Continuous powder feeding for pharmaceutical solid dosage form manufacture: a short review

There has been a noticeable shift from pharmaceutical batch processing towards a more continuous mode of manufacture for solid oral dosage forms. Continuous solid oral dose processes would not be possible in the absence of a highly accurate feeding system. The performance of feeders defines the content of formulations and is therefore a critical operation in continuous manufacturing of solid dosage forms. It was the purpose of this review to review the role of the initial powder feeding step in a continuous manufacturing process. Different feeding mechanisms are discussed with a particular emphasis on screw controlled loss in weight (LIW) feeding. The importance of understanding the physical properties of the raw materials and its impact on the feeding process is reviewed. Prior knowledge of materials provides an initial indication of how the powders will behave through processing and facilitates in the selection of the most suitable (i) feeder (capacity), (ii) feeding mechanism, and (iii) in the case of screw feeder - screw type. The studies identified in this review focus on the impact of material on powder feeding performance

Evaluating Established Methods for Rumen 16S rRNA Amplicon Sequencing With Mock Microbial Populations

peer-reviewedThe rumen microbiome scientific community has utilized amplicon sequencing as an aid in identifying potential community compositional trends that could be used as an estimation of various production and performance traits including methane emission, animal protein production efficiency, and ruminant health status. In order to translate rumen microbiome studies into executable application, there is a need for experimental and analytical concordance within the community. The objective of this study was to assess these factors in relation to selected currently established methods for 16S phylogenetic community analysis on a microbial community standard (MC) and a DNA standard (DS; ZymoBIOMICSTM). DNA was extracted from MC using the RBBC method commonly used for microbial DNA extraction from rumen digesta samples. 16S rRNA amplicon libraries were generated for the MC and DS using primers routinely used for rumen bacterial and archaeal community analysis. The primers targeted the V4 and V3–V4 region of the 16S rRNA gene and samples were subjected to both 20 and 28 polymerase chain reaction (PCR) cycles under identical cycle conditions. Sequencing was conducted using the Illumina MiSeq platform. As the bacteria contained in the microbial mock community were well-classified species, and for ease of explanation, we used the results of the Basic Local Alignment Search Tool classification to assess the DNA, PCR cycle number, and primer type. Sequence classification methodology was assessed independently. Spearman’s correlation analysis indicated that utilizing the repeated bead beating and column method for DNA extraction in combination with primers targeting the 16S rRNA gene using 20 first-round PCR cycles was sufficient for amplicon sequencing to generate a relatively accurate depiction of the bacterial communities present in rumen samples. These results also emphasize the requirement to develop and utilize positive mock community controls for all rumen microbiomic studies in order to discern errors which may arise at any step during a next-generation sequencing protocol

The EMT-activator ZEB1 is unrelated to platinum drug resistance in ovarian cancer but is predictive of survival

The IGROVCDDP cisplatin-resistant ovarian cancer cell line is an unusual model, as it is also cross-resistant to paclitaxel. IGROVCDDP, therefore, models the resistance phenotype of serous ovarian cancer patients who have failed frontline platinum/taxane chemotherapy. IGROVCDDP has also undergone epithelial-mesenchymal transition (EMT). We aim to determine if alterations in EMT-related genes are related to or independent from the drug-resistance phenotypes. EMT gene and protein markers, invasion, motility and morphology were investigated in IGROVCDDP and its parent drug-sensitive cell line IGROV-1. ZEB1 was investigated by qPCR, Western blotting and siRNA knockdown. ZEB1 was also investigated in publicly available ovarian cancer gene-expression datasets. IGROVCDDP cells have decreased protein levels of epithelial marker E-cadherin (6.18-fold, p = 1.58e−04) and higher levels of mesenchymal markers vimentin (2.47-fold, p = 4.43e−03), N-cadherin (4.35-fold, p = 4.76e−03) and ZEB1 (3.43-fold, p = 0.04). IGROVCDDP have a spindle-like morphology consistent with EMT. Knockdown of ZEB1 in IGROVCDDP does not lead to cisplatin sensitivity but shows a reversal of EMT-gene signalling and an increase in cell circularity. High ZEB1 gene expression (HR = 1.31, n = 2051, p = 1.31e−05) is a marker of poor overall survival in high-grade serous ovarian-cancer patients. In contrast, ZEB1 is not predictive of overall survival in high-grade serous ovarian-cancer patients known to be treated with platinum chemotherapy. The increased expression of ZEB1 in IGROVCDDP appears to be independent of the drug-resistance phenotypes. ZEB1 has the potential to be used as biomarker of overall prognosis in ovarian-cancer patients but not of platinum/taxane chemoresistance

Sequence embedding for fast construction of guide trees for multiple sequence alignment

