introduction, objectives, and key requirements...
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1INTRODUCTION, OBJECTIVES,AND KEY REQUIREMENTSFOR GLP REGULATIONS
1.1 INTRODUCTION
1.1.1 Good Laboratory Practices
Good laboratory practices (GLPs 21 CFR PART 58) is a standard by which laboratory
studies are designed, implemented, and reported to assure the public that the results
are accurate/reliable and the experiment can be reproduced accordingly [1], at any
time in the future. In less technical terms, GLP is the cornerstone of all laboratory-
based activities in any organization that prides itself on the quality of the work it
performs. And, despite its immediate association with the pharmaceutical sector,
GLPs can (and should) be applied to virtually all industries in which laboratory work
is conducted, including companies involved in drug development, manufacturing,
foods, pesticides (agrochemicals), drink production, and engineering testing. In
addition, commercial testing laboratories (for toxicology, metabolism, materials,
and safety, for example), research establishments, and universities—in fact, all
laboratories engaged in product or safety testing or research and development—
should adopt and apply the doctrines of GLP.
GLP is not a luxury. It is a necessity for any professional laboratorywishing to gain
and retain the respect of its employees, clients, regulators, and perhaps most
importantly, its competitors. If a company is seen to be applying and adhering to
the highest standards of laboratory practice, it will gain significant competitive
advantage and will compete successfully for business and recognition within its
Regulated Bioanalytical Laboratories: Technical and Regulatory Aspects from Global Perspectives,
By Michael Zhou
Copyright � 2011 John Wiley & Sons, Inc.
1
operational environment. Conversely, without rigidly enforced GLPs, good clinical
practice (GCP) [2], good manufacturing practices (GMPs) [3], or GxPs—a scientific
organizationwill not achieve the commercial success and respect that its products and
personnel deserve.
Published GLP regulations and guidelines have a significant impact on the daily
operations of analytical and/or bioanalytical laboratories. GLP is a regulation that
enhances good analytical practice. Good analytical/bioanalytical practice is impor-
tant, but it is not enough. For example, the laboratory must have a specific
organizational structure and procedures to perform and document laboratory work.
The objective is not only quality of data but also traceability and integrity of data.
However, the biggest difference between GLP and non-GLP work is the type and
amount of documentation. GLP functions as a regulation, which deals with the
specific organizational structure and documents related to laboratory work in order to
maintain integrity and confidentiality of the data. The entire cost of GLP-based work
is about 40% or more additional (from case to case) when compared to non-GLP
operations. For aGLP inspector, it should be possible to look at the documentation and
to easily find out the following:
. Who has done a study
. How the experiment was carried out
. Which procedures have been used, and
. Whether there has been any problem and if so
. How it has been addressed and solved where applicable
And this should not only be possible during and right after the study has been
finished but also 5–10 or more years later.
From worldwide perspectives, good practice rules govern drug/product develop-
ment activities in many parts of theworld.World Health Organization (WHO), which
has published documents on current good manufacturing practices (cGMPs) and
GCPs, has not previously recommended or endorsed any quality standard governing
the nonclinical phases of drug/product development. GLPs are recognized rules
governing the conduct of nonclinical safety studies, ensuring the quality, integrity, and
reliability of their data. To introduce the concepts of GLP to scientists in developing
countries, workshops onGLP have been organized in these regions. As an outcome of
the workshops (industries and regulatory bodies), it became apparent that some
formal guidance would be needed for the successful implementation of the GLP
regulations.
The first scientific working group on GLP issues was convened on November 25,
1999, in Geneva, to discuss quality issues in general and the necessity for a WHO
guidance document on GLP in particular. The working group concluded that it was
important to avoid the coexistence of two GLP standards, the Principles of good
laboratory practice of the Organization for Economic Cooperation and Development
(OECD) [4] being the internationally recognized and accepted standard, and
recommended that theOECDPrinciples be adopted byWHOforResearch&Training
2 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
in Tropical Disease (TDR) as the basis of this guidance document. The experts also
recognized the need to address quality issues in areas other than the strictly regulated
safety studies for regulatory submission, and recommended that some explanation be
included in this guidance document. The working group further recommended that
WHO/TDR should request OECD’s permission to publish the existing OECD GLP
textwith aWHOendorsement, and to supplement it with an explanatory introduction.
Classical drug development (drug life cycle) is characterized by four well-defined
stages as follows:
Stage 1: The first stage, the discovery of potential new drug products, is neither
covered by a regulatory standard, nor are studies demonstrating proof of
concept. This area may well require some international standards or guidance
documents in the future.
Stage 2: The position of GLP studies within the drug development process is
specific to the second stage. These studies are termed “nonclinical” as they are
not performed in human. Their primary purpose is safety testing. Toxicology
and safety pharmacology studies, with a potential extension to pharmacoki-
netics and bioavailability, are those studies where the compliance with GLP is
required, which is the rather restricted scope of GLP.
Stage 3: The third stage, following on from safety studies, encompasses the clinical
studies in human. Here, GCP is the basis for quality standards, ethical conduct,
and regulatory compliance. GCP must be instituted in all clinical trials from
Phase I (to demonstrate tolerance of the test drug and to define human
pharmacokinetics), through Phase II (where the dose–effect relationship is
confirmed), to Phase III (full-scale, often multicenter, clinical efficacy trials in
hundreds and thousands of patients).
Stage 4: The fourth stage is postapproval. Here the drug is registered and available
on the market. However, even after marketing, the use of the drug is monitored
through formalized pharmacovigilance procedures. Any subsequent clinical
trials (Phase IV) must also comply with GCP.
A brief summary of different stages is shown in Table 1.1.
TABLE 1.1 Stages Defined Within Discovery and Development Programs
Stage I Stage II Stage III Stage IV
Establish discovery
assessment of
compounds with
in vitro and/or
in vivo data
(not regulated
under GxP)
Demonstrate
efficacy, identify
side effects
including Tox and
assessment of
pharmacokinetics
(GLP and GCP)
Gain more data on
safety and
effectiveness in
multicenters with
thousands of
patients (GLP,
GCP, cGMP)
Monitor claims or
demonstrate new
indications;
examine special
drug–drug
interactions; assess
pharmacokinetics
(GLP, GCP, cGMP)
INTRODUCTION 3
1.1.2 Bioanalytical Laboratories—Bioanalysis
Bioanalytical laboratories have increasingly become center of excellence and
critically important in data generation for discovery, preclinical and clinical devel-
opment in life science industries. Bioanalysis is a broad term that is derived from
analytical applications to biologicalmaterials (matrices) such as human and/or animal
biological fluids and materials (blood, plasma, serum, urine, feces, tissues, etc.),
biopharmaceutical (peptides, protein, etc.), and biochemistry (DNA, RNA, organo-
nucleotides, etc.). The main focus of this book is within the aspects of liquid
chromatography–tandem mass spectrometry (LC–MS/MS) and to certain extent
of immunochemistry assays—enyzme-linked immunosorbent assays (ELISA) or
ligand-binding assays (LBAs). Bioanalysis is mainly referred to the quantitative
determination of drugs and their metabolites, and other life science products in
various sample matrices. However, it should also apply to qualitative analysis
(identification and elucidations) of drug degradants, metabolites, impurities, and
other analytes of interests. The techniques (chromatographic-based and ligand-
binding-based assays) are used very early in the drug discovery and development
process to provide support to product discovery programs on metabolite fate and
pharmacokinetics of chemicals in living cells and animals. They are referred by FDA
Guidance for Industry Bioanalytical Method Validation for chromatographic-based
and ligand-binding-based assays [5]. Their uses continue throughout the nonclinical
and clinical product development phases into postmarketing support and may
sometimes extend into clinical therapeutic monitoring. Recent developments and
industry trends for rapid sample throughput and data generation are introduced and
discussed in following chapters, together with examples of how these high throughput
needs are met in bioanalysis.
