In recent years, with the continuous innovation of technology, it has helped the pharmaceutical industry change the current situation, reduce costs and realize greater value. From personalized treatment to prevention, technological development has brought challenges to the traditional business model of pharmaceutical companies.

Among many emerging technologies, artificial intelligence and advanced analysis have attracted more and more attention in the pharmaceutical industry. The value of these technologies is that they can quickly process a large number of complex structured and unstructured data and provide suggestions for the actual operation of relevant personnel, so as to reduce costs, shorten the time to market and gain competitive advantage in the market.

FICCI is the abbreviation of the Federation of Indian Chambers of Commerce & industry. Founded in 1927, FICCI is the oldest national chamber of Commerce in India. The Federation has more than 500 members of regional and industrial chambers of Commerce, covering all industries in India, representing 250000 companies in India, with a total employment of about 20 million people.

The six scenes of AI + medicine have landed and ushered in the "outbreak period". There is still a long way to go in the future

FICCI also has extensive contacts with the business community of various countries and has established joint Business Council (JBC) with 74 countries and regions. Through this report, readers can see the picture of AI + medicine in FICCI’s eyes.

Artificial intelligence and advanced analysis become a new outlet for medical digitization

At present, the global pharmaceutical industry is undergoing two major changes: the first is the transformation of the whole medical value chain. As the central force, the government and insurance companies exert pressure on pharmaceutical companies to reduce prices and enhance drug value. Secondly, the medical model is gradually changing from treatment to prevention, diagnosis and cure, and has attracted a large number of competitors inside and outside the industry.

This shift is driven by three trends: breakthrough innovative therapies, technological advances, and medical consumption through the acquisition and analysis of patient data.

Although most technologies are still applied in the field of drug discovery, the practical application of artificial intelligence is also being further explored in other fields such as drug dosage and drug safety, manufacturing and supply chain and commercialization. In terms of artificial intelligence and advanced analytical technology, pharmaceutical companies need to choose their business areas and partners. We can see that pharmaceutical enterprises are increasingly emphasizing cooperative relations, the most of which is the cooperation with science and technology start-ups.

Looking ahead, the market of “science and technology + medicine” has great development potential. The reason is that the leader has obtained high returns. Although it has brought great pressure to other competitors, it also encourages more people to join the market competition. On the other hand, driven by start-ups, these technologies are widely used. At the same time, regulators need to change their traditional way of approving medical devices and equip relevant technical knowledge and professionals to evaluate and approve these emerging technologies faster.

Advances in science and technology bring possibilities to many complex things in different ways. Digital technologies, including mobile communications, cloud computing, advanced analytics and the Internet of things (IOT), are subverting traditional industries such as industrial manufacturing, retail, telecommunications, banking and pharmaceutical manufacturing. Various driving forces are accelerating the digital transformation of Medicine:

Improve efficiency and reduce the cost of pharmaceutical R & D;

Optimize the product quality and make the production process more in line with the specifications;

Increase the interaction with patients and improve the repurchase rate;

Improve the level of disease diagnosis and treatment;

Determine the needs of patients and reduce the gap between supply and demand;

Broaden the range of products and services.

In the field of emerging technologies, artificial intelligence and advanced analysis are challenging the traditional business model of pharmaceutical companies. Therefore, some technology companies may be different from traditional pharmaceutical companies. They will propose new business models and let pharmaceutical companies accept them as much as possible. The amount of data generated by the pharmaceutical industry is growing exponentially. Therefore, the primary task of pharmaceutical enterprises is to use these data to drive value. Its ultimate goal is to simplify the pharmaceutical value chain, improve drug production efficiency and approval rate, and reduce costs.

In order to better develop emerging technologies, top pharmaceutical companies have invested heavily in this field and formed strategic alliances with AI companies to integrate AI technologies into their value chain.

The six scenes of AI + medicine have landed and ushered in the "outbreak period". There is still a long way to go in the future

Figure 1: layout of global top pharmaceutical enterprises in AI + medicine

Most of these companies choose to form strategic alliances with AI companies to use AI for drug research and development. Because these partnerships are at the core of pharmaceutical companies, some pharmaceutical companies also believe that they need to develop relevant technologies internally, such as drug dosage and drug safety.

