Identification of chalcone derivatives as putative non-steroidal aromatase inhibitors potentially useful against breast cancer by molecular docking and ADME prediction

283-293Aromatase is an influential target to overcome estrogen receptor positive breast cancer, as the enzyme is responsible for conversion of androstenedione to estrone, a promising drug target for therapeutic management of breast cancer. Chalcones are prominent biosynthetic compounds and parent candidate for the synthesis of heterocycles with diversified biological activities. The prime objective of the present study is to evaluate the binding interaction of 2-hydroxyphenyl- prop-2-en-1-one (1A-1X), 2-hydroxy-4-methoxyphenyl- prop-2-en-1-one (3A-3X), 2,4-dihydroxyphenyl- prop-2-en-1-one (9A-9X) and 1-hydroxynaphthalen-2-yl-prop-2-en-1-one (5A-5X) derivatives with aromatase enzyme by molecular docking study and also check their ADME properties by maestro suit. The designed chalcones derivatives have been docked against our target protein with PDB id 3S7S retrieved from the protein data bank, whereas exemestane has been taken as the positive control. As docking data revealed that docking score of 1K, 1U, 1B 3K 3N, 5K, 5U, 9S, 9K, 9N and 9F compounds found less than exemestane and all of these compounds with appropriate ADME properties have proven their excellent absorption as well as solubility characteristics. The present findings provided valuable information about binding interactions of chalcones derivatives to the active site of aromatase. These compounds may serve as potential lead compound for developing new aromatase inhibitors in breast cancer treatment

Identification of chalcone derivatives as putative non-steroidal aromatase inhibitors potentially useful against breast cancer by molecular docking and ADME prediction

Aromatase is an influential target to overcome estrogen receptor positive breast cancer, as the enzyme is responsible for conversion of androstenedione to estrone, a promising drug target for therapeutic management of breast cancer. Chalcones are prominent biosynthetic compounds and parent candidate for the synthesis of heterocycles with diversified biological activities. The prime objective of the present study is to evaluate the binding interaction of 2-hydroxyphenyl- prop-2-en-1-one (1A-1X), 2-hydroxy-4-methoxyphenyl- prop-2-en-1-one (3A-3X), 2,4-dihydroxyphenyl- prop-2-en-1-one (9A-9X) and 1-hydroxynaphthalen-2-yl-prop-2-en-1-one (5A-5X) derivatives with aromatase enzyme by molecular docking study and also check their ADME properties by maestro suit. The designed chalcones derivatives have been docked against our target protein with PDB id 3S7S retrieved from the protein data bank, whereas exemestane has been taken as the positive control. As docking data revealed that docking score of 1K, 1U, 1B 3K 3N, 5K, 5U, 9S, 9K, 9N and 9F compounds found less than exemestane and all of these compounds with appropriate ADME properties have proven their excellent absorption as well as solubility characteristics. The present findings provided valuable information about binding interactions of chalcones derivatives to the active site of aromatase. These compounds may serve as potential lead compound for developing new aromatase inhibitors in breast cancer treatment

Molecular modeling and ADMET predictions of flavonoids as prospective aromatase inhibitors

192-200With the advent of a myriad of treatment possibilities for breast cancer, enzyme inhibition turns out to be the prevailing strategy for inhibiting estrogen biosynthesis. Aromatization of ring A of androstenedione, testosterone and 16-hydroxytestosterone results in increased estrogen level, which embraces the risk for breast cancer. In this present research, we have targeted human placental aromatase complexed with HDDG046 (PDB ID: 4GL7) for its inhibition by several inhibitors of flavonoid derivatives and further screening those molecules for ADMET properties for assessing its credibility for acceptance in successive steps of drug discovery. Novel flavonoid derivative molecules have been designed using Maestro 10.4, based on the literature review. Further, their molecular modeling studies have been performed against the imported target PDB ID: 4GL7 using the GLIDE platform and have been subjected to ADMET assessment using the QikProp and pkCSM program. From all the series exposed to molecular modeling; 2K, 4K, 6K, 8W and 10K molecules have been subjected to ADMET study based on their interaction profile. Successively screening of these molecules led to selection of 8W molecule for further validation by pkCSM. The results obtained have been compared with the reported molecule HDDG046 which presents substantially positive outcomes for 8W in terms of CaCo2 permeability, water solubility, P- glycoprotein; hERG I, II and CYP interactions, hepatotoxicity, LD50 value and so forth. Juxtaposing the results of all the designed molecules under study, we have established that these prospective molecules especially 8W of flavonoid derivatives have the potency to inhibit the target under study, which can be useful in the treatment of breast cancer. This has been estimated based on the in silico approaches performed using Molecular Modeling which utilizes the integral function of Molecular Mechanics and Quantum Mechanics. In addition, the ADMET predictions validate their integrity for being the lead molecules in drug discovery stages in the near future

Subtype-Specific and Co-Occurring Genetic Alterations in B-cell Non-Hodgkin Lymphoma

B-cell non-Hodgkin lymphoma (B-NHL) encompasses multiple clinically and phenotypically distinct subtypes of malignancy with unique molecular etiologies. Common subtypes of B-NHL, such as diffuse large B-cell lymphoma, have been comprehensively interrogated at the genomic level, but rarer subtypes, such as mantle cell lymphoma, remain less extensively characterized. Furthermore, multiple B-NHL subtypes have thus far not been comprehensively compared using the same methodology to identify conserved or subtype-specific patterns of genomic alterations. Here, we employed a large targeted hybrid-capture sequencing approach encompassing 380 genes to interrogate the genomic landscapes of 685 B-NHL tumors at high depth, including diffuse large B-cell lymphoma, mantle cell lymphoma, follicular lymphoma, and Burkitt lymphoma. We identified conserved hallmarks of B-NHL that were deregulated in the majority of tumors from each subtype, including frequent genetic deregulation of the ubiquitin proteasome system. In addition, we identified subtype-specific patterns of genetic alterations, including clusters of co-occurring mutations and DNA copy number alterations. The cumulative burden of mutations within a single cluster were more discriminatory of B-NHL subtypes than individual mutations, implicating likely patterns of genetic cooperation that contribute to disease etiology. We therefore provide the first cross-sectional analysis of mutations and DNA copy number alterations across major B-NHL subtypes and a framework of co-occurring genetic alterations that deregulate genetic hallmarks and likely cooperate in lymphomagenesis

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

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