The drug development industry faces multiple challenges in the realization of safe effective drugs. Computational modeling approaches can be used to support these efforts. One approach, mechanistic modeling, is new to the realm of drug safety. It holds the promise of not only predicting toxicity for novel compounds, but also illuminating the mechanistic underpinnings of toxicity. To increase the scientific community’s familiarity with mechanistic modeling in drug safety, this article seeks to provide perspective on the type of data used, how they are used and where they are lacking. Examples are derived from the development of DILIsym(®) software, a mechanistic model of drug-induced liver injury (DILI). DILIsym(®) simulates the mechanistic interactions and events from compound administration through the progression of liver injury and regeneration. Modeling mitochondrial toxicity illustrates the type and use of in vitro data to represent biological interactions, as well as insights on key differences between in vitro and in vivo conditions. Modeling bile acid toxicity illustrates a case in which the over-arching mechanism is well accepted, but many mechanistic details are lacking. Modeling was used to identify measurements predicted to strongly impact toxicity. Finally, modeling innate immune responses illustrates the importance of time-series data, particularly in the presence of positive and negative feedback loops, as well as the need for data from different animal species for better translation. These concepts are germane to most mechanistic models, although the details will vary. The use of mechanistic models is expected to improve the rational design of new drugs.

By Lisl Shoda, Jeffery Woodhead, Scott Q Siler, Paul B Watkins, Brett A Howell