Ortho-Substituent Effects on Halogen Bond Geometry for N-Haloimide⋯2-Substituted Pyridine Complexes

The nature of (imide)N-X⋯N(pyridine) halogen-bonded complexes formed by six N-haloimides and sixteen 2-substituted pyridines are studied using X-ray crystallography (68 crystal structures), Density Functional Theory (DFT) (86 complexation energies), and NMR spectroscopy (90 association constants). Strong halogen bond (XB) donors such as N-iodosuccinimide form only 1:1 haloimide:pyridine crystalline complexes, but even stronger N-iodosaccharin forms 1:1 haloimide:pyridine and three other distinct complexes. In 1:1 haloimide:pyridine crystalline complexes, the haloimide's N─X bond exhibits an unusual bond bending feature that is larger for stronger N-haloimides. DFT complexation energies (ΔEXB ) for iodoimide-pyridine complexes range from -44 to -99 kJ mol-1 , while for N-bromoimide-pyridine, they are between -31 and -77 kJ mol-1 . The ΔEXB of I⋯N XBs in 1:1 iodosaccharin:pyridine complexes are the largest of their kind, but they are substantially smaller than those in [bis(saccharinato)iodine(I)]pyridinium salts (-576 kJ mol-1 ), formed by N-iodosaccharin and pyridines. The NMR association constants and ΔEXB energies of 1:1 haloimide:pyridine complexes do not correlate as these complexes in solution are heavily influenced by secondary interactions, which DFT studies do not account for. Association constants follow the σ-hole strengths of N-haloimides, which agree with DFT and crystallography data. The haloimide:2-(N,N-dimethylamino)pyridine complex undergoes a halogenation reaction resulting in 5-iodo-2-dimethylaminopyridine.

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.

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, M_w, 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.

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, k_D, and the second osmotic virial coefficient, B₂₂. As a measure encompassing the effect of intermolecular interactions on dilute solution viscosity, the Huggins coefficient, k_h, 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, k_h, 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 k_D 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 k_h but this was not observed for the k_D or the b₂₂, which have been previously used as indicators of high concentration viscosity. Trends observed in [η] and k_h values as a function of pH and ionic strength are rationalised in terms of protein-protein interactions.

Short X···N Halogen Bonds With Hexamethylenetetraamine as the Acceptor

Hexamethylenetetramine (HMTA) and N-haloimides form two types of short (imide)X···N and X–X···N (X = Br, I) halogen bonds. Nucleophilic substitution or ligand-exchange reaction on the peripheral X of X–X···N with the chloride of N-chlorosuccinimide lead to Cl–X···N halogen-bonded complexes. The 1:1 complexation of HMTA and ICl manifests the shortest I···N halogen bond [2.272(5) Å] yet reported for an HMTA acceptor. Two halogen-bonded organic frameworks are prepared using 1:4 molar ratio of HMTA and N-bromosuccinimide, each with a distinct channel shape, one possessing oval and the other square grid. The variations in channel shapes are due to tridentate and tetradentate (imide)Br···N coordination modes of HMTA. Density Functional Theory (DFT) studies are performed to gain insights into (imide)X···N interaction strengths (ΔEint). The calculated ΔEint values for (imide)Br···N (−11.2 to −12.5 kcal/mol) are smaller than the values for (imide)I···N (−8.4 to −29.0 kcal/mol). The DFT additivity analysis of (imide)Br···N motifs demonstrates Br···N interaction strength gradually decreasing from 1:1 to 1:3 HMTA:N-bromosuccinimide complexes. Exceptionally similar charge density values ρ(r) for N–I covalent bond and I···N non-covalent bond of a (saccharin)N–I···N motif signify the covalent character for I···N halogen bonding.

Short X···N Halogen Bonds With Hexamethylenetetraamine as the Acceptor

Hexamethylenetetramine (HMTA) and N-haloimides form two types of short (imide)X···N and X-X···N (X = Br, I) halogen bonds. Nucleophilic substitution or ligand-exchange reaction on the peripheral X of X-X···N with the chloride of N-chlorosuccinimide lead to Cl-X···N halogen-bonded complexes. The 1:1 complexation of HMTA and ICl manifests the shortest I···N halogen bond [2.272(5) Å] yet reported for an HMTA acceptor. Two halogen-bonded organic frameworks are prepared using 1:4 molar ratio of HMTA and N-bromosuccinimide, each with a distinct channel shape, one possessing oval and the other square grid. The variations in channel shapes are due to tridentate and tetradentate (imide)Br···N coordination modes of HMTA. Density Functional Theory (DFT) studies are performed to gain insights into (imide)X···N interaction strengths (ΔE_int). The calculated ΔE_int values for (imide)Br···N (-11.2 to -12.5 kcal/mol) are smaller than the values for (imide)I···N (-8.4 to -29.0 kcal/mol). The DFT additivity analysis of (imide)Br···N motifs demonstrates Br···N interaction strength gradually decreasing from 1:1 to 1:3 HMTA:N-bromosuccinimide complexes. Exceptionally similar charge density values ρ(r) for N-I covalent bond and I···N non-covalent bond of a (saccharin)N-I···N motif signify the covalent character for I···N halogen bonding.

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

•

Combination of SAXS and MD simulations can explain the molecular background for the stability studies.

•

Destabilizing excipients in general induce opening of the N-lobe by binding to specific residues in the protein.

•

The fully open conformation of transferrin is not seen without aggregation.

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 (T_m). 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.

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.