IMR Press / FBL / Volume 27 / Issue 8 / DOI: 10.31083/j.fbl2708228
Open Access Short Communication
Using the BMD Approach to Derive Acceptable Daily Intakes of Cannabidiol (CBD) and Tetrahydrocannabinol (THC) Relevant to Electronic Cigarette Liquids
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1 Chemisches und Veterinäruntersuchungsamt Karlsruhe, 76187 Karlsruhe, Germany
2 Abteilung Lebensmittelchemie, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
*Correspondence: (Dirk W. Lachenmeier)
Academic Editor: Marcello Iriti
Front. Biosci. (Landmark Ed) 2022, 27(8), 228;
Submitted: 6 June 2022 | Revised: 16 June 2022 | Accepted: 8 July 2022 | Published: 25 July 2022
(This article belongs to the Special Issue Bioactive Phytochemicals and Botanicals in Health and Disease)
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

Background: In the past 60 years, Cannabis sativa L. has been an object of increasing interest because of the psychotropic effects of some of its constituents. These effects mainly arise from the cannabinoid Δ9-tetrahydrocannabinol (Δ9-THC). C. sativa species also synthesize and accumulate the non-psychotropic compound cannabidiol (CBD). Due to their therapeutic potential, both cannabinoids are an object of medical research and drug development. More recently, CBD has received increasing interest as an ingredient in electronic cigarette liquids (e-liquids). This trend may have been reinforced by health and disease-related claims, often based on clinical studies, which are used to advertise CBD. CBD liquids may be based on full-spectrum hemp extracts, CBD isolates, or synthetic CBD, all of which may contain some residual levels of Δ9-THC from either natural content (in the extracts) or from possible degradation of CBD to Δ9-THC, which may occur during storage. There is uncertainty about safety regarding the consumption of CBD (and Δ9-THC) in e-liquids. The aim of this publication was to present an approach for a toxicological risk assessment of CBD and Δ9-THC relevant to e-liquids by using the benchmark dose (BMD) approach. Materials and Methods: Before an analysis to estimate a reference dose (RfD) for both cannabinoids, a systematic review of dose-response data was conducted. The data obtained were analyzed using the BMD approach to derive a benchmark dose lower confidence limit (BMDL). The BMDL was used as a point of departure to estimate the RfD. Results: No adequate human data suitable for dose-response modeling were identified. Based on animal data, the RfD values for the most sensitive endpoints were selected. For CBD, an RfD for acute exposure of 1 mg/kg body weight (bw) was estimated. For Δ9-THC, an acute RfD was found to be 0.006 mg/kg bw. Additionally, the RfD for chronic exposure to CBD was estimated to be 4 mg/kg bw per day. The respective endpoints for CBD were a reduction in norepinephrine turnover and a reduction in uterus weight. The endpoint for Δ9-THC was a change in blood pressure. Conclusions: Because of the limited availability and quality of dose-response data, it cannot be excluded that the estimated RfD values might be afflicted with considerable uncertainties. Therefore, it is recommended to conduct further research on dose-response data, preferably from human studies.

risk assessment
benchmark dose
1. Introduction

During recent years, cannabinoids such as cannabidiol (CBD) and Δ9-tetrahydrocannabidiol (Δ9-THC) in particular have faced increasing research interest, due to several beneficial effects attributed to the compounds [1]. Cannabinoid formulations are currently used as a therapeutic approach for treating neuropathic disorders, such as severe forms of treatment-resistant epilepsy [2, 3]. In 2018, the prescription anticonvulsant Epidiolex, a drug containing CBD as the pharmacologically active compound, was approved by the Food and Drug Administration (FDA) in the United States of America [4]. Since then, the drug has been used to treat the Lennox-Gastaut syndrome (LGS) and the Dravet syndrome (DS) in children [5]. In addition to the reported antiepileptic effects of CBD, anxiolytic, antipsychotic, and anti-inflammatory effects have been reported in humans [2, 6]. Further potentially beneficial effects have been mentioned in several controlled clinical trials, which investigated the therapeutic effects of CBD preparations administered to patients suffering from schizophrenia [2].

While Δ9-THC directly interacts with the endocannabinoid receptors CB1 and CB2, molecular interactions of CBD occur only with low affinity at those targets [7, 8]. A major benefit of the administration of CBD-based drugs is a low incidence of adverse events in patients and volunteers in randomized controlled trials (RCTs) [9].

Different modes of action (MOAs) for CBD have been elucidated. CBD interacts with multiple cellular pathways. For example, through interaction with cytochrome P450 metabolic pathways, CBD can increase effects of Δ9-THC [10]. Other target receptors for CBD are TRPA1, as well as TRPV1/V2 and TRPM8, which are involved in the nociceptive system.

CBD is further known to increase beneficial effects of THC observed in studies co-administering CBD and Δ9-THC [11]. The drug Nabiximols is used in the oromucosal spray Sativex to treat spasticity and pain in multiple sclerosis and contains CBD and Δ9-THC in equal amounts. Sativex delivers 2.7 mg Δ9-THC and 2.5 mg CBD per application [12]. Administration of both compounds in combination allows for administering greater amounts of Δ9-THC to patients [2].

Due to strict legislative regulations in most countries and its analgesic effects, Δ9-THC is primarily administered as the prescription drug dronabinol during chemotherapy in cancer treatment as an antinociceptive and antiemetic agent and as an antiemetic in patients being affected by AIDS [13, 14, 15].

In the last decade, the use of electronic cigarettes (e-cigarettes) has experienced a significant increase in many countries as an attractive alternative to smoking. E-cigarettes or vaporizers are deemed a reduced risk alternative to conventional cigarettes and have found a wide application as a substitute delivery system. The devices operate by heating so-called e-liquids until vaporization. The aerosol is inhaled by the consumer. E-liquids contain dissolved aroma compounds in an organic matrix such as glycerol and propylene glycol [16]. Recent cases of e-cigarette associated lung injury (EVALI) raised concerns about the safety of e-cigarettes. In 2020, 2807 cases of EVALI have been reported, resulting in the deaths of 68 consumers in the United States [17]. In other countries e-cigarette associated cases of hospitalization and even deaths have been reported [18]. Some mechanisms leading to EVALI have been investigated in animal models, such as oxidative stress and inflammatory reactions in lung epithelia resulting in a damaging of alveolar tissues and cells [19], which is assumed to be the result of the accumulation of vitamin E acetate in alveolar tissue.

The beneficial effect of CBD concerning bone health is subject to current research. CBD preparations were found to reduce bone loss and promote bone healing in rats [20, 21]. However, it needs to be elucidated, whether exposure to CBD via inhalation of CBD liquids has the same effect on humans. This is relevant concerning the discussed osteotoxic effect of nicotine and flavoring agents in e-liquids on bone integrity [22].

