To increase manufacturing flexibility and system understanding in pharmaceutical development, the FDA launched the quality by design (QbD) initiative. Within QbD, the design space is the multidimensional region (of the input variables and process parameters) where product quality is assured. Given the high cost of extensive experimentation, there is a need for computational methods to estimate the probabilistic design space that considers interactions between critical process parameters and critical quality attributes, as well as model uncertainty. In this paper we propose two algorithms that extend the flexibility test and flexibility index formulations to replace simulation-based analysis and identify the probabilistic design space more efficiently. The effectiveness and computational efficiency of these approaches is shown on a small example and an industrial case study.

No abstract

Due to their high spatial resolution and precise application of force, optical traps are widely used to study the mechanics of biomolecules and biopolymers at the single‐molecule level. Recently, core–shell particles with optical properties that enhance their trapping ability represent promising candidates for high‐force experiments. To fully harness their properties, methods for functionalizing these particles with biocompatible handles are required. Here, a straightforward synthesis is provided for producing functional titania core–shell microparticles with proteins and nucleic acids by adding a silane–thiol chemical group to the shell surface. These particles display higher trap stiffness compared to conventional plastic beads featured in optical tweezers experiments. These core–shell microparticles are also utilized in biophysical assays such as amyloid fiber pulling and actin rupturing to demonstrate their high‐force applications. It is anticipated that the functionalized core–shells can be used to probe the mechanics of stable proteins structures that are inaccessible using current trapping techniques.

No abstract

The problem of performing model-based process design and optimization in the pharmaceutical industry is an important and challenging one both computationally and in the choice of solution implementation. In this work, a framework is presented to directly utilize a process simulator via callbacks during derivative-based optimization. The framework allows users with little experience in translating mechanistic ordinary differential equations and partial differential equations to robust and fully discretized algebraic formulations, required for executing simultaneous equation-oriented optimization and to obtain mathematically guaranteed optima at a competitive solution time when compared with the existing derivative-free and derivative-based frameworks. The effectiveness of the framework in the accuracy of the optimal solution as well as the computational efficiency is analyzed in two case studies: (i) an integrated 2-unit reaction synthesis train used for the synthesis of an anti-cancer active pharmaceutical ingredient and (ii) a more complex flowsheet representing a common synthesis–purification–isolation train of a pharmaceutical manufacturing process.