Recent developments on multifunctional nano-systems have opened new perspectives for tumor control by proposing new nano-actuators and nano-sensors in in vivo anti-cancer treatments. One of these challenging perspectives is the use of nanoparticles as vector to deliver the right dose of PS (Photosensitizer agent) into the tumor. But the delivery control of these nano-agents into the cancer cells is one of the major factors that directly affect the efficiency of the photodynamic therapy. Several previous works have already shown that the intracellular uptake could not be predicted from the nano-object chemical properties. In 2004, the FDA's Critical Path Report proposed, among other solutions, the increased use of model-based approaches to drug development, including pharmacokinetic and pharmacodynamic (PK/PD) modeling. In this study, we propose a non-supervised estimation method, developed to estimate the pharmacokinetic parameters of two multifunctional nanoparticles involved in photodynamic therapy. We show that continuous-time model identification methods (available in the CONTSID toolbox for Matlab), generally used in control engineering, can bring efficient solutions to help biologists to estimate crucial parameters of the nanoparticles pharmacokinetics from experimental data. The in vivo results presented clearly emphasize the relevance of these data-driven modeling approaches associated with magnetic resonance imaging.