Bioanalytical Method Validation

Table of Contents
I. INTRODUCTION.................................................................................................................................................................1
II. BACKGROUND...................................................................................................................................................................1
A. FULL VALIDATION .............................................................................................................................................................2
B. PARTIAL VALIDATION .......................................................................................................................................................2
C. CROSS-VALIDATION ..........................................................................................................................................................3
III. REFERENCE STANDARD............................................................................................................................................4
IV. METHOD DEVELOPMENT: CHEMICAL ASSAY........................................................................................................4
A. SELECTIVITY......................................................................................................................................................................4
B. ACCURACY, PRECISION, AND RECOVERY........................................................................................................................5
C. CALIBRATION/STANDARD CURVE.....................................................................................................................................5
D. STABILITY..........................................................................................................................................................................6
E. PRINCIPLES OF BIOANALYTICAL METHOD VALIDATION AND ESTABLISHMENT ............................................................8
F. SPECIFIC RECOMMENDATIONS FOR METHOD VALIDATION........................................................................................... 10
V. METHOD DEVELOPMENT: MICROBIOLOGICAL AND LIGAND-BINDING ASSAYS ..................................11
A. SELECTIVITY ISSUES ....................................................................................................................................................... 11
B. QUANTIFICATION ISSUES ................................................................................................................................................ 12
VI. APPLICATION OF VALIDATED METHOD TO ROUTINE DRUG ANALYSIS....................................................13
ACCEPTANCE CRITERIA FOR THE RUN..................................................................................................................................... 15
VII. DOCUMENTATION.....................................................................................................................................................16
A. SUMMARY INFORMATION................................................................................................................................................ 16
B. DOCUMENTATION FOR METHOD ESTABLISHMENT ....................................................................................................... 17
C. APPLICATION TO ROUTINE DRUG ANALYSIS ................................................................................................................. 17
D. OTHER INFORMATION..................................................................................................................................................... 19
GLOSSARY...................................................................................................................................................................................20
GUIDANCE FOR INDUSTRY1
Bioanalytical Method Validation
I. INTRODUCTION
This guidance provides assistance to sponsors of investigational new drug applications (INDs), new
drug applications (NDAs), abbreviated new drug applications (ANDAs), and supplements in
developing bioanalytical method validation information used in human clinical pharmacology,
bioavailability (BA), and bioequivalence (BE) studies requiring pharmacokinetic (PK) evaluation. This
guidance also applies to bioanalytical methods used for non-human pharmacology/toxicology studies
and preclinical studies. For studies related to the veterinary drug approval process, this guidance
applies only to blood and urine BA, BE, and PK studies.
The information in this guidance generally applies to bioanalytical procedures such as gas
chromatography (GC), high-pressure liquid chromatography (LC), combined GC and LC mass
spectrometric (MS) procedures such as LC-MS, LC-MS-MS, GC-MS, and GC-MS-MS performed
for the quantitative determination of drugs and/or metabolites in biological matrices
such as blood, serum, plasma, or urine. This guidance also applies to other bioanalytical methods, such
as immunological and microbiological procedures, and to other biological matrices, such as tissue and
skin samples.
This guidance provides general recommendations for bioanalytical method validation. The
recommendations can be adjusted or modified depending on the specific type of analytical method used.
II. BACKGROUND
1 This guidance has been prepared by the Biopharmaceutics Coordinating Committee in the Center for Drug Evaluation and
Research (CDER) in cooperation with the Center for Veterinary Medicine (CVM) at the Food and Drug Administration.
This guidance represents the Food and Drug Administration's current thinking on this topic. It
does not create or confer any rights for or on any person and does not operate to bind FDA or the
public. An alternative approach may be used if such approach satisfies the requirements of the
applicable statutes and regulations.
2
This guidance has been developed based on the deliberations of two workshops: (1) Analytical
Methods Validation: Bioavailability, Bioequivalence, and Pharmacokinetic Studies (held on December
3B5, 19902 ) and (2) Bioanalytical Methods Validation C A Revisit With a Decade of Progress (held
on January 12B14, 20003).
Selective and sensitive analytical methods for the quantitative evaluation of drugs and their metabolites
(analytes) are critical for the successful conduct of preclinical and/or biopharmaceutics and clinical
pharmacology studies. Bioanalytical method validation includes all of the procedures that demonstrate
that a particular method used for quantitative measurement of analytes in a given biological matrix, such
as blood, plasma, serum, or urine, is reliable and reproducible for the intended use. The fundamental
parameters for this validation include (1) accuracy, (2) precision, (3) selectivity, (4) sensitivity, (5)
reproducibility, and (6) stability. Validation involves documenting, through the use of specific laboratory
investigations, that the performance characteristics of the method are suitable and reliable for the
intended analytical applications. The acceptability of analytical data corresponds directly to the criteria
used to validate the method.
Published methods of analysis are often modified to suit the requirements of the laboratory performing
the assay. These modifications should be validated to ensure suitable performance of the analytical
method. When changes are made to a previously validated method, the analyst should exercise
judgment as to how much additional validation is needed. During the course of a typical drug
development program, a defined bioanalytical method undergoes many modifications. The evolutionary
changes to support specific studies and different levels of validation demonstrate the validity of an
assay’s performance. Different types and levels of validation are defined and characterized as follows:
A. Full Validation
· Full validation is important when developing and implementing a bioanalytical method for
the first time.
· Full validation is important for a new drug entity.
· A full validation of the revised assay is important if metabolites are added to an existing
assay for quantification.
B. Partial Validation
Partial validations are modifications of already validated bioanalytical methods. Partial validation
can range from as little as one intra-assay accuracy and precision determination to a nearly full
2 Workshop Report: Shah, V.P. et al., Pharmaceutical Research: 1992; 9:588-592.
3 Workshop Report: Shah, V.P. et al., Pharmaceutical Research: 2000; 17:in press.
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validation. Typical bioanalytical method changes that fall into this category include, but are not
limited to:
· Bioanalytical method transfers between laboratories or analysts
· Change in analytical methodology (e.g., change in detection systems)
· Change in anticoagulant in harvesting biological fluid
· Change in matrix within species (e.g., human plasma to human urine)
· Change in sample processing procedures
· Change in species within matrix (e.g., rat plasma to mouse plasma)
· Change in relevant concentration range
· Changes in instruments and/or software platforms
· Limited sample volume (e.g., pediatric study)
· Rare matrices
· Selectivity demonstration of an analyte in the presence of concomitant medications
· Selectivity demonstration of an analyte in the presence of specific metabolites
C. Cross-Validation
Cross-validation is a comparison of validation parameters when two or more bioanalytical methods
are used to generate data within the same study or across different studies. An example of crossvalidation
would be a situation where an original validated bioanalytical method serves as the
reference and the revised bioanalytical method is the comparator. The comparisons should be
done both ways.
When sample analyses within a single study are conducted at more than one site or more than one
laboratory, cross-validation with spiked matrix standards and subject samples should be conducted
at each site or laboratory to establish interlaboratory reliability. Cross-validation should also be
considered when data generated using different analytical techniques (e.g., LC-MS-MS vs.
ELISA4) in different studies are included in a regulatory submission.
All modifications should be assessed to determine the recommended degree of validation. The
analytical laboratory conducting pharmacology/toxicology and other preclinical studies for regulatory
submissions should adhere to FDA=s Good Laboratory Practices (GLPs)5 (21 CFR part 58) and to
sound principles of quality assurance throughout the testing process. The bioanalytical method for
human BA, BE, PK, and drug interaction studies must meet the criteria in 21 CFR 320.29. The
analytical laboratory should have a written set of standard operating procedures (SOPs) to ensure a
complete system of quality control and assurance. The SOPs should cover all aspects of analysis
from the time the sample is collected and reaches the laboratory until the results of the analysis are
reported. The SOPs also should include record keeping, security and chain of sample custody
4 Enzyme linked immune sorbent assay
5 For the Center for Veterinary Medicine, all bioequivalence studies are subject to Good Laboratory Practices.
4
(accountability systems that ensure integrity of test articles), sample preparation, and analytical tools
such as methods, reagents, equipment, instrumentation, and procedures for quality control and
verification of results.
The process by which a specific bioanalytical method is developed, validated, and used in routine
sample analysis can be divided into (1) reference standard preparation, (2) bioanalytical method
development and establishment of assay procedure, and (3) application of validated bioanalytical
method to routine drug analysis and acceptance criteria for the analytical run and/or batch. These
three processes are described in the following sections of this guidance.
III. REFERENCE STANDARD
Analysis of drugs and their metabolites in a biological matrix is carried out using samples spiked with
calibration (reference) standards and using quality control (QC) samples. The purity of the reference
standard used to prepare spiked samples can affect study data. For this reason, an authenticated
analytical reference standard of known identity and purity should be used to prepare solutions of known
concentrations. If possible, the reference standard should be identical to the analyte. When this is not
possible, an established chemical form (free base or acid, salt or ester) of known purity can be used.
Three types of reference standards are usually used: (1) certified reference standards (e.g., USP
compendial standards); (2) commercially supplied reference standards obtained from a reputable
commercial source; and/or (3) other materials of documented purity custom-synthesized by an analytical
laboratory or other noncommercial establishment. The source and lot number, expiration date,
certificates of analyses when available, and/or internally or externally generated evidence of identity and
purity should be furnished for each reference standard.
IV. METHOD DEVELOPMENT: CHEMICAL ASSAY
The method development and establishment phase defines the chemical assay. The fundamental
parameters for a bioanalytical method validation are accuracy, precision, selectivity, sensitivity,
reproducibility, and stability. Measurements for each analyte in the biological matrix should be
validated. In addition, the stability of the analyte in spiked samples should be determined. Typical
method development and establishment for a bioanalytical method include determination of (1)
selectivity, (2) accuracy, precision, recovery, (3) calibration curve, and (4) stability of analyte in spiked
samples.
