Model-based Parameters Research Articles

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis

PurposeTo evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer. MethodsThis prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses. Resultskep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944). ConclusionThe inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Normalizing Flow-Based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

The pharmacokinetic (PK) parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide valuable information for clinical research and diagnosis. However, these estimated PK parameters suffer from many sources of variability. Thus, the estimation of the posterior distributions of these PK parameters could provide a way to simultaneously quantify the values and uncertainties of the PK parameters. Our objective is to develop an efficient and flexible method to more closely approximate and estimate the underlying posterior distributions of the PK parameters. The normalizing flow model-based parameters distribution estimation neural network (FPDEN) is proposed to adaptively learn and estimate the posterior distributions of the PK parameters. The maximum likelihood estimation (MLE) loss is directly constructed based on the parameter distributions learned by the normalizing flow model, rather than pre-defined distributions. Experimental analysis shows that the proposed method can improve parameter estimation accuracy. Moreover, the uncertainty derived from the parameter distribution constitutes an effective indicator to exclude unreliable parametric results. A successful demonstration is the improved classification performance of the glioma World Health Organization (WHO) grading task, specifically in terms of distinguishing between low and high grades, as well as between Grade III and Grade IV. The FPDEN method offers improved accuracy for estimation of PK parameters and boosts the performance of the glioma grading task. By enhancing the precision and reliability of DCE-MRI, the proposed method promotes its further applications in clinical practice.

Glucometabolism in Kidney Transplant Recipients with and without Posttransplant Diabetes: Focus on Beta-Cell Function.

Posttransplant diabetes mellitus (PTDM) is a common complication after kidney transplantation. Pathophysiologically, whether beta-cell dysfunction rather than insulin resistance may be the predominant defect in PTDM has been a matter of debate. The aim of the present analysis was to compare glucometabolism in kidney transplant recipients with and without PTDM. To this aim, we included 191 patients from a randomized controlled trial who underwent oral glucose tolerance tests (OGTTs) 6 months after transplantation. We derived several basic indices of beta-cell function and insulin resistance as well as variables from mathematical modeling for a more robust beta-cell function assessment. Mean ± standard deviation of the insulin sensitivity parameter PREDIM was 3.65 ± 1.68 in PTDM versus 5.46 ± 2.57 in NON-PTDM. Model-based glucose sensitivity (indicator of beta-cell function) was 68.44 ± 57.82 pmol∙min-1∙m-2∙mM-1 in PTDM versus 143.73 ± 112.91 pmol∙min-1∙m-2∙mM-1 in NON-PTDM, respectively. Both basic indices and model-based parameters of beta-cell function were more than 50% lower in patients with PTDM, indicating severe beta-cell impairment. Nonetheless, some defects in insulin sensitivity were also present, although less marked. We conclude that in PTDM, the prominent defect appears to be beta-cell dysfunction. From a pathophysiological point of view, patients at high risk for developing PTDM may benefit from intensive treatment of hyperglycemia over the insulin secretion axis.

Characteristics and Influence Factors of Soil Water and Salt Movement in the Yellow River Irrigation District

Climate change and human activities lead to freshwater shortage, soil salinization, and food security crises in arable land. To explore the natural and irrigation factors on soil water and salt movement, this study quantitatively analyzed the dynamic characteristics of soil water and salt movement under precipitation, groundwater irrigation, and brackish water irrigation conditions for the next 30 years using Hydrus-1D model-based parameters obtained from the winter wheat–summer maize rotation experiments in the Yellow River Irrigation District. The results showed that precipitation was the key factor of climate change affecting soil water and salt migration, especially in the 0–20 cm soil layer. Under both SSP585 and SSP245 climate scenarios, rainfall in normal and wet years promoted salt leaching up to 1 m below the surface soil. But in dry years, salt washing treatment was required for the tillage layer to prevent salt accumulation. The higher the groundwater level was, the higher the soil water and salt content was in the 0–100 cm soil layer. In this soil layer, a 2 m groundwater level contributed 30% to wheat water needs, while a 3 m groundwater level contributed 18%, and no significant contribution was observed for a 4 m groundwater level. The salinity of the soil profile showed an overall increasing trend with irrigation using 1–3 g/L brackish water for 30 years. However, the salinity in the 0–100 cm soil layer was below the salt tolerance threshold of winter wheat and summer maize with salts accumulated in the 1–2 m soil layer. Considering the salinization of the root zone and crop water needs, it is recommended that the safe groundwater level for brackish water irrigation should be 3 m in the study region. This study provides scientific reference for groundwater–farmland ecosystems to utilize brackish water and treat saline–alkali lands.

