Jack A. Cook

Drug-drug interactions mediated through P-glycoprotein: clinical relevance and in vitro-in vivo correlation using digoxin as a probe drug.

The clinical pharmacokinetics and in vitro inhibition of digoxin were examined to predict the P‐glycoprotein (P‐gp) component of drug–drug interactions. Coadministered drugs (co‐meds) in clinical trials (N = 123) resulted in a small, ≤100% increase in digoxin pharmacokinetics. Digoxin is likely to show the highest perturbation, via inhibition of P‐gp, because of the absence of metabolic clearance. In vitro inhibitory potency data (concentration of inhibitor to inhibit 50% P‐gp activity; IC50) were generated using Caco‐2 cells for 19 P‐gp inhibitors. Maximum steady‐state inhibitor systemic concentration [I], [I]/IC50 ratios, hypothetical gut concentration ([I2], dose/250 ml), and [I2]/IC50 ratios were calculated to simulate systemic and gut‐based interactions and were compared with peak plasma concentration (Cmax),i,ss/Cmax, ss and area under the curve (AUC)i/AUC ratios from the clinical trials. [I]/IC50 < 0.1 shows high false‐negative rates (24% AUC, 41% Cmax); however, to a limited extent, [I2]/IC50 < 10 is predictive of negative digoxin interaction for AUC, and [I]/IC50 > 0.1 is predictive of clinical digoxin interactions (AUC and Cmax).

Pharmacokinetics of Pregabalin in Subjects with Various Degrees of Renal Function

The objectives of this study were to determine the single‐dose pharmacokinetics of pregabalin in subjects with various degrees of renal function, determine the relationship between pregabalin clearance and estimated creatinine clearance (CLcr), and measure the effect of hemodialysis on plasma levels of pregabalin. Results form the basis of recommended pregabalin dosing regimens in patients with decreased renal function. Thirty‐eight subjects were enrolled to ensure a wide range of renal function (CLcr < 30 mL/min, n = 8; 30–50, n = 5; 50–80, n = 7; and > 80, n = 6). Also enrolled were 12 subjects with renal impairment requiring hemodialysis. Each subject received 50 mg of pregabalin as two 25‐mg capsules in this open‐label, parallel‐group study. Pregabalin concentrations were measured using previously validated liquid chromatographic methods. Pregabalin pharmacokinetic parameters were evaluated by established noncompartmental methods. Pregabalin was rapidly absorbed in all subjects. Total and renal pregabalin clearance were proportional (56% and 58%, respectively) to CLcr. As a result, area under the plasma concentration‐time profile (AUC) and terminal elimination half‐life (t1/2) values increased with decreasing renal function. Pregabalin dosage adjustment should be considered for patients with CLcr < 60 mL/min. A 50% reduction in pregabalin daily dose is recommended for patients with CLcr between 30 and 60 mL/min compared to those with CLcr > 60 mL/min. Daily doses should be further reduced by approximately 50% for each additional 50% decrease in CLcr. Pregabalin was highly cleared by hemodialysis. Supplemental pregabalin doses may be required for patients on chronic hemodialysis treatment after each hemodialysis treatment to maintain steady‐state plasma pregabalin concentrations within desired ranges.

Comparison of Different Algorithms for Predicting Clinical Drug-Drug Interactions, Based on the Use of CYP3A4 in Vitro Data: Predictions of Compounds as Precipitants of Interaction

Cytochrome P450 3A4 (CYP3A4) is the most important enzyme in drug metabolism and because it is the most frequent target for pharmacokinetic drug-drug interactions (DDIs) it is highly desirable to be able to predict CYP3A4-based DDIs from in vitro data. In this study, the prediction of clinical DDIs for 30 drugs on the pharmacokinetics of midazolam, a probe substrate for CYP3A4, was done using in vitro inhibition, inactivation, and induction data. Two DDI prediction approaches were used, which account for effects at both the liver and intestine. The first was a model that simultaneously combines reversible inhibition, time-dependent inactivation, and induction data with static estimates of relevant in vivo concentrations of the precipitant drug to provide point estimates of the average magnitude of change in midazolam exposure. This model yielded a success rate of 88% in discerning DDIs with a mean -fold error of 1.74. The second model was a computational physiologically based pharmacokinetic model that uses dynamic estimates of in vivo concentrations of the precipitant drug and accounts for interindividual variability among the population (Simcyp). This model yielded success rates of 88 and 90% (for “steady-state” and “time-based” approaches, respectively) and mean -fold errors of 1.59 and 1.47. From these findings it can be concluded that in vivo DDIs for CYP3A4 can be predicted from in vitro data, even when more than one biochemical phenomenon occurs simultaneously.

Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Numerous retrospective analyses have shown the utility of in vitro systems for predicting potential drug-drug interactions (DDIs). Prediction of DDIs from in vitro data is commonly obtained using estimates of enzyme K(i), inhibitor and substrate concentrations and absorption rate for substrate and inhibitor. WHAT THIS STUDY ADDS Using a generic approach for all test compounds, the findings from the current study showed the use of recombinant P450s provide a more robust in vitro measure of P450 contribution (fraction metabolized, f(m)) than that achieved when using chemical inhibitors in combination with human liver microsomes, for the prediction of potential CYP3A4 drug-drug interactions prior to clinical investigation. The current study supported the use of SIMCYP(R), a modelling and simulation software in utilizing the in vitro measures in the prediction of potential drug-drug interactions. AIMS The aim of this study was to explore and optimize the in vitro and in silico approaches used for predicting clinical DDIs. A data set containing clinical information on the interaction of 20 Pfizer compounds with ketoconazole was used to assess the success of the techniques. METHODS The study calculated the fraction and the rate of metabolism of 20 Pfizer compounds via each cytochrome P450. Two approaches were used to determine fraction metabolized (f(m)); 1) by measuring substrate loss in human liver microsomes (HLM) in the presence and absence of specific chemical inhibitors and 2) by measuring substrate loss in individual cDNA expressed P450s (also referred to as recombinant P450s (rhCYP)) The fractions metabolized via each CYP were used to predict the drug-drug interaction due to CYP3A4 inhibition by ketoconazole using the modelling and simulation software SIMCYP. RESULTS When in vitro data were generated using Gentest supersomes, 85% of predictions were within two-fold of the observed clinical interaction. Using PanVera baculosomes, 70% of predictions were predicted within two-fold. In contrast using chemical inhibitors the accuracy was lower, predicting only 37% of compounds within two-fold of the clinical value. Poorly predicted compounds were found to either be metabolically stable and/or have high microsomal protein binding. The use of equilibrium dialysis to generate accurate protein binding measurements was especially important for highly bound drugs. CONCLUSIONS The current study demonstrated that the use of rhCYPs with SIMCYP provides a robust in vitro system for predicting the likelihood and magnitude of changes in clinical exposure of compounds as a consequence of CYP3A4 inhibition by a concomitantly administered drug.

P-glycoprotein related drug interactions: clinical importance and a consideration of disease states

Importance of the field: P-glycoprotein (P-gp) is the most characterized drug transporter in terms of its clinical relevance for pharmacokinetic disposition and interaction with other medicines. Clinically significant P-gp related drug interactions appear restricted to digoxin. P-gp may act as a major barrier to current and effective drug treatment in a number of diseases including cancer, AIDS, Alzheimers and epilepsy due to its expression in tumors, lymphocytes, cell membranes of brain capillaries and the choroid plexus. Areas covered in this review: This review summarizes the current understanding of P-gp structure/function, clinical importance of P-gp related drug interactions and the modulatory role this transporter may contribute towards drug efficacy in disease states such as cancer, AIDS, Alzheimers and epilepsy. What the reader will gain: The reader will gain an understanding that the clinical relevance of P-gp in drug interactions is limited. In certain disease states, P-gp in barrier tissues can modulate changes in regional distribution. Take home message: P-gp inhibition in isolation will not result in clinically important alterations in systemic exposure; however, P-gp transport may be of significance in barrier tissues (tumors, lymphocytes, brain) resulting in attenuated efficacy.

Application of the Biopharmaceutical Classification System in Clinical Drug Development—An Industrial View

The biopharmaceutical classification system (BCS) classifies compounds based on their solubility and permeability. Regulatory agencies and health organizations have utilized this classification system to allow dissolution to be used to establish bioequivalence for highly soluble and highly permeable compounds. The pharmaceutical industry has taken advantage of this and BCS-based waivers are becoming more routine and result in significant savings. Further, there is strong scientific rationale to allow BCS-based waivers for even more compounds to realize even more savings. Yet just as clear as the benefits are the barriers that limit application: lack of international regulatory harmonization, uncertainty in regulatory approval, and organizational barriers within the pharmaceutical industry. Once these barriers are overcome and additional applications are fully allowed, the full benefits of BCS applications will be realized.

Refining the in vitro and in vivo critical parameters for P-glycoprotein, [I]/IC50 and [I2]/IC50, that allow for the exclusion of drug candidates from clinical digoxin interaction studies.

The objective of this work was to further investigate the reasons for disconcordant clinical digoxin drug interactions (DDIs) particularly for false negative where in vitro data suggests no P-glycoprotein (P-gp) related DDI but a clinically relevant DDI is evident. Applying statistical analyses of binary classification and receiver operating characteristic (ROC), revised cutoff values for ratio of [I]/IC(50) < 0.1 and [I(2)]/IC(50) < 5 were identified to minimize the error rate, a reduction of false negative rate to 9% from 36% (based on individual ratios). The steady state total C(max) at highest dose of the inhibitor is defined as [I] and the ratio of the nominal maximal gastrointestinal concentration determined for highest dose per 250 mL volume defined [I(2)](.) We also investigated the reliability of the clinical data to see if recommendations can be made on values that would allow predictions of 25% change in digoxin exposure. The literature derived clinical digoxin interaction studies were statistically powered to detect relevant changes in exposure associated with digitalis toxicities. Our analysis identified that many co-meds administered with digoxin are cardiovascular (CV) agents. Moreover, our investigations also suggest that the presence of CV agents may alter cardiac output and/or kidney function that may act alone or are additional components to enhance digoxin exposure along with P-gp interaction. While we recommend digoxin as the probe substrate to define P-gp inhibitory potency for clinical assessment, we observed high concordance in P-gp inhibitory potency for calcein AM as a probe substrate.

