Covariate pharmacokinetic model building in oncology and its potential clinical relevance

Generation of realistic virtual adult populations using a model-based copula approach

Incorporating realistic sets of patient-associated covariates, i.e., virtual populations, in pharmacometric simulation workflows is essential to obtain realistic model predictions. Current covariate simulation strategies often omit or simplify dependency structures between covariates. Copula models are multivariate distribution functions suitable to capture dependency structures between covariates with improved performance compared to standard approaches. We aimed to develop and evaluate a copula model for generation of adult virtual populations for 12 patient-associated covariates commonly used in pharmacometric simulations, using the publicly available NHANES database, including sex, race-ethnicity, body weight, albumin, and several biochemical variables related to organ function. A multivariate (vine) copula was constructed from bivariate relationships in a stepwise fashion. Covariate distributions were well captured for the overall and subgroup populations. Based on the developed copula model, a web application was developed. The developed copula model and associated web application can be used to generate realistic adult virtual populations, ultimately to support model-based clinical trial design or dose optimization strategies.

Effect of poly(ε-caprolactone) microspheres on population pharmacokinetic/pharmacodynamic model of a simple coumarin

This study aims to evaluated the impact of poly(ε-caprolactone) (PCL) microspheres on the pharmacokinetics and pharmacodynamics (PopPK/PD) of 6-methylcoumarin (6MC). For this, PCL microspheres loaded with 6MC were prepared using the emulsification-evaporation method. Particle size, zeta potential, drug loading, and entrapment efficiency were characterised by dynamic light scattering and UV spectrophotometry. In vitro release and pharmacokinetics in Wistar rats were assessed for free and encapsulated 6MC. Anti-inflammatory activity was evaluated using the carrageenan-induced paw edoema model, with PopPK and PopPK/PD models developed. Microspheres showed diameters between 2.9 and 7.1 µm, zeta potentials of -10 to -15 mV, and drug loading of 0.24 mg/mg. Encapsulation efficiency was 45.5% to 75.9%. PopPK models showed enhanced absorption and distribution, with increased anti-inflammatory potency of encapsulated 6MC. PCL microspheres significantly improved the pharmacokinetic and pharmacodynamic profiles of 6MC, enhancing its therapeutic potential for lipophilic drugs.

Early Identification of Patients at Risk of Cabazitaxel-induced Severe Neutropenia

BACKGROUND

Cabazitaxel frequently causes severe neutropenia. A higher cabazitaxel systemic exposure is related to a lower nadir absolute neutrophil count (ANC).

OBJECTIVE

To describe the effect of cabazitaxel systemic exposure on ANC by a population pharmacokinetic/pharmacodynamic (POP-PK/PD) model, and to identify patients at risk of severe neutropenia early in their treatment course using a PK threshold.

DESIGN, SETTING, AND PARTICIPANTS

Data from five clinical studies were pooled to develop a POP-PK/PD model using NONMEM, linking both patient characteristics and cabazitaxel systemic exposure directly to ANC.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

A PK threshold, predictive of severe neutropenia (grade ≥3), was determined using a receiver operating characteristic curve.

RESULTS AND LIMITATIONS

Ninety-six patients were included with a total of 1726 PK samples and 1081 ANCs. The POP-PK/PD model described both cabazitaxel PK and ANC accurately. A cabazitaxel plasma concentration of >4.96 ng/ml at 6 h after the start of infusion was found to be predictive of severe neutropenia, with a sensitivity of 76% and a specificity of 65%.

CONCLUSIONS

Early cabazitaxel plasma levels are predictive of severe neutropenia. Implementation of the proposed PK threshold results in early identification of almost 76% of all severe neutropenias. If prospectively validated, patients at risk could benefit from prophylactic administration of granulocyte colony stimulating factors, preventing severe neutropenia in an early phase of treatment. Implementation of this threshold permits a less restricted use of the 25 mg/m2 dose, potentially increasing the therapeutic benefit.

PATIENT SUMMARY

Treatment with cabazitaxel chemotherapy often causes neutropenia, leading to susceptibility to infections, which might be life threatening. We found that a systemic cabazitaxel concentration above 4.96 ng/ml 6 h after the start of infusion is predictive of the occurrence of severe neutropenia. Measurement of systemic cabazitaxel levels provides clinicians with the opportunity to prophylactically stimulate neutrophil growth.

Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling

Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.

Go beyond the limits of genetic algorithm in daily covariate selection practice

Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. However, SCM is based on a local search strategy, in which the model-building process iteratively tests the addition or elimination of a single covariate at a time given all the others. This introduces a heuristic to limit the searching space and then the computational complexity, but, at the same time, can lead to a suboptimal solution. The application of genetic algorithms (GAs) for covariate selection has been proposed as a possible solution to overcome these limitations. However, their actual use during model building is limited by the extremely high computational costs and convergence issues, both related to the number of models being tested. In this paper, we proposed a new GA for covariate selection to address these challenges. The GA was first developed on a simulated case study where the heuristics introduced to overcome the limitations affecting currently available GA approaches resulted able to limit the selection of redundant covariates, increase replicability of results and reduce convergence times. Then, we tested the proposed GA on a real-world problem related to remifentanil. It obtained good results both in terms of selected covariates and fitness optimization, outperforming the SCM.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Population pharmacokinetics of methotrexate and 7-hydroxymethotrexate and delayed excretion in infants and young children with brain tumors

PURPOSE

The objectives of this study were to develop a population pharmacokinetic model of methotrexate (MTX) and its primary metabolite 7-hydroxymethotrexate (7OHMTX) in children with brain tumors, to identify the sources of pharmacokinetic variability, and to assess whether MTX and 7OHMTX systemic exposures were related to toxicity.

METHODS

Patients received 2.5 or 5 g/m2 MTX as a 24-hour infusion and serial samples were analyzed for MTX and 7OHMTX by an LC-MS/MS method. Pharmacokinetic parameters were estimated using nonlinear mixed-effects modeling. Demographics, laboratory values, and genetic polymorphisms were considered as potential covariates to explain the pharmacokinetic variability. Association between MTX and 7OHMTX systemic exposures and MTX-related toxicities were explored using random intercept logistic regression models.

RESULTS

The population pharmacokinetics of MTX and 7OHMTX were adequately characterized using two-compartment models in 142 patients (median 1.91 y; age range 0.09 to 4.94 y) in 513 courses. The MTX and 7OHMTX population clearance values were 4.6 and 3.0 l/h/m2, respectively. Baseline body surface area and estimated glomerular filtration rate were significant covariates on both MTX and 7OHMTX plasma disposition. Pharmacogenetic genotypes were associated with MTX pharmacokinetic parameters but had only modest influence. No significant association was observed between MTX or 7OHMTX exposure and MTX-related toxicity.

CONCLUSIONS

MTX and 7OHMTX plasma disposition were characterized for the first time in young children with brain tumors. No exposure-toxicity relationship was identified in this study, presumably due to aggressive clinical management which led to a low MTX-related toxicity rate.

[Therapy of mental disorders in patients with hematological malignancies]

OBJECTIVE

To assess the possibilities of therapy with minimal effective doses (MED) of psychotropic drugs for mental disorders (MD) that manifest during the treatment of hematological malignancies (HM).

