Optimization-based framework for resin selection strategies in biopharmaceutical purification process development.

Songsong Liu,Nigel J Titchener‐Hooker,David Gruber,Lazaros G Papageorgiou,Richard Turner,Spyridon Gerontas

doi:10.1002/btpr.2479

Abstract

This work addresses rapid resin selection for integrated chromatographic separations when conducted as part of a high‐throughput screening exercise during the early stages of purification process development. An optimization‐based decision support framework is proposed to process the data generated from microscale experiments to identify the best resins to maximize key performance metrics for a biopharmaceutical manufacturing process, such as yield and purity. A multiobjective mixed integer nonlinear programming model is developed and solved using the ε‐constraint method. Dinkelbach's algorithm is used to solve the resulting mixed integer linear fractional programming model. The proposed framework is successfully applied to an industrial case study of a process to purify recombinant Fc Fusion protein from low molecular weight and high molecular weight product related impurities, involving two chromatographic steps with eight and three candidate resins for each step, respectively. The computational results show the advantage of the proposed framework in terms of computational efficiency and flexibility. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1116–1126, 2017

Highlights

In the early stages of purification process development, different types of resins need to be tested at small scale (1.5–5000 mL) under various operating conditions, including different pH values, salt concentrations, and flow rates, to establish the resin most suited for process application at VC 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers
We address the rapid selection of optimal resins for integrated chromatographic separations by proposing the use of mathematical programming techniques
Using the Dinkelbach’s algorithm, each mixed integer linear fractional programming (MILFP) was solved by solving 2–4 mixed integer linear programming (MILP) models, each of which took less than 1 s to locate the optimal solution

Summary

Introduction

Overall, based on the data from the HTS experiments, we consider the resin selection problem as an optimization problem, which is described as follows: Given: target protein, and impurity proteins; a purification process including multiple chromatographic steps; a number of chromatography resins for each chromatographic step; chromatography operating conditions of each resin; protein mass loaded under each condition for each resin; protein mass collected under each condition in each time interval for each resin; gradient eluent salt concentration in each time interval for each resin; to determine: best resin at each step; best chromatography operating conditions; starting and finishing time for protein collection; so as to: maximize key performance metrics of the chromatography sequence, including the yield and purity of the target protein. T t where scst is the corresponding salt concentration of time interval t at chromatographic separation step s

Objective functions

Results

Conclusion

Literature Cited