1.1.2.1 High-Throughput Bioanalytical Sample Preparation Methods and
automation strategies are authoritative reference on the current state-of-the-art in
sample preparation techniques for bioanalysis. The following related chapters focus
on high-throughput (rapid productivity) techniques and describe exactly how to
perform and automate these methodologies, including useful strategies for method
development and optimization. A thorough review of the literature is included
describing high-throughput sample preparation techniques: protein removal by
precipitation; equilibrium dialysis and ultrafiltration; liquid–liquid extraction; solid
phase extraction; and various online techniques. A schematic diagram of analytical/
bioanalytical techniques used in automation is shown in Figure 1.1.
Among the sample preparation scheme, protein precipitation (PPT) is the most
commonly used approach for a simple, fast, and unique process of removing
unwanted materials from analyte(s) of interest for analysis or in some case for
further cleanup. High selectivity and sensitivity are also imperative for bioanalytical
laboratories to deal with sample analyses with great demand in method limits of
quantitation (LOQ), wide dynamic range (linearity and range), free of interferences
(specificity and selectivity), and other highly challenging requirements such as
multiple compounds (analytes—parent drugs, prodrugs, and their degradants/
4 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
metabolites), various sample types (matrices), and different analytical techniques
including LC–MS/MS,GC–MS/MS, LC–NMR, ICP–MS, and other advanced hybrid
techniques. In addition to above analytical techniques, immunoassays (ELISA and/or
ligand-binding assays—LBAs or alike) are also widely used within bioanalytical
laboratories, especially in biopharmaceutical and biotechnology industries where
relatively large molecules are dealt such as peptides and proteins as part of
drug development compounds and applying to different therapeutic areas. Rapid
advances in chromatographic as well as ligand-binding assay technologies have
been observed to meet the needs in product research and development processes.
More details of description are elaborated on above analytical and bioanalytical
techniques as powerful methodologies in trace level qualitative and quantitative
analyses.
There have been varieties of separation and detection techniques involved in
analytical and bioanalytical methodologies as indicated in Figure 1.2. More recent
years, biomarker analysis in various therapeutic areas has become incredibly
significant in drug/product development and monitoring programs. Without any
doubt, this has increasingly become part of bioanalytical capabilities. Biomarker
Analytical/Bioanalytical Sample Prep. Chemistry and Techniques
Protein Precipitation Liquid–LiquidExtraction
Solid Phase Extraction
Automation: Liquid Handling Workstations and Robots
TomtecQuadra 96 Plus
Packard MultiProbe IIEx
TecanGenesis Freedom
HamiltonSTAR
FIGURE 1.1 General schematic of analytical/bioanalytical techniques used in laboratory
operations/automation.
Analytical/Bioanalytical Separation/Detection Techniques
Spectroscopy/LBAsSpectrophotometryChromatography
GC, HPLC, CE, UV–VIS, FT-IR, NMR, MS/MS, LBAs, etc.
Small and Large Molecules
Ionic and PolarSpecies
Volatile andNonvolatile
Liquid, Gas, and Solids
FIGURE 1.2 Commonly used techniques in analytical/bioanalytical separation and
detection.
INTRODUCTION 5
measurements now support key decisions throughout the drug development process,
from lead optimization to regulatory approvals. They are essential for documenting
exposure–response relationships, specificity and potency toward themolecular target,
untoward effects, and therapeutic applications. In a broader sense, biomarkers
constitute the basis of clinical pathology and laboratory medicine. The utility of
biomarkers is limited by their specificity and sensitivity toward the drug or disease
process and by their overall variability. Understanding and controlling sources of
variability is not only imperative for delivering high-quality assay results, but
ultimately for controlling the size and expense of research studies. Variability in
biomarker measurements is affected by biological and environmental factors (e.g.,
gender, age, posture, diet, and biorhythms), sample collection factors (e.g., preser-
vatives, transport and storage conditions, and collection technique), and analytical
factors (e.g., purity of reference material, pipetting precision, and antibody speci-
ficity). The quality standards for biomarker assays used in support of nonclinical
safety studies fall under GLP (FDA) regulations, whereas, those assays used to
support human diagnostics and healthcare are established by Clinical Laboratory
Improvement Amendments (CLIAs) and Centers for Medicare &Medicaid Services
(CMSs) regulations and accrediting organizations such as the College of American
Pathologists (CAPs). While most research applications of biomarkers are not
regulated, biomarker laboratories in all settings are adopting similar laboratory
practices in order to deliver high-quality data. Because of the escalation in demand
for biomarker measurements, the highly parallel (multiplexed) assay platforms that
have fueled the rise of genomics will likely evolve into the analytical engines that
drive the biomarker laboratories of tomorrow. The role of biomarkers in drug
discovery and development has gained precedence over the years. As biomarkers
become integrated into drug development and clinical trials, quality assurance and, in
particular, assay validation become essential with the need to establish standardized
guidelines for bioanalytical methods used in biomarker measurements. New bio-
markers can revolutionize both the development and use of therapeutics but are
contingent on the establishment of a concrete validation process that addresses
technology integration and method validation as well as regulatory pathways for
efficient biomarker development. Perspective focuses on the general principles of the
biomarker validation process with an emphasis on assay validation and the collab-
orative efforts undertaken by various sectors to promote the standardization of this
procedure for efficient biomarker development. It is important to point out that
biomarker method validation is distinct from pharmacokinetic validation and routine
laboratory validation. The FDA has issued guidance for industry [5] on bioanalytical
method validation for assays that support pharmacokinetic studies that are specific for
chromatographic and ligand-binding assays, and that are not directly related to the
qualification or validation of biomarker assays.Whereas routine laboratory validation
refers to laboratories that do testing on human specimens for diagnosis, prevention, or
treatment of any disease and falls under the jurisdiction of the Clinical Laboratory
Improvement Amendments of 1988, there is little regulatory guidance on biomarker
assay validation. Hence, a “fit-for-purpose” approach for biomarker method devel-
opment and validation is derived with the idea that assay qualification or validation
6 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
should be tailored to meet the intended purpose of the biomarker study. Numerous
applications using bioanalytical techniques have generated enormous interests and
some case reveal ultimate solutions in drug efficacies and other indications that are
critical to the success in drug/product development and approval processes.
