[garrykillian/Adobe Stock]
In this Q&A that follows, Chris Boone, Ph.D., GVP of Research Services at Oracle Life Sciences, shares how the tool helps companies visualize drug uptake over time, inform go-to-market strategies, understand physician prescribing behaviors, identify unmet medical needs, and access valuable outcomes data to enhance strategic planning in oncology. In the interview, he shares how the tool can reveal critical insights into evolving treatment paradigms, such as the rapid shift from crizotinib to alectinib, and how this information can be used to accelerate the development of more effective cancer therapies.
Can you say more on how the CancerMPact Treatment Architecture Trends specifically helps biopharma companies quantify the impact of new drug approvals?
Chris Boone, Ph.D.
Boone: Drug approvals are a significant driver of market share in oncology, and accurately anticipating how past and recent approvals can shape the market is complex. CancerMPact Treatment Architecture Trends helps address this by providing a robust analytical framework that allows companies to visualize how historical drug uptake has evolved over time. It provides insights into how different types of physicians and in different treatment settings have adopted new treatments when they have become available in the past. By analyzing historical and current data, the tool enables companies to look at analogous situations that may inform how future drug approvals will shift treatment paradigms, giving them a clearer understanding of potential market share impacts.
Can you provide a concrete example of how a pharma company could tap the historical data within Treatment Architecture Trends to inform a go-to-market strategy for a new oncology drug?
Boone: Understanding the evolution of the standard of care (SoC) is critical for shaping an effective go-to-market strategy. By leveraging historical data from Treatment Architecture Trends, pharmaceutical companies can assess how past drug launches have shifted treatment patterns across different lines of therapy and apply those learnings to their current situation. A prime example is the rapid replacement of crizotinib by alectinib in the first line treatment of ALK+ Non-Small Cell Lung Cancer (NSCLC) in 2017-2018 as a result of the alectinib approval. This event highlights not only how quickly the SoC can change but also the differences in uptake, duration of therapy, and peak market share between regions, such as the U.S. versus Japan. Additionally, Treatment Architecture Trends helps identify which physician specialties have prescribed certain therapies, like comparing the use of hormonal treatments (such as abiraterone vs. enzalutamide), which may vary between urologists and oncologists. These insights allow companies to refine their marketing, targeting, and rollout strategies for greater impact.
Beyond market analysis and strategic planning, how can CancerMPact Treatment Architecture Trends be used to improve patient outcomes or accelerate drug development?
Boone: The strength of Treatment Architecture Trends lies in its ability to provide rapid and contextual market analysis, while also allowing companies to pressure test insights across different tumor types and geographies. By understanding how new therapies have been adopted in clinical practice, companies can better anticipate analogous shifts in future treatment. Furthermore, the tool allows one to evaluate unmet needs and gaps in existing treatment regimens, based on analysis of historical use of chemotherapy, immunotherapy, and targeted therapies — which can in turn inform drug development plans within biopharma companies to spur drug development in those settings, which would in turn improve patient outcomes.
Can you elaborate on the “outcomes data” that Treatment Architecture Trends provides?
Boone: Treatment Architecture Trends provides outcomes data, including physician-reported response rates and disease progression across different lines of therapy. These metrics give biopharma companies valuable insights into how all patients perform in real-world settings and the impact on long-term patient outcomes. Key indicators such as overall response (including complete and partial response) rates, and progression-free survival (PFS) across all therapies offer critical insights into how well a drug extends measures of survival or delays disease progression. Additionally, the data allows analysis of the proportion of patients, at each line of therapy, who receive further treatment versus those who do not, due to death, long-term response, or no further treatment. And in early-stage disease, metrics like response duration and recurrence type (local or metastatic) provide companies with a deeper understanding of shifting outcome dynamics.
Filed Under: clinical trials, Drug Discovery, machine learning and AI, Oncology
