Pharmacometabolomic Pathway Response of Effective Anticancer Agents on Different Diets in Rats with Induced Mammary Tumors

Abstract

Metabolomics is an effective approach to characterize the metabotype which can reflect the influence of genetics, physiological status, and environmental factors such as drug intakes, diet. Diet may change the chemopreventive efficacy of given agents due to the altered physiological status of the subject. Here, metabolomics response to a chemopreventive agent targretin or tamoxifen, in rats with methylnitrosourea-induced tumors on a standard diet (4% fat, CD) or a high fat diet (21% fat, HFD) was evaluated, and found that (1) the metabolome was substantially affected by diet and/or drug treatment; (2) multiple metabolites were identified as potential pharmacodynamic biomarkers related to targretin or tamoxifen regardless of diet and time; and (3) the primary bile acid pathway was significantly affected by targretin treatment in rats on both diets, and the lysolipid pathway was significantly affected by tamoxifen treatment in rats on the high fat diet.

Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC–MS based untargeted metabolomics practitioners

“…Unlike measures generated from genomics, small molecules represent the products of endogenous metabolism, in addition to products of environmental exposure (including lifestyle, diet, and other environmental exposures), individual-specific metabolism driven by underlying genetics, gut microbial influences, and the presence or absence of disease or pathological processes. Data derived from untargeted metabolomics have been used to interrogate the mechanisms underlying exposure-disease relationships and treatment or intervention effects as well as to understand the metabolic phenotype at the human population level (Dunn et al, 2015, Ilhan et al, 2019, Gafson et al, 2019, Bouhifd et al, 2013, Ramirez et al, 2013, Crestani et al, 2019, Hollister et al, 2019, Hu et al, 2019, Lains et al, 2019, Rangel-Huerta et al, 2019, Yu et al, 2019, Cao et al, 2019, Shi et al, 2019, Blacher et al, 2019, Wilmanski et al, 2019, Tang et al, 2019, Plaza-Diaz et al, 2019, de Groot et al, 2019, Wittemans et al, 2019, Tziotzios et al, 2019, Burrage et al, 2019, McCullough et al, 2019, Olson et al, 2018, Zambrana et al, 2019, Sato et al, 2019, Rebholz et al, 2019, Isganaitis et al, 2019, Gangler et al, 2019, Cirulli et al, 2019, Shin et al, 2014.…”

Effects of High-Fructose Diet vs. Teklad Diet in the MNUInduced Rat Mammary Cancer Model: Altered Tumorigenesis, Metabolomics and Tumor RNA Expression

“…This is unlike the other mouse models, triple-negative breast cancer or the mouse colon study, in which high fructose resulted in substantial increases in body weight [8]. We recently reported data comparing the SD to a Western diet in control groups as well as groups treated with five different preventive agents (tamoxifen, the aromatase inhibitor vorozole, the RXR agonist Targretin, Lipitor and metformin) [7,24]. Those studies showed that the Western diet altered tumor development, decreasing tumor latency and increasing tumor multiplicity and final tumor weights.…”

“…This is particularly important since it has been proposed that a HFD induces a variety of metabolic changes that may contribute to the development of diabetes or heart disease. Second, our prior studies [7,24] had shown that the use of the high-fat diet, which also enhances tumor development in the MNU-induced model, altered a variety of metabolic pathways, and the question arose whether exposure to a HFD might elicit some of the same metabolic changes.…”

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

“…In addition, there are a minority of authors who are using the term pharmacometabonomics or pharmacometabonomics to describe diagnostic metabonomics experiments with no prognostic elements. LC-MS and GC-MS prediction of efficacy with anti-TNF therapies in rheumatoid arthritis (Kapoor et al 2013) human NMR prediction of thiopurine-S-methyltransferase phenotype in Estonian volunteers (Karas-Kuzelicki et al 2014) human HPLC prediction of efficacy of L-carnitine therapy for patients with sepsic shock (Evans et al 2019;Puskarich et al 2018;Puskarich et al 2015) human NMR and LC-MS prediction of acamprosate treatment outcomes in alcoholdependent patients (Nam et al 2015) human LC-MS prediction of blood pressure lowering in hypertensive patients treated with atenolol and hydrochlorothiazide (Rotroff et al 2015) human GC-MS prediction of response in lung cancer patients (Hao et al 2016a) human NMR and GC-MS prediction of patient response to trastuzumab-paclitaxel neoadjuvant therapy in HER-2 positive breast cancer (Miolo et al 2016) human LC-MS prediction of patient response in SSRI treatment of major depressive disorder (Gupta et al 2016) human LC-ECA prediction of Clopidogrel high on treatment platelet reactivity (HTPR) in CAD patients [NMR] (Amin et al 2017) human NMR prediction of chemosensitivity of treatment of AML patients with cytarabine and anthracycline (Tan et al 2017) human LC-MS prediction of efficacy in pancreatic ductal adenocarcinoma patients receiving gemcitabine (Phua et al 2017) human GC-TOFMS (Jiang et al 2018) human NMR prediction of gemcitabine efficacy in pancreatic ductal adenocarcinoma patients (Phua et al 2018) human GC-MS prediction of response to metformin treatment in early T2DM patients (Park et al 2018) human GC-MS prediction of efficacy of propranolol in reducing hepatic venous pressure gradient (HPVG) in patients with liver cirrhosis (Reverter et al 2019) human LC-MS prediction of efficacy of meglumine antimonite efficacy if patients with cutaneous Leishmaniasis (Alejandro Vargas et al 2019) human LC-MS QUASI-prediction of dexamethasone steroid treatment efficacy in pre-term infants with respiratory syndrome (Cao et al 2019) human GC-TOF-MS prediction of warfarin efficacy in atrial fibrillation patients (Bawadikji et al 2019) human NMR prediction of adverse events prediction of toxicity from paracetamol/acetaminophen dosing (Clayton et al 2006…”