Integrated Selection of Coformers, Solvents, and Operating Conditions for Optimal Process and Product Performance of Pharmaceutical Cocrystals

The poor aqueous solubility of many modern active pharmaceutical ingredients (APIs) can severely limit bioavailability. Cocrystals are emerging as a promising means of overcoming this limitation. Solution crystallization is a common technique for cocrystal synthesis. The type of solvent to be used and the operating conditions are important design decisions. In cocrystal systems, choosing a coformer is also nontrivial, as it affects process efficiency and possibly enhanced bioavailability. However, existing model-based crystallization process optimization approaches focus only on solvent and operating condition selection. Product performance optimization is typically not addressed simultaneously. In this work, a new model-based optimization framework is presented for the integrated selection of coformers, solvents, and process operating conditions. This approach considers the various trade-offs arising from the competing process and product performance criteria of cocrystal systems. A perturbed-chain statistical associating fluid theory-based equilibrium process model describes the cocrystallization process. The API dissolution behavior of the cocrystal in a rotating-disk apparatus is modeled by using a dynamic model. A hybrid branch-and-bound-continuous mapping approach is proposed as the mixed-integer nonlinear programming (MINLP) solution strategy, which involves decomposing the original MINLP problem into a series of computationally tractable nonlinear programming problems. The results show that the proposed solution strategy can successfully solve the optimization problem to identify a list of coformer and solvent candidates. Furthermore, the good predictive performance of the model is demonstrated experimentally. The optimal solvent provides a substantially higher solubility for the API, and thus a higher attainable yield than the ones reported in the literature. Finally, the impact of various coformer feeding strategies and the dissolution medium composition on the optimal solution is revealed. The presented approach is especially impactful during the early stages of process and product design, as limited experimental input is required.