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TEACHING STUDENTS BASIC LAB SKILLS FOR A REGULATED ENVIRONMENT

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Title: TEACHING STUDENTS BASIC LAB SKILLS FOR A REGULATED ENVIRONMENT


1
TEACHING STUDENTS BASIC LAB SKILLS FOR A
REGULATED ENVIRONMENT
  • BIOMAN 2007
  • Lisa Seidman
  • Madison Area Technical College
  • Madison, WI

2
WHY THE BASICS?
  • Needs of students
  • Needs of employers

3
MYTH 1
  • Basics means simple, easy, obvious
  • If this were true, far fewer problems in
    companies and in research labs

4
BASIC MEANS
  • Vital
  • Essential
  • Fundamental
  • Primary
  • Staple
  • Must

5
MYTH 2
  • Most of this does not apply in research labs

6
MYTH 3
  • We all learn the basics in high school, or
    someone elses class, or by osmosis

7
MYTH 4
  • Basics are boring

8
BASICS
  • What are basics?
  • Different answers, but some common themes

9
HOW TO TEACH BASICS?
  • Consciously
  • Systematically
  • model 1 way teach children music
  • model 2 way grad students are taught
  • Underlying principles

10
THIS WORKSHOP
  • Teaching basics consciously
  • Systematically
  • Underlying principles

11
TOPICS FOR THIS WORKSHOP
  • Quality
  • Basic lab task making a solution
  • Metrology (unifying principles)

12
STORY OF FRANCES KELSEY
  • Case study

13
KELSEY
  • Purpose
  • introduce GMP
  • introduce process of developing drug
  • most important idea of quality
  • Bureaucrat who understood quality

14
  • QUALITY THE BIG (BUT BRIEF) PICTURE

15
WHAT IS BIOTECHNOLOGY?
  • The biotechnology industry transforms scientific
    knowledge into useful products

16
OVERVIEW
  • Talk about product quality systems
  • In broad way
  • Apply ideas to the various work places we talked
    about

17
QUALITY SYSTEMS
  • Broad systems of regulations, standards, or
    policies that ensure the quality of the final
    product
  • GMP/GLP/GCP are examples of quality systems

18
WHAT IS PRODUCT QUALITY?
  • What is a good product in biotechnology?
  • That depends
  • Consider biotech
  • Research labs
  • Testing labs
  • Production facilities

19
QUALITY PRODUCT RESEARCH LAB
  • Research lab, knowledge is product
  • Knowledge of nature (basic research)
  • Understanding of technology (applied research,
    RD)

20
QUALITY SYSTEMS IN RESEARCH LABS
  • Quality system in research
  • Ensure meaningful data
  • has been around a long time
  • It is called

21
  • DOING GOOD SCIENCE
  • Less formalized than other quality systems
  • No one book spells it out
  • No laws to obey
  • But it exists

22
INFORMAL SYSTEM
  • Consequences of poor quality product not
    life-threatening so
  • Government seldom involved in monitoring research
    quality
  • Oversight not generally by outside inspectors or
    auditors

23
BUT THERE IS OVERSIGHT
  • Oversight is by peers
  • Grant review
  • Publications
  • Reputation

24
  • Compare and contrast situation in research labs
    and other work places

25
PRODUCT QUALITY TESTING LAB
  • Testing lab
  • Information about samples
  • Good product result that can be relied on when
    making decisions

26
CONSEQUENCES
  • A poor quality product can be life-threatening or
    have serious effects

27
QUALITY SYSTEMS IN TESTING LABS
  • Include most of what we call doing good science
    plus
  • Specific formal requirements
  • Personnel
  • Equipment
  • Training
  • Facilities
  • Documentation

28
  • You can find a book that spells it out for
  • Clinical labs
  • Forensic labs
  • Environmental labs

29
ENFORCEMENT TESTING LABS
  • Since consequences of poor product can be
    life-threatening
  • Is outside oversight
  • FBI
  • EPA
  • Etc.

