Osteoporosis is a chronic progressive disorder and is regarded as an important worldwide health issue. The development of novel treatments and the comparison of the effects of novel and existing treatments in osteoporosis are complicated by the difficulties of establishing drug effects on disease progression, as reflected in the slowly changing primary biomarker, bone mineral density. In recent years, research has considerably improved our understanding of the pathophysiology of osteoporosis. Specifically, various biomarkers have been identified that reflect bone physiology at the cellular level. These biomarkers mirror the dynamics of bone formation and degradation on a shorter timescale than bone mineral density as a composite measure. These markers can therefore, in principle, be used to characterize the underlying regulatory system and to quantify drug effects in osteoporosis. Recently, the concept of disease system analysis has been proposed as a novel approach to characterize, in a strictly quantitative manner, drug effects on disease progression. This approach integrates physiology, disease progression and drug treatment in a comprehensive mechanism-based model, using dynamic information on a network of biomarkers. This review focuses on the use of disease system analysis for the characterization of drug effects on osteoporosis. It is concluded that, although the development of fully mechanistic disease system models may be practically impossible, parsimonious--but mechanism-based--disease system models may ultimately be used to adequately predict the long-term effects of drug treatment on clinical outcomes.