Search

Purpose Chloroquine and hydroxychloroquine are effective against respiratory viruses in vitro. However, they lack antiviral efficacy upon oral administration. Translation of in vitro to in vivo exposure is necessary for understanding the disconnect between the two to develop effective therapeutic strategies. Methods We employed an in vitro ion-trapping kinetic model to predict the changes in the cytosolic and lysosomal concentrations of chloroquine and hydroxychloroquine in cell lines and primary human airway cultures. A physiologically based pharmacokinetic model with detailed respiratory physiology was used to predict regional airway exposure and optimize dosing regimens. Results At their reported in vitro effective concentrations in cell lines, chloroquine and hydroxychloroquine cause a significant increase in their cytosolic and lysosomal concentrations by altering the lysosomal pH. Higher concentrations of the compounds are required to achieve similar levels of cytosolic and lysosomal changes in primary human airway cells in vitro. The predicted cellular and lysosomal concentrations in the respiratory tract for in vivo oral doses are lower than the in vitro effective levels. Pulmonary administration of aerosolized chloroquine or hydroxychloroquine is predicted to achieve high bound in vitro-effective concentrations in the respiratory tract, with low systemic exposure. Achieving effective cytosolic concentrations for activating immunomodulatory effects and adequate lysosomal levels for inhibiting viral replication could be key drivers for treating viral respiratory infections. Conclusion Our analysis provides a framework for extrapolating in vitro effective concentrations of chloroquine and hydroxychloroquine to in vivo dosing regimens for treating viral respiratory infections. Graphical abstract

Purpose This work describes a staged approach to the application of pharmacokinetic-pharmacodynamic (PK-PD) modeling in the voltage-gated sodium ion channel (NaV1.7) inhibitor drug discovery effort to address strategic questions regarding in vitro to in vivo translation of target modulation. Methods PK-PD analysis was applied to data from a functional magnetic resonance imaging (fMRI) technique to non-invasively measure treatment mediated inhibition of olfaction signaling in non-human primates (NHPs). Initial exposure-response was evaluated using single time point data pooled across 27 compounds to inform on in vitro to in vivo correlation (IVIVC). More robust effect compartment PK-PD modeling was conducted for a subset of 10 compounds with additional PD and PK data to characterize hysteresis. Results The pooled compound exposure-response facilitated an early exploration of IVIVC with a limited dataset for each individual compound, and it suggested a 2.4-fold in vitro to in vivo scaling factor for the NaV1.7 target. Accounting for hysteresis with an effect compartment PK-PD model as compounds advanced towards preclinical development provided a more robust determination of in vivo potency values, which resulted in a statistically significant positive IVIVC with a slope of 1.057 ± 0.210, R-squared of 0.7831, and p value of 0.006. Subsequent simulations with the PK-PD model informed the design of anti-nociception efficacy studies in NHPs. Conclusions A staged approach to PK-PD modeling and simulation enabled integration of in vitro NaV1.7 potency, plasma protein binding, and pharmacokinetics to describe the exposure-response profile and inform future study design as the NaV1.7 inhibitor effort progressed through drug discovery.

Purpose This study explored several strategies to improve the performance of literature QSAR models for plasma protein binding (PPB), such as a suitable endpoint transformation, a correct representation of chemicals, more consistency in the dataset, and a reliable definition of the applicability domain. Methods We retrieved human fraction unbound (Fu) data for 670 compounds from the literature and carefully checked them for consistency. Descriptors were calculated taking account of the ionization state of molecules at physiological pH (7.4), in order to better estimate the affinity of molecules to blood proteins. We used different algorithms and chemical descriptors to explore the most suitable strategy for modeling the endpoint. SMILES (simplified molecular input line entry system)-based string descriptors were also tested with the CORAL software (CORelation And Logic). We did an outlier analysis to establish the models to use (or not to use) in case of well recognized families. Results Internal validation of the selected models returned Q2 values close to 0.60. External validation also gave r2 values always greater than 0.60. The CORAL descriptor based model for √fu was the best, with r2 0.74 in external validation. Conclusions Performance in prediction confirmed the robustness of all the derived models and their suitability for real-life purposes, i.e. screening chemicals for their ADMET profiling. Optimization of descriptors can be useful in order to obtain the correct results with a ionized molecule. Electronic supplementary material The online version of this article (10.1007/s11095-018-2561-8) contains supplementary material, which is available to authorized users.

