The application of AI in the pharmaceutical industry is regarded as a major export for artificial intelligence companies, and pharmaceutical companies are also regarded as the potential largest payers.
According to statistics, from the perspective of capital inflows, as of July this year, the newly inflow of funds in the "AI + drugs" field exceeded 600 million US dollars, which is more than the whole year of last year, showing a spurt of growth. More than 100 companies have been labeled "AI + New Drugs" at home and abroad.
AI can be applied to many fields such as drug discovery, preclinical research, clinical trials, rational drug use decision-making, pharmacovigilance, and drug restructuring discovery.
On September 26, at the "2018 Artificial Intelligence + Pharmaceutical Enterprise Innovation Forum", several guests from medical and health-related experts and scholars, medical and health innovation companies, investment companies, and medical institution industry representatives shared their insights and jointly discussed the impact of AI on pharmaceutical companies, The value of the hospital and how to solve the challenge of breaking through AI applications.
Speaker Ross Rothmeier is the vice president of technical solutions and innovation laboratory of Medidata.
He brought you a new data: In the 10 years from 2006 to 2015, the conventional method for new drug research and development, the success rate from Phase I to Phase II was 63%, and from Phase II to Phase III it was 28.8%. 55% can enter the new drug application stage, but even at this stage, only 83.9% can finally be approved. If you combine these percentages, you will find that only 8.4% of all Phase I study drugs can succeed.
However, if AI is used to select biomarkers in the research and development process, the success rate of each of the above stages can be greatly improved, reaching 76.7%, 46.7%, 76.5%, and 94.5%, respectively. The combination of these percentages means that the success rate of the first phase study drug can reach 25.9%, which is three times higher than the original!
It is this amazing effect that pharmaceutical companies have joined forces with AI companies.
Medidata has more than 1,000 customers all over the world. On its software platform, more than 13,000 studies are being conducted, and 3.8 million patients can provide valuable data.
In China, Medidata has helped 870 clinical trials and has 146 customers, including leading national pharmaceutical companies and contract research organizations (CROs) such as Hisun Pharmaceuticals, Fosun Pharma, WuXi AppTec.
Internationally, among the pharmaceutical giants, GSK, Novartis, Johnson & Johnson have cooperated with AI company Insilico Medicine, Merck and Abbvie are Atomwise and cooperation; AstraZeneca, Pfizer, Takeda Pharmaceutical and other pharmaceutical companies have also cooperated with artificial intelligence companies. Cooperation.
Embrace AI and become a rigid demand in all links
The penetration of artificial intelligence will reshape every industry, and it is no exception in the medical field. The fact that artificial intelligence and deep learning can greatly improve efficiency is no longer a hype gimmick, but a fact. At present, the application trend and mode of AI have gradually become clear, and the value to all parties has also begun to be highlighted.
From the perspective of patients, artificial intelligence can better solve their medical needs and provide better medical services, which is welcomed by patients. Fan Xiaolei, co-founder of Suo Wen Bo Shi, said: “Patients are increasingly accepting diagnosis and treatment decisions and recommendations based on analysis based on big data and artificial intelligence. Participating in the treatment management process through new technologies has led to an increasing acceptance of patients. According to the data, our patients are very open and tolerant towards the development of new technologies and treatment data. This is a conclusion that makes us happy."
For pharmaceutical companies, the most direct benefit of artificial intelligence for pharmaceutical companies lies in the research and development of new drugs. Mr. Li Yunfei, Director of the IPD Management Office of Tasly Pharmaceutical Group’s Product Integration R&D, commented on the role of AI in discovering targets: “People are constantly aware of diseases, technology development, treatment paths, and treatment methods are constantly changing. Development of drugs More and more complex, more and more high-end technology is required. Informatics, including the so-called big data and AI, will definitely play a role in the future, and it will play an important role."
In addition, Li Yunfei also said that in addition to being a tool for new drug research and development, artificial intelligence can also be used as a tool for rediscovering the value of Chinese medicine. For example, network pharmacology is a tool that integrates pharmacoinformatics, software information, molecular biology, big data, artificial intelligence and other technologies. It can predict the target of traditional Chinese medicine, identify the active ingredient group of traditional Chinese medicine, clarify the mechanism of action of traditional Chinese medicine, explain the rationality of prescription and the law of traditional Chinese medicine prescription, and help find new indications.