<p>Abstract</p> <p>Background</p> <p>The most widely used multiple sequence alignment methods require sequences to be clustered as an initial step. Most sequence clustering methods require a full distance matrix to be computed between all pairs of sequences. This requires memory and time proportional to <it>N</it>²for <it>N </it>sequences. When <it>N </it>grows larger than 10,000 or so, this becomes increasingly prohibitive and can form a significant barrier to carrying out very large multiple alignments.</p> <p>Results</p> <p>In this paper, we have tested variations on a class of embedding methods that have been designed for clustering large numbers of complex objects where the individual distance calculations are expensive. These methods involve embedding the sequences in a space where the similarities within a set of sequences can be closely approximated without having to compute all pair-wise distances.</p> <p>Conclusions</p> <p>We show how this approach greatly reduces computation time and memory requirements for clustering large numbers of sequences and demonstrate the quality of the clusterings by benchmarking them as guide trees for multiple alignment. Source code is available for download from <url>http://www.clustal.org/mbed.tgz</url>.</p

Identifying novel hypoxia-associated markers of chemoresistance in ovarian cancer

BACKGROUND Ovarian cancer is associated with poor long-term survival due to late diagnosis and development of chemoresistance. Tumour hypoxia is associated with many features of tumour aggressiveness including increased cellular proliferation, inhibition of apoptosis, increased invasion and metastasis, and chemoresistance, mostly mediated through hypoxia-inducible factor (HIF)-1α. While HIF-1α has been associated with platinum resistance in a variety of cancers, including ovarian, relatively little is known about the importance of the duration of hypoxia. Similarly, the gene pathways activated in ovarian cancer which cause chemoresistance as a result of hypoxia are poorly understood. This study aimed to firstly investigate the effect of hypoxia duration on resistance to cisplatin in an ovarian cancer chemoresistance cell line model and to identify genes whose expression was associated with hypoxia-induced chemoresistance. METHODS Cisplatin-sensitive (A2780) and cisplatin-resistant (A2780cis) ovarian cancer cell lines were exposed to various combinations of hypoxia and/or chemotherapeutic drugs as part of a 'hypoxia matrix' designed to cover clinically relevant scenarios in terms of tumour hypoxia. Response to cisplatin was measured by the MTT assay. RNA was extracted from cells treated as part of the hypoxia matrix and interrogated on Affymetrix Human Gene ST 1.0 arrays. Differential gene expression analysis was performed for cells exposed to hypoxia and/or cisplatin. From this, four potential markers of chemoresistance were selected for evaluation in a cohort of ovarian tumour samples by RT-PCR. RESULTS Hypoxia increased resistance to cisplatin in A2780 and A2780cis cells. A plethora of genes were differentially expressed in cells exposed to hypoxia and cisplatin which could be associated with chemoresistance. In ovarian tumour samples, we found trends for upregulation of ANGPTL4 in partial responders and down-regulation in non-responders compared with responders to chemotherapy; down-regulation of HER3 in partial and non-responders compared to responders; and down-regulation of HIF-1α in non-responders compared with responders. CONCLUSION This study has further characterized the relationship between hypoxia and chemoresistance in an ovarian cancer model. We have also identified many potential biomarkers of hypoxia and platinum resistance and provided an initial validation of a subset of these markers in ovarian cancer tissues

Coevolution with bacteriophages drives genome-wide host evolution and constrains the acquisition of abiotic-beneficial mutations

This is the author accepted manuscript. The final version is available from OUP via the DOI in this record.Studies of antagonistic coevolution between hosts and parasites typically focus on resistance and infectivity traits. However, coevolution could also have genome-wide effects on the hosts due to pleiotropy, epistasis, or selection for evolvability. Here, we investigate these effects in the bacterium Pseudomonas fluorescens SBW25 during approximately 400 generations of evolution in the presence or absence of bacteriophage (coevolution or evolution treatments, respectively). Coevolution resulted in variable phage resistance, lower competitive fitness in the absence of phages, and greater genome-wide divergence both from the ancestor and between replicates, in part due to the evolution of increased mutation rates. Hosts from coevolution and evolution treatments had different suites of mutations. A high proportion of mutations observed in coevolved hosts were associated with a known phage target binding site, the lipopolysaccharide (LPS), and correlated with altered LPS length and phage resistance. Mutations in evolved bacteria were correlated with higher fitness in the absence of phages. However, the benefits of these growth-promoting mutations were completely lost when these bacteria were subsequently coevolved with phages, indicating that they were not beneficial in the presence of resistance mutations (consistent with negative epistasis). Our results show that in addition to affecting genome-wide evolution in loci not obviously linked to parasite resistance, coevolution can also constrain the acquisition of mutations beneficial for growth in the abiotic environment.This work was funded by European Research Council and NERC (UK)

