Computational fluid dynamics (CFD) models are useful for predicting site-specific dosimetry of airborne materials in the respiratory tract and elucidating the importance of species differences in anatomy, physiology, and breathing patterns. We improved the imaging and model development methods to the point where CFD models for the rat, monkey, and human now encompass airways from the nose or mouth to the lung. A total of 1272, 2172, and 135 pulmonary airways representing 17±7, 19±9, or 9±2 airway generations were included in the rat, monkey and human models, respectively. A CFD/physiologically based pharmacokinetic model previously developed for acrolein was adapted for these anatomically correct extended airway models. Model parameters were obtained from the literature or measured directly. Airflow and acrolein uptake patterns were determined under steady-state inhalation conditions to provide direct comparisons with prior data and nasal-only simulations. Results confirmed that regional uptake was sensitive to airway geometry, airflow rates, acrolein concentrations, air:tissue partition coefficients, tissue thickness, and the maximum rate of metabolism. Nasal extraction efficiencies were predicted to be greatest in the rat, followed by the monkey, and then the human. For both nasal and oral breathing modes in humans, higher uptake rates were predicted for lower tracheobronchial tissues than either the rat or monkey. These extended airway models provide a unique foundation for comparing material transport and site-specific tissue uptake across a significantly greater range of conducting airways in the rat, monkey, and human than prior CFD models.

Computational fluid dynamics (CFD) modeling is well suited for addressing species-specific anatomy and physiology in calculating respiratory tissue exposures to inhaled materials. In this study, we overcame prior CFD model limitations to demonstrate the importance of realistic, transient breathing patterns for predicting site-specific tissue dose. Specifically, extended airway CFD models of the rat and human were coupled with airway region-specific physiologically based pharmacokinetic (PBPK) tissue models to describe the kinetics of 3 reactive constituents of cigarette smoke: acrolein, acetaldehyde and formaldehyde. Simulations of aldehyde no-observed-adverse-effect levels for nasal toxicity in the rat were conducted until breath-by-breath tissue concentration profiles reached steady state. Human oral breathing simulations were conducted using representative aldehyde yields from cigarette smoke, measured puff ventilation profiles and numbers of cigarettes smoked per day. As with prior steady-state CFD/PBPK simulations, the anterior respiratory nasal epithelial tissues received the greatest initial uptake rates for each aldehyde in the rat. However, integrated time- and tissue depth-dependent area under the curve (AUC) concentrations were typically greater in the anterior dorsal olfactory epithelium using the more realistic transient breathing profiles. For human simulations, oral and laryngeal tissues received the highest local tissue dose with greater penetration to pulmonary tissues than predicted in the rat. Based upon lifetime average daily dose comparisons of tissue hot-spot AUCs (top 2.5% of surface area-normalized AUCs in each region) and numbers of cigarettes smoked/day, the order of concern for human exposures was acrolein > formaldehyde > acetaldehyde even though acetaldehyde yields were 10-fold greater than formaldehyde and acrolein.

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno‐oncology (IO) the aim is to direct the patient’s own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD‐L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug‐development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds’ pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

A reversible CO2-triggered volume expansion significantly lowers the fracture initiation pressure in highly impermeable igneous rock as compared to conventional fracturing fluids.