Exposure Profiles Research Articles

Abstract 5087: ILB-3101, a novel B7H3-targeting ADC with Eribulin as payload, demonstrates strong tumor killing activities and favorable PK/TOX profile in preclinical evaluation

Abstract B7H3, a B7 family member of immune checkpoint proteins, is overexpressed in a broad spectrum of malignant tumors. It has become an attractive target for cancer therapies in recent years. ILB-3101, a potential best-in-class ADC, was constructed by conjugating a potent mitotic inhibitor Eribulin to a proprietary anti-B7H3 mAb through a proprietary linker. This ADC molecule has been developed and preclinically evaluated as both the 1st line ADC treatment with B7H3 expressed patients in Eribulin sensitive cancer types and the later line treatment for patients whose cancer are B7H3 positive but TopoI inhibitor resistant. The humanized antibody of ILB-3101 is IgG1 type and shows high affinity to both human and monkey B7H3 proteins. It also shows better internalization than benchmark antibodies such as that of DS-7300 analog. The DAR value of ILB-3101 is 3.5. ILB-3101 has potent and dose-dependent cytotoxicity to B7H3-positive tumor cells and shows better activities than DS-7300 analog as positive control. In vivo studies based on xenograft tumor models reveal that ILB-3101 has potent anti-tumor activities in various tumor types including TNBC, sarcoma, ovarian cancer and AML etc., which are superior or comparable to DS-7300 analog. Furthermore, ILB-3101 shows strong anti-tumor activity on Dxd based ADC resistant tumors in xenograft tumor models. The PK study of ILB-3101 in cynomolgus monkeys shows dose-dependent exposure and desirable PK profile. ILB-3101 was well tolerated in cynomolgus monkeys with HNSTD at 6 mg/kg. No serious adverse events were observed, and the main safety findings were limited and reversible hematological and immunological effects, which are consistent with the safety profile of Eribulin as an approved chemotherapy. The in vitro and in vivo preclinical results above demonstrate that ILB-3101 has good efficacy and safety profile and it is a promising and potentially best-in-class B7H3 targeting ADC drug candidate. ILB-3101 is worthy of further translational research and clinical trials. Its IND application has been submitted in China, followed by US FDA filing soon. Citation Format: Yushuan Shang, Junzhuan Qiu, Yanfei Xin, Min Zhao, Qiang Liu, Zhengsheng Wang, Xiaojing Gong, Yan Xu, Yan Li, Mingde Xia, Rulei Chen. ILB-3101, a novel B7H3-targeting ADC with Eribulin as payload, demonstrates strong tumor killing activities and favorable PK/TOX profile in preclinical evaluation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5087.

Expected Spesolimab Plasma Exposure following Intravenous and Subcutaneous Dosing in Patients with Generalized Pustular Psoriasis

Introduction: Generalized pustular psoriasis (GPP) is a chronic, potentially life-threatening inflammatory skin disease characterized by widespread flares of sterile pustules. Spesolimab is a first-in-class anti-interleukin-36 receptor monoclonal antibody approved to treat GPP flares in adults via intravenous (IV) infusion. The aim of this study was to simulate the plasma pharmacokinetics (PK) of IV versus subcutaneous (SC) doses of spesolimab to compare drug exposure profiles and support dosing strategies in patients with GPP.
 Methods: A population PK model was developed using individual-level PK, anti-drug antibody, and covariate data from eighteen studies in which subjects were treated with IV or SC spesolimab.1 The resulting PK model was used to simulate concentration-time profiles following varying doses of IV and SC spesolimab. Peak plasma concentration (Cmax), time to peak plasma concentration (Tmax), and area under the curve (AUC) were summarized for each dose regimen.
 Results: Simulated spesolimab exposures demonstrated that the Cmax and AUC of the single dose 900 mg IV infusion consistently exceeded that of various single doses of SC spesolimab tested. A SC dose greater than two-fold higher would be required to attain a Cmax comparable to that of spesolimab 900 mg IV. The difference in exposure between the two administration routes was most pronounced in the first 2 weeks after administration. Slow absorption is expected with SC injection, with Tmax attained at later time points after SC dosing compared to immediately following the 90-minute infusion for IV dosing. Results were the same when simulating a two-dose regimen administered one week apart.
 Conclusions: PK simulations suggest that treatment with IV and SC spesolimab can result in differences in drug exposure in clinical practice. The immediate and high exposure of IV spesolimab compared with the lower exposure of SC spesolimab are supportive of IV dosing with spesolimab in acute GPP flare treatment vs SC dosing.

