Automated scale reduction of nonlinear QSP models with an example of a bone biology system

QSP models are increasingly used in drug development to provide a deeper understanding of the mechanism of action of drugs and their likely effects on the system as well as to identify appropriate disease targets in preclinical settings. Irrespective of the purpose of development, such models are generally not suitable for estimation purposes due to the large number of states and parameters to be handled, even if all unidentifiable parameters were fixed.
Based on identifying a specific input–output relationship, however, the system may be reduced to fewer states and parameters that may then be suitable for estimation purposes. Proper lumping has been used for order reduction of complicated linear models. This technique is however not straightforward to apply for nonlinear differential equations that are not uncommon in QSP models.In this presentation, I will be discussing the simplification of a nonlinear systems bone biology model by inductively linearizing the system followed by automated lumping. The reduced model will then be utilized to extrapolate long-term bone mineral density responses.