Exploration and evaluation of modular concepts for the design of full‐scale pharmaceutical manufacturing facilities

Abstract

The design of biopharmaceutical processes is predominantly driven by the domain of experimental process design. This approach can be further improved by combining multiple domain information such as experiments, unit models, and flowsheet models. Approaches consisting of methods and flowsheet models provide the framework for exploring, analyzing, and ultimately evaluating the combinatorial space of all possible designs within the molecule‐to‐manufacturing value chain. In recent years, modular process designs are of interest in the pharmaceutical industry because of the shift toward multiproduct, mutiprocess processes. Therefore, a systematic approach for how to evaluate the utilization of the modular plug‐n‐play concept provides metrics that can propel modular design from a viable design alternative to the selected alternative for full‐scale manufacturing. The objective of this paper is to present such an in silico approach for the evaluation of modular designs. The approach is presented as a systematic method and then, is exemplified through the manufacture of an active pharmaceutical ingredient (API). The application of the method shows how to transition from a typical design‐for‐purpose design alternative to a modular design through the utilization of data, modeling, simulation, and uncertainty/sensitivity analyses for quantification of various selection metrics such as process robustness and flexibility.

A General Framework and Optimization Models for Integrated Capacity and Production Planning

“…7,8 Capacity needs can be reached by combined investments in inhouse and external manufacturing capability. 9,10 Investments in in-house manufacturing can be used to design and construct greenfield 11 or retrofit existing units. 12 Both options provide full control over the manufacturing landscape, but they are capital-intensive and could involve long construction lead times.…”

“…Production planning determines how to use existing resources most efficiently, but demand increases or new product introductions could result in a mismatch between installed system capacity and product demands. Demand forecasts are therefore developed and used to determine how to meet future demands while maximizing profitability. , Capacity needs can be reached by combined investments in in-house and external manufacturing capability. , Investments in in-house manufacturing can be used to design and construct greenfield or retrofit existing units . Both options provide full control over the manufacturing landscape, but they are capital-intensive and could involve long construction lead times .…”