1.1.3 Good Laboratory Practices Versus Bioanalytical Labs/Bioanalysis
Recently, more and more debate and discussion around the connection between GLP
and Bioanalysis are surfaced. It is noted that there is no direct reference from GLP
regulations to bioanalysis. However, it has become common terminology and
acceptance when people refer to GLP–Bioanalysis. In a regulatory term, it may be
referred as regulated bioanalysis to support programs or studies under GLP compli-
ance. There is a misconception in some quarters that GLP is required for the conduct
of clinical studies. This is not correct. The introduction to the OECD Principles of
GLP (and the introduction to the USFDA GLPs in 21 CFR part 58) makes clear that
they apply only to the portions of nonclinical (preclinical) studies. The relevant
documents for clinical studies are the various codes of GC(R)P (e.g., ICH; TGA). The
USFDA and other registration authorities do require a demonstration of the quality of
test data from clinical studies. In the United States, this may well be by means of
conformance with Clinical Laboratories Improvement Act (CLIA) [6]. In Australia,
this is best demonstrated by the testing laboratory’s NATA accreditation (in Medical
Testing, Chemical Testing, etc.)1. Nevertheless, bioanalytical laboratories generate
data in support of clinical studies and ultimately as part of data submissions to
regulatory agencies. More detailed discussions on above techniques and guidelines
are available in respective chapters of this book. The regulatory environment in which
clinical trials are conducted continues to evolve. The changes are generally focused on
requiring more rigorous control within the organizations performing clinical trials in
order to ensure patient safety and the reliability of data produced. The global
acceptance of the ICH Guideline for GCP and the implementation of the European
Union Clinical Trials Directive (2001/20/EC) are two clear examples of such change.
For some years, it has been internationally recognized that clinical laboratories
processing specimens from clinical trials require an appropriate set of standards to
guide good practices. With that aim in mind, the Good Clinical Laboratory Practice
Guidelines [7] were drafted and published in 2003 by a working party of the Clinical
Committee of the British Association of ResearchQuality Assurance (BARQA). This
guidance identifies systems required and procedures to be followed within an
organization conducting analysis of samples from clinical trials in compliance with
the requirements of GCP. It thus provides sponsors, laboratory management, project
managers, clinical research associates (CRAs), and quality assurance personnel with
the framework for a quality system in analysis of clinical trial samples, ensuring GCP
compliance overall of processes and results.
1 The National Association of Testing Authorities (NATA)—Australia’s national laboratory accreditation
authority. NATA accreditation recognizes and promotes facilities competent in specific types of testing,
measurement, inspection, and calibration.
INTRODUCTION 7
1.2 OBJECTIVES AND KEY REQUIREMENTS FOR GLP
REGULATIONS
The ability to provide timely, accurate, and reliable data is essential to the role of
analytical and bioanalytical chemists and is especially true in the discovery, devel-
opment, and manufacture of pharmaceuticals and life science products. Analytical
and bioanalytical data are used to screen potential drug candidates, aid in the
development of drug syntheses, support formulation studies, animal PK/Tox, clinical
safety and efficacy programs, monitor the stability of bulk pharmaceuticals and
formulated products, and test final products for release. The quality of analytical and
bioanalytical data is a key factor in the success of a drug or product development
program. The process of method development and validation has a direct impact on
the quality of these data.
Although a thorough validation cannot rule out some potential problems, the
process ofmethod development and validation should address themost commonones.
Examples of typical problems that can be minimized or avoided are synthesis
impurities that coelute with the analyte peak in an HPLC assay; a particular type
of column that no longer produces the separation needed because the supplier of the
column has changed themanufacturing process; an assaymethod that is transferred to
a second laboratory where they are unable to achieve the same detection limit; and a
quality assurance audit of a validation report that finds no documentation on how the
method was performed during the validation.
Problems increase as additional people, laboratories, and equipment are used
to perform the method. When the method is used in the developer’s laboratory, a
small adjustment can usually be made to make the method work, but the flexibility
to change it is lost once the method is transferred to other laboratories or used for
official product testing. This is especially true in the pharmaceutical and life
science industries, where methods are submitted to regulatory agencies and changes
may require formal approval before they can be implemented for official testing/
intended use. The best way to minimize method problems is to perform adequate
validation experiments during development and establishment. Analysis of chemi-
cals/drugs in the complex environments/matrices in which they occur are carried out
by a vast range of institutions for a variety of purposes, from pharmaceutical and
agrochemical companies to hospital biochemistry labs and industry laboratories, from
environmental monitoring to safety and toxicity testing of new drugs/products. The
range of compounds for analysis is enormous, from naturally occurring compounds
such as vitamins to man-made chemicals from the pharmaceutical and agrochemical
industries. The following chapters offer an integrated, readable reference text
describing the full range of analytical techniques and regulatory requirements
available for such small molecules (mostly) and large molecules in an up-to-date
manner and should be useful and appeal to all involved in the rapidly growing field of
bioanalytical sciences.
. Responsibilities should be defined for the sponsor management, for the study
management, and for the quality assurance unit.
8 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
. All routine work should follow written standard operating procedures (SOPs).
. Facilities such as laboratories should be large enough and have the right
construction/facility to ensure the integrity of a study, for example, to avoid
cross contamination during implementation and processes.
. Test and control articles should have the right quality and instruments should be
calibrated and well maintained.
. People should be trained or otherwise qualified for the job.
. Raw data and other data should be acquired, processed, and archived to ensure
integrity of data.
The main objective is clearly stated within GLP regulations and guidelines—
embodies a set of principles that provides a framework forAquality system concerned
with the organizational process and the conditions underwhich laboratory studies are
planned, performed, monitored, recorded, reported, and archived. These studies are
undertaken to generate data by which the hazards and risks to users, consumers, and
third parties, including the environment, can be assessed for pharmaceuticals,
agrochemicals, veterinary medicines, industrial chemicals, cosmetics, food and feed
additives, and biocides. GLP helps assure regulatory authorities that the data
submitted are a true reflection of the results obtained during the study and can
therefore be relied upon whenmaking risk/safety assessments. GLP regulations were
established by the regulatory bodies to ensure that research submitted to them is not
only properly executed but also documented thoroughly enough so that any scientist/
organization skilled or qualified can follow the documentation and replicate the
results. The level of detail required to achieve this level of documentation is
substantial to ensure the integrity, quality, and accuracy of data for product approval.
Unfortunately most laboratories are in situations where they have had to interpret
the regulations. Procedures have been developed on an ad hoc basis, in isolation, in
response to inspections by both their company’s Quality Assurance Unit (QAU) and
regulatory bodies. Under such duress, many scientists in industry have developed
procedures to validate their instrumentation even though the same approach will
already have been applied at the instrument manufacturer’s sites. SOPs written to
accompany such validation efforts often duplicate extracts from operationmanuals—
why don’t the manufacturers provide the SOPs directly? When it comes to validating
the instrument’s application software, the person responsible has to take the man-
ufacturer’s word for it that the software has been validated and hope that supporting
documents, such as test results and source code are available to regulatory agencies
upon request, as part of basic requirements for GLP quality system and
implementation.
