The Backfill i3+3 Design for Dose-Finding Trials in Oncology

We consider a formal statistical design that allows simultaneous enrollment of a main cohort and a backfill cohort of patients in a dose-finding trial. The goal is to accumulate more information at various doses to facilitate dose optimization. The proposed design, called Bi3+3, combines the simple dose-escalation algorithm in the i3+3 design and a model-based inference under the framework of probability of decisions (POD), both previously published. As a result, Bi3+3 provides a simple algorithm for backfilling patients to lower doses in a dose-finding trial once these doses exhibit safety profile in patients. The POD framework allows dosing decisions to be made when some backfill patients are still being followed with incomplete toxicity outcomes, thereby potentially expediting the clinical trial. At the end of the trial, Bi3+3 uses both toxicity and efficacy outcomes to estimate an optimal biological dose (OBD). The proposed inference is based on a dose-response model that takes into account either a monotone or plateau dose-efficacy relationship, which are frequently encountered in modern oncology drug development. Simulation studies show promising operating characteristics of the Bi3+3 design in comparison to existing designs

Targeted molecular therapy of anaplastic thyroid carcinoma with AEE788

Anaplastic Thyroid Carcinoma (ATC) is one of the most aggressive human malignancies with a mean survival of only 6 months. The poor prognosis of patients with ATC reflects the current lack of curative therapeutic options and the need for development of novel therapeutic strategies. In this study, we report the results of a preclinical study of AEE788, a dual inhibitor of Epidermal Growth Factor Receptor (EGFR) and Vascular Endothelial Growth Factor Receptor (VEGFR) tyrosine kinases, against ATC. AEE788 was able to inhibit the proliferation and induce apoptosis of ATC cell lines in vitro. Administration of AEE788, alone and in combination with paclitaxel, to athymic nude mice bearing s.c. ATC xenografts inhibited the growth of ATC xenografts by 44% and 69%, respectively, compared with the control group. Furthermore, tumors from mice treated with AEE788, alone and in combination with paclitaxel, showed increase in apoptosis of tumor cells by ∼6- and 8-fold, respectively, compared with the control group. The microvessel density within the ATC xenografts was decreased by >80% in the mice treated with AEE788 alone and in combination with paclitaxel compared with the control group. Lastly, immunofluorescence microscopy showed the inhibition of EGFR autophosphorylation on the tumor cells as well as the inhibition of VEGFR-2 autophosphorylation on tumor endothelium. Considering the fact that curative options seldom exist for patients with ATC, concurrent inhibition of EGFR and VEGFR tyrosine kinases seems to be a valid and promising anticancer strategy for these patients

Combination Treatment with MEK and AKT Inhibitors Is More Effective than Each Drug Alone in Human Non-Small Cell Lung Cancer In Vitro and In Vivo

AZD6244 and MK2206 are targeted small-molecule drugs that inhibit MEK and AKT respectively. The efficacy of this combination in lung cancer is unknown. Our previous work showed the importance of activated AKT in mediating resistance of non-small cell lung cancer (NSCLC) to AZD6244. Thus we hypothesized that dual inhibition of both downstream MEK and AKT pathways would induce synergistic antitumor activity. In this study, we evaluated the efficacy of AZD6244 and MK2206 individually on a large panel of lung cancer cell lines. Then, we treated 28 human lung cancer cell lines with a combination of AZD6244 and MK2206 at clinically applicable drug molar ratios. The AZD6244-MK2206 combination therapy resulted in a synergistic effect on inhibition of lung cancer cell growth compared to the results of single drug treatment alone. MK2206 enhanced AZD6244-induced Bim overexpression and apoptosis in A549 and H157 cells. When we tested the combination of AZD6244 and MK2206 at ratios of 8∶1, 4∶1, 2∶1, and 1∶8, we found that the synergistic effect of the combination therapy was ratio-dependent. At ratios of 8∶1, 4∶1, and 2∶1, the drug combination consistently demonstrated synergy, whereas decreasing the ratio to 1∶8 resulted in a loss of synergy and produced an additive or antagonistic effect in most cell lines. Furthermore, the AZD6244-MK2206 combination therapy showed synergy in the suppression of A549 and H157 xenograft tumor growth and increased mean animal survival time. The AZD6244-MK2206 combination therapy resulted in effective inhibition of both p-ERK and p-AKT expression in tumor tissue. In addition, a significant increase of apoptosis was detected in tumor tissue from mice treated with AZD6244-MK2206 compared with that from the single agent treated mice. Our study suggests that the combination of AZD6244 and MK2206 has a significant synergistic effect on tumor growth in vitro and in vivo and leads to increased survival rates in mice bearing highly aggressive human lung tumors

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Use of Bayesian Net Benefit Regression Model to Examine the Impact of Generic Drug Entry on the Cost Effectiveness of Selective Serotonin Reuptake Inhibitors in Elderly Depressed Patients

