Sampling of pharmaceuticals

This presentation is compiled by “ Drug Regulations” a
non profit organization which provides free online
resource to the Pharmaceutical Professional.
Visit http://www.drugregulations.org for latest
information from the world of Pharmaceuticals.
3/26/2014 1

 This presentation is compiled from freely
available resources like the websites of
FDA, EMA, WHO.
 “Drug Regulations” is a non profit
organization which provides free online
 Visit http://www.drugregulations.org for
latest information from the world of
Pharmaceuticals.
3/26/2014 2
Drug Regulations : Online
Resource for Latest Information

◦ Sampling plays an Important role in implementing GMP’s.
◦ FDA
 211.84 (b) :Testing and approval or rejection of components, drug
product containers, and closures.
 Representative samples of each shipment of each lot shall be collected
for testing or examination. The number of containers to be
sampled, and the amount of material to be taken from each
container, shall be based upon appropriate criteria such as statistical
criteria for component variability, confidence levels, and degree of
precision desired, the past quality history of the supplier, and the
quantity needed for analysis and reserve where required by §211.170.
3/26/2014 3

 211.84 (c) Samples shall be collected in accordance with the following
procedures:
 (1) The containers of components selected shall be cleaned when
necessary in a manner to prevent introduction of contaminants into the
component.
 (2) The containers shall be opened, sampled, and resealed in a manner
designed to prevent contamination of their contents and contamination
of other components, drug product containers, or closures.
 (3) Sterile equipment and aseptic sampling techniques shall be used
when necessary.
3/26/2014 4

 211.84 (c) Samples shall be collected in accordance with the following
procedures:
 (4) If it is necessary to sample a component from the top, middle, and
bottom of its container, such sample subdivisions shall not be composited
for testing.
 (5) Sample containers shall be identified so that the following information
can be determined: name of the material sampled, the lot number, the
container from which the sample was taken, the date on which the sample
was taken, and the name of the person who collected the sample.
 (6) Containers from which samples have been taken shall be marked to show
that samples have been removed from them.
3/26/2014 5

 211.110 : Sampling and testing of in-process
materials and drug products.
 (a) To assure batch uniformity and integrity of drug
products, written procedures shall be established and
followed that describe the in-process controls, and
tests, or examinations to be conducted on appropriate
samples of in-process materials of each batch.
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 211.122 (a) : Materials examination and usage criteria.
 There shall be written procedures describing in sufficient
detail the
receipt, identification, storage, handling, sampling, exami
nation, and/or testing of labeling and packaging
materials; such written procedures shall be followed.
Labeling and packaging materials shall be representatively
sampled, and examined or tested upon receipt and before
use in packaging or labeling of a drug product.
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 211.165 ( c ) : Testing and release for distribution.
 Any sampling and testing plans shall be described in
written procedures that shall include the method of
sampling and the number of units per batch to be tested;
such written procedure shall be followed.
3/26/2014 8

 EudraLex - Volume 4 Good manufacturing practice (GMP)
Guidelines.
 6.11 The sample taking should be done in accordance with
approved written procedures that describe:
 the method of sampling;
 the equipment to be used;
 the amount of the sample to be taken;
 instructions for any required sub-division of the sample;
 the type and condition of the sample container to be used;
 the identification of containers sampled;
3/26/2014 9

 EudraLex - Volume 4 Good manufacturing practice (GMP)
Guidelines.
 6.11 The sample taking should be done in accordance with
approved written procedures that describe:
 any special precautions to be observed, especially with regard
to the sampling of sterile or noxious materials;
 the storage conditions;
 instructions for the cleaning and storage of sampling
equipment
3/26/2014 10
Drug Regulations : Online Resource for Latest Information

