A preliminary regional PBPK model of lung metabolism for improving species dependent descriptions of 1,3-butadiene and its metabolites

Urinary total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanonol is positively associated with tooth loss

BACKGROUND/AIM

Epidemiological studies had confirmed a fundamental association between smoking and tooth loss, but it remains unclear whether metabolites of tobacco products such as total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanonol (TNNAL) play a role in the incidence and progress of tooth loss. This study aims to investigate the relationship between TNNAL and tooth loss as well as how systemic inflammatory indexes mediate this process.

METHODS

The cross-sectional study data were collected from the National Health and Nutrition Examination Survey conducted in the United States. After screening and comparing the baseline data, zero-inflated negative binomial regression models were utilized to evaluate the relationship between urinary TNNAL level and missing teeth among whole population and participants with different smoking status. Furthermore, bootstrapping was applied to test the mediation effect of systemic inflammatory indexes between TNNAL level and missing teeth.

RESULTS

7726 participants were included, having 2958 individuals belonging to the TNNAL = 0 group and 4768 in the TNNAL > 0 group. In the model with covariates fully adjusted (model 3) among whole population, TNNAL level was found to be positively correlated with tooth loss [Incidence Rate Ratio (IRR) = 1.107, 95% confidence interval (95% CI) = 1.074-1.140], especially in the fourth quartile (Q4) of TNNAL level (IRR = 1.715; 95%CI = 1.535-1.916) compared to the Q1. Red blood cell distribution width (RDW) and monocyte/highdensity lipoprotein cholesterol ratio (MHR) played partial mediating role in the association between TNNAL and tooth loss, and the indirect effect of each was 0.0242 (RDW, 95%CI = 0.0076-0.0612) and 0.0151 (MHR, 95%CI = 0.0034-0.0426), respectively. The mediating effect was 0.393 (95%CI = 0.0179-0.958). In the regression model 3 among group of TNNAL > 0, higher concentration of urinary TNNAL was associated with increasing tooth loss (IRR = 1.079, 95%CI = 1.047-1.112). When group of TNNAL > 0 was further divided into subgroups according to the smoking status, a positive correlation was found between TNNAL and missing teeth among current active-smokers (Model 3: IRR = 1.508, 95%CI = 1.341-1.696), as well as passive former-smokers (Model 3: IRR = 1.127, 95%CI = 1.021-1.243).

CONCLUSIONS

Our study revealed a positive relationship between urinary TNNAL and tooth loss, and further demonstrated the mediating role of RDW and MHR between the TNNAL and the number of missing teeth in the whole popualtion. These findings will provide new theoretical insights for policy formulation and clinical therapeutic for the target prevention and intervention of related diseases.

Exhaled breath analysis for the discrimination of asthma and chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) and asthma are the most common chronic respiratory diseases. In middle-aged and elderly patients, it is difficult to distinguish between COPD and asthma based on clinical symptoms and pulmonary function examinations in clinical practice. Thus, an accurate and reliable inspection method is required. In this study, we aimed to identify breath biomarkers and evaluate the accuracy of breathomics-based methods for discriminating between COPD and asthma. In this multi-center cross-sectional study, exhaled breath samples were collected from 89 patients with COPD and 73 with asthma and detected on a high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform from 20 October 2022, to 20 May 2023, in four hospitals. Data analysis was performed from 15 June 2023 to 16 August 2023. The sensitivity, specificity, and accuracy were calculated to assess the overall performance of the volatile organic component (VOC)-based COPD and asthma discrimination models. Potential VOC markers related to COPD and asthma were also analyzed. The age of all participants ranged from to 18-86 years, and 54 (33.3%) were men. The age [median (minimum, maximum)] of COPD and asthma participants were 66.0 (46.0, 86.0), and 44.0 (17.0, 80.0). The male and female ratio of COPD and asthma participants were 14/75 and 40/33, respectively. Based on breathomics feature selection, ten VOCs were identified as COPD and asthma discrimination biomarkers via breath testing. The joint panel of these ten VOCs achieved an area under the curve of 0.843, sensitivity of 75.9%, specificity of 87.5%, and accuracy of 80.0% in COPD and asthma discrimination. Furthermore, the VOCs detected in the breath samples were closely related to the clinical characteristics of COPD and asthma. The VOC-based COPD and asthma discrimination model showed good accuracy, providing a new strategy for clinical diagnosis. Breathomics-based methods may play an important role in the diagnosis of COPD and asthma.

