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 Role of analytical methods in drug development processdevelopment process


AQbD- Drug Development Process


AQbD- Benefits


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.


Traditional versus AQbD


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


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 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


› 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.

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


PAT and AQbD

Analytical Quality by Design Approach in RP-HPLC Method Development for the Assay of

Etofenamate in Dosage FormsStep 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 Chromatogra

m

AQbD- Case Study


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


AQbD- Summary


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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

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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.44161808.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.

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