A. Selectivity
Selectivity is the ability of an analytical method to differentiate and quantify the analyte in the
presence of other components in the sample. For selectivity, analyses of blank samples of the
appropriate biological matrix (plasma, urine, or other matrix) should be obtained from at least
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six sources. Each blank sample should be tested for interference, and selectivity should be
ensured at the lower limit of quantification (LLOQ).
Potential interfering substances in a biological matrix include endogenous matrix components,
metabolites, decomposition products, and in the actual study, concomitant medication and other
exogenous xenobiotics. If the method is intended to quantify more than one analyte, each
analyte should be tested to ensure that there is no interference.
B. Accuracy, Precision, and Recovery
The accuracy of an analytical method describes the closeness of mean test results obtained by
the method to the true value (concentration) of the analyte. Accuracy is determined by replicate
analysis of samples containing known amounts of the analyte. Accuracy should be measured
using a minimum of five determinations per concentration. A minimum of three concentrations in
the range of expected concentrations is recommended. The mean value should be within 15%
of the actual value except at LLOQ, where it should not deviate by more than 20%. The
deviation of the mean from the true value serves as the measure of accuracy.
The precision of an analytical method describes the closeness of individual measures of an
analyte when the procedure is applied repeatedly to multiple aliquots of a single homogeneous
volume of biological matrix. Precision should be measured using a minimum of five
determinations per concentration. A minimum of three concentrations in the range of expected
concentrations is recommended. The precision determined at each concentration level should
not exceed 15% of the coefficient of variation (CV) except for the LLOQ, where it should not
exceed 20% of the CV. Precision is further subdivided into within-run, intra-batch precision or
repeatability, which assesses precision during a single analytical run, and between-run, interbatch
precision or repeatability, which measures precision with time, and may involve different
analysts, equipment, reagents, and laboratories.
The recovery of an analyte in an assay is the detector response obtained from an amount of the
analyte added to and extracted from the biological matrix, compared to the detector response
obtained for the true concentration of the pure authentic standard. Recovery pertains to the
extraction efficiency of an analytical method within the limits of variability. Recovery of the
analyte need not be 100%, but the extent of recovery of an analyte and of the internal standard
should be consistent, precise, and reproducible. Recovery experiments should be performed by
comparing the analytical results for extracted samples at three concentrations (low, medium, and
high) with unextracted standards that represent 100% recovery.
C. Calibration/Standard Curve
A calibration (standard) curve is the relationship between instrument response and known
concentrations of the analyte. A calibration curve should be generated for each analyte in the
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sample. A sufficient number of standards should be used to adequately define the relationship
between concentration and response. A calibration curve should be prepared in the same
biological matrix as the samples in the intended study by spiking the matrix with known
concentrations of the analyte. The number of standards used in constructing a calibration curve
will be a function of the anticipated range of analytical values and the nature of the
analyte/response relationship. Concentrations of standards should be chosen on the basis of the
concentration range expected in a particular study. A calibration curve should consist of a blank
sample (matrix sample processed without internal standard), a zero sample (matrix sample
processed with internal standard), and six to eight non-zero samples covering the expected
range, including LLOQ.
1. Lower Limit of Quantification (LLOQ)
The lowest standard on the calibration curve should be accepted as the limit of
quantification if the following conditions are met:
C The analyte response at the LLOQ should be at least 5 times the response
compared to blank response.
C Analyte peak (response) should be identifiable, discrete, and reproducible with
a precision of 20% and accuracy of 80-120%.
2. Calibration Curve/Standard Curve/Concentration-Response
The simplest model that adequately describes the concentration-response relationship
should be used. Selection of weighting and use of a complex regression equation should
be justified. The following conditions should be met in developing a calibration curve:
C #20% deviation of the LLOQ from nominal concentration
C #15% deviation of standards other than LLOQ from nominal concentration
At least four out of six non-zero standards should meet the above criteria, including the
LLOQ and the calibration standard at the highest concentration. Excluding the
standards should not change the model used.
D. Stability
Drug stability in a biological fluid is a function of the storage conditions, the chemical properties
of the drug, the matrix, and the container system. The stability of an analyte in a particular
matrix and container system is relevant only to that matrix and container system and should not
be extrapolated to other matrices and container systems. Stability procedures should evaluate
the stability of the analytes during sample collection and handling, after long-term (frozen at the
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intended storage temperature) and short-term (bench top, room temperature) storage, and after
going through freeze and thaw cycles and the analytical process. Conditions used in stability
experiments should reflect situations likely to be encountered during actual sample handling and
analysis. The procedure should also include an evaluation of analyte stability in stock solution.
All stability determinations should use a set of samples prepared from a freshly made stock
solution of the analyte in the appropriate analyte-free, interference-free biological matrix. Stock
solutions of the analyte for stability evaluation should be prepared in an appropriate solvent at
known concentrations.
1. Freeze and Thaw Stability
Analyte stability should be determined after three freeze and thaw cycles. At least three
aliquots at each of the low and high concentrations should be stored at the intended
storage temperature for 24 hours and thawed unassisted at room temperature. When
completely thawed, the samples should be refrozen for 12 to 24 hours under the same
conditions. The freeze–thaw cycle should be repeated two more times, then analyzed
on the third cycle. If an analyte is unstable at the intended storage temperature, the
stability sample should be frozen at -700C during the three freeze and thaw cycles.
2. Short-Term Temperature Stability
Three aliquots of each of the low and high concentrations should be thawed at room
temperature and kept at this temperature from 4 to 24 hours (based on the expected
duration that samples will be maintained at room temperature in the intended study) and
analyzed.
3. Long-Term Stability
The storage time in a long-term stability evaluation should exceed the time between the
date of first sample collection and the date of last sample analysis. Long-term stability
should be determined by storing at least three aliquots of each of the low and high
concentrations under the same conditions as the study samples. The volume of samples
should be sufficient for analysis on three separate occasions. The concentrations of all
the stability samples should be compared to the mean of back-calculated values for the
standards at the appropriate concentrations from the first day of long-term stability
testing.
4. Stock Solution Stability
The stability of stock solutions of drug and the internal standard should be evaluated at
room temperature for at least 6 hours. If the stock solutions are refrigerated or frozen
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for the relevant period, the stability should be documented. After completion of the
desired storage time, the stability should be tested by comparing the instrument
response with that of freshly prepared solutions.
5. Post-Preparative Stability
The stability of processed samples, including the resident time in the autosampler, should
be determined. The stability of the drug and the internal standard should be assessed
over the anticipated run time for the batch size in validation samples by determining
concentrations on the basis of original calibration standards.
Although the traditional approach of comparing analytical results for stored samples with
those for freshly prepared samples has been referred to in this guidance, other statistical
approaches based on confidence limits for evaluation of an analyte=s stability in a
biological matrix can be used. SOPs should clearly describe the statistical method and
rules used. Additional validation may include investigation of samples from dosed
subjects.
E. Principles of Bioanalytical Method Validation and Establishment
· The fundamental parameters to ensure the acceptability of the performance of a
bioanalytical method validation are accuracy, precision, selectivity, sensitivity,
reproducibility, and stability.
· A specific, detailed description of the bioanalytical method should be written. This can be in
the form of a protocol, study plan, report, and/or SOP.
· Each step in the method should be investigated to determine the extent to which
environmental, matrix, material, or procedural variables can affect the estimation of analyte
in the matrix from the time of collection of the material up to and including the time of
analysis.
· It may be important to consider the variability of the matrix due to the physiological nature
of the sample. In the case of LC-MS-MS-based procedures, appropriate steps should be
taken to ensure the lack of matrix effects throughout the application of the method,
especially if the nature of the matrix changes from the matrix used during method validation.
· A bioanalytical method should be validated for the intended use or application. All
experiments used to make claims or draw conclusions about the validity of the method
should be presented in a report (method validation report).
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· Whenever possible, the same biological matrix as the matrix in the intended samples should
be used for validation purposes. (For tissues of limited availability, such as bone marrow,
physiologically appropriate proxy matrices can be substituted.)
· The stability of the analyte (drug and/or metabolite) in the matrix during the collection
process and the sample storage period should be assessed, preferably prior to sample
analysis.
· For compounds with potentially labile metabolites, the stability of analyte in matrix from
dosed subjects (or species) should be confirmed.
· The accuracy, precision, reproducibility, response function, and selectivity of the method for
endogenous substances, metabolites, and known degradation products should be
established for the biological matrix. For selectivity, there should be evidence that the
substance being quantified is the intended analyte.
· The concentration range over which the analyte will be determined should be defined in the
bioanalytical method, based on evaluation of actual standard samples over the range,
including their statistical variation. This defines the standard curve.
· A sufficient number of standards should be used to adequately define the relationship
between concentration and response. The relationship between response and concentration
should be demonstrated to be continuous and reproducible. The number of standards used
should be a function of the dynamic range and nature of the concentration-response
relationship. In many cases, six to eight concentrations (excluding blank values) can define
the standard curve. More standard concentrations may be recommended for nonlinear than
for linear relationships.
· The ability to dilute samples originally above the upper limit of the standard curve should be
demonstrated by accuracy and precision parameters in the validation.
· In consideration of high throughput analyses, including but not limited to multiplexing,
multicolumn, and parallel systems, sufficient QC samples should be used to ensure control
of the assay. The number of QC samples to ensure proper control of the assay should be
determined based on the run size. The placement of QC samples should be judiciously
considered in the run.
· For a bioanalytical method to be considered valid, specific acceptance criteria should be set
in advance and achieved for accuracy and precision for the validation of QC samples over
the range of the standards.