 EudraLex: Volume 4 Good manufacturing practice (GMP) Guidelines. Annex 8
 Personnel who take samples should receive initial and on-going regular
training in the disciplines relevant to correct sampling. This training should
include:
 sampling plans,
 written sampling procedures,
 the techniques and equipment for sampling,
 the risks of cross-contamination,
 the precautions to be taken with regard to unstable and/or sterile substances,
 the importance of considering the visual appearance of materials, containers &
 labels,
 the importance of recording any unexpected or unusual circumstances.
3/26/2014 11

 EudraLex: Volume 4 Good manufacturing practice (GMP)
Guidelines. Annex 8 : Starting materials
 The identity of a complete batch of starting materials can
normally only be ensured if
 Individual samples are taken from all the containers
 An identity test performed on each sample
 It is permissible to sample only a proportion of the containers
where a validated procedure has been established to ensure
that no single container of starting material has been
incorrectly labelled.
3/26/2014 12

 This validation should take account of at least the
following aspects:
 The nature and status of the manufacturer and of the supplier
 Their understanding of the GMP requirements of the
Pharmaceutical Industry;
 The Quality Assurance system of the manufacturer of the
starting material;
3/26/2014 13

 This validation should take account of at least the
following aspects:
 The manufacturing conditions under which the starting
material is produced and controlled;
 The nature of the starting material and the medicinal products
in which it will be used.
3/26/2014 14

 EudraLex: Volume 4 Good manufacturing practice (GMP) Guidelines.
Annex 8 : Starting materials
 Under such a system, it is possible that a validated procedure
exempting identity testing of each incoming container of starting
material could be accepted for:
 starting materials coming from a single product manufacturer or
plant;
 starting materials coming directly from a manufacturer or in the
manufacturer’s sealed container where there is a history of reliability
and regular audits of the manufacturer’s Quality Assurance system
are conducted by the purchaser (the manufacturer of the medicinal
product) or by an officially accredited body.)
3/26/2014 15

 It is improbable that a procedure could be satisfactorily
validated for:
 Starting materials supplied by intermediaries such as
brokers where the source of manufacture is unknown or
not audited;
 Starting materials for use in parenteral products
3/26/2014 16

 EudraLex: Volume 4 Good manufacturing practice (GMP) Guidelines. Annex 8
: Starting materials
 The quality of a batch of starting materials may be assessed by taking and
testing a representative sample.
 The samples taken for identity testing could be used for this purpose.
 The number of samples taken for the preparation of a representative sample
should be determined statistically and specified in a sampling plan.
 The number of individual samples which may be blended to form a composite
sample should also be defined, taking into account the nature of the
material, knowledge of the supplier and the homogeneity of the composite
sample.
3/26/2014 17

Guidelines. Annex 8 : Packaging Materials
 The sampling plan for packaging materials should take account
of at least the following:
 The quantity received, the quality required, the nature of the
material (e.g. primary packaging materials and/or printed
packaging materials), the production methods, and
 what is known of the Quality Assurance system of the packaging
materials manufacturer based on audits.
 The number of samples taken should be determined statistically
and specified in a sampling plan.
3/26/2014 18

 Imagine, for example, an experiment to test the
effects of a new education technique on
schoolchildren.
 It would be impossible to select the entire school age
population of a country, divide them into groups and
perform research.
 A research group sampling the diversity of flowers in
the African savannah could not count every single
flower, because it would take many years.

 A population is generally a large collection of
individuals or objects that is the main focus of a
scientific query or analysis.
 However, due to the large sizes of populations, we
often cannot test every individual or object in the
population because it is too expensive and time-
consuming.
 This is the reason why we rely on sampling
techniques.

 This is where statistical sampling comes in
 The idea of trying to take a representative
section of the population,
 Perform the experiment and
 Extrapolate it back to the population as a whole.

 A sample group can be defined as a subset of a population.
 The population, or target population, is the total population about
which information is required.
 The "study population" is the population from which sample is to be
drawn.
 Commonly, the population is found to be very large and in any
study, studying all population is often impractical or impossible.
 Therefore, sample unit gives us a manageable and representative
subset of population.

