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How to Leverage AI Throughout the Pharma Treatment Pipeline

We’ve made incredible advancements in healthcare over the past few decades thanks to the introduction of new technology. Now, artificial intelligence (AI) presents another major opportunity to continue driving this trend to further improve patient lives. There are a wide variety of applications of AI when it comes to understanding and treating health conditions. In fact, AI can be leveraged throughout the entire pipeline when researchers set out to treat a new disease. The technology can be particularly useful for discovering new drugs, understanding emerging diseases, and measuring the outcomes of treatments.

AI in drug discovery

Long before manufacturers can bring a drug to market, researchers are working to identify the right molecules. AI can be applied to drug discovery and development, particularly for the purpose of making the process more efficient and less expensive. In the typical process of discovery, researchers may spend years testing different molecules, only to realize the one selected for a clinical trial doesn’t have the intended effect. AI can play a role in this process by predicting the bioactivity and interactions of different molecules. By leveraging existing data, a predictive model may be able to identify a molecule that has a higher likelihood of having the impact a researcher and the medical community is hoping for, even before anyone steps foot in the lab.

The use of AI in drug development is still in the relatively early stages, and no drugs discovered by AI are currently on the market. That being said, quite a few healthcare and research organizations have already begun incorporating AI into the process and are reaching clinical trials with AI-developed drugs. For example, a drug for idiopathic pulmonary fibrosis (IPF) that was identified using AI entered phase 1 trials in 2022 and gained FDA Orphan Drug Designation earlier this year. As the industry becomes more comfortable with AI, its applications in drug development will likely expand even further, and we may eventually see drugs developed with AI being given to patients.

AI in epidemiology and clinical trial management

Another key step in bringing a therapy to market and getting it into patient hands is gaining an understanding of a disease and how it’s impacting health outcomes at the population level. This is where epidemiologists come in – the group of researchers responsible for quantifying and monitoring therapeutic risk management across target populations and indications.

Utilizing AI and machine learning (ML) techniques, epidemiologists can explore real-world data (RWD) – among other types of available data – and identify trends relevant for commercial and clinical decision-making. Because ML is optimized for exploring data in a hypothesis-free manner, it enables researchers to discover novel patterns, generate better predictions for key trends such as disease prevalence, and identify the risk factors associated with poor outcomes. These insights are critical for researchers to develop treatments that will most effectively address the needs of their target population.

AI can also automate parts of the clinical trial phase of drug development, which is critical for establishing the safety and efficacy of a new therapy before it reaches patients. For example, AI can be utilized to ensure that the correct patients are being recruited for a clinical trial, and that the study group represents the general population while taking diversity and equity into account. AI can also help in the review of safety reports from a trial in a manner that is more reliable than a human team. Not all of epidemiology and clinical trial design can be automated, but AI can make certain aspects of the process more efficient.

AI in evaluating treatment outcomes

Once a clinical trial has demonstrated effectiveness, it’s critical to understand the value of a new intervention within the healthcare marketplace. By this point, researchers have spent countless hours and hundreds of millions, if not billions, of dollars developing a therapy – but they still need to ensure that the correct patients are able to access it when they need it. This is where health economics and outcomes research (HEOR) – the study of the value of healthcare interventions – plays a crucial role in the drug development pipeline.

The ultimate goal of HEOR analyses is to assist payers and others tasked with financing healthcare to optimize the health of their populations while minimizing costs. Without it, health systems would not be financially stable, and the timely delivery of care would be compromised. AI can play a role in HEOR analyses by uncovering patterns in the data that help to quantify the incremental benefit of a treatment, such as identifying unique subpopulations that experience an increased improvement in outcomes relative to the general population.

For example, ML was utilized in a study among people with type 2 diabetes to investigate which subpopulations could benefit from a behavioral intervention aimed at weight loss. While no significant impact was found among the general population of people with type 2 diabetes, researchers found that a subgroup with specific characteristics could avoid complications from cardiovascular disease following the intervention. These insights helped clinicians and health plans know which specific patients would benefit the most from the intervention, helping to improve patient outcomes and save costs overall.

The future of AI in the pharma pipeline

There are clearly a multitude of applications of AI when it comes to understanding and treating disease, and researchers are committed to further advancing the technology. In fact, the leading organization for HEOR, ISPOR, recently established guidelines for using machine learning within the area. This demonstrates a commitment to expanding the use of AI and ML in order to maximize its potential.

Epidemiologists, researchers, health economists, and others who play a role in the drug development pipeline can all find value from incorporating AI into their work. And if we can use AI to better understand diseases and develop more effective and targeted treatments, patients stand to benefit immensely at the end of the day. AI holds limitless potential within healthcare and pharma for improving lives – and it’s our responsibility to leverage it to its greatest capacity.

The post How to Leverage AI Throughout the Pharma Treatment Pipeline appeared first on Unite.AI.

 Thought Leaders, clinical, clinical trial, epidemiology, healthcare, HEOR, medical, thought leaders Unite.AI 

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