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F. Specific Recommendations for Method Validation
· The matrix-based standard curve should consist of a minimum of six standard points,
excluding blanks, using single or replicate samples. The standard curve should cover the
entire range of expected concentrations.
· Standard curve fitting is determined by applying the simplest model that adequately
describes the concentration-response relationship using appropriate weighting and statistical
tests for goodness of fit.
· LLOQ is the lowest concentration of the standard curve that can be measured with
acceptable accuracy and precision. The LLOQ should be established using at least five
samples independent of standards and determining the coefficient of variation and/or
appropriate confidence interval. The LLOQ should serve as the lowest concentration on
the standard curve and should not be confused with the limit of detection and/or the low QC
sample. The highest standard will define the upper limit of quantification (ULOQ) of an
analytical method.
· For validation of the bioanalytical method, accuracy and precision should be determined
using a minimum of five determinations per concentration level (excluding blank samples).
The mean value should be within ±15% of the theoretical value, except at LLOQ, where it
should not deviate by more than ±20%. The precision around the mean value should not
exceed 15% of the CV, except for LLOQ, where it should not exceed 20% of the CV.
Other methods of assessing accuracy and precision that meet these limits may be equally
acceptable.
· The accuracy and precision with which known concentrations of analyte in biological matrix
can be determined should be demonstrated. This can be accomplished by analysis of
replicate sets of analyte samples of known concentrations C QC samples C from an
equivalent biological matrix. At a minimum, three concentrations representing the entire
range of the standard curve should be studied: one within 3x the lower limit of quantification
(LLOQ) (low QC sample), one near the center (middle QC), and one near the upper
boundary of the standard curve (high QC).
· Reported method validation data and the determination of accuracy and precision should
include all outliers; however, calculations of accuracy and precision excluding values that are
statistically determined as outliers can also be reported.
· The stability of the analyte in biological matrix at intended storage temperatures should be
established. The influence of freeze-thaw cycles (a minimum of three cycles at two
concentrations in triplicate) should be studied.
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· The stability of the analyte in matrix at ambient temperature should be evaluated over a time
period equal to the typical sample preparation, sample handling, and analytical run times.
· Reinjection reproducibility should be evaluated to determine if an analytical run could be
reanalyzed in the case of instrument failure.
· The specificity of the assay methodology should be established using a minimum of six
independent sources of the same matrix. For hyphenated mass spectrometry-based
methods, however, testing six independent matrices for interference may not be important.
In the case of LC-MS and LC-MS-MS-based procedures, matrix effects should be
investigated to ensure that precision, selectivity, and sensitivity will not be compromised.
Method selectivity should be evaluated during method development and throughout method
validation and can continue throughout application of the method to actual study samples.
· Acceptance/rejection criteria for spiked, matrix-based calibration standards and validation
QC samples should be based on the nominal (theoretical) concentration of analytes.
Specific criteria can be set up in advance and achieved for accuracy and precision over the
range of the standards, if so desired.
V. METHOD DEVELOPMENT: MICROBIOLOGICAL AND LIGAND-BINDING
ASSAYS
Many of the bioanalytical validation parameters and principles discussed above are also applicable to
microbiological and ligand-binding assays. However, these assays possess some unique characteristics
that should be considered during method validation.
A. Selectivity Issues
As with chromatographic methods, microbiological and ligand-binding assays should be shown
to be selective for the analyte. The following recommendations for dealing with two selectivity
issues should be considered:
1. Interference From Substances Physiochemically Similar to the Analyte
· Cross-reactivity of metabolites, concomitant medications, or endogenous
compounds should be evaluated individually and in combination with the analyte
of interest.
· When possible, the immunoassay should be compared with a validated reference
method (such as LC-MS) using incurred samples and predetermined criteria for
agreement of accuracy of immunoassay and reference method.
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· The dilutional linearity to the reference standard should be assessed using study
(incurred) samples.
· Selectivity may be improved for some analytes by incorporation of separation
steps prior to immunoassay.
2. Matrix effects Unrelated to the Analyte
· The standard curve in biological fluids should be compared with standard in
buffer to detect matrix effects.
· Parallelism of diluted study samples should be evaluated with diluted standards to
detect matrix effects.
· Nonspecific binding should be determined.
B. Quantification Issues
Microbiological and imunoassay standard curves are inherently nonlinear and, in general, more
concentration points may be recommended to define the fit over the standard curve range than
for chemical assays. In addition to their nonlinear characteristics, the response-error
relationship for immunoassay standard curves is a nonconstant function of the mean response
(heteroscadisticity). For these reasons, a minimum of six non-zero calibrator concentrations,
run in duplicate, is recommended. The concentration-response relationship is most often fitted
to a 4- or 5-parameter logistic model, although others may be used with suitable validation. The
use of anchoring points in the asymptotic high- and low-concentration ends of the standard
curve may improve the overall curve fit. Generally, these anchoring points will be at
concentrations that are below the established LLOQ and above the established ULOQ.
Whenever possible, calibrators should be prepared in the same matrix as the study samples or
in an alternate matrix of equivalent performance. Both ULOQ and LLOQ should be defined by
acceptable accuracy, precision, or confidence interval criteria based on the study requirements.
For all assays the key factor is the accuracy of the reported results. This accuracy can be
improved by the use of replicate samples. In the case where replicate samples should be
measured during the validation to improve accuracy, the same procedure should be followed as
for unknown samples.
The following recommendations apply to quantification issues:
· If separation is used prior to assay for study samples but not for standards, it is important to
establish recovery and use it in determining results. Possible approaches to assess efficiency
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and reproducibility of recovery are (1) the use of radiolabeled tracer analyte (quantity too
small to affect the assay), (2) the advance establishment of reproducible recovery, (3) the
use of an internal standard that is not recognized by the antibody but can be measured by
another technique.
· Key reagents, such as antibody, tracer, reference standard, and matrix should be
characterized appropriately and stored under defined conditions.
· Assessments of analyte stability should be conducted in true study matrix (e.g., should not
use a matrix stripped to remove endogenous interferences).
· Acceptance criteria: At least 67% (4 out of 6) of QC samples should be within 15% of
their respective nominal value, 33% of the QC samples (not all replicates at the same
concentration) may be outside 15% of nominal value. In certain situations, wider
acceptance criteria may be justified.
· Assay reoptimization or validation may be important when there are changes in key
reagents, as follows:
Labeled analyte (tracer)
· Binding should be reoptimized.
· Performance should be verified with standard curve and QCs.
Antibody
· Key cross-reactivities should be checked.
· Tracer experiments above should be repeated.
Matrix
· Tracer experiments above should be repeated.
Method development experiments should include a minimum of six runs conducted over several days,
with at least four concentrations (LLOQ, low, medium, and high) analyzed in duplicate in each run.
VI. APPLICATION OF VALIDATED METHOD TO ROUTINE DRUG ANALYSIS
Assays of all samples of an analyte in a biological matrix should be completed within the time period for
which stability data are available. In general, biological samples can be analyzed with a single
determination without duplicate or replicate analysis if the assay method has acceptable variability as
defined by validation data. This is true for procedures where precision and accuracy variabilities
routinely fall within acceptable tolerance limits. For a difficult procedure with a labile analyte where high
precision and accuracy specifications may be difficult to achieve, duplicate or even triplicate analyses
can be performed for a better estimate of analyte.
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A calibration curve should be generated for each analyte to assay samples in each analytical run and
should be used to calculate the concentration of the analyte in the unknown samples in the run. The
spiked samples can contain more than one analyte. An analytical run can consist of QC samples,
calibration standards, and either (1) all the processed samples to be analyzed as one batch or (2) a
batch composed of processed unknown samples of one or more volunteers in a study. The calibration
(standard) curve should cover the expected unknown sample concentration range in addition to a
calibrator sample at LLOQ. Estimation of concentration in unknown samples by extrapolation of
standard curves below LLOQ or above the highest standard is not recommended. Instead, the
standard curve should be redefined or samples with higher concentration should be diluted and
reassayed. It is preferable to analyze all study samples from a subject in a single run.
Once the analytical method has been validated for routine use, its accuracy and precision should be
monitored regularly to ensure that the method continues to perform satisfactorily. To achieve this
objective, a number of QC samples prepared separately should be analyzed with processed test
samples at intervals based on the total number of samples. The QC samples in duplicate at three
concentrations (one near the LLOQ (i.e., #3 x LLOQ), one in midrange, and one close to the high end
of the range) should be incorporated in each assay run. The number of QC samples (in multiples of
three) will depend on the total number of samples in the run. The results of the QC samples provide the
basis of accepting or rejecting the run. At least four of every six QC samples should be within "15% of
their respective nominal value. Two of the six QC samples may be outside the "15% of their respective
nominal value, but not both at the same concentration.
The following recommendations should be noted in applying a bioanalytical method to routine drug
analysis:
· A matrix-based standard curve should consist of a minimum of six standard points,
excluding blanks (either single or replicate), covering the entire range.
· Response Function: Typically, the same curve fitting, weighting, and goodness of fit
determined during prestudy validation should be used for the standard curve within the
study. Response function is determined by appropriate statistical tests based on the actual
standard points during each run in the validation. Changes in the response function
relationship between prestudy validation and routine run validation indicate potential
problems.
· The QC samples should be used to accept or reject the run. These QC samples are matrix
spiked with analyte.
· System suitability: Based on the analyte and technique, a specific SOP (or sample) should
be identified to ensure optimum operation of the system used.
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· Any required sample dilutions should use like matrix (e.g., human to human) obviating the
need to incorporate actual within-study dilution matrix QC samples.
