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Chimeric antigen receptor (CAR)-T cell therapies have shown tremendous results against various hematologic cancers. Prior to cell infusion, a host preconditioning regimen is required to achieve lymphodepletion and improve CAR-T cell pharmacokinetic exposure, leading to greater chances of therapeutic success. To better understand and quantify the impact of the preconditioning regimen, we built a population-based mechanistic pharmacokinetic-pharmacodynamic model describing the complex interplay between lymphodepletion, host immune system, homeostatic cytokines, and pharmacokinetics of UCART19, an allogeneic product developed against CD19+ B cells. Data were collected from a phase I clinical trial in adult relapsed/refractory B-cell acute lymphoblastic leukemia and revealed three different UCART19 temporal patterns: (i) expansion and persistence, (ii) transient expansion with subsequent rapid decline, and (iii) absence of observed expansion. On the basis of translational assumptions, the final model was able to capture this variability through the incorporation of IL-7 kinetics, which are thought to be increased owing to lymphodepletion, and through an elimination of UCART19 by host T cells, which is specific to the allogeneic context. Simulations from the final model recapitulated UCART19 expansion rates in the clinical trial, confirmed the need for alemtuzumab to observe UCART19 expansion (along with fludarabine cyclophosphamide), quantified the importance of allogeneic elimination, and suggested a high impact of multipotent memory T-cell subpopulations on UCART19 expansion and persistence. In addition to supporting the role of host cytokines and lymphocytes in CAR-T cell therapy, such a model could help optimizing the preconditioning regimens in future clinical trials. Significance: A mathematical mechanistic pharmacokinetic/pharmacodynamic model supports and captures quantitatively the beneficial impact of lymphodepleting patients before the infusion of an allogeneic CAR-T cell product. Mediation through IL-7 increase and host T lymphocytes decrease is underlined, and the model can be further used to optimize CAR-T cell therapies lymphodepletion regimen.

Chimeric antigen receptor (CAR)-T cell therapies have shown tremendous results against various hematologic cancers. Prior to cell infusion, a host preconditioning regimen is required to achieve lymphodepletion and improve CAR-T cell pharmacokinetic exposure, leading to greater chances of therapeutic success. To better understand and quantify the impact of the preconditioning regimen, we built a population-based mechanistic pharmacokinetic-pharmacodynamic model describing the complex interplay between lymphodepletion, host immune system, homeostatic cytokines, and pharmacokinetics of UCART19, an allogeneic product developed against CD19+ B cells. Data were collected from a phase I clinical trial in adult relapsed/refractory B-cell acute lymphoblastic leukemia and revealed three different UCART19 temporal patterns: (i) expansion and persistence, (ii) transient expansion with subsequent rapid decline, and (iii) absence of observed expansion. On the basis of translational assumptions, the final model was able to capture this variability through the incorporation of IL-7 kinetics, which are thought to be increased owing to lymphodepletion, and through an elimination of UCART19 by host T cells, which is specific to the allogeneic context. Simulations from the final model recapitulated UCART19 expansion rates in the clinical trial, confirmed the need for alemtuzumab to observe UCART19 expansion (along with fludarabine cyclophosphamide), quantified the importance of allogeneic elimination, and suggested a high impact of multipotent memory T-cell subpopulations on UCART19 expansion and persistence. In addition to supporting the role of host cytokines and lymphocytes in CAR-T cell therapy, such a model could help optimizing the preconditioning regimens in future clinical trials.Significance:A mathematical mechanistic pharmacokinetic/pharmacodynamic model supports and captures quantitatively the beneficial impact of lymphodepleting patients before the infusion of an allogeneic CAR-T cell product. Mediation through IL-7 increase and host T lymphocytes decrease is underlined, and the model can be further used to optimize CAR-T cell therapies lymphodepletion regimen.

Supplementary Data

Purpose: Flotetuzumab (MGD006 or S80880) is a bispecific molecule that recognizes CD3 and CD123 membrane proteins, redirecting T cells to kill CD123-expressing cells for the treatment of acute myeloid leukemia. In this study, we developed a mathematical model to characterize MGD006 exposure–response relationships and to assess the impact of its immunogenicity in cynomolgus monkeys.Experimental Design: Thirty-two animals received multiple escalating doses (100-300-600-1,000 ng/kg/day) via intravenous infusion continuously 4 days a week. The model reflects sequential binding of MGD006 to CD3 and CD123 receptors. Formation of the MGD006/CD3 complex was connected to total T cells undergoing trafficking, whereas the formation of the trimolecular complex results in T-cell activation and clonal expansion. Activated T cells were used to drive the peripheral depletion of CD123-positive cells. Anti-drug antibody development was linked to MGD006 disposition as an elimination pathway. Model validation was tested by predicting the activity of MGD006 in eight monkeys receiving continuous 7-day infusions.Results: MGD006 disposition and total T-cell and CD123-positive cell profiles were well characterized. Anti-drug antibody development led to the suppression of T-cell trafficking but did not systematically abolish CD123-positive cell depletion. Target cell depletion could persist after drug elimination owing to the self-proliferation of activated T cells generated during the first cycles. The model was externally validated with the 7-day infusion dosing schedule.Conclusions: A translational model was developed for MGD006 that features T-cell activation and expansion as a key driver of pharmacologic activity and provides a mechanistic quantitative platform to inform dosing strategies in ongoing clinical studies. Clin Cancer Res; 24(11); 2631–41. ©2018 AACR.

