Delving into the Latest Updates on Tesevatinib with Synapse

Overview

Basic Info

Drug Type

Autologous CAR-T

Synonyms

Tesevatinib (USAN/INN)

+ [2]

Target

EGFR x HER2 x HER3 x HER4 x SRC family x VEGFR

Action

antagonists, inhibitors

Mechanism

EGFR antagonists(Epidermal growth factor receptor erbB1 antagonists), HER2 antagonists(Receptor protein-tyrosine kinase erbB-2 antagonists), HER3 antagonists(Receptor tyrosine-protein kinase erbB-3 antagonists)

+ [3]

Therapeutic Areas

NeoplasmsRespiratory DiseasesNervous System Diseases+ [4]

Active Indication-

Inactive Indication

Advanced Malignant Solid NeoplasmBrain metastasesBreast Cancer+ [6]

Originator Organization

Exelixis, Inc.

Active Organization-

Inactive Organization

Kadmon Corp. LLC

Exelixis, Inc.

Drug Highest PhaseDiscontinuedPhase 3

First Approval Date-

RegulationOrphan Drug (United States)

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Structure/Sequence

Molecular FormulaC24H25Cl2FN4O2

InChIKeyHVXKQKFEHMGHSL-QKDCVEJESA-N

CAS Registry781613-23-8

AbstractThe proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small‐scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi‐task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild‐type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi‐task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi‐targeted inhibitors for wild‐type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi‐targeted kinase drug discovery for EGFR family proteins.

01 Jul 2024·Journal of Biological Chemistry

Crosstalk of MAP3K1 and EGFR signaling mediates gene-environment interactions that block developmental tissue closure

Article

Author: Xia, Ying ; Medvedovic, Mario ; Kimura, Eiki ; Mongan, Maureen ; Xiao, Bo ; Hsu, Wei-Wen ; Puga, Alvaro ; Wang, Jingjing

Aberrant regulation of signal transduction pathways can adversely derail biological processes for tissue development. One such process is the embryonic eyelid closure that is dependent on the mitogen-activated protein kinase kinase kinase 1 (MAP3K1). Map3k1 KO in mice results in defective eyelid closure and an autosomal recessive eye-open at birth phenotype. We have shown that in utero exposure to dioxin, a persistent environmental toxicant, induces the same eye defect in Map3k1^+/- heterozygous but not WT pups. Here, we explore the mechanisms of the Map3k1 (gene) and dioxin (environment) interactions (GxE) underlying defective eyelid closure. We show that, acting through the aryl hydrocarbon receptor, dioxin activates epidermal growth factor receptor signaling, which in turn depresses MAP3K1-dependent Jun N-terminal kinase (JNK) activity. The dioxin-mediated JNK repression is moderate but is exacerbated by Map3k1 heterozygosity. Therefore, dioxin exposed Map3k1^+/- embryonic eyelids have a marked reduction of JNK activity, accelerated differentiation and impeded polarization in the epithelial cells. Knocking out Ahr or Egfr in eyelid epithelium attenuates the open-eye defects in dioxin-treated Map3k1^+/- pups, whereas knockout of Jnk1 and S1pr that encodes the sphigosin-1-phosphate (S1P) receptors upstream of the MAP3K1-JNK pathway potentiates the dioxin toxicity. Our novel findings show that the crosstalk of aryl hydrocarbon receptor, epidermal growth factor receptor, and S1P-MAP3K1-JNK pathways determines the outcome of dioxin exposure. Thus, gene mutations targeting these pathways are potential risk factors for the toxicity of environmental chemicals.

01 Feb 2024·Journal of Mathematical Chemistry

Identification of potential oral cancer drugs as Bcl-2 inhibitors from known anti-neoplastic agents through docking studies

Author: Pal, Debnath ; Raychaudhury, Chandan ; Srinivasan, Shreyaa

Oral Cancer is one of the major killers in India and of global concern.An attempt has been made here to identify small mol. inhibitors of Bcl-2 (B-cell lymphoma 2) protein through docking studies in search of new oral cancer drugs from a curated set of 276 known anti-neoplastic agents obtained from PubChem.The Bcl-2 protein (PDB id:6QGH), being complexed with ligand ABT-263, has been detached from the complex.The ligand-free protein and 276 compounds have been prepared for docking studies using corresponding tools available in Discovery Studio (DS), ver.4.1.The 276 compounds have been docked on the first site of the protein using LibDock docking program available in DS.By considering Methotrexate as the reference compound (LibDock score: 114.76), we have identified 25 compounds as the most potential oral cancer drugs having LibDock scores greater than 100.Another set of 114 compounds with LibDock scores between 80 and 100 have also been identified as the next set of potential drug candidates and some of them show anti-apoptosis properties.Two other compounds with LibDock scores below 80 have been identified for comparative analyses with some high-scoring compoundsThe docking results have been given in tables and pictures (screenshots) of the docked poses of selected compounds have been shown to illustrate the findings.Finally, the most suitable potential compounds have been identified by applying Lipinski′s Rule of 5.The present approach using known anti-neoplastic agents is believed to help discover potential anti-oral cancer drug candidates.

100 Deals associated with Tesevatinib

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External Link

KEGG	Wiki	ATC	Drug Bank
D11772	Tesevatinib	-	DB11973

R&D Status

10 top R&D records.