MATERIAL AND METHODS

A prospective study was conducted at the National Medical Research Center for Hematology of the Russian Ministry of Health (Moscow), which included 204 (39.4%) men and 314 (60.6%) women (518 patients in total), aged 17 to 83 years (median 45 years), with various HM, in which the manifestation of MD occurred during the treatment of the underlying disease. To minimize the side-effects of psychotropic drugs and given the relatively mild level of MD, psychopharmacotherapy of patients was carried out mainly at MED. The severity of MD, manifested in patients, was assessed by the illness severity scale of the Clinical Global Impression (CGI) scale, and the effectiveness of the treatment was assessed by the improvement scale (CGI-I).

RESULTS

Mainly mild (188, 36%) and moderately pronounced (270, 52%) MD were noted in patients with HM during the treatment of the underlying disease. Severe psychopathological disorders (60, 12%) were observed much less often. Because of psychopharmacotherapy with MED, patients experienced a very significant (97, 19%) and significant improvement (354, 68%) of their mental state, less often the improvement was regarded as minimal (67, 13%). Therefore, almost all patients showed a stable relief of MD; in 87% (95% CI 84-90) of patients, this improvement was significant.

CONCLUSION

The tactics of treatment MD that manifest in patients with HM with MED of psychotropic drugs turned out to be therapeutically effective according to the results of the assessment on CGI scales.

Topotecan clearance based on a single sample and a population pharmacokinetic model: Application to a pediatric high-risk neuroblastoma clinical trial

BACKGROUND

Topotecan, an antitumor drug with systemic exposure (SE)-dependent activity against many pediatric tumors has wide interpatient pharmacokinetic variability, making it challenging to attain the desired topotecan SE. The study objectives were to update our topotecan population pharmacokinetic model, to evaluate the feasibility of determining individual topotecan clearance using a single blood sample, and to apply this approach to topotecan data from a neuroblastoma trial to explore exposure-response relationships.

PROCEDURE

Our previous population pharmacokinetic and covariate model was updated using data from 13 clinical pediatric studies. A simulation-based Bayesian analysis was performed to determine if a single blood sample could be sufficient to estimate individual topotecan clearance. Following the Bayesian approach, single pharmacokinetic samples collected from a Children's Oncology Group Phase III clinical trial (ANBL0532; NCT0056767) were analyzed to estimate individual topotecan SE. Associations between topotecan SE and toxicity or early response were then evaluated.

RESULTS

The updated population model included the impact of patient body surface area (BSA), age, and renal function on topotecan clearance. The Bayesian analysis with the updated model and single plasma samples showed that individual topotecan clearance values were estimated with good precision (mean absolute prediction error ≤16.2%) and low bias (mean prediction error ≤7.2%). Using the same approach, topotecan SE was derived in patients from ANBL0532. The exposure-response analysis showed an increased early response after concomitant cyclophosphamide and topotecan up to a topotecan SE of 45 h ng/mL.

CONCLUSIONS

A simple single-sample approach during topotecan therapy could guide dosing for patients, resulting in more patients reaching target attainment.

Population pharmacokinetic modeling of treosulfan and rationale for dose recommendation in children treated for conditioning prior to allogeneic hematopoietic stem cell transplantation

Intravenously infused treosulfan was evaluated in adult and pediatric patients for conditioning regimen prior to allogeneic hematopoietic stem cell transplantation. A population pharmacokinetic (PK) model was initially developed on 116 adult and pediatric PK profiles from historical trials, to support treosulfan dose recommendations for children in 2 prospective trials. The aim was to assess and update the initial population PK model by inclusion of additional 83 pediatric PK profiles from these 2 trials. The final population PK model was 2-compartmental with dosing in the central compartment, linear elimination, and inter-compartmental clearance. Inter-individual variability was included on clearance (CL), central volume (V1), peripheral volume (V2), and inter-compartmental clearance (Q). The final model described an effect of the body surface area (BSA) on CL, V1, V2, and Q. The final model resulted in a modified dose recommendation for children and advises treosulfan doses of 10 g/m2, 12 g/m2, and 14 g/m2 for BSAs of <0.4 m2, ≥0.4 to <0.9 m2, and ≥0.9 m2, respectively. This simplified BSA-dependent dose recommendation was developed for children, ensuring a well comparable treosulfan exposure as a dose of 14 g/m2 in adults - irrespective of their age and without applying individual therapeutic drug monitoring.

A Population Pharmacokinetic Model of Pentobarbital for Children with Status Epilepticus and Severe Traumatic Brain Injury

BACKGROUND

Pentobarbital pharmacokinetics (PK) remain elusive and the therapeutic windows narrow. Administration is frequent in critically ill children with refractory status epilepticus (SE) and severe traumatic brain injury (sTBI).

OBJECTIVES

To investigate pentobarbital PK in SE and sTBI patients admitted to the paediatric intensive care unit (PICU) with population-based PK (PopPK) modelling and dosing simulations.

METHODS

Develop a PopPK model with non-linear mixed-effects modelling (NONMEM®) with retrospective data (n = 36; median age 1.3 years; median weight 10 kg; 178 blood samples) treated with continuous intravenous pentobarbital. An independent dataset was used for external validation (n = 9). Dosing simulations with the validated model evaluated dosing regimens.

RESULTS

A one-compartment PK model with allometrically scaled weight on clearance (CL; 0.75) and volume of distribution (Vd; 1) captured data well. Typical CL and Vd values were 3.59 L/70 kg/h and 142 L/70 kg, respectively. Elevated creatinine and C-reactive protein (CRP) levels significantly correlated to decreased CL, explaining 84% of inter-patient variability, and were incorporated in the final model. External validation using stratified visual predictive checks showed good results. Simulations demonstrated patients with elevated serum creatinine and CRP failed to achieve steady state yet progressed to toxic levels with current dosing regimens.

CONCLUSIONS

The one-compartment PK model of intravenous pentobarbital described data well whereby serum creatinine and CRP significantly correlated with pentobarbital CL. Dosing simulations formulated adjusted dosing advice in patients with elevated creatinine and/or CRP. Prospective PK studies with pharmacodynamic endpoints, are imperative to optimise pentobarbital dosing in terms of safety and clinical efficacy in critically ill children.

Mini-PBPK-Based Population Model and Covariate Analysis to Assess the Complex Pharmacokinetics and Pharmacodynamics of RO7449135, an Anti-KLK5/KLK7 Bispecific Antibody in Cynomolgus Monkeys

RO7449135, an anti-kallikrein (KLK)5/KLK7 bispecific antibody, is in development as a potential therapy against Netherton's syndrome (NS). In cynomolgus monkey studies, RO7449135 bound to KLK5 and KLK7, causing considerable accumulation of total KLKs, but with non-dose-proportional increase. To understand the complex PKPD, a population model with covariate analysis was developed accounting for target binding in skin and migration of bound targets from skin to blood. The covariate analysis suggested the animal batch as the categorical covariate impacting the different KLK5 synthesis rates between the repeat-dose study and single-dose study, and the dose as continuous covariate impacting the internalization rate of the binary and ternary complexes containing KLK7. To comprehend the mechanism underlying, we hypothesized that inhibition of KLK5 by RO7449135 prevented its cleavage of the pro-enzyme of KLK7 (pro-KLK7) and altered the proportion between pro-KLK7 and KLK7. Besides the pro-KLK7, RO7449135 can interact with other proteins like LEKTI through KLK7 connection in a dose-dependent manner. The different high-order complexes formed by RO7449135 interacting with pro-KLK7 or LEKTI-like proteins can be subject to faster internalization rate. Accounting for the dose and animal batch as covariates, the model-predicted free target suppression is well aligned with the visual target engagement check. The population PKPD model with covariate analysis provides the scientific input for the complex PKPD analysis, successfully predicts the target suppression in cynomolgus monkeys, and thereby can be used for the human dose projection of RO7449135.

