RT Journal Article
SR Electronic
T1 Utilizing Drug-Drug Interaction Prediction Tools during Drug Development: Enhanced Decision Making Based on Clinical Risk
JF Drug Metabolism and Disposition
JO Drug Metab Dispos
FD American Society for Pharmacology and Experimental Therapeutics
SP 2076
OP 2084
DO 10.1124/dmd.111.039214
VO 39
IS 11
A1 Carole E. Shardlow
A1 Grant T. Generaux
A1 Christopher C. MacLauchlin
A1 Nicoletta Pons
A1 Konstantine W. Skordos
A1 Jackie C. Bloomer
YR 2011
UL http://dmd.aspetjournals.org/content/39/11/2076.abstract
AB Several reports in the literature present the utility and value of in vitro drug-metabolizing enzyme inhibition data to predict in vivo drug-drug interactions in humans. A retrospective analysis has been conducted for 26 GlaxoSmithKline (GSK) drugs and drug candidates for which in vitro inhibition parameters have been determined, and clinical drug interaction information, from a total of 46 studies, is available. The dataset, for drugs with a diverse range of physiochemical properties, included both reversible and potentially irreversible cytochrome P450 inhibitors for which in vitro inhibition parameters (IC50 or KI/kinact as appropriate) were determined using standardized methodologies. Mechanistic static models that differentiated reversible and metabolism-dependent inhibition, and also considered the contribution of intestinal metabolism for CYP3A4 substrates, were applied to estimate the magnitude of the interactions. Several pharmacokinetic parameters, including total Cmax, unbound Cmax, as well as estimates of hepatic inlet and liver concentration, were used as surrogates for the inhibitor concentration at the enzyme active site. The results suggest that estimated unbound liver concentration or unbound hepatic inlet concentration, with consideration of intestinal contribution, offered the most accurate predictions of drug-drug interactions (occurrence and magnitude) for the drugs in this dataset. When used with epidemiological information on comedication profiles for a given therapeutic area, these analyses offer a quantitative risk assessment strategy to inform the necessity of excluding specific comedications in early clinical studies and the ultimate requirement for clinical drug-drug interaction studies. This strategy has significantly reduced the number of clinical drug interaction studies performed at GSK.