Process design of a fully integrated continuous biopharmaceutical process using economic and ecological impact assessment.

Continuous biomanufacturing is a promising alternative to current batch operation as it offers benefits in terms of improved productivity, product quality, and reduced footprint. This study aims to build a fully integrated continuous platform for monoclonal antibody (mAb) production incorporating novel technologies (like intensified seed expansion and continuous high cell density perfusion operations, single-pass tangential flow filtration, and single-use technologies) as well as media and buffer preparation steps. Economic assessment is performed on the basis of the total cost of goods (COGs), which is $102.2/g in the base-case scenario with a bioreactor scale of 500 L. E-factor is used as an environmental indicator and the result shows that 4865.6kg of process water and 11.1 kg of consumables are required to produce 1 kg mAbs. After the development and analysis of the benchmark process, scenario analysis is performed to assess the impacts of the bioreactor scale (60-2000 L) and upstream titers (1.12-2.08 g/L) on the process economics as well as on the environmental footprint. With the increase of bioreactor scale and mAb titer, the operating COGs per unit product decrease. Moreover, increasing the mAb titer is more favorable in terms of the ecological impacts. To investigate the production capacity, the upstream production is increased and the downstream bottlenecks are determined. It is found that only the multicolumn chromatographic (MCC) operations become the process bottleneck and the order of the MCC unit operation that becomes the process bottleneck depends on capacity utilization for that step. Finally, a new platform is built with the integration of membrane chromatography and the two designed processes are compared in terms of economic and ecological impacts.

Bioinformatics analysis of transcriptional regulation of circadian genes in rat liver.

BACKGROUND: The circadian clock is a critical regulator of biological functions controlling behavioral, physiological and biochemical processes. Because the liver is the primary regulator of metabolites within the mammalian body and the disruption of circadian rhythms in liver is associated with severe illness, circadian regulators would play a strong role in maintaining liver function. However, the regulatory structure that governs circadian dynamics within the liver at a transcriptional level remains unknown. To explore this aspect, we analyzed hepatic transcriptional dynamics in Sprague-Dawley rats over a period of 24 hours to assess the genome-wide responses. RESULTS: Using an unsupervised consensus clustering method, we identified four major gene expression clusters, corresponding to central carbon and nitrogen metabolism, membrane integrity, immune function, and DNA repair, all of which have dynamics which suggest regulation in a circadian manner. With the assumption that transcription factors (TFs) that are differentially expressed and contain CLOCK:BMAL1 binding sites on their proximal promoters are likely to be clock-controlled TFs, we were able to use promoter analysis to putatively identify additional clock-controlled TFs besides PARF and RORA families. These TFs are both functionally and temporally related to the clusters they regulate. Furthermore, we also identified significant sets of clock TFs that are potentially transcriptional regulators of gene clusters. CONCLUSIONS: All together, we were able to propose a regulatory structure for circadian regulation which represents alternative paths for circadian control of different functions within the liver. Our prediction has been affirmed by functional and temporal analyses which are able to extend for similar studies.

Design space determination of pharmaceutical processes: Effects of control strategies and uncertainty.

The identification of process Design Space (DS) is of high interest in highly regulated industrial sectors, such as pharmaceutical industry, where assurance of manufacturability and product quality is key for process development and decision-making. If the process can be controlled by a set of manipulated variables, the DS can be expanded in comparison to an open-loop scenario, where there are no controls in place. Determining the benefits of control strategies may be challenging, particularly when the available model is complex and computationally expensive - which is typically the case of pharmaceutical manufacturing. In this study, we exploit surrogate-based feasibility analysis to determine whether the process satisfies all process constraints by manipulating the process inputs and reduce the effect of uncertainty. The proposed approach is successfully tested on two simulated pharmaceutical case studies of increasing complexity, i.e., considering (i) a single pharmaceutical unit operation, and (ii) a pharmaceutical manufacturing line comprised of a sequence of connected unit operations. Results demonstrate that different control actions can be effectively exploited to operate the process in a wider range of inputs and mitigate uncertainty.

Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing.

Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.

Pathway modeling of microarray data: a case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP).

