Patients with type 1 diabetes mellitus (T1DM) need to supply their body with insulin from external sources in order to manage their blood glucose (BG) concentration and mitigate the long-term effects of a chronically increased BG level. Doing so is challenging and a heavy burden for those patients, which led to efforts of automating (parts of) this task. The trend to automated choice of insulin dosage, e.g. in Artificial pancreas (AP) systems, opens many new possibilities, but also challenges in terms of validation, as the number of freely tunable parameters (e.g. settings in an AP) can be so large that no clinical trial can assess the efficiency of all possible choices. As a consequence, computer model based trials - so called in silico evaluations - are getting increasingly popular. In recent times, several authors have tried to improve the quality of in silico evaluations by using 'Deviation Analyses', a term used to refer to methods that extrapolate the effect of a modified insulin therapy using real measurement data together with simple, linear models of insulin action. However, due to the inherent linear model assumption in all methods proposed so far, large deviations compared to the insulin dosing scheme of the recorded data can lead to unphysiological results, e.g. to negative values in the computed glucose traces. Against this background a new, nonlinear methodology is proposed which effectively avoids the common pitfalls of linear Deviation Analyses approaches, i.e. The constant mode of insulin action.