· Repeat Analysis: It is important to establish an SOP or guideline for repeat analysis and
acceptance criteria. This SOP or guideline should explain the reasons for repeating sample
analysis. Reasons for repeat analyses could include repeat analysis of clinical or preclinical
samples for regulatory purposes, inconsistent replicate analysis, samples outside of the assay
range, sample processing errors, equipment failure, poor chromatography, and inconsistent
pharmacokinetic data. Reassays should be done in triplicate if sample volume allows. The
rationale for the repeat analysis and the reporting of the repeat analysis should be clearly
documented.
· Sample Data Reintegration: An SOP or guideline for sample data reintegration should be
established. This SOP or guideline should explain the reasons for reintegration and how the
reintegration is to be performed. The rationale for the reintegration should be clearly
described and documented. Original and reintegration data should be reported.
Acceptance Criteria for the Run
The following acceptance criteria should be considered for accepting the analytical run:
· Standards and QC samples can be prepared from the same spiking stock solution,
provided the solution stability and accuracy have been verified. A single source of matrix
may also be used, provided selectivity has been verified.
· Standard curve samples, blanks, QCs, and study samples can be arranged as considered
appropriate within the run.
· Placement of standards and QC samples within a run should be designed to detect assay
drift over the run.
· Matrix-based standard calibration samples: 75%, or a minimum of six standards, when
back-calculated (including ULOQ) should fall within ±15%, except for LLOQ, when it
should be ±20% of the nominal value. Values falling outside these limits can be discarded,
provided they do not change the established model.
· Acceptance criteria for accuracy and precision as outlined in section IV.F, “Specific
Recommendation for Method Validation,” should be provided for both the intra-day and
intra-run experiment.
· Quality Control Samples: Quality control samples replicated (at least once) at a minimum of
three concentrations (one within 3x of the LLOQ (low QC), one in the midrange (middle
16
QC), and one approaching the high end of the range (high QC)) should be incorporated into
each run. The results of the QC samples provide the basis of accepting or rejecting the run.
At least 67% (four out of six) of the QC samples should be within 15% of their respective
nominal (theoretical) values; 33% of the QC samples (not all replicates at the same
concentration) can be outside the ±15% of the nominal value. A confidence interval
approach yielding comparable accuracy and precision is an appropriate alternative.
The minimum number of samples (in multiples of three) should be at least 5% of the number of
unknown samples or six total QCs, whichever is greater.
· Samples involving multiple analytes should not be rejected based on the data from one
analyte failing the acceptance criteria.
· The data from rejected runs need not be documented, but the fact that a run was rejected
and the reason for failure should be recorded.
VII. DOCUMENTATION
The validity of an analytical method should be established and verified by laboratory studies, and
documentation of successful completion of such studies should be provided in the assay validation
report. General and specific SOPs and good record keeping are an essential part of a validated
analytical method. The data generated for bioanalytical method establishment and the QCs should be
documented and available for data audit and inspection. Documentation for submission to the Agency
should include (1) summary information, (2) method development and establishment, (3) bioanalytical
reports of the application of any methods to routine sample analysis, and (4) other information
applicable to method development and establishment and/or to routine sample analysis.
A. Summary Information
Summary information should include:
· Summary table of validation reports, including analytical method validation, partial
revalidation, and cross-validation reports. The table should be in chronological sequence,
and include assay method identification code, type of assay, and the reason for the new
method or additional validation (e.g., to lower the limit of quantitation).
· Summary table with a list, by protocol, of assay methods used. The protocol number,
protocol title, assay type, assay method identification code, and bioanalytic report code
should be provided.
· A summary table allowing cross-referencing of multiple identification codes should be
provided (e.g., when an assay has different codes for the assay method, validation reports,
17
and bioanalytical reports, especially when the sponsor and a contract laboratory assign
different codes).
B. Documentation for Method Establishment
Documentation for method development and establishment should include:
· An operational description of the analytical method
· Evidence of purity and identity of drug standards, metabolite standards, and internal
standards used in validation experiments
· A description of stability studies and supporting data
· A description of experiments conducted to determine accuracy, precision, recovery,
selectivity, limit of quantification, calibration curve (equations and weighting functions used, if
any), and relevant data obtained from these studies
· Documentation of intra- and inter-assay precision and accuracy
· In NDA submissions, information about cross-validation study data, if applicable
· Legible annotated chromatograms or mass spectrograms, if applicable
· Any deviations from SOPs, protocols, or GLPs (if applicable), and justifications for
deviations
C. Application to Routine Drug Analysis
Documentation of the application of validated bioanalytical methods to routine drug analysis
should include:
· Evidence of purity and identity of drug standards, metabolite standards, and internal
standards used during routine analyses
· Summary tables containing information on sample processing and storage. Tables should
include sample identification, collection dates, storage prior to shipment, information on
shipment batch, and storage prior to analysis. Information should include dates, times,
sample condition, and any deviation from protocols.
· Summary tables of analytical runs of clinical or preclinical samples. Information should
include assay run identification, date and time of analysis, assay method, analysts, start and
18
stop times, duration, significant equipment and material changes, and any potential issues or
deviation from the established method.
· Equations used for back-calculation of results
· Tables of calibration curve data used in analyzing samples and calibration curve summary
data
· Summary information on intra- and inter-assay values of QC samples and data on intra- and
inter-assay accuracy and precision from calibration curves and QC samples used for
accepting the analytical run. QC graphs and trend analyses in addition to raw data and
summary statistics are encouraged.
· Data tables from analytical runs of clinical or preclinical samples. Tables should include
assay run identification, sample identification, raw data and back-calculated results,
integration codes, and/or other reporting codes.
· Complete serial chromatograms from 5B20% of subjects, with standards and QC samples
from those analytical runs. For pivotal bioequivalence studies for marketing,
chromatograms from 20% of serially selected subjects should be included. In other studies,
chromatograms from 5% of randomly selected subjects in each study should be included.
Subjects whose chromatograms are to be submitted should be defined prior to the analysis
of any clinical samples.
· Reasons for missing samples
· Documentation for repeat analyses. Documentation should include the initial and repeat
analysis results, the reported result, assay run identification, the reason for the repeat
analysis, the requestor of the repeat analysis, and the manager authorizing reanalysis.
Repeat analysis of a clinical or preclinical sample should be performed only under a
predefined SOP.
· Documentation for reintegrated data. Documentation should include the initial and repeat
integration results, the method used for reintegration, the reported result, assay run
identification, the reason for the reintegration, the requestor of the reintegration, and the
manager authorizing reintegration. Reintegration of a clinical or preclinical sample should be
performed only under a predefined SOP.
· Deviations from the analysis protocol or SOP, with reasons and justifications for the
deviations
19
D. Other Information
Other information applicable to both method development and establishment and/or to routine
sample analysis could include:
· Lists of abbreviations and any additional codes used, including sample condition codes,
integration codes, and reporting codes
· Reference lists and legible copies of any references
· SOPs or protocols covering the following areas:
· Calibration standard acceptance or rejection criteria
· Calibration curve acceptance or rejection criteria
· Quality control sample and assay run acceptance or rejection criteria
· Acceptance criteria for reported values when all unknown samples are assayed in
duplicate
· Sample code designations, including clinical or preclinical sample codes and
bioassay sample code
· Assignment of clinical or preclinical samples to assay batches
· Sample collection, processing, and storage
· Repeat analyses of samples
· Reintegration of samples
20
GLOSSARY
Accuracy: The degree of closeness of the determined value to the nominal or known true value under
prescribed conditions. This is sometimes termed trueness.
Analyte: A specific chemical moiety being measured, which can be intact drug, biomolecule or its
derivative, metabolite, and/or degradation product in a biologic matrix.
Analytical run (or batch): A complete set of analytical and study samples with appropriate number of
standards and QCs for their validation. Several runs (or batches) may be completed in one day, or one
run (or batch) may take several days to complete.
Biological matrix: A discrete material of biological origin that can be sampled and processed in a
reproducible manner. Examples are blood, serum, plasma, urine, feces, saliva, sputum, and various
discrete tissues.
Calibration standard: A biological matrix to which a known amount of analyte has been added or
spiked. Calibration standards are used to construct calibration curves from which the concentrations of
analytes in QCs and in unknown study samples are determined.
Internal standard: Test compound(s) (e.g. structurally similar analog, stable labeled compound)
added to both calibration standards and samples at known and constant concentration to facilitate
quantification of the target analyte(s).
Limit of detection (LOD): The lowest concentration of an analyte that the bioanalytical procedure
can reliably differentiate from background noise.
Lower limit of quantification (LLOQ): The lowest amount of an analyte in a sample that can be
quantitatively determined with suitable precision and accuracy.
Matrix effect: The direct or indirect alteration or interference in response due to the presence of
unintended analytes (for analysis) or other interfering substances in the sample.
Method: A comprehensive description of all procedures used in sample analysis.
Precision: The closeness of agreement (degree of scatter) between a series of measurements
obtained from multiple sampling of the same homogenous sample under the prescribed conditions.
Processed: The final extract (prior to instrumental analysis) of a sample that has been subjected to
various manipulations (e.g., extraction, dilution, concentration).
21
Quantification range: The range of concentration, including ULOQ and LLOQ, that can be reliably
and reproducibly quantified with accuracy and precision through the use of a concentration-response
relationship.
Recovery: The extraction efficiency of an analytical process, reported as a percentage of the known
amount of an analyte carried through the sample extraction and processing steps of the method.
Reproducibility: The precision between two laboratories. It also represents precision of the method
under the same operating conditions over a short period of time.
Sample: A generic term encompassing controls, blanks, unknowns, and processed samples, as
described below:
Blank: A sample of a biological matrix to which no analytes have been added that is used to
assess the specificity of the bioanalytical method.
Quality control sample (QC): A spiked sample used to monitor the performance of a
bioanalytical method and to assess the integrity and validity of the results of the unknown
samples analyzed in an individual batch.
Unknown: A biological sample that is the subject of the analysis.
