The Future of Biomanufacturing: In Silico Modelling

Whilst the market holds much potential, it is currently only in the early stages of development and is largely driven by academic efforts. Key commercial players offering services to the industry include:

Company	In Silico Offerings
	iTope™ and T Cell Epitope Database (TCED™) are in silico tools to rapidly screen antibodies and proteins for potential immunogenicity.
	Codexis’ proprietary machine learning software helps determine the additive effect of individual mutations.
	Sensitive Immunogenicity Assessment Technology® (SIAT®). SIAT® in silico immunogenicity assessment is based on modern bioinformatics techniques in combination with experimental approaches and can be applied to the prediction of the immunogenicity of biotherapeutic drug candidates including protein, enzyme, antibody, ADC, etc.
	Provides ChromX a versatile simulation software application for preparative liquid chromatography processes, that simplifies model-based process development.
	Insilico's technology is built around Insilico Cells™, which are genome-based mechanistic network models of common host organisms that are used to produce biologics. These models are refined and calibrated using real-life bioprocess measurements to become an individualized representation of the specific cells in silico. Based on simulations using such models, the behaviour of cells in a bioprocess can be simulated and understood for an almost unlimited number of scenarios.
	SuperPro Designer is used throughout the Life Cycle of Product Development and Commercialization to facilitate process optimization, cycle time reduction, improve team collaboration, and shorten the time to market.
	Lonza’s Epibase® In Silico platform is a patented T cell epitope screening tool for the identification of potential epitopes in biotherapeutic protein and antibody targets. The tool is driven by sequence information, structural bioinformatics, and experimental data.

Most commercial service developments are focused on epitopic screening to identify the optimal antibody for manufacturing purposes. It should be noted that the industry, as defined by companies developing biotherapeutics, is focused on in silico approaches/applications to chromatographic design and fluid dynamics. This is due the fact that the scale up of both is non-linear.

Table 2 below outlines industry players that are currently developing in silico approaches in-house:

Company	In Silico Offerings
	Developing a highly predictive model for a mAb CEX process. The model uses a novel pH dependent multi-state steric mass action isotherm, calibrated with a limited set of targeted bench scale experiments. The model is validated against a larger set of bench scale experiments. Finally, the model is applied to commercial scale, the results of which can be used for regulatory filing. Amgen are also developing Computational Fluid Dynamics (CFD)-based modelling frameworks for optimizing mixing and aeration-agitation strategies in mixing tanks and bioreactors.
	Implementing computational fluid dynamics (CFD) to guide scale up of cell culture bioreactors.
	Developing mathematical models to identify the fouling mechanism in depth filters for cell culture clarification.
	Developing machine learning approaches for streamlining downstream process development.
	Developing mechanistic modelling to study the factors leading to differences in pool sizes observed between scales, and to make predictions on lab scale pool sizes from miniature column data. Also investigating biologics process development enabled via data mining and quantitative structure analysis.
	Developing models based on the Donnan equilibrium (DIX) or the Steric Mass Action (SMA) principle were used to predict the elution behaviour of these mAb variants.
	Developing process simulations in a clinical scale single-use TFF system examine blend time efficiency.
	Developing predictive adsorption models which can reduce screening spaces to specific windows of interest tailored to individual molecules prior to process development.
	Using Bayesian techniques, chromatography data from high-throughput and traditional lab-scale DOE studies are combined with manufacturing-scale data to generate models accounting for scaling-offsets.
	Developing phase-appropriate in silico models.

For the concept to expand and provide a holistic system that can facilitate modelling from beginning to end (i.e.: USP through to DSP at scale), access to information such as current process informatics and data historian systems will need to be accessible industry-wide. Such data is the largely the intellectual property of organisations that are known manufacturers of market scale biologics, and as such the sharing of key intellectual property that could simplify the process of competing companies creating biosimilars is unlikely to occur.