Optimizing Drug Exposure

Transcription

1 Optimizing Drug Exposure All Patients Are Not Average Michael Neely, MD, MSc Associate Professor, Pediatrics Keck School of Medicine, University of Southern California Laboratory of Applied Pharmacokinetics and Bioinformatics Saban Research Institute, Children s Hospital Los Angeles

2 How We Are Taught to Prescribe Mel Brooks in History of the World, Part 1, 1981

3 Today Reduced adherence No dosing flexibility Trial and patient FAILURES Multiple exposures Ineffective Toxic

4 Dose Exposure visual predictive check of the model s ability to predict the data. The predictive check was conditional on dose (450 mg, Wilkins, J. J. et al. Population pharmacokinetics of rifampin in pulmonary tuberculosis patients, including a semimechanistic model to describe variable absorption. Antimicrob Agents Chemother 52, (2008).

5 Change Focus

6 How can we optimize individual patient dosing?

7 The parametric approach Population CL Data Posterior CL

8 MAP-Bayesian Parametric model Maximum a posteriori Bayesian probability One version of the patient Shrinkage of variability No predictive probability of success vs. failure

9 Non-Normal Populations Simulated population ( ) Non-parametric estimation of population values ( ) - Size proportional to probability Vol The entire population is accurately and precisely described Kel Neely MN, van Guilder MG, Yamada WM, Schumitzky A, Jelliffe RW. Accurate detection of outliers and subpopulations with Pmetrics, a nonparametric and parametric pharmacometric modeling and simulation package for R. Ther Drug Monit. 2012;34(4):

10 Non-Normal Populations Simulated population ( ) Mean (+) and percentile distributions of parametric population parameter estimates Vol Percentile Missed the outlier completely. Nobody is at the mean! Kel

11 Make a Non-Parametric Model

12 TB LZD+RIF Therapy Drusano, G. L. et al. Analysis of combination drug therapy to develop regimens with shortened duration of treatment for tuberculosis. PLoS ONE 9, e e (2014).

13 Obs vs. Pred LZD RIF Total MTb LZD-R MTb RIF-R MTb

14 Use the Model

15 Multiple Models

16 Multiple Models

17 Sparse data

18 Moderate data

19 Very rich data

20 Dose Exposure 13 month old bone marrow transplant patient on long-term oral voriconazole for pulmonary nodules with +Galactomannan Weight 8.76 kg Voriconazole trough concentration repeatedly <0.5 mg/l. Dose gradually increased to 17 mg/kg/dose PO tid (51 mg/ kg/day), compared to usual of ~20 mg/kg/day A 75 kg adult at this dose would be getting 2200 mg/day compared to usual dose of 400 mg/day (by allometric scaling)

21 AUC 67.8 AUC ref 40.6 (69%) (45%) 2 1 Vfend package insert mean (CV%) adult AUC 200 mg PO q12h, setid=dac26bcd-7a59-401f-9f66-a cece (accessed 9/10/13) 2 Vfend package insert mean (CV%) adult AUC 300 mg PO q12h

22 Studies with BestDose Condition/Indication Drug and Class Key 5indings Ref Adults/fungal infections n=10 Pediatrics/fungal infections n=33 Critically ill adults n=8 Hospitalized adults n=77 Renal transplant patients n=79 HIV infected children (n=2) and adults (n=2) Voriconazole Antifungal Voriconazole Antifungal Piperacillin Beta- lactam antibiotic Vancomycin Antibiotic Tacrolimus Immuno- suppressant 4 different antiretrovirals BestDose explains 86% of the variability in measured concentrations BestDose accurately predicts both dose (- 0.7%) and concentrations (1.1%) compared to actual doses and BestDose measured calculations concentrations for doses had average bias of % to 38% depending on number of available measured BestDose predicted concentrations concentrations used to with average of - 3.1% bias. "Lower bias than other Bayesian prediction Prospective, algorithms." randomized study BestDose vs. expert clinician. BestDose group with more patients BestDose in can goal assist concentration clinicians in 4 scenarios: patients with unexpectedly reduced drug clearance, adolescents

23 References Published studies 1. Hope, W. W. et al. Software for dosage individualization of voriconazole for immunocompromised patients. Antimicrob Agents Chemother 57, (2013). 2. Neely, M. et al. Achieving target voriconazole concentrations more accurately in children and adolescents. Antimicrob Agents Chemother AAC (2015). doi: /aac Felton, T. W. et al. Individualization of Piperacillin Dosing for Critically Ill Patients: Dosing Software To Optimize Antimicrobial Therapy. Antimicrob Agents Chemother 58, (2014). 4. Nunn, M. O. et al. Vancomycin dosing: assessment of time to therapeutic concentration and predictive accuracy of pharmacokinetic modeling software. The Annals of Pharmacotherapy 45, (2011). 5. Størset, E. et al. Improved Tacrolimus Target Concentration Achievement Using Computerized Dosing in Renal Transplant Recipients-A Prospective, Randomized Study. Transplantation 1 (2015). doi: /tp Neely, M. & Jelliffe, R. Practical therapeutic drug management in HIV-infected patients: use of population pharmacokinetic models supplemented by individualized Bayesian dose optimization. 48, (2008). Ongoing (not yet published) 1. Busulfan in children 2. Beta-lactams in critically ill adults 3. Vancomycin in adults 4. Tacrolimus in adults 5. Valganciclovir in children 6. Factor replacement in hemophilia 7. Cefepime in critically ill children 8. Tobramycin in adults with CF

24 Effect on study design Dose-based Phase 1 Exposure-based Phase 1 Based on pre-clinical data Serial small studies Homogenous populations Single large adaptive study Heterogenous population Target exposure groups The dose primary Exposures secondary Exposure(s) primary The doses secondary

25 Ripple Effect Phase II Concentration control Richer sampling Confirm PK, PK-PD Phase III Concentration control Sparse sampling Package Label Effective concentrations Dose range Starting dose based on PTA

26 Acknowledgements Support from NIH-NICHD R01 HD and NIH-NIGMS R01 GM LAPK - Mike van Guilder, Alan Schumitzky, David Bayard, Jay Bartroff, Alona Kryschenko, Tatiana Tatarinova, Walter Yamada, Elliott Keeter, Roger Jelliffe Collaborators - George Drusano (University of Florida), William Hope (University of Liverpool), Anders Asberg (University of Oslo), Marc Scheetz (Midwestern University), Andrés Zuluaga (University of Medellin), Denny Fleming and Binu Philip (Christian Medical College of Vellore, India), Giles Fitch (University of Leeds), Sylvain Goutelle (University of Lyon), Jean-Baptiste Woillard (University of Limoges)