A comprehensive physiologically based pharmacokinetic (PBPK) model for nicotine in humans from using nicotine-containing products with different routes of exposure

Dose Estimation Utility in a Population Pharmacokinetic Analysis of Inhaled Δ9-Tetrahydrocannabinol Cannabis Market Products in Occasional and Daily Users

BACKGROUND

Unusually high variability in blood Δ9-tetrahydrocannabinol (THC) concentrations have been observed in subjects inhaling similar cannabis products over similar time periods when consumption is ad libitum. This makes simple gravimetric dose estimation a poor predictor of THC exposure. Population pharmacokinetic analyses of blood THC concentration versus time data are routinely used to estimate pharmacokinetic parameters. The aim of this study was to estimate the inhaled dose of THC in occasional and daily users of high potency market cannabis.

METHODS

Blood THC concentrations were measured for 135 minutes from 29 participants who either smoked high concentration flower or inhaled concentrates ad libitum during a 15-minute session. Frequent blood samples were obtained over the following 135 minutes.

RESULTS

The estimated central and rapidly equilibrating volumes of distribution of a 3-compartment model were 19.9 ± 1.2 and 51.6 ± 4.7 L whereas the intercompartmental clearances were 1.65 ± 0.14 and 1.75 ± 0.10 L/min, respectively. Covariate-adjusted analysis revealed that the estimated inhaled THC dose was considerably less among occasional users compared with daily users.

CONCLUSIONS

Three-compartment pharmacokinetics of THC did not differ among the 3 user groups, and the early phase (first 135 minutes postinception of inhalation) kinetics were similar to those previously described after smoking low potency cannabis products. Therefore, inhaled THC dose can be estimated from pharmacokinetic data and covariate-driven adjustments can be used to estimate THC doses, based on the participant cannabis usage pattern (occasional versus daily), improving the accuracy of THC exposure estimates compared with those derived from weighed THC content alone.

Nicotine flux and pharmacokinetics-based considerations for early assessment of nicotine delivery systems

In the past few years, technological advancements enabled the development of novel electronic nicotine delivery systems (ENDS). Several empirical measures such as "nicotine flux" are being proposed to evaluate the abuse liability potential of these products. We explored the applicability of nicotine flux for clinical nicotine pharmacokinetics (PK) and 52-week quit success from cigarettes for a wide range of existing nicotine delivery systems. We found that the differences in nicotine flux for various nicotine delivery systems are not related to changes in PK, as nicotine flux does not capture key physiological properties such as nicotine absorption rate. Further, the 52-week quit success and abuse liability potential of nicotine nasal sprays (high nicotine flux product), and nicotine inhalers (nicotine flux similar to ENDS) are low, suggesting that nicotine flux is a poor metric for the assessment of nicotine delivery systems. PK indices are more dependable for characterizing nicotine delivery systems, and a nicotine plasmaC max T max > 1 could improve 52-week quit success from cigarettes. However, a single metric may be inadequate to fully assess the abuse liability potential of nicotine delivery systems and needs to be further studied. A combination of in vitro and in silico approaches could potentially address the factors influencing the inhaled aerosol dosimetry and resulting PK of nicotine to provide early insights for ENDS assessments. Further research is required to understand nicotine dosimetry and PK for ad libitum product use, and abuse liability indicators of nicotine delivery systems. This commentary is intended to (1) highlight the need to think beyond a single empirical metric such as nicotine flux, (2) suggest potential PK-based metrics, (3) suggest the use of in vitro and in silico tools to obtain early insights into inhaled aerosol dosimetry for ENDS, and (4) emphasize the importance of considering comprehensive clinical pharmacology outcomes to evaluate nicotine delivery systems.

