AstraZeneca's latest report: Artificial intelligence and machine learning are disrupting drug development

From drug design to clinical trials, AI artificial intelligence brings breakthroughs in all phases of drug development. With the aid of deep learning, the machine began to mimic the activity of human brain neurons to create an artificial "nerve system." We use AI methods in a large number of trials in pharmaceutical R&D to reduce costs and accelerate data supply.

We also use AI to help us analyze a large amount of data from imaging studies and biomarker studies of pathological samples to achieve proper drug use.

In our clinical trials, AI allows us to continuously monitor the safety data we receive, alerting scientists to safety signals that need attention. In response, the arterial network translated a research report from AstraZeneca.  

Predicted compound biological activity space and target interaction

Michael Kossenjans, head of AstraZeneca iLAB, said, “I feel energetic and enthusiastic at DMTA Hackweek. We are preparing for DMTA Hackweek day and night. It seems almost impossible to build an automated DMTA platform in a week, but we This is the first step in our research on new laboratory automation techniques and machine learning, and more work needs to be done."

Drug development: coexistence of human and machine readable data

Through machine learning, our state-of-the-art drug development robots and other automation devices can adjust operations and react to the data they receive, allowing us to work faster and more efficiently.

AI-driven automation is helping us solve some of the chemistry complexities that aim to accelerate the cycle of compound synthesis, design-manufacture-test-analysis (DMTA), to facilitate fast and fair decision making.

Our DMTA platform is just the first step in leveraging new laboratory automation technologies and machine learning, which will accelerate the construction and testing of drugs. It will be used to continually improve treatment-related compounds. For a common project, hundreds of DMTA cycles are required to find compounds that meet the drug candidate criteria. When these cycles are done manually, it can take several weeks, and our goal is to reduce the time between compound design and test data reception from 4 to 6 weeks to no more than 5 days.

In 2017, we hosted the DMTA “Hackweek”. The scientists at each research site worked with our in-house experts to build the first "DMTA machine" model through their scientific knowledge and expertise.

In the innovative laboratory in Gothenburg, they worked continuously for five days, completely changing the way we discovered drugs. In the spirit of "hacking", the team has overcome countless difficulties and combined hardware and software accumulated over 20 years to create a machine model. This machine can complete the entire DMTA cycle of the research project within two hours.

A thousand miles begins with a single step. Through this simple model system, we are developing machine learning to optimize the efficacy of new compounds, predict different synthetic routes, and make automation more advanced so that we can make more complex molecules and collect more data for screening.

Quantum Computing: Using Structural Chemistry to Discover Important Molecules

Establishing a chemical three-dimensional structure of a potential new drug is key to drug development because the size and shape of the drug is very important. They affect many different properties, including interactions with biological systems, and the way in which molecules of matter needed to form a drug are aggregated.

However, just as a skydiver needs multiple attempts to find the deepest valley. We must evaluate all possible molecular shapes again and again to find the low conformation needed to optimize the drug.

The emerging field of quantum computing is expected to help us solve this problem. Quantum computers can simultaneously explore all possible compound structures and focus on the most likely structures in a single operation, according to appropriate standards.

By adjusting the standards, you can find a range of high quality solutions. At present, quantum computing is limited by the amount of information that can be processed. We still need to use existing accurate methods on standard computers to perform some post-evaluation analysis on the solutions it provides.

Future applications of machine learning are expected to bring quantum computing to the next stage. Our current approach seems to allow us to master the most relevant solutions and help us choose the best chemical structure.

Use AI to help IMED scientists

In the research, we also use AI to make existing processes more efficient and turn data into knowledge. We are using AI to make reliable predictions of routine assay results, such as human plasma protein binding (hPPB) testing, to help our scientists give them more time to focus on those that will bring AstraZeneca more The problem of a big competitive advantage.

The hPPB test developed in the field of drug safety and metabolism can help us understand how potential drug molecules are distributed in patients. We are working with the world's leading organizations to use the most advanced research in the AI ​​field to predict results.

We are currently evaluating the use of AI for safety screening, protein generation, image analysis, and CRISPR gene editing. In the future, we hope to use AI to reform the process of data collection in our drug development and turn it into knowledge.

