A mathematical model can be defined as a theoretical approximation of an observed pattern. The specific form of the model and the associated mathematical methods are typically dictated by the question(s) to be addressed by the model and the underlying data. In the context of research and development of new medicines, these questions often focus on the dose-exposure-response relationship.The general workflow for model development and application can be delineated in three major elements: defining the model, qualifying the model, and performing simulations. These elements may vary significantly depending on modeling objectives. Quantitative systems pharmacology (QSP) models address the formidable challenge of quantitatively and mechanistically characterizing human and animal biology, pathophysiology, and therapeutic intervention.QSP model development, by necessity, relies heavily on preexisting knowledge, requires a comprehensive understanding of current physiological concepts, and often makes use of heterogeneous and aggregated datasets from multiple sources. This reliance on diverse datasets presents an upfront challenge: the determination of an optimal model structure while balancing model complexity and uncertainty. Additionally, QSP model calibration is arduous due to data scarcity (particularly at the human subject level), which necessitates the use of a variety of parameter estimation approaches and sensitivity analyses, earlier in the modeling workflow as compared to, for example, population modeling. Finally, the interpretation of model-based predictions must be thoughtfully aligned with the data and the mathematical methods applied during model development.The purpose of this chapter is to provide readers with a high-level yet comprehensive overview of a QSP modeling workflow, with an emphasis on the various challenges encountered in this process. The workflow is centered around the construction of ordinary differential equation models and may be extended beyond this framework. It includes the fundamentals of systematic literature reviews, the selection of appropriate structural model equations, the analysis of system behavior, model qualification, and the application of various types of model-based simulations. The chapter concludes with details on existing software options suitable for implementing the described methodologies.This workflow may serve as a valuable resource to both newcomers and experienced QSP modelers, offering an introduction to the field as well as operating procedures and references for routine analyses.