Prevention of drug-induced toxicities is a major component in the drug development pipeline and in clinical patient care. The toxicity potential of drugs is routinely accessed with in vitro assays and in vivo animal experiments before the first-in-human administration. Although these experiments produce valuable information, a significant amount of false positive toxicity predictions results when translating these findings to the human situation. The lack of patient-specific in vitro models that allow mimicking the patient-specific in vivo situation, as well as computational methods that translate these model findings to the patient level are missing to date. In this work, the development of computational multi-scale concepts is presented and applied to estimate the impact of drug concentrations-time profiles on the organ-specific cellular biochemistry. The presented concepts are used to estimate drug-induced metabolic perturbations on the cellular level after drug administration, enabling the prediction of to be expected changes of metabolite pools in organs, tissues, and blood plasma. Besides the prediction of drug-induced metabolic perturbations on the cellular biochemistry and metabolite concentrations, metabolic changes on the whole-body level can be estimated and thus allow the mechanistic characterization of the underlaying processes. The presented concepts and approaches allow the integration of time-resolved experimental data from in vitro and in vivo high-throughput experiments together with drug concentration-time profiles. Thereby, supporting a translation from in vitro findings into an in vivo context. The presented computational concepts span multiple orders of biological organization ranging from the whole-body over the organ and tissue scale down to the cellular level. If applied during the drug development process, the presented concepts could help to identify potential toxic compounds early in the drug development pipeline. Therefore, the presented work might help to develop targeted therapies aiming to reduce or even prevent drug-induced toxicities. This work may also contribute to our mechanistic understanding of organ-specific drug-induced metabolic perturbations that can set the molecular base for drug-induced toxicity