Selectivity: The ability of the bioanalytical method to measure and differentiate the analytes in the
presence of components that may be expected to be present. These could include metabolites,
impurities, degradants, or matrix components.
Stability: The chemical stability of an analyte in a given matrix under specific conditions for given time
intervals.
Standard curve: The relationship between the experimental response value and the analytical
concentration (also called a calibration curve).
System suitability: Determination of instrument performance (e.g., sensitivity and chromatographic
retention) by analysis of a reference standard prior to running the analytical batch.
Upper limit of quantification (ULOQ): The highest amount of an analyte in a sample that can be
quantitatively determined with precision and accuracy.
Validation:
Full validation: Establishment of all validation parameters to apply to sample analysis for the
bioanalytical method for each analyte.
22
Partial validation: Modification of validated bioanalytical methods that do not necessarily call
for full revalidation.
Cross-validation: Comparison validation parameters of two bioanalytical methods.

Process Validation: General Principles and Practices

TABLE OF CONTENTS
I.
INTRODUCTION
1
II.
BACKGROUND
2
III.
STATUTORY AND REGULATORY REQUIREMENTS FOR PROCESS
VALIDATION
4
IV.
RECOMMENDATIONS
6
A.
General Considerations for Process Validation
6
B.
Specific Stages and Activities of Process Validation in the Product Lifecycle
6
1.
Stage 1 – Process Design
7
2.
Stage 2 – Process Qualification
9
3.
Stage 3 – Continued Process Verification
13
V. CONCURRENT RELEASE OF PERFORMANCE QUALIFICATION BATCHES
14
VI.
DOCUMENTATION
15
VII.
ANALYTICAL METHODOLOGY
16
Contains Nonbinding Recommendations
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1 Guidance for Industry1
2
3 Process Validation: General Principles and Practices
4
5
6
This draft guidance, when finalized, will represent the Food and Drug Administration's (FDA's) current
7
thinking on this topic. It does not create or confer any rights for or on any person and does not operate to 8
bind FDA or the public. You can use an alternative approach if the approach satisfies the requirements of 9
the applicable statutes and regulations. If you want to discuss an alternative approach, contact the FDA 10
staff responsible for implementing this guidance. If you cannot identify the appropriate FDA staff, call 11
the appropriate number listed on the title page of this guidance. 12
13 14 15 16 17 18 I. INTRODUCTION 19 20 This guidance outlines the general principles and approaches that FDA considers to be 21 appropriate elements of process validation for the manufacture of human and animal drug and 22 biological products, including active pharmaceutical ingredients (API or drug substance), 23 collectively referred to in this guidance as drugs or products. This guidance incorporates 24 principles and approaches that all manufacturers can use in validating a manufacturing process. 25 26 This guidance aligns process validation activities with the product lifecycle concept and with 27 existing FDA guidance.2 The lifecycle concept links product and process development, 28 qualification of the commercial manufacturing process, and maintenance of the process in a state 29 of control during routine commercial production. This guidance promotes modern 30 manufacturing principles, process improvement, innovation, and sound science. 31
1 This guidance has been prepared by the Division of Manufacturing and Product Quality, Center for Drug Evaluation and Research (CDER), in cooperation with the Center for Biologics Evaluation and Research (CBER) and the Center for Veterinary Medicine (CVM) at the Food and Drug Administration. FDA’s Office of Regulatory Affairs (ORA) also contributed significantly to the development of this guidance.
2 See the FDA/International Conference on Harmonisation (ICH) guidances for industry: Q8 Pharmaceutical Development, Q9 Quality Risk Management, and when finalized, Q10 Pharmaceutical Quality System (a notice of availability for the May 2007 ICH draft guidance, Q10 Pharmaceutical Quality System, published in the Federal Register on July 13, 2007 (72 FR 38604)). We update guidance documents periodically. To make sure you have the most recent version of a guidance, check the CDER guidance page at http://www.fda.gov/cder/guidance/index.htm, the CBER guidance page at http://www.fda.gov/cber/guidelines.htm, or the CVM guidance page at http://www.fda.gov/cvm/Guidance/published.htm.
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32 The following categories of drugs are within the scope of this guidance: 33 34 • Human drugs 35 • Veterinary drugs 36 • Biological and biotechnology products 37 • Finished products and active pharmaceutical ingredients (API or drug substance)3 38 • The drug constituent of a combination (drug and medical device) product 39 40 The following categories of products are not covered by this guidance: 41 42 • Type A medicated articles and medicated feed 43 • Medical devices 44 • Dietary supplements 45 • Human tissues intended for transplantation regulated under section 361 of the Public Health 46 Service Act4 47 48 This guidance does not specify what information should be included as part of a regulatory submission. 49 Interested persons can refer to the appropriate guidance or contact the appropriate Center in determining 50 what information should be included in a submission. 51 52 This guidance also does not specifically discuss the validation of automated process control systems 53 (i.e., computer hardware and software interfaces), which are commonly integrated into modern drug 54 manufacturing equipment. This guidance is relevant, however, to the validation of processes that 55 include automated equipment in processing. 56 57 FDA's guidance documents, including this guidance, do not establish legally enforceable 58 responsibilities. Instead, guidances describe the Agency's current thinking on a topic and should 59 be viewed only as recommendations, unless specific regulatory or statutory requirements are 60 cited. The use of the word should in Agency guidances means that something is suggested or 61 recommended, but not required. 62 63 II. BACKGROUND 64 65 In the Federal Register of May 11, 1987 (52 FR 17638), FDA issued a notice announcing the 66 availability of a guidance entitled Guideline on General Principles of Process Validation (the
3 Separate current good manufacturing practice (CGMP) regulations for drug components such as APIs (drug substances) and intermediates have not published as of the date of this guidance, but these components are subject to the statutory CGMP requirements of section 501(a)(2)(B) of the Federal Food, Drug, and Cosmetic Act (the Act) (21 U.S.C. 351(a)(2)(B)). Process validation for APIs is discussed in the FDA/ICH guidance for industry, Q7A Good Manufacturing Practice Guidance for Active Pharmaceutical Ingredients (ICH Q7A), available on the Internet at http://www.fda.gov/cder/guidance/index.htm. Section XII of ICH Q7A describes in detail the principles to be followed in validating API processes.
4 See the FDA guidance for industry, Validation of Procedures for Processing of Human Tissues Intended for
Transplantation, available on the Internet at http://www.fda.gov/cber/guidelines.htm.
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67 1987 guidance).5 Since then, we have obtained additional experience through our regulatory 68 oversight that allows us to update our recommendations to industry on this topic. This revised 69 guidance conveys FDA’s current thinking on process validation and is consistent with basic 70 principles first introduced in the 1987 guidance. This guidance also provides recommendations 71 that reflect some of the goals of FDA’s initiative entitled “Pharmaceutical CGMPs for the 21st 72 Century – A Risk-Based Approach,” particularly with regard to the use of technological 73 advances in pharmaceutical manufacturing, as well as implementation of modern risk 74 management and quality system tools and concepts. When finalized, this guidance will replace 75 the 1987 guidance. 76 77 FDA has the authority and responsibility to inspect and evaluate process validation performed by 78 manufacturers. The CGMP regulations for validating pharmaceutical (drug) manufacturing 79 require that drug products be produced with a high degree of assurance of meeting all the 80 attributes they are intended to possess (21 CFR 211.100(a) and 211.110(a)). Effective process 81 validation contributes significantly to assuring drug quality. The basic principle of quality 82 assurance is that a drug should be produced that is fit for its intended use; this principle 83 incorporates the understanding that the following conditions exist: 84 85 • Quality, safety, and efficacy are designed or built into the product. 86 87 • Quality cannot be adequately assured merely by in-process and finished-product 88 inspection or testing. 89 90 • Each step of a manufacturing process is controlled to assure that the finished product 91 meets all design characteristics and quality attributes including specifications. 92 93 For purposes of this guidance, process validation is defined as the collection and evaluation of 94 data, from the process design stage throughout production, which establishes scientific 95 evidence that a process is capable of consistently delivering quality products. Process 96 validation involves a series of activities taking place over the lifecycle of the product and 97 process. This guidance describes the process validation activities in three stages. 98 99 • Stage 1 – Process Design: The commercial process is defined during this stage based on 100 knowledge gained through development and scale-up activities. 101 102 • Stage 2 – Process Qualification: During this stage, the process design is confirmed as 103 being capable of reproducible commercial manufacturing. 104 105 • Stage 3 – Continued Process Verification: Ongoing assurance is gained during routine 106 production that the process remains in a state of control.
5 The 1987 guidance was prepared by a working group that included representation from the Center for Devices and Radiological Health (CDRH). Since that time, CDRH elected to publish its own process validation guidance through the Global Harmonization Task Force. The principles and recommendations in that document, Quality Management Systems – Process Validation, edition 2 (available on the Internet at http://www.ghtf.org/sg3/sg3final.html), are also useful to consider for drug manufacturing processes.
