Design of a drug-drug interaction study of vincristine with azole antifungals in pediatric cancer patients using clinical trial simulation

Virtual Patient Simulation Using Copula Modeling

Virtual patient simulation is increasingly performed to support model-based optimization of clinical trial designs or individualized dosing strategies. Quantitative pharmacological models typically incorporate individual-level patient characteristics, or covariates, which enable the generation of virtual patient cohorts. The individual-level patient characteristics, or covariates, used as input for such simulations should accurately reflect the values seen in real patient populations. Current methods often make unrealistic assumptions about the correlation between patient's covariates or require direct access to actual data sets with individual-level patient data, which may often be limited by data sharing limitations. We propose and evaluate the use of copulas to address current shortcomings in simulation of patient-associated covariates for virtual patient simulations for model-based dose and trial optimization in clinical pharmacology. Copulas are multivariate distribution functions that can capture joint distributions, including the correlation, of covariate sets. We compare the performance of copulas to alternative simulation strategies, and we demonstrate their utility in several case studies. Our work demonstrates that copulas can reproduce realistic patient characteristics, both in terms of individual covariates and the dependence structure between different covariates, outperforming alternative methods, in particular when aiming to reproduce high-dimensional covariate sets. In conclusion, copulas represent a versatile and generalizable approach for virtual patient simulation which preserve relationships between covariates, and offer an open science strategy to facilitate re-use of patient data sets.

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

Pharmacometrics-Based Considerations for the Design of a Pharmacogenomic Clinical Trial Assessing Irinotecan Safety

PURPOSE

Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity.

METHODS

Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50-400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing.

RESULTS

The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·109 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar's test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power.

CONCLUSIONS

The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.

Population Pharmacokinetics of Vincristine Related to Infusion Duration and Peripheral Neuropathy in Pediatric Oncology Patients

Vincristine (VCR) is frequently used in pediatric oncology and can be administered intravenously through push injections or 1 h infusions. The effects of administration duration on population pharmacokinetics (PK) are unknown. We described PK differences related to administration duration and the relation between PK and VCR-induced peripheral neuropathy (VIPN). PK was assessed in 1-5 occasions (1-8 samples in 24 h per occasion). Samples were analyzed using high-performance liquid chromatography/tandem mass spectrometry. Population PK of VCR and its relationship with administration duration was determined using a non-linear mixed effect. We estimated individual post-hoc parameters: area under the concentration time curve (AUC) and maximum concentration (C_max) in the plasma and peripheral compartment. VIPN was assessed using Common Terminology Criteria for Adverse Events (CTCAE) and the pediatric-modified total neuropathy score (ped-mTNS). Overall, 70 PK assessments in 35 children were evaluated. The population estimated that the intercompartmental clearance (IC-Cl), volume of the peripheral compartment (V₂), and C_max were significantly higher in the push group. Furthermore, higher IC-Cl was significantly correlated with VIPN development. Administration of VCR by push led to increased IC-Cl, V₂, and C_max, but were similar to AUC, compared to 1 h infusions. Administration of VCR by 1 h infusions led to similar or higher exposure of VCR without increasing VIPN.

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.

Optimizing drug development in oncology by clinical trial simulation: Why and how?

In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after an extensive and expensive preclinical development period. Methodologies such as computer modeling and clinical trial simulation (CTS) might represent a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the preclinical phase to postmarketing. However, they are barely used and are poorly regarded for drug approval, despite Food and Drug Administration and European Medicines Agency recommendations. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by clinical trial studies and hospital databases. Data sharing and data merging raise legal, policy and technical issues that will need to be addressed. Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increased computing speed should allow this leap forward. The realization of CTS requires not only bioinformatics tools to allow interconnection and global integration of all clinical data but also a universal legal framework to protect the privacy of every patient. While recognizing that CTS can never replace 'real-life' trials, they should be implemented in future drug development schemes to provide quantitative support for decision-making. This in silico medicine opens the way to the P4 medicine: predictive, preventive, personalized and participatory.

