Drug models

We did focus on the Tucuxi core computing engine to make it as efficient as possible, while being generic enough to allow new models to be added without struggling with programming languages. Therefore the drug models are described in external files.

Below you find a list of current drug models that have been implemented by various people.

If you wish to create your own model, you are very welcome to use the drug model editor : https://drugeditor.tucuxi.ch . It allows to create your own model and ensures your model will give some results in Tucuxi.

  • Apixaban. Based on “Population Pharmacokinetics of Apixaban in Subjects With Nonvalvular Atrial Fibrillation”, Cirincione B, Kowalski K, Frost C et al. .
  • Busulfan, Based on “Pharmacokinetic Modeling and Simulation with Pharmacogenetic Insights Support the Relevance of Therapeutic Drug Monitoring for Myeloablative Busulfan Dosing in Adult HSCT”, Khalil Ben Hassine and Claire Seydoux and Sonia Khier and Youssef Daali and Michael Medinger and Joerg Halter and Dominik Heim and Yves Chalandon and Urs Schanz and Gayathri Nair and Nathan Cantoni and Jakob R. Passweg and Chakradhara Rao Satyanarayana Uppugunduri and Marc Ansari .
  • Busulfan, Based on “Precision dosing of intravenous busulfan in pediatric hematopoietic stem cell transplantation: Results from a multicenter population pharmacokinetic study”, Khalil Ben Hassine, Tiago Nava, Yves Théoret, Christa E. Nath, Youssef Daali, Nastya Kassir, Victor Lewis, Robbert G. M. Bredius, Peter J. Shaw, Henrique Bittencourt, Maja Krajinovic, Chakradhara Rao Satyanarayana Uppugunduri, Marc Ansari .
  • Busulfan. Based on “Pharmacokinetic behavior and appraisal of intravenous busulfan dosing in infants and older children: the results of a population pharmacokinetic study from a large pediatric cohort undergoing hematopoietic stem-cell transplantation”, Paci A., Vassal G., Moshous D., Dalle J.H., Bleyzac N., Neven B., Galambrun C., Kemmel V., Abdi Z.D., Broutin S., Pétain A., Nguyen L. .
  • Cefepime. Based on a meta-analysis performed by Thierry Buclin.
  • Daptomycin. Based on “Population pharmacokinetics of daptomycin”, Dvorchik B. and Arbeit R.D., Chung J., Liu S., Knebel W., Kastrissios H. .
  • Darunavir. Based on “Darunavir Population Pharmacokinetic Model Based on HIV Outpatient Data”, Daskapan, Alper and Tran, Quynh T. D. and Cattaneo, Dario and Gervasoni, Cristina and Resnati, Chiara and Stienstra, Ymkje and Bierman, Wouter F. W. and Kosterink, Jos G. W. and van der Werf, Tjip S. and Proost, Johannes H. and Alffenaar, Jan-Willem C. and Touw, Daniel J.
  • Dolutegravir. Based on “Population pharmacokinetics of dolutegravir: influence of drug-drug interactions in a real-life setting”, Catalina Barcelo, Manel Aouri, Perrine Courlet, Monia Guidi, Dominique L Braun, Huldrych F Günthard, Rein J Piso, Matthias Cavassini, Thierry Buclin, Laurent A Decosterd, Chantal Csajka, Swiss HIV Cohort Study.
  • Doravirine. Based on “Population pharmacokinetics of doravirine and exposure-response analysis in individuals with HIV-1”, Yee, K. L. and Ouerdani, A. and Claussen, A. and de Greef, R. and Wenning, L. .
  • Gentamicin. Based on “Population pharmacokinetic study of gentamicin in a large cohort of premature and term neonates”, Aline Fuchs and Monia Guidi and Eric Giannoni and Dominique Werner and Thierry Buclin and Nicolas Widmer and Chantal Csajka.
  • Imatinib. Based on “Therapeutic Drug Monitoring of Imatinib. Bayesian and Alternative Methods to Predict Trough Levels”, Verena Gotta, Nicolas Widmer, Michael Montemurro, Serge Leyvraz, Amina Haouala, Laurent A. Decosterd, Chantal Csajka and Thierry Buclin.
