A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs.

In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.

Unraveling the mechanism and the risk behind seizure liability of lead compounds in a neuroscience project.

INTRODUCTION: Several compounds from a neuroscience project induced convulsions in animals, at low exposure levels via a hypothetical off-target mechanism. A set of in vitro and in vivo experiments were conducted in order to 1) identify the mechanism behind convulsions; 2) characterize the convulsions, 3) detect premonitory signs that could be monitored clinically, and 4) assess the development of tolerance after repeat dosing. METHODS: Patch clamp assays were conducted on 12 different ion channels (e.g. sodium, potassium, calcium, AMPA, NMDA, GABAA and purinergic receptors) known to be associated with seizures, to identify the off-target culprit. A multiphase study was conducted with UCB-A and UCB-B in Beagle dogs telemetered for video EEG/EMG monitoring to further characterize the convulsive pattern. First, both compounds were administered by intravenous constant infusion (dose: 5 mg/kg/h) over 2 h. Thereafter, the same dogs received a daily oral administration of UCB-A (8 mg/kg/day) for 7 days. RESULTS: Compounds inducing convulsions showed strong inhibitory activity on GABAA channels (IC50 values <10 µM), whereas compounds with partial or no inhibitory effect on these channels did not induce seizures. In EEG experiments, convulsions were preceded by premonitory clinical signs (e.g. tremors, myoclonic jerks) and morphological EEG abnormalities (e.g. sharp waves, spike and wave patterns), confirming their CNS origin. No attenuation of the seizurogenic effects was observed over the 7-day treatment period. DISCUSSION: A well-designed set of experiments including electrophysiological assays on seizure-related ion channels and EEG/EMG assessment in telemetered dogs allowed a proper seizure liability risk assessment, leading to a rapid no go decision for the two most advanced leads.

Translational approach from preclinical to clinical: comparison of dose finding methods of a new Bcl2 inhibitor using PK-PD modeling and interspecies extrapolation.

The attrition rate of anticancer drugs during the clinical development remains very high. Interspecies extrapolation of anticancer drug pharmacodynamics (PD) could help to bridge the gap between preclinical and clinical settings and to improve drug development. Indeed, when combined with a physiologically-based-pharmacokinetics (PBPK) approach, PD interspecies extrapolation could be a powerful tool for predicting drug behavior in clinical trials. The present study aimed to explore this field for anticipating the clinical efficacy of a new Bcl-2 inhibitor, S 55746, for which dose ranging studies in xenografted mice and clinical data from a phase 1 trial involving cancer patients were available. Different strategies based on empirical or more mechanistic assumptions (based on PBPK-PD modelling) were developped and compared: the Rocchetti approach (ROC); the Orthogonal Rocchetti approach (oROC), a variant of ROC based on an orthogonal regression; the Consistent across species approach, bringing out an efficacy parameter assumed to be consistent across species; and the Scaling species-specific parameters approach, assuming the concentration-efficacy link is the same in mice as in humans, after allometric scaling. Empirical approaches (ROC and oROC) gave similar predictive performances and seemed to overestimate the active S 55746 dose compared to mechanistic approaches, while strategies elaborated from semi-mechanistic concepts and PBPK-PD modelling did not seem to be invalidated by clinical efficacy data. Also, empirical methods only predict a single dose level for the subsequent clinical studies, whereas mechanism-based strategies are more informative about the dose response relationship, highlighting the potential interest of such approaches in drug development.

Prediction of Human Nonlinear Pharmacokinetics of a New Bcl-2 Inhibitor Using PBPK Modeling and Interspecies Extrapolation Strategy.

