Drug-drug interaction pattern recognition.
OBJECTIVE
Drug-drug interaction (DDI) is an important aspect of drug development, especially for safety. When a drug is used concomitantly with other drug(s), one of the major concerns is the change of exposures, including the rate and extent of drug absorption, distribution, metabolism and elimination. To address the concerns, a common practice is to measure and report the differences between the exposure in the presence and in the absence of concomitant medication (COMED). The area under the plasma concentration versus time curve (AUC), maximum plasma concentration (C(max)) and time to reach the C(max) (t(max)) changes are usually measured in DDI studies. A usual observation is the different extents of changes among AUC, C(max) and t(max), which may raise concerns in certain therapeutic areas or some special agents. The objective of this study was to investigate the variation among changes of AUC, C(max) and t(max) in DDI studies, and its pharmacokinetic manifestation.


METHODS
Based on a list of DDI results from the literature, with the assumptions that the primary parameters of a drug of interest were altered during a DDI, two sets of simulated data were generated according to a single oral dose, one-compartment model. The first set including 24 cases with different half-lives and absorption constants (k(a)) considered the exposure changes upon independent variation of bioavailability (F), clearance (CL), volume of distribution (V(d)) and k(a) up to 50-fold increases or decreases. The second set considered the exposure changes with simultaneous variation of F, CL, V(d), and k(a) within 5-fold range (increase or decrease) for a case selected from the first set.
Parameter fold changes (defined in a fashion showing fold increase or fold decreases, including CL fold change, F fold change, V(d) fold change and k(a) fold change) and exposure changes (AUC fold change, C(max) fold change, t(max) fold change and fold change difference [AUC fold change - C(max) fold change]) were used to generate plots demonstrating various relationships between parameter fold changes and exposure changes. Based on the observations that AUC was influenced by CL and F, C(max) was affected by all four parameters, t(max) was mainly determined by CL and k(a), F did little for t(max) and k(a) was unrelated to AUC, a chart was created for DDI pattern recognition.


CONCLUSIONS
An approach, named DDI pattern recognition, is proposed for didactical purposes. It provides a quick initial estimate for interpreting the DDI results based on the exposure changes. This approach entails the following stages: (i) performing a drug interaction study; (ii) calculating the exposure changes in the presence of COMED compared to those in the absence of COMED, and the fold change difference; (iii) selecting the parameter fold changes that may play important roles in a specific DDI, by estimating their possible ranges; and (iv) interpreting the DDI by integrating all the information available, such as the possible mechanism involved. A quicker and better understanding about the processes, which dominate a DDI, has been achieved using this approach by focusing on integration of all information available and mechanistic interpretation.
Duan
John Z
JZ
Office of New Drug Quality Assessment, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA. john.duan@fda.hhs.gov
D003198:Computer Simulation; D004347:Drug Interactions; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D010599:Pharmacokinetics
D004364:Pharmaceutical Preparations