30
PRODUCT QUALITY PRODUCTION FACILITY
  • Make tangible items
  • Quality product fulfills intended purpose
  • Ex. reagent grade salt vs road salt vs table
    salt

31
QUALITY SYSTEMS IN PRODUCTION FACILITIES
  • Depends on nature of product
  • Poor product may or may not have life-threatening
    consequences

32
SO, FOR EXAMPLE
  • Products for research use, not generally
    regulated
  • Agricultural products are regulated in one way
  • Pharmaceutical products are regulated in another

33
VOLUNTARY STANDARDS
  • Companies that are not regulated may choose to
    comply with a product quality system for business
    reasons

34
ISO 9000
  • ISO 9000
  • Formal product quality system
  • Extensive
  • Exists in a series of books
  • Companies comply voluntarily to improve the
    quality of products
  • and to make more money

35
OVERSIGHT ISO 9000
  • Oversight by outside auditors, paid by company

36
BIOTECH AND MEDICAL PRODUCTS
  • Many biotech companies that make money make
    medical/pharmaceutical products
  • Consequences of poor product can be
    life-threatening

37
SO
  • These products are highly regulated by the
    government
  • But, it wasnt always this way

38
  • history
  • CFR, handout

39
HOW IS QUALITY BUILT INTO A PRODUCT?
  • No single answer
  • Requires
  • Skilled personnel
  • Well-designed and maintained facility
  • Well-constructed processes
  • Proper raw materials
  • Documentation
  • Change control
  • Validation

40
ENFORCEMENT
  • Compliance is enforced by government
  • FDA

41
QUALITY IS BASIC
  • Details may not be essential right now
  • Idea of quality is essential

42
LETS GO TO THE LABVERY BASIC LAB TASKS
  • 1. Write procedure to make 100 mL of a buffer
    solution that is
  • 100 mM Tris, pH 7.5
  • 2 NaCl
  • 10 µg/mL of proteinase K
  • QC your solution by checking its conductivity
  • Check the pH of a Tris buffer solution

43
PROCEDURE
  • For 100 mL of 100 mM Tris solution (FW 121.1)
    weigh out 1.211 g of Tris base. Dissolve in
    about 60 mL of water and adjust pH to 7.5.
  • Add 2g of NaCl
  • 10 µg/mL of proteinase K X 100 mL 1000 µg 1
    mg. Weigh and add to Tris.
  • Dissolve, BTV, check pH

44
VARIABILITY IN APPROACHES?
  • Value of SOPs in ensuring consistency
  • Value of communicating among various lab workers
  • Documentation

45
WHAT DO STUDENTS NEED TO KNOW?
  • Conceptual
  • Why they are making solution, context
  • How to interpret recipe
  • Basic calculations
  • Instrumentation
  • How to maintain, use, calibrate balance
  • How to maintain, use, calibrate pH meter
  • How to measure volume
  • How to maintain, use, calibrate conductivity
    meter
  • Quality control
  • How to ensure that solution is what it should be
  • How to document work

46
TEACHING
  • Concrete skills
  • calculations
  • using equipment
  • etc.
  • These are activities in the lab manual to
    systematically build these skills

47
VARIABILITY
  • Mike Fino

48
UNDERLYING PRINCIPLES
  • Quality ideas (e.g. reducing variability and
    documentation, following directionsSOPs)
  • Math calculations/ideas that repeat over and over
    again
  • Safety practices
  • Metrology principles

49
INTRODUCTION TO METROLOGYLisa SeidmanBioman 2007
50
DEFINITIONS
  • Metrology is the study of measurements
  • Measurements are quantitative observations
    numerical descriptions

51
OVERVIEW
  • Begin with general principles
  • Next weight, volume, pH, light transmittance
    (spectrophotometry)

52
WE WANT TO MAKE GOOD MEASUREMENTS
  • Making measurements is woven throughout daily
    life in a lab.
  • Often take measurements for granted, but
    measurements must be good.
  • What is a good measurement?

53
EXAMPLE
  • A man weighs himself in the morning on his
    bathroom scale, 172 pounds.
  • Later, he weighs himself at the gym,173 pounds.

54
QUESTIONS
  • How much does he really weigh?
  • Do you trust one or other scale? Which one?
    Could both be wrong? Do you think he actually
    gained a pound?

