Single Use bioreactors: Geometry does matter

The first generation of single use bioreactors (SUBs) departed from conventional stirred tank bioreactor (STR) geometry in terms of impeller number, and orientation and sparger hole diameter. Moreover, one marked feature of SUB bioreactors was that they could be operated at lower volumes than conventional STRs, bringing considerable operational flexibility. This practice, however, further negates the principle of geometric similarity. Whilst some processes may be able to remain within a SUB for the whole product life cycle, many products will require scale up to larger-scale vessels as demand for the product increases. This poster considers the implications of changing reactor geometry on scale up of mammalian cell culture processes using multivariate data analysis to compare different geometries and different fill volumes. This approach uncovered a surprising result when working at half volume, which may not have been spotted using conventional data analysis methods. The first generation of SUBs challenged two of the industry’s key principles of scale up: geometric similarity and maintenance of KLa. There is now a wider variety of SUBs on the market, including vessels that display a higher degree of geometrical similarity to conventional STR geometry. As a result a study was performed to evaluate similarity of process performance between systems with different geometries in order to support Lonza’s expansion of single use upstream capacity. In this study we have compared performance of two SUB systems; one with a conventional STR geometry (SUB 1) and one with a non‑conventional geometry (SUB 2). Mass transfer studies performed with both systems using the gassing-out approach demonstrated that empirical models built to describe KLa performance in Lonza’s conventional STRs (10 to 20,000 L) were better able to predict KLa’s in SUB 1 than in SUB 2, as would be expected given the geometries. Cell culture evaluations were performed with a model cell line in both SUB systems. Multivariate analysis of the data showed that the behavior of the cultures performed in the SUB 1 was closer to behavior of cultures performed in Lonza’s conventional scale-down model than those performed in SUB 2. However, Hoteling’s T2 and Q residuals analysis suggested that difference in behavior in SUB 2 was not extreme. The impact of operating SUB 1 at half volume was investigated for two different vessel volumes. Multivariate data analysis showed that there was considerable difference in behavior of the cultures performed at half volume when compared to cultures performed in the conventional scale-down model. At several time points towards the end of the cultures, Q residual values were outside the 95% confidence interval, indicating significantly different culture behavior. Furthermore, the analysis indicated that there was also a difference in behavior of the half-volume cultures in different size vessels. This indicated a lack of scalability between half-volume cultures performed in different scale vessels of SUB 1, which was not apparent when the same vessels were run at full volume. It was concluded that SUB geometry does matter when scaling processes up and should be a key consideration in a quality by design approach to minimizing differences in culture behavior during cell culture process scale up. Moreover, multivariate data analysis can provide useful supplemental insight in bioreactor process performance comparison

Scale-up in the single use age: Does geometry matter?

Singe use bioreactors (SUBs) are becoming standard work horses in the biopharmaceutical industry. These SUBs are supplied by vendors as off the shelf designs limiting the cell culture engineer’s ability to match the geometry of the SUB to the geometry of their existing stirred tank reactor (STR) capacity. The first generation of SUBs departed from conventional stirred tank bioreactor (STR) geometry in terms of impeller number, and orientation and sparger hole diameter. Moreover, one marked feature of SUB bioreactors was that they could be operated at lower volumes than conventional STRs, bringing considerable operational flexibility. This practice, however, further negated the principle of geometric similarity. This presentation considers the implications of changing reactor geometry on scale up of mammalian cell culture processes using multivariate data analysis to compare different geometries and different fill volumes. This approach uncovered a surprising result when working at half volume, which may not have been spotted using conventional data analysis methods. The first generation of SUBs challenged two of the industry’s key principles of scale up: geometric similarity and maintenance of KLa. As an early adopter of SUBs Lonza had to overcome these challenges. This was done by following an approach advocated by the SUB manufactures which departs from a conventional scale up strategy. Conditions were found empirically that matched the oxygen mass transfer in a conventional STR as closely as possible. There is now however a wider variety of SUBs on the market, including vessels that display a higher degree of geometrical similarity to conventional STR geometry. As a result a study was performed to evaluate similarity of process performance between systems with different geometries in order to support Lonza’s expansion of single use upstream capacity. In this study we have compared performance of two SUB systems; one with a conventional STR geometry (SUB 1) and one with a non‑conventional geometry (SUB 2). Mass transfer studies were performed with both systems using the gassing-out approach. Results demonstrated that empirical models built to describe KLa performance in Lonza’s conventional STRs (10 to 20,000 L) were better able to predict KLa’s in SUB 1 than in SUB 2, as would be expected given the geometries. Cell culture evaluations were performed with a model cell line in both SUB systems. Multivariate analysis of the data showed that the behavior of the cultures performed in the SUB 1 was closer to behavior of cultures performed in Lonza’s conventional scale-down model than those performed in SUB 2. However, Hoteling’s T2 and Q residuals analysis suggested that difference in behavior in SUB 2 was not extreme. The impact of operating SUB 1 at half volume was investigated for two different vessel volumes. Multivariate data analysis showed that there was considerable difference in behavior of the cultures performed at half volume when compared to cultures performed in the conventional scale-down model. At several time points towards the end of the cultures, Q residual values were outside the 95% confidence interval, indicating significantly different culture behavior. Furthermore, the analysis indicated that there was also a difference in behavior of the half-volume cultures in different size vessels. This indicated a lack of scalability between half-volume cultures performed in different scale vessels of SUB 1, which was not apparent when the same vessels were run at full volume. It was concluded that SUB geometry does matter when scaling processes up and should be a key consideration in a quality by design approach to minimizing differences in culture behavior during cell culture process scale up. Moreover, multivariate data analysis can provide useful supplemental insight in bioreactor process performance comparisons