Trapped on the seashore, seaborne evacuation, impact of exposure to PM2.5: demonstration of the urbanEXODUS evacuation model

The 2021 wildfire season affected large communities in over ten countries around the Mediterranean basin consuming an area almost double the area burnt by wildfires over the past twelve years. In many cases, people were exposed to hazardous combustion products that caused mass multimodal evacuations, including pedestrian, vehicle, and seaborne evacuations as well as a large number of fatalities. Evacuation modelling can be used to better understand the processes involved, including the interactions between those processes. Such a model is urbanEXODUS, utilised during the final exercise (FSX3) for the European Commission’s Horizon 2020 project IN-PREP. The tool was used as part of a training platform for incident managers in collaborative response to large scale disasters. The scenario deployed during the FSX3, and presented in this work, involved a traffic accident and cascading effects that start a wildfire at a forested area, initiating a multi-modal evacuation of the local community. The model, able to simulate multi-modal evacuations, includes pedestrian and vehicle evacuation, and through the development of a flow model, a simplistic representation of boat evacuation. The model is also able to determine the effect of wildfire products using two different datasets that include (a) wildfire perimeter data and (b) smoke plume data that include PM2.5 concentration levels. The former limits the escape routes, causing engulfment and fatalities. The latter, through the development of a novel fractional dose model, determines the acute exposure of agents to PM2.5 in relation to the World Health Organisation (WHO) daily mean Air Quality Guidelines (AQG). The model demonstrates key evacuation performance results, including evacuation times, escape route usage and number and locations of fatalities. The results indicate that 6% of the entire population were unable to leave the area and are considered as fatalities. With regard to the evacuees, 69% utilised the road network to leave the area, while 31% utilised the seaborne evacuation. Exposure to PM2.5 was zero for 84% of the evacuees, while for 1% it was less than the AQG. However, 15% of the agents received a dosage of PM2.5 on average of 7.6 times the AQG (range 1.0 – 28.3, SD = 5.8). This level of exposure is expected to cause health problems including respiratory, cardiovascular and cerebrovascular disorders. The model offers detailed evacuation information that is practically impossible to obtain otherwise, allowing crisis managers to make risk-informed decisions when planning for a crisis

Trapped on the seashore, seaborne evacuation, and impact of exposure to PM2.5: Live demonstration of the urbanEXODUS large-scale evacuation model

Wildfires can trigger large-scale pedestrian, vehicle and seaborne evacuations, and cause injuries and fatalities. Evacuation models are employed to better understand the involved processes and their interactions. During the final exercise of the European Commission’s H2020 IN-PREP project, urbanEXODUS was used within a training platform, by incident managers, to aid their response to a simulated disaster. The scenario involved a traffic accident escalating to a wildfire, causing the local community to evacuate. The model combined pedestrian and vehicle evacuation, and through a flow model, a simplistic representation of boat evacuation. The effects of wildfire on escape routes and possible fatalities were evaluated using fire perimeter data. The development of a novel fractional dose model allowed the software to determine agents’ acute exposure to PM2.5, in relation to the WHO daily mean Air Quality Guidelines (AQG). The simulation results comprise key evacuation performance parameters including evacuation times, fatalities, and escape route usage. Results indicate that 6% of the population was unable to leave the area and are treated as fatalities. The road network and boats were used by 69% and 31% of the evacuees respectively. PM2.5 exposure was zero for 84% of the evacuees, and below the AQG, for 1%, while 15% received, on average, a dosage of 7.6 times the AQG (range 1.0 – 28.3, SD = 5.8), which may cause respiratory and cardiovascular disorders. The model offers detailed evacuation information that is practically impossible to obtain otherwise, allowing crisis managers to take risk-informed decisions when planning for a crisis

The development of pedestrian gap acceptance and midblock pedestrian road crossing behavior utilizing SUMO

While there are several published studies for modelling pedestrian behavior at signalized crossings in SUMO, the behavior of pedestrians crossing a road at a location other than a designated crossing, has not been considered to date. This work looks at how to represent pedestrian agents selecting to cross a road at arbitrary locations along the length of the road. The pedestrian agents utilize a gap acceptance model that represents how a pedestrian decides when to cross a road, based on the frequency and speed of approaching vehicles, while considering the spacing between them. Furthermore, the gap acceptance model allows the pedestrians to choose to cross all lanes in one go, when safe to do so, known as Double Gap or one stage crossing. Alternatively, if an agent is identified as a risk-taker, they may choose to cross lane by lane, sometimes waiting in the middle of the road, known as Rolling Gap or risk-taker crossing behavior. The inclusion of these two crossing behaviors allows for situations where urgency plays an important role in behavioral decision making, such as in emergencies, rush hour or in crowd management events. The outlined pedestrian crossing model is attained by integrating the pedestrian model EXODUS with SUMO, via the TraCI API