Pharmacometrics in Pediatrics

Pharmacometrics have advanced from compartmental analysis to noncompartmental analysis and population pharmacokinetics that require complicated mathematical programs. These sophisticated mathematical analyses determine not only the usual pharmacometric measures of clearance and volume of distribution but also the effects of covariates on these measures. Although these analyses are very suitable to studies of small patient populations often encountered in pediatric studies, most pediatric clinicians have not been trained in how these analyses are conducted or the meaning of the results of these analyses expressed in terms of error measures and fixed and variable effects. In addition, clinicians may not be able to evaluate whether their patient population is adequately represented in the analysis. Thorough and clear descriptions of the methodology and the strengths and weaknesses of these analyses need to be published in journals read by clinicians.

Systems Pharmacology: Defining the Interactions of Drug Combinations

The majority of diseases are associated with alterations in multiple molecular pathways and complex interactions at the cellular and organ levels. Single-target monotherapies therefore have intrinsic limitations with respect to their maximum therapeutic benefits. The potential of combination drug therapies has received interest for the treatment of many diseases and is well established in some areas, such as oncology. Combination drug treatments may allow us to identify synergistic drug effects, reduce adverse drug reactions, and address variability in disease characteristics between patients. Identification of combination therapies remains challenging. We discuss current state-of-the-art systems pharmacology approaches to enable rational identification of combination therapies. These approaches, which include characterization of mechanisms of disease and drug action at a systems level, can enable understanding of drug interactions at the molecular, cellular, physiological, and organismal levels. Such multiscale understanding can enable precision medicine by promoting the rational development of combination therapy at the level of individual patients for many diseases.

Evidence-based drug treatment for special patient populations through model-based approaches

The majority of marketed drugs remain understudied in some patient populations such as pregnant women, paediatrics, the obese, the critically-ill, and the elderly. As a consequence, currently used dosing regimens may not assure optimal efficacy or minimal toxicity in these patients. Given the vulnerability of some subpopulations and the challenges and costs of performing clinical studies in these populations, cutting-edge approaches are needed to effectively develop evidence-based and individualized drug dosing regimens. Five key issues are presented that are essential to support and expedite the development of drug dosing regimens in these populations using model-based approaches: 1) model development combined with proper validation procedures to extract as much valid information from available study data as possible, with limited burden to patients and costs; 2) integration of existing data and the use of prior pharmacological and physiological knowledge in study design and data analysis, to further develop knowledge and avoid unnecessary or unrealistic (large) studies in vulnerable populations; 3) clinical proof-of-principle in a prospective evaluation of a developed drug dosing regimen, to confirm that a newly proposed regimen indeed results in the desired outcomes in terms of drug concentrations, efficacy, and/or safety; 4) pharmacodynamics studies in addition to pharmacokinetics studies for drugs for which a difference in disease progression and/or in exposure-response relation is anticipated compared to the reference population; 5) additional efforts to implement developed dosing regimens in clinical practice once drug pharmacokinetics and pharmacodynamics have been characterized in special patient populations. The latter remains an important bottleneck, but this is essential to truly realize evidence-based and individualized drug dosing for special patient populations. As all tools required for this purpose are available, we have the moral and societal obligation to make safe and effective pharmacotherapy available for these patients too.

Hyperlipidemia Alters the Pharmacokinetics of Posaconazole and Vincristine Upon Co-Administration in Rats

Objectives

Co-administration of posaconazole (PSZ) and vincristine (VCR) in the treatment of patients with acute lymphoblastic leukemia increases the neurotoxicity of VCR. Our aim is to study the effect of increased lipoprotein levels on the pharmacokinetics of PSZ and VCR upon co-administration in rats.

Methods

Rats were assigned to three groups, normolipidemic (NL), intermediate hyperlipidemic (IHL), and extreme hyperlipidemic (HL) groups. All rats were administered PSZ orally followed by VCR intravenously 4 h later. For the pharmacokinetic study, serial plasma samples were collected over 96 h and for tissue distribution study; plasma, lung, and liver tissues were collected over 48 h post oral dosing.

