Model-based meta-analysis of HbA1c reduction across SGLT2 inhibitors using dose adjusted by urinary glucose excretion.

This study was aimed to evaluate whether the dose-response relationship of the sodium glucose co-transporter-2 inhibitors (SGLT2is) in patients with type 2 diabetes mellitus (T2DM)-canagliflozin, dapagliflozin, empagliflozin, ipragliflozin, luseogliflozin, and tofogliflozin-can be explained in a unified manner based on their ability to promote urinary glucose excretion (UGE). Information on HbA1c reduction at various doses of each SGLT2i was collected from literatures on randomized controlled trials and was normalized based on the daily UGE data from phase I studies. After normalizing doses, the dose-response relationship of HbA1c reduction of most of SGLT2is was represented by a unified nonlinear mixed-effect model, with the estimated maximum HbA1c (%) reduction (Emax) of 0.796 points, whereas covariate analysis showed that canagliflozin had a 1.33-fold higher Emax than those of the other drugs. Other covariates included baseline HbA1c levels, body weight, disease duration, prior treatment, and renal function. Findings from this study would influence drug selection and adjustment in clinical practice. As with SGLT2is, in cases where the efficacy cannot be easily evaluated but an appropriate pharmacodynamic marker was assessed in early clinical trials, similar approaches for other drug classes can guide strategic and evidence-based dose selection in phase III trials.

Analyzing the worldwide progression of COVID-19 cases and deaths using nonlinear mixed-effects model.

COVID-19 is an infectious disease that continues to spread worldwide. A precise estimation of the cases and deaths due to COVID-19 would allow for appropriate consideration of healthcare resource allocation, public health response, and vaccination and economic planning, to minimize social damage. In this study, we analyzed the progression of COVID-19 cases and deaths until January 2022 in 156 countries using a nonlinear mixed-effect model based on the SIR framework. Given the major changes in mortality from infection, risk of re-infection and social responses, the analysis was limited to the period before the emergence of the Omicron variant. The impact of infection prevention measures in various countries was assessed, with a specific focus on estimating the effectiveness of lockdowns, where the effect was assumed to change over time. By accounting for excess mortality, our analysis allowed the estimation of unreported cases and deaths, and thus providing a more comprehensive understanding of the impact of pandemic. In the analysis, we identified gross domestic product (GDP), proportion of people aged 65 years or older, latitude of the capital city on transmissibility of infection, and city population and cardiovascular death rate on mortality rate as significant influencing factors. Furthermore, the differences in transmissibility and mortality rates by variants and the effect of vaccination on the mortality rate were assessed. The transmissibility has increased by odds ratios of 1.2 to 1.4 in Beta, Gamma, and Delta variants; mortality rate has increased by odds ratios of 1.7, 2.2, and 1.4 in Beta, Gamma, and Delta variants, respectively; and vaccination decreased the mortality rate by odds ratios of 0.4 and 0.1 in Delta and other variants, respectively.

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases.

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

Predicting the in vivo developmental toxicity of fenarimol from in vitro toxicity data using PBTK modelling-facilitated reverse dosimetry approach.

In vitro methods are widely used in modern toxicological testing; however, the data cannot be directly employed for risk assessment. In vivo toxicity of chemicals can be predicted from in vitro data using physiologically based toxicokinetic (PBTK) modelling-facilitated reverse dosimetry (PBTK-RD). In this study, a minimal-PBTK model was constructed to predict the in-vivo kinetic profile of fenarimol (FNL) in rats and humans. The model was verified by comparing the observed and predicted pharmacokinetics of FNL for rats (calibrator) and further applied to humans. Using the PBTK-RD approach, the reported in vitro developmental toxicity data for FNL was translated to in vivo dose-response data to predict the assay equivalent oral dose in rats and humans. The predicted assay equivalent rat oral dose (36.46 mg/kg) was comparable to the literature reported in vivo BMD10 value (22.8 mg/kg). The model was also employed to derive the chemical-specific adjustment factor (CSAF) for interspecies toxicokinetics variability of FNL. Further, Monte Carlo simulations were performed to predict the population variability in the plasma concentration of FNL and to derive CSAF for intersubject human kinetic differences. The comparison of CSAF values for interspecies and intersubject toxicokinetic variability with their respective default values revealed that the applied uncertainty factors were adequately protective.