Robotic assembly control reconfiguration based on transfer reinforcement learning for objects with different geometric features

Robotic force-based compliance control is a preferred approach to achieve high-precision assembly tasks. When the geometric features of assembly objects are asymmetric or irregular, reinforcement learning (RL) agents are gradually incorporated into the compliance controller to adapt to complex force-pose mapping which is hard to model analytically. Since force-pose mapping is strongly dependent on geometric features, a compliance controller is only optimal for current geometric features. To reduce the learning cost of assembly objects with different geometric features, this paper is devoted to answering how to reconfigure existing controllers for new assembly objects with different geometric features. In this paper, model-based parameters are first reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL). Then the RL agent is transferred based on the proposed Weighted Dimensional Policy Distillation (WDPD) method. The experiment results demonstrate that the control reconfiguration method costs less time and achieves better control performance, which confirms the validity of proposed methods.

In Silico Biomarkers of Motor Function to Inform Musculoskeletal Rehabilitation and Orthopedic Treatment.

In this review, we elaborate on how musculoskeletal (MSK) modeling combined with dynamic movement simulation is gradually evolving from a research tool to a promising in silico tool to assist medical doctors and physical therapists in decision making by providing parameters relating to dynamic MSK function and loading. This review primarily focuses on our own and related work to illustrate the framework and the interpretation of MSK model-based parameters in patients with 3 different conditions, that is, degenerative joint disease, cerebral palsy, and adult spinal deformities. By selecting these 3 clinical applications, we also aim to demonstrate the differing levels of clinical readiness of the different simulation frameworks introducing in silico model-based biomarkers of motor function to inform MSK rehabilitation and treatment, with the application for adult spinal deformities being the most recent of the 3. Based on these applications, barriers to clinical integration and positioning of these in silico technologies within standard clinical practice are discussed in the light of specific challenges related to model assumptions, required level of complexity and personalization, and clinical implementation.

Prosocial learning: Model-based or model-free?

Prosocial learning involves the acquisition of knowledge and skills necessary for making decisions that benefit others. We asked if, in the context of value-based decision-making, there is any difference between learning strategies for oneself vs. for others. We implemented a 2-step reinforcement learning paradigm in which participants learned, in separate blocks, to make decisions for themselves or for a present other confederate who evaluated their performance. We replicated the canonical features of the model-based and model-free reinforcement learning in our results. The behaviour of the majority of participants was best explained by a mixture of the model-based and model-free control, while most participants relied more heavily on MB control, and this strategy enhanced their learning success. Regarding our key self-other hypothesis, we did not find any significant difference between the behavioural performances nor in the model-based parameters of learning when comparing self and other conditions.

Aging and rejuvenation effects on the rheological response and chemical parameters of bitumen

The rejuvenation efficiency of aged bitumen is the main concern when developing rejuvenating agents. It is necessary to develop a method to assess the efficiency of rejuvenators using rheological parameters in the whole frequency region. To this end, the 2S2P1D micromechanics model is adopted to fit the entire G∗ master curves of various rejuvenated bitumen, and the influence of rejuvenator type/dosage and aging grade of bitumen on the whole G∗ master curve and chemical indices are investigated. Critical parameters for evaluating rejuvenation efficiency derived from viscoelastic models and chemical characterizations are proposed. Furthermore, the potential relationships between the rheological model-based parameters (E∞, δ, β, and τ) and chemical indices (carbonyl index CI and sulfoxide index SI) are explored. The results indicate that rejuvenators restore the δ, τ, CI, and SI values of aged bitumen towards the virgin bitumen level. The E∞ parameter is not applicable to evaluate the rejuvenation efficiency of engine-oil and naphthenic-oil rejuvenators, but the positive E∞R values of bio-oil and aromatic-oil rejuvenated bitumen are detected. All rejuvenators fail to restore the β parameter of aged bitumen. The τ and CI parameters are selected as critical evaluation indicators from the perspective of viscoelastic models and chemical characteristics. Linear correlations between all rheological parameters and chemical indices are observed and established.

Effects of the Molecular Structure of Starch in Foods on Human Health.

