Unbound Brain-to-Plasma Partition Coefficient, Kp,uu,brain-a Game Changing Parameter for CNS Drug Discovery and Development.

PURPOSE: More than 15 years have passed since the first description of the unbound brain-to-plasma partition coefficient (Kp,uu,brain) by Prof. Margareta Hammarlund-Udenaes, which was enabled by advancements in experimental methodologies including cerebral microdialysis. Since then, growing knowledge and data continue to support the notion that the unbound (free) concentration of a drug at the site of action, such as the brain, is the driving force for pharmacological responses. Towards this end, Kp,uu,brain is the key parameter to obtain unbound brain concentrations from unbound plasma concentrations. METHODS: To understand the importance and impact of the Kp,uu,brain concept in contemporary drug discovery and development, a survey has been conducted amongst major pharmaceutical companies based in Europe and the USA. Here, we present the results from this survey which consisted of 47 questions addressing: 1) Background information of the companies, 2) Implementation, 3) Application areas, 4) Methodology, 5) Impact and 6) Future perspectives. RESULTS AND CONCLUSIONS: From the responses, it is clear that the majority of the companies (93%) has established a common understanding across disciplines of the concept and utility of Kp,uu,brain as compared to other parameters related to brain exposure. Adoption of the Kp,uu,brain concept has been mainly driven by individual scientists advocating its application in the various companies rather than by a top-down approach. Remarkably, 79% of all responders describe the portfolio impact of Kp,uu,brain implementation in their companies as 'game-changing'. Although most companies (74%) consider the current toolbox for Kp,uu,brain assessment and its validation satisfactory for drug discovery and early development, areas of improvement and future research to better understand human brain pharmacokinetics/pharmacodynamics translation have been identified.

Lung pharmacokinetics of inhaled and systemic drugs: A clinical evaluation.

BACKGROUND AND PURPOSE: Human pharmacokinetic studies of lung-targeted drugs are typically limited to measurements of systemic plasma concentrations, which provide no direct information on lung target-site concentrations. We aimed to evaluate lung pharmacokinetics of commonly prescribed drugs by sampling different lung compartments after inhalation and oral administration. EXPERIMENTAL APPROACH: Healthy volunteers received single, sequential doses of either inhaled salbutamol, salmeterol and fluticasone propionate (n = 12), or oral salbutamol and propranolol (n = 6). Each participant underwent bronchoscopies and gave breath samples for analysis of particles in exhaled air at two points after drug administration (1 and 6, 2 and 9, 3 and 12, or 4 and 18 h). Lung samples were taken via bronchosorption, bronchial brush, mucosal biopsy and bronchoalveolar lavage during each bronchoscopy. Blood samples were taken during the 24 h after administration. Pharmacokinetic profiles were generated by combining data from multiple individuals, covering all sample timings. KEY RESULTS: Pharmacokinetic profiles were obtained for each drug in lung epithelial lining fluid, lung tissue and plasma. Inhalation of salbutamol resulted in approximately 100-fold higher concentrations in lung than in plasma. Salmeterol and fluticasone concentration ratios in lung versus plasma were higher still. Bronchosorption- and bronchoalveolar-lavage-generated profiles of inhaled drugs in epithelial lining fluid were comparable. For orally administered drugs, epithelial-lining-fluid concentrations were overestimated in bronchoalveolar-lavage-generated profiles. CONCLUSION AND IMPLICATIONS: Combining pharmacokinetic data derived from several individuals and techniques sampling different lung compartments enabled generation of pharmacokinetic profiles for evaluation of lung targeting after inhaled and oral drug delivery.

Deep Learning With Conformal Prediction for Hierarchical Analysis of Large-Scale Whole-Slide Tissue Images.

With the increasing amount of image data collected from biomedical experiments there is an urgent need for smarter and more effective analysis methods. Many scientific questions require analysis of image sub-regions related to some specific biology. Finding such regions of interest (ROIs) at low resolution and limiting the data subjected to final quantification at full resolution can reduce computational requirements and save time. In this paper we propose a three-step pipeline: First, bounding boxes for ROIs are located at low resolution. Next, ROIs are subjected to semantic segmentation into sub-regions at mid-resolution. We also estimate the confidence of the segmented sub-regions. Finally, quantitative measurements are extracted at full resolution. We use deep learning for the first two steps in the pipeline and conformal prediction for confidence assessment. We show that limiting final quantitative analysis to sub-regions with full confidence reduces noise and increases separability of observed biological effects.