Purpose Changes in drug absorption and first-pass metabolism have been reported throughout the pediatric age range. Our aim is to characterize both intestinal and hepatic CYP3A-mediated metabolism of midazolam in children in order to predict first-pass and systemic metabolism of CYP3A substrates. Methods Pharmacokinetic (PK) data of midazolam and 1-OH-midazolam from 264 post-operative children 1–18 years of age after oral administration were analyzed using a physiological population PK modelling approach. In the model, consisting of physiological compartments representing the gastro-intestinal tract and liver,intrinsic intestinal and hepatic clearances were estimated to derive values for bioavailability and plasma clearance. Results The whole-organ intrinsic clearance in the gut wall and liver were found to increase with body weight, with a 105 (95% confidence interval (CI): 5–405) times lower intrinsic gut wall clearance than the intrinsic hepatic clearance (i.e. 5.08 L/h (relative standard error (RSE) 10%) versus 527 L/h (RSE 7%) for a 16 kg individual, respectively). When expressed per gram of organ, intrinsic clearance increases with increasing body weight in the gut wall, but decreases in the liver, indicating that CYP3A-mediated intrinsic clearance and local bioavailability in the gut wall and liver do not change with age in parallel. The resulting total bioavailability was found to be age-independent with a median of 20.8% in children (95%CI: 3.8–50.0%). Conclusion In conclusion, the intrinsic CYP3A-mediated gut wall clearance is substantially lower than the intrinsic hepatic CYP3A-mediated clearance in children from 1 to 18 years of age, and contributes less to the overall first-pass metabolism compared to adults. Electronic supplementary material The online version of this article (10.1007/s11095-018-2458-6) contains, which is available to authorized users.

Purpose Currently, the FDA allows biowaivers for Class I (high solubility and high permeability) and Class III (high solubility and low permeability) compounds of the Biopharmaceutics Classification System (BCS). Scientific evidence should be provided to support biowaivers for BCS Class I and Class III (high solubility and low permeability) compounds. Methods Data on the effects of excipients on drug permeability are needed to demonstrate that commonly used excipients do not affect the permeability of BCS Class III compounds, which would support the application of biowaivers to Class III compounds. This study was designed to generate such data by assessing the permeability of four BCS Class III compounds and one Class I compound in the presence and absence of five commonly used excipients. Results The permeability of each of the compounds was assessed, at three to five concentrations, with each excipient in two different models: Caco-2 cell monolayers, and in situ rat intestinal perfusion. No substantial increases in the permeability of any of the compounds were observed in the presence of any of the tested excipients in either of the models, with the exception of disruption of Caco-2 cell monolayer integrity by sodium lauryl sulfate at 0.1 mg/ml and higher. Conclusion The results suggest that the absorption of these four BCS Class III compounds would not be greatly affected by the tested excipients. This may have implications in supporting biowaivers for BCS Class III compounds in general.

This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s11095-016-1992-3, PURPOSE: For passive dry powder inhalers (DPIs) entrainment and emission of the aerosolized drug dose depends strongly on device geometry and the patient's inhalation manoeuvre. We propose a computational method for optimizing the entrainment part of a DPI. The approach assumes that the pulmonary delivery location of aerosol can be determined by the timing of dose emission into the tidal airstream. METHODS: An optimization algorithm was used to iteratively perform computational fluid dynamic (CFD) simulations of the drug emission of a DPI. The algorithm seeks to improve performance by changing the device geometry. Objectives were to achieve drug emission that was: A) independent of inhalation manoeuvre; B) similar to a target profile. The simulations used complete inhalation flow-rate profiles generated dependent on the device resistance. The CFD solver was OpenFOAM with drug/air flow simulated by the Eulerian-Eulerian method. RESULTS: To demonstrate the method, a 2D geometry was optimized for inhalation independence (comparing two breath profiles) and an early-bolus delivery. Entrainment was both shear-driven and gas-assisted. Optimization for a delay in the bolus delivery was not possible with the chosen geometry. CONCLUSIONS: Computational optimization of a DPI geometry for most similar drug delivery has been accomplished for an example entrainment geometry.

Objective Respiratory depression is a serious and potentially life-threatening side-effect of opioid therapy. The objective of this investigation was to characterize the relationship between buprenorphine or fentanyl exposure and the effectiveness and safety outcome in rats. Methods Data on the time course of the antinociceptive and respiratory depressant effect were analyzed on the basis of population logistic regression PK–PD models using non-linear mixed effects modeling software (NONMEM). The pharmacokinetics of buprenorphine and fentanyl were described by a three- and two-compartment model, respectively. A logistic regression model (linear logit model) was used to characterize the relationship between drug exposure and the binary effectiveness and safety outcome. Results For buprenorphine, the odds ratios (OR) were 28.5 (95% CI, 6.9–50.1) and 2.10 (95% CI, 0.71–3.49) for the antinociceptive and respiratory depressant effect, respectively. For fentanyl these odds ratios were 3.03 (95% CI, 1.87–4.21) and 2.54 (95% CI, 1.26–3.82), respectively. Conclusion The calculated safety index (ORantinociception/ORrespiratory depression) for fentanyl of 1.20 suggests that fentanyl has a low safety margin, implicating that fentanyl needs to be titrated with caution. For buprenorphine the safety index is 13.54 suggesting that buprenorphine is a relatively safe opioid.

