Results from a Validated in vitro Gastrointestinal Model (TIM) used as input Data for in silico Modeling Give Highly Predictive Information for the Human Situation

Publication: Medical Research Archives
Software: GastroPlus®
Division: Simulations Plus


The aim of this review paper is to evaluate the predictive quality of a combination of in vitro dynamic gastrointestinal models, mucosal transit models and in silico kinetic modeling. The TNO gastro-Intestinal Model (TIM) is a computer-controlled system, mimicking essential gastrointestinal parameters of the stomach, small intestine and large intestine. The systems have dialysis or filtration units connected to the intestinal compartments. TIM settings are adapted to the condition that has to be simulated, such as fasted and fed state, age, and co-medication. In this way the transit and digestibility of food, release, dissolution, and bioaccessibility of nutrients, drugs, and metabolites can be studied. The TIM Systems have been validated in comparison to human studies for various food products and oral drugs, published in peer-reviewed journals. The results show the potential availability for absorption, called ‘bioaccessibility’. Combining TIM with mucosal transit assays, it is possible to also analyze the intestinal absorption. But for predicting bioavailability and plasma concentrations in time it needs additional kinetic data, such as distribution, metabolism, and excretion. TIM bioaccessibility data and (published) kinetic data can be used as input in commercial in silico models or specifically developed in silico modeling. Validation studies show a high predictive quality for human nutrient and drug bioavailability and plasma concentrations. Maybe not (yet) in all cases the predictions will cover for 100% the human data, so there is room for improvement. However, the reviewed studies clearly show the strength of combining a validated gastrointestinal model with physiological kinetic data in in silico modeling. It certainly will replace animal experiments and will strongly increase the success rate of follow-up human studies, saving time and costs.

By Robert Havenaar & Bellmann S