Assessment of Drug–drug Interaction and Optimization in Capecitabine and Irinotecan Combination Regimen using a Physiologically Based Pharmacokinetic Model

Capecitabine and irinotecan (CPT-11) combination regimen (XELIRI) is used for colorectal cancer treatment. Capecitabine is metabolized to 5-fluorouracil (5-FU) by three enzymes, including carboxylesterase (CES). CES can also convert CPT-11 to 7-ethyl-10-hydroxycamptotecin (SN-38). CES is involved in the metabolic activation of both capecitabine and CPT-11, and it is possible that drug–drug interactions occur in XELIRI. Here, a physiologically based pharmacokinetic (PBPK) model was developed to evaluate drug–drug interactions. Capecitabine (180 mg/kg) and CPT-11 (180 mg/m²) were administered to rats, and blood (250 μL) was collected from the jugular vein nine times after administration. Metabolic enzyme activities and K_i values were calculated through in vitro experiments. The plasma concentration of 5-FU in XELIRI was significantly decreased compared to capecitabine monotherapy, and metabolism of capecitabine by CES was inhibited by CPT-11. A PBPK model was developed based on the in vivo and in vitro results. Furthermore, a PBPK model-based simulation was performed with the capecitabin dose ranging from 0 to 1000mol/kg in XELIRI, and it was found that an approximately 1.7-fold dosage of capecitabine was required in XELIRI for comparable 5-FU exposure with capecitabine monotherapy. PBPK model-based simulation will contribute to the optimization of colorectal cancer chemotherapy using XELIRI.     

Pharmacokinetic Drug–Drug Interaction of the Novel Anticancer Agent E7070 and Acenocoumarol

E7070 is a novel sulfonamide anticancer agent that arrests cancer cells at the G1/S boundary of the cell cycle. Three patients receiving chronic therapy with the oral anticoagulant acenocoumarol experienced bleeding and/or a prolonged prothrombin time after treatment with E7070 at a dose of 700mg/m² given as a 1-h infusion. In vitro studies have shown that E7070 has the potential to inhibit several cytochrome P450 (CYP)-enzymes, including CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4. The major enzyme involved in the metabolism of acenocoumarol in man is CYP2C9. This study was performed to investigate the interaction between E7070 and acenocoumarol.Blood samples were obtained from two patients receiving daily oral maintenance treatment with acenocoumarol both prior to and following treatment with E7070. In addition, we incubated acenocoumarol enantiomers with pooled human microsomes with and without E7070 and measured the in vitro plasma protein binding of acenocoumarol after incubation with E7070. Pharmacokinetic parameters of acenocoumarol were calculated by noncompartmental analysis and revealed that in both patients the area under the concentration–time curve up to 24h after the acenocoumarol administration was higher following E7070 (2.56 and 1.58h*mol/L) compared to the systemic exposure in the absence of E7070 (1.87 and 1.23h*mol/l). The formation of acenocoumarol metabolites was retarded by E7070 at already low concentrations (2.1M). The plasma protein binding of acenocoumarol was reduced at higher concentrations of E7070 (259M).These results indicate that E7070 may primarily interact with acenocoumarol by reducing its systemic clearance. Displacement of acenocoumarol's plasma protein binding by E7070 may also occur but to a minor extent. In the absence of careful monitoring this drug–drug interaction may result in hypoprothrombinemia and a hemorrhagic tendency.     

Physiologically Based Pharmacokinetic Modelling of Drug Penetration Across the Blood–Brain Barrier—Towards a Mechanistic IVIVE-Based Approach

Predicting the penetration of drugs across the human blood–brain barrier (BBB) is a significant challenge during their development. A variety of in vitro systems representing the BBB have been described, but the optimal use of these data in terms of extrapolation to human unbound brain concentration profiles remains to be fully exploited. Physiologically based pharmacokinetic (PBPK) modelling of drug disposition in the central nervous system (CNS) currently consists of fitting preclinical in vivo data to compartmental models in order to estimate the permeability and efflux of drugs across the BBB. The increasingly popular approach of using in vitro–in vivo extrapolation (IVIVE) to generate PBPK model input parameters could provide a more mechanistic basis for the interspecies translation of preclinical models of the CNS. However, a major hurdle exists in verifying these predictions with observed data, since human brain concentrations can’t be directly measured. Therefore a combination of IVIVE-based and empirical modelling approaches based on preclinical data are currently required. In this review, we summarise the existing PBPK models of the CNS in the literature, and we evaluate the current opportunities and limitations of potential IVIVE strategies for PBPK modelling of BBB penetration.     

