模拟青霉素发酵过程中菌体生长动态的细胞自动机模型

在青霉素发酵生产机理及其动力学微分方程模型的基础上,建立了模拟青霉素分批发酵过程中菌体生长动态的细胞自动机模型(CABGM)。CABGM采用三维细胞自动机作为菌体生长空间,采用Moore型邻域作为细胞邻域,其演化规则根据青霉素分批发酵过程中菌体生长机理和动力学微分方程模型设计。CABGM中的每一个细胞既可代表单个的青霉素产生菌,又可代表特定数量的青霉素产生菌,它具有不同的状态。对CABGM进行了统计特性的理论分析和仿真实验,理论分析和仿真实验结果均证明了CABGM能一致地复现动力学微分方程模型所描述的青霉素分批发酵菌体生长过程。最后,对所建模型在实际生产过程中的应用问题进行了分析,指出了需要进一步研究的问题。     

Modeling growth,lipid accumulation and lipid turnover in submerged batch cultures of <Emphasis Type="Italic">Umbelopsis isabellina</Emphasis>

The production of lipids by oleaginous yeast and fungi becomes more important because these lipids can be used for biodiesel production. To understand the process of lipid production better, we developed a model for growth, lipid production and lipid turnover in submerged batch fermentation. This model describes three subsequent phases: exponential growth when both a C-source and an N-source are available, carbohydrate and lipid production when the N-source is exhausted and turnover of accumulated lipids when the C-source is exhausted. The model was validated with submerged batch cultures of the fungus Umbelopsis isabellina (formerly known as Mortierella isabellina) with two different initial C/N-ratios. Comparison with chemostat cultures with the same strain showed a significant difference in lipid production: in batch cultures, the initial specific lipid production rate was almost four times higher than in chemostat cultures but it decreased exponentially in time, while the maximum specific lipid production rate in chemostat cultures was independent of residence time. This indicates that different mechanisms for lipid production are active in batch and chemostat cultures. The model could also describe data for submerged batch cultures from literature well.     

重组毕赤酵母产青霉素G酰化酶的分批发酵动力学研究

构建了重组毕赤酵母产青霉素G酰化酶的分批发酵动力学模型。实验考察了分批发酵过程中甘油消耗、甲醇浓度、菌体浓度、溶氧、补料时间对青霉素G酰化酶活力的影响。应用Matlab软件,对菌体生长、基质消耗和产物生成方程进行最优参数估算和非线性拟合,得到相应的动力学模型。模型的计算值与实验值能较好地拟合,表明所建模型能较好反映重组毕赤酵母产青霉素G酰化酶的分批发酵过程。     

On the Shelf Life of Pharmaceutical Products

This article proposes new terminology that distinguishes between different concepts involved in the discussion of the shelf life of pharmaceutical products. Such comprehensive and common language is currently lacking from various guidelines, which confuses implementation and impedes comparisons of different methodologies. The five new terms that are necessary for a coherent discussion of shelf life are: true shelf life, estimated shelf life, supported shelf life, maximum shelf life, and labeled shelf life. These concepts are already in use, but not named as such. The article discusses various levels of "product" on which different stakeholders tend to focus (e.g., a single-dosage unit, a batch, a production process, etc.). The article also highlights a key missing element in the discussion of shelf life-a Quality Statement, which defines the quality standard for all key stakeholders. Arguments are presented that for regulatory and statistical reasons the true product shelf life should be defined in terms of a suitably small quantile (e.g., fifth) of the distribution of batch shelf lives. The choice of quantile translates to an upper bound on the probability that a randomly selected batch will be nonconforming when tested at the storage time defined by the labeled shelf life. For this strategy, a random-batch model is required. This approach, unlike a fixed-batch model, allows estimation of both within- and between-batch variability, and allows inferences to be made about the entire production process. This work was conducted by the Stability Shelf Life Working Group of the Product Quality Research Institute.     

Order Release and Product Mix Coordination in a Complex PCB Manufacturing Line with Batch Processors

In this paper, we study the role of order releases and product mix coordination in a complex manufacturing line with batch processors. We develop a planning methodology for synchronizing production in such manufacturing lines and discuss the decision-making process in the context of a PCB production environment at Northern Telecom's Fiberworld Division. The planning methodology includes developing mathematical programming models for determining a configuration of batch processors, order releases to the shop floor, and daily loading decisions at the batch processors. The optimization models are linked to a simulation model of the shop, which provides key statistics like lead time, work in process, and utilization rates. The objective is to reduce lead time for manufacturing different products in this environment while meeting the demand. We analyze the performance of such a line, study the efficacy of various types of shop floor synchronization policies, and establish the role of batch processors in managing such complex lines effectively. We exhibit how batch processors (which are bottleneck operations) could be scheduled effectively to incorporate the logical constraints that govern their operations and react to variabilities in the manufacturing line.     

