Imagine that you are the BU leader of a large pharmaceutical company based in Europe and you need to solve a crisis that your company is facing: a new drug that you recently launched is having a quality problem in the batches that were produced in a plant located in North America, due to a maintenance issue on that specific production line. You’re getting a lot of complaints from patients and pharmacies, and you need to act quickly.
The first thing you do is track all the batches that were produced in that plant through the blockchain, and in one minute you are able to know exactly the list of items to recall: after all, thanks to the blockchain, you have all the transactions and movements of your supply chain tracked in real time. You then request an urgent recall of the exact lots affected by the issue.
At the same time, you have to deal with some patients who are having side effects because of this issue, so you go into the metaverse and access the real-time health data of all the patients who have reported these side effects, and your medical team analyzes all of them in real time. Through Artificial Intelligence, you make simulations of molecules in the laboratory, and manage to discover a molecule that minimizes these side effects: you then produce this molecule and offer it free of charge to all these patients.
Last but not least, you have to solve the problem that your equipment has in the North American plant: because of that, you generate a “digital twin” of that equipment to appear in Augmented Reality within your office room in Europe, and can run the real-time maintenance simulations. You then understand that due to a loose screw, the equipment is vibrating more than usual and this is causing the problem: you then tighten the loose screw through a robot, and within a minute the equipment in North America is back to normal.
I’m sure you might be thinking, “Andrea, how is all of this possible? How can you solve problems in minutes that the industry today takes weeks, if not months, to solve?” This sounds more like a script from Netflix’s “Black Mirror” series, right?
But it’s not: it’s much more real than Black Mirror and represents some of the real-world applications of Web3 technologies for the pharmaceutical industry.
Let’s review this step by step:
First of all , what is Web3? Well, Web3 is considered by many to be the 3rd iteration of the internet, which we are approaching thanks to its new technologies: blockchain, Metaverse, DAOs, digital twins, cryptography, dApps (decentralized applications), NFTs, all powered by AI and ML (Machine Learning), and so on: basically, a new generation of Internet services that are built on decentralized technologies.
But how did we get here? Let’s look at the evolution of the Web: Web 1.0 came with the birth of the Internet and was a phase in which information was fundamentally digitized, submitting knowledge to the power of algorithms (this phase came to be dominated by Google) and making the Web mostly only a content consumption environment. Web 2.0 came with social media, running mainly on smartphones, digitized people and subjected human behavior and relationships to the power of algorithms (this phase was dominated by Facebook), and made the internet not just a place to consume content, but also to create it.
And Web3? This third phase will fundamentally digitize the rest of the world and render it in 3D. On Web3, all objects and places will be replicable and machine readable and subject to the power of algorithms. And by whom will the metaverse be dominated? Most likely by anyone and anyone at the same time – precisely because it is a decentralized web, as well as a place for people to consume content, produce it, but most importantly: own it and be rewarded for it. It has certain characteristics, in particular that it is decentralized (as we mentioned), immersive (that is, it is 3D and not just 2D like the internet is today) and persistent (that is, things happen even when we are not online).
Recent statistics show the opportunity for brands to delve deeply into Web3 and some of its underlying technologies, such as the metaverse: for example, a new report by research firm Gartner predicts that by 2026, 25% of people will spend at least an hour a day in the metaverse for work, shopping, education, social and/or entertainment. 30% of organizations globally are also expected to have metaverse-ready products and services by 2026.
When it comes to blockchain, while the financial sector accounts for more than 30% of the technology’s total market cap (a market cap expected to reach $67.4 billion by 2026, according to Markets and Markets), the value of this technology it also began to spread to other sectors such as manufacturing (17.6%), distribution and services (14.6%) and the public sector (4.2%). When it comes to the healthcare industry as a whole, the opportunity is huge: a report published by market research Vantage in 2022 estimated that the global healthcare blockchain market size is expected to reach around $11 billion by 2028.
