TY - JOUR A1 - Reel, Smarti A1 - Reel, Parminder S. A1 - Erlic, Zoran A1 - Amar, Laurence A1 - Pecori, Alessio A1 - Larsen, Casper K. A1 - Tetti, Martina A1 - Pamporaki, Christina A1 - Prehn, Cornelia A1 - Adamski, Jerzy A1 - Prejbisz, Aleksander A1 - Ceccato, Filippo A1 - Scaroni, Carla A1 - Kroiss, Matthias A1 - Dennedy, Michael C. A1 - Deinum, Jaap A1 - Eisenhofer, Graeme A1 - Langton, Katharina A1 - Mulatero, Paolo A1 - Reincke, Martin A1 - Rossi, Gian Paolo A1 - Lenzini, Livia A1 - Davies, Eleanor A1 - Gimenez-Roqueplo, Anne-Paule A1 - Assié, Guillaume A1 - Blanchard, Anne A1 - Zennaro, Maria-Christina A1 - Beuschlein, Felix A1 - Jefferson, Emily R. T1 - Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios JF - Metabolites N2 - Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing’s syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification. KW - metabolomics KW - machine learning KW - hypertension KW - primary aldosteronism KW - pheochromocytoma/paraganglioma KW - Cushing syndrome KW - biomarkers Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-286161 SN - 2218-1989 VL - 12 IS - 8 ER - TY - JOUR A1 - Isberner, Nora A1 - Gesierich, Anja A1 - Balakirouchenane, David A1 - Schilling, Bastian A1 - Aghai-Trommeschlaeger, Fatemeh A1 - Zimmermann, Sebastian A1 - Kurlbaum, Max A1 - Puszkiel, Alicja A1 - Blanchet, Benoit A1 - Klinker, Hartwig A1 - Scherf-Clavel, Oliver T1 - Monitoring of dabrafenib and trametinib in serum and self-sampled capillary blood in patients with BRAFV600-mutant melanoma JF - Cancers N2 - Simple Summary In melanoma patients treated with dabrafenib and trametinib, dose reductions and treatment discontinuations related to adverse events (AE) occur frequently. However, the associations between patient characteristics, AE, and exposure are unclear. Our prospective study analyzed serum (hydroxy-)dabrafenib and trametinib exposure and investigated its association with toxicity and patient characteristics. Additionally, the feasibility of at-home sampling of capillary blood was assessed, and a model to convert capillary blood concentrations to serum concentrations was developed. (Hydroxy-)dabrafenib or trametinib exposure was not associated with age, sex, body mass index, or AE. Co-medication with P-glycoprotein inducers was associated with lower trough concentrations of trametinib but not (hydroxy-)dabrafenib. The applicability of the self-sampling of capillary blood was demonstrated. Our conversion model was adequate for estimating serum exposure from micro-samples. The monitoring of dabrafenib and trametinib may be useful for dose modification and can be optimized by at-home sampling and our new conversion model. Abstract Patients treated with dabrafenib and trametinib for BRAF\(^{V600}\)-mutant melanoma often experience dose reductions and treatment discontinuations. Current knowledge about the associations between patient characteristics, adverse events (AE), and exposure is inconclusive. Our study included 27 patients (including 18 patients for micro-sampling). Dabrafenib and trametinib exposure was prospectively analyzed, and the relevant patient characteristics and AE were reported. Their association with the observed concentrations and Bayesian estimates of the pharmacokinetic (PK) parameters of (hydroxy-)dabrafenib and trametinib were investigated. Further, the feasibility of at-home sampling of capillary blood was assessed. A population pharmacokinetic (popPK) model-informed conversion model was developed to derive serum PK parameters from self-sampled capillary blood. Results showed that (hydroxy-)dabrafenib or trametinib exposure was not associated with age, sex, body mass index, or toxicity. Co-medication with P-glycoprotein inducers was associated with significantly lower trough concentrations of trametinib (p = 0.027) but not (hydroxy-)dabrafenib. Self-sampling of capillary blood was feasible for use in routine care. Our conversion model was adequate for estimating serum PK parameters from micro-samples. Findings do not support a general recommendation for monitoring dabrafenib and trametinib but suggest that monitoring can facilitate making decisions about dosage adjustments. To this end, micro-sampling and the newly developed conversion model may be useful for estimating precise PK parameters. KW - dabrafenib KW - trametinib KW - hydroxy-dabrafenib KW - melanoma KW - BRAF mutation KW - volumetric absorptive micro-sampling (VAMS) KW - at-home sampling KW - drug monitoring KW - population pharmacokinetics Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288109 SN - 2072-6694 VL - 14 IS - 19 ER - TY - JOUR A1 - Bliziotis, Nikolaos G. A1 - Kluijtmans, Leo A. J. A1 - Tinnevelt, Gerjen H. A1 - Reel, Parminder A1 - Reel, Smarti A1 - Langton, Katharina A1 - Robledo, Mercedes A1 - Pamporaki, Christina A1 - Pecori, Alessio A1 - Van Kralingen, Josie A1 - Tetti, Martina A1 - Engelke, Udo F. H. A1 - Erlic, Zoran A1 - Engel, Jasper A1 - Deutschbein, Timo A1 - Nölting, Svenja A1 - Prejbisz, Aleksander A1 - Richter, Susan A1 - Adamski, Jerzy A1 - Januszewicz, Andrzej A1 - Ceccato, Filippo A1 - Scaroni, Carla A1 - Dennedy, Michael C. A1 - Williams, Tracy A. A1 - Lenzini, Livia A1 - Gimenez-Roqueplo, Anne-Paule A1 - Davies, Eleanor A1 - Fassnacht, Martin A1 - Remde, Hanna A1 - Eisenhofer, Graeme A1 - Beuschlein, Felix A1 - Kroiss, Matthias A1 - Jefferson, Emily A1 - Zennaro, Maria-Christina A1 - Wevers, Ron A. A1 - Jansen, Jeroen J. A1 - Deinum, Jaap A1 - Timmers, Henri J. L. M. T1 - Preanalytical pitfalls in untargeted plasma nuclear magnetic resonance metabolomics of endocrine hypertension JF - Metabolites N2 - Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing’s syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies. KW - confounders KW - metabolomics KW - multicenter KW - plasma NMR KW - preanalytical conditions Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-282930 SN - 2218-1989 VL - 12 IS - 8 ER -