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

Abstract

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.