High-Concentration, Low-Volume Subcutaneous Formulation

Regulatory/GMP Compliance

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Neotropix, Inc. (Malvern, PA, www.neotropix.com), a biotechnology company dedicated to the development and commercialization of virus-based therapeutics for the treatment of cancer and other diseases, received a warning letter (http://www.fda.gov/foi/warning_letters/b6308d.pdf) on March 23, 2007, citing deviations from good laboratory practices (GLP) regulations governing the proper conduct of nonclinical studies as published under 21 CFR Part 58.

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A series of ICH guidances are encouraging industry to adopt quality-based approaches for achieving the "desired state" of drug and biotech manufacturing: more efficient and flexible operations that can reliably produce high quality therapies with less regulatory oversight.

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The recent FDA decision that meat from cloned animals is safe for human consumption seems logical enough. A protein is a protein. But even if we can eat such meat, it doesn't necessarily make economic or ecological sense to do so.

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The pharmaceutical industry is well aware that FDA is trying to take a risk-based approach to enforcing the current good manufacturing practices (cGMP) regulations. This approach is driven by an economic reality: FDA simply does not have the resources to inspect every facility every other year. The organization doesn't even have the resources to inspect facilities every three years. Likewise, it is not cost-effective for our companies to carry out a complete, documentation-oriented revalidation for every process change, regardless of its significance or risk.

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How much do regulatory agencies know about nanotechnology or microfluidics? Yesterday, the answer was probably, "not much." Tomorrow, it may be "a lot." The reason is that new technologies push the agencies to expand their expertise.

Vagueness in the ICH Q2A and Q2B guidelines necessitates effective protocol design and data analysis. For specificity (detection in the presence of interfering substances), the goal is statistical differences with meaningful implications on assay performance. Linearity (results directly proportional to concentration of analyte in the sample) is typically demonstrated via least squares regression. Accuracy (difference between measured and true values) usually is presented as a percent of nominal. Precision analysis is vital because it supports claims of accuracy and linearity. A well-designed experiment and statistically relevant methods will facilitate method validation in accordance with ICH guidelines.