"Of Mice and Measures": A Project to Improve How We Advance Duchenne Muscular Dystrophy Therapies to the Clinic

A new line of dystrophic mdx mice on the DBA/2J (D2) background has emerged as a candidate to study the efficacy of therapeutic approaches for Duchenne muscular dystrophy (DMD). These mice harbor genetic polymorphisms that appear to increase the severity of the dystropathology, with disease modifiers that also occur in DMD patients, making them attractive for efficacy studies and drug development. This workshop aimed at collecting and consolidating available data on the pathological features and the natural history of these new D2/mdx mice, for comparison with classic mdx mice and controls, and to identify gaps in information and their potential value. The overall aim is to establish guidance on how to best use the D2/mdx mouse model in preclinical studies

Drug Absorption Modeling as a Tool to Define the Strategy in Clinical Formulation Development

The purpose of this mini review is to discuss the use of physiologically-based drug absorption modeling to guide the formulation development. Following an introduction to drug absorption modeling, this article focuses on the preclinical formulation development. Case studies are presented, where the emphasis is not only the prediction of absolute exposure values, but also their change with altered input values. Sensitivity analysis of technologically relevant parameters, like the drug’s particle size, dose and solubility, is presented as the basis to define the clinical formulation strategy. Taking the concept even one step further, the article shows how the entire design space for drug absorption can be constructed. This most accurate prediction level is mainly foreseen once clinical data is available and an example is provided using mefenamic acid as a model drug. Physiologically-based modeling is expected to be more often used by formulators in the future. It has the potential to become an indispensable tool to guide the formulation development of challenging drugs, which will help minimize both risks and costs of formulation development

Gene Expression Signatures of Radiation Response Are Specific, Durable and Accurate in Mice and Humans

Background: Previous work has demonstrated the potential for peripheral blood (PB) gene expression profiling for the detection of disease or environmental exposures. Methods and Findings: We have sought to determine the impact of several variables on the PB gene expression profile of an environmental exposure, ionizing radiation, and to determine the specificity of the PB signature of radiation versus other genotoxic stresses. Neither genotype differences nor the time of PB sampling caused any lessening of the accuracy of PB signatures to predict radiation exposure, but sex difference did influence the accuracy of the prediction of radiation exposure at the lowest level (50 cGy). A PB signature of sepsis was also generated and both the PB signature of radiation and the PB signature of sepsis were found to be 100 % specific at distinguishing irradiated from septic animals. We also identified human PB signatures of radiation exposure and chemotherapy treatment which distinguished irradiated patients and chemotherapy-treated individuals within a heterogeneous population with accuracies of 90 % and 81%, respectively. Conclusions: We conclude that PB gene expression profiles can be identified in mice and humans that are accurate i

Systemic Signature of the Lung Response to Respiratory Syncytial Virus Infection

Respiratory Syncytial Virus is a frequent cause of severe bronchiolitis in children. To improve our understanding of systemic host responses to RSV, we compared BALB/c mouse gene expression responses at day 1, 2, and 5 during primary RSV infection in lung, bronchial lymph nodes, and blood. We identified a set of 53 interferon-associated and innate immunity genes that give correlated responses in all three murine tissues. Additionally, we identified blood gene signatures that are indicative of acute infection, secondary immune response, and vaccine-enhanced disease, respectively. Eosinophil-associated ribonucleases were characteristic for the vaccine-enhanced disease blood signature. These results indicate that it may be possible to distinguish protective and unfavorable patient lung responses via blood diagnostics

Human Gastrointestinal Juices Intended for Use in In Vitro Digestion Models

The aim of this study was to characterise the individual human gastric and duodenal juices to be used in in vitro model digestion and to examine the storage stability of the enzymes. Gastroduodenal juices were aspirated, and individual variations in enzymatic activities as well as total volumes, pH, bile acids, protein and bilirubin concentrations were recorded. Individual pepsin activity in the gastric juice varied by a factor of 10, while individual total proteolytic activity in the duodenal juice varied by a factor of 5. The duodenal amylase activity varied from 0 to 52.6 U/ml, and the bile acid concentration varied from 0.9 to 4.5 mM. Pooled gastric and duodenal juices from 18 volunteers were characterised according to pepsin activity (26.7 U/ml), total proteolytic activity (14.8 U/ml), lipase activity (951.0 U/ml), amylase activity (26.8 U/ml) and bile acids (4.5 mM). Stability of the main enzymes in two frozen batches of either gastric or duodenal juice was studied for 6 months. Pepsin activity decreased rapidly and adjusting the pH of gastric juice to 4 did not protect the pepsin from degradation. Lipase activity remained stable for 4 months, however decreased rapidly thereafter even after the addition of protease inhibitors. Glycerol only marginally stabilised the survival of the enzymatic activities. These results of compositional variations in the individual gastrointestinal juices and the effect of storage conditions on enzyme activities are useful for the design of in vitro models enabling human digestive juices to simulate physiological digestion

Recent translational research: microarray expression profiling of breast cancer – beyond classification and prognostic markers?

Genomic expression profiling has greatly improved our ability to subclassify human breast cancers according to shared molecular characteristics and clinical behavior. The logical next question is whether this technology will be similarly useful for identifying the dominant signaling pathways that drive tumor initiation and progression within each breast cancer subtype. A major challenge will be to integrate data generated from the experimental manipulation of model systems with expression profiles obtained from primary tumors. We highlight some recent progress and discuss several obstacles in the use of expression profiling to identify pathway signatures in human breast cancer

Ataluren delays loss of ambulation and respiratory decline in nonsense mutation Duchenne muscular dystrophy patients

Aim: We investigated the effect of ataluren plus standard of care (SoC) on age at loss of ambulation (LoA) and respiratory decline in patients with nonsense mutation Duchenne muscular dystrophy (nmDMD) versus patients with DMD on SoC alone. / Patients & methods: Study 019 was a long-term Phase III study of ataluren safety in nmDMD patients with a history of ataluren exposure. Propensity score matching identified Study 019 and CINRG DNHS patients similar in disease progression predictors. / Results & conclusion: Ataluren plus SoC was associated with a 2.2-year delay in age at LoA (p = 0.0006), and a 3.0-year delay in decline of predicted forced vital capacity to <60% in nonambulatory patients (p = 0.0004), versus SoC. Ataluren plus SoC delays disease progression and benefits ambulatory and nonambulatory patients with nmDMD. / ClinicalTrials.gov: NCT01557400

An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer

INTRODUCTION. Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation. METHODS. We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies. RESULTS. We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient. CONCLUSIONS. Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.V Foundation for Cancer Research (Partners in Excellence grant

An Integrated Approach to the Prediction of Chemotherapeutic Response in Patients with Breast Cancer

BACKGROUND: A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective. METHODS AND RESULTS: Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy. CONCLUSIONS: Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities