Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches.

Asparagine (Asn) deamidation and aspartic acid (Asp) isomerization are common degradation pathways that affect the stability of therapeutic antibodies. These modifications can pose a significant challenge in the development of biopharmaceuticals. As such, the early engineering and selection of chemically stable monoclonal antibodies (mAbs) can substantially mitigate the risk of subsequent failure. In this study, we introduce a novel in silico approach for predicting deamidation and isomerization sites in therapeutic antibodies by analyzing the structural environment surrounding asparagine and aspartate residues. The resulting quantitative structure-activity relationship (QSAR) model was trained using previously published forced degradation data from 57 clinical-stage mAbs. The predictive accuracy of the model was evaluated for four different states of the protein structure: (1) static homology models, (2) enhancing low-frequency vibrational modes during short molecular dynamics (MD) runs, (3) a combination of (2) with a protonation state reassignment, and (4) conventional full-atomistic MD simulations. The most effective QSAR model considered the accessible surface area (ASA) of the residue, the pKa value of the backbone amide, and the root mean square deviations of both the alpha carbon and the side chain. The accuracy was further enhanced by incorporating the QSAR model into a decision tree, which also includes empirical information about the sequential successor and the position in the protein. The resulting model has been implemented as a plugin named "Forecasting Reactivity of Isomerization and Deamidation in Antibodies" in MOE software, completed with a user-friendly graphical interface to facilitate its use.

Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics.

Adequate stability, manufacturability, and safety are crucial to bringing an antibody-based biotherapeutic to the market. Following the concept of holistic in silico developability, we introduce a physicochemical description of 91 market-stage antibody-based biotherapeutics based on orthogonal molecular properties of variable regions (Fvs) embedded in different simulation environments, mimicking conditions experienced by antibodies during manufacturing, formulation, and in vivo. In this work, the evaluation of molecular properties includes conformational flexibility of the Fvs using molecular dynamics (MD) simulations. The comparison between static homology models and simulations shows that MD significantly affects certain molecular descriptors like surface molecular patches. Moreover, the structural stability of a subset of Fv regions is linked to changes in their specific molecular interactions with ions in different experimental conditions. This is supported by the observation of differences in protein melting temperatures upon addition of NaCl. A DEvelopability Navigator In Silico (DENIS) is proposed to compare mAb candidates for their similarity with market-stage biotherapeutics in terms of physicochemical properties and conformational stability. Expanding on our previous developability guidelines (Ahmed et al. Proc. Natl. Acad. Sci. 2021, 118 (37), e2020577118), the hydrodynamic radius and the protein strand ratio are introduced as two additional descriptors that enable a more comprehensive in silico characterization of biotherapeutic drug candidates. Test cases show how this approach can facilitate identification and optimization of intrinsically developable lead candidates. DENIS represents an advanced computational tool to progress biotherapeutic drug candidates from discovery into early development by predicting drug properties in different aqueous environments.

Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties along the entire pharmaceutical process chain.

The discovery of therapeutic monoclonal antibodies (mAbs) primarily focuses on their biological activity favoring the selection of highly potent drug candidates. These candidates, however, may have physical or chemical attributes that lead to unfavorable chemistry, manufacturing, and control (CMC) properties, such as low product titers, conformational and colloidal instabilities, or poor solubility, which can hamper or even prevent development and manufacturing. Hence, there is an urgent need to consider the developability of mAb candidates during lead identification and optimization. This work provides a comprehensive proof of concept study for the significantly improved developability of a mAb variant that was optimized with the help of sophisticated in silico tools relative to its difficult-to-develop parental counterpart. Interestingly, a single amino acid substitution in the variable domain of the light chain resulted in a three-fold increased product titer after stable expression in Chinese hamster ovary cells. Microscopic investigations revealed that wild type mAb-producing cells displayed potential antibody inclusions, while the in silico optimized variant-producing cells showed a rescued phenotype. Notably, the drug substance of the in silico optimized variant contained substantially reduced levels of aggregates and fragments after downstream process purification. Finally, formulation studies unraveled a significantly enhanced colloidal stability of the in silico optimized variant while its folding stability and potency were maintained. This study emphasizes that implementation of bioinformatics early in lead generation and optimization of biotherapeutics reduces failures during subsequent development activities and supports the reduction of project timelines and resources.

Adaption of human antibody λ and κ light chain architectures to CDR repertoires.