Use of quantitative in vitro to in vivo extrapolation (QIVIVE) for the assessment of non-combustible next-generation product aerosols

With the use of in vitro new approach methodologies (NAMs) for the assessment of non-combustible next-generation nicotine delivery products, new extrapolation methods will also be required to interpret and contextualize the physiological relevance of these results. Quantitative in vitro to in vivo extrapolation (QIVIVE) can translate in vitro concentrations into in-life exposures with physiologically-based pharmacokinetic (PBPK) modelling and provide estimates of the likelihood of harmful effects from expected exposures. A major challenge for evaluating inhalation toxicology is an accurate assessment of the delivered dose to the surface of the cells and the internalized dose. To estimate this, we ran the multiple-path particle dosimetry (MPPD) model to characterize particle deposition in the respiratory tract and developed a PBPK model for nicotine that was validated with human clinical trial data for cigarettes. Finally, we estimated a Human Equivalent Concentration (HEC) and predicted plasma concentrations based on the minimum effective concentration (MEC) derived after acute exposure of BEAS-2B cells to cigarette smoke (1R6F), or heated tobacco product (HTP) aerosol at the air liquid interface (ALI). The MPPD-PBPK model predicted the in vivo data from clinical studies within a factor of two, indicating good agreement as noted by WHO International Programme on Chemical Safety (2010) guidance. We then used QIVIVE to derive the exposure concentration (HEC) that matched the estimated in vitro deposition point of departure (POD) (MEC cigarette = 0.38 puffs or 11.6 µg nicotine, HTP = 22.9 puffs or 125.6 µg nicotine) and subsequently derived the equivalent human plasma concentrations. Results indicate that for the 1R6F cigarette, inhaling 1/6th of a stick would be required to induce the same effects observed in vitro, in vivo. Whereas, for HTP it would be necessary to consume 3 sticks simultaneously to induce in vivo the effects observed in vitro. This data further demonstrates the reduced physiological potency potential of HTP aerosol compared to cigarette smoke. The QIVIVE approach demonstrates great promise in assisting human health risk assessments, however, further optimization and standardization are required for the substantiation of a meaningful contribution to tobacco harm reduction by alternative nicotine delivery products.

Using available in vitro metabolite identification and time course kinetics for β-chloroprene and its metabolite, (1-chloroethenyl) oxirane, to include reactive oxidative metabolites and glutathione depletion in a PBPK model for β-chloroprene

Introduction: ß-chloroprene (2-chloro-1,3-butadiene; CP) causes lung tumors after inhalation exposures in rats and mice. Mice develop these tumors at lower exposures than rats. In rats CP exposures cause depletion of lung glutathione (GSH). Methods: PBPK models developed to relate the appearance of mouse lung tumors with rates of CP metabolism to reactive metabolites or total amounts metabolized during exposures have been expanded to include production of reactive metabolites from CP. The extended PBPK model describes both the unstable oxirane metabolite, 2-CEO, and metabolism of the more stable oxirane, 1-CEO, to reactive metabolites via microsomal oxidation to a diepoxide, and linked production of these metabolites to a PK model predicting GSH depletion with increasing CP exposure. Key information required to develop the model were available from literature studies identifying: 1) microsomal metabolites of CP, and 2) in vitro rates of clearance of CP and 1-CEO from active microsomal preparations from mice, rats, hamsters and humans. Results: Model simulation of concentration dependence of disproportionate increases in reactive metabolite concentrations as exposures increases and decreases in tissue GSH are consistent with the dose-dependence of tumor formation. At the middle bioassay concentrations with a lung tumor incidence, the predicted tissue GSH is less than 50% background. These simulations of reduction in GSH are also consistent with the gene expression results showing the most sensitive pathways are Nrf2-regulation of oxidative stress and GSH metabolism. Discussion: The PBPK model is used to correlate predicted tissue exposure to reactive metabolites with toxicity and carcinogenicity of CP.

