(603o) Rapid Prediction of Facility Fit and Debottlenecking of Antibody Purification Facilities

Facility fit challenges in legacy biopharmaceutical purification suites can occur in purification suites with capacities originally matched to lower titre processes and lower concentration formulations. Higher titre and higher concentration formulations can pose bottlenecks that result in unexpected product losses and hence discarding expensive product. This presentation describes a data mining and optimisation decisional tool for rapid prediction of facility fit issues and debottlenecking of antibody purification facilities. Two industrial case studies will be presented. The first focuses on facility fit challenges in legacy facilities exposed to higher titres as well as batch-to-batch variability. The predictive tool was comprised of advanced multivariate analysis techniques to interrogate Monte Carlo stochastic simulation datasets that mimicked batch fluctuations in cell culture titres, step yields and chromatography eluate volumes. A decision tree classification method, CART (Classification and Regression Tree) was introduced to explore the impact of these process fluctuations on product mass loss and reveal the root causes of bottlenecks. The resulting pictorial decision tree determined a series of if-then rules of the critical combinations of factors that lead to different mass loss levels. Three different debottlenecking solutions were compared in relation to their impact on mass output, cost of goods and processing time, as well as consideration of extra capital investment and space requirements. The second case study focuses on facility fit challenges in multiproduct facilities catering for both low and high concentration formulations. The work explores the capability of a particular TFF system to reach high concentration product formulations and to apply multiobjective optimisation to find the optimal final UF/DF design for different target product concentrations with both maximum annual product output and minimum cost of goods (COG).