Developing a Computer System to Support the Registration of Orphan Drugs

New drug development is an expensive activity for pharmaceutical companies: novel drugs can cost more than $1billion to develop. For economic reasons, there is often a focus upon the development of treatments for common diseases rather than for rare ones.

It can, however, make economic sense to develop what is known as an orphan drug: one that treats a disease affecting a smaller number of individuals. Developers of orphan drugs are given a closed market for 10 years so the development of these drugs can make economic sense to pharmaceutical companies. An increasing number of orphan drugs are now becoming available, for example as treatments for rare forms of cancer.

Under EU rules, orphan drugs for a specific disease can only be registrated for use with patients if they are not similar to each other, and tests are carried out to assess levels of similarity. In the past these tests have been conducted by human experts and this has been a time consuming, subjective process. Professor Peter Willett of the Information School at the University of Sheffield was approached regarding identification of an automatic process that could be used to calculate the similarity between orphan drugs by looking at their structures. Working with PhD student Pedro Franco, a computer system has been developed that calculates the similarity between potential orphan drugs and other orphan drugs already approved, based upon past decisions that human experts at a European regulatory competence authority have made. This system is now being used at this European regulatory competence authority as a quantitative measure for making decisions as to whether a compound can be given registered orphan drug status and made available to patients throughout the EU.

Pedro Franco’s work has created a system which gives a reliable evidence base for orphan drug decisions which affect the lives of people suffering from chronic or terminal illnesses, and demonstrates the real-world impact of research from the Information School.