Drug-drug interaction between metformin and sorafenib alters antitumor effect in hepatocellular carcinoma cells
Ontology highlight
ABSTRACT: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and is one of the leading causes of cancer-related deaths worldwide. The multi‐target inhibitor sorafenib is a first-line treatment for patients with advanced unresectable HCC. Recent clinical studies have evidenced that patients treated with sorafenib together with the anti-diabetic drug metformin have a survival disadvantage compared to patients receiving sorafenib only. Here, we examined whether a clinically relevant dose of metformin (50 mg/kg/d) could influence the antitumoral effects of sorafenib (15 mg/kg/d) in a subcutaneous xenograft model of human HCC growth using two different sequences of administration, i.e concomitant versus sequential dosing regimens. We observed that the administration of metformin six hours prior to sorafenib was significantly less effective in inhibiting tumor growth than concomitant administration of the two drugs. In vitro experiments confirmed that pretreatment of different human HCC cell lines with metformin reduced the effects of sorafenib on cell viability, proliferation and signaling. Transcriptomic analysis confirmed significant differences between xenografted tumors obtained under the concomitant and the sequential dosing regimens. Taken together, these observations call into question the benefit of parallel use of metformin and sorafenib in patients with advanced HCC and diabetes, as the interaction between the two drugs could ultimately compromise patient survival. To better characterize the molecular signatures driving the differential responses to concomitant and sequential biotherapies, we conducted a transcriptomic analysis on RNA extracted from tumor xenografts mice xenografed and treated with vehicle (control, n=3), metformin (50 mg/kg/day) combined to sorafenib (15 mg/kg/day) (concomitant schedule, n=3) or metformin (50 mg/kg/day) followed 6 hours later by sorafenib (15 mg/kg/day) (sequential schedule, n=3).
Project description:We investigated the effects of metformin treatment on polycystic kidney disease in a mouse model of Pkd1-deficiency. Kidneys are harvested in postnatal day 63 after 28 days of intraperitoneal injection of metformin (150 mg/kg/day) or saline for control. Total RNA was extracted for microarray analysis. Gene expression profiles were different between treatment and control groups.
Project description:Diffuse midline glioma (DMG) is a uniformly fatal pediatric, adolescent, and young adult cancer diagnosed in the midline structures of the brain. PI3K/Akt signaling is an overarching contributor to the poor outcomes of DMG patients due to their dependance on insulin and recurring mutations and amplifications in PI3K/Akt genes. Paxalisib (GDC-0084) is a potent PI3K/Akt inhibitor developed to penetrate the brain’s protective blood-brain barrier (BBB). We performed paxalisib regimen optimization by assessment of blood glucose levels, analysis in vivo pharmacokinetics/pharmacodynamics properties, and employed DMG patient derived xenograft (PDX) mouse models to determine preclinical efficacy. To identify novel combination strategies, we conducted high-resolution quantitative phosphoproteomics of DMG cells treated with paxalisib. Elevated blood glucose levels promoting hyperglycemia was seen in mice using paxalisib 10 mg/kg/day (~mouse equivalent human maximum tolerated dose- NCT03696355). Whereas 5 mg/kg/day, or 5mg/kg/b.i.d. (twice daily) non-significantly increased blood glucose levels compared to controls. Pharmacokinetic analysis of mouse tissues showed 5 mg/kg/b.i.d. increased paxalisib acumination in the brainstem, suppressing PI3K/Akt signaling to significantly extend the survival of DMG PDX models compared to all other regimens; a survival benefit amplified when combined with the anti-glycemic therapy metformin. Phosphoproteomic profiling identified increased Protein kinase C (PKC) signaling following paxalisib treatment. The combination of paxalisib and enzastaurin (PKC inhibitor) synergistically extended the survival of PDX models. Our optimized dosing regimen increased the preclinical benefits of paxalisib, with the combination of paxalisib with metformin, or paxalisib with enzastaurin, heralding rationally designed combination strategies for the treatment of DMG
Project description:Metformin is now the most widely prescribed oral anti-diabetic agent worldwide, taken by over 150 million people annually. Although metformin has been used clinically to treat type 2 diabetes for over 60 years. Its mechanism of action remains only partially understood and controversial. In particular, this includes whether AMPK plays a role in metformin suppression of liver glucose production. To address this issue, we knocked out the AMPK catalytic alpha1 and alpha 2 subunits in the liver of HFD-fed adult homozygous mice. These mice were treated with a physiological relevent metformin dose (50 mg/kg/day) for 3 weeks. Liver samples were collected.
