Your Prescription Is Ready for Download
You may be familiar with Moore's Law, the prediction made by Intel co-founder Gordon Moore that computer chips would get faster and cheaper with each passing year. That's been borne out by the explosive growth of the tech industry, but you may not know that there is an inverse Moore's Law for drug development.
What if there were a way to apply the fast-moving, low-cost techniques of software development to drug discovery?
Eroom's Law—yes that's "Moore" spelled backward—is the observation that drug discovery has become slower and more expensive over time, despite technological improvements. And just like Moore's Law, it's been borne out by experience—from the 1950s to today, the number of drugs that can be developed per billion dollars in spending has steadily decreased, contributing to the continued growth of health care costs.
But what if there were a way to apply the fast-moving, low-cost techniques of software development to drug discovery? That's what a group of startups in the new field of digital therapeutics are promising. They develop apps that are used—either on their own or in conjunction with conventional drugs—to treat chronic disorders like addiction, diabetes and mental health that have so far resisted a pharmaceutical approach. Unlike the thousands of wellness and health apps that can be downloaded to your phone, digital therapeutics are developed and are meant to be used like drugs, complete with clinical trials, FDA approval and doctor prescriptions.
The field is hot—in 2017 global investment in digital therapeutics jumped to $11.5 billion, a fivefold increase from 2012, and major pharma companies like Novartis are developing their own digital products or partnering with startups. One such startup is the bicoastal Pear Therapeutics. Last month, Pear's reSET-O product became the first digital therapeutic to be approved for use by the millions of Americans who struggle with opioid use disorder, and the company has other products addressing addiction and mental illness in the pipeline.
I spoke with Dr. Corey McCann, Pear's CEO, about the company's efforts to meld software and medicine, designing clinical trials for an entirely new kind of treatment, and the future of digital therapeutics.
The interview has been edited and condensed for clarity and length.
"We're looking at conditions that currently can't be cured with drugs."
BRYAN WALSH: What makes a digital therapeutic different than a wellness app?
COREY MCCANN: What we do is develop therapeutics that are designed to be used under the auspices of a physician, just as a drug developed under good manufacturing would be. We do clinical studies for both safety and efficacy, and then they go through the development process you'd expect for a drug. We look at the commercial side, at the role of doctors. Everything we do is what would be done with a traditional medical product. It's a piece of software developed like a drug.
WALSH: What kind of conditions are you first aiming to treat with digital therapeutics?
MCCANN: We're looking at conditions that currently can't be cured with drugs. A good example is our reSET product, which is designed to treat addiction to alcohol, cannabis, stimulants, cocaine. There really aren't pharmaceutical products that are approved to treat people addicted to these substances. What we're doing is functional therapy, the standard of care for addiction treatment, but delivered via software. But we can also work with medication—our reSET-O product is a great example. It's for patients struggling with opioid addiction, and it's delivered in concert with the drug buprenorphine.
WALSH: Walk me through what the patient experience would be like for someone on a digital therapeutic like reSET.
MCCANN: Imagine you're a patient who has been diagnosed with cocaine addiction by a doctor. You would then receive a prescription for reSET during the same office visit. Instead of a pharmacy, the script is sent to the reSET Connect Patient Service Center, where you are onboarded and given an access code that is used to unlock the product after downloading it onto your device. The product has 60 different modules—each one requiring about a 10 to 15-minute interaction—all derived from a form of cognitive behavioral therapy called community reinforcement approach. The treatment takes place over 90 days.
"The patients receiving the digital therapeutic were more than twice as likely to remain abstinent as those receiving standard care."
Patients report their substance abuse, cravings and triggers, and they are also tested on core proficiencies through the therapy. Physicians have access to all of their data, which helps facilitate their one-on-one meetings. We know from regular urine tests how effective the treatment is.
WALSH: What kind of data did you find when you did clinical studies on reSET?
MCCANN: We had 399 patients in 10 centers taking part in a randomized clinical trial run by the National Institute on Drug Abuse. Every patient enrolled in the study had an active substance abuse disorder. The study was randomized so that patients either received the best current standard of care, which is three hours a week of face-to-face therapy, or they received the digital therapeutic. The primary endpoint was abstinence in weeks 9 to 12—if the patient had a single dirty urine screen in the last month, they counted as a failure.
In the end, the patients receiving the digital therapeutic were more than twice as likely to remain abstinent as those receiving standard care—40 percent versus 17 percent. Those receiving reSET were also much more likely to remain in treatment through the entire trial.
WALSH: Why start by focusing your first digital therapeutics on addiction?
MCCANN: We have tried to build a company that is poised to make a difference in medicine. If you look at addiction, there is little to nothing in the drug pipeline to address this. More than 30 million people in the U.S. suffer from addiction disorders, and not only is efficacy a concern, but so is access. Many patients aren't able to receive anything like the kind of face-to-face therapy our control group received. So we think digital therapeutics can make a difference there as well.
WALSH: reSET was the first digital therapeutic approved by the FDA to treat a specific disorder. What has the approval process been like?
MCCANN: It's been a learning process for all involved, including the FDA. Our philosophy is to work within the clinical trials structure, which has specific disease targets and endpoints, and develop quality software, and bring those two strands together to generate digital therapeutics. We now have two products that have been FDA-approved, and four more in development. The FDA is appropriately cautious about all of this, balancing the tradeoff between patient risk and medical value. As we see it, our company is half tech and half biotech, and we follow regulatory trials that are as rigorous as they would be with any drug company.
"This is a new space, but when you look back in 10 years there will be an entire industry of prescription digital therapeutics."
WALSH: How do you balance those two halves, the tech side and the biology side? Tech companies are known for iterating rapidly and cheaply, while pharma companies develop drugs slowly and expensively.
MCCANN: This is a new space, but when you look back in 10 years there will be an entire industry of prescription digital therapeutics. Right now for us we're combining the rigor of the pharmaceutical model with the speed and agility of a tech company. Our product takes longer to develop than an unverified health app, but less time and with less clinical risk than a new molecular entity. This is still a work in progress and not a day goes by where we don't notice the difference between those disciplines.
WALSH: Who's going to pay for these treatments? Insurers are traditionally slow to accept new innovations in the therapeutic space.
MCCANN: This is just like any drug launch. We need to show medical quality and value, and we need to get clinician demand. We want to focus on demonstrating as many scripts as we can in 2019. And we know we'll need to be persistent—we live in a world where payers will say no to anything three times before they say yes. Demonstrating value is how you get there.
WALSH: Is part of that value the possibility that digital therapeutics could be much cheaper than paying someone for multiple face-to-face therapy sessions?
MCCANN: I believe the cost model is very compelling here, especially when you can treat diseases that were not treatable before. That is something that creates medical value. Then you have the data aspect, which makes our product fundamentally different from a drug. We know everything about every patient that uses our product. We know engagement, we can push patient self-reports to clinicians. We can measure efficiency out in the real world, not just in a measured clinical trial. That is the holy grail in the pharma world—to understand compliance in practice.
WALSH: What's the future of digital therapeutics?
MCCANN: In 10 years, what we think of as digital medicine will just be medicine. This is something that will absolutely become standard of care. We are working on education to help partners and payers figure out where go from here, and to incorporate digital therapeutics into standard care. It will start in 2019 and 2020 with addiction medicine, and then in three to five years you'll see treatments designed to address disorders of the brain. And then past the decade horizon you'll see plenty of products that aim at every facet of medicine.
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.