Preclinical Optimization of the PI3K/Akt inhibitor Paxalisib for the Treatment of Diffuse Midline Glioma
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive
ORGANISM(S): Homo Sapiens (human)
TISSUE(S): Brain, Primary Cell Line Cell
DISEASE(S): Diffuse Midline Glioma
SUBMITTER: 
Zac Germon
LAB HEAD: Matthew Dun
PROVIDER: PXD036114 | Pride | 2024-02-20
REPOSITORIES: Pride
Similar Datasets
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.
2024-02-20 | GSE211565 | GEO
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.
2024-02-20 | GSE246057 | GEO
Project description: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).
2020-12-03 | GSE162557 | GEO
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".
2021-08-10 | BIOMD0000001029 | BioModels
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".
2021-08-10 | BIOMD0000001028 | BioModels
Project description:We performed RNA sequencing on tumors harvested from OV5398 (ovarian) PDX treated for 40-50 days with either control vehicle (PEG), 100 mg/kg BID INX-315 or 200 mg/kg QD INX315.
2023-11-22 | GSE243151 | GEO
Project description:In this dose series pregnant CD-1 (outbred) dams were exposed to methylmercury (MeHg) as monomethylmercuric chloride injected intraperitoneally on 9 d.p.c. Test doses were 10.0 mg/kg and below. This regimen induces symptoms of Fetal Minimata Disease (FMD) in term fetuses. Risks with respect to FMD for the various test doses were 10.0 mg/kg (40% risk), 5.0 mg/kg (20% risk), 2.5 mg/kg (NOAEL), and 1.25 mg/kg (subNOAEL). All measurements were on RNA from the embryonic forebrain (prosencephalon and surrounding tissue) at 3.0h post-treatment. Keywords = dose series Keywords = mercury Keywords = embryo Keywords = Fetal Minamata Disease Keywords: dose response
2004-03-11 | GSE1075 | GEO
Project description:Histone deacetylation is crucial for the regulation of chromatin structure, gene transcription and the epigenetic state of the cell. With the emergence of the neuroendocrine (NE) phenotype in castration-resistant prostate cancer (CRPC), we hypothesized that the inhibition of HDAC1/2 would disrupt the epigenetic events involved in the conversion of prostate adenocarcinoma to a NE prostate cancer (NEPC) phenotype. Additionally, monotherapies tend to have limited efficacy in cancer treatment and combination treatments show more promise. Since the PI3K/AKT/mTOR pathway is a well-established tumor growth-promoting pathway and a promising target in CRPC and NEPC, we investigated the tumor suppressing activities of fimepinostat, a dual HDAC and PI3K inhibitor, on both prostate adenocarcinoma and NEPC. We evaluated multiple cell lines and patient-derived xenograft (PDX) models of prostate adenocarcinoma and NEPC to determine the roles of HDAC1/2 and AKT inhibition, both individually and in combination, in suppressing CRPC and NEPC tumor growth in vitro and in vivo. HDAC1/2 inhibition in combination with PI3K/AKT inhibition was more efficacious than targeting each pathway alone, induced growth inhibitory effects (in some cases synergistic) through cell cycle inhibition and apoptosis across an array of prostate adenocarcinoma and NEPC patient-derived xenografts (PDX) and cell lines in vitro and in vivo. Molecular profiling revealed on-target effects of combined HDAC1/2 and PI3K/AKT inhibition as well as a common downregulation of Myc, E2F, DNA repair, and G2M checkpoint targets, independent of prostate cancer phenotype. Fimepinostat therapy was also associated with the suppression of lineage transcription factor expression including androgen receptor (AR) in prostate adenocarcinoma and Achaete-scute homolog 1 (ASCL1) in NEPC. Our findings demonstrate that dual HDAC1/2 and PI3K/AKT pathway inhibition leads to robust tumor growth inhibition in both adenocarcinoma and NEPC, suggesting that fimepinostat may represent a novel therapeutic for both prostate adenocarcinoma and NEPC phenotypes of CRPC.
2023-10-12 | GSE223758 | GEO
Project description:Transcriptome analysis was used to identify gene expression changes during development of sunitinib resistance in a renal cell carcinoma patient-derived xenograft (PDX) model. During the response phase, tumors exhibited a 91% reduction in volume, characterized by induction of TNFRSF1A, TNFAIP3, NFKB2, CCL2, CCL20, BIRC3, and MOAP1. Ingenuity Pathway Analysis indicated decreased expression of cell survival genes during tumor response to sunitinib. In this model, after 4 weeks of treatment, tumors developed resistance despite continued administration of the tyrosine kinase inhibitor (TKI) sunitinib (40 mg/kg/d p.o.). Resistance was associated with increased expression of VEGFA, EPO, IL-8, ANGPT2, TNFRSF12, MAPK3/7, MAPKBP1, and increased cell survival genes, suggesting activation of angiogenesis and MAPK/ERK pathways. Tumor lysate mRNA evaluated for murine gene expression to examine the contribution of host effects, indicated that tumor response was associated with downregulation of immune cell trafficking, cellular movement, and inflammatory response genes. During tumor escape, genes associated with cellular movement, inflammatory response, and immune cell trafficking were strongly induced, along with intratumoral accumulation of myeloid derived suppressor cells (MDSC), indicating a role for host factors during emergence of sunitinib resistance. The same PDX model was used to assess anti-tumor efficacy of sunitinib combined with MEK inhibitor (MEKi) PD-0325901 (4 mg/kg/d p.o.) using different schedules. The most effective treatment regimen was either continuous treatment with both drugs or switching from sunitinib to PD-0325901 monotherapy at d30, which reduced tumor volume by 78.6% (p=0.0241) and 88.5% (p=0.0068), respectively. The combination of MEKi with TKI (sunitinib, axitinib, or pazopanib) suppressed levels of phospho-MEK1/2 and phospho-ERK1/2, and decreased intratumoral MDSC. Thus, continuous treatment with sunitinib alone did not maintain tumor response, and addition of a MEKi abrogated resistance leading to prolonged survival. Study was comprised of three experimental groups (pre-treatment, response, escape). All tumors came from the same PDX model. There were four biological replicates in each group. Four mice were used, with each of the 3 groups per mouse. There were no control or reference samples.
2015-12-17 | E-GEOD-76068 | biostudies-arrayexpress
Project description:Diffuse midline gliomas (DMG), including diffuse intrinsic pontine gliomas (DIPGs), are the most lethal of all childhood cancers. Palliative radiotherapy is the only proven life-prolonging treatment, with patient survival 9-11 months. ONC201 shows preclinical and emerging clinical efficacy in DIPG. Currently, little is known about the mechanisms of sensitivity/resistance of DIPGs to ONC201, or whether recurring genomic features influence response. Using a systems-biological approach, we show ONC201 elicits potent agonism of the mitochondrial protease, CLPP, driving proteolysis of electron transport chain (ETC) and tricarboxylic acid (TCA) cycle proteins. DIPGs harboring TP53-mutations show reduced sensitivity to ONC201. Molecular mechanisms identify metabolic adaptation and resistance to ONC201 regulated by redox-activated PI3K/Akt signaling, counteracted using the brain penetrant PI3K/Akt inhibitor, paxalisib, in both wt-TP53 and TP53-mutant DIPGs. The discoveries described within, coupled with the powerful anti-DIPG/DMG pharmacokinetic and pharmacodynamic properties of ONC201 and paxalisib inform the DIPG/DMG phase II combination clinical trial NCT05009992.
2023-05-25 | PXD036245 | Pride