Design of experiments reveals critical parameters for pilot‐scale freeze‐and‐thaw processing of L‐lactic dehydrogenase

Freezing constitutes an important unit operation of biotechnological protein production. Effects of freeze‐and‐thaw (F/T) process parameters on stability and other quality attributes of the protein product are usually not well understood. Here a design of experiments (DoE) approach was used to characterize the F/T behavior of L‐lactic dehydrogenase (LDH) in a 700‐mL pilot‐scale freeze container equipped with internal temperature and pH probes. In 24‐hour experiments, target temperature between –10 and –38°C most strongly affected LDH stability whereby enzyme activity was retained best at the highest temperature of –10°C. Cooling profile and liquid fill volume also had significant effects on LDH stability and affected the protein aggregation significantly. Parameters of the thawing phase had a comparably small effect on LDH stability. Experiments in which the standard sodium phosphate buffer was exchanged by Tris‐HCl and the non‐ionic surfactant Tween 80 was added to the protein solution showed that pH shift during freezing and protein surface exposure were the main factors responsible for LDH instability at the lower freeze temperatures. Collectively, evidence is presented that supports the use of DoE‐based systematic analysis at pilot scale in the identification of F/T process parameters critical for protein stability and in the development of suitable process control strategies.     

Process development in the QbD paradigm: Role of process integration in process optimization for production of biotherapeutics

Biotherapeutics have become the focus of the pharmaceutical industry due to their proven effectiveness in managing complex diseases. Downstream processes of these molecules consist of several orthogonal, high resolution unit operations designed so as to be able to separate variants having very similar physicochemical properties. Typical process development involves optimization of the individual unit operations based on Quality by Design principles in order to define the design space within which the process can deliver product that meets the predefined specifications. However, limited efforts are dedicated to understanding the interactions between the unit operations. This paper aims to showcase the importance of understanding these interactions and thereby arrive at operating conditions that are optimal for the overall process. It is demonstrated that these are not necessarily same as those obtained from optimization of the individual unit operations. Purification of Granulocyte Colony Stimulating Factor (G‐CSF), a biotherapeutic expressed in E. coli., has been used as a case study. It is evident that the suggested approach results in not only higher yield (91.5 vs. 86.4) but also improved product quality (% RP‐HPLC purity of 98.3 vs. 97.5) and process robustness. We think that this paper is very relevant to the present times when the biotech industry is in the midst of implementing Quality by Design towards process development. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 32:355–362, 2016     

Drying Dynamics of Solution‐Processed Perovskite Thin‐Film Photovoltaics: In Situ Characterization,Modeling, and Process Control

A key challenge for the commercialization of perovskite photovoltaics is the transfer of high‐quality spin coated perovskite thin‐films toward applying industry‐scale thin‐film deposition techniques, such as slot‐die coating, spray coating, screen printing, or inkjet printing. Due to the complexity of the formation of polycrystalline perovskite thin‐films from the precursor solution, efficient strategies for process transfer require advancing the understanding of the involved dynamic processes. This work investigates the fundamental interrelation between the drying dynamics of the precursor solution thin‐film and the quality of the blade coated polycrystalline perovskite thin‐films. Precisely defined drying conditions are established using a temperature‐stabilized drying channel purged with a laminar flow of dry air. The dedicated channel is equipped with laser reflectometry at multiple probing positions, allowing for in situ monitoring of the perovskite solution thin‐film thickness during the drying process. Based on the drying dynamics as measured at varying drying parameters, namely at varying temperature and laminar air flow velocity, a quantitative model on the drying of perovskite thin‐films is derived. This model enables process transfer to industry‐scale deposition systems beyond brute force optimization. Via this approach, homogeneous and pinhole‐free blade coated perovskite thin‐films are fabricated, demonstrating high power conversion efficiencies of up to 19.5% (17.3% stabilized) in perovskite solar cells.     

