Finding Real-World Response End Points in NSCLC Data Sets With Janet Espirito, PharmD

At the recent American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago, Janet Espirito, PharmD, a Senior Medical Director at Ontada, sat down with Oncology Data Advisor to discuss her presentation on identifying real-world end points for assessing response in patients receiving novel therapies for metastatic non–small cell lung cancer (NSCLC) outside of clinical trials.  

Oncology Data Advisor: Welcome to Oncology Data Advisor. Today, we’re here at the ASCO Annual Meeting, and I’m joined by Janet Espirito. Thank you so much for joining me today.

Janet Espirito, PharmD: Thank you for having me.

Oncology Data Advisor: So, you have an abstract here titled Real-World Response End Points in Patients With Metastatic NSCLC Treated With Chemotherapy Across Real-World Data Sets. What was the background of this study, and why did you decide to investigate this?

Dr. Espirito: We are really excited that we were able to participate in this study. This was in collaboration with the Friends of Cancer Research Group, along with seven other electronic health real-world data partners. The background is really trying to understand how to use real-world data for assessment of response to oncology therapeutics. We know that in clinical trials, the assessment of responses is very protocol-driven in uniform. In real-world clinical practice, there’s variability seen in clinical practice, and assessment of response may not be performed in the same manner as in clinical trials.

Therefore, there’s really no consensus approach when real-world data groups are seeking to assess response outcomes using real-world data. The background of this study was really to inform the development of an approach that could be consistently applied across real-world data partners in terms of being able to assess response using real-world data.

Oncology Data Advisor: Great. How did you and the team go about designing and conducting the study?

Dr. Espirito: As part of the collaboration, the seven electronic health record data partners, including Ontada, all met and developed a consistent protocol and statistical analysis plan such that we could identify an aligned population. Certainly, there’s heterogeneity in patients in the real world, so we needed to align on a consistent population for inclusion in terms in for the study.

Oncology Data Advisor: What were the results that were found?

Dr. Espirito: Interestingly, the results were that we were able to find consistent, despite different, data sources and heterogeneity in patient populations. By aligning on a patient population, we were able to find consistent results in response rates across all of the data sources.

Oncology Data Advisor: Having this knowledge, how do you recommend that it can be used to inform trial design and drug efficacy evaluation?

Dr. Espirito: That is actually a next step where we are seeking to put out best practices on how to approach doing this. I think the takeaway points are that we’re able to demonstrate the feasibility of using real-world data and specifically a specific method of clinician assessment in terms of evaluating real-world response. We’re able to demonstrate the feasibility of doing that. Certainly, the next steps are to continue exploration and develop best practices so that we can continue to evaluate how to use real-world data to inform decision making, ultimately for effectiveness.

Oncology Data Advisor: Great. Well, thanks so much for talking about this today. It’s great to hear about.

Dr. Espirito: Thank you very much.

About Dr. Espirito

Janet Espirito, PharmD, is a Senior Medical Director with Ontada, an oncology data science and technology business dedicated to improving the lives of cancer patients through real-world data and evidence generation.

For More Information

McKelvey BA, Garrett-Mayer E, Belli AJ, et al (2023). Real-world response endpoints in patients with mNSCLC treated with chemotherapy across real-world datasets. J Clin Oncol, 41(suppl_16). Abstract 6595. DOI:10.1200/JCO.2023.41.16_suppl.6595

Transcript edited for clarity. Any views expressed above are the speaker’s own and do not necessarily reflect those of Oncology Data Advisor. 

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