How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.

Therapeutic monoclonal antibodies and their derivatives are key components of clinical pipelines in the global biopharmaceutical industry. The availability of large datasets of antibody sequences, structures, and biophysical properties is increasingly enabling the development of predictive models and computational tools for the "developability assessment" of antibody drug candidates. Here, we provide an overview of the antibody informatics tools applicable to the prediction of developability issues such as stability, aggregation, immunogenicity, and chemical degradation. We further evaluate the opportunities and challenges of using biopharmaceutical informatics for drug discovery and optimization. Finally, we discuss the potential of developability guidelines based on in silico metrics that can be used for the assessment of antibody stability and manufacturability.

In Silico Prediction of Diffusion Interaction Parameter (kD), a Key Indicator of Antibody Solution Behaviors.

PURPOSE: To develop resource-sparing in silico approaches that aim to reduce experimental effort and material required by developability assessments (DA) of monoclonal antibody (mAb) drug candidates. METHODS: A battery of standardized biophysical experiments was performed on high concentration formulations of 16 drug product development stage mAbs using a platform buffer. Full-length molecular models of these mAbs were also generated via molecular modeling. These models were used to computationally estimate molecular descriptors of these 16 mAbs. Pairwise and multi-parameter correlations among experimentally measured biophysical attributes and calculated molecular descriptors were obtained via statistical analyses. RESULTS: Diffusion interaction parameter (kD) showed statistically significant pairwise correlations (p-values <0.005) with thermal stability, viscosity, isoelectric point, and apparent solubility of the antibodies in our dataset. kD also showed statistically significant pairwise correlations (p-values <0.005) with several computationally calculated molecular descriptors (pI, net charge, charge on the Fv region, and zeta potential.) These pairwise correlations were further refined by multivariate analyses. These analyses yielded several useful equations for prediction of kD from antibody sequences, structural models, and experimentally measured biophysical attributes. CONCLUSIONS: Diffusion interaction parameter (kD) was found to be a key biophysical property for the mAbs in our dataset. It connects conformational heterogeneity of an antibody with its colloidal and rheological behaviors. The equations derived in this work shall enable rapid, resource-sparing, and cost-effective DAs of biologic drug candidates.

Biopharmaceutical Informatics: supporting biologic drug development via molecular modelling and informatics.

OBJECTIVES: The purpose of this article is to introduce an emerging field called 'Biopharmaceutical Informatics'. It describes how tools from Information technology and Molecular Biophysics can be adapted, developed and gainfully employed in discovery and development of biologic drugs. KEY FINDINGS: The findings described here are based on literature surveys and the authors' collective experiences in the field of biologic drug product development. A strategic framework to forecast early the hurdles faced during drug product development is weaved together and elucidated using chemical degradation as an example. Efficiency of translating biologic drug discoveries into drug products can be significantly improved by combining learnings from experimental biophysical and analytical data on the drug candidates with molecular properties computed from their sequences and structures via molecular modeling and simulations. SUMMARY: Biopharmaceutical Informatics seeks to promote applications of computational tools towards discovery and development of biologic drugs. When fully implemented, industry-wide, it will enable rapid materials-free developability assessments of biologic drug candidates at early stages as well as streamline drug product development activities such as commercial scale production, purification, formulation, analytical characterization, safety and in vivo performance.