Investigating the Impact of Drug Crystallinity in Amorphous Tacrolimus Capsules on Pharmacokinetics and Bioequivalence Using Discriminatory In Vitro Dissolution Testing and Physiologically Based Pharmacokinetic Modeling and Simulation

Delivering a drug in amorphous form in a formulated product is a strategy used to enhance the apparent solubility of a drug substance and its oral bioavailability. Drug crystallization in such products may occur during the manufacturing process or on storage, reducing the solubility advantage of the amorphous drug. However, the impact of partial drug crystallization in the drug product on the resulting bioavailability and pharmacokinetics is unknown. In this study, dissolution testing of commercial tacrolimus capsules (which are formulated to contain amorphous drug), both fresh and those containing different amounts of crystalline drug, was conducted using both United States Pharmacopeia and noncompendial dissolution tests with different dissolution media and volumes. A physiologically based pharmacokinetic (PBPK) absorption model was developed to predict the impact of crystallinity extent on the oral absorption of the products and to evaluate the discriminatory ability of the different dissolution methods. Virtual bioequivalence simulations between partially crystallized tacrolimus capsules versus fresh Prograf or generic tacrolimus capsules were performed using the PBPK model and in vitro dissolution data of the various fresh and partially crystallized capsules under United States Pharmacopeia and noncompendial dissolution conditions. The results suggest that compendial dissolution tests may not be sufficiently discriminatory with respect to the presence of crystallinity in an amorphous formulation. Nonsink dissolution tests using lower dissolution volumes generate more discriminatory profiles that predict different pharmacokinetics of tacrolimus capsules containing different extents of drug crystallinity. In conclusion, the PBPK modeling approach can be used to assess the impact of partial drug crystallinity in the formulated product and to guide the development of appropriate dissolution methods.     

Investigation of the Drug–Drug Interaction Between α-Lipoic Acid and Valproate via Mitochondrial β-oxidation

Purpose  To investigate the potential drug–drug interaction (DDI) between lipoic acid (LA) and valproate (VA) via the mitochondrial β-oxidation pathway in rats. Methods   In vitro mitochondrial assays were performed to compare the biotransformation of VA to valproyl-CoA (VA-CoA), in the absence and presence of LA. In vitro microsomal and protein binding assays were performed to elucidate their potential DDI at the microsomal metabolism and distribution levels. A pharmacokinetic study was conducted in Lister Hooded rats to ascertain the in vivo DDI between LA and VA. Results  LA was shown to decrease significantly (p < 0.05) the in vitro formation of VA-CoA in a concentration-dependent manner. Our in vitro assay results confirmed that there was minimal interaction between LA and VA in microsomal metabolism and protein binding. Based on the pharmacokinetic data, the absolute bioavailability of VA was determined to be 1.3 in the presence of LA. Conclusions  Our study demonstrated for the first time that there is a potential DDI between LA and VA at the mitochondrial β-oxidation level. While further clinical study is essential, our preliminary finding suggested that medical practitioners need to be prudent when managing epileptic patients who are co-administered with both VA and LA.     

Integrated Population Pharmacokinetic Model of Both Cyclophosphamide and Thiotepa Suggesting a Mutual Drug–Drug Interaction

PURPOSE/AIMS: Cyclophosphamide (CP) and thiotepa (TT) are frequently administered simultaneously in high-dose chemotherapy regimens. The prodrug CP shows strong autoinduction resulting in increased formation of its activated metabolite 4-hydroxycyclophosphamide (4OHCP). TT inhibits this bioactivation of CP. Previously, we successfully modelled CP bioactivation and the effect of TT on the autoinduction. Recently we suggested that CP may also induce the conversion of TT in to its metabolite tepa (T). The aim of the current study was to investigate whether the influence of CP on TT metabolism can be described with a population pharmacokinetic model and whether this interaction can be incorporated in an integrated model describing both CP and TT pharmacokinetics. METHODS: Plasma samples were collected from 49 patients receiving 86 courses of a combination of high-dose CP (4000 or 6000 mg/m2), TT (320 or 480 mg/m2) and carboplatin (1067 or 1600 mg/m2) given in short infusions during four consecutive days. For each patient, approximately 20 plasma samples were available per course. Concentrations of CP, 4OHCP, TT and T were determined using GC and HPLC. Kinetic data were processed using NONMEM. RESULTS: The pharmacokinetics of TT and T were described with a two-compartment model. TT was eliminated through a non-inducible and an inducible pathway, the latter resulting information of T (ClindTT = 12.4 l/hr, ClnonindTT = 17.0 l/hr). Induction of TT metabolism was mediated by a hypothetical amount of enzyme, different from that involved in CP induction, whose amount increased with time in the presence of CP. The amount of enzyme followed a zero-order formation and a decrease with a first-order elimination rate constant of 0.0343 hr(-1) (t1/2 = 20 hr). This model was significantly better than a model lacking the induction by CP. The model was successfully incorporated into the previously published pharmacokinetic model for CP, and resulted in comparable parameter estimates for this compound and its metabolite 4OHCP. CONCLUSION: The pharmacokinetics of TT, when administered in combination with CP, were successfully described. The model confirms induction of TT metabolism with time and it appears likely that CP is responsible for this phenomenon. The existence of a mutual pharmacokinetic interaction between CP and TT, as described in our integrated model, may be relevant in clinical practice.     

Physiologically Based Pharmacokinetic Modeling in Regulatory Science: An Update From the U.S. Food and Drug Administration’s Office of Clinical Pharmacology

This commentary provides an update on the status of physiologically based pharmacokinetic modeling and simulation at the U.S. Food and Drug Administration's Office of Clinical Pharmacology. Limitations and knowledge gaps in integration of physiologically based pharmacokinetic approach to inform regulatory decision making, as well as the importance of scientific engagement with drug developers who intend to use this approach, are highlighted.     

Mechanistic Physiologically Based Pharmacokinetic Modeling of the Dissolution and Food Effect of a Biopharmaceutics Classification System IV Compound—The Venetoclax Story

Venetoclax, a selective B-cell lymphoma-2 inhibitor, is a biopharmaceutics classification system class IV compound. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to mechanistically describe absorption and disposition of an amorphous solid dispersion formulation of venetoclax in humans. A mechanistic PBPK model was developed incorporating measured amorphous solubility, dissolution, metabolism, and plasma protein binding. A middle-out approach was used to define permeability. Model predictions of oral venetoclax pharmacokinetics were verified against clinical studies of fed and fasted healthy volunteers, and clinical drug interaction studies with strong CYP3A inhibitor (ketoconazole) and inducer (rifampicin). Model verification demonstrated accurate prediction of the observed food effect following a low-fat diet. Ratios of predicted versus observed C_max and area under the curve of venetoclax were within 0.8- to 1.25-fold of observed ratios for strong CYP3A inhibitor and inducer interactions, indicating that the venetoclax elimination pathway was correctly specified. The verified venetoclax PBPK model is one of the first examples mechanistically capturing absorption, food effect, and exposure of an amorphous solid dispersion formulated compound. This model allows evaluation of untested drug-drug interactions, especially those primarily occurring in the intestine, and paves the way for future modeling of biopharmaceutics classification system IV compounds.     

An Investigation of Oral Exposure Variability and Formulation Strategy: A Case Study of PI3Kδ Inhibitor and Physiologically Based Pharmacokinetic Modeling in Beagle Dogs

It is well acknowledged that drugs with poor aqueous solubility are often associated with poor oral absorption. Fortunately, drugs with a basic pKa can take advantage of solubilization in the stomach under the acidic environment to improve exposure. Consequently, high in vivo variability is often observed when stomach pH is altered. When issue encountered, enabling formulations are often used to solve the problem. However, each enabling formulation has its limitations and the situation can be further complicated by other absorption distribution metabolism elimination parameters. Therefore, formulation strategies need to consider various scenarios in order to be effective. Compound 1 is a potent phosphoinositide 3-kinase delta inhibitor with poor intrinsic solubility and 2 basic pKas. It was dosed as a suspension in dogs and found to have mediocre oral bioavailability with high variability. It was hypothesized that this variability was caused by their stomach pH variability. Pharmacokinetic modeling suggested that the issue could be improved with particle size reduction. Meanwhile, it was found that although the Madin-Darby canine kidney permeability was reasonable, Madin-Darby canine kidney transfected with human MDR1 gene (MDCK-MDR1) suggested that Compound 1 is an efflux transporter substrate. Findings were integrated into the design for in vivo studies in dogs. Data obtained from those studies allowed us to quickly narrow down the formulation approaches.     

