Supplementary Data from Glycogen Synthase Kinase-3 Inhibition Sensitizes Pancreatic Cancer Cells to Chemotherapy by Abrogating the TopBP1/ATR-Mediated DNA Damage Response

Supplemental Figure S3 9-ING-41 in combination with either gemcitabine or liposomal-formulated irinotecan (IRT-LP) enhances survival of orthotopically implanted PDAC tumors. (A) Schematic representation of experimental design. Once tumors were palpable, mice were randomly divided into 4 groups with one mouse in each group. Mice were then treated 2 times a week for four weeks by i.p. injection with either vehicle, gemcitabine (10 mg/kg), IRT-LP (15 mg/kg), 9-ING-41 (40 mg/kg), both gemcitabine (10 mg/kg) and 9-ING-41 (40 mg/kg), or IRT-LP (15 mg/kg) and 9-ING-41 (40 mg/kg). Following the last treatment, animals were monitored for survival and euthanized when IACUC endpoints were met. (B) Swimmer plots depicting days of survival following final treatment. (*) Denotes that vehicle-treated 6741 met IACUC endpoint criteria and had to be euthanized following the last day of treatment.</p

Supplementary Data from Glycogen Synthase Kinase-3 Inhibition Sensitizes Pancreatic Cancer Cells to Chemotherapy by Abrogating the TopBP1/ATR-Mediated DNA Damage Response

Supplemental Table S1</p

Supplementary Data from Glycogen Synthase Kinase-3 Inhibition Sensitizes Pancreatic Cancer Cells to Chemotherapy by Abrogating the TopBP1/ATR-Mediated DNA Damage Response

Supplemental Figure S1 9-ING-41 treatment synergizes with gemcitabine to inhibit PDAC proliferation and colony formation. (A) The 5160 PDAC cell line was plated and treated with 1 μM 9-ING-41 alone or with increasing concentration of gemcitabine (nM) for 48 and 72 hours. Cell proliferation was determined by MTS assay. Data was quantified as percentage of control and expressed as mean {plus minus} SEM. n=6. *P<0.05 gemcitabine and 9-ING-41 versus gemcitabine alone. #P<0.05 gemcitabine and 9-ING-41 versus 9-ING-41 alone. n=6. CI: combination index. (B) L3.6 and 6741 PDAC cells were seeded in a 6-well plate and treated with DMSO or increasing concentration of 9-ING-41 (nM) for 48 hours. Supernatant was then removed and remaining cells were allowed to form colonies, which were enumerated and displayed graphically in Figure 1C. Shown is representative crystal violet staining from each treatment condition. (C) Clonogenic assays were carried out as described in (B) but 200 nM 9-ING-41 was added together with increasing concentration of gemcitabine. Colonies that formed were counted and displayed graphically in Figure 1D. Shown is representative crystal violet staining from each treatment condition.</p

Supplementary Data from Glycogen Synthase Kinase-3 Inhibition Sensitizes Pancreatic Cancer Cells to Chemotherapy by Abrogating the TopBP1/ATR-Mediated DNA Damage Response

Supplemental Figure S2 GSK-3 inhibition leads to cell death and sensitizes PDAC cells to gemcitabine in vitro. (A) 5160, 6741 cells were treated with DMSO, 9-ING-41 (5 µM), gemcitabine (1 µM), both 9-ING-41 (5 µM) and gemcitabine (1 µM) for 24 or 48 hours. The treated cells were collected and stained with annexin V-APC and PI to evaluate cell death. Shown are representative Flowjo analyses from each treatment condition. (B) Percentage of live cells (duel negative), early apoptotic cells (Annexin V positive/PI negative), necrotic cells (Annexin V negative/PI positive) and late apoptotic cells (duel positive) from 5160 and 6741 cells were quantified and expressed as mean {plus minus} SEM. n=3. (C) 5160 and 6741 cells were treated as indicated in supplement Figure S3A and lysates were prepared and immunoblotted with the indicated antibodies.</p

Supplementary Data from Glycogen Synthase Kinase-3 Inhibition Sensitizes Pancreatic Cancer Cells to Chemotherapy by Abrogating the TopBP1/ATR-Mediated DNA Damage Response

