Streamlining process characterization efforts using the high throughput ambr® crossflow system for ultrafiltration and diafiltration processing of monoclonal antibodies.

Commercial process development for biopharmaceuticals often involves process characterization (PC) studies to gain process knowledge and understanding in preparation for process validation. One common approach to conduct PC activities is by using design-of-experiment, which can help determine the impact process parameter deviations may have on product quality attributes. Qualified scale-down systems are typically used to conduct these studies. For an ultrafiltration/diafiltration (UF/DF) application, however, a traditional scale-down still requires hundreds of milliliters of material per run and can only conduct one experiment at a time. This poses a challenge in resources as there could be 20+ experiments required for a typical UF/DF PC study. One solution to circumvent this is the use of high-throughput systems, which enable parallel experimentation by only using a fraction of the resources. Sartorius Stedim Biotech has recently commercialized the ambr® crossflow high-throughput system to meet this need. In this study, the performance of this system during a monoclonal antibody UF/DF step was first compared with a pilot- and a manufacturing-scale tangential flow filtration (TFF) system at a single operating condition. Due to material limitations, it was then compared to only the pilot-scale TFF system across wider ranges of transmembrane pressure; crossflow rate; and diafiltration concentration in a PC study. Permeate flux, aggregate content, process yield, pH/conductivity traces, retentate concentration, axial pressure drop, and turbidity values were measured at both scales. A good agreement was attained across scales, further supporting its potential use as a scale-down system.

Prediction of lab and manufacturing scale chromatography performance using mini-columns and mechanistic modeling.

Chromatography is a cornerstone of biologics downstream purification processes, and there is an ever increasing demand for improved speed and efficiency in process development. Scale-down models are used in process development to optimize operating conditions and study process robustness while expending as little time and material as possible. The advent of automated liquid handling systems and miniature columns has taken the efficiency of process development to another level by allowing up to eight column runs in parallel with column volumes under 1 ml. As expected, results between these miniature columns and typical lab/manufacturing scale columns can deviate due to scale dependent and/or configuration dependent differences. Regulatory guidelines do not require an exact match in scale-down and large scale data, but do require that small scale models account for scale effects, be representative of the commercial process, and be scientifically justified. Therefore, it is important to gain insight into what causes differences between scales and account for them during development. Mechanistic models can be used to understand the physics of the process (fluid flow, mass transfer, etc.) as a function of scale, and provide explanation for deviations that may be observed. We have used mechanistic modeling to study the factors leading to differences in pool sizes observed between scales, and to make predictions on lab scale pool sizes from miniature column data. Results indicate that changes in mass transfer parameters, specifically axial dispersion, between scales leads to the observed differences in pool size. Additionally, we have studied the effect of system differences between automated liquid handling systems and conventional preparative chromatography systems on elution pool volume. This work provides new insight into the fundamental differences observed between scales and overcomes the challenge of enabling the use of miniature column chromatography as a scale-down model for process characterization.

Development of a Coarse-Grained Model of Chitosan for Predicting Solution Behavior.

A new coarse-grained (CG) model of chitosan has been developed for predicting solution behavior as a function of degree of acetylation (DA). A multiscale modeling approach was used to derive the energetic and geometric parameters of this implicit-solvent, CG model from all-atom simulations of chitosan and chitin molecules in explicit water. The model includes representations of both protonated d-glucosamine (GlcN(+)) and N-acetyl-d-glucosamine (GlcNAc) monomers, where each monomer consists of three CG sites. Chitosan molecules of any molecular weight, DA, and monomer sequence can be built using this new CG model. Discontinuous molecular dynamics simulations of chitosan solutions show increased self-assembly in solution with increasing DA and chitosan concentration. The chitosan solutions form larger percolated networks earlier in time as DA and concentration increase, indicating "gel-like" behavior, which qualitatively matches experimental studies of chitosan gel formation. Increasing DA also results in a greater number of monomer-monomer associations, which has been predicted experimentally based on an increase in the storage modulus of chitosan gels with increasing DA. Our model also gives insight into how the monomer sequence affects self-assembly and the frequency of interaction between different pairs of monomers.

Simulation Study of Hydrophobically Modified Chitosan as an Oil Dispersant Additive.

Hydrophobically modified chitosan (HMC) is being considered as a possible oil dispersant additive to reduce the volume of dispersant required in oil spill remediation. We present the results of discontinuous molecular dynamics (DMD) simulations intended to determine how the HMC architecture affects its ability to prevent oil aggregation. The HMCs have a comb copolymer architecture with hydrophobic side chains (modification chains) of various lengths (5-15 spheres) to represent alkane chains that are attached to the chitosan backbone. We calculated the oil's solvent accessible surface area (SASA), aggregate size distribution, and aggregate asymmetry at various values of the HMC modification chain length, modification density, and concentration to determine HMC efficacy. HMCs with long modification chains result in larger oil SASA than HMCs with short modification chains. For long modification chains, there is no increase in oil SASA with increasing modification density above a saturation value. The size distribution of the oil aggregates depends on the modification chain length; systems with long modification chains lead to large aspherical aggregates, while systems with short modification chains lead to small tightly packed aggregates. A parametric analysis reveals that the most important factor in determining the ability of HMCs to prevent oil aggregation is the interaction between the HMC's modification chains and the oil molecules, even when using short modification chains. We conclude that HMCs with long modification chains are likely to be more effective at preventing oil aggregation than HMCs with short modification chains, and that long modification chains impede spherical oil droplet formation.