Publications

Papers on behalf of DDMoRe

1. ProbOnto – Ontology and Knowledge Base of Probability Distributions. Maciej J Swat, Pierre Grenon, Sarala Wimalaratne. Bioinformatics 2016. April 3
2. Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development. MJ Swat, S Moodie, SM Wimalaratne, NR Kristensen, M Lavielle, A Mari, P Magni, MK Smith, R Bizzotto, L Pasotti, E Mezzalana, E Comets, C Sarr, N Terranova, E Blaudez, P Chan, J Chard, K Chatel, M Chenel, D Edwards, C Franklin, T Giorgino, M Glont, P Girard, P Grenon, K Harling, AC Hooker, R Kaye, R Keizer, C Kloft, JN Kok, N Kokash, C Laibe, C Laveille, G Lestini, F Mentré, A Munafo, R Nordgren, HB Nyberg, ZP Parra-Guillen, E Plan, B Ribba, G Smith, IF Trocóniz, F Yvon, PA Milligan, L Harnisch, M Karlsson, H Hermjakob and N Le Novère, CPT: Pharmacometrics & Systems Pharmacology, 2015, 4, 316-319
3. A Review of Mixed-Effects Models of Tumor Growth and Effects of Anticancer Drug Treatment Used in Population Analysis. B Ribba, N H Holford, P Magni, I Trocóniz, I Gueorguieva, P Girard,C Sarr, M Elishmereni, C Kloft and L E Friberg, CPT: Pharmacometrics & Systems Pharmacology, 2014, 3, e113, doi:10.1038/psp.2014.12
4. Current Use and Developments Needed for Optimal Design in Pharmacometrics: A study Performed Among DDMoRe's European Federation of Pharmaceutical Industries and Associations Members. F Mentré, M Chenel, E Comets, J Grevel, A Hooker, MO Karlsson, M Lavielle and I Gueorguieva. CPT: Pharmacometrics & Systems Pharmacology 2013, 2, e46
5. White Paper: Landscape on Technical and Conceptual Requirements and Competence Framework in Drug/Disease Modeling and Simulation. G Vlasakakis, E Comets, A Keunecke, I Gueorguieva, P Magni, N Terranova, O Della Pasqua, E C de Lange and C Kloft.  CPT: Pharmacometrics & Systems Pharmacology 2013, 2, e40
6. Drug and Disease Model Resources: A Consortium to Create Standards and Tools to Enhance Model-Based Drug development. L. Harnisch, I. Matthews, J. Chard and M.O. Karlsson on behalf of the DDMoRe consortium Partners and Contributors.  CPT: Pharmacometrics & Systems Pharmacology 2013, 2, e34

Papers related to DDMoRe
Good Practices in Model-Informed Drug Discovery andDevelopment: Practice, Application, and Documentation. EFPIA MID3 Workgroup: SF Marshall, R Burghaus, V Cosson, SYA Cheung, M Chenel, O DellaPasqua, N Frey,B Hamren, L Harnisch, F Ivanow, T Kerbusch, J Lippert, PA Milligan, S Rohou, A Staab, JL Steimer, C Tornøe and SAG Visser CPT: Pharmacometrics & Systems Pharmacology 2016, 5, 93–122

