Hierarchical cluster analysis and nonlinear mixed-effects modelling for candidate biomarker detection in preclinical models of cancer

BACKGROUND

Preclinical models of cancer can be of translational benefit when assessing how different biomarkers are regulated in response to particular treatments. Detection of molecular biomarkers in preclinical models of cancer is difficult due inter-animal variability in responses, combined with limited accessibility of longitudinal data.

METHODS

Nonlinear mixed-effects modelling (NLME) was used to analyse tumour growth data based on expected tumour growth rates observed 7 days after initial doses (DD7) of Radiotherapy (RT) and Combination of RT with DNA Damage Response Inhibitors (DDRi). Cox regression was performed to confirm an association between DD7 and survival. Hierarchical Cluster Analysis (HCA) was then used to identify candidate biomarkers impacting responses to RT and RT/DDRi and these were validated using NLME.

RESULTS

Cox regression confirmed significant associations between DD7 and survival. HCA of RT treated samples, combined with NLME confirmed significant associations between DD7 and Cluster specific CD8+ Ki67 MFI, as well as DD7 and cluster specific Natural Killer cell density in RT treated mice.

CONCLUSION

Application of NLME, as well as HCA of candidate biomarkers may provide additional avenues to assess the effect of RT in MC38 syngeneic tumour models. Additional studies would need to be conducted to confirm association between DD7 and biomarkers in RT/DDRi treated mice.

Covariates in population pharmacokinetic studies of critically ill adults receiving β-lactam antimicrobials: a systematic review and narrative synthesis

Introduction

Population pharmacokinetic studies of β-lactam antimicrobials in critically ill patients derive models that inform their dosing. In non-linear mixed-effects modelling, covariates are often used to improve model fit and explain variability. We aimed to investigate which covariates are most commonly assessed and which are found to be significant, along with global patterns of publication.

Methods

We conducted a systematic review, searching MEDLINE, Embase, CENTRAL and Web of Science on 01 March 2023, including studies of critically ill adults receiving β-lactam antimicrobials who underwent blood sampling for population pharmacokinetic studies. We extracted and categorized all reported covariates and assessed reporting quality using the ClinPK checklist.

Results

Our search identified 151 studies with 6018 participants. Most studies reported observational cohorts (120 studies, 80%), with the majority conducted in high-income settings (136 studies, 90%). Of the 1083 identified covariate instances, 237 were unique; the most common categories were patient characteristics (n = 404), biomarkers (n = 206) and physiological parameters (n = 163). Only seven distinct commonly reported covariates (CLCR, weight, glomerular filtration rate, diuresis, need for renal replacement, serum albumin and C-reactive protein) were significant more than 20% of the time.

Conclusions

Covariates are most commonly chosen based on biological plausibility, with patient characteristics and biomarkers the most frequently investigated. We developed an openly accessible database of reported covariates to aid investigators with covariate selection when designing population pharmacokinetic studies. Novel covariates, such as sepsis subphenotypes, have not been explored yet, leaving a research gap for future work.

Mixed effects modeling of radiotherapy in combination with immune checkpoint blockade or inhibitors of the DNA damage response pathway

Dosage optimization to maximize efficacy and minimize toxicity is a potential issue when administering radiotherapy (RT) in combination with immune checkpoint blockade (ICB) or inhibitors of the DNA Damage Response Pathway (DDRi) in the clinic. Preclinical models and mathematical modeling can help identify ideal dosage schedules to observe beneficial effects of a tri-therapy. The aim of this study is to describe a mathematical model to capture the impact of RT in combination with inhibitors of the DNA Damage Response Pathway or blockade of the immune checkpoint protein - programmed death ligand 1 (PD-L1). This model describes how RT mediated activation of antigen presenting cells can induce an increase in cytolytic T cells capable of targeting tumor cells, and how combination drugs can potentiate the immune response by inhibiting the rate of T cell exhaustion. The model was fitted using preclinical data, where MC38 tumors were treated in vivo with RT alone or in combination with anti-PD-L1 as well as with either olaparib or the ataxia telangiectasia mutated (ATM) inhibitor-AZD0156. The model successfully described the observed data and goodness-of-fit, using visual predictive checks also confirmed a successful internal model validation for each treatment modality. The results demonstrated that the anti-PD-L1 effect in combination with RT was maximal in vivo and any additional benefit of DDRi at the given dosage and schedule used was undetectable. Model fit results indicated AZD0156 to be a more potent DDRi than olaparib. Simulations of alternative doses indicated that reducing efficacy of anti-PD-L1 by 68% would potentially provide evidence for a benefit of ATM inhibition in combination with ICB and increase the relative efficacy of tri-therapy.

Exploring the Boundaries for In Vitro-In Vivo Extrapolation: Use of Isolated Rat Hepatocytes in Co-culture and Impact of Albumin Binding Properties in the Prediction of Clearance of Various Drug Types

Prediction of hepatic clearance of drugs (via uptake or metabolism) from in vitro systems continues to be problematic, particularly when plasma protein binding is high. The following work explores simultaneous assessment of both clearance processes, focusing on a commercial hepatocyte-fibroblast co-culture system (HμREL) over a 24-hour period using six probe drugs (ranging in metabolic and transporter clearance and low-to-high plasma protein binding). A rat hepatocyte co-culture assay was established using drug depletion (measuring both medium and total concentrations) and cell uptake kinetic analysis, both in the presence and absence of plasma protein (1% bovine serum albumin). Secretion of endogenous albumin was monitored as a marker of viability, and this reached 0.004% in incubations (at a rate similar to in vivo synthesis). Binding to stromal cells was substantial and required appropriate correction factors. Drug concentration-time courses were analyzed both by conventional methods and a mechanistic cell model prior to in vivo extrapolation. Clearance assayed by drug depletion in conventional suspended rat hepatocytes provided a benchmark to evaluate co-culture value. Addition of albumin appeared to improve predictions for some compounds (where fraction unbound in the medium is less than 0.1); however, for high-binding drugs, albumin significantly limited quantification and thus predictions. Overall, these results highlight ongoing challenges concerning in vitro hepatocyte system complexity and limitations of practical expediency. Considering this, more reliable measurement of hepatically cleared compounds seems possible through judicious use of available hepatocyte systems, including co-culture systems, as described herein; this would include those compounds with low metabolic turnover but high active uptake clearance. SIGNIFICANCE STATEMENT: Co-culture systems offer a more advanced tool than standard hepatocytes, with the ability to be cultured for longer periods of time, yet their potential as an in vitro tool has not been extensively assessed. We evaluate the strengths and limitations of the HμREL system using six drugs representing various metabolic and transporter-mediated clearance pathways with various degrees of albumin binding. Studies in the presence/absence of albumin allow in vitro-in vivo extrapolation and a framework to maximize their utility.

