Cost-effectiveness of large-panel next-generation sequencing in guiding first-line treatment decisions for patients with nonsquamous advanced non-small cell lung cancer.

BACKGROUND: Clinical practice guidelines recommend broad-panel genomic profiling to identify actionable genomic alterations for patients with advanced non-small cell lung cancer (aNSCLC). OBJECTIVE: To assess the cost-effectiveness of large-panel next-generation sequencing (LP-NGS) compared with current empirical single-gene test (SGT) patterns to inform first-line treatment decisions for patients with aNSCLC from a US commercial payer perspective, accounting for the effect of testing turnaround time and time to treatment initiation. METHODS: We developed a discrete-event simulation model to estimate the impact of LP-NGS vs SGT for patients with nonsquamous aNSCLC. Discrete events and timing included testing patterns, receipt of the initial test result, treatment initiation (targeted vs nontargeted therapies), switching, retesting, rebiopsies, clinical trial participation, progression on therapy, and death. LP-NGS and SGT cohorts each comprised 100,000 adults with aNSCLC simulated over a 5-year postdiagnosis period, assumed to have the same distribution of genomic alterations. The model predicted the proportion of patients receiving appropriate first-line therapy according to clinical practice guidelines. Economic outcomes included expected life-years gained, quality-adjusted life-years, and the total costs of care over 5 years. Sensitivity and scenario analyses explored the robustness of the base-case model results. RESULTS: In the base-case model, LP-NGS was likely to identify more alterations than SGT. Total 5-year costs per patient were $539,658 for LP-NGS and $544,550 for SGT (net difference, $4,892 lower costs per patient for LP-NGS), which is likely to be cost-effective 95.1% of the time. The most influential model parameters on the 5-year total costs of care were preprogression nondrug medical costs on nontargeted therapy, NGS turnaround time, and clinical trial participation. CONCLUSIONS: This study suggests that LP-NGS to guide first-line treatment decisions is clinically more appropriate (more likely to identify alterations and subsequently allocate patients to clinically appropriate therapy) and provides a dominant cost-effectiveness treatment strategy over 5 years for patients with newly diagnosed aNSCLC in the United States.

Real-world total cost of care by line of therapy in relapsed/refractory diffuse large B-cell lymphoma.

AIMS: There are multiple recently approved treatments and a lack of clear standard-of-care therapies for relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL). While total cost of care (TCC) by the number of lines of therapy (LoTs) has been evaluated, more recent cost estimates using real-world data are needed. This analysis assessed real-world TCC of R/R DLBCL therapies by LoT using the IQVIA PharMetrics Plus database (1 January 2015-31 December 2021), in US patients aged ≥18 years treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or an R-CHOP-like regimen as first-line therapy. METHODS: Treatment costs and resources in the R/R setting were assessed by LoT. A sensitivity analysis identified any potential confounding of the results caused by the impact of the COVID-19 pandemic on healthcare utilization and costs. Overall, 310 patients receiving a second- or later-line treatment were included; baseline characteristics were similar across LoTs. Inpatient costs represented the highest percentage of total costs, followed by outpatient and pharmacy costs. RESULTS: Mean TCC per-patient-per-month generally increased by LoT ($40,604, $48,630, and $59,499 for second-, third- and fourth-line treatments, respectively). Costs were highest for fourth-line treatment for all healthcare resource utilization categories. Sensitivity analysis findings were consistent with the overall analysis, indicating results were not confounded by the COVID-19 pandemic. LIMITATIONS: There was potential misclassification of LoT; claims data were processed through an algorithm, possibly introducing errors. A low number of patients met the inclusion criteria. Patients who switched insurance plans, had insurance terminated, or whose enrollment period met the end of data availability may have had truncated follow-up, potentially resulting in underestimated costs. CONCLUSION: Total healthcare costs increased with each additional LoT in the R/R DLBCL setting. Further improvements of first-line treatments that reduce the need for subsequent LoTs would potentially lessen the economic burden of DLBCL.

