The effect of fluconazole on ritonavir and saquinavir pharmacokinetics in HIV-1-infected individuals

To study the effect of fluconazole on the steady-state pharmacokinetics of the protease inhibitors ritonavir and saquinavir in HIV-1-infected patients. Five subjects treated with saquinavir and three with ritonavir received the protease inhibitor alone (saquinavir 1200 mg three times daily, ritonavir 600 mg twice daily) on day 1, and the same protease inhibitor in combination with fluconazole (400 mg on day 2 and 200 mg on days 3 to 8). Pharmacokinetic parameters were determined on days 1 and 8. In the saquinavir group, the median increase in the area under the plasma concentration vs time curve was 50% from 1800 microg l(-1) h to 2700 microg l(-1) h (P = 0.04, median increase: 900 microg l(-1) h; 2.5 and 97.5 percentile: 500-1300), and 56% for the peak concentration in plasma (from 550 to 870 microg l(-1), P = 0.04; median increase: 320 microg l(-1) h, 2.5 and 97.5 percentile: 60-450 microg l(-1)). In the ritonavir group, there were no detectable changes in the pharmacokinetic parameters on addition of fluconazole. Because of the favourable safety profile of saquinavir, dose adjustments are probably not necessary with concomitant use of fluconazole, as is the case for ritonavi

Consensus Recommendations for Systematic Evaluation of Drug–Drug Interaction Evidence for Clinical Decision Support

BACKGROUND: Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations. OBJECTIVE: To provide recommendations for systematic evaluation of evidence from the scientific literature, drug product labeling, and regulatory documents with respect to DDIs for clinical decision support. METHODS: A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 15 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations. RESULTS: We developed expert-consensus answers to three key questions: 1) What is the best approach to evaluate DDI evidence?; 2) What evidence is required for a DDI to be applicable to an entire class of drugs?; and 3) How should a structured evaluation process be vetted and validated? CONCLUSION: Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug information systems that implement these recommendations should be able to provide higher quality information about DDIs in drug compendia and clinical decision support tools