PA401, a novel CXCL8-based biologic therapeutic with increased glycosaminoglycan binding, reduces bronchoalveolar lavage neutrophils and systemic inflammatory markers in a murine model of LPS-induced lung inflammation.

RATIONALE: Neutrophils play a fundamental role in a number of chronic lung diseases. Among the mediators of their recruitment to the lung, CXCL8 (IL-8) is considered to be one of the major players. CXCL8 exerts its chemotactic activity by binding to its GPCR receptors (CXCR1/R2) located on neutrophils, as well as through interactions with glycosaminoglycans (GAGs) on cell surfaces including those of the microvascular endothelium. Binding to GAG co-receptors is required to generate a solid-phase haptotactic gradient and to present IL-8/CXCL8 in a proper conformation to its receptors on circulating neutrophils. METHODS: We have engineered increased GAG-binding affinity into human CXCL8, thereby obtaining a competitive inhibitor that displaces wild-type IL-8/CXCL8 from GAGs. By additionally knocking-out the GPCR binding domain of the chemokine, we generated a dominant negative protein (dnCXCL8; PA401) with potent anti-inflammatory characteristics proven in vivo in a murine model of LPS-induced lung inflammation (Adage et al., 2015). Here we have further investigated PA401 activity in this pulmonary model by evaluating plasma changes induced by LPS on white blood cells (WBC) and a broad range of inflammatory markers, especially chemokines, by addressing immediate effects of PA401 on these parameters in healthy and LPS exposed mice. RESULTS: Aerosolized LPS induced a significant increase in bronchoalveolar lavage (BAL) neutrophils after 3 and 7h, as well as an increase in total WBC and changes in 21 of the 59 measured plasma markers, mostly belonging to the chemokine family. PA401 treatment in saline exposed mice didn't induce major changes in any of the measured parameters. When administered to LPS aerosolized mice, PA401 caused a significant normalization of KC/mCXCL1 and other inflammatory markers, as well as of blood WBC count. In addition, BAL neutrophils were significantly reduced, confirming the previously observed lung anti-inflammatory activity of PA401 in this experiment. CONCLUSIONS: PA401 is a new promising biologic therapeutic with a novel and unique mechanism of action for interfering with neutrophilic lung inflammation, that also normalizes plasma inflammatory markers.

Preclinical studies in the mdx mouse model of duchenne muscular dystrophy with the histone deacetylase inhibitor givinostat.

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.

Population pharmacokinetic-pharmacodynamic analysis of givinostat.

BACKGROUND: Givinostat (ITF2357), an oral, synthetic histone deacetylase inhibitor, significantly improved all histological muscle biopsy parameters in a Phase II study in boys with Duchenne muscular dystrophy (DMD). RESEARCH DESIGN AND METHODS: A population pharmacokinetic (PK) model, including seven clinical studies, was developed to explore the effect of covariates on givinostat PK. The final model was qualified to simulate pediatric dosing recommendations. A PK/pharmacodynamic (PD) model was developed to simulate the link between givinostat plasma concentration and platelet time course in 10-70-kg children following 6 months of givinostat 20-70 mg twice daily. RESULTS: A two-compartment model, with first-order input with lag and first-order elimination from the central compartment, described givinostat PK, demonstrating increasing apparent clearance with increasing body weight. The PK/PD model well-described platelet count time course. Weight-based dosing (arithmetic mean systemic exposure of 554-641 ng·h/mL) produced an average platelet count decrease from baseline of 45% with maximum decrease within 28 days. After 1 week and 6 months, ~1% and ~14-15% of patients, respectively, had a platelet count <75 × 109/L. CONCLUSIONS: Based on these data, givinostat dosing will be body weight adjusted and include monitoring of platelet counts to support efficacy and safety in a Phase III DMD study.

PK/PD analysis of trazodone and gabapentin in neuropathic pain rodent models: Translational PK-PD modeling from nonclinical to clinical development.

A pharmacokinetic/pharmacodynamic (PK/PD) model was developed to describe the time course of writhings after intraperitoneal injection of acetic acid in mice. The model was applied to investigate the antinociceptive effect of trazodone and gabapentin alone and in combination. Writhings time course was described by a transit compartment model with the delay due to the transit of the acetic acid being represented by a chain of intermediate compartments. In the drug-treated animals, the number of writhings decreases according to a k2 factor linking drug concentration and antinociceptive effect. Compounds' potency parameters were 10.9 and 0.0459 L/µmoles/min for trazodone and gabapentin, respectively, indicating a much higher in vivo potency of trazodone in the PD writhing test. The PK/PD parameters were used to simulate the expected writhing counts in mice at combined doses without efficacy alone, assuming pharmacological additivity. Simulation results indicated that, at low dose combinations, experimental data were mostly below the simulated writhings median, suggesting possible synergic effect. Such hypothesis was tested by adding the γ parameter in the PK/PD model to represent the deviation from the assumption of no-interaction, leading to a reduction of the objective function compared to the additive model. On this basis, several simulations were performed to identify possible starting dose combinations of trazodone and gabapentin in humans, by selecting doses yielding systemic exposures close to those being synergic in the mouse. Simulations indicated that doses of 50-100 mg trazodone could enhance gabapentin antinociceptive effect in humans, supporting the development of a low dose combination for optimal analgesia treatment.

