Rational development of a serum-free medium and fed-batch process for a GS-CHO cell line expressing recombinant antibody

A serum-free medium (CHO-SFM) together with a fed-batch process was developed for the cultivation of a recombinant GS-CHO cell line producing TNFR-Fc. According to the metabolic characteristics of GS-CHO cell, a basal medium was prepared by supplementing DMEM:F12:RPMI1640 (2:1:1) with amino acids, insulin, transferrin, Pluronic F68 and some other ingredients. Statistical optimization approaches based on Plackett–Burman and central composite designs were then adopted to identify additional positive determinants and determine their optimal concentrations, which resulted in the final CHO-SFM medium formulations. The maximum antibody titer reached was 90.95 mg/l in the developed CHO-SFM, which was a 18 % and 10 fold higher than that observed in the commercial EX-CELL™ 302 medium (76.95 mg/l) and basal medium (8.28 mg/l), respectively. Subsequently, a reliable, reproducible and robust fed-batch strategy was designed according to the offline measurement of glucose, giving a final antibody yield of 378 mg/l, which was a threefold improvement over that in conventional batch culture (122 mg/l) using CHO-SFM. In conclusion, the use of design of experiment (DoE) method facilitated the development of CHO-SFM medium and fed-batch process for the production of recombinant antibody using GS-CHO cells.     

Selective removal of closely related clipped protein impurities using poly(ethylenimine)- grafted anion-exchange chromatography resin

Abstract

Proteolytic degradation is a serious problem that complicates downstream processing during production of recombinant therapeutic proteins. It can lead to decreased product yield, diminished biological activity, and suboptimal product quality. Proteolytic degradation or protein truncation is observed in various expression hosts and is mostly attributed to the activity of proteases released by host cells. Since these clipped proteins can impact pharmacokinetics and immunogenicity in addition to potency, they need to be appropriately controlled to ensure consistency of product quality and patient safety. A chromatography step for the selective removal of clipped proteins from an intact protein was developed in this study. Poly(ethylenimine)-grafted anion- exchange resins (PolyQUAT and PolyPEI) were evaluated and compared to traditional macroporous anion-exchange and tentacled anion-exchange resins. Isocratic retention experiments were conducted to determine the retention factors (k′) and charge factors (Z) were determined through the classical stoichiometric displacement model. High selectivity in separation of closely related clipped proteins was obtained with the PolyQUAT resin. A robust design space was established for the PolyQUAT chromatography through Design-Of-Experiments (DoE) based process optimization. Results showed a product recovery of up to 63% with purity levels >99.0%. Approximately, one-log clearance of host cell protein and two-logs clearance of host cell DNA were also obtained. The newly developed PolyQUAT process was compared with an existing process and shown to be superior with respect to the number of process steps, process time, process yield, and product quality.     

The simplex algorithm for the rapid identification of operating conditions during early bioprocess development: case studies in FAb' precipitation and multimodal chromatography

This study describes a data-driven algorithm as a rapid alternative to conventional Design of Experiments (DoE) approaches for identifying feasible operating conditions during early bioprocess development. In general, DoE methods involve fitting regression models to experimental data, but if model fitness is inadequate then further experimentation is required to gain more confidence in the location of an optimum. This can be undesirable during very early process development when feedstock is in limited supply and especially if a significant percentage of the tested conditions are ultimately found to be sub-optimal. An alternative approach involves focusing solely upon the feasible regions by using the knowledge gained from each condition to direct the choice of subsequent test locations that lead towards an optimum. To illustrate the principle, this study describes the application of the Simplex algorithm which uses accumulated knowledge from previous test points to direct the choice of successive conditions towards better regions. The method is illustrated by two case studies; a two variable precipitation example investigating how salt concentration and pH affect FAb' recovery from E. coli homogenate and a three-variable chromatography example identifying the optimal pH and concentrations of two salts in an elution buffer used to recover ovine antibody bound to a multimodal cation exchange matrix. Two-level and face-centered central composite regression models were constructed for each study and statistical analysis showed that they provided a poor fit to the data, necessitating additional experimentation to confirm the robust regions of the search space. By comparison, the Simplex algorithm identified a good operating point using 50% and 70% fewer conditions for the precipitation and chromatography studies, respectively. Hence, data-driven approaches have significant potential for early process development when material supply is at a premium.     

Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks

The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements.     

Taguchi DoE methodology for modeling of synthetic dye biosorption from aqueous effluents: parametric and phenomenological studies

Abstract

Biosorption technology has been acknowledged as one of the most successful treatment approaches for colored industrial effluents. The problems such as its high manufacturing cost and poor regeneration capability in the use of activated carbon as a biosorbent have prompted the environmental scientists to develop alternative biosorbent materials. In this context, as a sustainable green generation alternative biosorbent source, the discarded seed biomass from pepper (Capsicum annuum L.) processing industry was explored for the biotreatment of colored aqueous effluents in this study. To test the wastewater cleaning ability of biosorbent, Basic red 46 was selected as a typical model synthetic dye. Taguchi DoE methodology was employed to study the effect of important operational parameters, contact time, pH and synthetic dye concentration, on the biosorption process and to develop a mathematical model for the estimation of biosorption potential of biosorbent. The percentage contribution of each of these process variables on the dye biosorption was found to be 19.31%, 41.39%, and 38.74%, respectively. The biosorption capacity under the optimum environmental conditions, contact time of 360?min, pH of 8 and dye concentration of 30?mg L^?1, was estimated to be 92.878?mg g^?1 (R²: 99.45). This value was very close to the experimentally obtained dye removal performance value (92.095?mg g^?1). These findings indicated the high ability of Taguchi DoE technique in the optimization and simulation of dye biosorption system. The kinetic and equilibrium modeling studies showed that the pseudo-second-order and Langmuir models were the best models for the elucidation of dye removal behavior of biosorbent. The thermodynamic studies displayed that the dye biosorption was a feasible, spontaneous and exothermic process. This parametric and phenomenological survey revealed that the discarded pepper seed biomass can be introduced as a potential and efficient biosorbent for the bioremediation of colored industrial effluents.     

A statistical approach to improve compound screening in cell culture media

The Chinese hamster ovary (CHO) cell line is widely used for the production of recombinant proteins due to its high growing capacity and productivity, as well as other cell lines derived later than CHO. Adapting cell culture media for each specific cell line is a key to exploit these features for cost effective and fast product generation. Media supplementation is generally addressed by means of one‐factor‐at‐a‐time or classical design of experiments approaches but these techniques may not be efficient enough in preliminary screening phases. In this study, a novel strategy consisting in folding over the Plackett–Burman design was used to increase cell growth and trastuzumab production of different CHO cell lines through supplementation with nonanimal recombinant compounds. Synergies between compounds could be detected with a reduced number of experiments by using this methodology in comparison to more conventional fractional factorial designs. In the particular case reported here, the sequential use of this modified Plackett–Burman in combination with a Box‐Behnken design led to a 1.5‐fold increase in cell growth (10 × 10⁶ cells/mL) and a two‐fold in trastuzumab titer (122 mg/L) in suspension batch culture.     

Design space determination to optimize DNA complexation and full capsid formation in transient rAAV manufacturing

Recombinant adeno-associated virus (rAAV) vectors are a promising platform for in vivo gene therapies. However, cost-effective, well-characterized processes necessary to manufacture rAAV therapeutics are challenging to develop without an understanding of how process parameters (PPs) affect rAAV product quality attributes (PQAs). In this work, a central composite orthogonal experimental design was employed to examine the influence of four PPs for transient transfection complex formation (polyethylenimine:DNA [PEI:DNA] ratio, total DNA/cell, cocktail volume, and incubation time) on three rAAV PQAs related to capsid content (vector genome titer, vector genome:capsid particle ratio, and two-dimensional vector genome titer ratio). A regression model was established for each PQA using partial least squares, and a design space (DS) was defined in which Monte Carlo simulations predicted < 1% probability of failure (POF) to meet predetermined PQA specifications. Of the three PQAs, viral genome titer was most strongly correlated with changes in complexation PPs. The DS and acceptable PP ranges were largest when incubation time and cocktail volume were kept at mid-high setpoints, and PEI:DNA ratio and total DNA/cell were at low-mid setpoints. Verification experiments confirmed model predictive capability, and this work establishes a framework for studying other rAAV PPs and their relationship to PQAs.