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AI is now playing a larger role in the strategies Big Pharma is adopting to achieve more equitable health outcomes. AstraZeneca is poised to collect and use data from cancer patients in neglected postcodes through a series of AI-based partnerships and screening initiatives.
Mohit Manrao, senior vice president and head of U.S. oncology at AstraZeneca, said innovation is currently outpacing health equity, widening the gap in oncology between patients who have access to new medicines and those who do not. He said he is doing so.
black Americans are high mortality rate According to the NIH’s National Cancer Institute, it is higher in many cancer types than any other race. Furthermore, people without access to medical care are more likely to be diagnosed later in the course of the disease and have a higher risk of death.
“Scientific progress is very fast, but the work isn’t over after that,” Manrao says. “The challenge we face at a local level is that inequality is widening and more and more people are falling into poverty. Everyone in the system has good intentions, but as an industry we need to capitalize on opportunities. It is important that we work together to resolve the issue.”
Manrao said AI is one of the most promising opportunities to improve health equity. By partnering with diagnostic manufacturers and AI companies, AstraZeneca hopes to use data to uncover health disparities and address the screening and access issues that exacerbate them. Manrao said AstraZeneca’s mission goes beyond developing oncology drugs like the blockbuster drug Enherz, which was originally approved for breast cancer in 2019. Recently acquired a new and expanded indicationand AI can find patients who need it.
“The secret sauce for us, and it’s not really a secret, is external collaboration. What’s remarkable for us, and for anyone who wants to move the needle for patients, is that we don’t work in silos. It means I can’t work.”
Mohit Manrao
Head of U.S. Oncology at AstraZeneca
“There is no reason why a black woman with breast cancer in New York would have a different outcome than a white woman in California or Atlanta. There is now a zip code lottery,” Manrao said.
Here, Manrao discusses the impact of data-driven AI on understanding and mitigating disparities in cancer care, the partnerships AstraZeneca has explored to incorporate AI, and how feedback loops can lead to better research that leads to more equitable outcomes. Learn how it can help drive development. road.
This interview has been edited for brevity and style.
PHARMAVOICE: How do you characterize the impact of AI on patients, especially from a health equity perspective?
Mohit Manrao: Data availability has increased in a number of ways, both in terms of genomic and multi-omics data at the patient and customer engagement levels. Similarly, we believe that science, technology and data are converging so rapidly that we can impact patients at every level. We want to look beyond the bonanza of biopharmaceutical innovation and ask new questions by looking at the entire patient pathway from a health equity perspective. Are patients being tested? What are their risk factors? Will they go to a testing center? Is there proper follow-up in the system at appropriate intervals to ensure early detection?
Because of the socio-economic determinants of health, individualized interventions are needed to address the specific barriers that stand in the way of specific patients.
AI learns using past examples, so it is prone to bias. find your way to the algorithm. How can we actually advance AI instead of staying where we are?
That’s a super important point. Recognizing the biases that can enter the system and putting checks and balances around them helps us consider health equity. It starts with comprehensive research and development across the entire pharmaceutical value chain. We have medicines developed based on biological samples, and we are committed to creating diverse biological samples that are representative of the populations we serve. Of course, we can’t do it alone. Therefore, bringing these diverse biological samples into drug development at the local grassroots and community level will help ensure patient recruitment achieves those goals.
For example, a clinical trial focused on a community in New Jersey, which is 18% Black, required me to not only ensure that 18% were recruited from that community, but also to assist with data discovery and collection. are working with them. All of this helps AI to make unbiased predictions.
Your AI strategy is partner-focused. How do potential partners stand out in the AI boom?
When ChatGPT came out, it visibly exploded into the world, and we’re already using AI in many parts of our value chain. The company has more than 700 data scientists who are AI experts working in various departments within the organization, from early drug discovery and development to commercial organization and operations. We incorporated them to think in terms of looking for partnerships. We cannot move forward alone. Therefore, it is critical to find the right partner with the capabilities, values, and vision to transform cancer care. For example, early detection presents many challenges. When we determine that tissue-based screening is a challenge, we work with blood-based companies to see how we can help improve their testing. That led us to Work with Grail In that space.
Companies like Qure.ai Using technology, X-rays can find lung nodules that are not visible to the naked eye. Companies like Clinicink It has natural language processing that can be incorporated into electronic medical records. These are all important steps.
At the same time, consider the example of lung cancer. Screening has been approved in the United States since 2013, but even 10 years later, the uptake rate is still a dismal 5-6%. Socioeconomic determinants pose barriers to lung cancer screening, so we are partnering with the Association of Cancer Treatment Centers to understand and collaborate with local communities and use their rich data to understand where the lung cancer pockets are. We provide information. Appalachian Rural Screening Program Lung Cancer Treatment in Kentucky.
Similarly, we just announced a partnership with the University of Maryland System across counties across the state to identify people at risk. These academic and community institutions play a large role in the data collection process.
Once you collect data, how does it feed back into the drug discovery and development process?
It is important to put patients at the center of drug discovery and development. All this data opens up opportunities to uncover patients’ true unmet needs in a variety of ways. And from a broader perspective, we want to eliminate cancer as a cause of death. Through these datasets, we can understand why the disease progresses and inform research and development by identifying patients early. For example, our partnership with Grail will help us not only identify patients, but also look at the data to see which patients within this group are at high risk and what interventions are needed. . It is therefore integrated throughout the company’s value chain.
The secret sauce for us, which is not really a secret, is external cooperation. We do not hesitate to partner with appropriate political parties. What stands out to us, and to everyone who wants to move the needle for patients, is that we cannot work in silos.
