Patent Pools and Clearinghouses in the Life Sciences: Back to the Future

Patents in the life sciences sector have sparked considerable debate over the past years. The grant of a series of patents for the screening of breast cancer (BRCA) genes led to wide controversy in Europe, the US and Australia. The grant of patents for plants resulting from essentially biological processes, also spurred stormy disputes. Decisions on the scope of plant biotech patents equally fueled a legal battle. Last but not least, the grant of patents for human embryonic stem cells in the US, triggered fierce discussions in Europe.In the ongoing debate, concern has been expressed about the potential hindering effect on innovation of the continuous increase of patents in the life sciences. The academic debate on the possible discouraging impact of the proliferation of patents was set in motion by the seminal article from Heller and Eisenberg ‘Can Patents Deter Innovation? The Anti-commons in Biomedical Research’ in 1998. Our past research aimed at contributing to the anti-commons debate in two ways. A first objective was to assess whether the prevailing assumption that an anti-commons problem was present in biomedical sciences held out in the field of human genetics. A second objective or our research was to explore solutions to the acclaimed anti-commons problem in the field of genetics. Rather than focusing on legislative (public ordering) measures, we explored to what extent collaborative licensing mechanisms (private ordering measures), such as patent pools and clearinghouses, could act as useful mechanisms to remedy possible adverse effects of fragmentation in the area of genetics.The present paper aims at re-visiting our former insights in a present-day context. First, we re-examine the patent proliferation phenomenon and related anti-commons problem by investigating the patent growth and re-assessing the existence of patent thickets in the life sciences. Second, and most importantly, we re-visit the collaborative license solution, by taking stock of new models and trends and by carrying out an in-depth analysis of operative models. We close by summarizing lessons learned from the past, which might be meaningful for (re-)writing the future.