15.13
Pharmacodynamic Models: Additive and Proportional Drug Effect Model
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inhere…
Typically, a baseline response exists even without drug administration.
Drug response models describe how this baseline combines with the drug’s effect, either in an additive or proportional manner.
In the additive model, the total observed response, R(t), is the sum of the baseline response, R(0), and the total drug effect, E. The total effect, E, can follow a linear or Emax-type relationship.
For stimulatory drugs such as argatroban, the plasma concentration-response curve often shows a sigmoidal increase, especially in Emax models.
Now, consider the baseline response in the proportional drug effect model. In this model, the drug amplifies or diminishes the baseline response.
The total effect may also follow a linear or Emax-type relationship.
In this model, the response depends on the baseline value, with a steeper decrease seen for the largest baseline compared to smaller baselines.
View the full transcript and gain access to JoVE Core videos
Q1: What is the difference between additive and proportional drug response models?
In the additive model, the total observed response combines the baseline response and drug effect independently, with the drug's contribution remaining constant regardless of baseline. In the proportional model, the drug amplifies or diminishes the baseline response, so the absolute change depends on the baseline value. Both models can follow linear or Emax-type relationships.
Q2: How does baseline response affect drug effect in the proportional model?
In the proportional model, a higher baseline response produces a steeper decrease when inhibitory drugs are applied compared to smaller baselines. The drug effect is a multiple of the baseline, meaning larger baseline values result in more significant absolute changes in effect. This aligns with physiological expectations where drug impact scales with existing activity.
Q3: What types of concentration-response relationships exist in additive models?
Additive models accommodate two primary concentration-response relationships: linear relationships where effect increases proportionally with drug concentration, and sigmoidal curves characteristic of Emax models. For stimulatory drugs like argatroban, plasma concentration-response curves often display sigmoidal increases, particularly in Emax-type relationships. These patterns reflect drug-receptor saturation.
Q4: Why is baseline response important in pharmacodynamic modeling?
Baseline response represents inherent physiological activity without drug influence and is essential for accurately predicting total observed effects. Different models combine baseline with drug effects in distinct ways: additively or proportionally. Understanding baseline is critical for optimizing therapeutic regimens and predicting drug efficacy across different patient populations.
Q5: How do stimulatory and inhibitory drug effects differ in the proportional model?
Stimulatory drugs amplify the baseline response, creating larger absolute changes with higher baseline values. Inhibitory drugs reduce the baseline response fractionally, meaning the proportional reduction remains consistent. Both effects depend on the baseline magnitude, distinguishing the proportional model from the additive model where drug effects are independent of baseline.
Q6: What does a sigmoidal curve represent in drug response models?
A sigmoidal curve characterizes Emax-type relationships where drug response increases gradually at low concentrations, then more steeply at intermediate concentrations, before plateauing at maximum effect. This S-shaped pattern is commonly observed in stimulatory drug responses and reflects the saturable nature of drug-receptor interactions.
Q7: How do linear and Emax relationships differ in describing drug effects?
Linear relationships assume drug effect increases proportionally with concentration without saturation, providing a simple but limited model. Emax relationships account for receptor saturation, producing a sigmoidal concentration-response curve that better reflects physiological reality. Both relationship types can be applied to additive and proportional drug response models.