Effect of Droplet-Wake Phenomena on Mixing-Sensitive Pharmaceutical Reactions - Pharmaceutical Technology

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Effect of Droplet-Wake Phenomena on Mixing-Sensitive Pharmaceutical Reactions


Pharmaceutical Technology




Chemical reactions in pharmaceutical and fine-chemical applications typically are carried out in systems that include at least one liquid phase. In many cases, another phase exists, and the reaction may be carried out in a gas–liquid, liquid–liquid, or solid–liquid system. In these systems, the mixing and the phase dynamics are highly complex and may have an impact on product distribution. Such reactions are termed mixing-sensitive. Problems associated with mixing-sensitive reactions often are seen during process scale-up, where impurities and byproducts that didn't exist at the laboratory scale suddenly appear at the production scale. Examples of mixing-sensitive GMP reactions include the hydrogenation of a dinitrile over a Raney-type nickel catalyst (1), the liquid-phase hydrogenation of benzene in metal-hydride slurry systems (2), and the diastereoselective iodohydroxylation of an alkyl enamide to form an intermediate for the HIV inhibitor "Crixivan" (Merck, Whitehouse Station, NJ) (3). In all these systems, the transport in the reactor can influence the selectivity of the chemical reactions, which causes the product distribution to deviate from the one prescribed by the kinetics. In addition, transport effects can influence the enantioselectivity of chemical reactions such as asymmetric catalytic hydrogenations. A specific example of such a reaction is the asymmetric hydrogenation of ethyl pyruvate (4).

For slow reactions, large-scale mixing patterns in the reactor are important for product distribution. For relatively fast reactions, however, such as reactions with shorter time scales than the mixing time scale, the problems associated with mixing-sensitive reactions are even more complicated because micromixing dictates the product distribution and the selectivity. Fast multiphase reactions occur close to the interface. Therefore, the local mixing environment has the strongest impact. Thus, for fast reactions taking place in liquid–liquid systems, the wake and the mixing behind the individual droplets determines the selectivity and byproduct formation. This article explores the impact of wake dynamics of individual droplets on the product distribution of a fast liquid–liquid reaction. To the best of the authors' knowledge, this is the first paper that addresses this effect.

Background

Multiphase reactions are an important and interesting class of reactions, and significant efforts in the literature have been devoted to this topic (5–9). The importance of mixing in reactive systems is paramount because it brings various reactive species in molecular contact, which is a prerequisite for a chemical reaction to take place. Better mixing brings reactants together faster, thus enhancing the rates at which reactions occur. Poor mixing may impede certain reactions, thus altering a process's product distribution (10–14). If fast side reactions are suppressed by low mixing rates (because the reactants for the side reactions are not brought into contact), however, then poor mixing can improve product quality.

Although micromixing and its influence on reaction behavior have been studied (15–18), the industrially important case of reactive micromixing in bubble and droplet swarms has received comparatively little attention. During the past five years, our group has investigated the micromixing effects close to individual bubbles and in bubble–droplet swarms (19–22). The main results of our studies have shown that the selectivity and yield of fast chemical reactions are determined by local micromixing—that is, by the local flow pattern around individual bubbles and droplets in a swarm. If the reaction is heterogeneously catalyzed, then the local density of the catalyst close to the bubble is important as well. Thus, for fast liquid–liquid–solid reactions, the liquid and particle flow close to individual droplets determines reactor performance (23).


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