Emanuele Tomba

General procedure to aid the development of continuous pharmaceutical processes using multivariate statistical modeling - an industrial case study.

Streamlining the manufacturing process has been recognized as a key issue to reduce production costs and improve safety in pharmaceutical manufacturing. Although data available from earlier developmental stages are often sparse and unstructured, they can be very useful to improve the understanding about the process under development. In this paper, a general procedure is proposed for the application of latent variable statistical methods to support the development of new continuous processes in the presence of limited experimental data. The proposed procedure is tested on an industrial case study concerning the development of a continuous line for the manufacturing of paracetamol tablets. The main driving forces acting on the process are identified and ranked according to their importance in explaining the variability in the available data. This improves the understanding about the process by elucidating how different active pharmaceutical ingredient pretreatments, different formulation modes and different settings on the processing units affect the overall operation as well as the properties of the intermediate and final products. The results can be used as a starting point to perform a comprehensive and science-based quality risk assessment that help to define a robust control strategy, possibly enhanced with the integration of a design space for the continuous process at a later stage.

Supporting the transfer of products between different equipment through latent variable model inversion

Abstract Product transfer is a problem commonly encountered in industry when a production has to be transferred from a source plant to a target plant. In this paper a strategy to assist product transfer is proposed. The strategy is based on latent variable models (LVMs) to relate the data available from one or more source plants with the (usually scarce) data available from the target plant, and on LVM inversion to estimate the target plant operating conditions. The inversion is performed within an optimization framework, which can handle constraints for both product quality variables and input variables. An experimental nanoparticle production process is used as a test bed to illustrate the benefits of the proposed strategy.

A general framework for latent variable model inversion to support product and process design

Abstract In this paper a general framework to perform the inversion of latent variable regression models (LVRMs) is proposed. The framework exploits the advantages of LVRMs in modeling the driving forces between databases of developed products and the raw materials/process conditions used. These relations are used to develop new products, by estimating the best combinations of input variables to obtain a desired product in output. The procedure can deal with several different constraints both in the predictor and in the quality spaces. A wet granulation particle design problem is used to illustrate the benefits of the proposed framework.