Epidermal growth factor receptor (EGFR) is an oncogenic drug target and plays a critical role in several cellular functions including cancer cell growth, survival, proliferation, differentiation, and motility. Several small-molecule tyrosine kinase inhibitors (TKIs) and monoclonal antibodies (mAbs) have been approved for targeting intracellular and extracellular domains of EGFR, respectively. However, cancer heterogeneity, mutations in the catalytic domain of EGFR, and persistent drug resistance limited their use. Different novel modalities are gaining a position in the limelight of anti-EGFR therapeutics to overcome such limitations. The current perspective reflects upon newer modalities, importantly the molecular degraders such as PROTACs, LYTACs, AUTECs, and ATTECs, etc., beginning with a snapshot of traditional and existing anti-EGFR therapies including small molecule inhibitors, mAbs, and antibody drug conjugates (ADCs). Further, a special emphasis has been made on the design, synthesis, successful applications, state-of-the-art, and emerging future opportunities of each discussed modality.

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In this paper an efficient face recognition technique is presented by integrating Discrete Wavelet Transform and Compressive Sensing based classifier. At first discrete wavelet transform has been applied on each face images. Then an image fusion technique has been applied on the decomposed image to provide better detail information of face images. Principal component Analysis is applied on fused face images to extract the feature vector. Finally, the feature vector of test images is extracted and classified by Compressive Sensing based Classifier. This proposed technique is also tested on two publicly available databases on AR and ORL. This technique is also tested on masked face images and experimental result shows improved performance compared to conventional PCA.