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

Abstract 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.

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Exploring a model-based analysis of patient derived xenograft studies in oncology drug development

PeerJ ◽

10.7717/peerj.10681 ◽

2021 ◽

Vol 9 ◽

pp. e10681

Author(s):

Jake Dickinson ◽

Marcel de Matas ◽

Paul A. Dickinson ◽

Hitesh B. Mistry

Keyword(s):

Simulation Study ◽

Statistical Power ◽

Tumour Growth ◽

Empirical Approach ◽

Series Data ◽

Ratio Test ◽

Data Set ◽

Model Based ◽

Patient Derived Xenograft ◽

Model Based Analysis

Purpose To assess whether a model-based analysis increased statistical power over an analysis of final day volumes and provide insights into more efficient patient derived xenograft (PDX) study designs. Methods Tumour xenograft time-series data was extracted from a public PDX drug treatment database. For all 2-arm studies the percent tumour growth inhibition (TGI) at day 14, 21 and 28 was calculated. Treatment effect was analysed using an un-paired, two-tailed t-test (empirical) and a model-based analysis, likelihood ratio-test (LRT). In addition, a simulation study was performed to assess the difference in power between the two data-analysis approaches for PDX or standard cell-line derived xenografts (CDX). Results The model-based analysis had greater statistical power than the empirical approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25% whereas the empirical approach required at least 50% TGI. The simulation study confirmed the findings and highlighted that CDX studies require fewer animals than PDX studies which show the equivalent level of TGI. Conclusions The study conducted adds to the growing literature which has shown that a model-based analysis of xenograft data improves statistical power over the common empirical approach. The analysis conducted showed that a model-based approach, based on the first mathematical model of tumour growth, was able to detect smaller size of effect compared to the empirical approach which is common of such studies. A model-based analysis should allow studies to reduce animal use and experiment length providing effective insights into compound anti-tumour activity.

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Analysis of patient derived xenograft studies in Oncology drug development: impact on design and interpretation of future studies

10.1101/579136 ◽

2019 ◽

Author(s):

Jake Dickinson ◽

Marcel de Matas ◽

Paul A Dickinson ◽

Hitesh Mistry

Keyword(s):

Time Series ◽

Drug Development ◽

Simulation Study ◽

Statistical Power ◽

T Test ◽

Model Based ◽

Patient Derived Xenograft ◽

Oncology Drug Development ◽

Paired T Test ◽

Model Based Analysis

AbstractBackgroundPreclinical Oncology drug development is heavily reliant on xenograft studies to assess the anti-tumour effect of new compounds. Patient derived xenograft (PDX) have become popular as they may better represent the clinical disease, however variability is greater than in cell-line derived xenografts. The typical approach of analysing these studies involves performing an un-paired t-test on the mean tumour volumes between the treated and control group at the end of the study. This approach ignores the time-series and may result in false conclusions, especially when considering the increased variability of PDX studies.AimTo test the hypothesis that a model-based analysis provides increased power than analysis of final day volumes and to provide insights into more efficient PDX study designs.MethodsData was extracted from tumour xenograft time-series data from a large publicly available PDX drug treatment database released by Novartis. For all 2-arm studies the percent tumour growth inhibition (TGI) at two time-points, day 10 and day 14 was calculated. For each study, the effect of treatment was calculated using an un-paired t-test and also a model-based analysis using the likelihood ratio-test. In addition a simulation study was also performed to assess the difference in power between the two data-analysis approaches for different levels of TGI for PDX or standard cell-line derived xenografts (CDX).ResultsThe model-based analysis had greater statistical power than the un-paired t-test approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25 percent whereas the un-paired t-test approach required at least 50 percent TGI. These findings were confirmed within the simulation study performed which also highlighted that CDX studies require less animals than PDX studies which show the equivalent level of TGI.ConclusionThe analysis of 59 2-arm PDX studies highlighted that taking a model-based approach gave increased statistical power over simply performing an un-paired t-test on the final study day. Importantly the model-based approach was able to detect smaller size of effect compared to the un-paired t-test approach is which maybe common of such studies. These findings were confirmed within simulated studies which also highlighted the same sample size used for CDX studies would lead to inadequately powered PDX studies. Application of a model-based analysis should allow studies to use less animals and run experiments for a shorter period thus providing effective insight into compound anti-tumour activity

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Population pharmacokinetic and pharmacodynamic analysis of osimertinib.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.15_suppl.e20536 ◽

2017 ◽

Vol 35 (15_suppl) ◽

pp. e20536-e20536

Author(s):

Martin Johnson ◽

Henning Schmidt ◽

Mikael Sunnaker ◽

Anthony F Nash ◽

Suman Nayak ◽

...

Keyword(s):

Exposure Model ◽

Population Pharmacokinetic ◽

Baseline Serum ◽

Model Based ◽

Exposure Response ◽

Number Of Patients ◽

Population Pk ◽

The Impact ◽

Egfr Tki ◽

Model Based Analysis

e20536 Background: Osimertinib is an oral, potent, irreversible, CNS active EGFR-TKI, selective for sensitizing (EGFRm) and T790M resistance mutations, indicated for the treatment of patients with T790M positive advanced non-small cell lung cancer who have progressed on or after EGFR-TKI therapy. Osimertinib pharmacokinetics (PK) were evaluated using a population approach and pharmacodynamic (PD) relationships using appropriate modeling approaches. Methods: To understand the impact of covariates on osimertinib PK, a population PK analysis was performed using data from patients who received osimertinib (20–240 mg) during the AURA studies. Exposure metrics were derived from a PK model and used to assess the exposure-response (safety/efficacy) relationship. Efficacy analysis included patients who were T790M positive (n = 710) and safety analysis included all dosed patients (n = 1088). The impact of covariates on exposure-response was assessed. Models accounting for rare safety events were applied to quantify the association between events and exposure. Results: Population PK analyses supported dose- and time-independent PK of osimertinib with no clinically meaningful covariates identified. Patients in the highest exposure quartile (Q4) had a numerically shorter median progression-free survival (8.3 months [95% CI 6.9, 10.5]) compared with patients in Q1, Q2 and Q3 (all 11.2 months [95% CIs 9.7, 12.7; 8.5, 15.6 and 8.7, 13.7, respectively]). A model-based analysis indicated that this effect is likely due to a larger number of patients in Q4 with poor prognostic features, i.e. worse performance status (WHO 1 or 2) and lower baseline serum albumin compared with Q1, Q2 and Q3, rather than to osimertinib exposure. Model-predicted probability of a relationship between osimertinib exposure and LVEF changes was not evident. Model-based analysis predicted that, compared with the median probability (0.03), the probability of a patient experiencing interstitial lung disease may increase with increasing osimertinib exposure (Q1 probability 0.01 [steady-state AUC 6361 nM*h] vs Q4 0.06 [24460 nM*h]) at the 80 mg dose. Conclusions: Population PK and PK-PD analysis is supportive of 80 mg as an appropriate dose for osimertinib. Clinical trial information: NCT01802632; NCT01802632; NCT02094261; NCT02151981.

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