Starch provides approximately half of humans' food energy, and its structural features influence human health. The most important structural feature is the chain length distribution (CLD), which affects properties such as the digestibility of starch-containing foods. The rate of digestion of such foods has a strong correlation with the prevalence and treatment of diseases such as diabetes, cardiovascular disease and obesity. Starch CLDs can be divided into multiple regions of degrees of polymerization, wherein the CLD in a given region is predominantly, but not exclusively, formed by a particular set of starch biosynthesis enzymes: starch synthases, starch branching enzymes and debranching enzymes. Biosynthesis-based models have been developed relating the ratios of the various enzyme activities in each set to the CLD component produced by that set. Fitting the observed CLDs to these models yields a small number of biosynthesis-related parameters, which, taken together, describe the entire CLD. This review highlights how CLDs can be measured and how the model-based parameters obtained from fitting these distributions are related to the properties of starch-based foods significant for health, and it considers how this knowledge could be used to develop plant varieties to provide foods with improved properties.

Correlations between intravoxel incoherent motion-derived fast diffusion and perfusion fraction parameters and VEGF- and MIB-1-positive rates in brain gliomas: an intraoperative MR-navigated, biopsy-based histopathologic study.

To explore the correlations between histopathologic findings and intravoxel incoherent motion (IVIM)-derived perfusion and diffusion parameters in brain gliomas. Thirty-two biopsy samples from twenty-one patients with newly diagnosed gliomas from a previous prospective cohort study were retrospectively analyzed. All patients underwent diffusion-weighted MRI with 22 b values (0-5000s/mm2), followed by intraoperative MR-guided biopsy surgery and surgical resection. All 32 biopsy samples underwent immunohistochemical staining followed by quantitative analysis of cell density (cellularity), percent of MIB-1 (Ki67)-positive expression (pMIB-1), number of CD34-stained vessels (CD34-MVD), and percent of VEGF-positive expressing cells (pVEGF) using a multispectral phenotyping microscope. Based on the co-registered localized biopsy, correlation analysis was performed between the IVIM-derived biexponential model-based parameters (Dfast1500 and Dfast5000, Dslow1500 and Dslow5000, PF1500 and PF5000) and the above four pathological biomarkers and glioma grades. Significant positive correlations were revealed between Dfast5000 and pVEGF (rho (r) = 0.466, p = 0.007), and Dfast1500 and pVEGF (r = 0.371, p = 0.037). A significant negative correlation was revealed between PF5000 with pMIB-1 (r = - 0.456, p = 0.01). Moderate to good positive correlations were shown between Dfast5000 and glioma grades (r = 0.509, p = 0.003) and Dfast1500 and glioma grades (r = 0.476, p = 0.006). IVIM-DWI-derived Dfast and PF correlate, respectively, with intratumor pVEGF and pMIB-1. When using the wide-high b value scheme, IVIM-derived Dfast and PF tend to demonstrate better efficacy in evaluating malignancy-related characteristics such as angiogenesis and cellular proliferation in gliomas. • Intravoxel incoherent motion-diffusion-weighted imaging (IVIM-DWI)-derived fast diffusion (Dfast) and perfusion fraction (PF) can quantitatively reflect intratumor pVEGF and pMIB-1. • IVIM-DWI-derived Dfast and PF tend to demonstrate better efficacy in evaluating glioma malignancy when an optimized scheme is used. • IVIM-DWI-derived Dfast5000 and PF5000 are promising non-invasive parameters correlating with pVEGF and pMIB-1 in gliomas.

Altered neural reward processing is associated with reward-related decision-making in adolescents with severe obesity

Obesity is associated with altered food-related reward processing, but its impact on non-food reward remains unclear. This question is both timely due to rising rates of severe obesity and important because adolescence is a period of heightened reward seeking behavior. We used computational modeling and functional magnetic resonance imaging (fMRI) to examine monetary reward processing using classic experimental tasks in 35 adolescents (14-18 years-old, 13 male) with severe obesity (OB; n=18) and without obesity (nonOB; n=17). Participants completed the Balloon Analog Risk Taking Task (BART) to assess reward-related decision-making and the Monetary Incentive Delay (MID) task to assess neural correlates of reward anticipation. BART model-based parameters revealed no differences in reward sensitivity but less adaptive decision-making (response consistency) in OB compared to nonOB. Other metrics (e.g., number of balloons popped, number of pumps, and total points) did not differ between groups. During MID reward anticipation, OB had lower activation than nonOB in ventral tegmental area (VTA) and prefrontal cortex, canonical regions for reward and cognitive control, respectively. Weight status moderated associations between VTA activation and BART metrics such that higher VTA activation was associated with more risky decision-making (more popped balloons) in OB but not nonOB. Functional connectivity of VTA with right inferior frontal gyrus (IFG) and left superior temporal gyrus (STG) was greater higher in OB than nonOB. Associations between value-related VTA-STG connectivity and BART metrics were moderated by weight status such that higher connectivity was associated with greater number of pumps and points for nonOB and less risky decision-making for OB. Therefore, differences in activation and connectivity between groups may suggest differences in decision-making strategies. Together, findings reveal that VTA, prefrontal, and temporal engagement during monetary reward anticipation differs in OB and nonOB adolescents and may contribute to individual differences in reward-related decision-making. Such domain-general alteration of reward processing may have far reaching consequences, not only for food intake but also functions central to motivational behavior such as learning and socialization during adolescence, a sensitive period in development. These findings highlight the importance of considering reward more broadly when designing and tailoring behavioral interventions in adolescent obesity

