Table 1.

The use of pharmacometabonomics to predict drug efficacy, toxicity, metabolism, and pharmacokinetics^a.

Class of experiment	Human studies	Pre-clinical studies
Prediction of pharmacokinetics (PK)	Prediction of tacrolimus PK in healthy volunteers (Phapale et al., 2010)	Prediction of pharmacokinetics of triptolide in rats (Liu et al., 2012)
	Prediction of atorvastatin pharmacokinetics in healthy volunteers (Huang et al., 2015)
	Prediction of methotrexate clearance in patients with lymphoid malignancies (Muhrez et al., 2016)
Prediction of drug metabolism	Prediction of metabolism of paracetamol/acetaminophen in human volunteers (Clayton et al., 2009)	Prediction of paracetamol/acetaminophen metabolism in rats (Clayton et al., 2006)
	**First demonstration of pharmacometabonomics in humans	**First demonstration of pharmacometabonomics
	Prediction of CYP3A4 induction in volunteer twins (Rahmioglu et al., 2011)
	Prediction of CYP3A activity in healthy volunteers (Shin et al., 2013)
Prediction of drug efficacy	Prediction of antipsychotic effects with olanzapine, risperidone and aripiprazole (Kaddurah-Daouk et al., 2007)
	**First detection of metabolic efficacy markers in baseline human samples but study was small and designated hypotheses generating rather than definitive by the authors
	Prediction of simvastatin efficacy in patients on the Cholesterol and Pharmacogenomics study (Kaddurah-Daouk et al., 2010; Trupp et al., 2012)
	Prediction of citalopram/escitalopram response in patients with major depressive disorder (MDD; Ji et al., 2011)
	${*}^{*}\text{First demonstration of pharmacometabonomics-informed pharmacogenomics approach to personalized medicine}$ (Abo et al., 2012) and (Gupta et al., 2016)
	Prediction of sertraline and placebo responses in patients with MDD (Kaddurah-Daouk et al., 2011), (Kaddurah-Daouk et al., 2013), and (Zhu et al., 2013)
	Prediction of efficacy of anti-psychotics in schizophrenia patients (Condray et al., 2011)
	Prediction of response to aspirin in healthy volunteers (Lewis et al., 2013; Yerges-Armstrong et al., 2013; Ellero-Simatos et al., 2014)
	Prediction of efficacy with anti-TNF therapies in rheumatoid arthritis (Kapoor et al., 2013)
	Prediction of thiopurine-S-methyltransferase phenotype in Estonian volunteers (Karas-Kuželički et al., 2014)
	Prediction of efficacy of L-carnitine therapy for patients with sepsic shock (Puskarich et al., 2015)
	Prediction of acamprosate treatment outcomes in alcohol-dependent patients (Nam et al., 2015)
	Prediction of blood pressure lowering in hypertensive patients treated with atenolol and hydrochlorothiazide (Rotroff et al., 2015)
	Prediction of response in lung cancer patients (Hao et al., 2016)
	Prediction of patient response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer (Miolo et al., 2016)
Prediction of adverse events	Prediction of weight gain in breast cancer patients undergoing chemotherapy (Keun et al., 2009)	Prediction of toxicity from paracetamol/acetaminophen dosing in rats (Clayton et al., 2006)
	**First demonstration of pharmacometabonomics in patients
	Prediction of liver injury markers in patients treated with ximelagatran (Andersson et al., 2009)	Prediction of onset of diabetes in rats administered with streptozotocin (Li et al., 2007)
	Prediction of toxicity of paracetamol/acetaminophen (“early-onset pharmacometabonomics”) (Winnike et al., 2010)	Prediction of nephrotoxicity of cisplatin in rats (Kwon et al., 2011)
	Prediction of toxicity in patients with inoperable colorectal cancer treated with capecitabine (Backshall et al., 2011)	Prediction of toxicity of isoniazid in rats (Cunningham et al., 2012)
		Prediction of variability in response to galactosamine treatment in rats (Coen et al., 2012)
		Prediction of toxicity from lipopolysaccharide treatment in rats (Dai et al., 2016)

^aSignificant papers are highlighted with a double asterisk with explanatory text in bold blue font.