Supplementary figures and tables Figure S1 PK/PD experimental design. This figure details the dose and sampling strategies for the PK/PD endpoints for all animal groups. Figure S2 Detailed structural PK/PD model of MGD006 administrated in cynomolgus monkeys. This figure supplements Fig. 1. Figure S3 Categorization of anti-drug antibody (ADA) development. This figure describes the method used to categorize the ADA effect in three different groups. Table S1 ADA development groups. This table summarizes the different identified groups and the number of animals in each group. Table S2 Parameter definitions of PK/ADA, total T-cell, and CD123+-cell models. Figure S4 /S5 /S6 Time-course of observed and model-fitted plasma MGD006 concentrations, total T-cell counts, and CD123+-cell counts following continuous 4-day infusion in cynomolgus monkeys. This figure supplements Fig. 3. Figure S7 Goodness-of-fit plots for PK, total T-cell, and CD123+-cell models: individual predictions vs. observations and individual weighted residuals vs. time. Figure S8 Prediction-corrected visual predictive checks (PC-VPC) of PK, total T-cell, and CD123+-cell models. Figure S9 /S10 /S11 External validation of the PK, total T-cell, and CD123+-cell models by VPC with data collected in animals following continous 7-day infusions. These figures supplement Fig. 4.

During development of a drug, the requirement of evaluating the proarrhythmic risk and delayed repolarization needs to be fulfilled. Would it be possible to create an alternative to a thorough QT (TQT) study or is there a need to perform a dedicated TQT study? How is an alternative approach generated, what information is available, and which instructions are considered missing today to generate such an approach? This tutorial describes the considerations and path followed to create an early and feasible alternative to a TQT study using experience-based insights from a successful application to the US Food and Drug Administration for GLPG1972, an ADAMTS-5 inhibitor, and discusses the approach used in light of the current guidelines and literature.

MET receptor tyrosine kinase inhibitors (TKIs) can restore sensitivity to gefitinib, a TKI targeting epidermal growth factor receptor (EGFR), and promote apoptosis in non‐small cell lung cancer (NSCLC) models resistant to gefitinib treatment in vitro and in vivo. Several novel MET inhibitors are currently under study in different phases of development. In this work, a novel tumor‐in‐host modeling approach, based on the Dynamic Energy Budget (DEB) theory, was proposed and successfully applied to the context of poly‐targeted combination therapies. The population DEB‐based tumor growth inhibition (TGI) model well‐described the effect of gefitinib and of two MET inhibitors, capmatinib and S49076, on both tumor growth and host body weight when administered alone or in combination in an NSCLC mice model involving the gefitinib‐resistant tumor line HCC827ER1. The introduction of a synergistic effect in the combination DEB‐TGI model allowed to capture gefitinib anticancer activity enhanced by the co‐administered MET inhibitor, providing also a quantitative evaluation of the synergistic drug interaction. The model‐based comparison of the two MET inhibitors highlighted that S49076 exhibited a greater anticancer effect as well as a greater ability in restoring sensitivity to gefitinib than the competitor capmatinib. In summary, the DEB‐based tumor‐in‐host framework proposed here can be applied to routine combination xenograft experiments, providing an assessment of drug interactions and contributing to rank investigated compounds and to select the optimal combinations, based on both tumor and host body weight dynamics. Thus, the combination tumor‐in‐host DEB‐TGI model can be considered a useful tool in the preclinical development and a significant advance toward better characterization of combination therapies.

Clinical assessment of drug-drug interactions (DDIs) in children is not a common practice in drug development. Therefore, physiologically-based pharmacokinetic (PBPK) modeling can be beneficial for informing drug labeling. Using ivabradine and its metabolite (both cytochrome P450 3A4 enzyme (CYP3A4) substrates), the objectives were (i) to scale ivabradine-metabolite adult PBPK/PD to pediatrics, (ii) to predict the DDIs with a strong CYP3A4 inhibitor, and (iii) to compare the sensitivity of children to DDIs using two CYP3A4 hepatic ontogeny functions: Salem and Upreti. A scaled parent-metabolite PBPK/PD model from adults to children satisfactorily predicted pharmacokinetics (PK) and pharmacodynamics (PD) in 74 children (0.5-18 years) regardless of CYP3A4 hepatic ontogeny function applied. However, using the Salem ontogeny, mean predicted parent and metabolite area under the concentration-time curve over 12 hours (AUC12h ) and heart rate change from baseline were 2-fold, 1.5-fold, and 1.4-fold higher in young children (0.5-3 years old) compared with Upreti ontogeny, respectively. Despite these differences, choice of appropriate hepatic CYP3A4 ontogeny was challenging due to sparse PK and PD data. Different sensitivity to ivabradine-ketoconazole DDIs was simulated in young children relative to adults depending on the choice of hepatic CYP3A4 ontogeny. Predicted ivabradine and metabolite AUCDDI /AUCcontrol were 2-fold lower in the youngest children (0.5-1 year old) compared with adults (Salem function). In contrast, the Upreti function predicted comparable ivabradine DDIs across all age groups, although predicted metabolite AUCDDI/ AUCcontrol was 1.3-fold higher between the youngest children and adults. In the case of PD, differences in predicted DDIs were minor across age groups and between both functions. Current work highlights the importance of careful consideration of hepatic CYP3A4 ontogeny function and implications on labeling recommendations in the pediatric population.

