Dr. Ron Bouchard is an Associate Professor in the Faculties of Law and of Medicine at the University of Manitoba, a CIHR New Investigator and an IP Osgoode Research Affiliate.
An earlier article on IP Osgoode by Chaubal gives admirable service to the issue of patent valuation, which presents to a wide audience as a kind of titanic contest of wills between those who prefer big incentives for innovation and those who focus of the social benefits, or outcomes, of innovation. The article focused on the tension between the business value known to be associated with patents and the ways of extracting value from those patents. In addition to the excellent survey of sources in the article, one can add fundamental work done by Mark Lemley at Stanford and Polk and Parchomovsky at Penn State.
In the world of life sciences products, there is, of course, a fundamental distinction to be made between an economic analysis (even one cast in a law and economics light) and a patent law analysis. This is because one is in service of utilitarian benefit and the other is in service of the patent bargain interpreted in light of the public health mandate of food and drug law. As noted by the Supreme Court of Canada in its seminal decisions in Biolyse and AstraZeneca, linkage regulations tying generic entry to brand-name patents must be made in a ‘patent-specific’ manner, thus highlighting the terms of the traditional patent bargain read in light of the so-called special provisions of linkage laws when parsing pharmaceutical patents.
Paul Grootendorst, Aidan Hollis, and I commented on the need for patent valuation in a recent issue of CMAJ. We discussed the limits of patent valuation in terms of Canada’s basket of intellectual property laws as it applies to pharmaceuticals. The article underscores the need for patent valuation and intellectual property law reform not only in regards to domestic drug costs and expenditures, but also in global jurisdictions where generic entry is controlled by linkage regulations.
In new work by our group, we have outlined a tandem of new methodological tools to identify and quantify new and follow-on drugs and patent valuation. The first is a harmonized method to quantify drug approvals, patents and associated chemical components that summarizes and extends our previous work on topic. The second provides a new “innovation index” that incrementally grades the value, not only for patents in the life sciences and other technology-intensive sectors, but also for associated regulatory approvals, chemical components, patent characteristics, etc. The innovation index values are based on evidentiary hurdles and prioritizations for several classes of “new” and “follow-on” drugs disclosed by drug regulators.
As indicated by the titles of the articles, one focuses on the quantitative side while the other focuses on the qualitative side of the analysis.
The Boston Article presents a harmonized method to collect, compare, and quantify regulatory approval data from multiple cohorts of new and follow-on drugs. We looked in some detail at about 2,000 regulatory approvals, 5,000 patents, and 130 chemical components. The analysis encompasses all drug classes enumerated, described and prioritized by domestic drug regulators. The drug classes were gleaned from the usual literature reviews, supplemented by several hours of consultation with Health Canada regulators and review of Health Canada Guidance Documents on topic. A second purpose of this work was to go beyond simplified descriptors of new and follow-on drugs found in the literature, to categorize classes of new, line extension and generic approvals according to the nomenclature used by regulators themselves. This is relevant, as we found different scholars use different approaches, and that these approaches were not always the same as those used by regulators themselves.
The innovation index work described in the accompanying Santa Clara Article was driven by the fact that almost all published patent assessment methods measure innovation using primarily quantitative methods, otherwise referred to as ‘counting methods.’ For reasons discussed in the references cited by Chaubal, and in Lemley and Polk & Parchomovsky, while quantitative models are widely considered to be problematic, a model that assesses patent value using qualitative methods has not yet emerged. A second reason for developing the methods is that is that even when many scholars and commentators do look at the “innovative” aspect of the data, they simply accept data provided by either Health Canada or the PMPRB in their respective annual reports (or those in other administrative bodies such as the U.S. FDA or E.M.E.A.).
While developing a novel scientific method for either obtaining or analyzing data is fraught with its own problems, this step nevertheless forms a necessary component of the “trial and error” heuristic typical in the hard sciences. As more individuals with prior experience in medical science enter law and legal scholarship, we will undoubtedly see more and more scientific studies of law, including importing of fundamental mathematical, statistical, curve fitting, modeling, and graphing methods.
In the Santa Clara Article, a qualitative innovation index is reported that we hope may fill some of the gaps in patent valuation. One of the figures, relating to regulatory approvals is shown below.
Fig. 1. Innovation Index Data for Total Approval Cohort. Bar graphs showing the number of total approvals expressed as a function of the level of innovation (LOI) before (a) and after (b) of generic approval data. c Brand approvals expressed as a function of LOI. Solid line is a fit of the data to a single exponential function. d Cumulative normalized brand approvals expressed as a function of LOI. Solid line is fit using a sigmoidal function.
The figure presents data for many classes of new and follow-on drugs and categorizes these classes using a linear scheme. Raw data values are given in the Y axis of Fig. 1a and 1b, the difference being generic data were subtracted in Fig. 1b to isolate data only from ‘innovator’ firms. The X axis in both panels represents the innovation index data. These data are referred to as transformed data, because the raw data pertaining to drug approvals, drug patents, and chemical components are transformed into qualitative values (0-15) using the methods outlined in the Santa Clara paper.
Fig. 1c shows the data in Fig. 1b fit to an exponential function. As can be seen by the close fit of the data to the function, the choice of an exponential relationship was well founded. The data are interesting as they demonstrate an exponential decline in the numbers of drugs in classes with relatively high innovation index values. In other words, the vast majority of drugs approved in Canada have a very low index value, and indeed are primarily follow-on Me Too drugs. Fig. 1d represents the normalized cumulative data, which is an approximation of “how fast” the innovation index data rise to their maximal peak – a fast rise, as we see here, suggests that most of the drugs approved are in the low index bins. Similar, though not identical, results were obtained with several Cohorts studied, including a wide Cohort of 2,087 drugs, a narrower Cohort of 95 of the most profitable drugs, and a similar Cohort of associated patents and chemical components.
The strengths and weaknesses of the hybrid “subjective-objective” nature of data transformation, and the similarities to subjective-objective hybrid models that are already widely accepted for use in the fields of drug approval, patent grant, and the adjudication of patent claims by the courts are discussed more fully in the Santa Clara Article.
The innovation index provides a means of weighing legitimate patent protection against perceived societal benefit. As such, it affords a qualitative measure of the innovative nature of drug patents that, when compared to counting methods, may more adequately reveal the outcome of development incentives for firms and regulating bodies insofar as these parties have conflicting interests.
The results from our analysis indicate that it is not the most innovative or even strongly innovative drugs that are attracting the greatest firm patenting effort. Rather, when gauged against development priorities disclosed by regulators, it is the least innovative drugs of all classes investigated that display the strongest patenting efforts.
In this manner, our data are contrary to the established dogma that the strength of patent protection is proportional to the level of innovation of a given product. The data obtained fully support the conclusion that cluster-based, or portfolio-based, drug development has become the dominant innovation strategy for both brand and generic firms.
Finally, the data suggest that the perception on the part of governments and the public to the effect that societal benefit comes as a kind of “natural consequence” of patenting may need to be reconsidered.