Clinical Pharmacokinetics

, Volume 52, Issue 1, pp 9–22 | Cite as

Benchmarking Therapeutic Drug Monitoring Software: A Review of Available Computer Tools

  • Aline Fuchs
  • Chantal Csajka
  • Yann Thoma
  • Thierry Buclin
  • Nicolas Widmer
Review Article

Abstract

Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation.

Supplementary material

40262_2012_20_MOESM1_ESM.pdf (474 kb)
Supplementary material 1 (PDF 473 kb)

References

  1. 1.
    Buclin T, Gotta V, Fuchs A, et al. Monitoring drug therapy. Br J Clin Pharmacol. 2012;73(6):917–23.PubMedCrossRefGoogle Scholar
  2. 2.
    Platt DR. Individualization of drug dosage regimens. Clin Lab Med. 1987;7(2):289–99.PubMedGoogle Scholar
  3. 3.
    International Association of Therapeutic Drug monitoring and Clinical Toxicology. Definition of TDM. http://www.iatdmct.org/index.php/publisher/articleview/frmArticleID/138/. Accessed 1 Dec 2011.
  4. 4.
    Burton M, Shaw LM, Schentag JJ, Evans WE, editors. Applied pharmacokinetics & pharmacodynamics. principles of therapeutic drug monitoring. 4th edn. Baltimore: Lippincott Williams & Wilkins; 2006.Google Scholar
  5. 5.
    Durieux P, Trinquart L, Colombet I, et al. Computerized advice on drug dosage to improve prescribing practice. Cochrane Database Syst Rev 2008;3:CD002894.Google Scholar
  6. 6.
    Kunz J, Shortliffe EH, Buchanan BG, et al. Computer-assisted decision making in medicine. J Med Philos. 1984;9(2):135–60.PubMedCrossRefGoogle Scholar
  7. 7.
    Bates D, Soldin SJ, Rainey PM, et al. Strategies for physician education in therapeutic drug monitoring. Clin Chem. 1998;44(2):401–7.PubMedGoogle Scholar
  8. 8.
    Anderson PO. Clinical pharmacokinetics computer programs. In: Anderson PO, McGuinness SM, Bourne PE, editors. Pharmacy informatics. Boca Raton: CRC Press Inc.; 2010. p. 199–216.Google Scholar
  9. 9.
    Frist WH. Shattuck Lecture: health care in the 21st century. N Engl J Med. 2005;352(3):267–72.PubMedCrossRefGoogle Scholar
  10. 10.
    Special report: health care and technology. Medicine goes digital. The Economist 2009 Apr 16.Google Scholar
  11. 11.
    Guiducci C, Temiz Y, Leblebici Y, et al. Integrating Bio-sensing functions on CMOS chips. 2010 Asia Pacific Conference on Circuits and Systems, Kuala Lumpur, 6–9 Dec 2010.Google Scholar
  12. 12.
    Buffington DE, Lampasona V, Chandler MHH. Computers in pharmacokinetics: choosing software for clinical decision making. Clin Pharmacokinet. 1993;25(3):205–16.PubMedCrossRefGoogle Scholar
  13. 13.
    Lenert LA, Klostermann H, Coleman RW, et al. Practical computer-assisted dosing for aminoglycoside antibiotics. Antimicrob Agents Chemother. 1992;36(6):1230–5.PubMedCrossRefGoogle Scholar
  14. 14.
    Peck CC, Sheiner LB, Martin CM, et al. Computer-assisted digoxin therapy. N Engl J Med. 1973;289(9):441–6.PubMedCrossRefGoogle Scholar
  15. 15.
    Sheiner LB, Rosenberg B, Melmon KL. Modelling of individual pharmacokinetics for computer-aided drug dosage. Comput Biomed Res. 1972;5(5):411–59.PubMedCrossRefGoogle Scholar
  16. 16.
    Hatton RC, Gotz VP, Robinson JD, et al. Conversion from intravenous aminophylline to sustained-release theophylline: computer simulation versus in vivo results. Clin Pharm. 1983;2 (4):347–52.Google Scholar
  17. 17.
    Gougnard T, Charlier C, Plomteux G. “CAPCIL”: posologic adjustment of aminoglycoside treatments [in French]. Acta Clin Belg Suppl. 1999;1:17–9.PubMedGoogle Scholar
  18. 18.
    Lacarelle B, Pisano P, Gauthier T, et al. Abbott PKS system: a new version for applied pharmacokinetics including Bayesian estimation. Int J Biomed Comput. 1994;36(1–2):127–30.PubMedCrossRefGoogle Scholar
  19. 19.
    Proost JH, Meijer DK. MW/Pharm, an integrated software package for drug dosage regimen calculation and therapeutic drug monitoring. Comput Biol Med. 1992;22(3):155–63.PubMedCrossRefGoogle Scholar
