Research Tool Patenting and Licensing and Biomedical Innovation

Over the last two decades changes in technology and policy have altered the landscape of drug discovery. These changes have led to concerns that the patent system may be creating difficulties for those trying to do research in biomedical fields. Using interviews and archival data, we examine the changes in patenting in recent years and how these have affected innovation in pharmaceuticals and related biotech industries. We find that there has in fact been an increase in patents on the inputs to drug discovery (“research tools”). However, we find that drug discovery has not been substantially impeded by these changes. We also find little evidence that university research has been impeded by concerns about patents on research tools. Restrictions on the use of patented genetic diagnostics, where we see some evidence of patents interfering with university research, are an important exception. There is, also, some evidence of delays associated with negotiating access to patented research tools, and there are areas in which patents over targets limit access and where access to foundational discoveries can be restricted. There are also cases in which research is redirected to areas with more intellectual property (IP) freedom. Still, the vast majority of respondents say that there are no cases in which valuable research projects were stopped because of IP problems relating to research inputs. We do not observe as much breakdown or even restricted access to research tools as one might expect because firms and universities have been able to develop “working solutions” that allow their research to proceed. These working solutions combine taking licenses, inventing around patents, infringement (often informally invoking a research exemption), developing and using public tools, and challenging patents in court. In addition, changes in the institutional environment, particularly new U.S. Patent and Trademark Office (USPTO) guidelines, active intervention by the National Institutes of Health (NIH), and some shift in the courts’ views toward research tool patents, appear to have further reduced the threat of breakdown and access restrictions although the environment remains uncertain. We conclude with a discussion of the potential social welfare effects of these changes in the industry and the adoption of these working solutions for dealing with a complex patent landscape. There are social costs associated with these changes, but there are also important benefits. Although we cannot rule out the possibility of new problems in the future, our results highlight some of the mechanisms that exist for overcoming these difficulties

How many cases of disease in a pedigree imply familial disease?

The ability to perform whole-exome and, increasingly, whole-genome sequencing on large numbers of individuals has led to increased efforts to identify rare genetic variants that affect the risk of both common and rare diseases. In such applications, it is important to identify families that are segregating the rare variants of interest. For rare diseases or rare familial forms of common diseases, pedigrees with multiple affected members are clearly harbouring risk variants. For more common diseases, however, it may be unclear whether a family with a few affected members is segregating a familial disease, is the result of multiple sporadic cases, or is a mixture of familial cases and phenocopies. We provide calculations for the probability that a family is harbouring familial disease, presented in general terms that admit working guidelines for selecting families for current sequencing studies. Using examples motivated by our own studies of thyroid cancer and published studies of colorectal cancer, we show that for common diseases, families with exactly two affected first-degree relatives have only a moderate probability of segregating familial disease, but this probability is higher for families with three or more affected relatives, and those families should therefore be prioritised in sequencing studies

How many cases of disease in a pedigree imply familial disease?

The ability to perform whole-exome and, increasingly, whole-genome sequencing on large numbers of individuals has led to increased efforts to identify rare genetic variants that affect the risk of both common and rare diseases. In such applications, it is important to identify families that are segregating the rare variants of interest. For rare diseases or rare familial forms of common diseases, pedigrees with multiple affected members are clearly harbouring risk variants. For more common diseases, however, it may be unclear whether a family with a few affected members is segregating a familial disease, is the result of multiple sporadic cases, or is a mixture of familial cases and phenocopies. We provide calculations for the probability that a family is harbouring familial disease, presented in general terms that admit working guidelines for selecting families for current sequencing studies. Using examples motivated by our own studies of thyroid cancer and published studies of colorectal cancer, we show that for common diseases, families with exactly two affected first-degree relatives have only a moderate probability of segregating familial disease, but this probability is higher for families with three or more affected relatives, and those families should therefore be prioritised in sequencing studies

Sites of action and effects of lipoic acid on brain glucose metabolism.

<p>The scheme shows the PI3K/Akt pathway of insulin signaling and the effects of lipoic acid on the different components investigated in this study: (A) glucose uptake, (B) total GLUT3 and GLUT4 expression, (C) translocation of GLUT3 and GLUT4 to the plasma membrane from intracellular vesicles, (D) changes in IRS-Tyr⁶⁰⁸ /IRS-Ser³⁰⁷ ratio, (E) activation of Akt, (F) phosphorylation of GSK3β at Ser⁹, and (G) synaptic plasticity.</p

Age-dependent decrease of whole brain glucose uptake and the restorative effect of lipoic acid.

<p>Standard uptake value (SUV) was calculated after [¹⁸F]-FDG injection followed by PET and CT scanning as described in the Materials and Methods section. (A) Young mice, <i>n</i> = 34, <i>n</i> ≥ 6/group. (B) Old mice, <i>n</i> = 27, <i>n</i> ≥ 6/group. Upper panel: Representative combined images from PET-CT scanning of nonTg and 3xTg-AD mice ± lipoic acid; lower panel: Average SUV values with the error bar indicating ± SEM. *<i>P</i> ≤ 0.05, **<i>P</i> ≤ 0.01.</p

Age dependent changes in the LTP of the 3xTg-AD mice and the lipoic acid effect.

