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Five computational developability guidelines for therapeutic antibody profiling.

Matthew I J Raybould1, Claire Marks1, Konrad Krawczyk1

  • 1Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America
|February 16, 2019
PubMed
Summary
This summary is machine-generated.

Antibody therapeutics require good developability. This study models antibody structures to create guidelines for five key properties, aiding in the design of stable and effective therapeutic antibodies.

Keywords:
developability guidelinesimmunoglobulin gene sequencingsurface chargesurface hydrophobicitytherapeutic monoclonal antibodies

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Area of Science:

  • Biopharmaceutical development
  • Computational biology
  • Protein engineering

Background:

  • Therapeutic monoclonal antibodies (mAbs) must possess favorable developability characteristics beyond target binding, including high stability and low aggregation.
  • Current small-molecule drug discovery utilizes predictive rules (e.g., Lipinski's rule of five), but analogous in silico tools for antibody design are lacking.
  • Developability issues can significantly hinder the progression of antibody therapeutics through clinical trials.

Purpose of the Study:

  • To establish in silico guidelines for antibody developability by analyzing clinical-stage antibody therapeutics (CSTs).
  • To develop a computational tool for predicting and flagging potential developability liabilities in antibody sequences.
  • To provide a framework for improving the success rate of antibody drug discovery.

Main Methods:

  • Modeling of variable domain structures for a large cohort of post-phase-I clinical-stage antibody therapeutics (CSTs).
  • Calculation of five in silico developability metrics: CDR length, surface hydrophobicity, CDR charges, and charge asymmetry between heavy and light chains.
  • Comparison of CST properties against the human antibody gene repertoire and derivation of guideline cutoffs.

Main Results:

  • Guideline values for five key developability metrics were established based on CST data.
  • A flagging system was proposed to identify antibody candidates with potential developability issues.
  • The Therapeutic Antibody Profiler (TAP) tool was developed, demonstrating success in highlighting sequences with known developability problems in independent datasets.

Conclusions:

  • The developed guidelines and the TAP tool offer a novel in silico approach to predict and mitigate antibody developability issues early in the design process.
  • TAP provides downloadable homology models and reports sequence liabilities, facilitating informed decision-making in antibody engineering.
  • This work contributes to improving the efficiency and success of therapeutic antibody development by addressing critical biophysical properties.