Mechanisms of a Sustained Anti-inflammatory Drug Response in Alveolar Macrophages Unraveled with Mathematical Modeling.

Both initiation and suppression of inflammation are hallmarks of the immune response. If not balanced, the inflammation may cause extensive tissue damage, which is associated with common diseases, e.g., asthma and atherosclerosis. Anti-inflammatory drugs come with side effects that may be aggravated by high and fluctuating drug concentrations. To remedy this, an anti-inflammatory drug should have an appropriate pharmacokinetic half-life or better still, a sustained anti-inflammatory drug response. However, we still lack a quantitative mechanistic understanding of such sustained effects. Here, we study the anti-inflammatory response to a common glucocorticoid drug, dexamethasone. We find a sustained response 22 hours after drug removal. With hypothesis testing using mathematical modeling, we unravel the underlying mechanism-a slow release of dexamethasone from the receptor-drug complex. The developed model is in agreement with time-resolved training and testing data and is used to simulate hypothetical treatment schemes. This work opens up for a more knowledge-driven drug development to find sustained anti-inflammatory responses and fewer side effects.

Revealing the Regional Localization and Differential Lung Retention of Inhaled Compounds by Mass Spectrometry Imaging.

Background: For the treatment of respiratory disease, inhaled drug delivery aims to provide direct access to pharmacological target sites while minimizing systemic exposure. Despite this long-held tenet of inhaled therapeutic advantage, there are limited data of regional drug localization in the lungs after inhalation. The aim of this study was to investigate the distribution and retention of different chemotypes typifying available inhaled drugs [slowly dissolving neutral fluticasone propionate (FP) and soluble bases salmeterol and salbutamol] using mass spectrometry imaging (MSI). Methods: Salmeterol, salbutamol, and FP were simultaneously delivered by inhaled nebulization to rats. In the same animals, salmeterol-d3, salbutamol-d3, and FP-d3 were delivered by intravenous (IV) injection. Samples of lung tissue were obtained at 2- and 30-minute postdosing, and high-resolution MSI was used to study drug distribution and retention. Results: IV delivery resulted in homogeneous lung distribution for all molecules. In comparison, while inhalation also gave rise to drug presence in the entire lung, there were regional chemotype-dependent areas of higher abundance. At the 30-minute time point, inhaled salmeterol and salbutamol were preferentially retained in bronchiolar tissue, whereas FP was retained in all regions of the lungs. Conclusion: This study clearly demonstrates that inhaled small molecule chemotypes are differentially distributed in lung tissue after inhalation, and that high-resolution MSI can be applied to study these retention patterns.

Understanding the Influence of Nanocarrier-Mediated Brain Delivery on Therapeutic Performance Through Pharmacokinetic-Pharmacodynamic Modeling.

This study aimed at evaluating how encapsulation in a regular nanocarrier (NC) (providing extended circulation time) or in a brain-targeting NC (providing prolonged circulation time and increased brain uptake) may influence the therapeutic index compared with the unformulated drug and to explore the key parameters affecting therapeutic performance using a model-based approach. Pharmacokinetic (PK) models were built with chosen PK parameters. For a scenario where central effect depends on area under the unbound brain concentration curve and peripheral toxicity relates to peak unbound plasma concentration, dose-effect and drug-side effect curves were constructed, and the therapeutic index was evaluated. Regular NC improved the therapeutic index compared with the unformulated drug due to reduced peripheral toxicity, while brain-targeting NC enhanced the therapeutic index by lowering peripheral toxicity and increasing central effect. Decreasing drug release rate or systemic clearance of NC with drug still encapsulated could increase the therapeutic index. Also, a drug with shorter half-life would therapeutically benefit more from a NC encapsulation. This work provides insights into how a NC for brain delivery should be optimized to maximize the therapeutic performance and is helpful to predict if and to what extent a drug with certain PK properties would obtain therapeutic benefit from nanoencapsulation.

