New York, United States - 05/05/2022 — An attempt to glimpse the future — the 2020s or even 2030s.
Today, clinical trials involving volunteers are the only way to prove the effectiveness and safety of a new medication. This was not always the case, however.
What was the world's history of clinical trials, and why does this format need an overhaul and a fresh start? What will they look like in the next few years, and how can big data contribute to that?
Clinical trials before modern-day protocols
It is impossible to predict with animal tests only how an unknown drug will affect a human body. To understand that, scientists needed years of trial and error; and some errors are considered unforgivable.
Clinical trials today: types and design
The full cycle of clinical development takes over 10 years on average before the new drug makes its way to the market. State regulators need to be confident that clinical trials adhere to the GCP standard before they authorize the medication.
Computer technologies: automation
Automation enables getting and analyzing multiple substances in the same conditions while minimizing the human factor and greatly reducing development time. Of course, this is closely tied to computing methods, mainly machine learning.
The next stage of automation would require uniting all laboratory equipment into one system controlled by a single software.
In silico: from animal to humans
When it comes to preclinical trials, nowadays developers can design a complete "human model" in virtual reality, digitally recreating an organism to study various biological processes in silico. While in vivo is understood in molecular biology as the colony of artificially cultivated cells, in vitro — as the system of cell-less synthesis in lab conditions, the in silico approach encompasses tasks on modeling the behavior of single molecules, biochemical processes, and even functioning of specific physiological systems.
This modeling is a costly process, yet it provides almost infinite opportunities for research, testing existing substances, and monitoring various types of therapy.
Big data as a resource
South Korean companies Hanshin Medipia and Infinity Care actively use Longenesis blockchain platform to facilitate their biomedical research. This technology automates the process of getting patients’ consent to medical intervention: platform users agree to partake in a medical study or a trial. The system works with pharmaceutical companies and research institutes to help them browse through anonymous metadata and see what information is available. Then the patients are offered to join a test or a study or provide their information for drug effectiveness assessment. This significantly speeds up the routine working procedures.
Big data also helps forecast adverse effects of certain components and compounds even before the trial. The analytical method includes checking hundreds of various substance characteristics and saves the manufacturer's time and money.
Author of the article
Rustam Gilfanov is a famous IT entrepreneur, a founder of a large IT company, and a partner of the LongeVC Fund.
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