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ANALYTICAL PROCEDURES AND
VALIDATION
HOW TO GUARANTEE THE ROBUSTNESS OF DATA AND
COMPLIANCE WITH REGULATIONS
Giovanni Boccardi
AFI and Istituto G. Ronzoni
UNIVERSITÀ DI PAVIA – WORKSHOP 23rd NOVEMBER 2018
SETTING BEFORE ANALYTICAL VALIDATIONS
• Assay specification of a tablet is 95.0 – 105.0 % label claim
• Assay result on batch L4/18 is 105.3 %
Possible reactions of the Head of production:
A. Something went wrong in production.
B. Was the analytical procedure correctly performed?
C. Are we sure of the analytical procedure?
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 2
The objective of validation of an analytical procedure is to
demonstrate that it is suitable for its intended purpose. (ICH Q2_R1)
Suitability means that no "bad" batches will be released and no "good"
batches will be rejected (a problem of OOS).
What are the questions we ask about the "suitability" of the
procedure?
• SPECIFICITY: can we apply the procedure to the matrix (the
medicinal product)? The problem is the potential interference of
matrix components to the analyte signal.
• UNCERTAINTY OF THE RESULT (quantitative procedures).
Includes linearity, precision and accuracy
• LIMIT OF DETECTION / QUANTITATION: the lowest analyte
concentration the procedure can quantify / quantitatively assess.
… not only today, but in all future applications of the procedure
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 3
Regulatory framework
official documents
• Guideline ICH Q2_R1 *
• FDA, Analytical Procedures and Methods Validation for Drugs and
Biologics – Guidance for industry, July 2015
• ANVISA RDC Nº 166, 24/07/2017
• USP <1010> Analytical data — Interpretation and treatment**
• USP <1210> Statistical tools for analytical validation
‪ *CH Press Release - Kobe, Japan, June 2018
The Assembly agreed to begin work on three new topics for ICH
harmonisation…
- Analytical Procedure Development and Revision of Q2(R1) Analytical
Validation (Q2(R2)/Q14
‪ ** Under revision: Pharmacopeial Forum 44(5) 2018
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 4
The analytical procedure lifecycle
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 5
The analytical development is part of the validation exercise
Validation is confirmed in the production phase
Correct language matters
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 6
USP <1210> Statistical tools for procedure validation.
The parameters of the analytical validation
Specificity
Specificity is the ability to assess unequivocally the analyte in the
presence of components which may be expected to be present.
Typically these might include impurities, degradants, matrix, etc.
(ICH Q2_R1).
• Includes degradation (stress testing).
• Robustness/quality by design of the chromatographic part of
the method ensures that specificity can be reasonably
maintained in all future applications of the analytical
methods.
• The specificity exercise, including robustness/QbD, should
afford a significant system suitability test to check specificity
in a single analysis.
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 7
The design of the quantitative power of the procedure
• ROBUSTNESS OF THE PREPARATION OF THE TEST SOLUTION
Can the procedure be easily repeated? (E.g. extraction)
• LINEARITY OF THE CALIBRATION (more in general: the
mathematical relationship between the analyte concentration
and the instrumental response).
• LINEARITY OF THE RESPONSE IN THE MATRIX
calibration: external or standard additions?
…essentially is relative accuracy… the primary focus in <1225>
and ICH Q2 is the first type of linearity, which can be called
calibration linearity [Pharmacopeial Forum 39(3)]
But: requested by ANVISA in case of complex matrixes (proof
of paralellism).
• STABILITY OF THE ANALYTICAL SOLUTIONS
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 8
PROCEDURE DESCRIPTION
You should describe analytical procedures in sufficient detail to
allow a competent analyst to reproduce the necessary conditions
and obtain results within the proposed acceptance criteria. You
should also describe aspects of the analytical procedures that
require special attention. (FDA 2015)
"Add 10 mL solvent and mix"
– does mixing time matter? No time should mean
instantaneous dissolution.
– mix with what system? Vortex? Manual? Magnetic?
This should have been investigated during robustness studies.
