DIRECTOR, Healr solutions & Center for Public Leadership Fellow, Harvard Kennedy School.
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With the increasing amount of data available, AI and machine learning have become critical tools for biopharmaceutical companies looking to stay competitive in the marketplace. These tools help researchers accelerate drug discovery, improve clinical trial design, and further personalize patient treatment.
Here are five areas where I see AI and machine learning changing the biopharmaceutical industry.
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Personalize treatment
AI and machine algorithms can analyze patient data and identify patterns and trends to help doctors personalize treatment. This data includes genetic information, medical history and other relevant factors.
For example, these technologies can help identify the most effective treatments for specific patients. This can reduce the risk of side effects and improve patient outcomes. AI, in particular, can help predict how a patient will respond to a given treatment by analyzing data with similar patients.
AI powered software developed by IBM researchers uses hospital databases and records to look at different patients diagnosed with one of three chronic diseases. Through this research, “they dissect that in the vast majority of cases of the three diseases, there were multiple treatment plans other than those selected by a specific physician.”
Optimization of clinical trials
Clinical trials are a critical part of drug development, but they are often time-consuming and expensive. Using AI and machine learning, clinical trials can be optimized by identifying the most suitable patients for specific treatments. Researchers can design more effective studies when analyzing data using AI, reducing the time and cost of clinical trials.
In addition, machine learning can identify patients by their specific genetic makeup and help predict those who are likely to respond to a given treatment, increasing the efficiency of clinical trials and leading to faster drug approval and more efficient use of resources .
As an example of this ability, Roche/Genentech created a predictive model to improve the effectiveness of their quality programs when it comes to monitoring adverse events in clinical trials. This machine learning method was able to identify the sites most at risk of under-reporting and enabled real-time safety reporting.
Improving drug production
AI and machine learning also improve drug production by analyzing data from manufacturing processes and identifying potential quality control issues. AI can be used to diagnose a wider range of problems in the manufacturing process, such as detecting malfunctions in machines, predict production line failures and optimize production times. The technology has been shown to effectively locate faulty machines. In addition, it can identify and reduce energy consumption, improve planning and identify potential cost savings.
When AI and machine learning diagnose issues early in the manufacturing process, product quality improves and the risk of recalls decreases, improving patient safety and reducing costs. A study by McKinsey has shown this 25% of the inspection costs and 10% of the annual maintenance can be reduced if AI is used.
Improve regulatory compliance
Product compliance is enhanced by AI and machine learning as they identify potential security risks. By detecting side effects and other safety issues early, these technologies can help prevent serious problems and improve patient safety. Machine learning can identify potential safety risks by analyzing data from clinical trials and ensuring that drugs are safe and effective before they are approved.
Improving regulatory compliance helps ensure that a clinical trial meets all regulatory requirements and is conducted quickly and cost-effectively, optimizing the clinical trial.
I see AI and machine learning methods becoming increasingly popular for regulatory compliance in clinical trials. In the future, I see us using both AI and machine learning models to automate specific tasks, reducing the amount of time and effort required for regulatory compliance. In addition, these technologies can be used to improve the accuracy of safety assessments.
Accelerating drug discovery
In recent years, AI and machine learning have been increasingly used by the biopharmaceutical industry to accelerate the drug discovery process.
An example is GSK is collaborating with the Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium to introduce AI into the process of identifying and developing treatments. This collaboration hopes to drastically reduce the time it takes to move from a drug target to a therapy ready for use by patients with an estimated timeline of less than a year.
These advances allow researchers to identify potential drug candidates by analyzing large data sets faster and more accurately. It has been made possible by deep learning algorithms that can analyze patterns in datasets to identify promising drug targets.
One of the key benefits of AI in drug discovery, such as that observed with GSK and ATOM, is that it allows researchers to analyze data from many sources, including patient data, clinical trial data, and public databases.
AI and machine learning are changing the future of the biopharmaceutical industry. These technologies help researchers accelerate drug discovery, personalize treatment for patients, optimize clinical trials, improve regulatory compliance and improve drug manufacturing. Progress is leading to more personalized, effective and efficient healthcare for patients around the world.
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