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Purpose:To determine the feasibility of T2-weighted magnetic resonance (MR) imaging in the noninvasive quantifi cation of renal infl ammation by using superparamagnetic iron oxide (SPIO) nanoparticles targeted to tissue-bound C3 activation fragments in a mouse model of lupus nephritis. Materials and Methods:All animal procedures were approved by the University of Colorado-Denver animal care and use committee. SPIO nanoparticles were encapsulated by using aminefunctionalized phospholipids. A recombinant protein containing the C3d-binding region of complement receptor type 2 (CR2) was then conjugated to the surface of the SPIO nanoparticle. Five MRL/lpr mice (a model of lupus nephritis) and six C57BL/6 wild-type mice were assessed with T2-weighted MR imaging at baseline and after SPIO injection. The same fi ve MRL/lpr mice and three C57BL/6 mice also underwent MR imaging after injection of CR2-targeted SPIO. A series of T2-weighted pulses with 16 echo times was used to enable precise T2 mapping and calculation of T2 relaxation times in the cortex and outer and inner medulla of the kidneys, as well as in the spleen, muscle, and fat. The effects of treatment and animal genotype on T2 relaxation times were analyzed with repeatedmeasures analysis of variance. Results:At baseline, the T2-weighted signal intensity in the kidneys of MRL/lpr mice was higher than that in the kidneys of wild-type mice. Injection of untargeted SPIO did not alter the T2-weighted signal in the kidneys in either strain of mice. Injection of CR2-targeted SPIO in MRL/lpr mice, however, caused a signifi cant accumulation of targeted iron oxide with a subsequent decrease in T2 relaxation times in the cortex and outer and inner medulla of the kidneys. No changes in T2 relaxation time were observed in the wild-type mice after injection of targeted SPIO. Conclusion:Injection of CR2-conjugated SPIO caused a signifi cant reduction in T2-weighted MR imaging signal and T2 relaxation time in nephritic kidneys.q RSNA, 2010Supplemental material: http://radiology.rsna.org/lookup /suppl

A cross‐industry survey was conducted to assess the landscape of preclinical quantitative systems pharmacology (QSP) modeling within pharmaceutical companies. This article presents the survey results, which provide insights on the current state of preclinical QSP modeling in addition to future opportunities. Our results call attention to the need for an aligned definition and consistent terminology around QSP, yet highlight the broad applicability and benefits preclinical QSP modeling is currently delivering.

Purpose: Docetaxel (Taxotere), an important chemotherapeutic agent with shown activity in a broad range of cancers, is being investigated for use in combination therapies and as an antiangiogenic agent. Docetaxel exhibits a complex pharmacologic profile with high interpatient variability. Pharmacokinetic models capable of predicting exposure under various dosing regimens would aid the rational development of clinical protocols. Experimental Design: A pharmacokinetic study of docetaxel at 5 and 20 mg/kg was carried out in female BALB/c mice. Tissues were collected at various time points and analyzed by liquid chromatography-tandem mass spectrometry. Time course tissue distribution and pharmacokinetic data were used to build and validate a physiologically based pharmacokinetic (PBPK) model in mice. Specific and nonspecific tissue partitioning, metabolism, and elimination data were coupled with mouse physiologic variables to develop a PBPK model that describes docetaxel plasma and tissue pharmacokinetic. The PBPK model was then modified with human model variables to predict the plasma distribution of docetaxel. Results: Resulting simulation data were compared with actual measured data obtained from our pharmacokinetic study (mouse), or from published data (human), using pharmacokinetic variables calculated using compartmental or noncompartmental analysis to assess model predictability. Conclusions:The murine PBPK model developed can accurately predict plasma and tissue levels at the 5 and 20 mg/kg doses. The human PBPK model is capable of estimating plasma levels at 30, 36, and 100 mg/m 2 . This will enable us to develop and test various dosing regimens (e.g., metronomic schedules and combination therapies) to achieve specific tissue and plasma concentrations to maximize therapeutic benefit while minimizing toxicity.

BackgroundThe complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach.ResultsA dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells.ConclusionsThe final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunologi...