Accelerating the Development and Transfer of Freeze-Drying Operations for the Manufacturing of Biopharmaceuticals by Model-Based Design of Experiments

Abstract

In the pharmaceutical industry, freeze-drying (also known as lyophilization) is often used to increase the shelf life of heat-sensitive biopharmaceuticals such as protein-based therapeutic drugs and vaccines. The most time-and energy-consuming step of a lyophilization process is primary drying. When a new system configuration (e.g., a new equipment, product formulation, or primary packaging) is to be tested in process development or when transferring to manufacturing facilities, one needs to characterize heat and mass transfer in the new configuration through a battery of experiments that can be very demanding. In this study, we use a model-based design of experiments (MBDoE) to design optimal experiments for fast primary-drying model parameter estimation. We show that MBDoE allows estimating all key heat-and mass-transfer parameters in a statistically meaningful way using one single, optimally designed experiment. Using data from industrial equipment, we test the effectiveness of the methodology on two typical problems to be faced in pharmaceutical process development and transfer: (i) transfer of product manufacturing between two different pieces of equipment and (ii) change of vial type for a given product in a given equipment. Results demonstrate that the proposed methodology can substantially ease the experimentation, thus accelerating the development of freeze-drying operations.

Data‐driven tools for the optimization of a pharmaceutical process through its knowledge‐driven model

“…This often implies that the shelf temperature and the chamber pressure are kept constant with time 14 . Yet, several studies demonstrated the potential of optimizing the drying recipe by considering time‐varying profiles of the manipulated inputs 2–4 …”

“…14 Yet, several studies demonstrated the potential of optimizing the drying recipe by considering time-varying profiles of the manipulated inputs. [2][3][4] In this study, we use the comprehensive knowledge-driven The model aims at describing the key performance indicators of the process, namely T p , _ m s , and the length of the frozen layer in the vials. As in the real equipment, the shelf temperature T s may be held constant during a lyophilization run, or changed according to a piecewise linear profile, with a maximum 1 K/min rate of change.…”

“…Most industrial freeze-driers are run at constant values of the manipulated inputs. However, several studies demonstrated that running the process with time-varying inputs can significantly reduce the duration of the freeze-drying and thus increase the process productivity, [2][3][4][5] yielding substantial economic benefits. The process is subject to upper constraints on some of its output variables and upper and lower bounds on the manipulated inputs, and this contributes to making the dynamic optimization problem computationally non-trivial.…”