Written by Andrea Iorio
Leading speaker (+150 keynotes/year) on Digital Transformation, Leadership and Web3 in Latin America and U.S. Formerly at Tinder & L’Oréal. Certified InΞvitable Instructor.
Imagine that you are the BU leader of a big Pharma company based in Europe, and you have to solve a crisis your company is facing: a new drug that you have recently launched is having a quality issue in the batches that have been produced at a plant in North America, due to a maintenance problem on that specific production line. You are getting a lot of complaints from patients, and pharmacies alike, and you have to act swiftly.
The first thing you do is you trace all the batches that have been produced in that plant through the blockchain, and in a minute you are able to know exactly the list of items to be recalled: after all, thanks to the blockchain, you have all of your supply chain transactions and movements traced in real time. You then order an urgent recall of the exact batches affected by the problem.
At the same time, you have to deal with some patients that are having side effects because of this problem, so you enter the metaverse and access the health data in real time of all the patients that reported these side effects, and your medical team analyzes these data in real-time and through Artificial Intelligence runs molecule simulations in the Lab and manages to discover a molecule that minimizes these side effects: you then produce it and offer it for free to all these patients.
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Last but not least, you have to solve the problem your equipment has in the North America plant: because of this, you generate a digital twin) of that equipment to show up in Augmented Reality within your office room in Europe, and are able to run the simulations in real-time of maintenance. You then understand that due to a loose screw, the equipment is vibrating more than normal and this is causing the problem: you then tighten the loose screw through a robot, and in a minute the equipment in North America is back to normal.
I am sure you might be like: “Andrea, how is all of this possible? How can you solve in minutes problems that the industry today takes weeks if not months to solve?”. This sounds more like a fictional script from the Netflix “Black Mirror” series, right?
But it is not: it is much more real than Black Mirror, and it represents some of the real-world applications of Web3 technologies to the Pharmaceutical sector.
Let’s go step by step.
Defining Web 3.0
Well, Web3 is considered by many the 3rd iteration of the internet, towards which we are approaching thanks to its underlying new technologies: blockchain, Metaverse, DAOs, digital twins, crypto, dApps (decentralized Apps), NFTs, all powered by A.I. and ML (Machine Learning), and so on: basically, a a new generation of Internet services that are built on top of decentralized technologies.
How did we get here, though? Let’s look at the evolution of the Web: Web 1.0 came with the birth of the Internet and fundamentally digitized information, submitting knowledge to the power of algorithms (this phase came to be dominated by Google) and making it read-only for the most part.
Web 2.0 came with social media, running mostly on Smartphones, and digitized people and subjected human behavior and relationships to the power of algorithms (this phase was dominated by Facebook), and made the internet not only a place to consume content, but also to create it.
What about Web3? This third phase will fundamentally digitize the rest of the world and render it in 3D. In Web3, all objects and places will be replicable and readable by machines and subject to the power of algorithms. And who will the metaverse be dominated by? Most likely by anyone and no one at the same time – exactly because it is a decentralized web, as well as it will be a place for people to consume content, produce it but most importantly: own it. It has certain characteristics, namely that it is decentralized (as we mentioned), immersive (namely it is 3D and not only 2D as the internet is today), and persistent (namely, things happen even while we are not online).
Recent statistics show the opportunity for brands to dive deep into the Web3 and some of its underlying technologies, such as the metaverse: for instance, a new report by research firm Gartner predicts that by 2026, 25% of people will spend at least one hour per day in the metaverse for work, shopping, education, social and/or entertainment. It’s also expected that 30% of the organizations in the world will have products and services ready for the metaverse by 2026.
When it comes to blockchain, although the financial sector accounts for more than 30% of the complete market value of the technology (a market value that is poised to reach $ 67.4 billions by 2026, according to Markets and Markets), the value of the ecosystem has also begun to spread to other technologies, such as manufacturing (17.6%), distribution and services, (14.6%) and the public sector (4.2%). When it comes to the health sector as a whole, the opportunity is huge: a report published by Vantage market research in 2022 found the global market size for blockchain in healthcare is expected to reach around $11 billion by 2028.