  20. 20.
    Bourne D. Pharmacokinetic and pharmacodynamic resources. Pharmacokinetic software. http://www.pharmpk.com/soft.html. Accessed 3 Apr 2012.
  21. 21.
    Jelliffe RW. The USC*PACK PC programs for population pharmacokinetic modeling, modeling of large kinetic/dynamic systems, and adaptive control of drug dosage regimens. Proc Annu Symp Comput Appl Med Care. 1991;922–4.Google Scholar
  22. 22.
    Duffull SB, Kirkpatrick CM, Begg EJ. Comparison of two Bayesian approaches to dose-individualization for once-daily aminoglycoside regimens. Br J Clin Pharmacol. 1997;43(2):125–35.PubMedCrossRefGoogle Scholar
  23. 23.
    Robinson JD, Hatton RC, Russell WL, et al. Accuracy of serum gentamicin concentration predictions generated by a personal-computer software system. Clin Pharm. 1984 Sep-Oct;3(5):509–16.Google Scholar
  24. 24.
    Sim SC, Ingelman-Sundberg M. Pharmacogenomic biomarkers: new tools in current and future drug therapy. Trends Pharmacol Sci. 2011;32(2):72–81.PubMedCrossRefGoogle Scholar
  25. 25.
    Gervasini G, Benitez J, Carrillo JA. Pharmacogenetic testing and therapeutic drug monitoring are complementary tools for optimal individualization of drug therapy. Eur J Clin Pharmacol. 2010;66(8):755–74.PubMedCrossRefGoogle Scholar
  26. 26.
    Zhou SF. Polymorphism of human cytochrome P450 2D6 and its clinical significance: part II. Clin Pharmacokinet. 2009;48(12):761–804.PubMedCrossRefGoogle Scholar
  27. 27.
    Schmidt LE, Dalhoff K. Food-drug interactions. Drugs. 2002;62(10):1481–502.PubMedCrossRefGoogle Scholar
  28. 28.
    Fujita K. Food-drug interactions via human cytochrome P450 3A (CYP3A). Drug Metab Drug Interact. 2004;20(4):195–217.CrossRefGoogle Scholar
  29. 29.
    Corti N, Taegtmeyer AB. Clinically important food-drug interactions: what the practitioner needs to know [in German]. Praxis. 2012;101(13):849–55.PubMedCrossRefGoogle Scholar
  30. 30.
    Bustad A, Terziivanov D, Leary R, et al. Parametric and nonparametric population methods: their comparative performance in analysing a clinical dataset and two Monte Carlo simulation studies. Clin Pharmacokinet. 2006;45(4):365–83.PubMedCrossRefGoogle Scholar
  31. 31.
    Rousseau A, Marquet P. Application of pharmacokinetic modelling to the routine therapeutic drug monitoring of anticancer drugs. Fundam Clin Pharmacol. 2002;16(4):253–62.PubMedCrossRefGoogle Scholar
  32. 32.
    Jelliffe RW, Schumitzky A, Bayard D, et al. Model-based, goal-oriented, individualised drug therapy. Linkage of population modelling, new ‘multiple model’ dosage design, Bayesian feedback and individualised target goals. Clin Pharmacokinet. 1998;34(1):57–77.PubMedCrossRefGoogle Scholar
  33. 33.
    Debord J, Voultoury JC, Lachatre G, et al. Pharmacokinetics and dosage regimens of amikacin in intensive care unit patients. Int J Biomed Comput. 1994;36(1–2):135–7.PubMedCrossRefGoogle Scholar
  34. 34.
    Bleyzac N, Souillet G, Magron P, et al. Improved clinical outcome of paediatric bone marrow recipients using a test dose and Bayesian pharmacokinetic individualization of busulfan dosage regimens. Bone Marrow Transplant. 2001;28(8):743–51.PubMedCrossRefGoogle Scholar
  35. 35.
    Neely M, Jelliffe R. Practical therapeutic drug management in HIV-infected patients: use of population pharmacokinetic models supplemented by individualized Bayesian dose optimization. J Clin Pharmacol. 2008;48(9):1081–91.PubMedCrossRefGoogle Scholar
  36. 36.
    Jelliffe RW. Some comments and suggestions concerning population pharmacokinetic modeling, especially of digoxin, and its relation to clinical therapy. Ther Drug Monit. 2012;34(4):368–77.PubMedCrossRefGoogle Scholar
  37. 37.
    Wright DF, Duffull SB. Development of a Bayesian forecasting method for warfarin dose individualization. Pharm Res. 2011;28(5):1100–11.PubMedCrossRefGoogle Scholar
  38. 38.
