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ImportanceCurrently available glucagon-like peptide 1 receptor (GLP-1R) agonists for treating type 2 diabetes (T2D) are peptide agonists that require subcutaneous administration or strict fasting requirements before and after oral administration.ObjectiveTo investigate the efficacy, safety, and tolerability of multiple dose levels of the novel, oral, small molecule GLP-1R agonist danuglipron over 16 weeks.Design, Setting, and ParticipantsA phase 2b, double-blind, placebo-controlled, parallel-group, 6-group randomized clinical trial with 16-week double-blind treatment period and 4-week follow-up was conducted from July 7, 2020, to July 7, 2021. Adults with T2D inadequately controlled by diet and exercise, with or without metformin treatment, were enrolled from 97 clinical research sites in 8 countries or regions.InterventionsParticipants received placebo or danuglipron, 2.5, 10, 40, 80, or 120 mg, all orally administered twice daily with food for 16 weeks. Weekly dose escalation steps were incorporated to achieve danuglipron doses of 40 mg or more twice daily.Main Outcomes and MeasuresChange from baseline in glycated hemoglobin (HbA1c, primary end point), fasting plasma glucose (FPG), and body weight were assessed at week 16. Safety was monitored throughout the study period, including a 4-week follow-up period.ResultsOf 411 participants randomized and treated (mean [SD] age, 58.6 [9.3] years; 209 [51%] male), 316 (77%) completed treatment. For all danuglipron doses, HbA1c and FPG were statistically significantly reduced at week 16 vs placebo, with HbA1c reductions up to a least squares mean difference vs placebo of −1.16% (90% CI, −1.47% to −0.86%) for the 120-mg twice daily group and FPG reductions up to a least squares mean difference vs placebo of −33.24 mg/dL (90% CI, −45.63 to −20.84 mg/dL). Body weight was statistically significantly reduced at week 16 compared with placebo in the 80-mg twice daily and 120-mg twice daily groups only, with a least squares mean difference vs placebo of −2.04 kg (90% CI, −3.01 to −1.07 kg) for the 80-mg twice daily group and −4.17 kg (90% CI, −5.15 to −3.18 kg) for the 120-mg twice daily group. The most commonly reported adverse events were nausea, diarrhea, and vomiting.Conclusions and RelevanceIn adults with T2D, danuglipron reduced HbA1c, FPG, and body weight at week 16 compared with placebo, with a tolerability profile consistent with the mechanism of action.Trial RegistrationClinicalTrials.gov Identifier: NCT03985293

The International Council for Harmonisation (ICH) guidelines have been revised allowing for modeling of concentration-QT (C-QT) data from Phase I dose-escalation studies to be used as primary analysis for QT prolongation risk assessment of new drugs. This work compares three commonly used Phase I dose-escalation study designs regarding their efficiency to accurately identify drug effects on QT interval through C-QT modeling. Parallel group design and 4-period crossover designs with sequential or interleaving cohorts were evaluated. Clinical trial simulations were performed for each design and across different scenarios (e.g. different magnitudes of drug effect, QT variability), assuming a pre-specified linear mixed effect (LME) model for the relationship between drug concentration and change from baseline QT (ΔQT). Analyses suggest no systematic bias in either the predictions of placebo-adjusted ΔQT (ΔΔQT) or the LME model parameter estimates across all evaluated designs. Additionally, false negative rates remained similar and adequately controlled across all evaluated designs. However, compared to the crossover designs, the parallel design had significantly less power to correctly exclude a clinically significant QT effect, especially in the presence of substantial intercept inter-individual variability. In such cases, parallel design is associated with increased uncertainty around ΔΔQT prediction, mainly attributed to the uncertainty around the estimation of the treatment-specific intercept in the model. Throughout all the evaluated scenarios, the crossover design with interleaving cohorts had consistently the best performance characteristics. The results from this investigation will further facilitate informed decision-making during Phase I study design and the interpretation of the associated C-QT modeling output.