Monoclonal antibodies bind with high specificity to a wide range of diverse antigens, primarily mediated by their hypervariable complementarity determining regions (CDRs). The defined antigen binding loops are supported by the structurally conserved ß-sandwich framework of the light chain (LC) and heavy chain (HC) variable regions. The LC genes are encoded by two separate loci, subdividing the entity of antibodies into kappa (LCκ) and lambda (LCλ) isotypes that exhibit distinct sequence and conformational preferences. In this work, a diverse set of techniques were employed including machine learning, force field analysis, statistical coupling analysis and mutual information analysis of a non-redundant antibody structure collection. Thereby, it was revealed how subtle changes between the structures of LCκ and LCλ isotypes increase the diversity of antibodies, extending the predetermined restrictions of the general antibody fold and expanding the diversity of antigen binding. Interestingly, it was found that the characteristic framework scaffolds of κ and λ are stabilized by diverse amino acid clusters that determine the interplay between the respective fold and the embedded CDR loops. In conclusion, this work reveals how antibodies use the remarkable plasticity of the beta-sandwich Ig fold to incorporate a large diversity of CDR loops.

Improved Solution-State Properties of Monoclonal Antibodies by Targeted Mutations.

Monoclonal antibody (mAb)-based therapeutics often require high-concentration formulations. Unfortunately, highly concentrated antibody solutions often have biophysical properties that are disadvantageous for therapeutic development, such as high viscosity, solubility limitations, precipitation issues, or liquid-liquid phase separation. In this work, we present a computational rational design principle for improving the thermodynamic stability of mAb solutions through targeted point mutations. Two publicly available IgG1 monoclonal antibodies that exhibit high viscosity at high concentrations were used as model systems. Guided by a computationally efficient approach that combines molecular dynamics simulations with three-dimensional reference interaction site model theory, point mutations of charged residues were introduced in the variable Fv regions in such a manner that the hydration free energy was optimized. Two selected point mutants were then produced by transient expression and characterized experimentally. Both engineered mAbs have reduced viscosity at high concentration, less negative second virial coefficient, and improved solubility compared to the respective wild-types. The results obtained with the suggested straightforward design principle underline the relevance of solvation effects for understanding, and ultimately optimizing, the properties of highly concentrated mAb solutions, with possible implications also for other biomolecular systems.

High-concentration protein formulations: How high is high?

High-concentration protein formulation (HCPF) is a term that is used to describe protein formulations, mostly monoclonal antibody (mAb) drugs, at high protein concentration. The concentration is rarely defined, with typical ranges varying between 50 and 150mg/ml for mAbs. The term HCPF is meant to include and express specific solution properties of formulations that are prone to appear at high protein concentrations such as high viscosity, high opalescence, phase separation, gel formation or the increased propensity for protein particle formation. Thus the term HCPF can be understood as a descriptor of protein formulations, usually at high protein (monoclonal antibody) concentrations, which have specific solution, stability and colloidal properties that differ from formulations at low protein concentration (e.g. at 10mg/ml). The current paper highlights in brief the development challenges that might occur for high-concentration protein/monoclonal antibody formulations. In particular, the maximum concentration regimes achievable in HCPF remained unclear. Based on geometrical considerations involving packing of monoclonal antibodies in a lattice we map out a maximum concentration range that might be theoretically achievable. Different geometrical assumptions and packing models are compared and their relevance is critically discussed, in particular concerning the influence of the physicochemical properties of the monoclonal antibodies on their solubility, which is neglected in the simple geometrical model. According to our estimates, monoclonal antibody concentration above 500mg/ml will be very challenging to achieve. Our results have implications for setting up realistic drug product development strategies and for preparing convincing drug target product profiles for development.

Prediction and Reduction of the Aggregation of Monoclonal Antibodies.

Protein aggregation remains a major area of focus in the production of monoclonal antibodies. Improving the intrinsic properties of antibodies can improve manufacturability, attrition rates, safety, formulation, titers, immunogenicity, and solubility. Here, we explore the potential of predicting and reducing the aggregation propensity of monoclonal antibodies, based on the identification of aggregation-prone regions and their contribution to the thermodynamic stability of the protein. Although aggregation-prone regions are thought to occur in the antigen binding region to drive hydrophobic binding with antigen, we were able to rationally design variants that display a marked decrease in aggregation propensity while retaining antigen binding through the introduction of artificial aggregation gatekeeper residues. The reduction in aggregation propensity was accompanied by an increase in expression titer, showing that reducing protein aggregation is beneficial throughout the development process. The data presented show that this approach can significantly reduce liabilities in novel therapeutic antibodies and proteins, leading to a more efficient path to clinical studies.