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Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study

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posted on 2023-10-23, 23:23 authored by MA Jaimes Campos, I Andújar, F Keller, G Mayer, P Rossing, JA Staessen, C Delles, J Beige, G Glorieux, AL Clark, W Mullen, JP Schanstra, A Vlahou, K Rossing, Karlheinz PeterKarlheinz Peter, A Ortiz, A Campbell, F Persson, A Latosinska, H Mischak, J Siwy, J Jankowski
(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient’s urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted.

Funding

Funding for this project was provided, in part, by the German ministry for education and science (BMBF), via grant 01DN21014, to H.M., J.S. and A.L. This project was also supported by the Federal Ministry of Education and Research (BMBF) via grant number 01KU2307 (SIG-NAL), under the frame of ERA PerMed to H.M. and J.S. Additional funding was provided by the European Union's Horizon 2020 research and innovation program under grant agreement No:848011 for the DC-ren project. M.A.J.C. was supported by the European Union's Horizon EuropeMarie Sklodowska-Curie Actions Doctoral Networks Industrial Doctorates Programme (HORIZON-MSCA-2021-DN-ID, Grant number: 101072828). A.O.'s research was supported by FIS/FondosFEDER ERA-PerMed-JTC2022 (SPAREKID AC22/00027), Comunidad de Madrid en Biomedicina P2022/BMD-7223, CIFRA_COR-CM, Instituto de Salud Carlos III (ISCIII) RICORS program to RI-CORS2040 (RD21/0005/0001), funded by the European Union-NextGenerationEU, Mecanismopara la Recuperacion y la Resiliencia (MRR), and SPACKDc PMP21/00109, FEDER funds, COST Action PERMEDIK CA21165, supported by COST (European Cooperation in Science and Technology),and PREVENTCKD Consortium. Project ID: 101101220, program: EU4H. DG/Agency: HADEA. J.J.was supported by grants from the German Research Foundation (DFG) (SFB/TRR 219 project ID:322900939 and SFB 1382 project ID 403224013), as well as by the European Union's Horizon 2020research and innovation program under the Marie Sklodowska-Curie grant agreement No: 764474(CaReSyAn) and No: 860329 (Strategy-CKD).

History

Publication Date

2023-09-14

Journal

Pharmaceuticals

Volume

16

Issue

9

Article Number

1298

Pagination

16p.

Publisher

MDPI

ISSN

1424-8247

Rights Statement

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).