The truth is that, although in Pharma we are still not there when it comes to maturity in Web3, we see a strong acceleration of Digital Transformation in the sector. As a speaker and researcher working with most Big Pharma globally (including Novartis, Janssen, AstraZeneca, Bayer, Abbott, Roche and many others), I am fully aware of the impact digitalization is having on the pharmaceutical industry, especially after Covid – 19: A recent Deloitte survey of 150 biopharmaceutical industry leaders points to the fact that certain digital technologies such as cloud computing (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 in their infancy. Another interesting statistic is that pharmaceutical manufacturers could spend $1.2 billion on data analytics by 2030, according to Pharma Manufacturing’s Smart Pharma Survey 2020 – which also found that “over 93% of manufacturers surveyed said that when they are designing or upgrading facilities, digitization is an important part of the discussion”.
But while we can agree that the digital transformation is underway right now (and accelerated by Covid-19), we still have to admit that – aside from some timid but much-needed experiments and pilot projects – the pharmaceutical industry is still not very clear about this. the potential impacts and opportunities of Web3 on your business, from drug Research and Development to clinical trials, from patient interaction in the Metaverse to using Blockchain for supply chain transformations – eventually helping to do what the industry has been aiming for since its beginning: to better cure (and prevent) diseases and improve the health of people in general.
That’s why I’ve spent the last few weeks talking to experts from the world’s largest pharmaceutical companies and compiled this article, which outlines what are the main impacts of Web3 technologies on the pharmaceutical industry.
1. Blockchain for Supply Chain Transformation
A few years ago in 2018, I came across an Accenture report 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 industry. sciences, primarily through supply chain transformation.
I must confess that at that time I was only in my early days of understanding about Web3 and its decentralization technology i.e. Blockchain, but these statistics really caught my attention, so I started to investigate more about Blockchain applications in the supply chain.
But before we get to its application in the Pharmaceutical market, let’s first better understand what Blockchain technology is: it is basically a distributed database that is shared between the nodes of a computer network, which stores information electronically in digital format. digital. A blockchain gathers information into groups, known as blocks, which contain sets of information and which have certain storage capacities and, when filled, are closed and linked to the previously filled block, forming a chain of data known as a blockchain. All new information following this newly added block is compiled into a newly formed block which will also be added to the chain once filled and when filled is irreversibly written and becomes part of that timeline. Each block in the chain is given an exact timestamp when it is added to the chain. You see? Blockchain is a Distributed Ledger Technology (DLT) where this database is distributed among multiple network nodes in multiple locations, which makes it decentralized.
And when it comes to its potential impacts on Pharma supply chains, we can list several like transparency (everything is tracked in real time), speed (you can access the information in real time and not wait for the middlemen to provide it to you) , and information sharing: I really like the definition of KPMG analyst Arun Ghosh, who said that blockchain in the pharmaceutical industry serves as a “book of truth” for sharing complex information with regulators, pharmaceutical benefits 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 track drugs and minimize the incidence of counterfeit drugs. A 2021 paper by a team of researchers at Khalifa University in Abu Dhabi titled “A Blockchain-based Approach to Drug Traceability in the Healthcare Supply Chain” points to the fact that most existing drug tracking systems are centralized (through the FDA in the US and regulators around the world) leading to issues of privacy, transparency and authenticity of data in the supply chain. They then present an approach based on the Ethereum blockchain, leveraging smart contracts and decentralized off-chain storage for efficient traceability of products in the healthcare supply chain: smart contracts guarantee the provenance of the data and eliminate the need for intermediaries and provide a secure and immutable transaction history for all interested parties. At the same time, think about this: drug recalls are much simpler through Blockchain technology. The product can be easily traced back to the manufacturer and associated with a production batch, allowing identification of other products and where they were shipped.
At the same time, as we mentioned, blockchain technology can help pharmaceutical companies apply “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 simplify price alignment and ensure first-time chargeback accuracy on MediLedger Blockchain Network. The MediLedger Network aligns trading partners in real-time on price agreements, eligible customer lists, and customer identity data such as HIN, DEA, and 340B identifiers. This data is then used by the blockchain to automatically enforce chargeback accuracy, eliminating most errors and escalations that create manual effort for providers.