. To assign responsibility for sponsor management, study management, and
quality assurance.
. Standard Operating Procedures must be followed.
. Calibration and maintenance of instruments.
. Right construction of laboratories to maintain integrity of the study.
OBJECTIVES AND KEY REQUIREMENTS FOR GLP REGULATIONS 9
. Raw data should be processed and achieved.
. Employee should be well qualified and trained as per the job assigned.
1.3 FUNDAMENTAL UNDERSTANDING OF GLP REGULATIONS
AND PRINCIPLES
Scientificmeasurements (whether they pertain tomonitoring contaminants and active
ingredients in pharmaceutical products, clinical determinations of diversified func-
tional elements, characterization of forensic evidence, or testing materials for
intermediates and/or final products) are generally recognized as affecting decisions
literally concerned with life and death issues. As personal acknowledgement of
their responsibility, scientists have traditionally adopted sound laboratory practices
directed at assuring the quality of their data. However, until recently these practices
were not consistently adopted, enforced, or audited. Because of some notorious
historic examples where erroneous data have lead to tragic consequences, national
and international agencies have developed guidelines directed at various industries
(food, agriculture, pharmaceutical, clinical, environmental, etc.), which fall in the
general category of GLPs.
Good laboratory practice regulations becamepart of the regulatory landscape in the
latter part of the 1970s in response tomalpractice in R&D activities of pharmaceutical
companies and contract facilities used by them. Themalpractice included some cases
of fraud, but by far the most important aspect of poor practice was the lack of proper
management and organization of studies used to complete regulatory dossiers. The
investigations of the US Food and Drug Administration (FDA) in the toxicology
laboratories in the United States demonstrated a lack of organization and poor
management which, it was decided, could only be dealt with by imposing regulations.
These regulations are the GLP regulations. First the US FDA, then the US Environ-
mental Protection Agency (EPA), instituted GLP regulations, and eventually many
nations of the world followed suit. In 1981, the OECD also published GLP Principles
and these have now dominated the international scene—so far 30 countries (the
member states of the OECD) have signed agreements that make the OECD GLP
Principles binding on them. This effectively makes the OECD Principles an interna-
tional text. The intent ofGLPwas to regulate the practices of scientists working on the
safety testing of prospective drugs. With the obvious potential impact on consumers
andpatients recruited for clinical trials, the safety of drugsbecameakey issue andGLP
was seen as ameans of ensuring that scientists did not invent ormanipulate safety data
and a means of ensuring that GLP compliant studies are properly managed and
conducted. Hence GLP became the champion of the consumer, the regulatory
safeguard, and the guarantee that the safety data were being honestly reported to the
registration or receiving authorities as the basis of a decision whether or not to allow a
newdrug onto themarket. GLPwas imposed on the industry by regulatory authorities,
in the same way as GMP had been before, and GCP was to be afterwards.
Within the United States, federal agencies such as FDA and EPA have produced
documents defining laboratory operational requirements, which must be met so that
10 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
technical data from laboratory studies may be acceptable by those agencies for any
legal or contractual purposes. Laboratories doing business with and/or for these
agenciesmust therefore complywith the specifiedGLP regulations. Not only the issue
of GLP is obviously so crucial to modern laboratory operations, but also most
importantly because good laboratory practice is an essential ingredient for any
professional scientist, this chapter will incorporate many of the principles that are
part of GLP in contemporary laboratories. A brief summary of GLP principles is
described and presented below.
1.3.1 Elements of Good Laboratory Practices
In general, basic elements of GLP may be defined as follows (but not limited to):
. Qualification of test facility management and personnel
. Standard operating procedures
. Quality assurance program
. Qualification of facilities (e.g., bioanalytical/analytical testing facilities)
. Qualification and validation of apparatus (equipment, computers, or comput-
erized systems), materials, and reagents
. Test systems, and test and reference items
. Performance of the study and reporting of study results
. Storage and retention of records and materials
. Documentation and maintenance of records
1.3.1.1 Qualification of Test Facility Management and Personnel The test
facility (TF) management means the person(s) who has (have) the authority and
formal responsibility for the organization and functioning of the TF according to the
GLP regulations. This requires the identification of management and the need of a
job description, qualification background, and training records (CVs or resumes).
The organization has to describe in an ad hoc document the way the TF is structured.
The TF management must ensure the availability of a master schedule, appropriate
facilities, equipment, and materials for the timely and proper conduct of the study.
A statement has to be in place that identifies the individual(s) within the TF by whom
the responsibilities of management are fulfilled.
1.3.1.2 Standard Operating Procedures SOPs provide standard working tools
that can be used to document routine quality system management and technical
activities. The development and use of SOPs are an integral part of a successful quality
system as it provides individuals with the information to perform a job or complete a
project properly, and facilitates consistency in the quality and integrity of a product or
end-result. The term “SOP” may not always be appropriate and terms such as
protocols, instructions, worksheets, and laboratory operating procedures may also
be used. SOPs detail the regularly recurringwork processes that are to be conducted or
FUNDAMENTAL UNDERSTANDING OF GLP REGULATIONS AND PRINCIPLES 11
followed within an organization. They document the way activities are to be
performed to facilitate consistent conformance to technical and quality system
requirements and to support data quality. They may describe, for example, funda-
mental programmatic actions and technical actions such as analytical processes, and
processes for maintaining, calibrating, and using equipment. SOPs are intended to be
specific to the organization or facility whose activities are described and assist that
organization to maintain their quality control and quality assurance processes and
ensure compliance with governmental regulations.
1.3.1.3 Quality Assurance The primary products of any laboratory concerned
with qualitative and quantitative analysis are the analytical data reported for speci-
mens examined by that laboratory. QA for such a laboratory includes all of the
activities associatedwith insuring that chemical and physicalmeasurements aremade
properly, interpreted correctly, and reported with appropriate estimates of error and
confidence levels. QA activities also include thosemaintaining appropriate records of
specimen/sample origins and history (sample tracking), as well as procedures, raw
data, and results associated with each specimen/sample. The various elements of
Quality Assurance are itemized here: (1) SOPs; (2) instrumentation validation; (3)
reagent/materials certification; (4) analyst qualification/certification; (5) lab facilities
qualification/certification; and (6) specimen/sample tracking.
Many volumes could be written regarding each of the QA elements itemized
above. However, a brief discussion is presented here. SOPs are what the name
implies . . . procedures which have been tested and approved for conducting a
particular determination. Often, these procedures will have been evaluated and
published by the regulatory agency involved (e.g., EPA or FDA); these agencies
may not accept analytical data obtained by other procedures for particular analytes.
Within the context of laboratory work, the experimental procedures provided in
LaboratoryManual correspond to the SOPs.Within any commercial laboratory, SOPs
should be either available or developed to acceptable standards, so that any analytical
data collected and reported can be tied according to a documented procedure.
Presumably, this implies that a given determination can be repeated at any later
time, for an identical specimen, using the SOP specified.