Pathway activity level analysis, the approach pursued in this study, focuses on all genes that are known to be members of metabolic and signaling pathways as defined by the KEGG database. The pathway activity level analysis entails singular value decomposition (SVD) of the expression data of the genes constituting a given pathway. We explore an extension of the pathway activity methodology for application to time-course microarray data. We show that pathway analysis enhances our ability to detect biologically relevant changes in pathway activity using synthetic data. As a case study, we apply the pathway activity level formulation coupled with significance analysis to microarray data from two different rat testes exposed in utero to Dibutyl Phthalate (DBP). In utero DBP exposure in the rat results in developmental toxicity of a number of male reproductive organs, including the testes. One well-characterized mode of action for DBP and the male reproductive developmental effects is the repression of expression of genes involved in cholesterol transport, steroid biosynthesis and testosterone synthesis that lead to a decreased fetal testicular testosterone. Previous analyses of DBP testes microarray data focused on either individual gene expression changes or changes in the expression of specific genes that are hypothesized, or known, to be important in testicular development and testosterone synthesis. However, a pathway analysis may inform whether there are additional affected pathways that could inform additional modes of action linked to DBP developmental toxicity. We show that Pathway activity analysis may be considered for a more comprehensive analysis of microarray data.

Use of genomic data in risk assessment case study: II. Evaluation of the dibutyl phthalate toxicogenomic data set.

An evaluation of the toxicogenomic data set for dibutyl phthalate (DBP) and male reproductive developmental effects was performed as part of a larger case study to test an approach for incorporating genomic data in risk assessment. The DBP toxicogenomic data set is composed of nine in vivo studies from the published literature that exposed rats to DBP during gestation and evaluated gene expression changes in testes or Wolffian ducts of male fetuses. The exercise focused on qualitative evaluation, based on a lack of available dose-response data, of the DBP toxicogenomic data set to postulate modes and mechanisms of action for the male reproductive developmental outcomes, which occur in the lower dose range. A weight-of-evidence evaluation was performed on the eight DBP toxicogenomic studies of the rat testis at the gene and pathway levels. The results showed relatively strong evidence of DBP-induced downregulation of genes in the steroidogenesis pathway and lipid/sterol/cholesterol transport pathway as well as effects on immediate early gene/growth/differentiation, transcription, peroxisome proliferator-activated receptor signaling and apoptosis pathways in the testis. Since two established modes of action (MOAs), reduced fetal testicular testosterone production and Insl3 gene expression, explain some but not all of the testis effects observed in rats after in utero DBP exposure, other MOAs are likely to be operative. A reanalysis of one DBP microarray study identified additional pathways within cell signaling, metabolism, hormone, disease, and cell adhesion biological processes. These putative new pathways may be associated with DBP effects on the testes that are currently unexplained. This case study on DBP identified data gaps and research needs for the use of toxicogenomic data in risk assessment. Furthermore, this study demonstrated an approach for evaluating toxicogenomic data in human health risk assessment that could be applied to future chemicals.

Hybrid model development for parameter estimation and process optimization of hydrophobic interaction chromatography.

Hydrophobic Interaction Chromatography (HIC) is often employed as a polishing step to remove aggregates for the purification of therapeutic proteins in the biopharmaceutical industry. To accelerate the process development and save the costs of performing time- and resource-intensive experiments, advanced model-based process design and optimization are necessary. Due to the unclear adsorption mechanism of the salt-dependent interaction between the protein and resin, the development of an accurate mechanistic model to describe the complex HIC behavior is challenging. In this work, an isotherm derived from Wang et al. is modified by adding three extra parameters together with an equilibrium dispersive model to represent the HIC process. To reduce the development effort of isotherm equations and extract missing information from the available data, a hybrid model is constructed by combining a simple and well-known multi-component Langmuir isotherm (MCL) with a neural network (NN). It is observed that the structure of the hybrid model is of critical importance to the accuracy of the developed model. During parameter estimation, a regularization strategy is incorporated to prevent overfitting. Furthermore, the impact of NN structures and regularization rates are comprehensively investigated. One of the interesting findings was that a simple NN with one hidden layer with two nodes and sigmoid as the activation function, significantly outperforms the mechanistic model, with a 62% improvement in accuracy in calibration and 31.4% in validation. To ensure the generalizability of the developed hybrid model, an in-silico dataset is generated using the mechanistic model to test the extrapolation capability of the hybrid model. Process optimization is also carried out to find the optimal operating conditions under product quality constraints using the developed hybrid model.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation.

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants.

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

Optimal quantification of residence time distribution profiles from a quality assurance perspective.

