Biology subjects

Antolin, A. A.

Publications and source records attributed to Antolin, A. A..

7 recordsLinked to original sources

bioRxiv2025

Multiple FGFR1 mutations modulate tumorigenic mechanisms in glioneuronal tumors.

FGFR1 genetic alterations are associated with human diseases, including brain tumors. We reported multiple FGFR1 mutations in familial and sporadic cases of low-grade glioneuronal tumors, suggesting intrinsic mechanisms of selective pressure toward FGFR1 multiple events arising in the context of a quiet genome. To decipher the molecular mechanisms triggered by multiple FGFR1 mutations, we have mapped the proximal interactome of wild-type, single-and double-mutant FGFR1 proteins through a BioID-MS approach. Our data reveals novel oncogenic functionality for the two hotspots N546K and K656E, linked to evasion of lysosomal degradation. We identified a modulatory role played by the susceptibility variant R661P, which hampers the oncogenic potential of both hotspot mutations by rescuing receptor degradation and reducing N546K affinity for the downstream effector PLC{gamma}. The R661P variant alone abolished the self-renewal capacity of oligodendroglioma cells and showed downregulation of genes involved in neurodevelopment and neuro-glial cell fate decisions, both aspects overcome in the double mutants. This study sheds light on the oncogenic effects associated with FGFR1 alterations and their recurrence in low-mutation burden and therapy naive tumors.

cancer biology↗

bioRxiv2023

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium

Drug Mechanism of Action (MoA) is often represented as a small repertoire of tissue-independent, high-affinity binding targets. Yet drug activity and polypharmacology are also manifested through a broad range of off-target and tissue-specific effector proteins. To address this dichotomy, we generated a compendium of drug perturbation profiles for >700 oncology drugs in cell lines representing high-fidelity models of 23 aggressive tumor subtypes, as well as an integrative framework for the proteome-wide assessment of drug-mediated, tissue-specific differential protein activity. Extending MoA and polypharmacology assessment via proteome-wide, multi-tissue analyses revealed novel polypharmacology, including for MAPK, PI3K, and folate pathways inhibitors--as supported by Western blots, thermal shift assays, and structural analysis--as well as functional inhibitors of challenging oncoprotein targets, including MYC, CTNNB1, CENPF and UHRF1. The dataset and associated computational framework might be extensible to additional compound libraries, supporting global, quantitative approaches to defining the mechanisms of action of many drugs.

systems biology↗

bioRxiv2023

Computational repurposing of oncology drugs through off-target drug binding interactions from pharmacological databases

PURPOSESystematic repurposing of approved medicine for another indication represents an attractive strategy to accelerating drug development in oncology. Herein we present a strategy of combining biomarker testing with drug repurposing to identify new treatments for patients with advanced cancer. METHODSTumours were sequenced with Illumina TruSight Oncology 500 (TSO-500) platform or the FoundationOne(R) CDx panel. Mutations were manually screened by two medical oncology clinicians and pathogenic mutations were categorised with reference to the literature. Variants of unknown significance were classified as potentially pathogenic if a plausible mechanism and computational prediction of pathogenicity existed. Gain of function mutations were evaluated through the repurposing databases Probe Miner, the Broad Institute Drug Repurposing Hub (Broad Institute DRH) and TOPOGRAPH. Gain of function mutations were classified as repurposing events if they were identified in Probe Miner, were not indexed in TOPOGRAPH which captures active clinical trial biomarkers and excluding mutations for which a known FDA-approved biomarker label exists. The performance of the computational repurposing approach was validated by evaluating its ability to identify known FDA-approved biomarkers. Exploratory functional analyses were performed with gene expression data and CRISPR-dependency data sourced from the DepMap portal. The total repurposable genome was identified by evaluating all possible gene-FDA drug approved combinations in the Probe Miner dataset. RESULTSThe computational repurposing approach was highly accurate at identifying FDA therapies with known biomarkers (94%). Using a real-world dataset of next-generation sequencing molecular reports (n = 94) and excluding the identification of mutations that would render patients eligible for FDA-licensed therapies or local clinical trials, it was found that a meaningful percentage of patients (14%) would have an off-label therapeutic identified through this approach. Exploratory analyses were performed, including the identification of drug-target interactions that have been previously described in the medicinal chemistry literature but are not well known, and the evaluation of the frequency of theoretical drug repurposing events in the TCGA pan-cancer dataset (73% of samples in the cohort). CONCLUSIONOverall, a computational drug repurposing approach may assist in identifying novel repurposing events in cancer patients with advanced tumours and no access to standard therapies. Further validation is needed to confirm the utility of a precision oncology approach using drug repurposing.

