Assessing airflow unsteadiness in the human respiratory tract under different expiration conditions.

To enhance the understanding of airflow characteristics in the human respiratory system, the expiratory airflow in a human respiratory tract model was simulated using large eddy simulation and dynamic mesh under different expiration conditions aligned with clinically measured data. The airflow unsteadiness was quantitatively assessed using power spectral density (PSD) and spectral entropy (SE). The following findings were obtained: (1) The airflow is highly turbulent in the mouth-pharynx region during expiration, with its dynamic characteristics being influenced by both the transient expiration flow pattern at mouth piece and the glottis motion. (2) PSD analysis reveals that the expiratory airflow is very unsteady, exhibiting a broad-band attenuation spectrum in the pharynx-trachea region. When only transient expiration or glottis motion is considered, the PSD spectrum changes slightly. When both are ignored, however, the change is significant, with the peak frequency reduced to 10% of the real expiration condition. (3) SE analysis indicates that the airflow transitions into turbulence in the trachea, and there may be multiple transitions in the region of soft palate. The transient expiration or glottis motion alone increases turbulence intensity by 2%-15%, while ignoring both reduces turbulence intensity by 10%-20%. This study implies that turbulence characteristics can be significantly different under different expiratory conditions, and therefore it is necessary to determine the expiratory flow characteristics using clinically measured expiratory data.

The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer.

Precision medicine has revolutionised cancer treatments; however, actionable biomarkers remain scarce. To address this, we develop the Oncology Biomarker Discovery (OncoBird) framework for analysing the molecular and biomarker landscape of randomised controlled clinical trials. OncoBird identifies biomarkers based on single genes or mutually exclusive genetic alterations in isolation or in the context of tumour subtypes, and finally, assesses predictive components by their treatment interactions. Here, we utilise the open-label, randomised phase III trial (FIRE-3, AIO KRK-0306) in metastatic colorectal carcinoma patients, who received either cetuximab or bevacizumab in combination with 5-fluorouracil, folinic acid and irinotecan (FOLFIRI). We systematically identify five biomarkers with predictive components, e.g., patients with tumours that carry chr20q amplifications or lack mutually exclusive ERK signalling mutations benefited from cetuximab compared to bevacizumab. In summary, OncoBird characterises the molecular landscape and outlines actionable biomarkers, which generalises to any molecularly characterised randomised controlled trial.

Numerical and Experimental Analysis of Drug Inhalation in Realistic Human Upper Airway Model.

The demand for a more efficient and targeted method for intranasal drug delivery has led to sophisticated device design, delivery methods, and aerosol properties. Due to the complex nasal geometry and measurement limitations, numerical modeling is an appropriate approach to simulate the airflow, aerosol dispersion, and deposition for the initial assessment of novel methodologies for better drug delivery. In this study, a CT-based, 3D-printed model of a realistic nasal airway was reconstructed, and airflow pressure, velocity, turbulent kinetic energy (TKE), and aerosol deposition patterns were simultaneously investigated. Different inhalation flowrates (5, 10, 15, 30, and 45 L/min) and aerosol sizes (1, 1.5, 2.5, 3, 6, 15, and 30 µm) were simulated using laminar and SST viscous models, with the results compared and verified by experimental data. The results revealed that from the vestibule to the nasopharynx, the pressure drop was negligible for flow rates of 5, 10, and 15 L/min, while for flow rates of 30 and 40 L/min, a considerable pressure drop was observed by approximately 14 and 10%, respectively. However, from the nasopharynx and trachea, this reduction was approximately 70%. The aerosol deposition fraction alongside the nasal cavities and upper airway showed a significant difference in pattern, dependent on particle size. More than 90% of the initiated particles were deposited in the anterior region, while just under 20% of the injected ultrafine particles were deposited in this area. The turbulent and laminar models showed slightly different values for the deposition fraction and efficiency of drug delivery for ultrafine particles (about 5%); however, the deposition pattern for ultrafine particles was very different.

Gene Expression-Based Molecular Test as Diagnostic Aid for the Differential Diagnosis of Psoriasis and Eczema in Formalin-Fixed and Paraffin-Embedded Tissue, Microbiopsies, and Tape Strips.

