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                     Artificial intelligence in pharmaceutical industry [latest version in 2022]

using Artificial intelligence in pharmaceutical industry

The market environment for the pharmaceutical industry is expected to become even more difficult due to the government's policy of curbing drug prices due to the expansion of medical expenses due to the declining birthrate and aging population. Therefore, the development of new drugs is becoming more necessary to find new markets.

There is a tailwind in the biotechnology and drug discovery fields of venture companies, and there are many cases in which major pharmaceutical companies have partnered with drug discovery ventures that have technologies in cutting-edge fields such as gene therapy. Digitalization into the medical field is progressing rapidly, and the utilization of artificial intelligence in pharmaceutical industry.

What is artificial intelligence (AI)?

The term artificial intelligence (AI) refers to intelligent actions performed by machines designed to reproduce the capabilities of the human brain through a combination of algorithms.

More specifically, artificial intelligence is about enabling certain machines to perceive their environment and react in a way similar to the human brain. It refers to the ability to perform functions such as reasoning, cognition, learning, and problem solving.

Computer science, logic, philosophy, and robotics have contributed to the creation and design of machines that can solve problems using artificial intelligence models.

The term artificial intelligence was first coined in 1956 by John MacCarthy, Marvin Minsky, and Claude Shannon. They defined it as "the science and ingenuity of creating intelligent computers, especially intelligent computer programs."

But the first inquiry goes back to the Greeks. In fact, Aristotle was the first to explain the functioning of human thought and the rules by which rational conclusions can be reached.

Examples of artificial intelligence

AI is present in most of today's technologies, especially smartphones , tablets, computers, and all kinds of devices with integrated electronic systems.

As an example of artificial intelligence in our daily life, we can refer to:

Home automation (intelligent air conditioning, programming to turn lights and devices on and off, etc.), autonomous vehicles, Google Assistant, voice assistants like Siri (Apple) or Alexa (Amazon Echo), Google Predictive Dictionary; image recognition software; security and fraud control software; habit analysis software; digital marketing forecasting; Forecasts and suggestions for consumption of news, music, movies, series, etc.

Types of artificial intelligence:

From a theoretical point of view, there are four types of artificial intelligence today, according to researcher Arend Hintze. see

Reactive machine

A machine designed to evaluate the information available in the environment and solve immediate problems based on that information. This type of AI doesn't store or memorize, so it doesn't learn. Your task is to analyze the information at any given moment, build a possible solution and choose the most efficient one.

In 1990, IBM developed a system with a feature called Deep Blue that won a match against champion chess player Garry Kasparov. Today, reactive AI is used , for example , in self-driving cars .

Machines with limited memory

It refers to the skills to use information obtained from databases, to record and learn basic information about the environment. An example is the case of GPS technology.

machine with a theory of mind

This is an AI type that is still in development. In the future, we hope that certain machines will be able to understand human thoughts and emotions and make decisions based on them. Therefore, social interaction is required. 

self-aware machine

A self-aware machine is a machine capable of having self-conscious awareness, thoughts and attitudes, i.e. machines capable of perceiving, reasoning and acting as humans.Here are some examples of how the pharmaceutical industry is actually using Artificial intelligence.

4 cases using Artificial intelligence in pharmaceutical industry

Here are some examples of how is actually using artificial intelligence in  pharmaceutical industry.

Artificial intelligence in pharmaceutical industry : Call center efficiency 

The first example of using Artificial intelligence in pharmaceutical industry:

Sawai Pharmaceutical Co., Ltd., a major generic drug company, has introduced and is utilizing the "TRAINA VOICE Digest" from Nomura Research Institute, Ltd., a major system development company.

The number of products handled by Sawai Pharmaceutical is increasing year by year, and the number of inquiries from medical professionals is also increasing, so it was introduced to improve productivity at the drug information center that handles it.

Inquiries were faced with a number of issues, including an increase in the number of inquiries, a wide variety of complex contents, and the inability to easily increase the number of staff due to the response of specialized staff with pharmacist qualifications.

TRAINA VOICE digest includes voice recognition and dialogue summarization technology, which previously required inputting telephone inquiries and answers into the system, but can significantly reduce the load of input work and produce. 

Artificial intelligence in pharmaceutical industry: Development of cancer vaccine 

The second example of using Artificial intelligence in pharmaceutical industry:

The cancer vaccine "TG4050" utilizing Artificial intelligence was jointly developed by French biotechnology company Transgene and NEC Corporation (NEC). This is a bespoke treatment that creates a personalized vaccine for each patient.

NEC has entered the drug discovery business utilizing Artificial intelligence, and the first full-scale entry is the TG4050.

By comparing normal cells of patients with cancer cells, Artificial intelligence predicts the abnormal protein "neoantigen" found only in cancer cells. We have developed a vaccine that increases the abnormal protein predicted by Artificial intelligence in the patient's body, and by increasing the abnormal protein in the body, immune cells attack as a foreign substance and it is also effective for cancer cells that originally existed.

NEC will continue to promote drug discovery business utilizing Artificial intelligence.

Artificial intelligence in pharmaceutical industry: maximize drug value.

The third example of using Artificial intelligence in pharmaceutical industry:Astellas Pharma Inc., a major pharmaceutical company, Doshisha University, and Wakayama Medical University have signed joint research agreements to maximize drug value by utilizing Artificial intelligence and statistics.

We will engage in research utilizing statistical models and simulations based on big data. Research with Doshisha University is the optimization of drug development decision making.

In drug research and development, there are many important choices such as target disease selection and clinical trial design. By analyzing these based on data, it is possible to evaluate the strengths and weaknesses of options and accelerate and optimize drug development decisions.

What we are doing with Wakayama Medical University is research on maximizing the therapeutic effect. If the effect of the drug can be predicted according to the patient's condition and the appropriate one can be selected, the therapeutic effect can be improved and the cost can be reduced.

It is possible to make decisions based on more accurate estimates by mutually utilizing the results and know-how obtained from these two joint researches.

Artificial intelligence in pharmaceutical industrtry: Creation of drug discovery themes 

The fourth example of using Artificial intelligence in pharmaceutical industry:

The LInC (Life Intelligence Consortium), an industry-academia collaboration project in which more than 100 domestic pharmaceutical and IT companies participate, is developing drug discovery Artificial intelligence.

At LInC, various projects are underway, and one of the major themes is "drug discovery theme creation." Further subdivided into four projects, in one project, "Creation of AI to discover collaborators from a huge amount of paper data", the prototype of LInC was developed, and G-Search Co., Ltd. announced "J Dream Expert Finder". We provide the service as.

This service is based on about 1 million authors of papers stored in the academic literature database, and when you enter a search theme, you can find out in what field the author has a clinical trial, who the co-author name is, and so on.

In addition, based on data such as co-authorship, a promising researcher with high growth potential from the relationship of human networks uses a calculation method called "mediation centrality" in which the more routes that pass through a certain point, the higher the centrality. You can find it.