Intangitopia is a knowledge platform initiated by an interdisciplinary group of 5 graduate scholars. Our primary focus is on the use of the legal, financial and technical arenas to leverage intellectual assets,
property and capital; in the light of the emerging knowledge economy.
Showing posts with label Tobias Thornblad. Show all posts
Showing posts with label Tobias Thornblad. Show all posts
Last month Pharmaceutical Executive published a summary based on its annual panel of heavy hitters in business development on best practices in licensing and M&A for the year ahead. The discussion was built around the latest findings from Cambell Alliance's 2012 Survey of Dealmaker Intentions. The survey had many interesting conclusions, and I thought that I would summarize a few of their thoughts here - I recommend reading the full article.
Most attractive in-licensing therapy areas
In-licensors expects most deals to be made in oncology, cardiovascular, CNS, metabolic and respiratory drugs. Oncology has a unique characteristic in that interest is high at all levels of the development cycle, including early stages. In fact, the interest in doing deals in pre-clinical, Phase I and II was found to be higher than for those in Phase III. All other major therapeutic segments (except immunology) showed a preference for candidates in phase III. An interesting side note from David Thomas (BIO) was that oncology, CV and CNS have the lowest success rates (less than one in 10 compounds make it from phase I to commercialization).
Trends within individualized treatments
FDA has approved 3 drugs in the last 12 months whose mechanisms are intended for a specialized target sub-population. Because the ability now exists (in some disease areas) to target and individualize therapies for patients, the per patient costs can be higher but it is also more likely the payers will support price premiums for some guarantee of better performance among a defined patient group. J&J did a deal with the UK NICE, to obtain payer buy in, in which it guaranteed that if Velcade did not work in a patient, it'd pay back the NHS.
About asset valuation
A decade ago the approach in valuing a target incorporated a lot of material that frankly is irrelevant, such as the number of patents on file, the number of employed scientists, or the square footage of lab space. R&D is not a numbers game. Pfizer consistently spent the most money on R&D and employed the most scientists, yet the return from its effort was poor. Nowadays companies are valued more on the basis of their strong cash flow, and little credit is given for development stage assets. The early 1990s saw enormous valuations for "ideas" with early stage IPOs, but with investors unable to sort through the good from the bad and value assets appropriately. Many companies subsequently failed.
Deal-making with academia
Universities have been empowered and push very hard on the IP front, taking a major interest in leveraging intellectual capital to generate profit. The IP and know-how from academia is important in drug development but often represents a small piece of a very complicated value equation, currently there seems to be little understanding that their contribution may only be a small part of the very long and expensive process to bring products to commercialization. Data packages from academia are rarely done to industry standards. Negotiations have on the other hand started to change. For a long time, academic partners insisted on terms that were entirely one-sided: take all the patent rights, refuse exclusivity in partnering, and reimburse them for 75 percent of the overhead costs. Today, the approach is more similar to those made with small biotech partners: with upfront payments plus royalties linked to milestones. Many TTOs now hire people with background in venture, biotech or Big Pharma - this could be a sign of an improving relationship.
Big Data is becoming an increasingly more popular concept and new companies are launched almost every week. The first to go public - Splunk - was recently valued at $3 billion when it launched earlier this year on Nasdaq. Many players besides Splunk are approaching Big Data and some of these include Wavii, Metamarkets, Palantir and IBM. It seems like there is a race to collect, curate and analyze the vast amounts of unstructured data out there. On the non-profit side, it is reported in the latest issue of Fast Company that the Wikimedia Foundation will complete its first phase of development of Wikidata in August. Wikidata will extract data from Wikipedia to create a database focused on facts and figures with less subjectivity.
At the same time, more data is becoming available from various sources. When Facebook launched its $100-billion initial public offering, its userbase surpassed 980 million users worldwide. This means that nearly one in seven of the world’s population seems to be comfortable sharing personal data over the internet. What started out as a social network where we shared photos has transformed into a forum where we are comfortable in sharing geographical coordinates of our current locations and even position on whether we are organ donors or not. It is perhaps not far fetched to predict that we will be comfortable to share even more in the future. In spite of this trend, most of us still are more circumspect when it comes to sharing genetic, physiological and medical information online. An editorial in this month's issue of Nature Biotech brings up this issue and states that one key reason for poor uptake could be that there is still no simple and transparent way to track how personal data are being used, let alone a means to opt into, or out of, research using the data.