Data-driven disease progression model of Parkinson's disease and effect of sex and genetic variants.

As Parkinson's disease (PD) progresses, there are multiple biomarker changes, and sex and genetic variants may influence the rate of progression. Data-driven, long-term disease progression model analysis may provide precise knowledge of the relationships between these risk factors and progression and would allow for the selection of appropriate diagnosis and treatment according to disease progression. To construct a long-term disease progression model of PD based on multiple biomarkers and evaluate the effects of sex and leucine-rich repeat kinase 2 (LRRK2) mutations, a technique derived from the nonlinear mixed-effects model (Statistical Restoration of Fragmented Time course [SReFT]) was applied to datasets of patients provided by the Parkinson's Progression Markers Initiative. Four biomarkers, including the Unified PD Rating Scale, were used, and a covariate analysis was performed to investigate the effects of sex and LRRK2-related mutations. A model of disease progression over ~30 years was successfully developed using patient data with a median of 6 years. Covariate analysis suggested that female sex and LRRK2 G2019S mutations were associated with 21.6% and 25.4% significantly slower progression, respectively. LRRK2 rs76904798 mutation also tended to delay disease progression by 10.4% but the difference was not significant. In conclusion, a long-term PD progression model was successfully constructed using SReFT from relatively short-term individual patient observations and depicted nonlinear changes in relevant biomarkers and their covariates, including sex and genetic variants.

Exercise training outcomes in patients with chronic heart failure with reduced ejection fraction depend on patient background.

Background: The aim of this study was to identify significant factors affecting the effectiveness of exercise training using information of the HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) study. Methods: Background factors influencing the effect of exercise training were comprehensively surveyed for 2,130 patients by multivariable Cox regression analysis with the stepwise variable selection, and only significant factors were selected that were statistically distinguished from dummy noise factors using the Boruta method. Results: The analysis suggested that the use of beta-blockers, pulse pressure, hemoglobin level, electrocardiography findings, body mass index, and history of stroke at baseline potentially influenced the exercise effect on all-cause death (AD). Therefore, a hypothetical score to estimate the effect of exercise training was constructed based on the analysis. The analysis suggested that the score is useful in identifying patients for whom exercise training may be significantly effective in reducing all-caused death and hospitalization (ADH) as well as AD. Such a subpopulation accounted for approximately 40% of the overall study population. On the other hand, in approximately 45% of patients, the effect of exercise was unclear on either AD or ADH. In the remaining 15% of patients, it was estimated that the effect of exercise might be unclear for ADH and potentially rather increase AD. Conclusions: This study is the first analysis to comprehensively evaluate the effects of various factors on the outcome of exercise training in chronic heart failure, underscoring the need to carefully consider the patient's background before recommending exercise training. However, it should be noted that exercise training can improve many outcomes in a wide variety of diseases. Therefore, given the limitations involved in post-hoc analyses of a single clinical trial, the characteristics of patients to whom the results of this analysis can be applied need attention, and also further research is necessary on the relationship between the degree of exercise and the outcomes. A new clinical trial would be needed to confirm the factors detected and the appropriateness of the score.

Involvement of multiple cytochrome P450 isoenzymes in drug interactions between ritonavir and direct oral anticoagulants.