Quantitative assessment of the exposure-efficacy relationship of glucocerebrosidase using Markovian elements in Gaucher patients treated with enzyme replacement therapy

AIMS

The aims of this study are (i) to develop a population pharmacokinetic model of enzyme activity in Gaucher-type 1 (GD1) patients after intravenous administration of enzyme replacement therapy (ERT) and, (ii) to establish an exposure-efficacy relationship for bone marrow infiltration to propose dose adjustments according to patient covariate values.

MATERIALS AND METHODS

A prospective follow-up, semi-experimental multi-centre study was conducted in four hospitals to evaluate the pharmacokinetics, efficacy and safety of ERT in GD1 patients. 25 individuals with 266 glucocerebrosidase (GCase) observations in plasma and leukocytes and 14 individuals with 68 Spanish Magnetic Resonance Imaging (S-MRI) observations were enrolled.

RESULTS

A two concatenated compartments with zero-order endogenous production and first-order distribution (CL₁ =3.85 x10^-1 L/d) and elimination (CL₂ = 1.25 L/d) allowed to describe GCase observations in plasma and leucocytes, respectively. An exponential time-dependency (k_T =6.14 x10^-1 d^-1 ) effect on CL₁ was incorporated. The final exposure-efficacy model was a longitudinal logistic regression model with a first-order Markov element. An Emax function (EC₅₀ =15.73 U/L and Emax=2.33) linked steady-state concentrations of GCase in leucocytes to the probability of transition across the different S-MRI stages.

CONCLUSION

A population pharmacokinetic model successfully characterized the leukocyte activity-time profiles of GCase following intravenous administration of ERT in GD1 patients together with an exposure-efficacy relationship in bone marrow using markovian elements. The information obtained from this study could be of high clinical relevance in individualization of ERT in GD1 patients, as this could lead to anticipate decision-making regarding clinical response in bone and optimal dosing strategy. NONSTANDARD ABBREVIATIONS: -2LL: -2xlog(likelihood); ERT: enzyme replacement therapy; GCase: glucocerebrosidase activity; GD1: Gaucher disease type 1; GOF: goodness-of-fit plots; IIV: inter-individual variability; NLME: non-linear mixed effects modelling; OFV: objective function value; pc-VPC: prediction-corrected visual predictive check; PK: pharmacokinetic; RSE: relative standard error; RUV: residual unexplained variability, S-MRI: Spanish Magnetic Resonance Imaging, TDM: therapeutic drug monitoring.

Serum Predose Metabolic Profiling for Prediction of Rosuvastatin Pharmacokinetic Parameters in Healthy Volunteers

Rosuvastatin is a well-known lipid-lowering agent generally used for hypercholesterolemia treatment and coronary artery disease prevention. There is a substantial inter-individual variability in the absorption of statins usually caused by genetic polymorphisms leading to a variation in the corresponding pharmacokinetic parameters, which may affect drug therapy safety and efficacy. Therefore, the investigation of metabolic markers associated with rosuvastatin inter-individual variability is exceedingly relevant for drug therapy optimization and minimizing side effects. This work describes the application of pharmacometabolomic strategies using liquid chromatography coupled to mass spectrometry to investigate endogenous plasma metabolites capable of predicting pharmacokinetic parameters in predose samples. First, a targeted method for the determination of plasma concentration levels of rosuvastatin was validated and applied to obtain the pharmacokinetic parameters from 40 enrolled individuals; then, predose samples were analyzed using a metabolomic approach to search for associations between endogenous metabolites and the corresponding pharmacokinetic parameters. Data processing using machine learning revealed some candidates including sterols and bile acids, carboxylated metabolites, and lipids, suggesting the approach herein described as promising for personalized drug therapy.

Evaluation of body-surface-area adjusted dosing of high-dose methotrexate by population pharmacokinetics in a large cohort of cancer patients

Background

The aim of this study was to identify sources of variability including patient gender and body surface area (BSA) in pharmacokinetic (PK) exposure for high-dose methotrexate (MTX) continuous infusion in a large cohort of patients with hematological and solid malignancies.

Methods

We conducted a retrospective PK analysis of MTX plasma concentration data from hematological/oncological patients treated at the University Hospital of Cologne between 2005 and 2018. Nonlinear mixed effects modeling was performed. Covariate data on patient demographics and clinical chemistry parameters was incorporated to assess relationships with PK parameters. Simulations were conducted to compare exposure and probability of target attainment (PTA) under BSA adjusted, flat and stratified dosing regimens.

Results

Plasma concentration over time data (2182 measurements) from therapeutic drug monitoring from 229 patients was available. PK of MTX were best described by a three-compartment model. Values for clearance (CL) of 4.33 [2.95–5.92] L h^− 1 and central volume of distribution of 4.29 [1.81–7.33] L were estimated. An inter-occasion variability of 23.1% (coefficient of variation) and an inter-individual variability of 29.7% were associated to CL, which was 16 [7–25] % lower in women. Serum creatinine, patient age, sex and BSA were significantly related to CL of MTX. Simulations suggested that differences in PTA between flat and BSA-based dosing were marginal, with stratified dosing performing best overall.

Conclusion

A dosing scheme with doses stratified across BSA quartiles is suggested to optimize target exposure attainment. Influence of patient sex on CL of MTX is present but small in magnitude.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08443-x.

Nonparametric goodness-of-fit testing for parametric covariate models in pharmacometric analyses

The characterization of covariate effects on model parameters is a crucial step during pharmacokinetic/pharmacodynamic analyses. Although covariate selection criteria have been studied extensively, the choice of the functional relationship between covariates and parameters, however, has received much less attention. Often, a simple particular class of covariate‐to‐parameter relationships (linear, exponential, etc.) is chosen ad hoc or based on domain knowledge, and a statistical evaluation is limited to the comparison of a small number of such classes. Goodness‐of‐fit testing against a nonparametric alternative provides a more rigorous approach to covariate model evaluation, but no such test has been proposed so far. In this manuscript, we derive and evaluate nonparametric goodness‐of‐fit tests for parametric covariate models, the null hypothesis, against a kernelized Tikhonov regularized alternative, transferring concepts from statistical learning to the pharmacological setting. The approach is evaluated in a simulation study on the estimation of the age‐dependent maturation effect on the clearance of a monoclonal antibody. Scenarios of varying data sparsity and residual error are considered. The goodness‐of‐fit test correctly identified misspecified parametric models with high power for relevant scenarios. The case study provides proof‐of‐concept of the feasibility of the proposed approach, which is envisioned to be beneficial for applications that lack well‐founded covariate models.

Population Pharmacokinetics of Oxycodone and Metabolites in Patients with Cancer-Related Pain

Simple Summary

Patients with moderate to severe cancer-related pain are frequently treated with oxycodone, a strong-acting opioid. However, treatment with oxycodone does not always lead to sufficient analgesic action. In order to determine which factors affect treatment outcomes, we performed an observational study and developed a population pharmacokinetic model. The model described oxycodone, nor-oxycodone and nor-oxymorphone pharmacokinetics. The association between oxycodone or oxycodone metabolites’ exposure with pain scores and adverse events was not significant. The combined oxycodone, nor-oxycodone and nor-oxymorphone model is a good starting point for further unravelling the factors that affect the pharmacokinetic/pharmacodynamic relation of oxycodone and its metabolites.

