Clinical relevance of DDIs
There has been some debate on how to determine when a DDI is clinically relevant.15 A conservative position is that only DDIs that result in a serious adverse event as defined by the FDA should be considered clinically relevant. The FDA's definition of a serious adverse event is any event that produces one or more of the following outcomes:
- Death (or is life-threatening).
- Persistent or significant disability/ incapacity.
- Hospitalization or prolongation of existing inpatient hospitalization.
- Congenital anomaly/birth defect.
- Clinically significant overdose.
- Other important medical events that occur in an emergency department or ambulatory surgical cen- ter and require medical or surgical intervention to prevent death or hospitalization.
Certainly, few would argue that a DDI that causes a serious adverse event is not clinically relevant. However, this threshold, while having high specificity, lacks sensitivity (ie, too many false negatives).
A more liberal approach is that a clinically relevant DDI is any DDI that results in an untoward or unfavorable outcome of any severity, including an outcome that is less desirable than could be reasonably expected. Some might argue that this approach, while highly sensitive, lacks specificity (ie, too many false positives).
A third approach is that a clinically relevant DDI is one that results in a change in treatment, whether by the physician, the patient, or a third party such as the FDA.2,3 Such a change could include:
- The discontinuation of one or more of the drugs involved.
- The addition of another drug to the treatment regimen.
- Additional office visit(s).
- Additional diagnostic tests.
- The removal of the drug from the market.
Parenthetically, several drugs were removed from the market over the past decade not because they were dangerous when used alone but because they were dangerous only when used in combination with other specific drugs (eg, terfenadine(Drug information on terfenadine) and ketoconazole(Drug information on ketoconazole)).
A conceptual model
Two equations that provide a framework for understanding the effect of any medication when used alone or in combination with other drugs are presented in Figure 1. Equation 1 identifies the 3 variables that determine the patient's response (ie, the effect) to any medication. They are (1) the affinity for and intrinsic activity of the drug at its site(s) of action, (2) the concentration of the drug achieved at its site(s) of action, and (3) the specific biology of the patient that can make him or her an outlier (ie, either sensitive or resistant) on the usual dose-response curve for a drug. Equation 2 makes the point that the concentration achieved at the site of action is a function of the dose patients take relative to their ability to clear the drug from their body.
In the first equation, the third variable (ie, the patient's biology) is a modifier of the first and/or second variables in the same equation. The equation further lists the 4 major sources of interindividual variability among patients: genetics, age, disease, and environment (internal).
Relative to the third variable in Equation 1, prescribers regularly take age (particularly when dealing with very young and very old patients) and disease (eg, liver or renal failure) into account when prescribing a medication. For example, the clinical adage about starting low and going slow when initiating therapy with a new medication in an elderly patient uses age as a surrogate for the physiological changes associated with advanced age that can make the patient more sensitive to the effects of a drug.
The internal environment under the third variable in Equation 1 is made of what the person consumes and includes the drugs they are taking. Figure 2 illustrates the ways in which drugs can interact either pharmacodynamically or pharmacokinetically.
Recently, there has been considerable interest and enthusiasm about the potential for pharmacogenomics to improve the efficacy, safety, and tolerability of medications by specifically selecting a medication based on the genetic uniqueness of the patient,or "individualized treatment." That enthusiasm is based on the anticipation that improved understanding of genetics will allow prescribers to better take into account genetically determined interindividual variability among patients, which can alter the outcome of drug therapy (ie, the genetic component under the third variable in Equation 1).
While there is little doubt that genetics, age, and disease are important sources of biological variance and can alter the outcome of drug therapy, there is an underappreciation of the importance of the biological variance produced by drug treatment. That source of interindividual biological variance is what underlies the occurrence of clinically relevant DDIs.