The truth is that, although in Pharma we might not be there yet when it comes to Web3 maturity, we see a strong acceleration of Digital Transformation in the sector. As a keynote speaker and researcher that works with most Big Pharma globally (including Novartis, Janssen, AstraZeneca, Bayer, Abbott, Roche and many others), I am fully aware of the impact that Digitalization is having on the Pharma industry, especially after Covid-19: a recent Deloitte survey with 150 biopharma industry leaders points to the fact that certain digital technologies such as the cloud (49%), AI (38%), data lakes (33%), and wearables (33%) have been adopted in day-to-day operations, while others such as quantum computing and digital twins are still nascent. Another interesting statistics is that pharmaceutical manufacturers could spend $1.2 billion on data analytics by 2030, according to Pharma Manufacturing’s 2020 Smart Pharma Survey – which also found that “over 93 percent of manufacturers surveyed said that when they are designing or upgrading facilities, digitalization is an important part of the discussion”.
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But if we can agree that Digital Transformation is underway at the moment (and accelerated by Covid-19), we still have to admit that – besides some timid but much-needed experiments and pilot projects – the Pharma industry is not very clear yet about the potential impacts and opportunities of Web3 on its business, from drug Research and Development to clinical trials, from interacting with the patient in the Metaverse to using the Blockchain for supply chain transformations – eventually helping to do what the industry aims for since its inception: better cure (and prevent) diseases, and improve people’s health overall.
This is why I have spent the last several weeks talking to experts from the biggest Pharma companies across the globe, and have put together this article that describes what are the main impacts of Web3 technologies on the Pharma industry.
1. Blockchain for Supply Chain Transformation
A couple of years ago, in 2018, I bumped into a report by Accenture titled: “In Blockchain We Trust: Transforming the Life Sciences Supply Chain“, which estimated that blockchain technology could provide a $3 billion opportunity by 2025 in the life science sector, mainly through Supply Chain transformation.
I must confess that by then I was just in my early days of understanding about Web3 and its underlying decentralization technology, namely Blockchain, but this statistics really caught my attention, so I started investigating further about the applications of Blockchain to supply chain.
But before getting to its application to the Pharma market, let’s first understand better what the Blockchain technology is: it is basically a distributed database that is shared among the nodes of a computer network, which stores information electronically in digital format. A blockchain collects information together in groups, known as blocks, that hold sets of information and that have certain storage capacities and, when filled, are closed and linked to the previously filled block, forming a chain of data known as the blockchain.
All new information that follows that freshly added block is compiled into a newly formed block that will then also be added to the chain once filled, and when it is filled, it is set in stone and becomes a part of this timeline. Each block in the chain is given an exact time stamp when it is added to the chain. See? The blockchain is a distributed ledger technology (DLT), where that database is spread out among several network nodes at various locations, which makes it decentralized.
And when it comes to its potential impacts on Pharma supply chains, we can list several ones such as transparency (everything is tracked in real-time), speed (you can access information real-time and not wait for intermediaries supply it to you), and sharing of information: I really like the definition by KPMG analyst Arun Ghosh, who said the blockchain in Pharma serves as a “ledger of truth” for sharing complex information with regulators, pharmacy benefit managers, contract manufacturers, physicians, patients, academic researchers and R&D collaborators, among others.
Let’s look at some of the practical applications: a first example comes from the opportunity to better trace drugs and minimize the incidence of counterfeit drugs. A 2021 paper from a team of researchers from the Khalifa University in Abu Dhabi titled “A Blockchain-based Approach for Drug Traceability in Healthcare Supply Chain”, points to the fact that most existing drug track and trace systems are centralized (trough FDA in the U.S. and regulators across the globe) leading to data privacy, transparency, and authenticity issues in supply chains.
They then present an Ethereum blockchain-based approach leveraging smart contracts and decentralized off-chain storage for efficient product traceability in the healthcare supply chain: the smart contracts guarantee data provenance and eliminate the need for intermediaries and provides a secure, immutable history of transactions to all stakeholders. At the same time, think about it: recalls of drugs are made much simpler through Blockchain technology. The product can be readily traced back to the manufacturer and associated with a production batch, allowing identification of other potentially problematic products and where they had been shipped.
