Highland games: A benchmarking exercise in predicting biophysical and drug properties of monoclonal antibodies from amino acid sequences.

Biopharmaceutical product and process development do not yet take advantage of predictive computational modeling to nearly the degree seen in industries based on smaller molecules. To assess and advance progress in this area, spirited coopetition (mutually beneficial collaboration between competitors) was successfully used to motivate industrial scientists to develop, share, and compare data and methods which would normally have remained confidential. The first "Highland Games" competition was held in conjunction with the October 2018 Recovery of Biological Products Conference in Ashville, NC, with the goal of benchmarking and assessment of the ability to predict development-related properties of six antibodies from their amino acid sequences alone. Predictions included purification-influencing properties such as isoelectric point and protein A elution pH, and biophysical properties such as stability and viscosity at very high concentrations. Essential contributions were made by a large variety of individuals, including companies which consented to provide antibody amino acid sequences and test materials, volunteers who undertook the preparation and experimental characterization of these materials, and prediction teams who attempted to predict antibody properties from sequence alone. Best practices were identified and shared, and areas in which the community excels at making predictions were identified, as well as areas presenting opportunities for considerable improvement. Predictions of isoelectric point and protein A elution pH were especially good with all-prediction average errors of 0.2 and 1.6 pH unit, respectively, while predictions of some other properties were notably less good. This manuscript presents the events, methods, and results of the competition, and can serve as a tutorial and as a reference for in-house benchmarking by others. Organizations vary in their policies concerning disclosure of methods, but most managements were very cooperative with the Highland Games exercise, and considerable insight into common and best practices is available from the contributed methods. The accumulated data set will serve as a benchmarking tool for further development of in silico prediction tools.

Biocompatible fluorescent nanoparticles for in vivo stem cell tracking.

Efficient application of stem cells to the treatment of neurodegenerative diseases requires safe cell tracking to follow stem cell fate over time in the host environment after transplantation. In this work, for the first time, fluorescent and biocompatible methyl methacrylate (MMA)-based nanoparticles (fluoNPs) were synthesized through a free-radical co-polymerization process with a fluorescent macromonomer obtained by linking Rhodamine B and hydroxyethyl methacrylate. We demonstrate that the fluoNPs produced by polymerization of MMA-Rhodamine complexes (1) were efficient for the labeling and tracking of multipotent human amniotic fluid cells (hAFCs); (2) did not alter the main biological features of hAFCs (such as viability, cell growth and metabolic activity); (3) enabled us to determine the longitudinal bio-distribution of hAFCs in different brain areas after graft in the brain ventricles of healthy mice by a direct fluorescence-based technique. The reliability of our approach was furthermore confirmed by magnetic resonance imaging analyses, carried out by incubating hAFCs with both superparamagnetic iron oxide nanoparticles and fluoNPs. Our data suggest that these finely tunable and biocompatible fluoNPs can be exploited for the longitudinal tracking of stem cells.

Protein adsorption in polyelectrolyte brush type cation-exchangers.

Ion exchange chromatography materials functionalized with polyelectrolyte brushes (PEB) are becoming an integral part of many protein purification steps. Adsorption onto these materials is different than that onto traditional materials, due to the 3D partitioning of proteins into the polyelectrolyte brushes. Despite this mechanistic difference, many works have described the chromatographic behavior of proteins on polyelectrolyte brush type ion exchangers with much of the same methods as used for traditional materials. In this work, unconventional chromatographic behavior on polyelectrolyte brush type materials is observed for several proteins: the peaks shapes reveal first anti-Langmuirian and then Langmuirian types of interactions, with increasing injection volumes. An experimental and model based description of these materials is carried out in order to explain this behavior. The reason for this behavior is shown to be the 3D partitioning of proteins into the polyelectrolyte brushes: proteins that fully and readily utilize the 3D structure of the PEB phase during adsorption show this behavior, whereas those that do not show traditional ion exchange behavior.

Model-based high-throughput design of ion exchange protein chromatography.

This work describes the development of a model-based high-throughput design (MHD) tool for the operating space determination of a chromatographic cation-exchange protein purification process. Based on a previously developed thermodynamic mechanistic model, the MHD tool generates a large amount of system knowledge and thereby permits minimizing the required experimental workload. In particular, each new experiment is designed to generate information needed to help refine and improve the model. Unnecessary experiments that do not increase system knowledge are avoided. Instead of aspiring to a perfectly parameterized model, the goal of this design tool is to use early model parameter estimates to find interesting experimental spaces, and to refine the model parameter estimates with each new experiment until a satisfactory set of process parameters is found. The MHD tool is split into four sections: (1) prediction, high throughput experimentation using experiments in (2) diluted conditions and (3) robotic automated liquid handling workstations (robotic workstation), and (4) operating space determination and validation. (1) Protein and resin information, in conjunction with the thermodynamic model, is used to predict protein resin capacity. (2) The predicted model parameters are refined based on gradient experiments in diluted conditions. (3) Experiments on the robotic workstation are used to further refine the model parameters. (4) The refined model is used to determine operating parameter space that allows for satisfactory purification of the protein of interest on the HPLC scale. Each section of the MHD tool is used to define the adequate experimental procedures for the next section, thus avoiding any unnecessary experimental work. We used the MHD tool to design a polishing step for two proteins, a monoclonal antibody and a fusion protein, on two chromatographic resins, in order to demonstrate it has the ability to strongly accelerate the early phases of process development.

Protein adsorption on ion exchange resins and monoclonal antibody charge variant modulation.