Project description:This model is supplementary material of publication "Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues"
by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.
This is a whole-body model representing the pharmacokinetics of metformin in the human body. The model is in the form of Ordinary differential equations and describes metformin concentration in 21 compartments.
The model consists of 21 compartments (“compartments” in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, red blood cells, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder, urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1L, but those are “volumeless” compartments as they are only necessary for the calculation of metformin amount, not concentration.
The model consists of 21 species (“species” in COPASI model) that correspond to the metformin concentrations in the 21 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration.
The model consists of 35 reactions – they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters – parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients.
The model consists of 62 global quantities – parameters involved in multiple reactions or necessary for another parameter calculation:
1.Parameters describing peroral metformin dose (Metformin Dose in Lumen in mg).
2.Parameter describing human physiology – body weight (in mL), cardiac output, blood flow to different compartments described as Q”compartment_name” (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate.
3.Parameters involved in the scale-up of the model
•Tissue:plasma partition coefficients (Ktp) that were estimated in the mice model.
•Kidney coefficient that is used for the scale-up of metformin elimination and is involved in the calculation of the rate parameters in the reactions “13.4. KidneyPlasma -> KidneyTissue” and “13.5. KidneyTissue -> KidneyTubular”. This parameter was determined using parameter estimation.
•Intestine coefficient that is involved in the calculation of the intestinal reaction rates of the reactions (03.2. IntestineLumen -> Enterocytes (PMAT OCT3), 03.3. Enterocytes -> IntestineVascular (OCT1), 03.4. IntestineLumen -> IntestineVascular (Saturable), 03.6. IntestineLumen -> Enterocytes (Diffusion) , 03.7. IntestineLumen -> IntestineVascular (Diffusion)). The parmaeters for these reactions are taken from Proctor publication and the intectine coefficient is used for the scale-up from the cell-culture to the human intestine.
4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg”Compartment_name” (for example mgLiver describes the metformin amount in mg in the liver tissues).
The time points of dose release are defined as “events” in COPASI and can be changed as necessary. The current model has 14 events and is set for a multiple-dose regimen for 7-day long twice-daily metformin administration.
Time course simulations can be accessed through the section “Time Course” in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created – Metformin amount in the 21 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see “Output Specifications” -> “Plots” to activate or deactivate plots). The time-course also includes multiple "Sliders" that allow to easily change 3 parameters - "Body Weight", "Cardiac Output", "Metformin Dose in Lumen in mg".
Project description:This model is supplementary material of publication "Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues"
by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.
This is a whole-body model representing the pharmacokinetics of metformin in the human body. The model is in the form of Ordinary differential equations and describes metformin concentration in 21 compartments.
The model consists of 21 compartments (“compartments” in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, red blood cells, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder, urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1L, but those are “volumeless” compartments as they are only necessary for the calculation of metformin amount, not concentration.
The model consists of 21 species (“species” in COPASI model) that correspond to the metformin concentrations in the 21 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration.
The model consists of 35 reactions – they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters – parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients.
The model consists of 62 global quantities – parameters involved in multiple reactions or necessary for another parameter calculation:
1.Parameters describing peroral metformin dose (Metformin Dose in Lumen in mg).
2.Parameter describing human physiology – body weight (in mL), cardiac output, blood flow to different compartments described as Q”compartment_name” (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate.