S 55746 ((S)-N-(4-hydroxyphenyl)-3-(6-(3-(morpholinomethyl)-1,2,3,4-tetrahydroisoquinoline-2-carbonyl)benzo[d][1,3]dioxol-5-yl)-N-phenyl-5,6,7,8-tetrahydroindolizine-1-carboxamide) is a new selective Bcl-2 (B-cell lymphoma 2) inhibitor developed by Servier Laboratories and used to restore apoptosis functions in cancer patients. The aim of this work was to develop a translational approach using physiologically based (PB) pharmacokinetic (PK) modeling for interspecies extrapolation to anticipate the nonlinear PK behavior of this new compound in patients. A PBPK mouse model was first built using a hybrid approach, defining scaling factors (determined from in vitro data) to correct in vitro clearance parameters and predicted Kp (partition coefficient) values. The qualification of the hybrid model using these empirically determined scaling factors was satisfactorily completed with rat and dog data, allowing extrapolation of the PBPK model to humans. Human PBPK simulations were then compared with clinical trial data from a phase 1 trial in which the drug was given orally and daily to cancer patients. Human PBPK predictions were within the 95% prediction interval for the eight dose levels, taking into account both the nonlinear dose and time dependencies occurring in S 55746 kinetics. Thus, the proposed PK interspecies extrapolation strategy, based on preclinical and in vitro information and physiologic assumptions, could be a useful tool for predicting human plasma concentrations at the early stage of drug development.

Tumor Growth Inhibition Modelling Based on Receptor Occupancy and Biomarker Activity of a New Bcl-2 Inhibitor in Mice.

The Bcl-2 inhibitor S 55746 ((S)-N-(4-hydroxyphenyl)-3-(6-(3-(morpholinomethyl)-1,2,3,4-tetrahydroisoquinoline-2-carbonyl)benzo[d][1,3]dioxol-5-yl)-N-phenyl-5,6,7,8-tetrahydroindolizine-1-carboxamide) is able to restore apoptosis functions impaired by tumorigenesis in mice. Data from pharmacokinetic (PK), biomarker, and tumor growth studies in a xenograft mouse model were considered for population modeling. The aim of the modeling exercise was to link the kinetics of the drug to the biomarker and tumor-size time profiles to better understand its dose-effect relationship. The PK, caspase kinetics, and tumor dynamics were successfully characterized by the proposed pharmacokinetic-pharmacodynamic model. The nonlinear plasma PK was best described by a two-compartment disposition model with both saturable absorption and elimination. Caspase was activated above the effective drug-concentration threshold (CTHRE ), at which near-maximal activity was reached. Increasing the dose did not increase the activation but better sustained it. Tumor growth followed a biphasic pattern, with caspase having an all-or-none inhibiting effect, consistent with the bistability property of the caspase pathway. For tumor eradication, the CTHRE in plasma was 2876 ng ml-1, and the relative caspase activity threshold (CaspTHRE) was 46.5. There was a strong relationship between the time spent above these thresholds and tumor growth inhibition. Tumor growth was inhibited by 50% when CaspTHRE was exceeded 13.8% of the time and when CTHRE was exceeded 8.1% of the time per dosing. This semimechanistic approach, based on experimental mice data and in vitro parameters, provides an interesting tool to quantify or simulate antitumor effects and, eventually, to plan phase 1 studies.

Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective.

This work provides a perspective on the qualification and verification of physiologically based pharmacokinetic (PBPK) platforms/models intended for regulatory submission based on the collective experience of the Simcyp Consortium members. Examples of regulatory submission of PBPK analyses across various intended applications are presented and discussed. European Medicines Agency (EMA) and US Food and Drug Administration (FDA) recent draft guidelines regarding PBPK analyses and reporting are encouraging, and to advance the use and acceptability of PBPK analyses, more clarity and flexibility are warranted.

Oral drug absorption in pediatrics: the intestinal wall, its developmental changes and current tools for predictions.

The dissolution, intestinal absorption and presystemic metabolism of a drug depend on its physicochemical characteristics but also on numerous physiological (e.g. gastrointestinal pH, volume, transit time, morphology) and biochemical factors (e.g. luminal enzymes and flora, intestinal wall enzymes and transporters). Over the past decade, evidence has accumulated indicating that these factors may differ in children and adults resulting in age-related changes in drug exposure and drug response. Thus, drug dosage may require adjustment for the pediatric population to ensure the desired therapeutic outcome and to avoid side-effects. Although tremendous progress has been made in understanding the effects of age on intestinal physiology and function, significant knowledge gaps remain. Studying and predicting pharmacokinetics in pediatric patients remains challenging due to ethical concerns associated with clinical trials in this vulnerable population, and because of the paucity of predictive in vitro and in vivo animal assays. This review details the current knowledge related to developmental changes determining intestinal drug absorption and pre-systemic metabolism. Supporting experimental approaches as well as physiologically based pharmacokinetic modeling are also discussed together with their limitations and challenges. Copyright © 2016 John Wiley & Sons, Ltd.

