It’s a weird feeling: this house has a full complement of stuff in it and now I can fine-tune the stuff so I don’t just have Minimum Viable Household going on here.
Biggest recent accomplishment is getting the Dutchman’s Pipe Vine to fill in the space on my side porch with the application of a little monofilament, plant velcro tape, an old metal gear, and nudging. Here’s a before-ish and an after picture.
Dear friends, audience members, venue staff, and all of the people in my life without whose wisdom and support I would not be here today – thank you so very much. It’s a deep honor to receive the 2024 Helmut Schmidt Future Prize and I am very grateful to the Jury for this recognition, and to you, Francesca, for your incredibly humbling remarks in your laudatory speech. I’m a big admirer of your work, so this is even more special to me.
I am accepting this award at a stark moment in history. We are watching bulwarks of global rules-based order moan, sway, and buckle as illiberal pressures mount. And it is with this in mind that I ask your forgiveness as I proceed to deliver an urgent call in a context generally reserved for acknowledgements, thanks, and optimistic visions.
Make no mistake – I am optimistic – but my optimism is an invitation to analysis and action, not a ticket to complacency.
With that in mind, I want to start with some definitions to make sure we’re all reading from the same score. Because so often, in this hype-based discourse, we are not. And too rarely do we make time for the fundamental questions – whose answers, we shall see, fundamentally shift our perspective. Questions like, what is AI? Where did it come from? And why is it everywhere, guaranteeing promises of omniscience, automated consciousness, and what can only be described as magic?
Well, first answer first: AI is a marketing term, not a technical term of art. The term “artificial intelligence” was coined in 1956 by cognitive and computer scientist John McCarthy – about a decade after the first proto-neural network architectures were created. In subsequent interviews McCarthy is very clear about why he invented the term. First, he didn’t want to include the mathematician and philosopher Norbert Wiener in a workshop he was hosting that summer. You see, Wiener had already coined the term “cybernetics,” under whose umbrella the field was then organized. McCarthy wanted to create his own field, not to contribute to Norbert’s – which is how you become the “father” instead of a dutiful disciple. This is a familiar dynamic for those of us familiar with “name and claim” academic politics. Secondly, McCarthy wanted grant money. And he thought the phrase “artificial intelligence” was catchy enough to attract such funding from the US government, who at the time was pouring significant resources into technical research in service of post-WWII cold war dominance.
Now, in the course of the term’s over 70 year history, “artificial intelligence” has been applied to a vast and heterogeneous array of technologies that bear little resemblance to each other. Today, and throughout, it connotes more aspiration and marketing than coherent technical approach. And its use has gone in and out of fashion, in time with funding prerogatives and the hype-to-disappointment cycle.
So why, then, is AI everywhere now? Or, why did it crop up in the last decade as the big new thing?
The answer to that question is to face the toxic surveillance business model – and the big tech monopolies that built their empires on top of this model.
The roots of this business model can be traced to the 1990s, as scholar Matthew Crain’s work illuminates. In a rush of enthusiasm to commercialize networked computation, the Clinton administration laid down the rules of the road for the profit-driven internet in 1996. In doing so, they committed two original sins – sins that we’re still paying for today.
First, even though they were warned by advocates and agencies within their own government about the privacy and civil liberties concerns that rampant data collection across insecure networks would produce, they put NO restrictions on commercial surveillance. None. Private companies were unleashed to collect and create as much intimate information about us and our lives as they wanted – far more than was permissible for governments. (Governments, of course, found ways to access this goldmine of corporate surveillance, as the Snowden documents exposed.) And in the US, we still lack a federal privacy law in 2024. Second, they explicitly endorsed advertising as the business model of the commercial internet – fulfilling the wishes of advertisers who already dominated print and TV media.
This combination was – and is – poison. Because, of course, the imperative of advertising is “know your customer,” in service of identifying the people most likely to be convinced to buy or do the things you want them to. And to know your customer you need to collect data on them. This incentivized mass surveillance, which now feeds governments and private industry well beyond advertising, with strong encryption serving as one of our few meaningful checks on this dynamic.