Herein, we aimed to determine the significance of drug interactions (DIs) between ritonavir and direct oral anticoagulants (DOACs) and identify the involved cytochrome P450 (CYP) isoenzymes. Using an in vitro cocktail method with human liver microsomes (HLM), we observed that ritonavir strongly inhibited CYPs in the following order: CYP3A, CYP2C8, CYP2D6, CYP2C9, CYP2C19, CYP2B6, and CYP2J2 (IC50: 0.023-6.79 µM). The degree of CYP2J2 inhibition was inconclusive, given the substantial discrepancy between the HLM and human expression system. Selective inhibition of CYP3A decreased the O-demethylation of apixaban by only 13.4%, and the involvement of multiple CYP isoenzymes was suggested, all of which were inhibited by ritonavir. Multiple CYP isoenzymes contributed also to the metabolism of rivaroxaban. Replacement of the incubation medium with phosphate buffer instead of HEPES enhanced apixaban hydroxylation. On surveying the FDA Adverse Event Reporting System, we detected that the signal of the proportional reporting ratio of "death" and found increase for "hemoglobin decreased" (12.5-fold) and "procedural hemorrhage" (201.9-fold) on administering apixaban with ritonavir; these were far less significant for other CYP3A inhibitors. Overall, these findings suggest that co-administration of ritonavir-boosted drugs with DOACs may induce serious DIs owing to the simultaneous inhibition of multiple CYP isoenzymes.

Model-based meta-analysis of ethnic differences and their variabilities in clearance of oral drugs classified by clearance mechanism.

In this study, the ethnic ratios (ERs) of oral clearance between Japanese and Western populations were subjected to model-based meta-analysis (MBMA) for 81 drugs evaluated in 673 clinical studies. The drugs were classified into eight groups according to the clearance mechanism, and the ER for each group was inferred together with interindividual variability (IIV), interstudy variability (ISV), and inter-drug variability within a group (IDV) using the Markov chain Monte Carlo (MCMC) method. The ER, IIV, ISV, and IDV were dependent on the clearance mechanism, and, except for particular groups such as drugs metabolized by polymorphic enzymes or their clearance mechanism is not confirmative, the ethnic difference was found to be generally small. The IIV was well-matched across ethnicities, and the ISV was approximately half of the IIV as the coefficient of variation. To adequately assess ethnic differences in oral clearance without false detections, phase I studies should be designed with full consideration of the mechanism of clearance. This study suggests that the methodology of classifying drugs based on the mechanism that causes ethnic differences and performing MBMA with statistical techniques such as MCMC analysis is helpful for a rational understanding of ethnic differences and for strategic drug development.

In vitro-based prediction of human plasma concentrations of food-related compounds.

Efforts have been made to replace animal experiments in safety evaluations, including in vitro-based predictions of human internal exposures, such as predicting peak plasma concentration (Cmax) values for xenobiotics and comparing these values with in vitro-based toxicity endpoints. Herein, the authors predicted the Cmax values of food-related compounds in humans based on existing and novel in vitro techniques. In this study, 20 food-related compounds, which have been previously reported in human pharmacokinetic or toxicokinetic studies, were evaluated. Human induced pluripotent stem cell-derived small intestinal epithelial cells (hiPSC-SIEC) and Caco-2 cells, HepaRG cells, equilibrium dialysis of human plasma, and LLC-PK1 cell monolayer were used to assess intestinal absorption and availability, hepatic metabolism, unbound plasma fraction, and secretion and reabsorption in renal tubular cells, respectively. After conversion of these parameters into human kinetic parameters, the plasma concentration profiles of these compounds were predicted using in silico methods, and the obtained Cmax values were found to be between 0.017 and 183 times the reported Cmax values. When the in silico-predicted parameters were modified with in vitro data, the predicted Cmax values came within 0.1-10 times the reported values because the metabolic activities of hiPSC-SIECs, such as uridine 5'-diphospho-glucuronosyl transferase, are more similar to those of human primary enterocytes. Thus, combining in vitro test results with the plasma concentration simulations resulted in more accurate and transparent predictions of Cmax values of food-related compounds than those obtained using in silico-derived predictions alone. This method facilitates accurate safety evaluation without the need for animal experiments.

Integrated Use of In Vitro and In Vivo Information for Comprehensive Prediction of Drug Interactions Due to Inhibition of Multiple CYP Isoenzymes.