A novel multicomponent adsorption equilibrium model for proteins on ion-exchange resins is developed on a statistical thermodynamic basis including surface coverage effects and protein-resin and protein-protein interactions. The resulting model exhibits a general competitive Langmuirian behavior and was applied to the study and optimization of the separation of monoclonal antibody charge variants on two strong cation exchangers. The model accounts explicitly for the effect of both pH and salt concentration, and its parameters can be determined in diluted conditions, that is, through physically sound assumptions, all model parameters can be obtained using solely experiments in diluted conditions, and be used to make predictions in overloaded conditions. The parameterization of the model and optimization of the separation is based on a two-step approach. First, gradient experiments in diluted conditions are undertaken in order to determine the model parameters. Based on these experiments and on information about the proteins of interest and the stationary phase used, all the model parameters can be estimated. Second, using the parameterized model, an initial Pareto optimization is undertaken where overloaded operating conditions are investigated. Experiments from this Pareto set are then used to refine the estimation of the model parameters. A second Pareto optimization can then be undertaken, this time with the refined parameters. This can be repeated until a satisfactory set of model parameters is found. This iterative approach is shown to be extremely efficient and to provide large amounts of knowledge based on only a few experiments. It is shown that due to the strong physical foundation of the model and the very low number of adjustable parameters, the number of iterations is expected to be at most two or three. Furthermore, the model based tool is improved as more experimental knowledge is provided, allowing for better estimations of the chromatographic processes considered at each iteration. This makes it a very suitable tool for the design and the development of preparative and industrial purification processes, including the determination of both the optimal operating conditions, as well as the allowable process operating space.

Optimization of reversed-phase chromatography methods for peptide analytics.

The analytical description and quantification of peptide solutions is an essential part in the quality control of peptide production processes and in peptide mapping techniques. Traditionally, an important tool is analytical reversed phase liquid chromatography. In this work, we develop a model-based tool to find optimal analytical conditions in a clear, efficient and robust manner. The model, based on the Van't Hoff equation, the linear solvent strength correlation, and an analytical solution of the mass balance on a chromatographic column describing peptide retention in gradient conditions is used to optimize the analytical scale separation between components in a peptide mixture. The proposed tool is then applied in the design of analytical reversed phase liquid chromatography methods of five different peptide mixtures.

Model-based description of peptide retention on doped reversed-phase media.

Reversed-phase (RP) chromatography is one of the main tools for the preparative purification of therapeutic peptides. In previous works [1,2], a new type of RP chromatography, doped reversed-phase chromatography (DRP) was presented. By adding small amounts (up to 15% of the surface ligands) of repulsive ion exchange ligands to a traditional RP material, significant improvements in peptide purification performance were observed, at the same or in similar operating conditions. These improvements included increases in selectivity in diluted conditions (up to twice as high), increases in yield in preparative conditions (up to 20% higher) and in productivity in preparative conditions (up to twice as high), when compared to RP materials [2]. A proper physical model is developed in this work to quantitatively explain and rationalize this behavior. The developed model is then used to correctly fit the retention data of several peptides in different buffering conditions. The increase in selectivity is related to a controlled decrease in free surface area available for adsorption due to the ionic ligands creating a repulsive sphere the analytes cannot enter. This decrease in adsorption surface is calculated using Debye-Hückel theory, and in combination with linear solvent strength theory, allows for the quantitative description of peptide retention on DRP media.

Strong cation-exchange chromatography of proteins on a sulfoalkylated monolithic cryogel.

A new strong cation exchanger (SCX) monolithic column was synthesized by at-line surface modification of a cryogel prepared by copolymerization of 2-hydroxyethylmethacrylate (HEMA) and glycidylmethacrylate (GMA). Sodium salt of 3-Mercaptopropane sulfonic acid (3-MPS) was used as the ligand to transform the surface of the monolith into a strong cation exchanger. The obtained material was characterized in terms of elemental analysis, infrared spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Brunauer-Emmett-Teller (BET) N2 adsorption, and used as a stationary phase for strong-cation exchange chromatography of some proteins, such as α-chymotrypsinogen, cytochrome c and lysozyme. Water permeability of the column was calculated according to Darcy's law (2.66×10(-13)m(2)). The performance of the monolithic cryogel column was evaluated on the basis of Height Equivalent to a Theoretical Plate (HETP). Retention behavior of the studied proteins was modeled on the basis of Yamamoto model to understand the role of the ion-exchange mechanism in retention behaviors. The considered proteins were successfully separated, and the obtained chromatogram was compared with that obtained with a non-functionalized cryogel column.

Doping reversed-phase media for improved peptide purification.

The purification of therapeutic peptides is most often performed using one or more reversed phase chromatography steps. This ensures high purities while keeping the costs of purification under control. In this paper, a doped reversed phase chromatographic material is tested and compared to traditional reversed phase materials. The doping consists of adding limited amounts of ion exchange ligands to the surface of the material to achieve orthogonal separation and increase the non-hydrophobic interactions with the surface. These ionic groups can either be attractive (opposite charge), or repulsive (same charge) to the peptide. The benefit of this new doped reversed phase material is shown through increases in selectivity in diluted conditions and yield and productivity in overloaded (i.e. industrial) conditions. It is the conjectured that all performance characteristics should increase using repulsive doping groups, whereas these characteristics should decrease when using attractive doping groups. This conjecture is shown to be true through several examples, including purifications of industrially relevant peptide crudes, in industrially relevant conditions. Moreover, the effect of ionic strength and organic modifier concentration was explored and shown to be in line with the expected behavior.