3.Parameters involved in the scale-up of the model
•Tissue:plasma partition coefficients (Ktp) that were estimated in the mice model.
•Kidney coefficient that is used for the scale-up of metformin elimination and is involved in the calculation of the rate parameters in the reactions “13.4. KidneyPlasma -> KidneyTissue” and “13.5. KidneyTissue -> KidneyTubular”. This parameter was determined using parameter estimation.
•Intestine coefficient that is involved in the calculation of the intestinal reaction rates of the reactions (03.2. IntestineLumen -> Enterocytes (PMAT OCT3), 03.3. Enterocytes -> IntestineVascular (OCT1), 03.4. IntestineLumen -> IntestineVascular (Saturable), 03.6. IntestineLumen -> Enterocytes (Diffusion) , 03.7. IntestineLumen -> IntestineVascular (Diffusion)). The parmaeters for these reactions are taken from Proctor publication and the intectine coefficient is used for the scale-up from the cell-culture to the human intestine.
4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg”Compartment_name” (for example mgLiver describes the metformin amount in mg in the liver tissues).
The time points of dose release are defined as “events” in COPASI and can be changed as necessary. The current model has 14 events and is set for a multiple-dose regimen for 7-day long twice-daily metformin administration.
Time course simulations can be accessed through the section “Time Course” in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created – Metformin amount in the 21 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see “Output Specifications” -> “Plots” to activate or deactivate plots). The time-course also includes multiple "Sliders" that allow to easily change 3 parameters - "Body Weight", "Cardiac Output", "Metformin Dose in Lumen in mg".
Project description:OSE-127 is a humanized monoclonal antibody targeting the IL-7Rα chain (CD127), under development for inflammatory and autoimmune disease treatment. It is a strict antagonist of the IL-7R pathway, is not internalized by target cells and is non-cytotoxic. Here, a first-in-human, phase I, randomized, double-blind, placebo-controlled, single-center study was carried out to determine the safety, pharmacokinetics, pharmacodynamics, and immunogenicity of OSE-127 administration.Sixty-three healthy subjects were randomly assigned to nine groups: six single ascending dose groups with intravenous (IV) administration (0.002-10 mg/kg), a single subcutaneous treatment group (1 mg/kg), and two double IV injection groups (6 or 10 mg/kg). Subjects were followed during < 146 days. OSE-127’s pharmacokinetic half-life after a single dose increased from 4.6 days (1mg/kg) to 11.7 days (10 mg/kg) and, after a second dose, from 12.5 days (6 mg/kg) to 16.25 days (10 mg/kg). Receptor occupancy was ≥ 95% at doses ≥ 0.02 mg/kg and this saturation level was maintained >100 days after two IV infusions at 10 mg/kg. IL-7 consumption was inhibited by OSE-127 administration, as demonstrated by a decreased IL-7 pathway gene signature in peripheral blood cells and by ex-vivo T-lymphocyte restimulation experiments. OSE-127 was well tolerated, with no evidence of cytokine-release syndrome and no significant alteration of blood lymphocyte counts or subset populations.Altogether, the observed lack of significant lymphopenia or serious adverse events, concomitant with the dose-dependent inhibition of IL-7 consumption by target cells highlight that OSE-127 may show clinical activity in IL-7R pathway-involved diseases.
Project description:Eight-week-old C57BL/6J and T2DM db/db mice randomly divided into 4 groups of 8 based on equal bodyweight and fasting blood glucose (FBG) levels (mice were fasted for 6 h). The C57BL/6J mice (normal group) were treated with saline. The T2DM db/db mice were treated with saline, DLE (200 mg/kg), WTE (180 mg/kg), or pioglitazone (PGZ, 20 mg/kg) for 12 weeks, and were called the model group, DLE group, WTE group and PGZ group, respectively. All mice received oral administration of the indicated treatments once a day. After 12 weeks drug administration, liver aliquots from four randomly selected mice from the normal, model, PGZ and DLE groups were ground into powder in liquid nitrogen, followed by RNAseq for differentially expressed gene determination and pathway enrichment analysis.