<p>LTP was induced at baseline intensity using theta burst stimulation (TBS) consisting of ten trains of five 100 Hz stimulation repeated at 5 Hz. Slope of EPSPs was measured and results normalized to the average value measured during the 10 min baseline period. Recording continued for at least 30 min following TBS and the last 5 min was used to calculate the LTP. Panels A, B, and C correspond to data from young mice, whereas, panels D, E, and F correspond to data from old mice (gray circles/bars – control (nonTg or 3xTg-AD), black circles/bars – fed lipoic acid (nonTg or 3xTg-AD + lipoic acid). A graph showing the first 10 min of baseline followed by the percentage of the baseline response elicited after TBS for 30 min for (A) young nonTg mice, (B) young 3xTg-AD mice, (D) old nonTg mice, (E) old 3xTg-AD mice. Bar graphs showing the measured LTP using % EPSP for the last 5 min of the response to TBS stimulation for (C) young mice and (F) old mice. Total <i>n</i> = 51 slices, <i>n</i> ≥ 5 slices/group and at least 3-4 animals/group. *<i>P</i> ≤ 0.05, **<i>P</i> ≤ 0.01.</p

Effect of lipoic acid on age-dependent changes in brain pAkt and pGSK3β.

<p>Western blot analyses of the levels of pAkt Ser⁴⁷³ and pGSK3β Ser⁹ in whole brain from nonTg and 3xTg-AD mice +/- lipoic acid. Left panels (A and B) correspond to data from young mice and right panels (C and D) to data from old mice. Bar graphs show the average pAkt Ser⁴⁷³ (normalized to loading control, Akt) and pGSK3β Ser⁹ (normalized to loading control, GSK3β) with error bars indicating ± SEM. Total <i>n</i> = 48, <i>n</i> ≥ 5/group. *<i>P</i> ≤ 0.05, **<i>P</i> ≤ 0.01.</p

Minimum EPSP, maximum EPSP, and stimulation intensity required to reach 1mV.

<p>Bar graphs of the levels of minimum EPSP, maximum EPSP, and stimulation intensity required to reach 1 mV as obtained during the I/O recordings in the stratum radiatum of the hippocampal CA1 region for nonTg and 3xTg-AD mice +/- lipoic acid. Left panels (A, B, and C) correspond to data from young mice and right panels (D, E, and F) to data from old mice. Bar graphs showing the minimum EPSP or the fEPSP slope values at 100 µA and the error bars indicating ± SEM for (A) young mice and (D) old mice. Bar graphs showing the maximum EPSP or the fEPSP slope values at 350 µA and the error bars indicating ± SEM for (B) young mice and (E) old mice. Bar graphs showing the stimulation intensity required to reach at least 1mV output and the error bars indicating ± SEM for (C) young mice (<i>p</i> < 0.01; F = 8.9 repeated measures ANOVA) (young nonTg <i>n</i> = 7, young 3xTg-AD <i>n</i> = 7) and (F) old mice (<i>p</i> < 0.003; F = 14.6 repeated measures ANOVA) (old nonTg <i>n</i> = 6, old 3xTg-AD <i>n</i> = 7). Total <i>n</i> = 51 slices, <i>n</i> ≥ 5 slices/group and at least 3-4 animals/group. *<i>P</i> ≤ 0.05, **<i>P</i> ≤ 0.01.</p

IRS activation status in the 3xTg-AD mice and the effect of lipoic acid.

<p>The levels of pIRS-Tyr⁶⁰⁸ (activated) and pIRS-Ser³⁰⁷ (inactivated) in whole brain from young and old nonTg and 3xTg-AD mice +/- lipoic acid were determined by western-blot analyses. Left panels (A, B, C, D, and E) correspond to data from young mice; right panels (F, G, H, I, and J) correspond to data from old mice. Bar graphs show the average pIRS Tyr⁶⁰⁸, pIRS Ser³⁰⁷, and pJNK Thr¹⁸³-Tyr¹⁸⁵ values after normalization with the loading control (IRS and JNK) and the error bars indicating ± SEM Total <i>n</i> = 48, <i>n</i> ≥ 5/group. *<i>P</i> ≤ 0.05, **<i>P</i> ≤ 0.01.</p

Membrane-associated GLUT3 and GLUT4 levels in brain.

<p>The levels of GLUT3 and GLUT4 in whole brain crude membranes from nonTg and 3xTg-AD mice +/- lipoic acid (young and old) were determined by western-blot analyses. Left panels (A, B, and C) correspond to data from young mice, whereas right panels (D, E, and F) correspond to data from old mice. Representative western blot images of GLUT3, GLUT4, and Na, K-ATPase (loading control) in whole brain crude membrane are shown. Bar graphs show the average membrane-associated GLUT3 and GLUT4 values after normalization with the loading control and the error bars indicating ± SEM. Total <i>n</i> = 32, <i>n</i> = 4/group. *<i>P</i> ≤ 0.05, **<i>P</i> ≤ 0.01.</p