Dataset Information

Monitoring drug-target interactions through target engagement-mediated amplification on arrays and in situ.

ABSTRACT: Drugs are designed to bind their target proteins in physiologically relevant tissues and organs to modulate biological functions and elicit desirable clinical outcomes. Information about target engagement at cellular and subcellular resolution is therefore critical for guiding compound optimization in drug discovery, and for probing resistance mechanisms to targeted therapies in clinical samples. We describe a target engagement-mediated amplification (TEMA) technology, where oligonucleotide-conjugated drugs are used to visualize and measure target engagement in situ, amplified via rolling-circle replication of circularized oligonucleotide probes. We illustrate the TEMA technique using dasatinib and gefitinib, two kinase inhibitors with distinct selectivity profiles. In vitro binding by the dasatinib probe to arrays of displayed proteins accurately reproduced known selectivity profiles, while their differential binding to fixed adherent cells agreed with expectations from expression profiles of the cells. We also introduce a proximity ligation variant of TEMA to selectively investigate binding to specific target proteins of interest. This form of the assay serves to improve resolution of binding to on- and off-target proteins. In conclusion, TEMA has the potential to aid in drug development and clinical routine by conferring valuable insights in drug-target interactions at spatial resolution in protein arrays, cells and in tissues.

SUBMITTER: Al-Amin RA

PROVIDER: S-EPMC9825164 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Monitoring drug-target interactions through target engagement-mediated amplification on arrays and in situ.

Al-Amin Rasel A RA Johansson Lars L Abdurakhmanov Eldar E Landegren Nils N Löf Liza L Arngården Linda L Blokzijl Andries A Svensson Richard R Hammond Maria M Lönn Peter P Haybaeck Johannes J Kamali-Moghaddam Masood M Jensen Annika Jenmalm AJ Danielson U Helena UH Artursson Per P Lundbäck Thomas T Landegren Ulf U

Nucleic acids research 20221201 22

Drugs are designed to bind their target proteins in physiologically relevant tissues and organs to modulate biological functions and elicit desirable clinical outcomes. Information about target engagement at cellular and subcellular resolution is therefore critical for guiding compound optimization in drug discovery, and for probing resistance mechanisms to targeted therapies in clinical samples. We describe a target engagement-mediated amplification (TEMA) technology, where oligonucleotide-conj ...[more]

PMID: 36189884

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Project description:Adverse drug effects (ADEs) are one of the leading causes of death in developed countries and are the main reason for drug recalls from the market, whereas the ADEs that are associated with action on the cardiovascular system are the most dangerous and widespread. The treatment of human diseases often requires the intake of several drugs, which can lead to undesirable drug-drug interactions (DDIs), thus causing an increase in the frequency and severity of ADEs. An evaluation of DDI-induced ADEs is a nontrivial task and requires numerous experimental and clinical studies. Therefore, we developed a computational approach to assess the cardiovascular ADEs of DDIs. This approach is based on the combined analysis of spontaneous reports (SRs) and predicted drug-target interactions to estimate the five cardiovascular ADEs that are induced by DDIs, namely, myocardial infarction, ischemic stroke, ventricular tachycardia, cardiac failure, and arterial hypertension. We applied a method based on least absolute shrinkage and selection operator (LASSO) logistic regression to SRs for the identification of interacting pairs of drugs causing corresponding ADEs, as well as noninteracting pairs of drugs. As a result, five datasets containing, on average, 3100 potentially ADE-causing and non-ADE-causing drug pairs were created. The obtained data, along with information on the interaction of drugs with 1553 human targets predicted by PASS Targets software, were used to create five classification models using the Random Forest method. The average area under the ROC curve of the obtained models, sensitivity, specificity and balanced accuracy were 0.837, 0.764, 0.754 and 0.759, respectively. The predicted drug targets were also used to hypothesize the potential mechanisms of DDI-induced ventricular tachycardia for the top-scoring drug pairs. The created five classification models can be used for the identification of drug combinations that are potentially the most or least dangerous for the cardiovascular system.

Project description:IntroductionGlucocorticoids are a mainstay of anti-inflammatory therapy, but significant adverse effects ultimately limit their utility. Previous efforts to design glucocorticoid structures with an increased therapeutic window have focused on dissociating anti-inflammatory transcriptional repression from adverse effects primarily driven by transcriptional activation. An alternative to this medicinal chemistry approach is a systems biology based strategy that seeks to amplify selectively the anti-inflammatory activity of very low dose glucocorticoid in immune cells without modulating alternative cellular networks that mediate glucocorticoid toxicity.MethodsThe combination of prednisolone and the antithrombotic drug dipyridamole was profiled using in vitro and in vivo models of anti-inflammatory activity and glucocorticoid-induced adverse effects to demonstrate a dissociated activity profile.ResultsThe combination synergistically suppresses release of proinflammatory mediators, including tumour necrosis factor-alpha, IL-6, chemokine (C-C motif) ligand 5 (RANTES), matrix metalloproteinase-9, and others, from human peripheral blood mononuclear cells and mouse macrophages. In rat models of acute lipopolysaccharide-induced endotoxemia and delayed-type hypersensitivity, and in chronic models of collagen-induced and adjuvant-induced arthritis, the combination produced anti-inflammatory activity that required only a subtherapeutic dose of prednisolone. The immune-specific amplification of prednisolone anti-inflammatory activity by dipyridamole did not extend to glucocorticoid-mediated adverse effects, including corticosterone suppression or increased expression of tyrosine aminotransferase, in vivo after repeat dosing in rats. After 8 weeks of oral dosing in mice, treatment with the combination did not alter prednisolone-induced reduction in osteocalcin and mid-femur bone density, which are markers of steroid-induced osteoporosis. Additionally, amplification was not observed in the cellular network of corticotroph AtT-20/D16v-F2 cells in vitro, as measured by pro-opiomelanocortin expression and adrenocorticotropic hormone secretion.ConclusionsThese data suggest that the multi-target mechanism of low-dose prednisolone and dipyridamole creates a dissociated activity profile with an increased therapeutic window through cellular network selective amplification of glucocorticoid-mediated anti-inflammatory signaling.

Dataset Information

Monitoring drug-target interactions through target engagement-mediated amplification on arrays and in situ.

Publications

Monitoring drug-target interactions through target engagement-mediated amplification on arrays and in situ.

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