Population:
 A set which includes all measurements of interest
◦ (The collection of all responses, measurements, or counts that are of
interest)
Sample:
 A subset of the population
Populationsample

 Choosing of sample size depends on non-
statistical considerations and statistical
considerations.
 The non-statistical considerations may include
availability of
resources, manpower, budget, ethics and
sampling frame.

 The statistical considerations will include
following three criteria
◦ The level of Precision
◦ The confidence level
◦ Degree of Variability

 Also called sampling error,
 Range in which the true value of the population
is estimated to be.
 Expressed in percentage points.

 The confidence interval is the statistical measure of the number of
times out of 100 that results can be expected to be within a specified
range.
 For example, a confidence interval of 90% means that results of an
action will probably meet expectations 90% of the time.
 The basic idea described in Central Limit Theorem is that when a
population is repeatedly sampled, the average value of an attribute
obtained is equal to the true population value.
 In other words, if a confidence interval is 95%, it means 95 out of 100
samples will have the true population value within range of precision.

 Depending upon the target population and attributes under
consideration, the degree of variability varies considerably.
 The more heterogeneous a population is, the larger the
sample size is required to get an optimum level of precision.
 A proportion of 55% indicates a high level of variability than
either 10% or 80%.
 This is because 10% and 80% means that a large majority does
not or does, respectively, have the attribute under
consideration.

 Let's suppose we have five chocolates bars and total 8 friends to
distribute these 5 chocolates to.
 How we are going to do this so the whole distribution process is with a
minimum of bias?
 We may write down names of each friend on a separate small piece of
paper;
 Fold all small pieces of papers so no one know what name is on any
paper.
 Then ask someone to pick 5 names and give chocolates to first 5 names.
 This will remove the bias without hurting any friend's feelings.
 The way we did this is randomization.

 In randomized controlled trials, the research participants
are assigned by chance, rather than by choice, to either the
experimental group or the control group.
 Randomization reduces bias as much as possible.
 Randomization is designed to "control" (reduce or
eliminate if possible) bias by all means.
 The fundamental goal of randomization is to acertain that
each treatment is equally likely to be assigned to any given
experimental unit.

 Different options to perform randomization.
 Can be achieved by use of random number
tables given in most statistical textbooks or
 Computers can also be used to generate
random numbers.

 There are two types of sampling risks
 Risk of incorrect acceptance of the research
hypothesis.
 Risk for incorrect rejection of hypothesis
 Risks pertain to the possibility that when a test is
conducted to a sample, the results and conclusions
may be different from the results and conclusions
when the test is conducted to the entire population.

 Risk of incorrect acceptance
◦ Risk that the sample can yield a conclusion that
supports a theory about the population when it is
actually not existent in the population.
 Risk of incorrect rejection
◦ Risk that the sample can yield a conclusion that
rejects a theory about the population when in
fact, the theory holds true in the population.

 Sampling distribution is the probability distribution of a
sample of a population instead of the entire population.
 From a given population take all possible samples of size n
 Compute a statistic (say mean) of all these samples.
 Probability distribution of this statistic, will give sampling
distribution
 Sampling distributions are important.
 Feasibility of an experiment dictates the sample size

 Sample size determines the properties of
sampling distribution.
 Generally the population is assumed to be
normally distributed.
 For a large sample size, the sampling
distribution will also be nearly be normal.

 In case of normal distribution of sample
sampling distribution can be totally determined
by two values
◦ the mean and
◦ the standard deviation.
 These two parameters are important to compute
for the sampling distribution if we are given the
normal distribution of the entire population.

 Non-Probability Sampling
◦ In non-probability sampling, the choice of sample group is
left to the researcher and thus element of bias always
shows up in such studies.
 Probability Sampling
◦ In probability sampling, the selection of the sample is made
using deliberate, unbiased process, so that each sample
unit in a group has an equal chance of being selected.
◦ This forms the basis of random sampling.