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107 108 This guidance describes activities typical in each stage, but in practice, some activities in 109 different stages might overlap. 110 111 Before any batch from the process is commercially distributed for use by consumers, a 112 manufacturer should have gained a high degree of assurance in the performance of the 113 manufacturing process such that it will consistently produce APIs and drug products meeting 114 those attributes relating to identity, strength, quality, purity, and potency. The assurance should 115 be obtained from objective information and data from laboratory-, pilot-, and/or commercial116 scale studies. Information and data should demonstrate that the commercial manufacturing 117 process is capable of consistently producing acceptable quality products within commercial 118 manufacturing conditions, including those conditions that pose a high risk of process failure. 119 120 A successful validation program depends upon information and knowledge from product and 121 process development. This knowledge and understanding is the basis for establishing an 122 approach to control that is appropriate for the manufacturing process. Manufacturers should: 123 124 • understand the sources of variation 125 • detect the presence and degree of variation 126 • understand the impact of variation on the process and ultimately on product attributes 127 • control the variation in a manner commensurate with the risk it represents to the process 128 and product 129 130 Each manufacturer should judge whether it has gained sufficient understanding to provide a high 131 degree of assurance in its manufacturing process to justify commercial distribution of the 132 product. Focusing on qualification efforts without understanding the manufacturing process may 133 not lead to adequate assurance of quality. After establishing and confirming the process, 134 manufacturers must maintain the process in a state of control over the life of the process, even as 135 materials, equipment, production environment, personnel, and manufacturing procedures 136 change.6 137 138 III. STATUTORY AND REGULATORY REQUIREMENTS FOR PROCESS 139 VALIDATION 140 141 Process validation for drugs (finished pharmaceuticals and components) is a legally enforceable 142 requirement under section 501(a)(2)(B) of the Act, which states the following: 143 144 A drug . . . shall be deemed to be adulterated . . . if . . . the methods used in, or the 145 facilities or controls used for, its manufacture, processing, packing, or holding do not 146 conform to or are not operated or administered in conformity with current good 147 manufacturing practice to assure that such drug meets the requirements of this Act as to
6 The statute and regulations described in section III of this guidance explain the requirement that the methods and facilities used for the manufacturing of drugs be operated and maintained under control sufficient to assure that the identity, strength, purity, and quality of a drug are as they purport or are represented to possess.
Contains Nonbinding Recommendations
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148 safety and has the identity and strength, and meets the quality and purity characteristics, 149 which it purports or is represented to possess. 150 151 FDA regulations describing current good manufacturing practice (CGMP) are provided in 21 152 CFR parts 210 and 211. 153 154 Process validation is required, in both general and specific terms, by the CGMP regulations in 155 parts 210 and 211. The foundation for process validation is provided in § 211.100(a), which 156 states that "[t]here shall be written procedures for production and process control designed to 157 assure that the drug products have the identity, strength, quality, and purity they purport or are 158 represented to possess" (emphasis added). This regulation requires that manufacturers design a 159 process including operations and controls that will result in a product meeting these attributes. 160 Product quality in the context of process validation means that product performance is consistent 161 from batch-to-batch and unit-to-unit. Many products are single-source or involve complicated 162 processes to manufacture. Validation also offers assurance that a process is reasonably 163 safeguarded from sources of variability affecting production output, the loss of which can cause 164 supply problems, thereby negatively affecting public health. 165 166 Other CGMP regulations define the various aspects of validation. Section 211.110(a), Sampling 167 and testing of in-process materials and drug products, requires that control procedures “. . . be 168 established to monitor the output and to validate the performance of those manufacturing 169 processes that may be responsible for causing variability in the characteristics of in-process 170 material and the drug product" (emphasis added). This regulation establishes the requirement 171 that even well-designed processes must include in-process control procedures to assure final 172 product quality. 173 174 CGMP regulations require that batch samples represent the batch under analysis (see, e.g., § 175 211.160(b)(3)) and that the sampling plan result in statistical confidence (§ 211.165(c) and (d)) 176 that the batch meets its predetermined specifications (§ 211.165(a)). Section 211.110(b) 177 provides two principles to follow when establishing in-process specifications. The first principle 178 is that “. . . in-process specifications for such characteristics [of in-process material and the drug 179 product] shall be consistent with drug product final specifications . . . .” Accordingly, in-process 180 material should be controlled to assure that the final drug product will meet its quality 181 requirements. The second principle in this regulation further requires that in-process 182 specifications “. . . shall be derived from previous acceptable process average and process 183 variability estimates where possible and determined by the application of suitable statistical 184 procedures where appropriate.” This requirement, in part, establishes the need for manufacturers 185 to analyze process performance and control batch-to-batch variability.7 186
7 In the Federal Register of September 29, 1978 (43 FR 45013 at 45052), FDA published a final rule on “Current Good Manufacturing Practice in Manufacture, Processing, Packing, or Holding” (available on the Internet at http://www.fda.gov/cder/dmpq/preamble.txt). In the preamble of the final rule, the Agency further explains this principle.
Contains Nonbinding Recommendations
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187 The CGMP regulations also describe and define activities connected with process design, 188 development, and maintenance. Section 211.180(e) requires that information and data about 189 product performance and manufacturing experience be periodically reviewed to determine 190 whether any changes to the established process are warranted. Ongoing feedback about product 191 performance is an essential feature of process maintenance. 192 193 In addition, the CGMP regulations require that facilities in which drugs are manufactured be of 194 suitable size, construction, and location to facilitate proper operations (21 CFR 211.42). 195 Equipment must be of appropriate design, adequate size, and suitably located to facilitate 196 operations for its intended use (21 CFR 211.63). Automated, mechanical, and electronic 197 equipment must be calibrated, inspected, or checked according to a written program designed to 198 assure proper performance (21 CFR 211.68). 199 200 In summary, the CGMP regulations require that manufacturing processes be designed and 201 controlled to assure that in-process materials and the finished product meet predetermined 202 quality requirements and do so consistently and reliably. 203 204 IV. RECOMMENDATIONS 205 206 A. General Considerations for Process Validation 207 208 In all stages of the product lifecycle, good project management and good archiving that capture 209 scientific knowledge will make the process validation program more effective and efficient. 210 These practices should ensure uniform collection and assessment of information about the 211 process, reduce the chance for redundant information gathering and analysis, and enhance the 212 accessibility of such information later in the product lifecycle. 213 214 We recommend an integrated8 team approach to process validation that includes expertise from a 215 variety of disciplines, including process engineering, industrial pharmacy, analytical chemistry, 216 microbiology, statistics, manufacturing, and quality assurance. Project plans, along with the full 217 support of senior management, are essential elements for success. 218 219 Throughout the product lifecycle, various studies can be initiated to discover, observe, correlate, 220 or confirm information about the product and process. All studies should be planned and 221 conducted according to sound scientific principles, appropriately documented, and should be 222 approved in accordance with the established procedure appropriate for the stage of the lifecycle. 223 224 B. Specific Stages and Activities of Process Validation in the Product Lifecycle 225 226 The following subsections describe the recommended stages and specific activities. 227
8 This concept is discussed in more detail in FDA’s guidance for industry, Quality Systems Approach to Pharmaceutical Current Good Manufacturing Practice Regulations, available on the Internet at http://www.fda.gov/cder/guidance/index.htm.
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228 1. Stage 1 – Process Design 229 230 a. Building and Capturing Process Knowledge and Understanding 231 232 Process design is the activity of defining the commercial manufacturing process that will 233 be reflected in the master production and control records. The goal of this stage is to 234 design a process suitable for routine commercial manufacturing that can consistently 235 deliver a product that meets its critical quality attributes. 236 237 Generally, early process design experiments do not need to be performed under CGMP 238 conditions. They should, however, be conducted in accordance with sound scientific 239 methods and principles, including good documentation practices. This recommendation 240 is consistent with ICH guidance for industry, Q10 Pharmaceutical Quality System.9 241 Decisions and justification of the controls should be sufficiently documented and 242 internally reviewed to verify and preserve their value for use later in the lifecycle of the 243 process and product. 244 245 There are exceptions, however. For example, viral and impurity clearance studies have a 246 direct impact on drug safety and should be performed under CGMP conditions, even 247 when performed at small scale. The quality unit should be involved with these studies as 248 is typical during commercial production. 249 250 Product-development activities provide key inputs to the design stage, such as the 251 intended dosage form, the quality attributes, and a general manufacturing pathway. 252 Process information available from the product-development stage can be leveraged in 253 the process-design stage. However, the full spectrum of input variability typical of 254 commercial production is not generally known at this stage. The functionality and 255 limitations of commercial manufacturing equipment should be considered, as well as the 256 contributions of variability by different component lots, production operators, 257 environmental conditions, and measurement systems in the production setting. 258 Laboratory or pilot-scale models designed to be representative of the commercial process 259 can be used to estimate variability. However, it is not a regulatory expectation that the 260 process be developed and tested until it fails, but rather that a process be controlled 261 within commercial manufacturing conditions, including those combinations of conditions 262 posing a high risk of process failure. 263 264 Designing an efficient process with an effective process control approach is dependent on 265 the process knowledge and understanding obtained. Design of Experiment (DOE) 266 studies can help develop process knowledge by revealing relationships, including 267 multifactorial interactions, between the variable inputs (e.g., component10 characteristics
9 A notice of availability for this draft ICH guidance published in the Federal Register on July 13, 2007 (72 FR 38604). When finalized, this guidance will represent FDA’s current thinking on this topic.
10 “Component means any ingredient [raw material] intended for use in the manufacture of a drug product, including those that may not appear in such drug product” (21 CFR 210.3(b)(3)).