BACKGROUND: Mechanistic static pharmacokinetic (MSPK) models are simple, have fewer data requirements, and have broader applicability; however, they cannot use in vitro information and cannot distinguish the contributions of multiple cytochrome P450 (CYP) isoenzymes and the hepatic and intestinal first-pass effects appropriately. We aimed to establish a new MSPK analysis framework for the comprehensive prediction of drug interactions (DIs) to overcome these disadvantages. METHODS: Drug interactions that occurred by inhibiting CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A in the liver and CYP3A in the intestine were simultaneously analyzed for 59 substrates and 35 inhibitors. As in vivo information, the observed changes in the area under the concentration-time curve (AUC) and elimination half-life (t1/2), hepatic availability, and urinary excretion ratio were used. As in vitro information, the fraction metabolized (fm) and the inhibition constant (Ki) were used. The contribution ratio (CR) and inhibition ratio (IR) for multiple clearance pathways and hypothetical volume (VHyp) were inferred using the Markov Chain Monte Carlo (MCMC) method. RESULT: Using in vivo information from 239 combinations and in vitro 172 fm and 344 Ki values, changes in AUC, and t1/2 were estimated for all 2065 combinations, wherein the AUC was estimated to be more than doubled for 602 combinations. Intake-dependent selective intestinal CYP3A inhibition by grapefruit juice has been suggested. By separating the intestinal contributions, DIs after intravenous dosing were also appropriately inferred. CONCLUSION: This framework would be a powerful tool for the reasonable management of various DIs based on all available in vitro and in vivo information.

Significance of Basal Membrane Permeability of Epithelial Cells in Predicting Intestinal Drug Absorption.

Drug absorption from the gastrointestinal tract is often restricted by efflux transport by P-glycoprotein (P-gp) and metabolism by CYP3A4. Both localize in the epithelial cells, and thus, their activities are directly affected by the intracellular drug concentration, which should be regulated by the ratio of permeability between apical (A) and basal (B) membranes. In this study, using Caco-2 cells with forced expression of CYP3A4, we assessed the transcellular permeation of A-to-B and B-to-A directions and the efflux from the preloaded cells to both sides of 12 representative P-gp or CYP3A4 substrate drugs and obtained the parameters for permeabilities, transport, metabolism, and unbound fraction in the enterocytes (fent) using simultaneous and dynamic model analysis. The membrane permeability ratios for B to A (RBA) and fent varied by 8.8-fold and by more than 3000-fold, respectively, among the drugs. The RBA values for digoxin, repaglinide, fexofenadine, and atorvastatin were greater than 1.0 (3.44, 2.39, 2.27, and 1.90, respectively) in the presence of a P-gp inhibitor, thus suggesting the potential involvement of transporters in the B membrane. The Michaelis constant for quinidine for P-gp transport was 0.077 µM for the intracellular unbound concentration. These parameters were used to predict overall intestinal availability (FAFG) by applying an intestinal pharmacokinetic model, advanced translocation model (ATOM), in which permeability of A and B membranes accounted separately. The model predicted changes in the absorption location for P-gp substrates according to its inhibition, and FAFG values of 10 of 12 drugs, including quinidine at varying doses, were explained appropriately. SIGNIFICANCE STATEMENT: Pharmacokinetics has improved predictability by identifying the molecular entities of metabolism and transport and by using mathematical models to appropriately describe drug concentrations at the locations where they act. However, analyses of intestinal absorption so far have not been able to accurately consider the concentrations in the epithelial cells where P-glycoprotein and CYP3A4 exert effects. In this study, the limitation was removed by measuring the apical and basal membrane permeability separately and then analyzing these values using new appropriate models.

Tumor-associated neutrophils and macrophages exacerbate antidrug IgG-mediated anaphylactic reaction against an immune checkpoint inhibitor.