Basic Business Statistics, 8e
© 2002 Prentice-Hall, Inc.
Chap 1-38
Quota
Samples
Non-Probability
Samples
Judgement Chunk
Probability Samples
Simple
Random
Systematic
Stratified
Cluster

 Probability sampling is most commonly used
 Randomization is performed to choose samples
 Provides each sample an equal chance of being
selected
 Minimizes or eliminates bias altogether

 Each member or object of the
population has an equal chance of
being selected.
 The entire process of sampling is done
in a single step
 Each subject/object is selected
independently of the other members of
the population.
 Lottery or use of random number
generator

Probability Sampling :Random Sampling

Table of Random Numbars
6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 1 4 0
5 8 2 0 3 2 1 5 4 7 8 5 9 6 2 0 2 4
3 6 2 3 3 3 2 5 4 7 8 9 1 2 0 3 2 5
9 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6 8 6

 Advantages
◦ Ease of assembling the sample.
◦ Fair way of selecting a sample from a given population
◦ Every member/object has equal opportunities of being selected.
◦ Representativeness of the population.
◦ Theoretically, the only thing that can compromise its
representativeness is luck.
◦ If the sample is not representative of the population, the random
variation is called sampling error.
into a uniﬁed procedure for lot acceptance through
the use of its switching rules.

 Disadvantages
◦ Need of a complete list of all the members/objects of
the population.
◦ List of the population must be complete and up-to-date.
◦ List is usually not available for large populations.
◦ In cases as such, it is wiser to use other sampling
techniques.

 A probability sampling technique
 Entire population is divided into different
subgroups or strata
 Then randomly select the final
subjects/objects proportionally from the
different strata.
 Common strata
◦ Age, Gender, Socioeconomic
status, Religion, nationality and educational
attainment.
◦ Initial , Middle, End
◦ Top , Middle , Bottom
◦ Coolest spot , Hottest spot, Dead Spot
◦ Near Exist , Near entry

 Highlights a specific subgroup within the population
 Observes existing relationships between two or more subgroups
 Representatively samples even the smallest and most inaccessible
subgroups in the population.
 Strata must be non-overlapping
 Overlapping strata will grant some individuals/objects higher
chances of being selected.
 Use simple probability sampling within the different strata.
 Higher statistical precision compared to simple random sampling.
◦ Variability within the subgroups is lower compared to the variations when dealing
with the entire population.
 Requires a small sample size which can save a lot of time, money and
effort

 Sample size of each stratum is proportionate to the
population size
 Each stratum has the same sampling fraction.
 Same sampling fraction for each stratum regardless of
the differences in population size of the strata
Stratum A B C
Population
Size
100 200 300
Sample
Fraction
1/2 1/2 1/2
Final
Sample Size
50 100 150

 The only difference between proportionate and
disproportionate stratified random sampling is their
sampling fractions.
 With disproportionate sampling, the different strata have
different sampling fractions.
 The precision of this design is highly dependent on the
sampling fraction allocation of the researcher.
 A stratum could either be overrepresented or
underrepresented which may result in skewed results.

 Involves randomly picking the the first item or subject
from the population.
 Then, select each nth subject/object from the list.
◦ 100th and then every 500th bottle from a packing line
◦ Initial & sample after every 30 minutes from a tableting press
◦ Initial & every 500th vial from a tunnel sterilizer
◦ Sample from every 5th container of API
 Very easy and can be done manually
 The results are representative of the population unless
certain characteristics of the population are repeated for
every nth individual, which is highly unlikely.

 Modified systematic random sampling
technique
 Involves first identify the needed sample size
 Then, dividing the total number of the
population with the sample size to obtain the
sampling fraction.
 The sampling fraction is then used as the
constant difference between subjects/objects

 Advantages
◦ Simplicity.
◦ Adds a degree of system or process into the random
selection of subjects/objects
◦ Assurance that the population will be evenly sampled.
◦ There exists a chance in simple random sampling that
allows a clustered selection of subjects.
◦ This is systematically eliminated in systematic
sampling.