  • Lopinavir. Based on “Comparison of Population Pharmacokinetics Based on Steady-State Assumption Versus Electronically Monitored Adherence to Lopinavir, Atazanavir, Efavirenz, and Etravirine: A Retrospective Study”, Fuchs, Aline and Rotzinger, Aurélie and Cavassini, Matthias and Bugnon, Olivier and Buclin, Thierry and Schneider, Marie Paule and Csajka, Chantal.
  • Meropenem. Based on “Population Pharmacokinetic Analysis and Dosing Regimen Optimization of Meropenem in Adult Patients”, Chonghua Li, Joseph L. Kuti, Charles H. Nightingale, David P. Nicolau.
  • Piperacillin. Based on “Development and validation of a clinical decision support tool for piperacillin”, Rong Chen, Qing Qian, Meng-ru Sun, Chun-yan Qian, Su-lan Zou, Ming-li Wang, Li-ying Wang.
  • Piperacillin. Based on “Population pharmacokinetics and pharmacodynamics of piperacillin/tazobactam in patients with complicated intra-abdominal infection”, Chonghua Li, Joseph L Kuti, Charles H Nightingale, Debra L Mansfield, Adrian Dana, David P Nicolau.
  • Ponatinib. Based on “Population Pharmacokinetics of Ponatinib in Healthy Adult Volunteers and Patients With Hematologic Malignancies and Model-Informed Dose Selection for Pediatric Development”, Michael J. Hanley, Paul M. Diderichsen, Narayana Narasimhan, Shouryadeep Srivastava, Neeraj Gupta, and Karthik Venkatakrishnan.
  • Rifampicin. Based on “A Population Pharmacokinetic Model Incorporating Saturable Pharmacokinetics and Autoinduction for High Rifampicin Doses”, Robin J. Svensson, Rob E. Aarnoutse, Andreas H. Diacon, Rodney Dawson, Stephen H. Gillespie, Martin J. Boeree and Ulrika S.H. Simonsson.
  • Tacrolimus, Based on “A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients”, Andrews et al. .
  • Tacrolimus, Based on “Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese lung transplant recipients”, Cai et al. .
  • Tacrolimus. Based on “Population Pharmacokinetic Modelling and Design of a Bayesian Estimator for Therapeutic Drug Monitoring of Tacrolimus in Lung Transplantation”, Caroline Monchaud, Brenda C. de Winter, Christiane Knoop, Marc Estenne, Martine Reynaud-Gaubert, Christophe Pison, Marc Stern, Romain Kessler, Romain Guillemain, Pierre Marquet and Annick Rousseau.
  • Tacrolimus, Based on “Toward a robust tool for pharmacokinetic-based personalization of treatment with tacrolimus in solid organ transplantation: A model-based meta-analysis approach”, Nanga et al. .
  • Tacrolimus, Based on “High Variability of Whole‑Blood Tacrolimus pharmacokinetics Early After Thoracic Organ transplantation”, Sikma et al. .
  • Tacrolimus, Based on “Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling”, Storset et al. .
  • Tacrolimus, Based on “Prediction of tacrolimus dosage in the early period after heart transplantation: a population pharmacokinetic approach with external validation”, Han et al. .
  • Teicoplanin. Based on “Population pharmacokinetics and pharmacodynamics of teicoplanin and C-reactive protein in hospitalized patients with Gram-positive infections”, Chika Ogami, Yasuhiro Tsuji, Yuichi Muraki, Akiko Mizoguchi, Masahiro Okuda, Hideto To.
  • Tobramycin. Based on “Population Pharmacokinetics of Tobramycin in Patients With and Without Cystic Fibrosis”, Stefanie Hennig, Joseph F. Standing, Christine E. Staatz, Alison H. Thomson.
  • Vancomycin. Based on “Vancomycin Pharmacokinetics Throughout Life: Results from a Pooled Population Analysis and Evaluation of Current Dosing Recommendations”, Colin PJ, Allegaert K, Thomson AH, Touw DJ, Dolton M, de Hoog M, Roberts JA, Adane ED, Yamamoto M, Santos-Buelga D, Martín-Suarez A, Simon N, Taccone FS, Lo YL, Barcia E, Struys MMRF, Eleveld DJ.