BACKGROUND: With the increased use of immune checkpoint inhibitors (ICIs), side effects and toxicity are a great concern. Anaphylaxis has been identified as a potential adverse event induced by ICIs. Anaphylaxis is a life-threatening medical emergency. However, the mechanisms and factors that can potentially influence the incidence and severity of anaphylaxis in patients with cancer remain unclear. METHODS: Healthy, murine colon 26, CT26, breast 4T1, EMT6, and renal RENCA tumor-bearing mice were treated with an anti-PD-L1 antibody (clone 10F.9G2). Symptoms of anaphylaxis were evaluated along with body temperature and mortality. The amounts of antidrug antibody and platelet-activating factor (PAF) in the blood were quantified via ELISA and liquid chromatography-mass spectrometry (LC-MS/MS). Immune cells were analyzed and isolated using a flow cytometer and magnetic-activated cell sorting, respectively. RESULTS: Repeated administration of the anti-PD-L1 antibody 10F.9G2 to tumor-bearing mice caused fatal anaphylaxis, depending on the type of tumor model. After administration, antidrug immunoglobulin G (IgG), but not IgE antibodies, were produced, and PAF was released as a chemical mediator during anaphylaxis, indicating that anaphylaxis was caused by an IgG-dependent pathway. Anaphylaxis induced by 10F.9G2 was treated with a PAF receptor antagonist. We identified that neutrophils and macrophages were PAF-producing effector cells during anaphylaxis, and the tumor-bearing models with increased numbers of neutrophils and macrophages showed lethal anaphylaxis after treatment with 10F.9G2. Depletion of both neutrophils and macrophages using clodronate liposomes prevented anaphylaxis in tumor-bearing mice. CONCLUSIONS: Thus, increased numbers of neutrophils and macrophages associated with cancer progression may be risk factors for anaphylaxis. These findings may provide useful insights into the mechanism of anaphylaxis following the administration of immune checkpoint inhibitors in human subjects.

Delivery of aPD-L1 antibody to i.p. tumors via direct penetration by i.p. route: Beyond EPR effect.

Chemotherapy for peritoneal dissemination is poorly effective owing to limited drug transfer from the blood to the intraperitoneal (i.p.) compartment after intravenous (i.v.) administration. i.p. chemotherapy has been investigated to improve drug delivery to tumors; however, the efficacy continues to be debated. As anticancer drugs have low molecular weight and are rapidly excreted through the peritoneal blood vessels, maintaining the i.p. concentration as high as expected is a challenge. In this study, we examined whether i.p. administration is an efficient route of administration of high-molecular-weight immune checkpoint inhibitors (ICIs) for the treatment of peritoneal dissemination using a model of peritoneal disseminated carcinoma. After i.p. administration, the amount of anti-PD-L1 antibody transferred into i.p. tumors increased by approximately eight folds compared to that after i.v. administration. Intratumoral distribution analysis revealed that anti-PD-L1 antibodies were delivered directly from the i.p. space to the surface of tumor tissue, and that they deeply penetrated the tumor tissues after i.p. administration; in contrast, after i.v. administration, anti-PD-L1 antibodies were only distributed around blood vessels in tumor tissues via the enhanced permeability and retention (EPR) effect. Owing to the enhanced delivery, the therapeutic efficacy of anti-PD-L1 antibody in the peritoneal dissemination models was also improved after i.p. administration compared to that after i.v. administration. This is the first study to clearly demonstrate an EPR-independent delivery of ICIs to i.p. tumors by which ICIs were delivered in a massive amount to the tumor tissue via direct penetration after i.p. administration.

Effects of ceramide kinase knockout on lipopolysaccharide-treated sepsis-model mice: Changes in serum cytokine/chemokine levels and increased lethality.

Ceramide, a central molecule of sphingolipid metabolism, is phosphorylated to ceramide-1-phosphate (C1P) by ceramide kinase (CerK). The CerK/C1P pathway regulates many cellular functions, but its roles in immune/inflammation-related (IIR) diseases in vivo are not well known. Sepsis is an acute systemic inflammatory disease accompanied by damage/dysfunction in multiple organs. In the present study, we investigated the effects of CerK knockout on the onset/progression of sepsis-related events in lipopolysaccharide (LPS)-treated sepsis-model mice. In CerK-null mice, the lethality at 48 h after i.v. injection of LPS was significantly increased compared with that in wild-type (WT) mice. The increased lethality by CerK knockout was reproduced in mice treated with i.p. injections of LPS. Changes in serum levels of 23 IIR molecules, including cytokines and chemokines, were measured. In WT mice, levels of these molecules increased 4 and/or 20 h after i.v. injection of LPS. Although the basal levels of IIR molecules were not affected, LPS-induced increases in interleukin-17 (IL-17), C-C motif chemokine ligands (CCL-2 and CCL-11), and tumor necrosis factor-α were significantly up-regulated, whereas IL-2 levels were slightly down-regulated by CerK knockout. Putative mechanisms for the CerK/C1P pathway-mediated regulation of IIR molecules and increased lethality in LPS-treated mice are discussed.