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 9
UNCERTAINTY COMPONENTS: PRECISION
• Not to forget: Precision should be investigated using
homogeneous, authentic samples. However, if it is not
possible to obtain a homogeneous sample it may be
investigated using artificially prepared samples or a sample
solution. (ICH Q2_R1)
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 10
precise
not precise
Interesting example:
a spiked sample is not
always a representative
sample.
PRECISION
• REPEATABILITY: same laboratory, same operator, same
equipment, short interval of time
• INTERMEDIATE PRECISION: within-laboratories variations:
different days, different analysts, different equipment, etc.
Estimated parameters:
• repeatability standard deviation and coefficient of variation,
• intermediate precision standard deviation and coefficient of
variation
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 11
UNCERTAINTY COMPONENTS: ACCURACY
• Result expression: recovery
• 3 levels: for the assay 80 % - 100 % -120 % of the nominal.
• True value:
– accurately prepared artificial samples
or
– comparison with results of an official method (less
common)
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 12
True value
True value
precise and accurate
precise but not accurate
LIMIT OF DETECTION (LOD) AND LIMIT OF QUANTITATON (LOQ)
• (For impurities and contaminants),
• the lowest concentration that can be detected or quantified
with suitable precision, respectively, in all future analyses
with reasonable confidence.
• ICH methods:
– based on the signal to noise in analytical procedures which
exhibit baseline noise,
– based on the calibration line (the ICH procedure is not
aligned with IUPAC and with best practice),
– in any LOQ should be confirmed with precision
experiments.
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 13
STATISTICS IN ANALYTICAL VALIDATION
Usual parametric statistics requires a known distribution of the
popolution (usually normal).
Statistics allow us:
– to estimate the parameters of the population (estimation
theory)  diagnosis
– to decide with reasonable confidence if our sample can belong
to an hypothesized population (hypothesis testing) decision
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 14
population
All analyses performed with
the procedure
sample,
the current analysis


m
s
statistical inference
sampling = performing the analysis
ACCEPTANCE CRITERIA
(not defined by ICH Q2_R1)
• Usual acceptance criteria:
• precision: standard deviation ≤ 2.0 %
• accuracy: mean recovery ≤ 2.0 %
• (specification: 95.0 % 105.0 %)
• Let suppose s = 1.9 %, m=1.9 %:
– we can expect a high number of out of specification results
even for a perfect batch (100 %)
– … the head of Production is right to doubt of our result!
… new criteria are necessary.
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 15
An interesting proposal
(but discussion is ongoing)
• Total measurement uncertitude is the combination of precision and
accuracy:
• 𝑇𝑀𝑈 = 𝑠𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛
2 + 𝑠𝑏𝑖𝑎𝑠
2
• the acceptance criterion is based on the joint interval of precision and
bias.
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 16
Lifecycle of the analytical procedure: analytical transfer and
verification of compendial methods
• Background: we are convinced to have exhaustively identified
variability sources (laboratory, instruments, reagents,
procedure description, operators skill,…), but is it true?
– Analytical transfer: the method has been validated in the
sending unit, but is it still suitable in the receiver unit?
– First application of a method described in a
pharmacopoeia: the method has been validated, but does
it work in our laboratory?
• Common statistical background: validation proofed that
parameters of the population are "suitable", but does our
laboratory belong to this population?
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 17
• In both cases we perform a risk analysis, i.e. a written and
documented assessment of critical factors which could result in a
(more frequent) failure of the method.
• Verification: factors identified as non negligible sources of
additional uncertainty will be experimentally checked, i.e.
– specificity is probably always critical,
– linearity can be non-critical for LC-UV and critical for LC-MS, etc.
• Operator training should always be at the top of attention.
• In analytical transfer an acceptable difference between sending and
receiving units should be set in advance and tests should show that
the measured difference with its uncertainty are within the
acceptable range (equivalence test or TOST).
