PATENT CLAIM ANALYSIS

Application Number: 15124288
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-01
Patent Classification: ["424", "133100"]

Abstract:
Disclosed herein are humanized anti-Epidermal Growth Factor (EGF) Receptor antibodies which can inhibit the proliferation of cells expressing the EGF receptor. Humanized anti-EGFR antibodies are capable of binding to the surface of cells and killing the EGF receptor overexpression cells. The invention presents the humanized anti-EGFR antibodies which bind to different epitope and inhibit the tumor formation in a different way than Erbitux. Most importantly, once bound to the surface EGFR, these new anti-EGFR antibodies will internalize rather quickly, which made them ideal candidate for antibody drug conjugation and other biotherapy. The invention also features method of humanization which leads to 90% of the amino acid sequences are human sequence, and significantly reduce the risk of human anti-mouse immunogenicity. The present invention also demonstrated that the humanized anti-EGFR antibodies have the affinity to EGFR in the range of 2.3 nM, which is very similar to the mouse anti-EGFR monoclonal antibody LA22.

Claim (Index 1):
A humanized anti-EGFR monoclonal antibody comprising the variable regions of heavy (SEQ ID NO.1) and light (SEQ ID NO.2) chains from mouse anti-EGFR monoclonal antibody, and the constant regions from human IgG1 kappa.

Metadata:
- Claim Count in Document: 26.0
- Percentile: 97.0
- Lexical Diversity: 1.70103
- Patent Class: 424.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['13814779', '14398441', '14558186', '15350801', '10994091']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7650906903972117
- 35 USC 102 Novelty (BERT): 0.5376447202752779
- Combined Prediction Score: 0.7423460933850183
- Mean Citation Score: 383.764416
- Max Citation Score: 392.24185
- Similarity Product: 283.97458030754325

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test