Age-Related Change in Hepatic Clearance Inferred from Multiple Population Pharmacokinetic Studies: Comparison with Renal Clearance and Their Associations with Organ Weight and Blood Flow.

OBJECTIVE: This study aimed to examine the magnitude of age-related change in hepatic clearance by integrating the data of multiple drugs and to compare this with renal clearance, considering associations with age-related changes in organ weight and blood flow. METHODS: The results of multiple population pharmacokinetic analyses that detected age-related clearance changes in hepatically eliminated drugs were collected. The relationship between hepatic clearance of the unbound drug and age was then analyzed using the nonlinear least-squares method, adjusting for interdrug differences. The obtained change in hepatic clearance was compared with age-related changes in liver weight and hepatic blood flow in Japanese and Westerners. For comparison, the changes in renal clearance were analyzed similarly. RESULTS: In total, 18 drugs were analyzed. The hepatic unbound clearance decreased by 32% at age 80 years and by 40% at age 90 years, compared with age 40 years, suggesting that it decreased by 0.80% per year with aging. The rate of the decrease was consistent with decreases in hepatic weight per person or blood flow per person, regardless of ethnicity and sex. Since age-related change in body weight varied somewhat by sex or ethnicity, hepatic weight per body weight was less consistent to account for age-related change in hepatic clearance. As for an index of renal clearance, the changes in inulin clearance with age were similar to those in renal blood flow, with a decrease of 0.97% per year from the age of 40 years. CONCLUSIONS: Hepatic clearance consistently decreased by 0.80% per year from the age of 40 years, with aging for multiple drugs analyzed in this study. Changes in organ weight and blood flow are considered to be primarily responsible for the age-related changes in hepatic and renal clearance.

Comparison of Predictions by BCS, rDCS and Machine Learning for the Effect of Food on Oral Drug Absorption Based on Features Calculated In silico.

In this study, observed food effects of 473 drugs were categorized into positive, negative, or no effects and compared with the predictions made by machine learning (ML), the Biopharmaceutics Classification System (BCS) and refined Developability Classification System (rDCS). All methods used primarily in silico estimates for prediction, and for ML, four algorithms were evaluated using nested cross-validation to select important information from 371 features calculated based on the chemical structure. Approximately 18 features, including estimated solubility in biorelevant media, were selected as important, and the random forest classifier was the best among four algorithms with 36.6% error rate (ER) and 10.8% opposite prediction rate (OPR). The prediction by rDCS utilizing solubility in a biorelevant medium was somewhat inferior, but not by much; 41.0% ER and 11.4% OPR. Compared with these two methods, the prediction by BCS was inferior; 54.5% ER and 21.4% OPR. ER was improved modestly by using measured features instead of in silico estimates when BCS was applied to a subset of 151 drugs (46.4% from 55.0%). ML and rDCS predicted the food effects of the same subset using in silico estimates with ERs of 37.7% and 42.4%, respectively, suggesting that the predictions by ML and rDCS using in silico features are similar or more accurate than those by BCS using measured features. These results suggest that ML was useful in revealing essential features from complex information and, together with rDCS, is effective in predicting food effects during drug development, including early drug discovery.

Classification of drugs for evaluating drug interaction in drug development and clinical management.

