Preclinical biomarkers for a cyclin-dependent kinase inhibitor translate to candidate pharmacodynamic biomarkers in phase I patients.

A genomics-based approach to identify pharmacodynamic biomarkers was used for a cyclin-dependent kinase inhibitory drug. R547 is a potent cyclin-dependent kinase inhibitor with a potent antiproliferative effect at pharmacologically relevant doses and is currently in phase I clinical trials. Using preclinical data derived from microarray experiments, we identified pharmacodynamic biomarkers to test in blood samples from patients in clinical trials. These candidate biomarkers were chosen based on several criteria: relevance to the mechanism of action of R547, dose responsiveness in preclinical models, and measurable expression in blood samples. We identified 26 potential biomarkers of R547 action and tested their clinical validity in patient blood samples by quantitative real-time PCR analysis. Based on the results, eight genes (FLJ44342, CD86, EGR1, MKI67, CCNB1, JUN, HEXIM1, and PFAAP5) were selected as dose-responsive pharmacodynamic biomarkers for phase II clinical trials.

A selective phosphatase of regenerating liver phosphatase inhibitor suppresses tumor cell anchorage-independent growth by a novel mechanism involving p130Cas cleavage.

The phosphatase of regenerating liver (PRL) family, a unique class of oncogenic phosphatases, consists of three members: PRL-1, PRL-2, and PRL-3. Aberrant overexpression of PRL-3 has been found in multiple solid tumor types. Ectopic expression of PRLs in cells induces transformation, increases mobility and invasiveness, and forms experimental metastases in mice. We have now shown that small interfering RNA-mediated depletion of PRL expression in cancer cells results in the down-regulation of p130Cas phosphorylation and expression and prevents tumor cell anchorage-independent growth in soft agar. We have also identified a small molecule, 7-amino-2-phenyl-5H-thieno[3,2-c]pyridin-4-one (thienopyridone), which potently and selectively inhibits all three PRLs but not other phosphatases in vitro. The thienopyridone showed significant inhibition of tumor cell anchorage-independent growth in soft agar, induction of the p130Cas cleavage, and anoikis, a type of apoptosis that can be induced by anticancer agents via disruption of cell-matrix interaction. Unlike etoposide, thienopyridone-induced p130Cas cleavage and apoptosis were not associated with increased levels of p53 and phospho-p53 (Ser(15)), a hallmark of genotoxic drug-induced p53 pathway activation. This is the first report of a potent selective PRL inhibitor that suppresses tumor cell three-dimensional growth by a novel mechanism involving p130Cas cleavage. This study reveals a new insight into the role of PRL-3 in priming tumor progression and shows that PRL may represent an attractive target for therapeutic intervention in cancer.

Genetic association meets RNA interference: large-scale genomic screens for causation and mechanism of complex diseases.

While the genomic era offers great promise for biomedicine in general and for biomarker discovery in particular, it has yet to significantly impact drug target discovery. Meanwhile, despite improvements over the past 20 years in reducing attrition in clinical trials due to adverse drug responses, the pharmaceutical industry continues to be beset by the high rate of attrition of compounds in late-stage development, primarily due to the lack of drug efficacy. Clearly, even highly potent drugs with ideal safety and pharmacokinetic profiles will fail to survive clinical trials if the drug target itself is not a key point of intervention for most patients. Genetic association studies and RNA interference are two scaleable genomic approaches that together can address the quality as well as quantity of candidate drug targets. Human genetic information has long been used to identify 'molecular bottlenecks' that can highlight the importance of a gene or pathway at the clinical level. The recent availability of the human HapMap and of high-throughput genotyping platforms now enables more systematic genetic screens for novel, clinically-relevant drug targets. In addition, RNA interference can help dissect the molecular role of a candidate drug target in preclinical model systems in vitro and in vivo. Wider applicability of RNA interference methods will closely follow continued progress on efficient delivery into appropriate cell models and target tissues.

The use of bioinformatics for identifying class II-restricted T-cell epitopes.

An important step in the design of subunit vaccines is the identification of promiscuous T helper cell epitopes in sets of disease-specific gene products. Most of the epitope prediction models are based on HLA-II peptide binding, which constitutes a major bottleneck in the natural selection of epitopes. Here we describe a computer model, TEPITOPE, that enables the systematic prediction of promiscuous peptide ligands for a broad range of HLA binding specificity. We show how to apply the TEPITOPE prediction model to identify T-cell epitopes, and provide examples of its successful application in the context of oncology, allergy, and infectious and autoimmune diseases.