Model-based approaches for ivabradine development in paediatric population, part I: study preparation assessment.

The main objective was to help design a paediatric study for ivabradine, a compound already marketed in adults, focusing on: the paediatric formulation evaluation, the doses to be administered, the sampling design and the sampling technique. A secondary objective was to perform a comparison of the prediction of ivabradine pharmacokinetics (PK) in children using a physiologically-based pharmacokinetic (PBPK) approach and allometric scaling of a population pharmacokinetic (PPK) model. A study was conducted in order to assess the relative bioavailability (Frel) of the paediatric formulation and a similar Frel was observed between the paediatric formulation and the adult marketed tablet. PBPK modelling was used to predict initial doses to be administered in the paediatric study and to select the most appropriate sample time collections. The dried blood spot technique was recommended in the clinical trial in children. Simulations obtained by both the PBPK approach and allometric scaling of a PPK model were compared a posteriori to the paediatric study observations. Both PPK and PBPK approaches allowed an adequate prediction of the PK of ivabradine and its metabolite in children.

Comparing translational population-PBPK modelling of brain microdialysis with bottom-up prediction of brain-to-plasma distribution in rat and human.

The prediction of brain extracellular fluid (ECF) concentrations in human is a potentially valuable asset during drug development as it can provide the pharmacokinetic input for pharmacokinetic-pharmacodynamic models. This study aimed to compare two translational modelling approaches that can be applied at the preclinical stage of development in order to simulate human brain ECF concentrations. A population-PBPK model of the central nervous system was developed based on brain microdialysis data, and the model parameters were translated to their corresponding human values to simulate ECF and brain tissue concentration profiles. In parallel, the PBPK modelling software Simcyp was used to simulate human brain tissue concentrations, via the bottom-up prediction of brain tissue distribution using two different sets of mechanistic tissue composition-based equations. The population-PBPK and bottom-up approaches gave similar predictions of total brain concentrations in both rat and human, while only the population-PBPK model was capable of accurately simulating the rat ECF concentrations. The choice of PBPK model must therefore depend on the purpose of the modelling exercise, the in vitro and in vivo data available and knowledge of the mechanisms governing the membrane permeability and distribution of the drug.

A physiologically based modeling strategy during preclinical CNS drug development.

Physiologically based pharmacokinetic (PBPK) modeling of the central nervous system (CNS) provides the opportunity to predict the relevant drug concentrations at the therapeutic target site during preclinical and clinical development. In order to successfully interpret model results, and to provide confidence in the subsequent human predictions, it is essential that an appropriate model structure is chosen at the preclinical stage which takes into account both physiological and drug-specific knowledge. However, the models published to date in the literature show significant variation in the approaches applied by different authors, which can lead to difficulties in the interpretation of model parameter estimates. We aimed to develop a coherent PBPK modeling approach in the rat, which would also be adaptable depending on the quantity and quality of in vivo data obtained during drug development. Based on a sensitivity analysis of the model parameters, and using three CNS drugs as case studies (atomoxetine, acetaminophen, and S 18986), we proposed a decision tree to aid in the appropriate parametrization and structure of the model according to the data available. We compared our parameter estimates to those originally published, and considered the impact of the respective approaches on the mechanistic interpretation of the parameter values. Since the measurement of brain extracellular fluid (ECF) concentrations using microdialysis is not routinely performed in the industrial environment, we also evaluated the bottom-up scaling of in vitro permeability data from the Caco-2 cell line to predict BBB passive permeability in the absence of measured ECF concentrations. Our strategy demonstrates the value of PBPK as a prediction tool throughout the development process of CNS-targeting drugs.

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.

S49076 is a novel kinase inhibitor of MET, AXL, and FGFR with strong preclinical activity alone and in association with bevacizumab.