Radiation in Combination with Immune Checkpoint Blockade and DNA Damage Response Inhibitors in Mice: Dosage Optimization in MC38 Syngeneic Tumors via Modelling and Simulation

Clinical trials assessing the impact of radiotherapy (RT) in combination with DNA damage response pathway inhibitors (DDRis) and/or immune checkpoint blockade are currently ongoing. However, current methods for optimizing dosage and schedule are limited. A mathematical model was developed to capture the impacts of RT in combination with DDRi and/or anti-PD-L1 [immune checkpoint inhibitor (ICI)] on tumor immune interactions in the MC38 syngeneic tumor model. The model was fitted to datasets that assessed the impact of RT in combination with the DNA protein kinase inhibitor (DNAPKi) AZD7648. The model was further fitted to datasets from studies that were used to assess both RT/ICI combinations as well as RT/ICI combinations followed by concurrent administration of the poly ADP ribose polymerase inhibitor (PARPi) olaparib. Nonlinear mixed-effects modeling was performed followed by internal validation with visual predictive checks (VPC). Simulations of alternative dosage regimens and scheduling were performed to identify optimal candidate dosage regimens of RT/DNAPKi and RT/PARPi/ICI. Model fits and VPCs confirmed a successful internal validation for both datasets and demonstrated very small differences in the median, lower, and upper percentile values of tumor diameters between RT/ICI and RT/PARPi/ICI, which indicated that the triple combination of RT/PARPi/ICI at the given dosage and schedule does not provide additional benefit compared with ICI in combination with RT. Simulation of alternative dosage regimens indicated that lowering the dosage of ICI to between 2 and 4 mg/kg could induce similar benefits to the full dosage regimen, which could be of translational benefit. SIGNIFICANCE STATEMENT: This work provides a mixed-effects model framework to quantify the effects of combination radiotherapy/DNA damage response pathway inhibitors/immune checkpoint inhibitors in preclinical tumor models and identify optimal dosage regimens, which could be of translational benefit.

Estimation of FMO3 Ontogeny by Mechanistic Population Pharmacokinetic Modelling of Risdiplam and Its Impact on Drug-Drug Interactions in Children

BACKGROUND AND OBJECTIVE

Spinal muscular atrophy (SMA) is a progressive neuromuscular disease caused by insufficient levels of survival motor neuron (SMN) protein. Risdiplam (EvrysdiTM) increases SMN protein and is approved for the treatment of SMA. Risdiplam has high oral bioavailability and is primarily eliminated through hepatic metabolism by flavin-containing monooxygenase3 (FMO3) and cytochrome P450 (CYP) 3A, by 75% and 20%, respectively. While the FMO3 ontogeny is critical input data for the prediction of risdiplam pharmacokinetics (PK) in children, it was mostly studied in vitro, and robust in vivo FMO3 ontogeny is currently lacking. We derived in vivo FMO3 ontogeny by mechanistic population PK modelling of risdiplam and investigated its impact on drug-drug interactions in children.

METHODS

Population and physiologically based PK (PPK and PBPK) modelling conducted during the development of risdiplam were integrated into a mechanistic PPK (Mech-PPK) model to estimate in vivo FMO3 ontogeny. A total of 10,205 risdiplam plasma concentration-time data from 525 subjects aged 2 months-61 years were included. Six different structural models were examined to describe the in vivo FMO3 ontogeny. Impact of the newly estimated FMO3 ontogeny on predictions of drug-drug interaction (DDI) in children was investigated by simulations for dual CYP3A-FMO3 substrates including risdiplam and theoretical substrates covering a range of metabolic fractions (fm) of CYP3A and FMO3 (fmCYP3A:fmFMO3 = 10%:90%, 50%:50%, 90%:10%).

RESULTS

All six models consistently predicted higher FMO3 expression/activity in children, reaching a maximum at the age of 2 years with an approximately threefold difference compared with adults. Different trajectories of FMO3 ontogeny in infants < 4 months of age were predicted by the six models, likely due to limited observations for this age range. Use of this  in vivo FMO3 ontogeny function improved prediction of risdiplam PK in children compared to in vitro FMO3 ontogeny functions. The simulations of theoretical dual CYP3A-FMO3 substrates predicted comparable or decreased CYP3A-victim DDI propensity in children compared to adults across the range of fm values. Refinement of FMO3 ontogeny in the risdiplam model had no impact on the previously predicted low CYP3A-victim or -perpetrator DDI risk of risdiplam in children.

CONCLUSION

Mech-PPK modelling successfully estimated in vivo FMO3 ontogeny from risdiplam data collected from 525 subjects aged 2 months-61 years. To our knowledge, this is the first investigation of in vivo FMO3 ontogeny by population approach using comprehensive data covering a wide age range. Derivation of a robust in vivo FMO3 ontogeny function has significant implications on the prospective prediction of PK and DDI in children for other FMO3 substrates in the future, as illustrated in the current study for FMO3 and/or dual CYP3A-FMO3 substrates.

CLINICAL TRIAL REGISTRY NUMBERS

NCT02633709, NCT03032172, NCT02908685, NCT02913482, NCT03988907.

Development of a sensitive biochemical assay for the detection of tofacitinib adherence

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease. Tofacitinib is a Janus Kinase inhibitor licensed for the treatment of RA that, unlike biologic anti-rheumatic drugs, is administered orally, but studies of long-term treatment adherence rates are lacking. The measurement of adherence, however, is challenging and there is currently no gold standard test for adherence. Here, we developed a novel HPLC MS/MS assay for the quantification of tofacitinib. The assay demonstrated a LLOQ for tofacitinib of 0.1 ng ml^-1, within run accuracy was 81-85% at LLOQ and 91-107% at all other levels. To investigate the ability of the assay to detect adherence, tofacitinib was measured in a random selection of serum samples (n = 10) of tofacitinib treated RA patients who self-reported adherent behaviour. The assay measured tofacitinib in all samples above the LLOQ demonstrating the potential of the assay to sensitively measure biochemical adherence in real-world patient samples. This method for detection of adherence has the potential to be a more objective measure that could be used in the future in the clinic but will require further studies to explore factors that may influence measurement of drug levels, such as clinical characteristics of patients.

Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug-Drug Interactions in Rats

Gadoxetate, a magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 and multidrug resistance-associated protein 2. Six drugs, with varying degrees of transporter inhibition, were used to assess gadoxetate dynamic contrast enhanced MRI biomarkers for transporter inhibition in rats. Prospective prediction of changes in gadoxetate systemic and liver AUC (AUCR), resulting from transporter modulation, were performed by physiologically-based pharmacokinetic (PBPK) modelling. A tracer-kinetic model was used to estimate rate constants for hepatic uptake (khe), and biliary excretion (kbh). The observed median fold-decreases in gadoxetate liver AUC were 3.8- and 1.5-fold for ciclosporin and rifampicin, respectively. Ketoconazole unexpectedly decreased systemic and liver gadoxetate AUCs; the remaining drugs investigated (asunaprevir, bosentan, and pioglitazone) caused marginal changes. Ciclosporin decreased gadoxetate khe and kbh by 3.78 and 0.09 mL/min/mL, while decreases for rifampicin were 7.20 and 0.07 mL/min/mL, respectively. The relative decrease in khe (e.g., 96% for ciclosporin) was similar to PBPK-predicted inhibition of uptake (97-98%). PBPK modelling correctly predicted changes in gadoxetate systemic AUCR, whereas underprediction of decreases in liver AUCs was evident. The current study illustrates the modelling framework and integration of liver imaging data, PBPK, and tracer-kinetic models for prospective quantification of hepatic transporter-mediated DDI in humans.