Portable legal consent (PLC)
To tackle this problem a new type of consent process - Portable Legal Consent (PLC) - has been launched. The PLC aims to simplify informed consent and allow feedback of results to any participants. This will put donors in greater control of their own data, which will hopefully lead to more data being shared. The feedback mechanism is thought to provide an incentive for individuals to donate data since the patients in the 'traditional model' usually learn nothing of the research outcomes from their specimens or data, which is particularly true if the results are never published. This, however, demands that informed consent is overhauled.
NBT writes that in the United States: informed consent is based on a uniform 20-year-old, almost pre-internet set of regulations, colloquially known as the ‘Common Rule’. Under the Common Rule, the patient’s signature on the consent form following an ‘informing’ conversation creates a legal agreement that allows research (or medical procedures) to go ahead. The scope of research depends on the consent form; in some cases, biological specimens and associated data can be used only in the research described in the original consent. Alternatively, consent can be broader, extending to future research, within or without some limits. Most donations of tissue and data can be used only once, in the original research project. Any subsequent analysis, reanalysis or pooling with other data is breaking the law.
The PLC (http://weconsent.us) provides a solution by permitting research participants to contribute their data to a common consented environment enabling broad and multiple research uses. Importantly, patients can withdraw their data from the database at any time. The withdrawal does not operate retrospectively, so derivatives of the data, or even copies of it held on computer drives, are likely to remain available. Publications based on their data would also be unaffected by data withdrawal.
Creating the incentives to share personal data
A crucial element, besides trust, in building a successful platform where personal data is shared is of course to provide incentives to share. Google Health failed to attract users because of many reasons but one may have been that people saw no real benefit in uploading their data. One incentive for donating data or specimens to medical research could be learning about how the data has been useful for the research community. Another may be to get access to a network of patients with the same condition (PatientsLikeMe) or learning about their genetic profile (23andMe). With the latest trends in the Quantified Self movement there will likely be multiple other incentives to share data from consumer-diagnostic tests, biometric devices to measure glucose levels, heart rate and indications of stress. The latest issue of Entrepreneur writes that Quantified Self company FitBit says that the average user takes 43% more steps per day when they use their device. The biggest incentive, however, likely is the fact that FitBit users can track their progress online through infographics, pie charts and stated goals. For some products it may even be enough to just compile data into Facebook statuses to show friends and family how healthy you are (e.g. RunKeeper, WOD of the day, etc).
The question is not if we will share, it is when we will share our clinical data and where.
Much of the
Life Science blogosphere has focused on the Supreme Court’s decision in Mayo
v. Prometheuslately. The decision
has jolted the biotech industry through a unanimous ruling that threw out two
medical-testing patents and has questioned the very concept of what a law of
nature is when it comes to medical testing. The case involved a group of drugs used to
treat diseases such as Crohn's and ulcerative colitis. For patients taking
thiopurine drugs for such immune system diseases, physicians must adjust the
dose to make sure the drug works while side effects are avoided. The Prometheus
patent described connecting the level of certain chemicals in the blood to the
thiopurine dosage to balance between too high or too low dosage. This led justice
Breyer to conclude that the Prometheus patents recited laws of nature, which
has sparked a big discussion. Personalized medicine and
companion diagnostics are examples of areas that are heavily affected by this
and given the fact that thousands of patents for diagnostic tests have been
issued in the past two decades - the court ruling could have dramatic effects
on the biotech industry. One of the most cited examples is of course Myriad
Genetics’ test for breast-cancer risk using information about the BRCA1 and
BRCA2 genes. To follow the debate I recommend visiting Patent Baristas,
Patently BIOtech or Holman’s Biotech IP Blog.
