Antibody PK Prediction Using Region-Specific Surface Properties
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Solution Overview
Problem
There is a need for an efficient, accurate, and rapid mechanism or apparatus for predicting the pharmacokinetic (PK) values of multi-specific antibodies, particularly for shortlisting candidates with unknown PK values, to reduce the need for extensive in vivo testing.
Innovation Solution
A computer-implemented method and system for generating a PK model that utilizes amino acid sequences and experimental data to compute surface properties, identify regions of interest, and establish a correlation for predicting PK values, enabling the shortlisting of candidate antibodies with desired properties for in vitro or in vivo trials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in vivo testing is conducted for a large panel of antibody candidates, then accurate PK evaluation is achieved, but resource consumption and time increase significantly
Solution Approach 1:
The patent applies preliminary action by performing in silico PK predictions and in vitro binding assays before in vivo testing. The system calculates surface properties, charge distributions, and FcRn binding affinities of antibody candidates computationally, and measures these properties experimentally, to pre-screen candidates and identify those most likely to succeed in vivo, thereby reducing the number of animals needed while maintaining evaluation accuracy
Solution Approach 2:
The patent introduces an intermediary layer of computational models and in vitro assays between the antibody candidates and in vivo testing. The in silico prediction system acts as a mediator that translates antibody structural properties into predicted PK parameters, while in vitro FcRn binding assays serve as intermediate measurements that correlate with in vivo PK behavior, allowing accurate screening without direct in vivo testing of all candidates
2Loss of information
If in vivo testing is performed on all antibody candidates, then comprehensive PK data is obtained, but ethical concerns and cost increase
Solution Approach 1:
The system performs preliminary computational PK predictions and in vitro characterizations for all antibody candidates before any in vivo testing. By calculating surface properties, charge distributions, and predicting FcRn binding behavior in silico, and measuring these properties in vitro, the system identifies a small subset of high-priority candidates that warrant in vivo testing, thereby obtaining comprehensive PK data for the most promising candidates while minimizing animal use and cost
Solution Approach 2:
The patent creates computational copies and in vitro models of the in vivo PK environment. The in silico prediction system generates virtual PK profiles based on antibody structural properties, and in vitro FcRn binding assays create simplified models of the recycling pathway, allowing comprehensive evaluation of PK-relevant properties without requiring in vivo testing of all candidates, thus reducing ethical concerns and costs while maintaining data completeness for prioritized candidates
3Device complexity
If conventional PK prediction methods based on overall surface properties are used, then the prediction process is simple, but accuracy for multi-specific antibodies is insufficient
Solution Approach 1:
The patent applies segmentation by dividing the antibody molecule into distinct functional regions: variable regions (VH, VL, CDRs) that bind antigens and constant regions (CH, CL, Fc) that mediate effector functions including FcRn binding. The system calculates surface properties and charge distributions specifically for each region, particularly focusing on the Fc region's interaction with FcRn, rather than treating the antibody as a uniform surface. This region-specific analysis significantly improves PK prediction accuracy for multi-specific antibodies while maintaining computational feasibility
Solution Approach 2:
The patent implements local quality by assigning different analytical treatments to different regions of the antibody. The system specifically analyzes the charge distribution and surface properties of the Fc region where FcRn binding occurs, using different calculation methods and thresholds for different domains. This localized, region-specific characterization allows the prediction model to capture the nuanced effects of local structural properties on PK behavior, improving accuracy without requiring excessive computational complexity
Data Source
AI summary
Methods, systems and apparatus are described for generating a pharmacokinetics (PK) model for predicting a PK value for an antibody of interest. An input dataset is received for a plurality of antibodies. The input dataset comprising data representative of the amino acid sequence of each of said antibodies and an experimentally determined PK value of each of said antibodies. One or more surface properties for each of said antibodies are computed based on the corresponding amino acid sequences. One or more region surface properties for one or more regions of interest are computed for each of said antibodies based on the one or more surface properties computed for each of said antibodies. A grouping from the one or more the regions of interest that produces a maximum correlation between the corresponding computed region surface properties of the grouping and the experimentally determined corresponding PK values is determined. This is used to establish a PK region surface property relationship for the plurality of antibodies. The PK model for predicting the PK value for the antibody of interest is generated based on the PK region surface property relationship. The PK model is configured to receive one or more input region surface properties of said determined grouping for said antibody of interest and output a predicted PK value for said antibody of interest by applying said inputted region surface properties to said relationship.