Abstract

Oxycodone is frequently used for treating cancer-related pain, while not much is known about the factors that influence treatment outcomes in these patients. We aim to unravel these factors by developing a population-pharmacokinetic model to assess the pharmacokinetics of oxycodone and its metabolites in cancer patients, and to associate this with pain scores, and adverse events. Hospitalized patients with cancer-related pain, who were treated with oral oxycodone, could participate. Pharmacokinetic samples and patient-reported pain scores and occurrence and severity of nine adverse events were taken every 12 h. In 28 patients, 302 pharmacokinetic samples were collected. A one-compartment model for oxycodone and each metabolite best described oxycodone, nor-oxycodone, and nor-oxymorphone pharmacokinetics. Furthermore, oxycodone exposure was not associated with average and maximal pain scores, and oxycodone, nor-oxycodone, and nor-oxymorphone exposure were not associated with adverse events (all p > 0.05). This is the first model to describe the pharmacokinetics of oxycodone including the metabolites nor-oxycodone and nor-oxymorphone in hospitalized patients with cancer pain. Additional research, including more patients and a more timely collection of pharmacodynamic data, is needed to further elucidate oxycodone (metabolite) pharmacokinetic/pharmacodynamic relationships. This model is an important starting point for further studies to optimize oxycodone dosing regiments in patients with cancer-related pain.

Model-based evaluation of image-guided fractionated whole-brain radiation therapy in pediatric diffuse intrinsic pontine glioma xenografts

Radiation therapy (RT) is currently the standard treatment for diffuse intrinsic pontine glioma (DIPG), the most common cause of death in children with brain cancer. A pharmacodynamic model was developed to describe the radiation-induced tumor shrinkage and overall survival in mice bearing DIPG. CD1-nude mice were implanted in the brain cortex with luciferase-labeled patient-derived orthotopic xenografts of DIPG (SJDIPGx7 H3F3AWT / K27 M and SJDIPGx37 H3F3AK27M / K27M ). Mice were treated with image-guided whole-brain RT at 1 or 2 Gy/fraction 5-days-on 2-days-off for a cumulative dose of 20 or 54 Gy. Tumor progression was monitored with bioluminescent imaging (BLI). A mathematical model describing BLI and overall survival was developed with data from mice receiving 2 Gy/fraction and validated using data from mice receiving 1 Gy/fraction. BLI data were adequately fitted with a logistic tumor growth function and a signal distribution model with linear radiation-induced killing effect. A higher tumor growth rate in SJDIPGx37 versus SJDIPGx7 xenografts and a killing effect decreasing with higher tumor baseline (p < 0.0001) were identified. Cumulative radiation dose was suggested to inhibit the tumor growth rate according to a Hill function. Survival distribution was best described with a Weibull hazard function in which the hazard baseline was a continuous function of tumor BLI. Significant differences were further identified between DIPG cell lines and untreated versus treated mice. The model was adequately validated with mice receiving 1 Gy/fraction and will be useful in guiding future preclinical trials incorporating radiation and to support systemic combination therapies with RT.

Model based dose personalization in clinical trials

BACKGROUND AND OBJECTIVE

Personalized medicine is an important area of medical research which consists of designing therapies specifically for a patient or a group of patients. For drugs having a narrow therapeutic index or for vulnerable patients, methods such as therapeutic drug monitoring are used in a hospital setting to ensure that the blood concentration of the drug is maintained within a pre-decided range. However, such methods can not be used for drugs which are still in the developmental phase since, generally, insufficient information is available about the pharmacokinetic behaviour of the drug.

METHODS

In this paper, we present a new methodology for explicit optimization of dose regimens during the course of the pharmacokinetic studies such that the resultant blood concentration of the drug in each subject is maintained around a desired target concentration or within a target range.

RESULTS

We demonstrate that our algorithm is able to achieve the clinical objective of PK estimation while simultaneously individualizing the dose to every subject in the trial. Our algorithm computes dose regimens that, on average, have a relative efficiency of 97% with a standard deviation of less than 5%. The results show that the algorithm can be relied upon to ensure that the subjects in the trial are minimally over- and under-exposed to the test therapy.

CONCLUSIONS

The proposed methodology can assist in ensuring correct dosing to each subject in a clinical trial so that each subject receives only the intended exposure to the drug while simultaneously estimating the PK profile of the drug. Our methodology can also be applied in randomized concentration-controlled trials where maintenance of the target concentration in the subjects is a fundamental requirement for conducting these trials.

Targeting drug delivery in the vascular system: Focus on endothelium

The bloodstream is the main transporting pathway for drug delivery systems (DDS) from the site of administration to the intended site of action. In many cases, components of the vascular system represent therapeutic targets. Endothelial cells, which line the luminal surface of the vasculature, play a tripartite role of the key target, barrier, or victim of nanomedicines in the bloodstream. Circulating DDS may accumulate in the vascular areas of interest and in off-target areas via mechanisms bypassing specific molecular recognition, but using ligands of specific vascular determinant molecules enables a degree of precision, efficacy, and specificity of delivery unattainable by non-affinity DDS. Three decades of research efforts have focused on specific vascular targeting, which have yielded a multitude of DDS, many of which are currently undergoing a translational phase of development for biomedical applications, including interventions in the cardiovascular, pulmonary, and central nervous systems, regulation of endothelial functions, host defense, and permeation of vascular barriers. We discuss the design of endothelial-targeted nanocarriers, factors underlying their interactions with cells and tissues, and describe examples of their investigational use in models of acute vascular inflammation with an eye on translational challenges.

Pharmacokinetics and population pharmacokinetics in pediatric oncology

Pharmacokinetic research has become increasingly important in pediatric oncology as it can have direct clinical implications and is a crucial component in individualized medicine. Population pharmacokinetics has become a popular method especially in children, due to the potential for sparse sampling, flexible sampling times, computing of heterogeneous data, and identification of variability sources. However, population pharmacokinetic reports can be complex and difficult to interpret. The aim of this article is to provide a basic explanation of population pharmacokinetics, using clinical examples from the field of pediatric oncology, to facilitate the translation of pharmacokinetic research into the daily clinic.

Dolutegravir Population Pharmacokinetics in a Real-Life Cohort of People Living With HIV Infection: A Covariate Analysis

BACKGROUND

There are growing concerns about dolutegravir (DTG)-related neuropsychiatric adverse events and about differences in the characteristics of people living with HIV infection (PLWH) potentially associated with higher risks of said side effects. Several studies have shown that DTG was stopped more frequently among women, older PLWH, and PLWH who initiated abacavir (ABC) at the same time. This study aimed to clarify the factors affecting the pharmacokinetics (PKs) of DTG in a real-life cohort of PLWH using a population PK approach.

METHODS

The model-building strategy was based on a previously published model developed from premarketing trials (1-compartment model with first-order absorption and a lag time). Sparse therapeutic drug monitoring data were obtained from a real-life cohort of 279 PLWH, and population PK analysis was performed using Monolix software. A stepwise covariate model-building strategy was used to evaluate any relevant effects of age, body weight, gender, total bilirubin, smoking status, formulations of DTG, morning versus evening dosing, backbone therapy, and other comedications including CYP/UGT inducers/inhibitors.