Systematic and Comparative Evaluation of Software Programs for Template‐Based Modeling of Protein Structures

Modeling protein structures is critical for understanding protein functions in various biological and biotechnological studies. Among representative protein structure modeling approaches, template‐based modeling (TBM) is by far the most reliable and most widely used approach to model protein structures. However, it still remains as a challenge to select appropriate software programs for pairwise alignments and model building, two major steps of the TBM. In this paper, pairwise alignment methods for TBM are first compared with respect to the quality of structure models built using these methods. This comparative study is conducted using comprehensive datasets, which cover 6185 domain sequences from Structural Classification of Proteins extended for soluble proteins, and 259 Protein Data Bank entries (whole protein sequences) from Orientations of Proteins in Membranes database for membrane proteins. Overall, a profile‐based method, especially PSI‐BLAST, consistently shows high performance across the datasets and model evaluation metrics used. Next, use of two model building programs, MODELLER and SWISS‐MODEL, does not seem to significantly affect the quality of protein structure models built except for the Hard group (a group of relatively less homologous proteins) of membrane proteins. The results presented in this study will be useful for more accurate implementation of TBM.     

A New Evaluation Method for 2‐D Fluorescence Spectra Based on Theoretical Modeling

This article presents a new evaluation procedure of 2‐D fluorescence spectra obtained during a yeast cultivation without performing a calibration measurement. The 2‐D fluorescence spectra are used to predict the process variables biomass, glucose and ethanol. The new calibration procedure uses a theoretical model of these process variables, i.e., differential equations, to replace any calibration measurement. The theoretical model parameters are identified simultaneously during the calculation of the chemometric models. The root mean square error of prediction of the chemometric models with respect to off‐line measurements are 1.5 g/L, 0.40 g/L and 0.56 g/L for glucose, biomass and ethanol, respectively.     

Integrated two‐liquid phase bioconversion and product‐recovery processes for the oxidation of alkanes: Process design and economic evaluation

Pseudomonas oleovorans and recombinant strains containing the alkane oxidation genes can produce alkane oxidation products in two‐liquid phase bioreactor systems. In these bioprocesses the cells, which grow in the aqueous phase, oxidize apolar, non‐water soluble substrates. The apolar products typically accumulate in the emulsified apolar phase. We have studied both the bioconversion systems and several downstream processing systems to separate and purify alkanols from these two‐liquid phase media. Based on the information generated in these studies, we have now designed bioconversion and downstream processing systems for the production of 1‐alkanols from n‐alkanes on a 10 kiloton/yr scale, taking the conversion of n‐octane to 1‐octanol as a model system. Here, we describe overall designs of fed‐batch and continuous‐fermentation processes for the oxidation of octane to 1‐octanol by Pseudomonas oleovorans, and we discuss the economics of these processes. In both systems the two‐liquid phase system consists of an apolar phase with hexadecene as the apolar carrier solvent into which n‐octane is dissolved, while the cells are present in the aqueous phase. In one system, multiple‐batch fermentations are followed by continuous processing of the product from the separated apolar phase. The second system is based on alkane oxidation by continuously growing cultures, again followed by continuous processing of the product. Fewer fermentors were required and a higher space‐time‐yield was possible for production of 1‐octanol in a continuous process. The overall performance of each of these two systems has been modeled with Aspen software. Investment and operating costs were estimated with input from equipment manufacturers and bulk‐material suppliers. Based on this study, the production cost of 1‐octanol is about 7 US$kg⁻¹ when produced in the fed‐batch process, and 8 US$kg⁻¹ when produced continuously. The comparison of upstream and downstream capital costs and production costs showed significantly higher upstream costs for the fed‐batch process and slightly higher upstream costs for continuous fermentation. The largest cost contribution was due to variable production costs, mainly resulting from media costs. The organisms used in these systems are P. putida alk+ recombinants which oxidize alkanes, but cannot oxidize the resulting alkanols further. Hence, such cells need a second carbon source, which in these systems is glucose. Although the continuous process is about 10% more expensive than the fed‐batch process, improvements to reduce overall cost can be achieved more easily for continuous than for fed‐batch fermentation by decreasing the dilution rate while maintaining near constant productivity. Improvements relevant to both processes can be achieved by increasing the biocatalyst performance, which results in improved overall efficiency, decreased capital investment, and hence, decreased production cost. © 1999 John Wiley & Sons, Inc. Biotechnol Bioeng 84: 459–477, 1999.     

