Biology subjects

Brimacombe, K. R.

Publications and source records attributed to Brimacombe, K. R..

7 recordsLinked to original sources

bioRxiv2026

Attenuating LRRK2 activity ameliorates progerin-induced aging phenotypes in HGPS models and during physiological aging

Hutchinson-Gilford progeria syndrome (HGPS) is an ultra-rare premature aging disorder caused by progerin, a truncated lamin A variant generated by a silent de novo mutation activating a cryptic splice site in LMNA. The resulting morphological, epigenetic, genomic, and proteostasic defects closely recapitulate some hallmarks of cellular aging. Here, we identify the Parkinsons disease-associated kinase LRRK2 as a critical regulator of HGPS pathology and physiological aging. Rab29-mediated LRRK2 hyperactivation exacerbates progerin-induced cellular aging, whereas LRRK2 knockdown or overexpression of its opposing phosphatase, PPM1H, ameliorates progerin-induced defects. Progerin-expressing cells exhibit altered intracellular trafficking, which is regulated by LRRK2 and links diverse aging hallmarks. Consistent with these findings, reducing LRRK2 levels mitigates cellular aging phenotypes in physiologically aged cells, and loss of the C. elegans ortholog lrk-1 preserves aging-associated loss of motility and extends organismal lifespan. Together, our findings establish LRRK2 as a central node in cellular aging and position it as a potential therapeutic target for aging-related defects in both HGPS and physiological aging.

cell biology↗

bioRxiv2023

A deep learning framework for predicting the neutralizing activity of COVID-19 therapeutics and vaccines against evolving SARS-CoV-2 variants

Understanding how viral variants evade neutralization is crucial for improving antibody-based treatments, especially with rapidly evolving viruses like SARS-CoV-2. Yet, conventional assays are limited in the face of rapid viral evolution, relying on a narrow set of viral isolates, and falling short in capturing the full spectrum of variants. To address this, we have developed a deep learning approach to predict changes in neutralizing antibody activity of COVID-19 therapeutics and vaccines against emerging viral variants. First, we trained a variational autoencoder (VAE) using all 67,885 unique SARS-CoV-2 spike protein sequences from the NCBI virus (up to October 31, 2022) database to encode spike protein variants into a latent space. Using this VAE and a curated dataset of 7,069 in vitro assay data points from the NCATS OpenData Portal, we trained a neural network regression model to predict fold changes in neutralizing activity of 40 COVID-19 therapeutics and vaccines against spike protein sequence variants, relative to their neutralizing activity against the ancestral strain (Wuhan-Hu-1). Our model also employs Bayesian inference to quantify prediction uncertainty, providing more nuanced and informative estimates. To validate the models predictive capacity, we assessed its performance on a test set of in vitro assay data collected up to eight months after the data included in the model training (N = 980). The model accurately predicted fold changes in neutralizing activity for this prospective dataset, with an R2 of 0.77. Expanding our methodology to include all available data from NCBI virus and NCATS OpenData Portal up to date, we assessed predicted changes in activity for current COVID-19 monoclonal antibodies and vaccines against newly identified SARS-CoV-2 lineages. Our predictions suggest that current therapeutic and vaccine-induced antibodies will have significantly reduced activity against newer XBB descendants, notably EG.5, FL.1.5.1, and XBB.1.16. Using the model, we were able to primarily attribute the observed predicted loss in activity to the F456L spike mutation found in EG.5 and FL.1.5.1 sequences. Conversely, mRNA-bivalent vaccines are predicted to be less susceptible to the recent BA.2.86 variant compared to new XBB descendants. These findings align closely with recent research, underscoring the potential of deep learning in shaping therapeutic and vaccine strategies for emerging viral variants.

immunology↗

bioRxiv2020

An OpenData portal to share COVID-19 drug repurposing data in real time

The National Center for Advancing Translational Sciences (NCATS) has developed an online open science platform - named the NCATS OpenData Portal (ODP) - for quickly and freely sharing complete NCATS translational datasets via an open-access, user-friendly interface. This paper describes the establishment of the ODP, initially deployed during the COVID-19 crisis, and provides a detailed analysis of COVID-19 drug repurposing screening datasets that served as the first large-scale use case for the platform. Over 10,000 compounds were tested across 17 quantitative high-throughput assays, covering a wide spectrum of the SARS-CoV-2 life cycle. In total, over 87,000 concentration-response curves and 426,000 data points were made publicly available on ODP in near real-time, enabling immediate access to complete datasets. The resource is flexible in accommodating various types and structures of data, and it has already expanded since its launch to host additional datasets for COVID-19, other viruses of pandemic potential, and beyond. The OpenData Portal has been designed as a scalable platform for real-time data sharing across drug discovery campaigns, regardless of disease area, with the overarching goal of accelerating discovery at NCATS, the NIH, and the greater scientific community.

microbiology↗

bioRxiv2019

A High-Throughput Screen of a Library of Therapeutics Identifies Substrates of P-glycoprotein