Intestinal disposition of small molecules involves interplay of drug metabolizing enzymes (DMEs), transporters, and host-microbiome interactions, which has spurred the development of in vitro intestinal models derived from primary tissue sources. Such models have been bioengineered from intestinal crypts, mucosal extracts, induced pluripotent stem cell (iPSC)-derived organoids, and human intestinal tissue. This minireview discusses the utility and limitations of these human-derived models in support of small molecule drug metabolism and disposition. Enteroids from human intestinal crypts, organoids derived from iPSCs using growth factors or small molecule compounds, and enterocytes extracted from mucosal scrapings show key absorptive cell morphology while are limited in quantitative applications due to the lack of accessibility to the apical compartment, the lack of monolayers, or low expression of key DMEs, transporters, and nuclear hormone receptors. Despite morphogenesis to epithelial cells, similar challenges have been reported by more advanced technologies that have explored the impact of flow and mechanical stretch on proliferation and differentiation of Caco-2 cells. Most recently, bioengineered human intestinal epithelial or ileal cells have overcome many of the challenges, as the DME and transporter expression pattern resembles that of native intestinal tissue. Engineering advances may improve such models to support longer-term applications and meet end-user needs. Biochemical characterization and transcriptomic, proteomic, and functional endpoints of emerging novel intestinal models, when referenced to native human tissue, can provide greater confidence and increased utility in drug discovery and development.

In this work, we first describe the population variability in hepatic drug metabolism using cryopreserved hepatocytes from five different donors cultured in a perfused three-dimensional human liver microphysiological system, and then show how the resulting data can be integrated with a modeling and simulation framework to accomplish in vitro-in vivo translation. For each donor, metabolic depletion profiles of six compounds (phenacetin, diclofenac, lidocaine, ibuprofen, propranolol, and prednisolone) were measured, along with metabolite formation, mRNA levels of 90 metabolism-related genes, and markers of functional viability [lactate dehydrogenase (LDH) release, albumin, and urea production]. Drug depletion data were analyzed with mixed-effects modeling. Substantial interdonor variability was observed with respect to gene expression levels, drug metabolism, and other measured hepatocyte functions. Specifically, interdonor variability in intrinsic metabolic clearance ranged from 24.1% for phenacetin to 66.8% for propranolol (expressed as coefficient of variation). Albumin, urea, LDH, and cytochrome P450 mRNA levels were identified as significant predictors of in vitro metabolic clearance. Predicted clearance values from the liver microphysiological system were correlated with the observed in vivo values. A population physiologically based pharmacokinetic model was developed for lidocaine to illustrate the translation of the in vitro output to the observed pharmacokinetic variability in vivo. Stochastic simulations with this model successfully predicted the observed clinical concentration-time profiles and the associated population variability. This is the first study of population variability in drug metabolism in the context of a microphysiological system and has important implications for the use of these systems during the drug development process., United States. Defense Advanced Research Projects Agency. Microphysiological Systems Program (Grant W911NF-12-2-0039), National Institutes of Health (U.S.). Microphysiological Systems Program (Grant 4-UH3-TR000496-03)

Microphysiological systems (MPSs) are in vitro models that capture facets of in vivo organ function through use of specialized culture microenvironments, including 3D matrices and microperfusion. Here, we report an approach to co-culture multiple different MPSs linked together physiologically on re-useable, open-system microfluidic platforms that are compatible with the quantitative study of a range of compounds, including lipophilic drugs. We describe three different platform designs – “4-way”, “7-way”, and “10-way” – each accommodating a mixing chamber and up to 4, 7, or 10 MPSs. Platforms accommodate multiple different MPS flow configurations, each with internal re-circulation to enhance molecular exchange, and feature on-board pneumatically-driven pumps with independently programmable flow rates to provide precise control over both intra- and inter-MPS flow partitioning and drug distribution. We first developed a 4-MPS system, showing accurate prediction of secreted liver protein distribution and 2-week maintenance of phenotypic markers. We then developed 7-MPS and 10-MPS platforms, demonstrating reliable, robust operation and maintenance of MPS phenotypic function for 3 weeks (7-way) and 4 weeks (10-way) of continuous interaction, as well as PK analysis of diclofenac metabolism. This study illustrates several generalizable design and operational principles for implementing multi-MPS “physiome-on-a-chip” approaches in drug discovery.