RESULTS

For a typical 70-kg PLWH, the apparent clearance (CL/F) and apparent volume of distribution (V/F) were 0.748 L/h and 14.6 L, respectively. Of the demographic factors evaluated, body weight was a significant covariate for CL/F and for V/F. Smokers had a 17% higher CL/F relative to nonsmokers. Both strong enzyme inhibitors (eg, atazanavir) and inducers (eg, rifampicin) had marked effects on DTG exposure, with potential clinical implications. Ritonavir-boosted darunavir was found to moderately increase clearance of DTG by 23%. No significant effect of ABC-based backbone therapy was observed on the PK parameters of DTG.

CONCLUSIONS

Our results did not support the hypothesis that ABC, by competing with the DTG metabolic pathway, may significantly increase DTG exposure leading to potential drug toxicity.

The Influence of Normalization Weight in Population Pharmacokinetic Covariate Models

In covariate (sub)models of population pharmacokinetic models, most covariates are normalized to the median value; however, for body weight, normalization to 70 kg or 1 kg is often applied. In this article, we illustrate the impact of normalization weight on the precision of population clearance (CL_pop) parameter estimates. The influence of normalization weight (70, 1 kg or median weight) on the precision of the CL_pop estimate, expressed as relative standard error (RSE), was illustrated using data from a pharmacokinetic study in neonates with a median weight of 2.7 kg. In addition, a simulation study was performed to show the impact of normalization to 70 kg in pharmacokinetic studies with paediatric or obese patients. The RSE of the CL_pop parameter estimate in the neonatal dataset was lowest with normalization to median weight (8.1%), compared with normalization to 1 kg (10.5%) or 70 kg (48.8%). Typical clearance (CL) predictions were independent of the normalization weight used. Simulations showed that the increase in RSE of the CL_pop estimate with 70 kg normalization was highest in studies with a narrow weight range and a geometric mean weight away from 70 kg. When, instead of normalizing with median weight, a weight outside the observed range is used, the RSE of the CL_pop estimate will be inflated, and should therefore not be used for model selection. Instead, established mathematical principles can be used to calculate the RSE of the typical CL (CL_TV) at a relevant weight to evaluate the precision of CL predictions.

A Pharmacokinetic Study of Methylphenidate Hydrochloride Multilayer Extended-Release Capsules (Aptensio XR®) in Preschool-Aged Children with Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

This was a single-dose, one-period, multicenter, pharmacokinetic (PK) study to evaluate the PK of methylphenidate (MPH) hydrochloride multilayer extended-release capsules (MPH-MLR) in preschool children aged 4 to < 6 years, previously diagnosed with attention-deficit/hyperactivity disorder (ADHD), and on a stable dose of MPH.

METHODS

Preschool-aged children (N = 10) received a single oral dose of MPH-MLR (10, 15, or 20 mg) sprinkled over applesauce; a dose equivalent to their pre-enrollment daily dose of MPH. Blood samples for the measurement of MPH concentrations were obtained pre-dose and at 0.5, 1, 2, 3, 4, 6, 8, 10, 12, and 24 h post-dose. No structural model was assumed in the derivation of PK values for analysis. Maximum plasma concentration (C_max), area under the concentration-time curve (AUC), elimination half-life, clearance (CL), and volume of distribution (V_d) data were compared with a historical group of older children aged 6-11 years (N = 11) and analyzed by bodyweight. Safety (adverse event monitoring, vital signs, electrocardiogram, clinical laboratory testing, physical examination) was assessed.

RESULTS

Mean dose-normalized C_max and area under the curve to the last measurable observation (AUC_0-t) values were similar across dose groups, ranging from 0.67 ng/mL/mg (MPH 15 mg) to 0.81 ng/mL/mg (MPH 10 mg) for C_max/dose, and from 7.80 h × ng/mL/mg (MPH 20 mg) to 8.92 h × ng/mL/mg (MPH 10 mg) for AUC_0-t/dose. PK results were integrated into a previously described pharmacostatistical population PK model. Visual predictive check plots showed greater variability in the 6- to 11-year-old group than the 4- to < 6-year-old group, and CL increased with increasing body weight in a greater than dose-proportional manner. Mean CL, normalized for body weight, was constant for all dose groups, ranging from 4.88 L/h/kg to 5.80 L/h/kg. Median time to C_max ranged from 2.00 to 3.00 h post-dose, and overall, dose-normalized C_max concentrations indicated greater systemic exposures of MPH-MLR in preschool children aged 4 to < 6 years compared with children aged 6-11 years. Children aged 4 to < 6 years had a lower V_d than children aged 6-11 years. There were no unexpected safety signals.

CONCLUSION

The PK of MPH-MLR in preschool children demonstrated the biphasic absorption profile described earlier in older children, and the PK profile in children with ADHD aged 4 to < 6 years was similar to the profile in those aged 6-11 years, apart from a lower V_d and relatively higher systemic MPH levels for children in the preschool group.

TRIAL REGISTRATION

Clinicaltrials.gov Identifier: NCT02470234.

Exposure-Toxicity Association of Cyclophosphamide and Its Metabolites in Infants and Young Children with Primary Brain Tumors: Implications for Dosing

PURPOSE

To characterize the population pharmacokinetics of cyclophosphamide, active 4-hydroxy-cyclophosphamide (4OH-CTX), and inactive carboxyethylphosphoramide mustard (CEPM), and their associations with hematologic toxicities in infants and young children with brain tumors. To use this information to provide cyclophosphamide dosing recommendations in this population.

PATIENTS AND METHODS

Patients received four cycles of a 1-hour infusion of 1.5 g/m² cyclophosphamide. Serial samples were collected to measure cyclophosphamide, 4OH-CTX, and CEPM plasma concentrations. Population pharmacokinetic modeling was performed to identify the patient characteristics influencing drug disposition. Associations between drug exposures and metrics reflecting drug-induced neutropenia, erythropenia, and thrombocytopenia were investigated. A Bayesian approach was developed to predict 4OH-CTX exposure using only cyclophosphamide and CEPM plasma concentrations.

RESULTS

Data from 171 patients (0.07-4.9 years) were adequately fitted by a two-compartment (cyclophosphamide) and one-compartment model (metabolites). Young infants (<6 months) exhibited higher mean 4OH-CTX exposure than did young children (138.4 vs. 107.2 μmol/L·h, P < 0.0001). No genotypes exhibited clinically significant influence on drug exposures. Worse toxicity metrics were significantly associated with higher 4OH-CTX exposures. Dosing simulations suggested decreased cyclophosphamide dosage to 1.2 g/m² for young infants versus 1.5 g/m² for children to attain similar 4OH-CTX exposure. Bayesian-modeled 4OH-CTX exposure predictions were precise (mean absolute prediction error 14.8% ± 4.2%) and had low bias (mean prediction error 4.9% ± 5.1%).

CONCLUSIONS

A 4OH-CTX exposure-toxicity association was established, and a decreased cyclophosphamide dosage for young infants was suggested to reduce toxicity in this population. Bayesian modeling to predict 4OH-CTX exposure may reduce clinical processing-related costs and provide insights into further exposure-response associations.

Pemetrexed exposure predicts toxicity in advanced non-small-cell lung cancer: A prospective cohort study

BACKGROUND

We explored whether total exposure to pemetrexed predicts effectiveness and toxicity in advanced non-small-cell lung cancer (NSCLC). Furthermore, we investigated alternative dosing schedules.

