Getting in Front of Pharma: Automated Public Discovery of Drug Candidates

»This time around, rather than a project about medical research abstractly, we focused specifically on the pharmaceutical industry: the 1.1 trillion-dollar business lying at the nexus of intellectual property law, predatory business practices, and the devaluing of human life.«

In 2018, Sean Raspet and I were invited to participate in Rhizome’s 7×7, this time in Beijing in collaboration with the Chinese Central Academy of Fine Arts (CAFA). I was very excited to collaborate with Sean again after our first collaboration in New York at the New Museum, and to have the chance to try something different together.

We thought about revisiting our previous project, cell.farm, which was a proposal for a cryptocurrency/distributed computing system for which the proof-of-work protocol involved computing simulation updates for an atomic-level model of a human cell (though our proposal initially suggested simulating a ribosome). Such detailed simulation of biological processes would be a boon for medical research, but simulating even the simplest cell at that resolution is so computationally demanding that it’s infeasible even for the world’s best supercomputers. But the aggregate computing power of the Bitcoin network is orders of magnitudes larger than any supercomputer, and might be able to run such a model in a reasonable amount of time. By adopting that model for in silico cells, a crucial part of medical research is essentially collectivized, and as part of our design, so too are the results of that research. The project bears similarity to Folding@Homeand its crypto-based derivatives (e.g. FoldingCoin), but as far as I know none of these projects explicitly distribute ownership of the research that results from the network. There were also some design details that we didn’t have time to hash out, and we left open a big question of computational verifiability: given a simulation update from a node, how can you be certain that they actually computed that value rather than returned some random value? This is a problem with any distributed computing system where nodes are out of your control and doesn’t have an easy solution.

This time around, rather than a project about medical research abstractly, we focused specifically on the pharmaceutical industry: the 1.1 trillion-dollar business lying at the nexus of intellectual property law, predatory business practices, and the devaluing of human life.

The Pharmaceutical Industry

This section leans heavily on the »Pill of Sale« episode of the Ashes Ashes podcast for background. The episode goes into more detail about the pharmaceutical industry—definitely worth a listen.

Most Americans are familiar with exorbitantly priced drugs—if not directly, then via one of the many horrifying stories of people crowdfunding their continued existence, flying elsewhere to access more reasonable prices, or using fish antibiotics from Amazon as a cheaper substitute. A hepatitis C cure from Gilead, Solvadi, costs $84,000 for a twelve-week course and is the subject of a recent Goldman Sachs report. The report describes cures as effective as Solvadi (up to 97 percent) as bad for business since you cure yourself out of a market. Even something as common as insulin can cost a significant portion of income—to the point where people die from needing to ration it.

This hostile environment is thinly justified with rhetoric around drug development costs and enforced through the patent law system, all under the implicit, sometimes explicit, assumption that it is necessary for drug companies to make a profit on their drugs. Patents provide exclusive rights for a company to sell a particular drug; this temporary monopoly essentially gives them carte blanche to set whatever price they want so that they recoup the drug development costs, so the story goes. These patents last 20 years and can basically be extended by »exclusivity« periods which add up to another 7 years. A drug may take 10–15 years to develop, leaving a window of at least five years of exclusive rights to produce and sell it. »Orphan drugs,« drugs that treat rare conditions, may have longer monopolies to compensate for the smaller market size. After this period generics are permitted to enter the market, which drives the cost down, but there are all sorts of tricks available that can prolong this protection period even further, a practice called »evergreening.« For example, slightly modifying how the drug is delivered (e.g. by tablet or capsule) can be enough for it to essentially be re-patented.

(It’s worth noting that prices can be high even for generics. For example, epinephrine — commonly known as an EpiPen, essential for severe allergic reactions — can be bought for about 0.10-0.95 USD outside the US, whereas generics in the US can cost about $70.)

»The narrative around high drug development costs also takes for granted that pharmaceutical companies are the ones bearing all of these costs. A considerable amount of the basic research that is foundational to drug development is funded publicly; (…). The amount of funding is estimated to be more than $100 billion.«

 

Drug development is expensive, averaging at over $2.5 billion per drug, and that’s only counting for those that gain FDA approval. However, these exclusivity rights are not merely used to recapture R&D costs, as is often said, but instead to flagrantly gouge prices such that the pharmaceutical industry is tied with banking for the largest profit margins of any industry (as high as 43 percent in the case of Pfizer).

The narrative around high drug development costs also takes for granted that pharmaceutical companies are the ones bearing all of these costs. A considerable amount of the basic research that is foundational to drug development is funded publicly; the linked study found that public funding contributed to every drug that received FDA approval from 2010–16. The amount of funding is estimated to be more than $100 billion.

