A mechanistic framework for a priori pharmacokinetic predictions of orally inhaled drugs

Predicting Maternal and Infant Tetrahydrocannabinol Exposure in Lactating Cannabis Users: A Physiologically Based Pharmacokinetic Modeling Approach

A knowledge gap exists in infant tetrahydrocannabinol (THC) data to guide breastfeeding recommendations for mothers who use cannabis. In the present study, a paired lactation and infant physiologically based pharmacokinetic (PBPK) model was developed and verified. The verified model was used to simulate one hundred virtual lactating mothers (mean age: 28 years, body weight: 78 kg) who smoked 0.32 g of cannabis containing 14.14% THC, either once or multiple times. The simulated breastfeeding conditions included one-hour post smoking and subsequently every three hours. The mean peak concentration (Cmax) and area under the concentration-time curve (AUC(0-24 h)) for breastmilk were higher than in plasma (Cmax: 155 vs. 69.9 ng/mL; AUC(0-24 h): 924.9 vs. 273.4 ng·hr/mL) with a milk-to-plasma AUC ratio of 3.3. The predicted relative infant dose ranged from 0.34% to 0.88% for infants consuming THC-containing breastmilk between birth and 12 months. However, the mother-to-infant plasma AUC(0-24 h) ratio increased up to three-fold (3.4-3.6) with increased maternal cannabis smoking up to six times. Our study demonstrated the successful development and application of a lactation and infant PBPK model for exploring THC exposure in infants, and the results can potentially inform breastfeeding recommendations.

Predicting Regional Respiratory Tissue and Systemic Concentrations of Orally Inhaled Drugs through a Novel PBPK Model

Oral inhalation (OI) of drugs is the route of choice to treat respiratory diseases or for recreational drug use (e.g., cannabis). After OI, the drug is deposited in and systemically absorbed from various regions of the respiratory tract. Measuring regional respiratory tissue drug concentrations at the site of action is important for evaluating the efficacy and safety of orally inhaled drugs (OIDs). Because such a measurement is routinely not possible in humans, the only alternative is to predict these concentrations, for example by physiologically based pharmacokinetic (PBPK) modeling. Therefore, we developed an OI-PBPK model to integrate the interplay between regional respiratory drug deposition and systemic absorption to predict regional respiratory tissue and systemic drug concentrations. We validated our OI-PBPK model by comparing the simulated and observed plasma concentration-time profiles of two OIDs, morphine and nicotine. Furthermore, we performed sensitivity analyses to quantitatively demonstrate the impact of key parameters on the extent and pattern of regional respiratory drug deposition, absorption, and the resulting regional respiratory tissue and systemic plasma concentrations. Our OI-PBPK model can be applied to predict regional respiratory tissue and systemic drug concentrations to optimize OID formulations, delivery systems, and dosing regimens. Furthermore, our model could be used to establish the bioequivalence of generic OIDs for which systemic plasma concentrations are not measurable or are not a good surrogate of the respiratory tissue drug concentrations. SIGNIFICANCE STATEMENT: Our OI-PBPK model is the first comprehensive model to predict regional respiratory deposition, as well as systemic and regional tissue concentrations of OIDs, especially at the drug's site of action, which is difficult to measure in humans. This model will help optimize OID formulations, delivery systems, dosing regimens, and bioequivalence assessment of generic OID. Furthermore, this model can be linked with organs-on-chips, pharmacodynamic and quantitative systems pharmacology models to predict and evaluate the safety and efficacy of OID.

Physiologically Based Modelling Framework for Prediction of Pulmonary Pharmacokinetics of Antimicrobial Target Site Concentrations

BACKGROUND AND OBJECTIVES

Prediction of antimicrobial target-site pharmacokinetics is of relevance to optimize treatment with antimicrobial agents. A physiologically based pharmacokinetic (PBPK) model framework was developed for prediction of pulmonary pharmacokinetics, including key pulmonary infection sites (i.e. the alveolar macrophages and the epithelial lining fluid).

METHODS

The modelling framework incorporated three lung PBPK models: a general passive permeability-limited model, a drug-specific permeability-limited model and a quantitative structure-property relationship (QSPR)-informed perfusion-limited model. We applied the modelling framework to three fluoroquinolone antibiotics. Incorporation of experimental drug-specific permeability data was found essential for accurate prediction.

RESULTS

In the absence of drug-specific transport data, our QSPR-based model has generic applicability. Furthermore, we evaluated the impact of drug properties and pathophysiologically related changes on pulmonary pharmacokinetics. Pulmonary pharmacokinetics were highly affected by physiological changes, causing a shift in the main route of diffusion (i.e. paracellular or transcellular). Finally, we show that lysosomal trapping can cause an overestimation of cytosolic concentrations for basic compounds when measuring drug concentrations in cell homogenate.

CONCLUSION

The developed lung PBPK model framework constitutes a promising tool for characterization of pulmonary exposure of systemically administrated antimicrobials.

Inferring pulmonary exposure based on clinical PK data: accuracy and precision of model-based deconvolution methods

Determining and understanding the target-site exposure in clinical studies remains challenging. This is especially true for oral drug inhalation for local treatment, where the target-site is identical to the site of drug absorption, i.e., the lungs. Modeling and simulation based on clinical pharmacokinetic (PK) data may be a valid approach to infer the pulmonary fate of orally inhaled drugs, even without local measurements. In this work, a simulation-estimation study was systematically applied to investigate five published model structures for pulmonary drug absorption. First, these models were compared for structural identifiability and how choosing an inadequate model impacts the inference on pulmonary exposure. Second, in the context of the population approach both sequential and simultaneous parameter estimation methods after intravenous administration and oral inhalation were evaluated with typically applied models. With an adequate model structure and a well-characterized systemic PK after intravenous dosing, the error in inferring pulmonary exposure and retention times was less than twofold in the majority of evaluations. Whether a sequential or simultaneous parameter estimation was applied did not affect the inferred pulmonary PK to a relevant degree. One scenario in the population PK analysis demonstrated biased pulmonary exposure metrics caused by inadequate estimation of systemic PK parameters. Overall, it was demonstrated that empirical modeling of intravenous and inhalation PK datasets provided robust estimates regarding accuracy and bias for the pulmonary exposure and pulmonary retention, even in presence of the high variability after drug inhalation.