1.3.1.4 Qualification/Certification of Laboratory Facilities These are normally
done by some external agency. For example, an analytical and bioanalytical labo-
ratory might be audited by representatives of a federal agency with which they have a
contract. An independent laboratorymight file documentationwith a responsible state
or federal agency. The evaluation is concerned with such issues as space (amount,
quality, and relevance), ventilation, equipment, storage, and hygiene. Routine
chemistry laboratories are generally evaluated by the American Chemical Society,
as part of the process of granting approval for the overall chemistry programpresented
by the college or university. This latter approval process is not as detailed regarding
analytical facilities as the certification processes pursued by agencies, concerned
specifically with quality assurance.
12 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
1.3.1.5 Instrumentation/Apparatus Qualification and Validation It is a process
inherently necessary for any analytical and bioanalytical laboratory. Data produced
by “faulty” instruments may give the appearance of valid data. These events are
particularly difficult to detect with modern computer-controlled systems, which
remove the analyst from the data collection/instrument control functions. Thus, it
is essential that some objective procedures be implemented for continuously asses-
sing the validity of instrumental data. These procedures, when executed on a regular
basis, will establish the continuing acceptable operation of laboratory instruments
within prescribed specifications.
1.3.1.6 Reagent/Materials Certification It is an obvious element of quality
assurance. However, GLP guidelines emphasize that certification must follow
accepted procedures, and must be adequately documented. Moreover, some guide-
lineswill specify that each container for laboratory reagents/materialsmust be labeled
with information related to its certification value, date, and expiration time. This
policy is meant to assure that reagents used are as specified in the SOPs.
1.3.1.7 Qualification/Certification of Analysts (Quality Personnel) This is a
required part of QA. Some acceptable proof of satisfactory training and/or compe-
tence with specific laboratory procedures must be established for each analyst.
Because the American Chemical Society does not currently have a policy regarding
“certification” of chemists or analysts, the requirements for “certification” vary, and
are usually prescribed by the laboratory in question. These standardswould have to be
accepted by any agency or client obtaining results from that laboratory. For routine
laboratory, the requirement for certification as an analyst is satisfactory completion of
the predefined assignments (specified by relevant SOPs). Execution of these basic
procedures will be repeated, if necessary, until satisfactory results are obtained
(evaluated based on analytical accuracy and precision).
1.3.1.8 Specimen/SampleTracking This is an aspect of quality assurance that has
received a great deal of attention with the advent of computer-based Laboratory
Information Management Systems (LIMSs). However, whether done by hand with
paper files, or by computer with modern bar-coding techniques, sample tracking is a
crucial part of quality assurance. The terms “specimen” and “sample” are often used
interchangeably. However, “specimen” usually refers to an item to be characterized
chemically; whereas “sample” usually refers to a finite portion of the specimen,which
is taken for analysis. When the specimen is homogeneous (such as a stable solution),
the sample represents the overall composition of the specimen. However, if specimens
are heterogeneous (e.g., metal alloys, rock, soil, textiles, foods, polymer composites,
vitamin capsules, etc.), then the samples may not represent the overall compositions.
Maintaining the distinction in records of analytical results can be crucial to the
interpretation of data.
Procedures for assuring adequate specimen/sample tracking will vary among
laboratories. The bottom line, however, is that these procedures must maintain the
FUNDAMENTAL UNDERSTANDING OF GLP REGULATIONS AND PRINCIPLES 13
unmistakable connection between a set of analytical data and the specimen and/or
samples from which they were obtained. In addition, the original source of the
specimen/sample(s) must be recorded and likewise unmistakably connected with the
set of analytical data. Finally, in many cases the “chain-of-custody” must be specified
and validated. This is particularly true for forensic samples (related to criminal
prosecution), but can also be essential for many other situations as well. For example,
a pharmaceutical companydeveloping a newproductmay be called upon at some time
to defend their interpretation of clinical trial tests. Such defense may require the
company to establish that specimens collected during these trials could not have been
deliberately tampered. That is, they may have to establish an unbroken chain-of-
custody, which would remove all doubt regarding the integrity of specimens
submitted to sample analysis.
1.3.1.9 Performance of the Study and Reporting of Study Results A critical
process to the success and quality of a study and data generated within a study. For
each study, a written protocol/plan should exist prior to the initiation of the study.
Acceptance criteria should be defined and followed. Deviations (e.g., amendments)
from the study plan and criteria should be justified, described, acknowledged,
approved (if necessary/applicable), and documented during the process/execution.
Upon completion of a study, results should be reported in a timely manner. Study
reports should be prepared in a format that is compliant with GLP requirements
(including GLP compliance statement where applicable) and signed/approved by
Study Director, which is a formal record to confirm that the study was/is conducted
and reported in accordance with GLP, clearly identifying, as appropriate, where the
study deviated from GLP. The GLP compliance statement should not be confused
with the QA statement, also presented in the study report, which is a distinct and
separate record of QA study monitoring.
1.3.1.10 Documentation and Maintenance of Records A central feature of GLP
guidelines is documentation along with the maintenance of records of specimen/
sampleorigins, chain-of-custody, rawanalyticaldata, processedanalyticaldata,SOPs,
instrument validation results, reagent certification results, analyst certification docu-
ments, etc. Maintenance of instrument and reagent certification records provides for
postevaluation of results, even after the passage of several years. Maintenance of all
records specified provides documentation, whichmay be required in the event of legal
challenges due to repercussions of decisions based on the original analytical results.
So important is this record-keeping feature of GLP that many vendors are now
providing many of these capabilities as part of computer packages for operating
modern instruments. For example, many modern computer-based instruments will
provide for the indefinite storage of raw analytical data for specific samples in a
protected (tamper proof) environment. They also provide for maintenance of his-
torical records of control chart data establishing the operational quality of instruments
in any period during which analytical data have been acquired by that instrument.
The length of time over which laboratory records should be maintained will vary
with the situation. However, the general guidelines followed in regulated laboratories
14 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
are to maintain records for at least 5 years. In practice, these records are being
maintained much longer. The development of higher density storage devices for
digitized data is making this kind of record-keeping possible. The increasing
frequency of litigation regarding chemistry-related commercial products is making
this kind of record-keeping essential. Moreover, establishing the integrity of the
stored data is becoming a high-level security issue for companies concerned about
future litigation.
1.3.1.11 Accountability GLP procedures inherently establish accountability for
laboratory results. Analysts, instruments, reagents, and analytical methods cannot
(and should not) maintain the anonymity that might be associated with a lack of GLP
policy. Responsibility for all aspects of the laboratory processes leading to technical
results and conclusions is clearly defined and documented. This situation should place
appropriate pressure on analysts to conduct studies with adequate care and concern.
Moreover, it allows the possibility of identifying more quickly and succinctly the
source(s) of error(s) and taking corrective action to maintain acceptable quality of
laboratory data.
The OCED GLP Principles simply state that the fundamental points of GLP
help the research to perform his/her work in compliance with reestablished plan
and standardized procedures worldwide. The regulations/principles do not con-
cern the scientific or technical content of the research programs. All GLP texts,
whatever their origins or the industry targeted, stress the importance of the
following points:
(1) Resources: Organization, personnel, facilities, and equipment.
