No abstract

The c-Met receptor tyrosine kinase (RTK) is a key regulator in cancer, in part, through oncogenic mutations. Eight clinically relevant mutants were characterized by biochemical, biophysical, and cellular methods. The c-Met catalytic domain was highly active in the unphosphorylated state (k(cat) = 1.0 s(-1)) and achieved 160-fold enhanced catalytic efficiency (k(cat)/K(m)) upon activation to 425000 s(-1) M(-1). c-Met mutants had 2-10-fold higher basal enzymatic activity (k(cat)) but achieved maximal activities similar to those of wild-type c-Met, except for Y1235D, which underwent a reduction in maximal activity. Small enhancements of basal activity were shown to have profound effects on the acquisition of full enzymatic activity achieved through accelerating rates of autophosphorylation. Biophysical analysis of c-Met mutants revealed minimal melting temperature differences indicating that the mutations did not alter protein stability. A model of RTK activation is proposed to describe how a RTK response may be matched to a biological context through enzymatic properties. Two c-Met clinical candidates from aminopyridine and triazolopyrazine chemical series (PF-02341066 and PF-04217903) were studied. Biochemically, each series produced molecules that are highly selective against a large panel of kinases, with PF-04217903 (>1000-fold selective relative to 208 kinases) being more selective than PF-02341066. Although these prototype inhibitors have similar potencies against wild-type c-Met (K(i) = 6-7 nM), significant differences in potency were observed for clinically relevant mutations evaluated in both biochemical and cellular contexts. In particular, PF-02341066 was 180-fold more active against the Y1230C mutant c-Met than PF-04217903. These highly optimized inhibitors indicate that for kinases susceptible to active site mutations, inhibitor design may need to balance overall kinase selectivity with the ability to inhibit multiple mutant forms of the kinase (penetrance).

Metabolic profiling has increasingly been used as a probe in disease diagnosis and pharmacological analysis. Herein, plasma fatty acid metabolic profiling including non-esterified fatty acid (NEFA) and esterified fatty acid (EFA) was investigated using gas chromatography/mass spectrometry (GC/MS) followed by multivariate statistical analysis. Partial least squares-linear discrimination analysis (PLS-LDA) model was established and validated to pattern discrimination between type 2 diabetic mellitus (DM-2) patients and health controls, and to extract novel biomarker information. Furthermore, the PLS-LDA model visually represented the alterations of NEFA metabolic profiles of diabetic patients with abdominal obesity in the treated process with rosiglitazone. The GC/MS-PLS-LDA analysis allowed comprehensive detection of plasma fatty acid, enabling fatty acid metabolic characterization of DM-2 patients, which included biomarkers different from health controls and dynamic change of NEFA profiles of patients after treated with medicine. This method might be a complement or an alternative to pathogenesis and pharmacodynamics research.

The HCV RNA-dependent RNA polymerase has emerged as one of the key targets for novel anti-HCV therapy development. Herein, we report the optimization of the dihydropyrone series inhibitors to improve compound aqueous solubility and reduce CYP2D6 inhibition, which led to the discovery of compound 24 (PF-00868554). Compound 24 is a potent and selective HCV polymerase inhibitor with a favorable pharmacokinetic profile and has recently entered a phase II clinical evaluation in patients with genotype 1 HCV.

Development of chromatographic fingerprint (CF) and related chemometric methods and their applications to quality control of traditional Chinese medicines (TCMs) were discussed. CF is essentially a kind of quality control method for TCMs (or Chinese herbal medicines). Also, it is a quality-relevant-data high-throughput and integral tool to explore chemically the complexity of TCMs. With the help of chemometrics, some difficulties in evaluation and analysis of CFs, such as calculation of information content, peak alignment, pattern analysis, deconvolution of overlapping peaks, etc. could be well solved. To further explore TCMs synergic quality, intensive study of CF coupled with chemometrics will create the possibility to achieve the aim to reveal the working mechanisms of TCMs and to further control and strengthen TCMs' intrinsic quality in a comprehensive manner.