CEO, Healr Solutions & Center for Public Leadership Fellow, Harvard Kennedy School.
With the increasing amount of available data, AI and machine learning have emerged as crucial tools for biopharmaceutical companies looking to remain competitive in the market. These tools are helping researchers speed up drug discovery, improve the clinical trial design and further personalize patient treatment.
Here are five areas where I see AI and machine learning changing the biopharmaceutical industry.
AI and machine algorithms can analyze patient data and identify patterns and trends in order to help doctors personalize treatment. This data includes genetic information, medical history and other relevant factors.
For example, these technologies can aid in identifying the most effective treatments for specific patients. This can reduce the risk of adverse reactions and improve patient outcomes. AI, in particular, can help predict how a patient will respond to a particular treatment by analyzing data with similar patients.
AI-powered software developed by IBM researchers is utilizing hospital databases and records to look at different patients diagnosed with one of three chronic diseases. Through this research, they “learned that in the vast majority of cases across the three diseases, there were multiple other treatment plans than the one a specific doctor had picked.”
Optimizing Clinical Trials
Clinical trials are a crucial part of drug development, but they are often time-consuming and expensive. With the help of AI and machine learning, clinical trials can be optimized by identifying the most suitable patients for specific treatments. Researchers can design more effective trials when analyzing data using AI, reducing the time and cost of clinical trials.
Furthermore, machine learning can identify patients by their specific genetic makeup and help predict those who are likely to respond to a particular treatment—increasing the efficiency of clinical trials and leading to faster drug approvals and more efficient use of resources.
As an example of this capability, Roche/Genentech created a predictive model to improve the effectiveness of their quality program leads when it comes to monitoring adverse events in clinical trials. This machine learning method was able to identify the sites that had the highest risk of underreporting and allowed for real-time safety reporting.
Improving Drug Manufacturing
AI and machine learning are also improving drug manufacturing 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 faults in machinery, predicting failures in production lines and optimizing production times. The technology has been shown to effectively locate faulty machinery. Furthermore, it can identify and reduce energy consumption, improve scheduling and identify potential cost savings.
When AI and machine learning diagnose problems early in the manufacturing process, the quality of products increases and the risk of recalls decreases—overall, improving patient safety and reducing costs. A McKinsey study has shown that 25% of inspection costs and 10% of annual maintenance could be reduced if AI is used.
Enhancing Regulatory Compliance
The compliance of products is improved by AI and machine learning as they identify potential safety risks. By detecting adverse reactions and other safety concerns early, these technologies can help prevent serious problems and improve patient safety. Machine learning could identify potential safety risks by analyzing data from clinical trials and ensuring that drugs are safe and effective before they are approved.
Enhancing regulatory compliance helps ensure that a clinical trial meets all regulatory requirements and is conducted promptly and cost-effectively, thus optimizing the clinical trial.
I see AI and machine learning methods becoming increasingly popular in regulatory compliance in clinical trials. In the future, I see us using both AI and machine learning models to automate specific tasks, helping reduce 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
During the last few years, AI and machine learning have increasingly been used by the biopharmaceutical industry to help accelerate the drug discovery process.
An example is GSK joining forces 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 therapy ready to be used by patients with an estimated timeline of less than one year.
These advances are making it so researchers can identify potential drug candidates by analyzing large datasets more quickly and accurately. It has been made possible through deep learning algorithms that can analyze patterns in datasets to identify promising drug targets.
One of the most significant advantages of AI in drug discovery, such as the one 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 reshaping the future of the biopharmaceutical industry. These technologies are helping researchers accelerate the drug discovery process, personalize treatment for patients, optimize clinical trials, enhance regulatory compliance and improve drug manufacturing. The advances are leading to more personalized, effective and efficient healthcare for patients worldwide.
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