Application of Minimal Physiologically-Based Pharmacokinetic Model to Simulate Lung and Trachea Exposure of Pyronaridine and Artesunate in Hamsters

A fixed-dose combination of pyronaridine and artesunate, one of the artemisinin-based combination therapies, has been used as a potent antimalarial treatment regimen. Recently, several studies have reported the antiviral effects of both drugs against severe acute respiratory syndrome coronavirus two (SARS-CoV-2). However, there are limited data on the pharmacokinetics (PKs), lung, and trachea exposures that could be correlated with the antiviral effects of pyronaridine and artesunate. The purpose of this study was to evaluate the pharmacokinetics, lung, and trachea distribution of pyronaridine, artesunate, and dihydroartemisinin (an active metabolite of artesunate) using a minimal physiologically-based pharmacokinetic (PBPK) model. The major target tissues for evaluating dose metrics are blood, lung, and trachea, and the nontarget tissues were lumped together into the rest of the body. The predictive performance of the minimal PBPK model was evaluated using visual inspection between observations and model predictions, (average) fold error, and sensitivity analysis. The developed PBPK models were applied for the multiple-dosing simulation of daily oral pyronaridine and artesunate. A steady state was reached about three to four days after the first dosing of pyronaridine and an accumulation ratio was calculated to be 1.8. However, the accumulation ratio of artesunate and dihydroartemisinin could not be calculated since the steady state of both compounds was not achieved by daily multiple dosing. The elimination half-life of pyronaridine and artesunate was estimated to be 19.8 and 0.4 h, respectively. Pyronaridine was extensively distributed to the lung and trachea with the lung-to-blood and trachea-to-blood concentration ratios (=Cavg,tissue/Cavg,blood) of 25.83 and 12.41 at the steady state, respectively. Also, the lung-to-blood and trachea-to-blood AUC ratios for artesunate (dihydroartemisinin) were calculated to be 3.34 (1.51) and 0.34 (0.15). The results of this study could provide a scientific basis for interpreting the dose–exposure–response relationship of pyronaridine and artesunate for COVID-19 drug repurposing.

Using quantitative modeling tools to assess pharmacokinetic bias in epidemiological studies showing associations between biomarkers and health outcomes at low exposures

Biomarkers of exposure can be measured at lower and lower levels due to advances in analytical chemistry. Using these sensitive methods, some epidemiology studies report associations between biomarkers and health outcomes at biomarker levels much below those associated with effects in animal studies. While some of these low exposure associations may arise from increased sensitivity of humans compared with animals or from species-specific responses, toxicology studies with drugs, commodity chemicals and consumer products have not generally indicated significantly greater sensitivity of humans compared with test animals for most health outcomes. In some cases, these associations may be indicative of pharmacokinetic (PK) bias, i.e., a situation where a confounding factor or the health outcome itself alters pharmacokinetic processes affecting biomarker levels. Quantitative assessment of PK bias combines PK modeling and statistical methods describing outcomes across large numbers of individuals in simulated populations. Here, we first provide background on the types of PK models that can be used for assessing biomarker levels in human population and then outline a process for considering PK bias in studies intended to assess associations between biomarkers and health outcomes at low levels of exposure. After providing this background, we work through published examples where these PK methods have been applied with several chemicals/chemical classes - polychlorinated biphenyls (PCBs), perfluoroalkyl substances (PFAS), polybrominated biphenyl ethers (PBDE) and phthalates - to assess the possibility of PK bias. Studies of the health effects of low levels of exposure will be improved by developing some confidence that PK bias did not play significant roles in the observed associations.

