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For many modern biopharmaceutical processes, manufacturers develop data-driven models using data analytics/machine learning (DA/ML) methods. The challenge is how to select the best methods for a specific dataset to construct the most accurate and reliable model. This article describes the application of smart process data analytics software to industrial end-to-end biomanufacturing datasets for monoclonal antibody production to automate the determination of the best DA/ML tools for model construction and process understanding. The application demonstrates that smart process data analytics software captures product- and process-specific characteristics for two different monoclonal antibody productions. This study provides tools that can be widely applied to biomanufacturing processes for root cause analysis, prediction, and control.

Crystallization is a potential cost-effective alternative to chromatography for the purification of biotherapeutic proteins. Crystallization kinetics are required for the design and control of such processes, but only a limited quantity of proteins is available during the initial stage of process development. This article describes the design of a droplet-based evaporative system for the evaluation of candidate crystallization conditions and the estimation of kinetics using only a droplet (on the order of μL) of protein solution. The temperature and humidity of air fed to a flow cell containing the droplet are controlled for evaporation and rehydration of the droplet, which are used for manipulating supersaturation. Dual-angle images of the droplet are taken and analyzed on-line to obtain the droplet volume and crystal sizes. Crystallization kinetics are estimated based on a first-principles process model and experimental data. Tight control of temperature and humidity of the air, fast and accurate image analysis, and accurate estimation of crystallization kinetics are experimentally demonstrated for a model protein lysozyme. The estimated kinetics are suitable for the model-based design and control of protein crystallization processes., A droplet-based evaporative system is designed for evaluating crystallization conditions and estimating kinetics using only a droplet of protein solution. The temperature and humidity are controlled for evaporation and rehydration of the droplet. Dual-angle images of the droplet are analyzed on-line to obtain the droplet volume and crystal sizes. The developed system is experimentally demonstrated for a model protein lysozyme.

Continuous slug-flow tubular crystallization has been explored by several research groups in academia and industry as a way to produce crystals while having low capital equipment costs. In this crystallization type, slugs of slurry and gas consecutively travel through a tube, with a high degree of mixing and temperature uniformity within each slug. This article presents an experimental system for slug-flow tubular crystallization that employs a spatial temperature profile and directed non-contact ultrasonication to induce primary nucleation to enable the generation of a wide variety of crystal size distributions. The crystal size distributions are compared for data collected from a full-factorial experimental design (27 experiments in total) to predictions from a population balance model that includes the effects of ultrasonication on primary nucleation. This population balance model for tubular crystallization is the first that incorporates the effects of ultrasonication and dissolution on the crystal size distribution. The crystal size distributions are reasonably consistent with the model, within 20% prediction error, for all experiments in which the spatial temperature profile is monotonically decreasing and at low to moderate supersaturation. Potential causes for weaker agreement for other experiments are discussed.

The optimization of upstream and downstream processes for production of recombinant adeno-associated virus (rAAV) with consistent quality depends on the ability to rapidly characterize critical quality attributes (CQAs). In the context of rAAV production, the virus titer, capsid content, and aggregation are identified as potential CQAs, affecting the potency, purity, and safety of rAAV-mediated gene therapy products. Analytical methods to measure these attributes commonly suffer from long turnaround times or low throughput for process development, although rapid, high-throughput methods are beginning to be developed and commercialized. These methods are not yet well established in academic or industrial practice, and supportive data are scarce. Here, we review both established and upcoming analytical methods for the quantification of rAAV quality attributes. In assessing each method, we highlight the progress toward rapid, at-line characterization of rAAV. Furthermore, we identify that a key challenge for transitioning from traditional to newer methods is the scarcity of academic and industrial experience with the latter. This literature review serves as a guide for the selection of analytical methods targeting quality attributes for rapid, high-throughput process characterization during process development of rAAV-mediated gene therapies., Graphical Abstract, This review discusses analytical methods for the characterization of rAAV-based products during their development. With the focus on vector titer, content ratio, and aggregation, the review assesses established and upcoming methods and serves as a guide for the selection of analytical methods for rAAV development.