Residence time distribution (RTD) has been widely applied across various fields of chemical engineering, including pharmaceutical manufacturing, for applications such as material traceability, quality assurance, system health monitoring, and fault detection. Determination of a representative RTD, in principle, requires an accurate process analytical technology (PAT) procedure capturing the entire range of tracer concentrations from zero to maximum. Such a wide concentration range creates at least two problems: i) decreased accuracy of the model across the entire range of concentrations, relating to limit of quantification, and ii) ambiguity associated with the detection of the tracer for low concentration levels, relating to limit of detection (LOD). These problems affect not only the RTD profile itself, but also RTD-based applications, which can potentially lead to erroneous conclusions. This article seeks to minimize the impact of these problems by understanding the relative importance of different features of RTD on the detection of out-of-specification (OOS) products. In this work, the RTD obtained experimentally was truncated at different levels, to investigate the impact of the truncation of RTD on funnel plots for OOS detection. The main finding is that the tail of the RTD can be truncated with no loss of accuracy in the determination of exclusion intervals. This enables the manufacturing scientist to focus entirely on the peak region, maximizing the accuracy of chemometric models.

Optimization of key energy and performance metrics for drug product manufacturing.

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors (Sampat et al., 2022), this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. The results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

Long-term dynamic profiling of inflammatory mediators in double-hit burn and sepsis animal models.

Burn injuries together with its subsequent complications, mainly bacterial infections originating from gastrointestinal tract, activate the host immune system through stimulation of a series of local and systemic responses, including the release of inflammatory mediators. To gain a more comprehensive understanding of these complex physiological changes and to propose therapeutic approaches to combat the deleterious consequences of burn and septic shocks, it is essential to analyze animal models of burn and sepsis. In this study, we analyzed the long term profiles of cytokines and chemokines in rat models which received burn injury followed 2 days later by cecal ligation and puncture (CLP) to induce sepsis and were sacrificed at different time points within 10 days (0, 1, 2, 3, 4, 7 and 10 days). It was observed that MCP-1 concentrations were elevated in all animal models following the burn injury or CLP treatment. IP-10 concentration was persistently decreased after CLP or sham-CLP treatments. GRO/KC concentration was also increased following the burn injury and CLP. It was elucidated that, in more severe injury model which received both burn and CLP treatments, GMCSF and MIP-1α (chemokines), IL-1α (a pro-inflammatory cytokine) and IL-6 (exhibiting both pro- and anti-inflammatory behaviors) were upregulated on the 7th and 10th days, which might be to protect the host system from the subsequent complications caused by burn and sepsis. In order to elucidate critical regulatory interactions, putative transcription factors of the inflammatory mediators which have been significantly changed following the injuries were further identified by analyzing the conserved regions of the promoters of cytokines and chemokines. In conclusion, the long term profiles of the inflammatory mediators were profoundly characterized in this study to gain a comprehensive understanding of inflammatory mediators' behaviors in various injury models.

Metabolic network analysis of perfused livers under fed and fasted states: incorporating thermodynamic and futile-cycle-associated regulatory constraints.

Isolated liver perfusion systems have been extensively used to characterize intrinsic metabolic changes in liver under various conditions, including systemic injury, hepatotoxin exposure, and warm ischemia. Most of these studies were performed utilizing fasted animals prior to perfusion so that a simplified metabolic network could be used in order to determine intracellular fluxes. However, fasting induced metabolic alterations might interfere with disease related changes. Therefore, there is a need to develop a "unified" metabolic flux analysis approach that could be similarly applied to both fed and fasted states. In this study we explored a methodology based on elementary mode analysis in order to determine intracellular fluxes and active pathways simultaneously. In order to decrease the solution space, thermodynamic constraints, and enzymatic regulatory properties for the formation of futile cycles were further considered in the model, resulting in a mixed integer quadratic programming problem. Given the published experimental observations describing the perfused livers under fed and fasted states, the proposed approach successfully determined that gluconeogenesis, glycogenolysis and fatty acid oxidation were active in both states. However, fasting increased the fluxes in gluconeogenic reactions whereas it decreased fluxes associated with glycogenolysis, TCA cycle, fatty acid oxidation and electron transport reactions. This analysis further identified that more pathways were found to be active in fed state while their weight values were relatively lower compared to fasted state. Glucose, lactate, glutamine, glutamate and ketone bodies were also found to be important external metabolites whose extracellular fluxes should be used in the hepatic metabolic network analysis. In conclusion, the mathematical formulation explored in this study is an attractive tool to analyze the metabolic network of perfused livers under various disease conditions. This approach could be simultaneously applied to both fasted and fed data sets.

