Predictive scaling of fiber-based protein A capture chromatography using mechanistic modeling.

Protein A affinity chromatography is an important step in the purification of monoclonal antibodies (mAbs) and mAb-derived biotherapeutics. While the biopharma industry has extensive expertise in the operation of protein A chromatography, the mechanistic understanding of the adsorption/desorption processes is still limited, and scaling up and scaling down can be challenging because of complex mass transfer effects in bead-based resins. In convective media, such as fiber-based technologies, complex mass transfer effects such as film and pore diffusions do not occur which facilitates the study of the adsorption phenomena in more detail and simplifies the process scale-up. In the present study, the experimentation with small-scale fiber-based protein A affinity adsorber units using different flow rates forms the basis for modeling of mAb adsorption and elution behavior. The modeling approach combines aspects of both stoichiometric and colloidal adsorption models, and an empirical part for the pH. With this type of model, it was possible to describe the experimental chromatograms on a small scale very well. An in silico scale-up could be carried out solely with the help of system and device characterization without feedstock. The adsorption model could be transferred without adaption. Although only a limited number of runs were used for modeling, the predictions of up to 37 times larger units were accurate.

Effect of operators' proficiency level and patients' related factors on possible complications, using a high frequency polyamide sonic intracanal irrigation device: A prospective clinical cohort study.

OBJECTIVES: Sonic/ultrasonic devices are essential tools in today's endodontics. This prospective trial evaluated for the first time the impact of practitioners' proficiency levels and patient-related factors on complications associated with a high frequency polyamide sonic irrigant activation device. METHODS: In total 334 patients (females:158, males:176; age:18-95 years) received in the course of their endodontic therapy an intracanal irrigation, using a high frequency polyamide sonic irrigant activation device, by practitioners of different proficiency levels (undergraduate students, general practitioners or endodontists). Intracanal bleeding (yes/no), postoperative pain (0-10 scale), emphysema (yes/no) and polyamide tip fractures (yes/no) were recorded and related to proficiency levels, age, gender, tooth type, smoking-status, systemic conditions affecting healing ability, baseline pain, swelling, fistula, sensitivity to percussion and diagnosis. RESULTS: Intracanal bleeding was associated with patients' age (p<0.05), baseline pain level (OR = 1.14, 95%CI = 0.91-1.22) and baseline swelling (OR = 2.73, 95%CI = 0.14-0.99; p<0.05) but not proficiency level, gender, tooth type, smoking, systemic conditions, baseline fistula or sensitivity to percussion (p>0.05). Postoperative pain development was related to proficiency level (p<0.05) and baseline pain level (p<0.001), with no influence of age, gender, tooth type, smoking, systemic conditions, baseline fistula, swelling or sensitivity to percussion (p>0.05). Emphysema and polyamide tip fractures were not reported. CONCLUSIONS: Within the current study's limitations, younger patients with higher baseline pain and swelling, were associated with higher intracanal bleeding. Apart from higher postoperative pain observed with less experienced practitioners, proficiency level had no influence on bleeding, polyamide tip fracture or emphysema, endorsing the high frequency polyamide sonic irrigation device as a safe therapeutic device.

Mechanistic modeling, simulation, and optimization of mixed-mode chromatography for an antibody polishing step.

Mixed-mode chromatography combines features of ion-exchange chromatography and hydrophobic interaction chromatography and is increasingly used in antibody purification. As a replacement for flow-through operations on traditional unmixed resins or as a pH-controlled bind-and-elute step, the use of both interaction modes promises a better removal of product-specific impurities. However, the combination of the functionalities makes industrial process development significantly more complex, in particular the identification of the often small elution window that delivers the desired selectivity. Mechanistic modeling has proven that even difficult separation problems can be solved in a computer-optimized manner once the process dynamics have been modeled. The adsorption models described in the literature are also very complex, which makes model calibration difficult. In this work, we approach this problem with a newly constructed model that describes the adsorber saturation with the help of the surface coverage function of the colloidal particle adsorption model for ion-exchange chromatography. In a case study, a model for a pH-controlled antibody polishing step was created from six experiments. The behavior of fragments, aggregates, and host cell proteins was described with the help of offline analysis. After in silico optimization, a validation experiment confirmed an improved process performance in comparison to the historical process set point. In addition to these good results, the work also shows that the high dynamics of mixed-mode chromatography can produce unexpected results if process parameters deviate too far from tried and tested conditions.