During new drug development, clinical drug interaction studies are carried out in accordance with the mechanism of potential drug interactions evaluated by in vitro studies. The obtained information should be provided efficiently to medical experts through package inserts and various information materials after the drug's launch. A recently updated Japanese guideline presents general procedures that are considered scientifically valid at the present moment. In this review, we aim to highlight the viewpoints of the Japanese guideline and enumerate drugs that were involved or are anticipated to be involved in evident pharmacokinetic drug interactions and classify them by their clearance pathway and potential intensity based on systematic reviews of the literature. The classification would be informative for designing clinical studies during the development stage, and the appropriate management of drug interactions in clinical practice.

Effects on Metabolism in Astrocytes Caused by cGAMP, Which Imitates the Initial Stage of Brain Metastasis.

The second messenger 2'3'-cyclic-GMP-AMP (cGAMP) is thought to be transmitted from brain carcinomas to astrocytes via gap junctions, which functions to promote metastasis in the brain parenchyma. In the current study, we established a method to introduce cGAMP into astrocytes, which simulates the state of astrocytes that have been invaded by cGAMP around tumors. Astrocytes incorporating cGAMP were analyzed by metabolomics, which demonstrated that cGAMP increased glutamate production and astrocyte secretion. The same trend was observed for γ-aminobutyric acid (GABA). Conversely, glutamine production and secretion were decreased by cGAMP treatment. Due to the fundamental role of astrocytes in regulation of the glutamine-glutamate cycle, such metabolic changes may represent a potential mechanism and therapeutic target for alteration of the central nervous system (CNS) environment and the malignant transformation of brain carcinomas.

Model-based meta-analysis of changes in circulatory system physiology in patients with chronic heart failure.

To characterize and compare various medicines for chronic heart failure (CHF), changes in circulatory physiological parameter during pharmacotherapy were investigated by a model-based meta-analysis (MBMA) of circulatory physiology. The clinical data from 61 studies mostly in patients with heart failure with reduced ejection fraction (HFrEF), reporting changes in heart rate, blood pressure, or ventricular volumes after treatment with carvedilol, metoprolol, bisoprolol, bucindolol, enalapril, aliskiren, or felodipine, were analyzed. Seven cardiac and vasculature function indices were estimated without invasive measurements using models based on appropriate assumptions, and their correlations with the mortality were assessed. Estimated myocardial oxygen consumption, a cardiac load index, correlated excellently with the mortality at 3, 6, and 12 months after treatment initiation, and it explained differences in mortality across the different medications. The analysis based on the present models were reasonably consistent with the hypothesis that the treatment of HFrEF with various medications is due to effectively reducing the cardiac load. Assessment of circulatory physiological parameters by using MBMA would be insightful for quantitative understanding of CHF treatment.

Investigation of energy metabolic dynamism in hyperthermia-resistant ovarian and uterine cancer cells under heat stress.

Despite progress in the use of hyperthermia in clinical practice, the thermosensitivity of cancer cells is poorly understood. In a previous study, we found that sensitivity to hyperthermia varied between ovarian and uterine cancer cell lines. Upon hyperthermia, glycolytic enzymes decreased in hyperthermia-resistant SKOV3 cells. However, the mechanisms of glycolysis inhibition and their relationship with thermoresistance remain to be explored. In this study, metabolomic analysis indicated the downregulation of glycolytic metabolites in SKOV3 cells after hyperthermia. Proteomic and pathway analyses predicted that the ubiquitin pathway was explicitly activated in resistant SKOV3 cells, compared with hyperthermia-sensitive A2780 cells, and STUB1, a ubiquitin ligase, potentially targeted PKM, a glycolytic rate-limiting enzyme. PKM is degraded via ubiquitination upon hyperthermia. Although glycolysis is inactivated by hyperthermia, ATP production is maintained. We observed that oxygen consumption and mitochondrial membrane potential were activated in SKOV3 cells but suppressed in A2780 cells. The activation of mitochondria could compensate for the loss of ATP production due to the suppression of glycolysis by hyperthermia. Although the physiological significance has not yet been elucidated, our results demonstrated that metabolomic adaptation from the Warburg effect to mitochondrial oxidative phosphorylation could contribute to thermoresistance in ovarian and uterine cancer cells.