Population pharmacokinetic analyses of methotrexate in pediatric patients: a systematic review.

Methotrexate is widely utilized in the chemotherapy of malignant tumors and autoimmune diseases in the pediatric population, but dosing can be challenging. Several population pharmacokinetic models were developed to characterize factors influencing variability and improve individualization of dosing regimens. However, significant covariates included varied across studies. The primary objective of this review was to summarize and discuss population pharmacokinetic models of methotrexate and covariates that influence pharmacokinetic variability in pediatric patients. Systematic searches were conducted in the PubMed and EMBASE databases from inception to 7 July 2023. Reporting Quality was evaluated based on a checklist with 31 items. The characteristics of studies and information for model construction and validation were extracted, summarized, and discussed. Eighteen studies (four prospective studies and fourteen retrospective studies with sample sizes of 14 to 772 patients and 2.7 to 93.1 samples per patient) were included in this study. Two-compartment models were the commonly used structural models for methotrexate, and the clearance range of methotrexate ranged from 2.32 to 19.03L/h (median: 6.86L/h). Body size and renal function were found to significantly affect the clearance of methotrexate for pediatric patients. There were limited reports on the role of other covariates, such as gene polymorphisms and co-medications, in the pharmacokinetic parameters of methotrexate pediatric patients. Internal and external evaluations were used to assess the performance of the population pharmacokinetic models. A more rigorous external evaluation needs to be performed before routine clinical use to select the appropriate PopPK model. Further research is necessary to incorporate larger cohorts or pool analyses in specific susceptible pediatric populations to improve the understanding of predicted exposure profiles and covariate identification.

Hair-Derived Exposome Exploration of Cardiometabolic Health: Piloting a Bayesian Multitrait Variable Selection Approach.

Cardiometabolic health is complex and characterized by an ensemble of correlated and/or co-occurring conditions including obesity, dyslipidemia, hypertension, and diabetes mellitus. It is affected by social, lifestyle, and environmental factors, which in-turn exhibit complex correlation patterns. To account for the complexity of (i) exposure profiles and (ii) health outcomes, we propose to use a multitrait Bayesian variable selection approach and identify a sparse set of exposures jointly explanatory of the complex cardiometabolic health status. Using data from a subset (N = 941 participants) of the nutrition, environment, and cardiovascular health (NESCAV) study, we evaluated the link between measurements of the cumulative exposure to (N = 33) pollutants derived from hair and cardiometabolic health as proxied by up to nine measured traits. Our multitrait analysis showed increased statistical power, compared to single-trait analyses, to detect subtle contributions of exposures to a set of clinical phenotypes, while providing parsimonious results with improved interpretability. We identified six exposures that were jointly explanatory of cardiometabolic health as modeled by six complementary traits, of which, we identified strong associations between hexachlorobenzene and trifluralin exposure and adverse cardiometabolic health, including traits of obesity, dyslipidemia, and hypertension. This supports the use of this type of approach for the joint modeling, in an exposome context, of correlated exposures in relation to complex and multifaceted outcomes.

