1.
1H NMR based pharmacometabolomics analysis of metabolic phenotype on predicting metabolism characteristics of losartan in healthy volunteers.
He, C, Liu, Y, Wang, Y, Tang, J, Tan, Z, Li, X, Chen, Y, Huang, Y, Chen, X, Ouyang, D, et al
Journal of chromatography. B, Analytical technologies in the biomedical and life sciences. 2018;:15-23
Abstract
Inter-individual variability in drug metabolism and disposition is common in both preclinical and clinical researches. Losartan and its active metabolite EXP3174 present a high degree of inter-individual differences in blood concentrations that affect drug efficacy and side effect. Pharmacometabolomics has been increasingly applied on predicting the drug responses by analyzing the differences in metabolic profile. A pre-dose metabolic phenotype was investigated to interpret inter-individual variations in the metabolism characteristics of losartan. 1H Nuclear Magnetic Resonance (NMR) spectroscopy-based metabolic profiles were performed on 36 healthy Chinese male volunteers by measuring their pre-dose plasma samples. After oral administration of losartan, the concentrations of losartan and its bioactive metabolite EXP3174 were monitored by liquid chromatography-mass spectrometry (LC-MS). Orthogonal partial least-squares (O-PLS) model was conducted to select potential biomarkers that substantially contributed to the inter-individual variations in the metabolism features via analyzing the ratio of pharmacokinetics (PK) parameters of its metabolite to parent drug. Potential metabolites such as glycine, phosphorylcholine, choline, creatine, creatinine, lactate, citrate, α-glucose, and lipids showed strong correlations with metabolism features of losartan. In addition, the pathway analysis revealed that baseline lipid metabolism, the glycine, serine and threonine pathway, and glycolysis or gluconeogenesis metabolism pathway were significantly associated with the ratio of PK parameters of EXP3174 to losartan. Step-wise multiple linear regression (MLR) was constructed to investigate the potential roles of the selected biomarkers in predicting individualized metabolism characteristics of losartan. These results showed that the pre-dose individual metabolic traits may be a useful approach for characterizing individual differences in losartan metabolism characteristics and therefore for expediting personalized dose-setting in further clinical studies.
2.
Comparison of magnetic resonance spectroscopy and positron emission tomography in detection of tumor recurrence in posttreatment of glioma: A diagnostic meta-analysis.
Wang, X, Hu, X, Xie, P, Li, W, Li, X, Ma, L
Asia-Pacific journal of clinical oncology. 2015;(2):97-105
Abstract
It is important to distinguish between tumor recurrence and treatment effects in posttreatment patients with high-grade gliomas. Several imaging modalities have been reported in differentiating between tumor recurrence and treatment effects. However, there were no consistent conclusions between different studies. We performed a meta-analysis of 23 studies that compared the diagnostic values of fluorine-18-fluorodeoxyglucose ((18)F-FDG) and (11)C-methionine ((11)C-MET) PET (positron emission tomography) or PET/CT (computed tomography) and magnetic resonance spectroscopy (MRS) in predicting tumor recurrence of gliomas. The pooled estimated sensitivity, specificity, positive likelihood ratios, negative likelihood ratios and summary receiver operating characteristic curves of (18)F-FDG and (11)C-MET PET or PET/CT and MRS in detecting tumor recurrence were calculated. In conclusion, MRS is highly sensitive in the detection of tumor recurrence in glioma.(18)F-FDG PET or PET/CT is highly specific in recurrence diagnosis. (11)C-MET does not have noticeable advantage over (18)F-FDG. The current evidence shows no statistical difference between MRS and PET on the accuracy.
3.
Identification of MRI and 1H MRSI parameters that may predict survival for patients with malignant gliomas.
Li, X, Jin, H, Lu, Y, Oh, J, Chang, S, Nelson, SJ
NMR in biomedicine. 2004;(1):10-20
Abstract
Although MR imaging (MRI) and MR spectroscopic imaging (MRSI) have been applied in the diagnosis and treatment planning for brain tumors, their prognostic significance has not yet been determined. The goal of this study was to identify pre-treatment MRI and MRSI parameters for patients with malignant glioma that may be useful in predicting survival. Two populations of patients with newly-diagnosed malignant glioma were examined with MRI and three-dimensional proton ((1)H) MRSI. Thirty-nine patients (22 grade 3 and 17 glioblastoma multiforme, GBM) were studied prior to surgery, and 33 GBM patients were studied after surgery but prior to treatment with radiation and chemotherapy. Signal intensities of choline (Cho), creatine (Cr), N-acetyl aspartate (NAA), and lactate/lipid (LL) were estimated from the spectra. Recursive partitioning methods were applied to parameters that included age, histological grade, MRI and MRSI variables to generate survival trees. Patients were grouped into high and low risk categories and the corresponding Kaplan-Meier curves were plotted for comparison between groups. The parameters that were selected by recursive partitioning as being predictive of poor outcome were older age, larger contrast enhancement, higher Cho-to-Cr, higher Cho-to-NAA, higher LL and lower Cr-to-NAA abnormalities. The survival functions were significantly different between the sub-groups of patients obtained from the survival tree for both pre-surgery and post-surgery data. The results of this study suggest that pre-treatment MRI and three-dimensional (1)H-MRSI provide information that predicts outcome for patients with malignant gliomas and have drawn attention to variables that should be examined prospectively in future studies using these techniques.