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!Open Source Pharma
Background White Paper
Rockefeller Foundation Bellagio
Center July 2014
Publicly licensed under CC BYi SA 4.0
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From Hierarchies To Networks !!
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!Abstract The contemporary pharmaceutical company is typically large, expensive, slow-‐moving, and a risky – if very occasionally incredibly lucrative – investment. Most firms fail, most projects fail, and the industry as a whole has trouble getting drugs to market at anything close to a fair price. !While the biology is fiendishly complex and difficult, and represents the largest single barrier to finding new drugs, there is a real argument to be made that the organizational structures of the firms themselves bear part of the blame. Firms are heavily centralized into command-‐control hierarchies derived from manufacturing concerns and chemical companies, and those structures are poorly fitted to the underlying complexity of the science. To these command-‐control hierarchies is added a relentless focus on privatizing knowledge as trade secret in order to protect the right to acquire patents, which are in turn used to protect prices high enough to pay off innumerable legacy failures – and which in turn price hundreds of millions of human beings out of the market for medicine. !This paper argues that the organizational and political systems associated with open source software and free culture bear significant promise as an alternative, competitive business structure for pharmaceutical investigation. By adopting a networked structure of “small parts, loosely joined” and a liberal approach to intellectual property, a firm might leverage networks of voluntary contributors (rather than paid full-‐time staff), networks of service providers (rather than cumbersome in-‐house facilities), and find routes to market that do not require complex, heavy corporations and cost structures. !!
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“The greatest critique that can be laid at the door of the current system is that it does not truly encourage the scientific process. In many ways, it promotes a culture that works contrary to the spirit of science.”
-‐ Manica Balasegaram
Background The pharmaceutical industry is in crisis. Despite decades of massive investments in new infrastructure technologies, productivity rates are down. It is now clear that discovering safe, effective new drugs is not yet something that can be effectively “industrialized” for higher throughput. Exacerbating the problem is the choice of targets: diseases and indications perceived to be more profitable, such as cholesterol management, dominate the research pipelines, leading to “me-‐too” drugs and ignoring the health needs of billions of people. !Every success still must pay off a legacy of failures, with prices skyrocketing into the hundreds of thousands of dollars per year for many new compounds. And those prices bring with them serious questions of access, fairness, and human rights: is the social view of drug discovery one that accepts pricing much of the world out of the market as an opportunity cost? The industry is thus caught on three sharp points at once. Its productivity is down, its prices are up, and those prices themselves are under withering attack from patient advocates, the public sector, and governments across the world. !There is also a new approach being deployed: sharing. It’s a simple concept, and a fundamental part of human culture. But as a business practice sharing touches politics, economics, law, technology, and society. Its emergence in software is one grounded in freedoms: the right to re-‐use, to change, to share with others, without asking for permission from a centralized authority. 1!Sharing has changed markets in software, textbooks, encyclopedias, stock photography, scholarly literature, and music. It has been touted since the early 1990s 2as a candidate to accelerate pharmaceutical knowledge development, and clear sharing inroads already exist in non-‐profit, public-‐private, government, and even fully private initiatives across the industry. !This should not be a surprise. There is no inherent reason that pharmaceutical companies must control all phases of the process, like a Hollywood studio in the old days. It’s simply been the case that until recently, doing everything inside the walls of a unified corporate structure created the best statistical odds to proceed from disease understanding to molecule discovery to clinical trials and marketing. But as the environment outside the firm becomes more powerful and able to support discovery elements, however, moving to a sharing approach is a natural experiment.
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This is the central concern – political and economic – of free software. But these freedoms in turn revealed unexpected 1
behaviors of people connected via computer networks: sometimes, a centralized firm is not the best way to collaborate on building something complex, or solving a complex problem. This is the central concern – management of production – of open source software. They are often combined into the acronym FLOSS, but the tensions between those involved for freedom and those involved for management purposes do not disappear in the combination.
Richard Jefferson’s CAMBIA project, in particular its work on open source biological licensing, was an early leader in this 2
space, and remains a guiding force in applying open source and open innovation concepts to biological research.
!The goal of this paper is to provide an overview of that ongoing natural experiment. The rise of peer production and sharing economies offers us a chance to define and differentiate the two in the context of the pharmaceutical industry, and to examine their powers to create knowledge and leverage slack resources to build an entirely new kind of drug discovery engine. This paper will attempt to identify where and when peer production might work, identify where and when sharing economy might work, identify barriers to emergence of new models, and present for discussion an alternative model for organizing drug discovery. !Part I is an overview of sharing practices. It begins with the roots of collaborative peer production (CBPP), which drives open source, in economic and legal theories of knowledge production. It also examines non-‐collaborative approaches to peer production, which are potentially relevant to drug discovery. Part I closes with an overview of the modern “sharing economy” which is often times more of a “renters economy” but again offers some tantalizing ideas for restructuring the drug discovery firm. !Part II is an overview of the drug discovery process and its sharing experiments. It uses the classic organization of the drug discovery and development process (DDP) that typically involves target discovery, target validation, screening, optimization, pre-‐clinical, clinical, and manufacturing stages. The paper describes a variety of projects ranging from classic “open source” peer production to classic “sharing economy” resource optimization to hybrid public-‐private partnerships. Part II also focuses on the intellectual property factors of each section of DDP and posits opportunities in each for application of various kinds of sharing. !Part III is an anticipatory look forward. It lays out one potential model for end-‐to-‐end drug discovery. It is not a complete model, the final model, or the only model. But it does describe a path from early stage biological knowledge to a compound in people that does not rely on a single, central organizing entity for its success. !!!