The ATP-binding cassette transporter P-glycoprotein (P-gp) is known to limit brain penetration of many chemotherapy drugs. Although Food and Drug Administration guidelines require that potential interactions of investigational drugs with P-gp be explored, often this information does not enter into the literature. As such, we developed a high-throughput screen (HTS) to identify substrates of P-gp from a series of chemical libraries, testing a total of 10,804 compounds, most of which have known mechanisms of action. We used the CellTiter-Glo viability assay to test library compounds against parental KB-3-1 human cervical adenocarcinoma cells and the colchicine-selected sub-line KB-8-5-11 that over-expresses P-gp. KB-8-5-11 cells were also tested in the presence of a P-gp inhibitor (tariquidar) to assess reversability of transporter-mediated resistance. Of the tested compounds, a total of 90 P-gp substrates were identified including 55 newly identified P-gp substrates. Substrates were confirmed using an orthogonal killing assay against HEK-293 cells transfected with P-gp. We confirmed that AT7159 (cyclin-dependent kinase inhibitor); AT9283, (Janus kinase 2/3 inhibitor); ispinesib (kinesin spindle protein inhibitor); gedatolisib (PKI-587, phosphoinositide 3-kinase/mammalian target of rampamycin inhibitor); GSK-690693 (AKT inhibitor); and KW-2478 (heat shock protein 90 inhibitor) were substrates, and direct ATPase stimulation was assessed. ABCG2 was also found to confer high levels of resistance to AT9283, GSK-690693 and gedatolisib, while ispinesib, AT7519 and KW-2478 were weaker substrates. Combinations of P-gp substrates and inhibitors were assessed to demonstrate on-target synergistic cell killing. This data will be of use in determining understanding how chemotherapeutic agents will cross the blood-brain barrier.

pharmacology and toxicology↗

bioRxiv2018

Discovery and optimization of piperazine-1-thiourea-based human phosphoglycerate dehydrogenase inhibitors

Proliferating cells, including cancer cells, obtain serine both exogenously and via the metabolism of glucose. By catalyzing the first, rate-limiting step in the synthesis of serine from glucose, phosphoglycerate dehydrogenase (PHGDH) controls flux through the biosynthetic pathway for this important amino acid and represents a putative target in oncology. To discover inhibitors of PHGDH, a coupled biochemical assay was developed and optimized to enable high-throughput screening for inhibitors of human PHGDH. Feedback inhibition was minimized by coupling PHGDH activity to two downstream enzymes (PSAT1 and PSPH), providing a significant improvement in enzymatic turnover. Further coupling of NADH to a diaphorase/resazurin system enabled a red-shifted detection readout, minimizing interference due to compound autofluorescence. With this protocol, over 400,000 small molecules were screened for PHGDH inhibition, and following hit validation and triage work, a piperazine-1-thiourea was identified. Following rounds of medicinal chemistry and SAR exploration, two probes (NCT-502 and NCT-503) were identified. These molecules demonstrated improved target activity and encouraging ADME properties, enabling both in vitro and in vivo assessment of the biological importance of PHGDH, and its role in the fate of serine in PHGDH-dependent cancer cells.

cancer biology↗

bioRxiv2017

Small Molecule Inhibitors of the Human Histone Lysine Methyltransferase NSD2 / WHSC1 / MMSET Identified from a Quantitative High-Throughput Screen with Nucleosome Substrate

The activity of the histone lysine methyltransferase NSD2 is thought to play a driving role in oncogenesis. Both overexpression of NSD2 and point mutations that increase its catalytic activity are associated with a variety of human cancers. While NSD2 is an attractive therapeutic target, no potent, selective and cell-active inhibitors have been reported to date, possibly due to the challenging nature of developing high-throughput assays for NSD2. To establish a platform for the discovery and development of selective NSD2 inhibitors, multiple assays were optimized and implemented. Quantitative high-throughput screening was performed with full-length wild-type NSD2 and a nucleosome substrate against a diverse collection of known bioactives comprising 16,251 compounds. Actives from the primary screen were further interrogated with orthogonal and counter assays, as well as activity assays with the clinically relevant NSD2 mutants E1099K and T1150A. Five confirmed inhibitors were selected for follow-up, which included a radiolabeled validation assay, surface plasmon resonance studies, methyltransferase profiling, and histone methylation in cells. The identification of NSD2 inhibitors that bind the catalytic SET domain and demonstrate activity in cells validates the workflow, providing a template for identifying selective NSD2 inhibitors.

cancer biology↗

bioRxiv2017

Assessing Inhibitors Of Mutant Isocitrate Dehydrogenase Using A Suite Of Pre-Clinical Discovery Assays

Isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) are key metabolic enzymes that are mutated in a variety of cancers to confer a gain-of-function activity resulting in the accumulation and secretion of an oncometabolite, D-2-hydroxyglutarate (2-HG). Accumulation of 2-HG can result in epigenetic dysregulation and a block in cellular differentiation, suggesting these mutations play a role in neoplasia. Based on its potential as a cancer target, a number of small molecule inhibitors have been developed to specifically inhibit mutant forms of IDH (mIDH1 and mIDH2). Here, a panel of mIDH inhibitors were systematically profiled using biochemical, cell-based, and tier-one ADME techniques. We quantified the biochemical effect of each inhibitor on mIDH1 (R132H and R132C) and mIDH2 (R172Q). The effect of these inhibitors on 2-HG concentrations in seven cell lines representing five different IDH1 mutations in both 2D and 3D cell cultures was assessed. Target engagement of these inhibitors was analyzed utilizing cellular thermal shift assays (CETSA), the effects of inhibitors on reversing 2-HG-induced block on leukemic cellular differentiation. We conclude from our mIDH1 assay panel that AG-120 and a Novartis inhibitor exhibited excellent activity in all biochemical and most cellular assays. While AG-120 has superior DMPK properties, it lacks efficacy a leukemic differentiation model. In conclusion, we present a comprehensive suite of in vitro preclinical drug development assays that can be used as a tool-box to identify lead compounds for mIDH drug discovery programs, as well as what we believe is the most comprehensive publically available dataset on the top mIDH inhibitors.

cancer biology↗