Antibody Binding Human c-MAF for Metastasis Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods fail to reliably identify markers for predicting bone metastasis in cancer, which is crucial for preventive therapeutic opportunities and understanding the metastasis process, as they do not explain why metastasis occurs, how it becomes tissue-specific, or what genes would serve as diagnostic markers and therapeutic targets.
Innovation Solution
Development of an antibody that specifically binds to human c-MAF, utilizing a heavy chain CDR1, CDR2, CDR3 and light chain CDR1, CDR2, CDR3 sequences, which is used to detect genetic abnormalities and predict metastasis in cancer, particularly breast, lung, prostate, and renal cancers, by determining the expression levels of the MAF gene in primary tumor samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current methods are used to identify markers for predicting bone metastasis, then the complexity of the metastasis process remains unexplained, but the ability to reliably identify diagnostic markers and therapeutic targets is insufficient
Solution Approach 1:
The patent replaces conventional, non-specific detection methods with a highly specific antibody-based detection system. The antibody INB-1-11-8 specifically binds to c-MAF protein, enabling reliable identification of this metastasis marker without requiring complex mechanistic understanding of the entire metastasis process. This substitution provides a practical diagnostic tool that overcomes the limitation of current methods.
2Measurement precision
If an antibody is developed to specifically bind c-MAF, then the ability to detect and quantify c-MAF expression is improved, but the complexity of antibody development and characterization increases
Solution Approach 1:
The patent performs preliminary actions by systematically characterizing the antibody's binding properties, specificity, and epitope recognition before clinical application. The detailed characterization data (binding affinity, specificity to c-MAF isoforms, epitope mapping) is established in advance, which simplifies subsequent diagnostic applications and reduces the need for repeated characterization studies.
Solution Approach 2:
The patent creates a simplified model system using recombinant c-MAF proteins and epitope peptides that replicate the behavior of endogenous c-MAF. This copying approach allows antibody characterization to be performed on purified, well-defined targets rather than complex tissue samples, thereby improving measurement precision while managing development complexity.
3Adaptability or versatility
If the antibody binds to multiple c-MAF isoforms, then the versatility of the detection method is improved, but the specificity for distinguishing between different isoforms decreases
Solution Approach 1:
The patent applies local quality by designing the antibody to recognize a specific epitope region (amino acids 64-158) that is common to multiple c-MAF isoforms. This localized epitope recognition provides versatility for detecting different isoforms while maintaining sufficient specificity for diagnostic purposes. The antibody's binding specificity is concentrated on this particular region rather than being distributed across the entire protein structure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The antibody effectively binds to c-MAF with high affinity, enabling the detection of genetic abnormalities and predicting metastasis, thereby providing a basis for customized therapy to prevent or treat bone metastasis and degradation.
Implementation Method 1
an antibody that specifically binds to human c-MAF
Data Source
Figure 1
Figure 2
Figure 3A~3C
AI summary
The disclosure relates to binding members, especially antibody molecules, which bind to human Maf. The binding members are useful for the determination of the expression level of Maf.