1. Quality Assurance
Chapter 29. Quantitative Chemical Analysis, Daniel C. Harris, 6th Edition, 2003.
New to this edition and a very important topic in industry today.
GLP/GMP in the pharmaceutical industry. Other quality standards ISO, CE and other quality systems.

2. Data Quality
How do we know that we have done a good job in lab? We are well trained scientists so we should be doing good work.
Are We????
This is where Quality Assurance comes in.
It will vary from lab to lab but it should be something that is taken very seriously.

3. Why has this become so important?
Let us look at the next three examples
There are many testing labs that one can have analyses done at. Let’s see how they do.

4. Pb in polyethylene film (lab self designation)

5. Pb in River Water (all having a quality management system)

6. Pb in River Water (National Measurement Institutes)

7. QC / QA
Quality Control – measures taken ensure that there is accuracy and precision in an analytical result
Quality Assurance – Assessment that the quality standards have been met.

8. Quality Control
In Pharmaceuticals this is usually the process of verifying the product quality. That the products meets specification.
That the label claim is correct. ( 1 mg table)
That impurities are below certain limits
That raw materials are in manufacturer specs
That returned materials are within specs

9. Quality Control Governed by detailed procedures.
Called SOPs – Standard Operating Procedures
There will be SOPs for 1) Equipment maintenance and repair, 2) Method Development and Validation, 3) Data tracking and record keeping, 4) Computer validation and data issues, 5) Setting of shelf life and other lab management issues, 6) Staff training requirements, 7) chain of custody of samples etc. etc. etc.
The real issue is record keeping and adherence to the procedures of the lab.

10. Quality Assurance
An independent function within the company to assure that the QC functions are working properly.
They hold routine audits and internal inspections.
They will be the staff that host visits from the FDA and coordinate the FDA site visit (every two years)
They are inspected first

11. GLP / GMP Good Laboratory and Good Manufacturing Practices.
Other agencies have different rules.
EPA will certify labs doing environmental tests. Many of these tests are specified and must be followed exactly

12. Quality Objectives
When rules are changed this might require that better methods be developed.
Perhaps with better detection limits or improved precision.
One must work to get the best information from the resources at hand.

13. Quality Objectives
It is not required that the best possible analysis be carried out. Only that it provide the data required, meet the data quality objective.
Cost considerations of course are important here. This must be considered when developing new methods such as new HPLC methods.

14. Method Validation
The process of proving that the analytical method is acceptable for the intended purpose. (Issues to addressed)
Specificity
Linearity
Accuracy
Precision
Range
Limit of detection
Limit of quantization
Robustness

15. Specificity
The ability to distinguish the analyte from everything else that might be in the sample.
Lets look at a separation for the drug Cefotaxime by MEKC.

18. Specificity
The drug is seen with the impurities that might show up in an assay.
Ideally you should get baseline resolution. This is when R > 1.5.
Failing this you might state that the unresolved impurities at their maximum level not effect the assay of the drug by more than 0.50%
A impurity assay might be specified with all expected impurities > 0.1% be resolved from the major component.

19. Specificity Just were do you get the impurities to test?
What is likely to be present in a drug formulation of bulk substance sample?
For a compound like aspirin this is fairly simple. We know what things to expect since it has been around for a long time. What about an NCE?
This is not a trivial challenge.
Make impurities.
Subject the compound to heat (solid phase), heat (in solution), light, humidity, acidic media, basic media and oxidants to the point of consuming about 20% of the parent.
Add Synthetic starting materials, by-products, synthesis intermediates and known synthesis degradation products.
Add all expected excipients.

20. Linearity
For bulk component you will want you standard curve to have five standards spanning 0.5 to 1.5 expected range. Include a blank
For impurity assay you will wish you standards to span 0.05% to 2% (wt/wt). Given that impurities might fall in the 0.5% to 1% range.
These ranges would be fixed in the SOP for the developed analysis and based on the product specification.

21. Linearity Testing We have discussed this.
R2 is a poor measure as we have stated before.
Look at residuals
For zero intercept
Bulk assay - 2% from the target value of the analyte
Impurity assay – 10% of the target value of the impurity.

22. Accuracy Analyze – Standard Reference Materials (if available)
Results of two or more different analytical methods.
Blank sample spiked with analyte. Ranges as in linearity.
Standard additions.
Spikes should be recovered at ( )% for major components and 0.1% absolute or + 10% relative for minor components

23. Precision
Expressed as relative standard deviations for at least 10 experiments.
Instrument precision (< 1%)
Intra-assay precision – prep each sample from start, same person, same day. (< 2%)
Intermediate precision (Ruggedness) – Different people, different days and different instruments.
Interlaboratory precision is also called just reproducibility (3% to perhaps 40% or more)

24. Horwitz Trumpet

25. Horwitz Trumpet
Where C is the weight fraction

26. Range
The range is the concentration interval where linearity, accuracy and precision are all acceptable.

27. Limits of Detection and Quantitation
Also called the lower limit of detection.
Smallest quantity that gives a signal significantly different from the blank.
This term had lead to much disagreement and controversy.

28. Finding the detection limit.
Prepare a sample that is 1 to 5 larger than the estimated detection limit.
Measure the signal for n replicate analyses (n > 7.
Calculate s for this data set.
Measure the signal from n replicate analyses of the blank. (n > 7) Calculate the mean value. (yblank)

29. Finding the detection limit.
ydl = yblank + ts
Where t is the Student’s t value for degrees of freedom at 98% confidence.
This gives you the signal detection limit.
Concentration Detection Limit.
Minimum detectable concentration = (ts)/slopecalib

31. Limit of Quantitation
We can find the analyte at this low end but it would be difficult to quantify it accurately so we have another term
Limit of Quantitation.
yloq = yblank + 10s

32. Limit of Detection (Alternate way)
Common shortcut
ydl = yblank + 3s
From Least Squares Line.
Signal detection limit
ydl = b + 3sy

33. Other consideration
Instrument detection limit
Method detection limit

34. Reporting Limit
A level below which you may report not detected. Regulatory specified.
This level might be different in different matrix systems.

35. Robustness The ability for a method to be unaffected by small changes.
Solvent composition pH
Buffer concentration
Temperature
Injection volume
Detector wavelength
Shelf life of solutions used

36. Quality Tracking SOP directs how you keep an assay under control.
Directs how samples are handled and stored.
Will dictate how and when the method should be checked.

37. Analytical Standards Can be purchased from NIST.
Must be handled with care.
Care should be taken with matrix effects.
Compound used to make up solutions should have no analyte.
Elemental purity measured on nines scale.
Four nines is pure. Only in terms of other metal and not other contaminants.

38. Standards You may elect to purchase NIST traced certified solutions.
There are many pitfalls to avoid and many are discussed on page 732.

41. Blanks Analysis of blanks can be very informative.
Will detect carry over.
Method Blank – all steps except addition of analyte.
Field Blank – all steps including being taken to the field.

42. Quality Control Samples
A sample that is known and analyzed along with the rest of the samples and standards.
Blind samples are best.
Collaborative and round robin testing will help assess analysis proficiency.

43. Control Charts.
Plot the results for a process or an analytical procedure. (with your quality control samples).

45. When do you shut down. Single observation outside action lines
2 out of 3 consecutive measurements between the warning and action lines
7 consecutive measurement all or above the center line
6 consecutive measurements all increasing or decreasing
14 consecutive measurements alternating up and down
An obvious nonrandom pattern.