Single dose of the antivascular agent, ZD6126 (N-acetylcolchinol-O-phosphate), reduces perfusion for at least 96 hours in the GH3 prolactinoma rat tumor model.

Tumor vasculature is an attractive therapeutic target as it differs structurally from normal vasculature, and the destruction of a single vessel can lead to the death of many tumor cells. The effects of antivascular drugs are frequently short term, with regrowth beginning less than 24 hours posttreatment. This study investigated the duration of the response to the vascular targeting agent, ZD6126, of the GH3 prolactinoma, in which efficacy and dose-response have previously been demonstrated. GH3 prolactinomas were grown in the flanks of eight Wistar Furth rats. All animals were treated with 50 mg/kg ZD6126. The tumors were examined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) 24 hours pretreatment and posttreatment, and at a single time between 48 and 96 hours posttreatment. No evidence of recovery of perfusion was observed even at the longest (96-hour) time point. Involvement of a statistician at the project planning stage and the use of DCE-MRI, which permits noninvasive quantitation of parameters related to blood flow in intact animals, allowed this highly significant result to be obtained using only eight rats.

In search of preclinical robustness.

Systematic engagement of statisticians in preclinical research could help address the weaknesses that are undermining the likelihood of subsequent success in drug discovery and development.

Proteomic biomarkers for acute interstitial lung disease in gefitinib-treated Japanese lung cancer patients.

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

Personalized medicine and proteomics: lessons from non-small cell lung cancer.

Personalized medicine allows the selection of treatments best suited to an individual patient and disease phenotype. To implement personalized medicine, effective tests predictive of response to treatment or susceptibility to adverse events are needed, and to develop a personalized medicine test, both high quality samples and reliable data are required. We review key features of state-of-the-art proteomic profiling and introduce further analytic developments to build a proteomic toolkit for use in personalized medicine approaches. The combination of novel analytical approaches in proteomic data generation, alignment and comparison permit translation of identified biomarkers into practical assays. We further propose an expanded statistical analysis to understand the sources of variability between individuals in terms of both protein expression and clinical variables and utilize this understanding in a predictive test.