The blood-brain barrier (BBB) is a highly selective membrane composed predominantly of brain capillary endothelial cells expressing drug efflux transporters that prevent substrates from accessing the brain. Inflammation is associated with central nervous system diseases and can impair BBB permeability via several mechanisms, including altered transporter and cell junction expression. This can modify the brain's exposure to drugs. However, comprehensive genomic analysis of the impact of interleukin (IL)-6, which plays a key role in the inflammatory response, on the BBB is lacking. In the present study, we analyzed the effects of exposure of hCMEC/D3 cells to 20 ng/mL IL-6 for 72 h. We performed RNA sequencing and ABC transporter efflux assays. Physiologically based pharmacokinetics (PBPK) simulations were conducted to evaluate the potential impact of IL-6 on the digoxin pharmacokinetics profile and brain exposure by decreasing BBB ABCB1 efflux activity. Exposure of hCMEC/D3 cells to IL-6 triggered the deregulation of numerous genes involved in barrier permeability, such as cell junctions, focal adherens complex, and cell adhesion molecules. We observed mild modification of the mRNA expression and efflux activities of ABC transporters. PBPK simulation showed that, if we only consider the impact of IL-6 on ABCB1 transporter, the modification of the digoxin pharmacokinetics profile and brain exposure is slight. IL-6 slightly affected the gene expression levels and activities of ABC transporters on BBB cells, exhibiting a weaker effect than on hepatic cells. However, inflammation may cause other modifications, such as altered BBB permeability, that could modify drug pharmacokinetics.

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.

Transforming Translation Through Quantitative Pharmacology for High-Impact Decision Making in Drug Discovery and Development

For decades, inflammation has been considered a cause of pharmacokinetic variability, mainly in relation to the inhibitory effect of pro-inflammatory cytokines on the expression level and activity of cytochrome P450 (CYP). In vitro and clinical studies have shown that two major CYPs, CYP2C19 and CYP3A4, are both impaired. The objective of the present study was to quantify the impact of the inflammatory response on the activity of both CYPs in order to predict the pharmacokinetic profile of their substrates according to systemic C-reactive protein (CRP). The relationships between CRP concentration and both CYPs activities were estimated and validated using clinical data first on midazolam then on voriconazole. Finally, clinical data on omeprazole were used to validate the findings. For each substrate, a physiologically based pharmacokinetics model was built using a bottom-up approach, and the relationships between CRP level and CYP activities were estimated by a top-down approach. After incorporating the respective relationships, we compared the predictions and observed drug concentrations. Changes in pharmacokinetic profiles and parameters induced by inflammation seem to be captured accurately by the models. These findings suggest that the pharmacokinetics of CYP2C19 and CYP3A4 substrates can be predicted depending on the CRP concentration.

Ivabradine and its metabolite both demonstrate heart rate–reducing effect (If current inhibitors) and undergo CYP3A4 metabolism. The purpose of this study was to develop a joint parent-metabolite physiologically based pharmacokinetic (PBPK)/pharmacodynamic (PD) model to predict the PK and PD of ivabradine and its metabolite following intravenous (i.v.) or oral administration (alone or co-administered with CYP3A4 inhibitors). Firstly, a parent-metabolite disposition model was developed and optimised using individual plasma concentration-time data following i.v. administration of ivabradine or metabolite within a Bayesian framework. Secondly, the model was extended and combined with a mechanistic intestinal model to account for oral absorption and drug-drug interactions (DDIs) with CYP3A4 inhibitors (ketoconazole, grapefruit juice). Lastly, a PD model was linked to the PBPK model to relate parent and metabolite PK to heart rate (HR) reduction. The disposition model described successfully parent-metabolite PK following i.v. administration. Following integration of a gut model, the PBPK model adequately predicted plasma concentration profiles and the DDI risk (92% and 85% of predicted AUC+inhibitor/AUCcontrol and Cmax+inhibitor/Cmaxcontrol for ivabradine and metabolite within the prediction limits). Ivabradine-metabolite PBPK model was linked to PD by using the simulated unbound parent-metabolite concentrations in the heart. This approach successfully predicted the effects of both entities on HR (observed vs predicted − 7.7/− 5.9 bpm and − 15.8/− 14.0 bpm, control and ketoconazole group, respectively). This study provides a framework for PBPK/PD modelling of a parent-metabolite and can be scaled to other populations or used for investigation of untested scenarios (e.g. evaluation of DDI risk in special populations).

Drug development in oncology commonly exploits the tools of molecular biology to gain therapeutic benefit through reprograming of cellular responses. In immuno-oncology (IO) the aim is to direct the patient's own immune system to fight cancer. After remarkable successes of antibodies targeting PD1/PD-L1 and CTLA4 receptors in targeted patient populations, the focus of further development has shifted toward combination therapies. However, the current drug-development approach of exploiting a vast number of possible combination targets and dosing regimens has proven to be challenging and is arguably inefficient. In particular, the unprecedented number of clinical trials testing different combinations may no longer be sustainable by the population of available patients. Further development in IO requires a step change in selection and validation of candidate therapies to decrease development attrition rate and limit the number of clinical trials. Quantitative systems pharmacology (QSP) proposes to tackle this challenge through mechanistic modeling and simulation. Compounds' pharmacokinetics, target binding, and mechanisms of action as well as existing knowledge on the underlying tumor and immune system biology are described by quantitative, dynamic models aiming to predict clinical results for novel combinations. Here, we review the current QSP approaches, the legacy of mathematical models available to quantitative clinical pharmacologists describing interaction between tumor and immune system, and the recent development of IO QSP platform models. We argue that QSP and virtual patients can be integrated as a new tool in existing IO drug development approaches to increase the efficiency and effectiveness of the search for novel combination therapies.

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.