On this toxic foundation, over the course of the 2000s, the Big Tech platforms established themselves through search, social media, marketplaces, ad exchanges, and much more. They invested in research and development to enable faster and bigger data collection, processing, and to build and maximize computational infrastructures and techniques that could facilitate such collection and ‘use’ of data. Economies of scale, network effects, and the self-reinforcing dynamics of communications infrastructures enabled the firms early to this toxic model to establish monopoly dominance. This was aided by the US government’s use of soft power, trade agreements, and imperial dominance to ensure that the EU and other jurisdictions adopted the US paradigm.
This history helps explain why the majority of the world’s big tech corporations are based in the US, with the rest emerging from China. The US got a head start, via military infrastructure and neoliberal policies and investment, while China built a self-contained market, capable of supporting its own platforms with its own norms for content that further limited external competition. Which also means that the story of the EU “stifling innovation via regulation” is both wrong, and suspiciously self-serving when it comes from the mouths of tech giants and their hype men.
Here you might pause, reflect, and ask … but what does this sordid history have to do with AI? Well, it has everything to do with AI.
In 2012, right as the surveillance platforms were cementing their dominance, researchers published a very important paper on AI image classification, which kicked off the current AI goldrush. The paper showed that a combination of powerful computers and huge amounts of data could significantly improve the performance of AI techniques – techniques that themselves were created in the late 1980s. In other words, what was new in 2012 were not the approaches to AI – the methods and procedures. What “changed everything” over the last decade was the staggering computational and data resources newly available, and thus newly able to animate old approaches.
Put another way, the current AI craze is a result of this toxic surveillance business model. It is not due to novel scientific approaches that–like the printing press–fundamentally shifted a paradigm. And while new frameworks and architectures have emerged in the intervening decade, this paradigm still holds: it’s the data and the compute that determine who “wins” and who loses.
What we call AI today grew out of this toxic model – and must be understood primarily as a way of marketing the derivatives of mass surveillance and concentrated platform and computational power. Currently, there are only a small handful of firms – based in the US and China – who have the resources to create and deploy large-scale AI from start to finish. These are the cloud and platform monopolies – those that established themselves early on the backs of the surveillance business model. Everyone else is licensing infrastructure, scrambling for data, and struggling to find market fit without massive cloud infrastructures through which AI can be licensed to customers or massive platforms into which AI can be integrated as a feature or service touching billions of people.
This is why even the most successful AI ‘startups’ – Open AI, Mistral, Inflection – are ultimately ending up as barnacles on the hull of the Big Tech ship – the Microsoft ship, in their case. It’s why Anthropic needs to be understood as a kind of subsidiary of Google and Amazon.
In addition to the current technologies that are being called “AI,” we also need to look at the AI narrative itself. The story that’s animating marketing and hype today – how is this marketing term being deployed? By wielding quasi-religious tales about conscious computers, artificial general intelligence, small elves that sit in our pocket and, servant-like, cater to our every desire, massive companies have paved the way for unprecedented dominance.
By narrating their products and services as the apex of “human progress” and “scientific advancement,” these companies and their boosters are extending their reach and control into nearly all sectors of life, across nearly every region on earth. Providing the infrastructure for governments, corporations, media, and militaries. They are selling the derivatives of the toxic surveillance business model as the product of scientific innovation.
And they are working to convince us that probabilistic systems that recognize statistical patterns in massive amounts of data are objective, intelligent, and sophisticated tools capable of nearly any function imaginable. Certainly more capable than we, mere mortals. And thus we should step aside and trust our business to them.
This is incredibly dangerous. The metastatic shareholder capitalism-driven pursuit of endless growth and revenue that ultimately propels these massive corporations frequently diverges from the path toward a liveable future.