Estimating the US Baseline Distribution of Health Inequalities Across Race, Ethnicity, and Geography for Equity-Informative Cost-Effectiveness Analysis.

OBJECTIVES: Information on how life expectancy, disability-free life expectancy, and quality-adjusted life expectancy varies across equity-relevant subgroups is required to conduct distributional cost-effectiveness analysis. These summary measures are not comprehensively available in the United States, given limitations in nationally representative data across racial and ethnic groups. METHODS: Through linkage of US national survey data sets and use of Bayesian models to address missing and suppressed mortality data, we estimate health outcomes across 5 racial and ethnic subgroups (non-Hispanic American Indian or Alaska Native, non-Hispanic Asian and Pacific Islander, non-Hispanic black, non-Hispanic white, and Hispanic). Mortality, disability, and social determinant of health data were combined to estimate sex- and age-based outcomes for equity-relevant subgroups based on race and ethnicity, as well as county-level social vulnerability. RESULTS: Life expectancy, disability-free life expectancy, and quality-adjusted life expectancy at birth declined from 79.5, 69.4, and 64.3 years, respectively, among the 20% least socially vulnerable (best-off) counties to 76.8, 63.6, and 61.1 years, respectively, among the 20% most socially vulnerable (worst-off) counties. Considering differences across racial and ethnic subgroups, as well as geography, gaps between the best-off (Asian and Pacific Islander; 20% least socially vulnerable counties) and worst-off (American Indian/Alaska Native; 20% most socially vulnerable counties) subgroups were large (17.6 life-years, 20.9 disability-free life-years, and 18.0 quality-adjusted life-years) and increased with age. CONCLUSIONS: Existing disparities in health across geographies and racial and ethnic subgroups may lead to distributional differences in the impact of health interventions. Data from this study support routine estimation of equity effects in healthcare decision making, including distributional cost-effectiveness analysis.

The Impact of Funding Inpatient Treatments for COVID-19 on Health Equity in the United States: A Distributional Cost-Effectiveness Analysis.

OBJECTIVES: We conducted a distributional cost-effectiveness analysis (DCEA) to evaluate how Medicare funding of inpatient COVID-19 treatments affected health equity in the United States. METHODS: A DCEA, based on an existing cost-effectiveness analysis model, was conducted from the perspective of a single US payer, Medicare. The US population was divided based on race and ethnicity (Hispanic, non-Hispanic black, and non-Hispanic white) and county-level social vulnerability index (5 quintile groups) into 15 equity-relevant subgroups. The baseline distribution of quality-adjusted life expectancy was estimated across the equity subgroups. Opportunity costs were estimated by converting total spend on COVID-19 inpatient treatments into health losses, expressed as quality-adjusted life-years (QALYs), using base-case assumptions of an opportunity cost threshold of $150 000 per QALY gained and an equal distribution of opportunity costs across equity-relevant subgroups. RESULTS: More socially vulnerable populations received larger per capita health benefits due to higher COVID-19 incidence and baseline in-hospital mortality. The total direct medical cost of inpatient COVID-19 interventions in the United States in 2020 was estimated at $25.83 billion with an estimated net benefit of 735 569 QALYs after adjusting for opportunity costs. Funding inpatient COVID-19 treatment reduced the population-level burden of health inequality by 0.234%. Conclusions remained robust across scenario and sensitivity analyses. CONCLUSIONS: To the best of our knowledge, this is the first DCEA to quantify the equity implications of funding COVID-19 treatments in the United States. Medicare funding of COVID-19 treatments in the United States could improve overall health while reducing existing health inequalities.

The Association Between Persistence and Adherence to Disease-Modifying Therapies and Healthcare Resource Utilization and Costs in Patients With Multiple Sclerosis.