In Silico Studies to Support Vaccine Development

The progress that has been made in computer science positioned in silico studies as an important and well-recognized methodology in the drug discovery and development process. It has numerous advantages in terms of costs and also plays a huge impact on the way the research is conducted since it can limit the use of animal models leading to more sustainable research. Currently, human trials are already being partly replaced by in silico trials. EMA and FDA are both endorsing these studies and have been providing webinars and guidance to support them. For instance, PBPK modeling studies are being used to gather data on drug interactions with other drugs and are also being used to support clinical and regulatory requirements for the pediatric population, pregnant women, and personalized medicine. This trend evokes the need to understand the role of in silico studies in vaccines, considering the importance that these products achieved during the pandemic and their promising hope in oncology. Vaccines are safer than other current oncology treatments. There is a huge variety of strategies for developing a cancer vaccine, and some of the points that should be considered when designing the vaccine technology are the following: delivery platforms (peptides, lipid-based carriers, polymers, dendritic cells, viral vectors, etc.), adjuvants (to boost and promote inflammation at the delivery site, facilitating immune cell recruitment and activation), choice of the targeted antigen, the timing of vaccination, the manipulation of the tumor environment, and the combination with other treatments that might cause additive or even synergistic anti-tumor effects. These and many other points should be put together to outline the best vaccine design. The aim of this article is to perform a review and comprehensive analysis of the role of in silico studies to support the development of and design of vaccines in the field of oncology and infectious diseases. The authors intend to perform a literature review of all the studies that have been conducted so far in preparing in silico models and methods to support the development of vaccines. From this point, it was possible to conclude that there are few in silico studies on vaccines. Despite this, an overview of how the existing work could support the design of vaccines is described.

Deconvolution of Systemic Pharmacokinetics Predicts Inhaled Aerosol Dosimetry of Nicotine

Absorption of inhaled compounds can occur from multiple sites based on upper and lower respiratory tract deposition, and clearance mechanisms leading to differential local and systemic pharmacokinetics. Deriving inhaled aerosol dosimetry and local tissue concentrations for nose-only exposure in rodents and inhaled products in humans is challenging. In this study we use inhaled nicotine as an example to identify regional respiratory tract deposition, absorption fractions, and their contribution toward systemic pharmacokinetics in rodents and humans. A physiologically based pharmacokinetic (PBPK) model was constructed to describe the disposition of nicotine and its major metabolite, cotinine. The model description for the lungs was simplified to include an upper respiratory tract region with active mucociliary clearance and a lower respiratory tract region. The PBPK model parameters such as rate of oral absorption, metabolism and clearance were fitted to the published nicotine and cotinine plasma concentrations post systemic administration and oral dosing. The fractional deposition of inhaled aerosol in the upper and lower respiratory tract regions was estimated by fitting the plasma concentrations. The model predicted upper respiratory tract deposition was 63.9% for nose-only exposure to nicotine containing nebulized aqueous aerosol in rats and 60.2% for orally inhaled electronic vapor product in humans. A marked absorption of nicotine from the upper respiratory tract and the gastrointestinal tract for inhaled aqueous aerosol contributed to the differential systemic pharmacokinetics in rats and humans. The PBPK model derived dosimetry shows that the current aerosol dosimetry models with their posteriori application using independent aerosol physicochemical characterization to predict aerosol deposition are insufficient and will need to consider complex interplay of inhaled aerosol evolutionary process. While the study highlights the needs for future research, it provides a preliminary framework for interpreting pharmacokinetics of inhaled aerosols to facilitate the analysis of in vivo exposure-responses for pharmacological and toxicological assessments.

Oral Absorption across Organotypic Culture Models of the Human Buccal Epithelium after E-cigarette Aerosol Exposure

Inhaled aerosols are absorbed across the oral cavity, respiratory tract, and gastrointestinal tract. The absorption across the oral cavity, which is one of the exposure routes, plays an important role in understanding pharmacokinetics and physiological effects. After aerosol exposure from e-cigarettes, tissue viability studies, morphological observation, and chemical analyses at the inner and outer buccal tissues were performed using organotypic 3D in vitro culture models of the buccal epithelium to better understand the deposition and absorption on the inner and outer buccal tissues. The aerosol exposures did not affect the tissue viability and had no change to the tissue morphology and structure. The deposition ratio at the buccal tissue surface is relatively low. This shows that majority of aerosol transfers to the airway tissues. The distribution from the inner tissue to the outer tissue has selectivity among various compounds, depending on the affinity with the liquid crystal structure of phospholipids and glucosylceramide. Although nicotine absorption in the aqueous solution was well known to increase as the unprotonated state of nicotine increased, the nicotine absorption after the aerosol exposure is irrelevant to the protonated-unprotonated state. Furthermore, the results showed that half of nicotine that adhered to the oral cavity transferred to the inner tissue via the oral epithelium and the other half transferred to the gastrointestinal tract accompanying multiple executions of swallowing, while majority of the water-soluble compounds with the hydroxyl group such as propylene glycol and benzoic acid that adhered to the oral cavity were eluted with the saliva and transferred to the gastrointestinal tract by swallowing.

Artificial intelligence-driven prediction of multiple drug interactions

When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug-drug, drug-food (excipients) and drug-microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug-drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.