Delving into the Latest Updates on Paterson Institute for Cancer Research with Synapse

Overview

Tags

Neoplasms

Urogenital Diseases

Small molecule drug

Disease domain score

A glimpse into the focused therapeutic areas

Technology Platform

Most used technologies in drug development

Targets

Most frequently developed targets

Disease Domain	Count

Neoplasms	1

Top 5 Drug Type	Count

Small molecule drug	1

Top 5 Target	Count

Protein prenyl transferase	1

O⁶-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine O⁶-alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [³H]-methylated-DNA-based MGMT inactivation assays and calculated the IC₅₀ values. Using the results of 370 compounds, we performed quantitative structure-activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC₅₀ values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability (R²_pr = 0.7474, Q²_Fn = 0.7375-0.7437, CCC_pr = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model (R²_pr = 0.7528, Q²_Fn = 0.7387-0.7449, CCC_pr = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment.

20 May 2021·Journal of Clinical Oncology

Antibiotic use reduces efficacy of tyrosine kinase inhibitors in patients with advanced melanoma and non-small cell lung cancer.

Author: Cook, Natalie ; Rack, Samuel ; Tan, Grace Chin Lay ; Lorigan, Paul ; Tinsley, Nadina ; Nahm, Sharon Hyo-Eun ; Zhou, Cong ; Blackhall, Fiona Helen

3100 Background: Antibiotic (ABX) use and disruption of the gut microbiome has been demonstrated to reduce the efficacy of immune checkpoint inhibitors and chemotherapeutics in cancer patients. Little is known about the impact of ABX use in patients treated with targeted therapies, such as tyrosine kinase inhibitors (TKI). Methods: Retrospective data analysis was performed on advanced melanoma and non-small cell lung cancer (NSCLC) patients treated with TKIs between January 2015 and April 2017 at The Christie NHS Foundation Trust, UK. Demographics, prior systemic treatment, extent of disease, lactate dehydrogenase level (LDH), presence of brain metastases, performance status, comorbidities, TKI agent and the use of ABX (class, route, duration) were collected. Progression free survival (PFS) and overall survival (OS) were compared between the ABX+ group (defined as patients treated with ABX within 2 weeks of TKI initiation or 6 weeks after) and the ABX – group (patients with no ABX during specified period). Statistical analyses were performed with univariate and multivariable models. Results: In total, 168 patients were included; 89 patients (53%) with advanced NSCLC and 79 patients (47%) with melanoma. Over a third of patients, (57/168, 34%) received ABX in the specified period (ABX+). On univariable analysis, ABX use was associated with shorter PFS (208 days vs 357 days, p = 0.008) and OS (294 days vs 438 days, p = 0.024). Increased age, poorer performance status, and higher LDH were also associated with shorter PFS and OS. On multivariable analysis, ABX use was independently associated with shorter PFS (HR 1.57, 95% CI 1.05-2.34, p = 0.028) and OS (HR 2.19, 95% CI 1.44-3.32, p = 0.0002). The negative impact of ABX on OS was particularly pronounced for patients with worse performance status (HR 3.82, 95% CI 1.18-12.36, p = 0.025). Conclusions: To our knowledge, this is the largest multivariable analysis showing ABX use independently reduces PFS and OS in patients treated with TKIs. It is the first analysis to demonstrate this phenomenon across two distinct tumour sites. The data suggests that ABX use could be an independent predictor of shorter PFS and OS in cancer patients treated with TKIs, and warrants further validation in a larger cohort.

20 May 2018·Journal of Clinical Oncology

Cumulative antibiotic use and efficacy of immune checkpoint inhibitors in patients with advanced cancer.

Author: Thistlethwaite, Fiona ; Zhou, Cong ; Elliott, Tony ; Blackhall, Fiona Helen ; Cook, Natalie ; Tinsley, Nadina ; Tan, Grace ; Krebs, Matthew ; Carter, Louise ; Hughes, Andrew ; Lorigan, Paul ; Villa, Shaun

3010Background: Efficacy of immune checkpoint inhibitors (CKIs) for treatment of cancer may be affected by the use of antibiotics (ABX). The gut microbiome is involved in immunotherapy responses th...

100 Deals associated with Paterson Institute for Cancer Research

Login to view more data

100 Translational Medicine associated with Paterson Institute for Cancer Research

Login to view more data

Corporation Tree

Boost your research with our corporation tree data.

login

or

view full example data

Boost your research with our corporation tree data.

Pipeline

Pipeline Snapshot as of 29 Dec 2024

The statistics for drugs in the Pipeline is the current organization and its subsidiaries are counted as organizations,Early Phase 1 is incorporated into Phase 1, Phase 1/2 is incorporated into phase 2, and phase 2/3 is incorporated into phase 3

Preclinical

1

Login to view more data