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Assessing DDI Risk in the Digital Age
Posted on August 9th, 2017 by Sherry Winter in Pharmacovigilance
In their 2012 Drug Regulations, the U.S. Food and Drug Administration recommended that “pharmacokinetic interactions between an investigational new drug and other drugs should be defined during drug development.”
This guideline aligned with an assessment need that successful pharmaceutical companies were already well aware of: proper evaluation of the safety and efficacy of a candidate drug meant considering all potential drug–drug interactions (DDIs).
Within an organism, drugs can influence each other. One drug can affect the pharmacokinetics of another drug. For example, by inhibiting a transporter, a drug can change how other drugs are absorbed and distributed, which can lead to unexpected accumulations, even to toxic levels. Or, if a drug induces a metabolizing enzyme needed by another drug, this can cause the second drug to be cleared out of the system, meaning reduced activity—or even no activity. Conversely, inhibition of a metabolizing enzyme can mean accumulation of toxic levels of a drug in the system.
Since most drugs are substrates, inhibitors or inducers of metabolizing enzymes and transporters, complying with the FDA guidelines on drug–drug interaction assessments requires a deep understanding of the dynamics of these proteins. Pharmaceutical developers must determine the exact role of the enzymes and transporters involved with a drug and the impact of changes in their activity on the pharmacokinetic parameters of the drug candidate.
In vitro methods traditionally focus on exposing candidate compounds to cellular extracts expressing the appropriate enzymes. In vivo methods use animal models with high doses of the molecules being administered to determine the adsorption, distribution, metabolism, and excretion (ADME) profile. But performing detailed in vitro and in vivo experiments to determine the interaction profile of a new drug is an expensive and time-consuming business, especially when one considers just how many interactions are possible.
Luckily for the pharmaceutical industry’s coffers, a less costly method of assessing DDI risk has emerged: in silico modeling. The information is available in searchable and comprehensive collections of data on drug pharmacokinetics, metabolizing enzymes and transporters. Tools have also been developed to calculate risks — with just a few keystrokes, it is possible to enter the in vitro data of an investigational drug into a risk calculator to get a full risk profile of predicted metabolism-based drug–drug interactions.
In the case study “Enabling Comprehensive Assessments of Drug-Drug Interaction Risks,” we learn that PharmaPendium is a trusted tool that provides this kind of support for making DDI risk assessments. Because new ways to use data are constantly emerging and improving the way pharmaceuticals are developed, it’s good to know that this critical area of drug development is now also supported by smarter data solutions.
All opinions shared in this post are the author’s own.
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