Papers supported by DDMoRe

1. dOFV distributions: a new diagnostic for the adequacy of parameter uncertainty in nonlinear mixed-effects models applied to the bootstrap. Dosne, A., Niebecker, R. & Karlsson, MO. J Pharmacokinet Pharmacodyn 2016 [Epub ahead of print]
2. Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling. Dosne AG, Bergstrand M, Harling K, Karlsson MO. J Pharmacokinet Pharmacodyn 2016 [Epub ahead of print]
3. Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches. JB Møller, NR Kristensen, S Klim, MO Karlsson, SH Ingwersen
   and MC Kjellsson CPT Pharmacometrics & Systems Pharmacology ERRATUM 2016 5, 388–389
4. A whole-body physiologically based pharmacokinetic (WB-PBPK) model of ciprofloxacin: a step towards predicting bacterial killing at sites of infection.Muhammad W. Sadiq, Elisabet I. Nielsen, Dalia Khachman, Jean-Marie Conil,  Bernard Georges, Georges Houin, Celine M. Laffont, Mats O. Karlsson, Lena E. Friberg. J Pharmacokinet Pharmacodyn. 2016 Aug 30. [Epub ahead of print]
5. Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations. Deng C, Plan EL, Karlsson MO. J Pharmacokinet Pharmacodyn. 2016 43:305-14.
6. Dynamic interaction of colistin and meropenem on a WT and a resistant strain of Pseudomonasaeruginosa as quantified in a PK/PD model. Ami F. Mohamed, Anders N. Kristoffersson, Matti Karvanen, Elisabet I. Nielsen, Otto Cars and Lena E. Friberg J Antimicrob Chemother. 2016 71:1279-90
7. A diagnostic tool for population models using non-compartmental analysis: The ncappc package for R. Acharya, Hooker A, Türkyılmaz GY, Jönsson S, Karlsson MO. Comput Methods Programs Biomed. 2016 127:83-93.
8. Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations. Aoki Y., Nordgren R., Hooker AC. AAPS J, 2016 18, 505--518
9. A strategy for residual error modeling incorporating scedasticity of variance and distribution shape. Dosne A.G., Bergstrand M., Karlsson MO. Journal of Pharmacokinetics and Pharmacodynamics, 2016 43,137--151
10. PK-PD Modeling of Individual Lesion FDG-PET Response to Predict Overall Survival in Patients With Sunitinib-treated Gastrointestinal Stromal Tumor. E Schindler, MA Amantea, MO Karlsson and LE Friberg CPT Pharmacometrics & Systems Pharmacology 2016 5, 173–181
11. Optimal Design for Informative Protocols in Xenograft Tumor Growth Inhibition Experiments in Mice. Lestini G, Mentré F, Magni P, AAPS J. 2016 18:1233-43
12. Glucose uptake saturation explains glucose kinetics profiles measured by different tests​. Bizzotto R, Natali A, Gastaldelli A, Muscelli E, Krssak M, Brehm A, Roden M, Ferrannini E, Mari Am J Physiol Endocr Metab, 2016 311:E346-57
13. Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer Zecchin C, Gueorguieva I, Enas N H, and Friberg L E Br J Clin Pharmacol 2016 82: 717–727
14. An MCMC method for the evaluation of the Fisher information matrix for non-linear mixed effect models. Riviere MK, Ueckert S, Mentré F. Biostatistics 2016 10:
15. Early change in tumour size predicts overall survival in patients with first-line metastatic breast cancer. Tate SC, Andre V, Enas N, Ribba B, Gueorguieva I. European Journal of Cancer. 2016. 66:95–103.
16. What do we mean by identifiability in mixed-effects models? Lavielle M., Aarons L.  Journal of Pharmacokinetics and Pharmacodynamics 2016 3: 111-122
17. Resistance Development: A Major Piece in the Jigsaw Puzzle of Tumor Size Modeling. N Terranova, P Girard, U Klinkhardt and A Munafo CPT: Pharmacometrics & Systems Pharmacology 2015, 4, 320
18. Optimal design for informative protocols in xenograft tumor growth inhibition experiments in mice. Giulia Lestini, France Mentré, Paolo Magni. AAPS J. 2016 18: 1233-1243.
19. Biomarker- versus drug-driven tumor growth inhibition models: an equivalence analysis. Maria Luisa Sardu , Italo Poggesi, Giuseppe De Nicolao, Journal of Pharmacokinetics and Pharmacodynamics 2015, 1-16
20. The Effects of a GLP-1 Analog on Glucose Homeostasis in Type 2 Diabetes Mellitus Quantified by an Integrated Glucose Insulin Model. RM Røge, S Klim, SH Ingwersen, MC Kjellsson and NR Kristensen, CPT Pharmacometrics & Systems Pharmacology 2015, 4, 28–36.
21. Influence of the Size of Cohorts in Adaptive Design for Nonlinear Mixed Effects Models: An Evaluation by Simulation for a Pharmacokinetic and Pharmacodynamic Model for a Biomarker in Oncology. Giulia Lestini, Cyrielle Dumont, France Mentré. Pharm Res. 2015 32:3159-69
22. The Open Physiology workflow: modeling processes over physiology circuitboards of interoperable tissue units. de Bono B, Safaei S, Grenon P, Nickerson DP, Alexander S, Helvensteijn M, Kok JN, Kokash N, Wu A, Yu T, Hunter P, Baldock RA. Front Physiol. 2015 6:24
23. Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches. JB Møller, NR Kristensen, S Klim, MO Karlsson, SH Ingwersen