Atherogenic Index as a Cardiovascular Biomarker in Mexican Workers from Marginalized Urban Areas Occupationally Exposed to Metals

Cardiovascular disease (CVD) is one of the main causes of death and disability worldwide. The etiology of CVD is often associated with multiple risk factors, with environmental factors receiving considerable attention. Individuals with precarious jobs are among the groups most affected by chronic exposure to environmental pollutants. This study aimed to evaluate occupational exposure to heavy metals among individuals in precarious job settings and investigate atherogenic indices as biomarkers of cardiovascular risk. A total of 137 workers participated in this cross-sectional study conducted in three work environments in San Luis Potosi, Mexico. Urine and blood samples were collected to assess metal exposure and biochemical profiles, including atherogenic indices. The results showed that workers in the brick sector exhibited the highest levels of metal exposure, particularly arsenic (44.06 µg/L), followed by stonecutters and garbage collectors (24.7 and 16.9 µg/L, respectively). Similarly, Castelli risk index (CRI) and the atherogenic index of plasma (AIP) were higher in brickmakers (3.883 and 0.499) compared to stonecutters (3.285 and 0.386) and garbage collectors (3.329 and 0.367). Evidence of exposure to heavy metals was observed in the three populations, in addition to the fact that individuals with greater exposure to arsenic also exhibited higher CRI and AIP.

Short- and medium-chain chlorinated paraffins in breast milk in Shanghai, China: Occurrence, characteristics, and risk assessment

As novel contaminants, short-chain chlorinated paraffins (SCCPs) and medium-chain chlorinated paraffins (MCCPs) have been of great concern in the past several years. Shanghai was one of the provinces with the largest chlorinated paraffins (CPs) emission in China; nevertheless, there is currently little information on the human exposure to SCCPs and MCCPs, particularly MCCPs. In this study, 25 breast milk samples were collected in Shanghai from 2016 to 2017. The concentrations of SCCPs and MCCPs were determined using two-dimensional gas chromatography coupled with orbitrap high-resolution mass spectrometry (GC × GC-orbitrap-HRMS) to investigate their characteristics and assess the associated health risks for breast-fed infants. Compared with the previous studies in other areas, the current study presented the higher CPs concentrations, with median concentrations of SCCPs and MCCPs up to 771 and 125 ng/g lipid weight (lw), respectively. The exposure profiles of the CPs were characterized by C10 and Cl6-7 as the predominant congeners of SCCPs, while C14 and Cl7–9 were identified as the dominant groups of MCCPs. CP-42 and CP-52 were identified as potential sources of CPs found in breast milk samples collected in Shanghai. The concentrations of MCCPs exhibited a positive correlation (p value < 0.05) with the dietary consumption of meat and poultry. No significant positive correlations were observed for SCCPs and MCCPs with polychlorinated dibenzodioxins/furans (PCDD/Fs) congeners. A preliminary exposure assessment showed that SCCPs in breast milk potentially posed high risks to the breast-fed infants in Shanghai.

EFFICIENT WRONG-WAY RISK MODELING FOR FUNDING VALUATION ADJUSTMENTS

Wrong-Way Risk (WWR) is an important component in Funding Valuation Adjustment (FVA) modeling. Yet, the standard assumption is independence between market risks and the counterparty defaults and funding costs. This typical industrial setting is our point of departure, where we aim to assess the impact of WWR without running a full Monte Carlo simulation with all credit and funding processes. We propose to split the exposure profile into two parts: an independent and a WWR-driven part. For the former, exposures can be re-used from the standard xVA calculation. We express the second part of the exposure profile in terms of the stochastic drivers and approximate these by a common Gaussian stochastic factor. Within the affine setting, the proposed approximation is generic, is an add-on to the existing xVA calculations and provides an efficient and robust way to include WWR in FVA modeling. Case studies for an interest rate swap and a representative multi-currency portfolio of swaps illustrate that the approximation method is applicable in a practical setting. We analyze the approximation error and use the approximation to compute WWR sensitivities, which are needed for risk management. The approach is equally applicable to other metrics such as Credit Valuation Adjustment.

Bayesian adaptive and interpretable functional regression for exposure profiles

Bayesian adaptive and interpretable functional regression for exposure profiles

Association between exposure to per- and polyfluoroalkyl substances and levels of lipid profile based on human studies.