A dynamic model linking cell growth to intracellular metabolism and extracellular by-product accumulation

Mathematical modeling of animal cell growth and metabolism is essential for the understanding and improvement of the production of biopharmaceuticals. Models can explain the dynamic behavior of cell growth and product formation, support the identification of the most relevant parameters for process design, and significantly reduce the number of experiments to be performed for process optimization. Few dynamic models have been established that describe both extracellular and intracellular dynamics of growth and metabolism of animal cells. In this study, a model was developed, which comprises a set of 33 ordinary differential equations to describe batch cultivations of suspension AGE1.HN.AAT cells considered for the production of α1-antitrypsin. This model combines a segregated cell growth model with a structured model of intracellular metabolism. Overall, it considers the viable cell concentration, mean cell diameter, viable cell volume, concentration of extracellular substrates, and intracellular concentrations of key metabolites from the central carbon metabolism. Furthermore, the release of metabolic by-products such as lactate and ammonium was estimated directly from the intracellular reactions. Based on the same set of parameters, this model simulates well the dynamics of four independent batch cultivations. Analysis of the simulated intracellular rates revealed at least two distinct cellular physiological states. The first physiological state was characterized by a high glycolytic rate and high lactate production. Whereas the second state was characterized by efficient adenosine triphosphate production, a low glycolytic rate, and reactions of the TCA cycle running in the reverse direction from α-ketoglutarate to citrate. Finally, we show possible applications of the model for cell line engineering and media optimization with two case studies.     

Biocatalytic process optimization based on mechanistic modeling of cholic acid oxidation with cofactor regeneration

Reduction and oxidation of steroids in the human gut are catalyzed by hydroxysteroid dehydrogenases of microorganisms. For the production of 12-ketochenodeoxycholic acid (12-Keto-CDCA) from cholic acid the biocatalytic application of the 12α-hydroxysteroid dehydrogenase of Clostridium group P, strain C 48-50 (HSDH) is an alternative to chemical synthesis. However, due to the intensive costs the necessary cofactor (NADP(+) ) has to be regenerated. The alcohol dehydrogenase of Thermoanaerobacter ethanolicus (ADH-TE) was applied to catalyze the reduction of acetone while regenerating NADP(+) . A mechanistic kinetic model was developed for the process development of cholic acid oxidation using HSDH and ADH-TE. The process model was derived by identifying the parameters for both enzymatic models separately using progress curve measurements of batch processes over a broad range of concentrations and considering the underlying ordered bi-bi mechanism. Both independently derived kinetic models were coupled via mass balances to predict the production of 12-Keto-CDCA with HSDH and integrated cofactor regeneration with ADH-TE and acetone as co-substrate. The prediction of the derived model was suitable to describe the dynamics of the preparative 12-Keto-CDCA batch production with different initial reactant and enzyme concentrations. These datasets were used again for parameter identification. This led to a combined model which excellently described the reaction dynamics of biocatalytic batch processes over broad concentration ranges. Based on the identified process model batch process optimization was successfully performed in silico to minimize enzyme costs. By using 0.1 mM NADP(+) the HSDH concentration can be reduced to 3-4 μM and the ADH concentration to 0.4-0.6 μM to reach the maximal possible conversion of 100 mM cholic acid within 48 h. In conclusion, the identified mechanistic model offers a powerful tool for a cost-efficient process design.     

Morphologically structured model for antitumoral retamycin production during batch and fed-batch cultivations of Streptomyces olindensis

A morphologically structured model is proposed to describe trends in biomass growth, substrate consumption, and antitumoral retamycin production during batch and fed-batch cultivations of Streptomyces olindensis. Filamentous biomass is structured into three morphological compartments (apical, subapical, and hyphal), and the production of retamycin, a secondary metabolite, is assumed to take place in the subapical cell compartment. Model accounts for the effect of glucose as well as complex nitrogen source on both the biomass growth and retamycin production. Laboratory data from bench-scale batch and fed-batch fermentations were used to estimate some model parameters by nonlinear regression. The predictive capability of the model was then tested for additional fed-batch and continuous experiments not used in the previous fitting procedure. The model predictions show fair agreement to the experimental data. The proposed model can be useful for further studies on process optimization and control.     