biochemistry↗

bioRxiv2022

Identification of differential polypharmacology between the PARP inhibitor rucaparib and its major metabolite

The (poly)pharmacology of drug metabolites is seldom comprehensively characterized in drug discovery and development. However, some drug metabolites can reach high plasma concentrations and display relevant in vivo activity, which can be distinct from its parent drug. Here, we use computational and experimental methods to comprehensively characterise the kinase polypharmacology of M324, the major metabolite of the FDA-approved PARP inhibitor rucaparib. We experimentally demonstrate that M324 displays a distinct in vitro kinome profile from its parent drug, characterized by potent in vitro inhibition of GSK3A and PLK2 at clinically-relevant concentrations. These confirmed kinase activities of M324 could have potential implications for the efficacy and safety of rucaparib and therefore warrant further clinical investigation. The study reported here highlights the importance of thoroughly characterizing the activity of significant drug metabolites to better understanding drug responses in the clinic and maximally exploit the current drug arsenal in personalized and precision medicine.

pharmacology and toxicology↗

bioRxiv2020

Evolution of kinase polypharmacology across HSP90 drug discovery

Most small molecules interact with several target proteins but this polypharmacology is seldom comprehensively investigated or explicitly exploited during drug discovery. Here, we use computational and experimental methods to systematically characterize the kinase cross-pharmacology of representative HSP90 inhibitors. We demonstrate that the resorcinol clinical candidates ganetespib and, to a lesser extent, luminespib, display unique off-target kinase pharmacology as compared to other HSP90 inhibitors. We also demonstrate that polypharmacology evolved during the optimisation to discover luminespib and that the hit, leads and clinical candidate all have different polypharmacological profiles. We conclude that the submicromolar target inhibition of protein kinases by ganetespib may have potential clinical significance and we recommend the computational and experimental characterization of polypharmacology earlier in drug discovery projects to unlock new multi-target drug design opportunities.

pharmacology and toxicology↗

bioRxiv2019

The off-target kinase landscape of clinical PARP inhibitors

Four PARP inhibitors have been approved by the FDA as cancer therapeutics, yet there are several clinical settings where no strong rationale exists to inform clinicians on which to choose in terms of clinical effectiveness and toxicity. The four drugs have largely similar PARP family inhibition profiles, but several differences at the molecular and clinical level have been reported that remain poorly understood. We previously hypothesized that PARP inhibitors could have an inherent capacity to inhibit kinases off-target. Here, we characterise the off-target kinase landscape of four FDA-approved PARP drugs. We demonstrate that all four PARP inhibitors have a unique polypharmacological profile across the kinome. Niraparib and rucaparib inhibit DYRK1s, CDK16 and PIM3 at clinically achievable submicromolar concentrations. These represent the most potently inhibited off-targets of PARP inhibitors identified to date and should be investigated further to clarify their potential implications for efficacy and safety in the clinic, including use of PARP inhibitors in combination with other agents, including immunotherapy.

pharmacology and toxicology↗

bioRxiv2017

Objective, Quantitative, Data-Driven Assessment of Chemical Probes

Chemical probes are essential tools for understanding biological systems and for target validation, yet selecting tools for biomedical research is largely biased and subjective. Here we describe the Probe Miner: Chemical Probes Objective Assessment resource - capitalising on the plethora of public medicinal chemistry data to empower quantitative, objective, Big Data-driven assessment of chemical probes. We assess >1.8m compounds for their suitability as chemical tools against 2,220 human targets and dissect their biases and limitations.

bioinformatics↗