 Disadvantages
◦ The process of selection can interact with a hidden
periodic trait within the population.
◦ If the sampling technique coincides with the
periodicity of the trait, the sampling technique will no
longer be random and representativeness of the
sample is compromised.

 All subjects/objects are not selected
from the entire population right off
 There are several steps in gathering
sample population.
 First select groups or clusters,
 Then from each cluster, select
individual subjects/objects
◦ Either simple random
◦ Systematic random sampling.
 The entire cluster and not just a
subset from it can be included

 The most common cluster used in research is a geographical
cluster.
 For example, consider a survey of academic performance of high
school students in Spain.
 The entire population of Spain can be divided into different clusters.
(cities).
 Then a number of clusters can be sampled by simple or systematic
random sampling.
 Then, from the selected clusters (randomly selected cities)
◦ All the high school students can be taken as subjects or
◦ A number of subjects from each cluster through simple or systematic random
sampling.

Section 4
Section 5
Section 3
Section 2Section 1

 Gives all the clusters equal chances of being
selected.
 One-Stage Cluster Sample
◦ One-stage cluster sample occurs when all the high school
students from all the randomly selected clusters as used
as sample.
 Two-Stage Cluster Sample
◦ Two-stage cluster sample is obtained when only a number
of students from each cluster are sampled by using simple
or systematic random sampling.

 Difference Between Cluster Sampling and Stratified Sampling
 The main difference lies with the inclusion of the cluster or
strata.
 In stratified random sampling, all the strata of the
population is sampled
 In cluster sampling, only randomly selected number of
clusters from the collection of clusters of the entire
population are sampled.
 Therefore, only a number of clusters are sampled, all the
other clusters are left unrepresented.

 This technique is the least representative of the
population.
 Overrepresented or underrepresented cluster
which can skew the results
 Possibility of high sampling error.
◦ Limited clusters included in the sample leaving off a
significant proportion of the population unsampled.

 A sampling technique where the samples are
gathered in a process that does not give all the
individuals in the population equal chances of
being selected.

 Convenience Sampling
◦ Most common of all sampling techniques.
◦ Samples are selected because they are accessible
 Consecutive Sampling
◦ Very similar to convenience sampling except that it seeks to
include ALL accessible subjects as part of the sample.
 Quota Sampling
◦ equal or proportionate representation of subjects/objects
depending on which trait is considered as basis of the quota.

 Judgmental Sampling
◦ Also known as purposive sampling.
◦ Subjects/objects are chosen to be part of the sample with a
specific purpose in mind.
 Snowball Sampling
◦ Usually done when there is a very small population size.
◦ Initial subject identifies another potential subject who also meets
the criteria of the research.

 When to Use Non-Probability Sampling
◦ To demonstrate that a particular trait exists in the population
◦ In a qualitative, pilot or exploratory study
◦ When randomization is impossible like when the population is
almost limitless
◦ When the results will not be used to create generalizations
pertaining to the entire population
◦ When the budget, time and workforce are limited
◦ In an initial study which will be carried out again using a
randomized, probability sampling.

 Military standard 105 (MIL-STD-105)
 Premier attribute sampling standard
 Led to a number of derivative standards
 Army discontinued support on February 27, 1995,
 Other standards
◦ American National Standards Institute (ANSI),
◦ International Organization for Standardization (ISO),
◦ International Electro technical Commission,
 Developed their own derivatives of 105 as civilian standards.

 Not a sampling plan but a sampling system
 Combines several individual sampling plans
 Motivates the producer to sustain quality at levels less that or equal to
the Acceptable Quality Level
 Procedure for switching between plans is essential to system
 Designed to take corrective action when quality fails below prescribed
levels
 Rewards in terms of reduced sample size for Quality Improvement.
 The standard ties together sets of three attribute sampling plans, each
at a different level of severity, into a uniﬁed procedure for lot
acceptance through the use of its switching rules.