Supplemental Figure S4 9-ING-41 reduces the activity of GSK-3β and maintains the phosphorylation of Plk1 with the combination of gemcitabine. (A) 5160 and 6741 cells were treated with DMSO or 9-ING-41 (5 µM) for 24 hours and lysates were collected and immunoblotted with the indicated antibodies. S.E.: short exposure. L.E.: long exposure. (B) 5160, 6741 cells were treated as indicated in Figure 3B and lysates were prepared and immunoblotted with the indicated antibodies. Lysates from M phase arrested 5160 and 6741 cells induced by thymidine and nocodazole block were used as a positive control. Noc: nocodazole.</p

Supplementary Data from Glycogen Synthase Kinase-3 Inhibition Sensitizes Pancreatic Cancer Cells to Chemotherapy by Abrogating the TopBP1/ATR-Mediated DNA Damage Response

Supplemental Figure S5 GSK-3 inhibition abrogates gemcitabine-induced cell cycle arrest. (A) 5160, 6741 and L3.6 cells were treated as indicated in Figure 3B and then stained with propidium iodide (PI). DNA content was measured by flow cytometry and analyzed with Modfit software. Data presented in A is representative of three independent experiments. (B) The percentage of G0/G1, S phase and G2/M phase cells from different treatments was expressed as mean {plus minus} SEM. n=3. (C) L3.6 cells were treated as indicated in Figure 3B and then provided EdU for 1 hour prior to harvesting. EdU incorporation was detected using the EdU detection kit followed by flow cytometry. (D) EdU positive cells were gated and the normalized MFI of the EdU peak is graphically displayed as are the percentage of EdU+ cells are expressed as mean {plus minus} SEM. *P<0.05 gemcitabine versus DMSO. #P<0.05 gemcitabine and GSK-3 inhibitor versus gemcitabine alone. Data presented in C and D is representative of three independent experiments.</p

Supplementary Data from Glycogen Synthase Kinase-3 Inhibition Sensitizes Pancreatic Cancer Cells to Chemotherapy by Abrogating the TopBP1/ATR-Mediated DNA Damage Response

Supplement Figure 6 GSK-3 inhibition maintains prolonged DNA damage signaling. (A) 5160 and 6741 cells were treated as indicated in Figure 5A and lysates were prepared and immunoblotted with the indicated antibodies. (B) 5160 and 6741 cells were grown on coverslips, treated with DMSO or gemcitabine (500 nM) for 2 hours, cells were washed with PBS and supplied with or without 9-ING-41 (5 µM) containing medium for 48 hours (gemcitabine withdrawal) prior to fixation. Fixed cells were subsequently stained with anti-gamma H2Ax antibodies and detected with an Alexa 488 conjugated donkey-anti-mouse rabbit secondary (green). DNA was visualized following Hoechst staining (blue). (C) The normalized MFI of nuclear gamma H2Ax was evaluated by ImageJ and expressed as mean {plus minus} SEM. *P<0.05 gemcitabine or 9-ING-41 versus DMSO. #P<0.05 gemcitabine and 9-ING-41 versus gemcitabine alone. n=500 cells per treatment group.</p

Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

BACKGROUND: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. METHODS: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. FINDINGS: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95$7·3 trillion (95% UI 7·2-7·4) in 2019; 293·7 times the $24·8 billion (95$43·1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, $1·8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and$37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health-related COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11-21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. INTERPRETATION: There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained. FUNDING: Bill & Melinda Gates Foundation

Burden of disease scenarios by state in the USA, 2022–50: a forecasting analysis for the Global Burden of Disease Study 2021