Physiologically Based Pharmacokinetic Models of 2′,3′-Dideoxyinosine

Purpose. The goal of this study was to develop physiologically based pharmacokinetic (PBPK) models for 2,3-dideoxyinosine (ddI) in rats when the drug was administered alone (ddI model) and with pentamidine (ddI + pentamidine model), and to use these models to evaluate the effect of our previously reported pentamidine-ddI interaction on tissue ddI exposure in humans. Methods. The PBPK models consisted of pharmacologically relevant tissues (blood, brain, gut, spleen, pancreas, liver, kidney, lymph nodes, muscle) and used the assumptions of perfusion-rate limited tissue distribution and linear tissue binding of ddI. The required physiologic model parameters were obtained from the literature, whereas the pharmacokinetic parameters and the tissue-to-plasma partition coefficients were calculated using plasma and tissue data. Results. The ddI model in rats yielded model-predicted concentration-time profiles that were in close agreement with the experimentally determined profiles after an intravenous ddI dose (5% deviation in plasma and 20% deviation in tissues). The ddI + pentamidine model incorporated the pentamidine-induced increases of ddI partition in pancreas and muscle. The two PBPK models were scaled-up to humans using human physiologic and pharmacokinetic parameters. A comparison of the model-predicted plasma concentration-time profiles with the observed profiles in AIDS patients who often received ddI with pentamidine showed that the ddI model underestimated the terminal half-life (t_1/2,) by 39% whereas the ddI + pentamidine model yielded identical t_1/2, and area-under-the-curve as the observed values (<1% deviation). Simulations of ddI concentration-time profiles in human tissues using the two models showed that pancreas and lymph nodes received about 2- to 30-fold higher ddI concentration than spleen and brain, and that coadministration of pentamidine increased the AUC of ddl in the pancreas by 20%. Conclusions. Data of the present study indicate that the plasma ddI concentration-time profile in patients were better described by the ddI + pentamidine model than by the ddI model, suggesting that the pentamidine-induced changes in tissue distribution of ddI observed in rats may also occur in humans.     

A Valid Equation for the Well-Stirred Perfusion Limited Physiologically Based Pharmacokinetic Model that Consistently Accounts for the Blood–Tissue Drug Distribution in the Organ and the Corresponding Valid Equation for the Steady State Volume of Distribution

A consistent account of the assumptions of the well-stirred perfusion limited model leads to the equation for the organ tissue that does not coincide with that often presented in books and papers. The difference in pharmacokinetic profiles calculated by the valid and the commonly used equations could be quite significant, particularly due to contribution of the organs with relatively large perfusion volume, and especially for drugs with small tissue–plasma partition coefficient and high blood–plasma concentration ratio. Application of the valid equation may result in much faster initial drop of drug plasma concentration time curve and significantly longer terminal half-life, especially for low extraction ratio drugs. An equation for the steady state volume of distribution consistent with the well-stirred model described by the valid equation is provided. © 2009 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 99:475–485, 2010     

Pharmacokinetic–Pharmacodynamic Modeling of the Effectiveness and Safety of Buprenorphine and Fentanyl in Rats

Objective Respiratory depression is a serious and potentially life-threatening side-effect of opioid therapy. The objective of this investigation was to characterize the relationship between buprenorphine or fentanyl exposure and the effectiveness and safety outcome in rats. Methods Data on the time course of the antinociceptive and respiratory depressant effect were analyzed on the basis of population logistic regression PK–PD models using non-linear mixed effects modeling software (NONMEM). The pharmacokinetics of buprenorphine and fentanyl were described by a three- and two-compartment model, respectively. A logistic regression model (linear logit model) was used to characterize the relationship between drug exposure and the binary effectiveness and safety outcome. Results For buprenorphine, the odds ratios (OR) were 28.5 (95% CI, 6.9–50.1) and 2.10 (95% CI, 0.71–3.49) for the antinociceptive and respiratory depressant effect, respectively. For fentanyl these odds ratios were 3.03 (95% CI, 1.87–4.21) and 2.54 (95% CI, 1.26–3.82), respectively. Conclusion The calculated safety index (OR_{antinociception}/OR_{respiratory depression}) for fentanyl of 1.20 suggests that fentanyl has a low safety margin, implicating that fentanyl needs to be titrated with caution. For buprenorphine the safety index is 13.54 suggesting that buprenorphine is a relatively safe opioid.     

Drug–Drug Interaction Potential of Marketed Oncology Drugs: In Vitro Assessment of Time-Dependent Cytochrome P450 Inhibition, Reactive Metabolite Formation and Drug–Drug Interaction Prediction

Purpose

To evaluate 26 marketed oncology drugs for time-dependent inhibition (TDI) of cytochrome P450 (CYP) enzymes. Evaluate TDI-positive drugs for potential to generate reactive intermediates. Assess clinical drug–drug interaction (DDI) risk using static mechanistic models.

Methods

Human liver microsomes and CYP-specific probes were used to assess TDI in a dilution shift assay followed by generation of K_I and k_inact. Reactive metabolite trapping studies were performed with stable label probes. Static mechanistic model was used to predict DDI risk using a 1.25-fold AUC increase as a cut-off for positive DDI.