The utilisation of physiologically-based pharmacokinetic models for the analysis of population data is an approach with progressively increasing impact. However, as we move from empirical to complex mechanistic model structures, incorporation of stochastic variability in model parameters can be challenging due to the physiological constraints that may arise. Here, we investigated the most common types of constraints faced in mechanistic pharmacokinetic modelling and explored techniques for handling them during a population data analysis. An efficient way to impose stochastic variability on the parameters of interest without neglecting the underlying physiological constraints is through the assumption that they follow a distribution with support and properties matching the underlying physiology. It was found that two distributions that arise through transformations of the normal, the logit-normal generalisation and the logistic-normal, are excellent for such an application as not only they can satisfy the physiological constraints but also offer high flexibility during characterisation of the parameters' distribution. The statistical properties and practical advantages/disadvantages of these distributions for such an application were clearly displayed in the context of different modelling examples. Finally, a simulation study clearly illustrated the practical gains of the utilisation of the described techniques, as omission of population variability in physiological systems parameters leads to a biased/misplaced stochastic model with mechanistically incorrect variance structure. The current methodological work aims to facilitate the use of mechanistic/physiologically-based models for the analysis of population pharmacokinetic clinical data.

Investigation of the pharmacokinetics (PK) of a compound is of significant importance during the early stages of drug development, and therefore several in vitro systems are routinely employed for this purpose. However, the need for more physiologically realistic in vitro models has recently fueled the emerging field of tissue-engineered 3D cultures, also referred to as organs-on-chips, or microphysiological systems (MPSs). We have developed a novel fluidic platform that interconnects multiple MPSs, allowing PK studies in multi-organ in vitro systems along with the collection of high-content quantitative data. This platform was employed here to integrate a gut and a liver MPS together in continuous communication, and investigate simultaneously different PK processes taking place after oral drug administration in humans (e.g., intestinal permeability, hepatic metabolism). Measurement of tissue-specific phenotypic metrics indicated that gut and liver MPSs can be fluidically coupled with circulating common medium without compromising their functionality. The PK of diclofenac and hydrocortisone was investigated under different experimental perturbations, and results illustrate the robustness of this integrated system for quantitative PK studies. Mechanistic model-based analysis of the obtained data allowed the derivation of the intrinsic parameters (e.g., permeability, metabolic clearance) associated with the PK processes taking place in each MPS. Although these processes were not substantially affected by the gut-liver interaction, our results indicate that inter-MPS communication can have a modulating effect (hepatic metabolism upregulation). We envision that our integrative approach, which combines multi-cellular tissue models, multi-MPS platforms, and quantitative mechanistic modeling, will have broad applicability in pre-clinical drug development., United States. Defense Advanced Research Projects Agency (Grant W911NF-12-2-0039), National Institutes of Health (U.S.) (Grant 4-UH3-TR000496-03)

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.

Aim: Ethnicity plays a modulating role in atorvastatin pharmacokinetics (PK), with Asian patients reported to have higher exposure compared with Caucasians. Therefore, it is difficult to safely extrapolate atorvastatin PK data and models across ethnic groups. This work aims to develop a population PK model for atorvastatin and its pharmacologically active metabolites specifically for the Japanese population. Subsequently, it aimed to identify genetic polymorphisms affecting atorvastatin PK in this population.Methods: Atorvastatin acid (ATA) and ortho-hydroxy-atorvastatin acid (o-OH-ATA) plasma concentrations, clinical/demographic characteristics and genotypes for 18 (3, 3, 1, 1, 7, 2 and 1 in the ABCB1, ABCG2, CYP3A4, CYP3A5, SLCO1B1, SLCO2B1 and PPARA genes, respectively) genetic polymorphisms were collected from 27 Japanese individuals (taking 10 mg atorvastatin once daily) and analysed using a population PK modelling approach.Results: The population PK model developed (one-compartment for ATA linked through metabolite formation to an additional compartment describing the disposition of o-OH-ATA) accurately described the observed data and the associated population variability. Our analysis suggested that patients carrying one variant allele for the rs2622604 polymorphism (ABCG2) show a 55% (95% confidence interval: 16–131%) increase in atorvastatin oral bioavailability relative to the value in individuals without the variant allele.Conclusion: The current work reports the identification in the Japanese population of a BCRP polymorphism, not previously associated with the PK of any statin, that markedly increases ATA and o-OH-ATA exposure. The model developed may be of clinical importance to guide dosing recommendations tailored specifically for the Japanese.