Computational modeling of lung deposition of inhaled particles in chronic obstructive pulmonary disease (COPD) patients: identification of gaps in knowledge and data.

Computational modeling together with experimental data are essential to assess the risk for particulate matter mediated lung toxicity and to predict the efficacy, safety and fate of aerosolized drug molecules used in inhalation therapy. In silico models are widely used to understand the deposition, distribution, and clearance of inhaled particles and aerosols in the human lung. Exacerbations of chronic obstructive pulmonary disease (COPD) have been reported due to increased particulate matter related air pollution episodes. Considering the profound functional, anatomical and structural changes occurring in COPD lungs, the relevance of the existing in silico models for mimicking diseased lungs warrants reevaluation. Currently available computational modeling tools were developed for the healthy adult (male) lung. Here, we analyze the major alterations occurring in the airway structure, anatomy and pulmonary function in the COPD lung, as compared to the healthy lung. We also scrutinize the various physiological and particle characteristics that influence particle deposition, distribution and clearance in the lung. The aim of this review is to evaluate the availability of the fundamental knowledge and data required for modeling particle deposition in a COPD lung departing from the existing healthy lung models. The extent to which COPD pathophysiology may affect aerosol deposition depends on the relative contribution of several factors such as altered lung structure and function, bronchoconstriction, emphysema, loss of elastic recoil, altered breathing pattern and altered liquid volumes that warrant consideration while developing physiologically relevant in silico models.

Building on the success of osimertinib: achieving CNS exposure in oncology drug discovery.

Due to the blood-brain barrier (BBB) limiting the exposure of therapeutics to the central nervous system (CNS), patients with brain malignancies are challenging to treat, typically have poor prognoses, and represent a significant unmet medical need. Preclinical data report osimertinib to have significant BBB penetration and emerging clinical data demonstrate encouraging activity against CNS malignancies. Here, we discuss the oncology drug candidates AZD3759 and AZD1390 as case examples of discovery projects designing in BBB penetrance. We demonstrate how these innovative kinase inhibitors were recognized as brain penetrant and outline our view of experimental approaches and strategies that can facilitate the discovery of new brain-penetrant therapies for the treatment of primary and secondary CNS malignancies as well as other CNS disorders.

Pulmonary PET imaging confirms preferential lung target occupancy of an inhaled bronchodilator.

BACKGROUND: Positron emission tomography (PET) is a non-invasive molecular imaging technique that traces the distribution of radiolabeled molecules in experimental animals and human subjects. We hypothesized that PET could be used to visualize the binding of the bronchodilator drug ipratropium to muscarinic receptors (MR) in the lungs of living non-human primates (NHP). The objectives of this study were two-fold: (i) to develop a methodology for quantitative imaging of muscarinic receptors in NHP lung and (ii) to estimate and compare ipratropium-induced MR occupancy following drug administration via intravenous injection and inhalation, respectively. METHODS: A series of PET measurements (n = 18) was performed after intravenous injection of the selective muscarinic radioligand 11C-VC-002 in NHP (n = 5). The lungs and pituitary gland (both rich in MR) were kept in the field of view. Each PET measurement was followed by a PET measurement preceded by treatment with ipratropium (intravenous or inhaled). RESULTS: Radioligand binding was quantified using the Logan graphical analysis method providing the total volume of distribution (VT). Ipratropium reduced the VT in the lung and pituitary in a dose-dependent fashion. At similar plasma ipratropium concentrations, administration by inhalation produced larger reductions in VT for the lungs. The plasma-derived apparent affinity for ipratropium binding in the lung was one order of magnitude higher after inhalation (Kiih = 1.01 nM) than after intravenous infusion (Kiiv = 10.84 nM). CONCLUSION: Quantitative muscarinic receptor occupancy imaging by PET articulates and quantifies the therapeutic advantage of the inhaled route of delivery and provides a tool for future developments of improved inhaled drugs.

Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling Accurately Predicts the Better Bronchodilatory Effect of Inhaled Versus Oral Salbutamol Dosage Forms.