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Why Open Source Pharma, and why now? !There is a glib way to explain the pharmaceutical industry’s recent embrace of sharing: they’ve tried everything else. Massive, non-‐recoverable capital investment in wave after wave of new “can’t miss” technologies ranging from nucleic acid arrays to RNA interference to angiogenesis and on have drastically increased the cost of discovery. But the irony of the post-‐genome industry is that all this information has not made the process of generating actionable knowledge less expensive…while it has made it arguably less effective.!Figure 1 3
Married to this cost boom has been an organizational restructuring boom. Every large pharmaceutical company still standing has endured reorganizations spawned by management consulting, whether around therapeutic area, centers of excellence within the drug discovery “chain” of processes, geography, and more. None of it worked. has. Massive mergers and acquisitions have come and gone, with little evidence of efficacy in terms of shareholder value and even less in terms of drug
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From (from http://vectorblog.org/2011/08/big-pharmas-changing-business-model-inviting-academia-to-take-the-3
lead/)
discovery capacity. Each of these re-‐organizations comes at enormous capital cost and opportunity – some very effective compounds have been shelved and sold off by companies in the midst of re-‐organization. 4!The third element pressing against the industry is the so-‐called “patent cliff” – the near-‐simultaneous evaporation of monopoly rights on a batch of blockbuster drugs whose revenues have been propping up the industry through its technology and organizational spending booms. Sanofi, Novartis, Roche, Astra Zeneca, and Eli Lilly collectively face $35.7 billion dollars in revenues threatened by blockbuster patent expirations – and that’s just in 2014. The emergent practice to deal with the patent 5
cliff is to acquire smaller companies with promising late-‐stage drugs, but that comes at high price and is itself fraught with risk. To make matters worse, there are often suggestions that the industry has already discovered the "low-‐hanging fruit", meaning it is likely that deeper R&D is required to tease out new compounds for complex diseases. !There are glimmers of hope in new kinds of compounds. After a brutal voyage through the Gartner Hype Cycle, RNA interference compounds are finally in phase III clinical trials and showing real promise. Gene therapies hold great promise, and even gene editing. But the reality of industrial structure inertia and regulatory time scales means even these new kinds of compounds won’t reach citizens, or transform prices, for a decade or more. !And prices matter. It is no longer socially acceptable to price the majority of the world out of a life-‐saving drug. Public debate around the fairness of the drug prices that emerge from the pharmaceutical industry is at a fever pitch. Access to medicines and drugs pricing have been for years issues well beloved to the non-‐governmental organization community, and to a few patient advocacy groups. While there has been some success around access to HIV anti-‐retrovirals, and most recently to HCV drugs, in the aggregate, drug companies remain in strong control of pricing regimes and continue to push punitive intellectual property provisions in international treaties, trade agreements, and more. !And as citizens of the west and the north begin to engage more directly in drug research, especially around rare diseases, the access to medicines issue begins to hit
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“Pfizer brought up more interesting compounds than it later was able to develop. It's a good question to wonder what 4
they could have done with these if they hadn't been pursuing their well-‐known merger strategy over these years, but we'll never know the answer to that one. The company got too big and spent too much money, and then tried to cure that by getting even bigger. Every one of those mergers was a big disruption, and you sometimes wonder how anyone kept their focus on developing anything. Some of its drug-‐development choices were disastrous and completely their fault (the Exubera inhaled-‐insulin fiasco, for example), but their decisions in their oncology portfolio, while retrospectively awful, were probably quite defensible at the time. But if they hadn't been occupied with all those upheavals over the last ten to fifteen years, they might have had a better chance on focusing on at least a few more of their own compounds.” From http://pipeline.corante.com/archives/2014/08/20/did_pfizer_cut_back_some_of_its_best_compounds.php
http://moneymorning.com/2014/02/18/patent-‐cliff-‐2014-‐chart-‐shows-‐much-‐revenue-‐big-‐pharma-‐will-‐lose/ 5
much closer to home. This exposes the rather ugly reality of a pharmaceutical industry CEO in the contemporary world: he or she must either price drugs out of the range of the vast majority of the world, or be replaced by someone who will. This has been conveniently ignorable as long as those suffering were distant, or un-‐connected. When they are neighbors, when they have social media, when they can collectively organize, it’s impossible to hide the access issue. It’s little wonder that sharing is emerging as a potential lifeline. !On a purely structural basis, a sharing approach to drug discovery has only recently become feasible. One side effect of the massive investment in technology has been a commodification of much of that technology, so that even as it does not increase efficiency, its costs have dropped. This makes it easier and cheaper to generate data outside a pharmaceutical firm than it has ever been. Networks of research instruments, robots, and other infrastructure key to the discovery process are now accessible to anyone with a credit card. And cheap cloud computing technology means that the data coming off these research networks can be stored, analyzed, and – crucially – shared with secondary analysts in ways that were simply impossible even a decade ago. !Taken together, we can reasonably say that there is now an incentive to try a new approach based on networks rather than hierarchies, and the external structures that might make a sharing approach functionally possible are now in place. The key is therefore to understand the sharing approaches themselves, and begin to map those approaches to the portions of the drug discovery process. !!
