(200m) Comparison of Batch and Continuous Biopharmaceutical Antibody Production Based on Techno-Economic Analysis

Authors: 
Yang, O., Rutgers, The State University of New Jersey
Ierapetritou, M., Rutgers, The State University of New Jersey

Continuous processing has advantages compared to batch mode of operation including reduced equipment sizes, increased productivity, improved product quality and high facility flexibility. These are the main reasons that continuous manufacturing has become an interesting alternative for biologic production where high productivity and process robustness is of great significance [1, 2]. In the recent years, a few studies have been published that compared batch and continuous biopharmaceutical processing including upstream, downstream and integrated process analysis that provide solutions to achieve continuous biopharmaceutical processing. Excel-based process and cost modeling software Bio-Solve (Biopharm Services, UK) [3] and a discrete event simulation tool ExtendSim (ExtendSim v8, Imagine That! Inc. San Jose, CA) [4, 5] have been applied to simulate different modes of operation and provide guidance using economic analysis to evaluate cost-effectiveness. However, many biopharmaceutical companies are still skeptical and need more analytical help to de-risk their decision-making.

This study uses data from a realistic case study as base scenario and builds a continuous biopharmaceutical manufacturing simulation flowsheet with detailed mass balance for all the streams and operation units using SuperPro Designer (Intelligen, Scotch Plains, NJ). Based on detailed process flow diagram with energy and material balance, the overall cost is calculated. This includes capital cost consisting of working capital, start-up and validation cost, and operating cost comprising of maintenance costs, equipment depreciation, labor costs, medium costs, consumables costs, waste and utilities costs [6]. Detailed economic analysis is performed using preliminary cost information that results in the evaluation of the cost-effectiveness of the alternative processes including quantifiable metrics with different cost categories. Investment profitability considering time value of money is analyzed to show the future cash flow and determine the discount rate with expected return of investment under different level of risks. Global Sensitivity analysis is applied to evaluate the importance of different model parameters and help with the risk assessment [7]. In sensitivity analysis, operating conditions and process parameters including upstream bioreactor perfusion rate, downstream process breakthrough capacities, integrated process running time and failure rate caused by contaminations are considered. This framework provides the platform for comparing cost sensitivity of different operating conditions in batch and continuous manufacturing processing to assist in decision-making.

Reference

  1. Konstantinov, K.B. and C.L. Cooney, White Paper on Continuous Bioprocessing. May 20–21, 2014 Continuous Manufacturing Symposium. Journal of Pharmaceutical Sciences, 2015. 104(3): p. 813-820.
  2. Jungbauer, A., Continuous downstream processing of biopharmaceuticals. Trends in Biotechnology, 2013. 31(8): p. 479-492.
  3. Xenopoulos, A., A new, integrated, continuous purification process template for monoclonal antibodies: Process modeling and cost of goods studies. J Biotechnol, 2015. 213: p. 42-53.
  4. Pollock, J., et al., Integrated continuous bioprocessing: Economic, operational, and environmental feasibility for clinical and commercial antibody manufacture. Biotechnol Prog, 2017.
  5. Pollock, J., S.V. Ho, and S.S. Farid, Fed-batch and perfusion culture processes: Economic, environmental, and operational feasibility under uncertainty. Biotechnology and Bioengineering, 2013. 110(1): p. 206-219.
  6. Petrides, D., Bioprocess Design and Economics, in Bioseparations Science and Engineering P.W.T. Roger G. Harrison, Scott R. Rudge and Demetri P. Petrides, Editor. 2015.
  7. Pannell, D.J., Sensitivity analysis: strategies, methods, concepts, examples. Agric Econ, 1997. 16: p. 139-152.