At the same time, as we mentioned, blockchain technology can help Pharma companies to enforce “smart contracts” and optimize costs related to supply chain transactions. A case study here comes from Amici Pharmaceuticals: on January 27, 2022, Chronicled, Inc., the technology company behind the leading pharmaceutical blockchain network MediLedger, and Amici Pharmaceuticals announced a partnership to streamline pricing alignment and ensure first time chargeback accuracy on the MediLedger Blockchain Network. The MediLedger Network aligns trading partners in real-time on pricing contracts, eligible customer lists and customer identity data, such as HIN, DEA and 340B identifiers. This data is then used by blockchain to automatically enforce chargeback accuracy, eliminating most of the errors and escalations that create manual effort for suppliers.
Now, when it comes to Big Pharma, they are not just sitting and watching, but are getting more and more open to Web3. Cynthia A. Challener, Ph.D., Scientific Content Director at Pharma’s Almanac, mapped out some of the main initiatives: Novartis using blockchain technology and the IoT to identify counterfeit medicines and track temperature with real-time visibility for all participants in the supply chain. Merck recently garnered a blockchain patent on its own covering technology for preventing counterfeit drugs by increasing supply chain security. In a combined effort, Pfizer, Amgen and Sanofi are investigating the use of blockchain technology to safely store patient health data to speed clinical trials and lower drug development costs. Blockchain startup Exochain offers a way to securely store and manage clinical trial patient data that also allows patients to control how researchers may interact with their medical data. Boehringer Ingelheim (Canada) has partnered with IBM to test the ability of the latter’s blockchain platform to “improve trust, transparency, patient safety and patient empowerment in clinical trials” by improving the management of clinical trial processes and records.
Recently, IBM announced that it is working with KPMG, Merck and Walmart to develop a pharmaceutical blockchain platform that can track drugs as they move through the global supply chain. There are several other FDA DSCSA projects utilizing blockchain technology. One of the most prominent is MediLedger, which has over 20 members, including Pfizer, Amgen and Gilead. The goal is to leverage blockchain’s capabilities to create an interoperable system in which multiple parties, including manufacturers, wholesale distributors, hospitals and pharmacies can register, verify and transfer pharmaceutical products with absolute trust in their authenticity and provenance.
Overall, we can forecast that as much as blockchain has been revolutionizing finance through crypto and Decentralized Finance (DeFi), we can forecast a revolution in how supply chains are managed globally through Blockchain – and not only in Pharma!
2. Metaverse for Drug Trials and Patient-centricity
“Meta-what?”: I am sure this was your reaction to Mark Zuckerberg’s recent announcement of Facebook’s rebranding to Meta. At least, that was mine. But interestingly enough, now we all talk about the Metaverse thanks to that announcement and although it is not a new idea, we only recently are able to better understand its implications for Pharma companies, especially for the way they conduct drug trials and get better patient data.
But let’s first understand what is the Metaverse: the term was born from the junction of the Greek prefix “meta” (meaning beyond) and “universe”, and fundamentally is a virtual and collective shared space, created by the convergence of virtually enhanced physical reality (represented by the “Digital Twins“, of which we will talk about), and the virtual space that already permeates the physical world (in particular Augmented Reality, also called AR). Confused?
Think of it this way: today we are basically online when we access the Internet, but with new devices, greater connectivity and cutting-edge technologies, we will be online all the time in decentralized, immersive and persistent worlds.
One of the great opportunities that the Metaverse is providing to the Pharma industry is to overall “get closer” to the patient – which is something with what the industry has been traditionally struggling with, let’s face it.
The truth is that, as Arghya Biswas, Global Trial Manager at Novartis, puts it in a great Linkedin article he wrote in February 2022 with the title “Metaverse will revolutionize Healthcare, including clinical trials, by 2030”, the Covid-19 pandemic has “accelerated the implementation of Decentralized Clinical Trials (DCT) or Virtual Trials, where the trial participants can take part from the comfort of their home or visit the hospital a few times (in case of Hybrid trials). The industry has now also started accepting and incorporating more digital options like eConsenting, ePRO, eSource, Electronic Health Records and Wearable devices in different clinical trials”. He added that “it might not be wrong to assume that one day the virtual clinical trials will happen within a metaverse. This will not only provide a real life-like interaction between the doctors, nurses and patients, but also the blockchain technology will add another layer of security to the trial data, making it difficult to tamper and thus improving the data credibility”.