Model-Free and Model-based Parameters Derived From CAIPIRINHA-Dixon-TWIST-VIBE DCE-MRI: Associations With Prognostic Factors and Molecular Subtypes of Invasive Ductal Breast Cancer.

CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) dynamic contrast-enhanced MRI (DCE-MRI) can be used to characterize breast cancer. However, the influence of the clinicopathologic factors and molecular subtypes of invasive breast carcinoma (IDC) on the model-free and model-based parameters has not been investigated. To compare model-free and model-based parameters of CDTV DCE-MRI with both clinicopathologic factors and molecular subtypes of IDC. Prospective. A total of 152 patients (mean age, 52 years) with IDC including 42 luminal A, 64 luminal B, 22 human epidermal growth factor receptor-2 (HER2) positive, and 24 triple-negative subtypes. A 3 T; turbo-FLASH, Dixon VIBE, and CDTV. Model-free parameters (initial enhancement rate [IER] and maximum slope [MS]) were estimated from the time-intensity curve. The mean, minimum, maximum, and range between the minimum and maximum values of inline model-based parameters (Ktrans , kep , and ve ) were measured to assess intratumoral heterogeneity of IDC lesions. Student's t tests, Mann-Whitney U tests, Kruskal-Wallis tests, post hoc Steel-Dwass tests, and receiver operating characteristic (ROC) curves. P < 0.05 was considered significant. No significant differences in IER and MS values were seen among the clinicopathologic factors and molecular subtypes (Bonferroni-corrected P=0.011-0.862, P=0.145-0.601, respectively). The minimum kep values in HER2-positive IDC were significantly lower than those in HER2-negative IDC. The mean and range kep values were independent predictors for distinguishing the high (grade 3) and low (grade 1 or 2) nuclear grade groups according to multivariable analyses. The post hoc test showed that the kep minimum and kep range values were significantly different between luminal A and HER2-positive tumor subtypes, yielding an area-under-the-curve of 0.820. Compared with the model-free parameters, inline kep related model-based parameters on CDTV DCE-MRI can be applied as a feasible tool to differentiate luminal A from HER2-positive breast cancers. 2. Stage 2.

Mechanism-Based Pharmacokinetic Modeling of Absorption and Disposition of a Deferoxamine-Based Nanochelator in Rats.

Deferoxamine (DFO) is an effective FDA-approved iron chelator. However, its use is considerably limited by off-target toxicities and an extremely cumbersome dose regimen with daily infusions. The recent development of a deferoxamine-based nanochelator (DFO-NP) with selective renal excretion has shown promise in ameliorating animal models of iron overload with a substantially improved safety profile. To further the preclinical development of this promising nanochelator and to inform on the feasibility of clinical development, it is necessary to fully characterize the dose and administration-route-dependent pharmacokinetics and to develop predictive pharmacokinetic (PK) models describing absorption and disposition. Herein, we have evaluated the absorption, distribution, and elimination of DFO-NPs after intravenous and subcutaneous (SC) injection at therapeutically relevant doses in Sprague Dawley rats. We also characterized compartment-based model structures and identified model-based parameters to quantitatively describe the PK of DFO-NPs. Our modeling efforts confirmed that disposition could be described using a three-compartment mamillary model with elimination and saturable reabsorption both occurring from the third compartment. We also determined that absorption was nonlinear and best described by parallel saturable and first-order processes. Finally, we characterized a novel pathway for saturable SC absorption of an ultrasmall organic nanoparticle directly into the systemic circulation, which offers a novel strategy for improving drug exposure for nanotherapeutics.

