Selection of DOE Tools› Method Operable Design Region (MODR) and Surface
Plots› Model Validation
Risk factor = Severity × Occurrence × Detestability
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AQbD Practical Aspects
Design of Experiments (DoE) › Screening, Optimization and Selection of DoE
tools Design Number of variables
and usageAdvantage Disadvantage
Full factorialdesign
Optimization/2–5 variables Identifying the main and interaction effect without any confounding
Experimental runs increase with increase in number of variables
Fractional factorialdesign or Taguchi methods
Optimization/and screening variables
Requiring lower number of experimental runs
Resolving confounding effects of interactions is a difficult job
Plackett-Burmanmethod
Screening/or identifying vital few factors from large number of variables
Requiring very few runs for large number of variables
It does not reveal interaction effect
Pseudo-Monte Carlo sampling(pseudorandom sampling) method
Quantitative risk analysis/optimization
Behaviour and changes to the model can be investigated with great ease and speed. This is preferred where exact calculation is possible
For nonconvex design spaces, this method of sampling can be more difficult to employ. Random numbers that can be produced from a random number generating algorithm
Full factorialdesign
Optimization/ 2–5 variables Identifying the main and interaction effect without any confounding
Experimental runs increase with increase in number of variables12Dept. of Quality Assurance, DLHHCOP
AQbD Practical Aspects
› Method Operable Design Region (MODR) and Surface Plot› Model Validation
Contour plot for MODR
Systematic simulation graph for retention time (X2-axis) as method response at constant X3 (0.8 mL/min as flow rate) with change in pH (X1--axis).
(Graph shows significant correlation between the predicted retention time and actual (experimental) retention time with good correlation coefficient.
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Method Operable Design Region (MODR) and Surface Plot
Model Validation
AQbD Practical Aspects
Method Verification/Validation Control Strategy- Continuous Method Monitoring
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AQbD Practical Aspects
S. No.
Pharmaceutical testing
Control strategy
1 Raw material testing
Specification based on product QTPP and CQAEffects of variability, including supplier variations, on process and method development are understood
2 In-process testing
Real time (at-, on-, or in-line) measurementsActive control of process to minimize product variation Criteria based on multivariate process understanding
3 Release testing Quality attributes predictable from process inputs (design space)Specification is only part of the quality control strategySpecification based on patient needs (quality, safety, efficacy, and performance)
4 Stability testing Predictive models at release minimize stability failuresSpecification set on desired product performance with time
Real-time Blend Uniformity by using TruProcess™ Analyzer
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PAT and AQbD
Analytical Quality by Design Approach in RP-HPLC Method Development for the Assay of
Etofenamate in Dosage FormsStep 1: Target measurement
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AQbD- Case Study
Step 2: DoE:Design of Experiment (Method Optimization and Development)
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Experimental Design
AQbD- Case Study
Step 3: Method Operable Design Region
pH of aqueous phase versus % of aqueous phase contour at 1.2ml/min flow rate of mobile phase
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AQbD- Case Study
Quadratic model was obtained on application of SigmaTech software with the polynomial equation:
Y=5.8778-0.0025X1+2.9925X2–0.8088X3–0.4925X1X2 0.075X1X3-0.125X2X3+0.1178X12 +1.1803X22+0.2768X32
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Step 4: DoE: Model validation using regression analysis
Developed Chromatogra
m
AQbD- Case Study
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Step 5: : Method validation
AQbD- Case Study
In a nutshell……Paramete
rTraditional Product QbD AQbD
Approach Based on empirical approach
Based on systematic approach
Based on systematic approach
Quality Quality is assured by end product testing
Quality is built in the product and process by
design and scientific approach
Robustness and reproducibility of the
method built in method development
stageFDA submission Including only data
for submissionSubmission with product knowledge and process
understanding
Submission with product knowledge
and assuring by analytical target
profileSpecifications Specifications are
based on batch history
Specifications are based on product performance
requirements
Based on method performance to ATP
criteria
Process Process is frozen and discourages changes
Flexible process with design space allows
continuous improvement
Method flexibility with MODR and
allowing continuous improvement
Targeted response
Focusing on reproducibility,
ignoring variation
Focusing on robustness which understands control
variation
Focus on robust and cost effective method
Advantage Limited and simple It is expended process analytical technology
(PAT) tool that replaces the need for end product
testing
Replacing the need of revalidation and minimizing OOT and
OOS
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AQbD- Summary
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% o
f re
sear
ch
AQbD- Summary
AQbD requires the right ATP and Risk Assessment and usage of right tools and performing the appropriate quantity of work within proper timelines.