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 18
Acknowledgements
• Jean-Pierre Vergnaud
• Michel Bauer
• Christophe Agut
• Marco Guerrini
• Lucio Mauri
• Anna Maria Naggi
• Giangiacomo Torri
UNIVERSITA' DI PAVIA - WORKSHOP 23rd
NOVEMBER 2018 19
• Daniele Fraioli
• Chiara Scotti
• Giorgio Marrubini
• Gruppo di Studio AFI
Controllo Qualità e
Sviluppo Analitico
• Camillo Melzi

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boccardi_analitytical-procedures.pdf

  • 1. ANALYTICAL PROCEDURES AND VALIDATION HOW TO GUARANTEE THE ROBUSTNESS OF DATA AND COMPLIANCE WITH REGULATIONS Giovanni Boccardi AFI and Istituto G. Ronzoni UNIVERSITÀ DI PAVIA – WORKSHOP 23rd NOVEMBER 2018
  • 2. SETTING BEFORE ANALYTICAL VALIDATIONS • Assay specification of a tablet is 95.0 – 105.0 % label claim • Assay result on batch L4/18 is 105.3 % Possible reactions of the Head of production: A. Something went wrong in production. B. Was the analytical procedure correctly performed? C. Are we sure of the analytical procedure? UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 2
  • 3. The objective of validation of an analytical procedure is to demonstrate that it is suitable for its intended purpose. (ICH Q2_R1) Suitability means that no "bad" batches will be released and no "good" batches will be rejected (a problem of OOS). What are the questions we ask about the "suitability" of the procedure? • SPECIFICITY: can we apply the procedure to the matrix (the medicinal product)? The problem is the potential interference of matrix components to the analyte signal. • UNCERTAINTY OF THE RESULT (quantitative procedures). Includes linearity, precision and accuracy • LIMIT OF DETECTION / QUANTITATION: the lowest analyte concentration the procedure can quantify / quantitatively assess. … not only today, but in all future applications of the procedure UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 3
  • 4. Regulatory framework official documents • Guideline ICH Q2_R1 * • FDA, Analytical Procedures and Methods Validation for Drugs and Biologics – Guidance for industry, July 2015 • ANVISA RDC Nº 166, 24/07/2017 • USP <1010> Analytical data — Interpretation and treatment** • USP <1210> Statistical tools for analytical validation ‪ *CH Press Release - Kobe, Japan, June 2018 The Assembly agreed to begin work on three new topics for ICH harmonisation… - Analytical Procedure Development and Revision of Q2(R1) Analytical Validation (Q2(R2)/Q14 ‪ ** Under revision: Pharmacopeial Forum 44(5) 2018 UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 4
  • 5. The analytical procedure lifecycle UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 5 The analytical development is part of the validation exercise Validation is confirmed in the production phase
  • 6. Correct language matters UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 6 USP <1210> Statistical tools for procedure validation.
  • 7. The parameters of the analytical validation Specificity Specificity is the ability to assess unequivocally the analyte in the presence of components which may be expected to be present. Typically these might include impurities, degradants, matrix, etc. (ICH Q2_R1). • Includes degradation (stress testing). • Robustness/quality by design of the chromatographic part of the method ensures that specificity can be reasonably maintained in all future applications of the analytical methods. • The specificity exercise, including robustness/QbD, should afford a significant system suitability test to check specificity in a single analysis. UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 7
  • 8. The design of the quantitative power of the procedure • ROBUSTNESS OF THE PREPARATION OF THE TEST SOLUTION Can the procedure be easily repeated? (E.g. extraction) • LINEARITY OF THE CALIBRATION (more in general: the mathematical relationship between the analyte concentration and the instrumental response). • LINEARITY OF THE RESPONSE IN THE MATRIX calibration: external or standard additions? …essentially is relative accuracy… the primary focus in <1225> and ICH Q2 is the first type of linearity, which can be called calibration linearity [Pharmacopeial Forum 39(3)] But: requested by ANVISA in case of complex matrixes (proof of paralellism). • STABILITY OF THE ANALYTICAL SOLUTIONS UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 8
  • 9. PROCEDURE DESCRIPTION You should describe analytical procedures in sufficient detail to allow a competent analyst to reproduce the necessary conditions and obtain results within the proposed acceptance criteria. You should also describe aspects of the analytical procedures that require special attention. (FDA 2015) "Add 10 mL solvent and mix" – does mixing time matter? No time should mean instantaneous dissolution. – mix with what system? Vortex? Manual? Magnetic? This should have been investigated during robustness studies. UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 9
  • 10. UNCERTAINTY COMPONENTS: PRECISION • Not to forget: Precision should be investigated using homogeneous, authentic samples. However, if it is not possible to obtain a homogeneous sample it may be investigated using artificially prepared samples or a sample solution. (ICH Q2_R1) UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 10 precise not precise Interesting example: a spiked sample is not always a representative sample.
