Chemometrics in Protein Formulation: Stability Governed by Repulsion and Protein Unfolding.

Therapeutic proteins can be challenging to develop due to their complexity and the requirement of an acceptable formulation to ensure patient safety and efficacy. To date, there is no universal formulation development strategy that can identify optimal formulation conditions for all types of proteins in a fast and reliable manner. In this work, high-throughput characterization, employing a toolbox of five techniques, was performed on 14 structurally different proteins formulated in 6 different buffer conditions and in the presence of 4 different excipients. Multivariate data analysis and chemometrics were used to analyze the data in an unbiased way. First, observed changes in stability were primarily determined by the individual protein. Second, pH and ionic strength are the two most important factors determining the physical stability of proteins, where there exists a significant statistical interaction between protein and pH/ionic strength. Additionally, we developed prediction methods by partial least-squares regression. Colloidal stability indicators are important for prediction of real-time stability, while conformational stability indicators are important for prediction of stability under accelerated stress conditions at 40 °C. In order to predict real-time storage stability, protein-protein repulsion and the initial monomer fraction are the most important properties to monitor.

Towards an improved prediction of concentrated antibody solution viscosity using the Huggins coefficient.

The viscosity of a monoclonal antibody solution must be monitored and controlled as it can adversely affect product processing, packaging and administration. Engineering low viscosity mAb formulations is challenging as prohibitive amounts of material are required for concentrated solution analysis, and it is difficult to predict viscosity from parameters obtained through low-volume, high-throughput measurements such as the interaction parameter, kD, and the second osmotic virial coefficient, B22. As a measure encompassing the effect of intermolecular interactions on dilute solution viscosity, the Huggins coefficient, kh, is a promising candidate as a parameter measureable at low concentrations, but indicative of concentrated solution viscosity. In this study, a differential viscometry technique is developed to measure the intrinsic viscosity, [η], and the Huggins coefficient, kh, of protein solutions. To understand the effect of colloidal protein-protein interactions on the viscosity of concentrated protein formulations, the viscometric parameters are compared to kD and B22 of two mAbs, tuning the contributions of repulsive and attractive forces to the net protein-protein interaction by adjusting solution pH and ionic strength. We find a strong correlation between the concentrated protein solution viscosity and the kh but this was not observed for the kD or the b22, which have been previously used as indicators of high concentration viscosity. Trends observed in [η] and kh values as a function of pH and ionic strength are rationalised in terms of protein-protein interactions.

Self-Interactions of Two Monoclonal Antibodies: Small-Angle X-ray Scattering, Light Scattering, and Coarse-Grained Modeling.

Using light scattering (LS), small-angle X-ray scattering (SAXS), and coarse-grained Monte Carlo (MC) simulations, we studied the self-interactions of two monoclonal antibodies (mAbs), PPI03 and PPI13. With LS measurements, we obtained the osmotic second virial coefficient, B22, and the molecular weight, Mw, of the two mAbs, while with SAXS measurements, we studied the mAbs' self-interaction behavior in the high protein concentration regime up to 125 g/L. Through SAXS-derived coarse-grained representations of the mAbs, we performed MC simulations with either a one-protein or a two-protein model to predict B22. By comparing simulation and experimental results, we validated our models and obtained insights into the mAbs' self-interaction properties, highlighting the role of both ion binding and charged patches on the mAb surfaces. Our models provide useful information about mAbs' self-interaction properties and can assist the screening of conditions driving to colloidal stability.

Characterization and purification of pentameric chimeric protein particles using asymmetric flow field-flow fractionation coupled with multiple detectors.

Porcine circovirus causes the post-weaning multi-systemic wasting syndrome. Despite the existence of commercial vaccines, the development of more effective and cheaper vaccines is expected. The usage of chimeric antigens allows serological differentiation between naturally infected and vaccinated animals. In this work, recombinant pentameric vaccination protein particles spontaneously assembled from identical subunits-chimeric fusion proteins derived from circovirus capsid antigen Cap and a multimerizing subunit of mouse polyomavirus capsid protein VP1 were purified and characterized using asymmetric flow field-flow fractionation (AF4) coupled with UV and MALS/DLS (multi-angle light scattering/dynamic light scattering) detectors. Various elution profiles were tested, including constant cross-flow and decreasing cross-flow (linearly and exponentially). The optimal sample retention, separation efficiency, and resolution were assessed by the comparison of the hydrodynamic radius (Rh) measured by online DLS with the Rh values calculated from the simplified retention equation according to the AF4 theory. The results show that the use of the combined elution profiles (exponential and constant cross-flow rates) reduces the time of the separation, prevents undesirable sample-membrane interaction, and yields better resolution. Besides, the results show no self-associations of the individual pentameric particles into larger clusters and no sample degradation during the AF4 separation. The Rg/Rh ratios for different fractions are in good correlation with morphological analyses performed by transmission electron microscopy (TEM). Additionally to the online analysis, the individual fractions were subjected to offline analysis, including batch DLS, TEM, and SDS-PAGE, followed by Western blot.

