The ability to use in vitro human cytochrome P450 (CYP) time-dependent inhibition (TDI) data for in vivo drug-drug interaction (DDI) predictions should be viewed as a prerequisite to generating the data. Important terms in making such predictions are k(inact) and K(I) but first-line screening assays typically involve characterisation of an IC(50) value or a time dependent shift in IC(50). In the work presented here, two key screening methods from the scientific literature were appraised both in terms of practicality and quality of k(inact)/K(I) estimation. The utility of TDI screening data in DDI predictions was investigated and particular reference given to a simple DDI simulation model based on a spreadsheet that calculates the systemic exposure of unbound inhibitor drug following the input of human pharmacokinetic parameters. Using several clinical mechanism-based CYP DDI examples, the effectiveness of the approach was assessed and compared to other widely available approaches (a simple algorithm that employs a single in vivo unbound inhibitor concentration, a seven-compartment physiologically based pharmacokinetic (PBPK) model that defines the extent of interaction as a result of hepatic inhibitor concentrations and the commercially available software SimCYP). All the methods gave predictions that compared favourably with the observed DDIs, but various advantages and disadvantages of each were also given full consideration. The new model facilitates rapid sensitivity analysis (parameters can be easily input and altered to give a visual representation of the impact on the active enzyme concentration) and it was therefore used to derive "rules of thumb" demonstrating the relationship between extent of DDI, time-dependent IC(50) and dose for typical acidic and basic drugs. Additionally, a TDI decision tree linking into reactive metabolite investigations is proposed for use in a Drug Discovery setting.