Chemical measurement data are often the basis for critical decisions on vital matters, including the health of individuals, environmental protection, and the production of safe, reliable, and useful products and services. As a result, the demand for measurement data is ever increasing, and, therefore, measurement data must be reliable with unequivocal evidence to prove accuracy and precision. The philosophy and procedures by which data users can obtain proof of the reliability and accuracy of measurements is commonly referred to as quality assurance.

The pharmaceutical industry in particular requires a higher level of quality assurance compared to most other industries. Pharmaceutical products require millions of dollars in research and development, and, still, most drugs never make it out of the pipeline and actually come to market. Adding further to the quality assurance task, the pharmaceutical manufacturing process is highly regulated and often, due to cost concerns, dependent upon outside services for vital business needs. Pharmaceutical manufacturers also rely on complex manufacturing and analytical tools to make chemical measurements. They need to defend the decisions they make based on such measurements to the FDA and, potentially, in court against plaintiffs. Therefore, the pharmaceutical industry must make extraordinary efforts to assure quality.

In order to achieve this goal, analytical chemists in the pharmaceutical sector need to think and work more like process engineers. Bringing analytical chemistry to the drug production line is one of the best ways to help assure quality. Testing end products is useful, but automated testing methods, such as Near-IR, should also be performed early in the manufacturing process.

To be successful, chemists and engineers must work together to identify and meet quality needs in pharmaceutical manufacturing processes. Since engineers work on the process units used in pharmaceutical manufacturing, chemists can best communicate with engineers by breaking down their measurements into a series of process units.

QC + QA = Quality Assurance
Quality assurance has two separate but related activities: quality control (QC) and quality assessment (QA). Quality control is a fairly well established practice, while quality assurance is a relatively new concept. To truly achieve measurement quality assurance, both QC and QA must be operational and coordinated.

• Quality control is the overall system of activities used to control the quality of a product for service so that it meets the requirements of users. The aim of QC is to provide quality that is satisfactory, adequate, dependable, and economic. QC in action: The mass spectroscopist calibrates the mass spectrometer before running samples to assure proper mass assignments, relative abundances, resolution, and sensitivity. This calibration is done by running a known standard compound on the instrument. The operator documents that the instrument is working before running a sample.

• Quality assessment is the system of activities used to ensure the overall quality control function is being performed effectively. It involves a continuing evaluation of the products produced and of the performance of the production system. QA in action: Management provides known standard samples for the instrument operator to check on their performance and adequacy of QC to get acceptable performance on the submitted known sample. Management has two options when they prepare the known QA sample, blind or blind-blind (or double-blind) samples. Using both options on a defined schedule to test the proficiency of a measurement process is a recommended best practice. A blind sample is useful because the operator knows it is a check on the measurement performance. A good operator will handle a blind sample with special care. In order to avoid bias and false security about the QA program, blind-blind samples should also be submitted to the operator. A blind-blind sample is a check sample, which appears to be a regular sample. Thus, the operator handles and reports the blind-blind sample in a batch without knowing it is a known standard. Blind-blind samples can be prepared by spiking real samples or submitting the same sample with different sample identifications.

• Quality assurance is the system of activities used to provide the producer or user of a product or a service the assurance that it meets defined standards of quality with a stated level of confidence. It is the end result of effective quality control and quality assessment programs.

Chemical Analysis System
In order to truly achieve quality assurance, measurements need to be established as a process analogous to a manufacturing process. Instead of having raw materials converted to finished products, the analytical chemist brings in relevant samples and converts them to defensible reports and valuable information. The chemical measurement process must be brought into a state of statistical control, i.e., the individual measurements must be defined by a statistical distribution, where characteristic precision and accuracy can be assigned to the data output. The analytical chemist needs to compare the precision and accuracy of the methodology to what the project needs to assure the proposed method meets requirements. If precision or accuracy needs to be improved, the sooner the problem is recognized, the better. Clearly, quality control relates to all that is done to attain and maintain the state of statistical control. Statistical control always has to be demonstrated before samples are run. Statistical control is demonstrated by running known standards, ideally with the same matrix and concentration range of interest to the project. Chemical analysis is more than a single process and is better characterized as a system consisting of a series of interdependent processes as indicated by the flow diagram in Table 1.


Table 1. There are many sequential and internally related parts to a measurement process. It is important to take the time to list the sequential and most important parts of individual processes. List the parts in a measurement on a lined piece of paper. Leave enough empty lines between each item in the initial list. This space will be filled in with more thought. Colleagues and outside consultants should also add parts to the list. Getting all stakeholders involved early shows good leadership. It is very important to develop trust and teamwork as part of the working process culture.

>> Problem Specification: Provide a detailed and exact statement of the specific problem/question to be solved/answered.

>> Correct Model: A state-of-the-art description of a theory or system that accounts for all of its known properties. The model should be updated periodically as new data becomes available.

