Abstract 2093: Imaging single cell drug target engagement in vivo reveals heterogeneous fractional occupancy

2016 ◽  
Author(s):  
Matt Dubach ◽  
Ralph Weissleder
Keyword(s):  
Single Cell ◽  
Drug Target ◽  
Target Engagement

scholarly journals CLAMPs allow single cell tracking of KRASG12C inhibition and endow druggability to KRAS mutants

2021 ◽  
Author(s):  
Marie Evangelista ◽  
Chris Davies ◽  
Angela Oh ◽  
Rana Mroue ◽  
Melinda Mulvihil ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Tracking ◽  
Molecular Probe ◽  
Target Engagement ◽  
Transient Nature ◽  
Open Conformation ◽  
Covalent Inhibitors ◽  
Direct Target ◽  
Covalent Inhibitor

Abstract The discovery of covalent inhibitors binding the switch II (SWII) pocket has enabled therapeutic intervention in KRASG12C driven tumors and represents a milestone in targeting KRAS-driven cancers. However, the transient nature and high energetic barrier required for binding this pocket has been an obstacle in successfully targeting other KRAS mutant oncoproteins. We report the discovery of KRAS Conformation Locking Antibodies for Molecular Probe discovery (CLAMP)s that specifically recognize the unique conformation of KRASG12C induced by covalent inhibitors. KRAS CLAMPs enable single cell resolution of covalent inhibitor-bound KRASG12C in cells and in vivo tumor models, providing a biomarker for direct target engagement of KRASG12C inhibition. KRAS CLAMPs bind multiple KRAS mutants and stabilize an open conformation of the SWII pocket increasing the affinity of weak non-covalent SWII pocket ligands. This work provides new insights into KRASG12C upon treatment with covalent inhibitors and offers a path towards targeting the SWII pocket in other RAS mutants.

scholarly journals Quantitating drug-target engagement in single cells in vitro and in vivo

2016 ◽  
Vol 13 (2) ◽  
pp. 168-173 ◽  
Author(s):  
J Matthew Dubach ◽  
Eunha Kim ◽  
Katherine Yang ◽  
Michael Cuccarese ◽  
Randy J Giedt ◽  
...  
Keyword(s):  
Drug Target ◽  
Single Cells ◽  
Target Engagement

scholarly journals Proteome thermal stability reflects organ physiology and identifies drug-target engagement in vivo

2018 ◽  
Author(s):  
Jessica Perrin ◽  
Thilo Werner ◽  
Nils Kurzawa ◽  
Dorothee D Childs ◽  
Mathias Kalxdorf ◽  
...  
Keyword(s):  
Thermal Stability ◽  
Drug Target ◽  
Molecular Level ◽  
Target Identification ◽  
Protein Homeostasis ◽  
Biological Processes ◽  
Target Engagement ◽  
Monitoring Drug ◽  
Deacetylase Inhibitor

Studying biological processes at a molecular level and monitoring drug-target interactions is established for simple cell systems but challenging in vivo. We introduce and apply a methodology for proteome-wide thermal stability measurements to characterize organ physiology and activity of many fundamental biological processes across tissues, such as energy metabolism and protein homeostasis. This method, termed tissue thermal proteome profiling (tissue-TPP), also enabled target and off-target identification and occupancy measurements in tissues derived from animals dosed with the non-covalent histone deacetylase inhibitor, panobinostat. Finally, we devised blood-CETSA, a thermal stability-based method to monitor target engagement in whole blood. Our study generates the first proteome-wide map of protein thermal stability in tissue and provides tools that will be of great impact for translational research.

scholarly journals Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through ‘reverse phenotyping’

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
David S. Fischer ◽  
Meshal Ansari ◽  
Karolin I. Wagner ◽  
Sebastian Jarosch ◽  
Yiqi Huang ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Rna Sequencing ◽  
Ex Vivo ◽  
Severe Disease ◽  
Human Leukocyte ◽  
Cell Receptor ◽  
Single Cell Rna Sequencing

AbstractThe in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for ‘reverse phenotyping’. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

scholarly journals Sulpiride Serves, a Substrate for the Gut Microbiome

Dose-Response ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 155932582098794
Author(s):  
Imran Mukhtar ◽  
Haseeb Anwar ◽  
Osman Asghar Mirza ◽  
Qasim Ali ◽  
Muhammad Umar Ijaz ◽  
...  
Keyword(s):  
Oral Administration ◽  
Gut Microbiome ◽  
Drug Target ◽  
Membrane Transporters ◽  
Intestinal Microbiome ◽  
Albino Rats ◽  
Body Organ ◽  
Total Absorbance ◽  
Research World

In the contemporary research world, the intestinal microbiome is now envisioned as a new body organ. Recently, the gut microbiome represents a new drug target in the gut, since various orthologues of intestinal drug transporters are also found present in the microbiome that lines the small intestine of the host. Owing to this, absorbance of sulpiride by the gut microbiome in an in vivo albino rats model was assessed after the oral administration with a single dose of 20mg/kg b.w. The rats were subsequently sacrificed at 2, 3, 4, 5 and 6 hours post oral administration to collect the gut microbial mass pellet. The drug absorbance by the gut microbiome was determined by pursuing the microbial lysate through RP-HPLC-UV. Total absorbance of sulpiride by the whole gut microbiome and drug absorbance per milligram of microbial pellet were found significantly higher at 4 hours post-administration as compared to all other groups. These results affirm the hypothesis that the structural homology between membrane transporters of the gut microbiome and intestinal epithelium of the host might play an important role in drug absorbance by gut microbes in an in vivo condition.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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