International audience; INTRODUCTION:In this paper, we studied the effect over time of agomelatine, an antidepressant drug administered in patient with major depressive disorder, through item response theory (IRT), taking into account a strong placebo effect and missing not at random. We also assessed the informativeness of the HAMD-17 scale's item.MATERIALS AND METHODS:The data includes five phase III clinical trials sponsored by Servier Institute, totalling 1549 patients followed during a maximum of 1 year. At each observation, individual scores for the 17 items of the HAMD scale were recorded. The probability for each score was modelled with IRT. A non-linear mixed effects model was used to describe the evolution of the disease and was coupled with a time to event model to predict dropout. Clinical trial simulations were then used to compare placebo and active treatment. Informativeness of each item was evaluated using the Fisher information theory.RESULTS:The best model combined an IRT model, a longitudinal model for underlying depression which describes the remission and then a possible relapse, and a hazard model for dropout depending on the evolution from baseline. The drug effect was best modelled as an effect on the remission and the relapse phases. The median predicted drop in HAMD between baseline and 6 weeks was 8.8 (90% PI, 8.3-9.2) when on placebo and 13.1 (90% PI, 12.8-13.4) when treated. Nine items were found to be the most informative.CONCLUSION:The IRT framework allowed to characterise the evolution of depression with time and estimate the effect of agomelatine, as well as the link between symptoms and disease.

Quantitative pharmacology (QP) applications in translational medicine, drug-development, and therapeutic use were crowd-sourced by the ASCPT Impact and Influence initiative. Highlighted QP case studies demonstrated faster access to innovative therapies for patients through 1) rational dose selection for pivotal trials; 2) reduced trial-burden for vulnerable populations; or 3) simplified posology. Critical success factors were proactive stakeholder engagement, alignment on the value of model-informed approaches, and utilizing foundational clinical pharmacology understanding of the therapy.

Background Dabigatran etexilate (DABE) has been suggested as a clinical probe for intestinal P-glycoprotein (P-gp)-mediated drug-drug interaction (DDI) studies and, as an alternative to digoxin. Clinical DDI data with various P-gp inhibitors demonstrated a dose-dependent inhibition of P-gp with DABE. The aims of this study were to develop a joint DABE (prodrug)-dabigatran reduced physiologically-based-pharmacokinetic (PBPK) model and to evaluate its ability to predict differences in P-gp DDI magnitude between a microdose and a therapeutic dose of DABE. Methods A joint DABE-dabigatran PBPK model was developed with a mechanistic intestinal model accounting for the regional P-gp distribution in the gastrointestinal tract. Model input parameters were estimated using DABE and dabigatran pharmacokinetic (PK) clinical data obtained after administration of DABE alone or with a strong P-gp inhibitor, itraconazole, and over a wide range of DABE doses (from 375 µg to 400 mg). Subsequently, the model was used to predict extent of DDI with additional P-gp inhibitors and with different DABE doses. Results The reduced DABE-dabigatran PBPK model successfully described plasma concentrations of both prodrug and metabolite following administration of DABE at different dose levels and when co-administered with itraconazole. The model was able to capture the dose dependency in P-gp mediated DDI. Predicted magnitude of itraconazole P-gp DDI was higher at the microdose (predicted vs. observed median fold-increase in AUC+inh/AUCcontrol (min-max) = 5.88 (4.29–7.93) vs. 6.92 (4.96–9.66) ) compared to the therapeutic dose (predicted median fold-increase in AUC+inh/AUCcontrol = 3.48 (2.37–4.84) ). In addition, the reduced DABE-dabigatran PBPK model predicted successfully the extent of DDI with verapamil and clarithromycin as P-gp inhibitors. Model-based simulations of dose staggering predicted the maximum inhibition of P-gp when DABE microdose was concomitantly administered with itraconazole solution; simulations also highlighted dosing intervals required to minimise the DDI risk depending on the DABE dose administered (microdose vs. therapeutic). Conclusions This study provides a modelling framework for the evaluation of P-gp inhibitory potential of new molecular entities using DABE as a clinical probe. Simulations of dose staggering and regional differences in the extent of intestinal P-gp inhibition for DABE microdose and therapeutic dose provide model-based guidance for design of prospective clinical P-gp DDI studies.

Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics. 1-3 Cur ...

Inflammatory response is characterized by an increase of several cytokines. Some are known to modify drugs pharmacokinetic by reducing the expression levels of drug-metabolizing enzymes (DMEs) and transporters. This impact of inflammatory signaling is well established in hepatic cells, but not in intestinal cells. EpiIntestinal is a 3D human small intestinal tissue model with epithelial polarity, allowing good evaluation of metabolism and drug transport. This study aimed to analyze the effect of IL-6 on this tissue model. RNA sequencing was performed in cells incubated with 5, 10, or 20 ng/mL IL-6 for 8 h to 72 h to study the impact of IL-6 on drug metabolism and pharmacokinetics gene expression. The influence of IL-6 on the activity of cytochromes P450 (CYPs) was studied by measuring metabolite formation of specific substrates with LC-HRMS. Its impact on ATP-binding cassette (ABC) transport was evaluated by measuring intra- and extracellular substrates using spectrofluorometry. Exposure of EpiIntestinal cells to IL-6 resulted in reduction of some CYP mRNAs, such as CYP2C19, CYP2C9, and CYP3A4, by 40% to 50%. Activities of these CYPs were also decreased in EpiIntestinal cells by 20% to > 75%. IL-6 exposure did not modify ABCB1 and ABCCs transporter activities in this model. This study shows that gene expression levels and activities of drug-metabolizing enzymes and ABC transporters may be altered by the pro-inflammatory cytokine IL-6 in intestinal cells. If these results are confirmed in vivo, it may result in pharmacokinetic modifications, such as pre-systemic metabolism, with clinical effects, and require dosage adaptation.

