Pharmacodynamics is the study of the relationship between the concentration of a drug and the response obtained in a patient. Originally, investigators examined the dose–response relationship of drugs in humans but found that the same dose of a drug usually resulted in different concentrations in individuals because of pharmacokinetic differences in clearance and volume of distribution. Examples of quantifiable pharmacodynamic measurements include changes in blood pressure during antihypertensive drug therapy, decreases in heart rate during -blocker treatment, and alterations in prothrombin time or international normalized ratio during warfarin therapy.

For drugs that exhibit a direct and reversible effect, the following diagram describes what occurs at the level of the drug receptor:

According to this scheme, there is a drug receptor located within the target organ or tissue. When a drug molecule "finds" the receptor, it forms a complex that causes the pharmacologic response to occur. The drug and receptor are in dynamic equilibrium with the drug–receptor complex.

The E_max and Sigmoid E_max Models

The mathematical model that comes from the classic drug receptor theory shown previously is known as the E_max model:

where E is the pharmacologic effect elicited by the drug, E_max is the maximum effect the drug can cause, EC₅₀ is the concentration causing one-half the maximum drug effect (E_max/2), and C is the concentration of drug at the receptor site. EC₅₀ can be used as a measure of drug potency (a lower EC₅₀, indicating a more potent drug), whereas E_max reflects the intrinsic efficacy of the drug (a higher E_max, indicating greater efficacy). If pharmacologic effect is plotted against concentration in the E_max equation, a hyperbola results with an asymptote equal to E_max (Fig. 8–14). At a concentration of zero, no measurable effect is present.

View Large Figure 8-14. Add to 'My Saved Images'  The Emax model [E = (Emax x C)/(EC50 + C)] has the shape of a hyperbola with an asymptote equal to Emax. EC50 is the concentration where effect = Emax/2.

When dealing with human studies in which a drug is administered to a patient, and pharmacologic effect is measured, it is very difficult to determine the concentration of the drug at the receptor site. Because of this, serum concentrations (total or unbound) usually are used as the concentration parameter in the E_max equation. Therefore, the values of E_max and EC₅₀ are much different than if the drug were added to an isolated tissue contained in a laboratory beaker.

The result is that a much more empirical approach is used to describe the relationship between concentration and effect in clinical pharmacology studies. After a pharmacodynamic experiment has been conducted, concentration–effect plots are generated. The shape of the concentration–effect curve is used to determine which pharmacodynamic model will be used to describe the data. Because of this, the pharmacodynamic models used in a clinical pharmacology study are deterministic in the same way that the shape of the serum-concentration-versus-time curve determines which pharmacokinetic model is used in clinical pharmacokinetic studies.

Sometimes a hyperbolic function does not describe the concentration–effect relationship at lower concentrations adequately.

When this is the case, the sigmoid E_max equation may be superior to the E_max model:

where n is an exponent that changes the shape of the concentration– effect curve. When n >1, the concentration–effect curve is S- or sigmoid-shaped at lower serum concentrations. When n <1, the concentration–effect curve has a steeper slope at lower concentrations (Fig. 8–15).

View Large Figure 8-15. Add to 'My Saved Images'  The sigmoid Emax model [E = (Emax x Cn)/(ECn50 + Cn)] has an S-shaped curve at lower concentrations. In this example, Emax and EC50 have the same values as in Fig. 8–14.

With both the E_max and sigmoid E_max models, the largest changes in drug effect occur at the lower end of the concentration scale. Small changes in low serum concentrations cause large changes in effect. As serum concentrations become larger, further increases in serum concentration result in smaller changes in effect. Using the E_max model as an example and setting E_max = 100 units and EC₅₀ = 20 mg/L, doubling the serum concentration from 5 to 10 mg/L increases the effect from 20 to 33 units (a 67% increase), whereas doubling the serum concentration from 40 to 80 mg/L only increases the effect from 67 to 80 units (a 19% increase). This is an important concept for clinicians to remember when doses are being titrated in patients.

Linear Models

When serum concentrations obtained during a pharmacodynamic experiment are between 20% and 80% of E_max, the concentration–effect curve may appear to be linear (Fig. 8–16). This occurs often because lower drug concentrations may not be detectable with the analytic technique used to assay serum samples, and higher drug concentrations may be avoided to prevent toxic side effects. The equation used is that of a simple line: E = S x C + I, where E is the drug effect, C is the drug concentration, S is the slope of the line, and I is the y intercept. In this situation, the value of S can be used as a measure of drug potency (the larger the value of S, the more potent the drug). The linear model can be derived from the E_max model. When EC₅₀ is much greater than C, E = (E_max/EC₅₀)C = S x C, where S = E_max/EC₅₀.

View Large Figure 8-16. Add to 'My Saved Images'  The linear model (E = S x C + I ) is often used as a pharmacodynamic model when the measured pharmacologic effect is 20% to 80% of Emax. In this situation, the determination of Emax and EC50 is not possible. To illustrate this, effect measurements from Fig. 8–14 between 20% and 80% of Emax are graphed using the linear pharmacodynamic model.

The linear model allows a nonzero value for effect when the concentration equals zero. This may be a baseline value for the effect that is present without the drug, the result of measurement error when determining effect, or model misspecification. Also, this model does not allow the prediction of a maximum response.