Summary Workshop Report: Bioequivalence, Biopharmaceutics Classification System, and Beyond

The workshop “Bioequivalence, Biopharmaceutics Classification System, and Beyond” was held May 21–23, 2007 in North Bethesda, MD, USA. This workshop provided an opportunity for pharmaceutical scientists to discuss the FDA guidance on the Biopharmaceutics Classification System (BCS), bioequivalence of oral products, and related FDA initiatives such as the FDA Critical Path Initiative. The objective of this Summary Workshop Report is to document the main points from this workshop. Key highlights of the workshop were (a) the described granting of over a dozen BCS-based biowaivers by the FDA for Class I drugs whose formulations exhibit rapid dissolution, (b) continued scientific support for biowaivers for Class III compounds whose formulations exhibit very rapid dissolution, (c) scientific support for a number of permeability methodologies to assess BCS permeability class, (d) utilization of BCS in pharmaceutical research and development, and (e) scientific progress in in vitro dissolution methods to predict dosage form performance.

Breast Cancer Resistance Protein (ABCG2) in Clinical Pharmacokinetics and Drug Interactions: Practical Recommendations for Clinical Victim and Perpetrator Drug-Drug Interaction Study Design

Breast cancer resistance protein (BCRP; ABCG2) limits intestinal absorption of low-permeability substrate drugs and mediates biliary excretion of drugs and metabolites. Based on clinical evidence of BCRP-mediated drug-drug interactions (DDIs) and the c.421C>A functional polymorphism affecting drug efficacy and safety, both the US Food and Drug Administration and European Medicines Agency recommend preclinical evaluation and, when appropriate, clinical assessment of BCRP-mediated DDIs. Although many BCRP substrates and inhibitors have been identified in vitro, clinical translation has been confounded by overlap with other transporters and metabolic enzymes. Regulatory recommendations for BCRP-mediated clinical DDI studies are challenging, as consensus is lacking on the choice of the most robust and specific human BCRP substrates and inhibitors and optimal study design. This review proposes a path forward based on a comprehensive analysis of available data. Oral sulfasalazine (1000 mg, immediate-release tablet) is the best available clinical substrate for intestinal BCRP, oral rosuvastatin (20 mg) for both intestinal and hepatic BCRP, and intravenous rosuvastatin (4 mg) for hepatic BCRP. Oral curcumin (2000 mg) and lapatinib (250 mg) are the best available clinical BCRP inhibitors. To interrogate the worst-case clinical BCRP DDI scenario, study subjects harboring the BCRP c.421C/C reference genotype are recommended. In addition, if sulfasalazine is selected as the substrate, subjects having the rapid acetylator phenotype are recommended. In the case of rosuvastatin, subjects with the organic anion–transporting polypeptide 1B1 c.521T/T genotype are recommended, together with monitoring of rosuvastatins cholesterol-lowering effect at baseline and DDI phase. A proof-of-concept clinical study is being planned by a collaborative consortium to evaluate the proposed BCRP DDI study design.

The Use of Clinical Trial Simulation to Support Dose Selection: Application to Development of a New Treatment for Chronic Neuropathic Pain

AbstractPurpose. Pregabalin is being evaluated for the treatment of neuropathic pain. Two phase 2 studies were simulated to determine how precisely the dose that caused a one-point reduction in the pain score could be estimated. The likelihood of demonstrating at least a one-point change for each available dose strength was also calculated. Methods. A pharmacokinetic-pharmacodynamic (PK/PD) model relating pain relief to gabapentin plasma concentrations was derived from a phase 3 study. The PK component of the model was modified to reflect pregabalin PK. The PD component was modified by scaling the gabapentin concentration-effect relationship to reflect pregabalin potency, which was based on preclincal data. Uncertainty about the potency difference and the steepness of the concentration-response slope necessitated simulating a distribution of outcomes for a series of PK/PD models. Results. Analysis of the simulated data suggested that after accounting for the uncertainty, there was an 80% chance that the dose defining the clinical feature was within 45% of the true value. The likelihood of estimating a dose that was within an acceptable predefined precision range relative to a known value approximated 60%. The minimum dose that should be studied to have a reasonable chance of estimating the dose that caused a one-point change was 300 mg. Conclusions. Doses that identify predefined response may be imprecisely estimated, suggesting that replication of a similar outcome may be elusive in a confirmatory study. Quantification of this precision provides a rationale for phase 2 trial design and dose selection for confirmatory studies.