Optimizing drug development of anti-cancer drugs in children using modelling and simulation

Pediatric oncology drug development and dosage optimization

Oncology drug discovery and development has always been an area facing many challenges. Phase 1 oncology studies are typically small, open-label, sequential studies enrolling a small sample of adult patients (i.e., 3-6 patients/cohort) in dose escalation. Pediatric evaluations typically lag behind the adult development program. The pediatric starting dose is traditionally referenced on the recommended phase 2 dose in adults with the incorporation of body size scaling. The size of the study is also small and dependent upon the prevalence of the disease in the pediatric population. Similar to adult development, the dose is escalated or de-escalated until reaching the maximum tolerated dose (MTD) that also provides desired biological activities or efficacy. The escalation steps and identification of MTD are often rule-based and do not incorporate all the available information, such as pharmacokinetic (PK), pharmacodynamic (PD), tolerability and efficacy data. Therefore, it is doubtful if the MTD approach is optimal to determine the dosage. Hence, it is important to evaluate whether there is an optimal dosage below the MTD, especially considering the emerging complexity of combination therapies and the long-term tolerability and safety of the treatments. Identification of an optimal dosage is also vital not only for adult patients but for pediatric populations as well. Dosage-finding is much more challenging for pediatric populations due to the limited patient population and differences among the pediatric age range in terms of maturation and ontogeny that could impact PK. Many sponsors defer the pediatric strategy as they are often perplexed by the challenges presented by pediatric oncology drug development (model of action relevancy to pediatric population, budget, timeline and regulatory requirements). This leads to a limited number of approved drugs for pediatric oncology patients. This review article provides the current regulatory landscape, incentives and how they impact pediatric drug discovery and development. We also consider different pediatric cancers and potential clinical trial challenges/opportunities when designing pediatric clinical trials. An outline of how quantitative methods such as pharmacometrics/modelling & simulation can support the dosage-finding and justification is also included. Finally, we provide some reflections that we consider helpful to accelerate pediatric drug discovery and development.

Developing Novel Anticancer Drugs for Targeted Populations: An Update

BACKGROUND

Due to higher failure rates, lengthy time and high cost of the traditional de novo drug discovery and development process, the rate of opportunity to get new, safe and efficacious drugs for the targeted population, including pediatric patients with cancer, becomes sluggish.

OBJECTIVES

This paper discusses the development of novel anticancer drugs focusing on the identification and selection of targeted anticancer drug development for the targeted population.

METHODS

Information presented in this review was obtained from different databases, including PUBMED, SCOPUS, Web of Science, and EMBASE. Various keywords were used as search terms.

RESULTS

The pharmaceutical companies currently are executing drug repurposing as an alternative means to accelerate the drug development process that reduces the risk of failure, time and cost, which take 3-12 years with almost 25% overall probability of success as compared to de novo drug discovery and development process (10- 17 years) which has less than 10% probability of success. An alternative strategy to the traditional de novo drug discovery and development process, called drug repurposing, is also presented.

CONCLUSION

Therefore, to continue with the progress of developing novel anticancer drugs for the targeted population, identification and selection of target to specific disease type is important. Considering the aspects of the age of the patient and the disease stages such as each cancer types are different when we study the disease at a molecular level. Drug repurposing technique becomes an influential alternative strategy to discover and develop novel anticancer drug candidates.

Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors

Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical impact in improving overall survival of several malignancies associated with poor outcomes; however, only 20–40% of patients will show long-lasting survival. Further clarification of factors related to treatment response can support improvements in clinical outcome and guide the development of novel immune checkpoint therapies. In this article, we have provided an overview of the pharmacokinetic (PK) aspects related to current ICIs, which include target-mediated drug disposition and time-varying drug clearance. In response to the variation in treatment exposure of ICIs and the significant healthcare costs associated with these agents, arguments for both dose individualization and generalization are provided. We address important issues related to the efficacy and safety, the pharmacodynamics (PD), of ICIs, including exposure–response relationships related to clinical outcome. The unique PK and PD aspects of ICIs give rise to issues of confounding and suboptimal surrogate endpoints that complicate interpretation of exposure–response analysis. Biomarkers to identify patients benefiting from treatment with ICIs have been brought forward. However, validated biomarkers to monitor treatment response are currently lacking.

