Pharma is Fundamentally Flawed

Diseases are a global phenomenon. They do not respect geographical boundaries, and in contrast with the world today, nor should the provision of medicine.

James Brodie
ID Theory

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This article highlights the pharmaceutical industry’s problems of misaligned incentives and institutional coercion. It offers a remedy, decentralised science, that could revolutionise transparency, realign incentives at a stakeholder level, and provide public goods where governments, institutions, and charities cannot. It is written as a technical companion to the following article, sharing my journey within the intersection of blockchain and science and outlining the current real-world manifestations of a proposed solution.

With developments in genetic sequencing, individuals will know what will afflict them with a high degree of probability. Before long, governments will embark upon “genomic literacy” to reduce healthcare costs.

A better understanding of the long-term disease burden will encourage investment in conditions (and assets), even if not near-term threats. Everyone becomes a stakeholder in your and your beloved’s future afflictions. These global stakeholder conditions should therefore leverage global stakeholder resources.

Cost per genome data — 2021. Source: DNA Sequencing Costs: Data. Credit: Darryl Leja, NHGRI.

A similar downward trend is emerging elsewhere, albeit one much more alarming and signifying an industry in deep trouble; the ROI of R&D spending within pharma has basically hit zero.

Source: EvaluatePharam, IRR Analysis

It costs between $1–5B to develop a drug, accompanied by significant attrition through the various stages. The long timelines and massive CapEx requirements make this pursuit prohibitive for biotech startups.

Typical pharmaceutical product development timeline and number of compounds needed in the different phases to obtain one FDA-approved drug.

Only five in 5,000 compounds that enter preclinical testing make it to human testing. Only one in five of those tested is approved.

This has orphaned many developmental prospects; numerous compounds are left to rot on the shelves of pharmaceutical houses; interesting signals are often not pursued due to safety concerns within the therapeutic focus of a given company, and natural compounds and nutraceuticals remain undeveloped for severe disease.

Governments recognised the problem in the early 1980’s and created an incentive for pharma companies to invest in orphaned diseases through the Orphan Drug Act. Exclusive marketing rights to the compounds were granted to the companies that developed them with or without patents.

This legislation was effective to a point but led to numerous cases of extortionate drug pricing from pharma companies. It is abundantly clear that the industry needs novel and sustainable incentives to initiate drug development, stimulate appropriate behaviours and reward the stakeholders who contribute most to the development of medicines.

DeSci’s Opportunity

Ethereum.org describes decentralised science (DeSci) as:

“…a movement that aims to build public infrastructure for funding, creating, reviewing, crediting, storing, and disseminating scientific knowledge fairly and equitably using the Web3 stack.”

The emerging protocol stack (which I describe in this accompanying essay) targets multiple facets across the drug discovery process. As a result, DeSci is well-positioned to address one of the biggest problems the industry faces — traversing the “valley of death”. This is the funding gap between basic scientific research and translation to novel therapeutics.

The table below summarises areas where the most pressing problems exist across the three main development stages of drug development. The following section outlines each problem,the opportunity they pose and how blockchain solutions can address these:

Academia is Broken

In essence, radical reforms are needed within the existing system. Good research getting access to funding, publishing and peer review is a bottleneck, and often conflicts with the interests of the institutions.

Universities are businesses

Motivated by publications/citations (as are academics) rather than translation and tangible impact. A once helpful practice has evolved into rent-seeking and self-reinforcing “prestige capture”. Instead, research should be tied directly to the delivery of technology in a granular way. DeSci “Lego blocks”, like those in DeFi, could sit on the balance sheet or even encourage new venues for scientists to affiliate with.

Funding

Researchers spend too much time fighting for ultra-competitive capital from governments now reigning in their spending. Avenues should be explored outside the “grants game” that don’t force scientists to lose focus and hop around subjects. For example, adopting quadratic funding will change how grants are awarded.

Outcomes

Peers have a limited ability to predict grant productivity. We need to change who grants are awarded to, reducing bias and improving grantee diversity. By encouraging enough liquidity and attention for them to be meaningful, prediction markets could help to adjudicate how reliable and robust potential research may be.

Peer review

Has become increasingly conservative as reviewers aren’t incentivised to take on risk. Covid demonstrated that preprints had minimal differences compared to post-review publications. An open infrastructure could motivate many to become reviewers, earn rewards, and establish transparent reputations.

