Process Optimization Reality Checks

What works in the lab may not always work after scaleup, says contributing editor Angelo De Palma.

By Angelo De Palma, Ph.D.

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Process optimization encompasses widely varying activities whose principal goal is to reduce costs by eliminating process steps, improving yields, shortening cycle times and producing higher-quality product. Companies with limited manufacturing capacity must look inside their manufacturing and logistic operations to squeeze more product from existing assets.

Process improvements usually range from the difficult to the next-to-impossible, so it is essential to address them as early as possible in the product’s life cycle. Dowpharma (Midland, Mich.) engages chemists and manufacturing engineers once the molecule enters the development pipeline, says Jeffrey Dudley, global business manufacturing director. “If we don’t, we risk getting stuck with very slick chemistry that’s a bear to run at production levels, simply because no one has thought enough about turning a reaction into a process,” he says. “You can’t scrape the sides of a reactor like you can a beaker.”

Process Optimization article: Dowpharma's 20-liter reactors
20-liter bioreactors for fermentation process development. Courtesy of Dowpharma.

As a first step, Dow likes to keep unit operations and chemical transformations as simple as possible by avoiding “university” reactions and obscure reagents. Next, downstream purification offers the most accessible improvements, according to Dudley. A team at Dow introduces what he calls “enabling” purification methods that provide meaningful yield improvements while helping to streamline the overall process. In addition, Dowpharma avoids purchasing equipment for one-off projects, or machinery that cannot be used by other processes. As for unit operations, Dudley avoids those that would restrict the use of facilities for other products.

Dow makes extensive use of modeling and high-throughput, parallel experimentation for all manufacturing-related activities. Everything, from reaction kinetics to separations, plus specialized operations like drying, solvent recovery and solids handling, goes into a model generated by the company’s Engineering Science division.

Any contract manufacturing organization (CMO) typically faces a gamut of process optimization projects, from being handed back-of-the-envelope chemical structures to fully developed processes. Many processes are fully functional when they arrive, but some “are so bad they make you weep,” Dudley quips. “We had one customer come in with a 17-step process that we had to cut to seven steps.” When those situations arise, his group will often invest their own resources to demonstrate a more efficient process to the client. Convincing customers to change, Dudley says, takes strong science and not a little persuasion. Demonstrating that the new process will require a more modest capital investment or contracting fees, in the case of outsourced production, generally works.

Optimizing a multistep chemical/pharmaceutical process requires balancing what was learned during discovery and chemical development against the desire for a robust process. Unfortunately, development scientists too often fall back on what worked in the lab. As a result, they may not have collected the right information, or examined it in a way that applies to the scaled-up process. “Then they’re disappointed when they don’t achieve the same performance,” says Lionel O’Young, Ph.D., CEO of process development firm ClearWaterBay Technology (Walnut, Calif.).

An example of this is in solvent selection. Bench chemists typically focus on reaction solvents, ignoring the impact of extraction and crystallization solvents on the process, and on product purity. Removing trace solvents is routine at the bench, but much more difficult in a finished product. “Whatever you put into the process must be removed, so one must consider its contribution,” O’Young observes. “Sometimes it’s better to use the second-best solvent when scaling up.”

Consider driving forces

Another scaleup mistake related to optimization is failure to reckon with or combine what O’Young calls “driving forces” — factors that favor product over raw materials or impurities. The driving force in distillation is the vapor-liquid equilibrium; for crystallization, it is the solid-solute equilibrium.

Chiral purifications provide a good example of exploiting multiple driving forces to advantage. Chiral chromatography is expensive, time-consuming and solvent-intensive if the goal is 99.9% optical purity, while chiral resolution is inefficient and may introduce impurities at an inopportune time. But by combining these two operations, development scientists can often reach the desired purity objective quickly: A quick pass through a chiral column that yields 95% enantiomeric excess, followed by complexing the unwanted isomer with a chiral reagent, might do the trick.

One complaint often heard from scaleup specialists is that molecules tend to be thrown “over a wall” between research and development, development and pilot plant, pilot and commercial manufacturing. This is in part a result of the competition, and lack of communication, between chemists and chemical engineers. “Although the objective of both groups is the best process for the best product, they don’t always communicate that idea,” O’Young observes.

Symyx (Santa Clara, Calif.), which is evolving into a drug discovery and process development company, offers optimization products and software for multi-dimensional process optimization that lessens the specialty segmentation across the development lifecycle.

Symyx uses robotics and parallel microreactors to test solubility, optimize catalysts and test reaction conditions with very small amounts of material. Tying the systems together is software that presents all relevant data on reaction conditions and yields, effectively bridging the knowledge gap between discovery, process development and pilot scale, and one hopes manufacturing. 

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