A predictive pharmacokinetic-pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination.

PURPOSE: In 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. METHODS: This 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. RESULTS: The 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. CONCLUSIONS: A 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.

A4S: a user-friendly graphical tool for pharmacokinetic and pharmacodynamic (PK/PD) simulation.

Effective communication of PK/PD principles and results in a biomedical research environment remains a significant challenge which can result in lack of buy-in and engagement from scientists outside the modeling and simulation communities. In our view, one of the barriers in this area is a lack of user-friendly tools which allow "non experts" to use PK/PD models without the need to develop technical skills and expertise in advanced mathematical principles and specialist software. The costs of commercial software may also prevent large-scale distribution. One attempt to address this issue internally in our research organizations has resulted in the development of the A4S ("Accelera for Sandwich") software, which is a simple-to-use, menu-drive Matlab-based PK/PD simulator targeted at biomedical researchers with little PK/PD experience.

Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts.

PURPOSE: Pharmacokinetic-pharmacodynamic (PK-PD) models able to predict the action of anticancer compounds in tumor xenografts have an important impact on drug development. In case of anti-angiogenic compounds, many of the available models show difficulties in their applications, as they are based on a cell kill hypothesis, while these drugs act on the tumor vascularization, without a direct tumor cell kill effect. For this reason, a PK-PD model able to describe the tumor growth modulation following treatment with a cytostatic therapy, as opposed to a cytotoxic treatment, is proposed here. METHODS: Untreated tumor growth was described using an exponential growth phase followed by a linear one. The effect of anti-angiogenic compounds was implemented using an inhibitory effect on the growth function. The model was tested on a number of experiments in tumor-bearing mice given the anti-angiogenic drug bevacizumab either alone or in combination with another investigational compound. Nonlinear regression techniques were used for estimating the model parameters. RESULTS: The model successfully captured the tumor growth data following different bevacizumab dosing regimens, allowing to estimate experiment-independent parameters. A combination model was also developed under a 'no-interaction' assumption to assess the effect of the combination of bevacizumab with a target-oriented agent. The observation of a significant difference between model-predicted and observed tumor growth curves was suggestive of the presence of a pharmacological interaction that was further accommodated into the model. CONCLUSIONS: This approach can be used for optimizing the design of preclinical experiments. With all the inherent limitations, the estimated experiment-independent model parameters can be used to provide useful indications for the single-agent and combination regimens to be explored in the subsequent development phases.

A model-based approach to the in vitro evaluation of anticancer activity.

PURPOSE: The use of in vitro screening tests for characterizing the activity of anticancer agents is a standard practice in oncology research and development. In these studies, human A2780 ovarian carcinoma cells cultured in plates are exposed to different concentrations of the compounds for different periods of time. Their anticancer activity is then quantified in terms of EC(50) comparing the number of metabolically active cells present in the treated and the control arms at specified time points. The major concern of this methodology is the observed dependency of the EC(50) on the experimental design in terms of duration of exposure. This dependency could affect the efficacy ranking of the compounds, causing possible biases especially in the screening phase, when compound selection is the primary purpose of the in vitro analysis. To overcome this problem, the applicability of a modeling approach to these in vitro studies was evaluated. METHODS: The model, consisting of a system of ordinary differential equations, represents the growth of tumor cells using a few identifiable and biologically relevant parameters related to cell proliferation dynamics and drug action. In particular, the potency of the compounds can be measured by a unique and drug-specific parameter that is essentially independent of drug concentration and exposure time. Parameter values were estimated using weighted nonlinear least squares. RESULTS: The model was able to adequately describe the growth of tumor cells at different experimental conditions. The approach was validated both on commercial drugs and discovery candidate compounds. In addition, from this model the relationship between EC(50) and the exposure time was derived in an analytic form. CONCLUSIONS: The proposed approach provides a new tool for predicting and/or simulating cell responses to different treatments with useful indications for optimizing in vitro experimental designs. The estimated potency parameter values obtained from different compounds can be used for an immediate ranking of anticancer activity.