Warfarin

Question 1

Target-specific covalent inhibition

Answer 1

• continuum or reversibility (irreversible model)
• turnover of E, I, and complex (PK/PD)
• relative conc of E and I (TTDD)
• genetic polymorphisms (pharmacogenomics)

Question 2

MD

Answer 2

1.5-2

Question 3

F

Answer 3

> 90

Question 4

Tmax

Answer 4

1-2

Question 5

Vd

Answer 5

0.08-0.12

Question 6

Protein binding

Answer 6

> 99

Question 7

Plasma conc

Answer 7

1.5-8

Question 8

Terminal half life

Answer 8

S: 24-33
R: 35-58

Question 9

Plasma Cl

Answer 9

S: 0.10-1 (more potent than R)
R: 0.07-0.35

Question 10

Target-mediated drug disposition

Answer 10

-extensive binding and binding to the target influenced the PK (disposition) of the drug

Question 11

TMDD PK characteristics

Answer 11

• nonlinear Vd and Cl
• Steep distributive phase for low doses
• long terminal elimination
• dose proportional on MD
• time-lag to css with low dose IV infusion

Question 12

Multiple dosing

Answer 12

• nonlinear PK goes away

- pk becomes more predictable

Question 13

Indirect PD response

Answer 13

• zero order production
• first order removal rate constant
• drug acts to inhibit this particular biomarker
• inhibiting a turnover process
• peak response is delayed relative to the peak conc of the drug
• delay in response makes monitoring warfarin difficult (due to indirect effect)

Question 14

Warfarin

Answer 14

• low hepatic extraction drug
• S-warfarin metabolized by CYP2C9
• R-warfarin metabolizedc by CYP1A1, 1A2, 2C19, 3A4, catechol-O-methyltransferase and ketoreductases
• Targets VKORC1
• slow onset of action and narrow therapeutic window
• lots of DDI (common:
• INR must be measured (target: 2-3)
• dietary interactions (intake of vitamin K)

Question 15

Genetic based dosing

Answer 15

• VKORC1
• CYP2C9
• no difference in genetic based dosing and traditional dosing (INR)

Question 16

Model based dosing

Answer 16

• population PKPD analysis
• estimate typical PKPD model parameters, along with the extent of inter-subject variability
• determine influence of patient characteristics in explaining inter-subject variability on model parameters
• address regulatory concerns
• provide scientific basis for individualized therapy
• Cl is depended on age and CYP2C9
• Bayesian dose individualization
• No method emerged as clearly superior in warfarin maintenance

Question 17

Bayesian Adaptive Feedback components

Answer 17

• Prior population-based PKPD model
• Feedback INR assessments
• Dosing info and patient characteristics
• Computer program for estimating individual patient parameters using the above and a Bayesian algorithm