Highly effective targeted therapies are available to treat noncommunicable chronic inflammatory skin diseases. In contrast, the exact diagnosis of noncommunicable chronic inflammatory skin diseases is complicated by its complex pathogenesis and clinical and histological overlap. Particularly, the differential diagnosis of psoriasis and eczema can be challenging in some cases, and molecular diagnostic tools need to be developed to support a gold standard diagnosis. The aim of this work was to develop a real-time PCR-based molecular classifier to distinguish psoriasis from eczema in formalin-fixed and paraffin-embedded-fixed skin samples and to evaluate the use of minimally invasive microbiopsies and tape strips for molecular diagnosis. In this study, we present a formalin-fixed and paraffin-embedded-based molecular classifier that determines the probability for psoriasis with a sensitivity/specificity of 92%/100%, respectively, and an area under the curve of 0.97, delivering comparable results to our previous published RNAprotect-based molecular classifier. The psoriasis probability, as well as levels of NOS2 expression, positively correlated with the disease hallmarks of psoriasis and negatively with eczema hallmarks. Furthermore, minimally invasive tape strips and microbiopsies were effectively used to differentiate psoriasis from eczema. In summary, the molecular classifier offers broad usage in pathology laboratories as well as outpatient settings and can support the differential diagnosis of noncommunicable chronic inflammatory skin diseases on a molecular level using formalin-fixed and paraffin-embedded tissue, microbiopsies, and tape strips.

Numerical and Machine Learning Analysis of the Parameters Affecting the Regionally Delivered Nasal Dose of Nano- and Micro-Sized Aerosolized Drugs.

The nasal epithelium is an important target for drug delivery to the nose and secondary organs such as the brain via the olfactory bulb. For both topical and brain delivery, the targeting of specific nasal regions such as the olfactory epithelium (brain) is essential, yet challenging. In this study, a numerical model was developed to predict the regional dose as mass per surface area (for an inhaled mass of 2.5 mg), which is the biologically most relevant dose metric for drug delivery in the respiratory system. The role of aerosol diameter (particle diameter: 1 nm to 30 µm) and inhalation flow rate (4, 15 and 30 L/min) in optimal drug delivery to the vestibule, nasal valve, olfactory and nasopharynx is assessed. To obtain the highest doses in the olfactory region, we suggest aerosols with a diameter of 20 µm and a medium inlet air flow rate of 15 L/min. High deposition on the olfactory epithelium was also observed for nanoparticles below 1 nm, as was high residence time (slow flow rate of 4 L/min), but the very low mass of 1 nm nanoparticles is prohibitive for most therapeutic applications. Moreover, high flow rates (30 L/min) and larger micro-aerosols lead to highest doses in the vestibule and nasal valve regions. On the other hand, the highest drug doses in the nasopharynx are observed for nano-aerosol (1 nm) and fine microparticles (1-20 µm) with a relatively weak dependence on flow rate. Furthermore, using the 45 different inhalation scenarios generated by numerical models, different machine learning models with five-fold cross-validation are trained to predict the delivered dose and avoid partial differential equation solvers for future predictions. Random forest and gradient boosting models resulted in R2 scores of 0.89 and 0.96, respectively. The aerosol diameter and region of interest are the most important features affecting delivered dose, with an approximate importance of 42% and 47%, respectively.

Effect of laryngeal jet on dry powder inhaler aerosol deposition: a numerical simulation.

Although pulmonary drug delivery has been deeply investigated, the effect of the laryngeal jet on particle deposition during drug delivery with dry powder inhalers (DPI) has not been evaluated profoundly. In this study, the flow structure and particle deposition pattern of a DPI in two airway models, one with mouth-throat region including the larynx and the other one without it, are numerically investigated. The results revealed that the laryngeal jet has a considerable effect on particle deposition. The presence of laryngeal jet leads to 4-fold and 2-fold higher deposition efficiencies for inlet flow rates of 30 and 90 L/min, respectively.

Targeted drug delivery with polydisperse particle transport and deposition in patient-specific upper airway during inhalation and exhalation.

Identifying the deposition pattern of inhaled pharmaceutical aerosols in the human respiratory system and understanding the effective parameters in this process is vital for more efficient drug delivery to this region. This study investigated aerosol deposition in a patient-specific upper respiratory airway and determined the deposition fraction (DF) and pressure drop across the airway. An experimental setup was developed to measure the pressure drop in the same realistic geometry printed from the patient-specific geometry. The unsteady simulations were performed with a flow rate of 15 L/min and different particle diameters ranging from 2 to 30 µm. The results revealed significant flow circulation after the nasal valve in the upper and oropharynx regions, and a maximum local velocity observed in the nasopharynx. Transient cumulative deposition fraction showed that after 2 s of the simulation, all particles deposit or escape the computational domain. About 30 % of the injected large particles (dp ≥ 20 µm) deposited in the first 1 cm away from the nostril and more than 95 % deposited in the nasal airway before entering the oropharynx region. While almost 94 % deposition in trachea was composed of particles smaller than 5 µm. Approximately 20 % of inhaled fine particles (2-5 µm) deposited in the upper airway and the rest deposited in oropharynx, larynx and trachea.