Now, when it comes to Big Pharma players, they’re not just sitting back and watching, but they’re increasingly open to Web3. Cynthia A. Challener, Ph.D., director of scientific content at Pharma’s Almanac, mapped out some of the key initiatives: Novartis using blockchain technology and IoT to identify counterfeit drugs and track temperature with real-time visibility to all supply chain participants . Merck recently obtained a blockchain patent to prevent counterfeit drugs by increasing supply chain security. In a joint effort, Pfizer, Amgen and Sanofi are investigating using blockchain technology to securely store patient health data to accelerate clinical trials and reduce drug development costs. Blockchain startup Exochain offers a secure way to store and manage clinical trial patient data that also allows patients to control how researchers can interact with their medical data. Boehringer Ingelheim (Canada) has partnered with IBM to test the latter’s blockchain platform’s ability to “improve trust, transparency, patient safety and patient empowerment in clinical trials” by improving process management and trial records. clinicians.
IBM recently announced that it is working with KPMG, Merck and Walmart to develop a blockchain pharmaceutical platform that can track medicines 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 aim is to leverage blockchain 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 confidence in their authenticity and provenance.
Overall, we can predict that as much as blockchain has revolutionized finance through cryptocurrencies and decentralized finance (DeFi), we can predict a revolution in the way supply chains are managed globally through Blockchain – and not just in pharmaceutical industry!
2. Metaverse for Clinical Trials and Patient Focus
“Meta-what?”: I’m sure this was your reaction to Mark Zuckerberg’s recent announcement about Facebook rebranding to Meta. At least, that was mine. But interestingly, we’re all now talking about Metaverse thanks to that announcement, and while it’s not a new idea, we’ve only recently been able to better understand its implications for pharmaceutical companies, especially the way they conduct drug trials and get better patient data.
But let’s first understand what the Metaverse is: the term was born from the junction of the Greek prefix “meta” (meaning beyond) and “universe”, and fundamentally it is a virtual and collective shared space, created by the convergence of virtually enhanced physical resources reality. (represented by the “digital twins”, which we will talk about), and the virtual space that already permeates the physical world (especially 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 big opportunities that the Metaverse is offering for the pharmaceutical industry is, in general, to “get closer” to the patient – which is something the industry has struggled with traditionally, let’s be honest.
The truth is, as Arghya Biswas, Global Trial Manager at Novartis, wrote in a great Linkedin article he wrote in February 2022 with the title “The Metaverse will revolutionize healthcare, including clinical trials, by 2030”, the pandemic of Covid-19 “has accelerated the implementation of Decentralized Clinical Trials (DCT) or Virtual Trials, where trial participants can participate from the comfort of their home or visit the hospital a few times (in the case of hybrid trials). The industry has now also started to accept and incorporate more digital options like eConsenting, ePRO, eSource, Electronic Health Records and wearable devices into different clinical trials.” He added that “it may not be wrong to assume that one day virtual clinical trials will take place within a metaverse. Not only will this provide real interaction between doctors, nurses and patients, but also blockchain technology will add another layer of security to the test data, making it harder to tamper and thus improving the credibility of the data.”
Biswas’ analysis is supported by McKinsey research on the impact of decentralization on clinical trials: Typically, 70% of participants live more than two hours from trial sites (data from Sanofi), so decentralization expands access to trials to reach a larger number and potentially a more diverse group of patients. Decentralization can also reduce the workload of study investigators, as traditional on-site activities (such as drug administration, assessments, and data verification) can be performed remotely by others or by study participants themselves. enabled by a plethora of evolving technologies and services: tools such as electronic consent, telemedicine, remote patient monitoring, and electronic assessments of clinical outcomes (eCOAs), and of course, Metaverse now also allows investigators to maintain links to study participants without on – personal visits.
The industry seems to be catching on to this shift: Prior to the pandemic, an Industry Standard Research survey in December 2019 found that only 38% of pharmaceutical and contract research organizations (CROs) expected virtual testing to be an important component of their portfolios. and 48% expected to carry out a test with most activities carried out in the participants’ homes. When McKinsey asked the same questions a year later at the McKinsey Clinical Operations Roundtable, the responses were 100% and 89%, respectively.
But how would trials of new molecules work in the Metaverse? For starters, I want you to think of a virtual “copy” of yourself and all of your health data in real time: that copy of yourself, which in the metaverse is called the “digital twin” (which we’ll talk about more about). below), would basically be a representation of yourself that not only the pharmaceutical industry, but also your doctor, can interact and monitor at any time during clinical trials. A kind of telemedicine in the extreme, isn’t it?