METHODS

In this prospective cohort study, patients with advanced NSCLC receiving first- or second-line pemetrexed(/platinum) were enrolled. Plasma sampling was performed weekly (cyclePK) and within 24 h (24hPK) after pemetrexed administration. With population pharmacokinetic/pharmacodynamic modelling, total exposure to pemetrexed during cycle 1 (area under the curve during chemotherapy cycle 1 [AUC₁]) was estimated and related to progression-free survival (PFS)/overall survival (OS). We compared mean AUC₁ (mg·h/L) in patients with and without severe chemotherapy-related adverse events (AEs) during total treatment. Second, different dosing schedules were simulated to minimise the estimated variability (coefficient of variation [CV]) of AUC.

RESULTS

For 106 of 165 patients, concentrations of pemetrexed were quantified (24hPK, n = 15; cyclePK, n = 106). After adjusting for prognostic factors, sex, disease stage and World Health Organisation performance score, AUC₁ did not predict PFS/OS in treatment-naive patients (n = 95) (OS, hazard ratio [HR] = 1.05, 95% confidence interval [CI]: 1.00-1.11; PFS, HR = 1.03, 95% CI: 0.98-1.08). Patients with severe chemotherapy-related AEs (n = 55) had significantly higher AUC₁ values than patients without them (n = 51) (226 ± 53 vs 190 ± 31, p < 0.001). Compared with body surface area-based dosing (CV: 22.5%), simulation of estimated glomerular filtration rate (eGFR)-based dosing (CV 18.5%) and fixed dose of 900 mg with 25% dose reduction, if the eGFR<60 mL/min (CV: 19.1%), resulted in less interindividual variability of AUC.

CONCLUSIONS

Higher exposure to pemetrexed does not increase PFS/OS but is significantly associated with increased occurrence of severe toxicity. Our findings suggest that fixed dosing reduces interpatient pharmacokinetic variability and thereby might prevent toxicity, while preserving effectiveness.

The Population Pharmacokinetics of High-Dose Methotrexate in Infants with Acute Lymphoblastic Leukemia Highlight the Need for Bedside Individualized Dose Adjustment: A Report from the Children's Oncology Group

BACKGROUND

Infants with acute lymphoblastic leukemia (ALL) treated with high-dose methotrexate may have reduced methotrexate clearance (CL) due to renal immaturity, which may predispose them to toxicity.

OBJECTIVE

The aim of this study was to develop a population pharmacokinetic (PK) model of methotrexate in infants with ALL.

METHODS

A total of 672 methotrexate plasma concentrations were obtained from 71 infants enrolled in the Children's Oncology Group (COG) Clinical Trial P9407. Infants received methotrexate 4 g/m2 intravenously for four cycles during weeks 4-12 of intensification. A population PK analysis was performed using NONMEM® version 7.4. The final model was evaluated using a non-parametric bootstrap and a visual predictive check. Simulations were performed to evaluate methotrexate dose and the utility of a bedside algorithm for dose individualization.

RESULTS

Methotrexate was best characterized by a two-compartment model with allometric scaling. Weight was the only covariate included in the final model. The coefficient of variation for interoccasion variability (IOV) on CL was relatively high at 25.4%, compared with the interindividual variability for CL and central volume of distribution (10.7% and 13.2%, respectively). Simulations identified that 21.1% of simulated infants benefitted from bedside dose adjustment, and adjustment of methotrexate doses during infusions can avoid supratherapeutic concentrations.

CONCLUSION

Infants treated with high-dose methotrexate demonstrated a relatively high degree of IOV in methotrexate CL. The magnitude of IOV in the CL of methotrexate suggests that use of a bedside algorithm may avoid supratherapeutic methotrexate concentrations resulting from high IOV in methotrexate CL.

Bayesian Population Pharmacokinetic Modeling of Eltrombopag in Chronic Hepatitis C Patients

BACKGROUND AND OBJECTIVES

Eltrombopag is a thrombopoietic growth factor that is approved for the treatment of thrombocytopenia in chronic hepatitis C virus (HCV) patients. We aimed to describe eltrombopag population pharmacokinetics in hepatitis C patients. Bayesian statistical approach will be applied to screen for patients' characteristics associated with eltrombopag pharmacokinetic parameters.

METHODS

A population pharmacokinetic analysis was conducted using WinBUGS version 1.4.3. Data from 483 individuals with chronic HCV infection were analyzed. This analysis is a secondary analysis of two clinical studies (ENABLE1 and ENABLE2) sponsored by GlaxoSmithKline. Several patients' characteristics were examined as possible covariates of the population pharmacokinetic model. Prior information from previous studies was incorporated in the bayesian model as prior distribution to estimate pharmacokinetic parameters.

RESULTS

A two-compartment pharmacokinetic model with first-order absorption with exponential error model best fit the data. We identified East Asian race and total bilirubin level as predictors of eltrombopag clearance. Typical value for distributional clearance was 0.762 L/h (95% Bayesian credible set, 0.703-0.826), for volume of distribution of the central and peripheral compartments were 12 L (10.9-13.4) and 10.9 L (10.4-11.5), and for absorption lag time was 0.947 h (0.918-0.977). Assuming an average total bilirubin of 21.7 µmol/L, the typical elimination clearance value for an East Asian patient was 0.14 L/h and for other races was 0.20 L/h.

CONCLUSIONS

Eltrombopag pharmacokinetic behavior was described using population bayesian approach. This model can be applied to optimize eltrombopag dosing in order to reduce the incidence of thrombocytopenia in HCV-infected patient receiving interferon-based therapy.

Population pharmacokinetic analysis of AR-67, a lactone stable camptothecin analogue, in cancer patients with solid tumors

Background AR-67 is a novel camptothecin analogue at early stages of drug development. The phase 1 clinical trial in cancer patients with solid tumors was completed and a population pharmacokinetic model (POP PK) was developed to facilitate further development of this investigational agent. Methods Pharmacokinetic data collected in the phase 1 clinical trial were utilized for the development of a population POP PK by implementing the non-linear mixed effects approach. Patient characteristics at study entry were evaluated as covariates in the model. Subjects (N = 26) were treated at nine dosage levels (1.2-12.4 mg/m²/day) on a daily × 5 schedule. Hematological toxicity data were modeled against exposure metrics. Results A two-compartment POP PK model best described the disposition of AR-67 by fitting a total of 328 PK observations from 25 subjects. Following covariate model selection, age remained as a significant covariate on central volume. The final model provided a good fit for the concentration versus time data and PK parameters were estimated with good precision. Clearance, inter-compartmental clearance, central volume and peripheral volume were estimated to be 32.2 L/h, 28.6 L/h, 6.83 L and 25.0 L, respectively. Finally, exposure-pharmacodynamic analysis using E_max models showed that plasma drug concentration versus time profiles are better predictors of AR-67-related hematologic toxicity were better predictors of leukopenia and thrombocytopenia, as compared to total dose. Conclusions A POP PK model was developed to characterize AR-67 pharmacokinetics and identified age as a significant covariate. Exposure PK metrics C_max and AUC were shown to predict hematological toxicity. Further efforts to identify clinically relevant determinants of AR-67 disposition and effects in a larger patient population are warranted.

