Cost Effectiveness of Sequencing Vedolizumab as First-Line Biologic in Ulcerative Colitis and Crohn's Disease in Canada: An Analysis Using Real-World Evidence from the EVOLVE Study.

INTRODUCTION: Vedolizumab is a gut-selective anti-lymphocyte trafficking biologic indicated for the treatment of adult patients with moderately to severely active ulcerative colitis (UC) and Crohn's disease (CD) in Canada. OBJECTIVE: The objective of this study was to evaluate the cost effectiveness of treatment sequencing for UC and CD from a public healthcare payer perspective, leveraging new real-world evidence from the literature and the EVOLVE study, a retrospective chart review. METHODS: Using separate decision tree/Markov models to assess cost effectiveness for UC and CD, two sequencing approaches were estimated for adult patients (≥ 18 years) diagnosed with UC or CD who were biologic-naïve: vedolizumab as first-line biologic followed by anti-tumor necrosis factor (TNF)-α versus first-line anti-TNFα followed by vedolizumab. Treatment effectiveness (response and remission), surgery rates, dose escalation and regain of response and safety inputs were estimated from EVOLVE, a retrospective chart review of real-world data, and evidence synthesis from the literature, whereas costs and utilities were estimated from health technology assessment reports, clinical trials, and the literature. Biosimilar costs were used for anti-TNFα. Both models simulated a 5-year time horizon and discounted costs and outcomes at 1.5%. Probabilistic base-case analyses (n = 10,000) reported total costs (2023 Canadian dollars) and quality-adjusted life-years (QALYs). Several scenario analyses were conducted to explore robustness of results. RESULTS: In UC, vedolizumab as a first-line biologic followed by anti-TNFα resulted in an incremental gain of 0.09 QALYs (2.46 vs. 2.55) and saved $7179 ($134,028 vs. $126,848), making this a dominant strategy compared with first-line anti-TNFα followed by vedolizumab. In CD, use of vedolizumab as a first-line biologic resulted in an incremental gain of 0.04 QALYs (3.35 vs. 3.39) at an incremental cost of $50,631 ($89,850 vs. $140,381) versus first-line anti-TNFα followed by vedolizumab (incremental cost-effectiveness ratio of $1,265,775 per QALY). CONCLUSIONS: Based on this analysis, sequencing vedolizumab as a first-line biologic prior to anti-TNFα in UC and CD provided additional clinical benefit to patients. In UC, vedolizumab as a first-line biologic also saved healthcare system costs compared with anti-TNFα, whereas in CD, vedolizumab provided incremental benefit at an incremental cost, which was not considered cost effective at a threshold of $50,000/QALY.

Does biomarker use in oncology improve clinical trial failure risk? A large-scale analysis.

PURPOSE: To date there has not been an extensive analysis of the outcomes of biomarker use in oncology. METHODS: Data were pooled across four indications in oncology drawing upon trial outcomes from www.clinicaltrials.gov: breast cancer, non-small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihood drugs would progress through the stages of clinical trial testing to approval based on biomarker status. This was done with multi-state Markov models, tools that describe the stochastic process in which subjects move among a finite number of states. RESULTS: Over 10000 trials were screened, which yielded 745 drugs. The inclusion of biomarker status as a covariate significantly improved the fit of the Markov model in describing the drug trajectories through clinical trial testing stages. Hazard ratios based on the Markov models revealed the likelihood of drug approval with biomarkers having nearly a fivefold increase for all indications combined. A 12, 8 and 7-fold hazard ratio was observed for breast cancer, melanoma and NSCLC, respectively. Markov models with exploratory biomarkers outperformed Markov models with no biomarkers. CONCLUSION: This is the first systematic statistical evidence that biomarkers clearly increase clinical trial success rates in three different indications in oncology. Also, exploratory biomarkers, long before they are properly validated, appear to improve success rates in oncology. This supports early and aggressive adoption of biomarkers in oncology clinical trials.

COVID-19: Adaptation of a model to predicting healthcare resources needs in Valle del Cauca, Colombia / COVID-19: Predicción de las necesidades de recursos sanitarios en el Valle del Cauca, Colombia

Abstract Background: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. Methods: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%); (2-3) partial restrictions (at 4% and 8% growth rates); and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. Results: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. Conclusion: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making