Introduction: Since their invention in the late 1980s and early 1990s, selective serotonin reuptake inhibitors (SSRIs) have become the primary form of pharmaceutical treatment for depression. As the patents of several top-selling SSRIs have expired or are soon to be expired, the SSRI market is expected to witness an increasing share of generic SSRIs. We explored the impact of generic drug entry on the cost effectiveness of SSRIs. Method: Using Medicare MarketScan claims data, we compared the cost effectiveness of sertraline, citalopram, escitalopram and fluoxetine with paroxetine in elderly depressed patients, before and after the entry of generic paroxetine. We followed users of SSRIs for 6 months, starting from the date of their first prescription of an SSRI. For each patient, we measured costs (Ci) as total medical costs and quantified effectiveness (Ei) as the avoidance of treatment failure, which was defined as having a break exceeding 45 days in the use of antidepressants. We then calculated individual net benefit as lambda_times_Ei - Ci and employed both net benefit and Bayesian net benefit regression models to examine the impact of generic paroxetine on the cost effectiveness of the other four SSRIs compared with paroxetine, while controlling for patients' sociodemographic characteristics, co-morbidities and patterns of medication switch. Results: Deterministic analysis showed that paroxetine was dominated by most SSRIs prior to the availability of generic paroxetine, and that, after the entry of generic paroxetine, citalopram and escitalopram were dominated by paroxetine. Net benefit regression analysis found that, at a number of lambda values ($US1000,$US5000 and $US10_000), sertraline and escitalopram were more cost effective than paroxetine in the pre-generic-entry period but not in the post-entry period, although the difference in net benefit between the two SSRIs and paroxetine was not statistically significant in both periods. The Bayesian net benefit regression analysis reached similar conclusions. At lambda =$US5000, the probability that sertraline, citalopram, escitalopram or fluoxetine was more cost effective than paroxetine was 96.7%, 77.6%, 96.3% and 97.0%, respectively, in the pre-entry period in the pooled analysis. These probabilities reduced to 36.7%, 62.7%, 33.0% and 60.1%, respectively, in the post-entry period. The probabilities became 94.1%, 71.9%, 89.1% and 92.1% in analysis using the pre-entry data as a prior to update the post-entry data rather than using the pooled data. Conclusion: Using generic drug entry as an example, our study demonstrated the importance of including the economic life cycle of pharmaceuticals in cost-effectiveness analyses. Additionally, the proposed Bayesian framework not only preserves the advantages of the net benefit regression framework, but more importantly, it introduces the possibility of conducting probabilistic cost-effectiveness analyses with claims data.Bayesian-analysis, Citalopram, Cost-effectiveness, Depression, Elderly, Escitalopram, Fluoxetine, Generic-substitution, Serotonin-reuptake-inhibitors, Paroxetine, Sertraline

Use of Bayesian Net Benefit Regression Model to Examine the Impact of Generic Drug Entry on the Cost Effectiveness of Selective Serotonin Reuptake Inhibitors in Elderly Depressed Patients

Introduction: Since their invention in the late 1980s and early 1990s, selective serotonin reuptake inhibitors (SSRIs) have become the primary form of pharmaceutical treatment for depression. As the patents of several top-selling SSRIs have expired or are soon to be expired, the SSRI market is expected to witness an increasing share of generic SSRIs. We explored the impact of generic drug entry on the cost effectiveness of SSRIs. Method: Using Medicare MarketScan claims data, we compared the cost effectiveness of sertraline, citalopram, escitalopram and fluoxetine with paroxetine in elderly depressed patients, before and after the entry of generic paroxetine. We followed users of SSRIs for 6 months, starting from the date of their first prescription of an SSRI. For each patient, we measured costs (Ci) as total medical costs and quantified effectiveness (Ei) as the avoidance of treatment failure, which was defined as having a break exceeding 45 days in the use of antidepressants. We then calculated individual net benefit as lambda_times_Ei - Ci and employed both net benefit and Bayesian net benefit regression models to examine the impact of generic paroxetine on the cost effectiveness of the other four SSRIs compared with paroxetine, while controlling for patients' sociodemographic characteristics, co-morbidities and patterns of medication switch. Results: Deterministic analysis showed that paroxetine was dominated by most SSRIs prior to the availability of generic paroxetine, and that, after the entry of generic paroxetine, citalopram and escitalopram were dominated by paroxetine. Net benefit regression analysis found that, at a number of lambda values ($US1000,$US5000 and $US10_000), sertraline and escitalopram were more cost effective than paroxetine in the pre-generic-entry period but not in the post-entry period, although the difference in net benefit between the two SSRIs and paroxetine was not statistically significant in both periods. The Bayesian net benefit regression analysis reached similar conclusions. At lambda =$US5000, the probability that sertraline, citalopram, escitalopram or fluoxetine was more cost effective than paroxetine was 96.7%, 77.6%, 96.3% and 97.0%, respectively, in the pre-entry period in the pooled analysis. These probabilities reduced to 36.7%, 62.7%, 33.0% and 60.1%, respectively, in the post-entry period. The probabilities became 94.1%, 71.9%, 89.1% and 92.1% in analysis using the pre-entry data as a prior to update the post-entry data rather than using the pooled data. Conclusion: Using generic drug entry as an example, our study demonstrated the importance of including the economic life cycle of pharmaceuticals in cost-effectiveness analyses. Additionally, the proposed Bayesian framework not only preserves the advantages of the net benefit regression framework, but more importantly, it introduces the possibility of conducting probabilistic cost-effectiveness analyses with claims data.Bayesian-analysis, Citalopram, Cost-effectiveness, Depression, Elderly, Escitalopram, Fluoxetine, Generic-substitution, Serotonin-reuptake-inhibitors, Paroxetine, Sertraline