Aberrant activity of the receptor tyrosine kinases MET, AXL, and FGFR1/2/3 has been associated with tumor progression in a wide variety of human malignancies, notably in instances of primary or acquired resistance to existing or emerging anticancer therapies. This study describes the preclinical characterization of S49076, a novel, potent inhibitor of MET, AXL/MER, and FGFR1/2/3. S49076 potently blocked cellular phosphorylation of MET, AXL, and FGFRs and inhibited downstream signaling in vitro and in vivo. In cell models, S49076 inhibited the proliferation of MET- and FGFR2-dependent gastric cancer cells, blocked MET-driven migration of lung carcinoma cells, and inhibited colony formation of hepatocarcinoma cells expressing FGFR1/2 and AXL. In tumor xenograft models, a good pharmacokinetic/pharmacodynamic relationship for MET and FGFR2 inhibition following oral administration of S49076 was established and correlated well with impact on tumor growth. MET, AXL, and the FGFRs have all been implicated in resistance to VEGF/VEGFR inhibitors such as bevacizumab. Accordingly, combination of S49076 with bevacizumab in colon carcinoma xenograft models led to near total inhibition of tumor growth. Moreover, S49076 alone caused tumor growth arrest in bevacizumab-resistant tumors. On the basis of these preclinical studies showing a favorable and novel pharmacologic profile of S49076, a phase I study is currently underway in patients with advanced solid tumors. Mol Cancer Ther; 12(9); 1749-62. ©2013 AACR.

Development of a physiologically based pharmacokinetic model for the rat central nervous system and determination of an in vitro-in vivo scaling methodology for the blood-brain barrier permeability of two transporter substrates, morphine and oxycodone.

A whole-body physiologically based pharmacokinetic (PBPK) model was developed for the prediction of unbound drug concentration-time profiles in the rat brain, in which drug transfer across the blood-brain barrier (BBB) was treated mechanistically by separating the parameters governing the rate (permeability) of BBB transfer from brain binding. An in vitro-in vivo scaling strategy based on Caco-2 cell permeability was proposed to extrapolate the active transporter-driven component of this permeability, in which a relative activity factor, RAF, was estimated by fitting the model to rat in vivo profiles. This scaling factor could be interpreted as the ratio of transporter activity between the in vitro system and the in vivo BBB, for a given drug in a given in vitro system. Morphine and oxycodone were selected to evaluate this strategy, as substrates of BBB-located efflux and influx transporters, respectively. After estimation of their respective RAFs using the rat model, the PBPK model was used to simulate human brain concentration profiles assuming the same RAF, and the implications of this were discussed.

Quantitative mass spectrometry imaging of propranolol and olanzapine using tissue extinction calculation as normalization factor.

In order to quantify small molecules at the early stage of drug discovery, we developed a quantitation approach based on mass spectrometry imaging (MSI) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) without the use of a labeled compound. We describe a method intended to respond to the main challenges encountered in quantification through MALDI imaging dedicated to whole-body or single heterogeneous organ samples (brain, eye, liver). These include the high dependence of the detected signal on the matrix deposition, the MALDI ionization yield of specific target molecules, and lastly, the ion suppression effect on the tissue. To address these challenges, we based our approach on the use of a normalization factor called the TEC (Tissue Extinction Coefficient). This factor takes into account the ion suppression effect that is both tissue- and drug-specific. Through this protocol, the amount of drug per gram of tissue was determined, which in turn, was compared with other analytical techniques such as Liquid Chromatography-Mass spectrometry (LC-MS/MS).

Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs?

In 2005, a survey compared a number of commercial PBPK software available at the time, mainly focusing on 'ready to use' modelling tools. Since then, these tools and software have been further developed and improved to allow modellers to perform WB-PBPK modelling including ADME processes at a high level of sophistication. This review presents a comparison of the features, values and limitations of both the 'ready to use' software and of the traditional user customizable software that are frequently used for the building and use of PBPK models, as well as the challenges associated with the various modelling approaches regarding their current and future use. PBPK models continue to be used more and more frequently during the drug development process since they represent a quantitative, physiologically realistic platform with which to simulate and predict the impact of various potential scenarios on the pharmacokinetics and pharmacodynamics of drugs. The 'ready to use' PBPK software has been a major factor in the increasing use of PBPK modelling in the pharmaceutical industry, opening up the PBPK approach to a broader range of users. The challenge is now to educate and to train scientists and modellers to ensure their appropriate understanding of the assumptions and the limitations linked both to the physiological framework of the 'virtual body' and to the scaling methodology from in vitro to in vivo (IVIVE).