A methodological framework for drug development in rare diseases. The CRESim program: Epilogue and perspectives

Based on the 'European Child-Rare-Euro-Simulation' (CRESim) project, this article proposes a generalizable strategy utilizing datasets analysis in combination with modeling and simulation, in order to optimize the clinical drug development applied in the field of rare diseases. The global process includes: (i) the simulation of a realistic virtual population of patients (modeled from a real dataset of patients), (ii) the modeling of disease pathophysiological components and of pharmacokinetic-pharmacodynamic relations of the drug(s) of interest, (iii) the modeling of several randomized controlled clinical trials (RCTs) designs and (iv) the analysis of the results (multi-dimensional approach for RCTs durations and precision of the estimation of the treatment effect). However, whereas modeling and numerical simulation may provide supplementary tools for drug development, they cannot be considered as a substitute for RCTs performed in 'real' patients.

Population pharmacokinetics, exposure-safety, and immunogenicity of atezolizumab in pediatric and young adult patients with cancer

BACKGROUND

The iMATRIX-atezolizumab study was a phase I/II, multicenter, open-label study designed to assess the safety and pharmacokinetics of atezolizumab in pediatric and young adult patients. We describe the pharmacokinetics (PK), exposure-safety, and immunogenicity of atezolizumab in pediatric and young adults with metastatic solid tumors or hematologic malignancies enrolled in this study.

METHODS

Patients aged < 18 years (n = 69) received a weight-adjusted dose of atezolizumab (15 mg/kg every 3 weeks [q3w]; maximum 1200 mg); those aged ≥ 18 years (n = 18) received a flat dose (1200 mg q3w). A prior two-compartment intravenous infusion input adult population-PK (popPK) model of atezolizumab was used as a basis to model pediatric data.

RESULTS

A total of 431 atezolizumab serum concentrations from 87 relapse-refractory pediatric and young adult patients enrolled in the iMATRIX-atezolizumab study were used for the popPK analysis. The dataset comprised predominantly patients aged < 18 years, including two infants aged < 2 years, with a wide body weight and age range. The clearance and volume of distribution estimates of atezolizumab were 0.217 L/day and 3.01 L, respectively. Atezolizumab geometric mean trough exposures were ~ 20% lower in pediatric patients versus young adults; this was not clinically meaningful as both groups achieved the target concentration (6 μg/mL). Safety was similar between pediatric and young adult patients with no exposure-safety relationship observed. Limited responses (4/87) precluded an exposure-response assessment on outcomes. A comparable rate (13% vs 11%) of atezolizumab anti-drug antibodies was seen in pediatric and young adult patients.

CONCLUSIONS

These findings demonstrate a similar exposure-safety profile of atezolizumab in pediatric and young adult patients, supportive of weight-based dosing in pediatric patients.

TRIAL REGISTRATION

NCT02541604.

Optimization of Cancer Treatment in the Frequency Domain

Thorough exploration of alternative dosing frequencies is often not performed in conventional pharmacometrics approaches. Quantitative systems pharmacology (QSP) can provide novel insights into optimal dosing regimen and drug behaviors which could add a new dimension to the design of novel treatments. However, methods for such an approach are currently lacking. Recently, we illustrated the utility of frequency-domain response analysis (FdRA), an analytical method used in control engineering, using several generic pharmacokinetic-pharmacodynamic case studies. While FdRA is not applicable to models harboring ever increasing variables such as those describing tumor growth, studying such models in the frequency domain provides valuable insight into optimal dosing frequencies. Through the analysis of three distinct tumor growth models (cell cycle-specific, metronomic, and acquired resistance), we demonstrate the application of a simulation-based analysis in the frequency domain to optimize cancer treatments. We study the response of tumor growth to dosing frequencies while simultaneously examining treatment safety, and found for all three models that above a certain dosing frequency, tumor size is insensitive to an increase in dosing frequency, e.g., for the cell cycle-specific model, one dose per 3 days, and an hourly dose yield the same reduction of tumor size to 3% of the initial size after 1 year of treatment. Additionally, we explore the effect of drug elimination rate changes on the tumor growth response. In summary, we show that the frequency-domain view of three models of tumor growth dynamics can help in optimizing drug dosing regimen to improve treatment success.

