1. Labetalol Pharmacokinetics in Hypertension During Pregnancy James H. Fischer, Pharm.D., FCCP May 17, 2011

2. Pregnancy: Therapeutic Challenges Determine: –whether physiologic changes impact PKs of drug –what are consequences for drug administration PK changes tend to be dynamic and unpredictable Conditions requiring continuous therapy become particularly challenging –diabetes, epilepsy, hypertension Anderson 2005

3. Hypertension in Pregnancy Hypertension complicates 10% of pregnancies Important cause of maternal and fetal morbidity 3% of pregnant women receive antihypertensive Lack of evidence for benefit of drug therapy on: –development of pre-eclampsia –adverse perinatal events Podymow & August 2008; Andrade et al 2008

4. Hypertension in Pregnancy Antihypertensive treatment targeted to: – Minimize fetus drug exposure – By administering lowest dose to control of blood pressure/prevent severe hypertension in mother Abalos et al 2007

5. Labetalol Treatment of Hypertension During Pregnancy α-1 and β adrenergic receptor antagonist First/ second line agent for treating hypertension during pregnancy PK properties: – Systemic availability: approximately 20-50% – Widely distributes into tissues – 50-60% bound to plasma proteins – Eliminated mainly by hepatic metabolism Glucuronidation via UGT 1A1 and 2B7 Goa 1989; Jeong et al 2008

6. Effect of Pregnancy on Labetalol Pharmacokinetics Four earlier studies examined labetalol in pregnancy – increased CL/F in pregnant women (1) – No difference in PK during pregnancy (3) Limitations of earlier studies: – Small number of subjects (<10) – Reliance on historical controls Rubin e al 1983; Nylund et al 1984; Rogers et al 1990; Saotome et al 1993

7. Objectives Compare pharmacokinetics of oral labetalol during and after pregnancy in women being treated for hypertension Identify any demographic or clinical factors influencing the variability in labetalol disposition in pregnancy

8. Rationale for Population PK Approach Less restrictive design components –unbalanced designs –sparse sampling Obtain informative PK information –population representative of patients –reduces impact of research on medical care

9. Research Team Co-Principal Investigator Gloria Sarto, M.D., Ph.D. UIC Co-Investigators Loraine Endres, M.D. Patricia Fischer, R.N. Stacie Geller, Ph.D. Jennifer Hardman, Pharm.D. Hyunyoung Jeong, Pharm.D., Ph.D. Sarah Kilpatrick, M.D. Keith Rodvold, Pharm.D. UW Co-Investigators Thomas Jenkins, M.D. Lori Wollett, R.N. FDA Margaret Miller, Ph.D.

10. Methods: Subjects Women at least 18 years old, between 12 th week of pregnancy and 3 months postpartum, and receiving labetalol for the treatment of chronic or gestational hypertension. Provide written informed consent

11. Methods: Study Design Prospective, open-label, longitudinal design Subjects entered study at any time from 12 th week of pregnancy through 3 months postpartum Routine clinical data, drug administration records and plasma concentrations collected at regularly scheduled perinatal or postnatal visits Treatment of hypertension occurred independent of study

12. Methods: Study Design Sparse sampling strategy –Sampling occurring within 1 of 5 sampling windows constructed from the D-optimal sampling times –Single plasma samples were collected at each clinic visit –3 plasma samples collected at 2 visits within each study period (i.e., 2 nd trimester, 3 rd trimester, postpartum) Drug intake was recorded by electronic monitoring (EDEM®) at UIC or diary at UWM

13. Methods: Laboratory Analysis Labetalol Plasma Concentrations : –HPLC with fluorescence detection –linear: 10 – 1020 ng/ml –LLOQ: 10 ng/ml –inter-assay precision: 1.7% to 7.5% Fraction unbound to plasma proteins –equilibrium dialysis at RT for 24 hours –recovery: 99.3% –binding independent of concentration: 50-400 ng/ml

14. Methods: Pharmacokinetic Analysis Nonlinear mixed effects modeling (NONMEM) with FOCE method Subjects with at least one evaluable labetalol plasma concentration Step 1: Identify structural (base) model –PK compartment model –Expressions for interindividual and residual error –Model selection Diagnostic plots SE for parameter estimates minimum value of objective function (MOF) –difference between competing models: χ 2 distributed

15. Methods: Pharmacokinetic Analysis Step 2: Covariate Analysis –Graphical/GAM screening (S-Plus) –Added alone to structural model –Stepwise forward addition (p<0.05) - backward deletion (p<0.005)

16. Covariates Body Size Measures total body weight lean body weight (Janmahasatian et al 2005) body mass index body surface area Categorical Variables pregnancy status ethnicity study site type of hypertension concurrent medications renal/hepatic disease compliance monitoring Continuous Variables age gestational age creatinine clearance serum albumin α-1 acid glycoprotein AST total bilirubin labetalol dose

17. Methods: Pharmacokinetic Analysis Step 3: Model validation –Bootstrap analysis: 1000 datasets constructed by resampling with replacement Fit to final PK model Bootstrap medians compared to population PK parameter estimates –Visual predictive check 250 datasets for labetalol regimen of 300 mg q12h for 6 doses from population model Median and 80% prediction intervals compared to observed labetalol plasma concentrations (normalized)