Contemporary Formulation Development for Inhaled Pharmaceuticals

Pulmonary delivery has gained increased interests over the past few decades. For respiratory conditions, targeted drug delivery directly to the site of action can achieve a high local concentration for efficacy with reduced systemic exposure and adverse effects. For systemic conditions, the unique physiology of the lung evolutionarily designed for rapid gaseous exchange presents an entry route for systemic drug delivery. Although the development of inhaled formulations has come a long way over the last few decades, many aspects of it remain to be elucidated. In particular, a reliable and well-understood method for in vitro-in vivo correlations remains to be established. With the rapid and ongoing advancement of technology, there is much potential to better utilise computational methods including different types of modelling and simulation approaches to support inhaled formulation development. This review intends to provide an introduction on some fundamental concepts in pulmonary drug delivery and inhaled formulation development followed by discussions on some challenges and opportunities in the translation of inhaled pharmaceuticals from preclinical studies to clinical development. The review concludes with some recent advancements in modelling and simulation approaches that could play an increasingly important role in modern formulation development of inhaled pharmaceuticals.

Pharmacokinetic and pharmacodynamic modeling of gut hormone peptide YY(3-36) after pulmonary delivery

Peptide YY_(3-36) (PYY_(3-36)) is an endogenous appetite suppressing peptide. The present research was to perform pharmacokinetic/pharmacodynamic (PK/PD) analysis for predicting the concentration- and response-time profiles of PYY_(3-36) after systemic and pulmonary delivery in mice, with the goal of suggesting a potential pulmonary dosing regimen in humans. A PK/PD model was developed to describe PYY_(3-36) plasma concentration - and relative food intake rate ratio (as % of control) - time profiles after intraperitoneal and subcutaneous administration, and inhalation in mice. The absorption of inhaled PYY_(3-36) from the lungs of mice could only be described with a combined slow (absorption rate of 0.147 L/h) and fast (absorption rate of 104.4 L/h) absorption process, presumably related to absorption from the central and peripheral regions of the lungs. The estimates for IC₅₀ and I_max were 6.8 ng/mL and 63.5%, respectively, based on inhibitory E_max model. The PK parameters, such as clearance (CL), volume of distribution at steady state (Vd_ss), and the absorption rates (k_a), were then scaled to human's. The scaled human CL and Vd_ss for obese subjects were 24.8 L/h and 9.0 L, respectively. The model predicted human plasma PYY_(3-36) concentrations agreed reasonably well with placebo-normalized plasma PYY_(3-36) concentrations after short-term infusion and SC injection in literature. An inhalation dose of PYY_(3-36) of about 100 µg was proposed for obese subjects based on simulations. This PK/PD analysis satisfactorily described PYY_(3-36) concentration-time and relative food intake rate ratio- time profiles at all doses and routes. The developed model might facilitate the inhalation dose selection of PYY_(3-36).

Development of a Region-Specific Physiologically Based Pharmacokinetic Brain Model to Assess Hippocampus and Frontal Cortex Pharmacokinetics

Central nervous system drug discovery and development is hindered by the impermeable nature of the blood-brain barrier. Pharmacokinetic modeling can provide a novel approach to estimate CNS drug exposure; however, existing models do not predict temporal drug concentrations in distinct brain regions. A rat CNS physiologically based pharmacokinetic (PBPK) model was developed, incorporating brain compartments for the frontal cortex (FC), hippocampus (HC), "rest-of-brain" (ROB), and cerebrospinal fluid (CSF). Model predictions of FC and HC C_max, t_max and AUC were within 2-fold of that reported for carbamazepine and phenytoin. The inclusion of a 30% coefficient of variation on regional brain tissue volumes, to assess the uncertainty of regional brain compartments volumes on predicted concentrations, resulted in a minimal level of sensitivity of model predictions. This model was subsequently extended to predict human brain morphine concentrations, and predicted a ROB C_max of 21.7 ± 6.41 ng/mL when compared to "better" (10.1 ng/mL) or "worse" (29.8 ng/mL) brain tissue regions with a FC C_max of 62.12 ± 17.32 ng/mL and a HC C_max of 182.2 ± 51.2 ng/mL. These results indicate that this simplified regional brain PBPK model is useful for forward prediction approaches in humans for estimating regional brain drug concentrations.