Epidemiological evidence suggests that exposure to per- and polyfluoroalkyl substances (PFAS) is associated with lipid profile levels, but with inconsistent conclusions from different studies. The aim of this study was to conduct a meta-analysis of the relationship between PFAS exposure and lipid profile levels based on population-based epidemiological studies. Embase, PubMed, Ovid database, The Cochrane Library and Web of Science database were used to search appropriate studies (before September 6, 2022) on the correlation between PFAS exposure and lipid profile levels. βvalue, odd ratio (OR) and 95 % confidence intervals (CIs) were extracted from studies. In this study, we found that higher low-density lipoprotein (LDL) levels were associated with exposure to perfluoroundecanoic acid (PFUnDA) (βvalue=0.13, 95 % CIs: 0.02, 0.24) and perfluorooctane sulfonic acid (PFOS) (β value=0.13, 95 % CIs: 0.04, 0.21). PFOA, PFOS and PFNA exposure were significantly related to the higher levels of total cholesterol (TC) with the pooled effect estimates of 0.08 (95 % CI: 0.02, 0.14), 0.13 (95 % CI: 0.05, 0.21) and 0.14 (95 % CI: 0.08, 0.20) respectively. In sum, our results identified that PFOA, PFOS, PFNA and PFUnDA were the most important risk factors for abnormal levels of lipid profile, indicating that we should prevent cerebrovascular disease by reducing and controlling PFAS exposure.

High pesticide exposure and risk to bees in pollinator plantings adjacent to conventionally managed blueberry fields

Wildflower plantings adjacent to agricultural fields provide diverse floral resources and nesting sites for wild bees. However, their proximity to pest control activities in the crop may result in pesticide exposure if pesticides drift into pollinator plantings. To quantify pesticide residues in pollinator plantings, we sampled flowers and soil from pollinator plantings and compared them to samples from unenhanced field margins and crop row middles. At conventionally managed farms, flowers from pollinator plantings had similar exposure profiles to those from unenhanced field margins or crop row middles, with multiple pesticides and high and similar risk quotient (RQ) values (with pollinator planting RQ: 3.9; without pollinator planting RQ: 4.0). Whereas samples from unsprayed sites had significantly lower risk (RQ: 0.005). Soil samples had overall low risk to bees. Additionally, we placed bumble bee colonies (Bombus impatiens) in field margins of crop fields with and without pollinator plantings and measured residues in bee-collected pollen. Pesticide exposure was similar in pollen from sites with or without pollinator plantings, and risk was generally high (with pollinator planting RQ: 0.5; without pollinator planting RQ: 1.1) and not significant between the two field types. Risk was lower at sites where there was no pesticide activity (RQ: 0.3), but again there was no significant difference between management types. The insecticide phosmet, which is used on blueberry farms for control of Drosophila suzukii, accounted for the majority of elevated risk. Additionally, analysis of pollen collected by bumble bees found no significant difference in floral species richness between sites with or without pollinator plantings. Our results suggest that pollinator plantings do not reduce pesticide risk and do not increase pollen diversity collected by B. impatiens, further highlighting the need to reduce exposure through enhanced IPM adoption, drift mitigation, and removal of attractive flowering weeds prior to insecticide applications.

Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA

Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017–2019), and the last five years (2014–2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health.

Integrated Analysis of Per- and Polyfluoroalkyl Substance Exposure and Metabolic Profiling of Elderly Residents Living near Industrial Plants.

Per- and polyfluoroalkyl substances (PFASs) are widely used in industrial production, causing potential health risks to the residents living around chemical industrial plants; however, the lack of data on population exposure and adverse effects impedes our understanding and ability to prevent risks. In this study, we performed screening and association analysis on exogenous PFAS pollutants and endogenous small-molecule metabolites in the serum of elderly residents living near industrial plants. Exposure levels of 11 legacy and novel PFASs were determined. PFOA and PFOS were major contributors, and PFNA, PFHxS, and 6:2 Cl-PFESA also showed high detection frequencies. Association analysis among PFASs and 287 metabolites identified via non-target screening was performed with adjustments of covariates and false discovery rate. Strongly associated metabolites were predominantly lipid and lipid-like molecules. Steroid hormone biosynthesis, primary bile acid biosynthesis, and fatty-acid-related pathways, including biosynthesis of unsaturated fatty acids, linoleic acid metabolism, α-linolenic acid metabolism, and fatty acid biosynthesis, were enriched as the metabolic pathways associated with mixed exposure to multiple PFASs, providing metabolic explanation and evidence for the potential mediating role of adverse health effects as a result of PFAS exposure. Our study achieved a comprehensive screening of PFAS exposure and associated metabolic profiling, demonstrating the promising application for integrated analysis of exposome and metabolome.

