Pharmacogenomics‐Driven Prediction of Antidepressant Treatment Outcomes: A Machine‐Learning Approach With Multi‐trial Replication

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied...

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Veröffentlicht in:	Clinical pharmacology and therapeutics 2019-10, Vol.106 (4), p.855-865
Hauptverfasser:	Athreya, Arjun P., Neavin, Drew, Carrillo‐Roa, Tania, Skime, Michelle, Biernacka, Joanna, Frye, Mark A., Rush, A. John, Wang, Liewei, Binder, Elisabeth B., Iyer, Ravishankar K., Weinshilboum, Richard M., Bobo, William V.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Algorithms Biomarkers, Pharmacological - blood Citalopram - pharmacokinetics Clinical Decision Rules Depressive Disorder, Major - blood Depressive Disorder, Major - drug therapy Depressive Disorder, Major - genetics Female Genetic Markers Genome-Wide Association Study Humans Machine Learning Male Pharmacogenomic Testing - methods Pharmacogenomic Variants Polymorphism, Single Nucleotide Remission Induction Serotonin Uptake Inhibitors - pharmacokinetics
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