Evaluating stability of human spine in static tasks: a combined in vivo-computational study

Various interpretations and parameters have been proposed to assess spinal stability such as antagonist muscle coactivity, trunk stiffness and spinal buckling load; however, the correlation between these parameters remains unknown. We evaluated spinal stability during different tasks while changing the external moment and load height and investigated likely relationships between different EMG- and model-based parameters (e.g., EMG coactivity ratio, trunk stiffness, force coactivity ratio) and stability margins. EMG and kinematics of 40 young healthy subjects were recorded during various quasi-static tasks. Muscle forces, trunk stiffness and stability margins were calculated by a nonlinear subject-specific EMG-assisted-optimization musculoskeletal model of the trunk. The load elevation and external moment increased muscle activities and trunk stiffness while all stability margins (i.e., buckling loads) decreased. The force coactivity ratio was strongly correlated with the hand-load stability margin (i.e., additional weight in hands to initiate instability; R 2 = 0.54) demonstrating the stabilizing role of abdominal muscles. The total trunk stiffness (Pearson’s r = 0.96) and the sum of EMGs of back muscles (Pearson’s r = 0.65) contributed the most to the T1 stability margin (i.e., additional required load at T1 for instability/buckling). Force coactivity ratio and trunk stiffness can be used as alternative spinal stability metrics.

3-D Model-Based Inversion Using Supervised Descent Method for Aspect-Limited Microwave Data of Metallic Targets

In this work, we apply the supervised descent method (SDM) to 3-D limited data inversion. In the measurement of the far field, the scattered field in 3-D measurement is small in magnitude and easily polluted by noise. Combining with limited observed data, the inversion problem is highly nonunique and ill-posed. To mitigate the ill-posedness, the model-based inversion is adopted by describing metallic targets based on prior information. Then, a series of generic descent directions is learned in the training stage iteratively using SDM. During inversion, the values of these model-based parameters are reconstructed directly using the learned directions. This approach is validated using both synthetic and experimental data. All simulations and experiments are conducted in a monostatic measurement setup to match real measurement conditions. The results show that by choosing proper prior information, the model-based SDM inversion can effectively compensate for the lack of data and achieve decent accuracy.

Characterization of multicellular breast tumor spheroids using image data-driven biophysical mathematical modeling

Multicellular tumor spheroid (MCTS) systems provide an in vitro cell culture model system which mimics many of the complexities of an in vivo solid tumor and tumor microenvironment, and are often used to study cancer cell growth and drug efficacy. Here, we present a coupled experimental-computational framework to estimate phenotypic growth and biophysical tumor microenvironment properties. This novel framework utilizes standard microscopy imaging of MCTS systems to drive a biophysical mathematical model of MCTS growth and mechanical interactions. By extending our previous in vivo mechanically-coupled reaction–diffusion modeling framework we developed a microscopy image processing framework capable of mechanistic characterization of MCTS systems. Using MDA-MB-231 breast cancer MCTS, we estimated biophysical parameters of cellular diffusion, rate of cellular proliferation, and cellular tractions forces. We found significant differences in these model-based biophysical parameters throughout the treatment time course between untreated and treated MCTS systems, whereas traditional size-based morphometric parameters were inconclusive. The proposed experimental-computational framework estimates mechanistic MCTS growth and invasion parameters with significant potential to assist in better and more precise assessment of in vitro drug efficacy through the development of computational analysis methodologies for three-dimensional cell culture systems to improve the development and evaluation of antineoplastic drugs.

Designing Heavy-Duty Vehicles’ Four-Parameter Driving Cycles to Best Represent Engine Distribution Consistency

The driving gear affects engine transient cycles. Current methods of designing engine transient cycles need to establish the shift model of the transmission system, and the differences from the actual driving shift law result in the quantitatively low consistency of engine transient test cycles. Then the representativeness and accuracy of the designed engine transient and steady-state test cycles will be worse. By expanding the Markov chain evolution (MCE) framework, in this study, four-parameter driving cycles with gear for heavy-duty vehicles are designed, which can represent the consistency of the engine cycle distribution. The Markov chain model-based multi-parameter state transition with gear information is constructed to be used as constraints to design the genetic operators; engine characteristic model-based parameters are calculated as the constraints for designing the objective function. Multi-parameter vehicle driving cycles are thus generated by the expanded MCE framework and then transformed into engine transient test cycles. The designed driving cycles were verified and analyzed using the data collected from a heavy-duty vehicle. The results showed that the driving parameters and fuel consumption per 100 km between the designed driving cycles and the collected database met the threshold deviation; the correlation coefficients of the distributions related to gear utilization, vehicle specified power (VSP), and engine cycles reached as high as 90%; and multiple results had the same effect as mentioned above. Compared with a conversion method based on the economical shift rule, the fuel consumption rate distribution in this study can be closer to the actual engine running conditions.

