Antibody Selection Using Molecular Dynamics for Low Viscosity
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Solution Overview
Problem
Existing methods struggle to predict and optimize the viscosity, clearance rate, and stability of monoclonal antibodies (mAbs) for stable, low-viscosity formulations suitable for subcutaneous delivery, which is crucial for improving patient compliance and efficacy.
Innovation Solution
Utilizing molecular dynamics simulations to analyze structural parameters such as charge, hydrophobicity, and asymmetry to predict and rank mAbs for desirable properties like viscosity, clearance, and stability, enabling efficient selection and manufacturing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If high concentrations of mAb are used in liquid formulation for subcutaneous delivery, then the volume required for delivery is reduced, but the viscosity increases making administration difficult and painful
Solution Approach 1:
The patent applies parameter changes by systematically varying formulation conditions including pH, ionic strength, and excipient concentrations to identify optimal ranges that maintain low viscosity at high mAb concentrations. Molecular dynamics simulations predict viscosity based on structural parameters, enabling selection of formulations that achieve the desired balance between concentration and viscosity.
2Stability of the object's composition
If the mAb solution is formulated for stability over extended shelf-life, then the therapeutic maintains efficacy, but the formulation may develop increased viscosity or degradation products
Solution Approach 1:
The patent employs preliminary action by using molecular dynamics simulations to predict viscosity and stability properties before actual formulation development. Structural parameters calculated from mAb sequences allow early identification of candidates likely to maintain low viscosity and stability, preventing later formulation failures and reducing the need for extensive empirical optimization.
Solution Approach 2:
The patent implements feedback through iterative refinement of formulation parameters based on simulated predictions and experimental validation. Viscosity and stability data from testing feed back into the simulation model to improve prediction accuracy and guide further optimization of formulation conditions for maintaining low viscosity over extended shelf-life.
3Measurement precision
If extensive experimental testing is performed to evaluate mAb viscosity and stability properties, then accurate data is obtained, but the time and resources required for selection increase significantly
Solution Approach 1:
The patent applies copying by using molecular dynamics simulations to create virtual models of mAb behavior that replicate experimental viscosity and stability measurements. These computational models serve as proxies for extensive physical testing, allowing rapid screening of multiple candidates and prediction of formulation performance without requiring exhaustive experimental evaluation of each candidate.
Solution Approach 2:
The patent employs preliminary action by performing computational predictions before experimental testing to prioritize candidates most likely to succeed. This pre-screening approach reduces the number of candidates requiring extensive experimental validation, thereby maintaining measurement precision for final selection while significantly reducing overall selection time and resource requirements.
Data Source
AI summary
Apparatus, systems, computer-readable media, articles of manufacture and methods for selecting an antibody. Apparatus, systems, computer-readable media, articles of manufacture and methods for producing the selected antibody. The selecting may include determining one or more physiochemical characteristics of the antibody. The determining may be based on antibody structural parameters.