PAT method to gather bioprocess parameters in real-time using simple input variables and first principle relationships

Post-approval quality-related regulatory actions for biopharmaceuticals approved in the European Union and the United States between 1995 and 2019

The quality of biopharmaceuticals is carefully monitored by manufacturers and regulators to ensure safety and efficacy throughout the entire product life cycle. Quality defects can lead to post-approval regulatory actions (RAs) to inform healthcare professionals (HCPs). The present study identified quality-related RAs for biopharmaceuticals approved in the European Union and United States between 1995 and 2019. Quality-related RAs were issued due to various quality defects and required different actions by HCPs. The quality defects were not identified due to a negative impact on efficacy and/or safety, which is reassuring. The findings reflect the capability of the stringent regulatory system and quality control to capture and counter various quality defects before the affected product and batches can harm patients.

State estimation and nonlinear tracking control simulation approach. Application to a bioethanol production system

Tracking control of specific variables is key to achieve a proper fermentation. This paper analyzes a fed-batch bioethanol production process. For this system, a controller design based on linear algebra is proposed. Moreover, to achieve a reliable control, on-line monitoring of certain variables is needed. In this sense, for unmeasurable variables, state estimators based on Gaussian processes are designed. Cell, ethanol and glycerol concentrations are predicted with only substrates measurement. Simulation results when the controller and estimators are coupled, are shown. Furthermore, the algorithms were tested with parametric uncertainties and disturbances in the control action, and are compared, in all cases, with neural networks estimators (previous work). Bayesian estimators show a performance improvement, which is reflected in a decrease of the total error. Proposed techniques give reliable monitoring and control tools, with a low computational and economic cost, and less mathematical complexity than neural network estimators.

Accelerating Biomanufacturing by Modeling of Continuous Bioprocessing—Piloting Case Study of Monoclonal Antibody Manufacturing

An experimental feasibility study on continuous bioprocessing in pilot-scale of 1 L/day cell supernatant, that is, about 150 g/year product (monoclonal antibody) based on CHO (Chinese hamster ovary) cells for model validation is performed for about six weeks including preparation, start-up, batch, and continuous steady-state operation for at least two weeks stable operation as well as final analysis of purity and yield. A mean product concentration of around 0.4 g/L at cell densities of 25 × 106 cells/mL was achieved. After perfusion cultivation with alternating tangential flow filtration (ATF), an aqueous two-phase extraction (ATPE) followed by ultra-/diafiltration (UF/DF) towards a final integrated counter-current chromatography (iCCC) purification with an ion exchange (IEX) and a hydrophobic interaction (HIC) column prior to lyophilization were successfully operated. In accordance to prior studies, continuous operation is stable and feasible. Efforts of broadly-qualified operation personal as well as the need for an appropriate measurement and process control strategy is shown evidently.

Model-Based Methods in the Biopharmaceutical Process Lifecycle

Model-based methods are increasingly used in all areas of biopharmaceutical process technology. They can be applied in the field of experimental design, process characterization, process design, monitoring and control. Benefits of these methods are lower experimental effort, process transparency, clear rationality behind decisions and increased process robustness. The possibility of applying methods adopted from different scientific domains accelerates this trend further. In addition, model-based methods can help to implement regulatory requirements as suggested by recent Quality by Design and validation initiatives. The aim of this review is to give an overview of the state of the art of model-based methods, their applications, further challenges and possible solutions in the biopharmaceutical process life cycle. Today, despite these advantages, the potential of model-based methods is still not fully exhausted in bioprocess technology. This is due to a lack of (i) acceptance of the users, (ii) user-friendly tools provided by existing methods, (iii) implementation in existing process control systems and (iv) clear workflows to set up specific process models. We propose that model-based methods be applied throughout the lifecycle of a biopharmaceutical process, starting with the set-up of a process model, which is used for monitoring and control of process parameters, and ending with continuous and iterative process improvement via data mining techniques.