Background: Persistence and adherence to disease-modifying therapies (DMTs) affects treatment efficacy and economic outcomes, both of which contribute to overall patient disease burden. Current literature suggests that patients with multiple sclerosis (MS) who adhere to DMT for 12 months have fewer relapses and reduced MS-related healthcare resource utilization (HCRU) and medical costs than nonadherent patients. Objective: To expand on previous research by estimating the association of persistence and adherence with all-cause and MS-related HCRU and non-DMT costs of patients with MS across 12 and 24 months of therapy use. Methods: This study was a retrospective analysis of adult patients with MS in the IBM MarketScan Commercial and Medicare Supplemental databases using claims data between April 2016 and December 2019. The index date was defined as the initiation of the DMT. Patients were required to have ≥12 months' continuous enrollment pre-index and ≥12 or ≥24 months' continuous enrollment post-index. Persistence was defined as no gap in DMT supply for ≥60 days within the post-index period or switch to another DMT. Adherence was calculated using the proportion of days covered (for this study, number of days covered by the DMT was 365 or 730 days), with ≥80% proportion of days covered considered adherent. Multivariable analyses were conducted to estimate total and individual components of non-DMT costs by persistence and adherence while controlling for baseline differences. Results: Patients who were persistent with medication for 12 months showed a reduction in mean total non-DMT medical costs of $10 022 compared with nonpersistent patients; these savings nearly doubled ($19 230) after 24 months of persistence. A similar pattern was observed for adherent vs nonadherent patients (reduction in costs at 12 months, $8543; at 24 months, $16 091). The largest reduction in all-cause HCRU costs was observed in the inpatient setting, while the largest reduction in MS-related costs was observed in the outpatient setting. Discussion: Patients with MS who were persistent and adherent to medication had substantially lower all-cause and MS-related non-DMT medical costs compared with those who were nonpersistent or nonadherent. Conclusions: These findings further support the importance of persistence and adherence to DMTs in patients with MS.

Trends in Use of Next-Generation Sequencing in Patients With Solid Tumors by Race and Ethnicity After Implementation of the Medicare National Coverage Determination.

Importance: In March 2018, Medicare issued a national coverage determination (NCD) for next-generation sequencing (NGS) to facilitate access to NGS testing among Medicare beneficiaries. It is unknown whether the NCD affected health equity issues for Medicare beneficiaries and the overall population. Objective: To examine the association between the Medicare NCD and NGS use by insurance types and race and ethnicity. Design, Setting, and Participants: A retrospective cohort analysis was conducted using electronic health record data derived from a real-world database. Data originated from approximately 280 cancer clinics (approximately 800 sites of care) in the US. Patients with advanced non-small cell lung cancer (aNSCLC), metastatic colorectal cancer (mCRC), metastatic breast cancer (mBC), or advanced melanoma diagnosed from January 1, 2011, through March 31, 2020, were included. Exposure: Pre- vs post-NCD period. Main Outcomes and Measures: Patients were classified by insurance type and race and ethnicity to examine patterns in NGS testing less than or equal to 60 days after diagnosis. Difference-in-differences models examined changes in average NGS testing in the pre- and post-NCD periods by race and ethnicity, and interrupted time-series analysis examined whether trends over time varied by insurance type and race and ethnicity. Results: Among 92 687 patients with aNSCLC, mCRC, mBC, or advanced melanoma, mean (SD) age was 66.6 (11.2) years, 51 582 (55.7%) were women, and 63 864 (68.9%) were Medicare beneficiaries. The largest racial and ethnic categories according to the database used and further classification were Black or African American (8605 [9.3%]) and non-Hispanic White (59 806 [64.5%]). Compared with Medicare beneficiaries, changes in pre- to post-NCD NGS testing trends were similar in commercially insured patients (odds ratio [OR], 1.03; 95% CI, 0.98-1.08; P = .25). Pre- to post-NCD NGS testing trends increased at a slower rate among patients in assistance programs (OR, 0.93; 95% CI, 0.87-0.99; P = .03) compared with Medicare beneficiaries. The rate of increase for patients receiving Medicaid was not statistically significantly different compared with those receiving Medicare (OR, 0.92; 95% CI, 0.84-1.01; P = .07). The NCD was not associated with statistically significant changes in NGS use trends by racial and ethnic groups within Medicare beneficiaries alone or across all insurance types. Compared with non-Hispanic White individuals, increases in average NGS use from the pre-NCD to post-NCD period were 14% lower (OR, 0.86; 95% CI, 0.74-0.99; P = .04) among African American and 23% lower (OR, 0.77; 95% CI, 0.62-0.96; P = .02) among Hispanic/Latino individuals; increases among Asian individuals and those with other races and ethnicities were similar. Conclusions and Relevance: The findings of this study suggest that expansion of Medicare-covered benefits may not occur equally across insurance types, thereby further widening or maintaining disparities in NGS testing. Additional efforts beyond coverage policies are needed to ensure equitable access to the benefits of precision medicine.