E-liquid preparations containing the non-psychotropic CBD are commercially available. In the European Union (EU), e-liquids containing CBD are not necessarily legal for sale because CBD may be a compound not in compliance with Art. 7 No. 6a of directive 2014/40/EU, which prohibits the placing on the market of tobacco products, which contain additives that create the impression that a tobacco product has a health benefit [23]. Due to narcotic laws in most countries, e-liquids are also not allowed to exceed certain thresholds of Δ9-THC, which vary between member states of the European Union and the United Kingdom [24]. Despite regulatory difficulties, a wide variety of commercially available e-liquids containing CBD is available on the market in different concentrations and the popularity of e-cigarettes has offered cannabis users new ways of consuming cannabis and its preparations [25].

This study aimed to conduct a benchmark dose modeling for CBD and Δ9-THC in order to find thresholds for an acceptable level of the 2 compounds in e-liquids.

2. Materials and Methods
2.1 Literature Research

The first step in this study was to obtain suitable data from the scientific literature. Only in vivo data from studies in different animal models and human data were considered appropriate. The accepted animal models were mice, rats, and rhesus monkeys. The data acquisition was carried out using the Google Scholar search engine, the PubMed database of the National Library of Medicine, and the Cochrane Library, as well as additional online resources such as databases provided by publishers, including Nature and Science.

The major search terms and keywords used are listed below. To complement the data acquisition, combinations and variations of search terms and keywords were used as well. Slashes separating keywords in the list below indicate, that the separated keywords were used individually or in combination with each other.

Search terms and keywords:

• toxicity of CBD/Δ9-THC

• acute/chronic toxicity of CBD/Δ9-THC

• acute/chronic toxicity of CBD/Δ9-THC

• CBD/Δ9-THC inhalation in rats/mice/monkeys/humans

• vaporized CBD/Δ9-THC in rats/mice/monkeys/humans

• oral CBD/Δ9-THC in rats/mice/monkeys/humans

• intravenous (i.v.) CBD/Δ9-THC in rats/mice/monkeys/humans

• bioavailability of oral and inhaled CBD/Δ9-THC in rats/mice/monkeys/humans

• randomized controlled trials for CBD/Δ9-THC administration in humans

• combinations of the search terms and keywords mentioned above with author names

A further research strategy, which has been found useful, was to directly search for publications found in references of studies and reviews.

To be included, the literature data had to meet several criteria. A study considered for inclusion in this research had to have administered at least 3 different doses and a control group receiving vehicle. Dose spacing was not considered relevant. Furthermore, applied doses had to be administered in mg/kg of body weight. Studies reported to have administered only fixed doses of CBD or Δ9-THC without considering the respective subject’s body weight were excluded, as precise information about dosage was preferred. The number of animals per dose group had to be declared. Studies reporting concomitant treatment with other compounds were not included.

The reported results were expected to be presented as a mean effect with a standard deviation. Alternatively, the standard error of a dose group could be converted into a standard deviation by multiplying the value of the error with the square root of the number of animals in the respective dose group.

The test substances that were used had to be as pure as possible. The results of formulations with combinations of CBD with Δ9-THC were considered unreliable. Studies that investigated the effects of mixtures such as hashish or marihuana preparations (smoked cannabis) were excluded. Studies in humans that received concomitant medication were also not included.

Data, which were only available plotted in diagrams, were included if data points for mean effect values and standard deviations were distinguishable from other plotted values. For numerical retrieval of plotted data, a dedicated plugin for ImageJ image processing software Version 1.8.0 (National Institutes of Health, Bethesda, MD, USA) was applied [26].

2.2 Benchmark Dose Modeling

In toxicological risk assessment, the benchmark dose (BMD) approach is a more advanced statistical method than the established No Observed Adverse Effect Level (NOAEL) approach [27]. The BMD approach was first introduced by Crump [28] as an alternative method to NOAEL.

The benchmark dose (BMD) is defined by the United States Environmental Protection Agency (EPA) as the exposure level that corresponds to a percentage increase (usually 5 or 10%) in the probability of an adverse event (response) compared to a control scenario with no exposure [27]. This change in response to an exposure is called a benchmark response (BMR). Therefore, a BMR of 5% would be defined as a 5% increase in the number of subjects with an incidence of an adverse event. Benchmark models provide not only a BMD but also a confidence interval, which contains the value of the respective BMD. The limits of the confidence intervals obtained are called the benchmark dose lower limit (BMDL) and the benchmark dose upper limit (BMDU). In toxicological risk assessment the BMDL is used for estimating a reference value for consumer safety.

The BMD and its respective confidence interval, with the BMDL being the Point of Departure (POD), are calculated by fitting multiple statistical models. The benchmark dose software (BMDS), which has been developed by the EPA [29], performs automated fitting of selected models to dose-response data retrieved from toxicological studies.

The output delivered by the software contains the results of the models, namely, the BMD, the BMDL and the BMDU, with a recommendation for the most suitable model. The most suitable model is determined by comparing the Akaike information criteria generated in the output.

Before a BMD analysis could be carried out by using the BMDS, settings needed to be adjusted in the main workspace window. The data type obtained from publications included in this assessment was found to be continuous. For the benchmark response, the default setting for continuous data of one standard deviation (1SD) was selected.

2.3 Dose-Conversion and Route-to-Route Extrapolation for Inhalation Exposure from Animal BMDLs

To obtain RfD values for inhalation in humans, a concept has been designed for converting animal BMDLs into RfD values. This was an important step because most BMDLs considered reliable were obtained from analyzing dose-response data from animal studies in other routes besides inhalation (i.e., oral or i.v.). The conversion is carried out in several steps.

Initially, route-specific RfDs in humans have been calculated from animal BMDL for the same route by using an uncertainty factor (UF) of 10 to account for interspecies variability and an additional UF of 10 to account for intraspecies variability.