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Assessing potential of open / sharing economies!There are multiple ways to categorize the variety of sharing-‐based approaches covered in this paper. The categorizations – like the phrase “open source” – come fraught with meaning from previous uses in software, in cultural works, and more. Thus we will attempt to use more granular terms to describe certain kinds of activities, and to map those activities to the drug discovery process. 6!Peer Production Classic open source software approaches represent a kind of commons-‐based peer production (CBPP). The central characteristic of CBPP is that groups of people can work together in complex ways: on complex tasks, for a complex variety of reasons, following a complex set of social signals mostly unrelated to intellectual property or traditional corporate organizational structures. !By one definition, "commons-‐based peer production refers to any coordinated, (chiefly) internet-‐based effort whereby volunteers contribute project components, and there exists some process to combine them to produce a unified intellectual work. CBPP covers many different types of intellectual output, from software to libraries of quantitative data to human-‐readable documents (manuals, books, encyclopedias, reviews, blogs, periodicals, and more)." 7!CBPP has resulted in enormous value creation without centralized firms: Wikipedia and the Linux operating system are the most obvious examples, but the economic value of CBPP in software alone is estimated in the tens (if not hundreds) of billions of dollars per year. 8!Where CBPP works, it can be more efficient than using market signals or centralized, command-‐and-‐control management. It works best in areas with several core
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To some ears, the use of the term “open source” outside of software is harsh. Open source inside software is a term of art 6
that was long fought over, and that has a precise definition. But outside software it has become a short-‐hand for a different, more open way of operating. We understand this difference, and use the term in the short-‐hand context: a way of doing the knowledge creation necessary for drug discovery in the open, using technical architectures and social signals to collaborate, with a way to unify the knowledge at the end. Open Source Pharma will include elements of liberal -‐ or no -‐ intellectual property, crowdsourcing, standardized contracting, sharing of pre-‐competitive knowledge, and more. The term is a catch-‐all for an idea. Its definition will likely only “harden” through its exploration and application by scientists, entrepreneurs, and policymakers.
http://www.freesoftwaremagazine.com/articles/fud_based_encyclopedia/ 7
An EU working group estimated a savings of €75,000,000,000 per year from the reuse of enterprise software code. http://8
www.slideshare.net/cdaffara/economic-‐value-‐of-‐open-‐source-‐14861646 slide 19
features. It works best when the problems are modular, granular, relatively low cost to integrate, and when the product is knowledge or culture. 9!CBPP is not simply a feature of problems with modularity and granularity. It also needs the required capital investment to be widely distributed – recall the explosion of personal computers, internet access, and enabling technologies that came together in the 1990s. This explosion lowered the capital costs of software development which in turn dramatically increased the population of programmers with capacity to participate in free/libre open source projects without asking permission. It is CBPP that most have in mind when they quote “open source” as a goal. However, not all peer production is commons-‐based. Peer production is simply the activity of leveraging lots of disparate individuals to create a knowledge or cultural product. What makes peer production commons-‐based is the use of legal tools, technologies, processes, and social norms that encode freedoms into the products – freedoms to use, to copy, to remix, to redistribute. !Facebook is a good example of a non-‐commons peer production system. All its users make the product, but no user has the freedom to take content out of Facebook, to export one’s friends list and take to another social network, and so forth. Similarly, Innocentive and other companies represent crowdsourcing systems in which networks of individuals attack problems, but those outcomes are not necessarily commons-‐based and the results are not necessarily recombinable into a larger knowledge product. !Both social networks and crowdsourcing are key elements of any eventual CBPP in pharmaceuticals, as additions and complements to more purely “open” processes. In this paper we will try to tease apart when CBPP is possible, when regular peer production is possible, and when one might be more or less desirable than the other. !CBPP in Drug Discovery and Development 10
There are clear differences between the activities involved in drug discovery and software development, and they present hurdles for CBPP in pharmaceuticals. These include the need to work in physical space rather than more purely virtual space; involvement with biological organisms rather than purely with code; the extremely expensive and centralized capital infrastructure; and the relative ease of large scale collaboration in software. However, the modular and granular requirements are actually very consistent with the way that pharmaceutical companies organize their scientific programs.
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There are many works on the subject. Perhaps the most well known is “Coase's Penguin, or, Linux and The Nature of the 9
Firm” by Yochai Benkler in 112 Yale L.J. 369.
