Delving into the Latest Updates on Tozasertib with Synapse

Overview

Basic Info

Drug Type

Small molecule drug

Synonyms

Tozasertib (USAN), Tozasertib Lactate

+ [1]

Target

Aurora A x Aurora B x Aurora C x Bcr-Abl T315I x FLT3

Mechanism

Aurora A inhibitors(Serine/threonine-protein kinase Aurora-A inhibitors), Aurora B inhibitors(Serine/threonine-protein kinase Aurora-B inhibitors), Aurora C inhibitors(Serine/threonine-protein kinase Aurora-C inhibitors)

+ [2]

Therapeutic Areas

NeoplasmsImmune System DiseasesDigestive System Disorders+ [3]

Active Indication-

Inactive Indication

Acute Lymphoblastic LeukemiaAdvanced cancerB-Cell Prolymphocytic Leukemia+ [9]

Originator Organization

Vertex Pharmaceuticals, Inc.

Active Organization-

Inactive Organization

Merck GmbH

Merck & Co., Inc.

Merck Sharp & Dohme Corp.

Drug Highest PhaseDiscontinuedPhase 2

First Approval Date-

Regulation-

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Structure

Molecular FormulaC23H28N8OS

InChIKeyGCIKSSRWRFVXBI-UHFFFAOYSA-N

CAS Registry639089-54-6

OBJECTIVEThis study aimed to evaluate the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of kinase inhibitors with warfarin and direct oral anticoagulants (DOACs).METHODSAn in vitro CYP probe substrate cocktail assay was used to study the inhibitory effects of fifteen kinase inhibitors on CYP2C9, 3A, and 1A2. Then, DDI predictions were performed using both mechanistic static and physiologically-based pharmacokinetic (PBPK) models.RESULTSLinsitinib, masitinib, regorafenib, tozasertib, trametinib, and vatalanib were identified as competitive CYP2C9 inhibitors (K_i = 1.4, 1.0, 1.1, 3.8, 0.5, and 0.1 μM, respectively). Masitinib and vatalanib were competitive CYP3A inhibitors (K_i = 1.3 and 0.2 μM), and vatalanib noncompetitively inhibited CYP1A2 (K_i = 2.0 μM). Moreover, linsitinib and tozasertib were CYP3A time-dependent inhibitors (K_I = 26.5 and 400.3 μM, k_inact = 0.060 and 0.026 min^-1, respectively). Only linsitinib showed time-dependent inhibition of CYP1A2 (K_I = 13.9 μM, k_inact = 0.018 min^-1). Mechanistic static models identified possible DDI risks for linsitinib and vatalanib with (S)-/(R)-warfarin, and for masitinib with (S)-warfarin. PBPK simulations further confirmed that vatalanib may increase (S)- and (R)-warfarin exposure by 4.37- and 1.80-fold, respectively, and that linsitinib may increase (R)-warfarin exposure by 3.10-fold. Mechanistic static models predicted a smaller risk of DDIs between kinase inhibitors and apixaban or rivaroxaban. The greatest AUC increases (1.50-1.74) were predicted for erlotinib in combination with apixaban and rivaroxaban. Linsitinib, masitinib, and vatalanib were predicted to have a smaller effect on apixaban and rivaroxaban AUCs (AUCR 1.22-1.53). No kinase inhibitor was predicted to increase edoxaban exposure.CONCLUSIONSOur results suggest that several kinase inhibitors, including vatalanib and linsitinib, can cause CYP-mediated drug-drug interactions with warfarin and, to a lesser extent, with apixaban and rivaroxaban. The work provides mechanistic insights into the risk of DDIs between kinase inhibitors and anticoagulants, which can be used to avoid preventable DDIs in the clinic.

01 Nov 2024·Journal of AOAC International

A One-Step Green Microwell Spectrophotometric Assay for the Determination of Certain New Chemotherapeutic Drug Formulations

Article

Author: Alsalhi, Mohammed S ; Darwish, Hany W ; Darwish, Ibrahim A

AbstractBackgroundThe formation of charge-transfer complexes (CTCs) of iodine with five chemotherapeutic drugs used for the treatment of different types of cancer has not been investigated. These drugs are olaparib, seliciclib, vandetanib, dasatinib, and tozasertib. Additionally, these drugs need an appropriate general spectrophotometric assay for their analysis in the dosage forms regardless of the differences in their chemical structures.ObjectiveThe aim of this study was the development of a novel microwell spectrophotometric assay (MW-SPA) for one-step determination of these drugs via their interactions with iodine, which resulted in instantaneous production of bright lemon-yellow CTCs.MethodsA spectrophotometric study of the CTCs was conducted, and all CTCs were characterized. Site(s) of interaction on each drug were assigned, and the MW-SPA was developed and applied to the analysis of dosage forms.ResultsThe findings confirmed that the reactions proceeded via CTC formation. Beer's law was obeyed over a general concentration range of 1–6 µg/mL. The LODs and LOQs were in the ranges of 0.5–2.1 and 1.5–6.4 µg/mL, respectively. The proposed MW-SPA demonstrated excellent precisions as the relative standard deviations were < 2.24 and 2.23% for the intra- and inter-assay precision, respectively. Recovery studies demonstrated the accuracy of MW-SPA. Successful determination of all drugs in bulk and tablet forms was achieved using the MW-SPA. The environmental sustainability of the proposed methodology was determined, providing evidence of the assay's alignment with the basis of green analytical chemistry. The high throughput of the assay was documented.ConclusionIn contrast to other existing methods, the MW-SPA described herein was valid for analyzing all drugs at the same wavelength.HighlightsThe assay is useful for routine analysis of drugs in their formulations in QC laboratories.

