The following publications are by members of the UK QSP Network. They cover a range of areas both directly and indirectly related to QSP research. Clicking on the title will take you to the respective download site for the publication, where this is possible.


Structural identifiability for mathematical pharmacology: models of myelosuppression. Neil D. Evans, S. Y. Amy Cheung, James W. T. Yates. Journal of Pharmacokinetics and Pharmacodynamics(2018).

Frequency-Domain Response Analysis for Quantitative Systems Pharmacology Models. Pascal Schulthess, Teun M. Post, James W.T. Yates, and Piet H. van der Graaf. CPT Pharmacometrics Syst. Pharmacol. (2018) 7, 111?123; doi:10.1002/psp4.12266

Outside-In Systems Pharmacology Combines Innovative Computational Methods With High-Throughput Whole Vertebrate Studies. Schulthess, P., van Wijk, R. C., Krekels, E. H. J., Yates, J. W. T., Spaink, H. P., & van der Graaf, P. H. CPT: Pharmacometrics and Systems Pharmacology (2018).

Understanding Haematological Toxicities With Mathematical Modelling. Clinical Pharmacology & Therapeutics. Fornari, C., Oplustil O?Connor, L., Yates, J. W. T., Amy Cheung, S. Y., Jodrell, D. I., Mettetal, J. T., & Collins, T. A. (2018).


Choosing an optimal input for an intravenous glucose tolerance test to aid parameter identification. E.C. Martin, J.W.T. Yates, K. Ogungbenro, L. Aarons. Journal of Pharmacy and Pharmacology (2017) . Early View. doi: 10.1111/jphp.12759.

Translational Modeling of Drug-Induced Myelosuppression and Effect of Pretreatment Myelosuppression for AZD5153, a Selective BRD4 Inhibitor. T.A. Collins, M.M. Hattersley, J.W.T. Yates, E. Clark, M. Mondal, J.T. Mettetal CPT: Pharmacometrics and Systems Pharmacology (2017). 6:357-364.

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4?5 December 2014). FT Musuamba, E. Manolis, N. Holford, S.Y.A. Cheung, L.E. Friberg, K. Ogungbenro, M. Posch, J.W.T. Yates, S. Berry, N. Thomas, S. Corriol-Rohou, B. Bornkamp, F. Bretz, A.C. Hooker, P.H. Van der Graaf, J.F. Standing, J. Hay, S. Cole, V. Gigante, K. Karlsson, T. Dumortier, N. Benda, F. Serone, S. Das, A. Brochot, F. Ehmann, R. Hemmings and I. Skottheim Rusten CPT: Pharmacometrics and Systems Pharmacology (2017). 6:418-429.

Multiscalar cellular automaton simulates in-vivo tumour-stroma patterns calibrated from in-vitro assay data. J.A. Delgado-SanMartin, J.I. Hare, E.J. Davies and J.W.T. Yates. BMC Medical Informatics and Decision Making (2017). 17:70.

A pharmacokinetic?pharmacodynamic model predicting tumour growth inhibition after intermittent administration with the mTOR kinase inhibitor AZD8055. J.W.T Yates, S.V. Holt, A. Logie, K. Payne, K. Woods, R.W. Wilkinson, B.R. Davies, S.M. Guichard. British Journal of Pharmacology (2017). 174:2652-2661.

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends. Snowden TJ, van der Graaf PH, Tindall MJ. Bull Math Biol. 2017 Jul;79(7):1449-1486. doi: 10.1007/s11538-017-0277-2.

Mathematical modeling of atopic dermatitis reveals "double-switch" mechanisms underlying 4 common disease phenotypes. Domínguez-Hüttinger E, Christodoulides P, Miyauchi K, Irvine AD, Okada-Hatakeyama M, Kubo M, Tanaka RJ. J Allergy Clin Immunol. 2017 Jun;139(6):1861-1872.e7. doi: 10.1016/j.jaci.2016.10.026. Epub 2016 Dec 5.

Quantitative analysis of lab-to-lab variability in Caco-2 permeability assays. Lee JB, Zgair A, Taha DA, Zang X, Kagan L, Kim TH, Kim MG, Yun HY, Fischer PM, Gershkovich P. Eur J Pharm Biopharm. 2017 May;114:38-42. doi: 10.1016/j.ejpb.2016.12.027. Epub 2017 Jan 12.

