Physicochemical properties of dietary phytochemicals can predict their passive absorption in the human small intestine

Abstract

A diet high in phytochemical-rich plant foods is associated with reducing the risk of chronic diseases such as cardiovascular and neurodegenerative diseases, obesity, diabetes and cancer. Oxidative stress and inflammation (OSI) is the common component underlying these chronic diseases. Whilst the positive health effects of phytochemicals and their metabolites have been demonstrated to regulate OSI, the timing and absorption for best effect is not well understood. We developed a model to predict the time to achieve maximal plasma concentration (T_max) of phytochemicals in fruits and vegetables. We used a training dataset containing 67 dietary phytochemicals from 31 clinical studies to develop the model and validated the model using three independent datasets comprising a total of 108 dietary phytochemicals and 98 pharmaceutical compounds. The developed model based on dietary intake forms and the physicochemical properties lipophilicity and molecular mass accurately predicts T_max of dietary phytochemicals and pharmaceutical compounds over a broad range of chemical classes. This is the first direct model to predict T_max of dietary phytochemicals in the human body. The model informs the clinical dosing frequency for optimising uptake and sustained presence of dietary phytochemicals in circulation, to maximise their bio-efficacy for positively affect human health and managing OSI in chronic diseases.

Figure 1

Prediction of T_max by the predictive model. (a) The log P model in liquid, (b) semi-solid and (c) solid intakes. (d) The PSA model in liquid, (e) semi-solid and (f) solid intakes.

Figure 2

Summary of variables included in datasets for the development and validation of the predictive model. Dot plots demonstrate distributions of (a) log P, (b) M_r and (c) PSA of four datasets: training (N = 67), PCv (N = 108), PHv-fasted (N = 60) and PHv-fed (N = 38) datasets.

Figure 3

Comparison of measured versus predicted values of T_max of the training dataset and the PCv dataset. Natural logarithm of T_max measured from the training dataset (N = 67) were plotted against natural logarithm of predicted T_max based on the log P model (black circle), the PSA model (clear circle) and compared to the regression of measured T_max = predicted T_max (dotted line) when intake as (a) liquid, (b) semi-solid and (c) solid forms. Similar comparison was plotted for the PCv dataset (N = 108) when intake as (d) liquid, (e) semi-solid and (f) solid forms.

Figure 4

Comparison of measured versus predicted values of T_max of the PHv datasets. Natural logarithm of T_max measured from (a) the PHv-fasted (N = 60) dataset and (b) the PHv-fed (N = 38) dataset were plotted against natural logarithm of predicted T_max based on the log P model (black circle), the PSA model (clear circle) and compared to the regression of measured T_max = predicted T_max (dotted line) when intake as solid forms.