Modeling Pediatric Systemic Drug Clearance as a Function of Child Age, Adult Pharmacokinetics

1. Modeling Pediatric Systemic Drug Clearance as a Function of Child Age, Adult Pharmacokinetics and In-Vitro Microsomal Metabolism Gene M. Williams, Ph.D. OCPB/CDER/FDA April 22, 2003 Meeting of the CDER Advisory Committee for Clinical Pharmacology

2. Questions 1. Is the general approach rational and logical (empirical  mechanistic) ? 2. What perils do you foresee (especially in the initial empiric approach) and how can they be avoided ? 3. Are there data sources you can recommend ? 4. Do you have any suggestions regarding the form of the non-PBPK mechanistic models ?

3. Objectives • Short term: • Construct a model that allows prediction of pediatric systemic drug clearance from adult pharmacokinetics and in vitro microsomal metabolism data. • Long term - aid: • Regulatory Scientists • Industry Scientists • Health Professionals

4. Data (I) • Clearance (from sparse or dense data) and age for each individual • Weight and height for each individual • Renal function for each individual • Demographic data for each individual - gender, race • In vitro microsomal metabolism data for each drug

5. Data (II) • Total approved active moieties granted pediatric exclusivity = 72 (3/14/2003) • http://www.fda.gov/cder/pediatric/exgrant.htm • Nature of data • raw - actual measurements of individuals, not summaries across individuals • reviewable - documented within the submission • Limitations of data • studies often not powered to compare PK between age groups • ages with the greatest differences from adults (very young) often most poorly represented • drugs are not “probe” substrates - may need to use Km as covariate

6. Data (III) Ginsberg et al., Toxicol. Sci, 66: 185-200, 2002 (21 - 27 drugs - metabolic routes vary, see next slide) Y-axis = child CL (ml/min/kg) / adult CL (ml/min/kg)

7. Data (IV) Ginsberg et al., Toxicol. Sci, 66: 185-200, 2002

8. Normalization of CL • Needed to allow for appropriate comparison of drugs whose adult clearances differ widely • Method -- divide each individual pediatric CL by mean adult CL

9. Models (I) Data adapted from Ginsberg et al., Clearance ratio vs Age Simple LS fit - no weighting, will use ELS (NONMEM) for project

10. Models (II) Rmax1·[1 - Exp(-K1·Kg)] + Rmax2·[1 - Exp(-K2·Yr)] Rmax1 + Rmax2 = FIXED = 1

11. Models (III) Speculative path (data driven) 1. Not sequential: - add exponentials - “half-life” of maturation of metabolic enzymes - add offset for age effect ( 0 at birth) 2. Investigate % excreted unchanged, Km, Km ratios, et al.

12. Models (IV) Mechanistic models - may require data outside FDA’s database 1. CL = f (age, “process constant1”) + f (age, “process constant2a”, “process constant2b”) + … CL = f (age, GFR) + f (age, Km, % non-renally eliminated) 2. CL = f ( age, Q, Clint, protein binding)

13. Questions 1. Is the general approach rational and logical (empirical  mechanistic) ? 2. What perils do you foresee (especially in the initial empiric approach) and how can they be avoided ? 3. Are there data sources you can recommend ? 4. Do you have any suggestions regarding the form of the non-PBPK mechanistic models ?