Maximum Unbound Concentration Research Articles

Prediction of renal transporter mediated drug-drug interactions for pemetrexed using physiologically based pharmacokinetic modeling.

Pemetrexed, an anionic anticancer drug with a narrow therapeutic index, is eliminated mainly by active renal tubular secretion. The in vitro to in vivo extrapolation approach used in this work was developed to predict possible drug-drug interactions (DDIs) that may occur after coadministration of pemetrexed and nonsteroidal anti-inflammatory drugs (NSAIDs), and it included in vitro assays, risk assessment models, and physiologically based pharmacokinetic (PBPK) models. The pemetrexed transport and its inhibition parameters by several NSAIDs were quantified using HEK-PEAK cells expressing organic anion transporter (OAT) 3 or OAT4. The NSAIDs were ranked according to their DDI index, calculated as the ratio of their maximum unbound concentration in plasma over the concentration inhibiting 50% (IC50) of active pemetrexed transport. A PBPK model for ibuprofen, the NSAID with the highest DDI index, was built incorporating active renal secretion in Simcyp Simulator. The bottom-up model for pemetrexed underpredicted the clearance by 2-fold. The model we built using a scaling factor of 5.3 for the maximal uptake rate (Vmax) of OAT3, which estimated using plasma concentration profiles from patients given a 10-minute infusion of 500 mg/m(2) of pemetrexed supplemented with folic acid and vitamin B12, recovered the clinical data adequately. The observed/predicted increases in Cmax and the area under the plasma-concentration time curve (AUC0-inf) of pemetrexed when ibuprofen was coadministered were 1.1 and 1.0, respectively. The coadministration of all other NSAIDs was predicted to have no significant impact on the AUC0-inf based on their DDI indexes. The PBPK model reasonably reproduced pemetrexed concentration time profiles in cancer patients and its interaction with ibuprofen.

Transporter-Mediated Drug–Drug Interactions Involving OATP Substrates: Predictions Based on In Vitro Inhibition Studies

Transporter-mediated drug–drug interactions (DDIs) are among the most important of the clinically relevant pharmacokinetic DDIs. We investigated the validity of a static prediction of area under the plasma concentration-time curve (AUC) ratios (AUCRs; AUC(with inhibitor)/AUC(control) using in vitro inhibition profiles, and selected the types of assumptions that improved the prediction accuracy with minimizing false-negative predictions. We used data from 58 DDI studies involving 12 substrates of hepatic organic anion–transporting polypeptides (OATPs). With original assumptions regarding the maximal increase in intestinal availability, maximum unbound concentration at the inlet to the liver, and inhibition of only the hepatic uptake process, the predicted AUCRs were comparable to those reported within a two/threefold error margin in 44/52 studies, whereas in 16 studies, the predictions were judged to be falsenegatives. When the inhibitory effects on both hepatic uptake and efflux/metabolisms were considered, the overall prediction accuracy became worse, although the false-negative prediction decreased to 11 studies. This illustrates that if appropriate assumptions are selected, unnecessary clinical DDI studies can be reasonably avoided.

Evaluation of Methods for Predicting Drug-drug Interactions by Monte Carlo Simulation

The ratio of the inhibitor concentration to the inhibition constant (K(i)) is used as the index for predicting drug-drug interactions involving metabolic inhibition. The maximum unbound concentration in the circulation (I(p, max, u)) and the maximum unbound concentration at the inlet to the liver (I(u, max)) have been used for the inhibitor concentration. In the present study, the methods for predicting drug-drug interactions using these concentrations were evaluated by Monte Carlo simulation. Information on the pharmacokinetic parameters of drugs and the K(i) values for cytochrome P450(CYP) were obtained from the literature. It was assumed that the pharmacokinetic parameters (intrinsic metabolic clearance, renal clearance and distribution volume for unbound fraction), serum protein binding and K(i) value for substrate and inhibitor are all log-normally distributed. Correlations among the parameters were assessed and were used for further simulations. A change in AUC of the substrate following co-administration of the inhibitor was simulated 1000 times using the physiologically based pharmacokinetic (PBPK) model. The percent of the drug combinations which exhibited a significant increase in the AUC (>125%) was 16.2% of the total combinations. The cases where the I/K(i) using I(u, max) and I(p, max, u) overestimated compared with the actual increased ratio of AUC (false positive prediction) were 41.2% and 16.7%, respectively. The cases where the predicted ratios of AUC from I/K(i) using I(u, max) and I(p, max, u) were comparable with the actual ratio were 3.2% and 8.7%, respectively. The prediction using I(p, max, u) was, thus, more reliable than that using I(u, max). However, in the case of I(u, max), there was no case where the actual increased ratio of AUC was greater than that predicted from I/K(i) (false negative prediction). On the other hand, for I(p, max, u), the rate of false negative prediction was 1.4%. The present study indicates that I(u, max) is better than I(p, max, u) for avoiding false negative predictions and I(p, max, u) is better than I(u, max) for increasing the probability of true positive and true negative predictions and avoiding false positive predictions.In conclusion, it is necessary to use both predictions involving I(u, max) and I(p, max, u) and to use them early on during the development stage of drug candidates. In order to finally choose which compound(s) to take forward to clinical trials, when predicting an interaction, the more quantitative and reliable method based on the PBPK model needs to be used.

