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Predictive Modeling in Pharmacokinetics and Pharmacodynamics: Linear vs. Non-linear Systems Study

Explore the comparison between linear and non-linear systems in pharmacokinetic and pharmacodynamic modeling, including prediction of responses and specific considerations. Learn about saturation kinetics, threshold effects, and the impact of induction on drug effects.

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Predictive Modeling in Pharmacokinetics and Pharmacodynamics: Linear vs. Non-linear Systems Study

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  1. Linear vs Non-linear System Linear single kinetic study + linearity can predict response to any input, including getting to SS NON-linear systems: CL, V, etc. not constant; depend on CSS, Dose requires testing at different doses; models time to SS not predicted by single dose study Common non-linearities Saturation kinetics (Michaelis-Menten) Saturable plasma protein, tissue binding Threshold effects (e.g., glucose spilling) Induction: Neuronal/hormonal regulation

  2. “Non-linear Kinetics” Example

  3. Pharmacokinetics Half Life (t1/2) 3

  4. PK/DP Modeling PK/PD Modeling • Procedure: • Estimate exposure and examine correlation between PD other endpoints (including AE rates) • Use mechanistic models • Purpose: • Estimate therapeutic window • Dose selection • Identify mechanism of action • Model probability of AE as function of exposure (and covariates) • Inform the label of the medicine

  5. SPECIFIC CONDIDERATIONS

  6. Specific considerations of PD/PK • Patient populations • Paediatric, gender, elderly • Other medications and foods • Drug-drug • Drug-food • Pharmacogenomics • Genetic polymorphism – Codeine Cyp2D6 ultrarapid • Other diseases, • Co-infections, etc

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