Dynamics of short-term gene expression profiling in liver following thermal injury.

BACKGROUND: Severe trauma, including burns, triggers a systemic response that significantly impacts on the liver, which plays a key role in the metabolic and immune responses aimed at restoring homeostasis. While many of these changes are likely regulated at the gene expression level, there is a need to better understand the dynamics and expression patterns of burn injury-induced genes in order to identify potential regulatory targets in the liver. Herein we characterized the response within the first 24 h in a standard animal model of burn injury using a time series of microarray gene expression data. METHODS: Rats were subjected to a full thickness dorsal scald burn injury covering 20% of their total body surface area while under general anesthesia. Animals were saline resuscitated and sacrificed at defined time points (0, 2, 4, 8, 16, and 24 h). Liver tissues were explanted and analyzed for their gene expression profiles using microarray technology. Sham controls consisted of animals handled similarly but not burned. After identifying differentially expressed probe sets between sham and burn conditions over time, the concatenated data sets corresponding to these differentially expressed probe sets in burn and sham groups were combined and analyzed using a "consensus clustering" approach. RESULTS: The clustering method of expression data identified 621 burn-responsive probe sets in four different co-expressed clusters. Functional characterization revealed that these four clusters are mainly associated with pro-inflammatory response, anti-inflammatory response, lipid biosynthesis, and insulin-regulated metabolism. Cluster 1 pro-inflammatory response is rapidly up-regulated (within the first 2 h) following burn injury, while Cluster 2 anti-inflammatory response is activated later on (around 8 h post-burn). Cluster 3 lipid biosynthesis is down-regulated rapidly following burn, possibly indicating a shift in the utilization of energy sources to produce acute phase proteins, which serve the anti-inflammatory response. Cluster 4 insulin-regulated metabolism was down-regulated late in the observation window (around 16 h post-burn), which suggests a potential mechanism to explain the onset of hypermetabolism, a delayed but well-known response that is characteristic of severe burns and trauma with potential adverse outcome. CONCLUSIONS: Simultaneous analysis and comparison of gene expression profiles for both burn and sham control groups provided a more accurate estimation of the activation time, expression patterns, and characteristics of a certain burn-induced response based on which the cause-effect relationships among responses were revealed.

Long-term gene expression profile dynamics following cecal ligation and puncture in the rat.

BACKGROUND: Despite the fact that the treatment options for septic patients have been significantly improved, the pathophysiologic changes caused by various septic cases have not been well understood. One commonly observed clinical phenomenon is the onset of a polymicrobial infection caused by bacteria that originate in the intestine but enter the peritoneum via translocation from the gut. This triggers a systemic inflammatory response via the innate immune system, which needs to be well characterized. Cecal ligation and puncture (CLP) is considered to be the gold-standard animal model by establishing infection with mixed bacterial flora and necrotic tissue to induce an inflammatory response. The aim of this study is to analyze the long-term gene expression dynamics in the rats subject to CLP in order to characterize the impact of sepsis upon liver function over an 8-d time period. METHODS: Rats received CLP or its control, sham CLP (SCLP), and then they were sacrificed at 9 am on days 0 (no treatment), 1, 2, 5, and 8 post injury to collect liver samples for microarray analysis. Differentially expressed probe sets in CLP versus SCLP (q value <0.001 and P value <0.001) were combined to form one single matrix, which was then clustered using the approach of "consensus clustering" to identify subsets of transcripts with coherent expression patterns. Finally, the gene expression patterns of the clusters were further transformed into principal components, which account for 65% of the total data. RESULTS: Three major clusters were obtained. The first cluster, which is mainly related to genes of anti-inflammatory response and antioxidative properties, is suppressed early in the CLP condition and later upregulated compared to the SCLP condition. Cluster 2 represents pro-inflammatory responses and signaling, along with amino acid metabolism. Cluster 3 is also associated with pro-inflammatory response. The genes of toll-like receptor signaling and hypermetabolism were identified in this cluster as well. Clusters 2 and 3 are both suppressed in the long-term response following CLP. Clusters 1 and 2 acting in concert return to the time 0 baseline in both groups, indicating resolution of both the anti-inflammatory and pro-inflammatory response; however, the SCLP response in cluster 3 shows persistent downregulation. CONCLUSIONS: Characterization of long-term hepatic responses to injury is critical to understanding the dynamics of transcriptional changes following the induction of the inflammatory response, and to monitoring its effective resolution. These results showed that each condition has unique dynamics that indicate fundamental differences in the response. Furthermore, the gene ontologies suggest a link to oxidative stress over the long term that may be able to be explored for clinical treatments.