18. Demographics Number57 Age (y) 30 (18-41) Gestational Age (wk) 20 (11-39) Total Body Weight (kg) 90 (49-161) BMI (kg/m 2 ) 32 (21-62) Ethnicity African American 34 Caucasian 15 Other 8 Hypertension Chronic 37 Gestational 20

19. Pharmacokinetic Data Study Period 2 nd or 3 rd + PP30 2 nd + 3 rd + PP17 Labetalol Doses50 - 2400 mg/day Labetalol Plasma Concentrations<10 - 1380 ng/ml Number of Concentrations11.4 /subject

20. Structural Model Two-Compartment PK model Inter-individual variability (IIV) – exponential error – full variance-covariance matrix Residual variability – proportional error Vc ka Vp CL D CL

21. One- versus Two-Compartment

22. Covariate Analysis: Oral Clearance (CL/F) Body Size Measures total body weight lean body weight body mass index body surface area Categorical Variables pregnancy status ethnicity study site type of hypertension concurrent medications renal/hepatic disease compliance monitoring Continuous Variables age gestational age creatinine clearance serum albumin α-1 acid glycoprotein AST total bilirubin labetalol dose

23. Covariate Analysis: Apparent Central (Vc/F) and Steady-State (Vss/F) Distribution Volumes Body Size Measures total body weight lean body weight body mass index body surface area Categorical Variables pregnancy status ethnicity study site type of hypertension concurrent medications renal/hepatic disease compliance monitoring Continuous Variables age gestational age creatinine clearance serum albumin α-1 acid glycoprotein AST total bilirubin labetalol dose

24. Covariate Models Oral Clearance (CL/F) CL/F = 188 L/h + (IND × (1+GA/40) × 55 L/h) × LBW/50 Distribution Volumes (Vc/F and Vss/F) V c /F = 197 l × (Preg + 0.9) × TBW/70 V ss /F = 691 l × (Preg + 0.9) × TBW/70 IND=indicator variable

25. Model Predicted Vc/F and Vss/F for 70- kg Total Body Weight (TBW) Woman PostpartumPregnancy Vc/F (l/70 kg TBW) 197374 Vss/F (l/70 kg TBW) 6911313

26. Model Predicted CL/F for a 50-kg Lean Body Weight (LBW) Woman

27. CL/F: Individual Bayesian Estimates

28. CL/F during Pregnancy Standardized by Postpartum CL/F

29. Population PK Parameters: Model vs. Bootstrap Estimates ParameterFinal ModelBootstrap (n=1000) EstimateMedian2.5 th - 97.5 th Percentiles k a (h -1 )1--- CL/F (l/h/50 kg LBW)188190161 – 235 Effect of gestational age545235 – 72 CL D /F (l/h)422420323 – 519 V c /F (l/70 kg TBW)197189110 – 316 V ss /F (l/70 kg TBW)691687617 – 1080 Effect of pregnancy1.901.890.3 – 1.6 Inter-Individual Variability (CV, %) CL/F 38 30 – 45 V c /F 84 8962 – 124 V ss /F 40 4125 – 59 Residual error (%) 36 31 - 42

30. Visual Predictive Check Second trimester Third trimester Postpartum

31. Relationship: CL/F vs Lean Body Weight

32. Relationship: CL/F vs Total Body Weight

33. Implications of Different Relationships between CL/F and LBW or TBW Lean body weight (LBW) provides more clinically useful indicator of differences in dose requirements among women of varying body sizes Excessive labetalol exposure may be reduced by adjusting labetalol doses based on LBW, or without regard to body size, rather than TBW

34. Mechanism for Pregnancy-Induced Change in CL/F Labetalol –high extraction ratio –hepatic metabolism Explanation for higher CL/F –reduced absorption from GI tract –decreased plasma protein binding –increased hepatic intrinsic clearance

35. Effect of Pregnancy on Labetalol Plasma Protein Binding Pregnancy Status Serum Albumin (g/dl) Serum α-1 Acid Glycoprotein (mg/100 ml) Labetalol Free Fraction (%) Lipoproteins (% of non- pregnant value) Globulins (% of non- pregnant value) 2 nd Trimester3.16938%118%*110%* 3rd Trimester2.95842%135%*124%* Post- partum3.612041%--- Fischer et al 2011 * Literature values

36. Mechanism for Pregnancy-Induced Change in CL/F Labetalol –high extraction ratio –hepatic metabolism Explanation for higher CL/F –reduced absorption from GI tract –decreased plasma protein binding –increased hepatic intrinsic clearance Explanation for greater Vc/F and Vss/F –increased hepatic intrinsic clearan ce

37. Pathway for Sex Hormone Regulation of Expression of UGT 1A1 and CYP 3A4 XREM Promoter Region Progesterone PXR RXR CYP 3A4 UGT 1A1 Jeong et al 2008

38. Conclusion Population PK analysis allowed successful characterization of labetalol pharmacokinetics during pregnancy Pregnancy significantly influenced labetalol PK –higher CL/F and greater Vc/F and Vss/F –increased hepatic intrinsic CL likely mechanism Wide individual variability for CL/F precludes providing specific dosage recommendations Lean body weight provides a more useful guide for adjusting labetalol doses in pregnant women than total body weight