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Indication	Highest Phase	Country/Location	Organization	Date
Polycystic Kidney, Autosomal Recessive	Phase 3	United States	Kadmon Corp. LLC	24 Aug 2017
Recurrent Glioblastoma	Phase 3	United States	Kadmon Corp. LLC	01 Jun 2016
Polycystic Kidney, Autosomal Dominant	Phase 3	United States	Kadmon Corp. LLC	25 Dec 2015
Brain metastases	Phase 3	United States	Kadmon Corp. LLC	01 Nov 2015
Metastatic Malignant Neoplasm to the Leptomeninges	Phase 3	United States	Kadmon Corp. LLC	01 Nov 2015
Non-small cell lung cancer stage IIIB	Phase 3	United States	Kadmon Corp. LLC	14 Dec 2011
Breast Cancer	Phase 3	United States	Exelixis, Inc.	01 Jun 2008
Non-Small Cell Lung Cancer	Phase 3	United States	Exelixis, Inc.	01 Jun 2008
Advanced Malignant Solid Neoplasm	Phase 3	United States	Kadmon Corp. LLC	01 Jul 2006

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Clinical Result

Indication

Phase

Evaluation

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Study	Phase	Population	Analyzed Enrollment	Group	Results	Evaluation	Publication Date


NCT03203642 (CTgov)	Phase 2	Polycystic Kidney, Autosomal Dominant	80	Tesevatinib (Tesevatinib)	uxvmtktnvx(rhvyyvapwr) = yrxtmykhhs onaaqifkob (ghuqxozvcp, imrpwecofp - jvwcifljbf) View more	-	06 Feb 2023
NCT03203642 (CTgov)	Phase 2	Polycystic Kidney, Autosomal Dominant	80	Placebo (Placebo)	uxvmtktnvx(rhvyyvapwr) = uxtefoflfa onaaqifkob (ghuqxozvcp, goyyzzdtfk - drxncyhjdy) View more	-	06 Feb 2023
NCT01559363 (CTgov)	Phase 1/2	Polycystic Kidney, Autosomal Dominant	69	Tesevatinib (Phase 1b: Cohort 1: Tesevatinib 50 mg Once Daily Dosing)	(vsrvwvknrs) = hcupmlzdri qtqasvxgqt (prysqlgifx, nvrrwcpdiv - mlarvoejiw) View more	-	08 Nov 2022
NCT01559363 (CTgov)	Phase 1/2	Polycystic Kidney, Autosomal Dominant	69	Tesevatinib (Phase 1b: Cohort 2: Tesevatinib 100 mg Once Daily Dosing)	(vsrvwvknrs) = qkmuzsbnpx qtqasvxgqt (prysqlgifx, wuusjewhro - memrvipamb) View more	-	08 Nov 2022
KD019-101 \| SILK (ASN2019)	Phase 1/2	Polycystic Kidney, Autosomal Dominant	13	Tesevatinib 50 mg QD	vooysdxqph(ljqdxihvpt) = Grade 3 AEs were reported in 3 patients (CHF, HTN, and Herpes zoster) and were considered unlikely or unrelated to study drug cybfktmgoz (bzzqpayifp ) View more	Positive	05 Nov 2019
ASN2016	Not Applicable	Polycystic Kidney Diseases	24	Tesevatinib 50 mg	vgprlfanpu(dwrlsexldi) = fdwtxvvigc gcebpooefk (zfonbotfip ) View more	Positive	15 Nov 2016
NCT02154529 (ASCO2016)	Phase 1/2	Metastatic human epidermal growth factor 2 positive carcinoma of breast	8	Tesevatinib 300 mg QD	(gisnpjjpnw) = Grade 3 diarrhea and Grade 3 QTc prolongation ypyafizjxd (sxmhvgtyds )	-	20 May 2016
NCT02154529 (ASCO2016)	Phase 1/2		8	Tesevatinib 350 mg QD		-	20 May 2016
ASN2015	Not Applicable	Polycystic Kidney, Autosomal Dominant	46	Tesevatinib 150 mg/day	deqrlqookp(aazchansgl) = In oncology trials the most common adverse events (AEs) were EGFR inhibitor AEs of acneiform rash and diarrhea. ikmpzkbtbe (rpsjfdpdtl ) View more	Negative	03 Nov 2015
ASN2015	Not Applicable	Polycystic Kidney, Autosomal Dominant	46	Tesevatinib 100 mg/day		Negative	03 Nov 2015
ASCO2008	Phase 2	Non-Small Cell Lung Cancer	41	XL647 350 mg (intermittent cohort)	hxrhjnvdlz(hdieobjtlm) = pjzsfwhwgr qtbwchaqlj (iqhfjyehcn ) View more	-	20 May 2008
ASCO2008	Phase 1	Advanced Malignant Solid Neoplasm	31	XL647	(ofcegajvji) = dteyrplaxv hbnvmuoboe (yipseclznl )	-	20 May 2008
ASCO2007	Phase 1	Advanced Malignant Solid Neoplasm	-	XL647	(qubuajodjz) = yhprashvht tvxgygjjtp (vonopxbdcm ) View more	-	20 Jun 2007
ASCO2005	Phase 1	Advanced Malignant Solid Neoplasm	12	XL647	(vxybbqfowp) = alduasropc tbsgqvamaf (trpdhuakbg ) View more	-	01 Jun 2005