Application of high‐throughput mini‐bioreactor system for systematic scale‐down modeling,process characterization,and control strategy development

High‐throughput systems and processes have typically been targeted for process development and optimization in the bioprocessing industry. For process characterization, bench scale bioreactors have been the system of choice. Due to the need for performing different process conditions for multiple process parameters, the process characterization studies typically span several months and are considered time and resource intensive. In this study, we have shown the application of a high‐throughput mini‐bioreactor system viz. the Advanced Microscale Bioreactor (ambr15^TM), to perform process characterization in less than a month and develop an input control strategy. As a pre‐requisite to process characterization, a scale‐down model was first developed in the ambr system (15 mL) using statistical multivariate analysis techniques that showed comparability with both manufacturing scale (15,000 L) and bench scale (5 L). Volumetric sparge rates were matched between ambr and manufacturing scale, and the ambr process matched the pCO₂ profiles as well as several other process and product quality parameters. The scale‐down model was used to perform the process characterization DoE study and product quality results were generated. Upon comparison with DoE data from the bench scale bioreactors, similar effects of process parameters on process yield and product quality were identified between the two systems. We used the ambr data for setting action limits for the critical controlled parameters (CCPs), which were comparable to those from bench scale bioreactor data. In other words, the current work shows that the ambr15^TM system is capable of replacing the bench scale bioreactor system for routine process development and process characterization. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1623–1632, 2015     

Modeling competition for infection sites on roots by nonpathogenic strains of Fusarium oxysporum

By use of plane and solid geometry and probability models, efficiencies of infection and competition for nutrients and infection sites by a nonpathogenic strain of Fusarium oxysporum (C14) with F. oxysporum f. sp. cucumerinum on the rhizoplane of cucumber were calculated. The model is derived from previously published data. Efficiencies for successful infection were 0.04 chlamydospores per infection site for both pathogen and nonpathogen. Observed successful infections by the pathogen in competition with the nonpathogen were close in values to the competition ratio (CR) calculated as the number of chlamydospores on the infection court of the pathogen divided by the total number of both pathogen and nonpathogen at relatively low densities. When total chlamydospores were, on average, closer than 175 μm apart, however, competition for nutrients/mutual inhibition occurred. At such densities there was an overestimation of the effect of competition for infection sites. These relationships were modeled at inoculum densities of pathogen and/or nonpathogen of 5000 chlamydospores per g soil and above, however, in the field, maximum densities of 1000 colony forming units/g (cfu) were observed. Most likely models of competition for infection sites at this density of the pathogen revealed that infection efficiency was only approximately halved, even when 0.98 of the possible 30 infection sites were occupied by the nonpathogen. It is conclude that competition for nutrients and/or infection sites is an insignificant factor in biocontrol of Fusarium wilt diseases by nonpathogenic fusaria.     

A predictive high‐throughput scale‐down model of monoclonal antibody production in CHO cells