Exposure of individuals aged 18–44 years to personal care products in Beijing, China: Exposure profiles, possible influencing factors, and risk assessment

Personal care products (PCPs) are a class of emerging pollutants that have attracted public concern owing to their harmful effects on humans and the environment. Biomonitoring data is valuable for insight the levels of PCPs in the human body and can be crucial for identifying potential health hazards. To gain a better understanding of timely exposure profiles and health risk of reproductive-age population to PCPs, we determined six parabens, six benzophenone-type ultraviolet filters, and three disinfectants in 256 urine samples collected from young adults aged 18–44 years in Beijing, China. The urinary levels of benzophenone-3 (BP-3) and 4-hydroxybenzophenone (4-OHBP) were significantly higher in summer compared to winter, suggesting these compounds have different seasonal usage patterns. Moreover, the total concentration of 15 PCPs in female was 430 ng/mL, approximately two times higher than that in male. P‑chloro-m-xylenol (PCMX), as a new type of antibacterial agent, has the greatest level among all target analytes, indicating the increasingly use of this antibacterial alternative recently. Five potential influencing factors that lead to the elevated exposure level of PCPs were identified. Over 19% of the target population had a high hazard index value (greater than 1) which was attributed to exposure to propyl paraben (PrP), benzophenone-1 (BP-1), BP-3 and PCMX, indicating that PCPs may pose a relatively high exposure risk at environmental levels that should be a cause for concern.

Daily light exposure profiles and the association with objective sleep quality in patients with Parkinson's disease: The PHASE study.

Light information crucially influences sleep initiation and continuity. The purpose of this study was to compare daily light exposure between patients with Parkinson's disease (PD) and non-PD older adults and evaluate the association of daily light exposure with objective sleep measures in patients with PD. In this cross-sectional study of 189 outpatients with PD and 1101 community-dwelling older adults (controls), daily light exposure was measured using wrist light meters during the daytime and light meters set in the bedrooms during the nighttime, and objective sleep quality was measured by wrist actigraphy. The median duration of exposure to ≥ 1000 lux light was significantly shorter in patients with PD than in controls. The median nighttime light intensity was higher in patients with PD than in controls. Among patients with PD, multivariable analysis suggested that the highest quartile of exposure to ≥ 1000 lux light during the daytime was linked to significantly higher sleep efficiency (SE) by 8.0% and shorter wake after sleep onset (WASO) by 36.9 minutes than the lowest quartile. During the nighttime, the highest quartile of mean light intensity had significantly lower SE by 6.8%, longer WASO by 24.1 minutes, longer sleep onset latency, and higher fragmentation index, than the lowest quartile. Importantly, daytime and nighttime light levels were independently associated with objective sleep measures. The present study illustrated that greater daytime light exposure and lower nighttime light exposure are significantly associated with better objective sleep measures in patients with PD.

Modelling patient drug exposure profiles in vitro to narrow the valley of death.

Modelling patient drug exposure profiles in vitro to narrow the valley of death.

National‐Scale Rainfall‐Triggered Landslide Susceptibility and Exposure in Nepal

AbstractNepal is one of the most landslide‐prone countries in the world, with year‐on‐year impacts resulting in loss of life and imposing a chronic impediment to sustainable livelihoods. Living with landslides is a daily reality for an increasing number of people, so establishing the nature of landslide hazard and risk is essential. Here we develop a model of landslide susceptibility for Nepal and use this to generate a nationwide geographical profile of exposure to rainfall‐triggered landslides. We model landslide susceptibility using a fuzzy overlay approach based on freely‐available topographic data, trained on an inventory of mapped landslides, and combine this with high resolution population and building data to describe the spatial distribution of exposure to landslides. We find that whilst landslide susceptibility is highest in the High Himalaya, exposure is highest within the Middle Hills, but this is highly spatially variable and skewed to on average relatively low values. Around 4 × 106 Nepalis (∼15% of the population) live in areas considered to be at moderate or higher degree of exposure to landsliding (&gt;0.25 of the maximum), and critically this number is highly sensitive to even small variations in landslide susceptibility. Our results show a complex relationship between landslides and buildings, that implies wider complexity in the association between physical exposure to landslides and poverty. This analysis for the first time brings into focus the geography of the landslide exposure and risk case load in Nepal, and demonstrates limitations of assessing future risk based on limited records of previous events.

