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 2):
The humanized anti-EGFR monoclonal antibody of  claim 1 , comprising any one of the humanized light chain amino acid sequences of SEQ ID NO. 12\u02dc14, and any one of the humanized heavy chain amino acid sequences of SEQ ID NO. 7\u02dc11.

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): 0.7651342196596428
- 35 USC 102 Novelty (BERT): 0.5375810804426103
- Combined Prediction Score: 0.7423789057379396
- Mean Citation Score: 383.764416
- Max Citation Score: 392.24185
- Similarity Product: 282.38557355117206

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