Engineering strategies for enhanced production of protein and bio-products in Pichia pastoris: A review

Pichia pastoris has been recognized as one of the most industrially important hosts for heterologous protein production. Despite its high protein productivity, the optimization of P. pastoris cultivation is still imperative due to strain- and product-specific challenges such as promoter strength, methanol utilization type and oxygen demand. To address the issues, strategies involving genetic and process engineering have been employed. Optimization of codon usage and gene dosage, as well as engineering of promoters, protein secretion pathways and methanol metabolic pathways have proved beneficial to innate protein expression levels. Large-scale production of proteins via high cell density fermentation additionally relies on the optimization of process parameters including methanol feed rate, induction temperature and specific growth rate. Recent progress related to the enhanced production of proteins in P. pastoris via various genetic engineering and cultivation strategies are reviewed. Insight into the regulation of the P. pastoris alcohol oxidase 1 (AOX1) promoter and the development of methanol-free systems are highlighted. Novel cultivation strategies such as mixed substrate feeding are discussed. Recent advances regarding substrate and product monitoring techniques are also summarized. Application of P. pastoris to the production of biodiesel and other value-added products via metabolic engineering are also reviewed. P. pastoris is becoming an indispensable platform through the use of these combined engineering strategies.

Latent variable modeling to analyze the effects of process parameters on the dissolution of paracetamol tablet

The dissolution is one of the critical quality attributes (CQAs) of oral solid dosage forms because it relates to the absorption of drug. In this paper, the influence of raw materials, granules and process parameters on the dissolution of paracetamol tablet was analyzed using latent variable modeling methods. The variability in raw materials and granules was understood based on the principle component analysis (PCA), respectively. A multi-block partial least squares (MBPLS) model was used to determine the critical factors affecting the dissolution. The results showed that the binder amount, the post granulation time, the API content in granule, the fill depth and the punch tip separation distance were the critical factors with variable importance in the projection (VIP) values larger than 1. The importance of each unit of the whole process was also ranked using the block importance in the projection (BIP) index. It was concluded that latent variable models (LVMs) were very useful tools to extract information from the available data and improve the understanding on dissolution behavior of paracetamol tablet. The obtained LVMs were also helpful to propose the process design space and to design control strategies in the further research.

Modeling of an aerobic bioprocess based on gas exchange and dynamics: a novel approach

Monitoring of the biological degradation of a substrate by microorganisms is a key issue, especially in the soil bioremediation area. Respiration measurement is the easiest way to obtain online information on the biological activity. Nevertheless, it is indirectly related to substrate consumption and microbial growth. To be able to link these phenomena, a robust and descriptive model has been developed. Both biological and gas/liquid transfer dynamics must be taken into account to link the online measurement with the actual biological respiration. For that, experimental evolution of the respiratory ouotient (RQ) during a biodegradation has been compared against general biodegradation knowledge. To obtain a reliable model, practical and structural sensitivity analyses have been conducted. The model can describe the evolution of both online measurable and non-measurable states. It also gives a new definition of the apparent RQ, measured in the gas phase, compared to the actual biological RQ.

Model-based analysis on the extractability of information from data in dynamic fed-batch experiments

Dynamic changes of physiological bioprocess parameters, e.g. a change in the specific growth rate μ, are frequently observed during industrial manufacturing as well as bioprocess development. A quantitative description of these variations is of great interest, since it can bring elucidation to the physiological state of the culture. The goal of this contribution was to show limitations and issues for the calculation of rates with regard to temporal resolution for dynamic fed-batch experiments. The impact of measurement errors, temporal resolution and the physiological activity on the signal to noise ratio (SNR) of the calculated rates was evaluated using an in-silico approach. To make use of that in practice, a generally applicable rule of thumb equation for the estimation of the SNR of specific rates was presented. The SNR calculated by this rule of thumb equation helps with definition of sampling intervals and making a decision whether an observed change is statistically significant or should be attributed to random error. Furthermore, a generic reconciliation approach to remove random as well as systematic error from data was presented. This reconciliation technique requires only little prior knowledge. The validity of the proposed tools was checked with real data from a fed-batch culture of E. coli with dynamic variations due to feed profile. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 2013

Real-time estimation of biomass and specific growth rate in physiologically variable recombinant fed-batch processes

The real-time measurement of biomass has been addressed since many years. The quantification of biomass in the induction phase of a recombinant bioprocess is not straight forward, since biological burden, caused by protein expression, can have a significant impact on the cell morphology and physiology. This variability potentially leads to poor generalization of the biomass estimation, hence is a very important issue in the dynamic field of process development with frequently changing processes and producer lines. We want to present a method to quantify “biomass” in real-time which avoids off-line sampling and the need for representative training data sets. This generally applicable soft-sensor, based on first principles, was used for the quantification of biomass in induced recombinant fed-batch processes. Results were compared with “state of the art” methods to estimate the biomass concentration and the specific growth rate µ. Gross errors such as wrong stoichiometric assumptions or sensor failure were detected automatically. This method allows for variable model coefficients such as yields in contrast to other process models, hence does not require prior experiments. It can be easily adapted to a different growth stoichiometry; hence the method provides good generalization, also for induced culture mode. This approach estimates the biomass (or anabolic bioconversion) in induced fed-batch cultures in real-time and provides this key variable for process development for control purposes.