Effect of variations in the amounts of P-glycoprotein (ABCB1), BCRP (ABCG2) and CYP3A4 along the human small intestine on PBPK models for predicting intestinal first pass.

It is difficult to predict the first-pass effect in the human intestine due to a lack of scaling factors for correlating in vitro and in vivo data. We have quantified cytochrome P450/3A4 (CYP3A4) and two ABC transporters, P-glycoprotein (P-gp, ABCB1) and the breast cancer resistant protein BCRP (ABCG2), throughout the human small intestine to determine the scaling factors for predicting clearance from intestinal microsomes and develop a physiologically based pharmacokinetic (PBPK) model. CYP3A4, P-gp and BCRP proteins were quantified by Western blotting and/or enzyme activities in small intestine samples from 19 donors, and mathematical trends of these expressions with intestinal localization were established. Microsome fractions were prepared and used to calculate the amount of microsomal protein per gram of intestine (MPPGI). Our results showed a trend in CYP3A4 expression decrease from the upper to the lower small intestine while P-gp expression is increasing. In contrast, BCRP expression did not vary significantly with position, but varied greatly between individuals. The MPPGI (mg microsomal protein per centimeter intestine) remained constant along the length of the small intestine, at about 1.55 mg/cm. Moreover, intrinsic clearance measured with specific CYP3A4 substrates (midazolam and an in-house Servier drug) and intestinal microsomes was well correlated with the amount of CYP3A4 (R(2) > 0.91, p < 0.01). In vivo data were more accurately predicted using PBPK models of blood concentrations of these two substrates based on the segmental distributions of these enzymes and MPPGI determined in this study. Thus, these mathematical trends can be used to predict drug absorption at different intestinal sites and their metabolism can be predicted with the MPPGI.

Predictions of metabolic drug-drug interactions using physiologically based modelling: Two cytochrome P450 3A4 substrates coadministered with ketoconazole or verapamil.

Nowadays, evaluation of potential risk of metabolic drug-drug interactions (mDDIs) is of high importance within the pharmaceutical industry, in order to improve safety and reduce the attrition rate of new drugs. Accurate and early prediction of mDDIs has become essential for drug research and development, and in vitro experiments designed to evaluate potential mDDIs are systematically included in the drug development plan prior to clinical assessment. The aim of this study was to illustrate the value and limitations of the classical and new approaches available to predict risks of DDIs in the research and development processes. The interaction of cytochrome P450 (CYP) 3A4 inhibitors (ketoconazole and verapamil) with midazolam was predicted using the inhibitor concentration/inhibition constant ([I]/K(i)) approach, the static approach with added variability (Simcyp(R)), and whole-body physiologically based pharmacokinetic (WB-PBPK) modelling (acslXtreme(R)). Then an in-house reference drug was used to challenge the different approaches based on the midazolam experience. Predicted values (pharmacokinetic parameters, the area under the plasma concentration-time curve [AUC] ratio and plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods. With the [I]/K(i) approach, the interaction risk was always overpredicted for the midazolam substrate, regardless of its route of administration and the coadministered inhibitor. However, the predictions were always satisfactory (within 2-fold) for the reference drug. For the Simcyp(R) calculations, two of the three interaction results for midazolam were overpredicted, both when midazolam was given orally, whereas the prediction obtained when midazolam was administered intravenously was satisfactory. For the reference drug, all predictions could be considered satisfactory. For the WB-PBPK approach, all predictions were satisfactory, regardless of the substrate, route of administration, dose and coadministered inhibitor. DDI risk predictions are performed throughout the research and development processes and are now fully integrated into decision-making processes. The regulatory approach is useful to provide alerts, even at a very early stage of drug development. The 'steady state' approach in Simcyp(R) improves the prediction by using physiological knowledge and mechanistic assumptions. The DDI predictions are very useful, as they provide a range of AUC ratios that include individuals at the extremes of the population, in addition to the 'average tendency'. Finally, the WB-PBPK approach improves the predictions by simulating the concentration-time profiles and calculating the related pharmacokinetic parameters, taking into account the time of administration of each drug - but it requires a good understanding of the absorption, distribution, metabolism and excretion properties of the compound.

Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 2: clinical trial results.