It used to be that inventions resulting from federal funding remained under federal ownership, but the 1980 Bayh–Dole Act offered businesses and other institutions the option to claim private ownership. The result is the »public paying twice« for these drugs. The Act does preserve »march-in rights« for the government, allowing the government to circumvent the patent and assign licenses independently if the invention is not made »available to the public on reasonable terms,« but as of now these rights have never been exercised. In 2016, there was an unsuccessful attempt to use these march-in rights to lower the price of a prostate cancer drug called Xtandi, priced at $129,000/year.

All of this isn’t to say that the work of the pharmaceutical industry isn’t valuable; drugs are a necessary part of so many peoples‘ lives. A couple years ago, I started using Sumatriptan to deal with debilitating migraines, and am hugely grateful it exists (and is not ridiculously expensive). It’s because pharmaceuticals are such a critical part of life that their development and distribution should not be dictated by the values that currently shape it.

One particularly egregious example of this mess is the nightmare scenario of Purdue Pharmaceuticals, owned by the Sackler family (who are also prolific patrons of the arts), producers of OxyContin (accounting for more than 80 percent of their sales last year), basically responsible for the ongoing opioid crisis (affecting at least 2.1 million Americans directly, and many more collaterally), and recently granted a patent for a drug that treats opioid addiction. The patented treatment is a small modification of an existing generic.

The day before our 7×7 presentation a story broke in the Guardian: »Sackler family members face mass litigation and criminal investigations over opioids crisis.«

matter.farm

matter.farm combines these two key components of the pharmaceutical industry: patent law and drug discovery, specifically the emerging practice of computational drug discovery.

One reason drug development is so difficult is that the space of possible drug compounds is extremely large, estimated to be between 1060 and 1063 compounds. For comparison, there are an estimated 1022 to 1024 stars in the entire universe, and according to this estimate about 1049–1050 atoms making up our entire world.

Drug development is in large part a search problem, looking to find useful compounds within this massive space. A brute-force search is impossible; even if it took only a couple seconds to examine each possible compound you’d see several deaths of our sun (a lifespan of about 10 billion years) before fully exploring that space.

More effective techniques for searching this space include slightly modifying existing drugs for different therapeutic applications (»me-too« compounds) and literally looking at plants and indigenous medical traditions for leads (this general practice is called »bioprospecting« and this particularly colonialist form is called »biopiracy«).

Of course with the proliferation of machine learning there is a big interest in searching this space computationally. Two main categories are virtual screening (looking through known compounds for ones that look promising) and molecular generation (generating completely new compounds that look promising).

Because drug development is so difficult, companies rely on patents to monopolize any promising results. One crucial criteria for a patent is that the invention must be novel; that is, the invention cannot have already been known to the public. An existing publicly-known instance of an invention is called »prior art« and can invalidate a patent claim. However, sufficient variations to an invention may qualify it as original enough to be patentable (this is the idea behind evergreening, described above).

If a drug is discovered and made public prior to a patent claim on it, it would function as prior art and make that compound unpatentable in its current form. If we were able to generate new molecules that could function as useful drugs, and make public those new molecules, then perhaps we can prevent companies from patenting them and maintaining a temporary monopoly on their distribution.

matter.farm is an open-source and public computational drug discovery system. One of our goals was to frame computational drug discovery as a potential mechanism for drug development for public good. Of course, there’s nothing about the current system that can’t become more public good oriented as-is, except for entrenched interests. But our drug discovery system, in combination with patent law, means that any beneficial compounds discovered by our system automatically become unpatentable. The system originally ran continuously, proposing new compounds and estimating their applications every few seconds, and published these compounds to the website matter.farm, thus placing them in the public and making them prior art (in theory, at least). Because the space of possible compounds is so large, it’s unlikely that any useful compound is produced by our system. Our limited resources mean that our training was not comprehensive and the rate of new compound generation is quite low. In theory, a dedicated set of hardware and more fine-tuning would have a better chance of worthwhile discoveries. The project is meant more to put forth a model for how drug discovery might move forward for common benefit. The machine learning component is just a means, the point is public ownership of essentials like medical discoveries.

Additional efforts

Other efforts to address the problems with the pharmaceutical industry can be found in initiatives like Medicare for All and the proposed Prescription Drug Price Relief Act, and the organizing happening around those. The issues with the pharmaceutical industry are just one piece of a more general hostility in American healthcare.

There is also a burgeoning DIY medicine movement which aims to build alternatives to industrialized medicine, providing autonomy, access, and reliability where those are normally withheld. For example, the artist Ryan Hammond is working on genetically modifying tobacco plants to produce estrogen and testosterone, and the Four Thieves Vinegar Collective (discussed in the Ashes Ashes »Pill of Sale« episode) provides instructions for a DIY EpiPen and a DIY lab (»MicroLab«) for synthesizing various pharmaceuticals, including Naloxone and Solvadi.

 

This post was originally written in 2018 and published on space & times,  https://spaceandtim.es/projects/matter_farm/ (accessed April 30, 2021).

Francis Tseng is a software engineer and lead independent researcher at the Jain Family Institute. His interests include simulation, games, political ecology, and technology.