A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models

Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

Investigation of simplified physiologically-based pharmacokinetic models in rat and human

Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

Coproporphyrin I as an Endogenous Biomarker to Detect Reduced OATP1B Activity and Shift in Elimination Route in Chronic Kidney Disease

Coproporphyrin I (CPI) is an endogenous biomarker of organic anion transporting polypeptide 1B transporter (OATP1B). CPI plasma baseline was reported to increase with severity of chronic kidney disease (CKD). Further, ratio of CPI area under the plasma concentration-time curve (AUCR) in the presence/absence of OATP1B inhibitor rifampin was higher in patients with CKD compared with healthy participants, in contrast to pitavastatin (a clinical OATP1B probe). This study investigated mechanism(s) contributing to altered CPI baseline in patients with CKD by extending a previously developed physiologically-based pharmacokinetic (PBPK) model to this patient population. CKD-related covariates were evaluated in a stepwise manner on CPI fraction unbound in plasma (f_u,p ), OATP1B-mediated hepatic uptake clearance (CL_active ), renal clearance (CL_R ), and endogenous synthesis (k_syn ). The CPI model successfully recovered increased baseline and rifampin-mediated AUCR in patients with CKD by accounting for the following disease-related changes: 13% increase in f_u,p , 29% and 39% decrease in CL_active in mild and moderate to severe CKD, respectively, decrease in CL_R proportional to decline in glomerular filtration rate, and 27% decrease in k_syn in severe CKD. Almost complete decline in CPI renal elimination in severe CKD increased its fraction transported by OATP1B, rationalizing differences in the CPI-rifampin interaction observed between healthy participants and patients with CKD. In conclusion, mechanistic modeling performed here supports CKD-related decrease in OATP1B function to inform prospective PBPK modeling of OATP1B-mediated drug-drug interaction in these patients. Monitoring of CPI allows detection of CKD-drug interaction risk for OATP1B drugs with combined hepatic and renal elimination which may be underestimated by extrapolating the interaction risk based on pitavastatin data in healthy participants.

POS0665 DEVELOPMENT OF A BIOCHEMICAL TOFACITINIB ADHERENCE ASSAY IN RHEUMATOID ARTHRITIS: THE ORAL ADHERE STUDY

Background

Tofacitinib is a potent inhibitor of the JAK1/JAK3 tyrosine kinases effective in the treatment of rheumatoid arthritis (RA). Unlike biologic DMARDs, tofacitinib is administered orally. Oral administration offers a major benefit to patients, removing the risk of injection site reactions and previous research has shown that patients prefer an oral DMARD which may affect patient’s adherence (1).

Non-adherence is a health behaviour that results in reduced response and increased healthcare costs but can be challenging to accurately measure. Direct tofacitinib measurement may be an accurate measure of adherence that could, in the future, be used in a clinical setting as part of a behaviour change intervention.

Tofacitinib can be measured using High Performance Liquid Chromatography Selected Reaction Monitoring Mass Spectrometry (HPLC-SRM-MS). Previous tofacitinib studies have demonstrated an assay sensitivity of 0.1ng/ml may be sufficient for the detection of adherence following 5mg twice daily administration (2).

Objectives

The aim of this study is to develop a HPLC-SRM-MS assay to measure biochemical tofacitinib adherence in patients with RA.

Methods

Human serum for method development was obtained from volunteers recruited to the collection of blood and urine samples from volunteers for the development of analytical methods study (UREC 12346) and the National Repository Study (REC 99/8/84) following informed consent.

Samples were spiked with Tofacitinib/Tofacitinib-d3 and subjected to protein precipitation. LC-MS/MS analysis was performed on a TSQ Vantage triple quadrupole mass spectrometer coupled with an Accela UHPLC system (Thermo Fisher Scientific, USA). Validation of the assay was tested as adapted from European Medicines Agency guidelines on Bioanalytical Validation. Specifically, the lower limit of quantification (LLOQ), carryover, accuracy, linearity, precision, recovery and stability of the assay was determined.

To investigate the ability of the assay to detect adherence, serum samples (n=10) of patients prescribed tofacitinib from the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS) were analysed (REC reference: 04/Q1403/37). Participants self-reported date and time of tofacitinib ingestion prior to venepuncture. Samples were analysed in triplicate.

Results

The assay demonstrated a tofacitinib LLOQ of 0.1ng/ml, carryover of sample following injection of a 1000 ng/ml tofacitinib was <1%, linearity of r2=0.998, within run accuracy was between 81-85% at LLOQ and between 91-107% at all other levels. Between run accuracy was within 14.9% at LLOQ and within 0.2-5.1% of the nominal concentration at all other levels. Samples of tofacitinib spiked in whole blood and left at room temperature for seven days were within 0.98-10.25% of serum samples spiked on the day of analysis for all concentrations. To demonstrate the potential of the assay to determine adherence, all 10 BRAGGSS samples revealed tofacitinib levels above 0.1ng/ml with CV<15% (Table 1).

Table 1.Sample IDTime difference between self-reported tofacitinib ingestion and blood sample (hours)Mean Tofacitanib (ng/ml, n=3)CV (%)497.026.432.82402.8100.555.51703.673.232.27681.5150.378.09201.5137.469.09301.8117.8514.97331.490.891.18795.4217.4711.40172.8108.437.43372.5335.043.48

Conclusion

A novel tofacitinib LC-MS/MS assay has been developed. The ability of the assay to measure biochemical adherence has been explored. Further research to establish the sensitivity of the assay and the ability of the assay to detect non-adherence are required.

References

[1]Alten R, Krüger K, Rellecke J, Schiffner-Rohe J, Behmer O, Schiffhorst G, et al. Examining patient preferences in the treatment of rheumatoid arthritis using a discrete-choice approach. Patient Prefer Adherence. 2016;10:2217-28.

[2]Suzuki M, Tse S, Hirai M, Kurebayashi Y. Application of Physiologically-Based Pharmacokinetic Modeling for the Prediction of Tofacitinib Exposure in Japanese. Kobe J Med Sci. 2017;62(6):E150-E61.

Acknowledgements

Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited

Disclosure of Interests

Stephanie Church Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited, Kimme Hyrich Speakers bureau: Honoraria as a speaker received from Abbvie, Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited.

Research grant award from BMS, Kayode Ogungbenro Consultant of: Afferent, Biogen, Buzzard, Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited., Richard Unwin Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited, Anne Barton Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited. AB has received grant funding from Scipher Medicine Ltd, Bristol Myers Squibb and Galapagos in the past 12 months., James Bluett Grant/research support from: Financial support was provided as an Investigator Sponsored Research Grant from Pfizer Limited. JB has received travel/conference fees from UCB, Pfizer and Eli Lilly

In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition

Drug development for the central nervous system (CNS) is a complex endeavour with low success rates, as the structural complexity of the brain and specifically the blood-brain barrier (BBB) poses tremendous challenges. Several in vitro brain systems have been evaluated, but the ultimate use of these data in terms of translation to human brain concentration profiles remains to be fully developed. Thus, linking up in vitro-to-in vivo extrapolation (IVIVE) strategies to physiologically based pharmacokinetic (PBPK) models of brain is a useful effort that allows better prediction of drug concentrations in CNS components. Such models may overcome some known aspects of inter-species differences in CNS drug disposition. Required physiological (i.e. systems) parameters in the model are derived from quantitative values in each organ. However, due to the inability to directly measure brain concentrations in humans, compound-specific (drug) parameters are often obtained from in silico or in vitro studies. Such data are translated through IVIVE which could be also applied to preclinical in vivo observations. In such exercises, the limitations of the assays and inter-species differences should be adequately understood in order to verify these predictions with the observed concentration data. This report summarizes the state of IVIVE-PBPK-linked models and discusses shortcomings and areas of further research for better prediction of CNS drug disposition.

