10th German Pharm-Tox Summit / 91. Jahrestagung der Deutschen Gesellschaft für Experimentelle und Klinische Pharmakologie und Toxikologie (DGPT)

Prof. Dr.

Chair

Prof. Dr. Dr.

PD. Dr.

Advanced tools for orphan GPCR drug discovery: a GPR3 conformational biosensor reveals new ligands.

G-protein coupled receptors

BR01

GPR3 is a recently de-orphanized class A GPCRs and plays a central role in both benign and malignant physiological processes including energy expenditure in adipocytes and Alzheimer"s disease pathology, respectively. Despite the therapeutic potential of both receptor agonists and inverse agonists, GPR3 lacks drug-like ligands and innovative screening technologies, hindering effective drug discovery efforts targeting this receptor. To overcome the limitations of conventional screening techniques, we developed a conformational GPR3 biosensor to monitor receptor activity in living cells with high-throughput screening (HTS)-compatible sensitivity and robustness. Combined with virtual compound screening and classical medicinal chemistry, this biosensor enabled us to identify new ligands, one of which modulates GPR3-dependent Gs activity with nanomolar potency. Our study not only presents new GPR3 hit ligands for future optimization efforts and paves the way for the development of novel GPR3 ligands, but our sensor approach also provides a blueprint for targeting other therapeutically attractive but challenging GPCRs.

Basic Research

Short talk

Prof.

Why is Imeglimin Insulinotropic (but not Metformin)?

Endocrine pharmacology

AP05

Imeglimin has recently been approved for use as an oral antidiabetic agent in Japan and India. It is a close structural analogue of metformin which is known to enhance insulin action but not to increase insulin secretion. Imeglimin in contrast has been described to exert metformin-like effect on insulin-sensitive organs and additionally, to increase insulin secretion. The mechanisms underlying the latter effect are not yet clarified. NMRI mouse islets were isolated by collagenase digestion und used after a 24h cell culture period. The islets were cultured in RPMI (5 mM Glucose) plus 10% FCS (control) or in the additional presence of either imeglimin or metformin (0.1 mM). The mitochondrial function was assessed by measuring the NAD(P)H- and FAD- autofluorescence and the fluorescence of TMRE (indicator the mitochondrial membrane potential) of perifused islets. The insulin secretion and the contents of ATP and ADP were measured after static incubation by ELISA or luciferase luminescence, respectively. Islets which had been cultured in the presence of imeglimin showed a stronger increase of insulin secretion in response to 25 mM glucose than control islets or islets cultured in the presence of metformin. The NAD(P)H/FAD ratio of autofluorescences increased in response to raising the glucose concentration from 5 to 25 mM. The strength of the increase was: metformin ˃˃ imeglimin ˃ control. The same sequence applied to the further increase as induced by the inhibitors of the respiratory chain, oligomycin followed by antimycin plus rotenone. The TMRE fluorescence of metformin-cultured islets, measured simultaneously with the autofluorescences, showed a more marked increase in response to 25 mM glucose and a stronger decrease in response to antimycin plus rotenone than imeglimin-cultured or control islets. The measurement of the adenine nucleotides after stimulation with 25 mM glucose revealed a significantly stronger increase of the ATP/ADP ratio for imeglimin-cultured islets than for metformin-cultured or control islets. The increase of the ratio was mainly due to the decrease of the ADP content. The stronger increase of the ATP/ADP ratio by imeglimin than by metformin fits to the different insulinotropic property of these compounds. The more marked increase of the NAD(P)H/FAD ratio by metformin suggests a backlog of reducing equivalents as a consequence of a diminished flow along the respiratory chain.

Applied Pharmacology

Is pharmacogenetic testing relevant for paediatric inpatient prescribing of drugs – a German perspective

