Applications of statistical regression and modeling in fill-finish process development of structurally related proteins.

In order to increase efficiency and reduce cost, many biotechnology and pharmaceutical companies utilize platform approaches for discovery and development of structurally related therapeutic proteins. In the case of the monoclonal antibody modality, retention and reuse of prior development knowledge is especially useful to gauge risks, improve speed and reduce cost for developing similar molecules in the future. In this paper, we present two applications of statistical regression and modeling to help decision making during antibody drug product fill-finish process development. The applications are for estimating viscosity and filter capacity (Vmax) values. Experiments were performed to obtain relevant data sets of viscosity, protein concentration, density, and Vmax values for various candidate antibodies. Then, statistical models were developed and optimized to estimate viscosity and filtration Vmax values for new antibodies. Viscosity of protein formulations is an important physical property that impacts almost all manufacturing operations, as well as delivery or administration of drug products. Vmax is a critical parameter for filter size selection in manufacturing processes. Development and optimization of both models followed similar steps: identifying multicollinearity and interactions, removing unnecessary explanatory variables, performing appropriate data transformation, and evaluating different model options. We obtained 95% prediction limits for the mean and individual values from the models, and further verified the models by comparing predicted values with additional experimental data. These applications of statistical tools enabled leveraging prior knowledge for process development of new molecules belonging to the same class of structurally related proteins. Although the two specific models presented here may not be directly applicable for all proteins, the approach and methodology presented can be broadly useful for structurally related protein products during their development.

Factors affecting the particle size and in vitro release of bovine serum albumin from polyethylene glycol microparticles.

Bovine serum albumin was encapsulated in polyethylene glycol (20,000 MW) microparticles using a modified double emulsion method. Particle size, release rate, and encapsulation efficiency were measured for microparticles when varying multiple process factors such as sonication time, sonication intensity and drug loading. For all procedures, ninety percent of the particles were less than 2 microns in diameter. Particle size was reduced by extending the preparation's exposure to sonication over a range of 30 to 120 s, increasing the sonication intensity from 5 to 20 W or varying the polymer to protein ratios from 10 to 100:1. Perhaps of more interest, the particle size distribution was substantially narrowed at the upper end of each parameter compared to the lower end. Substantial differences were observed at the upper and lower end of each varied process parameter. Upon examination of protein release from microparticles over 120 minutes, the burst effect was minimized at the upper end of each parameter. Encapsulation efficiency and the time to release 50% of the entrapped protein were greatest at the lower end. In conclusion, production process parameters significantly affect protein release rates while their effect on particle size is less significant. These particles could be utilized when delayed, but not necessarily extended, release period is desired.