For brevity and readability, we amend “discovery and development” to “discovery” and “DDP” in this document.10
Problems are broken down into the smallest feasible possible components so that progress can be measured: a certain compound in a certain strain of mouse for a certain amount of time, to be measured by a certain kind of blood test for a certain kind of outcome. !Nevertheless, there is an additional hurdle to CBPP in pharmaceuticals, namely, decision-‐making. The decision process that creates the needed modularity in pharmaceuticals has yet to be successfully translated into a consensus-‐based model like those associated with successful CBPP. There have been formalisms developed for criteria against which projects can be measured, such as the MMV compound progression criteria for malaria, (http://www.mmv.org/research-‐development/essential-‐information-‐scientists) and these probably arose from a committee, the decision processes are not openly debated and indeed are subject to the command and control processes of the committee. !CBPP is also not popularly associated with industries that have high-‐risk “go/no go” decisions as a regular part of product development. The level of risk tends to correlate with information control among the upper echelons of firm management who have access to enough information to make such a decision, as well as a general unwillingness to “reverse” analyze failed decisions for indicators of future failures. This element has significant conflicts with classic “open source” CBPP, where in the event of a disagreement, the community divides (known as “forking”) into two groups, each taking their own desired decision forward. !Last, CBPP’s lack of a centralized planning authority can create issues with regulatory filings: who prepares and signs the paperwork that submits a drug to the FDA? Who answers the questions? Who pays for manufacturing and distribution in advance of revenues? Who is liable if a marketed compound must be removed from the market due to side effects, like Vioxx? !It is very possible that the creation of scientific assignments, the expense of capital infrastructure, the need for expensive and consequential “yes or no” decisions, and the innate structure of the regulatory and manufacturing process create a set of transaction costs that bias in favor of the centralized firm – even if that firm is regularly failing at its core mission of discovering drugs. A model built on CBPP – in part or in whole – must be able to plausibly explain how these obligations can be met in the absence of a single organizing firm. !Collaborative ConsumptionThe rise of what is popularly called the “sharing” economy can create confusion with peer production. In the sharing economy, it’s actually not about sharing, but about selling: it’s a class of economic arrangements in which participants share access to products or services in return for compensation. It is similar to CBPP in that the 11
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From “The Case Against Sharing” at https://medium.com/the-‐nib/the-‐case-‐against-‐sharing-‐9ea5ba3d216d 11
central organizer is not a firm selling an asset, but differs in that the key signaler is a market force, not a collaborative desire to co-‐create. The market forces work to enable access to resources that were previously impossible to tap to work in a two-‐sided market (many sellers to many buyers): apartments with spare rooms, cars with empty seats, people with time on their hands and a willingness to assemble flatpack furniture. The transaction costs of reaching the number of sellers and buyers used to be so high that a firm was required to create capital-‐intensive resources (like a hotel or taxi fleet) to provide the service. But the ubiquity of mobile devices has brought a change in transaction costs. Those low costs combined with the idea of a multi-‐sided platform (the firm that mediates the connections between sellers and buyers through technology) to underpin the emergence of the sharing economy. The sharing economy is dominated by redistribution and peer sharing enabled by ubiquitous digital networks. Redistribution firms facilitate the transfer of goods – designer handbags, Pez dispensers, free furniture – without needing garage sales, flea markets, antique stores, or trunk shows. Peer sharing firms facilitate short-‐term rentals and services: a clean room for the night, a car ride, an Ikea cabinet assembled. Sharing Economy and Drug Discovery The sharing economy appears to have extraordinary potential for drug development. The underlying characteristics of industrial drug discovery are very well suited to redistribution and peer sharing, and indeed, one can see evidence of those activities dating back decades in pharmaceuticals. But the implementation of redistribution and peer sharing has either been highly formal (mergers and acquisitions, divestments, cross-‐licensing) or highly informal (conversations in the bar at a scholarly conference). What’s been missing is the emergence of multi-‐sided platform brokers to facilitate the connections between buyers and sellers. The massive cost of investment in machinery and tools cuts across the drug discovery process. Every process has been hit with parallelization, miniaturization, and robotics to make the creation of data faster and cheaper. The scale of the data has exploded as a result. Simultaneously, research institutions have invested in core facilities replicating many of the same infrastructure elements. But these are facilities that can sit idle for periods of time, creating an ideal space for peer sharing. The same assets can become liabilities when taking a smaller company into acquisition or bankruptcy, creating an ideal space for redistribution markets. Indeed, a quick search on eBay for DNA sequencers returns more than 100 results, most with next-‐day shipping available. !!
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Block by block analysis of DDP Having limned the contours of CBPP and the sharing economy, we now turn to an analysis of the drug discovery process. The classic view of the process is that of an industrialized, linear process in which drug targets are identified, validated, and screened, where compounds emerge and are optimized and prepared for the clinic, then moved into human trials and out into the market. This is most often visualized as a series of chevrons, immortalized in power points and Figure 2 below (from Nature Reviews Drug Discovery ). 12!Figure 2
!We will proceed through the process and explore the applicability and potential, or inapplicability and lack of potential, of CBPP and sharing economy techniques to each element. !Target Identification Modern drug discovery classically starts with the identification of a drug target – if the drug is the key, the target is the lock to be opened. The goal is to identify the biological roots of the disease, and to find areas of the genome and their associated proteins that appear to drive the mechanistic action of disease. Typically the goal is either to increase (in the event of a protective target) or decrease (in the event of a harmful target) the effect of the protein. !Some recent drug discovery programs, particularly in infectious diseases, have involved a phenotypic approach where screening is conducted on live organisms and active compounds are pursued in the absence of any knowledge of the relevant biological target (10.1126/science.1194923). Such strategies have given rise to unusual depositions of data obtained in the private sector into the public domain with
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Cooper, Matthew “Optical biosensors in drug discovery” Nature Reviews Drug Discovery 1, 515-528 (July 2002) 12
doi:10.1038/nrd838
significant impact on the direction of drug discovery collaborations between academic and industrial groups in malaria, tuberculosis and other tropical diseases. The resulting drug development programs have then been pursued outside the public domain. This phenotypic screening approach coupled with public domain databases has not yet extended outside infectious disease. Drug discovery is wildly complex work. Estimates of the total number of proteins in the human body range up to 2,000,000, and each protein can exist in many states depending on temperature, time, and more. This is in contrast to the number of genes in the human body, which are estimated at a far more tractable 20,000, and which are far more stable to study. One classic method of target identification is to compare genomes (either sequence or activity) of the sick to genomes of the healthy, to generate hypotheses about which genes or mutated genes are responsible for disease. Another method is to silence the activity of one gene at a time and see if anything “breaks” afterwards. It’s also common to start with a known quantity of chemical and explore its genetic effects (aspirin for inflammation, for example) to learn about other potential targets for new drugs. Once a set of genes and proteins has been identified, it is turned over to a different segment of the firm to be validated. Before the sequencing of the genome and its associated explosion of data and content, target identification was a slow, laborious process that created strong incentives for aggressive intellectual property approaches. In the US and elsewhere, courts allowed patents on genetic sequences to create incentives to identify useful areas of natural genomes. But just as the advance of genomics since the late 1990s has lowered the cost of target identification by orders of magnitude, the courts have increasingly recognized naturally occurring sequences as being in the public domain. There is also systemic public investment in genetics research by governments around the world that creates vast databases of papers about targets as well as raw and processed data about targets. Even more, funders of the public research typically require open access to outputs of the research process, which creates the possibility for significant reuse and reanalysis of those outputs. This means that at least in theory, target validation meets many of the criteria for CBPP. The resources being created are inherently knowledge resources, which is the first step. Second, the capital required to create new knowledge is relatively low – with the advent of services like Assay Depot and Science Exchange, a motivated group of individuals can engage in data creation and analysis. Then, once the data exist and are shared, new data mining is well within the reach of a typical programmer in the US or Europe. The ability to practice CBPP in the real world depends on the terms of the capital investment in targets and on the creation of appropriate incentives for data analysts to attack the data.
