Risk-Based Cleaning Validation in Biopharmaceutical API Manufacturing - - BioPharm International

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Risk-Based Cleaning Validation in Biopharmaceutical API Manufacturing


BioPharm International



A. Hamid Mollah
Cleaning validation (CV) is driven by regulatory expectations to ensure that residues from one product will not carry over and cross contaminate the next product.1,2 Regulatory scrutiny is more rigorous in a multiproduct facility compared to a single product establishment. Companies are usually cited either for not having a sound cleaning validation or not meeting the protocol acceptance criteria. Because failing a protocol acceptance criteria is considered a substantial regulatory risk, companies are forced to spend money and resources even though there is minimal or no product risk.

It is vital for a successful cleaning validation to have appropriate acceptance criteria. In developing the acceptance criteria, companies may adopt a conservative approach either to prove that they have a sound cleaning validation program or to ensure that field data (results) will reflect the acceptance criteria. The Food and Drug Administration's (FDA) guidance for determining residue limits is that they must be logical (based on understanding of the process), practical, achievable, and verifiable.3 In validation, adequacy of each cleaning procedure requires demonstration that it can reliably and effectively remove or reduce residues to an acceptable level such that residues from the production of one product will not carry over in significant amounts to the next product. Companies today are faced with the challenge of reducing validation costs in an environment that demands increased compliance with current good manufacturing practices (cGMP). FDA's initiative in pharmaceutical cGMPs for the 21st Century is a science and risk-based approach to product quality regulation. The risk- based approach will enhance the ability to focus on identifying and controlling critical factors that effect process and product quality.




When setting the specification for a production process it must reflect product quality so appropriate action can be taken when it deviates from specification. The purpose of setting process input limits is to consistently produce a quality product. On the other hand, the purpose of acceptance criteria in cleaning validation is to ensure acceptable levels of carryover residue. If the same acceptance criteria are used for cleaning validation in cell culture, purification, and formulation/filling processes, a good cleaning validation program can be penalized because the acceptance criteria are too rigid. Therefore, a cleaning validation program based on risk assessment with justifiable and achievable acceptance criteria is crucial. This paper addresses cleaning validation in biopharmaceutical active pharmaceutical ingredients (APIs).

PROCESS RESIDUES IN BIOPHARMACEUTICAL APIS

Many biopharmaceutical products are proteinaceous, and their manufacturing involves media and buffer preparation, cell growth, cell harvest and processing, product purification, and other steps. Biopharmaceutical residues are often composed of proteins, lipids, simple and complex sugars, and salts. For biologically derived products, contaminants could be any one or more of various organic and/or inorganic residues introduced or derived during the production process. Many biopharmaceutical processes also require decontamination with steam prior to cleaning; heat denatured residues are a common challenge. Proteinaceous residue or polysaccharide removal can be difficult due to denaturation, insolubility, foaming, and other factors. The removal of lipid, simple sugar, and salt residues is often less difficult. Validation of a cleaning process requires the demonstration that the cleaning process can reliably and effectively remove or reduce residues to an acceptable level. The validation of a multiproduct cleaning process is more challenging compared to a single product.

Worst-case validation testing strategies reduce the total quantity of validation studies for a system or process. By demonstrating that the cleaning process is consistent and effective for the worst-case conditions, all other conditions are, by default, assured of consistent, effective operation. Worst case may include highest soil (product and others) concentrations in the hardest to clean area. Due to a large number of variable parameters that exist for entire cleaning processes, the worst-case cleaning concept is applied to unit operations or to equipment matrices.


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