Purpose To evaluate the nanoparticle tracking analysis (NTA) technique, compare it with dynamic light scattering (DLS) and test its performance in characterizing drug delivery nanoparticles and protein aggregates. Methods Standard polystyrene beads of sizes ranging from 60 to 1,000 nm and physical mixtures thereof were analyzed with NTA and DLS. The influence of different ratios of particle populations was tested. Drug delivery nanoparticles and protein aggregates were analyzed by NTA and DLS. Live monitoring of heat-induced protein aggregation was performed with NTA. Results NTA was shown to accurately analyze the size distribution of monodisperse and polydisperse samples. Sample visualization and individual particle tracking are features that enable a thorough size distribution analysis. The presence of small amounts of large (1,000 nm) particles generally does not compromise the accuracy of NTA measurements, and a broad range of population ratios can easily be detected and accurately sized. NTA proved to be suitable to characterize drug delivery nanoparticles and protein aggregates, complementing DLS. Live monitoring of heat-induced protein aggregation provides information about aggregation kinetics and size of submicron aggregates. Conclusion NTA is a powerful characterization technique that complements DLS and is particularly valuable for analyzing polydisperse nanosized particles and protein aggregates.

Purpose To identify the magnitude and sources of variability of a generic, aseptic manufacturing process for experimental anticancer agents employed at our facility, and to estimate the effects on product quality. Materials and Methods In-process and quality control data of all products manufactured according to this generic process (composed of weighing, dissolution, filtration, filling, semi-stoppering and lyophilization) over a 3-year period were retrospectively analyzed using mixed-effects analysis. Results Variability in the filling process was shown to be marginal and of minor importance for product quality in terms of content and content uniformity. An overall content of 101% was found with batch-to-batch and vial-to-vial variability up to 4.21% and 2.57%, respectively. Estimation of the overall batch failure revealed that structural bias in content and a high batch-to-batch variability in content were the most prominent factors determining batch failure. Furthermore, content and not content uniformity was shown to be most important parameter influencing batch failure. Calculated Process Capability Indices (CpKs) calculated for each product showed that the process is capable of manufacturing products which will routinely comply with the specification of 90–110% for content. However, the CpK values decreased dramatically using the specification of 95–105% as required for approved drug products. Conclusion These results indicate that at the early stage of product development less tight specification limits must be applied to prevent unnecessary batch rejection of investigational agents.

BACKGROUND AND PURPOSE The selection of the most suitable animal species and subsequent translation of the concentration-effect relationship to humans are critical steps for accurate assessment of the pro-arrhythmic risk of candidate molecules. The objective of this investigation was to assess quantitatively the differences in the QTc prolonging effects of moxifloxacin between cynomolgus monkeys, dogs and humans. The impact of interspecies differences is also illustrated for a new candidate molecule. EXPERIMENTAL APPROACH Pharmacokinetic data and ECG recordings from pre-clinical protocols in monkeys and dogs and from a phase I trial in healthy subjects were identified for the purpose of this analysis. A previously established Bayesian model describing the combined effect of heart rate, circadian variation and drug effect on the QT interval was used to describe the pharmacokinetic-pharmacodynamic relationships. The probability of a ≥ 10 ms increase in QT was derived as measure of the pro-arrhythmic effect. KEY RESULTS For moxifloxacin, the concentrations associated with a 50% probability of QT prolongation ≥ 10 ms (Cp50) varied from 20.3 to 6.4 and 2.6 μM in dogs, monkeys and humans, respectively. For NCE05, these values were 0.4 μM vs 2.0 μM for monkeys and humans, respectively. CONCLUSIONS AND IMPLICATIONS Our findings reveal significant interspecies differences in the QT-prolonging effect of moxifloxacin. In addition to the dissimilarity in pharmacokinetics across species, it is likely that differences in pharmacodynamics also play an important role. It appears that, regardless of the animal model used, a translation function is needed to predict concentration-effect relationships in humans.