Contains Nonbinding Recommendations
Draft — Not for Implementation
268 or processing parameters) and the resulting outputs (e.g., in-process material, 269 intermediates, or the final product). Risk analysis tools can be used to screen potential 270 variables for DOE studies to minimize the total number of experiments conducted while 271 maximizing knowledge gained. The results of DOE studies can provide justification for 272 establishing ranges of incoming component quality, equipment parameters, and in273 process material quality attributes. 274 275 Other activities, such as experiments or demonstrations at laboratory or pilot scale, allow 276 evaluation of certain conditions and prediction of performance of the commercial 277 process. These activities also provide information that can be used to model or simulate 278 the commercial process. Computer-based or virtual simulations of certain unit operations 279 or dynamics can provide process understanding and avoid problems at commercial scale. 280 It is important to understand the degree to which models represent the commercial 281 process, including any differences that might exist, as this may have an impact on the 282 relevance of information derived from the studies. 283 284 It is essential that activities and studies resulting in product understanding be 285 documented. Documentation should reflect the basis for decisions made about the 286 process. For example, manufacturers should document the variables studied for a unit 287 operation and the rationale for those variables identified as significant. This information 288 is useful during the process qualification and continued process verification stages, 289 including when the design is revised or the strategy for control is refined or changed. 290 291 b. Establishing a Strategy for Process Control 292 293 Process knowledge and understanding is the basis for establishing an approach to process 294 control for each unit operation and the process overall. Strategies for process control can 295 be designed to reduce input variation, adjust for input variation during manufacturing 296 (and so reduce its impact on the output), or combine both approaches. 297 298 Process controls address variability to assure quality of the product. Controls can consist 299 of material analysis and equipment monitoring at significant processing points designed 300 to assure that the operation remains on target and in control with respect to output quality. 301 Special attention to control of the process through operational limits and in-process 302 monitoring is essential (1) where the product attribute is not readily measurable due to 303 limitations of sampling or detectability (e.g., viral clearance or microbial contamination), 304 or (2) when intermediates and products cannot be highly characterized and well-defined 305 quality attributes cannot be identified. These controls are included in the master 306 production and control records (see 21 CFR 211.186(a) and (b)(9)). 307 308 More advanced strategies, such as process analytical technology (PAT), use timely 309 analysis and control loops to adjust the processing conditions so that the output remains 310 constant. Manufacturing systems of this type can provide a higher degree of process 311 control. In the case of PAT strategy, the approach to process qualification will be 312 different from that for other process designs. Further information on PAT processes can
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313 be found in FDA’s guidance for industry on PAT – A Framework for Innovative 314 Pharmaceutical Development, Manufacturing, and Quality Assurance (available on the 315 Internet at http://www.fda.gov/cder/guidance/index.htm). 316 317 The planned commercial production and control records, which contain the operational 318 limits and overall strategy for process control, should be carried forward to the next stage 319 for confirmation. 320 321 2. Stage 2 – Process Qualification 322 323 During the process qualification stage of process validation, the process design is 324 confirmed as being capable of reproducible commercial manufacture. This stage has two 325 elements: (1) design of the facility and qualification of the equipment and utilities, and 326 (2) performance qualification (PQ). During this stage, CGMP-compliant procedures 327 must be followed and successful completion of this stage is necessary before commercial 328 distribution.11 Products manufactured during this stage, if acceptable, can be released. 329 330 a. Design of a Facility and Qualification of Utilities and Equipment 331 332 Proper design of a manufacturing facility is required under 21 CFR part 211, subpart C, 333 of the CGMP regulations on Buildings and Facilities. It is essential that activities 334 performed to assure proper facility design and commissioning precede PQ. Activities 335 undertaken to demonstrate that utilities and pieces of equipment are suitable for their 336 intended use and perform properly is referred to in this guidance as qualification. These 337 activities necessarily precede manufacturing products at the commercial scale. 338 339 Qualification of utilities and equipment generally includes the following activities: 340 341 • Selecting utilities and equipment construction materials, operating principles, and 342 performance characteristics based on whether they are appropriate for their specific 343 use. 344 345 • Verifying that utility systems and equipment are built and installed in compliance 346 with the design specifications (e.g., built as designed with proper materials, capacity, 347 and functions, and properly connected and calibrated). 348 349 • Verifying that the utility system and equipment operate in accordance with the 350 process requirements in all anticipated operating ranges. This should include 351 challenging the equipment or system functions while under load comparable to that 352 expected during routine production. It should also include the performance of 353 interventions, stoppage, and start-up as is expected during routine production.
11 As discussed in section III of this guidance, process validation (including process qualification) is legally enforceable under section 501(a)(2)(B) of the Act. FDA regulations require that process validation procedures be established and followed (21 CFR 211.100) before a batch can be distributed (21 CFR 211.22 and 211.165).
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354 Operating ranges should be shown capable of being held as long as would be 355 necessary during routine production. 356 357 Qualification of utilities and equipment can be covered under individual plans or as part 358 of an overall project plan. The plan should consider the requirements of use and can 359 incorporate risk management to prioritize certain activities and to identify a level of effort 360 in both the performance and documentation of qualification activities. The plan should 361 identify (1) the studies or tests to use, (2) the criteria appropriate to assess outcomes, (3) 362 the timing of qualification activities, (4) responsibilities, and (5) the procedures for 363 documenting and approving the qualification. It should also include the firm’s 364 requirements for the evaluation of changes. Qualification activities should be 365 documented and summarized in a report with conclusions that address criteria in the plan. 366 The quality control unit must review and approve the qualification plan and report (21 367 CFR 211.22). 368 369 b. Performance Qualification Approach 370 371 The PQ is the second element of stage 2, process qualification. The PQ combines the 372 actual facility, utilities, equipment (each now qualified), and the trained personnel with 373 the commercial manufacturing process, control procedures, and components to produce 374 commercial batches. A successful PQ will confirm the process design and demonstrate 375 that the commercial manufacturing process performs as expected. 376 377 Success at this stage signals an important milestone in the product lifecycle and needs to 378 be completed before a manufacturer commences commercial distribution of the drug 379 product.12 The decision to begin commercial distribution should be supported by data 380 from commercial batches. Data from laboratory and pilot studies can provide additional 381 assurance. 382 383 The approach to PQ should be based on sound science and the manufacturer’s overall 384 level of product and process understanding. The cumulative data from all relevant 385 studies (e.g., designed experiments; laboratory, pilot, and commercial batches) should be 386 used to establish the manufacturing conditions in the PQ. For example, to have sufficient 387 understanding of the commercial process, the manufacturer will need to consider the 388 effects of scale; however, it is not typically necessary to explore the entire operating 389 range at commercial scale if assurance can be provided by other data. Previous credible 390 experience with sufficiently similar products and processes can also be considered. In 391 addition, we strongly recommend firms employ objective measures (e.g., statistical 392 metrics), wherever feasible and meaningful to achieve adequate assurance. 393 394 In most cases, PQ will have a higher level of sampling, additional testing, and greater 395 scrutiny of process performance. The level of monitoring and testing should be sufficient 396 to confirm uniform product quality throughout the batch during processing. This greater
12 See section III of this guidance, Statutory and Regulatory Requirements for Process Validation.
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397 scrutiny accompanied by a higher level of sampling should continue through the process 398 verification stage, as appropriate. 399 400 The extent to which some materials, such as column resins or molecular filtration media, 401 can be re-used without adversely affecting product quality can be assessed in relevant 402 laboratory studies, and their usable lifetime should be confirmed by an ongoing PQ 403 protocol during commercial manufacture. 404 405 A manufacturing process that uses PAT may warrant a different PQ approach. Such a 406 process is one that is designed to measure in real time the attributes of an in-process 407 material and then adjust the process in a timely control loop so the process maintains the 408 desired quality of the output material. The process design stage and the process 409 qualification stage should have as a focus the measurement system and control loop. 410 Regardless, the goal remains the same: establishing scientific evidence that the process is 411 reproducible and will consistently deliver quality products. 412 413 c. Performance Qualification Protocol 414 415 A written protocol that specifies the manufacturing conditions, controls, testing, and 416 expected outcomes is essential for this stage of process validation. We recommend that 417 the protocol discuss: 418 419 • The manufacturing conditions including operating parameters, processing limits, and 420 component (raw material) inputs. 421 422 • The data to be collected and when and how it will be evaluated. 423 424 • Tests to be performed (in-process, release, characterization) and acceptance criteria 425 for each significant processing step. 426 427 • The sampling plan including sampling points, number of samples, and the frequency 428 of sampling for each unit operation and attribute. The number of samples should be 429 adequate to provide sufficient statistical confidence of quality both within a batch and 430 between batches. The confidence level selected can be based on risk analysis as it 431 relates to the particular attribute under examination. Sampling during this stage 432 should be more extensive than is typical during routine production. 433 434 • Criteria that provide for a rational conclusion of whether the process consistently 435 produces quality products. The criteria should include: 436 437 o A description of the statistical methods to be used in analyzing all collected 438 data (e.g., statistical metrics defining both intra-batch and inter-batch 439 variability). 440
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441 o Provision for addressing deviations from expected conditions and handling of 442 nonconforming data. Data should not be excluded from further consideration 443 in terms of PQ without a documented, science-based justification. 444 445 • Design of facilities and the qualification of utilities and equipment, personnel training 446 and qualification, and verification of material sources (components and 447 container/closures), if not previously accomplished. 448 449 • Status of the validation of analytical methods used in measuring the process, in450 process materials, and the product. 451 452 • Review and approval by appropriate departments and the quality unit. 453 454 d. Protocol Execution and Report 455 456 Protocol execution should not begin until the protocol has been reviewed and approved 457 by all appropriate departments, including the quality unit. Departure from the established 458 protocol must be made according to established procedure or provisions in the protocol. 459 Such departures must be justified and approved by all appropriate departments and the 460 quality unit before implementation (§ 211.100). 461 462 The commercial manufacturing process and routine procedures must be followed (§§ 463 211.100(b) and 211.110(a)). The PQ lots should be manufactured under normal 464 conditions by personnel expected to routinely perform each step of each unit operation in 465 the process. Normal operating conditions should cover the utility systems (e.g., air 466 handling and water purification), material, personnel, environment, and manufacturing 467 procedures. 468 469 A report documenting and assessing adherence to the written protocol should be prepared 470 in a timely manner after the completion of the protocol. This report should: 471 472 • Discuss and cross-reference all aspects of the protocol. 473 474 • Summarize data collected and analyze the data, as specified by the protocol. 475 476 • Evaluate any unexpected observations and additional data not specified in the 477 protocol. 478 479 • Summarize and discuss all manufacturing nonconformances such as deviations, 480 aberrant test results, or other information that has bearing on the validity of process. 481 482 • Describe in sufficient detail any corrective actions or changes that should be made to 483 existing procedures and controls. 484
