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Background: Patients with non-small cell lung cancer harbouring mesenchymal-epithelial transition exon 14 (METex14) skipping typically demonstrate poorer prognosis than overall non-small cell lung cancer. Until recently, no targeted treatments were available for patients with non-small cell lung cancer harbouring METex14 skipping in the UK, with limited treatments available., Objective: This study estimates the long-term survival and quality-adjusted life-year benefit of MET inhibitor tepotinib versus current standard of care from a UK perspective., Methods: A partitioned-survival model assessed the survival and quality-adjusted life-year benefits of tepotinib versus immunotherapy ± chemotherapy and chemotherapy for untreated and previously treated patients, respectively, using evidence from the single-arm VISION trial (NCT02864992). Two approaches were used to inform an indirect treatment comparison: (1) published clinical trials in overall non-small cell lung cancer and (2) real-world evidence in the METex14 skipping population. Results are presented as median and total quality-adjusted life-year gain and survival for progression-free survival and overall survival. Survival curves were validated against the external literature and uncertainty assessed using a probabilistic sensitivity analysis., Results: Using the indirect treatment comparison against the published literature, tepotinib is estimated to have a median progression-free survival gain versus pembrolizumab ± chemotherapy (11.0 and 9.2 months) in untreated patients, and docetaxel ± nintedanib (5.1 and 6.4 months) in previously treated patients. Across the populations, tepotinib is estimated to have a median survival gain of 15.4 and 9.2 months versus pembrolizumab ± chemotherapy in untreated patients and 12.8 and 5.1 months versus docetaxel ± nintedanib in previously treated patients. The total quality-adjusted life-year gain ranges between 0.56 and 1.17 across the untreated and previously treated populations. Results from the real-world evidence of indirect treatment comparisons are consistent with these findings., Conclusions: Despite the limitations of the evidence base, the numerous analyses conducted have consistently indicated positive outcomes for tepotinib versus the current standard of care., (© 2024. The Author(s).)

Aims: To estimate the cost impact of non-medical switching from originator to biosimilar etanercept in stable patients with rheumatoid arthritis (RA) in the UK. Materials and methods: A cohort-based decision tree model was developed with a 1-year time horizon. The model population included patients with stable RA (patients who responded to originator etanercept treatment with no treatment changes in the previous 6 months). Patients could undergo a non-medical switch to a biosimilar and then switch treatment again, if medically required, after 3-6 months. Data on the proportion of patients switching therapies, baseline healthcare resource use, and impact of switching on resource use were sourced from a survey of 150 rheumatologists from EU5 markets (France, Germany, Italy, Spain, UK). The average impact of switching was evaluated as mean values for change in resource utilization due to switching. Also, low- and high-impact scenarios (lower and upper values of the 95% confidence intervals for change in resource utilization due to switching) were modelled as sensitivity analyses. Cost data came from published UK sources. Results: The model assumed that 5,000 patients were treated with originator etanercept, with 1,259 (25.2%) switching to a biosimilar. Of those, 875 (69.5%) and 384 (30.5%) switched to SB4 and GP2015, respectively. After 3 months, 26.3% of patients who switched treatments did so again: 8.3% back to originator, 3.8% to the other biosimilar, and 14.2% to another biologic. Although originator etanercept was more expensive than the biosimilars, switching was more costly than continuous originator treatment across all impact scenarios. Switching treatment chains had higher overall annual per-patient costs than continuous originator treatment. Switching was associated with increased healthcare resource use. Limitations: Results from this analysis are not transferable to other (non-RA) etanercept indications. Conclusion: Non-medical switching can result in increased payer costs because of increased healthcare resource use following switching.

Background: Chronic spontaneous urticaria (CSU) negatively impacts patient quality of life and productivity and is associated with considerable indirect costs to society., Objective: The aim of this study was to assess the cost utility of add-on omalizumab treatment compared with standard of care (SOC) in moderate or severe CSU patients with inadequate response to SOC, from the UK societal perspective., Methods: A Markov model was developed, consisting of health states based on Urticaria Activity Score over 7 days (UAS7) and additional states for relapse, spontaneous remission and death. Model cycle length was 4 weeks, and total model time horizon was 20 years in the base case. The model considered early discontinuation of non-responders (response: UAS7 ≤6) and retreatment upon relapse (relapse: UAS7 ≥16) for responders. Clinical and cost inputs were derived from omalizumab trials and published sources, and cost utility was expressed as incremental cost-effectiveness ratios (ICERs). Scenario analyses included no early discontinuation of non-responders and an altered definition of response (UAS7 <16)., Results: With a deterministic ICER of £3183 in the base case, omalizumab was associated with increased costs and benefits relative to SOC. Probabilistic sensitivity analysis supported this result. Productivity inputs were key model drivers, and individual scenarios without early discontinuation of non-responders and adjusted response definitions had little impact on results. ICERs were generally robust to changes in key model parameters and inputs., Conclusions: In this, the first economic evaluation of omalizumab in CSU from a UK societal perspective, omalizumab consistently represented a treatment option with societal benefit for CSU in the UK across a range of scenarios.

Background: Natalizumab is a highly effective treatment for patients with relapsing-remitting multiple sclerosis (RRMS). Treatment with natalizumab has been associated with progressive multifocal leukoencephalopathy (PML), a rare yet serious disease of the brain. Published studies have quantified the PML risk by the presence of anti-JC virus antibodies, previous immunosuppressant use, and duration of natalizumab treatment., Objectives: The aim of this analysis was to evaluate the net benefits and risks for patients with RRMS receiving natalizumab treatment compared with fingolimod, interferon-β, and no treatment across PML risk sub-groups., Research Design and Methods: Based on previously validated MS model structures, a Markov cohort model was developed to assess the impact of treatment on quality-adjusted life years (QALYs). Natalizumab-treated patients were classified by PML risk sub-groups and analysed separately for short-term (2 years) and long-term (20 years) time horizons., Main Outcome Measures: Main outcome measures included total QALYs by PML risk sub-group and the increase in PML risk associated with natalizumab treatment which offsets the quality of life benefit of comparator treatments., Results: Results showed higher QALYs with natalizumab versus all other comparators across PML risk sub-groups over both time horizons. For the QALYs of natalizumab to equal the QALYs of fingolimod, interferon-β, and no treatment, the risk of PML would have to increase 4.6-84.2 times, 24.0-444.3 times, and 5.7-106.1 times, respectively (short term), and 1.4-123.4 times, 1.5-138.3 times, and 2.2-193.7 times, respectively (long term)., Conclusion: This study shows that natalizumab generates the most net health benefits in terms of quality-adjusted life years compared with fingolimod, interferon-β, or no treatment, even when the risk of natalizumab-associated PML is taken into consideration. This study is limited by the availability of published data around natalizumab-associated PML, as well as the constraints of the model used to conduct the analysis.