Multi‐factorial experimentation is essential in understanding the link between mammalian cell culture conditions and the glycoprotein product of any biomanufacturing process. This understanding is increasingly demanded as bioprocess development is influenced by the Quality by Design paradigm. We have developed a system that allows hundreds of micro‐bioreactors to be run in parallel under controlled conditions, enabling factorial experiments of much larger scope than is possible with traditional systems. A high‐throughput analytics workflow was also developed using commercially available instruments to obtain product quality information for each cell culture condition. The micro‐bioreactor system was tested by executing a factorial experiment varying four process parameters: pH, dissolved oxygen, feed supplement rate, and reduced glutathione level. A total of 180 micro‐bioreactors were run for 2 weeks during this DOE experiment to assess this scaled down micro‐bioreactor system as a high‐throughput tool for process development. Online measurements of pH, dissolved oxygen, and optical density were complemented by offline measurements of glucose, viability, titer, and product quality. Model accuracy was assessed by regressing the micro‐bioreactor results with those obtained in conventional 3 L bioreactors. Excellent agreement was observed between the micro‐bioreactor and the bench‐top bioreactor. The micro‐bioreactor results were further analyzed to link parameter manipulations to process outcomes via leverage plots, and to examine the interactions between process parameters. The results show that feed supplement rate has a significant effect (P < 0.05) on all performance metrics with higher feed rates resulting in greater cell mass and product titer. Culture pH impacted terminal integrated viable cell concentration, titer and intact immunoglobulin G titer, with better results obtained at the lower pH set point. The results demonstrate that a micro‐scale system can be an excellent model of larger scale systems, while providing data sets broader and deeper than are available by traditional methods. Biotechnol. Bioeng. 2009; 104: 1107–1120. © 2009 Wiley Periodicals, Inc.     

Modeling of microalgal shear‐induced flocculation and sedimentation using a coupled CFD‐population balance approach

In this study, shear‐induced flocculation modeling of Chlorella sp. microalgae was conducted by combination of population balance modeling and CFD. The inhomogeneous Multiple Size Group (MUSIG) and the Euler–Euler two fluid models were coupled via Ansys‐CFX‐15 software package to achieve both fluid and particle dynamics during the flocculation. For the first time, a detailed model was proposed to calculate the collision frequency and breakage rate during the microalgae flocculation by means of the response surface methodology as a tool for optimization. The particle size distribution resulted from the model was in good agreement with that of the jar test experiment. Furthermore, the subsequent sedimentation step was also examined by removing the shear rate in both simulations and experiments. Consequently, variation in the shear rate and its effects on the flocculation behavior, sedimentation rate and recovery efficiency were evaluated. Results indicate that flocculation of Chlorella sp. microalgae under shear rates of 37, 182, and 387 s^?1 is a promising method of pre‐concentration which guarantees the cost efficiency of the subsequent harvesting process by recovering more than 90% of the biomass. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:160–174, 2018     

Partial‐Likelihood Analysis of Spatio‐Temporal Point‐Process Data

Summary We investigate the use of a partial likelihood for estimation of the parameters of interest in spatio‐temporal point‐process models. We identify an important distinction between spatially discrete and spatially continuous models. We focus our attention on the spatially continuous case, which has not previously been considered. We use an inhomogeneous Poisson process and an infectious disease process, for which maximum‐likelihood estimation is tractable, to assess the relative efficiency of partial versus full likelihood, and to illustrate the relative ease of implementation of the former. We apply the partial‐likelihood method to a study of the nesting pattern of common terns in the Ebro Delta Natural Park, Spain.     

Soft sensor based on 2D‐fluorescence and process data enabling real‐time estimation of biomass in Escherichia coli cultivations

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D‐fluorescence data and process data, was developed for a comprehensive study with a 20‐L experimental design, for Escherichia coli fed‐batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D‐fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D‐fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression‐model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on‐line, enabling Quality by Design.     

Bayesian Nonparametric Modeling for Comparison of Single-Neuron Firing Intensities

Summary .  We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.     

Photoluminescence‐Based Characterization of Halide Perovskites for Photovoltaics

Photoluminescence spectroscopy is a widely applied characterization technique for semiconductor materials in general and halide perovskite solar cell materials in particular. It can give direct information on the recombination kinetics and processes as well as the internal electrochemical potential of free charge carriers in single semiconductor layers, layer stacks with transport layers, and complete solar cells. The correct evaluation and interpretation of photoluminescence requires the consideration of proper excitation conditions, calibration and application of the appropriate approximations to the rather complex theory, which includes radiative recombination, non‐radiative recombination, interface recombination, charge transfer, and photon recycling. In this article, an overview is given of the theory and application to specific halide perovskite compositions, illustrating the variables that should be considered when applying photoluminescence analysis in these materials.