Accelerating Drug Development in Pediatric Oncology With the Clinical Pharmacology Storehouse

Pediatric drug development is a challenging process due to the rarity of the population, the need to meet regulatory requirements across the globe, the associated uncertainty in extrapolating data from adults, the paucity of validated biomarkers, and the lack of systematic testing of drugs in pediatric patients. In oncology, pediatric drug development has additional challenges that have historically delayed availability of safe and effective medicines for children. In particular, the traditional approach to pediatric oncology drug development involves conducting phase 1 studies in children once the drug has been characterized and in some cases approved for use in adults. The objective of this article is to describe clinical pharmacology factors that influence pediatric oncology trial design and execution and to highlight efficient approaches for designing and expediting oncology drug development in children. The topics highlighted in this article include (1) study design considerations, (2) updated dosing approaches, (3) ways to overcome the significant biopharmaceutical challenges unique to the oncology pediatric population, and (4) use of data analysis strategies for extrapolating data from adults, with case studies. Finally, suggestions for ways to use clinical pharmacology approaches to accelerate pediatric oncology drug development are provided.

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.

Physiologically Based Pharmacokinetic Modeling for Olaparib Dosing Recommendations: Bridging Formulations, Drug Interactions, and Patient Populations

We report physiologically based pharmacokinetic-modeling analyses to determine olaparib (tablet or capsule) drug-drug interactions (DDIs). Verified DDI simulations provided dose recommendations for olaparib coadministration with clinically relevant CYP3A4 modulators to eliminate potential risk to patient safety or olaparib efficacy. When olaparib is given with strong/moderate CYP3A inhibitors, the dose should be reduced to 100/150 mg b.i.d. (tablet), and 150/200 mg b.i.d. (capsule). Olaparib administration is not recommended with strong/moderate CYP3A inducers. No dose reductions are required with weak CYP3A inhibitors/inducers. Olaparib was shown to be a weak inhibitor of CYP3A (1.6-fold increase in exposure of a sensitive CYP3A probe) and to have no effect on P-glycoprotein or UGT1A1 substrates. Finally, this model was used to simulate exposure in scenarios where clinical data of olaparib are lacking, such as severe renal or hepatic impairment populations, and provided initial dosing recommendations in pediatric patients.

Lack of value of juvenile animal toxicity studies for supporting the safety of pediatric oncology phase I trials

Toxicity studies in juvenile animals (JAS) are sometimes performed to support clinical trials in pediatric oncology patients, and there are differing conclusions on the value of JAS for pediatric drug development. This manuscript provides a review of the pediatric clinical data for 25 molecularly-targeted and 4 biologic anticancer therapeutics. Other publications that evaluated the value of JAS in pediatric drug development focus on differences in toxicity between juvenile animals and adult animals. The present paper examines pediatric-specific clinical findings to focus on dose setting in pediatric oncology patients and safety monitoring in terms of the potential value of JAS. Our assessment demonstrates that pediatric starting doses were safe for all 29 therapeutics examined in that no life-threatening toxicities occurred in the first cohort, and overall the ratio of the pediatric maximum tolerated dose (MTD) to the recommended adult dose was close to 1. In addition, the 4 serious adverse events (SAEs) that weren't detectable with standard monitoring plans for pediatric oncology trials would not have been detectable in a standard JAS. This review demonstrates that safe starting doses in pediatric oncology patients for these therapeutics could have been solely based on adult doses without any knowledge of findings in JAS.

Risk and surrogate benefit for pediatric Phase I trials in oncology: A systematic review with meta-analysis

BACKGROUND

Pediatric Phase I cancer trials are critical for establishing the safety and dosing of anti-cancer treatments in children. Their implementation, however, must contend with the rarity of many pediatric cancers and limits on allowable risk in minors. The aim of this study is to describe the risk and benefit for pediatric cancer Phase I trials.