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In many ways, the industry practices peer production. Experts have argued for years that science is basically a giant wiki, with a paper being the unit of knowledge produced rather than a wiki page, and with each paper representing a small edit to the scholarly paradigm. This is a slow, inefficient form of peer production, but the technology paradigms do not support true commons-‐based creativity and innovation. Publishers assert vigorous copyrights against text miners, the most common format (PDF) is often impermeable to machine-‐reading, sharing of negative data is generally discouraged and scientists themselves are slow to move to new methods of indexing and reading. Similarly, the sharing economy echoes many systems already in place in the industry. Peer to peer resource sharing and redistribution are regular elements of cross-‐company collaboration, but those collaborations are usually one-‐to-‐one deals rather than one-‐to-‐many offers like those associated with the sharing economy. !IP factors in target identification include copyright on papers and databases, database rights in some jurisdictions, desire for trade secrecy ahead of disclosure / filing, historical-‐but-‐fading patenting on sequences, desire to protect unknown upstream opportunities. Functional opportunities include text mining, data integration and mining, reuse of sequencing-‐phenotype studies, patient-‐powered research. Scientific opportunities include increasing the number of proteins with known function and discovering more targets through CBPP and PP: text mining, data mining, crowdsourcing, and competitions. !Target validation Target validation is the filtering of a set of potential targets to a single target (or family of related ones). Validation is inherently about separating potential targets that are correlated with disease from those that are causally involved. Processes include a variety of cellular and molecular tests that can systematically weed out correlations, and can operate as both “forward” and “reverse” genetics processes. In the forward process, the investigation starts with observed physiology (a.k.a. “phenotype”) and moves to the genes, while the reverse goes the other way from genetic studies that identify genes to finding those individuals and querying their physical states. The forward can proceed from data integration of existing phenotypes that have genomes, while the reverse requires ability to recontact and then confirm a related phenotype. Validation hinges on choices informed by highly designed and controlled experiments, and both choice and design complicate CBPP in validation. The negotiation of consensus to a single decision in commons-‐based governance process is often very slow, and contentious. In comparison, the ability to tightly control the design of an experiment is a feature that firms are familiar with and have developed systems for completing, and the processes that make choices with tremendous
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financial and scientific decisions are a key part of the industry. These factors combine to challenge the emergence of CBPP in validation. Some non-‐commons based peer production seems applicable to validation, however. Contests to generate standardized experimental data, or to design analytic and decision support models, are not inherently commons based but could plausibly fill the roles played by assay and informatics departments inside firms. The ability to use contest rules to dictate formats and experimental standards is significant; there is far less need for content negotiation and integration among multiple vendors. Contests can also be made to feed the commons itself through their rules and terms of use (e.g. compelling code sharing or open source licensing for submitted code). !Validation is well suited to sharing economy approaches. The high price of creating, maintaining, and staffing the validation phase drives up the overall cost of directly engaging in validation. The instruments for validation are well distributed across large firms, small firms, and academia. The assets rapidly depreciate, and in many cases, may not be in heavy usage. This maps well to the peer to peer rental model and, indeed, there is already a multi-‐sided platform play funded and operating in the market (Science Exchange), with a focus on academic resources. As more resources become available for rental, more organizational structures will be able to take a target from identification to validation thanks to lower overall costs of capital. It is plausible that some of these organizational structures will be far more amenable to commons-‐based decision-‐making and support the emergence of CBPP in validation a decade from now. !The redistribution market holds significant possibility here as well. As targets are deprecated, they become zero-‐benefit assets to those investigating them, but the knowledge around those targets would have significant value to the rest of the world. Reduction of duplicate investigation is the obvious benefit, but it’s also possible to imagine a market emerging of target recycling amongst organizations with interest in biologically related diseases. This is indeed much how the industry works today, though again it operates in a non-‐platform context, mediated more by attorneys than software. The adoption of redistribution markets may require the emergence of a new trusted entity that can implement and enforce standard downstream royalty terms to properly incent flow of discarded targets out for re-‐evaluation. !IP factors include extreme trade secrecy around knowledge that a target is actually druggable and useful (particularly in order to file patent claims on entire pathways). As with the rest of the system, copyrights on papers containing key validation data usually restrict re-‐distribution, machine analysis of the papers, and extraction of data. Patents obtained by universities are more and more frequently entering the secondary market (in which they are asserted by non-‐practicing entities known as “trolls”) when their licensees are acquired and assets liquidated.
