chapter  2
34 Pages

Artificial Neural Networks for Therapeutic Protein Engineering

In recent years, molecular medicine and biotechnology have been transformed by discoveries from the studies of the human genome and proteomics. The understanding of disease mechanisms at the cellular level is accelerating the discovery and development of new therapeutic agents. Of these, therapeutic proteins are a major focus of research and pose great challenges in development, delivery, safety, and stability. Over the last few years, there has been a substantial increase in the number of therapeutic peptides and proteins that are reaching the market. This tendency is likely to continue and even escalate in the near future. Approximately $20 billion is spent on biopharmaceutical research and development annually [1]. Much of this spending has resulted in approximately 2500 biotechnology-based drugs currently in development, 900 in preclinical trials, and over 1600 in clinical trials [2]. It is predicted that over the next decade the major activity in biotechnology

will be in human health care, involving both therapeutics and diagnostics [3]. The ability to formulate and manufacture therapeutic proteins is an issue of paramount importance.