This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of "good practice" recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit RD ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.

We show through a simulation study how the joint analysis of data from phase I and phase II studies enhances the power of pharmacogenetic tests in pharmacokinetic (PK) studies. PK profiles were simulated under different designs along with 176 genetic markers. The null scenarios assumed no genetic effect, while under the alternative scenarios, drug clearance was associated with six genetic markers randomly sampled in each simulated dataset. We compared penalized regression Lasso and stepwise procedures to detect the associations between empirical Bayes estimates of clearance, estimated by nonlinear mixed effects models, and genetic variants. Combining data from phase I and phase II studies, even if sparse, increases the power to identify the associations between genetics and PK due to the larger sample size. Design optimization brings a further improvement, and we highlight a direct relationship between η-shrinkage and loss of genetic signal.

The objectives of this work were first to describe the pharmacokinetic (PK) of ivabradine and its active metabolite in a paediatric patient population after repeated oral administration of ivabradine using a population PK approach, and secondly to assess whether the blood/plasma ratio and the pharmacokinetic/pharmacodynamic (PK/PD) relationship are preserved in the paediatric population in comparison to adult. PK data for 70 patients were obtained after blood sampling using dried blood spot and one plasma sample in order to assess the relationship between blood and plasma concentration. In order to describe ivabradine and its metabolite blood concentrations in children, a joint population PK model was developed taking into account weight & age effects on PK parameters. Plasma PK exposure parameters were calculated in children using plasma PK profiles. In order to assess the PK/PD relationship in children, an adult PK/PD model was used. The relationship between blood and plasma concentrations was described using linear mixed effect models. Two and one-compartment models best described parent and metabolite dispositions. Weight effects were fixed to the allometric values of ¾ on clearance (CL) and 1 on volume. A maturation function was added on metabolite formation clearance (CL PM ) reflecting enzyme maturation. Plasma exposure comparison indicated that higher dose/kg were necessary to achieve a similar exposure between younger and older children. No differences between age classes were observed in terms of range of exposure at the maintenance dose. The PK/PD relationship in adult patients is conserved in children.

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.

The middle initial in the fifth author's name is incorrect in the original article. "Lena F. Friberg" should be "Lena E. Friberg". The original article was corrected.

Design of phase 1 combination therapy trials is complex compared to single therapy trials. In this work, model-based adaptive optimal design (MBAOD) was exemplified and evaluated for a combination of paclitaxel and a hypothetical new compound in a phase 1 study to determine the best dosing regimen for a phase 2 trial. Neutropenia was assumed as the main toxicity and the dose optimization process targeted a 33% probability of grade 4 neutropenia and maximal efficacy (based on preclinical studies) by changing the dose amount of both drugs and the dosing schedule for the new drug. Different starting conditions (e.g., initial dose), search paths (e.g., maximal change in dose intensity per step), and stopping criteria (e.g., "3 + 3 rule") were explored. The MBAOD approach was successfully implemented allowing the possibility of flexible designs with the modification of doses and dosing schedule throughout the trial. The 3 + 3 rule was shown to be highly conservative (selection of a dosing regimen with at least 90% of the possible maximal efficacy in less than 21% of the cases) but also safer (selection of a toxic design in less than 2% of the cases). Without the 3 + 3 rule, better performance was observed (>67% of selected designs were associated with at least 90% of possible maximal efficacy) while the proportion of DLTs per trial was similar. Overall, MBAOD is a promising tool in the context of dose finding studies of combination treatments and was showed to be flexible enough to be associated with requirements imposed by clinical protocols. Correction in: AAPS JOURNAL, Volume:20, Issue:3, Article Number:55, DOI:10.1208/s12248-018-0218-5

International audience; Purpose - Normalised prediction distribution errors (npde) are used to graphically and statistically evaluate mixed-effect models for continuous responses. In this study, our aim was to extend npde to time-to-event (TTE) models and evaluate their performance.Methods - Let V denote a dataset with censored TTE observations. The null hypothesis (H) is that observations in V can be described by model M. We extended npde to TTE models using imputations to take into account censoring. We then evaluated their performance in terms of type I error and power to detect model misspecifications for TTE data by means of a simulation study with different sample sizes.Results - Type I error was found to be close to the expected 5% significance level for all sample sizes tested. The npde were able to detect misspecifications in the baseline hazard as well as in the link between the longitudinal variable and the survival function. The ability to detect model misspecifications increased as the difference in the shape of the survival function became more apparent. As expected, the power also increased as the sample size increased. Imputing the censored events tended to decrease the percentage of rejections.Conclusions - We have shown that npde can be readily extended to TTE data and that they perform well with an adequate type I error.

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Purpose: Flotetuzumab (MGD006 or S80880) is a bispecific molecule that recognizes CD3 and CD123 membrane proteins, redirecting T cells to kill CD123-expressing cells for the treatment of acute myeloid leukemia. In this study, we developed a mathematical model to characterize MGD006 exposure–response relationships and to assess the impact of its immunogenicity in cynomolgus monkeys. Experimental Design: Thirty-two animals received multiple escalating doses (100-300-600-1,000 ng/kg/day) via intravenous infusion continuously 4 days a week. The model reflects sequential binding of MGD006 to CD3 and CD123 receptors. Formation of the MGD006/CD3 complex was connected to total T cells undergoing trafficking, whereas the formation of the trimolecular complex results in T-cell activation and clonal expansion. Activated T cells were used to drive the peripheral depletion of CD123-positive cells. Anti-drug antibody development was linked to MGD006 disposition as an elimination pathway. Model validation was tested by predicting the activity of MGD006 in eight monkeys receiving continuous 7-day infusions. Results: MGD006 disposition and total T-cell and CD123-positive cell profiles were well characterized. Anti-drug antibody development led to the suppression of T-cell trafficking but did not systematically abolish CD123-positive cell depletion. Target cell depletion could persist after drug elimination owing to the self-proliferation of activated T cells generated during the first cycles. The model was externally validated with the 7-day infusion dosing schedule. Conclusions: A translational model was developed for MGD006 that features T-cell activation and expansion as a key driver of pharmacologic activity and provides a mechanistic quantitative platform to inform dosing strategies in ongoing clinical studies. Clin Cancer Res; 24(11); 2631–41. ©2018 AACR.

