Model based process design for a monoclonal antibody-producing cell line :optimisation using hybrid modelling and an agent based system

Abstract

The biopharmaceutical industry has seen rapid growth over the last 10 years in the area of therapeutic medicines. These include products such as monoclonal antibodies (mAbs) produced using mammalian cell lines such as Chinese Hamster Ovary (CHO). In order to comply with the regulatory authority (FDA) Quality by Design (QbD) and Process Analytical Technology (PAT) requirements, modelling can be used in the development and operation of the bioprocess. A model can assist in both the design, scale up and control of these complex, non-linear processes. A predictive model can be used to identify optimal operating conditions, which is vital for a contract manufacturer. Traditionally industry has approached modelling through the one-unit-at-a-time method, which can fail to capture unit interactions. The research reported in this work addresses this issue by using a whole system approach, which can also capture the interactions between units. Predictive models for each of the process units are combined within an overall framework allowing for the integration of the models, predicting how changes in the output of one unit influence the performance of subsequent units. These predictions can serve as the basis for the modifications to the standard operating procedures to achieve the required performance of the whole process. In this thesis three distinct studies are presented; the first utilises a hybridoma data set and presents a model to predict and characterise the various critical quality attributes (CQAs), such as final product glycosylation profile, and critical process parameters (CPPs) including titre and viable cell count. The second data set concerns the purification of lactoferrin using ion-exchange chromatography as a model system for developing downstream iii processing models. The output of this data set varied widely, and has led to the development of a novel peak isolation methodology, which can ultimately be used to characterise the elution. The final data set contains various CQAs and CPPs for multiple units within one process. This data set has been employed within a proof of concept study to show how an agent based framework can be developed to allow for overall process optimisation. The results showed that it is possible to link process units using a common CPP or CQA. This work shows that using a agent based system of two layers of modelling i.e. individual process unit models connected with a higher level agent model that links via a common measurement allows for the influences between units to be considered. The model presented in this work considers the use of titre, HCP, measure of heterogeneity, and molecular weight as the common measurement. It is shown that it is possible to link the units in this way with the goal of predicting and controlling the glycosylation profile of the Bulk Drug Substance (BDS).