Digital twins of trial participants would be created with health data from different sources, such as electronic patient medical records and wearables that measure physical parameters in real time (such as oxygen saturation, which is easily available through the oximeter on the devices). Samsung’s latest), and they would replicate how they would behave and respond in specific situations. You can track your health, diagnose illnesses, plan preventive treatments and, of course, monitor your reactions to a new drug that is being developed and tested.
The truth is, we urgently need innovation in clinical trials, because, as Ganes Kesari, co-founder 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 are not an accurate representation of the real world;
2. Few studies recruit needed patients on time (recruitment challenges delay nearly 80% of all studies);
3. Not every patient is treated with a new study drug (usually half are treated with placebo);
4. Not all experimental drugs work safely enough.
Well, digital twins in Metaverse can solve all this through some of their features: infinite coverage (digital twins can simulate a wide variety of patient characteristics, providing a representative view of a drug’s impact on a wider population), speed (AI can simplify study 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 all patients in a study can be confident of the new treatment) and, last but not least, safety (by reducing the number of patients who need testing in the real world, digital twins can minimize the dangerous impact of stage drugs initial).
But what is the current situation? The truth is that we are still in the early days of applying digital twins to the life sciences. Today, pilots use single twins to model the molecular and cellular functions of the human body, rather than simulating a patient’s entire response in clinical trials.
In the same Forbes article, Charles Fisher, CEO of Unlearn.AI, a startup that has raised more than $17 million to build digital twins for testing, said: “We are not yet at a stage where we can simulate the real biochemistry of a person. There’s a lot of biology we still don’t understand and there’s no data. Therefore, we are not working to predict how patients will respond to the new treatment.” But the impact could be huge, as he added: “I see the potential to safely and reliably reduce the size of clinical trials by, say, 25%, which can have a multiplier effect on medical research and patients. This will allow all biotech and pharmaceutical companies to run clinical trials faster and at lower cost.”
Therefore, we can conclude that just as telemedicine will also be revolutionized by the metaverse – totally changing doctor/patient interactions -, we can foresee a paradigm shift in clinical trials thanks to immersive and decentralized technologies like the Metaverse.
3. Digital twins for R&D and manufacturing
In 2019, Kevin Kelly, founder of Wired magazine, wrote an amazing cover story for the magazine called “Welcome to the Mirrorworld” where he described 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 the world.
“Digital twin?”, you might be asking yourself, especially after having read about this concept earlier in the article.
Well, let me introduce one of the first building blocks behind the metaverse, namely the concept of “digital twins”. A digital twin is, according to IBM’s definition, a virtual representation of an object or system, or even a person as we have seen, that spans its lifecycle, is updated from real-time data and uses simulation, Machine Learning. and reasoning to help decide what to do. Imagine a large manufacturing company having digital twins of its equipment: through them, an engineer at home will be able to solve problems in a factory on another continent across the Metaverse. The same technologies will enable much more productive office meetings than current two-dimensional video conferencing tools. Customer-facing applications can include creating digital twins in retail, offering customer service experiences that wouldn’t be possible in the physical world, and even engineering companies like Siemens are using digital twins to simulate the impact of falling trees. on their 5G antennas. Awesome, right?
And when we get to the Pharma sector and look at the potential implications for the industry, we can use digital twins both in the production building, in the laboratory, in the product itself and even in the patient itself: that is, they have numerous applications.
But for now, let’s focus on R&D first: when we look at pharmaceutical R&D, cost and resource issues often hamper research projects, as researchers cannot do everything with the tools at their disposal. As a result, when researchers embark on a new molecule discovery project, the odds are largely against them. Approximately 90% of new drug research fails – a substantial amount, given that the global pharmaceutical industry spent nearly $200 billion on research and development in 2020. This is where digital twins can be a useful asset in R&D. While researchers can take months, even years, of dedicated focus to classify and analyze data, advances in computing allow digital twins to run multiple test scenarios simultaneously. In addition, test automation allows clinicians to rapidly recreate and reproduce test scenarios, often conducted in highly controlled environments, as we saw in the previous chapter.
