Qualification & Validation-A brief intro

1. QUALIFCATION
&
VALIDATION
QUALITY WEEK 14-19 NOVEMBER 2016

2. Definitions
 Qualification - Action of proving that any premises, systems and items of
equipment work correctly and actually lead to the expected results
 Validation - Action of proving, in accordance with the principles of GMP,
that any procedure, process, equipment, material, activity or system
actually leads to the expected results

3. Qualification
 DQ – Design Qualification
 To be done while planning procurement
 IQ- Installation Qualification
 At the time of installation, by OEM
 OQ – Operational Qualification
 After installation, preferably by OEM
 PQ – Performance Qualification
 After installation, may be done internally

4. Scope
 Documented evidence to prove that, e.g.
 Premises
 Supporting utilities
 Equipment
 have been designed in accordance with requirement (DQ)
 have been built and installed in accordance with their design
specifications (IQ)
 operate in accordance with their design specifications (OQ)
 perform consistently in accordance with their design specifications
(PQ)

5. Qualification
 Method Qualification
 Instrument Qualification
 Analyst Qualification

6. Analyst Qualification
 Minimum 2 analyst to run test
 4 replicates for each analyst
 Standard result to be obtained from supplier or, already qualified analyst
 Accuracy 98 – 102 % (95 – 105% for content <1%)
 RSD maximum 2%

7. Validation
 Prospective Validation
 Retrospective Validation
 Concurrent Validation
 Revalidation

8. Validation – Postulates & Protocol
 Process Validation
 Validation Master Plan
 To simulate / achieve optimum operational environment with minimum interim
variation
 Reference / supporting process or, machines must be pre – validated or, calibrated to
desired level of operation to minimize variation & uncertainty
 Generate ample data
 Statistical analysis of data and conclusion

9. Uncertainty
 parameter, associated with the result of a measurement, that characterizes the dispersion of
the values that could reasonably be attributed to the measurand
 The parameter may be, for example, a standard deviation (or a given multiple of it), or the
half-width of an interval having a stated level of confidence.
 Uncertainty of measurement comprises, in general, many components. Some of these
components may be evaluated from the statistical distribution of the results of a series of
measurements and can be characterized by experimental standard deviations. The other
components, which also can be characterized by standard deviations, are evaluated from
assumed probability distributions based on experience or other information.
 It is understood that the result of the measurement is the best estimate of the value of the
measurand, and that all components of uncertainty, including those arising from
systematic effects, such as components associated with corrections and reference
standards, contribute to the dispersion.