Machine Learning and Pharmacometrics for Prediction of Pharmacokinetic Data: Differences, Similarities and Challenges Illustrated with Rifampicin.

Pharmacometrics (PM) and machine learning (ML) are both valuable for drug development to characterize pharmacokinetics (PK) and pharmacodynamics (PD). Pharmacokinetic/pharmacodynamic (PKPD) analysis using PM provides mechanistic insight into biological processes but is time- and labor-intensive. In contrast, ML models are much quicker trained, but offer less mechanistic insights. The opportunity of using ML predictions of drug PK as input for a PKPD model could strongly accelerate analysis efforts. Here exemplified by rifampicin, a widely used antibiotic, we explore the ability of different ML algorithms to predict drug PK. Based on simulated data, we trained linear regressions (LASSO), Gradient Boosting Machines, XGBoost and Random Forest to predict the plasma concentration-time series and rifampicin area under the concentration-versus-time curve from 0-24 h (AUC0-24h) after repeated dosing. XGBoost performed best for prediction of the entire PK series (R2: 0.84, root mean square error (RMSE): 6.9 mg/L, mean absolute error (MAE): 4.0 mg/L) for the scenario with the largest data size. For AUC0-24h prediction, LASSO showed the highest performance (R2: 0.97, RMSE: 29.1 h·mg/L, MAE: 18.8 h·mg/L). Increasing the number of plasma concentrations per patient (0, 2 or 6 concentrations per occasion) improved model performance. For example, for AUC0-24h prediction using LASSO, the R2 was 0.41, 0.69 and 0.97 when using predictors only (no plasma concentrations), 2 or 6 plasma concentrations per occasion as input, respectively. Run times for the ML models ranged from 1.0 s to 8 min, while the run time for the PM model was more than 3 h. Furthermore, building a PM model is more time- and labor-intensive compared with ML. ML predictions of drug PK could thus be used as input into a PKPD model, enabling time-efficient analysis.

Pulsatile Bi-Directional Aerosol Flow Affects Aerosol Delivery to the Intranasal Olfactory Region: A Patient-Specific Computational Study.

The nasal olfactory region is a potential route for non-invasive delivery of drugs directly from the nasal epithelium to the brain, bypassing the often impermeable blood-brain barrier. However, efficient aerosol delivery to the olfactory region is challenging due to its location in the nose. Here we explore aerosol delivery with bi-directional pulsatile flow conditions for targeted drug delivery to the olfactory region using a computational fluid dynamics (CFD) model on the patient-specific nasal geometry. Aerosols with aerodynamic diameter of 1 µm, which is large enough for delivery of large enough drug doses and yet potentially small enough for non-inertial aerosol deposition due to, e.g., particle diffusion and flow oscillations, is inhaled for 1.98 s through one nostril and exhaled through the other one. The bi-directional aerosol delivery with steady flow rate of 4 L/min results in deposition efficiencies (DEs) of 50.9 and 0.48% in the nasal cavity and olfactory region, respectively. Pulsatile flow with average flow rate of 4 L/min (frequency: 45 Hz) reduces these values to 34.4 and 0.12%, respectively, and it mitigates the non-uniformity of right-left deposition in both the cavity (from 1.77- to 1.33-fold) and the olfactory region (from 624- to 53.2-fold). The average drug dose deposited in the nasal cavity and the olfactory epithelium region is very similar in the right nasal cavity independent of pulsation conditions (inhalation side). In contrast, the local aerosol dose in the olfactory region of the left side is at least 100-fold lower than that in the nasal cavity independent of pulsation condition. Hence, while pulsatile flow reduces the right-left (inhalation-exhalation) imbalance, it is not able to overcome it. However, the inhalation side (even with pulsation) allows for relatively high olfactory epithelium drug doses per area reaching the same level as in the total nasal cavity. Due to the relatively low drug deposition in olfactory region on the exhalation side, this allows either very efficient targeting of the inhalation side, or uniform drug delivery by performing bidirectional flow first from the one and then from the other side of the nose.

Effect of swirling flow and particle-release pattern on drug delivery to human tracheobronchial airways.