A PBPK modelling approach was used to predict organic anion transporter (OAT) mediated drug-drug interactions involving S44121, a substrate and an inhibitor of OAT1 and OAT3. Model predictions were then compared to the results of a clinical DDI study which was carried out to investigate the interaction of S44121 with probenecid, tenofovir and ciprofloxacin. PBPK models were developed and qualified using existing clinical data, and inhibition constants were determined in vitro. The model predictions for S44121 as an OAT inhibitor were similar to the results obtained from the clinical DDI study, with no interaction observed for tenofovir or ciprofloxacin in the presence of S44121. An observed AUC ratio of 2.2 was obtained for S44121 in the presence of probenecid, which was slightly higher than the model predicted AUC ratio of 1.6. A DDI study in the monkey was also carried out for the interaction between S44121 and probenecid, since the monkey has previously been reported to be a good preclinical model for OAT-mediated DDI. However, this study highlighted a species difference in the major route of S44121 elimination between monkey (mainly hepatic metabolism) and human (mainly renal excretion of unchanged drug), rendering a comparison between the two DDI studies difficult. Overall, for S44121 the PBPK modelling approach gave a better prediction of the extent of DDI than the static predictions based on inhibitor Cmax and IC50, therefore this can be considered a potentially valuable tool within drug development.

International audience; Nonlinear mixed-effect models are used increasingly during drug development. For design, an alternative to simulations is based on the Fisher information matrix. Its expression was derived using a first-order approach, was then extended to include covariance and implemented into the R function PFIM. The impact of covariance on standard errors, amount of information, and optimal designs was studied. It was also shown how standard errors can be predicted analytically within the framework of rich individual data without the model. The results were illustrated by applying this extension to the design of a pharmacokinetic study of a drug in pediatric development.

Central nervous system (CNS) drug disposition is dictated by a drug’s physicochemical properties and its ability to permeate physiological barriers. The blood–brain barrier (BBB), blood-cerebrospinal fluid barrier and centrally located drug transporter proteins influence drug disposition within the central nervous system. Attainment of adequate brain-to-plasma and cerebrospinal fluid-to-plasma partitioning is important in determining the efficacy of centrally acting therapeutics. We have developed a physiologically-based pharmacokinetic model of the rat CNS which incorporates brain interstitial fluid (ISF), choroidal epithelial and total cerebrospinal fluid (CSF) compartments and accurately predicts CNS pharmacokinetics. The model yielded reasonable predictions of unbound brain-to-plasma partition ratio (Kpuu,brain) and CSF:plasma ratio (CSF:Plasmau) using a series of in vitro permeability and unbound fraction parameters. When using in vitro permeability data obtained from L-mdr1a cells to estimate rat in vivo permeability, the model successfully predicted, to within 4-fold, Kpuu,brain and CSF:Plasmau for 81.5% of compounds simulated. The model presented allows for simultaneous simulation and analysis of both brain biophase and CSF to accurately predict CNS pharmacokinetics from preclinical drug parameters routinely available during discovery and development pathways.

Abexinostat, an oral pan-histone deacetylase inhibitor (HDACi), was evaluated in patients with advanced solid tumours in two single agent phase I studies (PCYC-402 and CL1-78454-002). In PCYC-402 study testing four different administration schedules, the maximum tolerated dose (MTD) was established at 75 mg/m(2) BID (twice daily) and the recommended dose at 60 mg/m(2) BID regardless of the schedule tested. The dose limiting toxicity (DLT), consistently observed across all these schedules, was reversible thrombocytopenia. The CL1-78454-002 study was initially investigating an additional schedule of 14 days on/7 days off. While testing two first cohorts, thrombocytopenia was observed without reaching DLT. To address this issue, a pharmacokinetic/pharmacodynamic (PK/PD) model was used to predict the optimal schedule allowing higher doses with minimal thrombocytopenia. Several administration schedules were simulated using this model. A 4 days on/3 days off schedule was associated with the smallest platelet decrease. Accordingly, the CL1-78454-002 study was amended. After reaching MTD1 (75 mg/m(2) BID) with the initial schedule, subsequent cohorts received abexinostat on a revised schedule of 4 days on/3 days off, starting at one dose level below MTD1 (60 mg/m(2) BID). As expected, the dose-escalation continued for two more dose levels beyond MTD1. The MTD2 reached for this optimised schedule was 105 mg/m(2) BID and the recommended dose 90 mg/m(2) BID. In conclusion, early understanding of toxicities and PK determination allowed us to build a PK/PD model of thrombocytopenia, which predicted the optimal administration schedule. This optimised schedule is currently used in the trials in solid tumours with abexinostat.