Pharmacoepidemiology and drug safety

AP07

Introduction Pharmacogenetic (PGx) guided drug therapy is a promising approach for improving drug response and safety of therapies. While the integration of PGx information into clinical practice is already advancing in adult medicine, its application in paediatrics remains limited. We aimed to analyse the utilisation of pharmacogenetically (PGx) relevant drugs in paediatric patients and to assess the potential impact of pre-emptive pharmacogenetic testing in this population. Methodology Routine medication data were retrieved from 25,339 admissions of 16,289 patients on general paediatric wards at a children"s university hospital between 2014 and 2019. 17,543 admissions of 11,611 patients who received medication during their hospital stay contributed to the analysis. The frequency of pharmacogenetically relevant drugs was calculated and stratified by age group. This analysis was based on the 74 currently on the PharmGKB.org listed drugs with a paediatric guideline annotation. In addition, we identified the underlying PGx target genes of all PGx relevant drugs used in our study population and extrapolated the number of possibly affected patients. Results 37.9% (n = 4,401) of the study patients (11,611) received at least one PGx drug. The total number of exposures to PGx drugs was 5,633. The three most commonly administered PGx drugs were ibuprofen (3,407/5,633, 60.5%), omeprazole (797/5,633, 14.1%), tobramycin (409/5,633, 7.3%). There were no age dependent differences except for aminoglycosides which were more often used in neonates, infants and toddlers. Only 35 of the 74 PGx drugs with paediatric recommendations were used. For these drugs 17 different pharmacogenetic test targets would have been needed to provide information about possible clinical consequences. CYP2C9 testing was relevant for n=10 (28.6%) and CYP2D6 for n=8 (22.9%) of the 35 PGx relevant drugs prescribed. Eight of the 17 genes account for over 95% of the required individual PGx tests. 12% of the treated patients with PGx drugs are expected to have a relevant pharmacogenetic variant. Conclusion PGx medications are widely used in paediatrics. For a large proportion of the PGx drugs used, only a limited number of different gene tests would be required. Thus, there is evidence that PGx contributes to paediatric medications which are used in the real world setting of a university hospital. Implementation strategies for PGx in children medicine are required. MS und FS are supported by the Robert Stiftung Stuttgart

Acylcarnitines as markers for metabolic shifts mediated by hepatic transporter OCT1

Ion channels and membrane transporters

BR05

Background: Organic cation transporter 1 (OCT1) is a polyspecific hepatic transporter with high genetic variability. Significant changes in the plasma concentration of acylcarnitines like isobutyrylcarnitine (IBC) have been reported in carriers of two reduced activity OCT1 alleles, so called poor transporters. IBC is not a substrate of human OCT1, leaving the mechanisms underlying this association unresolved. In this project, we addressed the mechanisms by which the reduction or lack of OCT1 activity affects the formation of acylcarnitines such as IBC.Methods: We analyzed the differences in the plasma concentration of selected acylcarnitines between poor (n=22), intermediate (n=22) and extensive (n=22) OCT1 transporters and in Oct1/Oct2 knockout mice. We performed untargeted proteomics on hOCT1 overexpressing HEK293 cells. Finally, we traced the formation of acylcarnitines from amino acids in HEK293 cells overexpressing human and murine OCT1 using pulse chaise analysis with non-radioactively labeled precursor metabolites.Results: Poor OCT1 transporters and Oct1 knockout mice showed significantly lower IBC concentrations (p=5.0x10-7 and p=4.1x10-4 for humans and mice, respectively), but higher concentrations of glutaryl-carnitine (GC; p=0.048 and p=5.6x10-3 for humans and mice, respectively). Extracellular accumulation of IBC formed from 13C-labled valine was 3.8-fold higher in hOCT1 expressing cells than control (p=8.6x10-4). The protein abundance of 14 proteins involved in valine metabolism were significantly altered in hOCT1 overexpressing cells. Three out if these were increased, and eleven decreased compared to control. Subunits of thiamine dependent dehydrogenases were also altered. The E1 subunit of the branched-chain ketoacid dehydrogenase complex was the only one increased (18% compared to control; p=1.23x10-4). However, the E1 subunit of the 2-oxoadipate deyhydrogenase complex, forming glutaryl-CoA in a step of lysine degradation, was decreased by 26% compared to control (p=0.001). Conclusion: This suggests that OCT1 deficiency affects the metabolism of selected amino acids by differential effects on the expression of key metabolizing enzymes.

Cellular fate of dietary heme versus non-heme iron and their contribution to spatio-temporal ROS formation in intestinal epithelial cells