Model-Based Drug-Drug Interaction Extrapolation Strategy From Adults to Children: Risdiplam in Pediatric Patients With Spinal Muscular Atrophy

Risdiplam (Evrysdi) improves motor neuron function in patients with spinal muscular atrophy (SMA) and has been approved for the treatment of patients ≥2 months old. Risdiplam exhibits time‐dependent inhibition of cytochrome P450 (CYP) 3A in vitro. While many pediatric patients receive risdiplam, a drug–drug interaction (DDI) study in pediatric patients with SMA was not feasible. Therefore, a novel physiologically‐based pharmacokinetic (PBPK) model‐based strategy was proposed to extrapolate DDI risk from healthy adults to children with SMA in an iterative manner. A clinical DDI study was performed in healthy adults at relevant risdiplam exposures observed in children. Risdiplam caused an 1.11‐fold increase in the ratio of midazolam area under the curve with and without risdiplam (AUCR)), suggesting an 18‐fold lower in vivo CYP3A inactivation constant compared with the in vitro value. A pediatric PBPK model for risdiplam was validated with independent data and combined with a validated midazolam pediatric PBPK model to extrapolate DDI from adults to pediatric patients with SMA. The impact of selected intestinal and hepatic CYP3A ontogenies on the DDI susceptibility in children relative to adults was investigated. The PBPK analysis suggests that primary CYP3A inhibition by risdiplam occurs in the intestine rather than the liver. The PBPK‐predicted risdiplam CYP3A inhibition risk in pediatric patients with SMA aged 2 months–18 years was negligible (midazolam AUCR of 1.09–1.18) and included in the US prescribing information of risdiplam. Comprehensive evaluation of the sensitivity of predicted CYP3A DDI on selected intestinal and hepatic CYP3A ontogeny functions, together with PBPK model‐based strategy proposed here, aim to guide and facilitate DDI extrapolations in pediatric populations.

Physiologically Based Pharmacokinetic Modeling of Transporter-Mediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats

Physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development to simulate changes in both systemic and tissue exposures that arise as a result of changes in enzyme and/or transporter activity. Verification of these model-based simulations of tissue exposure is challenging in the case of transporter-mediated drug–drug interactions (tDDI), in particular as these may lead to differential effects on substrate exposure in plasma and tissues/organs of interest. Gadoxetate, a promising magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2). In this study, we developed a gadoxetate PBPK model and explored the use of liver-imaging data to achieve and refine in vitro–in vivo extrapolation (IVIVE) of gadoxetate hepatic transporter kinetic data. In addition, PBPK modeling was used to investigate gadoxetate hepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced (DCE) MRI data of gadoxetate in rat blood, spleen, and liver were used in this analysis. Gadoxetate in vitro uptake kinetic data were generated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyte uptake unbound Michaelis–Menten constant (K_m,u) of gadoxetate was 106 μM (17%) (n = 4 rats), and active saturable uptake accounted for 94% of total uptake into hepatocytes. PBPK–IVIVE of these data (bottom-up approach) captured reasonably systemic exposure, but underestimated the in vivo gadoxetate DCE–MRI profiles and elimination from the liver. Therefore, in vivo rat DCE–MRI liver data were subsequently used to refine gadoxetate transporter kinetic parameters in the PBPK model (top-down approach). Active uptake into the hepatocytes refined by the liver-imaging data was one order of magnitude higher than the one predicted by the IVIVE approach. Finally, the PBPK model was fitted to the gadoxetate DCE–MRI data (blood, spleen, and liver) obtained with and without coadministered rifampicin. Rifampicin was estimated to inhibit active uptake transport of gadoxetate into the liver by 96%. The current analysis highlighted the importance of gadoxetate liver data for PBPK model refinement, which was not feasible when using the blood data alone, as is common in PBPK modeling applications. The results of our study demonstrate the utility of organ-imaging data in evaluating and refining PBPK transporter IVIVE to support the subsequent model use for quantitative evaluation of hepatic tDDI.

Evaluation of area under the concentration curve adjusted by the terminal-phase as a metric to reduce the impact of variability in bioequivalence testing

AIM

To quantify the utility of a terminal-phase adjusted area under the concentration curve method in increasing the probability of a correct and conclusive outcome of a bioequivalence (BE) trial for highly variable drugs when clearance (CL) varies more than the volume of distribution (V).

METHODS

Data from a large population of subjects were generated with variability in CL and V, and used to simulate a two-period, two-sequence crossover BE trial. The 90% confidence interval for formulation comparison was determined following BE assessment using the area under the concentration curve (AUC) ratio test, and the proposed terminal-phase adjusted AUC ratio test. An outcome of bioequivalent, nonbioequivalent or inconclusive was then assigned according to predefined BE limits.

RESULTS

When CL varied more than V, the proposed approach enhanced the probability of correctly assigning bioequivalent or nonbioequivalent and reduced the risk of an inconclusive trial. For a hypothetical drug with between-subject variability of 35% for CL and 10% for V, when the true test-reference ratio of bioavailability was 1.15, a crossover study of n = 14 subjects analysed by the proposed method would have 80% or 20% probability of claiming bioequivalent or nonbioequivalent, compared to 22%, 46% or 32% probability of claiming bioequivalent, nonbioequivalent or inconclusive using the standard AUC ratio test.

CONCLUSIONS

The terminal-phase adjusted AUC ratio test represents a simple and readily applicable approach to enhance the BE assessment of drug products when CL varies more than V.

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/AUC_control (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/AUC_control = 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.

Population pharmacokinetic modeling and simulation to support qualification of pyridoxic acid as endogenous biomarker of OAT1/3 renal transporters

Renal clearance of many drugs is mediated by renal organic anion transporters OAT1/3 and inhibition of these transporters may lead to drug‐drug interactions (DDIs). Pyridoxic acid (PDA) and homovanillic acid (HVA) were indicated as potential biomarkers of OAT1/3. The objective of this study was to develop a population pharmacokinetic model for PDA and HVA to support biomarker qualification. Simultaneous fitting of biomarker plasma and urine data in the presence and absence of potent OAT1/3 inhibitor (probenecid, 500 mg every 6 h) was performed. The impact of study design (multiple vs. single dose of OAT1/3 inhibitor) and ability to detect interactions in the presence of weak/moderate OAT1/3 inhibitors was investigated, together with corresponding power calculations. The population models developed successfully described biomarker baseline and PDA/HVA OAT1/3‐mediated interaction data. No prominent effect of circadian rhythm on PDA and HVA individual baseline levels was evident. Renal elimination contributed greater than 80% to total clearance of both endogenous biomarkers investigated. Estimated probenecid unbound in vivo OAT inhibitory constant was up to 6.4‐fold lower than in vitro values obtained with PDA as a probe. The PDA model was successfully verified against independent literature reported datasets. No significant difference in power of DDI detection was found between multiple and single dose study design when using the same total daily dose of 2000 mg probenecid. Model‐based simulations and power calculations confirmed sensitivity and robustness of plasma PDA data to identify weak, moderate, and strong OAT1/3 inhibitors in an adequately powered clinical study to support optimal design of prospective clinical OAT1/3 interaction studies.

