The aim of this work was to develop a phosphate buffer based dissolution method for enteric-coated formulations with improved biopredictivity for fasted conditions. Two commercially available enteric-coated aspirin products were used as model formulations (Aspirin Protect 300 mg, and Walgreens Aspirin 325 mg). The disintegration performance of these products in a physiological 8 mM pH 6.5 bicarbonate buffer (representing the conditions in the proximal small intestine) was used as a standard to optimize the employed phosphate buffer molarity. To account for the fact that a pH and buffer molarity gradient exists along the small intestine, the introduction of such a gradient was proposed for products with prolonged lag times (when it leads to a release lower than 75% in the first hour post acid stage) in the proposed buffer. This would allow the method also to predict the performance of later-disintegrating products. Dissolution performance using the accordingly developed method was compared to that observed when using two well-established dissolution methods: the United States Pharmacopeia (USP) method and blank fasted state simulated intestinal fluid (FaSSIF). The resulting dissolution profiles were convoluted using GastroPlus software to obtain predicted pharmacokinetic profiles. A pharmacokinetic study on healthy human volunteers was performed to evaluate the predictions made by the different dissolution setups. The novel method provided the best prediction, by a relatively wide margin, for the difference between the lag times of the two tested formulations, indicating its being able to predict the post gastric emptying onset of drug release with reasonable accuracy. Both the new and the blank FaSSIF methods showed potential for establishing in vitro-in vivo correlation (IVIVC) concerning the prediction of Cmax and AUC0-24 (prediction errors not more than 20%). However, these predictions are strongly affected by the highly variable first pass metabolism necessitating the evaluation of an absorption rate metric that is more independent of the first-pass effect. The Cmax/AUC0-24 ratio was selected for this purpose. Regarding this metric’s predictions, the new method provided very good prediction of the two products’ performances relative to each other (only 1.05% prediction error in this regard), while its predictions for the individual products’ values in absolute terms were borderline, narrowly missing the regulatory 20% prediction error limits (21.51% for Aspirin Protect and 22.58% for Walgreens Aspirin). The blank FaSSIF-based method provided good Cmax/AUC0-24 ratio prediction, in absolute terms, for Aspirin Protect (9.05% prediction error), but its prediction for Walgreens Aspirin (33.97% prediction error) was overwhelmingly poor. Thus it gave practically the same average but much higher maximum prediction errors compared to the new method, and it was strongly overdiscriminating as for predicting their performances relative to one another. The USP method, despite not being overdiscriminating, provided poor predictions of the individual products’ Cmax/AUC0-24 ratios. This indicates that, overall, the new method is of improved biopredictivity compared to established methods.