55
  • Are these good measurements?

56
NOT SURE
  • We are not exactly certain of the mans true
    weight because
  • Maybe his weight really did change always
    sample issues
  • Maybe one or both scales are wrong always
    instrument issues

57
DO WE REALLY CARE?
  • Do you care if he really gained a pound?
  • How many think give or take a pound is OK?

58
ANOTHER EXAMPLE
  • Suppose a premature baby is weighed. The weight
    is recorded as 5 pounds 3 ounces and the baby is
    sent home.
  • Do we care if the scale is off by a pound?

59
GOOD MEASUREMENTS
  • A good measurement is one that can be trusted
    when making decisions.
  • We just made judgments about scales.
  • We make this type of judgment routinely.

60
IN THE LAB
  • Anyone who works in a lab makes judgments about
    whether measurements are good enough
  • but often the judgments are made subconsciously
  • differently by different people
  • Want to make decisions
  • Conscious
  • Consistent

61
QUALITY SYSTEMS
  • All laboratory quality systems are concerned with
    measurements
  • All want good measurements

62
NEED
  • Awareness of issues so can make good
    measurements.
  • Language to discuss measurements.
  • Tools to evaluate measurements.

63
METROLOGY VOCABULARY
  • Very precise science with imprecise vocabulary
  • (word precise has several precise meanings that
    are, without uncertainty, different)
  • Words have multiple meanings, but specific
    meanings

64
VOCABULARY
  • Units of measurement
  • Standards
  • Calibration
  • Traceability
  • Tolerance
  • Accuracy
  • Precision
  • Errors
  • Uncertainty

Instrumentation
Measurement itself
65
UNITS OF MEASUREMENT
  • Units define measurements
  • Example, gram is the unit for mass
  • What is the mass of a gram? How do we know?

66
DEFINITIONS MADE BY AGREEMENT
  • Definitions of units are made by international
    agreements, SI system
  • Example, kilogram prototype in France
  • K10 and K20 at NIST

67
EXTERNAL AUTHORITY
  • Measurements are always made in accordance with
    external authority
  • Early authority was Pharaohs arm length

68
  • A standard is an external authority
  • Also, standard is a physical embodiment of a unit

69
STANDARDS ARE
  • Physical objects, the properties of which are
    known with sufficient accuracy to be used to
    evaluate other items.

70
STANDARDS ARE AFFECTED BY THE ENVIRONMENT
  • Units are unaffected by the environment, but
    standards are
  • Example, Pharaohs arm length might change
  • Example, a ruler is a physical embodiment of
    centimeters
  • Can change with temperature
  • But cm doesnt change

71
STANDARDS ALSO ARE
  • In chemical and biological assays, substances or
    solutions used to establish the response of an
    instrument or assay method to an analyte
  • See these in spectrophotometry labs

72
STANDARDS ALSO ARE
  • Documents established by consensus and approved
    by a recognized body that establish rules to make
    a process consistent
  • Example ISO 9000
  • ASTM standard method calibrating micropipettor

73
CALIBRATION IS
  • Bringing a measuring system into accordance with
    external authority, using standards
  • For example, calibrating a balance
  • Use standards that have known masses
  • Relate response of balance to units of kg
  • Do this in lab

74
PERFORMANCE VERIFICATION IS
  • Check of the performance of an instrument or
    method without adjusting it.
  • Do this in lab.

75
TOLERANCE IS
  • Amount of error that is allowed in the
    calibration of a particular item. National and
    international standards specify tolerances.