Results

Negative TDI across CYPs was observed for 13/26 drugs; the rest were time-dependent inhibitors of, predominantly, CYP3A. The k_inact/K_I ratios for 11 kinase inhibitors ranged from 0.7 to 42.2 ml/min/μmol. Stable label trapping agent–drug conjugates were observed for ten kinase inhibitors. DDI predictions gave no false negatives, one true negative, four false positives and three true positives. The magnitude of DDI was overestimated irrespective of the inhibitor concentration selected.

Conclusions

13/26 oncology drugs investigated showed TDI potential towards CYP3A, formation of reactive metabolites was also observed. An industry standard static mechanistic model gave no false negative predictions but did not capture the modest clinical DDI potential of kinase inhibitors.     

Microdosing Cocktail Assay Development for Drug–Drug Interaction Studies

Methodology for analysis of a microdosing drug cocktail designed to evaluate the contribution of drug transporters and drug metabolizing enzymes to disposition was developed using liquid chromatography–mass spectrometry–based detection. Fast and sensitive methods were developed and qualified for the quantification of statins (pitavastatin, pitavastain lactone, rosuvastatin, atorvastatin, 2-hydroxy, and 4-hydroxy atorvastatin), midazolam, and dabigatran in human plasma. Chromatographic separation was accomplished using reversed-phase liquid chromatography or hydrophilic interaction liquid chromatography with gradient elution and detection by tandem mass spectrometry in the positive ionization mode using electrospray ionization. The lower limit of quantitation (LLOQ) for the statins assay was 1 pg/mL for the 6 analytes with a linear range from 1 to 1000 pg/mL processing 250 μL plasma sample. The midazolam assay LLOQ was 0.5 pg/mL with a linear range of 0.5 to 1000 pg/mL. For the dabigatran assay, the LLOQ was 10 pg/mL with a linear range of 10 to 5000 pg/mL processing 100 μL plasma sample. The intraday and interday precision and accuracy of the assays were within acceptable ranges, and the assays were successfully applied to support a study where a microdose cocktail was dosed to healthy human subjects for simultaneous assessment of clinical drug-drug interactions mediated by major drug transporters and CYP3A.     

An In Silico Transwell Device for the Study of Drug Transport and Drug–Drug Interactions

PURPOSE: Validate and exemplify a discrete, componentized, in silico, transwell device (ISTD) capable of mimicking the in vitro passive transport properties of compounds through cell monolayers. Verify its use for studying drug-drug interactions. METHODS: We used the synthetic modeling method. Specialized software components represented spatial and functional features including cell components, semi-porous tight junctions, and metabolizing enzymes. Mobile components represented drugs. Experiments were conducted and analyzed as done in vitro. RESULTS: Verification experiments provided data analogous to those in the literature. ISTD parameters were tuned to simulate and match in vitro urea transport data; the objects representing tight junction (effective radius of 6.66 A) occupied 0.066% of the surface area. That ISTD was then tuned to simulate pH-dependent, in vitro alfentanil transport properties. The resulting ISTD predicted the passive transport properties of 14 additional compounds, individually and all together in one in silico experiment. The function of a two-site enzymatic component was cross-validated with a kinetic model and then experimentally validated against in vitro benzyloxyresorufin metabolism data. Those components were used to exemplify drug-drug interaction studies. CONCLUSIONS: The ISTD is an example of a new class of simulation models capable of realistically representing complex drug transport and drug-drug interaction phenomena.     

A Bayesian Meta-Analysis on Published Sample Mean and Variance Pharmacokinetic Data with Application to Drug–Drug Interaction Prediction

In drug-drug interaction (DDI) research, a two-drug interaction is usually predicted by individual drug pharmacokinetics (PK). Although subject-specific drug concentration data from clinical PK studies on inhibitor or inducer and substrate PK are not usually published, sample mean plasma drug concentrations and their standard deviations have been routinely reported. Hence there is a great need for meta-analysis and DDI prediction using such summarized PK data. In this study, an innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The three levels model sample means and variances, between-study variances, and prior distributions. Through a ketoconazle-midazolam example and simulations, we demonstrate that our meta-analysis model can not only estimate PK parameters with small bias but also recover their between-study and between-subject variances well. More importantly, the posterior distributions of PK parameters and their variance components allow us to predict DDI at both population-average and study-specific levels. We are also able to predict the DDI between-subject/study variance. These statistical predictions have never been investigated in DDI research. Our simulation studies show that our meta-analysis approach has small bias in PK parameter estimates and DDI predictions. Sensitivity analysis was conducted to investigate the influences of interaction PK parameters, such as the inhibition constant Ki, on the DDI prediction.