Analysis of complex protein elution behavior in preparative ion exchange processes using a colloidal particle adsorption model.

A fundamental understanding of the protein retention mechanism in preparative ion exchange (IEX) chromatography columns is essential for a model-based process development approach. For the past three decades, the mechanistic description of protein retention has been based predominantly on the steric mass action (SMA) model. In recent years, however, retention profiles of proteins have been reported more frequently for preparative processes that are not consistent with the mechanistic understanding relying on the SMA model. In this work, complex elution behavior of proteins in preparative IEX processes is analyzed using a colloidal particle adsorption (CPA) model. The CPA model is found to be capable of reproducing elution profiles that cannot be described by the traditional SMA model. According to the CPA model, the reported complex behavior can be ascribed to a strong compression and concentration of the elution front in the lower unsaturated part of the chromatography column. As the unsaturated part of the column decreases with increasing protein load density, exceeding a critical load density can lead to the formation of a shoulder in the peak front. The general applicability of the model in describing preparative IEX processes is demonstrated using several industrial case studies including multiple monoclonal antibodies on different IEX adsorber systems. In this context, the work covers both salt controlled and pH-controlled protein elution.

Protein adsorption on ion exchange adsorbers: A comparison of a stoichiometric and non-stoichiometric modeling approach.

For mechanistic modeling of ion exchange (IEX) processes, a profound understanding of the adsorption mechanism is important. While the description of protein adsorption in IEX processes has been dominated by stoichiometric models like the steric mass action (SMA) model, discrepancies between experimental data and model results suggest that the conceptually simple stoichiometric description of protein adsorption provides not always an accurate representation of nonlinear adsorption behavior. In this work an alternative colloidal particle adsorption (CPA) model is introduced. Based on the colloidal nature of proteins, the CPA model provides a non-stoichiometric description of electrostatic interactions within IEX columns. Steric hindrance at the adsorber surface is considered by hard-body interactions between proteins using the scaled-particle theory. The model's capability of describing nonlinear protein adsorption is demonstrated by simulating adsorption isotherms of a monoclonal antibody (mAb) over a wide range of ionic strength and pH. A comparison of the CPA model with the SMA model shows comparable model results in the linear adsorption range, but significant differences in the nonlinear adsorption range due to the different mechanistic interpretation of steric hindrance in both models. The results suggest that nonlinear adsorption effects can be overestimated by the stoichiometric formalism of the SMA model and are generally better reproduced by the CPA model.

Defective metabolic programming impairs early neuronal morphogenesis in neural cultures and an organoid model of Leigh syndrome.

Leigh syndrome (LS) is a severe manifestation of mitochondrial disease in children and is currently incurable. The lack of effective models hampers our understanding of the mechanisms underlying the neuronal pathology of LS. Using patient-derived induced pluripotent stem cells and CRISPR/Cas9 engineering, we developed a human model of LS caused by mutations in the complex IV assembly gene SURF1. Single-cell RNA-sequencing and multi-omics analysis revealed compromised neuronal morphogenesis in mutant neural cultures and brain organoids. The defects emerged at the level of neural progenitor cells (NPCs), which retained a glycolytic proliferative state that failed to instruct neuronal morphogenesis. LS NPCs carrying mutations in the complex I gene NDUFS4 recapitulated morphogenesis defects. SURF1 gene augmentation and PGC1A induction via bezafibrate treatment supported the metabolic programming of LS NPCs, leading to restored neuronal morphogenesis. Our findings provide mechanistic insights and suggest potential interventional strategies for a rare mitochondrial disease.

Toward in silico CMC: An industrial collaborative approach to model-based process development.