Background Cough in asthmatic patients is a common and troublesome symptom. It is generally assumed coughing occurs as a consequence of bronchial hyperresponsiveness and inflammation, but the possibility that airway nerves are dysfunctional has not been fully explored. Objectives We sought to investigate capsaicin-evoked cough responses in a group of patients with well-characterized mild-to-moderate asthma compared with healthy volunteers and assess the influences of sex, atopy, lung physiology, inflammation, and asthma control on these responses. Methods Capsaicin inhalational challenge was performed, and cough responses were analyzed by using nonlinear mixed-effects modeling to estimate the maximum cough response evoked by any concentration of capsaicin (E max ) and the capsaicin dose inducing half-maximal response (ED 50 ). Results Ninety-seven patients with stable asthma (median age, 23 years [interquartile range, 21-27 years]; 60% female) and 47 healthy volunteers (median age, 38 years [interquartile range, 29-47 years]; 64% female) were recruited. Asthmatic patients had higher E max and lower ED 50 values than healthy volunteers. E max values were 27% higher in female subjects ( P = .006) and 46% higher in patients with nonatopic asthma ( P = .003) compared with healthy volunteers. Also, patients with atopic asthma had a 21% lower E max value than nonatopic asthmatic patients ( P = .04). The ED 50 value was 65% lower in female patients ( P = .0001) and 71% lower in all asthmatic patients ( P = .0008). ED 50 values were also influenced by asthma control and serum IgE levels, whereas E max values were related to 24-hour cough frequency. Age, body mass index, FEV 1 , PC 20 , fraction of exhaled nitric oxide, blood eosinophil counts, and inhaled steroid treatment did not influence cough parameters. Conclusion Patients with stable asthma exhibited exaggerated capsaicin-evoked cough responses consistent with neuronal dysfunction. Nonatopic asthmatic patients had the highest cough responses, suggesting this mechanism might be most important in type 2–low asthma phenotypes.

Whole-body physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development for their ability to predict drug concentrations in clinically relevant tissues and to extrapolate across species, experimental conditions and sub-populations. A whole-body PBPK model can be fitted to clinical data using a Bayesian population approach. However, the analysis might be time consuming and numerically unstable if prior information on the model parameters is too vague given the complexity of the system. We suggest an approach where (i) a whole-body PBPK model is formally reduced using a Bayesian proper lumping method to retain the mechanistic interpretation of the system and account for parameter uncertainty, (ii) the simplified model is fitted to clinical data using Markov Chain Monte Carlo techniques and (iii) the optimised reduced PBPK model is used for extrapolation. A previously developed 16-compartment whole-body PBPK model for mavoglurant was reduced to 7 compartments while preserving plasma concentration-time profiles (median and variance) and giving emphasis to the brain (target site) and the liver (elimination site). The reduced model was numerically more stable than the whole-body model for the Bayesian analysis of mavoglurant pharmacokinetic data in healthy adult volunteers. Finally, the reduced yet mechanistic model could easily be scaled from adults to children and predict mavoglurant pharmacokinetics in children aged from 3 to 11 years with similar performance compared with the whole-body model. This study is a first example of the practicality of formal reduction of complex mechanistic models for Bayesian inference in drug development.

To develop a population physiologically-based pharmacokinetic (PBPK) model for simvastatin (SV) and its active metabolite, simvastatin acid (SVA), that allows extrapolation and prediction of their concentration profiles in liver (efficacy) and muscle (toxicity). SV/SVA plasma concentrations (34 healthy volunteers) were simultaneously analysed with NONMEM 7.2. The implemented mechanistic model has a complex compartmental structure allowing inter-conversion between SV and SVA in different tissues. Prior information for model parameters was extracted from different sources to construct appropriate prior distributions that support parameter estimation. The model was employed to provide predictions regarding the effects of a range of clinically important conditions on the SV and SVA disposition. The developed model offered a very good description of the available plasma SV/SVA data. It was also able to describe previously observed effects of an OATP1B1 polymorphism (c.521 T > C) and a range of drug-drug interactions (CYP inhibition) on SV/SVA plasma concentrations. The predicted SV/SVA liver and muscle tissue concentrations were in agreement with the clinically observed efficacy and toxicity outcomes of the investigated conditions. A mechanistically sound SV/SVA population model with clinical applications (e.g., assessment of drug-drug interaction and myopathy risk) was developed, illustrating the advantages of an integrated population PBPK approach.

The aim of this work was to develop a joint population pharmacokinetic model for simvastatin (SV) and its active metabolite, simvastatin acid (SVA), that incorporates the effects of multiple genetic polymorphisms and clinical/demographic characteristics. SV/SVA plasma concentrations, demographic/clinical data, and genotypes for 18 genetic variants were collected from 74 individuals (three clinical trials) and analyzed using a nonlinear mixed-effects modeling approach. The structural model that best described the data included a two- and a one-compartment disposition model for SV and SVA, respectively. Age, weight, Japanese ethnicity, and seven genetic polymorphisms-rs4149056 (SLCO1B1), rs776746 (CYP3A5), rs12422149 (SLCO2B1), rs2231142 (ABCG2), rs4148162 (ABCG2), rs4253728 (PPARA), and rs35599367 (CYP3A4)-were identified as significantly affecting model parameters. The developed model was used to assess combinations of these covariates, highlighting specific risk factors associated with altered SV/SVA pharmacokinetics, and consequently myopathy cases that cannot be solely attributed to the rs4149056 CC genotype.