Nonreplicating rotavirus vaccine (NRRV) candidates are being developed with the aim of serving the needs of developing countries. A significant proportion of the cost of manufacturing such vaccines is the purification in multiple chromatography steps. Crystallization has the potential to reduce purification costs and provide new product storage modality, improved operational flexibility, and reduced facility footprints. This communication describes a systematic approach for the design of the crystallization of an NRRV candidate, VP8 subunit proteins fused to the P2 epitope of tetanus toxin, using first‐principles models and preliminary experimental data. The first‐principles models are applied to literature data to obtain feasible crystallization conditions and lower bounds for nucleation and growth rates. Crystallization is then performed in a hanging‐drop vapor diffusion system, resulting in the nucleation and growth of NRRV crystals. The crystals obtained in a scaled‐up evaporative crystallization contain proteins truncated in the P2 region, but have no significant differences with the original samples in terms of antibody binding and overall conformational stability. These results demonstrate the promise of evaporative crystallization of the NRRV., The crystallization of an nonreplicating rotavirus vaccine (NRRV) candidate was designed by a systematic approach using first‐principles models and preliminary experimental data. The first‐principles models are applied to literature data to obtain feasible crystallization conditions and lower bounds for nucleation and growth rates. Proof‐of‐concept experiments resulted in the nucleation and growth of NRRV crystals which have no significant differences with the original samples in terms of antibody binding and overall conformational stability.

Accompanied with the growth of the biopharmaceuticals market has been the interest in developing processes with increased control of product quality attributes at low manufacturing cost, with one of the approaches being through genuinely continuous manufacturing processes. Part of this interest is in new drug product formulations that extend shelf-life and improve the patient experience. Some of these drug product formulations require the production of protein crystals of controlled size distribution. This article describes a continuous tubular crystallizer in which the size distribution of the produced protein crystals is tuned by controlling the spatial temperature along the tube. Under appropriate buffer and pH conditions, the magnitude and dispersion of product protein crystals are reproducibly manipulated using a fully controlled temperature profile over a residence time of 25 to 30 minutes, and the formation of amorphous precipitates can be achieved under higher supersaturation conditions via the addition of a concentrated precipitant for drug products in which higher solubility is desired. The tunable continuous process for protein crystallization has the potential to become a low-cost platform technology for producing protein crystals for a variety of biologic drug product formulations.

The methylotrophic yeast Pichia pastoris is widely used as a microbial host for recombinant protein production. Bioreactor models for P. pastoris can inform understanding of cellular metabolism and can be used to optimize bioreactor operation. This article constructs an extensive macroscopic bioreactor model for P. pastoris which describes substrates, biomass, total protein, other medium components, and off-gas components. Species and elemental balances are introduced to describe uptake and evolution rates for medium components and off-gas components. Additionally, a pH model is constructed using an overall charge balance, acid/base equilibria, and activity coefficients to describe production of recombinant protein and precipitation of medium components. The extent of run-to-run variability is modeled by distributions of a subset of the model parameters, which are estimated using the maximum likelihood method. Model prediction from the extensive macroscopic bioreactor model well describes experimental data with different operating conditions. The probability distributions of the model predictions quantified from the parameter distribution are quantifiably consistent with the run-to-run variability observed in the experimental data. The uncertainty description in this macroscopic bioreactor model identifies the model parameters that have large variability and provides guidance as to which aspects of cellular metabolism should be the focus of additional experimental studies. The model for medium components with pH and precipitation can be used for improving chemically defined medium by minimizing the amount of components needed while meeting cellular requirements.

Artificial neural networks are black-box models widely used to approximate nonlinear dynamical system behavior. This article proposes sufficient design criteria for stabilizing dynamic output error feedback controllers and optimal output observers for systems described by dynamic artificial neural networks (DANNs). DANNs can be written in the standard nonlinear operator form (SNOF) and as diagonal norm-bounded linear differential inclusions (DNLDIs). Expanding on past work on Lyapunov theory and matrix inequalities, criteria are derived for designing two-step observer-based dynamic output feedback controllers for discrete-time DNLDIs. The applicability of the theory is demonstrated for a nonlinear multistep chemical synthesis process modeled by a DANN under the presence of disturbances and parametric uncertainty.

Batch low-pH hold is a common processing step to inactivate enveloped viruses for biologics derived from mammalian sources. Increased interest in the transition of biopharmaceutical manufacturing from batch to continuous operation resulted in numerous attempts to adapt batch low-pH hold to continuous processing. However, control challenges with operating this system have not been directly addressed. This article describes a low-cost, column-based continuous viral inactivation system constructed with off-the-shelf components. Model-based, reaction-invariant pH controller is implemented to account for the nonlinearities with Bayesian estimation addressing variations in the operation. The residence time distribution is modeled as a plug flow reactor with axial dispersion in series with a continuously stirred tank reactor, and is periodically estimated during operation through inverse tracer experiments. The estimated residence time distribution quantifies the minimum residence time, which is used to adjust feed flow rates. Controller validation experiments demonstrate that pH and minimum residence time setpoint tracking and disturbance rejection are achieved with fast and accurate response and no instability. Viral inactivation testing demonstrates tight control of logarithmic reduction values over extended operation. This study provides tools for the design and operation of continuous viral inactivation systems in service of increasing productivity, improving product quality, and enhancing patient safety.