Innovation

Science is ossifying into group think, exemplified by the explosion of co-authorship. Smaller teams should be engaged in innovative research to attract talent from adjacent fields. Cloud-based and decentralised labs (LabDAO) record inventions on-chain and can structure ownership without lawyers.

Reproducibility

Significant studies fail to recapitulate findings creating institutional knowledge that is factually wrong — the replication crisis. Award scientists for testing old ideas as well as novel experiments. Bounties can incentivise validation studies and scientific hygiene by awarding shared ownership of work outcomes.

The Translation Gap

Translational attrition must be avoided. Crypto rails may provide the capital bridge to take many projects over the valley of death from the promising discovery phase to the expensive clinical phase.

Signals

A lack of robustness exists whereby animal models do not recapitulate the biology of the human state. Development in rare diseases (where models don’t exist, and the need is desperate) demonstrated the power of early clinical signals. Communities should sponsor the creation of accurate models or fund investigator-initiated studies to obtain early human signals. As web2 enabled the community sharing of information on neglected diseases, web3 facilitates the pooling of capital around them.

Data composability

With the industry relying on manuscripts and a system of citations, important information falls in between the gaps. Code review and data validation are missing from the peer review system. The creation of DeSci “Lego blocks”, modular research objects and new data primitives that span academia and pharma as pioneered by DeSci labs, would enable better financialisation.

Open information

A lack of transparency around ownership of data and IP fuels the current replication crisis. Researchers should open their datasets (positive and negative), allowing others to build on the shoulders of giants. Staking systems can incentivise good data, with stakers getting slashed for incorrect data.

Collaboration

Ownership of IP and data within companies and university tech transfer offices (TTO) prevents collaboration and open research. It should be possible to create alternative democratic research structures that distribute value and governance more evenly. Decentralised autonomous organisations (DAOs) provide an alternative.

Antitrust

Pharma can acquire and shelve promising compounds because they are competitive. We must address the fundamental problem of academic discoveries not being translated and potential medicines not being delivered to patients. Using open marketplaces and bidding by investment DAOs could allow for unutilized compounds to be developed and brought to market.

Accessibility

Thirty years ago, Donald Hayes evidenced how inaccessible science had become to the masses. Neutral platforms where information is transparently shared would be optimised for impact. By incentivising experts to distil and disseminate information, speculative discovery and crowd-analysis is possible — by tapping into the hive mind.

Drug Industry Poised for Disruption

Fundamentally, value creation in biopharma is driven by two core assets: intellectual property (IP) and R&D data. Medicines remain undeveloped unless profit can be secured and risk mitigated through patents and data exclusivity.

Shareholders vs Stakeholders

Drug development is financed through markets and equity, with decisions not necessarily in the best interest of patients. Science should be used to improve societal outcomes and not drive profit for a mere few. Capital and talent can be organised to pursue a common goal by realigning incentives of the neglected stakeholder groups, patients and scientists.

Expense

The significant failure rate, immense costs, and diminishing ROI is leading to an innovation crisis despite advancements in biotech (genomics, high throughput screening, transgenic animals). Either we have depleted our targets or are unproductive, or both! DAOs can fund otherwise underserviced research, reform infrastructure requirements, and leverage pay-for-success (PFS) retroactive public goods funding.

Clinical trials

Achieving statistical significance in complex and chronic conditions has become challenging, given the heterogenicity of the patient populations. New regulatory pathways and trial designs could be embraced that may also facilitate prophylactic and generational medicines. Trial participants could be incentivised for their data with tokenised equity in any resulting therapies.

Efficacy

Many drugs are coming off patent, making it exceptionally hard to beat the standard of care in clinical studies. Natural compounds are not pursued in severe disease because there is no payoff. Generics/nutraceuticals should be tested observationally or through wearables. Personalised data and a focus on precision medicine are needed to develop the next phase of drugs with comprehensive data tying cause to effect on an individual basis.

Safety

Regulatory barriers have risen significantly since the thalidomide incident, coupled with arduous post-approval side effect reporting. Interesting signals are often not pursued due to safety concerns within chosen indications, which should be transparent and provide opportunities for others to pursue. Smart contracts can easily manage this type of sub-licensing.

