Acute diseases, as a rule, differ from chronic ones in their rapid course. If detected
Pharmaceutical companies are called to play thisimportant role. With the help of artificial intelligence (AI) and other advanced analytical technologies, they can accelerate the development and dissemination of needed treatments.
Vaccine development stages
The very sense of urgency thatassociated with acute infectious diseases, can speed up the process of obtaining a vaccine. The need for immediate action unites and motivates a range of actors - from researchers and clinicians to regulators and manufacturers - to develop effective treatments for healthcare professionals as soon as possible.
However, even with tireless and purposefulthese teams can take years to complete a full development cycle. Before the product is ready to go to market, you need to go through several stages. Namely:
• Research stage:development of thousands of potential formulas for the selection of vaccine candidates, as well as research of the immune response.
•Preclinical stage:laboratory analysis to determine the appropriate antigens for developing the concept and structure of the vaccine.
•Clinical trials:testing a vaccine on test groups with different characteristics.
•Compliance Check & Certification:checking vaccine safety and legal compliance.
• Production and quality control:production of medicines to prepare for mass vaccination.
Each step is essential to ensure efficiency andthe safety of the vaccine being produced, in order to detect all side effects in a timely manner and that the mass production of the vaccine is carried out on an ongoing basis until the threat of disease is minimized.
Historical complexity, regulation and costeach of these stages slowed down the response to emerging medical problems. Now, thanks to artificial intelligence technologies, we can quickly accelerate the process of obtaining and introducing new drugs.
How does AI help in this process?
Although one cannot expect instant success withBy addressing the daunting challenge of developing a vaccine, we can take steps to overcome a number of constraints and bottlenecks that prevent us from getting the job done. Advances in automated data analysis and step-by-step visualization of all stages of vaccine search help remove some of these limitations, thereby accelerating the vaccine development process and simplifying production scale-up.
Here are some of the challenges that AI can solve at each stage.
Research / preclinical stage
The initial phases of drug development are ofteninclude a process for selecting candidate vaccines based on previous research and treatment results. Researchers can use AI to manipulate vast libraries of digital data (for example, to analyze the properties of thousands of pharmaceutical compounds in screening) with significantly greater precision than manual processing.
Thus, in March, the White House and a group of researchinstitutes and companies invited ML engineers working with text recognition to analyze a dataset of 30,000 scientific publications on COVID-19 to summarize the necessary information that would help the development of vaccines and drugs. DeepMind joined the research. Using an algorithm trained on large genomic data, they were able to recognize the structure of the proteins associated with the virus.
AI can also be used in these steps to sequence DNA based on data from large numbers of people, allowing doctors to perform tests for genetic matching and immune response.
Clinical development and trials
After finding suitable connections, starttests in real conditions. Different patients will respond differently to the drug depending on factors such as age and medical history. Therefore, tests must be comprehensive and cover even those unlikely cases where the patient may not be well aware of the drug.
By practicing deep learning algorithms,researchers can perform these trials on a previously unthinkable scale, even before physically applying the candidate vaccine to the test patients. The digital twin of the patient can greatly facilitate clinical trials. By placing a digital analog of a drug in a digital analog of a human body, researchers can see how the drug would interact with that organism. These algorithms can be used to identify and select antibodies to fight infectious diseases, dramatically improving speed and cost. In addition, advanced analytics and visualization of human response to potential vaccines can be used to speed up testing, allowing for more sophisticated analysis and less error.
Production and quality control
After the vaccine is approved by the regulatory bodybegins the intense process of developing and supplying drugs to a wide network of hospitals and clinics. At this stage, drug manufacturers are faced with significant factors that require quick decision-making, such as available volume of production, drug quality and optimal packaging decisions.
Combining AI and application-based technologiessensors, manufacturers can use detailed data to improve the efficiency of supply chains. Artificial intelligence helps to predict the duration of the production cycle of a particular drug and build an optimal plan for loading production lines. In addition, AI tasks include identifying critical production areas, finding defective products, predicting downtime, equipment breakdowns, and planning repairs. This can lead to better optimization of energy consumption, reduce unnecessary operating costs, eliminate the imbalance between supply and demand during production, and reduce the risk of product spoilage during sale.
Universal assistants
AI isn't just helping with vaccine development. For example, during a pandemic, the Botkin.AI platform used AI to detect the initial symptoms of pneumonia on images of the lungs. Thanks to this, patients could receive the necessary treatment in a short time at the earliest stages of the disease and avoid complications.
In addition, modern technologies are widelyused in other areas of medicine. For example, in the production of insulin, it is necessary to ensure high accuracy of work with components, as well as ideal cleanliness of production lines and workshops. To meet all the requirements, Biopharmax implemented intelligent controllers, which, together with operator workstations and distributed control systems, helped to achieve detailed visualization of production processes. This allows in real time to accurately monitor all stages of insulin release and to comply with high criteria of sanitary standards and accuracy.
Another problem to deal withdigital technologies, - a long wait when receiving treatment for patients with cancer, from examination to therapy. In one of the clinics in Tennessee (USA), visitors to the oncology department had to wait five hours for an examination, and in the case of such a serious diagnosis as cancer, time is the most valuable resource. To reduce the terms of service, the medical organization has integrated specialized software. After analyzing the current state, the solution made recommendations for various procedures and processes: drawing up an optimal schedule, reorganizing the space in the building, hiring additional personnel for certain positions, investing in new equipment for analysis. After the implementation of all actions, the waiting time was reduced by 25%.
Faster treatment in emergencies
An outbreak of a viral infection can createunforeseen challenges for those involved in public health management, from politicians and health authorities to doctors and manufacturers. While the former can take emergency measures to identify the infection and localize the identified foci, the latter often find themselves under increased pressure with the requirement to quickly find an effective treatment. One has only to find a new effective way to develop vaccines - and how the picture of treatment of detected cases will change, the burden on the health infrastructure will decrease and recovery will accelerate.
The capabilities of innovative technologies allowthose involved in development work faster in difficult circumstances. Techniques such as deep learning and advanced data visualization allow scientists to build on the full body of existing research while overcoming the challenges of finding suitable treatments for new viruses. Artificial intelligence can process huge amounts of data, which allows doctors to start testing with the most promising drugs. For example, while searching for a cure for coronavirus, AI suggested drugs with apoptosis inducers - substances that kill cancer cells. IT is beneficial in drug manufacturing and distribution where manufacturers are working vigorously to accelerate drug adoption in a challenging pandemic.
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