James M. B. Evans

End-to-End Continuous Manufacturing of Pharmaceuticals: Integrated Synthesis, Purification, and Final Dosage Formation†

A series of tubes: The continuous manufacture of a finished drug product starting from chemical intermediates is reported. The continuous pilot-scale plant used a novel route that incorporated many advantages of continuous-flow processes to produce active pharmaceutical ingredients and the drug product in one integrated system.

Capacity Planning for Continuous Pharmaceutical Manufacturing Facilities

Abstract In this work, we develop an integrated optimization under uncertainty framework to address the problem of capacity planning in pharmaceutical supply chains that employ novel integrated continuous production schemes. Since the outcomes of clinical trials are uncertain, the problem naturally leads to a stochastic programming problem, which is formulated as a scenario-based multi-period mixed-integer linear programming (MILP) model. The number of scenarios grows exponentially with the number of potential products, resulting in large-scale MILP models that are intractable with standard solvers. We propose a novel solution method to solve the above industrial-scale problems that contain up to 10 potential products and nearly 60,000 scenarios.

The application of an automated plant-wide control strategy for a continuous pharmaceutical pilot plant

Continuous manufacturing has the potential to provide substantial improvements to the manufacturing of pharmaceutical products compared to traditional batch-wise manufacturing. A key challenge is the development of effective control strategies for this new type of process. This contribution presents the experimental application of an automated plant-wide control strategy for a continuous pharmaceutical pilot plant that produces a pharmaceutical product from start to finish in a continuous fashion. The performance of the automated control loops demonstrate the maintenance of the critical material attributes close to their desired setpoints for a sustained period of operation.

An Efficient Solution Algorithm for Large-Scale Stochastic Mixed-Integer Linear Programs1

Abstract In this work, we develop a decomposition strategy that exploits the underlying structure of two-stage stochastic programming (TSSP) formulations with mixed-integer linear programming (MILP) recourse problems. We demonstrate the effectiveness of the proposed algorithm using several examples of a capacity planning problem that arises in the pharmaceutical industry. The examples contain up to 10 potential products and nearly 60,000 scenarios. With the proposed decomposition algorithm, the solution time scales linearly with the number of scenarios, whereby a 10-product example with over 36 million binary variables, nearly 176 million continuous variables, and over 150 million constraints was solved in less than 3 hr of solver time.