(1) R f D h u m a n , r o u t e = B M D L a n i m a l , r o u t e U F i n t e r s p e c i e s * U F i n t r a s p e c i e s

This equation yields an RfD for exposure in humans by the same administration route as used in the animal model. The next step calculates a theoretical RfD for the same dose-dependent response after an intravenous (i.v.) administration in humans (RfDhuman,i.v.). By definition, the intravenous administration of a chemical compound results in systemic bioavailability (F) of the substance of 100%, which means, that 100% of the injected compound is available in a subject’s blood circuit [30]. This means that the concentration value of the RfDhuman,i.v. can equal the plasma concentration of a substance that is injected intravenously. The RfDhuman,i.v. can be calculated by dividing the previously determined RfDhuman,route by the route-specific bioavailability in humans designated here as Fhuman,route for the same route the test substance was administered in the animal model:

(2) R f D h u m a n , i . v . = R f D a n i m a l , r o u t e F h u m a n , r o u t e

This step is necessary because it allows one to calculate the final estimated RfD for inhalation in humans (RfDhuman,inhal.). The RfDhuman,inhal. can be determined by dividing the RfDhuman,i.v. by the bioavailability for a substance after inhalation in humans:

(3) R f D h u m a n , i n h a l a t i o n = R f D h u m a n , i . v . F h u m a n , i n h a l a t i o n

This approach allows for the estimation of an RfD for inhalation in humans from BMDLs derived from animal models for different administration routes and can also be used to convert applied doses, documented in the literature, to doses for inhalation, which would lead to the same observed adverse effect. This facilitated a comparison of the estimated RfDs with the RfDs in the literature. To perform this approach for extrapolation, the values of the respective bioavailability for each route need to be known. The respective bioavailability values for human exposure to CBD and Δ9-THC were retrieved from literature and can be found in Table 1 (Ref. [1, 6, 31, 32]). The table shows the values of the respective bioavailability obtained from the references. When a range between 2 values was presented, the highest value was selected.

Table 1.Bioavailability of CBD and Δ9-THC in humans after oral administration and inhalation.
Test substance Administration route Bioavailability Reference
CBD oral 19% Mechoulam et al. [6]
inhalation 31% Lucas et al. [31]
Δ9-THC oral 20% Grotenhermen et al. [1]
inhalation 37% Lunn et al. [32]
3. Results

Before conducting this risk assessment, a literature review was performed to obtain usable dose-response data (Fig. 1). Data that met the inclusion criteria were included for a BMD analysis using the benchmark dose software (BMDS) provided by the EPA [29]. A total of 609 publications were screened for usable dose-response data, of which 567 publications were excluded. Of the remaining 42 studies, 2 studies did not contain dose-response data and 23 studies had no usable dose-response data. Six studies were included for exposure assessment and have been used to develop the dose conversion model, which has been introduced before. Among 11 studies that were considered to be used for a risk assessment using BMD modeling, 4 animal studies contained sufficient dose-response data for a BMD analysis.

Fig. 1.

Flow-chart diagram of all studies reviewed for this risk assessment. Publications were finally included after published dose-response data were considered suitable for a risk assessment using the BMD approach.

This article presents the lowest BMD and BMDL values obtained by conducting BMD analyses with dose-response data from animal studies that administered CBD or Δ9-THC via different administration routes. The results obtained from the BMD analysis of the included dose-response data are presented in Table 2 (Ref. [33, 34, 35]). The lowest BMDLs for long-term inhalation of CBD in humans was found to be approximately 26 mg/kg bw (p-value: 0.7). The BMDL was derived from an oral-study by Marx et al. [34], who administered CBD preparations in rats for 90 consecutive days. The observed endpoint was a change in the weight of the uterus. A single acute BMDL of 4.3 mg/kg bw was derived from a study by Steger et al. [33] (p-value: 0.06). The respective endpoint was found to be a change in norepinephrine (NE) turnover. This BMDL was derived from a study by Steger et al. [33] who administered oral CBD ranging from concentrations of 0 mg/kg bw (Control) to 10 mg/kg bw to rats. A BMDL of acute exposure to Δ9-THC has been derived from Siqueira et al. [35] who injected solutions of Δ9-THC in mice with a dose range of 0 mg/kg bw to 10 mg/kg bw. The observed adverse effect was a change in mean arterial blood pressure at a BMDL of 0.22 mg/kg bw (p-value: 0.4).

Table 2.Results of the BMD modeling of data obtained from animal studies in rats.
Author Model Study type Compound Route of administration Doses [mg/kg bw] Endpoint BMD BMDL p-value a
Marx et al. [34] *b rats 90-day study; daily administration CBD Oral 0 (Control), 100, 360, 720 Uterus weight 79 26 0.7
Steger et al. [33] rats acute CBD Single oral dose 0 (Control), 0.1, 1, 10 Norepinephrine turnover 6.8 4.3 0.06
Siqueira et al. [35] *b rats acute Δ9-THC Intravenous 0 (Control), 1, 2, 5, 10 Change in mean arterial blood pressure 3.05 0.22 0.4
a The p-values provide information about accuracy of predicted BMD models. A p-value greater than 0.1 indicates that the model fits the data (p-value 1.0 = perfect fit).
b Asterisks (*) indicate that the authors reported some statistically significant responses among their published dose-response data, which result from the administration of the corresponding doses.
4. Discussion
4.1 Dose-Response Modeling of CBD and Δ9-THC

During research for retrieving published data for deriving an RfD, different toxicological endpoints regarding physiological and psychological parameters were considered. However, not all parameters are considered equally relevant. To achieve an appropriate evaluation of the safety of CBD and Δ9-THC, toxicological endpoints were prioritized. Endpoints of higher clinical importance were considered changes in blood pressure, heart rate, blood parameters, cell titers in blood specimens, endocrinological parameters, as well as changes in organ weights, as these findings might indicate long-term health effects for consumers. The endpoints of lesser significance are supposedly changes in nociception, body temperature, and food intake. Another prioritization refers to the experimental model and administration route of the compounds. Per definition, dose-response data retrieved from human studies (e.g., RCTs) is considered to be more reliable than data from animal models. Furthermore, data from inhalation and i.v. application of both compounds are preferred over RfD values retrieved from studies using oral administration.

This study might present one of the first approaches for a risk assessment for CBD and Δ9-THC in e-liquids. From 11 publications included in this risk assessment, 4 studies provided dose-response data, which were considered to provide usable BMDL values. Depending on the respective toxicological endpoint, the RfD values obtained clearly differ for both CBD and Δ9-THC.

This finding indicates different susceptibilities of tissues and organs to inhaled CBD and Δ9-THC. Comparison with the literature referred to in this publication supports this observation, as tissue or organ-specific dose-depending responses have been reported. The results for CBD suggest that blood parameters, hormone levels and enzyme titers are in general less susceptible to CBD than changes in physiological parameters [34].

Uterus weight in animal models was altered by a relatively low dose of CBD (RfD 4 mg/kg bw). This endpoint may be relevant, as hormones and therefore hormone producing glands, tissues and organs have an essential function in the development of an organism, especially during prenatal development and adolescence.