The artificial intelligence of the pharmaceutical industry is gradually changing from the initial R & D stage to the consumption stage.

Some trends in the industry include:

Drug R & D: large pharmaceutical companies choose to develop their own AI technology or cooperate with AI start-ups to speed up the drug R & D process and realize individualized drug use;

Drug dose and drug safety: artificial intelligence can customize the corresponding drug dose for each patient according to the patient’s condition and characteristics. Artificial intelligence is applied to all stages of the safety value chain to improve overall quality and drug compliance;

Drug production and supply chain: artificial intelligence is being used to optimize drug verification and counterfeit drug identification in the whole production process;

Commercialization: artificial intelligence is increasingly used in patient classification to improve drug efficacy and reduce adverse reactions;

Regulatory approval: simplify the approval process of clinical drugs to make it faster and more transparent.

In India, pharmaceutical companies have only recently begun to apply AI to drug R & D and product supply chain. Drug discovery is still the focus of the digital transformation of the pharmaceutical industry, because artificial intelligence can scan the database to find specific molecules of drugs.

The landing of six AI application scenarios ushered in the “outbreak period”

1. AI + drug development

The drug discovery process usually involves the identification of a large number of compounds. Artificial intelligence can simplify this process by using algorithms to check the chemical characteristics of molecules to determine whether they can be used to make drugs. Pharmaceutical companies such as GSK, Sanofi, Takeda pharma and Merck have established various cooperative relationships with AI start-ups:

GSK cooperated with exscientia, a British AI start-up, and invested US $43 million for drug research and development to identify small molecules for 10 selected targeted drugs in undisclosed treatment areas. Sanofi and ExscienTIa signed a strategic cooperation agreement worth $283 million to develop new therapies for diabetes and other metabolic diseases.

Drug reuse is another common use case – new use of old drugs. Different algorithms can identify new potential applications for existing drugs or candidate drugs in later development.

It is the preferred strategy for many biopharmaceutical companies to use drugs in later development in new treatment fields. For example, Sanofi cooperates with artificial intelligence start-up recursion pharmaceuticals to jointly carry out drug research and development, aiming to use Sanofi’s clinical stage small molecules in the treatment of various inherited diseases. Astellas Pharma cooperates with numedii, a big data bioinformatics company, to reuse drugs by using machine learning technology.

The development of biomarkers is an important stage of drug research and development. Artificial intelligence is applied more and more in this field. Sanofi Pasteur, the global leader of influenza vaccine, uses Berg health’s platform and artificial intelligence tools to identify molecular features and develop potential biomarkers in order to evaluate the immune response of influenza vaccine.

In addition, pharmaceutical enterprises pay more and more attention to digital biomarkers, which is conducive to obtaining objective data with clinical significance and improving cost-effectiveness.

2. AI + medication safety

Drug dosage: the National University of Singapore has created an artificial intelligence platform called “cure. AI”. It can use the patient’s clinical data, such as historical records, to quickly identify the drug dose, and modify the tumor size or tumor biomarker level on this basis. These data can also be used to customize different courses of treatment according to the needs of patients.

Clinical safety: Agios pharmaceuticals uses natural language processing (NLP) to help its system make rapid and comprehensive decisions. The technology can also identify safety signals through exploratory research for preclinical drug research and development. In addition, natural language processing can also be used to study the patient’s symptom patterns to help identify whether the patient is in a high-risk situation.

Non clinical safety: Merck uses NLP technology to automate the workflow, combine unstructured data and structured data for analysis, and create a visual business intelligence dashboard for the safety assessment team. This process enables the company to identify anomalies that can only be identified in long-term testing.

Pharmacovigilance: GSK’s clinical safety team continuously determines relevant safety signals by studying medical literature. GSK has nearly 200 product portfolios. It can use NLP to improve research efficiency and language processing speed, make the search process more standardized, and quickly determine the relationship between drugs and adverse events.