On the
pharma/biotech side of the industry there is also activity in the IP-realm. Due
to the commotion around Prometheus, IP analysis around pharma/bio seems to have
been in the background lately. Therefore, this month, Intangitopia is providing
you with an overview of IP lawsuits, settlements and infringements in the pharma/biotech
space covering Jan-June 2012:
As can be
seen in the diagram above, most of the activity has been in the disease groups:
1) nervous system diseases, 2) nutritional and metabolic diseases, and 3) respiratory
tract diseases. Below are summaries of the top IP news in media within these disease groups.
Crowdsourcing is a concept that is used ever more often when knowledge intensive industries are discussed. Arturas Vedrickas today briefly describes a location based social network known as Foursquare on the CIP FORUM blog that is planning to harness its large base of users. There are currently many interesting examples of crowdsourcing initiatives in the IT-industry where of course Wikipedia is one of my personal favorites. However, this concept is certainly spreading into other knowledge intensive industries such as the biomedical society. This is perhaps not surprising, given that the biotech revolution has transformed the whole pharma industry into a data driven reality where knowledge is key.
Crowdsourcing
Crowdsourcing refers to outsourcing tasks that would usually be performed by people within a company or institution to an outside 'crowd' of people, outside the organization. This way of harnessing the power of the many differs from other types of open innovation in that members of the crowd nowadays has grown accustomed to generally expect some kind of incentive or reward. This has been a rather quick transition given that the term 'crowdsourcing' itself was coined less than 5 years ago, by Jeff Howe writing for Wired magazine.
Harnessing the Global Brain in Life Science
Early efforts in the biomedical field to use this innovation strategy was - not surprisingly - implemented in the fields most closely similar to the IT-industry. Namely bioinformatics. Some of these efforts included BioJava, BioPerl, BioPython, Bio-SPICE and BioRuby. Two early initiatives, in 2000, without the word "bio" in their names were Screensaver Lifesaver and Folding@Home. Both of these harnessed the power of volunteers. Foldin@Home models the thermodynamics of protein folding, while the Screensaver Livesaver used 3 500 000+ volunteers to run molecular modeling simulations, docking potential ligands into the binding sites of known drug targets for various diseases.
Indianapolis based pharma giant Eli Lilly was one of the first Life Science companies to implement this way of thinking. In fact, it is more accurate to say that Eli Lilly was part of creating and defining the field of openness within Life Science. At the same time as Folding@Home and SL were launched, Sidney Junell, then head of Lilly, organized a group of executives to explore new ways of working. Impressively, no fewer than three successful open innovation companies—InnoCentive, based in Waltham, Massachusetts, YourEncore, in Indianapolis and Cincinnati, and Collaborative Drug Discovery, based in Burlingame, California—sprang from these discussions. For the past decade, Eli Lilly has maintained a leading position in the Life Science field of internet-led open innovation.
Several interesting initiatives have sprung up over the last few years in this industry. Within genomics, 23andMe (a model I have written about here on Intangitopia in the past) is a company that accumulates data from its customers through crowdsourcing. Customers of the personal genomics startup who submit samples of their saliva for genotyping have the opportunity to take part in surveys, which, when combined with their genetic information, can provide useful information to the wider group about genetic linkage. This approach of course becomes even more powerful still when genetic data are combined with contributions from patients. For Parkinson's disease 23andMe tries to achieve this through to collecting genetic data from individuals in a partnership with PatientsLikeMe and the Michael J. Fox Foundation. Patients Like Me, in turn, is also a crowdsourcing site that allows its - by now 80 000 - members to share details of symptoms and treatments with each other, as well as with the research and medical communities. The reward in this case is to learn more about one's condition through the experience of others.
Business implications and using crowdsourcing as innovation strategy
Given the intellectual property difficulties that is generated by the volunteer computing models (Folding@Home and Screensaver Livesaver), these have largely been embraced by the academic and not-for-profit sectors. But what about the other models: are these also incompatible with IP? Of course not.