The costs of care from a US claims database in patients with neuromyelitis optica spectrum disorder.

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) is a rare autoimmune disease of the central nervous system that often leads to severe disability. Patients with highly active NMOSD have approximately a 10-times higher hospital inpatient admission rate compared with patients without NMOSD. Accurate assessments of the impact of NMOSD treatments on the burdens of illness require quantitative metrics of these burdens, including costs of care. METHODS: This study evaluated claims data from the IBM MarketScan Commercial and Medicare Supplemental Databases between 2014 and 2018. Patients were included based on inpatient or outpatient claims meeting criteria defined for NMOSD. Non-NMOSD controls were matched 5:1 to patients with NMOSD. Total costs of healthcare services in consumer price index-adjusted 2019 US dollars during the 1-year postindex follow-up period were calculated for patients and controls. RESULTS: Patients with NMOSD required more healthcare services and incurred significantly greater costs for inpatient hospitalizations (annual mean [SD] cost: $29,054 [$144,872] vs controls $1521 [$10,759]), outpatient services ($24,881 [$35,463] vs $4761 [$26,447]), and emergency department (ED) visits ($2400 [$7771] vs $408 [$2579]). Almost 12% of patients with NMOSD were further burdened with plasma exchange or intravenous immunoglobulin G treatments, costing an annual median (interquartile range) of $1684 ($566-$3817) and $24,353 ($5425-$42,975), respectively. CONCLUSIONS: Compared with controls, patients with NMOSD had significantly higher costs associated with hospitalizations, ED visits, and prescriptions. These results highlight the considerable economic burden of NMOSD, which may be favorably impacted by disease-modifying therapies that are regulatory-approved to be safe and effective.

Burden and cost of comorbidities in patients with neuromyelitis optica spectrum disorder.

BACKGROUND: Neuromyelitis optica spectrum disorder (NMOSD) is associated with various comorbidities, including non-autoimmune and autoimmune conditions. The burden and cost of illness for NMOSD are unclear, particularly in the context of comorbidities. METHODS: Claims data from IBM MarketScan Commercial and Medicare Supplemental Databases between 2014 and 2018 were analyzed. Patients with NMOSD were specified as having inpatient or outpatient claims for NMOSD diagnosis or specific NMOSD symptoms claims and no subsequent claims for multiple sclerosis (MS) or use of MS disease-modifying therapy (DMT). Continuous enrollment ≥ 6 months before and ≥ 1 year after the first claim (index date) was required for study inclusion. Total costs stratified by comorbidities within 12 months post-index date were calculated per patient and compared 1:5 with matched non-NMOSD controls. RESULTS: A total of 162 patients with NMOSD and 810 non-NMOSD controls were evaluated. A significantly higher proportion of NMOSD patients had comorbidities than non-NMOSD controls (66.7% vs 41.5%; P < 0.001). Concomitant autoimmune disease occurred in 19.1% vs 4.9% (P < 0.001) of patients with NMOSD vs non-NMOSD controls. NMOSD patients incurred significantly higher total median (interquartile range) healthcare costs per patient ($68,386.48 [$23,373.54-$160,862.70]) than matched non-NMOSD controls with autoimmune disease ($17,215.13 [$6715.48-$31,441.93]; P < 0.001) or patients with NMOSD without autoimmune comorbidity ($23,905.42 [$8632.82-$67,251.54]; P = 0.022). Similarly, patients with NMOSD and non-autoimmune comorbidities incurred higher median healthcare costs than matched controls. CONCLUSIONS: Patients with NMOSD experience significant disease burden and cost that are amplified by comorbidities. Effective therapies are needed, particularly for patients with concomitant autoimmune disease.