A population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to describe the thrombocytopenia (dose-limiting toxicity) of abexinostat, a new histone deacetylase inhibitor. An optimal administration schedule of the drug was determined using a simulation-based approach. Early PK and PK/PD data were analysed using a sequential population modeling approach (NONMEM 7), allowing for the description of a PK profile and platelet-count decrease after abexinostat administration with various administration schedules. Simulations of platelet count with several administration schedules over 3-week treatment cycles (ASC) and over a day (ASD) were computed to define the optimal schedule that limits the depth of thrombocytopenia. An intermediate PK/PD model accurately described the data. The administration of abexinostat during the first 4 days of each week in a 3-week cycle resulted in fewer adverse events (with no influence of ASD on platelet count profiles), and corresponded to the optimal treatment schedule. This administration schedule was clinically evaluated in a phase I clinical trial and allowed for the definition of a new maximum tolerated dose (MTD), leading to a nearly 30% higher dose-intensity than that of another previously tested schedule. Lastly, a final model was built using all of the available data. The final model, characterizing the dose-effect and the dose-toxicity relationships, provides a useful modeling tool for clinical drug development.

To develop a physiologically based pharmacokinetic (PBPK) model to describe the disposition of Strontium--a bone seeking agent approved in 2004 (as its Ranelate salt) for treatment of osteoporosis in post-menopausal women.The model was developed using plasma and bone exposure data obtained from ovariectomised (OVX) female rats--a preclinical model for post-menopausal osteoporosis. The final PBPK model incorporated elements from literature models for bone seeking agents allowing for description of the heterogeneity of bone tissue and also for a physiological description of bone remodelling processes. The model was implemented in MATLAB in open and closed loop configurations, and fittings of the model to exposure data to estimate certain model parameters were carried out using nonlinear regression, treating data with a naïve-pooled approach.The PBPK model successfully described plasma and bone exposure of Strontium in OVX rats with parameter estimates and model behaviour in keeping with known aspects of the distribution and incorporation of Strontium into bone.The model describes Strontium exposure in a physiologically rationalized manner and has the potential for future uses in modelling the PK-PD of Strontium, and/or other bone seeking agents, and for scaling to model human Strontium bone exposure.

The purpose of this study was to explore the impact of censoring due to animal sacrifice on parameter estimates and tumor volume calculated from two diameters in larger tumors during tumor growth experiments in preclinical studies. The type of measurement error that can be expected was also investigated. Different scenarios were challenged using the stochastic simulation and estimation process. One thousand datasets were simulated under the design of a typical tumor growth study in xenografted mice, and then, eight approaches were used for parameter estimation with the simulated datasets. The distribution of estimates and simulation-based diagnostics were computed for comparison. The different approaches were robust regarding the choice of residual error and gave equivalent results. However, by not considering missing data induced by sacrificing the animal, parameter estimates were biased and led to false inferences in terms of compound potency; the threshold concentration for tumor eradication when ignoring censoring was 581 ng.ml(-1), but the true value was 240 ng.ml(-1).

The purpose of this study was to explore the interval censoring induced by caliper measurements on smaller tumors during tumor growth experiments in preclinical studies and to show its impact on parameter estimations. A new approach, the so-called interval-M3 method, is proposed to specifically handle this type of data. Thereby, the interval-M3 method was challenged with different methods (including classical methods for handling below quantification limit values) using Stochastic Simulation and Estimation process to take into account the censoring. In this way, 1000 datasets were simulated under the design of a typical of tumor growth study in xenografted mice, and then, each method was used for parameter estimation on the simulated datasets. Relative bias and relative root mean square error (relative RMSE) were consequently computed for comparison purpose. By not considering the censoring, parameter estimations appeared to be biased and particularly the cytotoxic effect parameter, k 2 , which is the parameter of interest to characterize the efficacy of a compound in oncology. The best performance was noted with the interval-M3 method which properly takes into account the interval censoring induced by caliper measurement, giving overall unbiased estimations for all parameters and especially for the antitumor effect parameter (relative bias = 0.49%, and relative RMSE = 4.06%).

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.

International audience; Genetic data is now collected in many clinical trials, especially in population pharmacokinetic studies. There is no consensus on methods to test the association between pharmacokinetics and genetic covariates. We performed a simulation study inspired by real clinical trials, using the PK of a compound under development having a nonlinear bioavailability along with genotypes for 176 Single Nucleotide Polymorphisms (SNPs). Scenarios included 78 subjects extensively sampled (16 observations per subject) to simulate a phase I study, or 384 subjects with the same rich design. Under the alternative hypothesis (H 1), 6 SNPs were drawn randomly to affect the log-clearance under an additive linear model. For each scenario 200 PK data sets were simulated under the null hypothesis (no gene effect) and H1. We compared 16 combinations of four association tests, a stepwise procedure and three penalised regressions (ridge regression, Lasso, HyperLasso), applied to four pharmacokinetic phenotypes, two observed concentrations, area under the curve estimated by noncompartmental analysis and model-based clearance. The different combinations were compared in terms of true and false positives and probability to detect the genetic effects. In presence of nonlinearity and/or variability in bioavailability, model-based phenotype allowed a higher probability to detect the SNPs than other phenotypes. In a realistic setting with a limited number of subjects, all methods showed a low ability to detect genetic effects. Ridge regression had the best probability to detect SNPs, but also a higher number of false positives. No association test showed a much higher power than the others.

