Analytical QbD

 Introduction
 Analytical Quality by Design (AQbD)
 Implementation of AQbD- Practical
aspects
 Case study
 Conclusion
 References
Dept. of Quality Assurance, DLHHCOP 2

3Dept. of Quality Assurance, DLHHCOP
Introduction

AQbD- Key components

 Role of analytical methods in drug developmentRole of analytical methods in drug development
processprocess
AQbD- Drug Development Process

AQbD- Benefits

Traditional versus AQbD

Steps Synthetic development (QbD) Analytical development (AQbD)
1 QTPP identification ATP (Analytical Target
Profile) identification
2 CQA/CMA identification,
Risk Assessment
CQA identification, Initial
Risk Assessment
3 Define product design space
with DoE
Method Optimization and
development with DOE
4 Refine product design space MODR (Method Operable
Design Region)
5 Control Strategy with Risk
Assessment
Control Strategy with Risk
Assessment
6 Process validation AQbD Method Validation
7 Continuous process Monitoring Continuous process Monitoring
 QbD tools for synthetic development and analytical development.
Traditional versus AQbD

AQbD Workflow

 Analytical Target Profile (ATP)
 Analytical Method Performance Characteristics
S. 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
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

 Design of Experiments (DoE)
› Screening, Optimization and Selection of DoE tools
Design Number of variables
and usage
Advantage Disadvantage
Full factorial
design
Optimization/2–5 variables Identifying the main and
interaction effect without
any confounding
Experimental runs
increase with increase in
number of variables
Fractional factorial
design or Taguchi
methods
Optimization/and screening
variables
Requiring lower number
of experimental runs
Resolving confounding
effects of interactions is a
difficult job
Plackett-Burman
method
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 factorial
design
Optimization/ 2–5 variables Identifying the main and
interaction effect without
any confounding
Experimental runs
increase with increase in
number of variables

› 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.
Method Operable Design Region (MODR) and Surface Plot Model Validation

 Method Verification/Validation
 Control Strategy- Continuous Method Monitoring
S.
No.
Pharmaceutic
al testing
Control strategy
1 Raw material
testing
Specification based on product QTPP and CQA
Effects of variability, including supplier variations,
on process and method development are
understood
2 In-process
testing
Real time (at-, on-, or in-line) measurements
Active 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 strategy
Specification based on patient needs (quality,
safety, efficacy, and performance)
4 Stability
testing
Predictive models at release minimize stability
failures
Specification set on desired product performance
with time

Real-time Blend
Uniformity by
using
TruProcess™
Analyzer
PAT and AQbD

Analytical Quality by Design Approach in RP-HPLC Method
Development for the Assay of Etofenamate in Dosage
Forms
Step 1: Target measurement
AQbD- Case Study

Step 2: DoE:Design of Experiment
(Method Optimization and Development)
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
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
Step 4: DoE: Model validation using regression analysis
Developed
Chromatogram
AQbD- Case Study

Step 5: : Method validation
AQbD- Case Study

 In a nutshell……
Parameter Traditional 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 stage
FDA submission Including only data for
submission
Submission with product
knowledge and process
understanding
Submission with product
knowledge and assuring
by analytical target
profile
Specifications 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
AQbD- Summary

%ofresearch
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’
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.
Chromatogr. Sci. 2011, 49 (6), 439–446..
 Monks, K. E.; Rieger, H.-J.; Molnár, I. J. Pharm. Biomed. Anal. 2011, 56 (5), 874–879.
 Reid G. L., Cheng G., Fortin et al D. T. J. Liq. Chromatogr. Relat. Tecnhologies 2013, 36
(18), 2612–2638.
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.4416180
8.pdf (accessed on Oct 26, 2016).
 Chatterjee, S. IFPAC Annu. Meet.2013
 Morgado, J.; Barnett, K.; Ph, D.; Harrington, B.; Wang, J.; Ph, D.; Harwood, J. 2013, 2, 1–14.
 Elder, D.; Borman, P. Pharm. Outsourcing 2013.
 Zlota, A. A.; Zlota, T.; Llc, C. 2014..
 ASME. 2010, https://www.asme.org/products/codesstandards/b89731-2001guidelines decision-
rules-considering (accessed on Oct 26, 2016).
 Guide, C.; Edition, F. Interpret. A J. Bible Theol. 2007, 18.
 Burnett K. L., Harrington B., and Graul T. W. 2013.
 Jadhav, M. L.; Tambe, S. R. Chromatogr. Res. Int. 2013, 2013, 1–9.
 ICH Expert Working Group. ICH Harmon. Tripart. Guidel. 2005, No. November, 1–23.
http://www.ssciinc.com/DrugSubstance/PATandPharmaceuticalQualityByDesign/
tabid/86/Default.aspx (accessed on Oct 26, 2016).
 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.
Biomed. Anal.2010, 52 (4), 493–507.
References

Analytical QbD

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