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Oss, Netherlands

Torti V.R.,Pfizer | Wojciechowicz D.,Pfizer | Hu W.,Pfizer | John-Baptiste A.,Pfizer | And 9 more authors.
Molecular Cancer Therapeutics

Clinical trials of selective RAF inhibitors in patients with melanoma tumors harboring activated BRAFV600E have produced very promising results, and a RAF inhibitor has been approved for treatment of advanced melanoma. However, about a third of patients developed resectable skin tumors during the course of trials. This is likely related to observations that RAF inhibitors activate extracellular signal-regulated kinase (ERK) signaling, stimulate proliferation, and induce epithelial hyperplasia in preclinical models. Because these findings raise safety concerns about RAF inhibitor development, we further investigated the underlying mechanisms. We showed that the RAF inhibitor PF-04880594 induces ERK phosphorylation and RAF dimerization in those epithelial tissues that undergo hyperplasia. Hyperplasia and ERK hyperphosphorylation are prevented by treatment with the mitogen-activated protein/extracellular signal-regulated kinase (MEK) inhibitor PD-0325901 at exposures that extrapolate to clinically well-tolerated doses. To facilitate mechanistic and toxicologic studies, we developed a three-dimensional cell culture model of epithelial layering that recapitulated the RAF inhibitor-induced hyperplasia and reversal by MEK inhibitor in vitro. We also showed that PF-04880594 stimulates production of the inflammatory cytokine interleukin 8 in HL-60 cells, suggesting a possible mechanism for the skin flushing observed in dogs. The complete inhibition of hyperplasia by MEK inhibitor in epithelial tissues does not seem to reduce RAF inhibitor efficacy and, in fact, allows doubling of the PF-04880594 dose without toxicity usually associated with such doses. These findings indicated that combination treatment with MEK inhibitors might greatly increase the safety and therapeutic index of RAF inhibitors for the treatment of melanoma and other cancers. ©2012 AACR. Source

Evers R.,Merck And Co. | Dallas S.,Janssen Research and Development LLC | Dickmann L.J.,Amgen Inc. | Fahmi O.A.,Pharmacokinetics | And 6 more authors.
Drug Metabolism and Disposition

Drug-drug interactions (DDIs) between therapeutic proteins (TPs) and small-molecule drugs have recently drawn the attention of regulatory agencies, the pharmaceutical industry, and academia. TP-DDIs are mainly caused by proinflammatory cytokine or cytokine modulator-mediated effects on the expression of cytochrome P450 enzymes. To build consensus among industry and regulatory agencies on expectations and challenges in this area, a working group was initiated to review the preclinical state of the art. This white paper represents the observations and recommendations of the working group on the value of in vitro human hepatocyte studies for the prediction of clinical TP-DDI. The white paper was developed following a "Workshop on Recent Advances in the Investigation of Therapeutic Protein Drug-Drug Interactions: Preclinical and Clinical Approaches" held at the Food and Drug Administration White Oak Conference Center on June 4 and 5, 2012. Results of a workshop poll, cross-laboratory data comparisons, and the overall recommendations of the in vitro working group are presented herein. The working group observed that evaluation of TP-DDI for anticytokine monoclonal antibodies is currently best accomplished with a clinical study in patients with inflammatory disease. Treatment-induced changes in appropriate biomarkers in phase 2 and 3 studies may indicate the potential for a clinically measurable treatment effect on cytochrome P450 enzymes. Cytokine-mediated DDIs observed with anti-inflammatory TPs cannot currently be predicted using in vitro data. Future success in predicting clinical TP-DDIs will require an understanding of disease biology, physiologically relevant in vitro systems, and more examples of well conducted clinical TP-DDI trials. Copyright © 2013 by The American Society for Pharmacology and Experimental Therapeutics. Source

Jones B.,Statistics | Smith D.A.,Pharmacokinetics | Schmid E.F.,Pfizer

In this paper we model the cost-benefit of excluding populations at risk through predictive toxicity biomarkers and diagnostics. False positives/negatives inherent in predictive markers and the frequency and nature of adverse events determine whether biomarkers are beneficial and economically viable. We present a model that takes these and other factors into account using data largely in line with real world cases. © 2012 Informa UK, Ltd. Source

An unbiased scanning methodology using ultra high-performance liquid chromatography coupled with high-resolution mass spectrometry was used to bank data and plasma samples for comparing the data generated at different dates. This method was applied to bank the data generated earlier in animal samples and then to compare the exposure to metabolites in animal versus human for safety assessment. With neither authentic standards nor prior knowledge of the identities and structures of metabolites, full scans for precursor ions and all ion fragments (AIF) were employed with a generic gradient LC method to analyze plasma samples at positive and negative polarity, respectively. In a total of 22 tested drugs and metabolites, 21 analytes were detected using this unbiased scanning method except that naproxen was not detected due to low sensitivity at negative polarity and interference at positive polarity; and 4′- or 5-hydroxy diclofenac was not separated by a generic UPLC method. Statistical analysis of the peak area ratios of the analytes versus the internal standard in five repetitive analyses over approximately 1 year demonstrated that the analysis variation was significantly different from sample instability. The confidence limits for comparing the exposure using peak area ratio of metabolites in animal plasma versus human plasma measured over approximately 1 year apart were comparable to the analysis undertaken side by side on the same days. These statistical analysis results showed it was feasible to compare data generated at different dates with neither authentic standards nor prior knowledge of the analytes. © 2015 American Chemical Society. Source

Lubin A.,Pharmacokinetics | Cabooter D.,Catholic University of Leuven | Augustijns P.,Catholic University of Leuven | Cuyckens F.,Pharmacokinetics
Journal of Mass Spectrometry

Structural elucidation of metabolites is an important part during the discovery and development process of new pharmaceutical drugs. Liquid Chromatography (LC) in combination with Mass Spectrometry (MS) is usually the technique of choice for structural identification but cannot always provide precise structural identification of the studied metabolite (e.g. site of hydroxylation and site of glucuronidation). In order to identify those metabolites, different approaches are used combined with MS data including nuclear magnetic resonance, hydrogen/deuterium exchange and chemical derivatization followed by LC-MS. Those techniques are often time-consuming and/or require extra sample pre-treatment. In this paper, a fast and easy to set up tool using desorption electrospray ionization-MS for metabolite identification is presented. In the developed method, analytes in solution are simply dried on a glass plate with printed Teflon spots and then a single drop of derivatization mixture is added. Once the spot is dried, the derivatized compound is analyzed. Six classic chemical derivatizations were adjusted to work as a one drop reaction and applied on a list of compounds with relevant functional groups. Subsequently, two successive reactions on a single spot of amoxicillin were tested and the methodology described was successfully applied on an in vitro incubated alprazolam metabolite. All reactions and analyses were performed within an hour and gave useful structural information by derivatizing functional groups, making the method a time-saving and efficient tool for metabolite identification if used in addition or in some cases as an alternative to common methods. Copyright © 2015 John Wiley & Sons, Ltd. Source

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