Small angle X-ray scattering and molecular dynamic simulations provide molecular insight for stability of recombinant human transferrin.

Transferrin is an attractive candidate for drug delivery due to its ability to cross the blood brain barrier. However, in order to be able to use it for therapeutic purposes, it is important to investigate how its stability depends on different formulation conditions. Combining high-throughput thermal and chemical denaturation studies with small angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations, it was possible to connect the stability of transferrin with its conformational changes. Lowering pH induces opening of the transferrin N-lobe, which results in a negative effect on the stability. Presence of NaCl or arginine at low pH enhances the opening and has a negative impact on the overall protein stability. STATEMENT OF SIGNIFICANCE: Protein-based therapeutics have become an essential part of medical treatment. They are highly specific, have high affinity and fewer off-target effects. However, stabilization of proteins is critical, time-consuming, and expensive, and it is not yet possible to predict the behavior of proteins under different conditions. The current work is focused on a molecular understanding of the stability of human serum transferrin; a protein which is abundant in blood serum, may pass the blood brain barrier and therefore with high potential in drug delivery. Combination of high throughput unfolding techniques and structural studies, using small angle X-ray scattering and molecular dynamic simulations, allows us to understand the behavior of transferrin on a molecular level.

Application of machine learning to predict monomer retention of therapeutic proteins after long term storage.

An important aspect of initial developability assessments as well formulation development and selection of therapeutic proteins is the evaluation of data obtained under accelerated stress condition, i.e. at elevated temperatures. We propose the application of artificial neural networks (ANNs) to predict long term stability in real storage condition from accelerated stability studies and other high-throughput biophysical properties e.g. the first apparent temperature of unfolding (Tm). Our models have been trained on therapeutic relevant proteins, including monoclonal antibodies, in various pharmaceutically relevant formulations. Further, we developed network architectures with good prediction power using the least amount of input features, i.e. experimental effort to train the network. This provides an empiric means to highlight the most important parameters in the prediction of real-time protein stability. Further, several models were developed by a different validation means (i.e. leave-one-protein-out cross-validation) to test the robustness and the limitations of our approach. Finally, we apply surrogate machine learning algorithms (e.g. linear regression) to build trust in the ANNs decision making procedure and to highlight the connection between the leading inputs and the outputs.

Characterization of Native Reversible Self-Association of a Monoclonal Antibody Mediated by Fab-Fab Interaction.

The native reversible self-association of monoclonal antibodies has been associated with high viscosity, liquid-liquid, and liquid-solid phase separation. We investigated the native reversible self-association of an IgG1, which exerts this association even at low protein concentrations, in detail to gain further understanding of this phenomenon by extensive characterization of the association as a function of multiple factors, namely pH, temperature, salt concentration, and protein concentration. The nature of the self-association of the full-length IgG1 as well as the corresponding Fab and Fc fragment was studied by viz. size exclusion chromatography combined with multiangle light scattering, batch dynamic and static light scattering, analytical ultracentrifugation, small angle X-ray scattering, asymmetric flow field flow fractionation coupled with multiangle light scattering, and intrinsic fluorescence. We rationalized the self-association as a combination of hydrophobic and electrostatic interactions driven by the Fab fragments. Finally, we investigated the long-term stability of the IgG1 molecule.

Advancing Therapeutic Protein Discovery and Development through Comprehensive Computational and Biophysical Characterization.

Therapeutic protein candidates should exhibit favorable properties that render them suitable to become drugs. Nevertheless, there are no well-established guidelines for the efficient selection of proteinaceous molecules with desired features during early stage development. Such guidelines can emerge only from a large body of published research that employs orthogonal techniques to characterize therapeutic proteins in different formulations. In this work, we share a study on a diverse group of proteins, including their primary sequences, purity data, and computational and biophysical characterization at different pH and ionic strength. We report weak linear correlations between many of the biophysical parameters. We suggest that a stability comparison of diverse therapeutic protein candidates should be based on a computational and biophysical characterization in multiple formulation conditions, as the latter can largely determine whether a protein is above or below a certain stability threshold. We use the presented data set to calculate several stability risk scores obtained with an increasing level of analytical effort and show how they correlate with protein aggregation during storage. Our work highlights the importance of developing combined risk scores that can be used for early stage developability assessment. We suggest that such scores can have high prediction accuracy only when they are based on protein stability characterization in different solution conditions.