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Opportunities include a secondary market for validated but abandoned targets, collaborative licensing approaches, Uber-‐style access to slack resources in core facilities in academia and companies, ex ante accession to open source-‐type IP arrangements by researchers, particularly in areas of low revenue or neglected disease, cloud-‐mediated access to real-‐world cohorts with stable electronic data and contactability / pre-‐consent to be deep phenotyped or genotyped. !Primary / Secondary screening and Lead optimization Once a target has been validated, the classic drug discovery process calls for handover of the target for screening: the search for a chemical or biomolecule that inhibits or excites the target. In high-‐throughput screening, large libraries of compounds, some proprietary, are robotically exposed to cell lines of interest and watched for activity. If a candidate compound “hits” in the cell then it is funneled via workflow into much deeper analysis to determine how and where it is creating a reaction. This process does not assume any hypothesis or knowledge about classes of chemicals and classes of targets, though that knowledge is often informally applied. Other processes formally incorporate knowledge of structure and activity relationships in attempting to design compound libraries with higher likelihood of success, or use only fragments of compounds to inform later chemical design processes. 13
Secondary screens deploy candidate drugs into various more complex environments including mouse, xenograph, and hollow fiber, then observed to see if effects are “real” or not. The US National Cancer Institute reports about 2% of all drugs it screens (2500 per year) pass primary phase into secondary screening. 14
Primary screening requires less than a week and often uses pre-‐existing libraries of compounds that are either openly sold (NCI, Pharmacopeia) or trade secret based pre-‐patent, non-‐disclosed chemicals that are used via collaborative licensing deals. Secondary screening typically requires less than a month, though this timing is deeply dependent on workflow systems implemented within a firm. External timing could be significantly longer. !IP factors include trade secrecy around the compound structures themselves, as well as trade secrecy around structure-‐activity relationships (SAR). Non-‐IP property factors include the complexity of synthesis of a compound (i.e. a good candidate that is hard to manufacture). But the primary IP issue in screening is the desire to file a strong composition of matter patent on a promising compound in advance of regulatory filing for new
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3058157/13
http://www.cancer.gov/cancertopics/factsheet/NCI/drugdiscovery14
chemical entities. The very knowledge required to spur CBPP in this sector (chemical structure of leads and related data) is the same knowledge that constitutes disclosure in the eyes of patent law. The need for such patents to support investment is a regular part of the debate over open source drug discovery, and attempts to reverse-‐license patents in genomics and drug discovery have not taken off. The whole screening phase of drug discovery is not particularly well suited to CBPP. As noted above, the disclosure/patent issue creates a disincentive to share knowledge, but CBPP is also limited by the deeply physical nature of screening. The 15
best examples we have of CBPP are those that create knowledge and culture. A compound is neither. It’s instead an artificially synthesized chemical that exists as a rivalrous resource in a library. Typically the library is quite literally that – compounds on shelves – and when exhausted, the compounds must be re-‐synthesized at laborious expense. Thus the chemicals are harder to copy, distribute, work on 16
modularly, and re-‐integrate – the features of CBPP. But though screening may not be ideal for CBPP as practiced elsewhere, one may adopt its ethos if not all its methods – and perhaps some new ones. !As the physical, capital-‐intensive resources are deployed to create knowledge about compound-‐target relationships, a different organizational model might well choose not to depend on patents and trade secrets as a foundational element. Most arguments for patents and secrecy assume that investment without strong IP rights is impossible. Alternative investment models exist, with prizes perhaps the best developed, that render strong IP rights unnecessary, and in that context adopting intentional knowledge flow from the funded laboratory to the commons is not just allowable but desirable. In this case, Open Notebook Science as a method is an essential practice. The primary benefit of opening up the notebooks is to attract CBPP to a well-‐fitted task: the finding and documenting of errors and improvement of methods, which are indeed knowledge products, unlike the compounds themselves. This is an important meta-‐point for open source drug discovery. When the product is chemistry, the assumption is that capital requirements, IP factors, and the nature of the underlying product mitigate against CBPP. But the process itself is just as vulnerable to frailty due to few eyes, and thus improvement via many eyes, as when the underlying product is knowledge. The IP factors must be minimized if one is to achieve this benefit however. The existence of alternative organizational and investment models is essential for this division to take place. Traditional models of science (academic, startup,
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Computational drug design does hold promise and bears future observation but as yet isn’t delivering drugs that can skip 15
the in vivo work.
3D printing is a long term trend that may radically affect the creation of libraries and patentabilty of compounds, but it’s 16
not yet able to print at the molecular level.
