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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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Related Experiment Video

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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QSAR Implementation for HIC Retention Time Prediction of mAbs Using Fab Structure: A Comparison between Structural

Micael Karlberg1, João Victor de Souza2, Lanyu Fan1,2

  • 1School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.

International Journal of Molecular Sciences
|October 31, 2020
PubMed
Summary
This summary is machine-generated.

Predicting monoclonal antibody (mAb) behavior early is crucial. This study developed a workflow using molecular dynamics simulations to accurately forecast mAb retention times, aiding in early risk assessment for biopharmaceutical development.

Keywords:
hydrophobic interaction chromatographymanufacturabilitymonoclonal antibodiesprocess developmentprotein dynamics analysisquantitative structure–activity relationship

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Area of Science:

  • Biopharmaceutical Development
  • Computational Chemistry
  • Protein Engineering

Background:

  • Monoclonal antibodies (mAbs) are vital biopharmaceuticals, but high failure rates in clinical trials and development pose significant challenges.
  • Early identification of problematic mAb candidates is essential to mitigate risks and improve success rates.
  • Current in silico tools for mAb risk assessment require enhancement for better predictive accuracy.

Purpose of the Study:

  • To design and validate a quantitative structure-activity relationship (QSAR) modeling workflow for predicting mAb hydrophobic interaction chromatography (HIC) retention times.
  • To evaluate the impact of different structural resolutions (primary sequence, homology modeling, molecular dynamics) on HIC retention time prediction accuracy.
  • To establish a robust in silico method for early-stage risk assessment of mAb candidates.

Main Methods:

  • Development of three novel descriptor sets based on primary sequence, homology modeling, and atomistic molecular dynamics (MD) simulations.
  • Application of QSAR modeling to correlate structural descriptors with experimental HIC retention times of mAbs.
  • Comparative analysis of descriptor sets to determine the optimal level of structural detail for accurate HIC prediction.

Main Results:

  • Descriptors derived from 3D structures obtained after MD simulations demonstrated the highest predictive power for HIC retention times (R² = 0.63 in an external test set).
  • Homology modeling resulted in biased 3D structures, highlighting the necessity of MD simulations for structural relaxation and natural conformation.
  • The proposed QSAR workflow effectively captured the relationship between mAb structure and HIC behavior.

Conclusions:

  • Molecular dynamics simulations provide essential structural information for accurate HIC retention time prediction in mAbs.
  • The developed QSAR workflow offers a valuable tool for early-stage risk assessment of mAb candidates in biopharmaceutical development.
  • This approach can help reduce late-stage failures by identifying potentially problematic mAbs earlier in the development pipeline.