From Steger et al. [33], an RfD for Δ9-THC of approximately 1 mg/kg bw for a reduction in plasma FSH has been derived. Lower levels in plasma FSH induced by surpassing a critical dose of Δ9-THC might be responsible for a reported reduction in fertility in both males and females. This finding might be a cause for a reduction in human fertility most likely due a reduction in reproductive hormones when Δ9-THC is consumed extensively over a long period. However, Δ9-THC induced effects on sex hormones have been reported to be reversible [36]. One reported mechanism, which could be responsible for the observed reduction in plasma FSH, is caused by Δ9-THC acutely inhibiting the release of gonadotropin-releasing hormone (GnRH) [37, 38] in the hypothalamus. GnRH stimulates the production of FSH and LH. The release of FSH and LH is responsible for the secretion of testosterone in males and estradiol and progesterone in females. This effect is probably mediated by CB1 receptors, which have been found in the hypothalamus and gonad cells [37, 39, 40].

Another effect observed after Δ9-THC exposure is an increase in (mean) blood pressure. According to the findings in this study, a change in mean blood pressure can be observable after surpassing an estimated RfD of approximately 0.006 mg/kg bw [35]. For a consumer with a body weight of 70 kg, this would be equivalent to a total intake of 0.42 mg Δ9-THC per administration session. In contrast to this finding, Johnson et al. [41] reported a significant increase in systolic and diastolic blood pressure in human subjects who inhaled more than 10 mg Δ9-THC in marijuana cigarettes. Solowij et al. [42] reported a significant increase in blood pressure in human subjects who inhaled 8 mg of Δ9-THC by vaporization, which would equal an intake of approximately 0.1 mg/kg bw assuming a body weight of 70 kg. In a study by Friedman et al. [43], the authors have found, that a total of approximately 0.25 mg/kg (about 0.1 mg/kg bw) of Δ9-THC administered intravenously to dogs had no significant effect on blood pressure. Converting this dose into an inhaled dose for humans by considering an uncertainty factor of 100 and bioavailability for inhalation of 37% would yield an estimated inhalation dose of approximately 0.007 mg/kg bw of Δ9-THC in humans. Note that these exemplary values are not actual RfD values, which were derived from points of departure but only serve as a comparison for the estimated RfD.

Data used for estimating an RfD for inhalation of 0.04 mg/kg bw Δ9-THC in rhesus monkeys [44] was unfortunately not based on statistically significant dose-response data but was still included in the discussion because a change in thymus weight may result in altered endocrinological parameters, which could lead to serious adverse events.

4.2 Comparison with Previous Assessments of CBD and Δ9-THC in the Literature

In 2015, the EFSA postulated an oral ARfD for Δ9-THC of 0.001 mg/kg bw using an UF of 30 with an increase in pulse rate as one of the respective endpoints [45]. Converting this dose for a 70 kg person into an (A)RfD for inhalation considering an oral bioavailability of Δ9-THC of 20% [1] and bioavailability for inhalation of 37% [32] by route-to-route extrapolation would deliver an acceptable dose of 0.135 μg/kg bw or 0.95 μg/d for a 70 kg person, respectively.

A published review of risk assessments [46] derived a LO(A)EL of 2.8 mg/d for cannabis inhalation from a study by Ramaekers et al. [47]. After conducting a dose conversion, the theoretical NOAEL would yield an RfD for the inhalation of cannabis of a total of 0.028 mg/d. In comparison, the RfD for inhalation of pure Δ9-THC estimated from Siqueira et al. [35] for a 70 kg person would be 0.42 mg/d according to the results of the BMD modelling presented in Table 2, which has been obtained after a dose conversion.

It is shown above that the RfD for Δ9-THC estimated from a BMDL was found to be above the dose values retrieved from literature and adjusted to (A)RfD values for inhalation for comparison [45, 47]. It is advisable to prefer the oral (A)RfD of 0.001 mg/kg bw from the EFSA study because EFSA provided an already determined ARfD, which only needs to be adjusted by conducting a dose conversion for a 70 kg person into an inhalation dose and extrapolated by using the same method applied for the BMDLs. Furthermore, EFSA is the only agency that mentioned an increase in heart rate as one of the observed endpoints.

In conclusion, the RfD of 0.006 mg/kg bw, which has been derived from dose-response data published by Siqueira et al. [35], was found to be less conservative than the (A)RfD derived from EFSA, which might be due to the respective BMDL (if converted to an oral BMDL) resulting from a BMD analysis with a 5% confidence interval.

It is recommended to further investigate whether a BMDL10 would deliver a more appropriate RfD, when more dose-response data are available.

The BMDL and RfD values estimated for hematological endpoints and endpoints regarding clinical chemistry observations were found to be on average much higher than the acute and chronic BMDL and RfD values found for more susceptible endpoints. This observation indicates that the respective organs and systems were less susceptible to CBD. Although endocrinologically active organs were found to be more susceptible to CBD, considerable deviations in estimated BMDL and RfD values for endocrinologically active organs were observed during this study. Endpoints that indicate high tolerability of CBD were assumed to be unreliable and were excluded from the further evaluation because adverse effects have already been observed at significantly lower doses of CBD in the literature [34]. Devinsky et al. [3] reported elevated levels of aspartate (AST) and alanine aminotransferase (ALT) in more than 30 seizure patients who received an oral dose of 10 or 20 mg/kg bw of CBD per day in a 14-day trial [3]. For comparison with the RfD values obtained from the BMD analysis of dose-response data obtained from Marx et al. [34] for the respective endpoints, the oral dose of 10 mg/kg bw of CBD could be converted into an inhalation dose of approximately 1.7 mg/kg bw. In comparison an RfD of 107 mg/kg bw for AST-levels was derived from Marx et al. [34] by using the BMD approach. Other adverse events reported in this study were diarrhea, vomiting, mild upper respiratory tract infection, and pyrexia. CBD concentrations in the study by Devinsky et al. [3] were far below the corresponding RfD values estimated by Marx et al. [34]. This discrepancy might be due to the use of different test subjects. As Devinsky investigated administration in humans [3], it should be assumed that humans have a higher sensitivity to CBD than rats. It appears in this case that the used uncertainty factor of 100 did not account for this difference in sensitivity.

For CBD, some limited NOAEL values have been published. The study by Marx et al. [34], which was also included in this risk assessment, derived an oral NOAEL in hemp extract of 100 mg/kg bw in male rats, which can be converted into an RfD for inhalation of 17 mg/kg bw in humans, which corresponds to a NOAEL for CBD of approximately 1 mg/kg bw after dose conversion. In 2019, the European Medicines Agency (EMA) [48] published a NOAEL for i.v. application of CBD in beagle dogs of 15 mg/kg bw/d for hepatological endpoints in a 14-day study, which can be converted via dose conversion to an RfD of approximately 0.5 mg/kg bw/d. In comparison, the lowest RfD estimated from the study in rats by Marx et al. [34] was 2 mg/kg bw for a statistically significant change in liver weight. The BMD model might therefore estimate a larger BMDL to be a more appropriate POD than NOAEL values obtained from literature. To account for consumer safety, it is recommended to prefer the more conservative NOAEL published by EMA [48] or the NOAEL from Marx et al. [34] as a more reliable POD over the BMDL derived in this study.