BACKGROUND: Predicting local lung tissue pharmacodynamic (PD) responses of inhaled drugs is a longstanding challenge related to the lack of experimental techniques to determine local free drug concentrations. This has prompted the use of physiologically based pharmacokinetic (PBPK) modeling to potentially predict local concentration and response. A unique opportunity for PBPK model evaluation is provided by the clinical PD data for salbutamol, which in its inhaled dosage form (400 µg), produces a higher bronchodilatory effect than in its oral dosage form (2 mg) despite lower drug concentrations in blood. The present study aimed at evaluating whether inhalation PBPK model predictions of free drug in tissue would be predictive of these observations. METHODS: A PBPK model, including 24 airway generations, was parameterized to describe lung, plasma, and epithelial lining fluid concentrations of salbutamol administered intratracheally and intravenously to rats (100 nmol/kg). Plasma and lung tissue concentrations of unbound (R)-salbutamol, the active enantiomer, were predicted with a humanized version of the model and related to effect in terms of forced expiratory volume in 1 second (FEV1). RESULTS: In contrast to oral dosing, the model predicted inhalation to result in spatial heterogeneity in the target site concentrations (subepithelium) with higher free drug concentrations in the lung as compared with the plasma. FEV1 of inhaled salbutamol was accurately predicted from the PK/PD relationship derived from oral salbutamol and PBPK predictions of free concentration in airway tissue of high resistance (e.g., 6th generation). CONCLUSION: An inhalation PBPK-PD model was developed and shown predictive of local pharmacology of inhaled salbutamol, thus conceptually demonstrating the validity of PBPK model predictions of free drug concentrations in lung tissue. This achievement unlocks the power of inhalation PBPK modeling to interrogate local pharmacology and guide optimization and development of inhaled drugs and their formulations.

Uncovering the regional localization of inhaled salmeterol retention in the lung.

Treatment of respiratory disease with a drug delivered via inhalation is generally held as being beneficial as it provides direct access to the lung target site with a minimum systemic exposure. There is however only limited information of the regional localization of drug retention following inhalation. The aim of this study was to investigate the regional and histological localization of salmeterol retention in the lungs after inhalation and to compare it to systemic administration. Lung distribution of salmeterol delivered to rats via nebulization or intravenous (IV) injection was analyzed with high-resolution mass spectrometry imaging (MSI). Salmeterol was widely distributed in the entire section at 5 min after inhalation, by 15 min it was preferentially retained in bronchial tissue. Via a novel dual-isotope study, where salmeterol was delivered via inhalation and d3-salmeterol via IV to the same rat, could the effective gain in drug concentration associated with inhaled delivery relative to IV, expressed as a site-specific lung targeting factor, was 5-, 31-, and 45-fold for the alveolar region, bronchial sub-epithelium and epithelium, respectively. We anticipate that this MSI-based framework for quantifying regional and histological lung targeting by inhalation will accelerate discovery and development of local and more precise treatments of respiratory disease.

Translational model to predict pulmonary pharmacokinetics and efficacy in man for inhaled bronchodilators.

Translational pharmacokinetic (PK) models are needed to describe and predict drug concentration-time profiles in lung tissue at the site of action to enable animal-to-man translation and prediction of efficacy in humans for inhaled medicines. Current pulmonary PK models are generally descriptive rather than predictive, drug/compound specific, and fail to show successful cross-species translation. The objective of this work was to develop a robust compartmental modeling approach that captures key features of lung and systemic PK after pulmonary administration of a set of 12 soluble drugs containing single basic, dibasic, or cationic functional groups. The model is shown to allow translation between animal species and predicts drug concentrations in human lungs that correlate with the forced expiratory volume for different classes of bronchodilators. Thus, the pulmonary modeling approach has potential to be a key component in the prediction of human PK, efficacy, and safety for future inhaled medicines.

Benchmarking of Human Dose Prediction for Inhaled Medicines from Preclinical In Vivo Data.