Evaluating the association between longitudinal exposure to a PFAS mixture and adolescent cardiometabolic risk in the HOME Study.

Exposure to per- and polyfluoroalkyl substances (PFAS) throughout gestation and childhood may impact cardiometabolic risk. In 179 HOME Study participants (Cincinnati, OH; recruited 2003-2006), we used latent profile analysis to identify two distinct patterns of PFAS exposure from serum concentrations of four PFAS measured at birth and ages 3, 8, and 12 years. We assessed the homeostatic model of insulin resistance, triglycerides-to-high-density lipoprotein cholesterol ratio, leptin-to-adiponectin ratio, systolic blood pressure, visceral fat, and hemoglobin A1c levels at age 12 years. We used multivariable linear regression to assess the association of membership in the longitudinal PFAS mixture exposure group with a summary measure of overall cardiometabolic risk and individual components. One PFAS exposure profile (n = 66, 39%) had higher geometric means of all PFAS across all visits than the other. Although adjusted associations were null in the full sample, child sex modified the association of longitudinal PFAS mixture exposure group with overall cardiometabolic risk, leptin-to-adiponectin ratio, systolic blood pressure, and visceral fat (interaction term P values: 0.02-0.08). Females in the higher exposure group had higher cardiometabolic risk scores (ß = 0.43; 95% CI = -0.08, 0.94), systolic blood pressures (ß = 0.6; 95% CI = 0.1, 1.1), and visceral fat (ß = 0.44; 95% CI = -0.13, 1.01); males had lower cardiometabolic risk scores (ß = -0.52; 95% CI = -1.06, -0.06), leptin-to-adiponectin ratios (ß = -0.7; 95% CI = -1.29, -0.1), systolic blood pressures (ß = -0.14; 95% CI = -0.7, 0.41), and visceral fat (ß = -0.52; 95% CI = -0.84, -0.19). Exposure to this PFAS mixture throughout childhood may have sex-specific effects on adolescent cardiometabolic risk.

Association between dietary pesticide exposure profiles and body weight change in French adults: Results from the NutriNet-Santé cohort

BackgroundPesticides cause a wide range of deleterious health effects, including metabolic disorders. Little is known about the effects of dietary pesticide exposure on body weight (BW) change in the general population.We aimed to investigate the role of dietary pesticide exposure in BW change among NutriNet-Santé participants, focusing on potential sexual dimorphism. MethodsParticipants completed a Food Frequency Questionnaire (2014), assessing conventional and organic food consumption. Dietary exposure from plant foods of 25 commonly used pesticides was estimated using a residue database, accounting for agricultural practices (conventional and organic).Exposure profiles based on dietary patterns were computed using Non-negative Matrix Factorization (NMF). Mixed models were used to estimate the associations between BW change and exposure to pesticide mixtures, overall and after stratification by sex and menopausal status. ResultsThe final sample included 32,062 participants (8,211 men, 10,637 premenopausal, and 13,214 postmenopausal women). The median (IQR) follow-up was 7.0 (4.4; 8.0) years. Four pesticides profiles were inferred.Overall, men and postmenopausal women lost BW during follow-up, whereas premenopausal women gained BW.Higher exposure to NMF3, reflecting a lower exposure to synthetic pesticides, was associated with a lower BW gain, especially in premenopausal women (β(95 %CI) = -0.04 (-0.07; 0) kg/year, p = 0.04).Higher exposure to NMF2, highly positively correlated with a mixture of synthetic pesticides (azoxystrobin, boscalid, chlorpropham, cyprodinil, difenoconazole, fenhexamid, iprodione, tebuconazole, and lamda-cyhalothrin), was associated with a higher BW loss in men (β(95 %CI) = -0.05 (-0.08; −0.03) kg/year, p < 0.0001). No associations were observed for NMF1 and 4. ConclusionsThis study suggests a role of pesticide exposure, inferred from dietary patterns, on BW change, with sexually dimorphic actions, including a potential role of a lower exposure to synthetic pesticides on BW change in women. In men, exposure to a specific pesticide mixture was associated with higher BW loss. The underlying mechanisms need further elucidation.