Biswas’ analysis is backed up by McKinsey’s research about the impact of decentralization on Clinical trials: typically, 70 percent of potential participants live more than two hours from trial sites (data from Sanofi), so decentralization broadens trial access to reach a larger number and potentially a more diverse pool of patients. Decentralization can also reduce the workload for trial investigators, since traditional site activities (such as drug administration, assessments, and data verification) can be performed remotely by others or by trial participants themselves.The shift of clinical-trial activities closer to patients has been enabled by a plethora of evolving technologies and services: tools such as electronic consent, telehealthcare, remote patient monitoring, and electronic clinical-outcome assessments (eCOAs), and of course now the Metaverse as well allow investigators to maintain links to trial participants without in-person visits.
The industry appears to be realizing this shift: before the pandemic, an Industry Standard Research survey in December 2019 found that 38 percent of pharma and contract-research organizations (CROs) expected virtual trials to be a major component of their portfolios, and 48 percent expected to run a trial with most activities conducted in participants’ homes. When McKinsey asked the same questions a year later at McKinsey’s Clinical Operations Roundtable, the responses were 100 percent and 89 percent, respectively.
But would drug trials work in the metaverse?
Digital twins of trial participants would be made of health data from different sources like patients’ electronic medical records and wearable devices measuring physical parameters in real-time (like for example oxygen saturation, which is easily available through the oximeter on Samsung’s newest devices), and they would replicate how they would behave and respond in specific situations. You could track their health, diagnose diseases, plan preventive treatments, and of course monitor their reactions to a new drug being developed and tested.
The truth is that we need urgent innovation in clinical trials, because as Ganes Kesari, cofounder and Chief Decision Scientist at Gramener, puts it in a great Forbes article titled “Meet Your Digital Twin: The Coming Revolution In Drug Development”, current drug trials have 4 shortcomings:
1. They’re not an accurate representation of the real world;
2. Few trials recruit the needed patients on time (recruitment challenges delay almost 80% of all trials);
3. Not every patient is treated by a trial’s new drug (usually half of it is treated with a placebo);
4. Not all experimental drugs work as safely as intended.
Well, digital twins in the Metaverse can solve all this through some of their unique characteristics: infinite coverage (Digital twins can simulate a wide variety of patient characteristics, providing a representative view of a drug’s impact on a broader population), speed (AI can simplify trial design by providing visibility into patient availability for a variety of inclusion and exclusion criteria), predictability (with digital twins predicting patient response, there will be no need for placebos or dummy drugs, so that every patient in a trial can be assured of the new treatment) and last but not least safety (by reducing the number of patients who need real-world testing, digital twins can minimize the hazardous impact of early-stage drugs).
But, what’s the current status? The truth is that we’re still in the early days of applying digital twins to life sciences. Today, pilots use simple twins to model the molecular and cellular functions of the human body, rather than simulate the entire response of a patient in clinical trials.
In the same Forbes piece, Charles Fisher, CEO of Unlearn.AI, a startup that has raised over $17 million to build digital twins for trials, said: “We are not at a stage yet where we can simulate the actual biochemistry of a person. There’s a lot of biology we don’t understand yet, and there’s no data. So, we’re not working on predicting how patients respond to brand new treatment”. But the impact can be huge, as he added: “I see the potential to reduce the size of clinical trials safely and reliably, say by 25%, which can have a multiplier effect on medical research and patients. It will enable every Biotech and Pharma company to run clinical trials faster and less expensively”.
Therefore, we can conclude that as much as telemedicine will also be revolutionized by the metaverse – totally changing doctors/patients interactions -, we can forecast a paradigm shift in Drug trials thanks to immersive and decentralized technologies such as the Metaverse.
3. Digital twins for R&D and manufacturing
In 2019, Kevin Kelly, the founder of Wired magazine, wrote an amazing cover story for the magazine called “Welcome to the Mirrorworld”, where he describes how Augmented Reality will unleash the next big tech platforms. He wrote: “We are building a 1-to-1 world map of almost unimaginable reach. When completed, our physical reality will merge with the digital universe.” In other words, get ready to meet your digital twin and the digital twin of your home, your country, your office, and even of the world.
