Development of intestinal bioavailability prediction (IBP) and phytochemical relative antioxidant potential prediction (PRAPP) models for optimizing functional food value of <i>Cannabis sativa</i> (hemp)

Wise, Kimber; Selby-Pham, Sophie; Selby‐Pham, Jamie; Gill, Harsharn

doi:10.1080/10942912.2020.1797783

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…For all of these factors and the low absorption in the gastrointestinal tract, phytochemicals have low bioavailability ( 5 ). Information about the bioavailability of specific phytochemicals is still limited, but we can predict the bioavailability of phytochemicals by using the Lipinski's rule ( 101 ); which predicts the drug-likeness of the passive absorption of a molecule considering five chemical characteristics: a molecular weight ≤ 500, partition coefficient (LogP) <5, hydrogen bond donors <5, and a maximum of 10 hydrogen receptors ( 102 ) (See Supplementary Table 1 ).…”

Section: Implications Of the Bioavailability Of Solanum Antioxidants And Their Health-promoting Propertiesmentioning

confidence: 99%

Solanum Fruits: Phytochemicals, Bioaccessibility and Bioavailability, and Their Relationship With Their Health-Promoting Effects

et al. 2021

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The Solanum genus is the largest in the Solanaceae family containing around 2,000 species. There is a great number of edibles obtained from this genus, and globally, the most common are tomato (S. lycopersicum), potato (S. tuberosum), and eggplant (S. melongena). Other fruits are common in specific regions and countries, for instance, S. nigrum, S. torvum, S. betaceum, and S. stramonifolium. Various reports have shown that flavonoids, phenolic acids, alkaloids, saponins, and other molecules can be found in these plants. These molecules are associated with various health-promoting properties against many non-communicable diseases, the main causes of death globally. Nonetheless, the transformations of the structure of antioxidants caused by cooking methods and gastrointestinal digestion impact their potential benefits and must be considered. This review provides information about antioxidant compounds, their bioaccessibility and bioavailability, and their health-promoting effects. Bioaccessibility and bioavailability studies must be considered when evaluating the bioactive properties of health-promoting molecules like those from the Solanum genus.

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Section: Implications Of the Bioavailability Of Solanum Antioxidants And Their Health-promoting Propertiesmentioning

confidence: 99%

Solanum Fruits: Phytochemicals, Bioaccessibility and Bioavailability, and Their Relationship With Their Health-Promoting Effects

et al. 2021

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“…Statistical modelling can be used to predict chemical activities or properties from their physicochemical data, which is known as quantitative structure–activity/property relationship (QSAR/QSPR) modelling. This process and relationship has been demonstrated for a range of properties including time of maximal phytochemical concentration in circulation following ingestion and inhalation [ 36 , 37 ], blood-to-liver partition coefficients of volatile compounds [ 38 ], intestinal bioavailability and antioxidant activity [ 39 ], and perception of chemical odour following inhalation [ 34 ]. Generation of these QSPR models is useful, particularly for measures such as ODT which are laborious to measure and are required for OI calculation which is an input for vector modelling [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour

et al. 2023

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Cannabis flower odour is an important aspect of product quality as it impacts the sensory experience when administered, which can affect therapeutic outcomes in paediatric patient populations who may reject unpalatable products. However, the cannabis industry has a reputation for having products with inconsistent odour descriptions and misattributed strain names due to the costly and laborious nature of sensory testing. Herein, we evaluate the potential of using odour vector modelling for predicting the odour intensity of cannabis products. Odour vector modelling is proposed as a process for transforming routinely produced volatile profiles into odour intensity (OI) profiles which are hypothesised to be more informative to the overall product odour (sensory descriptor; SD). However, the calculation of OI requires compound odour detection thresholds (ODT), which are not available for many of the compounds present in natural volatile profiles. Accordingly, to apply the odour vector modelling process to cannabis, a QSPR statistical model was first produced to predict ODT from physicochemical properties. The model presented herein was produced by polynomial regression with 10-fold cross-validation from 1,274 median ODT values to produce a model with R2 = 0.6892 and a 10-fold R2 = 0.6484. This model was then applied to terpenes which lacked experimentally determined ODT values to facilitate vector modelling of cannabis OI profiles. Logistic regression and k-means unsupervised cluster analysis was applied to both the raw terpene data and the transformed OI profiles to predict the SD of 265 cannabis samples and the accuracy of the predictions across the two datasets was compared. Out of the 13 SD categories modelled, OI profiles performed equally well or better than the volatile profiles for 11 of the SD, and across all SD the OI data was on average 21.9% more accurate (p = 0.031). The work herein is the first example of the application of odour vector modelling to complex volatile profiles of natural products and demonstrates the utility of OI profiles for the prediction of cannabis odour. These findings advance both the understanding of the odour modelling process which has previously only been applied to simple mixtures, and the cannabis industry which can utilise this process for more accurate prediction of cannabis odour and thereby reduce unpleasant patient experiences.