Carcinogenesis

TOX05

The consumption of red meat has been linked to the development of colorectal cancer (CRC). Red meat contains high amounts of heme iron, which is thought to be the main factor contributing to its carcinogenic potential. In a previous study, we showed that hemin (Fe3+ protoporphyrin), but not non-heme iron, is genotoxic and selectively cytotoxic to human colon epithelial cells (HCEC). This study investigated the cellular fate of heme vs. non–heme iron (FeCl3, FeSO4) and its spatio-temporal ROS formation using different intestinal epithelial cell models. HCEC and CRC cells were exposed to hemin, the physiological relevant form, and non-heme iron. Intracellular labile hemin and ferrous iron were detected by fluorescent probes (Ac-HFluNox, Calcein-AM) using FACS and immunofluorescence (IF) microscopy. The total iron level was measured by inductively coupled plasma mass spectrometry (ICP-MS). ROS formation was monitored by different fluorescent probes (CM-H2HCFDA, MitoSOX, Dithydroethidium) and genetically encoded reporters (roGFP2-Grx1, roGFP2-Orp1). Label-free quantitative proteomics was used to analyze the cellular proteome. Labile ferrous iron was increased after hemin exposure, which resulted from heme degradation by heme oxygenase-1 (HO) as proved by chemical HO-1 inhibition. Intracellular free hemin accumulated, which was potentiated by chemical HO-1 inhibition. The uptake of hemin measured by total iron showed concentration and time-dependent increase, while non-heme iron showed no uptake in Caco2. Hemin, but not non-heme iron led to time-dependent general ROS production in HCEC. According to this oxidative stress, hemin augmented glutathione disulfide levels in the cytosol. Hemin caused preferably mitochondrial superoxide formation and less cytosolic superoxides. Importantly, the superoxide scavenger MITO-Tempo blocked hemin-induced mitochondrial superoxide formation, while HO-1 inhibition increased the levels. Furthermore, a hemin-dependent increase in mitochondrial, but not cytosolic H2O2 was observed. Interestingly, proteomics revealed an upregulation of the NRF2 antioxidant response pathway including HO-1, glutathione biosynthesis enzymes and SOD2. Hemin, but not non-heme iron, causes time-dependent ROS formation, mitochondrial superoxides and H2O2 formation leading to the activation of NRF2-dependent antioxidative pathways in intestinal epithelial cells. Our future work will focus on the relevance of SODs using genetically engineered cell models.

Toxicology

Countless trees, several forests? – A comparison of popular enrichment analysis methods for in vitro untargeted metabolomics in the context of toxicological and pharmacological testing.

General Toxicology

TOX08

Metabolomics is the discipline that studies small molecules within a biological system, also referred to as metabolites[1]. It can be broadly classified into targeted and untargeted approaches: while targeted experiments focus on a small subset of metabolites, untargeted experiments aim to identify as many metabolites as possible, thereby revealing a comprehensive overview of the state of the biological system as a whole. This often results in complex datasets which are hard to interpret and require sophisticated analysis workflows. To facilitate this task, enrichment analysis has gained interest in the metabolomics community in the recent past[2]. The purpose of these approaches is to identify pathways of interest rather than individual metabolites. Several statistical methods are available to perform enrichment analysis. These include over-representation analysis (ORA), metabolite set enrichment analysis (MSEA) and the mummichog algorithm[3,4]. In a recent study, Xia et al. benchmarked several methods using simulated and authentic in vivo datasets but to the best of our knowledge, a comparison for in vitro data, especially with a focus on toxicological and pharmacological testing has not been reported yet[2].One objective of this work was to compare several methods for enrichment analysis for data from in vitro untargeted metabolomics experiments. Another question was if enrichment analysis can elucidate the mechanism of action (MoA) of pharmacologically and toxicologically relevant compounds.For this, HepG2 hepatoblastoma cells were treated with several compounds with a broad range of well-studied MoA, namely ROS generators, HMG-CoA reductase inhibitors, glycolysis inhibitors, inhibitors of the respiratory chain and compounds that interfere with the nucleotide metabolism. Samples were analyzed using liquid chromatography coupled to a timsTOF mass spectrometer. Several enrichment analyses were performed, and the results were compared.This contribution will highlight similarities and differences in the results of several enrichment methods and help researchers make a sound decision for their data.[1] X. Liu, J. W. Locasale, Trends Biochem. Sci. 2017, 42, 274–284.[2] Y. Lu, Z. Pang, J. Xia, Brief. Bioinform. 2023, 24, bbac553.[3] J. J. Goeman, P. Bühlmann, Bioinformatics 2007, 23, 980–987.[4] S. Li, Y. Park, S. Duraisingham, F. H. Strobel, N. Khan, Q. A. Soltow, D. P. Jones, B. Pulendran, PLoS Comput. Biol. 2013, 9, e1003123.

Hörsaal B

Short talk 11 - Young Investigator Lectures

Young Investigator Lectures

Short talks

Short talk 11 - Young Investigator Lectures
Young Investigator Lectures

Termin

Chair

Programm

Short talk 11 - Young Investigator LecturesYoung Investigator Lectures

Termin

Chair

Programm

Short talk 11 - Young Investigator Lectures
Young Investigator Lectures