METHODS AND FINDINGS

Our protocol was prospectively registered in PROSPERO (CRD42015015961). We systematically searched Embase and PubMed for solid and hematological malignancy Phase I pediatric trials published between 1 January 2004 and 1 March 2015. We included pediatric cancer Phase I studies, defined as "small sample size, non‑randomized, dose escalation studies that defined the recommended dose for subsequent study of a new drug in each schedule tested." We measured risk using grade 3, 4, and 5 (fatal) drug-related adverse events (AEs) and benefit using objective response rates. When possible, data were meta-analyzed. We identified 170 studies meeting our eligibility criteria, accounting for 4,604 patients. The pooled overall objective response rate was 10.29% (95% CI 8.33% to 12.25%), and was lower in solid tumors, 3.17% (95% CI 2.62% to 3.72%), compared with hematological malignancies, 27.90% (95% CI 20.53% to 35.27%); p < 0.001. The overall fatal (grade 5) AE rate was 2.09% (95% CI 1.45% to 2.72%). Across the 4,604 evaluated patients, there were 4,675 grade 3 and 4 drug-related AEs, with an average grade 3/4 AE rate per person equal to 1.32. Our study had the following limitations: trials included in our review were heterogeneous (to minimize heterogeneity, we separated types of therapy and cancer types), and we relied on published data only and encountered challenges with the quality of reporting.

CONCLUSIONS

Our meta-analysis suggests that, on the whole, AE and response rates in pediatric Phase I trials are similar to those in adult Phase I trials. Our findings provide an empirical basis for the refinement and review of pediatric Phase I trials, and for communication about their risk and benefit.

Towards a Model-Based Dose Recommendation for Doxorubicin in Children

Following the publication of our paper regarding a population-based model of doxorubicin pharmacokinetics in children in Clinical Pharmacokinetics last year (Voller et al. 54:1139-1149, 2015), we have received many inquiries on the practical clinical consequences of this model; however, a population-based model is only one of the aspects to be taken into account when developing dosing algorithms. In addition, any new method of dose calculation would need clinical validation and, subsequently, a new clinical trial. However, such a trial, especially with regard to burden to the children involved, requires optimal preparation and the selection of the best hypotheses. The European Paediatric Oncology Off-Patent Medicines Consortium (EPOC), represented by the authors, would therefore like to initiate an interdisciplinary discussion on the clinical and pharmacological goals for dose calculation. This current opinion summarizes the existing knowledge on the pharmacokinetics and pharmacodynamics of doxorubicin. Our aim was to define the clinical needs as precisely as possible, with the intention of stimulating discussion between the clinical pediatric oncologist and the pediatric pharmacologist. By doing so, we hope to define surrogates for best practice of a common doxorubicin dose in children. The intent is for a trial to validate a rational dose calculation rule, leading to a regulatory process and subsequent labeling.

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.

Population Pharmacokinetics of Vancomycin in Postoperative Neurosurgical Patients and the Application in Dosing Recommendation

Our previous study indicates that cerebrospinal fluid (CSF) albumin level is a determinant of CSF vancomycin concentration for postoperative neurosurgical patients. We aimed to develop an improved vancomycin population pharmacokinetic model with incorporation of more covariates, and to provide dosing guidance for clinicians. Vancomycin was administered intravenously to 20 patients with external ventricular drains after neurosurgical operation. Blood and CSF were collected and vancomycin concentrations were measured by HPLC. A separate CSF compartment was considered, and was linked to the central compartment by a first-order process (Q_CSF). The clearance of the CSF compartment (Cl_CSF) was used to characterize vancomycin elimination from CSF through external ventricular drain. Nonlinear mixed-effects modeling approach was used to develop the model. The CSF albumin level (mg/dL) was the covariate influencing Q_CSF: Q_CSF=0.0049+0.000021×(CSF albumin-279). The effect of body weight (BW, kg) was significant on central volume (V_C): V_C=27.84+0.96×(BW-69). All parameters were estimated with an acceptable precision (relative standard error: RSE% < 30.26). The performance of the final model was acceptable with our previous dataset. A simple to use dosage regimen table was created to guide clinicians with vancomycin dosing. This model incorporates variables of both CSF albumin and BW, which offers improvements to the previous pharmacokinetics model.

Optimizing pharmacokinetic bridging studies in paediatric oncology using physiologically-based pharmacokinetic modelling: application to docetaxel

AIM

Applying physiologically-based pharmacokinetic (PBPK) modelling in paediatric cancer drug development is still challenging. We aimed to demonstrate how PBPK modelling can be applied to optimize dose and sampling times for a paediatric pharmacokinetic (PK) bridging study in oncology and to compare with the allometric scaling population PK (AS-popPK) approach, using docetaxel as an example.

METHODS

A PBPK model for docetaxel was first developed for adult cancer patients using Simcyp® and subsequently used to predict its PK profiles in children by accounting for age-dependent physiological differences. Dose (mg m(-2) ) requirements for children aged 0-18 years were calculated to achieve targeted exposure in adults. Simulated data were then analyzed using population PK modelling with MONOLIX® in order to perform design optimization with the population Fisher information matrix (PFIM). In parallel, the AS-popPK approach was performed for the comparison.

