title: Data mining for rapid prediction of facility fit and debottlenecking of biomanufacturing facilities.
abstract: Higher titre processes can pose facility fit challenges in legacy biopharmaceutical purification suites with capacities originally matched to lower titre processes. Bottlenecks caused by mismatches in equipment sizes, combined with process fluctuations upon scale-up, can result in discarding expensive product. This paper describes a data mining decisional tool for rapid prediction of facility fit issues and debottlenecking of biomanufacturing facilities exposed to batch-to-batch variability and higher titres. The predictive tool comprised 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 for the critical combinations of factors that lead to different mass loss levels. Three different debottlenecking strategies were investigated involving changes to equipment sizes, using higher capacity chromatography resins and elution buffer optimisation. The analysis compared the impact of each strategy on mass output, direct cost of goods per gram and processing time, as well as consideration of extra capital investment and space requirements.
authors.affiliation: Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
authors.affiliation: The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, University College London, Torrington Place, London WC1E 7JE, UK. Electronic address: s.farid@ucl.ac.uk.
authors.forename: Suzanne S
authors.initials: SS
authors.lastname: Farid
authors.affiliation: Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK. Electronic address: n.thornhill@imperial.ac.uk.
authors.forename: Nina F
authors.initials: NF
authors.lastname: Thornhill
mesh_terms: D000911:Antibodies, Monoclonal / Q000378:metabolism; D061249:Batch Cell Culture Techniques; D057005:Bioengineering / Q000379:methods; D019149:Bioreactors; D001709:Biotechnology / Q000379:methods; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D066192:Manufacturing Industry; D009010:Monte Carlo Method
chemical_list: D000911:Antibodies, Monoclonal
keywords: Biopharmaceutical manufacture; Data mining; Decision tree classification; Multivariate data analysis; Stochastic discrete-event simulation