76
EXAMPLE
  • Standards for balance calibration can have slight
    variation from true value
  • Highest quality 100 g standards have a tolerance
    of 2.5 mg
  • 99.99975-100.00025 g
  • Leads to uncertainty in all weight measurements

77
TRACEABILITY IS
  • The chain of calibrations, genealogy, that
    establishes the value of a standard or
    measurement
  • In the U.S. traceability for most physical and
    some chemical standards goes back to NIST

78
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79
TRACEABILITY
  • Note in this catalog example, traceable to NIST

80
VOCABULARY
  • Standards
  • Calibration
  • Traceability
  • Tolerance
  • Play with these ideas in labs

81
MEASUREMENT
  • What are the characteristics of good measurement?
  • Accuracy
  • Precision

82
ACCURACY AND PRECISION ARE
  • Accuracy is how close an individual value is to
    the true or accepted value
  • Precision is the consistency of a series of
    measurements

83
EXPRESS ACCURACY
  • error True value measured value X 100
  • True value
  • Will calculate this in volume lab

84
EXPRESS PRECISION
  • Standard deviation (p. 187-190)
  • Expression of variability
  • Take the mean (average)
  • Calculate how much each measurement deviates from
    mean
  • Take an average of the deviation, so it is the
    average deviation from the mean
  • Try this in the volume lab

85
ERROR IS
  • Error is responsible for the difference between a
    measured value and the true value

86
CATEGORIES OF ERRORS
  • Three types of error
  • Gross
  • Random
  • Systematic

87
GROSS ERROR
  • Blunders

88
RANDOM ERROR
  • In U.S., weigh particular 10 g standard every
    day. They see
  • 9.999590 g, 9.999601 g, 9.999592 g .
  • What do you think about this?

89
RANDOM ERROR
  • Variability
  • No one knows why
  • They correct for humidity, barometric pressure,
    temperature
  • Error that cannot be eliminated. Called random
    error

90
RANDOM ERROR
  • Do you think that repeating the measurement over
    and over would allow us to be more certain of the
    true weight of this standard?

91
RANDOM ERROR
  • Yes, because in the presence of only random
    error, the mean is more likely to be correct if
    repeat the measurement many times
  • Standard is probably really a bit light
  • Average of all the values is a good estimate of
    its true weight

92
RANDOM ERROR AND ACCURACY
  • In presence of only random error, average value
    will tend to be correct
  • With only one or a few measurements, may or may
    not be accurate

93
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94
THERE IS ALWAYS RANDOM ERROR
  • If cant see it, system isnt sensitive enough
  • Less sensitive balance 10.00 g,
  • 10.00 g, 10.00 g
  • Versus 9.999600 g

95
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96
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97
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98
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99
Mean Median Mode
100
SO
  • Can we ever be positive of true weight of that
    standard?
  • No
  • There is uncertainty in every weight measurement

101
RELATIONSHIP RANDOM ERROR AND PRECISION
  • Random error
  • Leads to a loss of precision

102
SYSTEMATIC ERROR
  • Defined as measurements that are consistently too
    high or too low, bias
  • Many causes, contaminated solutions,
    malfunctioning instruments, temperature
    fluctuations, etc., etc.

103
SYSTEMATIC ERROR
  • Technician controls sources of systematic error
    and should try to eliminate them, if possible
  • Temperature effects
  • Humidity effects
  • Calibration of instruments
  • Etc.

104
  • In the presence of systematic error, does it help
    to repeat measurements?

105
SYSTEMATIC ERROR
  • Systematic error
  • Does impact accuracy
  • Repeating measurements with systematic error does
    not improve the accuracy of the measurements

106
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107
Match these descriptions with the 4 distributions
in the figure Good precision, poor
accuracy Good accuracy, poor precision Good
accuracy, good precision Poor accuracy, poor
precision
108
ANOTHER DEFINTION OF ERROR IS
  • Error is the difference between the measured
    value and the true value due to any cause
  • Absolute error True value - measured value
  • Percent error is
  • True value - measured value (100 )
  • True value

109
ERRORS AND UNCERTAINTY
  • Errors lead to uncertainty in measurements
  • Can never know the exact, true value for any
    measurement.
  • Idea of a true value is abstract never
    knowable.
  • In practice, get close enough

110
UNCERTAINTY IS
  • Estimate of the inaccuracy of a measurement that
    includes both the random and systematic
    components.

111
UNCERTAINTY ALSO IS
  • An estimate of the range within which the true
    value for a measurement lies, with a given
    probability level.