Association Between Medicare's National Coverage Determination and Utilization of Next-Generation Sequencing.

PURPOSE: In 2018, Medicare issued a national coverage determination (NCD) providing reimbursement for next-generation sequencing (NGS) tests for beneficiaries with advanced or metastatic cancer and no previous NGS testing. We examined the association between NCD implementation and NGS utilization trends in Medicare beneficiaries versus commercially insured patients. METHODS: This was a retrospective study of patients with advanced non-small-cell lung cancer (aNSCLC), metastatic colorectal cancer (mCRC), metastatic breast cancer (mBC), or advanced melanoma with a de novo or recurrent advanced diagnosis from January 1, 2011, through December 30, 2019, using a nationwide US electronic health record-derived deidentified database. Patients were classified by insurance and by advanced diagnosis date. NGS testing was assessed by receipt of first NGS test result ≤ 60 days of advanced diagnosis. Interrupted time series analysis assessed NGS utilization pre- and post-NCD effective date by insurance type. RESULTS: The utilization and repeat NGS testing analysis included 70,290 and 4,295 patients, respectively. Use of NGS rose from < 1% in 2011 to > 45% in Q4 2019 in aNSCLC while remaining < 20% in mBC and advanced melanoma. Among patients with aNSCLC, mCRC, or mBC, NGS testing increased post-NCD versus pre-NCD (P < .05). There was no significant difference in trends pre- and post-NCD between Medicare beneficiaries and commercially insured patients in any tumor. Repeat NGS testing was similar before the NCD (Medicare v commercial: 24.8% v 28.5%). Post-NCD, fewer Medicare beneficiaries had repeat NGS testing (27.7% v 36.0%; P < .01). CONCLUSION: Trends in NGS utilization significantly changed post-NCD, although the magnitude of change was not significantly different by insurance type, indicating private insurers may also be incorporating NCD guidance. Implementation of the NCD may have limited use of repeat NGS testing in Medicare beneficiaries.

Identification and temporal trends of patients with neuromyelitis optica spectrum disorder in a US insurance claims database.

BACKGROUND: This US claims-based study aimed to identify and characterize temporal trends in diagnostic pathways for patients likely to have neuromyelitis optica spectrum disorder (NMOSD). METHODS: Patients were identified from IBM MarketScan Commercial Databases if, within 1 year, they had two NMOSD claims separated by ≥ 60 days; two transverse myelitis (TM) or optic neuritis (ON) claims separated by ≥ 60 days, and one additional symptom (TM, ON, or area postrema syndrome); or one NMOSD claim and one additional symptom. The first NMOSD or TM/ON claim was the index date, and the second claim was the diagnosis date. Similar methodology was used in temporal trend and incidence and prevalence analyses. RESULTS: Among 1,901 patients with NMOSD, 34.2% were identified by two NMO claims, 53.2% by ON or TM +1 symptom, and 12.6% by one NMOSD claim +1 symptom. Anti-aquaporin-4 immunoglobin G (AQP4-IgG) autoantibody tests and magnetic resonance imaging was used for 23.0% and 71.9% of cases, respectively. Across cohorts, 21.4-49.1% had multiple sclerosis (MS) diagnosis claims prior to index date, and 37.3-60.6% had an MS diagnosis, 14.9-31.0% had MS disease-modifying therapy (DMT) claims and 6.3-44.8% had immunosuppressive therapy (IST) claims <1 year after diagnosis. Over time, there were slight changes in MS diagnosis claims, AQP4-IgG autoantibody testing, and DMT and IST use before and after NMOSD diagnosis. LIMITATIONS: This study is limited by the information available in US claims databases, which included the potential for misclassification of NMOSD based solely on claims codes and lack of reimbursement for AQP4-IgG testing by insurance companies. CONCLUSIONS: Among patients likely to have NMOSD, low AQP4-IgG testing rates, IST use, frequent MS diagnosis claims, and DMT use highlight the need for a diagnostic algorithm and timely treatment of NMOSD.