“Digital twin?”, you might be asking yourself, especially after having read about this concept previously in the article.
Well, let me then introduce you to one the first building blocks behind the metaverse, that is, the concept of “digital twins”. A digital twin is, according to IBM’s definition, a virtual representation of an object or system, or even person as we saw, that spans its lifecycle, is updated from real-time data, and uses simulation, Machine Learning and reasoning to help decision-making. Imagine a large manufacturing company having digital twins of its equipment: through them, an engineer from his home will be able to solve problems in a factory on another continent through the Metaverse. The same technologies will enable office meetings that are much more productive than using today’s two-dimensional video conferencing tools. Customer-facing applications can include creating Digital Twins in retail, offering customer service experiences that would not be possible in the physical world, and even engineering companies such as Siemens are using digital twins to simulate the impact of trees falling on their 5G antennas. Amazing, right?
And when we get to Pharma and look at the potential implications for the industry, we can use Digital twins either in the production building, in the Lab, in the product itself, and even in the patient itself: so that there are countless applications.
But for now, let us focus on R&D first: when we look at Pharma R&D, issues with costs and resources often hamper research projects, as clinicians can only do so much with the tools they have at their disposal. As a result, when researchers embark on a new drug discovery project, the odds are largely stacked against them. Approximately 90% of new drug research fails – a substantial amount given that the global pharma industry spent almost $200 billion on research and development in 2020. This is where digital twins can be a useful asset in R&D. Whereas it may take life science researchers months, even years, of dedicated focus to sort and analyze data, advances in computing mean that digital twins can run multiple test scenarios simultaneously. Moreover, automating testing allows clinicians to rapidly recreate and reproduce trial scenarios, often conducted in highly controlled environments, across locations and personnel, as we saw in the previous chapter.
But what’s most interesting, is that digital twins are a vital tool to help engineers and operators understand not only how products are performing, but how they will perform in the future: analysis of the data from the connected sensors, combined with other sources of information, allows us to make these predictions.
An example? Big pharma firms use computational fluid dynamics (CFD) software to model momentum, energy, and mass transport within engineering and biological systems, and when we look at this, CFD software is one of the most popular types of digital twin solution.
Digital twins organize bioprocess development, suggest experimental designs, and manage new knowledge, and this all dramatically reduces process development costs, achieved by combining previous platform knowledge to predict future process results.
When it comes to the use of digital twins in Pharma manufacturing, the impact is also enormous: in order to accelerate time to market, reduce batch waste and increase quality and reliability in vaccines manufacturing, Atos, GlaxoSmithKline and Siemens have come together to bring digital transformation to pharmaceutical development and manufacturing. Using in-line sensors at each step in the process, they can now collect data to understand exactly what is happening in real-time, and by combining this data with physical, chemical and biological models they have built a digital twin of the pharmaceutical process: a live in-silica replica of the physical process which enables optimization of operations and the simulation of changes, providing new insights for development and full control over the pharmaceutical manufacturing process.
Matt Harrison, head of sciences, digital innovation and business strategy at GSK Vaccines said in the press release: “With digital twins, you’re able to do huge amounts of digital experiments and minimize the number of wet experiments that you do”. Digital twin–based experiments also can eliminate the need to build a test facility, which can potentially take years. “We can run multiple experiments — modeling and simulating — rather than going to a laboratory,” he added.
When we look at it, digital twins are truly a technology that is poised to revolutionize the world, and when I say that I mean it literally: NVIDIA, the leading A.I. computing company in the world, is building a digital twin of the Earth, called Earth-2, in order to simulate exactly the impact of climate change. Amazing, right?
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4. Artificial Intelligence, Machine Learning and Big Data for Drug marketing and CRM
I would like you to imagine the following scenario: you are the pilot of an airplane, and one day, while in the middle of the flight, one of your engines breaks down. Terrible, right? It happened suddenly, and seemingly nothing would have been able to preview that.
But the truth is that, yes, it might have likely been possible to preview it if the airplane was filled with sensors that capture data in real time, and through A.I., it would be able to anticipate an engine stopping through correlations and simulations based on the Big Data it collects (pretty much like a Tesla is able to do, differently from most cars).