corporate) simply do not have the incentives or internal structures to benefit from CBPP in this manner. !Screening is already a fee-‐for-‐service market as well as an in-‐house service, which makes it very amenable to sharing economy approaches with the caveat that some chemicals in the library are harder to make than others – long processes, expensive precursors, unusual techniques. Peer to peer rentals would require a very trusted multi-‐side platform vendor; the market already brokers many bilateral sharing relationships mediated by lawyers. Opportunities for “open source ethos” development via sharing economy include building large public libraries, leveraging existing public library resources, running contests to convert hard-‐to-‐make chemicals into easy-‐to-‐make chemicals, creating structures to liberally share under trade secret with wider audiences, and more. Opportunities for CBPP include open notebook science, pre-‐competitive collaborations, and incentive based models. !Preclinical development At the preclinical phase there has been a conscious choice of compound (family) and the work begins on lining up for a clinical trial process. Generally speaking both property filings (patents on either composition of matter or indication) and regulatory filings (NCE / new chemical entity) or NME / new molecular entity) filing have been made by the owner before the investment in preclinical work begins. !Key aspects of preclinical build on elements touched on in lead development, but take them to scale: how active is the molecule, what organ systems does it hit in model animals, what are the likely toxic effects and how severe are they, how will the molecule be delivered (pill/shot/cream etc), how will the molecule be manufactured, and so forth. Significant tacit knowledge is deployed here to “know” from the data whether model organism drug is promising or not. !The process seems to yield more false positives than false negatives, indicating that reactions from tacit knowledge saying “this won’t work” tend to be accurate while “this will work” may be a result of confirmation bias as well as simple lack of knowledge about mechanisms of disease. It is possible that the higher understanding of toxicity (studied in all cases, whereas disease is only studied in its own case) allows researchers to recognize it in advance more accurately than efficacy. !IP is not the primary block to “open source” in pre-‐clinical development, as the patent filings are typically already complete. But there is still enormous importance attached to trade secrecy and fear of sharing – pre-‐clinical can give clues to investors and competitors, or provide grist for lawsuits downstream over post-‐approval side effects. Worse, the tacit knowledge required to effectively interpret the data is rare and unevenly distributed. !
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Within the space of CBPP on pre-‐clinical knowledge, data related to the bioactivity of the compound is much more sensitive than the data related to the manufacturing and potential bioavailability (absorption, distribution, metabolism, excretion) of the compound. The latter may be amenable to CBPP in an open system context. It is also possible to open source the bioactivity data but requires a commitment to either an unpatented compound (and thus another incentive system to get investments to get through the clinic) or trust that a patent owner will somehow share the benefits of CBPP. !The sharing economy here seems divided: peer to peer rentals perform best when there is a large population on each side, and that does not exist in pre-‐clinical. There is a small population of those with the necessary expertise in both the chemistry of the molecule and the biology of the disease to make informed choices about taking a molecule forward. Additionally, the number of total compounds that make it to this stage is low. Redistribution markets for shelved compounds do exist already but are brokered primarily through the social networks of pharmaceutical executives and scientists. At the close of this phase, some group at some point has to come to a decision as to whether or not the compound should be promoted to phase I clinical trials, which is a decision costing tens of millions of dollars. That same group also has to decide whether or not to file with patent and regulatory offices, which is also not something that CBPP or sharing economy has been shown to do effectively. There is real movement towards consortium-‐based experimentation with CBPP and sharing economy in this space to create alternatives to the firm that are able to make and support these decisions. The implicit hypothesis being tested is that a more open clinical process will allow stop-‐go decisions to be made more quickly, saving time and money and improving quality. !At the manufacturing end of the pipeline, we must also consider the generics industry as relevant to CBPP. Generic drug manufacturers take nonproprietary information – for example the relevant IP from off-‐patent or never-‐patented drugs – and take those drugs forward on a market basis. While they may have their own proprietary processes, or even process patents, they can be considered a vehicle for bringing unpatented health technologies to market. A loose analogy would be that they are the Red Hats, or a Red Hat, of the pharmaceutical industry, in that they market and distribute products built on nonproprietary IP. !!
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An Open Source Drug Discovery Model, End To End The goal of part 3 of this paper is to imagine a set of interlinked public and private structures that might be capable of moving from early stage target identification all the way through to approval and post-‐marketing surveillance. The contemporary model for drug development is an attempt to convert a fundamentally uncertain scientific process into a quantifiable, replicable, and scalable business. It represents a Taylorist view of science – rational, empirical, improvable. But although scientists themselves must be rational and empirical, the conversion of scientific experiments into medicines that work, pass regulatory approval, and can be sold in the market has turned out to be at least semi-‐random. And the pharmaceutical firm itself does not exist in a vacuum. It sits in an ecosystem of knowledge creation and investment with many players, most notably the federal governments of many countries investing billions of dollars in science research and development and the investment of private capital into startup biotechnology companies that play a key intermediate role between academic knowledge creation and industrial drug discovery. When knowledge is created in academia, its primary form is copyrighted content via the academic paper. In rare cases the knowledge is valuable enough to be patented and licensed, most often to a startup company funded by private capital. Most academic patents are unlicensed however and startups in biotechnology have a high failure rate. There is thus an active secondary market in resale of related patents to non-‐practicing entities (“trolls”). When a biotechnology startup’s R&D program shows success, it can attract larger and larger private capital and eventually make it to the public markets. But the expertise needed to navigate regulatory phases and the high failure rates of compounds in phase I-‐III trials often force integration into larger firms via merger and acquisitions. The marketed drug thus sits at the end of a long, expensive process. The firm that owns the drug is dominated by the need to navigate regulatory systems, produce, market, and distribute drugs worldwide, and to pay off failed bets inside and outside its own walls. Rare are the drugs that are marketed by the same firm that began the target identification process. Although networked systems now represent alternative paths to knowledge creation, at transaction costs lower than previously possible, the emergence of CBPP and sharing economy effects won’t necessarily lead to open source industrial models. A new industrial model will require either systemic change by existing players or the creation of new institutional players friendly to commons-‐based and sharing economy knowledge creation.
