One of the most important aims within the pharmaceutical industry is the on-time delivery of medicine with expected quality to patients. Medicine production is a highly regulated process that requires quality of incoming materials, intermediates, and the final product to be verified for every produced batch. All of these quality control analysis results need to be within defined limits, to release a batch to the market. This quality control process is very time-consuming mainly due to the laboratory-centered analysis.
As part of the doctoral thesis, we have investigated whether historical production and laboratory data for selected medicine could be used for quality predictions of future batches. We have acquired the data for 1005 industry-scale batches. The data for each batch included the results of 53 different laboratory tests of incoming raw materials, intermediates, and final products. Additionally, identification spectra of incoming raw materials and process time series were collected. The data were acquired from different databases, suitably cleaned, and structured for use in prediction models.
Identification spectra of incoming raw materials have been successfully used for the prediction of raw materials' quality. Process time series were used for building models that can optimize key process steps. And finally, laboratory data were included in models for the prediction of certain quality attributes of the final product.
We have demonstrated that by using only historical data collected and kept by the industry for every single manufactured batch, we can build reliable prediction models which can replace current laboratory analysis. Implementation of models presented in this thesis would save a lot of time, reduce required laboratory capacities and enable the delivery of medicine to patients in a much shorter time.
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