There are many examples that illustrate just how dangerous turning our core infrastructures and sensitive governance decisions over to these few centralized actors is. Scholars like Abeba Birhane, Seda Gürses, Alex Hannah, Khadijah Abdurahman, Jathan Sadowski, Dan McQuillian, Amba Kak and Sarah Myers West, among many others, have explicated such examples at length and with care.
Here, I will pause and reflect on one particularly distressing trend, which in my view exposes the most dangerous tendency of all. These massive surveillance AI companies are moving to become defense contractors, providing weapons and surveillance infrastructures to militaries and governments they choose to arm and cooperate with.
We are all familiar with being shown ads in our feeds for yoga pants (even though you don’t do yoga) or a scooter (even if you just bought one), or whatever else. We see these because the surveillance company running the ad market or social platform has determined that these are things “people like us” are assumed to want or be attracted to, based on a model of behavior built using surveillance data. Since other people with data patterns that look like yours bought a scooter, the logic goes, you will likely buy a scooter (or at least click on an ad for one). And so you’re shown an ad. We know how inaccurate and whimsical such targeting is. And when it’s an ad it’s not a crisis when it’s mistargeted.
But when it’s more serious, it’s a different story.
We can trace this story to the post-9/11 US drone war, with the concept of the Signature Strike. A signature strike uses the logic of ad targeting, determining targets for death based not on knowledge of the target or certainty about their culpability, but based on data patterns and surveillance of behavior that the US, in this case, assumes to be associated with terrorist activity. Signature strikes kill people based on their data profiles. And AI, and the large scale surveillance platforms that feed AI systems, are supercharging this capability in incredibly perilous ways.
We know of one shocking example thanks to investigative work from the Israeli publication 972, which reported that the Israeli Army, following the Oct 7th attacks, is currently using an AI system named Lavender in Gaza, alongside a number of others. Lavender applies the logic of the pattern recognition-driven signature strikes popularized by the United States, combined with the mass surveillance infrastructures and techniques of AI targeting. Instead of serving ads, Lavender automatically puts people on a kill list based on the likeness of their surveillance data patterns to the data patterns of purported militants – a process that we know, as experts, is hugely inaccurate. Here we have the AI-driven logic of ad targeting, but for killing. According to 972’s reporting, once a person is on the Lavender kill list, it’s not just them who’s targeted, but the building they (and their family, neighbours, pets, whoever else) live is subsequently marked for bombing, generally at night when they (and those who live there) are sure to be home. This is something that should alarm us all.
While a system like Lavender could be deployed in other places, by other militaries, there are conditions that limit the number of others who could practically follow suit. To implement such a system you first need fine-grained population-level surveillance data, of the kind that the Israeli government collects and creates about Palestinian people. This mass surveillance is a precondition for creating ‘data profiles’, and comparing millions of individual’s data patterns against such profiles in service of automatically determining whether or not these people are added to a kill list. Implementing such a system ultimately requires powerful infrastructures and technical prowess – of the kind that technically capable governments like the US and Israel have access to, as do the massive surveillance companies. Few others also have such access. This is why, based on what we know about the scope and application of the Lavender AI system, we can conclude that it is almost certainly reliant on infrastructure provided by large US cloud companies for surveillance, data processing, and possibly AI model tuning and creation. Because collecting, creating, storing, and processing this kind and quantity of data all but requires Big Tech cloud infrastructures – they’re “how it's done” these days. This subtle but important detail also points to a dynamic in which the whims of Big Tech companies, alongside those of a given US regime, determines who can and cannot access such weaponry.
This is also why it’s imperative that we recognize mass surveillance – and ultimately the surveillance business model – as the root of the large-scale tech we’re currently calling “AI”.
Which brings us to political economy – of course. Because this turn to defense contracting that the large tech companies have been making is a key part of their revenue model. Work by Tech Inquiry recently revealed that the five largest US military contracts to major tech firms between 2019 and 2022 have contract ceilings of $53 billion. And those are the ones we know of. Due to the combination of military classification and corporate secrecy – transparency – let alone accountability – is very hard to come by.
