Meta-analysis of published efficacy and safety data for docetaxel in second-line treatment of patients with advanced non-small-cell lung cancer

Utility of Physiologically Based Pharmacokinetic Modeling to Investigate the Impact of Physiological Changes of Pregnancy and Cancer on Oncology Drug Pharmacokinetics

BACKGROUND

The treatment of cancer during pregnancy remains challenging with knowledge gaps in drug dosage, safety, and efficacy due to the under-representation of this population in clinical trials. Our aim was to investigate physiological changes reported in both pregnancy and cancer populations into a PBPK modeling framework that allows for a more accurate estimation of PK changes in pregnant patients with cancer.

METHODS

Paclitaxel and docetaxel were selected to validate a population model using clinical data from pregnant patients with cancer. The validated population model was subsequently used to predict the PK of acalabrutinib in pregnant patients with cancer.

RESULTS

The Simcyp pregnancy population model reasonably predicted the PK of docetaxel in pregnant patients with cancer, while a modified model that included a 2.5-fold increase in CYP2C8 abundance, consistent with the increased expression during pregnancy, was needed to reasonably predict the PK of paclitaxel in pregnant patients with cancer. Changes in protein binding levels of patients with cancer had a minimal impact on the predicted clearance of paclitaxel and docetaxel. PBPK modeling predicted approximately 60% lower AUC and Cmax for acalabrutinib in pregnant versus non-pregnant patients with cancer.

CONCLUSIONS

Our results suggest that PBPK modeling is a promising approach to investigate the effects of pregnancy and cancer on the PK of oncology drugs and potentially inform dosing for pregnant patients with cancer. Further evaluation and refinement of the population model are needed for pregnant patients with cancer with additional compounds and clinical PK data.

Applications of Model-Based Meta-Analysis in Drug Development

Model-based meta-analysis (MBMA) is a quantitative approach that leverages published summary data along with internal data and can be applied to inform key drug development decisions, including the benefit-risk assessment of a treatment under investigation. These risk-benefit assessments may involve determining an optimal dose compared against historic external comparators of a particular disease indication. MBMA can provide a flexible framework for interpreting aggregated data from historic reference studies and therefore should be a standard tool for the model-informed drug development (MIDD) framework.In addition to pairwise and network meta-analyses, MBMA provides further contributions in the quantitative approaches with its ability to incorporate longitudinal data and the pharmacologic concept of dose-response relationship, as well as to combine individual- and summary-level data and routinely incorporate covariates in the analysis.A common application of MBMA is the selection of optimal dose and dosing regimen of the internal investigational molecule to evaluate external benchmarking and to support comparator selection. Two case studies provided examples in applications of MBMA in biologics (durvalumab + tremelimumab for safety) and small molecule (fenebrutinib for efficacy) to support drug development decision-making in two different but well-studied disease areas, i.e., oncology and rheumatoid arthritis, respectively.Important to the future directions of MBMA include additional recognition and engagement from drug development stakeholders for the MBMA approach, stronger collaboration between pharmacometrics and statistics, expanded data access, and the use of machine learning for database building. Timely, cost-effective, and successful application of MBMA should be part of providing an integrated view of MIDD.

Model-informed drug repurposing: A pharmacometric approach to novel pathogen preparedness, response and retrospection

During a pandemic caused by a novel pathogen (NP), drug repurposing offers the potential of a rapid treatment response via a repurposed drug (RD) while more targeted treatments are developed. Five steps of model‐informed drug repurposing (MIDR) are discussed: (i) utilize RD product label and in vitro NP data to determine initial proof of potential, (ii) optimize potential posology using clinical pharmacokinetics (PK) considering both efficacy and safety, (iii) link events in the viral life cycle to RD PK, (iv) link RD PK to clinical and virologic outcomes, and optimize clinical trial design, and (v) assess RD treatment effects from trials using model‐based meta‐analysis. Activities which fall under these five steps are categorized into three stages: what can be accomplished prior to an NP emergence (preparatory stage), during the NP pandemic (responsive stage) and once the crisis has subsided (retrospective stage). MIDR allows for extraction of a greater amount of information from emerging data and integration of disparate data into actionable insight.

Benchmarking single-arm studies against historical controls from non-small cell lung cancer trials - an empirical analysis of bias

Background: Recent trials of novel agents in 'rare' molecular subtypes of non-small cell lung cancer (NSCLC) have used single-arm trial designs and benchmarked outcomes against historical controls. We assessed the consistency of historical control outcomes using docetaxel data from published NSCLC randomized controlled trials (RCTs).Material and methods: Advanced NSCLC RCTs including a docetaxel monotherapy arm were included. Heterogeneity in tumor objective response rates (ORRs), progression-free survival (PFS) and overall survival (OS), and correlations between outcomes and year of trial commencement were assessed.Results: Among 63 trials (N = 10,633) conducted between 2000 and 2017, ORR ranged from 0% to 26% (I² = 76.1%, p_{heterogeneity} < .0001). Mean of the median PFS was 3.0 months (range: 1.4-6.4), 3-month PFS ranged from 25% to 85% (I² = 86.0%, p_{heterogeneity} < .0001). Mean of the median OS was 9.1 months (range: 4.7-22.9), 9-month OS ranged from 23% to 79% (I² = 83.0%, p_{heterogeneity} < .0001). Each later year of trial commencement was associated with 0.3% (p = .046), 0.5% (p = .11) and 0.9% (p = .001) improvement in ORR, 3-month PFS and 9-month OS rates, respectively.Conclusions: There was significant heterogeneity and an improving trend in docetaxel outcomes across trials conducted over 20 years. Benchmarking biomarker-targeted agents against historical controls may not be a valid approach to replace RCTs. Innovative study designs involving a concurrent control arm should be considered.

The role of docetaxel in the treatment of non-small cell lung cancer lung cancer: an update

INTRODUCTION

Non-small cell lung cancer lung cancer (NSCLC) is a devastating disease, with poor prognosis for patients with metastatic disease. The management of these patients has evolved during the past decade, challenging the role of cytotoxic chemotherapy as the only available treatment option. Nevertheless, chemotherapy still retains a dominant position for the majority of both treatment naïve and pretreated patients. Among the chemotherapeutic agents, docetaxel is one of the most commonly used in 1st and subsequent treatment lines, even in the current era of precision medicine. Areas covered: We searched Medline, Embase, Scopus and Cochrane Library for randomized phase III trials that evaluated docetaxel in various clinical settings of NSCLC and for meta-analyses of such trials and we present all relevant data regarding the pharmacology and clinical use of docetaxel in NSCLC. Expert commentary: Despite its diminishing role, docetaxel in combination with novel targeted agents remains an important option of the therapeutic armamentarium in advanced NSCLC.