To investigate the effect of OATP1B1 genotype as a covariate on repaglinide pharmacokinetics and drug-drug interaction (DDIs) risk using a reduced physiologically-based pharmacokinetic (PBPK) model. Twenty nine mean plasma concentration-time profiles for SLCO1B1 c.521T>C were used to estimate hepatic uptake clearance (CLuptake) in different genotype groups applying a population approach in NONMEM v.7.2. Estimated repaglinide CLuptake corresponded to 217 and 113 μL/min/106 cells for SLCO1B1 c.521TT/TC and CC, respectively. A significant effect of OATP1B1 genotype was seen on CLuptake (48% reduction for CC relative to wild type). Sensitivity analysis highlighted the impact of CLmet and CLdiff uncertainty on the CLuptake optimization using plasma data. Propagation of this uncertainty had a marginal effect on the prediction of repaglinide OATP1B1-mediated DDI with cyclosporine; however, sensitivity of the predicted magnitude of repaglinide metabolic DDI was high. In addition, the reduced PBPK model was used to assess the effect of both CYP2C8*3 and SLCO1B1 c.521T>C on repaglinide exposure by simulations; power calculations were performed to guide prospective DDI and pharmacogenetic studies. The application of reduced PBPK model for parameter optimization and limitations of this process associated with the use of plasma rather than tissue profiles are illustrated.

PP144—ASSeSSiNg iN ViVo eNzyme geNeTiC VAriATioN iN The PreSeNCe of TrANSPorTer effeCT R. Chauhan; N. Tsamandouras; and A. Rostami-Hodjegan School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester; and Simcyp Ltd, Sheffield, United Kingdom Introduction: Interindividual variability in drug efficacy and toxicity can be partly attributed to genetic variation in metabolic enzymes, such as CYP2D6. The administration of probe substrates such as debrisoquine has been widely used in vivo to phenotype CYP2D6 metabolic activity. Recently, it was revealed that debrisoquine is also a substrate for the OCT1 uptake transporter. Therefore, the principal focus of this study was to analyze the misphenotyping that may arise in the complex situation, where drugs are substrates to both polymorphic enzymes and hepatic uptake transporters. Patients (or Materials) and Methods: A physiologically based pharmacokinetic modeling and simulation approach was carried out using the Simcyp Simulator (v12). A virtual study population (n = 1000) was generated including extensive, poor, and ultrarapid CYP2D6 metabolizers (86.5%, 8.2%, and 5.3% of the population, respectively). CYP2D6 activity was phenotyped using dextromethorphan as a probe substrate and monitoring indirect metrics such as the urinary recovery or plasma area under the curve metabolite/drug (dextrorphan/dextromethorphan) ratios. Multimodality in the frequency distribution of these metrics in the study population was assessed with the NTV (normal test variable) graphical method. Using a permeability-limited liver model and gradually decreasing the influx of the probe substrate into the liver, we investigated the effects of this decrease on the modality of the metrics frequency distribution. Results: Phenotyping successfully identified a clear multimodal frequency distribution in the study population with regard to CYP2D6 activity, when no hepatic permeability restrictions are applied to the probe substrate. However, when the uptake of the probe substrate into the liver is not passive but mainly transporter-mediated, the multimodality on the frequency distribution was clearly affected, confounding the distinction between the poor and extensive metabolizers. Specifically, when the contribution of the active uptake to total uptake was > 90%, multimodality was distorted to a unimodal frequency distribution. Conclusion: In conclusion, the findings suggest that when using a probe drug to phenotype the activity of a metabolic enzyme the hepatic permeability characteristics of the compound should be also considered. When uptake of the probe drug is predominately transporter mediated, influx into hepatocytes and not metabolism is the step that mostly contributes to the phenotype. Taking also into account that uptake transporters can also be polymorphically expressed the possibility that transporter variation can produce a flawed perception of enzyme activity should not be neglected. Disclosure of Interest: None declared.