The use of probabilistic techniques to determine who is worthy of death – wherever they’re used – is, to me, the most chilling example of the serious dangers of the current centralized AI industry ecosystem, and of the very material risks of believing the bombastic claims of intelligence and accuracy that are used to market these inaccurate systems. And to justify carnage under the banner of computational sophistication. As UN Secretary General Antonio Gutiérrez put it, “machines that have the power and the discretion to take human lives are politically unacceptable, are morally repugnant, and should be banned by international law.”
It’s because of this that I join the German Forum of Computer Scientists for Peace and Social Responsibility in demanding that “practices of targeted killing with supporting systems be outlawed as war crimes.”
Particularly given the very real possibility of a more authoritarian government in the US, where these companies are homed. A place where the right wing in the country has already broadcast plans to bring the two major tech regulators in the US – the Federal Trade Commission and the Federal Communications Commission – under direct presidential control in the future. Where four of the top five social media platforms are housed, alongside cloud giants that currently control 70 percent of the global cloud market. And where a federal abortion ban is on the right wing agenda, accompanied by ongoing campaigns of book banning and censorship of LGBTQ resources and expression already shaping legislation at the state level.
But! None of this is a reason to give up hope! I’m working every day, giving talks like this in place of soft-focus thanks and praise, because I am hopeful. I am hopeful – but I refuse to plant my hope in the shallow soil of illusion and false comfort.
There is still time to create a lovely and liveable future. To question these narratives and the control they give these monopolistic actors. We can dismantle this toxic surveillance business model, and the AI derivatives being pushed into the nervous system of our lives and institutions. There’s time, for example, to audit and understand the EU’s dependencies on US surveillance AI giants, and to forge a better way forward.
Because an ending is the beginning of another thing. And recognizing that something isn’t working – whether it’s a relationship, or a job, or a whole tech paradigm – is the point where a new world can begin being born. With the veil torn and the benevolent rhetoric of the big tech companies revealed to be more marketing than reality, we can start in earnest revising and rebuilding our technological future.
It does not have to be this way! Computational technology is cool, it can be wonderful, and it can take many, many, many forms.
We don’t have to compete with the giants on their terms – why let them set the terms to begin with? We can make our own terms! From socially supported infrastructure that breeds more locally governed applications and communications platforms, to a redefinition – or reclamation – of the concept of AI, away from the surveillance-dependent bigger is better approach, toward smaller models built with intentionally-created data that serve real social needs. To open, accountable and shared core infrastructures that, instead of being abandoned and extracted, are supported and nurtured.
Here, Signal serves as a model. And I am so honored and proud to be able to dedicate my time and energy to nurturing and caring for Signal. The world's most widely used truly private messaging app, and the only cool tech company.
Signal is a mighty example proving that tech can be done otherwise. Signal’s massive success demonstrates that tech that prioritizes privacy, rejects the surveillance AI business model, and is accountable to the people who use it is not only possible – but can flourish and thrive as a nonprofit, supported by the people who rely on it, not ad dollars and military contracts.
So, as I said, I am optimistic. My optimism drives me. And I am particularly inspired by the young people here today, and those I talked with earlier this afternoon, whose energy and vision provides the leadership we need to both sit with and address the serious challenges we face, while simultaneously (re)building and reimagining technology that reflects the world we want, not the world surveillance advertising monopolies tell us we’re stuck with.
In materials science, a disappearing polymorph is a form of a crystal structure that is suddenly unable to be produced, instead transforming into a different crystal structure with the same chemical composition (a polymorph) during nucleation.[2][3] Sometimes the resulting transformation is extremely hard or impractical to reverse, because the new polymorph may be more stable.[4] It is hypothesized that contact with a single microscopic seed crystal of the new polymorph can be enough to start a chain reaction causing the transformation of a much larger mass of material.[5] Widespread contamination with such microscopic seed crystals may lead to the impression that the original polymorph has "disappeared."
