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THERAPEUTIC DRUG MONITORING – WHAT YOU CAN DO WITH IT TODAY Target goal-oriented, model-based TDM

THERAPEUTIC DRUG MONITORING – WHAT YOU CAN DO WITH IT TODAY Target goal-oriented, model-based TDM. Roger Jelliffe, Laboratory of Applied Pharmacokinetics USC Keck School of Medicine Los Angeles CA. Important points. Must use models of drug behavior, and software.

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THERAPEUTIC DRUG MONITORING – WHAT YOU CAN DO WITH IT TODAY Target goal-oriented, model-based TDM

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  1. THERAPEUTIC DRUG MONITORING – WHAT YOU CAN DO WITH IT TODAYTarget goal-oriented, model-based TDM Roger Jelliffe, Laboratory of Applied Pharmacokinetics USC Keck School of Medicine Los Angeles CA

  2. Important points • Must use models of drug behavior, and software. • Clinician must set target goal(s): Must SEE the pt! • Assay error pattern – no LOQ! • Better TDM strategies–not just troughs • Nonparametric pop PK/PD models • Multiple Model dosage design • Nonparametric Bayesian posteriors • IMM Bayesian posteriors • Three illustrative cases

  3. Determining the assay error pattern

  4. Fisher Information • Fisher Info = x/Varx • So need to know, or have a good estimate, of the SD of every serum level • Var = SD2 • Weightx = 1/Varx

  5. Assay CVSD Concentration

  6. Determining the Assay SD polynomial • Measure blank, low, medium, high, and very high samples in at least quadruplicate. • Get mean + SD for each sample. • Fit a polynomial to the mean and SD data. • SD = A0C0 + A1C1 + A2C2 + A3C3 • Then can weight each measurement by the reciprocal of its variance (Fisher Info) • No lower detectable limit for PK work!

  7. Better TDM strategies–not just troughs • Optimal Times to get Serum Samples: • D - optimal experimental Design

  8. When and why to get Serum Samples • To achieve target goals precisely. • To get good AUC values for individual patients. • And good AUC’s from population models. • Many drug regimens target desired AUC’s. • The AUC’s MUST be reliable!

  9. Example: Methotrexate • Most samples obtained to see if patient needs leucovorin rescue, not for good PK models. • A real problem! • How reliable are target AUC’s when samples not obtained before 6, 8, 12, 24 hours? • How reliable are patient AUC’s when only trough samples are drawn?

  10. Monitoring Lidocaine Optimally – D’Argenio, 1981 • Loading infusion over 1 min, then 1.45 mg/min. 10 subjects. • Conventional strategy – 8 measurements, at 5, 10, 30, 60, 120, 180, 360, and 720 min into the regimen. • D – Optimal strategy – 4 samples, each in duplicate, at 1, 10, 70, and 720 min. • Assay SD = 0.0078 + 0.1236 x conc.

  11. Good TDM strategies • Can check achievement of target goals • Can get good parameter and AUC values for individual patient PK models • Can make good population models from TDM data • Permit reliable target AUC values • Permit reliable target goals for individual patients.

  12. What is the IDEAL Pop Model? • The correct structural PK/PD Model. • The collection of each subject’s exactly known parameter values for that model. • Therefore, multipleindividualmodels, up to one for each subject. • Usual statistical summaries can also be obtained, but usually will lose info. • How best approach this ideal?

  13. A Parametric Population Model Joint Density

  14. A Population Model, as made by Breugel

  15. An NPML Population Model, as made by Mallet

  16. Nonparametric Population Models • Get the entire ML distribution, a DiscreteJoint Density: one param set per subject, + its prob. • Shape of distribution not determined by some equation, only by the dataitself. • Multiple indiv models, up to one model per subject. • Can discover, locate, unsuspected subpopulations. • The multiple models permit multiple predictions. • Can predict precision of goal achievement by a dosage regimen.

  17. The Separation Principle • Whenever you separate the process of controlling the behavior of a system into • First, getting the best single point parameter estimates, and then… • Second, using those point values to control the system to achieve target goals, • The control is usually done suboptimally. • No performance criterion is optimized.

  18. The Way around the Separation Principle • Use a pop model with discrete multiple models - an NP pop model, for example. • Give a candidate regimen to each model. • Predict each result. • Compute weighted squared error of failure to hit target goal at target time. • Find the regimen having the minimal weighted squared error. This is multiple model (MM) dosage design.

  19. Lido Regimen based on Param means: Predicted response of full lido pop model

  20. MM lido regimen:Predicted response of full lido pop model

  21. Three illustrative cases • A patient on gentamicin -changing renal function • A patient on tobramycin – changing clinical status • A patient on Digoxin – converting A Fib

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