Some investigators have used a log-linear model in pharmacodynamic experiments: E = S x (log C) + I, where the symbols have the same meaning as in the linear model. The advantages of this model are that the concentration scale is compressed on concentration–effect plots for experiments where wide concentration ranges were used, and the concentration values are transformed so that linear regression can be used to compute model parameters. The disadvantages are that the model cannot predict a maximum effect or an effect when the concentration equals zero. With the increased availability of nonlinear regression programs that can compute the parameters of nonlinear functions such as the E_max model easily, use of the log-linear model has been discouraged.⁵³

Baseline Effects

At times, the effect measured during a pharmacodynamic study has a value before the drug is administered to the patient. In these cases, the drug changes the patient's baseline value. Examples of these types of measurements are heart rate and blood pressure. In addition, a given drug may increase or decrease the baseline value. Two basic techniques are used to incorporate baseline values into pharmacodynamic data. One way incorporates the baseline value into the pharmacodynamic model; the other transforms the effect data to take baseline values into account.

Incorporation of the baseline value into the pharmacodynamic model involves the addition of a new term to the previous equations. E₀ is the symbol used to denote the baseline value of the effect that will be measured. The form that these equations takes depends on whether the drug increases or decreases the pharmacodynamic effect. When the drug increases the baseline value, E₀ is added to the equations:

When E₀ is not known with any better certainty than any other effect measurement, it should be estimated as a model parameter similar to the way that one would estimate the values of E_max, EC₅₀, S, or n.^54,55 If the baseline effect is well known and has only a small amount of measurement error, it can be subtracted from the effect determined in the patient during the experiment and not estimated as a model parameter. This approach can lead to better estimates of the remaining model parameters.⁵⁵ Using the linear model as an example, the equation used would be E – E₀ = S x C.

If the drug decreases the baseline value, the drug effect is subtracted from E₀ in the pharmacodynamic models:

where E_max represents the maximum reduction in effect caused by the drug, and IC₅₀ is the concentration that produces a 50% inhibition of E_max. These forms of the equations have been called the inhibitory E_max and inhibitory sigmoidal E_max equations, respectively. In this arrangement of the pharmacodynamic model, E₀ is a model parameter and can be estimated. If the baseline effect is well known and has little measurement error, the effect in the presence of the drug can be subtracted from the baseline effect and not estimated as a model parameter. Using the inhibitory E_max model as an example, the formula would be E₀ – E = (E_max x C)/(IC₅₀ + C).

When using the inhibitory E_max model, a special situation occurs if the baseline effect can be obliterated completely by the drug (e.g., decreased premature ventricular contractions during antiarrhythmic therapy). In this situation, E_max = E₀, and the equation simplifies to a rearrangement known as the fractional E_max equation:

This form of the model relates drug concentration to the fraction of the maximum effect.

An alternative approach to the pharmacodynamic modeling of drugs that alter baseline effects is to transform the effect data so that they represent a percentage increase or decrease from the baseline value.⁵⁵ For drugs that increase the effect, the following transformation equation would be used: percent effect_t = [(treatment_t – baseline)/baseline] x 100. For drugs that decrease the effect, the following formula would be applied to the data: percent inhibition_t = [(baseline – treatment_t)/baseline] x 100. The subscript indicates the treatment, effect, or inhibition that occurred at time t during the experiment. If the study included a placebo control phase, baseline measurements made at the same time as treatment measurements (i.e., heart rate determined 2 hours after placebo and 2 hours after drug treatment) could be used in the appropriate transformation equation.⁵⁵ The appropriate model (excluding E₀) then would be used.

Hysteresis

Concentration–effect curves do not always follow the same pattern when serum concentrations increase as they do when serum concentrations decrease. In this situation, the concentration–effect curves form a loop that is known as hysteresis. With some drugs, the effect is greater when serum concentrations are increasing, whereas with other drugs, the effect is greater while serum concentrations are decreasing (Fig. 8–17). When individual concentration–effect pairs are joined in time sequence, this results in clockwise and counterclockwise hysteresis loops.

View Large Figure 8-17. Add to 'My Saved Images'  Hysteresis occurs when effect measurements are different at the same concentration. This is commonly seen after short-term IV infusions or extravascular doses where concentrations increase and subsequently decrease. Counterclockwise hysteresis loops are found when concentration–effect points are joined as time increases (shown by arrows) and effect is larger at the same concentration but at a later time. Clockwise hysteresis loops are similar, but the concentration–effect points are joined in clockwise order, and the effect is smaller at a later time.

Clockwise hysteresis loops usually are caused by the development of tolerance to the drug. In this situation, the longer the patient is exposed to the drug, the smaller is the pharmacologic effect for a given concentration. Therefore, after an extravascular or short-term infusion dose of the drug, the effect is smaller when serum concentrations are decreasing compared with the time when serum concentrations are increasing during the infusion or absorption phase.

Accumulation of a drug metabolite that acts as an antagonist also can cause clockwise hysteresis.

Counterclockwise hysteresis loops can be caused by the accumulation of an active metabolite, sensitization to the drug, or delay in time in equilibration between serum concentration and concentration of drug at the site of action. Combined pharmacokinetic/pharmacodynamic models have been devised that allow equilibration lag times to be taken into account.