Physiologically based pharmacokinetic toolkit to evaluate environmental exposures: Applications of the dioxin model to study real life exposures.
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ABSTRACT:
SUBMITTER: Emond C
PROVIDER: S-EPMC8195324 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A computational framework was developed to assist in screening and prioritizing chemicals based on their dosimetry, toxicity, and potential exposures. The overall strategy started with contextualizing chemical activity observed in high-throughput toxicity screening (HTS) by mapping these assays to biological events described in Adverse Outcome Pathways (AOPs). Next, in vitro to in vivo (IVIVE) extrapolation was used to convert an in vitro dose to an external exposure level, which was compared with potential exposure levels to derive an AOP-based margins of exposure (MOE). In this study, the framework was applied to estimate MOEs for chemicals that can potentially cause developmental toxicity following a putative AOP for fetal vasculogenesis/angiogenesis. A physiologically based pharmacokinetic (PBPK) model was developed to describe chemical disposition during pregnancy, fetal, neonatal, and infant to adulthood stages. Using this life-stage PBPK model, maternal exposures were estimated that would yield fetal blood levels equivalent to the chemical concentration that altered in vitro activity of selected HTS assays related to the most sensitive vasculogenesis/angiogenesis putative AOP. The resulting maternal exposure estimates were then compared with potential exposure levels using literature data or exposure models to derive AOP-based MOEs.
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Project description:Sparsentan is a dual endothelin/angiotensin II receptor antagonist indicated to reduce proteinuria in patients with primary IgA nephropathy at high risk of disease progression. In vitro data indicate that sparsentan is likely to inhibit or induce various CYP enzymes at therapeutic concentrations. Sparsentan as a victim and perpetrator of CYP3A4 mediated drug-drug interactions (DDIs) has been assessed clinically. A mechanistic, bottom-up, physiologically-based pharmacokinetic (PK) model for sparsentan was developed based on in vitro data of drug solubility, formulation dissolution and particle size, drug permeability, inhibition and induction of metabolic enzymes, and P-glycoprotein (P-gp) driven efflux. The model was verified using clinical PK data from healthy adult volunteers administered single and multiple doses in the fasted and fed states for a wide range of sparsentan doses. The model was also verified by simulation of clinically observed DDIs. The verified model was then used to test various DDI simulations of sparsentan as a perpetrator and victim of CYP3A4 using an expanded set of inducers and inhibitors with varying potency. Additional perpetrator and victim DDI simulations were performed using probes for CYP2C9 and CYP2C19. Simulations were conducted to predict the effect of complete inhibition of P-gp inhibition on sparsentan absorption and clearance. The predictive simulations indicated that exposure of sparsentan could increase greater than two-fold if co-administered with a strong CYP3A4 inhibitor, such as itraconazole. Other potential DDI interactions as victim or perpetrator were all within two-fold of control. The effect of complete P-gp inhibition on sparsentan PK was negligible.
| S-EPMC10864932 | biostudies-literature
Project description:Selumetinib (ARRY-142886), an oral, potent and highly selective allosteric mitogen-activated protein kinase kinase 1/2 inhibitor, is approved by the US Food and Drug Administration for the treatment of pediatric patients aged ≥2 years with neurofibromatosis type 1 with symptomatic, inoperable plexiform neurofibromas. A physiologically based pharmacokinetic (PBPK) model was constructed to predict plasma concentration-time profiles of selumetinib, and to evaluate the impact of coadministering moderate cytochrome P450 (CYP) 3A4/2C19 inhibitors/inducers. The model was also used to extrapolate pharmacokinetic exposures from older children with different body surface area to guide dosing in younger children. This model was built based on physiochemical data and clinical in vivo drug-drug interaction (DDI) studies with itraconazole and fluconazole, and verified against data from an in vivo rifampicin DDI study and an absolute bioavailability study. The pediatric model was updated by changing system-specific input parameters using the Simcyp pediatric module. The model captured the observed selumetinib pharmacokinetic profiles and the interactions with CYP inhibitors/inducers. The predictions from the PBPK model showed a DDI effect of 30% to 40% increase or decrease in selumetinib exposure when coadministered with moderate CYP inhibitors or inducers, respectively, which was used to inform dose management and adjustments. The pediatric PBPK model was applied to simulate exposures in specific body surface area brackets that matched those achieved with a 25 mg/m2 dose in SPRINT clinical trials. The pediatric PBPK model was used to guide the dose for younger patients in a planned pediatric clinical study.