This is of concern to both the pharmaceutical and computer hardware industry, where disappearing polymorphs can ruin the effectiveness of their products, and make it impossible to manufacture the original product if there is any contamination. There have been cases of laboratories growing crystals of a particular structure and when they try to recreate this, the original crystal structure is not created but a new crystal structure is.[6] The drug paroxetine was subject to a lawsuit that hinged on such a pair of polymorphs, and multiple life-saving drugs, such as ritonavir, have been recalled due to unexpected polymorphism.[7] Although it may seem like a so-called disappearing polymorph has disappeared for good, it is believed that it is always possible in principle to reconstruct the original polymorph, though doing so may be impractically difficult.[3] Disappearing polymorphs are generally metastable forms that are replaced by a more stable form.[3]
It is hypothesized that "unintentional seeding" may also be responsible for the phenomenon in which it often becomes easier to crystallize synthetic compounds over time.[5]
Pharmaceutical and legal impact
In the United States, the first company to develop a drug ("pioneer") must demonstrate the drug is safe and effective by extensive and expensive trials. After that, there would be a period of exclusive rights to sell the drug, after which other companies ("generics") can market the same drug as a generic chemical under the Abbreviated New Drug Application. The pioneer companies often attempt to evergreen the patent drug by many methods. One of them is to argue that if a new polymorph of a drug with identical pharmaceutical effects is discovered later than the original, it should not count as the original drug.[note 1] Since with disappearing polymorphism, it is practically impossible for anyone to produce the original drug without it turning into the new one, producers are effectively barred from selling generics until the patent for the new polymorph has run out. Alternatively, they may try to argue that a new polymorph needs to undergo the same trials as new drugs, potentially delaying release of a generic for years. Since the appearance of generics can decrease revenue rate of patented drugs by as much as 80%, delaying the appearance of generic competitors is very profitable.[9]
Case studies
Paroxetine hydrochloride
Paroxetine hydrochloride was developed in the 1970s by scientists in Ferrosan, and patented as US4007196A in 1976.[10] Ferrosan licensed this patent to the Beecham Group, which later merged into GSK (GlaxoSmithKline at the time).
The Paroxetine developed at that time was paroxetine anhydrate, which is a chalky powder that was hygroscopic. This made it difficult to handle. In late 1984, while scaling up the production of Paroxetine, a new crystal form (hemihydrate) suddenly appeared at two Beecham sites in the UK within a few weeks of each other. In the presence of water or humidity, mere contact with hemihydrate converts anhydrate into hemihydrate.
Alan Curzons, working for GSK, wrote down the "Paroxetine Polymorphism" memorandum on May 29, 1985, a memorandum vital to later litigations.[11]
When the patent for paroxetine anhydrate (the "original" polymorph) ran out, other companies wanted to make generic antidepressants using the chemical. The only problem was that by the time other companies began manufacturing, Earth's atmosphere was already seeded with microscopic quantities of paroxetine hemihydrate from GSK's manufacturing plants, which meant that anyone trying to manufacture the original polymorph would find it transformed into the still-patented version, which GSK refused to give manufacturing rights for. As it is illegal to manufacture or sell anyone's patented product without their permission, GSK sued the Canadian generic pharmaceutical company Apotex for patent infringement by producing quantities of the newer paroxetine polymorph in their generic pills, asking for their products to be blocked from entering the market.[11][7] GSK eventually lost the case on a technicality in the U.S. Federal Circuit Court, but many abstract legal questions were raised in the process which may not yet be fully resolved.[clarification needed]
Later research showed that the "anhydrate" was in fact a nonstoichiometric hydrate that rapidly dehydrates and rehydrates. The hemihydrate form is more stable due to a higher number of hydrogen bonds.[12]
Paroxetine mesylate
In order to avoid patent issues, some companies developed alternative salts of paroxetine. In the mid-1990s SmithKline Beecham (now a part of GSK) and Synthon independently developed paroxetine mesylate. They obtained two separate patents.