Modeling bispecific monoclonal antibody interaction with two cell membrane targets indicates the importance of surface diffusion. Sengers BG, McGinty S, Nouri FZ, Argungu M, Hawkins E, Hadji A, Weber A, Taylor A, Sepp A. MAbs. 2016 Jul;8(5):905-15. doi: 10.1080/19420862.2016.1178437. Epub 2016 Apr 20.
**This paper is an output from tackling the problem "Understanding polypharmacology of antibodies: what are the benefits of using a bispecific vs combination of monospecifics?" at the 2015 UK QSP Meeting at Alderley Edge **

Mathematical Modeling of Streptococcus pneumoniae Colonization, Invasive Infection and Treatment. Domínguez-Hüttinger E, Boon NJ, Clarke TB, Tanaka RJ. Front Physiol. 2017 Mar 2;8:115. doi: 10.3389/fphys.2017.00115. eCollection 2017.

ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behaviour. Evans S, Alden K, Cucurull-Sanchez L, Larminie C, Coles MC, Kullberg MC, Timmis J. PLoS Comput Biol. 2017 Feb 3;13(2):e1005351. doi: 10.1371/journal.pcbi.1005351.

Extending and Applying Spartan to Perform Temporal Sensitivity Analyses for Predicting Changes in Influential Biological Pathways in Computational Models. Alden K, Timmis J, Andrews P, Veiga-Fernandes H, Coles M. IEEE/ACM Trans Comput Biol Bioinform. Vol 14(2), 431-442, 2017.

Identifying new antiepileptic drugs through genomics-based drug repurposing. Mirza N, Sills GJ, Pirmohamed M, Marson AG. Hum Mol Genet. 2017 Feb 1;26(3):527-537. doi: 10.1093/hmg/ddw410.

A combined model reduction algorithm for controlled biochemical systems. Snowden TJ, van der Graaf PH, Tindall MJ. BMC Syst Biol. 2017 Feb 13;11(1):17. doi: 10.1186/s12918-017-0397-1.

The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions. Lloret-Villas A, Varusai TM, Juty N, Laibe C, Le NovÈre N, Hermjakob H, Chelliah V. CPT Pharmacometrics Syst Pharmacol. 2017 Feb;6(2):73-86. doi: 10.1002/psp4.12155. Epub 2017 Jan 7.

Atrial fibrillation dynamics and ionic block effects in six heterogeneous human 3D virtual atria with distinct repolarization dynamics. C. Sánchez, A. Bueno-Orovio, E. Pueyo, B. Rodriguez. Frontiers in Bioengineering and Biotechnology. 5:29. doi: 10.3389/fbioe.2017.00029. 2017.

The electrogenic Na+/K+ pump is a key determinant of repolarization abnormality susceptibility in human ventricular cardiomyocytes: a population-based simulation study. O.J. Britton, A. Bueno-Orovio, L. Virág, A. Varró, B. Rodriguez. Frontiers in Physiology. 8:278.doi: 10.3389/fphys.2017.00278. 2017

Balance between Sodium and Calcium Currents Underlying Chronic Atrial Fibrillation Termination: An In Silico Inter–subject Variability Study. A. Liberos, A. Bueno-Orovio, M. Rodrigo, U. Ravens, I. Hernandez-Romero, F. Fernandez-Aviles, M.S. Guillem, B. Rodriguez*, A.M. Climent*. *Equal Contribution as senior authors. Heart Rhythm. s1547-5271(16)30749-4S1547-5271(16)30671-3 doi: 2016.


Building Confidence in Quantitative Systems Pharmacology Models: An Engineer's Guide to Exploring the Rationale in Model Design and Development. Timmis J, Alden K, Andrews P, Clark E, Nellis A, Naylor B, Coles M, Kaye P. CPT Pharmacometrics Syst Pharmacol. 2016 Nov 11. doi: 10.1002/psp4.12157.

Irreversible inhibition of EGFR: Modelling the combined Pharmacokinetic-Pharmacodynamic relationship of osimertinib and its active metabolite AZ5104 . Yates, J.W.T Ashton, S., Cross, D., Mellor, M.J. Powell, S.J., Ballard, P. Molecular Cancer Therapeutics (2016). 15:2378-2387.

Up-regulation of miR-31 in human atrial fibrillation begets the arrhythmia by depleting dystrophin and neuronal nitric oxide synthase. Reilly SN, X. Liu, R. Carnicer, A. Recalde, A. Muszkiewicz, R. Jayaram, M.C. Carena, M. Stefanini, N.C. Surdo, R. Wijesurenda, O. Lomas, C. Ratnatunga, R. Sayeed, G. Krasopoulos, T. Rajakumar, A. Bueno-Orovio, S. Verheule, T.A. Fulga, B. Rodriguez, U. Schotten, B. Casadei. Science Translation Medicine, 8(340), 340ra74. doi: 10.1126/scitranslmed.aac42962016. 2016

Development of a Novel Simplified PBPK Absorption Model to Explain the Higher Relative Bioavailability of the OROS Formulation of Oxybutynin. A. Olivares-Morales, A. Ghosh, L. Aarons, A. Rostami-Hodjegan, D. The AAPS Journal 18, 1532-1549 (2016).