The challenges of performing high density perfusion processes in single use bioreactors

Single use technologies (SUT) have become embedded in the biopharmaceutical industry over the last 20 years. An important recent trend in the industry is a pivot towards continuous processing. SUT are being deployed for continuous perfusion cultures where expected viable cell concentrations are higher than 1 x 108 /mL. Although SUT provides many advantages, there are also some challenges associated with its application to intensified perfusion processes. In particular, the single use bioreactor has to provide enough oxygen to the culture to maintain aerobic respiration. The main challenges of adapting single use systems for perfusion culture are limited power dissipation (P/V) and limited gas flow rates. These problems arise because plastic is a weak material of construction limiting torque on the simpler shaft and back pressure on the gas outlet filters. These problems are compounded at larger scales due to unfavourable cross-sectional area to volume ratios. Lonza have characterised a number of single use bioreactor systems for their ability to achieve sufficient mass transfer to support perfusion culture. In this poster we will present results from one such system. Oxygen mass transfer was characterised for different sparger and impeller configurations over a range of power dissipations and superficial gas velocities at various levels of oxygen enrichment. A DoE approach was used to characterise the design space for oxygen mass transfer in two different bioreactor scales. Response surface models were used to quantify the contributions of different factors on the overall oxygen mass transfer. Whilst the response of oxygen mass transfer was broadly in line with expectations certain interesting observations were made. For example, an interaction between impeller type and power dissipation was observed. This caused us to look more closely at the impact of impeller design on the distribution of the gas phase. The experiments we have carried out so far enabled us to design a new approach to manipulate the oxygen transfer rate via multiple spargers. It was concluded that the single use bioreactor system under evaluation was capable of supporting between 0.4 and 1 x 108 viable cells/mL depending on the cell specific oxygen consumption rate

Challenges of mass transfer for perfusion cultures in single use bioreactors part 1:Oxygen

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Controlling pCO2 in high density perfusion cultures

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May Measurement Month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension

Aims Raised blood pressure (BP) is the biggest contributor to mortality and disease burden worldwide and fewer than half of those with hypertension are aware of it. May Measurement Month (MMM) is a global campaign set up in 2017, to raise awareness of high BP and as a pragmatic solution to a lack of formal screening worldwide. The 2018 campaign was expanded, aiming to include more participants and countries. Methods and results Eighty-nine countries participated in MMM 2018. Volunteers (≥18 years) were recruited through opportunistic sampling at a variety of screening sites. Each participant had three BP measurements and completed a questionnaire on demographic, lifestyle, and environmental factors. Hypertension was defined as a systolic BP ≥140 mmHg or diastolic BP ≥90 mmHg, or taking antihypertensive medication. In total, 74.9% of screenees provided three BP readings. Multiple imputation using chained equations was used to impute missing readings. 1 504 963 individuals (mean age 45.3 years; 52.4% female) were screened. After multiple imputation, 502 079 (33.4%) individuals had hypertension, of whom 59.5% were aware of their diagnosis and 55.3% were taking antihypertensive medication. Of those on medication, 60.0% were controlled and of all hypertensives, 33.2% were controlled. We detected 224 285 individuals with untreated hypertension and 111 214 individuals with inadequately treated (systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg) hypertension. Conclusion May Measurement Month expanded significantly compared with 2017, including more participants in more countries. The campaign identified over 335 000 adults with untreated or inadequately treated hypertension. In the absence of systematic screening programmes, MMM was effective at raising awareness at least among these individuals at risk

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

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