(638a) New Theoretical Methodologies for Pharmaceutical Plant Cleaning

Plant cleaning in the pharmaceutical industry is an undervalued but critical stage of processing. Cleaning with solvents and/or surfactants is often the only method capable of removing residual particles. This is often necessary prior to further processing being undertaken in the same equipment. Regulatory bodies have stringent guidelines not only for cleaning equipment but also, as importantly, relating to carryover and/or the potential contamination of subsequent batches of other products from carried over active pharmaceutical ingredients. In a typical multipurpose pharmaceutical plant there are significant implications if cleaning is not achieved effectively and efficiently, ranging from reduced plant availability through to financial penalties and possible plant closure.

Cleaning in pharmaceutical plant is often only considered once the process chemistry has been optimised. The product is regarded as the main source of financial gain in a process, but the question that is rarely asked is - what happens if cleaning is suboptimal? Cleaning challenges can cost companies millions of dollars’ as they must clean plant equipment effectively to satisfy regulatory constraints. Failure to do this right first time can result in missed stage gates with processing schedules pushed back, often with their own financial consequences.  The empirical approach generally adopted for cleaning protocol development results in plant that is often over cleaned, with cost implications in terms of plant availability, cleaning agent use and disposal.

More reliable methods for plant cleaning are required and thus it is necessary to consider the basic science involved in the production processes carried out prior to defining the cleaning strategy namely (1) structural features of raw materials, intermediates, product or by-products and (2) physiochemical properties of the process materials which may affect cleanability. The research in this presentation describes a new approach to cleaning using multivariate data analysis.

The first approach utilises the fact that cleaning protocols can be determined based on the identification of chemical functional groups within pharmaceutical products. A database of pharmaceutical products was created and a multivariate analysis of the variables related to structure and composition of the functional groups was undertaken. Using principal component analysis (PCA), a set of products were identified which could potentially be cleaned utilising the same approach. This provides a means to determine how products may react to certain cleaning methods with solvents and thus it can be inferred as to how easy or challenging a product may prove to clean from plant equipment.

The second approach is based on the physicochemical properties of process materials. Using a similar PCA-based approach, physiochemical properties are identified that can potentially be used to determine how process materials can be best cleaned from plant and also which products might be difficult to clean from plant equipment.  

Both techniques can be used to help determine cleaning protocols based on fundamental scientific principles. Adopting this approach ensures that the cleaning agent used is appropriate to the process chemistry and, therefore cleaning is carried out right first time and as cost effectively as possible. These approaches will enable the inference of worst-case scenario products or intermediates to clean from a processing plant prior to the start of processing. The ultimate goal is for these techniques to be used to support the design of manufacturing processes taking cleaning into account from early stages of development thereby saving time and money during the processing stages.

Beta testing is currently being carried out in collaboration with a number of companies across the pharmaceutical supply chain to validate the practical implementation of this research. Through increased knowledge and understanding of industrial process cleaning, this research will support an industry-wide move towards a target of a more sustainable future.