The Third Modeling Workshop focusing on bioprocess modeling was held in Kenilworth, NJ in May 2019. A summary of these Workshop proceedings is captured in this manuscript. Modeling is an active area of research within the biotechnology community, and there is a critical need to assess the current state and opportunities for continued investment to realize the full potential of models, including resource and time savings. Beyond individual presentations and topics of novel interest, a substantial portion of the Workshop was devoted toward group discussions of current states and future directions in modeling fields. All scales of modeling, from biophysical models at the molecular level and up through large scale facility and plant modeling, were considered in these discussions and are summarized in the manuscript. Model life cycle management from model development to implementation and sustainment are also considered for different stages of clinical development and commercial production. The manuscript provides a comprehensive overview of bioprocess modeling while suggesting an ideal future state with standardized approaches aligned across the industry.

Adsorption of colloidal proteins in ion-exchange chromatography under consideration of charge regulation.

Mechanistic modeling of protein adsorption has gained increasing importance in the development of ion-exchange (IEX) chromatography processes. The most common adsorption models use a stoichiometric representation of the adsorption process based on the law of mass action. Despite the importance of these models in model-based development, the stoichiometric representation of the adsorption process is not accurate for the description of long-range electrostatic interactions in IEX chromatography, limiting the application and mechanistic extension of these models. In this work an adsorption model is introduced describing the non-stoichiometric electrostatic interaction in IEX chromatography based on the linear Poisson-Boltzmann equation and a simplified colloidal representation of the protein. In contrast to most recent non-stoichiometric models, the introduced model accounts for charge regulation during the adsorption process. Its capability of describing the adsorption equilibrium is demonstrated by simulating partitioning coefficients of multiple proteins on different adsorber systems as a function of ionic strength and pH. Despite model simplifications the physical meaning and predictive value of the model could be preserved. By transferring model parameters of a monoclonal antibody (mAb) from one adsorber system to another, it could be demonstrated that protein parameters are theoretically not only valid on a specific adsorber system but freely transferable to other adsorbers. The predictive value of the mechanistic model on the new adsorber system was highlighted by predicting the elution behavior of charge variants of the mAb.

Facile Method for the Investigation of Temperature-Dependent C60 Diffusion in Conjugated Polymers.

We developed a novel all-optical method for monitoring the diffusion of a small quencher molecule through a polymer layer in a bilayer architecture. Experimentally, we injected C60 molecules from a C60 layer into the adjacent donor layer by stepwise heating, and we measured how the photoluminescence (PL) of the donor layer becomes gradually quenched by the incoming C60 molecules. By analyzing the temporal evolution of the PL, the diffusion coefficient of C60 can be extracted, as well as its activation energy and an approximate concentration profile in the film. We applied this technique to three carbazole-based low-bandgap polymers with different glass temperatures with a view to study the impact of structural changes of the polymer matrix on the diffusion process. We find that C60 diffusion is thermally activated and not driven by WFL-type collective motion above Tg but rather by local motions mediated by the sidechains. The results are useful as guidance for material design and device engineering, and the approach can be adapted to a wide range of donor and acceptor materials.

Kinetic Parameters for the Selective Hydrogenation of Acetylene on GaPd2 and GaPd.

Intermetallic GaPd2 is a highly selective and stable catalyst for the semi-hydrogenation of acetylene. Knowledge of the underlying reaction kinetics is essential to gain a deeper understanding of the selective hydrogenation on this catalytic material. To date, there has been no experimental kinetic data published for this reaction on a well-defined intermetallic catalyst possessing isolated active sites. Kinetic measurements are performed at 140-200 °C, revealing an apparent activation energy of 29(2) kJ mol-1 . GaPd2 is shown to be the first binary catalyst material, which shows a positive reaction order (0.89) with respect to acetylene at 200 °C. The influences on the extent of acetylene conversion, specific activity and selectivity to ethylene, ethane, and higher hydrocarbons are determined by a 24 factorial experiment following a design of experiments approach. Temperature and pressure have the strongest impact on these values. The results allow optimal operation for achieving high ethylene yields. A comparison of the reaction kinetics on GaPd2 with experimental results obtained for GaPd reveals different orders of reaction of H2 and C2 H2 on the two compounds.

Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks.

In protein chromatography, process variations, such as aging of column or process errors, can result in deviations of the product and impurity levels. Consequently, the process performance described by purity, yield, or production rate may decrease. Based on visual inspection of the UV signal, it is hard to identify the source of the error and almost unfeasible to determine the quantity of deviation. The problem becomes even more pronounced, if multiple root causes of the deviation are interconnected and lead to an observable deviation. In the presented work, a novel method based on the combination of mechanistic chromatography models and the artificial neural networks is suggested to solve this problem. In a case study using a model protein mixture, the determination of deviations in column capacity and elution gradient length was shown. Maximal errors of 1.5% and 4.90% for the prediction of deviation in column capacity and elution gradient length respectively demonstrated the capability of this method for root cause investigation.

Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks.

Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, such as system characteristics, can be determined reliably by applying well-established experimental methods, whereas others cannot be measured directly. In common practice of protein chromatography modeling, these parameters are identified by applying time-consuming methods such as frontal analysis combined with gradient experiments, curve-fitting, or combined Yamamoto approach. For new components in the chromatographic system, these traditional calibration approaches require to be conducted repeatedly. In the presented work, a novel method for the calibration of mechanistic models based on artificial neural network (ANN) modeling was applied. An in silico screening of possible model parameter combinations was performed to generate learning material for the ANN model. Once the ANN model was trained to recognize chromatograms and to respond with the corresponding model parameter set, it was used to calibrate the mechanistic model from measured chromatograms. The ANN model's capability of parameter estimation was tested by predicting gradient elution chromatograms. The time-consuming model parameter estimation process itself could be reduced down to milliseconds. The functionality of the method was successfully demonstrated in a study with the calibration of the transport-dispersive model (TDM) and the stoichiometric displacement model (SDM) for a protein mixture.

Modeling of complex antibody elution behavior under high protein load densities in ion exchange chromatography using an asymmetric activity coefficient.

A main requirement for the implementation of model-based process development in industry is the capability of the model to predict high protein load densities. The frequently used steric mass action isotherm assumes a thermodynamically ideal system and, hence constant activity coefficients. In this manuscript, an industrial antibody purification problem under high load conditions is considered where this assumption does not hold. The high protein load densities, as commonly applied in industrial downstream processing, may lead to complex elution peak shapes. Using Mollerup's generalized ion-exchange isotherm (GIEX), the observed elution peak shapes could be modeled. To this end, the GIEX isotherm introduced two additional parameters to approximate the asymmetric activity coefficient. The effects of these two parameters on the curvature of the adsorption isotherm and the resulting chromatogram are investigated. It could be shown that they can be determined by inverse peak fitting and conform with the mechanistic demands of model-based process development.

A mechanistic model of ion-exchange chromatography on polymer fiber stationary phases.

Fibers are prominent among novel stationary phase supports for preparative chromatography. Several recent studies have highlighted the potential of fiber-based adsorbents for high productivity downstream processing in both batch and continuous mode, but so far the development of these materials and of processes employing these materials has solely been based on experimental data. In this study we assessed whether mechanistic modeling can be performed on fiber-based adsorbents. With a column randomly filled with short cut hydrogel grafted anion exchange fibers, we tested whether tracer, linear gradient elution, and breakthrough data could be reproduced by mechanistic models. Successful modeling was achieved for all of the considered experiments, for both non-retained and retained molecules. For the fibers used in this study the best results were obtained with a transport-dispersive model in combination with a steric mass action isotherm. This approach accurately accounted for the convection and dispersion of non-retained tracers, and the breakthrough and elution behaviors of three different proteins with sizes ranging from 6 to 160kDa were accurately modeled, with simulation results closely resembling the experimental data. The estimated model parameters were plausible both from their physical meaning, and from an analysis of the underlying model assumptions. Parameters were determined within good confidence levels; the average confidence estimate was below 7% for confidence levels of 95%. This shows that fiber-based adsorbents can be modeled mechanistically, which will be valuable for the future design and evaluation of these novel materials and for the development of processes employing such materials.