But most interestingly, digital twins are a vital tool to help engineers and operators understand not only how products will perform, but how they will work in the future: analyzing data from connected sensors, combined with other sources of information, gives us allows you to make these predictions.
An example? Big pharmaceutical companies use computational fluid dynamics (CFD) software to model momentum, energy, and mass transport in engineering and biological systems, and when we look at this, CFD software is one of the most popular types of digital twin solutions.
Digital twins organize the development of bioprocesses, suggest experimental designs, and manage new knowledge, all of which dramatically reduce development costs, achieved by combining the platform’s prior knowledge to predict future process outcomes.
When it comes to the use of digital twins in pharmaceutical manufacturing, the impact is also huge: to accelerate time-to-market, reduce batch waste, and increase quality and reliability in vaccine manufacturing, Atos, GlaxoSmithKline and Siemens teamed up to bring “digital twins” to the manufacturing process in the pharmaceutical industry. Using inline sensors at every step of the process, they can now collect data to understand exactly what is happening in real time, and by combining that data with physical, chemical and biological models, they have built a digital twin of the pharmaceutical process: a replica of the silica of the physical process that allows the optimization of operations and the simulation of changes, providing new insights for the development and total control over the pharmaceutical manufacturing process.
Matt Harrison, chefe de ciências, inovação digital e estratégia de negócios da GSK Vaccines, disse no comunicado à imprensa: “Com gêmeos digitais, você pode fazer grandes quantidades de experimentos digitais e minimizar o número de experimentos reais que você faz”. Experimentos baseados em gêmeos digitais também podem eliminar a necessidade de construir uma instalação de teste, o que pode levar anos.” Ele acrescentou: “Podemos executar vários experimentos – modelagem e simulação – em vez de ir a um laboratório”.
Quando olhamos para isso, os gêmeos digitais são realmente uma tecnologia que está pronta para revolucionar o mundo, e quando digo isso, quero dizer literalmente: NVIDIA, a líder em Inteligência Artificial,, está construindo um gêmeo digital da Terra, chamado Earth-2, para simular exatamente o impacto das mudanças climáticas. Incrível, não é?
4. Artificial Intelligence, Machine Learning and Big Data for Marketing and CRM
I would like you to imagine the following scenario: you are the pilot of an airplane and one day, in the middle of the flight, one of your engines fails. Terrible, right? It happened suddenly, and apparently nothing could have predicted it.
But the truth is, yes, it would probably be possible to visualize it if the plane were full of sensors that capture data in real time and, through AI, would be able to anticipate an engine stop – through correlations and simulations based on on the Big Data that is collected (pretty much like a Tesla is capable of doing, unlike most cars).
See the power of Big Data being processed by Artificial Intelligence? This helps us predict more and blindly react 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 have reached the point of 97 Zettabytes of data by the end of 2022 of according to Statista (just to give you an idea, a Zettabyte is a number with 12 zeros… that’s a lot of data!).
So how Big Data and A.I. can help in Pharma? The truth is, there are a plethora of applications, especially after Covid-19: a Deloitte survey of the scaling of AI adoption across the Pharma value chain found that COVID-19 has put AI in the spotlight. Companies have used AI extensively to optimize site selection for COVID-19 vaccines and manage the impact of disruptions to their clinical development operations. Novartis, for example, used AI to analyze data about test operations stored in data lakes to predict where disruptions (such as staff shortages, enrollment delays) would likely occur and intervene early to reduce their impact on test deadlines. Additionally, Deloitte’s 2020 “Measuring the Return on Pharmaceutical Innovation” study found that investments in AI and the digitization of test operations allowed most of the top 20 companies (in terms of R&D spend) to keep key trials going without affecting expected release deadlines.
The truth is that pharmaceutical companies have a lot of data, accumulated over years of operation (especially internal data, but we are seeing more and more external data such as patient data): in R&D, for example, digital discovery and molecule testing with advanced modeling and simulation techniques will be common (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 included in registry records and value dossiers to provide an early indication of real-world effectiveness.