Results

Posaconazole showed higher plasma concentrations than VCR at all time points. Co-administration of VCR with PSZ reduced PSZ weight normalized oral clearance, increased PSZ area under the plasma concentration–time curve (AUC) from time zero to infinity, showed higher PSZ liver concentrations, and increased VCR volume of distribution of the central compartment. Upon increasing the lipoprotein levels, PSZ showed higher plasma availability and delayed tissue distribution, whereas VCR had shown a significant decrease in PSZ AUC_0-24h, AUC_0-tlast, and AUC_o-inf (NL = IHL > HL) and a significant increase in the volume of distribution (NL = IHL < HL). Vincristine has shown higher tissue uptake and concentrations.

Conclusion

Monitoring cholesterol and triglyceride levels in patients with acute lymphoblastic leukemia is advisable to decrease VCR neurological side effect incidences and delay the activity of both PSZ and VCR.

The effect of increased lipoproteins levels on the disposition of vincristine in rat

Background

Vincristine (VCR), an antineoplastic agent, is a key component in the treatment of acute lymphoblastic leukemia, lymphomas, rhabdomyosarcoma, neuroblastoma, and Wilms’ tumor diseases. Recently, high incidence of hyperlipidemia was reported to be associated with allogenic hematopoietic stem cell transplantation and VCR/L-asparaginase therapy.

The aim of this study is to test the effects of incremental increase in lipoproteins levels on vincristine disposition in rat.

Method

To study VCR pharmacokinetics and protein binding, rats (n = 25) were assigned to three groups, normal lipidemic (NL), intermediate (IHL) and extreme hyperlipidemic (HL). Hyperlipidemia was induced by ip injection of (1 g/Kg) poloxamer 407 in rats. Serial blood samples were collected using the pre-inserted jugular vein cannula for 72 h post VCR (0.15 mg/Kg) i.v. dose. VCR unbound fractions in NL, IHL and HL plasma were determined using ultrafiltration kits.

Results

VCR demonstrated a rapid distribution phase (6–8 h) followed by a slower elimination phase with a mean elimination t½ of ~ 14 h. VCR exhibited moderate binding to plasma proteins ~ 83 %. It showed a relatively small Vc (~0.17 L/Kg) and a larger Vβ (1.53 L/Kg) indicating good tissue distribution. As the lipoproteins levels were increased, no significant changes were noted in VCR unbound fraction, plasma concentration, or volume of distribution indicating low affinity to lipoprotein binding. Induced HL also did not affect VCR elimination where similar VCR AUC_0-∞, Cl and elimination phase t½ were reported along the different lipemic groups.

Conclusion

Incremental increase in lipoprotein levels resulted in no significant effect on VCR disposition as such ALL malignant lymphoma and allogenic hematopoietic stem cell transplantation patients need not to worry about HL-VCR interaction. Whether, HL can potentiate another drug-drug or drug-disease interaction involving VCR warrants further studying and monitoring to ensure therapeutic safety and efficiency.

Towards integrative systems pharmacology models in oncology drug development

Quantitative systems pharmacology (QSP) modeling represents an emerging area of value to further streamline knowledge integration and to better inform decision making processes in drug development. QSP models reside at the interface between systems biology models and pharmacological models, yet their concrete implementation still needs to be established further. This review outlines key modeling techniques in both of these areas and to subsequently discuss challenges and opportunities for further integration, in oncology drug development.

Disease Progression/Clinical Outcome Model for Castration-Resistant Prostate Cancer in Patients Treated With Eribulin

Frameworks that associate cancer dynamic disease progression models with parametric survival models for clinical outcome have recently been proposed to support decision making in early clinical development. Here we developed such a disease progression clinical outcome model for castration-resistant prostate cancer (CRPC) using historical phase II data of the anticancer agent eribulin. Disease progression was captured using the dynamics of prostate-specific antigen (PSA). For clinical outcome, overall survival (OS) was used. The model for PSA dynamics comprised parameters for baseline PSA (23.2 ng/ml, relative standard error (RSE) 16.5%), growth rate (0.00879 day(-1), RSE 12.6%), drug effect (0.241 µg·h·l(-1) day(-1), RSE 32.6%), and resistance development (0.0113 day(-1), RSE 44.3%). OS was modeled according to a Weibull distribution. Predictors for survival included model-predicted PSA time to nadir (TTN), PSA growth rate, Eastern Cooperative Oncology Group (ECOG) score, and baseline PSA. The developed framework can be considered to support informative design and analysis of drugs developed for CRPC.