With the advent of new drugs, it is now possible to treat diseases that used to have a high fatality rate.
In March 2018, a new drug “ Xofluza ” was launched for influenza, which is familiar to everyone. Not only does it have a good reputation for being more effective than any other drug, but it is also used by many medical institutions because it allows for speedy treatment .
In this way, new drugs can be said to reduce the risk of contracting a disease or exacerbating symptoms.
AI is finally beginning to be applied to the pharmaceutical industry .
Therefore, in this article, we will delve into the challenges of new drug development and what impact the introduction of AI will have on the drug discovery field .
Current state of drug discovery
Currently, the field of drug discovery is still in the growth stage. Further development is aimed at by introducing AI into the field of drug discovery.
Before explaining the application of AI to the drug discovery field, it is first necessary to understand the current state of drug discovery .
What is the flow of drug development and what challenges are there?
10-year process to new drug development
Drug development is carried out in the following flow.
In this way, it is a matter of course that it takes more than 10 years from “target search” to “new drug approval” . In fact, the odds of a lead compound ultimately becoming a drug are less than approximately 1/25,000.
- Target search : Many pharmaceuticals are compounds that bind to proteins in the body and regulate their actions. In target discovery, the protein to which such a drug binds is selected.
- Lead compound : A compound that has a strong binding force to the target protein in target search and is likely to be used for the development of new drugs is called a lead compound.
Issues in new drug development
New drug development poses not only high risks, but also various challenges.
- It costs an average of 100 billion yen or more
- Difficulty in predicting efficacy and toxicity at the earliest possible stage in the drug development process
- Unexpected side effects may occur due to differences between experimental animals and humans
- If an AI system makes an incorrect decision or prediction, who will take responsibility and who will incorporate the safety features. There are ethical considerations and legal issues to be resolved, such as how the economy will react to the advent of AI if certain jobs become useless .
Such
That’s all there is to it.
It is necessary to develop technology to solve the current problems.
Why apply AI to drug discovery?
To reduce the cost and labor of drug discovery
Using AI can reduce the cost and effort of drug discovery.
For example, the success rate of drug discovery can be greatly increased by conducting simulations using AI in the initial stage of verification. AI also plays an active role in the screening work of narrowing down effective combinations from tens of thousands of compounds.
Using AI to organize and utilize vast amounts of data on chemical compounds, drugs, and diseases will be of great help in solving the challenges of new drug development.
To efficiently verify the effects of new drugs
AI is very useful not only for drug development, but also for efficacy verification.
For example, when a new drug is experimentally administered to a patient, image diagnosis using AI makes it possible to efficiently verify the effects of the new drug.
How to apply AI to drug discovery
AI utilization methods in the drug discovery field include the following.
- Target identification
- drug design
- drug development
- big data analysis
- Research risk prediction
- patient matching
AI can be used in these fields.
Specifically, it predicts the activity of a protein from the compound factory, visualizes the effective and ineffective parts of activity expression, and proposes compounds suitable for drug discovery from existing compounds. can do.
Examples of companies working on AI in the drug discovery field
What kind of companies are working on AI in the drug discovery field?
Life Intelligence Consortium (LINK) – Harnessing Big Data
The Life Intelligence Consortium (LINK) is an initiative aimed at AI drug discovery through industry-government-academia collaboration. In order to support the development of the Japanese pharmaceutical industry, LINK’s efforts began as it was essential to improve the efficiency and success rate of new drug development using AI.
The specific goals of LINK are to develop AI and big data technology in the pharmaceutical field to develop the pharmaceutical industry, and to match the IT industry and the life industry to increase the industrial competitiveness of each area. increase.
Efforts are underway to develop, coordinate and integrate 30 AI systems that support each pharmaceutical process.
MOLCURE Co., Ltd. – Drug Discovery AI Venture
MOLCURE Co., Ltd. is a venture company that provides technology for new drug development using AI. The biopharmaceutical molecular design technology developed by MOLCURE utilizes AI and robots for screening and compound design.
By using this technology, screening time can be reduced to 1/10 compared to existing methods, and new drug candidates can be discovered by 10 times.
In August 2021, MOLCURE plans to raise a total of 800 million yen in funds from JAFCO Group, STRIVE, SBI Investment, Japan Post Capital, GMO Venture Partners, and Nippon Chemiphar to strengthen its global business development. is expressed.
Impact of AI on drug discovery
AI can be used to automate various aspects such as molecular design, speeding up drug development . It is expected that the development period, which used to take more than 10 years, can be shortened by 4 years.
If the speed of drug development increases, development costs can be reduced. For the industry as a whole, we can save 1.2 trillion yen . This represents a reduction of 60 billion yen per item.
Also, if the introduction of AI in the drug discovery field progresses, it may become possible to realize precision medicine .
Precision medicine is to analyze each person’s genes in detail and prescribe the best medicine based on the results. Translated as “precision medicine”. The power of AI will be greatly demonstrated in prescribing medicines that suit each individual.
summary
Currently, there are many problems in the field of drug discovery, such as the enormous amount of time and money required and the inability to predict new drugs.
AI is beginning to be applied as a solution.
By utilizing AI, it is possible not only to solve problems, but also to propose medicines that suit individuals.
Keep an eye on AI, which will continue to develop further in the future.
