Introduction to Pharmaceutical Thermal Analysis: a Teaching Tool

Significant Thermal Analysis-physical chemical data needs to be acquired by the new analyst whether an entry level chemist or a new function for the experienced pharmaceutical scientist. This teaching tool describes the introductory use of Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA) and Thermomechanical Analysis (TMA) for characterizing pharmaceuticals. Optimum Experimental conditions for DSC, TGA and TMA will focus on collecting the best results and interpretations. Does the sample contain volatiles? Evaporation creates endothermic peaks, 2 water or solvent can lower the glass transition temperature (Tg) by up to 100°C and affect the crystallization temperature on cooling. The decomposition temperature can be determined by DSC and TGA. Decomposition, not volatilization, can result in 5 weight loss and render no meaningful DSC data. The upper DSC temperature for practical use is based on the decomposition temperature. Identical materials can look totally different based on their storage temperature and time, cooling rate from a temperature above the Tg or above the melting temperature (Tm). TMA determines the dimensional change of a sample with respect to temperature. The heating rate, an essential feature of DSC, TGA and TMA can cause multiple variations in transitions. Thermal history of chemicals can affect the ultimate thermal analysis results. TGA can provide information about bound and unbound (free) water due to evaporation, desorption and dehydration. Calibration of DSC and TGA are vital in establishing the precision and accuracy of these unique methods: You must learn and follow the standard protocol ASTM E968 for the heat of fusion and heat capacity as well as ASTM E967 for the determining the transition or phase temperatures of pharmaceuticals. DSC can determine the Tm, crystallization temperature Tc, Tg and the their heats of transition, e.g., fusion and crystallization.A statistical optimum method was developed based on a great deal of supportive data was collect

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

Introduction to Pharmaceutical Thermal Analysis: A Teaching Tool

Significant Thermal Analysis-physical chemical data needs to be acquired by the new analyst whether an entry level chemist or a new function for the experienced pharmaceutical scientist. This teaching tool describes the introductory use of Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA) and Thermomechanical Analysis (TMA) for characterizing pharmaceuticals. Optimum Experimental conditions for DSC, TGA and TMA will focus on collecting the best results and interpretations. Does the sample contain volatiles? Evaporation creates endothermic peaks, 2 water or solvent can lower the glass transition temperature (Tg) by up to 100°C and affect the crystallization temperature on cooling. The decomposition temperature can be determined by DSC and TGA. Decomposition, not volatilization, can result in 5 weight loss and render no meaningful DSC data. The upper DSC temperature for practical use is based on the decomposition temperature. Identical materials can look totally different based on their storage temperature and time, cooling rate from a temperature above the Tg or above the melting temperature (Tm). TMA determines the dimensional change of a sample with respect to temperature. The heating rate, an essential feature of DSC, TGA and TMA can cause multiple variations in transitions. Thermal history of chemicals can affect the ultimate thermal analysis results. TGA can provide information about bound and unbound (free) water due to evaporation, desorption and dehydration. Calibration of DSC and TGA are vital in establishing the precision and accuracy of these unique methods: You must learn and follow the standard protocol ASTM E968 for the heat of fusion and heat capacity as well as ASTM E967 for the determining the transition or phase temperatures of pharmaceuticals. DSC can determine the Tm, crystallization temperature Tc, Tg and the their heats of transition, e.g., fusion and crystallization.A statistical optimum method was developed based on a great deal of supportive data was collect

Not Available

Not AvailableIn the manufacture of tablets, tableting processes, design of reliable solids handling equipment such as hoppers, silos, and storage bins the flowability of powders and other flow property data are most important characteristics. In the case of powder discharge from silos or hoppers, arches and ratholes may be formed, especially in the presence of humid air, resulting in poor flow of the powder. Keeping in this view flow properties of gum arabic powder was evaluated by powder flow tester in which gum arabic powder with particle size 700 micron to 300 micron was tested. It was found that gum arabic powder follow two type of flow behaviour first easy flowing between 0.6 to 3 kPa major principal consolidating stress, σ1 and 0.07 to 0.30 kPa unconfined faliure strength, σc then free flowing between 3 to 9 kPa major principal consolidating stress, σ1 and 0.30 to 0.80 kPa unconfined faliure strength, σc. The critical arching values for gum arabic were density 717.3 kg/m3, effective angle of internal friction 38oC, effective lengh 0.011m at stress level 0.038 kPa and critical rathole values were density 797 kg/m3, effective angle of internal friction 36.5oC, diameter 0.29 m at stress level 0.80 kPa. All these properties can be used in handling and processing equipments.Science & Technology Society for Integrated Rural Improvemen

Solid-state Mechanical Properties of Crystalline Drugs and Excipients

Thermal mechanical analysis (TMA) of crystalline drugs and excipients in their pre-melt temperature range performed in this study corroborate their newly found linear dielectric conductivity properties with temperature. TMA of crystalline active pharmacy ingredients (APIs) or excipients shows softening at 30–100 °C below the calorimetric melting phase transition, which is also observed by dielectric analysis (DEA). Acetophenetidin melts at 135 °C as measured calorimetrically by DSC, but softens under a low mechanical stress at 95 °C. At this pre-melting temperature, the crystals collapse under the applied load, and the TMA probe shows rapid displacement. The mechanical properties yield a softening structure and cause a dimensionally slow disintegration resulting in a sharp dimensional change at the melting point. In order to incorporate these findings into a structure–property relationship, several United States Pharmacopeia (USP) melting-point standard drugs were evaluated by TMA, DSC, and DEA, and compared to the USP standard melt temperatures. The USP standard melt temperature for vanillin (80 °C) [1], acetophenetidin (135 °C) [2], and caffeine (235 °C) [3] are easily verified calorimetrically via DSC. The combined thermal analysis techniques allow for a wide variety of the newly discovered physical properties of drugs and excipients

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

BackgroundRegular, 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.MethodsThe 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.FindingsThe 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.InterpretationLong-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