The objective of this paper is to determine optimal blood sampling time windows for the estimation of pharmacokinetic (PK) parameters by a population approach within the clinical constraints. A population PK model was developed to describe a reference phase II PK dataset. Using this model and the parameter estimates, D-optimal sampling times were determined by optimising the determinant of the population Fisher information matrix (PFIM) using PFIM_ _M 1.2 and the modified Fedorov exchange algorithm. Optimal sampling time windows were then determined by allowing the D-optimal windows design to result in a specified level of efficiency when compared to the fixed-times D-optimal design. The best results were obtained when K(a) and IIV on K(a) were fixed. Windows were determined using this approach assuming 90% level of efficiency and uniform sample distribution. Four optimal sampling time windows were determined as follow: at trough between 22 h and new drug administration; between 2 and 4 h after dose for all patients; and for 1/3 of the patients only 2 sampling time windows between 4 and 10 h after dose, equal to [4 h-5 h 05] and [9 h 10-10 h]. This work permitted the determination of an optimal design, with suitable sampling time windows which was then evaluated by simulations. The sampling time windows will be used to define the sampling schedule in a prospective phase II study.

The aim of the present study was to investigate amoxicillin (AMX) distribution in muscle interstitial fluid by microdialysis in healthy, awake rats. Microdialysis probes were inserted into the jugular vein and hind leg muscle. Probe recoveries in each rat were determined by retrodialysis with cefadroxil. AMX was administered as a bolus dose of 50 mg · kg −1 , and microdialysis samples were collected during 180 min. Concentrations of unbound drug in blood and muscle were analyzed simultaneously by a population approach. Simulations were conducted using a hybrid, physiologically based pharmacokinetic model to investigate the potential impact of tissue blood flow on muscle AMX distribution. A two-compartment pharmacokinetic model described adequately the unbound amoxicillin concentration-time profiles in blood and muscle. Muscle AMX distribution equilibrium was rapidly achieved. Consequently, the best results were obtained by considering concentrations in muscle as part of the central compartment. The ratio of the concentration of unbound drug in muscle to that in blood ( R model ) was estimated to 0.80 by the model, which is close to the mean value obtained by noncompartmental data analysis ( R area = 0.86 ± 0.29). Simulations conducted with a hybrid, physiologically based pharmacokinetic model suggest that a muscle blood flow reduction of 30% to 50%, such as could be encountered in critical care patients, has virtually no effect on muscle AMX concentration profiles. In conclusion, this study has clearly demonstrated that AMX distributes rapidly and extensively within muscle interstitial fluid, consistent with theory, and that altered muscle blood flow seems unlikely to have a major effect on these distribution characteristics.

The purpose of this study was to investigate the effect of dose on norfloxacin pharmacokinetics and distribution into the brain extracellular fluid (ECF), in freely moving rats. Unbound concentrations of norfloxacin in hippocampus were determined by microdialysis after an i.v. bolus dose of 12.5, 25, 50, 100, or 150 mg/kg in rats. In vivo recovery of norfloxacin was determined by retrodialysis by calibrator. Among three fluoroquinolones (enoxacin, pefloxacin, and ciprofloxacin) selected as potential calibrators, ciprofloxacin was selected as the best one. Maximum ECF brain norfloxacin concentrations are rapidly obtained but the ECFbrain/plasma areas under curves (AUC) ratios are low and independent of dose with a mean value of 8.2 ± 5.8%. By contrast, norfloxacin systemic pharmacokinetics was nonlinear, with total plasma clearance decreasing significantly from 23.0 ± 3.4 to 14.4 ± 3.8 mL/min/kg when dose increased from 12.5 to 150 mg/kg. © 2003 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 92:2458–2465, 2003

A previously developed pharmacokinetic-pharmacodynamic (PK-PD) modeling approach was used to investigate the epileptogenic activity of norfloxacin as a representative antibiotic with concentration-dependent antimicrobial activity. Rats received an intravenous infusion of norfloxacin at a rate of 5 mg kg of body weight −1 min −1 over 30 min. Blood samples were collected for drug assay, and an electroencephalogram (EEG) was recorded during infusion and postinfusion. An important delay was observed between concentrations of norfloxacin in plasma and the EEG effect. Indirect effect models failed to describe these data, which were successfully fitted by using an effect compartment model with a spline function to describe the relationship between effect and concentration at the effect site, as previously observed with imipenem. The robustness of the PK-PD model was then assessed by keeping the dose constant but increasing the duration of infusion to 120 and 240 min. Although this was accompanied by PK modifications, PD parameters did not vary significantly, and the PK-PD model still applied. In conclusion, the successful PK-PD modeling of the norfloxacin EEG effect in rats should be considered to predict and reduce the epileptogenic risk associated with this antibiotic as a representative fluoroquinolone (E. Fuseau and L. B. Sheiner, Clin. Pharmacol. Ther. 35:733-741, 1984).

Background In the clinical development of oncology drugs, the recommended dose is usually determined using a 3 + 3 dose-escalation study design. However, this phase I design does not always adequately describe dose-toxicity relationships. Methods 125 patients, with either solid tumours or lymphoma, were included in the study and 1217 platelet counts were available over three treatment cycles. The data was used to build a population pharmacokinetic/pharmacodynamic (PKPD) model using a sequential modeling approach. Model-derived Recommended Doses (MDRD) of abexinostat (a Histone Deacetylase Inhibitor) were determined from simulations of different administration schedules, and the higher bound for the probability of reaching these MDRD with a 3 + 3 design were obtained. Results The PKPD model developed adequately described platelet kinetics in both patient populations with the inclusion of two platelet baseline counts and a disease progression component for patients with lymphoma. Simulation results demonstrated that abexinostat administration during the first 4 days of each week in a 3-week cycle led to a higher MDRD compared to the other administration schedules tested, with a maximum probability of 40 % of reaching these MDRDs using a 3 + 3 design. Conclusions The PKPD model was able to predict thrombocytopenia following abexinostat administration in both patient populations. A model-based approach to determine the recommended dose in phase I trials is preferable due to the imprecision of the 3 + 3 design.