| S-EPMC9290801 | biostudies-literature
Project description:BackgroundGrowing evidence exists about the fetal and environmental origins of hypertension, but mainly limited to single-exposure studies. The exposome has been proposed as a more holistic approach by studying many exposures simultaneously.ObjectivesThis study aims to evaluate the association between a wide range of prenatal and postnatal exposures and blood pressure (BP) in children.MethodsSystolic and diastolic BP were measured among 1,277 children from the European HELIX (Human Early-Life Exposome) cohort aged 6 to 11 years. Prenatal (n = 89) and postnatal (n = 128) exposures include air pollution, built environment, meteorology, natural spaces, traffic, noise, chemicals, and lifestyles. Two methods adjusted for confounders were applied: an exposome-wide association study considering the exposures independently, and the deletion-substitution-addition algorithm considering all the exposures simultaneously.ResultsDecreases in systolic BP were observed with facility density (β change for an interquartile-range increase in exposure: -1.7 mm Hg [95% confidence interval (CI): -2.5 to -0.8 mm Hg]), maternal concentrations of polychlorinated biphenyl 118 (-1.4 mm Hg [95% CI: -2.6 to -0.2 mm Hg]) and child concentrations of dichlorodiphenyldichloroethylene (DDE: -1.6 mm Hg [95% CI: -2.4 to -0.7 mm Hg]), hexachlorobenzene (-1.5 mm Hg [95% CI: -2.4 to -0.6 mm Hg]), and mono-benzyl phthalate (-0.7 mm Hg [95% CI: -1.3 to -0.1 mm Hg]), whereas increases in systolic BP were observed with outdoor temperature during pregnancy (1.6 mm Hg [95% CI: 0.2 to 2.9 mm Hg]), high fish intake during pregnancy (2.0 mm Hg [95% CI: 0.4 to 3.5 mm Hg]), maternal cotinine concentrations (1.2 mm Hg [95% CI: -0.3 to 2.8 mm Hg]), and child perfluorooctanoate concentrations (0.9 mm Hg [95% CI: 0.1 to 1.6 mm Hg]). Decreases in diastolic BP were observed with outdoor temperature at examination (-1.4 mm Hg [95% CI: -2.3 to -0.5 mm Hg]) and child DDE concentrations (-1.1 mm Hg [95% CI: -1.9 to -0.3 mm Hg]), whereas increases in diastolic BP were observed with maternal bisphenol-A concentrations (0.7 mm Hg [95% CI: 0.1 to 1.4 mm Hg]), high fish intake during pregnancy (1.2 mm Hg [95% CI: -0.2 to 2.7 mm Hg]), and child copper concentrations (0.9 mm Hg [95% CI: 0.3 to 1.6 mm Hg]).ConclusionsThis study suggests that early-life exposure to several chemicals, as well as built environment and meteorological factors, may affect BP in children.
| S-EPMC8713646 | biostudies-literature
Project description:Considerable interest remains across the pharmaceutical industry and regulatory landscape in capabilities to model oral contraceptives (OCs), whether combined (COCs) with ethinyl estradiol (EE) or progestin-only pill. Acceptance of COC drug-drug interaction (DDI) assessment using physiologically-based pharmacokinetic (PBPK) is often limited to the estrogen component (EE), requiring further verification, with extrapolation from EE to progestins discouraged. There is a paucity of published progestin component PBPK models to support the regulatory DDI guidance for industry to evaluate a new chemical entity's (NCE's) DDI potential with COCs. Guidance recommends a clinical interaction study to be considered if an investigational drug is a weak or moderate inducer, or a moderate/strong inhibitor, of CYP3A4. Therefore, availability of validated OC PBPK models within one software platform, will be useful in predicting the DDI potential with NCEs earlier in the clinical development. Thus, this work was focused on developing and validating PBPK models for progestins, DNG, DRSP, LNG, and NET, within Simcyp, and assessing the DDI potential with known CYP3A4 inhibitors (e.g., ketoconazole) and inducers (e.g., rifampicin) with published clinical data. In addition, this work demonstrated confidence in the Simcyp EE model for regulatory and clinical applications by extensive verification in 70+ clinical PK and CYP3A4 interaction studies. The results provide greater capability to prospectively model clinical CYP3A4 DDI with COCs using Simcyp PBPK to interrogate the regulatory decision-tree to contextualize the potential interaction by known perpetrators and NCEs, enabling model-informed decision making, clinical study designs, and delivering potential alternative COC options for women of childbearing potential.
| S-EPMC11015076 | biostudies-literature
Project description:As one of the key components in model-informed drug discovery and development, physiologically-based pharmacokinetic (PBPK) modeling linked with in vitro-to-in vivo extrapolation (IVIVE) is widely applied to quantitatively predict drug-drug interactions (DDIs) on drug-metabolizing enzymes and transporters. This study aimed to investigate an IVIVE for intestinal P-glycoprotein (Pgp, ABCB1)-mediated DDIs among three Pgp substrates, digoxin, dabigatran etexilate, and quinidine, and two Pgp inhibitors, itraconazole and verapamil, via PBPK modeling. For Pgp substrates, assuming unbound Michaelis-Menten constant (Km ) to be intrinsic, in vitro-to-in vivo scaling factors for maximal Pgp-mediated efflux rate (Jmax ) were optimized based on the clinically observed results without co-administration of Pgp inhibitors. For Pgp inhibitors, PBPK models utilized the reported in vitro values of Pgp inhibition constants (Ki ), 1.0 μM for itraconazole and 2.0 μM for verapamil. Overall, the PBPK modeling sufficiently described Pgp-mediated DDIs between these substrates and inhibitors with the prediction errors of less than or equal to ±25% in most cases, suggesting a reasonable IVIVE for Pgp kinetics in the clinical DDI results. The modeling results also suggest that Pgp kinetic parameters of both the substrates (Km and Jmax ) and the inhibitors (Ki ) are sensitive to Pgp-mediated DDIs, thus being key for successful DDI prediction. It would also be critical to incorporate appropriate unbound inhibitor concentrations at the site of action into PBPK models. The present results support a quantitative prediction of Pgp-mediated DDIs using in vitro parameters, which will significantly increase the value of in vitro studies to design and run clinical DDI studies safely and effectively.