Accounting for dropout in xenografted tumour efficacy studies: integrated endpoint analysis, reduced bias and better use of animals. E.C. Martin, L. Aarons, J.W.T. Yates. Cancer Chemother Pharmacol 78, 131-141 (2016).

Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data. T. Wendling, H. Mistry, K. Ogungbenro, L. Aarons. Cancer Chemother Pharmacol 17, 927-938 (2016).

Reduction of a whole-body physiologically based pharmacokinetic model to stabilise the Bayesian analysis of clinical data. T. Wendling, N. Tsamandouras, S. Dumitras, E. Pigeolet, K. Ogungbenro, L. Aarons, The AAPS Journal 18, 196-209 (2016).

What do we mean by identifiability in mixed effects models? M. Lavielle, L. Aarons. J Pharmacokinet Pharmacodyn 43, 111–122 (2016).

Mechanisms of pro-arrhythmic abnormalities in ventricular repolarisation and anti-arrhythmic therapies in human hypertrophic cardiomyopathy. E. Passini, A. Mincholé, R. Coppini, E. Cerbai, B. Rodriguez, S. Severi, A. Bueno-Orovio. Journal of Molecular and Cellular Cardiology.Journal of Molecular and Cellular Cardiology 2016;96:72-81. doi: 10.1016/j.yjmcc.2015.09.003. 2016.

Experimentally-based computational investigation into beat-to-beat variability in ventricular repolarization and its response to ionic current inhibition. E. Pueyo, C. Dangerfield, O.J. Britton, L. Virág, K. Kistamás, N. Szentandrássy, A. Varró, P.P. Nánási, K. Burrage, B. Rodríguez. PLoS One. 11(3):e0151461. doi: 10.1371/journal.pone.0151461. 2016.

Parameter identifiability of fundamental pharmacodynamic models. D. Janzen, L. Bergenholm, M. Jirstrand, J. Parkinson, J. Yates, N.D. Evans, M.J. Chappell. Frontiers in Physiology (2016). In press. DOI: 10.3389/fphys.2016.00590.

Early afterdepolarizations promote transmural reentry in ischemic human ventricles with reduced repolarization reserve. S. Dutta, A. Mincholé, E. Zacur, T.A. Quinn, P. Taggart, B. Rodriguez. Progress in Biophysics and Molecular Biology. doi:10.1016/j.pbiomolbio.2016.01.008. 2016.

In vivo and in silico investigation into mechanisms of frequency dependence of repolarization alternans in human ventricular cardiomyocytes. X. Zhou, A. Bueno-Orovio, M. Orini, B. Hanson, M. Hayward, P. Taggart, P.D. Lambiase, K. Burrage, B. Rodriguez. Circulation Research: 2016;118:266-278. doi: 10.1161/CIRCRESAHA.115.307836. 2016.


Agent-Based Modeling in Systems Pharmacology. Cosgrove J, Butler J, Alden K, Read M, Kumar V, Cucurull-Sanchez L, Timmis J, Coles M. CPT: Pharmacometrics & Systems Pharmacology, Nov 4(11), DOI: 10.1002/psp4.12018, 2015.

Modelling and Simulation Approaches for Cardiovascular Function and Their Role in Safety Assessment. T.A. Collins, L. Bergenholm, T. Abdulla, J.W.T. Yates, N. Evans, M.J. Chappell and J.T. Mettetal. CPT Pharmacometrics and Systems Pharmacology (2015). 4: e18

Systems toxicology: modelling biomarkers of glutathione homeostasis and paracetamol metabolism. Simone H. Stahl, James W. Yates, Andrew W. Nicholls, J. Gerry Kenna, Muireann Coen, Fernando Ortega, Jeremy K. Nicholson, Ian D. Wilson. Drug Discovery Today: Technologies (2015). 15:9-14.

Validation of a predictive modeling approach to demonstrate the relative efficacy of three different schedules of the AKT inhibitor AZD536. James W. T. Yates, Phillippa Dudley, Jane Cheng, Celina D’Cruz, Barry R. Davies. Cancer Chemotherapy and Pharmacology (2015). 76: 343-356.

Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738. S. Checkley, L. MacCallum, J. Yates, P. Jasper, H. Luo, J. Tolsma, C. Bendtsen. Nature Scientific Reports(2015). 5, 13545.

Oxygen-Driven Tumour Growth Model: A Pathology-Relevant Mathematical Approach. Juan A. Delgado-San Martin, Jennifer I. Hare, Alessandro P.S. de Moura, James W.T. Yates. PLoS Computational Biology (2015). 11(10):e1004550.