See the power of Big Data being processed by Artificial Intelligence? It helps us to predict more and react blindly less. And consider that we already live in a world with lots and lots of data, where more than 90% of the data generated since the beginning of humanity was generated in the last decade, and where today we got to the point of 97 Zettabytes of data by the end of 2022 according to Statista (which just to give you an idea, a Zettabyte is a number with 12 zeros…that’s a lot of data!).
So how can Big Data and A.I. can help us in Pharma? The truth is that there are a plethora of applications, especially after Covid-19: a Deloitte research on Scaling AI adoption across the life science value chain found that COVID-19 put a spotlight on AI. Companies have used AI to optimize site selection for COVID-19 vaccines and manage the impact of disruptions to their clinical development operations. Novartis, for instance, used AI to analyze data on trial operations housed in data lakes to predict where disruptions (such as staff shortages, enrollment delays) were likely to occur, and intervene early to reduce their impact on trial timelines. In addition to that, Deloitte’s 2020 “Measuring the return from pharmaceutical innovation” study found investments in AI and digitalizing trial operations enabled most of the top 20 companies by R&D spend to keep pivotal trials moving without affecting anticipated launch timings.
The truth is that pharmaceutical companies have a lot of data, accumulated over years of operations (especially internal data, but we are seeing more and more external data, such as patient data): in R&D, for example, digital discovery and testing of molecules with advanced modeling and simulation techniques will be commonplace (as we saw in the previous chapter). For example, physiological simulation will accelerate product development, and 3D tissue modeling will help assess potential toxicity using computer simulation. In clinical trials, in vivo clinical trial sensor data streams captured by wearables will be factored into registry records and value dossiers to provide an early indication of real-world effectiveness.
GSK took a step in that direction in 2018 with an investment of USD 300m in 23andMe to, among other things, gain access to the 5 million people database that the startup owns. And we know that big data feeds Machine Learning algorithms: GSK is also one of the companies that is scaling the most in Artificial Intelligence, with a team of more than 100 people working on AI. Eli Lilly has also been working hard in this area: she is a member of MLDPS, the Machine Learning for Pharmaceutical Discovery and Synthesis Consortium, which is a collaboration with MIT to develop software to automate the discovery and synthesis of small molecules.
In marketing and sales, the role of Big Data is also essential to understand the prescribing behavior and the profiles of potential patients, enabling a more precise segmentation of suppliers and increasing the number of prescriptions filled. For example, a “patient search” technology that exploits electronic medical records to identify patients with specific rare diseases will allow sales forces and medical science contacts to focus on care providers who care for patients likely to have these rare diseases. diseases, although they have not yet been diagnosed. An example is what Novartis has been doing in Brazil with multiple sclerosis: they have put QR codes in ophthalmology clinics, for patients to scan and fill a form with their symptoms, which can help Novartis predict much better than individual doctors the chances for multiple sclerosis, through an A.I. algorithm using the patient data.
But beware! Tech companies like Apple, IBM, and Qualcomm Technologies are pushing hard into healthcare, and are already generating a lot of data, They can engage with patients through apps, health and fitness devices, and online communities, for example. Just think of Apple, with the Health Kit on the iPhone and Apple Watch! And they are able to collect petabytes of data from these and other sources, such as electronic medical records and insurance claims, capturing valuable information.
For example, the IBM Watson Health platform – recently at the center of a partnership with Apple and its health sensor data platform HealthKit – is using advanced analytics and natural language processing capabilities to support clinical decisions. The opportunity is there: whoever first among the pharmaceutical companies collaborates with these players in putting together an analytical and Big Data culture, will gain an important competitive advantage. Sanofi took a step in that direction by starting a partnership in 2017 with Evidation Health, a “behavioral analytics” company that, through its Real Life Study platform, collects data about patients through apps on smartphones and wearables.