PBPK Model of Coproporphyrin I: Evaluation of the Impact of SLCO1B1 Genotype, Ethnicity, and Sex on its Inter-Individual Variability

Coproporphyrin I (CPI) is an endogenous biomarker of OATP1B activity and associated drug-drug interactions. In this study, a minimal physiologically-based pharmacokinetic model was developed to investigate the impact of OATP1B1 genotype (c.521T>C), ethnicity, and sex on CPI pharmacokinetics and interindividual variability in its baseline. The model implemented mechanistic descriptions of CPI hepatic transport between liver blood and liver tissue and renal excretion. Key model parameters (e.g., endogenous CPI synthesis rate, and CPI hepatic uptake clearance) were estimated by fitting the model simultaneously to three independent CPI clinical datasets (plasma and urine data) obtained from white (n = 16, men and women) and Asian-Indian (n = 26, all men) subjects, with c.521 variants (TT, TC, and CC). The optimized CPI model successfully described the observed data using c.521T>C genotype, ethnicity, and sex as covariates. CPI hepatic active was 79% lower in 521CC relative to the wild type and 42% lower in Asian-Indians relative to white subjects, whereas CPI synthesis was 23% higher in male relative to female subjects. Parameter sensitivity analysis showed marginal impact of the assumption of CPI synthesis site (blood or liver), resulting in comparable recovery of plasma and urine CPI data. Lower magnitude of CPI-drug interaction was simulated in 521CC subjects, suggesting the risk of underestimation of CPI-drug interaction without prior OATP1B1 genotyping. The CPI model incorporates key covariates contributing to interindividual variability in its baseline and highlights the utility of the CPI modeling to facilitate the design of prospective clinical studies to maximize the sensitivity of this biomarker.

Dose individualisation in oncology using chemotherapy-induced neutropenia: Example of docetaxel in non-small cell lung cancer patients

AIMS

Chemotherapy-induced neutropenia has been associated with an increase in overall survival in non-small cell lung cancer patients. Therefore, neutrophil counts could be an interesting biomarker for drug efficacy as well as linked directly to toxicity. For drugs where neutropenia is dose limiting, neutrophil counts might be used for monitoring drug effect and for dosing optimisation.

METHODS

The relationship between drug effect on the first cycle neutrophil counts and patient survival was explored in a Phase III clinical trial where patients with non-small cell lung cancer were treated with docetaxel. Once the association has been established, dosing optimisation was performed for patients with severe toxicities (neutropenia) without compromising drug efficacy (overall survival).

RESULTS

After taking into account baseline prognostic factors, such as Eastern Cooperative Oncology Group performance status, smoking status, liver metastasis, tumour burden, neutrophil counts and albumin levels, a model-predicted drug effect on the first cycle neutrophil counts was strongly associated with patient survival (P = .005). Utilising this relationship in a dose optimisation algorithm, 194 out of 366 patients would have benefited from a dose reduction after the first cycle of docetaxel. The simulated 1-year survival probabilities associated with the original dose and the individualised dose were not different.

CONCLUSION

The strong relationship between drug effect on the first cycle neutrophil counts and patient survival suggests that this variable could be used to individualise dosing, possibly without needing pharmacokinetic samples. The algorithm highlights that doses could be reduced in case of severe haematological toxicities without compromising drug efficacy.

Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study

OBJECTIVES

Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19.

DESIGN

The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.

SETTING

3 secondary and tertiary level centres in Greater Manchester, the UK.

PARTICIPANTS

392 hospitalised patients with a diagnosis of COVID-19.

RESULTS

392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.

CONCLUSIONS

This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.

Does "Birth" as an Event Impact Maturation Trajectory of Renal Clearance via Glomerular Filtration? Reexamining Data in Preterm and Full-Term Neonates by Avoiding the Creatinine Bias

Glomerular filtration rate (GFR) is an important measure of renal function. Various models for its maturation have recently been compared; however, these have used markers, which are subject to different renal elimination processes. Inulin clearance data (a purer probe of GFR) collected from the literature were used to determine age‐related changes in GFR aspects of renal drug excretion in pediatrics. An ontogeny model was derived using a best‐fit model with various combinations of covariates such as postnatal age, gestational age at birth, and body weight. The model was applied to the prediction of systemic clearance of amikacin, gentamicin, vancomycin, and gadobutrol. During neonatal life, GFR increased as a function of both gestational age at birth and postnatal age, hence implying an impact of birth and a discrepancy in GFR for neonates with the same postmenstrual age depending on gestational age at birth (ie, neonates who were outside the womb longer had higher GFR, on average). The difference in GFR between pre‐term and full‐term neonates with the same postmenstrual age was negligible from beyond 1.25 years. Considering both postnatal age and gestational age at birth in GFR ontogeny models is important because postmenstrual age alone ignores the impact of birth. Most GFR models use covariates of body size in addition to age. Therefore, prediction from these models will also depend on the change in anthropometric characteristics with age. The latter may not be similar in various ethnic groups, and this makes the head‐to‐head comparison of models very challenging.

Impact of tumour size measurement inter-operator variability on model-based drug effect evaluation

Purpose

During oncology clinical trials, tumour size (TS) measurements are commonly used to monitor disease progression and to assess drug efficacy. We explored inter-operator variability within a subset of a phase III clinical trial conducted from August 1995 to February 1997 and its impact on drug effect evaluation using a tumour growth inhibition model.

Methods

One hundred twenty lesions were measured twice at each time point; once at the hospital and once at the centralised centre. A visual analysis was performed to identify trends within the profiles over time. Linear regression and relative error ratios were used to explore the inter-operator variability of raw TS measurements and model-based estimates.

Results

While correlation between patient-level estimates of drug effect was poor (r² = 0.28), variability between the study-level estimates was much less affected (9%).

Conclusions

The global evaluation of drug effect using modelling approaches might not be affected by inter-operator variability. However, the exploration of covariates for drug effect and the characterisation of an exposure–tumour shrinkage relationship seems limited by the high measurement variability that translates to a poor correlation of individual drug effect estimates. This might be addressed by the use of more precise computer-aided measurement methods.

Electronic supplementary material

The online version of this article (10.1007/s00280-020-04049-5) contains supplementary material, which is available to authorized users.

Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution

In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fu_p) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.

Comparison of piperacillin exposure in the lungs of critically ill patients and healthy volunteers

Background

Severe infections of the respiratory tracts of critically ill patients are common and associated with excess morbidity and mortality. Piperacillin is commonly used to treat pulmonary infections in critically ill patients. Adequate antibiotic concentration in the epithelial lining fluid (ELF) of the lung is essential for successful treatment of pulmonary infection.