Dynamics of hepatic gene expression profile in a rat cecal ligation and puncture model.

BACKGROUND: Sepsis remains a major clinical challenge in intensive care units. The difficulty in developing new and more effective treatments for sepsis exemplifies our incomplete understanding of the underlying pathophysiology of it. One of the more widely used rodent models for studying polymicrobial sepsis is cecal ligation and puncture (CLP). While a number of CLP studies investigated the ensuing systemic inflammatory response, they usually focus on a single time point post-CLP and therefore fail to describe the dynamics of the response. Furthermore, previous studies mostly use surgery without infection (herein referred to as sham CLP, SCLP) as a control for the CLP model, however, SCLP represents an aseptic injurious event that also stimulates a systemic inflammatory response. Thus, there is a need to better understand the dynamics and expression patterns of both injury- and sepsis-induced gene expression alterations to identify potential regulatory targets. In this direction, we characterized the response of the liver within the first 24 h in a rat model of SCLP and CLP using a time series of microarray gene expression data. METHODS: Rats were randomly divided into three groups: sham, SCLP, and CLP. Rats in SCLP group are subjected to laparotomy, cecal ligation, and puncture while those in CLP group are subjected to the similar procedures without cecal ligation and puncture. Animals were saline resuscitated and sacrificed at defined time points (0, 2, 4, 8, 16, and 24 h). Liver tissues were explanted and analyzed for their gene expression profiles using microarray technology. Unoperated animals (Sham) serve as negative controls. After identifying differentially expressed probesets between sham and SCLP or CLP conditions over time, the concatenated data sets corresponding to these differentially expressed probesets in sham and SCLP or CLP groups were combined and analyzed using a "consensus clustering" approach. Promoters of genes that share common characteristics were extracted and compared with gene batteries comprised of co-expressed genes to identify putatative transcription factors, which could be responsible for the co-regulation of those genes. RESULTS: The SCLP/CLP genes whose expression patterns significantly changed compared with sham over time were identified, clustered, and finally analyzed for pathway enrichment. Our results indicate that both CLP and SCLP triggered the activation of a proinflammatory response, enhanced synthesis of acute-phase proteins, increased metabolism, and tissue damage markers. Genes triggered by CLP, which can be directly linked to bacteria removal functions, were absent in SCLP injury. In addition, genes relevant to oxidative stress induced damage were unique to CLP injury, which may be due to the increased severity of CLP injury versus SCLP injury. Pathway enrichment identified pathways with similar functionality but different dynamics in the two injury models, indicating that the functions controlled by those pathways are under the influence of different transcription factors and regulatory mechanisms. Putatively identified transcription factors, notably including cAMP response element-binding (CREB), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and signal transducer and activator of transcription (STAT), were obtained through analysis of the promoter regions in the SCLP/CLP genes. Our results show that while transcription factors such as NF-κB, homeodomain transcription factor (HOMF), and GATA transcription factor (GATA) were common in both injuries for the IL-6 signaling pathway, there were many other transcription factors associated with that pathway which were unique to CLP, including forkhead (FKHD), hairy/enhancer of split family (HESF), and interferon regulatory factor family (IRFF). There were 17 transcription factors that were identified as important in at least two pathways in the CLP injury, but only seven transcription factors with that property in the SCLP injury. This also supports the hypothesis of unique regulatory modules that govern the pathways present in both the CLP and SCLP response. CONCLUSIONS: By using microarrays to assess multiple genes in a high throughput manner, we demonstrate that an inflammatory response involving different dynamics and different genes is triggered by SCLP and CLP. From our analysis of the CLP data, the key characteristics of sepsis are a proinflammatory response, which drives hypermetabolism, immune cell activation, and damage from oxidative stress. This contrasts with SCLP, which triggers a modified inflammatory response leading to no immune cell activation, decreased detoxification potential, and hyper metabolism. Many of the identified transcription factors that drive the CLP-induced response are not found in the SCLP group, suggesting that SCLP and CLP induce different types of inflammatory responses via different regulatory pathways.

Improvement of tablet coating uniformity using a quality by design approach.

