Securing, controlling and monetising genomic data with blockchain technology
Blockchain and other Web3 tech...
Revisiting existing network topologies in order to lay the foundations for a more secure future for individuals’ data.
Blockchain and other Web3 technologies, particularly in the past two years, have risen to prominence as solutions for a myriad of long-standing issues. Indeed, innovators in the burgeoning industry have already begun to apply the technology so as to streamline, secure and render a number of processes more transparent. Where are these the most significant? In the realm of data security.
We’re contributing to an infrastructure that, sooner rather than later, will yield potentially catastrophic consequences. On one hand, we’re constantly developing new software and devices designed to handle and analyse data points pertaining to a myriad of a given individual’s activities – from financial and identity information to health and shopping preferences. On the other, we’re storing and processing this data in incredibly unsafe environments.
If there’s one thing that’s certain, it’s that the model of standalone centralised databases that’s been the standard for decades is problematic, and requires a significant overhaul. All too often, convenience is prioritised at the expense of security. Even if an individual has no qualms with regards to the lack of privacy caused by the asymmetry of total trust in data custodians, they should absolutely be concerned with the security risks that this creates.
The threat of data breaches shouldn’t be underestimated. It’s time to accept the reality that no wholly centralised data silo is safe. One need only look at recent events – companies such as Facebook and Google+ suffered from critical breaches that left user data exposed. It’s by no means an anomaly, but rather an addition to the ever-growing string of data leaks occurring (whether by malice or incompetence on the custodian’s part). It’s critical, in moving forward, that these databases are phased out, or at least strengthened with robust encryption or hybridised models that incorporate decentralised protocols to ensure greater security.
With the rising popularity of mHealth applications, biometrics and online genetic testing, it’s more important than ever that vastly more secure systems are adopted, lest we wish to see another event on par with the recent Aadhaar one, where hackers were able to spoof login credentials to gain access to a wealth of personal information. This should also raise questions surrounding the custodian’s ability to sell such data to third parties.
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Fortunately, we have the technology to undo years of flawed database architecture – blockchain. Where a regular database takes information from many and stores it in a single instance, which is overseen by one party, a blockchain (or distributed ledger) leverages cryptography and a distributed network topology to instead connect peers directly with each other. Each participant keeps a copy of the ledger, synchronising it with peers as new entries are appended.
Not only does this robust protocol preserve the integrity of the data kept on it (no one party controls the ledger), but it also allows for users to remain entirely self-sovereign over the information they store in the digital realm, with heavy encryption clearly defining ownership. Of course, inbuilt mechanisms allow for granular control, so granting permissions to certain parties or upon the adherence to set parameters is possible.
In the realm of precision medicine, the blockchain offering is highly valuable. Big Data is predicted to turbocharge efforts in the field, and is already making strides in training machine learning algorithms that can analyse genomic information and return results. Of course, the more data made available, the more accurate the insights generated will be.
Ingrained into the functionality of a blockchain network is the possibility for issuing tokens providing utility. Combined with a distributed storage medium anchored in the blockchain, this presents some interesting options for users to control and monetise their genomic and other healthcare data, all whilst contributing to research in various fields of medicine – whether for clinical testing, pharmaceutical development or training any number of algorithms. Smart contracts (trustless and self-executing bits of code) can be established to automatically grant access to pieces of anonymized genomic data once an interested institution pays a requisite amount of data into it.
Large data sets are key to medical breakthroughs. With an ecosystem of interoperable blockchain-based platforms and incentivised sharing, researchers, healthcare providers, and businesses gain access to troves of region-specific genetic information they would not otherwise be privy to, assisting them in their pursuits to deliver cutting-edge precision medicines and improving the quality of predictive techniques for identifying diseases early on, while simultaneously rewarding donors.
Dr Axel Schumacher, Founder & Chief Scientific Officer of Shivom, has over 25 years of Research and Development leadership experience in genomics, epigenetics, biomarker discovery, Bio-IT, aging & longevity. He is the Author of the ‘Blockchain & Healthcare Strategy Guide’. Axel is also a Member of the Blockchain Research Institute in Toronto. He holds a Ph.D. in Human Genetics from the University of Cologne.
Introducing Dosis - the AI powered dosing platform
Cloud-based platform Dosis uses AI to help patients and clinicians tailor their medication plans. Shivrat Chhabra, CEO and co-founder, tells us how it works.
When and why was Dosis founded?
Divya, my co-founder and I founded Dosis in 2017 with the purpose of creating a personalised dosing platform. We see personalisation in so many aspects of our lives, but not in the amount of medication we receive. We came across some research at the University of Louisville that personalised the dosing of a class of drugs called ESAs that are used to treat chronic anaemia. We thought, if commercialised, this could greatly benefit the healthcare industry by introducing precision medicine to drug dosing.
The research also showed that by taking this personalised approach, less drugs were needed to achieve the same or better outcomes. That meant that patients were exposed to less medication, so there was a lower likelihood of side effects. It also meant that the cost of care was reduced.
What is the Strategic Anemia Advisor?
Dosis’s flagship product, Strategic Anemia Advisor (SAA), personalises the dosing of Erythropoiesis Stimulating Agents (ESAs). ESAs are a class of drugs used to treat chronic anaemia, a common complication of chronic kidney disease.
SAA takes into account a patient’s previous ESA doses and lab levels, determines the patient’s unique response to the drug and outputs an ESA dose recommendation to keep the patient within a specified therapeutic target range. Healthcare providers use SAA as a clinical decision support tool.
What else is Dosis working on?
In the near term, we are working on releasing a personalised dosing module for IV iron, another drug that’s used in tandem with ESAs to treat chronic anaemia. We’re also working on personalising the dosing for the three drugs used to treat Mineral Bone Disorder. We’re very excited to expand our platform to these new drugs.
What are Dosis' strategic goals for the next 2-3 years?
We strongly believe that personalised dosing will be the standard of care within the next decade, and we’re honored to be a part of making that future a reality. In the next few years, we see Dosis entering partnerships with other companies that operate within value-based care environments, where tools like ours that help reduce cost while maintaining or improving outcomes are extremely useful.
What do you think AI's greatest benefits to healthcare are?
If designed well, AI in healthcare allows for a practical and usable way to deploy solutions that would not be feasible otherwise. For example, it’s possible for someone to manually solve the mathematical equations necessary to personalise drug dosing, but it is just not practical. AI in healthcare offers an exciting path forward for implementing solutions that for so long have appeared impractical or impossible.