PURPOSE: To compare results of population PK analyses obtained with a full empirical design (FD) and an optimal sparse design (MD) in a Drug-Drug Interaction (DDI) study aiming to evaluate the potential CYP3A4 inhibitory effect of a drug in development, SX, on a reference substrate, midazolam (MDZ). Secondary aim was to evaluate the interaction of SX on MDZ in the in vivo study. Methods To compare designs, real data were analysed by population PK modelling technique using either FD or MD with NONMEM FOCEI for SX and with NONMEM FOCEI and MONOLIX SAEM for MDZ. When applicable a Wald test was performed to compare model parameter estimates, such as apparent clearance (CL/F), across designs. To conclude on the potential interaction of SX on MDZ PK, a Student paired test was applied to compare the individual PK parameters (i.e. log(AUC) and log(C(max))) obtained either by a non-compartmental approach (NCA) using FD or from empirical Bayes estimates (EBE) obtained after fitting the model separately on each treatment group using either FD or MD. RESULTS: For SX, whatever the design, CL/F was well estimated and no statistical differences were found between CL/F estimated values obtained with FD (CL/F = 8.2 l/h) and MD (CL/F = 8.2 l/h). For MDZ, only MONOLIX was able to estimate CL/F and to provide its standard error of estimation with MD. With MONOLIX, whatever the design and the administration setting, MDZ CL/F was well estimated and there were no statistical differences between CL/F estimated values obtained with FD (72 l/h and 40 l/h for MDZ alone and for MDZ with SX, respectively) and MD (77 l/h and 45 l/h for MDZ alone and for MDZ with SX, respectively). Whatever the approach, NCA or population PK modelling, and for the latter approach, whatever the design, MD or FD, comparison tests showed that there was a statistical difference (P < 0.0001) between individual MDZ log(AUC) obtained after MDZ administration alone and co-administered with SX. Regarding C(max), there was a statistical difference (P < 0.05) between individual MDZ log(C(max)) obtained under the 2 administration settings in all cases, except with the sparse design with MONOLIX. However, the effect on C(max) was small. Finally, SX was shown to be a moderate CYP3A4 inhibitor, which at therapeutic doses increased MDZ exposure by a factor of 2 in average and almost did not affect the C(max). CONCLUSION: The optimal sparse design enabled the estimation of CL/F of a CYP3A4 substrate and inhibitor when co-administered together and to show the interaction leading to the same conclusion as the full empirical design.

Drug-drug interaction predictions with PBPK models and optimal multiresponse sampling time designs: application to midazolam and a phase I compound. Part 1: comparison of uniresponse and multiresponse designs using PopDes.

PURPOSE: To determine the optimal sampling time design of a drug-drug interaction (DDI) study for the estimation of apparent clearances (CL/F) of two co-administered drugs (SX, a phase I compound, potentially a CYP3A4 inhibitor, and MDZ, a reference CYP3A4 substrate) without any in vivo data using physiologically based pharmacokinetic (PBPK) predictions, population PK modelling and multiresponse optimal design. METHODS: PBPK models were developed with AcslXtreme using only in vitro data to simulate PK profiles of both drugs when they were co-administered. Then, using simulated data, population PK models were developed with NONMEM and optimal sampling times were determined by optimizing the determinant of the population Fisher information matrix with PopDes using either two uniresponse designs (UD) or a multiresponse design (MD) with joint sampling times for both drugs. Finally, the D-optimal sampling time designs were evaluated by simulation and re-estimation with NONMEM by computing the relative root mean squared error (RMSE) and empirical relative standard errors (RSE) of CL/F. RESULTS: There were four and five optimal sampling times (=nine different sampling times) in the UDs for SX and MDZ, respectively, whereas there were only five sampling times in the MD. Whatever design and compound, CL/F was well estimated (RSE < 20% for MDZ and <25% for SX) and expected RSEs from PopDes were in the same range as empirical RSEs. Moreover, there was no bias in CL/F estimation. Since MD required only five sampling times compared to the two UDs, D-optimal sampling times of the MD were included into a full empirical design for the proposed clinical trial. A joint paper compares the designs with real data. CONCLUSION: This global approach including PBPK simulations, population PK modelling and multiresponse optimal design allowed, without any in vivo data, the design of a clinical trial, using sparse sampling, capable of estimating CL/F of the CYP3A4 substrate and potential inhibitor when co-administered together.