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

Abstract

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.

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

“…MD is important for a fuller understanding of antibody systems as it provides insight into the flexibility and fluctuations of antibody structure and developability parameters ( 29 , 33 , 34 , 117 , 119 , 120 ). Specifically, Park and Izadi found that antibody developability surface descriptor parameters (e.g., positive electrostatic potential on the surface of the CDR region ( 121 )) vary extensively as a function of the structure prediction method used, which is in line with the findings in this manuscript (Supplementary File).…”

“…However, mAbs are usually exposed to a wider range of solution pH (4.8-9) during production and formulation (10,97). Also, antibody variable region charge in acidic pH has proven to be a critical factor in IgG mAb pharmacokinetics (98)(99)(100) and product formulation (29). Therefore, we included the sequence-based variable region charge measures in 14 pH points (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) in our analysis.…”

“…Traditionally, developability screening is performed through a series of laborious in vitro assays (17,22,23). Therefore, major efforts have been invested into developing real-world-relevant in silico tools and machine learning (ML) algorithms that can computationally quantify or predict DP values using antibody sequence and/or structure information (7,(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35). Given the current high throughput of antibody structure prediction at repertoire scale (36)(37)(38), tools for computational developability determination have become available, which can be used to identify potential design shortfalls during development and selection of lead mAb candidates (39)(40)(41)(42).…”

“…Furthermore, given previous low-sample size investigations, we are unaware to what extent sequence changes affect a given DP and which sequence or structure-based design restrictions may limit all-vs-all DP optimization with a given antibody sequence. Furthermore, many studies have focused on extracting developability guidelines from a limited number of successful mAbs, considering them as a "ground truth" of desired developability (7,10,17,29,52). In addition, most studies have focused on a small number of DPs (17,53), and apply their hypotheses to limited antibody datasets comprising of a few 100s to 1000s antibody sequences (7,10,34,52) or datasets not including patent-submitted antibodies or antibodies that failed during early clinical trials (7,10,54).…”

SUMO –In SilicoSequence Assessment Using Multiple Optimization Parameters

“…This option requires the ability to discriminate candidates that have liabilities. Many experimental [22][23][24][25][26][27][28] and computational [29][30][31][32][33][34][35][36][37][38][39] methods of candidate assessment are available and have been previously discussed at length [40][41][42][43] . The second option is to generate drug candidates de-risked for developability by design.…”