RESULTS

The PBPK model developed for docetaxel adequately predicted its PK profiles in both adult and paediatric cancer patients (predicted clearance and volume of distribution within 1.5 fold of observed data). The revised dose of docetaxel for a child over 1.5 years old was higher than the adult dose. Considering clinical constraints, the optimal design contained two groups of 15 patients, having three or four sampling times and had good predicted relative standard errors (RSE<30%) for almost all parameters. The AS-popPK approach performed reasonably well but could not predict for very young children.

CONCLUSION

This research shows the clinical utility of PBPK modelling in combination with population PK modelling and optimal design to support paediatric oncology development.

Integrated Simulation Framework for Toxicity, Dose Intensity, Disease Progression, and Cost Effectiveness for Castration-Resistant Prostate Cancer Treatment With Eribulin

Quantitative model-based analyses are helpful to support decision-making in drug development. In oncology, disease progression/clinical outcome (DPCO) models have been used for early predictions of clinical outcome, but most of such approaches did not include adverse events or dose intensity. In addition, cost-effectiveness evaluations of investigational compounds are becoming increasingly important. Here, we developed an integrated model-based framework including relevant treatment effects for patients with castration-resistant prostate cancer treated with the anticancer agent eribulin. The framework included (i) a DPCO model relating prostate-specific antigen (PSA) dynamics to survival; (ii) models for adverse events including dose-limiting neutropenia and other graded toxicities; (iii) a model for Eastern Cooperative Oncology Group (ECOG) performance score; (iv) a model for dropout; (v) the consideration of cost effectiveness. The model allowed simulation of realistic treatment courses. Subsequently, simulations evaluating alternative treatment protocols or patient characteristics were performed in order to derive inferences on expected efficacy and cost effectiveness.

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

BACKGROUND

The aim of the current work was to perform a clinical trial simulation (CTS) analysis to optimize a drug-drug interaction (DDI) study of vincristine in children who also received azole antifungals, taking into account challenges of conducting clinical trials in this population, and, to provide a motivating example of the application of CTS in the design of pediatric oncology clinical trials.

PROCEDURE

A pharmacokinetic (PK) model for vincristine in children was used to simulate concentration-time profiles. A continuous model for body surface area versus age was defined based on pediatric growth curves. Informative sampling time windows were derived using D-optimal design. The CTS framework was used to different magnitudes of clearance inhibition (10%, 25%, or 40%), sample size (30-500), the impact of missing samples or sampling occasions, and the age distribution, on the power to detect a significant inhibition effect, and in addition, the relative estimation error (REE) of the interaction effect.

RESULTS

A minimum group specific sample size of 38 patients with a total sample size of 150 patients was required to detect a clearance inhibition effect of 40% with 80% power, while in the case of a lower effect of clearance inhibition, a substantially larger sample size was required. However, for the majority of re-estimated drug effects, the inhibition effect could be estimated precisely (REE < 25%) in even smaller sample sizes and with lower effect sizes.

CONCLUSION

This work demonstrated the utility of CTS for the evaluation of PK clinical trial designs in the pediatric oncology population.

Pharmacokinetic/pharmacodynamic modelling approaches in paediatric infectious diseases and immunology

Pharmacokinetic/pharmacodynamic (PKPD) modelling is used to describe and quantify dose-concentration-effect relationships. Within paediatric studies in infectious diseases and immunology these methods are often applied to developing guidance on appropriate dosing. In this paper, an introduction to the field of PKPD modelling is given, followed by a review of the PKPD studies that have been undertaken in paediatric infectious diseases and immunology. The main focus is on identifying the methodological approaches used to define the PKPD relationship in these studies. The major findings were that most studies of infectious diseases have developed a PK model and then used simulations to define a dose recommendation based on a pre-defined PD target, which may have been defined in adults or in vitro. For immunological studies much of the modelling has focused on either PK or PD, and since multiple drugs are usually used, delineating the relative contributions of each is challenging. The use of dynamical modelling of in vitro antibacterial studies, and paediatric HIV mechanistic PD models linked with the PK of all drugs, are emerging methods that should enhance PKPD-based recommendations in the future.