Drug-Drug Interaction Pattern Recognition

Abstract

Background and 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. Data Sources: 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. Study Selection, Data Extraction and Synthesis: 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.

Perspectivas da enfermagem sobre o protocolo da sepse materna: análise à luz da teoria da complexidade

“…Categoricamente, diversos estudos mencionam a ocorrência de atrasos na realização do antibiótico e o aumento do risco de vida em casos de sepse (4,6,7). Em um destes estudos desenvolvidos no Oeste da China sobre a sepse bacteriana, evidenciou-se a necessidade do início precoce do antibiótico apropriado, do qual dependerá de cada microrganismo causador (4).…”

“…Atualmente, as ocorrências de mortes maternas por sepse estão aumentando proporcionalmente à taxa de mortalidade observada em outros grupos, sendo responsável por 28 % dos óbitos e 15 % das internações em unidades de terapia intensiva nos eUA (4). É considerada uma das cinco principais causas de morte materna no mundo, com maior frequência em países subdesenvolvidos da Ásia, da África, da América Latina e do Caribe (5).…”

Effect of ritonavir on pharmacokinetics of antimalarial drug combinations in rats

“…As there was no change in half-life, it can be inferred that the observed changes in exposure may not be related to inhibition of postabsorptive metabolism by ritonavir. While the exact reason is unknown, it is possible that the increased C max and exposure of artemether when dosed with ritonavir result from an overall increase in the oral bioavailability/fraction absorbed of artemether (Duan, 2010), which may be due to one or more of several factors including local inhibition of gut CYP enzymes, possible inhibition of ef lux transporters, or a local formulation interaction that in luences fraction absorbed. Though ritonavir has been demonstrated to also inhibit transporters likely P-glycoprotein, this is purely conjectured in the current work as this was not addressed directly.…”

Utility of population pharmacokinetic modeling in the assessment of therapeutic protein-drug interactions

“…The PPK approach can be useful to address these challenges by pooling relevant data from multiple studies assuming sufficient understanding of the drug disposition . The major advantage of this approach is that it allows assessment of TPDI along with PK inter‐ and intra‐subject variability, influential covariates, effect of study conditions (e.g., PK sample schedules, dose levels, patient populations), and the presence of anti‐drug antibodies.…”

“…When both drugs are offset and the perpetrator drug is administered too early or too late relative to the victim, the extent of the interaction may be underpredicted . Dose amounts, frequency and duration are important to establish the proper PK covariate model structure . It is critical for determining whether clinically relevant exposures of the concomitant medication were studied.…”