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“…[5][6][7] There are also reports of repeated dose toxicity of marijuana roots [8] of genotoxicity, [9,10] mutagenicity and carcinogenicity, [11] and pharmacokinetic studies of CBD. [12,13] On the other hand, studies have been published on the use of in silico models to make predictions of cannabis effects, such as bioavailability, [14] ADMET parameters (Administration, Distribution, Metabolism, Excretion, and Toxicity), [15] SAR studies (structure activity relationship) of some components, [16,17] QSKR pharmacokinetic studies. [18] It is evident that toxicological aspects, especially in consumption that could be considered pathological, are linked to controversy in health regulation, such as the cases reported in the United States, [19] and also considering that the term "natural is not synonymous with safe" [20] makes it necessary to contribute to obtaining additional data that are significant weight when making decisions that impact the population that currently consumes these products recreationally or medicinally.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, studies have been published on the use of in silico models to make predictions of cannabis effects, such as bioavailability, [14] ADMET parameters (Administration, Distribution, Metabolism, Excretion, and Toxicity), [15] SAR studies (structure activity relationship) of some components, [16,17] QSKR pharmacokinetic studies [18] …”

Section: Introductionmentioning

confidence: 99%

In Silico Predictability of Toxicity Parameters Using the OECD QSAR Toolbox of Some Components of Cannabis sativa

et al. 2023

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In this paper, the quantitative structure‐activity relationship predictive models (QSAR) are presented: the prediction model for some of the main components of cannabis sativa, acute toxicity LD50 (n=45, r2=0.917, q2=0.902), chronic toxicity LOAEL (n=35, r2=0.911, q2=0.868), NOAEL(n=22, r2=0.964, q2=0.937), LOEL (n=26, r2=0.911, q2=0.863) and NOEL (n=25, r2=0.867, q2=0.802), in addition to carcinogenicity and mutagenicity effects. Only for the case of LD50, the values reported in the literature of the delta 9‐THC compound present in Cannabis sativa were taken into account and this model was applied to the other compounds present in the plant, which we consider the most relevant since they fell from the allowed domain for QSAR Toolbox to predict the LD50 of each of them, while the other additional endpoints were established according to the methodology that marks the QSAR Toolbox software. The application of these models and predictions allowed us to predict the permissible daily exposure (PDE) and acceptable daily intake (ADI) values of some of the cannabis components. This work aims to present important elements of the toxicology of some of the components of the plant that serve only as a reference and does not intend to favor or inhibit the use of cannabis for medicinal or recreational use.

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Development of intestinal bioavailability prediction (IBP) and phytochemical relative antioxidant potential prediction (PRAPP) models for optimizing functional food value of Cannabis sativa (hemp)

Cited by 8 publications

References 38 publications

Solanum Fruits: Phytochemicals, Bioaccessibility and Bioavailability, and Their Relationship With Their Health-Promoting Effects

Solanum Fruits: Phytochemicals, Bioaccessibility and Bioavailability, and Their Relationship With Their Health-Promoting Effects

Utilisation of QSPR ODT modelling and odour vector modelling to predict Cannabis sativa odour

In Silico Predictability of Toxicity Parameters Using the OECD QSAR Toolbox of Some Components of Cannabis sativa

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