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Modeling the economic outcomes of immuno-oncology drugs: alternative model frameworks to capture clinical outcomes.

ABSTRACT: Background:Economic models in oncology are commonly based on the three-state partitioned survival model (PSM) distinguishing between progression-free and progressive states. However, the heterogeneity of responses observed in immuno-oncology (I-O) suggests that new approaches may be appropriate to reflect disease dynamics meaningfully. Materials and methods:This study explored the impact of incorporating immune-specific health states into economic models of I-O therapy. Two variants of the PSM and a Markov model were populated with data from one clinical trial in metastatic melanoma patients. Short-term modeled outcomes were benchmarked to the clinical trial data and a lifetime model horizon provided estimates of life years and quality adjusted life years (QALYs). Results:The PSM-based models produced short-term outcomes closely matching the trial outcomes. Adding health states generated increased QALYs while providing a more granular representation of outcomes for decision making. The Markov model gave the greatest level of detail on outcomes but gave short-term results which diverged from those of the trial (overstating year 1 progression-free survival by around 60%). Conclusion:Increased sophistication in the representation of disease dynamics in economic models is desirable when attempting to model treatment response in I-O. However, the assumptions underlying different model structures and the availability of data for health state mapping may be important limiting factors.

SUBMITTER: Gibson EJ

PROVIDER: S-EPMC5848668 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Modeling the economic outcomes of immuno-oncology drugs: alternative model frameworks to capture clinical outcomes.

Gibson E J EJ Begum N N Koblbauer I I Dranitsaris G G Liew D D McEwan P P Tahami Monfared A A AA Yuan Y Y Juarez-Garcia A A Tyas D D Lees M M

ClinicoEconomics and outcomes research : CEOR 20180308

<h4>Background</h4>Economic models in oncology are commonly based on the three-state partitioned survival model (PSM) distinguishing between progression-free and progressive states. However, the heterogeneity of responses observed in immuno-oncology (I-O) suggests that new approaches may be appropriate to reflect disease dynamics meaningfully.<h4>Materials and methods</h4>This study explored the impact of incorporating immune-specific health states into economic models of I-O therapy. Two varian ...[more]

PMID: 29563820

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Project description:BackgroundExisting economic model frameworks may not adequately capture the atypical treatment response patterns in immuno-oncology (I-O) compared with conventional therapies and thus may fail to represent the full clinical value associated with disease dynamics and improved survival.ObjectiveA cost-effectiveness analysis (CEA) of the I-O Regimen (nivolumab/ipilimumab) versus ipilimumab alone in advanced melanoma was carried out by applying a 5-state partitioned survival model (PSM) as a case study, to explore the I-O treatment response and clinical outcomes. The findings were compared with those of a conventional 3-state PSM.Materials and methodsThe case study extends the conventional 3-state PSM, by separating the pre-progression state into non-responders and responders, and the post-progression state into normal and I-O progression to account for delayed treatment effects preceding clinical response. Model states were populated using patient-level data (where possible), mapping from the best overall response (BOR), and survival analysis with flexible and traditional parametric methods. Survival functions were applied to progression-free survival (PFS) and overall survival (OS) endpoints across treatment arms using the 4-year follow-up data (data available at the time of the research; since then 5-year follow-up data have been published) from the CheckMate 067 trial. Information on BOR was used as a means of differentiating the I-O treatment response in addition to the outcomes of progression-free and progressed disease. A UK National Health Service and personal social services (NHS/PSS) perspective over a lifetime horizon was used with outcomes discounted at 3.5% annually.ResultsThe 5-state PSM generated an increase in quality adjusted life years (QALYs) in both treatment arms and gave a more granular description of patients' health profiles compared with the traditional 3-state PSM. The incremental QALY increased by 13% (from 2.62 to 2.95 QALYs) and the incremental cost decreased by 12% (£29,125 to £25,678) with the 5-state model. In both models, the Regimen had an incremental cost-effectiveness ratio (ICER) relative to ipilimumab alone within the lower bound of the National Institute for Health and Care Excellence (NICE) reference range (£20,000 per QALY gained).ConclusionA 5-state economic model, incorporating relevant I-O health states, can be more informative to gain insight into treatment response and progression differences that are not commonly captured in existing economic models. Clinical trial endpoints, including those relating to treatment response, which are not directly reported in ongoing I-O trials, can be mapped on to the proposed modelled health states (although assumptions are required to do so). Improvements in reporting treatment response in future I-O clinical trials could help to further validate and improve the proposed model framework.

Project description:Immune checkpoint inhibitors (ICI) revolutionized the field of immuno-oncology and opened new avenues towards the development of novel assets to achieve durable immune control of cancer. Yet, the presence of tumor immune evasion mechanisms represents a challenge for the development of efficient treatment options. Therefore, combination therapies are taking the center of the stage in immuno-oncology. Such combination therapies should boost anti-tumor immune responses and/or target tumor immune escape mechanisms, especially those created by major players in the tumor microenvironment (TME) such as tumor-associated macrophages (TAM). Natural killer (NK) cells were recently positioned at the forefront of many immunotherapy strategies, and several new approaches are being designed to fully exploit NK cell antitumor potential. One of the most relevant NK cell-activating receptors is NKG2D, a receptor that recognizes 8 different NKG2D ligands (NKG2DL), including MICA and MICB. MICA and MICB are poorly expressed on normal cells but become upregulated on the surface of damaged, transformed or infected cells as a result of post-transcriptional or post-translational mechanisms and intracellular pathways. Their engagement of NKG2D triggers NK cell effector functions. Also, MICA/B are polymorphic and such polymorphism affects functional responses through regulation of their cell-surface expression, intracellular trafficking, shedding of soluble immunosuppressive isoforms, or the affinity of NKG2D interaction. Although immunotherapeutic approaches that target the NKG2D-NKG2DL axis are under investigation, several tumor immune escape mechanisms account for reduced cell surface expression of NKG2DL and contribute to tumor immune escape. Also, NKG2DL polymorphism determines functional NKG2D-dependent responses, thus representing an additional challenge for leveraging NKG2DL in immuno-oncology. In this review, we discuss strategies to boost MICA/B expression and/or inhibit their shedding and propose that combination strategies that target MICA/B with antibodies and strategies aimed at promoting their upregulation on tumor cells or at reprograming TAM into pro-inflammatory macrophages and remodeling of the TME, emerge as frontrunners in immuno-oncology because they may unleash the antitumor effector functions of NK cells and cytotoxic CD8 T cells (CTL). Pursuing several of these pipelines might lead to innovative modalities of immunotherapy for the treatment of a wide range of cancer patients.

Dataset Information

Modeling the economic outcomes of immuno-oncology drugs: alternative model frameworks to capture clinical outcomes.

Publications

Modeling the economic outcomes of immuno-oncology drugs: alternative model frameworks to capture clinical outcomes.

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