Water on hydrophobic surfaces: Mechanistic modeling of hydrophobic interaction chromatography.

Mechanistic models are successfully used for protein purification process development as shown for ion-exchange column chromatography (IEX). Modeling and simulation of hydrophobic interaction chromatography (HIC) in the column mode has been seldom reported. As a combination of these two techniques is often encountered in biopharmaceutical purification steps, accurate modeling of protein adsorption in HIC is a core issue for applying holistic model-based process development, especially in the light of the Quality by Design (QbD) approach. In this work, a new mechanistic isotherm model for HIC is derived by consideration of an equilibrium between well-ordered water molecules and bulk-like ordered water molecules on the hydrophobic surfaces of protein and ligand. The model's capability of describing column chromatography experiments is demonstrated with glucose oxidase, bovine serum albumin (BSA), and lysozyme on Capto™ Phenyl (high sub) as model system. After model calibration from chromatograms of bind-and-elute experiments, results were validated with batch isotherms and prediction of further gradient elution chromatograms.

A versatile noninvasive method for adsorber quantification in batch and column chromatography based on the ionic capacity.

Within the Quality by Design (QbD) framework proposed by the International Conference on Harmonisation (ICH), high-throughput process development (HTPD) and mechanistic modeling are of outstanding importance for future biopharmaceutical chromatography process development. In order to compare the data derived from different column scales or batch chromatographies, the amount of adsorber has to be quantified with the same noninvasive method. Similarly, an important requirement for the implementation of mechanistic modeling is the reliable determination of column characteristics such as the ionic capacity Λ for ion-exchange chromatography with the same method at all scales and formats. We developed a method to determine the ionic capacity in column and batch chromatography, based on the adsorption/desorption of the natural, uv-detectable amino acid histidine. In column chromatography, this method produces results comparable to those of classical acid-base titration. In contrast to acid-base titration, this method can be adapted to robotic batch chromatographic experiments. We are able to convert the adsorber volumes in batch chromatography to the equivalent volume of a compressed column. In a case study, we demonstrate that this method increases the quality of SMA parameters fitted to batch adsorption isotherms, and the capability to predict column breakthrough experiments. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:666-677, 2016.

GMP-grade α-TEA lysine salt: a 28-Day oral toxicity and toxicokinetic study with a 28-Day recovery period in Beagle dogs.

BACKGROUND: Alpha-tocopheryloxyacetic acid (α-TEA) is a semi-synthetic derivative of naturally occurring vitamin E (alpha-tocopherol) that can be delivered via an oral route. Preclinical in vitro and in vivo data demonstrated that α-TEA is a potent anti-tumor agent with a safe toxicity profile in mice. We report a comprehensive study to evaluate the toxokinetics of good manufacturing practice (GMP)-grade α-TEA in dogs after daily oral administration for 28 days, followed by a 28-day recovery period. METHODS: Male and female beagle dogs received capsules of α-TEA Lysine Salt at doses of 100, 300, 1500 mg/kg/day. α-TEA plasma levels were determined by high-performance liquid chromatography (HPLC) with mass spectrometric detection. During the treatment, animals were observe for clinical signs, food consumption, body weight, and subjected to ophthalmoscopic, and electrocardiographic assessments. At the end of the dosing period, blood was taken and toxicokinetic analyses and histopathology assessments were performed when animals were necropsied. RESULTS: Our findings showed that there was no α-TEA-related mortality or moribundity. At the highest dose, increases in white blood cells and fibrinogen levels were observed. These levels returned to normal at the end of the recovery period. Histopathological evaluation of major organs revealed no significant lesions related to α-TEA-treatment. CONCLUSION: We demonstrate that for designing clinical trials in patients, the highest non-severely toxic dose (HNSTD) of α-TEA is 1500 mg/kg/day in Beagle dogs and this data informed the design of dose-escalation studies of α-TEA in patients with advanced cancer.