112
UNCERTAINTY
  • Not surprisingly, it is difficult to state, with
    certainty, how much uncertainty there is in a
    measurement value.
  • But that doesnt keep metrologists from trying

113
METROLOGISTS
  • Metrologists try to figure out all the possible
    sources of uncertainty and estimate their
    magnitude
  • One or another factor may be more significant.
    For example, when measuring very short lengths
    with micrometers, care a lot about repeatability.
    But, with measurements of longer lengths,
    temperature effects are far more important

114
REPORT VALUES
  • Metrologists come up with a value for uncertainty
  • You may see this in catalogues or specifications
  • Example
  • measured value an estimate of uncertainty

115
UNCERTAINTY ESTIMATES
  • Details are not important to us now
  • But principle is any measurement, need to know
    where the important sources of error might be

116
SIGNIFICANT FIGURES
  • One cause of uncertainty in all measurements is
    that the value for the measurement can only read
    to a certain number of places
  • This type of uncertainty. It is called
    resolution error. (It is often evaluated using
    Type B methods.)

117
SIIGNIFICANT FIGURE CONVENTIONS
  • Significant figure conventions are used to record
    the values from measurements
  • Expression of uncertainty
  • Also apply to very large counted values
  • Do not apply to exact values
  • Counts where are certain of value
  • Conversion factors

118
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119
ROUNDING CONVENTIONS
  • Combine numbers in calculations
  • Confusing
  • Look up rules when they need them

120
RECORDING MEASURED VALUES
  • Record measured values (or large counts) with
    correct number of significant figures
  • Dont add extra zeros dont drop ones that are
    significant
  • With digital reading, record exactly what it
    says assume the last value is estimated
  • With analog values, record all measured values
    plus one that is estimated
  • Discussed in Laboratory Exercise 1

121
ROUNDING
  • A Biotechnology company specifies that the level
    of RNA impurities in a certain product must be
    less than or equal to 0.02. If the level of RNA
    in a particular lot is 0.024, does that lot meet
    the specifications?

122
  • The specification is set at the hundredth decimal
    place. Therefore, the result is rounded to that
    place when it is reported. The result rounded is
    therefore 0.02, and it meets the specification.

123
GOOD WEB SITE FOR SIGNIFICANT FIGURES
  • http//antoine.frostburg.edu/cgi-bin/senese/tutori
    als/sigfig/index.cgi

124
THERMOMETERS
  • Look at the values for the thermometers on the
    board.
  • Significant figure conventions can guide us in
    how to record the value that we read off any
    measuring instrument.
  • With these thermometers, correct number of sig
    figs is _______.

125
THERMOMETERS
  • Were they accurate?
  • How could we figure out the true value for the
    temperature?

126
REPEATING MEASUREMENTS
  • Would repeating measurements with these
    thermometers, assuming we did not calibrate them,
    improve our ability to trust them?
  • Is their error an example of random or systematic
    error?

127
CALIBRATION
  • Calibration of the thermometers could lead to
    increased accuracy
  • This is a type of systematic error
  • In the presence of systematic error, repeating
    the measurement will not improve its accuracy

128
TOLERANCE
  • Here is a catalog description of mercury
    thermometers.
  • Are these thermometers out of the range for which
    their tolerance is specified?

129
PRECISION
  • Were they precise? How could precision be
    measured?
  • Would calibration help to make them more precise?

130
CALIBRATION
  • Calibration would probably not improve their
    precision

131
RETURN TO OUR ORIGINAL TYPE OF QUESTION
  • Are our temperature measurements good
    measurements?
  • How do you make that judgment?
  • Can we trust them?

132
THERMOMETERS GOOD ENOUGH?
  • Are times that we need to be very close in
    temperature measurements. For example PCR is
    fairly picky.
  • Other times we can be pretty far off and process
    will still work.

133
EXPLORE SOME OF THESE IDEAS
  • In lab
  • Calibrate instruments
  • Use standards
  • Check performance of pipettors
  • Record measurement values
  • Calculate per cent errors
  • Calculate repeatability

134
ASSAYS
135
SAME IDEAS APPLY
  • A good assay is one can trust when making a
    decision
  • Accuracy and precision
  • Linearity
  • Limits

136
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