A recent example of an implemented corporate model for harnessing crowd-sourcing is that of Life Technologies (LT). The company announced in December a $7 million crowdsourcing initiative called the Life Grand Challenges Contest. Focus of the contest is on LT's new Personal Genome Machine acquired from Connecticut–based startup Ion Torrent. The sequencing technology costs $50,000 to buy and can sequence a sample at a cost of $500 in just two hours. But that is apparently not good enough for Jonathan Rothberg, founder and CEO of Ion Torrent. The first three $1-million challenges in the contest ask innovators to devise ways to make Ion Torrent's technology even faster, cheaper and more accurate.
Implementing crowdsourcing in your IP-strategy
A model that I have seen successfully implemented in the IP-strategy of a large biotech company actually used crowdsourcing. This particular company often used the Innocentive platform for this very purpose. Innocentive connects a community of solvers with seekers (companies that post technically challenging research or management problems). Any individual may register as a solver. Solvers pay no fees, but most formally register for a challenge before they receive the full, confidential outline of the project. While seekers pay to register on the site and again to register each challenge. If a problem is solved, pre-defined reward(s) is/are paid to one or more solvers out of the registration fee. Intellectual property is thus protected under secrecy agreements (formal registration for solvers) and transacted to the seeker as a reward is paid to a solver.
When the company had made a new discovery it posted the problem (not its solution/discovery) on Innocentive. This way, the company was typically able to "purchase" additional solutions to the same problem by paying out Innocentive rewards. An approach that was much cheaper than inventing these solutions in-house. Patent applications covering the various solutions would then be filed and consequently a much stronger position against invent-around risks resulted.
Alternative IP-strategies and IP-based business will be discussed during CIP FORUM in May.
(Btw, don't miss the early bird fee before the end of March)
Intangitopia has unfortunately remained dormant for quite a while now with all authors being busy with their jobs. This has certainly been true for myself too as I recently changed jobs to work in a startup within the medical device industry. But since I still do some research on the topic IP strategy within the Life Science industry I thought that I should write a blog post based on my latest article (co-published with Ulf Petrusson and Henrik Rosén at the Center for Intellectual Property, University of Gothenburg).
Global Technology Markets – the role of open intellectual property platforms
In the article we analyze the business phenomena where multiple stakeholders collaborate, package and transact upon technology in systems with ‘venture-market hybrid’ characteristics. We term these complex interactions, including the structures and stakeholders who build and participate in them, ‘open IP platforms’ in lack of a previous descriptive term. As many of our Intangitopia readers will recognize this concept captures much of the processes that are seen in open innovation, distributed innovation, open source, public-private partnerships and crowd-sourcing concepts. Activities on these platforms typically include collective gathering, creation and development of knowledge assets to which openness is regulated through the determined level of access, ownership and utilisation rights. We analyse how IPRs (Intellectual Property Rights) and contracts operate as a set of self-regulatory tools in the construction of platforms where technology is accessed openly but still is priced on what could be described as technology markets.
The impact of these platforms on new and developing technology intensive markets is evident and growing, though it is not yet fully clear what it is that drives the creation of such platforms, how the specific characteristics of the platforms should be understood, and what kind of markets arise as a result of these platform interactions, whether on the platform itself or in a market context where the platform participants can act as a single entity. If one wishes to understand the future of knowledge based business it will be necessary to understand and begin to answer these questions, as it is our strong belief that relating to open IP platforms, whether through participating or acting on the markets shaped by the platforms, will increasingly become a necessity. For this reason, the article presents a first attempt at answering some of these questions, by outlining the trends that have driven the emergence of the business phenomena we define as open IP platforms, and by presenting an initial concept for a framework model to understand and classify these platforms by their common characteristics.
A new collaborative logic - case study: GSM
A case study of the GSM (Global System for Mobile Communication) platform has been incorporated to illustrate a transition over time as a new logic for collaborative, market-based development coalesces. We describe a historic development that views patents as monopoly interventions in the free operation of the market. Something we argue to be a danger that need to be limited or surrendered if a common standard is to be developed. Our literature review shows a perspective on the patent as a tool through which actors can build a standard or platform for developing the standard as notably absent; at best, patents are seen as a necessary evil incentivizing innovative contribution. While it would be going much too far to say that there is not an inherent danger in the combination of strong individual IPR-based protection and the technology lock-in effects of standardization, it is our view of that it is possible and necessary to see patents as the building blocks enabling collaborative platforms such as GSM.