Population Pharmacokinetic Analyses and Model Validation of Tolvaptan in Subjects With Autosomal Dominant Polycystic Kidney Disease

Tolvaptan is the first approved drug treatment to slow kidney function decline in adults at risk of rapidly progressing autosomal dominant polycystic kidney disease (ADPKD). The objective is to develop (1091 subjects, 7335 observations) and validate (678 subjects, 3012 observations) a population pharmacokinetic model to describe tolvaptan pharmacokinetics in ADPKD subjects. The final model was evaluated with a bootstrapping method. The final model was internally and externally evaluated using visual predictive checks (VPC). Pharmacokinetics was best described by a 1‐compartmental model with 0‐order absorption, nonlinear relative bioavailability (F1), and first‐order elimination. Accounting for changes in F1 significantly improved the model: as the dose increased from 15 mg to 120 mg, F1 decreased by 36%. Population estimates for clearance/F (CL/F), volume of distribution/F (Vd/F), duration of absorption (D1), the highest dose at which F1 is lowest, and the amount of dose at which F1 is 50% were 12.6 L·h^‐1, 110 L, 0.58 hour, 182 mg, and 166 mg, respectively. The interindividual variability was 64% in CL/F, 70% in Vd/F, and 238% in D1. Residual variability was described by a combined‐error model. The VPC (500 data sets simulated) showed that 76% to 92% of the observed data fell within the 90% prediction intervals. The model stability assessed by a 1000‐run bootstrap analysis showed that the mean parameter estimates of data were within 10% of those obtained with the final model. The developed model is robust and stable. Internal and external validation confirmed the model ability to describe the data optimally.

Dose adjustment in orphan disease populations: the quest to fulfill the requirements of physiologically based pharmacokinetics

INTRODUCTION

While the media is engaged and fascinated by the idea of 'Precision Medicine', the nuances related to 'Precision Dosing' seem to be largely ignored. Assuming the 'right drug' is selected, clinicians still need to decide on the 'right dose' for individuals. Ideally, optimal dosing should be studied in clinical trials; however, many drugs on the market lack evidence-based dosing recommendations, and small groups of patients (orphan disease populations) are dependent on local guidance and clinician experience to determine drug dosage adjustments. Areas Covered: This report explores the current understanding of dosing adjustment in special populations and examines the requirements for developing 'in silico' models for pediatric, elderly and pregnant patients. The report also highlights current use of modeling to provide evidence-based recommendations for drug labeling in the absence of complete clinical trials in orphan disease populations. Expert Opinion: Physiologically based pharmacokinetics (PBPK) is an attractive prospect for determining the best drug dosage adjustments in special populations. However, it is not sufficient for individualized, or even stratified dosing, unless the systems (drug-independent) data required to build robust PBPK models are obtained. Such models are not a substitute for clinical trials, but they are an alternative to undocumented and inconsistent guesswork.

Understanding Hematological Toxicities Using Mathematical Modeling

Balancing antitumor efficacy with toxicity is a significant challenge, and drug-induced myelosuppression is a common dose-limiting toxicity of cancer treatments. Mathematical modeling has proven to be a powerful ally in this field, scaling results from animal models to humans, and designing optimized treatment regimens. Here we outline existing mathematical approaches for studying bone marrow toxicity, identify gaps in current understanding, and make future recommendations to advance this vital field of safety research further.

Full covariate modelling approach in population pharmacokinetics: understanding the underlying hypothesis tests and implications of multiplicity

AIMS

To clarify the hypothesis tests associated with the full covariate modelling (FCM) approach in population pharmacokinetic analysis, investigate the potential impact of multiplicity in population pharmacokinetic analysis, and evaluate simultaneous confidence intervals (SCI) as an approach to control multiplicity.

METHODS

Clinical trial simulations were performed using a simple one-compartment pharmacokinetic model. Different numbers of covariates, sample sizes, effect sizes of covariates, and correlations among covariates were explored. The false positive rate (FPR) and power were evaluated.

RESULTS

The FPR for the FCM approach dramatically increases with number of covariates. The chance of incorrectly selecting ≥1 seemingly clinically relevant covariates can be increased from 5% to a 40-70% range for 10-20 covariates. The SCI approach may provide appropriate control of the family-wise FPR, allowing more appropriate decision making. As a result, the power detecting real effects without incorrectly identifying non-existing effects can be greatly improved by the SCI approach compared to the approach in current practice. The performance of the SCI approach is driven by the ratio of sample size to number of covariates. The FPR can be controlled at 5% and 10% using the SCI approach when the ratio was ≥20 and 10, respectively.

CONCLUSION

The FCM approach still lies within the framework of statistical testing, and therefore multiplicity is an issue for this approach. It is imperative to consider multiplicity reporting and adjustments in FCM modelling practice to ensure more appropriate decision making.

Drugs Being Eliminated via the Same Pathway Will Not Always Require Similar Pediatric Dose Adjustments

For scaling drug plasma clearance (CLp) from adults to children, extrapolations of population pharmacokinetic (PopPK) covariate models between drugs sharing an elimination pathway have enabled accelerated development of pediatric models and dosing recommendations. This study aims at identifying conditions for which this approach consistently leads to accurate pathway specific CLp scaling from adults to children for drugs undergoing hepatic metabolism. A physiologically based pharmacokinetic (PBPK) simulation workflow utilizing mechanistic equations defining hepatic metabolism was developed. We found that drugs eliminated via the same pathway require similar pediatric dose adjustments only in specific cases, depending on drugs extraction ratio, unbound fraction, type of binding plasma protein, and the fraction metabolized by the isoenzyme pathway for which CLp is scaled. Overall, between‐drug extrapolation of pediatric covariate functions for CLp is mostly applicable to low and intermediate extraction ratio drugs eliminated by one isoenzyme and binding to human serum albumin in children older than 1 month.

Combining "Bottom-up" and "Top-down" Approaches to Assess the Impact of Food and Gastric pH on Pictilisib (GDC-0941) Pharmacokinetics

Pictilisib, a weakly basic compound, is an orally administered, potent, and selective pan-inhibitor of phosphatidylinositol 3-kinases for oncology indications. To investigate the significance of high-fat food and gastric pH on pictilisib pharmacokinetics (PK) and enable label recommendations, a dedicated clinical study was conducted in healthy volunteers, whereby both top-down (population PK, PopPK) and bottom-up (physiologically based PK, PBPK) approaches were applied to enhance confidence of recommendation and facilitate the clinical development through scenario simulations. The PopPK model identified food (for absorption rate constant (Ka )) and proton pump inhibitors (PPI, for relative bioavailability (Frel ) and Ka ) as significant covariates. Food and PPI also impacted the variability of Frel . The PBPK model accounted for the supersaturation tendency of pictilisib, and gastric emptying physiology successfully predicted the food and PPI effect on pictilisib absorption. Our research highlights the importance of applying both quantitative approaches to address critical drug development questions.

What is the role for population pharmacokinetics in hemophilia?

Prevention of bleeding in hemophilia requires that plasma levels of the deficient factor exceed the desired minimum target level. Large interindividual variability suggests that knowledge of individual pharmacokinetic (PK) would help to achieve this goal, simultaneously minimizing infusion frequency and the amount of concentrate used. Population PK (PopPK) allows for the incorporation of determinants of interpatient variability and eliminates the need for extensive postinfusion plasma sampling. Barriers to implementation of PopPK are the need for concentrate specific models, Bayesian calculation power, specific expertise for validation and appraisal of forecasted estimates. The Web Accessible Population Pharmacokinetic Service – Hemophilia ( www.wapps-hemo.org ), developed by an international research network of hemophilia centers will test if PK-guided dose individualization can improve patient important outcomes in hemophilia.

