It is important to determine if the drug's desired pharmacological effects occur at dose levels that humans can tolerate. Without this information, the estimated MTD cannot be put into context of a therapeutic window. For drugs with reversible pharmacological action that is readily quantifiable, PD becomes an important assessment in FIH studies. Desirable or undesirable pharmacodynamic effects may only be measurable in patients (e.g., anti-hypertensive agents). Often with antagonists, pharmacological activity can only be demonstrated with a provocative challenge. For example in exercise-induced asthma, a patient undergoes an exercise challenge to assess the pharmacological activity of a leukotriene antagonist as the targeted leukotriene pathway responsible for bronchoconstriction is operative only in the disease state (Adelroth et al., 1997).

Pharmacological effects, if these are related to exposure and are predictable from animal data, should be monitored by carefully observing subjects. If no exaggerated pharmacological effects are seen in healthy subjects and patients in early Phases I and II studies, then these exaggerated effects are unlikely to be seen in Phase III. However, it is possible that a drug could have an effect that might become apparent in patients, but was not seen in healthy subjects. The healthy subject's counter-regulatory system may be able to compensate whereas that of the patient may not. For example, counter-regulatory mechanisms induced by hypothermia include shivering, which can induce a fourfold increase in heat production, but at the expense of a 40 to 100% increase in oxygen consumption. Patients with coronary artery disease often have worse outcomes in hypothermia. However, for certain treatment-emergent events counter-regulatory mechanisms may be ineffective even in the healthy subject.

Major sources of variability in a patient's response to a given treatment are derived from PK, PD or the disease state itself provided that the patient is compliant. The drug may have a variable effect on the disease over time. For drugs having greater variability in PK than PD parameters, plasma concentration data may be better able than dose to predict the magnitude and duration of PD effects (FDA, 2003a). On the other hand, if PD variability is greater than PK variability, plasma concentration data may not predict the PD effect well. Sources of PK variability could include demographic factors (age, gender, and race), other diseases (renal or hepatic), diet, concomitant medications, and disease characteristics. Thus, assessing variability and identifying the sources of variability allows for a better understanding of the individual dose-response relationship for PD or efficacy endpoints.

Understanding a drug's pharmacological response is challenging due to the multifaceted nature of this endeavor. As a practical matter, it is easier to demonstrate a dose-response relationship for a PD effect that can be measured as a continuous or categorical variable, if the effect is obtained relatively rapidly after dosing and dissipates rapidly after therapy is stopped (e.g., blood pressure, analgesia, or bronchodilation) (FDA, 2003a). For drugs acting on the central nervous system, measuring the intensity of the pharmacological response is not always possible and several of the frequently used psychomotor performance tests suffer from limitations related to learning and practice effects (Di Bari et al., 2002). For this reason, it may not be possible to apply these tests repeatedly within the same subject.

For drugs used in the treatment of depression, anxiety and pain, rating scales are often used. The responses to rating scales may be subjective and variables such as motivation or fatigue can influence the results (Demyttenaere et al., 2005). The assessment of visual acuity in age-related macular degeneration requires the use of sham or placebo-control to minimize bias as the patient may try harder to see and lean forward during visual acuity assessments if he believes he is benefiting from treatment (Gragoudas et al., 2004). Knowledge of the disease state in relation to the selection of PD endpoints and examples of successful efforts with other drugs for the same indication or having the same mechanism of action provides a greater certainty that these data will be collected and analyzed appropriately and be ultimately usable.

PD endpoints which can be readily measured and exhibit the ideal characteristics (continuity, repeatability or the ability to obtain multiple measurements over time, reproducibility, sensitivity, and objectivity) often have an unclear relationship to the primary efficacy endpoint (Lesko et al., 2000). Sometimes the efficacy endpoint is delayed, persistent, or irreversible (e.g., stroke prevention, arthritis treatments with late onset response, survival in cancer, treatment of depression). Thus, it is not inconceivable that the shape of the dose or exposure or concentration-response relationship for the PD endpoint differs from that of the efficacy dose or concentration-response relationship (Figure 3.4).

Figure 3.4. PD effect vs. clinical efficacy dose- or exposure-response relationships.

Clinical PK/PD data arise from complex and dynamic systems. Data from early studies are limited to single and short-term multiple dosing from a small number of individuals, and these data are unlikely to represent the full breadth of the intended patient population. Nonetheless, these data are invaluable in establishing exposure-response relationships that are further characterized in Phases II and III to provide a basis for dosage adjustment in subpopulations of interest and a rationale for the intended clinical dose (see Chapter 6). Various approaches have been used to model PK-PD or PD versus dose data (e.g., effect compartment, lag-time, PK-PD link, physiological feedback, indirect response models). These models in their most general form can be seen as relating PD effects to dose or exposure (see Chapter 14 for Emax model) to more extensive modeling efforts with successive links from dose to exposure to PD or efficacy endpoints (see Chapter 6).

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