PURPOSE: A scientifically robust prediction of human dose is important in determining whether to progress a candidate drug into clinical development. A particular challenge for inhaled medicines is that unbound drug concentrations at the pharmacological target site cannot be easily measured or predicted. In the absence of such data, alternative empirical methods can be useful. This work is a post hoc analysis based on preclinical in vivo pharmacokinetic/pharmacodynamic (PK/PD) data with the aim to evaluate such approaches and provide guidance on clinically effective dose prediction for inhaled medicines. METHODS: Five empirically based methodologies were applied on a diverse set of marketed inhaled therapeutics (inhaled corticosteroids and bronchodilators). The approaches include scaling of dose based on body weight or body surface area and variants of PK/PD approaches aiming to predict the therapeutic dose based on having efficacious concentrations of drug in the lung over the dosing interval. RESULTS: The most robust predictions of dose were made by body weight adjustment (90% within 3-fold) and by a specific PK/PD approach aiming for an average predicted 75% effect level during the dosing interval (80% within 3-fold). Scaling of dose based on body surface area consistently under predicted the therapeutic dose. CONCLUSIONS: Preclinical in vivo data and empirical scaling to man can be used as a baseline method for clinical dose predictions of inhaled medicines. The development of more sophisticated translational models utilizing free drug concentration and target engagement data is a desirable build.

Current Progress Toward a Better Understanding of Drug Disposition Within the Lungs: Summary Proceedings of the First Workshop on Drug Transporters in the Lungs.

The School of Pharmacy and Pharmaceutical Sciences at Trinity College Dublin hosted the "1st Workshop on Drug Transporters in the Lungs" in September 2016 to discuss the impact of transporters on pulmonary drug disposition and their roles as drug targets in lung disease. The workshop brought together about 30 scientists from academia and pharmaceutical industry from Europe and Japan and addressed the primary questions: What do we know today, and what do we need to know tomorrow about transporters in the lung? The 3 themes of the workshop were: (1) techniques to study drug transporter expression and actions in the lungs; (2) drug transporter effects on pulmonary pharmacokinetics-case studies; and (3) transporters as drug targets in lung disease. Some of the conclusions of the workshop were: suitable experimental models that allow studies of transporter effects are available; data from these models convincingly show a contribution of both uptake and efflux transporters on pulmonary drug disposition; the effects of transporters on drug lung PK is now better conceptualized; some transporters are associated with lung diseases. However, more work is needed to establish which of the available models best translate to the clinical situation.

Lung Retention by Lysosomal Trapping of Inhaled Drugs Can Be Predicted In Vitro With Lung Slices.

Modulating and optimizing the local pharmacokinetics of inhaled drugs by chemical design or formulation is challenged by the lack of predictive in vitro systems and in vivo techniques providing a detailed description of drug location in the lung. The present study investigated whether a new experimental setup of freshly prepared agarose-filled lung slices can be used to estimate lung retention in vitro, by comparing with in vivo lung retention after intratracheal instillation. Slices preloaded with inhaled ß-adrenergic compounds (salbutamol, formoterol, salmeterol, indacaterol or AZD3199) were incubated in a large volume of buffer (w/wo monensin to assess the role of lysosomal trapping), and the amount remaining in slices at different time points was determined with liquid chromatography-tandem mass spectrometry. The in vitro lung retention closely matched the in vivo lung retention (half-lives within 3-fold for 4/5 compounds), and monensin shortened the half-lives for all compounds. The results suggest that freshly prepared rat lungs slices can be used to predict lung retention and that slow kinetics of lysosomal trapping is a key mechanism by which retention in the lung and the effect duration of inhaled ß-adrenergic bronchodilators are prolonged.

Pharmacokinetic considerations of nanodelivery to the brain: Using modeling and simulations to predict the outcome of liposomal formulations.

The use of nanocarriers is an intriguing solution to increase the brain delivery of novel therapeutics. The aim of this paper was to use pharmacokinetic analysis and simulations to identify key factors that determine the effective drug concentration-time profile at the target site in the brain. Model building and simulations were based on experimental data obtained from the administration of the opioid peptide DAMGO in glutathione tagged PEGylated liposomes to rats. Different pharmacokinetic models were investigated to explore the mechanisms of increased brain delivery. Concentration-time profiles for a set of formulations with varying compound and carrier characteristics were simulated. By controlling the release rate from the liposome, the time profile and the extent of brain delivery can be regulated. The modeling did not support a mechanism of the liposomes passing the brain endothelial cell membrane in an intact form through endocytosis or transcytosis. The most likely process was found to be fusion of the liposome with the endothelial luminal membrane. The simulations revealed that low permeable compounds, independent on efflux, will gain the most from a nanocarrier formulation. The present model based approach is useful to explore and predict possibilities and limitations of carrier-based systems to the brain.