Overall, digital transformation is enabling pharmaceutical companies to generate value for the patient beyond the drug, such as providing personalized and real-time treatment through sensors and digital services. Many drugs will be part of a digital ecosystem that constantly monitors patient conditions and provides real-time feedback. The result of this? Greater effectiveness as it will customize therapy based on the patient’s clinical and lifestyle needs, and allow remote monitoring by healthcare professionals. There are already many IoT sensors and devices on the market that can measure the patient’s biophysical signals: from wearables like the Apple Watch, FitBit and the likes, to chips under the skin that also allow for the injection of medicine where most needed, to one I know well since I have been working at L’Oréal: La Roche-Posay’s My Skin Track UV – a sensor combined with a mobile app that monitors the skin’s exposure to ultraviolet rays.
The consequence of all this? That treatments will be more and more personalized and more precise, based on the needs of each patient. By coupling IoT with Big Data, it will be possible to predict how patients will react to treatments and even run clinical trials of new drugs more quickly. Personalizing drugs by following an individual’s genetic makeup is part of the precision (or personalized) medicine initiative, and much of this has been made possible by advances in understanding the human microbiome, especially the way the human gut flora interacts with pharmaceuticals. That is, in the future it will not be important to just produce the drug, but it will be even more important to provide patients and people with comprehensive, customized solutions for healthcare products and services. And A.I. and Machine Learning are exactly there for that.
5. NFTs for Data sharing and IP verification
Who hasn’t heard of the NFT buzzword lately? Impossible not to have been impacted by this term, which for the most part is related to “digital art”- and I am sure you are like now: “Andrea, what does this have to do with Pharma?”.
Well, to start off we have to understand what are NFTs, or Non-fungible Tokens, in order to understand that their applications go much beyond only art and gaming, and are not only the speculative bubble that we are seeing now.
What are NFTs, exactly? NFTs can be thought of as a signature for digital assets, which rely on blockchain technology to prove authenticity through a ledger. By confirming authenticity, NFTs establish ownership of one-of-a-kind online assets which can range from a simple pixelated image to a complex set of data, making it impossible to duplicate without permission (to clarify, this means that a set of data can be imitated, but the original is always clearly identifiable: for instance you’d be able to read Jack Dorsey’s first Tweet on Twitter all over the internet, but the original one has been auctioned for $ 2.9 millions and is owned by crypto entrepreneur Sina Estavi. Identifying where data comes from and verifying its validity is a key pillar of the industry today, making it likely that it will continue to be a major topic of interest going forward.
Contrary to the wide draw of NFTs as investments, the applications of NFTs in healthcare and pharma marketing are not meant to inherently just generate profit. Instead, NFTs would serve as a solution for verification of digitized health services, authentication of credentials and data, and protection of intellectual property (IP). At its most basic level, this technology can help shorten the healthcare journey and eliminate human error, and at its peak, it can improve transparency in the space for health care providers and patients alike.
What are some of its applications? As a great PM360 article called “Beyond trendy investments: three applications of NFTs in Healthcare and Pharma marketing” puts it, a first example is the use of NFTs for verification of offers and service: see, telemedicine and prescription delivery services are on the rise, and NFTs can be valuable in verifying these services between virtual and physical transactions. For example, “tokenized” prescription orders and OTC purchases can be definitively linked back to their initial writing and manufacture respectively, guaranteeing quality until the orders reach the consumer. In this scenario, the NFT would be associated with the physical product and tracked online, including information about recalls, replacements, and expiration dates. Do you recall the blockchain application for Pharma supply chain transparency? That’s exactly it.
Also, NFTs can be used for the authentication of credentials and data. The truth is that the applications of NFTs extend beyond tracking products to verifying health care providers credentials and patient records. With these credentials, NFTs can be used to validate educational backgrounds, from institutional degrees and certifications to continued medical education credits (CMEs). When issued by the organization directly, these credentials are unsusceptible to manipulation, maintaining their integrity independent of any physical records. This will minimize the possibility of fraudulent practitioners and improve patient safety at all levels of the healthcare journey.
Furthermore, patient records may also be verifiable by NFTs, marking one original set of patient data as authentic, and potentially allowing the patient to own that data set themselves. As owners of that data, patients could grant third-party access to use their records across healthcare offices, pharmacies, and other applicable institutions. This would ultimately minimize human error in between office transfers, improving patient treatment efficiencies, and it would also minimize transfer time, speeding up the treatment journey in the process.
