Computational prediction of local drug effect on carcinogenic acetaldehyde in the mouth based on in vitro/in vivo results of freely soluble L-cysteine.

Background: The computational models for predicting oral drug absorption in humans using in vitro and in vivo data have been published. However, only a limited number of studies are available on the prediction of local drug efficacy in the mouth using computational models.

Aim: The goal of this study was to develop a simulation model for prediction of drug amount and effect on carcinogenic acetaldehyde in the mouth.

Methods: The model was based partly on our previous studies in which we showed in vivo that l-cysteine-containing tablets can eliminate carcinogenic salivary acetaldehyde in the mouth during smoking. To develop as informative a model as possible, we also investigated whether a lower saliva pH (4.7) can affect the freely soluble l-cysteine dissolution rate and cysteine stability profile in the mouth, compared to the normal saliva pH of 7.4.

Results: Stability of the active drug is not pH dependent and thus users with normal, healthy saliva pH and those with lower pH can benefit from cysteine-containing products. The simulated saliva profiles of l-cysteine and acetaldehyde corresponded to the in vivo results.

Conclusions: The model developed can be used as an alternative tool to obtain faster and cheaper answers on how freely soluble drugs affect local conditions in the mouth. Because tobacco smoke contains more than 60 carcinogenic compounds, the model developed can offer a new view in eliminating or reducing not only one toxic compound from smoke but also many others compounds using only one formulation containing various active compounds.