Efficient feeding profile optimization for recombinant protein production using physiological information

A multivariate study was performed aiming at the optimization of a recombinant rhamnose inducible E. coli induction system with alkaline phosphatase as target product. The effects of typical factors with impact on post- as well as pre-induction feeding rates were investigated with respect to the space–time yield of the target product. The goal was increased understanding as well as quantitative characterization of these factors with respect to their physiological impact on the model system. The optical density (OD) at which the culture was induced had a strong positive effect on the space–time yield. Pre-induction growth rate (k) had a second-order effect, while induction feed rate drop (J), a factor defining the linear post-induction feed rate, was interacting with (k). However, explanation of the observed effects to acquire more understanding regarding their effect on cell metabolism was not straight forward. Hence, the original process parameters were transformed into physiological more meaningful parameters and served as the basis for a multivariate data analysis. The observed variance with respect to observed volumetric activity was fully explained by the specific substrate uptake rate (q_s) and induction OD, merging the process parameters pre-induction growth rate (k) and feed rate drop (J) into the physiological parameter specific substrate uptake rate (q_s). After transformation of the response volumetric activity (U/ml) into the biomass specific activity (U/g_biomass), the observed variance was fully explained solely by the specific substrate uptake rate (q_s). Due to physiological multivariate data analysis, the interpretation of the results was facilitated and factors were reduced. On the basis of the obtained results, it was concluded that the physiological parameter q_s rather than process parameters (k, J, induction OD) should be used for process optimization with respect to the feeding profile.

Switching industrial production processes from complex to defined media: method development and case study using the example of Penicillium chrysogenum

BACKGROUND

Filamentous fungi are versatile cell factories and widely used for the production of antibiotics, organic acids, enzymes and other industrially relevant compounds at large scale. As a fact, industrial production processes employing filamentous fungi are commonly based on complex raw materials. However, considerable lot-to-lot variability of complex media ingredients not only demands for exhaustive incoming components inspection and quality control, but unavoidably affects process stability and performance. Thus, switching bioprocesses from complex to defined media is highly desirable.

RESULTS

This study presents a strategy for strain characterization of filamentous fungi on partly complex media using redundant mass balancing techniques. Applying the suggested method, interdependencies between specific biomass and side-product formation rates, production of fructooligosaccharides, specific complex media component uptake rates and fungal strains were revealed. A 2-fold increase of the overall penicillin space time yield and a 3-fold increase in the maximum specific penicillin formation rate were reached in defined media compared to complex media.

CONCLUSIONS

The newly developed methodology enabled fast characterization of two different industrial Penicillium chrysogenum candidate strains on complex media based on specific complex media component uptake kinetics and identification of the most promising strain for switching the process from complex to defined conditions. Characterization at different complex/defined media ratios using only a limited number of analytical methods allowed maximizing the overall industrial objectives of increasing both, method throughput and the generation of scientific process understanding.

Model-based analysis on the relationship of signal quality to real-time extraction of information in bioprocesses

Quality by design (QbD) is a current structured approach to design processes yielding a quality product. Knowledge and process understanding cannot be achieved without proper experimental data; hence requirements for measurement error and frequency of measurement of bioprocess variables have to be defined. In this contribution, a model-based approach is used to investigate impact factors on calculated rates to predict the obtainable information from real-time measurements (= signal quality). Measurement error, biological activity, and averaging window (= period of observation) were identified as biggest impact factors on signal quality. Moreover, signal quality has been set in context with a quantifiable measure using statistical error testing, which can be used as a benchmark for process analytics and exploitation of data. Results have been validated with data from an E. coli batch process. This approach is useful to get an idea which process dynamics can be observed with a given bioprocess setup and sampling strategy beforehand.