Objectives

To compare piperacillin pharmacokinetics/pharmacodynamics in the serum and ELF of healthy volunteers and critically ill patients.

Methods

Piperacillin concentrations in the serum and ELF of healthy volunteers and critically ill patients were compared using population methodologies.

Results

Median piperacillin exposure was significantly higher in the serum and the ELF of critically ill patients compared with healthy volunteers. The IQR for serum piperacillin exposure in critically ill patients was six times greater than for healthy volunteers. The IQR for piperacillin exposure in the ELF of critically ill patients was four times greater than for healthy volunteers. The median pulmonary piperacillin penetration ratio was 0.31 in healthy volunteers and 0.54 in critically ill patients.

Conclusions

Greater variability in serum and ELF piperacillin concentrations is observed in critically ill patients compared with healthy adult subjects and must be considered in the development of dosage regimens. Pulmonary penetration of antimicrobial agents should be studied in critically ill patients, as well as healthy volunteers, during drug development to ensure appropriate dosing of patients with pneumonia.

A population pharmacokinetic model for simvastatin and its metabolites in children and adolescents

Purpose

Poor adherence to dietary/behaviour modifications as interventions for hypercholesterolemia in paediatric patients often necessitates the initiation of statin therapy. The aim of this study was to develop a joint population pharmacokinetic model for simvastatin and four metabolites in children and adolescents to investigate sources of variability in simvastatin acid exposure in this patient population, in addition to SLCO1B1 genotype status.

Methods

Plasma concentrations of simvastatin and its four metabolites, demographic and polymorphism data for OATP1B1 and CYP3A5 were analysed utilising a population pharmacokinetic modelling approach from an existing single oral dose (10 mg < 17 years and 20 mg ≥ 18 years) pharmacokinetic dataset of 32 children and adolescents.

Results

The population PK model included a one compartment disposition model for simvastatin with irregular oral absorption described by two parallel absorption processes each consisting of sequential zero and first-order processes. The data for each metabolite were described by a one-compartment disposition model with the formation and elimination apparent parameters estimated. The model confirmed the statistically significant effect of c.521T>C (rs4149056) on the pharmacokinetics of the active metabolite simvastatin acid in children/adolescents, consistent with adult data. In addition, age was identified as a covariate affecting elimination clearances of 6-hydroxymethyl simvastatin acid and 3, 5 dihydrodiol simvastatin metabolites.

Conclusion

The model developed describes the pharmacokinetics of simvastatin and its metabolites in children/adolescents capturing the effects of both c.521T>C and age on variability in exposure in this patient population. This joint simvastatin metabolite model is envisaged to facilitate optimisation of simvastatin dosing in children/adolescents.

Electronic supplementary material

The online version of this article (10.1007/s00228-019-02697-y) contains supplementary material, which is available to authorized users.

Development and validation of a methotrexate adherence assay

BACKGROUND

The first-line therapy for rheumatoid arthritis (RA) is weekly oral methotrexate (MTX) at low dosages (7.5-25 mg/week). However, ~40% of patients are non-adherent which may explain why some do not respond and need to start more expensive biological therapies. To monitor adherence more accurately and develop strategies to improve it, a validated objective MTX adherence test is required.

OBJECTIVE

To develop and validate the diagnostic accuracy of a novel MTX adherence assay using high-performance liquid chromatography-selected reaction monitoring- mass spectrometry (HPLC-SRM-MS) based biochemical analysis of drug levels.

METHODS

20 patients with RA underwent MTX pharmacokinetic assessment using HPLC-SRM-MS MTX plasma concentration analysis over a 6-day period. Directly observed therapy was the reference standard. Pharmacokinetic model validation was performed using independent plasma samples from real-world patients (n=50) with self-reported times of drug administration. Following assay optimisation, the sensitivity of the assay to detect adherence was established using samples from an observational cohort study (n=138).

RESULTS

A two-compartment pharmacokinetic model was developed and validated. Simulations described the sensitivity required for MTX detection over 7 days; subsequent assay optimisation and retesting of samples confirmed that all patients were correctly identified as MTX adherers. Using real-world samples, the assays sensitivity was 95%.

CONCLUSION

Non-adherence to MTX is common and can have a significant effect on disease activity. HPLC-SRM-MS plasma analysis accurately detects MTX adherence. The validated objective test could easily be used in clinic to identify patients requiring adherence support.

An evaluation of cetuximab dosing strategies using pharmacokinetics and cost analysis

OBJECTIVES

Cetuximab dosing is based on body surface area (BSA), an approach that is associated with significant wastage due to available vial sizes. NHS England recently introduced an alternative strategy for cetuximab dosing based on dose banding. The aim of this work was to investigate approaches to cetuximab dosing to improve its cost-effectiveness.

METHODS

A simulation study using a population pharmacokinetic model was used to assess the performance of dosing strategies using exposure, probability of target attainment and cost. Two dosage regimens (500 and 400/250 mg/m² ) were investigated; 5% and 10% dose banding, fixed and optimised dosing strategies were evaluated and compared to BSA strategy.

KEY FINDINGS

The percentage of the total cost associated with wastage for the 400/250 mg/m² regimen were 8.75%, 5.13%, 3.61%, 9.2% and 0% for BSA; 5 and 10% bands; fixed and optimal strategies, respectively. Similar results were obtained for 500 mg/m² regimen. In comparison with BSA strategy, other strategies have comparable or improved performance. Optimised strategy showed consistent performance and ensures equal exposure and probability of target attainment.

CONCLUSIONS

Cost-effectiveness of cetuximab treatment can be improved with alternative strategies by reducing wastage without compromising exposure.

A study of the dosage and duration for levobupivacaine infusion by the caudal-epidural route in infants aged 3-6 months

BACKGROUND

The local anesthetic, levobupivacaine, is the safer enantiomer of racemic bupivacaine. Present protocols for levobupivacaine are based on studies and pharmacokinetic modeling with racemic bupivacaine.

AIMS

The aim is to investigate total serum levobupivacaine concentrations after a caudalepidural loading dose followed by a maintenance infusion over 48 hours in infants aged 3-6 months.

METHODS

The clinical trial was conducted in eight infants aged 3-6 months, undergoing bladder exstrophy repair. Pharmacokinetic modeling allowed optimization of clinical sampling to measure total levobupivacaine and α₁ -acid glycoprotein and prediction of the effect of α₁ -acid glycoprotein on levobupivacaine plasma protein binding.

RESULTS

The observed median total levobupivacaine serum concentration was 0.30 mg/L (range: 0.20-0.70 mg/L) at 1 hour after the loading dose of 2 mg/kg. The median total levobupivacaine concentration after 47 hours of infusion, at 0.2 mg/kg/h, was 1.21 mg/L (0.07-1.85 mg/L). Concentrations of α₁ -acid glycoprotein were found to rise throughout the study period. Pharmacokinetic modeling suggested that unbound levobupivacaine quickly reached steady state at a concentration of approximately 0.03 mg/L.

CONCLUSION

The study allows the development of a pharmacokinetic model, combining levobupivacaine and α₁ -acid glycoprotein data. Modeling indicates that unbound levobupivacaine quickly reaches steady state once the infusion is started. Simulations suggest that it may be possible to continue the infusion beyond 48 hours.