Project description:Early epigenetic changes and DNA damage do not predict clinical response in an overlapping schedule of 5-azacytidine and entinostat in patients with myeloid malignancies. The patients with MDS, chronic myelomonocytic leukemia (CMMoL), and high risk AML were treated with sequential administration of methylation inhibitor drugs (5AC and entinostat). To study gene expresion regulation in treated patients, microarray analysis was done on RNA samples extracted from CD34+ cells from 18 patients before and 15 days after treatment using Affymetrix U133Plus2.0. 18 enrolled patients were treated with sequential administration of 5AC and entinostat and microarray analysis were done on RNA samples from CD34+ cells before and 15 days after treatment using Affymetrix U133Plus2.0. One chip was used for one sample and there was no technical replicates. Twelve pairs of day 0 and day 15 specimens passed quality control for hybridization and RNA integrity. A list of differentially regulated genes was created after GC-RMA normalization by t-test paired using a 1.5 fold cut-off with p<0.005.
Project description:Hepatocellular carcinoma (HCC) activates platelets through the action of adjacent sinusoidal cells. Activated platelets bind to tumor-associated endothelial cells and release growth factors that promote tumor progression. We hypothesized that tumor inhibitors encapsulated in platelets would function as drug carriers for tumor therapy. We propose a therapeutic strategy for HCC using autologous platelets encapsulating multiple tyrosine kinase inhibitors in a rat chemically-induced HCC model. Sorafenib or lenvatinib was encapsulated in platelets isolated from tumor-bearing rats in vitro. The rats were divided into groups that received repeated intravenous injections (twice a week for 10 weeks) of the following materials: placebo, sorafenib (SOR), lenvatinib (LEN), autologous platelets, autologous platelets encapsulating sorafenib (SOR-PLT), and autologous platelets encapsulating lenvatinib (LEN-PLT). The therapeutic effect was then analyzed by ultrasonography (US) and histopathological analysis. Histopathological and US analysis demonstrated extensive tumor necrosis in the tumor tissue of SOR-PLT or LEN-PLT, but not in other experimental groups. By liquid chromatography-mass spectrometry, more abundant sorafenib was detected in tumor tissues after SOR-PLT administration than in surrounding normal tissues, but no such difference in sorafenib level was observed with SOR administration. Therefore, the use of autologous platelets encapsulating drugs might be a novel therapeutic strategy for HCC. We investigated the effect of the drug encapsulation process on the degradation of resident mRNA inherited from megakaryocytes in platelets.
Project description:Background New approaches are needed to improve prognostic accuracy for treatment response and to explore the complexities of drug effects on human tumours. We developed a strategy of global genomic investigation of sequential tumor biopsies at baseline and 21 days post-treatment, and applied this approach in a phase I study of sorafenib plus dacarbazine in patients with solid tumours. Methods 23 patients received 21-day cycles of oral sorafenib, 400 mg twice daily and dacarbazine, 1000 mg/m2 by 1-h intravenous infusion on day 1. Efficacy was assessed using response evaluation criteria in solid tumours. Differential gene expression was assessed through genomic microarray analysis of biopsy tissue from the same tumour at baseline and on day 21. Seven parameters characterizing vascularisation using dynamic enhanced-contrast ultrasonography (DCE-US) were assessed. Changes from baseline in the two parameters were characterized for patients with and without a clinical response to treatment at 3 months.Findings 23 patients were evaluable for efficacy and 17 for gene expression and DCE-US analyses. One patient had a partial response; 14 had stable disease. Genomic analyses identified a 247-gene signature that distinguished progressors from nonprogressors at 3 months. Expression of four genes was significantly associated with progression at 3 months. Functional parameters of DCE-US representing blood volume at baseline, day 8, and day 21 correlated with 3-month disease progression status.Interpretation This novel approach of sequential investigations in a phase I trial was feasible, detecting early changes in gene expression and tumour vascularity that may be predictive of clinical outcome.