A Cost-Effectiveness Framework for COVID-19 Treatments for Hospitalized Patients in the United States.

INTRODUCTION: The COVID-19 pandemic is a global crisis impacting population health and the economy. We describe a cost-effectiveness framework for evaluating acute treatments for hospitalized patients with COVID-19, considering a broad spectrum of potential treatment profiles and perspectives within the US healthcare system to ensure incorporation of the most relevant clinical parameters, given evidence currently available. METHODS: A lifetime model, with a short-term acute care decision tree followed by a post-discharge three-state Markov cohort model, was developed to estimate the impact of a potential treatment relative to best supportive care (BSC) for patients hospitalized with COVID-19. The model included information on costs and resources across inpatient levels of care, use of mechanical ventilation, post-discharge morbidity from ventilation, and lifetime healthcare and societal costs. Published literature informed clinical and treatment inputs, healthcare resource use, unit costs, and utilities. The potential health impacts and cost-effectiveness outcomes were assessed from US health payer, societal, and fee-for-service (FFS) payment model perspectives. RESULTS: Viewing results in aggregate, treatments that conferred at least a mortality benefit were likely to be cost-effective, as all deterministic and sensitivity analyses results fell far below willingness-to-pay thresholds using both a US health payer and FFS payment perspective, with and without societal costs included. In the base case, incremental cost-effectiveness ratios (ICER) ranged from $22,933 from a health payer perspective using bundled payments to $8028 from a societal perspective using a FFS payment model. Even with conservative assumptions on societal impact, inclusion of societal costs consistently produced ICERs 40-60% lower than ICERs for the payer perspective. CONCLUSION: Effective COVID-19 treatments for hospitalized patients may not only reduce disease burden but also represent good value for the health system and society. Though data limitations remain, this cost-effectiveness framework expands beyond current models to include societal costs and post-discharge ventilation morbidity effects of potential COVID-19 treatments.

Estimated Impact of Public and Private Sector COVID-19 Diagnostics and Treatments on US Healthcare Resource Utilization.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has imposed a considerable burden on the United States (US) health system, with particular concern over healthcare capacity constraints. METHODS: We modeled the impact of public and private sector contributions to developing diagnostic testing and treatments on COVID-19-related healthcare resource use. RESULTS: We estimated that public sector contributions led to at least 30% reductions in COVID-19-related healthcare resource utilization. Private sector contributions to expanded diagnostic testing and treatments led to further reductions in mortality (- 44%), intensive care unit (ICU) and non-ICU hospital beds (- 30% and - 28%, respectively), and ventilator use (- 29%). The combination of lower diagnostic test sensitivity and proportions of patients self-isolating may exacerbate case numbers, and policies that encourage self-isolating should be considered. CONCLUSION: While mechanisms exist to facilitate research, development, and patient access to diagnostic testing, future policies should focus on ensuring equitable patient access to both diagnostic testing and treatments that, in turn, will alleviate COVID-19-related resource constraints.

HER2-Positive Metastatic Breast Cancer: A Retrospective Cohort Study of Healthcare Costs in the Targeted-Therapy Age.