In 2017, the development of the virtual screening tool FastVS demonstrates the prospect of machine creation efficiency. The new "Google-style" networking tool developed in collaboration with OpenEye Scientific Software has shortened the time to search and filter entries in many large molecular databases, ranging from hours to seconds, to optimize the process of drug discovery.

Big data analysis helps traditional pathology enter the 21st century

In a science-driven environment, the ability to quickly identify and learn signals and patterns in data is key to building knowledge and influencing the direction of future science. To achieve this, we need to collect a diverse set of big data sets in a single available model.

In the past, the assessment of integrated gene expression, protein and individual organ metabolism data was limited by our limited data analysis and computational capabilities. This is the first time AI can handle big data, analyze all endpoints and their spatial relationships.

We are using mass spectrometry imaging (MSI) to spatially localize molecules for biological and tissue sections, such as sections for pathological assessment. These comprehensive spatial data information can be a good link between tissue microenvironment, drug positioning, efficacy and safety. However, existing data mining methods have high requirements for computer systems, and we can only analyze small, single data sets.

To solve this problem, we have developed a new computational algorithm that can segment a large number of MSI data accurately and efficiently to improve our ability to learn from multiple endpoints, as we recently mentioned in Analytical Chemistry.

This enhances our ability to accurately quantify molecular changes in specific regions of tissues and organs and to provide data for increasingly complex spatial relationships. Researchers in the field of drug safety and metabolism work closely with external experts in the fields of computer and pathology and are an important part of the entire study.

Looking ahead, we plan to combine deep learning algorithms with image analysis to accelerate the assessment of animal models of chronic kidney disease and provide more reliable data for downstream multispectral image analysis. This will increase the speed, confidence, and reproducibility of quantitative analysis of data and detect biological relationships and their outcomes through integrated multimodal image mining.

In 2017, our scientists collaborated with the team of the UK Cancer Research (CRUK) to present a tomographic map of the tumor, using Google's map approach to study cancer-related information. This has the potential to make pathology, one of the most traditional safety disciplines, into the 21st century.

Use AI to use drugs correctly

More and more tissue biomarkers are being used to match patients and the right drugs. However, the current technology involves the pathologist manually marking the image, which is subjective, time consuming, and complicated. We used AI to solve this problem and developed a new deep learning algorithm that automates the organization of biomarkers using digital pathology.

In a proof-of-concept study of 71 patient tumor samples, we found that AI can automatically label human epidermal growth factor receptor-2 (HER2), a biomarker for breast cancer. The algorithm also identifies samples with the risk of misdiagnosis, proving that it can make tissue biomarkers faster, simpler, and more accurate.

In 2017, we presented this achievement at two world-leading science conferences and published them in the Science Report.

We will continue to use the most advanced science and technology to conduct similar research by working with leading academic institutions. By combining cloud computing with the latest graphics processing unit (GPU) hardware, we intend to turn automated analysis of digital pathology into a high-throughput process and incorporate AI algorithms into the development of diagnostic tests. Our goal is to use AI to influence patient care and target the drug to the patients most in need.

Watcher: Always monitor drug safety

In our early clinical trials, the AI-based decision support system, Watcher, constantly monitors incoming safety data and alerts scientists to safety signals that require attention.

Watcher is an innovative AI alarm system that helps doctors and scientists embed clinical decision rules into logic in clinical trials. AstraZeneca, the University of Manchester Cancer Institute, the Centre for Cancer Biomarker Sciences and the Christie National Health Service Foundation Trust have reached a five-year collaboration and are an important part of our iDecide research project.

The CRUK Manchester Institute's Digital Experimental Cancer Medicine team (digitalECMT) is responsible for the iDecide program, which works directly with patients to develop new methods that enable better clinical trial decisions to benefit patients directly.

Watcher also used another iDecide tool, REACT 4, which collated and visualized the safety, effectiveness and biomarker data for the first and second phases of the trial and is currently being used in AstraZeneca. More than 140 studies.

REACT 4 can be clinically stated on demand and depends on the system being used. However, Watcher's continuous monitoring can detect the signal and provide timely notification when the signal is sent.

In the future, we plan to develop Watcher through clinical rules and machine learning to enhance and extend current capabilities. These developments will enable it to be brought into the patient's home along with timely care equipment, allowing the patient to further self-monitor while participating in clinical trials.

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