Unfortunately not enough adequate dose response data could be retrieved from online sources in ordert to conduct BMD modelling for further toxicological endpoints like local (airway) effects or cognitive and psychological effects. Most animal studies retrieved for this BMD analysis only reported acute systemic effects. A change in uterus weight in rats [34] was the only long-term effect reported in this study, as this was found to be the lowest RfD for a relevant endpoint.

It should be emphasized that this comparison between extrapolated RfDs from BMDL values and published PODs or dose-response data in humans and animals can only represent an attempt to elucidate, whether BMD modelling of the data available provided RfD values, which may be comparable to literature data. Therefore, RfDs obtained in this study should not be considered as alternative doses to already established threshold doses of CBD or Δ9-THC. In order to obtain reliable threshold doses, BMD modelling using more appropriate dose-response data is recommended.

4.3 Initial Risk Assessment of E-Liquids

To suggest an initial approach for a risk assessment of CBD applicable to e-liquids, a theoretical consumption scenario will be considered. E-liquids contain different amounts of CBD in a glycerol/propylene glycol matrix. During our analyses, e-liquids with the highest concentrations of CBD typically contained about 100 g/L CBD in a 10 ml container. The consumer safety of these products should be assessed in a hypothetical consumption scenario with the following considerations:

The pulmonary system will be exposed to the entire amount of CBD contained in the e-liquid, and the hypothetical consumer will be exposed to an entire 10 mL of CBD-liquid. Exposure to 10 ml of e-liquid per day was also assumed in this case, considering the worst-case exposure to vaping e-liquids ranging between 5 and 25 mL/day.

When considering bioavailability after inhalation of 31% [31], a total of approximately 31 mg CBD should reach the blood circuit of consumers. With an assumed average body weight of 70 kg, a human would inhale approximately 1.43 mg/kg bw/d CBD.

The amount of CBD in the respective e-liquid would exceed the lowest estimated RfD for inhalation of approximately 1 mg/kg bw/d of CBD by a factor of two. This approach refers to an RfD for inhalation derived from a study in Sprague-Dawley rats [33]. The lowest RfD for inhalation derived from a study in humans was found to be approximately 87 mg/kg bw/d. This dose would allow a safe inhalation of the mentioned CBD-liquid. It should be emphasized, however, that endocrinological endpoints appear more susceptible to lower doses of both CBD and Δ9-THC, than physiological parameters. The lowest RfD derived from the 90-day trial in rats by Marx et al. [34] was estimated to be 5 mg/kg bw/d. Among animal studies, this RfD may be considered the most suitable dose because a 90-day study is a more realistic approach to assess the potential long-term health risks of a consumer product, which is most likely to be consumed daily. It is therefore questionable, whether the lower RfD of 1 mg/kg bw/d for CBD derived from the rat model in Steger et al. [33] should be preferred over an alternative RfD obtained from another study, especially because the dose-response data in the study has not been statistically significant. It should be further noted that dose-response data considered reliable enough was rare in animal and human studies. Furthermore, strict precautions were taken when estimating the respective RfDs for inhalation by considering the highest value for bioavailability after inhalation [31]. It is also rather unrealistic to assume that 30% of all CBD contained in the e-liquid is fully absorbed because a significant proportion of vaporized e-liquid is usually exhaled, before pulmonary absorption can be completed, as was assumed in this scenario. These considerations could have led to an overestimation of the toxicity of CBD inhaled in humans. Therefore using this preliminary approach, an e-liquid containing 100 mg of CBD per milliliter could still be consumed with only a very low risk of adverse effects, especially when using an RfD of approximately 5 mg/kg bw/d derived from Marx et al. [34].

4.4 Limitations of the Assessment

A major challenge in conducting this risk assessment was the limited availability of sufficient usable toxicological data on inhaled CBD and Δ9-THC to derive a BMDL for both compounds. Therefore, data from studies on animal models was required, which used various routes of administration and different formulations containing the compounds. This makes considerations regarding species and administration route dependent metabolism of both compounds relevant, as metabolites of CBD and Δ9-THC can have observable effects on animals and humans. Because most of the results, which were considered to be reliable, were obtained from studies in rats, this section focuses mainly on the differences in drug metabolism of Δ9-THC and CBD between rats and humans.

The metabolism of CBD and Δ9-THC differs from humans to animals and is route dependent. After oral administration, both compounds face extensive metabolism resulting in significantly reduced bioavailability, as chemical compounds face more enzymatic reactions and different reactive environments such as saliva and gastric juices. Oral bioavailability of Δ9-THC is limited and ranges between 4 and 20% due to extensive metabolism in the hepatic first pass effect [1, 49]. Due to its lipophilicity, Δ9-THC is quickly distributed and accumulated in fat tissues, as well as in the brain and muscle, further reducing the plasma concentration of the compounds after the administration [50, 51, 52, 53]. Δ9-THC is largely metabolized in the hepatic first-pass effect by CYP 450-mediated microsomal hydroxylation and oxidation. One of the main metabolites of Δ9-THC formed by CYP2C9 is 11-OH-THC [1, 31, 54]. Measured levels of this compound are higher after oral ingestion compared to inhalation [55, 56]. THC-COOH is the predominant glucuronide conjugate and is assumed to be an inactive metabolite. For 11-OH-THC, psychotropic effects comparable to those of Δ9-THC have been reported [57]. However, since BMDLs for psychotropic effects were not included, this finding was considered not relevant for estimated RfD values in this study.

Furthermore, it should be considered that Δ9-THC can be redistributed back into the blood circuit from the tissue in which it has accumulated [58, 59, 60]. This is particularly relevant for heavy regular cannabis consumers, to whom a greater bioaccumulation in adipose tissue can be expected.

Regarding the knowledge about the toxicology of Δ9-THC metabolites, it has been reported, that 11-OH-THC has similar or even greater psychotropic effects compared to Δ9-THC [61]. 11-COOH-THC, in contrast, has no reported psychological effects, but presumable analgesic and anti-inflammatory effects could not be excluded [62].

Regarding Δ9-THC, oral bioavailability and bioavailability after inhalation of cannabis is limited mainly due to the lipophilic properties of the compounds. Harvey et al. [63] found that CBD undergoes similar metabolic reactions in humans, rats and dogs, namely, carboxylation to carbonic acids, epoxidation, hydroxylation at multiple sites, oxidation, and beta-oxidation as well as conjugation [63]. However, differences between humans and rats regarding CBD metabolism have been observed. CBD metabolism in rats appears not to favor conjugation reactions of CBD, unlike in human subjects. The major metabolites in rats are acid metabolites of CBD originating from beta-oxidation. C-6 and C-7 hydroxylation was found to be more frequent in rat urine, whereas in humans, hydroxylated 7-COOH-CBD derivatives were more common [64]. The CBD metabolites 7-OH-CBD and 7-COOH-CBD were mentioned to have detectable antinociceptive and anti-inflammatory activities in animals and patients [65].