Subsequently, all attempts to produce Synthon's version of paroxetine mesylate ended up with Beecham's version. There were two possibilities: either Synthon's version is a disappearing polymorph, or Synthon's patent application contained erroneous data. Many litigations later, there was no legal consensus on which possibility was correct.[3]
Form II was of sufficiently lower energy that it became impossible to produce Form I in any laboratory where Form II was introduced, even indirectly. Scientists who had been exposed to Form II in the past seemingly contaminated entire manufacturing plants by their presence, probably because they carried over microscopic seed crystals of the new polymorph.[3] The drug was temporarily recalled from the market, and tens of thousands of AIDS patients went without medication for their condition, until ritonavir was reformulated, approved, and re-released to the market in 1999. It is estimated that Abbott, the company which produced ritonavir under the brand name Norvir, lost over $250 million USD as a result of the incident.[3]
A later study found 3 additional morphs: a metastable polymorph, a trihydrate, and a formamide solvate.[15]
Rotigotine
Rotigotine (sold under the brand name Neupro among others) is a dopamine agonist indicated for the treatment of Parkinson's disease (PD) and restless legs syndrome (RLS).[16][17] In 2007, the Neupro patch was approved by the Food and Drug Administration (FDA) as the first transdermal patch treatment of Parkinson's disease in the United States. The drug had been established in 1980, and no prior polymorphism had been observed. In 2008, a more stable polymorph unexpectedly emerged, which was described as resembling "snow-like crystals".[3] The new polymorph did not display any observable reduction in efficacy, but nonetheless, Schwarz Pharma recalled all Neupro patches in the United States and some in Europe. Those with remaining patches in Europe were told to refrigerate their stock, since refrigeration seemed to reduce crystallization rates. The patch was reformulated in 2012, as per FDA recommendations, and was reintroduced in the United States without requiring refrigeration.[18]
Progesterone
Progesterone is a naturally occurring steroid hormone and is used in hormone therapy and birth control pills, among other applications. There are two known forms of naturally-occurring progesterone (or nat‐progesterone), and other synthetic polymorphs of the hormone have also been created and studied.[19] Early scientists reported being able to crystallize both forms of nat‐progesterone, and could convert form 2 into form 1 (which is more thermodynamically stable and melts at a different temperature). When later scientists tried to replicate the crystallization of form 2 from pure materials, they found themselves completely unable to do so. Attempts to replicate older instructions (and variations on those instructions) for crystallization of form 2 would invariably produce form 1 instead, sometimes even leading to crystals of exceptional purity but of the wrong polymorph. Researchers have tentatively suggested that form 2 became gradually harder to produce around 1975, based on a review of production difficulties documented or alluded to in existing literature.[19]
Form 2 was eventually successfully synthesized by using pregnenolone, a structurally similar compound, as an additive in the crystallization process.[3] The additive seemed to overturn the order of stability of the polymorphs. Multiple theories were proposed for why earlier research was able to produce form 2 from "pure" ingredients, ranging from the possibility that the early researchers were unintentionally working with impure materials, to the possibility that seed crystals of form 1 had become more common in the atmosphere of laboratories since the 1970s.[19]
Beta-melibiose
Pfanstiehl Chemical Company in Waukegan, Illinois, was known for isolating and purifying natural substances, including melibiose. The final step of purifying melibiose was to crystallize it. However, one day, all new melibiose crystals appeared in a different morph. The old morph was called beta-melibiose and the new morph, alpha-melibiose. The chemists theorized that tiny traces of the alpha morph in the air or on the lab equipment could be causing this change, but they never found out where it was coming from. Ultimately, the company gave up. However, they suggested that if the process were attempted in a different location, where there was absolutely no trace of alpha morph, it might still be possible to successfully crystallize the beta morph.
