Randomized exposure-controlled trials; impact of randomization and analysis strategies

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.

Recent advances in the ontogeny of drug disposition

Developmental changes that occur throughout childhood have long been known to impact drug disposition. However, pharmacokinetic studies in the paediatric population have historically been limited due to ethical concerns arising from incorporating children into clinical trials. As such, much of the early work in the field of developmental pharmacology was reliant on difficult-to-interpret in vitro and in vivo animal studies. Over the last 2 decades, our understanding of the mechanistic processes underlying age-related changes in drug disposition has advanced considerably. Progress has largely been driven by technological advances in mass spectrometry-based methods for quantifying proteins implicated in drug disposition, and in silico tools that leverage these data to predict age-related changes in pharmacokinetics. This review summarizes our current understanding of the impact of childhood development on drug disposition, particularly focusing on research of the past 20 years, but also highlighting select examples of earlier foundational research. Equally important to the studies reviewed herein are the areas that we cannot currently describe due to the lack of research evidence; these gaps provide a map of drug disposition pathways for which developmental trends still need to be characterized.

Dose-Response Mixed Models for Repeated Measures – a New Method for Assessment of Dose-Response

Purpose

In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations.

Methods

The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM.

Results

The DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS).

Conclusions

DR-MMRM is a promising method for dose-response analysis.

Electronic supplementary material

The online version of this article (10.1007/s11095-020-02882-0) contains supplementary material, which is available to authorized users.

Determining causal exposure-response relationships with randomized concentration-controlled trials

Determining causal effects in exposure-response relationships is an important but also a challenging task since confounding factors that affect both drug exposure and response often exist and lead to confounding biases in causal effect estimation. Randomized concentration control (RCC) trials are designed to eliminate or to reduce the confounding bias. However, statistical issues in the design and analysis of these trials have not been examined closely in the literature. Analysis of dose-exposure relationship may also be affected by confounding factors if they affect dose adjustments. We examined these issues and suggest methodological and practical solutions. In particular, we proposed using instrumental variables (IV) for the estimation of causal effects in both exposure-response and dose-exposure relationships. We also examined the impacts of confounded treatment heterogeneity on the IV estimate for RCC trials. We illustrated these approaches with a trial design scenario showing the importance of considering multiple practical factors that may alter the performance of the IV estimate. The performance of the IV estimates was examined by simulations for a wide range of scenarios. The results showed clear advantages for the IV estimates over routine estimates. Some situations in which the IV estimates may fail were also identified.

Adaptive trials in paediatric development: dealing with heterogeneity and uncertainty in pharmacokinetic differences in children

AIMS

To assess whether an adaptive design in early clinical trials based on the paradigm of variable dosing and controlled exposure can provide better dosing recommendations compared with the standard fixed dose approach.

METHODS

In a clinical trial simulation setting, a paediatric study was simulated using a pharmacokinetic model previously developed for abacavir. Plasma concentrations following the current recommended dose (8 mg kg⁻¹) were taken at standard sampling times, exposures (AUC) were calculated and doses individually adapted to reach the target exposure (i.e. effective exposure in adults). A second round of simulations followed with the adapted doses, and the resulting concentrations were fitted again with the same model. Exposure distributions in both conditions (i.e. fixed dose and controlled exposure) were compared with the target exposure.

RESULTS

The AUC distribution after the current dose resulted in a median exposure of 6.43 mg h l⁻¹ (90th percentile 3.13-10.67 mg h l⁻¹). A total of 61 of 128 subjects showed AUC values either too low or to high compared with the target exposure. After dose adjustment, the median exposure was 6.94 mg h l⁻¹ (5.57-8.25 mg h l⁻¹), and only 14 subjects deviated from the target range.

CONCLUSIONS

Adaptive randomization can be used to optimize dosing regimens in early paediatric clinical trials. The randomization of patients to target exposure rather than dose increases the probability of demonstrating efficacy (i.e. study power) compared with dose-controlled trials. Furthermore, it contributes to further understanding of the role of dose on the total heterogeneity in clinical response.