The present study aims to investigate the effect of swirling flow on particle deposition in a realistic human airway. A computational fluid dynamic (CFD) model was utilized for the simulation of oral inhalation and particle transport patterns, considering the k-ω turbulence model. Lagrangian particle tracking was used to track the particles' trajectories. A normal breathing condition (30 L/min) was applied, and two-micron particles were injected into the mouth, considering swirling flow to the oral inhalation airflow. Different cases were considered for releasing the particles, which evaluated the impacts of various parameters on the deposition efficiency (DE), including the swirl intensity, injection location and pattern of the particle. The work's novelty is applying several injection locations and diameters simultaneously. The results show that the swirling flow enhances the particle deposition efficiency (20-40%) versus no-swirl flow, especially in the mouth. However, releasing particles inside the mouth, or injecting them randomly with a smaller injection diameter (dinj) reduced DE in swirling flow condition, about 50 to 80%. Injecting particles inside the mouth can decrease DE by about 20%, and releasing particles with smaller dinj leads to 50% less DE in swirling flow. In conclusion, it is indicated that the airflow condition is an important parameter for a reliable drug delivery, and it is more beneficial to keep the inflow uniform and avoid swirling flow.

Flow Structure and Particle Deposition Analyses for Optimization of a Pressurized Metered Dose Inhaler (pMDI) in a Model of Tracheobronchial Airway.

Inhalation therapy plays an important role in management or treatment of respiratory diseases such asthma and chronic obstructive pulmonary diseases (COPDs). For decades, pressurized metered dose inhalers (pMDIs) have been the most popular and prescribed drug delivery devices for inhalation therapy. The main objectives of the present computational work are to study flow structure inside a pMDI, as well as transport and deposition of micron-sized particles in a model of human tracheobronchial airways and their dependence on inhalation air flow rate and characteristic pMDI parameters. The upper airway geometry, which includes the extrathoracic region, trachea, and bronchial airways up to the fourth generation in some branches, was constructed based on computed tomography (CT) images of an adult healthy female. Computational fluid dynamics (CFD) simulation was employed using the k-ω model with low-Reynolds number (LRN) corrections to accomplish the objectives. The deposition results of the present study were verified with the in vitro deposition data of our previous investigation on pulmonary drug delivery using a hollow replica of the same airway geometry as used for CFD modeling. It was found that the flow structure inside the pMDI and extrathoracic region strongly depends on inhalation flow rate and geometry of the inhaler. In addition, regional aerosol deposition patterns were investigated at four inhalation flow rates between 30 and 120 L/min and for 60 L/min yielding highest deposition fractions of 24.4% and 3.1% for the extrathoracic region (EX) and the trachea, respectively. It was also revealed that particle deposition was larger in the right branches of the bronchial airways (right lung) than the left branches (left lung) for all of the considered cases. Also, optimization of spray characteristics showed that the optimum values for initial spray velocity, spray cone angle and spray duration were 100 m/s, 10° and 0.1 sec, respectively. Moreover, spray cone angle, more than any other of the investigated pMDI parameters can change the deposition pattern of inhaled particles in the airway model. In conclusion, the present investigation provides a validated CFD model for particle deposition and new insights into the relevance of flow structure for deposition of pMDI-emitted pharmaceutical aerosols in the upper respiratory tract.

Simulation of patient-specific bi-directional pulsating nasal aerosol dispersion and deposition with clockwise 45° and 90° nosepieces.

Numerical simulations of the dispersion and deposition of poly-disperse particles in a patient-specific human nasal configuration are performed. Computed tomography (CT) images are used to create a realistic configuration of the nasal cavity and paranasal sinuses. The OpenFOAM software is used to perform unsteady Large Eddy Simulations (LES) with the dynamic sub-grid scale Smagorinsky model. For the numerical analysis of the particle motion, a Lagrangian particle tracking method is implemented. Two different nosepieces with clockwise inclinations of 45° and 90° with respect to the horizontal axis are connected to the nostrils. A sinusoidal pulsating airflow profile with a frequency of 45 Hz is imposed on the airflow which carries the particles. Flow partition analysis inside the sinuses show that ventilation of the sinuses is improved slightly when the 45° nosepiece is used instead of the 90° nosepiece. The flow partition into the right maxillary is improved from 0.22% to 0.25%. It is observed that a closed soft palate increases the aerosol deposition efficiency (DE) in the nasal cavity as compared to an open soft palate condition. The utilization of pulsating inflow leads to more uniform deposition pattern in the nasal airway and enhances the DE by 160% and 44.6%, respectively, for the cases with clockwise 45° and 90° nosepieces, respectively. The bi-directional pulsating drug delivery with the same particle size distribution and inflow rates as the PARI SINUS device results in higher total DEs with 45° nosepiece than with the 90°. Thus, the numerical simulation suggests that the 45° nosepiece is favorable in terms of the delivered dose.