Development of a Novel Lung Slice Methodology for Profiling of Inhaled Compounds.

The challenge of defining the concentration of unbound drug at the lung target site after inhalation limits the possibility to optimize target exposure by compound design. In this study, a novel rat lung slice methodology has been developed and applied to study drug uptake in lung tissue, and the mechanisms by which this occurs. Freshly prepared lung slices (500 µm) from drug-naive rats were incubated with drugs followed by determination of the unbound drug volume of distribution in lung (Vu,lung), as the total concentration of drug in slices divided by the buffer (unbound) concentration. Vu,lung determined for a set of inhaled drug compounds ranged from 2.21 mL/g for salbutamol to 2970 mL/g for dibasic compound A. Co-incubation with monensin, a modulator of lysosomal pH, resulted in inhibition of tissue uptake of basic propranolol to 13%, indicating extensive lysosomal trapping. Partitioning into cells was particularly high for the cation MPP+ and the dibasic compound A, likely because of the carrier-mediated transport and lysosomal trapping. The results show that different factors are important for tissue uptake and the presented method can be used for profiling of inhaled compounds, leading to a greater understanding of distribution and exposure of drug in the lung.

A novel in vivo receptor occupancy methodology for the glucocorticoid receptor: toward an improved understanding of lung pharmacokinetic/pharmacodynamic relationships.

Investigation of pharmacokinetic/pharmacodynamic (PK/PD) relationships for inhaled drugs is challenging because of the limited possibilities of measuring tissue exposure and target engagement in the lung. The aim of this study was to develop a methodology for measuring receptor occupancy in vivo in the rat for the glucocorticoid receptor (GR) to allow more informative inhalation PK/PD studies. From AstraZeneca's chemical library of GR binders, compound 1 [N-(2-amino-2-oxo-ethyl)-3-[5-[(1R,2S)-2-(2,2-difluoropropanoylamino)-1-(2,3-dihydro-1,4-benzodioxin-6-yl)propoxy]indazol-1-yl]-N-methyl-benzamide] was identified to have properties that are useful as a tracer for GR in vitro. When given at an appropriate dose (30 nmol/kg) to rats, compound 1 functioned as a tracer in the lung and spleen in vivo using liquid chromatography-tandem mass spectrometry bioanalysis. The methodology was successfully used to show the dose-receptor occupancy relationship measured at 1.5 hours after intravenous administration of fluticasone propionate (20, 150, and 750 nmol/kg) as well as to characterize the time profile for receptor occupancy after a dose of 90 nmol/kg i.v. The dose giving 50% occupancy was estimated as 47 nmol/kg. The methodology is novel in terms of measuring occupancy strictly in vivo and by using an unlabeled tracer. This feature confers key advantages, including occupancy estimation not being influenced by drug particle dissolution or binding/dissociation taking place postmortem. In addition, the tracer may be labeled for use in positron emission tomography imaging, thus enabling occupancy estimation in humans as a translatable biomarker of target engagement.

Exploring in silico prediction of the unbound brain-to-plasma drug concentration ratio: model validation, renewal, and interpretation.

Recently, we built an in silico model to predict the unbound brain-to-plasma concentration ratio (Kp,uu,brain), a measure of the distribution of a compound between the blood plasma and the brain. Here, we validate the previous model with new additional data points expanding the chemical space and use that data also to renew the model. The model building process was similar to our previous approach; however, a new set of descriptors, molecular signatures, was included to facilitate the model interpretation from a structure perspective. The best consensus model shows better predictive power than the previous model (R(2) = 0.6 vs. R(2) = 0.53, when the same 99 compounds were used as test set). The two-class classification accuracy increased from 76% using the previous model to 81%. Furthermore, the atom-summarized gradient based on molecular signature descriptors was proposed as an interesting new approach to interpret the Kp,uu,brain machine learning model and scrutinize structure Kp,uu,brain relationships for investigated compounds.