  • 11. PRECISION • REPEATABILITY: same laboratory, same operator, same equipment, short interval of time • INTERMEDIATE PRECISION: within-laboratories variations: different days, different analysts, different equipment, etc. Estimated parameters: • repeatability standard deviation and coefficient of variation, • intermediate precision standard deviation and coefficient of variation UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 11
  • 12. UNCERTAINTY COMPONENTS: ACCURACY • Result expression: recovery • 3 levels: for the assay 80 % - 100 % -120 % of the nominal. • True value: – accurately prepared artificial samples or – comparison with results of an official method (less common) UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 12 True value True value precise and accurate precise but not accurate
  • 13. LIMIT OF DETECTION (LOD) AND LIMIT OF QUANTITATON (LOQ) • (For impurities and contaminants), • the lowest concentration that can be detected or quantified with suitable precision, respectively, in all future analyses with reasonable confidence. • ICH methods: – based on the signal to noise in analytical procedures which exhibit baseline noise, – based on the calibration line (the ICH procedure is not aligned with IUPAC and with best practice), – in any LOQ should be confirmed with precision experiments. UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 13
  • 14. STATISTICS IN ANALYTICAL VALIDATION Usual parametric statistics requires a known distribution of the popolution (usually normal). Statistics allow us: – to estimate the parameters of the population (estimation theory)  diagnosis – to decide with reasonable confidence if our sample can belong to an hypothesized population (hypothesis testing) decision UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 14 population All analyses performed with the procedure sample, the current analysis   m s statistical inference sampling = performing the analysis
  • 15. ACCEPTANCE CRITERIA (not defined by ICH Q2_R1) • Usual acceptance criteria: • precision: standard deviation ≤ 2.0 % • accuracy: mean recovery ≤ 2.0 % • (specification: 95.0 % 105.0 %) • Let suppose s = 1.9 %, m=1.9 %: – we can expect a high number of out of specification results even for a perfect batch (100 %) – … the head of Production is right to doubt of our result! … new criteria are necessary. UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 15
  • 16. An interesting proposal (but discussion is ongoing) • Total measurement uncertitude is the combination of precision and accuracy: • 𝑇𝑀𝑈 = 𝑠𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 2 + 𝑠𝑏𝑖𝑎𝑠 2 • the acceptance criterion is based on the joint interval of precision and bias. UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 16
  • 17. Lifecycle of the analytical procedure: analytical transfer and verification of compendial methods • Background: we are convinced to have exhaustively identified variability sources (laboratory, instruments, reagents, procedure description, operators skill,…), but is it true? – Analytical transfer: the method has been validated in the sending unit, but is it still suitable in the receiver unit? – First application of a method described in a pharmacopoeia: the method has been validated, but does it work in our laboratory? • Common statistical background: validation proofed that parameters of the population are "suitable", but does our laboratory belong to this population? UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 17
  • 18. • In both cases we perform a risk analysis, i.e. a written and documented assessment of critical factors which could result in a (more frequent) failure of the method. • Verification: factors identified as non negligible sources of additional uncertainty will be experimentally checked, i.e. – specificity is probably always critical, – linearity can be non-critical for LC-UV and critical for LC-MS, etc. • Operator training should always be at the top of attention. • In analytical transfer an acceptable difference between sending and receiving units should be set in advance and tests should show that the measured difference with its uncertainty are within the acceptable range (equivalence test or TOST). UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 18
  • 19. Acknowledgements • Jean-Pierre Vergnaud • Michel Bauer • Christophe Agut • Marco Guerrini • Lucio Mauri • Anna Maria Naggi • Giangiacomo Torri UNIVERSITA' DI PAVIA - WORKSHOP 23rd NOVEMBER 2018 19 • Daniele Fraioli • Chiara Scotti • Giorgio Marrubini • Gruppo di Studio AFI Controllo Qualità e Sviluppo Analitico • Camillo Melzi