(2) Rules: Protocols and written procedures.
(3) Characterization: Test items and test systems.
(4) Documentation: Raw data, final report, and archives.
(5) Quality assurance unit.
The training program of the WHO takes each of these five fundamental points in
turn and explains the rules of GLP in each case. The major points are summarized
here.
(1) Resources (Organization and Personnel): GLP regulations require that the
structure of the research organization and the responsibilities of the research
personnel be clearly defined. GLP also stresses that staffing levels must be
sufficient to perform the tasks required. The qualifications and the training
of staff must also be defined and documented. Facilities and Equipment—
The regulations emphasize the need for sufficient facilities and equipment in
order to perform the studies. All equipment must be in working order. A strict
program of qualification, calibration, and maintenance attains this.
(2) Rules (Protocols and Written Procedures): The main steps of research
studies are described in the study plan or protocol. However, the protocol
FUNDAMENTAL UNDERSTANDING OF GLP REGULATIONS AND PRINCIPLES 15
does not contain all the technical details necessary to exactly repeat the study.
Since being able to repeat studies and obtain similar results is a sine qua non of
mutual acceptance of data (and, indeed, a central tenet in the scientific
method), the routine procedures are described in written SOPs. Laboratories
may also need to standardize certain techniques to facilitate comparison of
results; here again written SOPs are an invaluable tool.
(3) Characterization: In order to perform a study correctly, it is essential to know
as much as possible about the materials used during the study. For studies to
evaluate the properties of pharmaceutical compounds during the preclinical
phase, it is a prerequisite to have details about the test item and about the test
system (often an animal or plant) to which it is administered.
(4) Documentation (Raw Data): All studies generate raw data. These are the
fruits of research and represent the basis for establishing results and arriving at
conclusions. The raw data must also reflect the procedures and conditions of
the study. Final Report—The study report, just like all other aspects of the
study, is the responsibility of the study director. He/she must ensure that the
contents of the report describe the study accurately. The study director is also
responsible for the scientific interpretation of the results. Archives—Storage
of records must ensure safekeeping for many years, coupled with logical and
prompt retrieval.
(5) Quality Assurance: QA as defined by GLP is a team of persons charged with
assuring management that GLP compliance has been attained within the
laboratory. They are organized independently of the operational and study
program, and function as witnesses to the whole preclinical research
process.
1.4 KEY ELEMENTS OF BIOANALYTICAL METHODS VALIDATION
It is apparent that the quality of bioanalytical data is critical to supporting regulatory
filing and approval process. BMV employed for the quantitative determination of
drugs and their metabolites in biological fluids plays a significant role in the
evaluation and interpretation of BA, bioequivalence (BE), PK, and toxicokinetic
(TK) study data. The quality of these studies is directly related to the quality and
integrity of the underlying bioanalytical data. It is therefore important that guiding
principles for the validation of these analytical methods be established and dissem-
inated to the pharmaceutical and life sciences communities.
FDA Bioanalytical Method Validation Guidance for Industry (May 2001) [5]
provides assistance to sponsors of investigational new drug (INDs) applications,
new drug applications (NDAs), abbreviated new drug applications (ANDAs), and
supplements indevelopingbioanalyticalmethodvalidation informationused inhuman
clinical pharmacology,BA,andBEstudies requiringPKevaluation.Theguidancealso
applies tobioanalyticalmethodsused fornonhumanpharmacology/toxicologystudies
16 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
and nonclinical studies. For studies related to theveterinary drug approval process, the
guidance applies only to blood and urine in BA, BE, and PK studies.
The information in the guidance generally applies to bioanalytical procedures such
as gas chromatography (GC), high-performance liquid chromatography (HPLC),
combinedGC and LCmass 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. The
guidance also applies to other bioanalytical methods, such as immunological and
microbiological procedures, and to other biological matrices, such as tissue, skin,
feces, and other samples/specimens. The guidance provides general recommenda-
tions for bioanalytical method validation. The recommendations may be adjusted or
modified depending on the specific type of analytical method for intended use. The
guidance should be an excellent reference to other similar method validation in life
science industries.
Selective and sensitive analytical methods for the quantitative evaluation of drugs
and their metabolites (analytes of interest) are critical for the successful conduct of
nonclinical and/or biopharmaceutics and clinical pharmacology studies. Bioanaly-
tical method validation includes all of the procedures that demonstrate that a
particular method used for quantitativemeasurement of analytes in a given biological
matrix, such as blood, plasma, serum, or urine, is reliable and reproducible for the
intended purpose, scope, and use. The fundamental parameters for validation include:
(1) accuracy, (2) precision, (3) selectivity, (4) sensitivity, (5) reproducibility, (6)
stability, and (7) ruggedness and robustness (not clearly addressed within the
Guidance). 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 or meet 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:
Full Validation: It is important when developing and implementing a bioanaly-
tical method for the first time or is considered to be method official establish-
ment? 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.
Partial Validations: These are modifications of already validated bioanalytical
methods. Partial validation can range from as little as one intra-assay accuracy
KEY ELEMENTS OF BIOANALYTICAL METHODS VALIDATION 17
and precision determination to a nearly full 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; and
. selectivity demonstration of an analyte in the presence of specificmetabolites.
Typical recommendation of Method Partial and Full Validation is given in
Table 1.2.
Cross-Validation: It 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 cross-validation 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.
TABLE 1.2 Typical Recommendation of Method Partial and Full Validation
Items Full Validation Partial Validation
Different matrices þExtend dynamic range þAdd metabolite(s) þReduce matrix volume þCheck for comed þDifferent anticoagulant þLLOQ þChange analysts þChange instruments þChange extraction (mechanism) þChange detection system þChange chromatography þPlease note that some of the partial validation may be up to a full validation (e.g., anticoagulant with
different types and chromatographic conditions).
18 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
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 consideredwhen data
generated using different analytical techniques (e.g., LC–MS/MS versus LBAs)
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 nonclinical studies for regulatory submissions should adhere to FDA’s GLPs
(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 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 generated
and reported. The SOPs also should include record keeping, security and chain of
sample custody (accountability systems that ensure integrity of test articles), sample
preparation, and analytical tools such as methods, reagents, equipment, instrumen-
tation, and procedures for quality control and verification of results. Here are typical
recommendations of what need to be performed as for a full validation or a partial
validation.
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 sample analysis and
acceptance criteria for the analytical run and/or batch. These three processes are
described below.
1.4.1 Reference Standards
Analysis of drugs and their metabolites in a biological matrix is carried out using
samples spiked with calibration (reference) standards and using 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 noncom-
mercial establishment. The source and lot number, expiration date, certificates of
analyseswhen available, and/or internally or externally generated evidence of identity
and purity should be furnished for each reference standard.
KEY ELEMENTS OF BIOANALYTICAL METHODS VALIDATION 19
1.4.2 Method Development—Chemical/Chromatographic Assay
The method development and establishment phase defines the chemical assay. The
fundamental parameters for a bioanalyticalmethod 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 establish-
ment for a bioanalytical method include determination of (1) selectivity; (2) accuracy,
precision, recovery; (3) calibration curve; and (4) stability of analyte in spiked
samples.
