Making Industrial Processes More Cost Efficient

Pharmaceuticals

Nonlinear models speed up development of an enantioselective enzymatic process for a pharmaceutical intermediate

Abstract
Good process development can result in better production economics than can be achieved by operating in countries with low labour costs. To be able to produce the product most cost efficiently while fulfilling the requirements on product properties and other constraints, it is very advantageous to have mathematical models which relate the important variables in the process.

Process development of biotechnological processes is often carried out by performing a large number of experiments, for the simple reason that development of mathematical models of most biotechnological processes is too complicated. Physical modeling is not very effective, neither is empirical modeling with conventional linear statistical techniques. New techniques of nonlinear modeling have altered this situation entirely, and offer a tremendous advantage in quantitatively describing complicated biotechnological processes. Nonlinear models have successfully been used for a large number of processes in various industrial sectors. In the case described in this article, they turn out to be an order of magnitude better than linear models.

How these models in combination with appropriate mathematical tools help in efficient process development with much less experimentation is demonstrated with an example. Besides a little bit of theory, the article also explains the strengths and limitations of these new techniques.

Introduction
Pharmaceutical intermediate production technology has undergone a lot of progress over the last couple of decades. Biotechnological processes are better routes for several kinds of products including some kinds of pharmaceutical intermediates. A particularly interesting category is that of enantiomers. There are enzymes which catalyse the conversion of primarily one enantiomer, which is also an effective means of separating enantiomers.

  Bio_Process.pdf