Manufacturing of recombinant adeno-associated virus (rAAV) viral vectors remains challenging, with low yields and low full:empty capsid ratios in the harvest. To elucidate the dynamics of recombinant viral production, we develop a mechanistic model for the synthesis of rAAV viral vectors by triple plasmid transfection based on the underlying biological processes derived from wild-type AAV. The model covers major steps starting from exogenous DNA delivery to the reaction cascade that forms viral proteins and DNA, which subsequently result in filled capsids, and the complex functions of the Rep protein as a regulator of the packaging plasmid gene expression and a catalyst for viral DNA packaging. We estimate kinetic parameters using dynamic data from literature and in-house triple transient transfection experiments. Model predictions of productivity changes as a result of the varied input plasmid ratio are benchmarked against transfection data from the literature. Sensitivity analysis suggests that (1) the poorly coordinated timeline of capsid synthesis and viral DNA replication results in a low ratio of full virions in harvest, and (2) repressive function of the Rep protein could be impeding capsid production at a later phase. The analyses from the mathematical model provide testable hypotheses for evaluation and reveal potential process bottlenecks that can be investigated., Graphical abstract, A mechanistic model for recombinant adeno-associated virus viral vector production is developed to gain insights into the viral synthesis dynamics in non-viral transfection culture. Parameter estimation and validation of the model against experimental data reveal potential bottlenecks of the process, and potential process improvements are proposed.

An approach is proposed for the design of feedback controllers for processes described by dynamic artificial neural networks (DANNs) using linear matrix inequalities. This approach uses the fact that DANNs are subsets of the standard nonlinear operator form. We then reformulate the DANN written in standard nonlinear operator form as a diagonal norm-bounded linear differential inclusion. The latter formulation is applied to linear matrix inequality-based optimizations derived using the Lyapunov method for analysis and controller synthesis. The approach is demonstrated for the design of a feedback control system for a highly nonlinear pH control problem.

This article provides a perspective on control and operations for biopharmaceutical manufacturing. Challenges and opportunities are described for (1) microscale technologies for high-speed continuous processing, (2) plug-and-play modular unit operations with integrated monitoring and control systems, (3) dynamic modeling of unit operations and entire biopharmaceutical manufacturing plants to support process development and plant-wide control, and (4) model-based control technologies for optimizing startup, changeover, and shutdown. A challenge is the ability to simultaneously address the uncertainties, nonlinearities, time delays, non-minimum phase behavior, constraints, spatial distributions, and mixed continuous-discrete operations that arise in biopharmaceutical operations. The design of adaptive and hybrid control strategies is discussed. Process data analytics and grey-box modeling methods are needed to deal with the heterogeneity and tensorial dimensionality of biopharmaceutical data. Novel bioseparations as discussed as a potential cost-effective unit operation, with a discussion of challenges for the widespread application of crystallization to therapeutic proteins.

For continuous manufacturing of pharmaceuticals, understanding the dynamics of how a material flows through the process is critical with respect to the development of a control strategy for product quality assurance. Such understanding of the process dynamics can be obtained by characterization of the residence time distribution (RTD). The RTD for a process is not fixed and can vary due to changes in operating conditions or physiochemical properties of the blend. As such the RTD needs to be evaluated over the range of operating condition that can impact process dynamics (e.g. throughput, impeller rotation rate etc.). In this paper, we demonstrate that the dimensionless RTD (normalized with respect to the mean residence time) is invariant with throughput and impeller rotation rates under certain conditions for the two continuous direct compression processes. We present a case study to illustrate the utility of this relationship for predicting the process dynamics at different operating conditions (i.e., throughputs) and evaluating the impact of variations in the process dynamics on the control strategy for a continuous direct compression process.

Microbioreactors are a promising technology to accelerate biologic drug development. In aerobic cellular respiration, a potential limit to the productivity of such systems is the transport of oxygen from an external gas to the most oxygen-deficient cells, and the potential for excessive spatially localized dissolved oxygen which can result in cellular damage. This article analytically solves a mechanistic model for the spatiotemporal transport of oxygen through a gas-permeable membrane to the cells within a microbioreactor. An analytical solution to the partial differential equations for oxygen transport is derived using the finite Fourier transform method. A parameter-adaptive extended Kalman filter is shown to produce highly accurate estimates of the oxygen uptake rate of the cells, with some fluctuation in estimates of the specific cell growth rate and the specific oxygen uptake rate. The estimates are fed to a model predictive control formulation that improves the spatial control of dissolved oxygen during cell growth by more than 30% compared to a PID controller.