Since 1982 GSC has helped pharmaceutical and biotechnology clients, and even food manufacturers. Prior to that our founder manufactured medical instrumentation – imaging systems for cardiac nuclear medicine.

GSC understands the industry and the FDA’s GLP/FMP requirements, the difference betwern QC and QA and other peculiarities of the industry

GSC projects have included:

• Pharmaceutical Labs
• Manufacturing Lines
• LIMS / HPLC / electrophoresis systems
• plant and lab utilities
• Pilot plant construction
• Scale-up problems

GSC does “process engineering” – but concentrates on the problems that occur outside the pipes and vessels.

There are other firms highly expert at assisting with the chemical and biological aspects of your process. That is not GSC. But we like to cooperate with them to bring your plant together.

Scale-up, from bench to pilot plant to full scale manufacturing, can involve internal chemical and biological process issues. But often there are major external issues. There is a big difference between Tygon® tubing running across a lab bench and stainless steel piping hung from the ceiling. Even single-use technology can have special problem. Even sensors can require a change with larger vessels. We can help with these issues.

SQC (sometimes called “trending”) is not just applying statistics and graphic data against limit lines.

There is significant engineering judgment (or “data science” if you prefer) involved in choosing limits and interpreting results.

• What does it mean when a result is outside trending limits but still in spec? It means that “something interesting” might have happened and you might want or need to do something about it. One of the hardest problems for many pharmaceutical people is realizing thus is is not the same as a result out of spec.
• Not all distributions are “normal” (Gaussian”). One non-pharma example is electronic components where 1% tolerance components are chosen by measuring each unit and selecting those within the closer limits. This leaves the rest of the population with a gap.
• How do you get started and choose initial limits. Is the current process “under control”? If not, calculating limits based on all existing data may make out of control data look normal

The FDA initiative Pharmaceutical Quality for the 21st Century - A Risk Based Approach introduced in 2002 is driving the biotech/pharma industry to a new risk-based validation paradigm.

The challenge is to provide a "science-based" and "risk-based" approach to testing.

It is not just that "risk" is considered. The testing should also be based on the "science" of the process, and testing should "challenge" the scientific design assumptions.

The "risk-based" approach maximizes risk mitigation for patients. Thus it might either minimize a risk or mitigate it.

One of the goals is the elimination of unnecessary IQ, OQ, and PQ deviations. Many of these are just "paper chases". If a deviation does not actually lead to a change in the system (or perhaps in documentation or training), then it hasn’t done anything to improve the system or reduce patient risk. In one sense this test is a waste because the patient outcome will be the same even if the test was never performed. If nothing is changed, the outcome is the same whether there is one sentence or 50 pages of deviation documentation. The goal then should be to quickly determine whether a change is required. If not, then quickly move on.

"Science-based" testing implies having an understanding of the internal characteristics of a system. The “black box” must be “opened” (either by the vendor’s tests or by user tests). Control Systems theory spent much of the first half of the 20th Century learning that complex technology can not be successfully employed without understanding the internal characteristics of the “black box” (technology perfected by the MIT Servomechanisms Laboratory and the NDRC during World War II).

Duplicate testing (e.g., repeating the same tests at the vendor and user sites) is not more or better testing - just duplication. Of course some tests (e.g., Installation Qualifications) may legitimately need to be repeated for each item or site (because an installation mistake can occur after 999 successful installations).Arbitrary testing (without a scientific basis for the tests conducted) will probably no longer be accepted as valid best practice.

Once upon a time, "alchemy" was considered to be real science and “magic spells” a valid lab technique. The witches in Macbeth were perhaps doing primitive biochemistry.

"Science-Based" testing requires the use of meaningful tests developed by, and with results evaluated by, subject matter experts. Otherwise the testing has little more scientific validity than the use of “magic spells”. Traditional validation has worked fairly well because dedicated, experienced, knowledgeable people have implicitly built good tests into their protocols. Often this has been in spite of, rather than a result of, the validation methodology.

As systems become more complex, industry can’t rely on this implicit coverage to catch everything. The new "science-based" and "risk-based" approaches are necessary to reduce costs and maintain quality. This new approach is described in

• ASTM E2500-07 Standard Guide for Specification, Design, and Verification of Pharmaceutical and Biopharmaceutical Manufacturing Systems and Equipment
• ISPE GAMP(r) 5: A Risk-Based Approach to Compliant GxP Computerized Systems

However, this type of analysis has been a feature of traditional/conventional engineering (e.g., civil, structural, mechanical, electrical) development for decades.

GSC has been including "Design for Verification/Validation" in its biotech/pharma products and projects since the 1980's. Forensic products have included concepts that anticipate legal challenges during courtroom cross-examination.

Let GSC help you transition to this new validation paradigm on your next project.

Flyer about Risk-based Validation (PDF)

Today, if you are doing anything related to biotechnology, then, for better or worse, you are probably using computers. All too often poor automation ruins good science. GSC helps keep that from happening. Whether you are conducting biotech research, developing a new product, or constructing a new manufacturing plant, computers and/or other automation are almost certainly involved. If the science is not working, then adding a computer, or a fancy graphic user interface, or web interface, won't solve the problem. The wrong technology can create a problem where one didn't exist.

All Problems are "Physics"

Problems may appear as mathematical, mechanical, electrical, thermodynamic, or chemical; ultimately physics is the issue. A robust solution for any requirement or problem requires an architectural design that deals with the physics. Voting on a list of requirements won't make it work. The automation system must be designed for the problem at hand, because the problem won't adapt to the computer. Integration into the final environment can't be neglected. New problems arise. No matter how many approval votes or signoffs were done at the factory, nature is casting another vote in the field. GSC first understands the physics of your system, then looks for the automation that will support the system. Sometimes GSC can suggest changes that will facilitate automation or improve reliability.

Connecting biotechnology and automation since 1982

Flyer about Protecting Good Science (PDF)