INTRODUCTION: Claims data (IBM MarketScan Commercial and MarketScan Medicare Supplemental databases) from June 30, 2011 to September 30, 2017 were used to evaluate the cost impact of human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC) in this retrospective cohort study. METHODS: The primary analysis compared short-term costs for patients diagnosed with HER2+ MBC at least 180 days after the end of first HER2-targeted treatment (MBC+ cohort) versus a propensity score matched cohort of patients with breast cancer who did not develop MBC (MBC- cohort). A pseudo-post period for patients in the HER2+ MBC- cohort was defined by indexing to the HER2+ treatment completion-MBC diagnosis time interval of the matched pair in the HER2+ MBC+ cohort; we then compared average monthly cost differences between these groups for the year preceding and following MBC diagnosis. In secondary analyses, we estimated medium-term aggregate and categorical healthcare costs for patients with HER2+ MBC up to 3 years post-diagnosis. RESULTS: In the short-term primary analysis, costs for the HER2+ MBC+ and HER2+ MBC- cohorts were largely comparable in the year preceding MBC diagnosis. Monthly direct costs were significantly higher for the HER2+ MBC+ cohort in the months immediately preceding MBC diagnosis, with differences in the range of $500-5000. Following diagnosis, total monthly costs were $13,000-34,000 higher for patients in the HER2+ MBC+ cohort vs. the HER2+ MBC- cohort. In the medium-term secondary analysis, mean per patient total costs were $218,171 [standard error (SE) $5450] in the first year following MBC diagnosis and $412,903 (SE $13,034) cumulatively over 3 years following diagnosis (among patients with complete follow-up). Primary cost contributors were outpatient visits ($195,162; SE $8043) and HER2-targeted therapy drug costs ($177,489; SE $8120). CONCLUSIONS: HER2+ MBC is associated with high short-term and medium-term direct healthcare costs. These could be alleviated with early diagnosis and optimal standard-of-care treatment for early breast cancer, which can significantly reduce the risk of recurrence.

Are National Comprehensive Cancer Network Evidence Block Affordability Ratings Representative of Real-World Costs? An Evaluation of Advanced Non-Small-Cell Lung Cancer.

PURPOSE: The National Comprehensive Cancer Network (NCCN) developed the Evidence Blocks framework to assess the value of oncology regimens. This study characterizes the relationship between real-world costs and NCCN affordability ratings (ARs) for advanced non-small-cell lung cancer (aNSCLC) treatments. METHODS: Using the MarketScan and PharMetrics Plus databases, we identified patients treated between 2012 and 2017 with an aNSCLC regimen evaluated by the NCCN Evidence Blocks. We estimated adjusted mean total per-patient-per-month (PPPM) costs and drug costs for each regimen using a log-linked gamma generalized linear model. Weighted regression was used to examine the correlation between adjusted mean PPPM costs per regimen and NCCN AR. RESULTS: A total of 25,162 patients with aNSCLC (mean age, 63 years [standard deviation, 10 years]; 52% male) had identifiable regimens. Mean total PPPM cost by therapeutic class ranged from $16,824 for epidermal growth factor receptors to $41,815 for immunotherapy-based treatment. Epidermal growth factor receptor and anaplastic lymphoma kinase inhibitor treatment had lower ARs compared with generic chemotherapy. No therapy was listed as AR group 5 (least expensive). In pairwise comparisons, AR group 1 had significantly higher PPPM total costs compared with AR groups 2 and 4. There were no significant differences in PPPM total cost among AR groups 2, 3, and 4. CONCLUSION: Real-world aNSCLC treatment costs are often inconsistent with the NCCN ARs. Given that NCCN Evidence Blocks are intended to inform provider-patient discussions and other decision support resources, such as the NCCN Categories of Preference, our results suggest that the NCCN ARs require further refinement and validation.