GSK took a step in that direction in 2018 with a $300 million investment in 23andMe to, among other things, gain access to the startup’s database of 5 million people. And we know that data fuels Machine Learning algorithms: GSK is also one of the fastest scaling companies in Artificial Intelligence, with a team of over 100 people working on AI. Eli Lilly has also done a lot of work 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 fundamental to understand the prescribing behavior and profiles of potential patients, enabling a more precise segmentation of suppliers and increasing the number of prescriptions made. For example, a “patient lookup” technology that exploits electronic medical records to identify patients with specific rare diseases will allow sales forces and medical science contacts to focus on service providers who care for patients who are likely to have these rare diseases. rare diseases, although they have not yet been diagnosed. An example is what Novartis has been doing in Brazil with multiple sclerosis: they have placed QR codes in eye clinics for patients to scan and fill in a form with their symptoms, which can help Novartis predict much better than doctors individual the odds of multiple sclerosis, through an A.I. that uses patient data.
But beware: Tech companies like Apple, IBM, and Qualcomm Technologies are putting a lot of effort 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 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: who first among the pharmaceutical companies will collaborate with these players in building an analytical and Big Data culture, will gain an important competitive advantage. Sanofi took a step in that direction by initiating 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 deliver patient value beyond the drug, such as providing real-time, personalized care through digital sensors and services. Many drugs will be part of a digital ecosystem that constantly monitors patients’ conditions and provides real-time feedback. The result of this? Greater effectiveness as it allows customizing therapy based on the patient’s clinical and lifestyle needs and will allow for remote monitoring by healthcare professionals. There are already many sensors and IoT devices on the market that can measure the patient’s biophysical signals: from wearables like the Apple Watch, FitBit and the like, to under-the-skin chips that also allow the injection of drugs where needed most, for one I know well. as I worked on La Roche-Posay’s L’Oréal: 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 are increasingly personalized and more precise, according to the needs of each patient. By coupling the IoT with Big Data, it will be possible to predict how patients will react to treatments and even conduct clinical trials of new drugs more quickly. Personalizing medicines 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. In other words, in the future it will not only be important to produce the drug, but it will be even more important to provide patients and people with complete and customized solutions for health products and services. And Artificial Intelligence and Machine Learning are exactly here for that.
5. NFTs for data sharing and IP protection
Who hasn’t heard of the word “NFT” lately? Impossible not to have been impacted by this term, which is most often related to “digital art” – and I’m sure you’re thinking right now: “Andrea, what does this have to do with Pharma?”.
Well, for starters we have to understand what NFTs, or Non-fungible Tokens are, to understand that their applications go far beyond art and games, and are not just the speculative bubble 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 record. By confirming authenticity, NFTs establish ownership of unique online assets that can range from a simple pixelated image to a complex set of data, making duplication without permission impossible (to clarify, this means a dataset can be imitated , but the original is always clearly identifiable: for example, you could read Jack Dorsey’s first Twitter Tweet all over the internet, but the original was auctioned for $2.9 million and is owned by cryptocurrency entrepreneur Sina Estavi ). Identifying where data comes from and verifying its validity is a fundamental pillar of the industry today, making it likely that it will continue to be a major topic of interest in the future.
Contrary to the great fad of NFTs as investments, the applications of NFTs in healthcare and pharmaceutical marketing are not just about making a profit. Instead, NFTs would serve as a solution for verifying digitized healthcare, authenticating credentials and data, and protecting 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 healthcare professionals and patients.
What are some of your applications? As the beautiful PM360 article entitled “Beyond Fashion Investments: Three Applications of NFTs in Healthcare and Pharmaceutical Marketing” describes, a first example is the use of NFTs to verify offers and services: see, telemedicine and delivery services. medicines 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 to their initial writing and manufacturing, respectively, ensuring quality until the orders reach the consumer. In this scenario, the NFT would be associated with the physical product and tracked online, including information on recalls, replacements, and expiration dates. Remember the blockchain used to have pharmaceutical supply chain transparency? That’s exactly it.
In addition, NFTs can be used for credential and data authentication: the truth is that applications of NFTs go beyond product tracking to verify healthcare provider credentials and patient records. With these credentials, NFTs can be used to validate educational backgrounds, from diplomas and institutional certifications like CRM. When issued directly by the organization, these credentials are impervious to manipulation, maintaining their integrity independent of any physical records. This will minimize the possibility of fraudulent professionals and improve patient safety at all levels of the healthcare journey.