Resumen Introducción: Valle del Cauca es el departamento con el cuarto mayor número de casos de COVID-19 en Colombia (>50,000 en septiembre 7, 2020). Debido a la ausencia de tratamientos efectivos para COVID-19, los tomadores de decisiones requieren de acceso a información actualizada para estimar la incidencia de la enfermedad, y la disponibilidad de recursos hospitalarios para contener la pandemia. Métodos: Adaptamos un modelo existente al contexto local para estimar la incidencia de COVID-19, y la demanda de recursos hospitalarios en los próximos meses. Para ello, modelamos cuatro escenarios hipotéticos: (1) el gobierno local implementa una cuarentena desde el primero de septiembre hasta el 15 de octubre (asumiendo una tasa promedio de infecciones diarias del 2%); (2-3) se implementan restricciones parciales (tasas de infección del 4% y 8%); (4) se levantan todas las restricciones (tasa del 10%). Los mismos escenarios fueron previamente evaluados entre julio y agosto, y los resultados fueron resumidos. Estimamos el número de casos nuevos, el número de pruebas diagnósticas requeridas, y el numero de camas de hospital y de unidad de cuidados intensivos (con y sin ventilación) disponibles, para cada escenario. Resultados: El modelo estimó 67,700 casos a octubre 15 al asumir la implementación de una nueva cuarentena, 80,400 y 101,500 al asumir restricciones parciales al 4 y 8% de infecciones diarias, respectivamente, y 208,500 al asumir ninguna restricción. La demanda por pruebas diagnósticas (de reacción en cadena de la polimerasa) fue estimada entre 202,000 y 1,610,600 entre septiembre 1 y octubre 15, a través de los diferentes escenarios evaluados. El modelo estimó un agotamiento de camas de cuidados intensivos para septiembre 20 al asumir una tasa de infecciones del 10%. Conclusión: Se estima que el levantamiento paulatino de las restricciones de distanciamiento social y la reapertura de la economía no debería causar el agotamiento de recursos hospitalarios si la tasa de infección diaria se mantiene por debajo del 8%. Sin embargo, incrementar la disponibilidad de camas permitiría al sistema de salud ajustarse rápidamente a potenciales picos inesperados de infecciones nuevas. Los modelos de predicción deben ser utilizados de manera iterativa para depurar las predicciones epidemiológicas y para proveer a los tomadores de decisiones con información actualizada.

Vancomycin-resistant enterococci (VRE) screening and isolation in the general medicine ward: a cost-effectiveness analysis.

Background: Vancomycin-resistant enterococci (VRE) are a serious antimicrobial resistant threat in the healthcare setting. We assessed the cost-effectiveness of VRE screening and isolation for patients at high-risk for colonisation on a general medicine ward compared to no VRE screening and isolation from the healthcare payer perspective. Methods: We developed a microsimulation model using local data and VRE literature, to simulate a 20-bed general medicine ward at a tertiary-care hospital with up to 1000 admissions, approximating 1 year. Primary outcomes were accrued over the patient's lifetime, discounted at 1.5%, and included expected health outcomes (VRE colonisations, VRE infections, VRE-related bacteremia, and deaths subsequent to VRE infection), quality-adjusted life years (QALYs), healthcare costs, and incremental cost-effectiveness ratio (ICER). Probabilistic sensitivity analysis (PSA) and scenario analyses were conducted to assess parameter uncertainty. Results: In our base-case analysis, VRE screening and isolation prevented six healthcare-associated VRE colonisations per 1000 admissions (6/1000), 0.6/1000 VRE-related infections, 0.2/1000 VRE-related bacteremia, and 0.1/1000 deaths subsequent to VRE infection. VRE screening and isolation accrued 0.0142 incremental QALYs at an incremental cost of $112, affording an ICER of $7850 per QALY. VRE screening and isolation practice was more likely to be cost-effective (> 50%) at a cost-effectiveness threshold of $50,000/QALY. Stochasticity (randomness) had a significant impact on the cost-effectiveness. Conclusion: VRE screening and isolation can be cost-effective in majority of model simulations at commonly used cost-effectiveness thresholds, and is likely economically attractive in general medicine settings. Our findings strengthen the understanding of VRE prevention strategies and are of importance to hospital program planners and infection prevention and control.

Applying Small Molecule Signal Transducer and Activator of Transcription-3 (STAT3) Protein Inhibitors as Pancreatic Cancer Therapeutics.

Constitutively activated STAT3 protein has been found to be a key regulator of pancreatic cancer and a target for molecular therapeutic intervention. In this study, PG-S3-001, a small molecule derived from the SH-4-54 class of STAT3 inhibitors, was found to inhibit patient-derived pancreatic cancer cell proliferation in vitro and in vivo in the low micromolar range. PG-S3-001 binds the STAT3 protein potently, Kd = 324 nmol/L by surface plasmon resonance, and showed no effect in a kinome screen (>100 cancer-relevant kinases). In vitro studies demonstrated potent cell killing as well as inhibition of STAT3 activation in pancreatic cancer cells. To better model the tumor and its microenvironment, we utilized three-dimensional (3D) cultures of patient-derived pancreatic cancer cells in the absence and presence of cancer-associated fibroblasts (CAF). In this coculture model, inhibition of tumor growth is maintained following STAT3 inhibition in the presence of CAFs. Confocal microscopy was used to verify tumor cell death following treatment of 3D cocultures with PG-S3-001. The 3D model was predictive of in vivo efficacy as significant tumor growth inhibition was observed upon administration of PG-S3-001. These studies showed that the inhibition of STAT3 was able to impact the survival of tumor cells in a relevant 3D model, as well as in a xenograft model using patient-derived cells. Mol Cancer Ther; 15(5); 794-805. ©2016 AACR.