Model-based meta-analysis for quantifying Paclitaxel dose response in cancer patients

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.

Implantable smart hyperthermia nanofibers for cancer therapy: Challenges and opportunities

Nanofibers (NFs) with practical drug-loading capacities, high stability, and controllable release have caught the attention of investigators due to their potential applications in on-demand drug delivery devices. Developing novel and efficient multidisciplinary management of locoregional cancer treatment through the design of smart NF-based systems integrated with combined chemotherapy and hyperthermia could provide stronger therapeutic advantages. On the other hand, implanting directly at the tumor area is a remarkable benefit of hyperthermia NF-based drug delivery approaches. Hence, implantable smart hyperthermia NFs might be very hopeful for tumor treatment in the future and provide new avenues for developing highly efficient localized drug delivery systems. Indeed, features of the smart NFs lead to the construction of a reversibly flexible nanostructure that enables hyperthermia and facile switchable release of antitumor agents to eradicate cancer cells. Accordingly, this study covers recent updates on applications of implantable smart hyperthermia NFs regarding their current scope and future outlook. This article is categorized under: Implantable Materials and Surgical Technologies > Nanomaterials and Implants.

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.

How translational modeling in oncology needs to get the mechanism just right

Translational model‐based approaches have played a role in increasing success in the development of novel anticancer treatments. However, despite this, significant translational uncertainty remains from animal models to patients. Optimization of dose and scheduling (regimen) of drugs to maximize the therapeutic utility (maximize efficacy while avoiding limiting toxicities) is still predominately driven by clinical investigations. Here, we argue that utilizing pragmatic mechanism‐based translational modeling of nonclinical data can further inform this optimization. Consequently, a prototype model is demonstrated that addresses the required fundamental mechanisms.

Optimization of clinical dosing schedule to manage neutropenia: learnings from semi-mechanistic modeling simulation approach

Neutropenia is a common side-effect of oncology drugs. We aimed to study the impact of exposure and dosing schedule on neutropenia to guide selection of dosing schedules that minimize neutropenia potential while maintaining the desired minimum concentration (C_min) required for target engagement. Dose, frequency and PK parameters were chosen for five hypothetical drugs of various half-lives to (1) achieve same exposure with continuous dosing and evaluate impact of 4 intermittent dosing schedules; and (2) achieve same nadir for continuous and intermittent dosing and evaluate impact on % time above C_min, a surrogate assumed to indicate target engagement. Absolute neutrophil count (ANC) profiles were simulated using Friberg model, a widely used semi-mechanistic myelosuppression model, assuming drug concentration directly reduce the proliferation rate of stem cells and progenitor cells in proliferation compartment. The correlations between different PK measures and neutropenia metrics were explored. In (1), when the same daily dose was used, intermittent schedules offered better management of ANC nadir. The reduced average drug exposure with intermittent dosing led to lower% time above C_min. In (2), when the dose was adjusted to achieve the same nadir, drugs with moderate half-life (8-48 h) showed similar % time above C_min regardless of schedule, while continuous dosing was better for a short half-life (4 h). Area under the concentration curve (AUC) was highly correlated with neutropenia. In summary, continuous dosing, with the dose selected correctly, is most effective to maintain % time above C_min while providing similar tolerability as intermittent dosing with a higher dose. But dose interruptions could be required to manage individual toxicities. Intermittent schedules, on the other hand, allow recovery of ANC, enabling more orderly schedules.

A randomized phase III trial in patients with recurrent platinum sensitive ovarian cancer comparing efficacy and safety of paclitaxel micellar and Cremophor EL-paclitaxel

OBJECTIVE

Paclitaxel micellar was developed to avoid Cremophor-EL (Cr-EL) associated dose limiting toxicity and to allow a shorter infusion time. The efficacy and safety of paclitaxel micellar (+carboplatin) was compared to Cr-EL paclitaxel (+carboplatin) in recurrent platinum-sensitive ovarian, fallopian tube or peritoneal carcinoma.

METHODS

This was a multicentre, open-label, randomized phase III trial. Adult patients with recurrent disease was assigned to six 3-week cycles of paclitaxel micellar (250 mg/m²) administered as 1-h infusion or Cr-EL paclitaxel (175 mg/m²) as 3-h infusion. Both arms received carboplatin (AUC 5-6). Primary objective was non-inferiority for progression free survival (PFS) using computed tomography scans. Overall survival (OS) was included as secondary endpoint.

RESULTS

Between 2009 and 2013, 789 patients were randomized to receive experimental (N = 397) or control (N = 392) treatment. PFS for paclitaxel micellar was non-inferior to Cr-EL paclitaxel with a hazard ratio of 0.86 (95% CI: 0.72;1.03) in the per protocol population (PP), favouring paclitaxel micellar (non-inferiority margin was 1.2). Non-inferiority of OS was shown in the PP population with a hazard ratio of 0.95 (95% CI: 0.78; 1.16), favouring paclitaxel micellar (non-inferiority margin was 1.185). The most common adverse event was neutropenia (grade ≥ 3); 245 patients (79%) for paclitaxel micellar vs 213 patients (66%) for Cr-EL paclitaxel. The frequency of peripheral sensory neuropathy (any grade) was similar between the arms; 16% for paclitaxel micellar and 20% for Cr-EL paclitaxel.