Population pharmacokinetics and humanized dosage regimens matching the peak, area, trough, and range of amikacin plasma concentrations in immune-competent murine bloodstream and lung infection models.

Amikacin is an FDA-approved aminoglycoside antibiotic that is commonly used. However, validated dosage regimens that achieve clinically relevant exposure profiles in mice are lacking. We aimed to design and validate humanized dosage regimens for amikacin in immune-competent murine bloodstream and lung infection models of Acinetobacter baumannii. Plasma and lung epithelial lining fluid (ELF) concentrations after single subcutaneous doses of 1.37, 13.7, and 137 mg/kg of body weight were simultaneously modeled via population pharmacokinetics. Then, humanized amikacin dosage regimens in mice were designed and prospectively validated to match the peak, area, trough, and range of plasma concentration profiles in critically ill patients (clinical dose: 25-30 mg/kg of body weight). The pharmacokinetics of amikacin were linear, with a clearance of 9.93 mL/h in both infection models after a single dose. However, the volume of distribution differed between models, resulting in an elimination half-life of 48 min for the bloodstream and 36 min for the lung model. The drug exposure in ELF was 72.7% compared to that in plasma. After multiple q6h dosing, clearance decreased by ~80% from the first (7.35 mL/h) to the last two dosing intervals (~1.50 mL/h) in the bloodstream model. Likewise, clearance decreased by 41% from 7.44 to 4.39 mL/h in the lung model. The humanized dosage regimens were 117 mg/kg of body weight/day in mice [administered in four fractions 6 h apart (q6h): 61.9%, 18.6%, 11.3%, and 8.21% of total dose] for the bloodstream and 96.7 mg/kg of body weight/day (given q6h as 65.1%, 16.9%, 10.5%, and 7.41%) for the lung model. These validated humanized dosage regimens and population pharmacokinetic models support translational studies with clinically relevant amikacin exposure profiles.

Model-based learn and confirm: designing effective treatment regimens against multidrug resistant Gram-negative pathogens

Over the last decade, there has been a growing appreciation for the use of in vitro and in vivo infection models to generate robust and informative nonclinical PK/PD data to accelerate the clinical translation of treatment regimens. The objective of this study was to develop a model-based “learn and confirm” approach to help with the design of combination regimens using in vitro infection models to optimise the clinical utility of existing antibiotics. Static concentration time-kill studies were used to evaluate the PD activity of polymyxin B (PMB) and meropenem against two carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates; BAA2146 (PMB-susceptible) and BRKP67 (PMB-resistant). A mechanism-based model (MBM) was developed to quantify the joint activity of PMB and meropenem. In silico simulations were used to predict the time-course of bacterial killing using clinically-relevant PK exposure profiles. The predictive accuracy of the model was further evaluated by validating the model predictions using a one-compartment PK/PD in vitro dynamic infection model (IVDIM). The MBM captured the reduction in bacterial burden and regrowth well in both the BAA2146 and BRKP67 isolate (R2 = 0.900 and 0.940, respectively). The bacterial killing and regrowth predicted by the MBM were consistent with observations in the IVDIM: sustained activity against BAA2146 and complete regrowth of the BRKP67 isolate. Differences observed in PD activity suggest that additional dose optimisation might be beneficial in PMB-resistant isolates. The model-based approach presented here demonstrates the utility of the MBM as a translational tool from static to dynamic in vitro systems to effectively perform model-informed drug optimisation.