CBD has been reported to be an antagonist of the type 1 vanilloid receptor in protein binding studies. Both CBD and 7-OH-CBD inhibit fatty acid amid hydrolase in rats [66]. CBD and 6-alpha/beta-OH-CBD and other hydroxylated metabolites are capable of inhibiting microsomal CYP 2C and 3A in in vivo mouse model studies [54]. Kraemer et al. [67] discovered that CBD can be metabolized to its decarbonylated form in humans. However, toxicological data on decarbonylated CBD have not been found.

In conclusion, the regarded metabolites may not be of toxicological relevance for the results obtained in this risk assessment, but it should be assumed that metabolism might affect the concentration of CBD and Δ9-THC respectively.

Note that the human metabolism of CBD and Δ9-THC can differ between individuals and depend on a person’s health status. For example, the metabolism of xenobiotics can be increased or compromised by diseases, particularly in diseases that affect the liver and gastrointestinal tract. Plasma concentrations of xenobiotics and their metabolites can vary from those in healthy individuals and might increase the risk of adverse effects, when administered. Other factors influencing metabolic rate are age and sex. Consumption of foods containing active enzymes, which may interact with xenobiotics (such as fruit juice), can also alter the bioavailability of compounds and metabolites [68].

Furthermore, it should not be excluded that other factors, which cannot be easily assessed, may impact the toxicology of CBD and Δ9-THC, such as consumer age, gender, health status as well as consumption habits of products containing cannabis or cannabinoid preparations.

5. Conclusions

This study presents a risk assessment for inhalation of Δ9-THC and CBD from e-liquids using the BMD approach. From a limited amount of dose-response data considered suitable for BMD modeling, several BMD values have been derived and used as points of departure for estimating RfDs. The obtained RfDs were compared with dose-response data, which have been adjusted to RfDs to facilitate comparison.

Generally, the RfDs obtained from BMD modeling were found to be higher than doses from the literature converted for comparison. To account for additional consumer safety, it is not advised to consider the RfDs from this study as reliable due to uncertainties arising from the insufficient amount and quality of dose-response data, which have been included in BMD modeling. Note that the BMD approach benefits from dose-response data, which cover a broad dose range. This would allow risk assessors to conduct a retrospective meta-analysis, which combines dose-response data from individual studies. As BMD modeling does not require a large number of test subjects per dose group, this would allow to combine dose-response data, especially from individual studies in human test subjects, to efficiently use limited dose-response data for a risk assessment. Considering that inhalation of vaporized Δ9-THC and CBD is discussed as a comfortable and practical approach to the treatment of neurological disorders, it is advised to include dose-response data from standardized clinical trials, which have administered vaporized drugs to patients and volunteers.

Further useful dose-response data could be provided by a subsequent study to Marx et al. [34] in rats, which uses additional or intermediate dose groups with fewer animals per dose group. The results should then be evaluated using BMD modeling.


CBD, Cannabidiol; Δ9-THC, Δ9-Tetrahydrocannabidiol; 7-OH-CBD, 7-hydroxy cannabidiol; CYP 450, Cytochrome P 450 isoenzymes; 11-OH-THC, 11-hydroxy tetrahydrocannabinol; THC-COOH or 11-COOH-THC, Carboxy-tetrahydrocannabinol or 11-nor-9-Carboxy-THC; FSH, Follicle-stimulating hormone; LH, Luteinizing hormone; TRPA1, Transient receptor potential channels, anakyrin-protein family; TRPM8, Transient receptor potential cation channel subfamily M member 8TRPV1/V2, Transient receptor potential channels, vanilloid subtype 1/subtype 2; POD, Point of departure; (A)RfD, (Acute) reference dose; BMD, Benchmark dose; BMDL, Benchmark dose lower limit; BMDU, Benchmark dose upper limit; BMR, Benchmark response; BMDS, Benchmark dose software; MOA, Mode of action; DS, Dravet syndrome; LGS, Lennox-Gastaut syndrome; i.v., Intravenous; NOAEL, No observed adverse effect level; LO(A)EL, Lowest observed (adverse) effect level; UF, Uncertainty factor; F, Bioavailability; RCT, randomized controlled trial; EFSA, European food safety authority; US EPA, United States environmental protection agency; FDA, Food and drug administration.

Author Contributions

Conceptualization—DWL; methodology—DWL; software—AS; validation—PH; formal analysis—PH; investigation—PH, AS; resources—SGW; data curation—PH, DWL; writing original draft preparation—PH, AS; writing review and editing—DWL, ER, SGW; visualization—PH, AS; supervision—SGW, DWL, ER; project administration—DWL. All authors have read and agreed to the published version of the manuscript.

Ethics Approval and Consent to Participate

Not applicable.


Not applicable.


This research received no external funding.

Conflict of Interest

The authors declare no conflict of interest. DWL is serving as one of the Guest Editor of this journal. We declare that DWL had no involvement in the peer review of this article and has no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to MI.

