Drug-drug interactions are difficult to evaluate in clinical trial settings The number of potential drug combinations in a real-life population is very large, and it doesn't make sense to include all
potential combinations in clinical trials unless there is a strong pharmacological basis for testing for a small number of
Better Databases Ahead
When a drug is approved, the manufacturer has an integrated safety database of all pre-marketing clinical trial results. But
to take full advantage of these data, manufacturers need to systematically enhance and utilize the database post-approval.
Too often, post-marketing safety data are not routinely integrated. The advent of electronic data capture (EDC), which encourages
standardization of data, has made it easier to merge data from multiple studies, and new results can be added almost in real
time. In addition, it has become easier to query databases for specific events because of the worldwide adoption of the Medical
Dictionary for Regulatory Activities (MedDRA) for coding adverse events. The database could be queried periodically and systematically
after a drug is introduced. Pre-marketing safety concerns, theoretical or actual, could be continually assessed.
An enhanced database of clinical trial data could also support evaluation of safety signals received as spontaneous reports.
Although only a small number of serious adverse reactions are typically reported through PV systems, when such an event occurs,
drug safety personnel need to evaluate quickly whether it represents an excess risk associated with the drug. Could it be
explained by the background rate (i.e., the incidence of events among patients with the same disease)? Is the adverse event
also associated with similar drugs (class effects)? Are there subgroups of patients with risk factors that predict occurrence
of the reaction? All of these factors must be reviewed to recommend a course of action.
Even an improved clinical trials database will have limitations. In the real world, an adverse event isn't necessarily caused
by the intrinsic toxicity of the drug. Many adverse events are caused by inappropriate use, such as taking the wrong dose
or taking a medication in spite of contraindications. These types of inappropriate use would not be assessed in a controlled
clinical trial environment. That can only happen after approval and extensive population drug exposure. And that is where
claims databases have their place.
Eventually, if electronic medical records become universal, and if they are implemented correctly, they may become the tool
of choice for assessing safety. In the meantime, thanks to the evolution of the healthcare financing system and advances in
information technology, healthcare claims databases have become a vast repository of information about a patient's health
and use of the healthcare system. The Health Insurance Portability and Accountability Act (HIPAA) allows the use of de-identified
data for public health research
Scientists trained in rigorous study design, data collection, and data analysis process may be skeptical about data they have
not collected first-hand. But routinely collected data, such as vital statistics, have played an important role in public-health
research for years. These secondary data are often as valuable as primary data in drug safety research.
FDA started to recognize the utility of large linked databases in the mid 1980s, when the agency initiated a number of cooperative
agreements to allow its officers to work with large data sources to carry out drug safety assessment. Since then, those data
sources have been used in many drug safety studies. On the industry side, pharmaceutical companies have sponsored many drug
safety studies using claims datasets.
In fall 2005, FDA awarded grants to i3 Drug Safety, Harvard Pilgrim Healthcare and HMO Research Network, Kaiser California,
and Vanderbilt University. The agency's goal was to (1) conduct drug safety analyses to the benefit of the public's health;
(2) respond expeditiously to urgent public safety concerns; (3) provide a mechanism for collaborative pharmacoepidemiological
research designed to test hypotheses, particularly those arising from suspected adverse reactions reported to FDA, and enable
rapid access to US population-based data sources.
Public policy must be based on solid science, but large-scale clinical trials are not the sole answer to important drug-safety
questions. Scientists are trained to design perfect studies, but public health scientists also understand the need for timely
data to guide regulatory decisions. The key is to strike a balance between the timeliness of data collection, the quality
of the data, and the validity of inference.