51 st Annual Scientific Meeting, Australasian Association of Clinical Biochemists,

Transcription

1 Tools for TDM, Present and Future. 51 st Annual Scientific Meeting, Australasian Association of Clinical Biochemists, Gold Coast, September A/Prof Ross Norris, Research Consultant, Australian Centre for Paediatric Pharmacokinetics.

2 Overview of presentation Example: Dosing tobramycin in paediatric cystic fibrosis patients. Challenges Population Pharmacokinetic modeling. Target Concentration Intervention (TCI)/predictive modeling (dose individualisation). Clinical assessment of TCI.

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4 Importance of correct information. 5 yo female. CF. 320 mg tobra iv daily. Time (h) AUC(0 (0.5 hr inf) AUC(1 (1.0 hr inf) AUC range = , Target AUC = 95.

5 Importance of correct information (Adult patient t on tobramycin). Date Dose AUC Se Creat (mg) (mg.l/hr) (µmol/l) 31/ / / / /

6 AUC - disadvantages 2 samples required 1 st sample: 1-2hrs after start of infusion 2 nd sample: 8-12hrs after start of infusion Exact documentation is essential When infusion was started Length of infusion Time of 1 st sample (often incorrect) Time of 2 nd sample Inconvenient, expensive, inaccurate

7 Challenge.

8 Target Concentration Intervention An extension of traditional TDM Targets a specific concentration using the patient s individual PK model. Individual PK model determined using Bayesian principles Model based, to forecast time-course of fdrug Limited it data required (i.e. 1 concentration) ti

9 TCIWorks

10 Population PK study (BJCP 2007)

11 Pop PK data required - Observational data - What is it? These data have very few design restrictions, and are often collected during routine clinical practice, or as a supplement in a study designed for another purpose Cyclosporin in heart Tx Oxycodone in palliative care 11

12 Mixed effects (population) model Mixed Effects Model Fixed Effects: PK parameters, e.g. CL = 14.5 L/h Covariates, e.g. CL = 14.5 (Weight) Random Effects: Intersubject variability Interoccasion variability Unexplained variability

13 NONMEM modelling What is produced? Typical population values for the PK model parameters, a e.g. Clearance, a Volume Variability of each individual s true but unknown PK parameter values about typical values Unexplained (residual) variability of observed from responses predicted from the population p model (e.g. drug level) Coefficients for factors that influence PK values Diagnostics Scatters, SE estim. 13

14 2007 Study Outcome - Simulations. Aim: To determine percentage of patients achieving an AUC of mg.h/l assuming initial dose is based on mg/kg only. mg/kg W/in Above Below

15 2007 Study Outcome. 1. PK estimates, t co-factors etc. needed d for TCIWorks. 2. Condition for effective TDM met: Between Observation Variability (within subject) less than Between Subject Variability.

16 TCIWorks Demonstration New patient t Demographic data Estimating initial iti dose Add data Individualising the model Predict new dose Graphical output

17 TCI new patient

18 TCI - demographics

19 TCI population estimation

20 TCI individual model

21 TCI dose prediction

22 TCI - prediction

23 TCI - output

24 TCI - output

25 Evaluation paediatric CF patients. Retrospective study. Evaluate the accuracy of an AUC calculated using 1 concentration compared to AUC calculated from 2 concentrations. AUC from 2 concentrations defined as gold standard. Also compare to in-house database AUC method CF children controlled study ygroup p( (n = 14) and group where dosing data was taken from charts (n = 30) Adults no CF model available so used general adult model. Ref: Barras et al. J Pharmacy Practice & Research 41(3) 2011 p 183.

26 Evaluation Evaluated all data included first admission 2 nd occasion data (when individualised model is used)

27 AUC CF children all data 120 all data, control group (n=14) 15 all data, control group (n=14) evels AUC, 2 l difference e AUC,1 level -15 Average r 2 = 0.86 Bias = 0.72, 95%CI = to 8.71

28 AUC children Controlled group only. AUC, 2 le evels occ. 2, control group (n=14) AUC,1 level differenc e occ. 2, control group (n=14) Average r 2 = 0.94 Bias = 1.00, 95%CI = to 6.91

29 Summary data. Population Median weight Gender Median age Correlation AUC1 vs AUC2 Bias (95%CI) Bias Control (14) 28.5 (14-57) 7m, 7f 11.5 (3-17) R 2 = to 6.91 Total (30) 30 (14-74) 14m,16f 11 (2-17) R 2 = to 8.71

30 Models for TCIWorks Gentamicin i / tobramycin - CF paediatric - Adults update due soon - CF adults due soon Enoxaparin Haemodialysis gentamicin Vancomycin being tested Phenytoin, digoxin in the future Program currently under major revision Due for release soon.