Mathematical model of T-cell lymphoblastic lymphoma: disease, treatment, cure or relapse of a virtual cohort of patients

T lymphoblastic lymphoma (T-LBL) is a rare type of lymphoma with a good prognosis with a remission rate of 85%. Patients can be completely cured or can relapse during or after a 2-year treatment. Relapses usually occur early after the remission of the acute phase. The median time of relapse is equal to 1 year, after the occurrence of complete remission (range 0.2-5.9 years) (Uyttebroeck et al., 2008). It can be assumed that patients may be treated longer than necessary with undue toxicity.The aim of our model was to investigate whether the duration of the maintenance therapy could be reduced without increasing the risk of relapses and to determine the minimum treatment duration that could be tested in a future clinical trial.We developed a mathematical model of virtual patients with T-LBL in order to obtain a proportion of virtual relapses close to the one observed in the real population of patients from the EuroLB database. Our simulations reproduced a 2-year follow-up required to study the onset of the disease, the treatment of the acute phase and the maintenance treatment phase.

Comprehensive Evaluation of the Utility of 20 Endogenous Molecules as Biomarkers of OATP1B Inhibition Compared with Rosuvastatin and Coproporphyrin I

Endogenous biomarkers can be clinically relevant tools for the assessment of transporter function in vivo and corresponding drug-drug interactions (DDIs). The aim of this study was to perform systematic evaluation of plasma data obtained for 20 endogenous molecules in the same healthy subjects (n = 8-12) in the absence and presence of organic anion transporting polypeptide (OATP) inhibitor rifampicin (600 mg, single dose). The extent of rifampicin DDI magnitude [the ratio of the plasma concentration-time area under the curve (AUCR)], estimated fraction transported (f_T), and baseline variability was compared across the biomarkers and relative to rosuvastatin and coproporphyrin I (CPI). Out of the 20 biomarkers investigated tetradecanedioate (TDA), hexadecanedioate (HDA), glycocholic acid, glycodeoxycholic acid (GDCA), taurodeoxycholic acid (TDCA), and coproporphyrin III (CPIII) showed the high AUCR (2.1-8.5) and f_T (0.5-0.76) values, indicative of substantial OATP1B-mediated transport. A significant positive correlation was observed between the individual GDCA and TDCA AUCRs and the magnitude of rosuvastatin-rifampicin interaction. The CPI and CPIII AUCRs were significantly correlated, but no clear trend was established with the rosuvastatin AUCR. Moderate interindividual variability (15%-62%) in baseline exposure and AUCR was observed for TDA, HDA, and CPIII. In contrast, bile acids demonstrated high interindividual variability (69%-113%) and significant decreases in baseline plasma concentrations during the first 4 hours. This comprehensive analysis in the same individuals confirms that none of the biomarkers supersede CPI in the evaluation of OATP1B-mediated DDI risk. Monitoring of CPI and GDCA/TDCA may be beneficial for dual OATP1B/sodium-taurocholate cotransporting polypeptide inhibitors with consideration of challenges associated with large inter- and intraindividual variability observed for bile acids. Benefit of monitoring combined biomarkers (CPI, one bile acid and one fatty acid) needs to be confirmed with larger data sets and against multiple OATP1B clinical probes and perpetrators.

Reduction of inflammation after administration of interleukin-1 receptor antagonist following aneurysmal subarachnoid hemorrhage: results of the Subcutaneous Interleukin-1Ra in SAH (SCIL-SAH) study

OBJECTIVE Aneurysmal subarachnoid hemorrhage (aSAH) is a devastating cerebrovascular event with long-term morbidity and mortality. Patients who survive the initial bleeding are likely to suffer further early brain injury arising from a plethora of pathological processes. These may result in a worsening of outcome or death in approximately 25% of patients and may contribute to longer-term cognitive dysfunction in survivors. Inflammation, mediated by the cytokine interleukin-1 (IL-1), is an important contributor to cerebral ischemia after diverse forms of brain injury, including aSAH. Its effects are attenuated by its naturally occurring antagonist, IL-1 receptor antagonist (IL-1Ra [anakinra]). The authors hypothesized that administration of additional subcutaneous IL-1Ra would reduce inflammation and associated plasma markers associated with poor outcome following aSAH. METHODS This was a randomized, open-label, single-blinded study of 100 mg subcutaneous IL-1Ra, administered twice daily in patients with aSAH, starting within 3 days of ictus and continuing until 21 days postictus or discharge from the neurosurgical center, whichever was earlier. Blood samples were taken at admission (baseline) and at Days 3-8, 14, and 21 postictus for measurement of inflammatory markers. The primary outcome was difference in plasma IL-6 measured as area under the curve between Days 3 and 8, corrected for baseline value. Secondary outcome measures included similar area under the curve analyses for other inflammatory markers, plasma pharmacokinetics for IL-1Ra, and clinical outcome at 6 months. RESULTS Interleukin-1Ra significantly reduced levels of IL-6 and C-reactive protein (p < 0.001). Fibrinogen levels were also reduced in the active arm of the study (p < 0.002). Subcutaneous IL-1Ra was safe, well tolerated, and had a predictable plasma pharmacokinetic profile. Although the study was not powered to investigate clinical effect, scores of the Glasgow Outcome Scale-extended at 6 months were better in the active group; however, this outcome did not reach statistical significance. CONCLUSIONS Subcutaneous IL-1Ra is safe and well tolerated in aSAH. It is effective in reducing peripheral inflammation. These data support a Phase III study investigating the effect of IL-1Ra on outcome following aSAH. Clinical trial registration no.: EudraCT: 2011-001855-35 ( www.clinicaltrialsregister.eu ).

Gaining Mechanistic Insight Into Coproporphyrin I as Endogenous Biomarker for OATP1B-Mediated Drug-Drug Interactions Using Population Pharmacokinetic Modeling and Simulation

This study evaluated coproporphyrin I (CPI) as a selective endogenous biomarker of OATP1B‐mediated drug–drug interactions (DDIs) relative to clinical probe rosuvastatin using nonlinear mixed‐effect modeling. Plasma and urine CPI data in the presence/absence of rifampicin were modeled to describe CPI synthesis, elimination clearances, and obtain rifampicin in vivo OATP Ki. The biomarker showed stable interoccasion baseline concentrations and low interindividual variability (<25%) in subjects with wildtype SLCO1B1. Biliary excretion was the dominant CPI elimination route (maximal >85%). Estimated rifampicin in vivo unbound OATP Ki (0.13 μM) using CPI data was 2‐fold lower relative to rosuvastatin. Model‐based simulations and power calculations confirmed sensitivity of CPI to identify moderate and weak OATP1B inhibitors in an adequately powered clinical study. Current analysis provides the most detailed evaluation of CPI as an endogenous OATP1B biomarker to support optimal DDI study design; further pharmacogenomic and DDI data with a panel of inhibitors are required.