23 Sep 2024·Journal of biomolecular structure & dynamics

Prognosis and immunotherapeutic implications of molecular classification of cervical cancer based on immunophenoscore-related genes.

Article

Author: Guo, Aihua ; Zhong, Peilin ; Lin, Xiurong ; Wang, Linghua ; Feng, Mei

The immunophenoscore (IPS) is an important indicator for evaluating immunotherapy response. This work was designed to establish a prognostic model based on IPS-related genes in cervical cancer. Weighted correlation network analysis (WGCNA) was utilized to identify key modules related to IPS in cervical cancer data from The Cancer Genome Atlas (TCGA). The results show that the yellow module (158 genes) had a high correlation with both IPS_CTLA4_blocker and IPS_CTLA4_and PC1/PDL1/PDL2 blocker. Univariate cox regression analysis and LASSO regression analysis were performed based on 158 genes, and 9 characteristic genes were finally identified to construct the model. According to the differentially expressed genes, cervical cancer samples were divided into high-risk and low-risk groups and cluster 1.2.3. Higher risk scores associated with poorer prognosis. cluster2 and cluster3 were identified as cervical cancer subtypes with significant survival differences. cluster2 had higher immune cell infiltration levels and better prognosis, with greater sensitivity to Cyclopamine, Imatinib, MG-13, Paclitaxel, PHA-665752, Rapamycin, Sorafenib, Sunitinib, and VX-680. In contrast, cluster3 had higher TTN and PIK3CA mutations and greater sensitivity to AZ628, Dasatinib, Doxorubicin, HG-6-64-1, JQ12, Midostaurin, PF-562271, TAE684, and WH-4-023. In conclusion, we developed a feasible risk score model based on IPS-related genes for cervical cancer prognosis and identified potential drugs for different cervical cancer subtypes.

100 Deals associated with Tozasertib

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External Link

KEGG	Wiki	ATC	Drug Bank
D08279	Tozasertib	-	-

R&D Status

10 top R&D records.

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Indication	Highest Phase	Country/Location	Organization	Date
Colorectal Cancer	Phase 2	US	Merck GmbH	-
Hematologic Neoplasms	Phase 2	US	Merck GmbH	-
Hematologic Neoplasms	Phase 2	SG	Merck GmbH	-
Hematologic Neoplasms	Phase 2	CO	Merck GmbH	-
Hematologic Neoplasms	Phase 2	EU	Merck GmbH	-
Hematologic Neoplasms	Phase 2	RU	Merck GmbH	-
Hematologic Neoplasms	Phase 2	JP	Merck GmbH	-
Hematologic Neoplasms	Phase 2	KR	Merck GmbH	-
Hematologic Neoplasms	Phase 2	IL	Merck GmbH	-
Non-Small Cell Lung Cancer	Phase 2	US	Merck GmbH	-

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Clinical Result

Indication

Phase

Evaluation

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Study	Phase	Population	Analyzed Enrollment	Group	Results	Evaluation	Publication Date


NCT00405054 (Pubmed)	Phase 2	Philadelphia positive acute lymphocytic leukaemia \| Philadelphia chromosome positive chronic myelogenous leukemia	52	MK-0457 at 40 mg/m(2)/h	(kcduvvpsda) = The most common grade 3/4 AEs were neutropenia (46%) and febrile neutropenia (35%) tjpvzmpakh (imevpluksb ) View more	-	15 Aug 2014
NCT00111683 (Pubmed \| Leukemia)	Phase 1/2	Leukemia	77	MK-0457	(skyvlvstak) = mrhscjxdtv fdkwjgmdnt (jecrkpkwls ) View more	-	01 Jan 2013
Pubmed \| Cancer Chemother Pharmacol	Phase 1	HR-positive/HER2-low Solid Tumors	-	MK-0457	(fvxujgeqbs) = eawtyjjhsp vkjnardzqw (hdzxycwupb ) View more	-	01 Feb 2011
ASCO2006	Phase 1	Neoplasms	16	MK-0457	(ualaoisdwa) = fivvvfrlmu usizxzznrl (gvwwtpkarj, ± 141) View more	-	20 Jun 2006