Towards better dose individualisation: metabolic phenotyping to predict cabazitaxel pharmacokinetics in men with prostate cancer

BACKGROUND

Cabazitaxel is approved for treatment of castration-resistant metastatic prostate cancer. The current dosing strategy of cabazitaxel is based on body surface area (BSA). Body surface area is known as a poor predictor for total systemic exposure to drugs, since it does not take into account variability in activity of metabolising enzymes, necessary for clearance of drugs. As exposure to cabazitaxel is related to treatment response, it is essential to develop a better individualised dosing strategy.

METHODS

Ten patients with metastatic castration-resistant prostate cancer, who received cabazitaxel dosed on BSA as a part of routine palliative care, were enrolled in this study. Midazolam was administered as phenotyping probe for cytochrome P450 isoenzyme 3A (CYP3A). The relationship between midazolam and cabazitaxel clearance was investigated using non-linear mixed effects modelling.

RESULTS

The clearance of Midazolam highly correlated with cabazitaxel clearance (R=0.74). Midazolam clearance significantly (P<0.004) explained the majority (∼60%) of the inter-individual variability in cabazitaxel clearance in the studied population.

CONCLUSIONS

Metabolic phenotyping of CYP3A using midazolam is a promising strategy to individualise cabazitaxel dosing. Before clinical application, a randomised study is warranted.

Tailoring treatment of haemophilia B: accounting for the distribution and clearance of standard and extended half-life FIX concentrates

The prophylactic administration of factor IX (FIX) is considered the most effective treatment for haemophilia B. The inter-individual variability and complexity of the pharmacokinetics (PK) of FIX, and the rarity of the disease have hampered identification of an optimal treatment regimens. The recent introduction of extended half-life recombinant FIX molecules (EHL-rFIX), has prompted a thorough reassessment of the clinical efficacy, PK and pharmacodynamics of plasma-derived and recombinant FIX. First, using longer sampling times and multi-compartmental PK models has led to more precise (and favourable) PK for FIX than was appreciated in the past. Second, investigating the distribution of FIX in the body beyond the vascular space (which is implied by its complex kinetics) has opened a new research field on the role for extravascular FIX. Third, measuring plasma levels of EHL-rFIX has shown that different aPTT reagents have different accuracy in measuring different FIX molecules. How will this new knowledge reflect on clinical practice? Clinical decision making in haemophilia B requires some caution and expertise. First, comparisons between different FIX molecules must be assessed taking into consideration the comparability of the populations studied and the PK models used. Second, individual PK estimates must rely on multi-compartmental models, and would benefit from adopting a population PK approach. Optimal sampling times need to be adapted to the prolonged half-life of the new EHL FIX products. Finally, costs considerations may apply, which is beyond the scope of this manuscript but might be deeply connected with the PK considerations discussed in this communication.

Lack of genetic association between OCT1, ABCB1, and UGT2B7 variants and morphine pharmacokinetics

AIM

A high inter-individual variation in the pharmacokinetics and pharmacodynamics of morphine has been observed. Genetic polymorphisms in genes encoding the organic cation transporter isoform 1 (OCT1), the efflux transporter p-glycoprotein (ABCB1), and the UDP-glucuronosyltransferase-2B7 (UGT2B7) may influence morphine pharmacokinetics and thus, also pharmacodynamics. The aim of this study was to evaluate the association between OCT1, ABCB1, and UGT2B7 variants, and morphine pharmacokinetics and -dynamics in healthy volunteers.

METHODS

Pharmacokinetic and pharmacodynamic data were collected from a double-blinded, randomized, crossover trial in 37 healthy subjects. Pharmacokinetic data were analyzed in NONMEM®, and the time-concentration relationship of morphine, morphine-3-glucuronide, and morphine-6-glucuronide was parameterized as the transit compartment rate constant (k_tr), clearance (CL), and volume of distribution (V_D). The area under the plasma concentration-time curve (AUC_0-150min) and the maximum plasma concentration (C_max) were also calculated. Pharmacodynamic data were measured as pain tolerance thresholds to mechanical stimulation of the rectum and muscle, as well as tonic cold pain stimulation ("the cold pressor test" where hand was immersed in cold water). Six different single nucleotide polymorphisms in three different genes (OCT1 (n=22), ABCB1 (n=37), and UGT2B (n=22)) were examined.

RESULTS

Neither AUC_0-150min, k_tr, CL, nor V_D were associated with genetic variants in OCT1, ABCB1, and UGT2B7 (all P>0.05). Similarly, the antinociceptive effects of morphine on rectal, muscle, and cold pressor tests were not associated with these genetic variants (all P>0.05).

CONCLUSIONS

In this experimental study in healthy volunteers, we found no association between different genotypes of OCT1, ABCB1, and UGT2B7, and morphine pharmacokinetics and pharmacodynamics. Nonetheless, due to methodological limitations we cannot exclude that associations exist.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

Development of a Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo): Study Protocol

BACKGROUND

Individual pharmacokinetic assessment is a critical component of tailored prophylaxis for hemophilia patients. Population pharmacokinetics allows using individual sparse data, thus simplifying individual pharmacokinetic studies. Implementing population pharmacokinetics capacity for the hemophilia community is beyond individual reach and requires a system effort.

OBJECTIVE

The Web-Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) project aims to assemble a database of patient pharmacokinetic data for all existing factor concentrates, develop and validate population pharmacokinetics models, and integrate these models within a Web-based calculator for individualized pharmacokinetic estimation in patients at participating treatment centers.

METHODS

Individual pharmacokinetic studies on factor VIII and IX concentrates will be sourced from pharmaceutical companies and independent investigators. All factor concentrate manufacturers, hemophilia treatment centers (HTCs), and independent investigators (identified via a systematic review of the literature) having on file pharmacokinetic data and willing to contribute full or sparse pharmacokinetic data will be eligible for participation. Multicompartmental modeling will be performed using a mixed-model approach for derivation and Bayesian forecasting for estimation of individual sparse data. NONMEM (ICON Development Solutions) will be used as modeling software.

RESULTS

The WAPPS-Hemo research network has been launched and is currently joined by 30 HTCs from across the world. We have gathered dense individual pharmacokinetic data on 878 subjects, including several replicates, on 21 different molecules from 17 different sources. We have collected sparse individual pharmacokinetic data on 289 subjects from the participating centers through the testing phase of the WAPPS-Hemo Web interface. We have developed prototypal population pharmacokinetics models for 11 molecules. The WAPPS-Hemo website (available at www.wapps-hemo.org, version 2.4), with core functionalities allowing hemophilia treaters to obtain individual pharmacokinetic estimates on sparse data points after 1 or more infusions of a factor concentrate, was launched for use within the research network in July 2015.

CONCLUSIONS

The WAPPS-Hemo project and research network aims to make it easier to perform individual pharmacokinetic assessments on a reduced number of plasma samples by adoption of a population pharmacokinetics approach. The project will also gather data to substantially enhance the current knowledge about factor concentrate pharmacokinetics and sources of its variability in target populations.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02061072; https://clinicaltrials.gov/ct2/show/NCT02061072 (Archived by WebCite at http://www.webcitation.org/6mRK9bKP6).

Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.