At the same time, in Pharma marketing and R&D, NFTs can protect intellectual property similar to how they can protect patient data. For marketers that develop unique solutions, assets and materials can be tokenized and subject to transfer of ownership as needed. In the same way, custom programs and algorithms can be tokenized to prevent copying and manipulation.
Online recruitment platforms with multiple data points, such as those that require individual registrations, can even be tokenized, ensuring that the data set is accurate and verifiable across marketing initiatives. In addition to improving data security, this application of NFTs to marketing could also increase the value of unique solutions among competitors in the space.
See? When we look at NFTs, we should not consider them only super expensive pieces of digital art, but a game-changer in the protection of data and IP in the Pharma and health industries as well.
6. DAOs and decentralization for collaboration in Pharma
Do you know how a cooperative works? I do a lot of speaking to cooperatives in Brazil, especially in the finance and agro sectors, and I have always been amazed by the way they are able to be more customer-centric and collaborative, because of their “ownership” structure – that, to explain as briefly as possible, is basically a model in which the organization is “owned” by its customers.
This definitely makes accountability much more important, makes the division of profits more egalitarian, and as I mentioned before it makes the organization more customer-focused (as the cooperates, namely the customers-owners, make decisions about the cooperative strategy during regular meetings).
And while traditional cooperativism was born in 1844 in England, we now see a new form of cooperativism on the rise through Web3: namely the one brought about by DAOs, or Decentralized Autonomous Organizations.
What are DAOs, to start off? A DAO is a new kind of organizational structure, built with blockchain technology, that is often described as a sort of crypto co-op. In their purest form, DAOs are groups that form for a common purpose, like investing in start-ups, managing a stablecoin or buying a bunch of NFTs. ConsenSys, a blockchain organization, defines DAOs as “governing bodies that oversee the allocation of resources tied to the projects they are associated with and are also tasked with ensuring the long term success of the project they support”. Once it’s formed, a DAO is run by its members, often through the use of crypto tokens. These tokens often come with certain rights attached, such as the ability to manage a common treasury or vote on certain decisions.
And how can DAOs make an impact in Pharma? Well, one of its uses can be of use in order to collaborate around IP and allows for better licensing and discoverability through collaboration in research.
Look at this example: recently, Molecule AG, which describes itself as a “biotech IP Web3 marketplace”, has announced a tripartite partnership with longevity-focused venture capital fund Apollo Health Ventures, and VitaDAO, a Decentralized Autonomous Organization funding early-stage longevity biotech R&D. The goal of the partnership is to collaborate on financing and building the longevity biotech ecosystem, as Molecule believes that inefficiencies in longevity biopharma R&D and university tech transfer can be addressed using Web3 marketplace tools. This includes new types of liquid asset class, such as the IP-NFT, a type of non-fungible token pioneered by Molecule that holds intellectual property.
New forms of governance via DAOs, such as VitaDAO, and valuation of IP, such as IP-NFTs, moves early-stage intellectual property into Web3 to allow for greater liquidity, discoverability, and reduced legal complexity by standardizing licensing terms.
At the same time, collaboration in Pharma can be boosted not only through DAOs, but also through Decentralized Medical Database apps: UK-based startup Innovative Bioresearch enables decentralized medical research for HIV, cancer, and COVID. The startup’s token, INNBC, combines decentralized finance (DeFi) and science to support biomedical research and decentralized app (dApp) development. Unlike conventional DeFi staking, the startup allows users to farm coins while contributing to drugs and therapies. Its decentralized database app allows medical researchers to share data over blockchain, ensuring safety and proof of origin. In this way, Innovative Bioresearch facilitates drug development and prevents intellectual property (IP) theft.
Also, Dutch startup Triall develops blockchain-integrated software for decentralized clinical research. It combines blockchain and self-sovereign identity (SSI) technologies to secure clinical research data. Unlike existing electronic systems that lack transparency and data integrity, the startup enables clinical data audits and immutable storage. For example, Triall’s clinical document management application, Verial eTMF, stores data on the blockchain and ensures its integrity and authenticity. Likewise, its solutions allow pharma companies, contract researchers, hospitals, and patients to collaborate in a more transparent manner and accelerate drug development.
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