Background: The capacity to anticipate future health issues is important for both policy makers and practitioners in the USA, as such insights can facilitate effective planning, investment, and implementation strategies. Forecasting trends in disease and injury burden is not only crucial for policy makers but also garners substantial interest from the general populace and leads to a better-informed public. Through the integration of new data sources, the refinement of methodologies, and the inclusion of additional causes, we have improved our previous forecasting efforts within the scope of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to produce forecasts at the state and national levels for the USA under various possible scenarios. Methods: We developed a comprehensive framework for forecasting life expectancy, healthy life expectancy (HALE), cause-specific mortality, and disability-adjusted life-years (DALYs) due to 359 causes of disease and injury burden from 2022 to 2050 for the USA and all 50 states and Washington, DC. Using the GBD 2021 Future Health Scenarios modelling framework, we forecasted drivers of disease, demographic drivers, risk factors, temperature and particulate matter, mortality and years of life lost (YLL), population, and non-fatal burden. In addition to a reference scenario (representing the most probable future trajectory), we explored various future scenarios and their potential impacts over the next several decades on human health. These alternative scenarios comprised four risk elimination scenarios (including safer environment, improved behavioural and metabolic risks, improved childhood nutrition and vaccination, and a combined scenario) and three USA-specific scenarios based on risk exposure or attributable burden in the best-performing US states (improved high adult BMI and high fasting plasma glucose [FPG], improved smoking, and improved drug use [encompassing opioids, cocaine, amphetamine, and others]). Findings: Life expectancy in the USA is projected to increase from 78·3 years (95% uncertainty interval 78·1–78·5) in 2022 to 79·9 years (79·5–80·2) in 2035, and to 80·4 years (79·8–81·0) in 2050 for all sexes combined. This increase is forecasted to be modest compared with that in other countries around the world, resulting in the USA declining in global rank over the 2022–50 forecasted period among the 204 countries and territories in GBD, from 49th to 66th. There is projected to be a decline in female life expectancy in West Virginia between 1990 and 2050, and little change in Arkansas and Oklahoma. Additionally, after 2023, we projected almost no change in female life expectancy in many states, notably in Oklahoma, South Dakota, Utah, Iowa, Maine, and Wisconsin. Female HALE is projected to decline between 1990 and 2050 in 20 states and to remain unchanged in three others. Drug use disorders and low back pain are projected to be the leading Level 3 causes of age-standardised DALYs in 2050. The age-standardised DALY rate due to drug use disorders is projected to increase considerably between 2022 and 2050 (19·5% [6·9–34·1]). Our combined risk elimination scenario shows that the USA could gain 3·8 additional years (3·6–4·0) of life expectancy and 4·1 additional years (3·9–4·3) of HALE in 2050 versus the reference scenario. Using our USA-specific scenarios, we forecasted that the USA could gain 0·4 additional years (0·3–0·6) of life expectancy and 0·6 additional years (0·5–0·8) of HALE in 2050 under the improved drug use scenario relative to the reference scenario. Life expectancy and HALE are likewise projected to be 0·4–0·5 years higher in 2050 under the improved adult BMI and FPG and improved smoking scenarios compared with the reference scenario. However, the increases in these scenarios would not substantially improve the USA's global ranking in 2050 (from 66th of 204 in life expectancy in the reference scenario to 63rd–64th in each of the three USA-specific scenarios), indicating that the USA's best-performing states are still lagging behind other countries in their rank throughout the forecasted period. Regardless, an estimated 12·4 million (11·3–13·5) deaths could be averted between 2022 and 2050 if the USA were to follow the combined scenario trajectory rather than the reference scenario. There would also be 1·4 million (0·7–2·2) fewer deaths over the 28-year forecasted period with improved adult BMI and FPG, 2·1 million (1·3–2·9) fewer deaths with improved exposure to smoking, and 1·2 million (0·9–1·5) fewer deaths with lower rates of drug use deaths. Interpretation: Our findings highlight the alarming trajectory of health challenges in the USA, which, if left unaddressed, could lead to a reversal of the health progress made over the past three decades for some US states and a decline in global health standing for all states. The evidence from our alternative scenarios along with other published studies suggests that through collaborative, evidence-based strategies, there are opportunities to change the trajectory of health outcomes in the USA, such as by investing in scientific innovation, health-care access, preventive health care, risk exposure reduction, and education. Our forecasts clearly show that the time to act is now, as the future of the country's health and wellbeing—as well as its prosperity and leadership position in science and innovation—are at stake. Funding: Bill & Melinda Gates Foundation.</p

Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050

BackgroundThe rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020.MethodsWe estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050.FindingsIn 2019, health spending globally reached$8·8 trillion (95% uncertainty interval [UI] 8·7–8·8) or $1132 (1119–1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total,$40·4 billion (0·5%, 95% UI 0·5–0·5) was development assistance for health provided to low-income and middle-income countries, which made up 24·6% (UI 24·0–25·1) of total spending in low-income countries. We estimate that $54·8 billion in development assistance for health was disbursed in 2020. Of this,$13·7 billion was targeted toward the COVID-19 health response. $12·3 billion was newly committed and$1·4 billion was repurposed from existing health projects. $3·1 billion (22·4$2·4 billion (17·9%) was for supply chain and logistics. Only $714·4 million (7·7$1519 (1448–1591) per person in 2050, although spending across countries is expected to remain varied.InterpretationGlobal health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all.</h4