Blockchain enthusiast developer and writer. My telegram: ksshilov
The global drug development market is worth more than 350 billion dollars a year, according to ClinicalTrials.gov, which includes the costs of running the studies in addition to the resources used.
Aside from money, these trials have plenty of other problems to deal with: patient recruitment and retention, incomplete/implausible data, and delays that can often push trials back by an average of nearly 11 months.
All of these things combined can cause a company to lose, on average, $8 million per day that the clinical trial is delayed.
Clinical trials can trace their modern history to the year 1747, so why haven’t we figured out faster, more accurate, and less expensive ways to complete them?
Clinical trials have many stages, and many (many) different places where they could be better optimized. To understand the overall picture, let’s take a look at what the standard clinical trial goes through.
This stage of the clinical trial assesses the safety of the drug and typically includes a small number of healthy volunteers that are paid for their participation. This stage is designed to ensure the drug is safe to take, and measures how it is absorbed, metabolized, and excreted in the subjects. Side effects are often discovered during this phase, and approximately 70% of drugs pass this phase of the study.
Key weaknesses: patient recruitment and retention, data management/analysis, and payment portals.
Drugs that pass the first phase come here to be tested on its efficacy, after all, what good is a drug if it doesn’t do what it’s supposed to do? Phase II studies are typically randomized, with one group of patients receiving the drug and the other receiving a placebo. In addition, these studies are completed “blind,” so neither the participants nor the researchers know who is receiving the drug. About 33% of drugs pass this phase of the study.
Key weaknesses: patient retention, data management/analysis, data integrity, and human error
On average the most expensive and largest official testing phase, Phase III can include several hundred to several thousand patients, often spread around the world. This phase exists to help the FDA better understand the drug’s effectiveness, and the budget for this phase often climbs due to having a large number of participants. Anywhere between 70 - 90% of drugs that made it this far typically pass this phase.
Key weaknesses: data tracking and analyzing, patient recruitment and retention, central laboratory management, investigator site management/integrity checks, and payment portals
This phase takes place after the drug is released to the public, and typically entails monitoring long-term use of the drug and the impact the drug has on a patient’s quality of life. Phase IV either ends with the drug being cleared for continual use or by being taken off the market and reevaluated.
Key weaknesses: Immense data tracking and reporting
Nearly all drugs on the market go through these phases, which is why nearly all clinical studies could benefit from modern blockchain technology solving many of the common problems and weaknesses that clinical studies experience. Given the database capabilities that are the core of blockchains, it should be no surprise that when it comes to data tracking and management they will surpass existing systems in terms of efficiency and immutability. But that isn’t all that blockchains have to offer to the clinical trial industry…
There are many companies trying to be the first one to claim the huge prize that is clinical trial spending, but the majority of them are approaching the problem with the same solutions.
To summarize the paragraphs above, clinical trials typically go through four phases and suffer from a number of different weaknesses.
Blockchains can address the majority of these problems directly, such as:
Here are four companies currently competing for this space, with a brief writeup on what they do and where they excel.
Although this company isn’t directly involved in the clinical trial process, it is a huge proponent of tracking and managing health data.
Given that patient recruitment and retention is a major weakness for clinical trials, Patientory has a massive opportunity to step in and fill that gap.
By helping people manage their health data on the blockchain, everything from steps tracked to heart test results, Patientory could easily shift this focus to helping clinical trials find participants for studies. Usually, studies have to rely on doctors, message boards, recruitment websites, advertisements, and more to find participants - Patientory could completely erase that need by offering up the ideal candidates already using Patientory services.
When it comes to creating provably secure blockchains, Guardtime has a long history of success. Guardtime Health is a branch of the company that focuses on five key things: blockchains for health data, personal control over data with privacy regulations, anonymizing personal data for secondary research use, time-stamped data transactions for audit trials, and EHR (electronic health record) integration with blockchain-backed transmissions.
Like Patientory, Trials.ai is all about compiling data and using it for the greater good. Trials.ai focuses on AI-infused data investigations to help clinical trials find the best sites, participants, and protocols to help their trial succeed. By combining AI and data mining, Trials.ai can give researchers insight into past trials, medical journals, regulations, and best practices to reduce costs and setbacks.
By combining many of the practices mentioned above, Clintex.io takes a hybrid approach to combine AI, ML, and blockchains to help researchers create and maintain a successful clinical trial. With builtin protocols for AI-based predictions, site investigations, patient recruitment and retention, risk-based monitoring, data visualization, and more, Clintex.io brings the true power of blockchain technology and artificial intelligence to clinical trials. Two specific applications within Clintex, CTi-OEM and CTi-PDA, help researchers save even more money and time. The former, CTi-OEM, provides AI-driven insights into high-waste areas of a clinical trial, while the latter, CTi-PDA, uses historical clinical trial data to predict future performance and price issues.
Although no companies are perfect, the clinical trial field needs a company that combines both blockchain technology and artificial intelligence to not only store data, but detect problems and costs using that data before they even arise. For a clinical trial to benefit from these technologies and save money, they’d have to have all the existing technologies and methods they currently use migrated over to a blockchain without any disruption. Because of the centrally orientated data flow of existing clinical trials, it may be easier to use a service that creates an internal blockchain to replace the existing data flows already utilized by clinical trials.
Most researchers and companies creating clinical trials don’t have the expertise and experience needed to create and maintain a blockchain, so they’d have to rely on external companies to not only create the blockchain for them, but also manage it to ensure the maximum benefit is reached. Relying on Guardtime to create blockchains is one solution, but to get the full benefit you’d have to then rely on Trials.ai to gain the benefit of AI. Another option is to use Clintex, which recently tested their software by integrating data from various trials available from the European Medicines Agency. Clintex was able to deliver operational insights into numerous late phase clinical trials, which really helps their case since they have market experience.
The winner will be the company that can first successfully integrate multiple clinical case studies into their systems, with irrefutable proof that blockchain and AI can help reduce costs and errors.
As we are seeing with Coronavirus, the efficiency of clinical trials could certainly be increased and the costs could certainly be lowered.
Blockchains will absolutely play a huge role in this, especially when paired with machine learning algorithms to further analyze massive amounts of data. The world is in need of a better way to complete clinical trials safely and quickly, and the company that offers the best solution will be rewarded with billions of dollars through clinical study funding in the US alone.
The author is not associated with any of the projects mentioned.
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