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485 • State a clear conclusion as to whether the data indicates the process met the 486 conditions established in the protocol and whether the process is considered to be in a 487 sufficient state of control. If not, the report should state what should be accomplished 488 before such a conclusion can be reached. This conclusion should be based on a 489 documented justification for the approval of the process, and release of lots produced 490 by it to the market in consideration of the entire compilation of knowledge and 491 information gained from the design stage through the process qualification stage. 492 493 • Include all appropriate department and quality unit review and approvals. 494 495 3. Stage 3 – Continued Process Verification 496 497 The goal of the third validation stage is to continually assure that the process remains in a 498 state of control (the validated state) during commercial manufacture. A system or 499 systems for detecting unplanned departures from the process as designed is essential to 500 accomplish this goal. Adherence to the CGMP requirements, specifically including the 501 collection and evaluation of information and data about the performance of the process 502 (see below), will allow detection of process drift. The evaluation should determine 503 whether action must be taken to prevent the process from drifting out of control (§ 504 211.180(e)). 505 506 An ongoing program to collect and analyze product and process data that relate to 507 product quality must be established (§ 211.180(e)). The data collected should include 508 relevant process trends and quality of incoming materials or components, in-process 509 material, and finished products. The data should be statistically trended and reviewed by 510 trained personnel. The information collected should verify that the critical quality 511 attributes are being controlled throughout the process. 512 513 We recommend that a statistician or person with adequate training in statistical process 514 control techniques develop the data collection plan and statistical methods and 515 procedures used in measuring and evaluating process stability and process capability. 516 Procedures should describe how trending and calculations are to be performed. 517 Procedures should guard against overreaction to individual events as well as against 518 failure to detect process drift. Production data should be collected to evaluate process 519 stability and capability. The quality unit should review this information. If done 520 properly, these efforts can identify variability in the process and/or product; this 521 information can be used to alert the manufacturer that the process should be improved. 522 523 Good process design and development should anticipate significant sources of variability 524 and establish appropriate detection, control, and/or mitigation strategies, as well as 525 appropriate alert and action limits. However, a process is likely to encounter sources of 526 variation that were not previously detected or to which the process was not previously 527 exposed. Many tools and techniques, some statistical and others more qualitative, can be 528 used to detect variation, characterize it, and determine the root cause. We recommend 529 that the manufacturer use quantitative, statistical methods whenever feasible. We also
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530 recommend that it scrutinize intra-batch as well as inter-batch variation as part of a 531 comprehensive continued process verification program. 532 533 We recommend continued monitoring and/or sampling at the level established during the 534 process qualification stage until sufficient data is available to generate significant 535 variability estimates. Once the variability is known, sampling and/or monitoring should 536 be adjusted to a statistically appropriate and representative level. Process variability 537 should be periodically assessed and sampling and/or monitoring adjusted accordingly. 538 539 Variation can also be detected by the timely assessment of defect complaints, out-of540 specification findings, process deviation reports, process yield variations, batch records, 541 incoming raw material records, and adverse event reports. Production line operators and 542 quality unit staff should be encouraged to provide feedback on process performance. 543 Operator errors should also be tracked to measure the quality of the training program; to 544 identify operator performance issues; and to look for potential batch record, procedural, 545 and/or process improvements that could help to reduce operator error. We recommend 546 that the quality unit meet periodically with production staff to evaluate data, discuss 547 possible trends or drifts in the process, and coordinate any correction or follow-up actions 548 by production. 549 550 Data gathered during this stage might suggest ways to improve and/or optimize the 551 process by altering some aspect of the process or product such as the operating conditions 552 (ranges and set-points), process controls, component, or in-process material 553 characteristics. A description of the planned change, a well-justified rationale for the 554 change, an implementation plan, and quality unit approval before implementation must 555 be documented (21 CFR 211.100). Depending on the significance to product quality, 556 modifications may warrant performing additional process design and process 557 qualification activities.13 558 559 Maintenance of the facility, utilities, and equipment is another important aspect of 560 ensuring that a process remains in control. Once established, qualification status must be 561 maintained through routine monitoring, maintenance, and calibration procedures and 562 schedules (21 CFR part 211, subparts C and D). The data should be assessed periodically 563 to determine whether re-qualification should be performed and the extent of that re564 qualification. Maintenance and calibration frequency should be adjusted based on 565 feedback from these activities. 566 567 V. CONCURRENT RELEASE OF PERFORMANCE QUALIFICATION BATCHES 568 569 In most cases, the PQ protocol needs to be completed before the commercial distribution of a 570 product. In special situations, the PQ protocol can be designed to release a PQ batch for
13 Certain manufacturing changes may call for a formal notification to the Agency before implementation, as directed by existing regulations and filing guidance (i.e., documents that describe procedures for filing information to an application).
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571 distribution before completion of the protocol. The conclusions about the manufacturing 572 process should be made when the protocol is completed and the data is fully evaluated. 573 574 FDA expects that concurrent release will be used rarely. Concurrent release might be 575 appropriate for processes used infrequently because of limited demand for the product (e.g., 576 orphan drugs), processes with necessarily low production volume per batch (e.g., 577 radiopharmaceuticals, including positron emission tomography drugs), and processes 578 manufacturing medically necessary drugs to alleviate a short supply, which should be 579 coordinated with the Agency. 580 581 When warranted and used, concurrent release should be accompanied by a system for careful 582 oversight of the distributed batch to facilitate rapid customer feedback. For example, customer 583 complaints and defect reports should be rapidly assessed to determine root cause and whether the 584 process should be improved or changed. We recommend that each batch in a concurrent release 585 program also undergo stability testing and that this test data be promptly evaluated to ensure 586 rapid detection and correction of any problems. 587 588 VI. DOCUMENTATION 589 590 Documentation at each stage of the process validation lifecycle is essential for effective 591 communication in complex, lengthy, and multidisciplinary projects. Documentation is important 592 so that knowledge gained about a product and process is accessible and comprehensible to others 593 involved in each stage of the lifecycle. In addition to being a fundamental tenet of following the 594 scientific method, information transparency and accessibility are essential so that organizational 595 units responsible and accountable for the process can make informed, science-based decisions 596 that ultimately support the release of a product to commerce. 597 598 The degree and type of documentation required by CGMP is greatest during stage 2, process 599 qualification, and stage 3, continued process verification. Studies during these stages must 600 conform to CGMPs and must be approved by the quality unit in accordance with the regulations 601 (see 21 CFR 211.22 and 211.100). Viral and impurity clearance studies, even when performed at 602 small scale, also require full quality unit oversight as is necessary during routine commercial 603 production. 604 605 CGMP documents for commercial manufacturing (i.e., the initial commercial master batch 606 production and control record (21 CFR 211.186) and supporting procedures) are key outputs of 607 stage 1, process design. We recommend that firms diagram the process flow for the full-scale 608 process. Process flow diagrams should describe each unit operation, its placement in the overall 609 process, monitoring and control points, and the component, as well as other processing material 610 inputs (e.g., processing aids) and expected outputs (i.e., in-process materials and finished 611 product). It is also useful to generate and preserve process flow diagrams of the various scales as 612 the process design progresses to facilitate comparison and decision making about their 613 comparability. 614
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615 VII. ANALYTICAL METHODOLOGY 616 617 Process knowledge is dependent on accurate and precise measuring techniques that are used to 618 test and examine the quality of drug components, in-process materials, and finished products. 619 For data to have value in predicting process outcomes, it is essential that the analytical tests be 620 scientifically sound (as required under 21 CFR 211.160). While validated analytical methods are 621 not required during product- and process-development activities, methods should be 622 scientifically sound (e.g., specific, sensitive, and accurate), suitable, and reliable for the specified 623 purpose. There should be assurance of proper equipment function for laboratory experiments. 624 Procedures for analytical method and equipment maintenance, documentation practices, and 625 calibration practices supporting process-development efforts should be documented or described. 626 Analytical methods supporting clinical supply production, particularly stage 2 and 3 studies, 627 must follow appropriate CGMPs in parts 210 and 211.
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628 REFERENCES 629 630 FDA, 1987 (CDER, CBER, and Center for Devices and Radiological Health (CDRH)), 631 Guideline on General Principles of Process Validation, guidance for industry, May 1987. 632 633 FDA, 2002 (CBER), Validation of Procedures for Processing of Human Tissues Intended for 634 Transplantation, guidance for industry, May 2002. 635 636 FDA, 2004 (CDER, CVM, and ORA), PAT — A Framework for Innovative Pharmaceutical 637 Development, Manufacturing, and Quality Assurance, guidance for industry, September 638 2004. 639 640 FDA, 2006 (CDER, CBER, CVM, and ORA), Quality Systems Approach to Pharmaceutical 641 Current Good Manufacturing Practice Regulations, guidance for industry, September 642 2006. 643 644 FDA/Global Harmonization Task Force (GHTF; medical devices), 2004, Quality Management 645 Systems – Process Validation, edition 2, guidance, January 2004. 646 647 FDA/ICH, 2001 (CDER and CBER), Q7A Good Manufacturing Practice, Guidance for Active 648 Pharmaceutical Ingredients, ICH guidance for industry, August 2001. 649 650 FDA/ICH, 2006 (CDER and CBER), Q8A Pharmaceutical Development, ICH guidance for 651 industry, May 2006. 652 653 FDA/ICH, 2006 (CDER and CBER), Q9A Quality Risk Management, ICH guidance for industry, 654 June 2006. 655 656 FDA/ICH (CDER and CBER) Q10 Quality Systems, ICH draft guidance for industry, May 2007 657 (when finalized, this guidance will convey FDA’s current thinking on this topic).