In addition, patient records can also be verifiable by NFTs, marking an original patient dataset as authentic and potentially allowing the patient to own that dataset. As owners of this data, patients can grant third-party access to use their records at healthcare practices, pharmacies and other applicable institutions. Ultimately, this would minimize human error between office handoffs, improving the efficiency of patient care, and it would also minimize handover time, accelerating the treatment journey in the process.
At the same time, in pharmaceutical marketing and R&D, NFTs can protect intellectual property in a similar way to protecting patient data: for marketers developing unique solutions, assets and materials can be tokenized and subject to transfer of ownership as required. Likewise, custom programs and algorithms can be tokenized to avoid copying and manipulation.
Online recruiting platforms with multiple data points, such as those that require individual registrations, can even be tokenized, ensuring the dataset is accurate and verifiable across all marketing efforts. In addition to improving data security, this application of NFTs to marketing can also increase the value of unique solutions among competitors.
You see? When we look at NFTs, we should not only consider them as super-expensive pieces of digital art, but also a game-changer in data and IP protection in the pharmaceutical and healthcare industries.
6. DAOs and Decentralization for Pharma Collaboration
Do you know how a cooperative works? I speak a lot for cooperatives in Brazil, especially in the financial and agri sectors, and I have always been amazed at how they manage to be more customer-centric and collaborative, because of their “ownership” structure – which, to explain as soon as possible , is basically a model in which the organization is “owned” by its customers.
This definitely makes accountability much more important, makes profit sharing more equitable, and, as I mentioned earlier, makes the organization more customer-focused (since the members, i.e. the customer-owners, make decisions about the strategy cooperative during its assemblies).
And while traditional cooperativism was born in 1844 in England, we now see a new form of cooperativism on the rise through the Web3: that brought about by DAOs, or Decentralized Autonomous Organizations.
What are DAOs, to begin with? A DAO is a new type of organizational structure, built on blockchain technology, which is often described as a kind of cryptographic cooperation. In their purest form, DAOs are groups that form for a common purpose, like investing in startups, managing a stablecoin, or buying a bunch of NFTs. ConsenSys, a blockchain organization, defines DAOs as “government 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 formed, a DAO is managed by its members, usually through the use of cryptographic tokens. These tokens often come with certain rights, such as the ability to manage a common equity or vote on certain decisions.
And how can DAOs impact the pharmaceutical industry? Well, one of its uses can be useful for collaborating around IP and allows for better licensing and discovery through research collaboration.
Take this example: Recently, Molecule AG, which describes itself as an “IP Web3 biotechnology marketplace,” announced a partnership with venture capital fund Apollo Health Ventures and VitaDAO, a decentralized autonomous organization that finances staged start-ups. initial R&D in the area of longevity. The aim of the partnership is to collaborate in funding and building the longevity biotechnology ecosystem, as Molecule believes that inefficiencies in longevity biopharmaceutical research and development and university technology transfer can be addressed using Web3 marketplace tools. This includes new types of liquid asset classes such as IP-NFT, a type of non-fungible token pioneered by Molecule that it owns intellectual property.
New forms of governance through DAOs such as VitaDAO and IP assessment such as IP-NFTs move early stage intellectual property to the Web3 to enable greater liquidity, discovery and reduced legal complexity through standardization of licensing terms .
At the same time, collaboration in the pharmaceutical industry can be boosted not only through DAOs, but also through decentralized medical databases: British 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 application (dApp) development. Contrary to the conventional attitude of DeFi, the startup allows users to mine coins while contributing medicines and therapies. Its decentralized database application allows medical researchers to share data over blockchain, ensuring security and proof of origin. In this way, Innovative Bioresearch facilitates drug development and prevents intellectual property (IP) theft.
In addition, Dutch startup Triall develops blockchain-integrated software for decentralized clinical research. It combines blockchain technologies and self-sovereign identity (SSI) to protect clinical research data. Unlike existing electronic systems that lack data transparency and integrity, it allows for 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 enable pharmaceutical companies, contract researchers, hospitals and patients to collaborate more transparently and accelerate drug development.