Model-based approach for predicting the impact of genetic modifications on product yield in biopharmaceutical manufacturing-Application to influenza vaccine production

Efficient Simulation of Viral Transduction and Propagation for Biomanufacturing

The design of biomanufacturing platforms based on viral transduction and/or propagation poses significant challenges at the intersection between synthetic biology and process engineering. This paper introduces vitraPro, a software toolkit composed of a multiscale model and an efficient numeric technique that can be leveraged for determining genetic and process designs that optimize transduction-based biomanufacturing platforms and viral amplification processes. Viral infection and propagation for up to two viruses simultaneously can be simulated through the model, considering viruses in either the lytic or lysogenic stage, during batch, perfusion, or continuous operation. The model estimates the distribution of the viral genome(s) copy number in the cell population, which is an indicator of transduction efficiency and viral genome stability. The infection age distribution of the infected cells is also calculated, indicating how many cells are in an infection stage compatible with recombinant product expression or viral amplification. The model can also consider the presence of defective interfering particles in the system, which can severely compromise the productivity of biomanufacturing processes. Model benchmarking and validation are demonstrated for case studies of the baculovirus expression vector system and influenza A propagation in suspension cultures.

Mechanistic modeling of viral particle production

Viral systems such as wild-type viruses, viral vectors, and virus-like particles are essential components of modern biotechnology and medicine. Despite their importance, the commercial-scale production of viral systems remains highly inefficient for multiple reasons. Computational strategies are a promising avenue for improving process development, optimization, and control, but require a mathematical description of the system. This article reviews mechanistic modeling strategies for the production of viral particles, both at the cellular and bioreactor scales. In many cases, techniques and models from adjacent fields such as epidemiology and wild-type viral infection kinetics can be adapted to construct a suitable process model. These process models can then be employed for various purposes such as in-silico testing of novel process operating strategies and/or advanced process control.

Quantification methods for viruses and virus-like particles applied in biopharmaceutical production processes

INTRODUCTION

Effective cell-based production processes of virus particles are the foundation for the global availability of classical vaccines, gene therapeutic vectors, and viral oncolytic treatments. Their production is subject to regulatory standards ensuring the safety and efficacy of the pharmaceutical product. Process analytics must be fast and reliable to provide an efficient process development and a robust process control during production. Additionally, for the product release, the drug compound and the contaminants must be quantified by assays specified by regulatory authorities.

AREAS COVERED

This review summarizes analytical methods suitable for the quantification of viruses or virus-like particles. The different techniques are grouped by the analytical question that may be addressed. Accordingly, methods focus on the infectivity of the drug component on the one hand, and on particle counting and the quantification of viral elements on the other hand. The different techniques are compared regarding their advantages, drawbacks, required assay time, and sample throughput.

EXPERT OPINION

Among the technologies summarized, a tendency toward fast methods, allowing a high throughput and a wide applicability, can be foreseen. Driving forces for this progress are miniaturization and automation, and the continuous enhancement of process-relevant databases for a successful future process control.

Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering

For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently developed model for biopolymer production. The results indicate that the the current state of the systems can be accurately reconstructed and therefore represent a promising tool for further application in advanced model-based control not only of the considered processes but also of related processes.

Approximate Moment Methods for Population Balance Equations in Particulate and Bioengineering Processes

Population balance modeling is an established framework to describe the dynamics of particle populations in disperse phase systems found in a broad field of industrial, civil, and medical applications. The resulting population balance equations account for the dynamics of the number density distribution functions and represent (systems of) partial differential equations which require sophisticated numerical solution techniques due to the general lack of analytical solutions. A specific class of solution algorithms, so-called moment methods, is based on the reduction of complex models to a set of ordinary differential equations characterizing dynamics of integral quantities of the number density distribution function. However, in general, a closed set of moment equations is not found and one has to rely on approximate closure methods. In this contribution, a concise overview of the most prominent approximate moment methods is given.