Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates.

PURPOSE: 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). METHODS: 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. RESULTS: Changes in pharmacokinetic profiles and parameters induced by inflammation seem to be captured accurately by the models. CONCLUSIONS: These findings suggest that the pharmacokinetics of CYP2C19 and CYP3A4 substrates can be predicted depending on the CRP concentration.

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.

Model-based approaches for ivabradine development in paediatric population, part II: PK and PK/PD assessment.

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.

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.

Pharmacokinetic/Pharmacodynamic modeling of abexinostat-induced thrombocytopenia across different patient populations: application for the determination of the maximum tolerated doses in both lymphoma and solid tumour patients.

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.

Influence of covariance between random effects in design for nonlinear mixed-effect models with an illustration in pediatric pharmacokinetics.

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.

Bioavailability of three novel oral, sustained-release pellets, relative to an immediate-release tablet containing 500 mg flucytosine: A randomized, open-label, crossover study in healthy volunteers.

The opportunistic fungal infection cryptococcal meningoencephalitis is a major cause of death among people living with HIV in sub-Saharan Africa. We report pharmacokinetic (PK) and safety data from a randomized, four-period crossover phase I trial of three sustained-release (SR) oral pellet formulations of 5-flucytosine conducted in South Africa. These formulations were developed to require less frequent administration, to provide a convenient alternative to the current immediate release (IR) formulation, A. Formulations B, C, and D were designed to release 5-flucytosine as a percentage of the nominal dose in vitro. We assessed their safety and PK profiles in a single dose (1 × 3000 mg at 0 h), relative to commercial IR tablets (Ancotil 500 mg tablets; 3 × 500 mg at 0 h and 3 × 500 mg at 6 h) in healthy, fasted participants. Forty-two healthy participants were included. All treatments were well-tolerated. The primary PK parameters, maximum observed plasma concentration (Cmax ) and area under the concentration-time profiles, were significantly lower for the SR formulations than for the IR tablets, and the geometric mean ratios fell outside the conventional bioequivalence limits. The median maximum time to Cmax was delayed for the SR pellets. Physiologically-based PK modeling indicated a twice-daily 6400 mg dose of SR formulation D in fasted condition would be optimal for further clinical development. This regimen is predicted to result in a rapid steady-state plasma exposure with effective and safe trough plasma concentration and Cmax values, within the therapeutic boundaries relative to plasma exposure after four times per day administration of IR tablets (PACTR202201760181404).

A physiologically based pharmacokinetic model for strontium exposure in rat.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Application of hematological toxicity modeling in clinical development of abexinostat (S-78454, PCI-24781), a new histone deacetylase inhibitor.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: The final model, characterizing the dose-effect and the dose-toxicity relationships, provides a useful modeling tool for clinical drug development.

Virtual patients, digital twins and causal disease models: Paving the ground for in silico clinical trials.

Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins are generated to simulate specific organs and to predict treatment efficacy at the individual patient level. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.

How to Successfully Generate an Alternative Approach to a Thorough QT Study: GLPG1972 as an Example.

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.

Some alternatives to asymptotic tests for the analysis of pharmacogenetic data using nonlinear mixed effects models.

Nonlinear mixed effects models allow investigating individual differences in drug concentration profiles (pharmacokinetics) and responses. Pharmacogenetics focuses on the genetic component of this variability. Two tests often used to detect a gene effect on a pharmacokinetic parameter are (1) the Wald test, assessing whether estimates for the gene effect are significantly different from 0 and (2) the likelihood ratio test comparing models with and without the genetic effect. Because those asymptotic tests show inflated type I error on small sample size and/or with unevenly distributed genotypes, we develop two alternatives and evaluate them by means of a simulation study. First, we assess the performance of the permutation test using the Wald and the likelihood ratio statistics. Second, for the Wald test we propose the use of the F-distribution with four different values for the denominator degrees of freedom. We also explore the influence of the estimation algorithm using both the first-order conditional estimation with interaction linearization-based algorithm and the stochastic approximation expectation maximization algorithm. We apply these methods to the analysis of the pharmacogenetics of indinavir in HIV patients recruited in the COPHAR2-ANRS 111 trial. Results of the simulation study show that the permutation test seems appropriate but at the cost of an additional computational burden. One of the four F-distribution-based approaches provides a correct type I error estimate for the Wald test and should be further investigated.

Mechanistic Modeling of the Interplay Between Host Immune System, IL-7 and UCART19 Allogeneic CAR-T Cells in Adult B-cell Acute Lymphoblastic Leukemia.

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.

Impact of Hepatic CYP3A4 Ontogeny Functions on Drug-Drug Interaction Risk in Pediatric Physiologically-Based Pharmacokinetic/Pharmacodynamic Modeling: Critical Literature Review and Ivabradine Case Study.

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.

Reduced physiologically-based pharmacokinetic model of dabigatran etexilate-dabigatran and its application for prediction of intestinal P-gp-mediated drug-drug interactions.

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.

Modeling restoration of gefitinib efficacy by co-administration of MET inhibitors in an EGFR inhibitor-resistant NSCLC xenograft model: A tumor-in-host DEB-based approach.

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.

Impact of interleukin-6 on drug transporters and permeability in the hCMEC/D3 blood-brain barrier model.

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.

Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.

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.