Application of spectral deconvolution and inverse mechanistic modelling as a tool for root cause investigation in protein chromatography.

In chromatographic protein purification, process variations, aging of columns, or processing errors can lead to deviations of the expected elution behavior of product and contaminants and can result in a decreased pool purity or yield. A different elution behavior of all or several involved species leads to a deviating chromatogram. The causes for deviations are however hard to identify by visual inspection and complicate the correction of a problem in the next cycle or batch. To overcome this issue, a tool for root cause investigation in protein chromatography was developed. The tool combines a spectral deconvolution with inverse mechanistic modelling. Mid-UV spectral data and Partial Least Squares Regression were first applied to deconvolute peaks to obtain the individual elution profiles of co-eluting proteins. The individual elution profiles were subsequently used to identify errors in process parameters by curve fitting to a mechanistic chromatography model. The functionality of the tool for root cause investigation was successfully demonstrated in a model protein study with lysozyme, cytochrome c, and ribonuclease A. Deviating chromatograms were generated by deliberately caused errors in the process parameters flow rate and sodium-ion concentration in loading and elution buffer according to a design of experiments. The actual values of the three process parameters and, thus, the causes of the deviations were estimated with errors of less than 4.4%. Consequently, the established tool for root cause investigation is a valuable approach to rapidly identify process variations, aging of columns, or processing errors. This might help to minimize batch rejections or contribute to an increased productivity.

Modeling and simulation of anion-exchange membrane chromatography for purification of Sf9 insect cell-derived virus-like particles.

Recombinant protein-based virus-like particles (VLPs) are steadily gaining in importance as innovative vaccines against cancer and infectious diseases. Multiple VLPs are currently evaluated in clinical phases requiring a straightforward and rational process design. To date, there is no generic platform process available for the purification of VLPs. In order to accelerate and simplify VLP downstream processing, there is a demand for novel development approaches, technologies, and purification tools. Membrane adsorbers have been identified as promising stationary phases for the processing of bionanoparticles due to their large pore sizes. In this work, we present the potential of two strategies for designing VLP processes following the basic tenet of 'quality by design': High-throughput experimentation and process modeling of an anion-exchange membrane capture step. Automated membrane screenings allowed the identification of optimal VLP binding conditions yielding a dynamic binding capacity of 5.7 mg/mL for human B19 parvovirus-like particles derived from Spodoptera frugiperda Sf9 insect cells. A mechanistic approach was implemented for radial ion-exchange membrane chromatography using the lumped-rate model and stoichiometric displacement model for the in silico optimization of a VLP capture step. For the first time, process modeling enabled the in silico design of a selective, robust and scalable process with minimal experimental effort for a complex VLP feedstock. The optimized anion-exchange membrane chromatography process resulted in a protein purity of 81.5%, a DNA clearance of 99.2%, and a VLP recovery of 59%.

High-throughput micro-scale cultivations and chromatography modeling: Powerful tools for integrated process development.

Upstream processes are rather complex to design and the productivity of cells under suitable cultivation conditions is hard to predict. The method of choice for examining the design space is to execute high-throughput cultivation screenings in micro-scale format. Various predictive in silico models have been developed for many downstream processes, leading to a reduction of time and material costs. This paper presents a combined optimization approach based on high-throughput micro-scale cultivation experiments and chromatography modeling. The overall optimized system must not necessarily be the one with highest product titers, but the one resulting in an overall superior process performance in up- and downstream. The methodology is presented in a case study for the Cherry-tagged enzyme Glutathione-S-Transferase from Escherichia coli SE1. The Cherry-Tag™ (Delphi Genetics, Belgium) which can be fused to any target protein allows for direct product analytics by simple VIS absorption measurements. High-throughput cultivations were carried out in a 48-well format in a BioLector micro-scale cultivation system (m2p-Labs, Germany). The downstream process optimization for a set of randomly picked upstream conditions producing high yields was performed in silico using a chromatography modeling software developed in-house (ChromX). The suggested in silico-optimized operational modes for product capturing were validated subsequently. The overall best system was chosen based on a combination of excellent up- and downstream performance.