7PeopleLogo MainMarieCurie

Publications

  1. S. Checkley, L. MacCallum, J. Yates, P. Jasper, H. Luo, J. Tolsma, C. Bendtsen (2015) Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738. Sci. Rep. 5, 13545
  2. Delgado San Martin, J. A., Hare, J. I., Yates, J. W., & Barry, S. T. (2015) Tumour stromal morphology impacts nanomedicine cytotoxicity in patient-derived xenografts. Nanomedicine: Nanotechnology, Biol. Med. 11, 1247-1252.
  3. Emma J. Davies, Meng Dong, Matthias Gutekunst, Katja Närhi,Hanneke J. A. A. van Zoggel, Sami Blom, Ashwini Nagaraj, Tauno Metsalu, Eva Oswald, Sigrun Erkens-Schulze, Juan A. Delgado San Martin, Riku Turkki, Stephen R. Wedge, Taija M. af Hällström, Julia Schueler, Wytske M. van Weerden, Emmy W. Verschuren, Simon T. Barry, Heiko van der Kuip. John A. Hickman (2015) Capturing complex tumour biology in vitro: histological and molecular characterisation of precision cut slices. Sci. Rep. 5, 17187
  4. Juan A. Delgado-SanMartin, Jennifer I. Hare, Alessandro P.S. de Moura, James W.T. Yates. (2015) Oxygen-Driven Tumour Growth Model: A Pathology-Relevant Mathematical Approach. PLoS Comp. Biol. 11, e1004550
  5. James W. T. Yates, Phillippa Dudley, Jane Cheng, Celina D'Cruz, Barry R. Davies. (2015) Validation of a predictive modeling approach to demonstrate the relative efficacy of three different schedules of the AKT inhibitor AZD5363. Cancer Chemother. Pharmacol. 76, 343-356
  6. Simone H. Stahl, James W. Yates, Andrew W. Nicholls, J. Gerry Kenna, Muireann Coen, Fernando Ortega, Jeremy K. Nicholson, Ian D. Wilson (2015) Systems toxicology: modelling biomarkers of glutathione homeostasis and paracetamol metabolism. Drug Discovery Today: Technol. 15, 9-14
  7. T.A. Collins, L. Bergenholm, T. Abdulla, J.W.T. Yates, N. Evans, M.J. Chappell and J.T. Mettetal (2015). Modelling and Simulation Approaches for Cardiovascular Function and Their Role in Safety Assessment. CPT Pharmacomet. Systems Pharmacol. 4, e18
  8. J.A. Delgado San Martin, P. Worthington and J.W.T. Yates (2015). Non-invasive 3D time-of-flight imaging technique for tumour volume assessment in subcutaneous models. Laboratory Animals 49, 168-171
  9. T.R. B. Grandjean, M.J. Chappell, A.M. Lench, J.W. T. Yates, and C. J. O'Donnell. (2014) Experimental and mathematical analysis of in vitro Pitavastatin hepatic uptake across species. Xenobiotica 44, 961-974
  10. N.D. Evans, R.J. Dimelow, J.W.T. Yates (2014). Modelling and tumour growth and cytotoxic effect of docetaxel in xenografts. Comp. Meth. Prog. Biomed. 114, e3-e13
  11. T.R.B. Grandjean, M.J. Chappell, J.W.T. Yates, N.D. Evans. (2014) Structural Identifiability analyses of candidate models for in vitro Pitavastatin uptake. Comp. Meth. Prog. Biomed. 114, e60-e69
  12. Ulloa, J.L., Stahl, S., Yates,J., Woodhouse, N., Kenna, J.G., Jones, H.B., Waterton, J.C., and Hockings, P.D. (2013) Assessment of gadoxetate DCE-MRI as a biomarker of hepatobiliary transporter inhibition. NMR Biomed. 26 (10), 1258-1270
  13. Parkinson, J., Visser, S.A.G., Jarvis, P., Pollard, C., Valentin, J.P., Yates, J.W.T., and Ewart, L.(2013) Translational pharmacokinetic-pharmacodynamic modeling of QTc effects in dog and human. J. Pharmacol. Toxicol. Meth. 68, 357-366
  14. Cheung, S.Y.A., Yates, J.W.T., and Aarons, L. (2013) The design and analysis of parallel experiments to produce structurally identifiable models. Journal of Pharmacokinetics and Pharmacodynamics. 40, 93-100.
  15. Yates, J.W.T., and Watson, E.M. (2013) Estimating insulin sensitivity from glucose levels only: Use of a non-linear mixed effects approach and maximum a posteriori (MAP) estimation. Comp. Meth. Prog. Biomed. 109, 134-143
  16. Geenen, S., Yates, J.W.T., Kenna, J.G., Bois, F.Y., Wilson, I.D., and Westerhoff, H. V. (2013) Multiscale modelling approach combining a kinetic model of glutathione metabolism with PBPK models of paracetamol and glutathione-depleting biomarkers ophthalmic acid and 5-oxoproline in humans and rats. Integrative Biol. 5, 877-888
  17. Visser, S.A.G., Aurell, M., Jones, R.D.O., Schuck, V.J.A., Egnell, A.-C., Peters, S. A., Brynn, L., Yates, J.W.T., Jansson-Loefmark, R., Tan, B., Cooke, M., Barry, S.T., Hughes, A., and Bredberg, U. (2013) Model-based drug discovery: implementation and impact. Drug Discovery Today. 18, 764-775
  18. Davies, R., Greenwood, H., Dudley, P., Crafter, C., Yu, D.H., Zhang, J., Li, J., Gao, B., Maynard, Q. Ji, J., Ricketts, S.-A., Cross, D., Cosulich, S. C., Chresta, C.M., Page, K., Yates, J.W.T., Lane, C., Watson, R., Luke, R., Ogilvie, D. J., and Pass, M. (2012). Preclinical Pharmacology of AZD5363, an Orally Bioavailable Inhibitor of AKT: Pharmacodynamics, Antitumor Activity and Correlation of Monotherapy Activity with Genetic Background. Mol. Cancer Therapeutics.11, 873-887
  19. Cheung, S.Y.A., Majid, O., Yates, J. W.T.,  and  Aarons, L. (2012) Structural identifiability analysis and reparameterisation (parameter reduction) of a cardiovascular feedback model.Eur. J. Pharmac. Sci. 34, 104-109
  20. Yates, J.W.T., Das, S., Mainwaring, G., and Kemp, J. (2012) Population pharmacokinetic/ pharmacodynamic modelling of the anti-TNF-α± polyclonal fragment antibody AZD9773 in patients with severe sepsis. J. Pharmacokin. Pharmacodyn. 39, 591-599
  21. Graham, H., Walker, M., Jones, O., Yates, J.W.T., Galetin A., and Aarons, L. (2012) Comparison of in-vivo and in-silico methods used for prediction of tissue: plasma partition coefficients in rat. J. Pharm. Pharmacol. 64, 383-396.