    and MC Kjellsson CPT Pharmacometrics & Systems Pharmacology 2014 3, e122
24. Concordance between criteria for covariate model building. Hennig, Karlsson MO. J Pharmacokinet Pharmacodyn. 2014 Apr;41(2):109-25
25. Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature Thu Thuy Nguyen, France Mentré CSDA 2014 80: 57–69
26. Improved Utilization of ADAS-Cog Assessment Data Through Item Response Theory Based Pharmacometric Modeling. Sebastian Ueckert, Elodie L. Plan, Kaori Ito, Mats O. Karlsson, Brian Corrigan, Andrew C. Hooker, and the Alzheimer’s Disease Neuroimaging Initiative, Pharm Res. 2014 31: 2152–2165
27. A note on BIC in mixed-effects models. M. Delattre, M. Lavielle and M.A. Poursat, EJS, 2014, 8, 456-475
28. Biophysical constraints on the evolution of tissue structure and function. P.J. Hunter and B. de Bono, JPhysiol, 2014, 592, 2389 - 2401
29. Use of a linearization approximation facilitating stochastic model building. Elin M Svensson, Mats O. Karlsson, JPKPD,2014, 2, 153-158
30. Modeling of 24-Hour Glucose and Insulin Profiles in Patients With Type 2 Diabetes Mellitus Treated With Biphasic Insulin Aspart. Rikke M. Røge, Søren Klim, Niels R. Kristensen, Steen H. Ingwersen, and Maria C. Kjellsson. J Clin Pharmacol. 2014, 54:809-17
31. A Population Pharmacodynamic Model for Lactate Dehydrogenase and Neuron Specific Enolase to Predict Tumor Progression in Small Cell Lung Cancer Patients. Núria Buil-Bruna, José-María López-Picazo, Marta Moreno-Jiménez, Salvador Martín-Algarra, Benjamin Ribba, and Iñaki F. Trocóniz AAPSJ 2014, 16, 609-619
32. Population pharmacokinetic pharmacodynamic modelling in oncology: a tool for predicting clinical response1. B C Bender, E Schindler, L E Friberg. BJCP 2013, DOI: 10.1111/bcp.12258
33. Modeling tumor response after combined administration of different immune-stimulatory agents. Parra-Guillen ZP, Berraondo P, Ribba B, Trocóniz IF. JPET 2013, 346:432
34. Mathematical Model Approach to Describe Tumour Response in Mice After Vaccine Administration and its Applicability to Immune-Stimulatory Cytokine-Based Strategies. Zinnia P. Parra-Guillen, Pedro Berraondo, Emmanuel Grenier, Benjamin Ribba, and Iñaki F. Troconiz. AAPSJ 2013, 15:797
35. Functional tissue units and their primary tissue motifs in multi-scale physiology. Bernard de Bono, Pierre Grenon, Richard Baldock, Peter Hunter. JBiomedSemantics 2013, 4:22
36. Comparison of Methods for Handling Missing Covariate Data. Johansson AM, Karlsson MO. AAPSJ 2013, 4:1232
37. Multiple Imputation of Missing Covariates in NONMEM and Evaluation of the Method's Sensitivity to η-Shrinkage. Johansson AM, Karlsson MO. AAPSJ 2013, 4:1035
38. Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs. Nielsen EI, Friberg LE. Pharmocol. Rev. 2013 65:1053
39. Longitudinal Modeling of the Relationship Between Mean Plasma Glucose and HbA1c Following Antidiabetic Treatments JB Møller, RV Overgaard, MC Kjellsson, NR Kristensen, S Klim, SH Ingwersen and MO Karlsson CPT Pharmacometrics & Systems Pharmacology 2013 2:e82
40. A predictive pharmacokinetic-pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination. Nadia Terranova, Massimiliano Germani, Francesca Del Bene, Paolo Magni. Cancer Chemother Pharmacol. 2013 72:471
41. A Model-Based Approach to Predict Longitudinal HbA1c, Using Early Phase Glucose Data From Type 2 Diabetes Mellitus Patients After Anti-Diabetic Treatment. Kjellsson MC, Cosson VF, Mazer NA, Frey N, Karlsson MO. J Clin Pharmacol. 2013 53:589
42. Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth after administration of an anti-angiogenic agent, bevacizumab, as single-agent and combination therapy in tumor xenografts. Maurizio Rocchetti, Massimiliano Germani, Francesca Del Bene, Italo Poggesi, Paolo Magni, Enrico Pesenti and Giuseppe De Nicolao. Cancer Chemother Pharmacol.  2013 71: 1147
43. Target-mediated disposition model describing the dynamics of IL12 and IFNy after administration of a Mifepristone-inducible adenoviral vector for IL-12 expression in mice. Zinnia Patricia Parra-Guillen, Alvaro Janda, Pilar Alzuguren, Pedro Berraondo, Ruben Hernandez-Alcoceba and Inaki F. Troconiz. AAPS J. 2013, 15: 183
44. A minimal model of tumor growth inhibition in combination regimens under the hypothesis of no interaction between drugs. Paolo Magni, Nadia Terranova, Franscesca Del Bene, Massimiliano Germani and Giuseppe De Nicolao. IEEE Trans Biomed Eng. 2012, 59: 2161
45. ApiNATOMY: a novel toolkit for visualizing sultiscale anatomy schematics with phenotype-related information. Bernard de Bono, Pierre Grenon and Stephen John Sammut. Hum Mutat. 2012, 38: 837
46. The RICORDO approach to semantic interoperability for biomedical data and models: strategy, standards and solutions. Bernard de Bono, Robert Hoehndorf, Sarala Wimalaratne, George Gkoutos and Pierre Grenon. BMC Res Notes 2011, 4: 313