Publisher’s Note: IMR Press stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Grotenhermen F. Pharmacokinetics and pharmacodynamics of cannabinoids. Clinical Pharmacokinetics. 2003; 42: 327–360.
Devinsky O, Cilio MR, Cross H, Fernandez-Ruiz J, French J, Hill C, et al. Cannabidiol: Pharmacology and potential therapeutic role in epilepsy and other neuropsychiatric disorders. Epilepsia. 2014; 55: 791–802.
Devinsky O, Patel AD, Cross JH, Villanueva V, Wirrell EC, Privitera M, et al. Effect of cannabidiol on drop seizures in the Lennox–Gastaut syndrome. New England Journal of Medicine. 2018; 378: 1888–1897.
Wise J. FDA approves its first cannabis based medicine. British Medical Journal. 2018; 361: k2827.
Sekar K. Epidiolex as adjunct therapy for treatment of refractory epilepsy: A comprehensive review with a focus on adverse effects. F1000Research. 2019; 8: F1000 Faculty Rev-234.
Mechoulam R, Parker LA, Gallily R. Cannabidiol: an overview of some pharmacological aspects. The Journal of Clinical Pharmacology. 2002; 42: 11S–19S.
Thomas A, Baillie GL, Phillips AM, Razdan RK, Ross RA, Pertwee R. Cannabidiol displays unexpectedly high potency as an antagonist of CB1 and CB2 receptor agonists in vitro. British Journal of Pharmacology. 2007; 150: 613–623.
Pertwee RG. The diverse CB1 and CB2 receptor pharmacology of three plant cannabinoids. Δ9-tetrahydrocannabinol, cannabidiol and Δ9-tetrahydrocannabivarin. British Journal of Pharmacology. 2008; 153: 199–215.
Chesney E, Oliver D, Green A, Sovi S, Wilson J, Englund A, et al. Adverse effects of cannabidiol: a systematic review and meta-analysis of randomized clinical trials. Neuropsychopharmacology. 2020; 45: 1799–1806.
Klein C, Karanges E, Spiro A, Wong A, Spencer J, Huynh T, et al. Cannabidiol potentiates Δ9-tetrahydrocannabinol (THC) behavioral effects and alters THC pharmacokinetics during acute and chronic treatment in adolescent rats. Psychopharmacology. 2011; 218: 443–457.
Karniol IG, Carlini EA. Pharmacological interaction between cannabidiol and Δ9-tetrahydrocannabinol. Psychopharmacologia. 1973; 33: 53–70.
Sastre-Garriga J. THC and CBD oromucosal spray (Sativex®) in the management of spasticity associated with multiple sclerosis. Expert Review of Neurotherapeutics. 2011; 11: 627–637.
Beal JE, Olson R, Laubenstein L, Morales JO, Bellman P, Yangco B, et al. Dronabinol as a treatment for anorexia associated with weight loss in patients with AIDS. Journal of Pain and Symptom Management. 1995; 10: 89–97.
de Vries M, van Rijckevorsel DC, Wilder-Smith OH, van Goor H. Dronabinol and chronic pain: importance of mechanistic considerations. Expert Opinion on Pharmacotherapy. 2014; 15: 1525–1534.
Badowski ME. A review of oral cannabinoids and medical marijuana for the treatment of chemotherapy-induced nausea and vomiting: a focus on pharmacokinetic variability and pharmacodynamics. Cancer Chemotherapy and Pharmacology. 2017; 80: 441–449.
Cahn Z, Siegel M. Electronic cigarettes as a harm reduction strategy for tobacco control: a step forward or a repeat of past mistakes? Journal of Public Health Policy. 2011; 32: 16–31.
Center for Disease Control. Outbreak of lung injury associated with the use of e-cigarette, or vaping, products. 2020. Available at: (Accessed: 24 June 2022).
Viswam D. Respiratory failure caused by lipoid pneumonia from vaping e-cigarettes. BMJ Case Reports. 2018; 2018: bcr2018224350.
Staudt MR, Salit J, Kaner RJ, Hollmann C, Crystal RG. Altered lung biology of healthy never smokers following acute inhalation of E-cigarettes. Respiratory Research. 2018; 19: 78.
Napimoga MH, Benatti BB, Lima FO, Alves PM, Campos AC, Pena-dos-Santos DR, et al. Cannabidiol decreases bone resorption by inhibiting RANK/RANKL expression and pro-inflammatory cytokines during experimental periodontitis in rats. International Immunopharmacology. 2009; 9: 216–222.
Kogan NM, Melamed E, Wasserman E, Raphael B, Breuer A, Stok KS, et al. Cannabidiol, a major non-psychotropic cannabis constituent enhances fracture healing and stimulates lysyl hydroxylase activity in osteoblasts. Journal of Bone and Mineral Research. 2015; 30: 1905–1913.
Otero CE, Noeker JA, Brown MM, Wavreil FDM, Harvey WA, Mitchell KA, et al. Electronic cigarette liquid exposure induces flavor-dependent osteotoxicity and increases expression of a key bone marker, collagen type I. Journal of Applied Toxicology. 2019; 39: 888–898.
Bundesinstitut für Risikobewertung. Tetrahydrocannabinol levels are too high in many hemp-containing foods - health impairments are possible. Bundesinstitut für Risikobewertung, Berlin, Germany. 2018.
Tallon MJ. Cannabis sativa L. and its extracts. Regulation of cannabidiol in the European Union and United Kingdom. Journal of Dietary Supplements. 2020; 17: 503–516.
Giroud C, de Cesare M, Berthet A, Varlet V, Concha-Lozano N, Favrat B. E-cigarettes. A review of new trends in cannabis use. International Journal of Environmental Research and Public Health. 2015; 12: 9988–10008.
Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nature Methods. 2012; 9: 671–675.
Davis JA, Gift JS, Zhao QJ. Introduction to benchmark dose methods and U.S. EPA’s benchmark dose software (BMDS) version 2.1.1. Toxicology and Applied Pharmacology. 2011; 254: 181–191.
Crump KS. A new method for determining allowable daily intakes. Toxicological Sciences. 1984; 4: 854–871.
United States Environmental Protection Agency. About Benchmark Dose Software (BMDS) - Version 3.2. 2022. Available at: (Accessed: 24 June 2022).
Flynn E. Pharmacokinetic parameters. In Enna SJ, Bylund DB (eds.) xPharm: The Comprehensive Pharmacology Reference. Elsevier: Amsterdam, the Netherlands. 2007.
Lucas CJ, Galettis P, Schneider J. The pharmacokinetics and the pharmacodynamics of cannabinoids. British Journal of Clinical Pharmacology. 2018; 84: 2477–2482.
Lunn S, Diaz P, O’Hearn S, Cahill SP, Blake A, Narine K, et al. Human pharmacokinetic parameters of orally administered Δ9-tetrahydrocannabinol capsules are altered by fed versus fasted conditions and sex differences. Cannabis and Cannabinoid Research. 2019; 4: 255–264.
Steger RW, Murphy LL, Bartke A, Smith MS. Effects of psychoactive and nonpsychoactive cannabinoids on the hypothalamic-pituitary axis of the adult male rat. Pharmacology Biochemistry and Behavior. 1990; 37: 299–302.