As of 1995, this issue might still exist. According to a survey of catalogs from various chemical companies including Merck, Fluka, BDH, Aldrich, and Sigma, only the alpha-melibiose is available.
Beta-melibiose is in fact an epimer of alpha-melibiose. However, since when in solution, alpha- and beta-melibiose rapidly convert to each other, this may still be productively considered a case of crystal polymorphism.[5]
Xylitol
Xylitol, a type of sugar, was first synthesized from beech wood chips in September 1890 in the form of syrups, but no one reported its crystal forms for 50 years. It has two different crystal morphs. One is a metastable, moisture-absorbing form that melts at 61 °C, and the other is a more stable form that melts at 94 °C. Notably, its metastable morph was prepared before the stable form.
When the metastable form was brought into a lab where the stable form had previously been made, it changed into the stable form after a few days in the open air. The structure of the stable crystal was determined by.[20][how?] They failed to obtain the metastable form from a solution of alcohol, either at room temperature or near freezing; they kept ending up with the stable form. This seems to be because once the stable form has been made in a lab, its "seeds" or nuclei can disperse in air, influencing new crystals to grow the same way.[5]
Ranitidine
Ranitidine, a medicine for peptic ulcers sold under the name of Zantac, was developed by Allen & Hanburys (then a part of Glaxo Group Research, now GSK), and patented in 1978 (US4128658A, Example 32[21]). Originally, its crystals were all in Form 1, but in the batch prepared on April 15, 1980 exhibited a new infrared spectrogram peak at 1045 , demonstrating that a new crystal appeared, designated Form 2. Subsequent batches produced more and more Form 2 despite using the same procedure, until Form 1 completely disappeared. The group patented Form 2 in 1985 (US4521431A [22]) and 1987 (US4672133A[23]).[24]
Interestingly, though it's very difficult to crystalize Form 1 in the presence of seeds of Form 2, once Form 1 crystals are obtained, they can be mixed with Form 2 crystals, and both forms would coexist indefinitely.[3]
As the 1978 patent was nearing its 1995 expiration, many generics companies attempted to develop generics using the procedure described in 1978 patent, but they all ended up with Form 2. Some generics companies (such as Novopharm) claimed that Glaxo never produced Form 1, and thus it should be the 1978 patent, not the 1985 patent, that covered Form 2. Depending on which patent applies, Form 2 could be marketed as generics either in 1995 or in 2002. Since an additional seven years of exclusive marketing is highly profitable, Glaxo fought back. There were multiple cases.
In order to win the first Glaxo, Inc. v. Novopharm, Ltd case,[25][26] Glaxo successfully demonstrated that Form 1 could be produced according to the 1978 patent procedure. The organic chemist Jack Baldwin, acting as a witness to Glaxo, had two of his postdoctoral researchers, for three times, produce Form 1 according to the 1978 patent procedure.[3] Consequently, the court ruled that Form 2 is covered by the 1985 patent.
Subsequent to losing the case, Novopharm attempted to bring Form 1 to market, so Glaxo sued them again in the second Glaxo, Inc. v. Novopharm, Ltd case. Glaxo argued that Novopharm could not market generics containing even trace amounts of Form 2. In particular, that means any generic Zantac containing an infrared spectrogram peak at 1045 infringes their 1985 patent. However, during the prosecution of the first case, Glaxo accepted that the 1985 patent covers only products containing chemicals with a specific, 29-peak infrared (IR) spectrum. This was intended to avoid double patenting—Glaxo had emphasized the unique aspects of Form 2 to distinguish it from the invention described in the 1978 patent, thus avoiding a situation where they were essentially trying to patent the same invention twice. Since Glaxo could not establish the presence of the 29-peak spectrogram in Novopharm's product, the court ruled in favor of Novopharm.[8][27]
the claims at issue all identify Form 2 RHCl by reference to a 29-peak IR spectrum.. proof of infringement requires proof that the drug alleged to infringe would exhibit all of those peaks, not a single, potentially meaningless peak.