 These action rules determine the level of severity to be
employed depending on the level of quality previously
submitted
 Inspection of succession of lots is intended to move among
the specified set of tightened , normal & reduced sampling
samples as quality level degenerate or improve over time.
 Switching between tightened and normal plans is made
mandatory by the standard, whereas use of reduced plans is
optional.

 The MIL-STD-105E system, as such, does not allow for
application of individual plans without use of the
switching rules, because such an approach can lead to
serious loss of protection from that achieved when the
system is properly applied
 Quality levels are speciﬁed in terms of AQL for the
producer, whereas consumer protection is afforded by
the switching rules, which lead to tighter plans when
quality is poor

 The D and E versions of MIL-STD-105 exhibit minor
editorial changes, though the sampling tables are
essentially the same;
 However the rule for discontinuing inspection was
modified from 10 lots to 5 rejections while on
tightened inspection.
 Unfortunately the standard is misused by selection &
use of normal plans only disregarding the tightened &
reduced plans & the switching rules.

 The operation of MIL-STD-105E is straightforward
 Lot sizes are linked to sample size by a system of code
letters.
 Matched sets of single, double, and multiple plans
provide a complete choice among these types of plans
in application.
 The average sample size of double and multiple plans
can be arrived at from the average sample number
curves provided.

 MIL-STD-105E also contains tables presenting the average
outgoing quality limit (AOQL) resulting from the use of its
normal plans together with 100% inspection of rejected lots.
 Complete sets of operating characteristic curves and
probability points of the normal and tightened plans are
contained in the standard
 The standard is written in terms of inspection for defectives
(expressed in percentage defective) and also for defects
(expressed in defects per 100 units)

 The approach and operation of the scheme is the same
for both, so they will be used interchangeably here for
economy of presentation.
 Their measures of performance, however, are based on
different probability distributions (binomial for
percentage defective and Poisson for defects per 100
units) so they must be addressed separately where
operating characteristics and other measures are
concerned.

 An ASTM standard that maintains the MIL-STD- 105E
content as closely as possible was created by ASTM
committee E11 in 2005.
 It is intended to provide a source for use in
conjunction with ASTM and other standards which
directly reference MILSTD-105E. It is best used in a
testing or laboratory environment and with
methodology in support of other standards

 The ANSI=ASQ Z1.4 standard (2008) is an American national
standard with direct lineage to MIL-STD-105E and is
recommended by the U.S. Department of Defense as the
replacement to MIL-STD-105E. It is best used in-house and
in domestic transactions. It differs from MIL-STD-105E and
E2234-09 in its deﬁnition of what constitutes a rejectable
item. The deﬁnitions and terminology employed in this
standard are in accord with ANSI=ASQ A3534-2:2006
(Statistics-Vocabulary and Symbols-Part 2, Applied
Statistics)

 The following two definitions are particularly important in
applying the standard
 Defect: A departure of a quality characteristic from its
intended level or state that occurs with a severity sufficient
to cause an associated product or service not to satisfy
intended normal, or foreseeable usage requirements.
 Nonconformity: A departure of a quality characteristic from
its intended level or state that occurs with severity sufficient
to cause an associated product or service not to meet a
specification requirement.

 These acceptance sampling plans for attributes are given in
terms of the percentage or proportion of product in a lot or
batch that departs from some requirement. The general
terminology used within the document is given in terms of
percentage of nonconforming units or number of
nonconformities, because these terms are likely to constitute
the most widely used criteria for acceptance sampling. Its
format is very similar to the MIL-STD-105E and E2234
standards.

 This presentation was compiled from freely
available resources like the websites of
FDA, EMA, WHO.
 “Drug Regulations” is a non profit
organization which provides free online
 Visit http://www.drugregulations.org for
latest information from the world of
Pharmaceuticals.
3/26/2014 75

Sampling of pharmaceuticals

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