31 Individualised Drug Dosing in the Cloud

32 Web, Tablet, Phone and more

33 Ever-growing Drug Support Drug Adult Paediatric Amikacin Busulfan Interested in another drug? DoseMe can help. Enoxaparin Gentamicin Itraconazol e Methotrexat e Coming soon Tobramycin Vancomycin Voriconazol e Warfarin

34 Rightdose/Bestdose A/Prof Michael Neely. Keck School of Medicine, University of Southern California Laboratory of Applied Pharmacokinetics Los Angeles.

35 Future Develop further pharmacokinetic/pharmacodynamic models using population PK modelling, for more drugs, in more patient groups. Include pharmacogenomic information as co-factors in PK modelling. Apply models to predictive pharmacokinetic application in clinical practice (i.e. dose individualisation /TCI) Assess these models in clinical practice for clinical and pharmacoeconomic outcomes.

36 Clinical Implications Initial dose will be better estimated using population p PK values and co-factors. Dose adjustment after first dose (two samples in the dosing interval) to establish the patients individual PK profile. Interpretation will more likely be in clinicians hands via iphone apps. etc.

37 IMPLICATION FOR LABORATORIES Importance of accuracy of assays is more apparent. That is, correlation with efficacy and/or toxicity is not enough The mathematical relationship between a given circulating concentration (not a range) and drug dose is used.

38 Acknowledgements: Dr Michael Barras A/Prof Bruce Charles Prof Carl Kirkpatrick Dr Stefanie Hennig Dr Margaret Harris Dr Carolyn Dakin Dr Hana Alraman Ms Katrina Jess Ms Melissa Pilbeam Staff and patients at Mater Children s Hospital Respiratory Unit. Staff of the Australian Centre for Paediatric Pharmacokinetics.

39 What makes up TCIWorks? TCIWorks is capable of running on any operating system that supports JAVA applications e.g. Windows,Mac TCIWorks comprises of a combination of proprietary freeware including JAVA runtime and an number of free available numerical applications for JAVA Numerical integrator Non-linear regression tools Graphics/plotting functions Database with three drug models

40 Traditional TDM. Traditional TDM targets a population range (usually wide) i.e. Therapeutic range (TR). TR usually developed from small, healthy populations Dose adjustment t to within a (wide?) range Population values of PK parameters (eg Clearance) are sometimes, but not regularly used.

41 What is population pharmacokinetics? A means to study the sources and correlates of variability in the PK responses ses (i.e. drug concentrations) ce o among a number of individuals (i.e. the population ), representative of those in whom the drug will be used clinically. 41

42 What are its strengths? In explaining variability, it can be used to help identify factors of demographic, environmental pathophysiological, h i l or drug-related d origin that may influence the PK behaviour (covariates). 42

43 Sources of variability Interindividual variability: Genetic Polypharmacy/drug interactions Characteristics of patients (e.g. weight, age) Pathophysiological (e.g. renal function) Random unexplained variability: Measurement error (drug assay) Model misspecification (e.g. 1 compartment instead of 2) Variation in dose, administration time, sampling time Interoccasion (day-to-day) y) variability: Factor X 43

44 Where might we use population PK? Population approaches are useful where there are sparse, observational data in patients mostly inaccessible ibl to normal PK studies... Palliative care Outpatients, ambulatory, home care Paediatrics Geriatrics Oncology Perinatal (premature infants) Intensive care Emergency medicine 44

45 Observational data - Advantages Expedited d ethics approval often possible Can combine rich data with sparse data Often cheaper to collect ( piggybacking ) Subjects/patients contributing to the data set represent the target population p that is receiving treatment of the drug(s) 45

46 Observational data - Disadvantages - Limited opportunity - Outpatient compliance - Retrospective data - Staffing issues - Samples per patient - Patient numbers - Tedious data collection - Verification i (medical records) - Multicentre data (logistics, cost) - Timing, design restrictions 46

47 Nonlinear mixed effects (NONMEM) modeling State-of-the-art in the population modeling world. Sparse/rich, unbalanced/unstructured clinical data can be modeled (with some qualifications) Considers the population rather than the individual id as the unit for the PK analysis But the individuality of the subjects is still preserved (via Bayesian posthoc estimation) 47