Impact

With industry players funding research, it can lack patient-centeredness, value for money, and transparency. The idea of pharma companies pooling money for research is not new, which would minimise conflicts of interest. This capital can be embraced by DAOs to firewall academics during study design and execution to achieve better outcomes.

A New Dawn: Open Source Science and DeSci

Drug development is where software development was 30 years ago. The open sourcing of R&D data will unlock new business models and reduce the time and cost of drug development. Ethereum helps to move value around and represent the ownership of property and data, thus making it a suitable platform to coordinate open R&D.

DAOs are vehicles for agent coordination around a common cause. They run a predefined ruleset with treasuries controlled by smart contracts, all enforceable on public blockchains. These tokenised composable structures enable data co-ownership and community-owned bottom-up governance. Instead of equity, cryptoassets have the flexibility and programmability to incentivise the stakeholders’ behaviours required to thrive.

DAOs act as a Schelling point for the community intersecting web3 and bioscience, empowering researchers, scientists and patients to collaborate.

Open composability with DeFi markets provides entirely new funding models to promising scientists and laboratories across the globe. Publicly funded DAOs are not focused on ROI or short-term outcomes but on disease impact, so they won’t divest promising assets readily. With DAOs, the responsibility and risks are shared by all stakeholders who have the most to gain from discovering new medicines.

By removing centralised storage of medical records (e.g. via IPFS or Arweave), data is protected from cyberattacks and, ironically, ransomware which cost US healthcare organisations $7.8Bn in 2021. Small pilot tests appear to perform well; we are starting to see donation models used in Europe. In addition, compliance with GDPR and HIPAA can be addressed via permissioned blockchains, while privacy is preserved using zero-knowledge proofs on public blockchains.

Challenges

Far from being a panacea, DeSci faces numerous challenges still to be overcome. Not least, the nascent technology-healthcare industry inertia. Convincing TTOs to work with DAOs and facilitating pharmaceutical companies to work in conjunction with DeSci efforts will ultimately enable stakeholder-driven inventions to come to life.

Throughput, data storage and scalability can be addressed by layer 2 and 3 architecture, sidechains, and other DLT technologies; DAGs, Arweave, IPFS, etc.

Data privacy is certainly addressed by zk proofs. Public web3 technology (IPFS, Ceramic, Ocean, Arweave, Kyve) isn’t compliant with HIPPA or GPRD, but data could be stored off-chain while a hash or proof is submitted on-chain. The industry must also agree on standards to uphold the semantic integrity of things such as blockchain-based EHRs.

As we have written extensively on, DAOs face myriad challenges; including governance capture and/or the draining of funds, incentivising participation and contribution, reducing noise:signal ratio, and legal ambiguity. In addition, DAOs don’t exist within a jurisdiction and are not regulated by any particular rules. This can create potential liabilities for DAO members resulting from the actions of the DAO itself, disincentivising individuals from joining due to substantial risk to their assets outside the DAO.

Concluding thoughts

The incentive and economic misalignment in big pharma concerning the drug development pathway is clear and widely understood. Blockchain and Web3 initiatives hold tremendous promise and a potential solution that can be disruptive, sustainable and scalable.

These problems are coming to a head precisely when blockchain-based solutions have matured and now offer tried and tested foundational technology upon which to build. From academia to drug efficacy and impact, blockchain offers numerous methods to fundamentally re-aligning incentives and economics.

The Web3 toolset of quadratic funding, tokenised incentives, staking and reputation systems, prediction markets, IP NFTs, and DAOs all enable the required drastic change.

Since its creation, crypto has struggled with any true product-market fit beyond finance. These novel financial rails serve as a necessary platform for societal reform to occur in other areas. We believe De-Sci is going to be the pre-eminent use case and will be truly world changing. In so doing so, it will aid in transforming the negative public perception of the technology.

At ID Theory, we are incredibly privileged to sit at the exciting epicenter of this collision between Web3 and Science.

Special thanks to Graham Stanton, Charlie Edwards and Harry Ephremsen for reviewing and providing critical feedback.

ID Theory may hold positions in some of the assets discussed in this post. This post is strictly for informational and educational purposes only. It does not in any way constitute an offer or solicitation of an offer to buy or sell any investment or cryptoassets discussed herein. Always perform your own research and conduct independent due diligence prior to making any investment decisions.

Interested in partnering with ID Theory or building something special? Get in touch through our website or at info@idtheory.io.

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