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

• Main Authors: Mayra Alejandra Jaimes Campos (Author), Iván Andújar (Author), Felix Keller (Author), Gert Mayer (Author), Peter Rossing (Author), Jan A. Staessen (Author), Christian Delles (Author), Joachim Beige (Author), Griet Glorieux (Author), Andrew L. Clark (Author), William Mullen (Author), Joost P. Schanstra (Author), Antonia Vlahou (Author), Kasper Rossing (Author), Karlheinz Peter (Author), Alberto Ortiz (Author), Archie Campbell (Author), Frederik Persson (Author), Agnieszka Latosinska (Author), Harald Mischak (Author), Justyna Siwy (Author), Joachim Jankowski (Author)
• Format: Book
• Published: MDPI AG, 2023-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.3390/ph16091298; 1424-8247
• Keywords: cardiovascular events; coronary artery disease; heart failure; chronic kidney disease; personalized medicine; urinary biomarkers; Medicine; R; Pharmacy and materia medica; RS1-441
• Source: Pharmaceuticals, Vol 16, Iss 9, p 1298 (2023)

(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.