In addition to pharmaceutical companies, artificial intelligence can also alleviate the plight of the hospital. On the hospital side, hospitals at different levels have different needs, and artificial intelligence is one of the key technologies to solve the problem. For secondary hospitals, the construction of a medical consortium will improve the level of diagnosis and treatment and better serve the needs of patients. After the flow of big data, artificial intelligence and deep learning can better replicate the clinical experience, diagnosis and treatment processes of higher-level hospitals, and empower grassroots medical staff. For the top three hospitals, a large number of doctors have a large number of scientific research needs, and artificial intelligence can make better use of real-world data.
The data problem is still the congenital deficiency of domestic AI
Although AI has broad application prospects, AI wants to achieve landing applications requires a large amount of standardized and structured data for "feeding". Secondly, all parties in the medical and health services also need to change their thinking and actively embrace artificial intelligence.
In terms of data, if artificial intelligence is divided into three dimensions: algorithm, computing power, and data, the main opportunities in the industry now focus on data and application. The core of competition lies in the quality and quantity of data. However, for Chinese medical artificial intelligence companies, there are large-scale potential data in the market, but they cannot be sorted and utilized. On the one hand, the number of hospitals in China is huge, but more than 75% are unstructured, and they cannot give full play to the value of "big data". On the other hand, whether it is modeling or training machines, they are inseparable from the real clinical environment. Most medical artificial intelligence products in China lack a clinical environment.
Fan Xiaolei explained: “Currently, a large amount of data is not standardized, which is greatly affected by doctors’ qualifications and personal style. The second is the lack of data and the superficiality. Now the HIS system big data only records basic things. The molecule is big. Biological testing, genetic testing data, and out-of-hospital follow-up data. These key data for scientific research are not recorded in the hospital system. The lack of a large amount of information has caused many difficulties in the application of this part of the data. Although there are also natural languages ​​nowadays. The processing technology, but for this part of the data processing, the value obtained from the investment on this part is relatively low."
Li Yishi from Haoyue Capital put forward the standard of "good" data: "We believe that high-quality data first exists in high-quality hospitals. It should be the experience of clinical experts. Combined with guidelines and evidence-based medicine, it contains more and more complete data. , Richer dimensional data, and high-quality data generated for the purpose of scientific research. In the past, clinical research was done, and the data accumulated in it was of a relatively high level."
Technology can catch up, but the change of concept is the invisible barrier. Liang Yi, Chief Commercial Officer of Zai Lab, said bluntly: "We can already imagine the future, 5G technology and the Internet of Things will allow everything to be interconnected. This will have an impact on the business model, production, and all aspects of the entire medical industry. The future of the Internet of Things It will be a big change. For all industries, companies that do not understand the Internet of Things, do not understand soft things, and only understand hard production will be eliminated. Especially the current domestic backward pharmaceutical companies ."
Liang Yi added: "When launching a new product, pharmaceutical companies still use the traditional approach, which is to communicate with doctors, and then the entire upstream and downstream links that match this product will not do any research and development in all aspects of the ecological environment. R&D is not only It only refers to product research and development. Research and development is also the research and development of business models. Your marketing department also needs to do research and development. This has not been done once in a pharmaceutical factory for decades."
As a tool that saves a lot of manpower and empowers all aspects of the medical industry, artificial intelligence is reflected in all industries of the medical and health industry. Artificial intelligence can help solve the problem of shortage of hospital doctors' resources, and provide patients with more accurate and efficient services. On the side of pharmaceutical companies, AI wants to meet the R&D needs of pharmaceutical companies and solve precise patient recruitment. The primary problem is to generate a large amount of data. In the future, artificial intelligence companies need to cooperate with big data companies or pharmaceutical companies that have accumulated a large amount of data. To achieve precise patient recruitment, specialized patient organizations are also needed.
Pharmaceutical companies can no longer leave artificial intelligence, but how to pass the data barrier is a top priority.
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