Initially developed for diabetes treatment, GLP-1 agonists have gained significant attention for their weight-loss benefits. The success of GLP-1 medications like Ozempic, Wegovy, Mounjaro, and Zepbound has spurred a wave of research exploring their potential beyond diabetes and weight loss.
Discovering secondary uses for GLP-1s
The headlines are coming at us fast and hard. Just in recent weeks, we’ve read that the GLP-1 agonists may help reduce sleep apnea, reduce pancreatitis risk in obese and diabetic patients, reduce rheumatoid arthritis symptoms, and potentially even boost fertility.
In other words, these medications are changing consumer habits and industry dynamics, and people just can’t get enough of them. While the pharmaceutical industry is eagerly investigating new applications for GLP-1 drugs, some think that the real opportunity lies in precision medicine. This approach promises to open numerous commercial pathways and significantly advance personalized patient care.
Why precision medicine?
Elliott Green, the co-founder and CEO of Dandelion Health, which collects and processes clinical data for the healthcare industry, is one of the believers. In a recent article he penned for Fast Company, he opined that, as the COVID-19 pandemic taught us, rapid innovation is crucial for saving and improving lives on a large scale. However, traditional clinical trials, while scientifically rigorous, are not designed for speed and cost-effectiveness.
In Green’s opinion, the challenge is accelerating precision medicine for GLP-1 drugs by applying lessons from the pandemic to achieve near-term, data-driven insights that lead to personalized treatments and care.
Learning from oncology
It’s complicated though. Green writes:
To understand just how “blackbox” GLP-1 drugs are today, one only needs to read or listen to the news. For example, early GLP-1 studies seem to appear daily, and they point to potential issues, such as unwanted side effects in some patients, like psychiatric issues, or opportunities — like GLP-1 agonists potentially being used to treat prostate cancer one day. The key word here? Potential.
With increasing access to data and advancements in AI, healthcare providers should be able to predict which patients will benefit most from specific weight loss drugs. Similarly, pharmaceutical companies should be able to identify new, effective uses for GLP-1 formulations. While progress is being made, it is not happening quickly enough to optimize patient outcomes or confirm new applications for these drugs.
Adopting a proactive approach from oncology, where precision medicine has had a significant impact, could be transformative. Oncologists select treatments based on the genetic profile of tumors. Similarly, GLP-1 drugs could be chosen based on a digital phenotype that predicts the best response with minimal side effects.
Addressing data gaps
The challenge in bringing precision medicine to GLP-1 drugs lies in the lack of real-world data. Although there is more real-world data (RWD) than ever before, much of it remains isolated and unreadable, locked in various systems within healthcare organizations.
RWD often comes from electronic health records (EHR), claims data, and disease-specific registries. However, the most valuable data — unstructured clinical data like waveforms (e.g., ECGs) and imaging data (e.g., MRIs, CT scans) — is typically outside the EHR. This data, which constitutes over 80{e60f258f32f4d0090826105a8a8e4487cca35cebb3251bd7e4de0ff6f7e40497} of healthcare data, holds immense potential for personalizing GLP-1 care and accelerating drug development.
Leveraging AI for precision medicine
In Green’s words,
[W]e can take these broad generalizations and turn them into more precise hypotheses to be tested, like: demonstrating GLP-1’s therapeutic effects beyond current uses, including secondary benefits derived from exploratory use or demonstrated with additional data modalities; and developing precision-medicine tools to identify patients with uncontrolled symptoms or to match patients to the right treatment plans.
The bottom line
To advance personalized weight loss treatments, there must be stronger integration of both structured and unstructured health data, and a robust approach to vetting AI algorithms trained on rich, unbiased datasets. This will provide the necessary insights for personalized patient care and help pharmaceutical companies quickly and cost-effectively explore new uses for GLP-1 drugs.
By embracing these strategies, we can drive a more personalized approach to weight loss and unlock new therapeutic potentials for GLP-1 drugs, benefiting patients and the healthcare industry alike.
Your responses and feedback are welcome!
Source: “How AI can power GLP-1’s next frontier in medicine,” Fast Company, 6/7/24
Source: “Ozempic and Wegovy May Help Reduce Rheumatoid Arthritis Symptoms,” Healthline, 6/27/24
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