A combination of analytical and statistical methods is used to improve a tablet coating process guided by quality by design (QbD) principles. A solid dosage form product was found to intermittently exhibit bad taste. A suspected cause was the variability in coating thickness which could lead to the subject tasting the active ingredient in some tablets. A number of samples were analyzed using a laser-induced breakdown spectroscopy (LIBS)-based analytical method, and it was found that the main variability component was the tablet-to-tablet variability within a lot. Hence, it was inferred that the coating process (performed in a perforated rotating pan) required optimization. A set of designed experiments along with response surface modeling and kriging method were used to arrive at an optimal set of operating conditions. Effects of the amount of coating imparted, spray rate, pan rotation speed, and spray temperature were characterized. The results were quantified in terms of the relative standard deviation of tablet-averaged LIBS score and a coating variability index which was the ratio of the standard deviation of the tablet-averaged LIBS score and the weight gain of the tablets. The data-driven models developed based on the designed experiments predicted that the minimum value of this index would be obtained for a 6% weight gain for a pan operating at the highest speed at the maximum fill level while using the lowest spraying rate and temperature from the chosen parametric space. This systematic application of the QbD-based method resulted in an enhanced process understanding and reducing the coating variability by more than half.

Characterization and propagation of RTD uncertainty for continuous powder blending processes.

Residence time distribution (RTD) is a probability density function that describes the time materials spend inside a system. It is a promising tool for mixing behavior characterization, material traceability, and real-time quality control in pharmaceutical manufacturing. However, RTD measurements are accompanied with some degree of uncertainties because of process fluctuation and variation, measurement error, and experimental variation among different replicates. Due to the strict quality control requirements of drug manufacturing, it is essential to consider RTD uncertainty and characterize its effects on RTD-based predictions and applications. Towards this end, two approaches were developed in this work, namely model-based and data-based approaches. The model-based approach characterizes the RTD uncertainty via RTD model parameters and uses Monte Carlo sampling to propagate and analyze the effects on downstream processes. To avoid bias and possible reduction of uncertainty during model fitting, the data-based approach characterizes RTD uncertainty using the raw experimental data and utilizes interval arithmetic for uncertainty propagation. A constrained optimization approach was also proposed to overcome the drawback of interval arithmetic in the data-based approach. Results depict probability intervals around the upstream disturbance tracking profile and the funnel plot, facilitating better decision-making for quality control under uncertainty.

Ambient-pressure lignin valorization to high-performance polymers by intensified reductive catalytic deconstruction.

Chemocatalytic lignin valorization strategies are critical for a sustainable bioeconomy, as lignin, especially technical lignin, is one of the most available and underutilized aromatic feedstocks. Here, we provide the first report of an intensified reactive distillation­reductive catalytic deconstruction (RD-RCD) process to concurrently deconstruct technical lignins from diverse sources and purify the aromatic products at ambient pressure. We demonstrate the utility of RD-RCD bio-oils in high-performance additive manufacturing via stereolithography 3D printing and highlight its economic advantages over a conventional reductive catalytic fractionation/RCD process. As an example, our RD-RCD reduces the cost of producing a biobased pressure-sensitive adhesive from softwood Kraft lignin by up to 60% in comparison to the high-pressure RCD approach. Last, a facile screening method was developed to predict deconstruction yields using easy-to-obtain thermal decomposition data. This work presents an integrated lignin valorization approach for upgrading existing lignin streams toward the realization of economically viable biorefineries.

The dynamics of the early inflammatory response in double-hit burn and sepsis animal models.

Severe burn trauma is generally associated with bacterial infections, which causes a more persistent inflammatory response with an ongoing hypermetabolic and catabolic state. This complex biological response, mediated by chemokines and cytokines, can be more severe when excessive interactions between the mediators take place. In this study, the early inflammatory response following the cecum ligation and puncture (CLP) or its corresponding control treatment (sham-CLP or SCLP) in burn (B) male rats was analyzed by measuring 23 different cytokines and chemokines. Cytokines and chemokines, including MCP-1, IP-10, leptin, TNF-α, MIP-1α, IL-18, GMCSF, RANTES and GCSF were significantly altered in both B+CLP and B+SCLP groups. IL-10 and IL-6 were significantly up-regulated in the B+CLP group when compared to the B+SCLP group. Down regulation of leptin and IP-10 concentrations were found to be related to surgery and/or infection. IL-18 and MCP-1 were elevated in all groups including previously published single injury models receiving similar treatments. In this study, insult-specific mediators with their characteristic temporal patterns were elucidated in double hit models.