Estimating (unidentifiable) enhanced EGP in glycaemic control modelling: Dancing with minions of the Dark Lord

Critically ill patients frequently experience stress-induced hyperglycaemia. Glycaemic control (GC) with insulin therapy can improve patient outcomes, but effective GC is not currently well achieved in most critical care units. STAR is a model-based decision support system, utilizing the ICING model, for glycaemic control in intensive care. Understanding model-based parameters and assumptions within their clinical context is important. The ICING model uses a population constant for endogenous glucose production (EGP), but EGP can vary considerably in patients during extreme stress and trauma. This study uses data from 145 patients on the SPRINT protocol to explore the assumptions around the EGP parameter and estimate minimum EGP values when the model is constrained to a minimum insulin sensitivity (SI) value. The model is frequently constrained when there is no nutritional input, highlighting the importance of the EGP parameter for glucose flux in the model equation. Minimum EGP values were calculated when SI was less than or equal to 1e-5 L/mU/min and ranged from 1.16 mmol/min to 2.72 mmol/min, where the median value is a 12% increase from the population value of 1.16 mmol/min. This analysis provides a relative indication of EGP changes in patients and supports the use of the EGP population value as only 2.3% of hours require EGP modification.

Single dose testosterone administration increases impulsivity in the intertemporal choice task among healthy males

Circulating levels of testosterone have been positively associated with impulsivity. The present study investigates the effect of testosterone administration on impulsivity in an intertemporal choice task, where participants are given a choice between smaller-sooner rewards and larger-later rewards. Healthy young male participants (n = 111) received a single-dose of 150 mg testosterone gel in a double-blind, placebo-controlled, between-subjects design. At 180 min post-administration, participants performed the decision-making task. Both model-free (i.e., higher indifference point) and model-based (i.e., steeper discounting rate) parameters revealed that testosterone administration increased impulsive choice. This finding supports the hypothesis that exogenous testosterone increases impulsivity among healthy young males in a laboratory task.

Brain Distributional Kinetics of a Novel MDM2 Inhibitor SAR405838: Implications for Use in Brain Tumor Therapy.

Achieving an effective drug concentration in the brain is as important as targeting the right pathway when developing targeted agents for brain tumors. SAR405838 is a novel molecularly targeted agent that is in clinical trials for various solid tumors. Its application for tumors in the brain has not yet been examined, even though the target, the MDM2-p53 interaction, is attractive for tumors that could occur in the brain, including glioblastoma and brain metastases. In vitro and in vivo studies indicate that SAR405838 is a substrate of P-glycoprotein (P-gp). P-gp mediated active efflux at the blood-brain barrier plays a dominant role in limiting SAR405838 brain distribution. Even though the absence of P-gp significantly increases the drug exposure in the brain, the systemic exposure, including absorption and clearance processes, were unaffected by P-gp deletion. Model-based parameters of SAR405838 distribution across the blood-brain barrier indicate the CLout of the brain was approximately 40-fold greater than the CLin The free fraction of SAR405838 in plasma and brain were found to be low, and subsequent Kpuu values were less than unity, even in P-gp/Bcrp knockout mice. These results indicate additional efflux transporters other than P-gp and Bcrp may be limiting distribution of SAR405838 to the brain. Concomitant administration of elacridar significantly increased brain exposure, also without affecting the systemic exposure. This study characterized the brain distributional kinetics of SAR405838, a novel MDM2 inhibitor, to evaluate its potential in the treatment of primary and metastatic brain tumors. SIGNIFICANCE STATEMENT: This paper examined the brain distributional kinetics of a novel MDM2-p53 targeted agent, SAR405838, to see its possible application for brain tumors by using in vitro, in vivo, and in silico approaches. SAR405838 is found to be a substrate of P-glycoprotein (P-gp), which limits its distribution to the brain. Based on the findings in the paper, manipulation of the function of P-gp can significantly increase the brain exposure of SAR405838, which may give an insight on its potential benefit as a treatment for primary and metastatic brain cancer.