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!One end to end path might look something like the following. First, a heavily crowdsourced, non-‐patented, computer-‐driven form of early-‐stage drug discovery. This could take the form of university-‐based labs working on early stage “in silico” drug discovery, as well as medicinal chemistry Collaborators would agree ex ante to place an “open” and likely patent free IP regime on their work. This stage might also be fueled by a prize, where those who agreed up front to place their work in the public domain would be eligible. !The second stage, where more expensive clinical trials must be conducted, could be largely publicly supported, where the drugs being developed were for important public health needs for which there were limited market incentives (e.g antibiotics, tuberculosis, malaria, Ebola). Funding could be provided to any “open soruce” candidate that had reached, for example, phase 1 clinical trials. An affordable pricing regime, or a patent free condition for low-‐income markets, would be imposed as a condition. The third stage would involve manufacture. The generics industry is already ready and willing to take forward patent-‐free drugs on a market basis, ensuring affordability. !Systemic change Systemic change by existing players is already happening, at least around the edges of the process. Governments now often attach requirements that make scientific publications available to the public, which is creating a steadily growing knowledge commons of biological knowledge. This is a new variation of open source in which a guild creates a reusable set of knowledge products (data and papers) and then is compelled to open them up, at which point a diverse set of individuals and institutions can begin to translate them into drug discovery. Similarly, large pharmaceutical firms have begun to share data and knowledge connected to clinical and pre-‐clinical compounds in hopes of leveraging CBPP of knowledge related to toxicity and disease mechanisms. This is an early-‐stage development, and most sharing is related not to compounds under investigation but to the “comparator” data gathered on standard of care or disease progression. The data are typically available under restrictive contracts for re-‐analysis and often times the firm providing retains significant rights. This is a long way from a true open source approach. But it is a major crack in the wall of trade secrecy and data retention that mark the modern pharmaceutical company. Both of these developments need to accelerate and demonstrate capacity to compliment existing discovery capacity if existing models are to approach an open source ecosystem. A potential systemic change by existing players would be to intentionally allow the late entry of non-‐profit organizations or governments into the existing discovery process. Patents related to shelved projects could be placed under non-‐assertion
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pledges for use in rare disease or diseases of the global poor, and related data made available as a club good under contract to accelerate repurposing of compounds. The economic effect to the firm is similar to the write-‐off but empowers new players to jump in with open source missions and investors. Indeed, this is already happening, as private firms are donating candidates and molecules to nonprofits for them to take forward. !There is already a strong market of in-‐licensing and out-‐licensing of leads and targets amongst the market. But the market is highly artisanal and dependent on relationships among scientists and executives. The outflow of information about failures and “shelved” projects from firms is weak, which keeps knowledge about failures scarce. This hampers the emergence of an ecosystem that supports open source – the expense concentrates capital into a few firms, and the scarcity of knowledge caps total cognitive supply at a low level. New industrial structures The emergence of new institutional structures – beyond federal investment, academic research, private capital and large firms – could be the key to moving to a truly open source approach. The lack of significant human resources available in chemistry and in regulatory is not a problem that can easily be addressed through contractual or licensing approaches. But we have some parallels against which to draw from other areas. 1. A public reserve for early-‐stage biological knowledge !The creation of national parks, biosphere reserves, and other structures in real property could inspire the creation of similar reserves in knowledge related to biology. In many ways, this already exists via the US government’s investment in the National Center for Biotechnology Information and the emerging movement to open access in the scientific literature. But it could be codified and most importantly economically valued as a public resource that supports downstream drug discovery at substantially reduced costs and time. !2. A public or private investment fund to support the emergence of non-‐exclusionary sharing economy startups in the lead discovery and optimization space. !Since the human resources in this space are scarce, and the capital resources relatively high and concentrated, the emergence of a network of companies providing services via standardized fee-‐for-‐service contract at low, pre-‐set prices could empower new groups with less money and expertise to work in lead development. This field is also already emerging, but the companies do not have typically have an incentive to work in a fee-‐for-‐service model, and often opt to ask for downstream royalties, which damages the low-‐price potential of open source discovery. A fund dedicated to social value as well as economic value that made
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standardized contracting part of its investment thesis would relatively quickly spur the creation of a resource sector. !3. A contract research organization operated as a public utility to assist non-‐profits, small corporations, and other parties to submit, navigate, and complete the regulatory process. !Small parties that might survive target identification via the knowledge commons and identify promising leads via a “virtual” contract model will likely run aground in the regulatory process. Even savvy companies with massive amounts of private capital regularly fail to navigate regulatory waters effectively. Much as public utilities broker access to resources in energy and real estate, a CRO operated as a public utility would create a large institutional partner with concentrated regulatory expertise that simultaneously had no incentive to demand punitive partnership agreements. And of course its costs would be much lower as the utility model caps profits. Another benefit of the utility model would be the ability to include contractual clauses that cap profits, require compulsory licenses for generic manufacture, and so on. !Using the utility would be optional – firms that did not want those caps would be free to navigate the system on their own – but the very existence of the utility would radically alter the marketplace. Funding would also be required in order to take drug candidates forward through clinical trials, particularly in areas of low revenue/neglected disease. This is where a consortium of national government funders (e.g. the G-‐20) could play a role. If each of these three new players emerged, it is possible to sketch a path from early stage open knowledge to late stage regulatory submission that does not require the massive firm size or access to stock market capital of the current system. !
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Lead Authors: John Wilbanks and Jaykumar Menon Supported by the Open Society Foundations Licensed to the public under Creative Commons BY-‐SA 4.0
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