Influence of culture modes on the microbial production of hyaluronic acid by <Emphasis Type="Italic">Streptococcus zooepidemicus</Emphasis>

The principal objective of this study was to assess the effects of culture modes including batch culture, pulse fed-batch culture, constant feeding rate fed-batch culture, and exponential fed-batch culture on the production of hyaluronic acid (HA) by Streptococcus zooepidemicus. Batch cultures had the highest levels of HA productivity, whereas fed-batch cultures were more favorable with regard to cell growth, and exponential fed-batch cultures evidenced the highest cell concentrations. A two-step culture model was proposed to enhance HA production: an exponential fed-batch culture was conducted prior to 8 h and then sucrose supplementation was applied for 8 h to start the batch fermentation of S. zooepidemicus. HA production and productivity were increased by 36 and 37% in the proposed two-step culture process as compared with that observed in the batch culture, respectively. The proposed two-step culture model can be applied in the production of secondary metabolites, and particularly of the exopolysaccharides.     

Implementation of chemometrics,design of experiments,and neural network analysis for prior process knowledge assessment,failure modes and effect analysis,scale-down model development,and process characterization for a chromatographic purification of Teriparatide

Process understanding and characterization forms the foundation, ensuring consistent and robust biologics manufacturing process. Using appropriate modeling tools and machine learning approaches, the process data can be monitored in real time to avoid manufacturing risks. In this article, we have outlined an approach toward implementation of chemometrics and machine learning tools (neural network analysis) to model and predict the behavior of a mixed-mode chromatography step for a biosimilar (Teriparatide) as a case study. The process development data and process knowledge was assimilated into a prior process knowledge assessment using chemometrics tools to derive important parameters critical to performance indicators (i.e., potential quality and process attributes) and to establish the severity ranking for the FMEA analysis. The characterization data of the chromatographic operation are presented alongwith the determination of the critical, key and non- key process parameters, set points, operating, process acceptance and characterized ranges. The scale-down model establishment was assessed using traditional approaches and novel approaches like batch evolution model and neural network analysis. The batch evolution model was further used to demonstrate batch monitoring through direct chromatographic data, thus demonstrating its application for continuos process verification. Assimilation of process knowledge through a structured data acquisition approach, built-in from process development to continuous process verification was demonstrated to result in a data analytics driven model that can be coupled with machine learning tools for real time process monitoring. We recommend application of these approaches with the FDA guidance on stage wise process development and validation to reduce manufacturing risks.     

Process performance models in the optimization of multiproduct protein production plants

In this work we propose a model that simultaneously optimizes the process variables and the structure of a multiproduct batch plant for the production of recombinant proteins. The complete model includes process performance models for the unit stages and a posynomial representation for the multiproduct batch plant. Although the constant time and size factor models are the most commonly used to model multiproduct batch processes, process performance models describe these time and size factors as functions of the process variables selected for optimization. These process performance models are expressed as algebraic equations obtained from the analytical integration of simplified mass balances and kinetic expressions that describe each unit operation. They are kept as simple as possible while retaining the influence of the process variables selected to optimize the plant. The resulting mixed-integer nonlinear program simultaneously calculates the plant structure (parallel units in or out of phase, and allocation of intermediate storage tanks), the batch plant decision variables (equipment sizes, batch sizes, and operating times of semicontinuous items), and the process decision variables (e.g., final concentration at selected stages, volumetric ratio of phases in the liquid-liquid extraction). A noteworthy feature of the proposed approach is that the mathematical model for the plant is the same as that used in the constant factor model. The process performance models are handled as extra constraints. A plant consisting of eight stages operating in the single product campaign mode (one fermentation, two microfiltrations, two ultrafiltrations, one homogenization, one liquid-liquid extraction, and one chromatography) for producing four different recombinant proteins by the genetically engineered yeast Saccharomyces cerevisiae was modeled and optimized. Using this example, it is shown that the presence of additional degrees of freedom introduced by the process performance models, with respect to a fixed size and time factor model, represents an important development in improving plant design.     

Microbial production of polyhydroxybutyrate with tailor-made properties: an integrated modelling approach and experimental validation

The microbial production of polyhydroxybutyrate (PHB) is a complex process in which the final quantity and quality of the PHB depend on a large number of process operating variables. Consequently, the design and optimal dynamic operation of a microbial process for the efficient production of PHB with tailor-made molecular properties is an extremely interesting problem. The present study investigates how key process operating variables (i.e., nutritional and aeration conditions) affect the biomass production rate and the PHB accumulation in the cells and its associated molecular weight distribution. A combined metabolic/polymerization/macroscopic modelling approach, relating the process performance and product quality with the process variables, was developed and validated using an extensive series of experiments and measurements. The model predicts the dynamic evolution of the biomass growth, the polymer accumulation, the consumption of carbon and nitrogen sources and the average molecular weights of the PHB in a bioreactor, under batch and fed-batch operating conditions. The proposed integrated model was used for the model-based optimization of the production of PHB with tailor-made molecular properties in Azohydromonas lata bacteria. The process optimization led to a high intracellular PHB accumulation (up to 95% g of PHB per g of DCW) and the production of different grades (i.e., different molecular weight distributions) of PHB.     

