DOSE RESPONSE CURVES (slides 1-23) - MARCH 11TH

Definitions and Concepts

  • Graded Response: A type of response that can take on a range of values.

    • Example: Measuring blood pressure can yield various values like 120, 110, or 118.

    • Applies to various physiological measurements like heart rate and body temperature.

  • Quantile Response: An all-or-nothing (binary) response.

    • Example: Conditions such as either being pregnant or not, or being alive or dead. There are no degrees of these responses.

Characteristics of Dose-Response Curves

  • Graded response vs. Quantile response: Both respond to values similarly, but they differ fundamentally.

  • Types of Dose-Response Curves:

    • Comparison of linear dose-response curves vs. logarithmic dose-response curves.

    • Plateau: A key characteristic of some response curves representing maximum effect.

Efficacy and Potency

  • Efficacy: A measure of how effectively a drug produces a desired response.

  • Potency: Refers to the amount of drug needed to produce an effect; inversely related to dose. Lower doses indicate higher potency.

  • Efficacy vs. Potency:

    • Sometimes different drugs within the same class will show varied efficacy and potency.

Graph Axes Definitions

  • X-axis: Typically represents the log of the concentration or dose administered.

  • Y-axis: Represents the response of measured outcomes (e.g., heart rate, blood pressure, cell death).

    • Maximum response is often defined in studies based on the data collected.

Sigmoidal Curve Representation

  • When plotting the log of the concentration, the resulting curve is typically sigmoidal.

  • Linear Portion of the Curve: Most linear between 16% to 84% of the maximum response. This area is crucial for accurate determination of important values such as ED50 (Effective Dose 50) and LD50 (Lethal Dose 50).

Key Values for Dose-Response Relationships

  • ED50: The effective dose at which 50% of the maximum response is observed.

  • LD50: The lethal dose at which 50% of subjects would experience death.

  • Therapeutic Index: A calculation that compares ED50 and LD50 helping to gauge drug safety.

Drug Responses and Curves

  • Various drugs can be plotted on a dose-response graph to compare efficacy and potency:

    • Red Curve: Represents Drug A, serves as a baseline for efficacy.

    • Blue Curve: Drug B, shifted to the left indicating higher potency than Drug A with a lower ED50.

    • Green Curve: Drug C is less potent and shows lower efficacy (not reaching max response).

Examples of Drug Comparisons

  • Analgesics Example:

    • Fentanyl: Highly potent with typical dosing in micrograms.

    • Morphine: Less potent than fentanyl, dosed in milligrams.

    • Codeine: Least potent, dosed at higher milligram levels, also less efficacious.

Calculation of ED50 and Therapeutic Considerations

  • Determining potent drugs:

    • Shifts to left indicate increased potency (lower ED50), while shifts right indicate decreased potency.

  • Understanding EC50 and toxic responses using dose-response graphs can guide drug development.

Agonists, Antagonists and Modulators

  • Full Agonist: Offers 100% activity at the target receptor.

  • Partial Agonist: Provides less than 100% activity, can act as competitive antagonists.

  • Neutral Antagonist: Exhibits no activity on its own but blocks other interventions.

  • Inverse Agonist: Acts to reduce activity at a receptor, opposite to that of an agonist.

Selectivity vs. Specificity of Drugs

  • Selectivity: Drug preferentially binds to one receptor type over others with some off-target interactions likely observed.

    • Example: Cardioselective beta-blockers primarily target beta-1 receptors but can interact with beta-2 receptors.

  • Specificity: A drug exclusively targets one receptor or process without off-target effects, often rare in practice.

Summary Remarks

  • Illustrations of dose-response curves play a critical role in pharmacological studies.

  • Understanding the relationship between drug parameters is essential for predicting therapeutic outcomes.

  • The role of dose-response curves and linear vs. log representations influences pharmacological and therapeutic investigations significantly.