Developability Index: A Rapid In Silico Tool for the Screening of Antibody Aggregation Propensity

Lauer, Timothy M.; Agrawal, Neeraj J.; Chennamsetty, Naresh; Egodage, Kamal; Helk, Bernhard; Trout, Bernhardt L.

doi:10.1002/jps.22758

Cited by 175 publications

(179 citation statements)

References 35 publications

(49 reference statements)

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“…If a given series of data for antibodies contains mutations on the hinge, or Fc domain, then these regions will need to be modeled, and the structural descriptors and protein QSPR could be applied for developability optimization. 10,15 These methods may also present a challenge when working with unusual CDR loops either in sequence or in size. In such cases, obtaining a crystal structure of one of the candidate Fabs as a template for modeling the others is recommended.…”

Section: Discussionmentioning

confidence: 99%

“…15–17 The earlier work of building single-parameter hydrophobic patch predictors was validated appropriately using experimental data for fewer than twenty sequences. 8,12 For the multi-parameter models that are being constructed and applied to experimental antibody property prediction, 10,15,16 large sets of sequences and experimental data are required for model building and validation. Ideally, such datasets would include negative data in order to robustly train predictive models and advance the field in the direction of data driven computational predictions, e.g., available protein thermostability benchmark datasets have allowed machine learning to be applied, resulting in accurate thermostability predictions.…”

Section: Introductionmentioning

confidence: 99%

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Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies

Jetha

Thorsteinson²,

Jmeian

et al. 2018

mAbs

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Monoclonal antibody (mAb) candidates from high-throughput screening or binding affinity optimization often contain mutations leading to liabilities for further development of the antibody, such as aggregation-prone regions and lack of solubility. In this work, we optimized a candidate integrin α11-binding mAb for developability using molecular modeling, rational design, and hydrophobic interaction chromatography (HIC). A homology model of the parental mAb Fv region was built, and this revealed hydrophobic patches on the surface of the complementarity-determining region loops. A series of 97 variants of the residues primarily responsible for the hydrophobic patches were expressed and their HIC retention times (RT) were measured. As intended, many of the computationally designed variants reduced the HIC RT compared to the parental mAb, and mutating residues that contributed most to hydrophobic patches had the greatest effect on HIC RT. A retrospective analysis was then performed where 3-dimentional protein property descriptors were evaluated for their ability to predict HIC RT using the current series of mAbs. The same descriptors were used to train a simple multi-parameter protein quantitative structure-property relationship model on this data, producing an improved correlation. We also extended this analysis to recently published HIC data for 137 clinical mAb candidates as well as 31 adnectin variants, and found that the surface area of hydrophobic patches averaged over a molecular dynamics sample consistently correlated to the experimental data across a diverse set of biotherapeutics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies

Jetha

Thorsteinson²,

Jmeian

et al. 2018

mAbs

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“…For monoclonal antibodies, these properties include high-level expression, high solubility, covalent integrity, conformational and colloidal stability, low polyspecificity, and low immunogenicity. The high cost of failing any of these criteria at a late stage in drug development has led to considerable efforts at predicting developability on the basis of sequence motifs and experimentally determined biophysical properties (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15).…”

mentioning

confidence: 99%

Biophysical properties of the clinical-stage antibody landscape

Jain

Sun

Durand

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

470

824

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Antibodies are a highly successful class of biological drugs, with over 50 such molecules approved for therapeutic use and hundreds more currently in clinical development. Improvements in technology for the discovery and optimization of high-potency antibodies have greatly increased the chances for finding binding molecules with desired biological properties; however, achieving drug-like properties at the same time is an additional requirement that is receiving increased attention. In this work, we attempt to quantify the historical limits of acceptability for multiple biophysical metrics of "developability." Amino acid sequences from 137 antibodies in advanced clinical stages, including 48 approved for therapeutic use, were collected and used to construct isotypematched IgG1 antibodies, which were then expressed in mammalian cells. The resulting material for each source antibody was evaluated in a dozen biophysical property assays. The distributions of the observed metrics are used to empirically define boundaries of drug-like behavior that can represent practical guidelines for future antibody drug candidates.monoclonal antibody | developability | biophysical properties | manufacturability | nonspecificity T arget binding is the predominant first concern in development of any drug. However, once a lead molecule attains the desired potency of biological modification, a suite of characteristics termed "developability" assumes critical importance. For monoclonal antibodies, these properties include high-level expression, high solubility, covalent integrity, conformational and colloidal stability, low polyspecificity, and low immunogenicity. The high cost of failing any of these criteria at a late stage in drug development has led to considerable efforts at predicting developability on the basis of sequence motifs and experimentally determined biophysical properties (1-15).In a landmark study of small-molecule drugs over 2,000 molecules with United States Adopted Names (USAN) designations and known to have oral availability were collected and computationally analyzed (16). A simple set of thresholds, encapsulated as the "Lipinski rule of fives," was formulated and has been used by many to prioritize small molecules for entry into clinical development. To date, analogous guiding principles for antibody drugs have not emerged-we therefore endeavor here to do so. By analogy to the Lipinski effort, we first collected the sequences of antibodies that had reached at least phase-2 trials and had USAN or WHO International Nonproprietary Names (INN) designations (137 in total as of the start of this project). As a common basis for comparison of intrinsic variable domain phenotypes we expressed each antibody as the human IgG1 isotype and formulated them in simple Hepes-buffered saline. Each antibody was then subjected to a battery of 12 different biophysical assays in common use for developability assessment.Unexpectedly, for many of the measures the distribution of values was not symmetrically Gaussian, but instead was lon...

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“…16 Cell-based screenings are typically performed in parallel with a suited negative control cell line, if available. In later phases of discovery, the biological activity, affinity, epitopes, 17,18 manufacturing and pharmaceutical development aspects 19,20 are evaluated. Extensive characterization of specificity on large protein panels, as described above, is expensive and time consuming and is reserved for the final few lead candidates, if at all.…”

Section: Resultsmentioning

confidence: 99%