Mathematical modeling of growth and alkaloid production in Claviceps purpurea batch fermentation

An new systematic approach for describing Claviceps purpurea growth and ergot alkaloid production during batch fermentation is presented. The model is based on microbial life, as the main characteristic for microbial development during fermentation process. The aging process of the microorganism is represented by life function, defined in microbial life space. The life space is defined as a measure in which the observer follows the development of a biosystem through physiological and morphological changes of a microorganism. As a consequence of such approach the relativistic theory is recognized. To validate the model developed, a test on growth and alkaloid synthesis data from an industrial batch fermentation was performed. (c) 1993 John Wiley & Sons, Inc.     

Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.     

Experimental design and metabolic flux analysis tools to optimize industrially relevant Haemophilus influenzae type b growth medium

Haemophilus influenzae type b (Hib), a Gram‐negative capsulated bacterium, is a causative agent of meningitis worldwide. The capsular polysaccharide, a high molecular mass polymer consisting of the repeated units of the polyribosyl‐ribitol‐phosphate, is considered the main virulence factor and it is used as an antigen to vaccines, conjugated to a carrier protein. The industrial production of the polysaccharide requires the cultivation of Hib in rich medium, which impacts process costs and product recovery. In this study, a central composite rotational experimental design strategy was used to access the influence of key components of culture medium (soy peptone, yeast extract and glucose) on biomass formation and polysaccharide production in shake‐flasks. The optimized medium formulation, containing half of the usual yeast extract and soytone concentrations, was further validated in batch bioreactor cultivations. High polysaccharide production (～500 mg/L) was obtained in a cheaper and more competitive production process for use in Hib vaccine production. In addition, simulations of a metabolic model describing Hib central metabolism were used to assess the role of key amino acids on growth. A chemically defined medium supplemented only with amino acids from α‐ketoglutarate and oxaloacetate families as well as phenylalanine was suggested as a promising alternative for reduced acetate accumulation and enhanced polysaccharide production in Hib cultures. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1508–1519, 2017     

Rational scale-up of a baculovirus-insect cell batch process based on medium nutritional depth

We have developed a serum-free cell culture process utilizing a recombinant baculovirus (AcNPV) expression vector to infect Trichoplusia ni insect cells for the production of the human lysosomal enzyme, glucocerebrosidase. The enzyme, which is harvested as a secreted protein in this process, can serve as a replacement therapy for the genetic deficiency Gaucher disease. In the course of pilot scale-up of a batch glucocerebrosidase process from 25-mL working volume shaker flask units to 25-L working volume stirred bioreactor units, a semi-empirical model was developed for the rational determination of scaleable process parameters, including host cell density at infection, multiplicity of infection (MOI), and harvest time. A key assumption of the model is that maximum protein production is limited by the serum-free medium's nutritional capacity, which can, in turn, be determined from the growth of uninfected cells. For the host cell/medium combination used in this study, the nutritional limit was determined to be 1.3 x 10(7) to 1.7 x 10(7) viable-cell-days/mL. Based on this, the model predicts that optimal protein expression is consistent with a 4-day batch process where the host cell density at the time of infection is 1.5 x 10(6) to 2.0 x 10(6) cells/mL and the MOI is 0.09-0.3. These parameters were empirically confirmed to give the highest achievable batch product yield, first in shaker flasks and then at larger scales. The low MOI allows at least one population doubling to take place post viral addition, so that the effective infected cell density producing product generally exceeds 4 x 10(6) cells/mL. It was also interesting to note that this process consistently achieved the same level of maximum protein production at the 25-L bioreactor scale in 4 days compared to 5 days at the shaker flask scale. This may be attributable to better control of the culture environment in the bioreactor. Unlike some other lepidopteran insect cells, such as Sf-9, T. ni cells were found to produce significant levels of the inhibitory metabolites ammonia and lactate. Our results suggest that reduction and/or removal of inhibitory metabolites might be beneficial for infection of high-density cultures of these cells and might also facilitate application of more sophisticated culture strategies, including fed-batch. (c) 1996 John Wiley & Sons, Inc.     