Predicting human skin permeability of chemical compounds accurately and efficiently is useful for developing dermatological medicines and cosmetics. However, previous work have two problems; 1) quality of databases used, and 2) methods for prediction models. In this paper, we attempt to solve these two problems. We first compile, by carefully screening from the literature, a novel dataset of chemical compounds with permeability coefficients, measured under consistent experimental conditions. We then apply machine learning techniques such as support vector regression (SVR) and random forest (RF) to our database to develop prediction models. Molecular descriptors are fully computationally obtained, and greedy stepwise selection is employed for descriptor selection. Prediction models are internally and externally validated. We generated an original, new database on human skin permeability of 211 different compounds from aqueous donors. Nonlinear SVR achieved the best performance among linear SVR, nonlinear SVR, and RF. The determination coefficient, root mean square error, and mean absolute error of nonlinear SVR in external validation were 0.910, 0.342, and 0.282, respectively. We provided one of the largest datasets with purely experimental log k p and developed reliable and accurate prediction models for screening active ingredients and seeking unsynthesized compounds of dermatological medicines and cosmetics.

This paper aims to elucidate quantitative relationships between small molecule solubility/water-uptake in triglyceride/monoglyceride lipid formulations, the chemical structure of the solute, and the solvent composition.Solubility and water uptake in tricaprylin/1-monocaprylin and tricaprylin/1-monocaprin mixtures in the "microemulsion" region at 37 degrees C were determined with HPLC and KF coulometry, respectively. Twelve model solutes varying in hydrogen bond acidity, basicity, polarity, and molecular volume were chosen. Linear free energy relationships (LFER) (Abraham type) were implemented to obtain solvent coefficients at various monoglyceride concentrations.Profiles for both solubility and water uptake (at different water activities) in lipid mixtures containing different monoglycerides were superimposable, producing a single master curve when the monoglyceride concentrations were plotted on a molar scale. The LFER derived solvent coefficients showed a systematic dependence on the lipid composition consistent with the view that relative solubility is determined largely by the molar concentrations of individual functional groups such as glyceride ester moieties and hydroxyl groups. At low RH, water uptake increased linearly with monoglyceride concentration while cooperativity was evident in water uptake profiles at high RH.This study provides a potential universal framework for predicting relative drug solubility in mixtures containing fully saturated triglycerides and monoglycerides.

International audience; PURPOSE: The aim of this study is to define and illustrate metrics for the external evaluation of a population model. MATERIALS AND METHODS: In this paper, several types of metrics are defined: based on observations (standardized prediction error with or without simulation and normalized prediction distribution error); based on hyperparameters (with or without simulation); based on the likelihood of the model. All the metrics described above are applied to evaluate a model built from two phase II studies of gliclazide. A real phase I dataset and two datasets simulated with the real dataset design are used as external validation datasets to show and compare how metrics are able to detect and explain potential adequacies or inadequacies of the model. RESULTS: Normalized prediction errors calculated without any approximation, and metrics based on hyperparameters or on objective function have good theoretical properties to be used for external model evaluation and showed satisfactory behaviour in the simulation study. CONCLUSIONS: For external model evaluation, prediction distribution errors are recommended when the aim is to use the model to simulate data. Metrics through hyperparameters should be preferred when the aim is to compare two populations and metrics based on the objective function are useful during the model building process.

Applications of transdermal drug delivery are limited by low skin permeability. Many chemicals have been used to enhance skin permeability, however, only a handful are actually used in practice. Combinations of chemicals are likely to be more efficient in enhancing skin permeability compared to individual enhancers. However, identification of efficient enhancer combinations is quite challenging because many chemical enhancers interact with each other and with the skin in a complex manner. In the absence of a fundamental knowledge of such interactions, we need to rely on rapid methods to screen various enhancer combinations for their effectiveness. In this paper, we report a novel high throughput (HTP) method that is at least 50-fold more efficient in terms of skin utilization and up to 30-fold more efficient in terms of holdup times than the current methods for formulation screening (Franz diffusion cells).A high throughput method was developed based on skin conductivity and mannitol penetration into the skin. This method was used to perform at least 100 simultaneous tests per day. Detailed studies were performed using two model enhancers, sodium lauryl sulfate (SLS) and dodecyl pyridinium chloride (DPC). The predictions of the high throughput method were validated using Franz diffusion cells.High throughput screening revealed that mixtures of SLS and DPC are significantly more effective in enhancing transdermal transport compared to each of them alone. Maximum efficiency was observed with near-equimolar mixtures of SLS: DPC. The predictions of the HTP method compared well against those made using Franz diffusion cells. Specifically, the effect of surfactant mixtures on skin conductivity and mannitol permeability measured using Franz cells also showed a maximum at near-equimolar mixtures of SLS: DPC.The novel HTP method allows rapid screening of enhancer formulations for transdermal applications. This method can be used to discover new and effective enhancer mixtures. At the same time, these data may also broaden our understanding of the effect of enhancers on skin permeability.