Previous work has established the existence of dystrophin-nitric oxide (NO) signaling to histone deacetylases (HDACs) that is deregulated in dystrophic muscles. As such, pharmacological interventions that target HDACs (that is, HDAC inhibitors) are of potential therapeutic interest for the treatment of muscular dystrophies. In this study, we explored the effectiveness of long-term treatment with different doses of the HDAC inhibitor givinostat in mdx mice-the mouse model of Duchenne muscular dystrophy (DMD). This study identified an efficacy for recovering functional and histological parameters within a window between 5 and 10 mg/kg/d of givinostat, with evident reduction of the beneficial effects with 1 mg/kg/d dosage. The long-term (3.5 months) exposure of 1.5-month-old mdx mice to optimal concentrations of givinostat promoted the formation of muscles with increased cross-sectional area and reduced fibrotic scars and fatty infiltration, leading to an overall improvement of endurance performance in treadmill tests and increased membrane stability. Interestingly, a reduced inflammatory infiltrate was observed in muscles of mdx mice exposed to 5 and 10 mg/kg/d of givinostat. A parallel pharmacokinetic/pharmacodynamic analysis confirmed the relationship between the effective doses of givinostat and the drug distribution in muscles and blood of treated mice. These findings provide the preclinical basis for an immediate translation of givinostat into clinical studies with DMD patients.

No abstract

No abstract

PurposeIn clinical oncology, combination treatments are widely used and increasingly preferred over single drug administrations. A better characterization of the interaction between drug effects and the selection of synergistic combinations represent an open challenge in drug development process. To this aim, preclinical studies are routinely performed, even if they are only qualitatively analyzed due to the lack of generally applicable mathematical models.MethodsThis paper presents a new pharmacokinetic–pharmacodynamic model that, starting from the well-known single agent Simeoni TGI model, is able to describe tumor growth in xenograft mice after the co-administration of two anticancer agents. Due to the drug action, tumor cells are divided in two groups: damaged and not damaged ones. The damaging rate has two terms proportional to drug concentrations (as in the single drug administration model) and one interaction term proportional to their product. Six of the eight pharmacodynamic parameters assume the same value as in the corresponding single drug models. Only one parameter summarizes the interaction, and it can be used to compute two important indexes that are a clear way to score the synergistic/antagonistic interaction among drug effects.ResultsThe model was successfully applied to four new compounds co-administered with four drugs already available on the market for the treatment of three different tumor cell lines. It also provided reliable predictions of different combination regimens in which the same drugs were administered at different doses/schedules.ConclusionsA good and quantitative measurement of the intensity and nature of interaction between drug effects, as well as the capability to correctly predict new combination arms, suggest the use of this generally applicable model for supporting the experiment optimal design and the prioritization of different therapies.Electronic supplementary materialThe online version of this article (doi:10.1007/s00280-013-2208-8) contains supplementary material, which is available to authorized users.

No abstract