Selectivity/specificity 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 biologicalmatrix (plasma, urine, or other
matrix) should be obtained from at least 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, concom-
itant medication and other exogenous xenobiotics. If the method is intended to
quantifymore than one analyte, each analyte should be tested to ensure that there is no
interference.
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 concen-
tration. Aminimum 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 bymore than 20%. The deviation of themean from
the true value serves as the measure of accuracy.
Precision of an analyticalmethod describes the closeness of individualmeasures 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 concentra-
tions 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, intrabatch 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.
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
20 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
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.
1.4.3 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 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 curvewill 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 nonzero samples covering
the expected range, including LLOQ.
(1) Lower Limit of Quantification: The lowest standard on the calibration curve
should be accepted as the limit of quantification if the following conditions
are met: The analyte response at the LLOQ should be at least five times the
response compared to blank response. 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 sim-
plest model that adequately describes the concentration–response relation-
ship 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: 20% deviation of the LLOQ from nominal
concentration; 15% deviation of standards other than LLOQ from nominal
concentration. At least four out of six nonzero 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.
1.4.4 Stability
Drug stability in a biological fluid is a function of the storage conditions, the chemical
properties of the drug, thematrix, and the container system. The stability of an analyte
in a particularmatrix and container system is relevant only to thatmatrix 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
KEY ELEMENTS OF BIOANALYTICAL METHODS VALIDATION 21
collection and handling, after long-term (frozen at the intended storage tempera-
ture) 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.
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 h and
thawed unassisted at room temperature. When completely thawed, the samples
should be refrozen for 12–24 h under the same conditions. The freeze–thaw
cycle should be repeated twomore times, and then analyzed on the third cycle. If
an analyte is unstable at the intended storage temperature, the stability sample
should be frozen at -70�C during the three freeze and thaw cycles.
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 h (based on the expected duration that samples will
be maintained at room temperature in the intended study) and analyzed.
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 or more 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.
Stock Solution Stability: The stability of stock solutions of drug and the internal
standard should be evaluated at room temperature for at least 6 h. If the stock
solutions are refrigerated or frozen 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.
Postpreparative Stability: The stability of processed samples, including the
resident time in the auto-sampler, 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
22 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
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.
1.4.5 Reproducibility
It is part of evaluation for the closeness of the agreement between the results of
successive measurements of the same analyte in identical material made by the same
method under different conditions, for example, different operators and different
laboratories and considerably separated in time. Incurred sample reanalysis (ISR) is
highly recommended for this investigation. Results should be expressed in terms of
the reproducibility standard deviation, the reproducibility coefficient of variation or
the confidence interval of the mean value.
1.4.6 Robustness or Ruggedness
It measures the capacity of a test to remain unaffected by small variations in the
procedures. It is measured by deliberately introducing small changes to the method
and examining the consequences, which is not mentioned within BMV Guidelines,
but it is important to evaluate and establish rugged/robust methods, especially for
pivotal clinical trial sample analyses.
1.5 BASIC 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 (test 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 towhich environmental, matrix,material, or procedural variables can affect
the estimation of analyte in thematrix 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 themethod should be presented in a report (method
validation report). 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
BASIC PRINCIPLES OF BIOANALYTICAL METHOD VALIDATION AND ESTABLISHMENT 23
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 evaluated and established.
The evaluation of accuracy, precision, reproducibility, response function, and
selectivity of the method for endogenous substances, metabolites, and known
degradation products should be performed and 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 multi-
plexing, 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/batch size. The placement ofQC samples
should be judiciously considered in the run/batch sequences. 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.
1.5.1 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 in support of various studies. Please note
that different dynamic ranges, possibly including dilution evaluation may be neces-
sary to cover various regulated programs/studies including nonclinical toxicology
studies. 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 confi-
dence interval. The LLOQ should serve as the lowest concentration on the standard
24 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
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 (exclud-
ing blank samples). The mean value should be within 15% of the theoretical value,
except at LLOQ, where it should not deviate bymore 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 QC samples from an equivalent biological matrix.
At a minimum, three concentrations representing the entire range of the standard
curve should be studied: one within 3� the LLOQ (low QC sample), one near the
center (middle QC), and one near the upper boundary of the standard curve (high
QC—being approximately 80% of ULOQ).
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 estab-
lished. The influence of freeze–thaw cycles (a minimum of three cycles at two
concentrations in triplicate) should be studied. The stability of the analyte in matrix at
ambient temperature should be evaluated over a time period equal to or even greater
than 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-
basedmethods, however, testing six independentmatrices for interferencemay not be
absolutely 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.
1.5.1.1 Method Development for Microbiological and Ligand-Binding AssaysMany of the bioanalytical validation parameters and principles discussed above are also
applicable tomicrobiological and ligand-binding assays.However, these assays possess
some unique characteristics that should be considered during method validation.
BASIC PRINCIPLES OF BIOANALYTICAL METHOD VALIDATION AND ESTABLISHMENT 25
1.5.1.2 Selectivity Issues Aswith chromatographicmethods,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:
Interference from Substances Physiochemically Similar to the Analyte: Cross-
reactivity of metabolites, concomitant medications, or endogenous compounds
should be evaluated individually and in combinationwith the analyte of interest.
When possible, the immunoassay should be compared with a validated refer-
ence method (such as LC–MS) using incurred samples and predetermined
criteria for agreement of accuracy of immunoassay and reference method. The
dilution linearity to the reference standard should be assessed using study
(incurred) samples. Selectivity may be improved for some analytes by incor-
poration of separation steps prior to immunoassay.
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 where
applicable.
1.5.1.3 Quantification Issues Microbiological and immunoassay standard curves
are inherently nonlinear and, in general, more concentration points may be recom-
mended 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 nonzero calibrator con-
centrations, run in duplicate, are recommended. The concentration–response rela-
tionship is most often fitted to a 4- or 5-parameter logisticmodel, 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 samematrix as the study samples or in an alternatematrix 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 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; and (3) the use of an internal standard that is not recognized by
26 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
the antibody but can be measured by another technique. Key reagents, such as
antibody, tracer, reference standard, andmatrix 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% (four out of six) 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 (1) labeled analyte (tracer); (2) binding should be reopti-
mized; (3) performance should be verified with standard curve and QCs; and (4)
antibody. Key cross-reactivities should be checked and evaluated. 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.
1.5.1.4 Application of Validated Method to Routine Sample 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 concentration. A typical
flowchart of method development and validation is shown in Figure 1.3.
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 intended 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�LLOQ), one inmidrange, and one close to the high end of the
BASIC PRINCIPLES OF BIOANALYTICAL METHOD VALIDATION AND ESTABLISHMENT 27
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/batch. 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 sample analysis: Amatrix-based standard curve should consist of a
minimum of six standard points, excluding blanks (either single or replicate),
covering the entire range.
ResponseFunction: 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/batch. 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.
Any required sample dilutions should use like/similar matrix (e.g., human to
human) obviating the need to incorporate actual within-study dilution matrix
QC samples.