A model that links growth and secondary metabolite production in plant cell suspension cultures

Plant cell suspensions of grape cells (Vitis vinifera L. cv. Gamay Fréaux) were grown in shake flasks operated both in the batch and semicontinuous mode. A mathematical model was developed to describe grape cell growth, sucrose uptake, and secondary metabolite (anthocyanin) production. Parameters were estimated from batch studies data. The model was able to predict results for semicontinuous experiments by only modifying the value of four of these parameters. The modified parameters (maximum specific rate of biomass production, maximum specific rate of substrate consumption for maintenance, maximum specific rate of anthocyanin production, and degradation constant of anthocyanins) were related to the kinetics rather than to the yield of the process. The model introduces the concept of primary and secondary metabolism substrate concentration-dependent competition for precursors. Further, the model was able to predict the evolution of the cell system when substrate is scarce, as the value of the different kinetic constants determines the portion of substrate that is used for biomass production, secondary metabolite production, and cell maintenance. (c) 1995 John Wiley & Sons, Inc.     

Modeling retrovirus production for gene therapy. 1. Determination Of optimal bioreaction mode and harvest strategy

Although retroviruses are a promising tool for gene therapy, there are two major problems limiting the establishment of viable industrial processes: retrovirus stability and low final yield in the supernatant. This fact emphasizes the need for an effective process optimization, not only at a genetic level but also at a bioprocess engineering level. In part 1 of this paper a mathematical model was developed to optimize the bioreaction yield by determining the best retrovirus harvest strategy in perfusion cultures. PA317 cells producing recombinant retroviruses were used to develop and test this model. Cell culture was performed in stirred tanks using porous supports. The parameters of the proposed model were experimentally determined for batch and perfusion cultures at 32 and 37 degrees C both with and without additives to enhance production; the model was then validated. This model allowed the determination of the optimal values of all operational variables included: batch and perfusion duration and perfusion rate. The highest productivity (2682 virus cm(-)(3) h(-)(1)) was obtained under the following conditions: batch at 37 degrees C for 53 h followed by perfusion at 32 degrees C for 23 h with a perfusion rate of 0.107 h(-)(1). This value was 3.5-fold higher than the best result obtained in batch cultures for the same conditions of titer and quality. A sensitivity analysis of the parameters showed that the parameters that affect most the final productivity depend on the bioreaction phase: cell growth in batch culture and production and product degradation in perfusion culture. In part 2 of this paper, this model is extended to the first step of downstream processing, and the addition of further steps to the process is discussed in order to achieve global process optimization.     

Kinetics of kojic acid fermentation byAspergillus flavus link S44-1 using sucrose as a carbon source under different pH conditions

Kojic acid production byAspergillus flavus strain S44-1 using sucrose as a carbon source was carried out in a 250-mL shake flask and a 2-L stirred tank fermenter. For comparison, production of kojic acid using glucose, fructose and its mixture was also carried out. Kojic acid production in shake flask fermentation was 25.8 g/L using glucose as the sole carbon source, 23.6 g/L with sucrose, and 6.4 g/L from fructose. Reduced kojic acid production (13.5 g/L) was observed when a combination of glucose and fructose was used as a carbon source. The highest production of kojic acid (40.2 g/L) was obtained from 150 g/L sucrose in a 2 L fermenter, while the lowest kojic acid production (10.3 g/L) was seen in fermentation using fructose as the sole carbon source. The experimental data from batch fermentation and resuspended cell system was analysed in order to form the basis for a kinetic model of the process. An unstructured model based on logistic and Luedeking-Piret equations was found suitable to describe the growth, substrate consumption, and efficiency of kojic acid production byA. flavus in batch fermentation using sucrose. From this model, it was found that kojic acid production byA. flavus was not a growth-associated process. Fermentation without pH control (from an initial culture pH of 3.0) showed higher kojic acid production than single-phase pH-controlled fermentation (pH 2.5, 2.75, and 3.0).     

法夫酵母产虾青素的反复分批及反复分批补料发酵

以生物量和虾青素产量为指标,考察法夫酵母多批次半连续培养产虾青素的稳定性。实验结果显示,在摇瓶上分别以4 d和5 d为周期反复分批培养法夫酵母,虾青素产量呈现先增加再下降的趋势,但第2代至第7代虾青素产量仍高于第1代,并且4 d为周期的虾青素平均产量略高于5 d的。在5 L罐法夫酵母进行反复分批补料发酵中,不管是补加30％的葡萄糖还是补加30％的淀粉水解糖,第2个批次发酵的生物量和虾青素产量均达到第1个批次的水平,表明菌种稳定性较好。