In Vivo Assessment of Antibody-Dependent Enhancement of Influenza B Infection.

A theoretical safety concern proposed in the influenza literature is that therapeutic antiviral antibodies could have the potential for antibody-dependent enhancement (ADE) of infection and disease. ADE may occur when virus-specific antibodies at subtherapeutic, nonneutralizing concentrations facilitate virus uptake and, in some cases, enhance replication, which can lead to an exacerbation of virus-mediated disease. Alternatively, ADE may occur due to antibody-dependent complement activation exacerbating virus-mediated disease in the absence of increased replication. As a result of this theoretical safety concern, safety assessment of anti-influenza antibodies may include an in vivo evaluation of ADE of infection and/or disease. These studies were conducted to investigate the potential of MHAB5553A, a broadly specific, neutralizing therapeutic anti-influenza B antibody, to elicit ADE of infection and disease in mouse models of influenza B infection. In parallel studies, female DBA/2J mice were infected with either influenza B/Victoria/504/2000 or influenza B/Brisbane/60/2008 representing distinct lineages. Assessment of ADE was based on an integration of results from multiple endpoints, including infectious lung viral titers and genomes, body weight, mortality, lung weight, and histopathology. In these studies, the high dose of 15 mg/kg MHAB5553A resulted in substantial attenuation of influenza pneumonia, with more modest effects at 1.5 mg/kg; whereas MHAB5553A treatment at 0.15 or 0.015 mg/kg was generally comparable to vehicle-treated controls. Our results demonstrate that MHAB5553A across a broad range of doses did not enhance primary influenza B infection or disease in this model, and represent a nonclinical de-risking of the ADE potential with this antibody.

In vitro assessment of chemotherapy-induced neuronal toxicity.

Neurotoxicity is a major concern during drug development, and together with liver and cardio-toxicity, it is one of the main causes of clinical drug attrition. Current pre-clinical models may not sufficiently identify and predict the risk for central or peripheral nervous system toxicity. One such example is clinically dose-limiting neuropathic effects after the administration of chemotherapeutic agents. Thus, the need to establish novel in vitro tools to evaluate the risk of neurotoxicities, such as neuropathy, remains unmet in drug discovery. Though in vitro studies have been conducted using primary and immortalized cell lines, some limitations include the utility for higher throughput methodologies, method reproducibility, and species extrapolation. As a novel alternative, human induced-pluripotent stem cell (iPSC)-derived neurons appear promising for testing new drug candidates. These iPSC-derived neurons are readily available and can be manipulated as required. Here, we describe a novel approach to assess neurotoxicity caused by different classes of chemotherapeutics using kinetic monitoring of neurite dynamic changes and apoptosis in human iPSC-neurons. These studies show promising changes in neurite dynamics in response to clinical inducers of neuropathy, as well as the ability to rank-order and gather mechanistic insight into class-specific compound induced neurotoxicity. This platform can be utilized in early drug development, as part of a weight of evidence approach, to screen drug candidates, and potentially reduce clinical attrition due to neurotoxicity.

Comparison of the performance of particle filter algorithms applied to tracking of a disease epidemic.

We present general methodology for sequential inference in nonlinear stochastic state-space models to simultaneously estimate dynamic states and fixed parameters. We show that basic particle filters may fail due to degeneracy in fixed parameter estimation and suggest the use of a kernel density approximation to the filtered distribution of the fixed parameters to allow the fixed parameters to regenerate. In addition, we show that "seemingly" uninformative uniform priors on fixed parameters can affect posterior inferences and suggest the use of priors bounded only by the support of the parameter. We show the negative impact of using multinomial resampling and suggest the use of either stratified or residual resampling within the particle filter. As a motivating example, we use a model for tracking and prediction of a disease outbreak via a syndromic surveillance system. Finally, we use this improved particle filtering methodology to relax prior assumptions on model parameters yet still provide reasonable estimates for model parameters and disease states.