Role of modelling and simulation: a European regulatory perspective

Modelling and simulation (M&S) of clinical data, e.g. pharmacokinetic, pharmacodynamic and clinical endpoints, is a useful approach for more efficient interpretation of collected data and for extrapolation of knowledge to the entire target population. This type of documentation is included in the majority of marketing authorization applications for new medicinal products. This article summarizes the current status of regulatory review with respect to the role of M&S in Europe from the perspective of the Swedish Medical Products Agency. At present, regulatory bodies in Europe encourage the application of the M&S approach during drug development. However, there is a lack of consensus and transparent guidance documents. The main regulatory usage is in the evaluation of dose choices in sub-populations and as support for the dosing regimen in general. The regulatory review of conestat alfa illustrates how the dose recommendation was revised during the approval procedure based on M&S information. A survey of marketing authorization applications for new medicinal products approved in 2010 revealed that the use of the information gained from M&S documentation varies with respect to both regulatory review and the applicants' presentation of the data in the submitted dossier. Increased utilization and broadened application of M&S is anticipated in pharmaceutical development, where one area of focus is medicines for paediatric patients. Accordingly, the regulatory agencies will need to increase their capability to assess and utilize this type of information, and an interactive process among regulatory agencies is warranted to provide more unified regulatory assessment and guidance. Moreover, applicants are encouraged to expand on the usage of exposure-response models to map the systemic exposure range that yields safe and efficacious treatment and to improve the presentation of the gained knowledge in summary documents of the marketing authorization applications.

Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models

Efficient power calculation methods have previously been suggested for Wald test-based inference in mixed-effects models but the only available alternative for Likelihood ratio test-based hypothesis testing has been to perform computer-intensive multiple simulations and re-estimations. The proposed Monte Carlo Mapped Power (MCMP) method is based on the use of the difference in individual objective function values (ΔiOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The ΔiOFV is sampled and summed (∑ΔiOFVs) for each study at each sample size of interest to study, and the percentage of ∑ΔiOFVs greater than the significance criterion is taken as the power. The power versus sample size relationship established via the MCMP method was compared to traditional assessment of model-based power for six different pharmacokinetic and pharmacodynamic models and designs. In each case, 1,000 simulated datasets were analysed with the full and reduced models. There was concordance in power between the traditional and MCMP methods such that for 90% power, the difference in required sample size was in most investigated cases less than 10%. The MCMP method was able to provide relevant power information for a representative pharmacometric model at less than 1% of the run-time of an SSE. The suggested MCMP method provides a fast and accurate prediction of the power and sample size relationship.

Comparison of dose-finding designs for narrow-therapeutic-index drugs: concentration-controlled vs. dose-controlled trials

This study compared the performances of randomized dose-controlled trials (DCTs) with those of concentration-controlled trials (CCTs) in dose finding for drugs with narrow therapeutic indexes. A simulation-based study was performed for a hypothetical immunosuppressant agent with two clinical end points. Different scenarios were simulated and analyzed, and three designs were compared: one DCT and two CCTs (a target-equivalent CCT and a variability-equivalent CCT). The DCT was consistently superior to the CCTs in the following aspects: (i) precision and bias reduction in parameter estimates, (ii) precision and bias reduction in the estimate of optimal exposure, (iii) bias reduction in prediction of the estimated therapeutic benefit at estimated optimal exposure, and (iv) bias reduction in prediction of the estimated benefit of therapeutic drug monitoring as compared with fixed dosing. DCT designs are more informative when describing the exposure-response relationship for drugs with narrow therapeutic indexes and provide a better basis for decision making with regard to dosing strategy.

The influence of underlying assumptions on evaluating the relative merits of concentration-controlled and dose-controlled trials

The objective of this study was to assess the relative performances of concentration-controlled trial (CCT) and dose-controlled clinical trial (DCT) designs with varying (i) interindividual variability (IIV) in clearance (CL), (ii) relative clinical importance of rejection and infection episodes, (iii) parameter values for the concentration-effect relationships, (iv) interindividual covariance between exposure and effect relationships, and (v) nonlinearity of the concentration-effect relationship. Different scenarios were simulated and analyzed for DCT and CCT designs, and these were compared with respect to bias, prediction, and power. The DCT design showed superiority across all the scenarios studied, with regard to precision and bias in parameter estimates, precision and bias in the estimate of optimal exposure, and bias in prediction of the therapeutic benefit at estimated optimal exposure. However, when a pharmacokinetic-pharmacodynamic (PKPD) covariance in the parameters was considered, either the variance-equivalent concentration-controlled trial (VCCT) or the DCT was the more useful design. Across a number of scenarios, the DCT design is the more informative one.