Alternative approaches for acute inhalation toxicity testing to address global regulatory and non-regulatory data requirements: An international workshop report

Inhalation toxicity testing, which provides the basis for hazard labeling and risk management of chemicals with potential exposure to the respiratory tract, has traditionally been conducted using animals. Significant research efforts have been directed at the development of mechanistically based, non-animal testing approaches that hold promise to provide human-relevant data and an enhanced understanding of toxicity mechanisms. A September 2016 workshop, "Alternative Approaches for Acute Inhalation Toxicity Testing to Address Global Regulatory and Non-Regulatory Data Requirements", explored current testing requirements and ongoing efforts to achieve global regulatory acceptance for non-animal testing approaches. The importance of using integrated approaches that combine existing data with in vitro and/or computational approaches to generate new data was discussed. Approaches were also proposed to develop a strategy for identifying and overcoming obstacles to replacing animal tests. Attendees noted the importance of dosimetry considerations and of understanding mechanisms of acute toxicity, which could be facilitated by the development of adverse outcome pathways. Recommendations were made to (1) develop a database of existing acute inhalation toxicity data; (2) prepare a state-of-the-science review of dosimetry determinants, mechanisms of toxicity, and existing approaches to assess acute inhalation toxicity; (3) identify and optimize in silico models; and (4) develop a decision tree/testing strategy, considering physicochemical properties and dosimetry, and conduct proof-of-concept testing. Working groups have been established to implement these recommendations.

Assessing modified risk tobacco and nicotine products: Description of the scientific framework and assessment of a closed modular electronic cigarette

Cigarette smoking causes many human diseases including cardiovascular disease, lung disease and cancer. Novel tobacco products with reduced yields of toxicants compared to cigarettes, such as tobacco-heating products, snus and electronic cigarettes, hold great potential for reducing the harms associated with tobacco use. In the UK several public health agencies have advocated a potential role for novel products in tobacco harm reduction. Public Health England has stated that "The current best estimate is that e-cigarettes are around 95% less harmful than smoking" and the Royal College of Physicians has urged public health to "Promote e-cigarettes widely as substitute for smoking". Health related claims on novel products such as 'reduced exposure' and 'reduced risk' should be substantiated using a weight of evidence approach based on a comprehensive scientific assessment. The US FDA, has provided draft guidance outlining a framework to assess novel products as Modified Risk Tobacco Products (MRTP). Based on this, we now propose a framework comprising pre-clinical, clinical, and population studies to assess the risk profile of novel tobacco products. Additionally, the utility of this framework is assessed through the pre-clinical and part of the clinical comparison of a commercial e-cigarette (Vype ePen) with a scientific reference cigarette (3R4F) and the results of these studies suggest that ePen has the potential to be a reduced risk product.

Advances in experimental and mechanistic computational models to understand pulmonary exposure to inhaled drugs

Prediction of local exposure following inhalation of a locally acting pulmonary drug is central to the successful development of novel inhaled medicines, as well as generic equivalents. This work provides a comprehensive review of the state of the art with respect to multiscale computer models designed to provide a mechanistic prediction of local and systemic drug exposure following inhalation. The availability and quality of underpinning in vivo and in vitro data informing the computer based models is also considered. Mechanistic modelling of local exposure has the potential to speed up and improve the chances of successful inhaled API and product development. Although there are examples in the literature where this type of modelling has been used to understand and explain local and systemic exposure, there are two main barriers to more widespread use. There is a lack of generally recognised commercially available computational models that incorporate mechanistic modelling of regional lung particle deposition and drug disposition processes to simulate free tissue drug concentration. There is also a need for physiologically relevant, good quality experimental data to inform such modelling. For example, there are no standardized experimental methods to characterize the dissolution of solid drug in the lungs or measure airway permeability. Hence, the successful application of mechanistic computer models to understand local exposure after inhalation and support product development and regulatory applications hinges on: (i) establishing reliable, bio-relevant means to acquire experimental data, and (ii) developing proven mechanistic computer models that combine: a mechanistic model of aerosol deposition and post-deposition processes in physiologically-based pharmacokinetic models that predict free local tissue concentrations.