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Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes.
Tremmel, Roman; Hofmann, Ute; Haag, Mathias; Schaeffeler, Elke; Schwab, Matthias.
Afiliación
  • Tremmel R; Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany; email: matthias.schwab@ikp-stuttgart.de.
  • Hofmann U; University of Tuebingen, Tuebingen, Germany.
  • Haag M; Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany; email: matthias.schwab@ikp-stuttgart.de.
  • Schaeffeler E; University of Tuebingen, Tuebingen, Germany.
  • Schwab M; Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany; email: matthias.schwab@ikp-stuttgart.de.
Annu Rev Pharmacol Toxicol ; 64: 65-87, 2024 Jan 23.
Article en En | MEDLINE | ID: mdl-37585662
ABSTRACT
Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Annu Rev Pharmacol Toxicol Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Annu Rev Pharmacol Toxicol Año: 2024 Tipo del documento: Article