Coupling Multi-Angle Light Scattering to Reverse-Phase Ultra-High-Pressure Chromatography (RP-UPLC-MALS) for the characterization monoclonal antibodies.

Multi-angle light scattering coupled with size-exclusion chromatography (SEC-MALS) is a standard approach for protein characterization. Recently MALS detection has been coupled with ion-exchange chromatography (IEX) which demonstrated the feasibility and high value of MALS in combination with non-sized-based fractionation methods. In this study we coupled reverse-phase ultra-high pressure liquid chromatography (RP-UPLC) with a low-dispersion MALS detector for the characterization of intact monoclonal antibody (mAbs) and their fragments. We confirmed a constant refractive index increment value for mAbs in RP gradients, in good agreement with the values in literature for other classes of proteins. We showed that the impurities eluting from a RP column can often be related to aggregated species and we confirmed that in most cases those oligomers are present also in SEC-MALS. Yet, in few cases small aggregates fractions in RP-UPLC are an artifact. In fact, proteins presenting thermal and physical stability not suitable for the harsh condition applied during the RP separation of mAbs (i.e. organic solvents at high temperature) can aggregate. Further, we applied RP-UPLC-MALS during a long term stability studies. The different principle of separation used in RP-UPLC- MALS provides an additional critical level of protein characterization compared to SEC-MALS and IEX-MALS.

Application of interpretable artificial neural networks to early monoclonal antibodies development.

The development of a new protein drug typically starts with the design, expression and biophysical characterization of many different protein constructs. The initially high number of constructs is radically reduced to a few candidates that exhibit the desired biological and physicochemical properties. This process of protein expression and characterization to find the most promising molecules is both expensive and time-consuming. Consequently, many companies adopt and implement philosophies, e.g. platforms for protein expression and formulation, computational approaches, machine learning, to save resources and facilitate protein drug development. Inspired by this, we propose the use of interpretable artificial neuronal networks (ANNs) to predict biophysical properties of therapeutic monoclonal antibodies i.e. melting temperature Tm, aggregation onset temperature Tagg, interaction parameter kD as a function of pH and salt concentration from the amino acid composition. Our ANNs were trained with typical early-stage screening datasets achieving high prediction accuracy. By only using the amino acid composition, we could keep the ANNs simple which allows for high general applicability, robustness and interpretability. Finally, we propose a novel "knowledge transfer" approach, which can be readily applied due to the simple algorithm design, to understand how our ANNs come to their conclusions.

A New Approach to Study the Physical Stability of Monoclonal Antibody Formulations-Dilution From a Denaturant.

The early-stage assessment of the physical stability of new monoclonal antibodies in different formulations is often based on high-throughput techniques that suffer from various drawbacks. Accordingly, new approaches that facilitate the protein formulation development can be of high value to the industry. In this study, a dynamic light scattering plate reader is used to measure the aggregation (by means of the increase in the hydrodynamic radius [Rh]) of monoclonal antibody samples that were subject to incubation and subsequent dilution from different concentrations of a denaturing agent, that is, guanidine hydrochloride. The increase in the Rh of the protein samples is dependent not only on the denaturant concentration used but also on the buffer in which the incubation/dilution was performed. We also compare the aggregation after dilution from a denaturant with other high-throughput stability-indicating methods and find good agreement between the techniques. The proposed approach to probe the physical stability of monoclonal antibodies in different formulation conditions offers a unique combination of features-it is isothermal, probes both the resistance to denaturant-induced unfolding and the colloidal protein stability, it is entirely label-free, does not rely on complex data evaluation, and requires very short instrument measurement time on standard equipment.

Self-Association of Apo A-1 Studied with Dynamic and Static Light Scattering.