  • Vancomycin. Based on “Optimisation of vancomycin exposure in neonates based on the best level of evidence”, Dao K, Guidi M, André P, Giannoni E, Basterrechea S, Zhao W, Fuchs A, Pfister M, Buclin T, Csajka C.
  • Vancomycin. Based on “A neonatal amikacin covariate model can be used to predict ontogeny of other drugs eliminated through glomerular filtration in neonates”, De Cock, R. and Allegaert, K. and Sherwin, C. and Nielsen, E. and de Hoog, M. and van den Anker, J. and Danhof, M. and Knibbe, C. .
  • Vancomycin. Based on “Association between vancomycin trough concentration and area under the concentration-time curve in neonates”, Frymoyer, A. and Hersh, A. and El-Komy, M. and Gaskari, S. and Su, F. and Drover, D. and Van Meurs, K. .
  • Vancomycin. Based on “Hospitalized Patients With and Without Hemodialysis Have Markedly Different Vancomycin Pharmacokinetics: A Population Pharmacokinetic Model-Based Analysis”, Vineet Goti, Ayyappa Chaturvedula, Michael J. Fossler, Steve Mok and Jesse T. Jacob.
  • Vancomycin. Based on “Pharmacokinetics and dose requirements of vancomycin in neonates”, Grimsley, C. and Thomson, A. .
  • Vancomycin. Based on “A population pharmacokinetic model of vancomycin for dose individualization based on serum cystatin C as a marker of renal function”, Tao-tao Liua , Hui-mei Panga, Li Jinga, Wen-xing Weia, Xiao-ling Qina, Qing Guoa, Hua Lua,Dao-hai Chenga and Wei-zhe Jiang.
  • Vancomycin. Based on “Population pharmacokinetic parameters of vancomycin in critically ill patients”, Llopis-Salvia, P. and Jiménez-Torres, N. V. .
  • Vancomycin. Based on “Population pharmacokinetic modelling of gentamicin and vancomycin in patients with unstable renal function following cardiothoracic surgery”, Staatz, Christine E. and Byrne, Colette and Thomson, Alison H. .
  • Vancomycin. Based on “Development and evaluation of vancomycin dosage guidelines designed to achieve new target concentrations”, A.H. Thomson, C.E. Staatz, C.M. Tobin, M. Gall, A.M. Lovering.
  • Vancomycin. Based on “Population pharmacokinetic analysis of vancomycin in patients with gram-positive infections and the influence of infectious disease type”, Yamamoto, M. and Kuzuya, T. and Baba, H. and Yamada, K. and Nabeshima, T. .
  • Vancomycin. Based on “Population pharmacokinetics of arbekacin, vancomycin, and panipenem in neonates”, Kimura, T. and Sunakawa, K. and Matsuura, N. and Kubo, H. and Shimada, S. and Yago, K. .
  • Vancomycin. Based on “Population pharmacokinetics and dose optimization of vancomycin in neonates”, Lee, S. M. and Yang, S. and Kang, S. and Chang, M. J. .
  • Vancomycin. Based on “Population pharmacokinetics of vancomycin in premature Malaysian neonates: identification of predictors for dosing determination”, Lo, Y.-L. and van Hasselt, J. and Heng, S.-C. and Lim, C.-T. and Lee, T.-C. and Charles, B. .
  • Vancomycin. Based on “Evaluation of vancomycin dosing regimens in preterm and term neonates using Monte Carlo simulations”, Mehrotra, N. and Tang, L. and Phelps, S. and Meibohm, B. .
  • Vancomycin. Based on “Assessment of vancomycin pharmacokinetics and dose regimen optimisation in preterm neonates”, Mulubwa, M. and Griesel, H. A. and Mugabo, P. and Dippenaar, R. and van Wyk, L. .
  • Venetoclax, Based on “Clinical Predictors of Venetoclax Pharmacokinetics in Chronic Lymphocytic Leukemia and Non-Hodgkin’s Lymphoma Patients: a Pooled Population Pharmacokinetic Analysis”, Brackman et al. .