FIGURE 1.3 Typical flowchart of method development and validation.
28 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
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 defined or justified and 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. Reintegration data
along with original result(s) should be reported.
1.5.1.5 Documentation The validity of an analytical method should be estab-
lished 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 submis-
sion 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.
1.5.2 Acceptance Criteria for Analytical Run
It is vitally important to establish analytical run (batch) acceptance criteria to ensure
the quality of data for regulated studies. An in-house SOP specifying batch pass/fail
criteria should be written in accordance with FDA BMV Guidelines. The following
acceptance criteria should be considered for accepting an 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 (pooled one)may also be used, provided selectivity has beenverified. 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
(includingULOQ) should fall within 15%, except for LLOQ,when it should bewithin
20% of the nominal value. Values falling outside these limits can be discarded,
provided they do not change the establishedmodel (e.g., regression/weighingmodel).
Acceptance criteria for accuracy and precision should be provided for both the
intraday and intrarun experiments.
BASIC PRINCIPLES OF BIOANALYTICAL METHOD VALIDATION AND ESTABLISHMENT 29
Quality Control Samples: Quality control samples replicated (at least once) at
a minimum of three concentrations (one within 3� of the LLOQ (low QC), one in
the midrange (middle 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 explained and
recorded.
1.5.2.1 Reasons for Repeat Analysis
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 reintegra-
tion, 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 should be documented for
the deviations.
Summary Information Summary information should include the following: sum-
mary tables of validation reports include (but not limited to) 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 should also include a list, by protocol, of assay
methods used and so on. The protocol number, protocol title, assay type, assay
method identification code, and bioanalytical 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 assaymethod, validation
reports, and bioanalytical reports, especially when the sponsor and a contract
laboratory assign different codes).
30 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
Documentation for Method Establishment Documentation for method develop-
ment and establishment should include the following:
(1) An operational description of the analytical method (e.g., lab or test method)
(2) Evidence of purity and identity of drug standards, metabolite standards, and
internal standards used in validation experiments
(3) A description of stability studies and supporting data
(4) 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
(5) Documentation of intra- and interassay precision and accuracy
(6) In NDA submissions, information about cross-validation study data, if
applicable
(7) Legible annotated chromatograms or mass spectrograms, if applicable
(8) Any deviations from SOPs, protocols, or GLPs (if applicable), and justifica-
tions for deviations.
Application to Sample Analysis Documentation of the application of validated
bioanalytical methods to routine drug/sample analysis should include: (1) evidence of
purity and identity of drug standards, metabolite standards, and internal standards
used during routine analyses; (2) summary tables should contain information on
sample processing and storage; (3) tables should also include sample identification,
collection dates, storage prior to shipment, information on shipment batch, and
storage prior to analysis; and (4) information should include dates, times, sample
condition, and any deviation from protocols.
For summary tables of analytical runs of clinical or preclinical samples,
information should include: (1) assay run identification, date and time of analysis,
assay method, analysts, start and stop times, duration, significant equipment and
material changes, and any potential issues or deviation from the establishedmethod;
(2) equations used for back calculation of results; (3) tables of calibration curve data
used in analyzing samples and calibration curve summary data; (4) summary
information on intra- and interassay values of QC samples and data on intra- and
interassay accuracy and precision from calibration curves and QC samples used for
accepting the analytical run; (5) QC graphs and trend analyses in addition to raw
data and summary statistics are encouraged; and (6) data tables from analytical runs
of clinical or preclinical samples. Tables should include (1) assay run identification,
sample identification, raw data and back-calculated results, integration codes, and/
or other reporting codes; and (2) complete serial chromatograms from 5% to 20%
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.
BASIC PRINCIPLES OF BIOANALYTICAL METHOD VALIDATION AND ESTABLISHMENT 31
1.5.2.2 Other Information Other information applicable to both method devel-
opment 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.
(1) Reference lists and legible copies of any references
(2) 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 should be defined for reported values when all unknown
samples are assayed in duplicate. Sample code designations, including clinical or
preclinical sample codes and bioassay sample code should be documented. Infor-
mation such as sample collection, processing, storage, and repeat analyses of samples;
reintegration of samples, and so on should also be included.
Bioanalysis and the production of pharmacokinetic, toxicokinetic, and metabolic
data play a fundamental role in pharmaceutical life sciences research and develop-
ment; therefore, the data must be produced to acceptable scientific standards and
regulatory (GLP) compliance. For this reason and the need to satisfy regulatory
authority requirements, all bioanalytical methods should be properly validated for
intended purposes and uses. It is hoped that these validation guidelines not only have
taken into account the statistical arguments described in the literature but also have
regard to the practicalities of performing bioanalytical method validations for the
pharmaceutical industry in this highly competitive era and that they aid further
standardization in this field.
It is very important to note that the validation of standard or collaboratively tested
methods should not be taken for granted, no matter how impeccable the method’s
pedigree—the laboratory should satisfy itself that the degree of validation of a
particular method is adequate for the required or intended purpose, and that the
laboratory is itself able tomatch any stated performance data. There are two important
requirements in this excerpt:
(1) The standard’s method validation data are adequate and sufficient to meet the
laboratory’s method requirements.
(2) The laboratory must be able to match the performance data as described in the
standard.
The main objectives of GLP regulations/principles and Bioanalytical Method
Validation Guidelines are to help scientists obtain results that are: (1) reliable, (2)
repeatable, (3) auditable, and (4) recognized by scientists worldwide. These may also
enhance the opportunity ofLimitingwaste of resources,Ensuring high quality of data,
Acquiring comparability of results, and Deriving to mutual recognition of scientific
findings worldwide, and ultimately Securing the health and well-being of our
societies, as being LEADS concept per author’s perspectives. The fundamental
32 INTRODUCTION, OBJECTIVES, AND KEY REQUIREMENTS FOR GLP REGULATIONS
requirements for GLP are to define conditions under which studies are planned,
performed, recorded, monitored, reported, and archived based on study’s designs.
REFERENCES
1. Code of Federal Regulation. Food Drug and Cosmetic Act, 21 CFR, Part 58—Good
laboratory practice for nonclinical laboratory studies, 1978.
2. International Conference on Harmonization (ICH)/World Health Organization (WHO).
Topic E 6 (R1) Guideline for Good Clinical Practice, 1996.
3. Code of Federal Regulation. FoodDrug andCosmetic Act 21 CFR Part 210 and 211 Current
Good Manufacturing Practices, 1995–2007.
4. Organization for EconomicCo-operation andDevelopment. Principles onGoodLaboratory
Practice (as revised in 1997) OECD, Paris, 1998 (Series on principles of GLP and
compliance monitoring, No. 1, ENV/MC/CHEM(98)17).
5. USFDA Guidance for Industry: Bioanalytical Method Validation, 2001.
6. Code of Federal Regulation. Department of Health and Human Services, Centers for
Disease Control and Prevention 21 CFR, Part 493—Clinical Laboratory Improvement Act,
2004.
7. World Health Organization (WHO). Good Clinical Laboratory Practices (GCLP), 2009.
REFERENCES 33