— 110 F. 3d 1562 - Glaxo Inc v. Novopharm Ltd
In fiction
The atoms had begun to stack and lock—to freeze—in a different fashion. The liquid that was crystallizing hadn't changed, but the crystals it was forming were, as far as industrial applications went, pure junk... The seed, which had come from God-only-knows where, taught the atoms the novel way in which to stack and lock, to crystallize, to freeze.
In the 1963 novel Cat's Cradle, by Kurt Vonnegut, the narrator learns about Ice-nine, an alternative structure of water that is solid at room temperature and acts as a seed crystal upon contact with ordinary liquid water, causing that liquid water to instantly freeze and transform into more Ice-nine. Later in the book, a character frozen in Ice-nine falls into the sea. Instantly, all the water in the world's seas, rivers, and groundwater transforms into solid Ice-nine, leading to a climactic doomsday scenario.[28]
Ice-nine has been described as a fictional parallel—a seed crystal triggering a chain reaction akin to the disappearing polymorph phenomenon.[5][29]
In an indirect homage to Cat's Cradle, Ice-nine and its doomsday scenario is also mentioned in the 2009 video game 999: Nine Hours, Nine Persons, Nine Doors. A character additionally describes a rumor that glycerin was not observed to crystallize until 1920, when a batch spontaneously crystallized independently of a seed crystal. From that incident forward, all glycerin globally was observed to crystallize when cooled to under 64 degrees Fahrenheit, regardless of whether it had come into contact with a seed crystal or not.[30]
^Legally, patents on pharmaceutical molecules usually specify the molecule by the location and amplitude of peaks in its X-ray diffraction spectrum, infrared spectrum, and other spectrographic data. The United States Pharmacopoeia states that two preparations of the same molecule usually have spectra with peaks at the same locations up to ± 0.10 degree, but relative intensities may vary up to 20%.[8]
^Seddon KR, Zaworotko M, eds. (1999). Crystal Engineering: The Design and Application of Functional Solids. Vol. 539. Springer Science & Business Media. ISBN.
^Lowe D (November 26, 2019). "Perverse Polymorphism". In the Pipeline. American Association for the Advancement of Science. Archived from the original on July 5, 2022. Retrieved 2022-07-04.{{cite web}}: CS1 maint: bot: original URL status unknown (link)
^Hilfiker R, ed. (2006). "14. Polymorphism and Patents from a Chemist's Point of View [1]". Polymorphism in the pharmaceutical industry. Weinheim: WILEY-VCH. ISBN.
^US4007196A, Christensen, Jorgen Anders & Squires, Richard Felt, "4-Phenylpiperidine compounds", issued 1977-02-08
^Pina MF, Pinto JF, Sousa JJ, Fábián L, Zhao M, Craig DQ (December 2012). "Identification and characterization of stoichiometric and nonstoichiometric hydrate forms of paroxetine HCl: reversible changes in crystal dimensions as a function of water absorption". Molecular Pharmaceutics. 9 (12): 3515–3525. doi:10.1021/mp3003573. PMID23051151.
^World Health Organization (2019). World Health Organization model list of essential medicines: 21st list 2019. Geneva: World Health Organization. hdl:10665/325771. WHO/MVP/EMP/IAU/2019.06. License: CC BY-NC-SA 3.0 IGO.
^"Down At the Crystal Surface". <a href="http://www.science.org" rel="nofollow">www.science.org</a>. Retrieved 2022-07-08.
^ abcLancaster RW, Karamertzanis PG, Hulme AT, Tocher DA, Lewis TC, Price SL (December 2007). "The polymorphism of progesterone: stabilization of a 'disappearing' polymorph by co-crystallization". Journal of Pharmaceutical Sciences. 96 (12): 3419–3431. doi:10.1002/jps.20983. PMID17621678.
Log in