Marx TK, Reddeman R, Clewell AE, Endres JR, Béres E, Vértesi A, et al. An assessment of the genotoxicity and subchronic toxicity of a supercritical fluid extract of the aerial parts of hemp. Journal of Toxicology. 2018; 2018: 8143582.
Siqueira SW. The Triple Effect induced by Δ9-tetrahydrocannabinol on the rat blood pressure. European Journal of Pharmacology. 1979; 58: 351–357.
Smith RN, Vaughan CG. The decomposition of acidic and neutral cannabinoids in organic solvents. Journal of Pharmacy and Pharmacology. 1977; 29: 286–290.
Murphy LL, Muñoz RM, Adrian BA, Villanúa MA. Function of cannabinoid receptors in the neuroendocrine regulation of hormone secretion. Neurobiology of Disease. 1998; 5: 432–446.
Brown TT, Dobs AS. Endocrine effects of marijuana. The Journal of Clinical Pharmacology. 2002; 42: 90S–96S.
Gérard CM, Mollereau C, Vassart G, Parmentier M. Molecular cloning of a human cannabinoid receptor which is also expressed in testis. Biochemical Journal. 1991; 279: 129–134.
Galiegue S, Mary S, Marchand J, Dussossoy D, Carriere D, Carayon P, et al. Expression of central and peripheral cannabinoid receptors in human immune tissues and leukocyte subpopulations. European Journal of Biochemistry. 1995; 232: 54–61.
Johnson S, Domino EF. Some cardiovascular effects of marihuana smoking in normal volunteers. Clinical Pharmacology and Therapeutics. 1971; 12: 762–768.
Solowij N, Broyd S, Greenwood LM, van Hell H, Martelozzo D, Rueb K. A randomized controlled trial of vaporized Δ9-tetrahydrocannabinol and cannabidiol alone and in combination in frequent and infrequent cannabis users. Acute intoxication effects. European Archives of Psychiatry and Clinical Neuroscience. 2019; 269: 17–35.
Friedman E, Gershon S, Hine B, TOrrelio M, Moss IR. Cardiovascular effects of Δ9-tetrahydrocannabinol in conscious and anaesthetized dogs. British Journal of Pharmacology. 1977; 59: 561–563.
Thompson GR, Fleischman RW, Rosenkrantz H, Braude MC. Oral and intravenous toxicity of Δ9-tetrahydrocannabinol in rhesus monkeys. Toxicology and Applied Pharmacology. 1974; 27: 648–665.
European Food Safety Authority. Scientific Opinion on the safety of hemp (Cannabis genus) for use as animal feed. EFSA Panel on Contaminants in the Food Chain (CONTAM). European Food Safety Authority (EFSA), Parma, Italia. EFSA Journal. 2015; 9: 255.
Skoczinski P, Carus M, Grotenhermen F, Beitzke B, Kruse D. Limit and guideline values for THC (tetrahydrocannabinol) in hemp foods. European Industrial Hemp Association: Hürth, Germany. 2019.
Ramaekers JG, Berghaus G, van Laar M, Drummer OH. Dose related risk of motor vehicle crashes after cannabis use. Drug and Alcohol Dependence. 2004; 73: 109–119.
Committee for Medicinal Products for Human Use. Epidyolex: EPAR - Public assessment report. European Medicines Agency. 2019. Available at: (Accessed: 24 June 2022).
Owens SM. 125I radioimmunoassay of Δ9-tetrahydrocannabinol in blood and plasma with a solid-phase second-antibody separation method. Clinical Chemistry. 1981; 27: 619–624.
Haggerty GC, Deskin R, Kurtz PJ, Fentiman AF, Leighty EG. The pharmacological activity of the fatty acid conjugate 11-palmitoyloxy-Δ9-tetrahydrocannabinol. Toxicology and Applied Pharmacology. 1986; 84: 599–606.
McBurney LJ, Bobbie BA, Sepp LA. GC/MS and EMIT Analyses for Δ9-Tetrahydrocannabinol Metabolites in Plasma and Urine of Human Subjects. Journal of Analytical Toxicology. 1986; 10: 56–64.
Chiarotti M, Costamagna L. Analysis of 11-nor-9-carboxy-Δ9-tetrahydrocannabinol in biological samples by gas chromatography tandem mass spectrometry (GC/MS-MS). Forensic Science International. 2000; 114: 1–6.
Huestis MA. Pharmacokinetics and metabolism of the plant cannabinoids, Δ9-tetrahydrocannabinol, cannabidiol and cannabinol. Handbook of Experimental Pharmacology. 2005; 168: 657–690.
Bornheim LM, Everhart ET, Li J, Correia MA. Characterization of cannabidiol-mediated cytochrome P450 inactivation. Biochemical Pharmacology. 1993; 45: 1323–1331.
Lemberger L. Δ9-Tetrahydrocannabinol: metabolism and disposition in long-term marihuana smokers. Science. 1971; 173: 72–74.
Hollister LE, Gillespie HK, Ohlsson A, Lindgren JE, Wahlen A, Agurell S. Do plasma concentrations of Δ9‐tetrahydrocannabinol reflect the degree of intoxication? The Journal of Clinical Pharmacology. 1981; 21: 171S–177S.
Perez-Reyez M, Reid White W, McDonald SA, Hicks RE, Robert Jeffcoat A, Edgar Cook C. The pharmacologic effects of daily marijuana smoking in humans. Pharmacology Biochemistry and Behavior. 1991; 40: 691–694.
Kreuz DS. Δ9-Tetrahydrocannabinol: localization in body fat. Science. 1973; 179: 391–393.
Hunt CA. Tolerance and disposition of tetrahydrocannabinol in man. The Journal of Pharmacology and Experimental Therapeutics. 1980; 215: 35–44.
Johansson E. Prolonged apparent half-life of delta 1-tetrahydrocannabinol in plasma of chronic marijuana users. The Journal of Pharmacy and Pharmacology. 1988; 40: 374–375.
Lemberger L, Martz R, Rodda B, Forney R, Rowe H. Comparative Pharmacology of Δ9-Tetrahydrocannabinol and its Metabolite, 11-OH-Δ9-Tetrahydrocannabinol. Journal of Clinical Investigation. 1973; 52: 2411–2417.
Ujváry I. 11-Nor-9-carboxy-Δ9-tetrahydrocannabinol – a ubiquitous yet underresearched cannabinoid. A Review of the Literature. Cannabinoids. 2014; 9: 1–8.
Harvey DJ, Samara E, Mechoulam R. Comparative metabolism of cannabidiol in dog, rat and man. Pharmacology Biochemistry and Behavior. 1991; 40: 523–532.
Wall ME. Identification of Δ9-tetrahydrocannabinol and metabolites in man. Journal of the American Chemical Society. 1972; 94: 8579–8581.
Ujváry I, Hanuš L. Human Metabolites of cannabidiol: a review on their formation, Biological Activity, and Relevance in Therapy. Cannabis and Cannabinoid Research. 2016; 1: 90–101.
Bisogno T, Hanuš L, De Petrocellis L, Tchilibon S, Ponde DE, Brandi I, et al. Molecular targets for cannabidiol and its synthetic analogues: effect on vanilloid VR1 receptors and on the cellular uptake and enzymatic hydrolysis of anandamide. British Journal of Pharmacology. 2001; 134: 845–852.
Kraemer M. Decarbonylation: A metabolic pathway of cannabidiol in humans. Drug Testing and Analysis. 2019; 11: 957–967.
Dahan A. Food–drug interaction: grapefruit juice augments drug bioavailability—mechanism, extent and relevance. European Journal of Clinical Nutrition. 2004; 58: 1–9.
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