>> Quality Assurance Plan: One of the most important parts of the overall planning process, as it provides the structure for obtaining defensible and useful data. The plan should address the types and number of samples to be taken, the details of collection, frequency of sample collection, and the procedures to be

>> Sampling Plan: Transport and storage of samples needs to be validated by a stability study for the analytes in the measurement process. Proper sample containers, glass or plastic, and chemical additions to samples to stabilize the analyte are important to measurement success.

>> Sample Transport & Storage: Transport and storage of samples needs to be validated by a stability study for the analytes in the measurement process. Proper sample containers, glass or plastic, and chemical additions to samples to stabilize the analyte are important to measurement success.

>> Analyte Isolation: Chemical measurements are often like finding a needle in a haystack. Separation science is used to isolate the analyte to avoid false positives or false negatives when making chemical measurements.

>> Instrument Calibration: Comparison of results from running a standard on the instrument with the acceptable result. The goal is to eliminate any deviations in the accuracy of the two results by having the operator make necessary adjustments to the instruments.

>> Quality Control: The summation of all of the things an analyst performs to assure the data user is satisfied. The standard operating procedure (SOP) documents all of the steps the analyst must follow. The SOP contains directions for the analyst to follow when unexpected measurement problems arise.

>> Concentration: Often the sample must be concentrated or diluted to obtain analyte detection. Detection limit is the smallest amount of analyte that is measurable at a stated level of confidence.

>> Fractionation: Used to get the purest possible product. In today’s market, being able to fractionate the optically active forms of drugs is important. One optical form may be beneficial to the patient, while another form may contribute negative side-effects. Fractionation can be the difference between market success and failure.

>> Derivitization: Some analytes need to be derivatized before they can be analyzed, because the physical-chemical properties do not allow the measurement. Derivatization changes the properties of the analyte and can also be used in structure elucidation, i.e., a mass shift.

>> Separation: The data validation process should be separated and independent of the original data providers. Management needs to obtain external performance evaluations on a defined regular schedule.

>> Qualitative Identification: Evidence for correct identification of the analyte is critical. Data providers need a document that specifies identification criterion and the handling of chemical interferences.

>> Quantitation: The analysis of a substance that determines the amounts or proportions of its chemical constituents. The degree of uncertainty must accompany the reported quanitative values.

>> Report Record Keeping: The archival process must meet the needs of all stakeholders. The organization of reports and records must have an SOP, and they must be in a readily accessible repository.

>> Validation of Data: Data should be validated before it leaves the data provider’s facility. Once the data user receives the information, it should be checked again to assure its quality for their applications.

>> Continuous Improvement: After each project or task is completed, it is important to consider how it might be improved for the next measurement. The aim is to create a culture where all stakeholders are continually striving to refine the process.

From Problem to
Model Solution
Chemical measurements are undertaken to answer questions necessary in solving problems. It is important to clearly and precisely define the problem. Much like when speaking with teenagers about their problems, it is important to practice active listening to correctly understand the real problem. The original problem is often not the real problem that needs to be understood, and a variety of perspectives must be considered to find an effective solution. One of the most important steps in the process is to ensure the correct actions are being taken to reach an effective solution. Correctly defining the problem and possible future iterations is the key to achieving this goal. A problem must be represented by a model that sets forth the questions that need to be answered and defines the data requirements and how they will be used to arrive at a solution. Based on this model, a measurement program can be planned to address sampling, calibration, the methodology to be used, and the quality assurance procedures to be followed. The design and implementation of the chemical measurement system is often an iterative process, and every measurement program should include a feedback mechanism, which provides for identification of deficiencies and the initiation of corrective actions for all parts of the system as such deficiencies are identified.

It is important to seek continuous improvement. This does not mean the stakeholders are continuously changing their mind, but rather that before the next measurement is performed the stakeholders revisit the plan with the hope of further refining the measurement process. History has shown that measurement improvement is required by regulators and users. Therefore, a system of continuous improvement is key. Continuing education is an important part of keeping up with the latest advances in chemical measurements. All components of the system are critically dependent on each other and are implicitly or explicitly present in every measurement activity. Failure to recognize the importance of each component and to properly design an appropriate system is a major defect of many analytical measurement programs.

Henry Nowicki, Ph.D., directs the laboratory testing and consulting services for PACS, a provider of process chemistry and analytical consulting services. Dr. Nowicki is a recognized activated carbon adsorption expert with 29 years in the field, 10 government research grants, and over 40 carbon cases as an expert witness. He can be reached at or 724 457-6576. Barbara Sherman directs the PACS short course program, conferences, and expositions. She can be reached at

For More Information:

PACS will be holding an exposition and short course titled “Laboratory Information Management Systems (LIMS)” on Oct. 2-5, 2006 at the Crowne Plaza Hotel near the Pittsburgh, Pa. airport. This event will be of particular interest to those involved with quality assurance for pharmaceutical manufacturing processes.