The patient receives a pharmaceutical product, not a drug. The pharmaceutical products are formulated with a drug, an active ingredient to produce the maximum therapeutic effect after oral absorption. Therefore, it is the product we must optimize for the patients. In order to assure the safety and efficacy of pharmaceutical products, we need an in vivo predictive tool for oral product performance in patients. Currently, we are a surprisingly long way from accomplishing that objective. If the 20th century was the ‘age of the drug’, i.e., the ‘magic bullet’, the 21st century must become the ‘age of the guided missile’, i.e., the delivery system, including the form of the active pharmaceutical ingredient (API) (‘drug’). The physical form of the drug and the delivery system must be optimized to maximize the therapeutic benefits of pharmaceutical products for humans. Oral immediate release (IR) dosage forms cannot be optimal for all drugs or likely even any drugs (APIs). Still, the formulation of pharmaceutical products has to be optimized for patients. But how do we optimize oral delivery of drugs? It is usually through ‘trial and error’, in humans! We need a better way to optimize the oral dosage forms. We have suggested to select different dissolution methodologies for this optimization based on BCS Subclasses. In this article, we present the predicted in vivo drug dissolution profile of ketoconazole as a model drug from our laboratory utilizing a gastrointestinal simulator (GIS), which is an adaptation of the ASD system. GIS consists of three chambers representing stomach, duodenum, and jejunum, to create the human gastrointestinal tract-like environment and enable the control the gastric emptying rate. This dissolution system allows the monitoring of the drug dissolution phenomena and the observation of the supersaturation and the precipitation of pharmaceutical products, which is useful information to predict in vivo dissolution of pharmaceutical products. This system can provide the actual input needed to accurately predict the input into the systemic circulation required by many of the absorption prediction packages available today.