Static and dynamic light scattering were employed to determine simultaneously the average relative molecular mass, Mr, and the average hydrodynamic radius, Rh, of protein molecules. The new method was applied to the association-dissociation equilibrium of apolipoprotein A-1 (Apo A-1) and its thermal unfolding. As a control, lysozyme was measured as a nonassociating protein. Apo A-1 forms oligomers as a function of concentration and temperature, and the equilibrium can be described by a cooperative association model, consisting of a nucleation step and a growth step. At concentrations of 1 and 2.7 mg/mL, the Apo A-1 solution contained mainly monomers and octamers, with intermediates occurring at very low concentrations. Oligomer formation was maximal at 22 °C and was characterized by a temperature-dependent association constant. The cooperative association model allows the quantitative analysis of both the average relative molecular mass, Mr, and the average hydrodynamic radius, Rh, with the same set of model parameters which, in turn, are also applicable to analytical ultracentrifugation experiments. The light scattering experiments were reversible as long as the Apo A-1 solution was not heated above 60 °C.

Two competing reactions of tetrabutylammonium alginate in organic solvents: Amidation versus γ-lactone synthesis.

Biocompatibility and thickening properties predetermine alginates as ingredients in food, cosmetic and pharmaceutical products. Further chemical modifications are often desired for a product optimization. The introduction of hydrophobic groups can be realized by employing organic tetrabutylammonium alginate (TBA-Alg) solutions. The synthesis of alginic acid alkyl amides from TBA-Alg with 2-chloro-1-methylpyridinium iodide (CMPI) as a coupling agent, however, has so far not resulted in a high degree of amidation. The analysis of the coupling reaction revealed the formation of mannuronic acid γ-lactone structures, which required a conformation change from (1)C4 to (4)C1. The opening of the γ-lactone required a high excess of butylamine. In the case of CMPI, triethylamine had to be added prior to the coupling agent in order to suppress the assumed alginic acid formation. The degrees of amidation achieved were up to 0.8, and for propylphosphonic anhydride as the coupling agent up to 1. The molecular weights of the alginic acid butyl amide were ≥35kDa.

Synthesis enhancements for generating highly soluble tetrabutylammonium alginates in organic solvents.

The adaptation of alginates to food and pharmaceutical specifications is limited to aqueous chemistry due to the insolubility of sodium alginate (Na-Alg) and the insufficient solubility of tetrabutylammonium alginate (TBA-Alg) in organic solvents. In the present investigation, these restrictions were resolved by optimizing the solubility of TBA-Alg by improving its synthesis from Na-Alg via heterogeneous acidification with hydrochloric and formic acid, followed by neutralization with tetrabutylammonium hydroxide. The best acidification results were achieved with formic acid, because the reaction controlling solubility of the by-product in the acidic solvent was improved in comparison to hydrochloric acid. The solubility of TBA-Alg in polar aprotic organic solvents improved by increasing the degree of TBA substitution (DSTBA), decreasing the molecular weight of TBA-Alg and increasing the relative permittivity of the solvent. The best TBA-Algs, with DSTBA=0.95 and relative high molecular weights, gave optically clear solutions with a turbidity of about 1 NTU.

Asymmetrical flow field-flow fractionation with multi-angle light scattering detection for the analysis of structured nanoparticles.

Synthesis and applications of new functional nanoparticles are topics of increasing interest in many fields of nanotechnology. Chemical modifications of inorganic nanoparticles are often necessary to improve their features as spectroscopic tracers or chemical sensors, and to increase water solubility and biocompatibility for applications in nano-biotechnology. Analysis and characterization of structured nanoparticles are then key steps for their synthesis optimization and final quality control. Many properties of structured nanoparticles are size-dependent. Particle size distribution analysis then provides fundamental analytical information. Asymmetrical flow field-flow fractionation (AF4) with multi-angle light scattering (MALS) detection is able to size-separate and to characterize nanosized analytes in dispersion. In this work we focus on the central role of AF4-MALS to analyze and characterize different types of structured nanoparticles that are finding increasing applications in nano-biotechnology and nanomedicine: polymer-coated gold nanoparticles, fluorescent silica nanoparticles, and quantum dots. AF4 not only size-fractionated these nanoparticles and measured their hydrodynamic radius (r(h)) distribution but it also separated them from the unbound, relatively low-M(r) components of the nanoparticle structures which were still present in the sample solution. On-line MALS detection on real-time gave the gyration radius (r(g)) distribution of the fractionated nanoparticles. Additional information on nanoparticle morphology was then obtained from the r(h)/r(g) index. Stability of the nanoparticle dispersions was finally investigated. Aggregation of the fluorescent silica nanoparticles was found to depend on the concentration at which they were dispersed. Partial release of the polymeric coating from water-soluble QDs was found when shear stress was induced by increasing flowrates during fractionation.