    Bjorkman S. Evaluation of the TCIWorks Bayesian computer program for estimation of individual pharmacokinetics of FVIII. Haemophilia. 2011;17(1):e239–40.PubMedCrossRefGoogle Scholar
  39. 39.
    Holford NH. Target concentration intervention: beyond Y2K. Br J Clin Pharmacol. 1999;48(1):9–13.PubMedCrossRefGoogle Scholar
  40. 40.
    Neef C, Jelliffe RW, van Laar T, et al. Comparison of two software programs to be used for the calculation of population pharmacokinetic parameters. Int J Biomed Comput. 1994;36(1–2):143–50.PubMedCrossRefGoogle Scholar
  41. 41.
    Gauthier T, Lacarelle B, Marre F, et al. Predictive performance of two software packages (USC*PACK PC and Abbott PKS system) for the individualization of amikacin dosage in intensive care unit patients. Int J Biomed Comput. 1994;36(1–2):131–4.PubMedCrossRefGoogle Scholar
  42. 42.
    Norris RL, Martin JH, Thompson E, et al. Current status of therapeutic drug monitoring in Australia and New Zealand: a need for improved assay evaluation, best practice guidelines, and professional development. Ther Drug Monit. 2010;32(5):615–23.PubMedCrossRefGoogle Scholar
  43. 43.
    Antibiotic Expert Group. Therapeutic guidelines: antibiotic. Version 14. Melbourne: Therapeutic Guidelines Limited; 2010. http://www.tg.org.au/index.php?sectionid=41. Accessed 29 Oct 2012.
  44. 44.
    Gotta V, Widmer N, Montemurro M, et al. Therapeutic drug monitoring of imatinib: bayesian and alternative methods to predict trough levels. Clin Pharmacokinet. 2012;51(3):187–201.PubMedCrossRefGoogle Scholar
  45. 45.
    Ensom MH, Davis GA, Cropp CD, et al. Clinical pharmacokinetics in the 21st century. Does the evidence support definitive outcomes? Clin Pharmacokinet. 1998;34(4):265–79.PubMedCrossRefGoogle Scholar
  46. 46.
    Touw D, Neef C, Thomson AH, et al. Cost-effectiveness of therapeutic drug monitoring: an update. EJHP Sci. 2007;13(4):83–91.Google Scholar
  47. 47.
    Shenfield GM. Therapeutic drug monitoring beyond 2000. Br J Clin Pharmacol. 2001;52(Suppl 1):3S–4S.PubMedGoogle Scholar
  48. 48.
    Murphy JE, Slack MK, Campbell S. National survey of hospital-based pharmacokinetic services. Am J Health Syst Pharm. 1996;53(23):2840–7.PubMedGoogle Scholar
  49. 49.
    Pedersen CA, Schneider PJ, Santell JP, et al. ASHP national survey of pharmacy practice in acute care settings: monitoring, patient education, and wellness–2000. Am J Health Syst Pharm. 2000;57(23):2171–87.PubMedGoogle Scholar
  50. 50.
    Elin RJ. Computer-assisted therapeutic drug monitoring. Clin Lab Med. 1987;7(2):485–92.PubMedGoogle Scholar
  51. 51.
    Nieuwlaat R, Connolly SJ, Mackay JA, et al. Computerized clinical decision support systems for therapeutic drug monitoring and dosing: a decision maker-researcher partnership systematic review. Implement Sci. 2011;6:90.Google Scholar
  52. 52.
    Bates DW. Improving the use of therapeutic drug monitoring. Ther Drug Monit. 1998;20(5):550–5.PubMedCrossRefGoogle Scholar
  53. 53.
    Special report: health care and technology. Fantastic voyage: technology is making health care more portable, precise and personal. The Economist 2009 Apr 16.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2012

Authors and Affiliations

  • Aline Fuchs
    • 1
  • Chantal Csajka
    • 1
    • 3
  • Yann Thoma
    • 2
  • Thierry Buclin
    • 1
  • Nicolas Widmer
    • 1
  1. 1.Division of Clinical Pharmacology, Service of Biomedicine, Department of Laboratory, Hôpital de BeaumontCentre Hospitalier Universitaire Vaudois and University of LausanneLausanneSwitzerland
  2. 2.Reconfigurable and Embedded Digital Systems Institute, School of Business and Engineering VaudUniversity of Applied Sciences Western SwitzerlandYverdon-les-BainsSwitzerland
  3. 3.School of Pharmaceutical SciencesUniversity of Geneva and LausanneGenevaSwitzerland

Personalised recommendations