Framework
To further understand the underlying building blocks and enabling legal tools that provide the foundation for open IP platforms, we suggest a framework. The ambition to build platforms can only be approached by understanding the building blocks that create the platforms and design the platform characteristics. These building blocks can take the form of relationships or tacit connections, but our focus remains on the explicit legal instruments that build the platforms, and the role of IPR’s in the platform as constructive elements.
In the article we present frameworks for measuring the following parameters;
·Level of system/tool leverage
·Level of collaboration
·Level of public responsibility
·Level of platform governance
·Level of IPR claims
By applying the framework to the Innovative Medicines Initiative platform the tools that govern openness and stimulate creation of new knowledge markets are made visible based on the information in;
·IPR policies;
·Collaboration policies;
·Policies on exploitation of background and foreground;
Our case study results shows that a novel creation of knowledge markets can be argued to occur whenever IP transactions – that provide access rights - take place between participants in IMI. By intellectually categorizing utilization of the generated project results in pre-competitive arenas (e.g. clinical trials and preclinical research) as IMI Research Use, an internal market is created where commercialization is not the end goal. The altruistic motives behind making tools available at little or no cost for the purpose of ensuring that pharmaceuticals are safe may be questioned by tool-supplying actors who do not normally perform clinical trials themselves. But the fact remain that an internal knowledge market with different conditions than standard commercially negotiated terms is the result. And by using the same research data with the intent to commercialize them (IMI Direct Exploitation) as therapeutic, diagnostic, screening, or recombinant tools - another knowledge market with completely different norms emerges.
The third, and arguably most radically different, ownership and access norms result when the generated results fall within the IMI Sideground definition. The outcome in this case can be interpreted as separating the generated results from the platform altogether and join the other assets of the creator’s proprietary portfolio, which have not been brought onto the platform. This means that the owner of Sideground can commercialize the results without having any access right obligations specified in the IMI IP Policy.
Since the rules and norms that govern the scope of the three knowledge markets are determined, before accession to a project, when drafting the Project Agreement – drafting this plays a major role. Successfully negotiating terms favorable to one’s self-interests is thus likely to be seen as a key activity for each participant. Developing the ability to clearly objectify and define Background, before negotiations are started, as well as implementing an intellectual asset management system to capture valuable results are consequently imperative factors to create strong market positions in these internal knowledge markets.
Conclusions
We see a development where intellectual property is increasingly used to claim early research. If we are to have platforms that increasingly make title claims on academic results, those platforms must also be capable of managing a structured contribution to the public domain to ensure that it is not impoverished by shortsighted commercial approaches. Both the ICT and Life Science markets are increasingly characterized by complex transactions of IP and cross-licensing, and we therefore need to develop and strengthen mechanisms for openness such asFRAND. This will be the only way to support proportionality and limit destructive ransoming of these platforms.
Our presented IMI and GSM examples demonstrate these issues from different perspectives. The GSM history, which by any measure is a successful collaboration, shows how a lack of structural clarity and central responsibility in open IP platforms can lead to unilateral royalty demands and the rise of complexities when trying to navigate the patent landscapes. In the case of IMI we can identify how mechanisms for sophisticated claiming of early stage research result. But also how this creates higher demands on the capabilities of universities and academic research institutions to wield these mechanisms appropriately and safeguard the public interest of independent and uninfluenced research.
Our collective abilities to develop open IP platform will in many ways define which kind of businesses that will be created and which business climate we will generate. The specifics of how to constructively develop these platforms to generate an appropriate business climate; how to safeguard public interests, build up public domain, ensure open and functioning markets, manage complex knowledge transfer and technology interdependence, etc., is a future discussion that we believe is both inevitable and vital to the construction of a functioning knowledge economy
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Opinions expressed here and in any corresponding comments are the personal opinions of the original authors and does not necessarily correspond with the ones of their employers, clients or related entities.
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