Reducing Pharmaceutical Production Costs Using Continuous Manufacturing and Computational Modeling

Continuous manufacturing is the typical industrial practice characterized by a flow of materials through an assembly line often operating 24/7 to produce a product. I have been familiar with continuous manufacturing most of my life: my dad builds tires in 12-hour rotating shifts at a Goodyear factory, and I spent a summer as a chemical engineering intern in continuous manufacturing for refining oil into fuels. A striking exception to the rule of using continuous manufacturing to produce chemical products is the pharmaceutical industry, which uses batch manufacturing. Like bakeries that bake many batches of different pastries using regular or jumbo-sized kitchen equipment like mixers and ovens to accommodate the varied tastes of many customers, the pharmaceutical industry uses jumbo-sized organic chemistry lab techniques for producing batches of specialty chemicals with a range of demands. These techniques do not necessarily scale well to larger production. The differences between batch and continuous manufacturing can be illustrated by sub sandwiches. Subway’s highly successful marketing campaign for the $5 foot long has made them the go-to restaurant for affordable, made-to-order sandwiches. Subway uses an assembly line to create customized sub sandwiches in bulk (continuous manufacturing). One of my friends on a tight budget did an experiment in the economics of creating a sandwich at home (batch manufacturing) with quality on par with Subway. His question: is it cost effective to make your own sandwich versus buying Subway’s foot long? His answer was a resounding no. Not only did it cost a lot for the groceries, but he had to go shopping, prepare the veggies, and think of ways to eat all the groceries before they spoiled or eat the same type of sandwich repeatedly. (A similar case study is available online.) Compared to continuous manufacturing the batch method of manufacturing a sandwich has more expensive raw ingredients, can create a lot of waste, and takes more time. Also, product quality may vary widely between batches. The same things are typical of the pharmaceutical industry. The Novartis-MIT Center for Continuous Manufacturing (CCM) focuses on shifting the pharmaceutical industry to the more cost effective practice of continuous manufacturing (for more on the CCM click here and here).

In my research in the CCM, I have developed a computational model to describe a chemical assembly line for making pharmaceuticals. We add medicines and flavors to a liquid mixture, which is spread on a conveyor belt. This thin liquid layer has to be carefully dried to form an edible film like Listerine Pocketpaks breath strips or Fruit Roll-Ups before it leaves the equipment, and the film must have uniform concentrations of medicine and precise physical properties. I use mathematical descriptions of the time-varying processes involved in drying the film (heating, evaporation, motion of water molecules, and movement along the conveyor belt) to aid in design and quality control of the system. We use computational tools to monitor changes to the input conditions and undesirable disturbances, quickly calculate how these changes should affect the output using my model, and make corrections to the temperatures or speeds within the equipment to ensure that the final product reliably meets its quality specifications, preventing waste of materials, time, and energy. Thus, fabrication of high quality pharmaceuticals through continuous manufacturing is enabled by computational science and applied mathematics and should lower production costs.