Dose Rationalization of Pembrolizumab and Nivolumab Using Pharmacokinetic Modeling and Simulation and Cost Analysis

Pembrolizumab and nivolumab are highly selective anti-programmed cell death 1 (PD-1) antibodies approved for the treatment of advanced malignancies. Variable exposure and significant wastage have been associated with body size dosing of monoclonal antibodies (mAbs). The following dosing strategies were evaluated using simulations: body weight, dose banding, fixed dose, and pharmacokinetic (PK)-based methods. The relative cost to body weight dosing for band, fixed 150 mg and 200 mg, and PK-derived strategies were -15%, -25%, + 7%, and -16% for pembrolizumab and -8%, -6%, and -10% for band, fixed, and PK-derived strategies for nivolumab, respectively. Relative to mg/kg doses, the median exposures were -1.0%, -4.6%, + 27.1%, and +3.0% for band, fixed 150 mg, fixed 200 mg, and PK-derived strategies, respectively, for pembrolizumab and -3.1%, + 1.9%, and +1.4% for band, fixed 240 mg, and PK-derived strategies, respectively, for nivolumab. Significant wastage can be reduced by alternative dosing strategies without compromising exposure and efficacy.

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014)

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late‐stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well‐established and regulatory‐acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4–5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP‐Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)‐based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well‐designed dose‐finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

Choosing an optimal input for an intravenous glucose tolerance test to aid parameter identification

OBJECTIVE

The minimal model is used to estimate insulin sensitivity in patients with diabetes, following an intravenous glucose tolerance test (IVGTT). Issues have been reported regarding parameter estimation, including correlation between insulin sensitivity and action parameters. The objective was to reduce these issues, by modifying the input of glucose in the test.

METHODS

Data were available for 24 volunteers following an IVGTT and glucose clamp test. Correlation between parameters was explored using likelihood heatmaps. An integrated glucose-insulin model was used to simulate glucose and insulin concentrations following new glucose inputs. The improved input for the test was selected by finding the minimum inverse of the determinant of the Fisher information matrix.

KEY FINDINGS

When the minimal model was fitted to the IVGTT data, there was clear correlation between the insulin parameters. With the glucose clamp, all parameters were correlated and badly estimated. The modified input, a bolus dose followed by constant infusion, resulted in improvement in parameter estimation and reduction in parameter correlation.

CONCLUSIONS

It is possible to reduce the issues with parameter estimation in the minimal model by modifying the glucose input, leading to a simplified test deign and a reduction in the total amount of glucose infused.

Population Pharmacokinetics of Imatinib in Nigerians With Chronic Myeloid Leukemia: Clinical Implications for Dosing and Resistance

Imatinib, a tyrosine kinase inhibitor, is the drug of choice for the treatment of chronic myeloid leukemia in Nigeria. Several studies have established interindividual and interpopulation variations in imatinib disposition although no pharmacokinetic study have been conducted in an African population since the introduction of the drug. This study explored a population pharmacokinetic approach to investigate the disposition of imatinib in Nigerians and examined the involvement of some covariates including genetic factors in the variability of the drug disposition with a view to optimize the use of the drug in this population. A total of 250 plasma concentrations from 126 chronic myeloid leukemia patients were quantified using a validated method. A population pharmacokinetic model was fitted to the data using NONMEM VII software, and the influences of 12 covariates were investigated. The mean population-derived apparent steady-state clearance, elimination half-life, area under the concentration-time curve over 24 hours, and volume of distribution were 17.2 ± 1.8 L/h., 12.05 ± 2.1 hours, 23.26 ± 0.6 μg·h/mL, and 299 ± 20.4 L, respectively. Whole blood count, ethnicity, CYP3A5*3, and ABCB1 C3435T were found to have significant influence on the apparent clearance, while the interindividual variability in clearance and interoccasion variability in bioavailability were 17.4% and 20.4%, respectively. There was a wide variability in apparent clearance and area under the curve compared to those reported in other populations. Thus, treatment with a standard dose of imatinib in this population may not produce the desired effect in most of the patients, whereas continuous exposure to a low drug concentration could lead to pharmacokinetic-derived resistance. The authors suggest the need for therapeutic drug monitoring-guided dose individualization in this population.

The effect of veno-venous ECMO on the pharmacokinetics of Ritonavir, Darunavir, Tenofovir and Lamivudine

INTRODUCTION

To our knowledge, there is no published data on the pharmacokinetic (PK) profile of antiretroviral (ART) drugs on patients undergoing extracorporeal membrane oxygenation (ECMO) therapy. We present PK analyses of Ritonavir, Darunavir, Lamivudine and Tenofovir in a patient with HIV who required veno-venous ECMO (VV ECMO).

METHODS

Plasma concentrations for Ritonavir, Darunavir, Tenofovir and Lamivudine were obtained while the patient was on ECMO following pre-emptive dose adjustments. Published population PK models were used to simulate plasma concentration profiles for the drugs. The population prediction and the observed plasma concentrations were then overlaid with the expected drug profiles using the individual Bayesian post-hoc parameter estimates.

RESULTS

Following dose adjustments, the PK profiles of Ritonavir, Darunavir and Tenofovir fell within the expected range and appeared similar to the population prediction, although slightly different for Ritonavir. The observed data for Lamivudine and its PK profile were completely different from the data available in the literature.

CONCLUSIONS

To our knowledge, this is the first study reporting the PK profile of ART drugs during ECMO therapy. Based on our results, dose adjustment of ART drugs while on VV ECMO may be advisable. Further study of the PK profile of Lamivudine is required.

Reduction of a Whole-Body Physiologically Based Pharmacokinetic Model to Stabilise the Bayesian Analysis of Clinical Data

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.

Physiologically based pharmacokinetic model for 6-mercpatopurine: exploring the role of genetic polymorphism in TPMT enzyme activity

AIMS

To extend the physiologically based pharmacokinetic (PBPK) model developed for 6-mercaptopurine to account for intracellular metabolism and to explore the role of genetic polymorphism in the TPMT enzyme on the pharmacokinetics of 6-mercaptopurine.

METHODS

The developed PBPK model was extended for 6-mercaptopurine to account for intracellular metabolism and genetic polymorphism in TPMT activity. System and drug specific parameters were obtained from the literature or estimated using plasma or intracellular red blood cell concentrations of 6-mercaptopurine and its metabolites. Age-dependent changes in parameters were implemented for scaling, and variability was also introduced for simulation. The model was validated using published data.

RESULTS

The model was extended successfully. Parameter estimation and model predictions were satisfactory. Prediction of intracellular red blood cell concentrations of 6-thioguanine nucleotide for different TPMT phenotypes (in a clinical study that compared conventional and individualized dosing) showed results that were consistent with observed values and reported incidence of haematopoietic toxicity. Following conventional dosing, the predicted mean concentrations for homozygous and heterozygous variants, respectively, were about 10 times and two times the levels for wild-type. However, following individualized dosing, the mean concentration was around the same level for the three phenotypes despite different doses.

CONCLUSIONS

The developed PBPK model has been extended for 6-mercaptopurine and can be used to predict plasma 6-mercaptopurine and tissue concentration of 6-mercaptopurine, 6-thioguanine nucleotide and 6-methylmercaptopurine ribonucleotide in adults and children. Predictions of reported data from clinical studies showed satisfactory results. The model may help to improve 6-mercaptopurine dosing, achieve better clinical outcome and reduce toxicity.