Which concentration of the inhibitor should be used to predict in vivo drug interactions from in vitro data?

When the metabolism of a drug is competitively or noncompetitively inhibited by another drug, the degree of in vivo interaction can be evaluated from the [I]u/Ki ratio, where [I]u is the unbound concentration around the enzyme and Ki is the inhibition constant of the inhibitor. In the present study, we evaluated the metabolic inhibition potential of drugs known to be inhibitors or substrates of cytochrome P450 by estimating their [I]u/Ki ratio using literature data. The maximum concentration of the inhibitor in the circulating blood ([I]max), its maximum unbound concentration in the circulating blood ([I]max,u), and its maximum unbound concentration at the inlet to the liver ([I]in,max,u) were used as [I]u, and the results were compared with each other. In order to calculate the [I]u/Ki ratios, the pharmacokinetic parameters of each drug were obtained from the literature, together with their reported Ki values determined in in vitro studies using human liver microsomes. For most of the drugs with a calculated [I]in,max,u/Ki ratio less than 0.25, which applied to about half of the drugs investigated, no in vivo interactions had been reported or "no interaction" was reported in clinical studies. In contrast, the [I]max,u/Ki and [I]max/Ki ratio was calculated to be less than 0.25 for about 90% and 65% of the drugs, respectively, and more than a 1.25-fold increase was reported in the area under the concentration-time curve of the co-administered drug for about 30% of such drugs. These findings indicate that the possibility of underestimation of in vivo interactions (possibility of false-negative prediction) is greater when [I]max,u or [I]max values are used compared with using [I]in,max,u values.

Quantitative prediction of in vivo drug-drug interactions from in vitro data based on physiological pharmacokinetics: use of maximum unbound concentration of inhibitor at the inlet to the liver.

To assess the degree to which the maximum unbound concentration of inhibitor at the inlet to the liver (I(inlet,u,max), used in the prediction of drug-drug interactions, overestimates the unbound concentration in the liver. The estimated value of I(inlet,u,max) was compared with the unbound concentrations in systemic blood, liver, and inlet to the liver, obtained in a simulation study based on a physiological flow model. As an example, a tolbutamide/sulfaphenazole interaction was predicted taking the plasma concentration profile of the inhibitor into consideration. The value of I(inlet,u,max) differed from the concentration in each compartment, depending on the intrinsic metabolic clearance in the liver, first-order absorption rate constant, non-hepatic clearance and liver-to-blood concentration ratio (Kp) of the inhibitor. The AUC of tolbutamide was predicted to increase 4-fold when co-administered with sulfaphenazole, which agreed well with in vivo observations and was comparable with the predictions based on a fixed value of I(inlet,u,max). The blood concentration of tolbutamide was predicted to increase when it was co-administered with as little as 1/100 of the clinical dose of sulfaphenazole. Although I(inlet,u,max) overestimated the unbound concentration in the liver, the tolbutamide/sulfaphenazole interaction could be successfully predicted by using a fixed value of I(inlet,u,max) indicating that the unbound concentration of sulfaphenazole in the liver after its clinical dose is by far larger than the concentration to inhibit CYP2C9-mediated metabolism and that care should be taken when it is co-administered with drugs that are substrates of CYP2C9.