| S-EPMC8752109 | biostudies-literature
Project description:BACKGROUND:Chemical and nonchemical environmental exposures are increasingly suspected to influence the development of obesity, especially during early life, but studies mostly consider single exposure groups. OBJECTIVES:Our study aimed to systematically assess the association between a wide array of early-life environmental exposures and childhood obesity, using an exposome-wide approach. METHODS:The HELIX (Human Early Life Exposome) study measured child body mass index (BMI), waist circumference, skinfold thickness, and body fat mass in 1,301 children from six European birth cohorts age 6-11 y. We estimated 77 prenatal exposures and 96 childhood exposures (cross-sectionally), including indoor and outdoor air pollutants, built environment, green spaces, tobacco smoking, and biomarkers of chemical pollutants (persistent organic pollutants, metals, phthalates, phenols, and pesticides). We used an exposure-wide association study (ExWAS) to screen all exposure-outcome associations independently and used the deletion-substitution-addition (DSA) variable selection algorithm to build a final multiexposure model. RESULTS:The prevalence of overweight and obesity combined was 28.8%. Maternal smoking was the only prenatal exposure variable associated with higher child BMI (z-score increase of 0.28, 95% confidence interval: 0.09, 0.48, for active vs. no smoking). For childhood exposures, the multiexposure model identified particulate and nitrogen dioxide air pollution inside the home, urine cotinine levels indicative of secondhand smoke exposure, and residence in more densely populated areas and in areas with fewer facilities to be associated with increased child BMI. Child blood levels of copper and cesium were associated with higher BMI, and levels of organochlorine pollutants, cobalt, and molybdenum were associated with lower BMI. Similar results were found for the other adiposity outcomes. DISCUSSION:This first comprehensive and systematic analysis of many suspected environmental obesogens strengthens evidence for an association of smoking, air pollution exposure, and characteristics of the built environment with childhood obesity risk. Cross-sectional biomarker results may suffer from reverse causality bias, whereby obesity status influenced the biomarker concentration. https://doi.org/10.1289/EHP5975.
| S-EPMC7313401 | biostudies-literature
Project description: Not available
| S-EPMC8249342 | biostudies-literature
Project description:BackgroundPhysiologically-based pharmacokinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics by continuous knowledge integration.ObjectiveThe objective of this study was to build a ciprofloxacin PBPK model for intravenous and oral dosing based on a comprehensive literature review, and evaluate the predictive performance towards pediatric and geriatric patients.MethodsThe aim of this report was to establish confidence in simulations of the ciprofloxacin PBPK model along the development process to facilitate reliable predictions outside of the tested adult age range towards the extremes of ages. Therefore, mean data of 69 published clinical trials were identified and integrated into the model building, simulation and verification process. The predictive performance on both ends of the age scale was assessed using individual data of 258 subjects observed in own clinical trials.ResultsCiprofloxacin model verification demonstrated no concentration-related bias and accurate simulations for the adult age range, with only 4.8% of the mean observed data points for intravenous administration and 12.1% for oral administration being outside the simulated twofold range. Predictions towards the extremes of ages for the area under the plasma concentration-time curve (AUC) and the maximum plasma concentration (Cmax) over the entire span of life revealed a reliable estimation, with only two pediatric AUC observations outside the 90% prediction interval.ConclusionOverall, this ciprofloxacin PBPK modeling approach demonstrated the predictive power of a thoroughly informed middle-out approach towards age groups of interest to potentially support the decision-making process.
| S-EPMC6267540 | biostudies-literature
Project description:There is a growing literature that suggests environmental exposure during key developmental periods could have harmful impacts on growth and development of humans. Understanding and estimating the relationship between early-life exposure and human growth is vital to studying the adverse health impacts of environmental exposure. We compare two statistical tools, mixed-effects models with interaction terms and growth mixture models, used to measure the association between exposure and change over time within the context of non-linear growth and non-monotonic relationships between exposure and growth. We illustrate their strengths and weaknesses through a real data example and simulation study. The data example, which focuses on the relationship between phthalates and the body mass index growth of children, indicates that the conclusions from the two models can differ. The simulation study provides a broader understanding of the robustness of these models in detecting the relationships between any exposure and growth that could be observed. Data-driven growth mixture models are more robust to non-monotonic growth and stochastic relationships but at the expense of interpretability. We offer concrete modeling strategies to estimate complex relationships with growth patterns.
| S-EPMC6296561 | biostudies-literature