3. AI + drug production and supply chain

Veripad uses machine learning technology to identify counterfeit drugs in the supply chain. The organization has designed a chemical test card to quickly detect the components of common drugs. Using this test card with the corresponding mobile application can identify counterfeit drugs. Finally, veripad uses data analysis technology to summarize the results of each test, so as to better understand the circulation of fake and inferior drugs.

For drug classification, veripad’s first generation application can achieve 80% accuracy. The research team of New York University also used machine learning technology to develop a new mechanism to identify the true and false of the same product.

4. AI + market development and commercialization

A global pharmaceutical company partnered with aktana, an artificial intelligence and analysis company, to simplify their multi-channel marketing (MCM) process. The company believes that doctors are more likely to open and reply to e-mail sent by representatives of pharmaceutical companies than an automatic e-mail. Based on this information, the company decided to simplify its patient tracking channel with the help of aktana.

Because these processes are complex and time-consuming, aktana helps pharmaceutical companies pre synthesize data, send scheduled emails, and track interactions in customer relationship management (CRM). After adopting aktana’s suggestions, the number of customer emails received by the company increased by 23 times and e-mail participation tripled.

5. AI + patient personalized diagnosis and treatment

Oncology has always been one of the main research fields in the pharmaceutical industry. The focus of this discipline is to find the best treatment for tumor and cancer. In order to achieve this goal, clinicians should first determine the appropriate treatment method according to the etiology of specific patients.

Using its advanced technology in data analysis and machine learning, IBM Watson can analyze the data in electronic health records (EHR) and related information in order to further study the best treatment scheme for a single patient.

In addition, artificial intelligence is also used to help patients match clinical trials, which helps to improve the results of cancer clinical trials. Novartis (Novartis) has collaborated with IBM Watson to apply such solutions in the field of advanced breast cancer, and plans to further expand to the wider field of oncology. The two companies aim to improve the treatment effect of patients by analyzing real-time patient data.

Hungary’s start-up company turbine cooperates with German pharmaceutical giant Bayer to combine artificial intelligence technology with cancer treatment. Turbine wants to create a simulated cancer cell through gene sequencing. Relevant software can help millions of simulations to develop the best treatment combination.

By significantly shortening the test cycle, this concept can help pharmaceutical companies achieve high return on investment. In addition, without a clear treatment plan, turbine’s AI platform can test millions of treatment combinations to find the most appropriate treatment.

6. AI + Realize patient connection through telemedicine and mobile medicine

ADA health, a manufacturer of AI health applications in London, has launched a telemedicine application. The program can use artificial intelligence and natural language processing (NLP) to generate relevant problems and suggestions according to patients’ symptoms.

Its design is inspired by the company’s awareness that the pharmaceutical and health industries are adopting a patient-centered model. This app allows doctors and AI assistants to work together to take care of patients.

In addition, the company also cooperates with karepack, a drugstore, to deliver prescription drugs prescribed by doctors to patients’ homes.

AI + medicine, there is still a long way to go

It is not enough for pharmaceutical companies to recognize these two changes – reducing prices and increasing the value of their treatments, and moving from treatment to prevention, diagnosis and cure. The biggest challenge facing pharmaceutical enterprises is how to quickly and decisively adapt to the impact of these changes on business and operation mode in a comprehensive way.

At the same time, pharmaceutical companies are increasingly using different technologies such as artificial intelligence and advanced analysis, which not only helps to improve efficiency and reduce costs, but also can adapt to the patient-centered business model. Several key factors such as automation, efficiency and collaboration will play an important role in reshaping the patient-centered pattern of the pharmaceutical industry.

The comprehensive application of these technologies will become the future trend and change the overall prospect of the pharmaceutical value chain. Whether working with AI start-ups or developing in-house technologies, pharmaceutical companies are undergoing digital transformation and investing in AI technologies. Although only a few pharmaceutical value chains have actually adopted these technologies, in the next few years, technologies such as artificial intelligence and advanced analysis are expected to change the traditional industry of medicine.

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