‘RIGHT ANALYTICS AT THE RIGHT TIME’
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AQbD- Conclusion
Raman, N. V. V. S. S.; Mallu, U. R.; Bapatu, H. R. J. Chem.2014, 2015 (1), 8. Torbeck L. D.J. Pharm.Tech.35 (10), 2011,46–47 ICH Harmon. Tripart. Guidel. 2009, 8 (August), 1–28. Jackson, P. 2013, Technical note,
http://www.gmpcompliance.org/daten/training/ECA_QbD_in_Analysis_2013 (accessed Oct 23, 2016).
Warf S. F. 2013, Conference note; http:// www.ISPE.org/2013QbDConference (accessed Oct 23, 2016).
Jadhav, M. L.; Tambe, S. R. Chromatogr. Res. Int. 2013, 2013 (2), 1–9. Borman, P.; Roberts, J.; Jones, C.; Hanna-Brown, M.; Szucs, R.; Bale, nd S. 2010, 2 (7), 2–4. Hanna-brown, M.; Borman, P.; Bale, S.; Szucs, R. Sep. Sci. 2010, 2, 12–20. Nethercote P.; Borman P.; Bennett T.; Martin G.; McGregor P. 2010, 1–9. Vogt, F. G.; Kord, A. S. Pharm. Sci. 2011, 100 (3), 797–812. Bhatt, D. A.; Rane, S. I. Int. J. Pharm. Pharm. Sci. 2011, 3 (1), 179–187. Swartz, M.; Lukulay, P. H.; Krull, I.; Joseph, T. LCGC North Am. 2008, 26 (12), 1190–1197. Meyer, C.; Soldo,T.; Kettenring, U. Chim. Int. J. Chem. 2010, 64 (11), 825–825. McBrien, M. A.; Ling, S.. The Column 2011, 7 (5), 16–20. Molnár, I.; Rieger, H. J.; Monks, K. E. J. Chromatogr. A 2010, 1217 (19), 3193–3200. Karmarkar, S.; Garber, R.; Genchanok, Y.; George, S.; Yang, X.; Hammond, R. J.
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References
Monks, K.; Molnár, I.; Rieger, H. J.; Bogáti, B.; Szabó, E. J. Chromatogr. A 2012, 1232, 218–230. Orlandini, S.; Pinzauti, S.; Furlanetto, S. Anal. Bioanal. Chem. 2013, 405 (2–3), 443–450. Musters, J.; Van Den Bos, L.; Kellenbach, E. Org. Process Res. Dev. 2013, 17 (1), 87–96. Xavier, C. M.; Basavaiah, K.; Vinay, K. B.; Swamy, N. ISRN Chromatogr. 2013, 2013, 1–10.. Xavier, C. M.; Basavaiah, K.; Xavier, C. M.; Basavaiah, K. ISRN Chromatogr. 2012, 2012, 1–11. Dasare, P.
http://sspcmsn.org/yahoo_site_admin/assets/docs/Analytical_approach_in_QbD_SSPC.44161808.pdf (accessed on Oct 26, 2016).
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Patel, G. M.; Shelat, P. K.; Lalwani, A. N. Eur. J. Pharm. Sci.2016. Li, Y.; Liu, D. Q.; Yang, S.; Sudini, R.; McGuire, M. A.; Bhanushali, D. S.; Kord, A. S. J. Pharm.
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References
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