CONCLUSION

Paclitaxel micellar (+ carboplatin) is non-inferior to Cr-EL paclitaxel (+ carboplatin) in terms of PFS and OS in the studied population. It provides a treatment option of a higher paclitaxel dose with a shorter infusion time without mandatory premedication.

TRIAL REGISTRATION NUMBER

2008-002668-32 (EudraCT), NCT00989131 (ClinicalTrials.gov).

Model-based meta-analysis to evaluate optimal doses of direct oral factor Xa inhibitors in atrial fibrillation patients

The noninferiority of direct oral factor Xa (FXa) inhibitors (rivaroxaban, apixaban, and edoxaban) in treatment of atrial fibrillation were demonstrated compared with warfarin by several large clinical trials; however, subsequent meta-analyses reported a higher risk of major bleeding with rivaroxaban than with the other FXa inhibitors. In the present study, we first estimated the changes of prothrombin time (PT) in 5 randomized trials based on reported population pharmacokinetic and pharmacodynamic models and then carried out a model-based meta-analysis to obtain models describing the relationship between PT changes and the event rates of ischemic stroke/systemic embolism (SE) and of major bleeding. By using the models, we simulated the optimal therapeutic doses for each FXa inhibitor. It was suggested that dose reduction of rivaroxaban from the current 20 mg/d to 10 mg/d would decrease patient deaths from major bleeding (hazard ratio [HR], 0.69; 95% confidence interval [CI], 0.64-0.74) with little increase in those for ischemic stroke/SE (HR, 1.11; 95% CI, 1.07-1.20). The overall decrease in the mortality caused by both events was estimated as 5.81 per 10 000 patient-years (95% CI, 3.92-8.16), with an HR of 0.87 (95% CI, 0.83-0.91). For apixaban and edoxaban, no distinct change in the overall mortality was simulated by dose modification. This study suggested that the current dose of rivaroxaban might be excessive and would need to be reduced to decrease the excess risk of major bleeding.

Model-Informed Drug Development for Ixazomib, an Oral Proteasome Inhibitor

Model-informed drug development (MIDD) was central to the development of the oral proteasome inhibitor ixazomib, facilitating internal decisions (switch from body surface area (BSA)-based to fixed dosing, inclusive phase III trials, portfolio prioritization of ixazomib-based combinations, phase III dose for maintenance treatment), regulatory review (model-informed QT analysis, benefit-risk of 4 mg dose), and product labeling (absolute bioavailability and intrinsic/extrinsic factors). This review discusses the impact of MIDD in enabling patient-centric therapeutic optimization during the development of ixazomib.

Model-Based Meta-Analysis for Multiple Myeloma: A Quantitative Drug-Independent Framework for Efficient Decisions in Oncology Drug Development

The failure rate for phase III trials in oncology is high; quantitative predictive approaches are needed. We developed a model‐based meta‐analysis (MBMA) framework to predict progression‐free survival (PFS) from overall response rates (ORR) in relapsed/refractory multiple myeloma (RRMM), using data from seven phase III trials. A Bayesian analysis was used to predict the probability of technical success (PTS) for achieving desired phase III PFS targets based on phase II ORR data. The model demonstrated a strongly correlated (R² = 0.84) linear relationship between ORR and median PFS. As a representative application of the framework, MBMA predicted that an ORR of ∼66% would be needed in a phase II study of 50 patients to achieve a target median PFS of 13.5 months in a phase III study. This model can be used to help estimate PTS to achieve gold‐standard targets in a target product profile, thereby enabling objectively informed decision‐making.

Multiscale Modeling in the Clinic: Drug Design and Development

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Pathological complete response rate in hormone receptor-negative breast cancer treated with neoadjuvant FEC, followed by weekly paclitaxel administration: A retrospective study and review of the literature

While tumor size, the presence of inflammatory carcinoma and lymph node involvement are the main prognostic factors of women with locally advanced breast cancer, the prognostic value of the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 (HER2) status has not been fully clarified. The present study examined the therapeutic efficacy of a neoadjuvant fluorouracil, epirubicin and cyclophosphamide regimen (FEC), followed by weekly paclitaxel and/or trastuzumab administration, in the treatment of hormone receptor-negative breast cancer patients. Between April 2012 and February 2014, 14 patients with hormone receptor-negative local breast cancer (triple-negative type, 9 patients; HER2 type, 5 patients) were included in the study. In all cases, the histological type of the primary cancer was invasive ductal carcinoma. Among the 14 women who received the regimen, 5 presented with stage I cancer (35.7%), 3 with stage IIA (21.4%), 3 with stage IIB (21.4%), 1 with stage IIIB (7.1%) and 2 with stage IIIC (14.3%), according to the American Joint Committee on Cancer staging system. With regard to the tumor-node-metastasis classification, 5 patients were T1N0M0 (35.7%), 3 were T2N0M0 (21.4%), 3 were T2N1M0 (21.4%), 2 were T3N3M0 (14.3%) and 1 was T4N1M0 (7.1%). The pathological response was evaluated using resected tissue following neoadjuvant chemotherapy, according to the criteria established by the Japanese Breast Cancer Society. Patients were classified into pathological responders (grades 2 and 3, 71.4% of all patients) and non-responders (grade 1, 28.6% of all patients). A pathological complete response (pCR) was achieved in 50.0% of all cases (7/14); 44.4% of triple-negative-type cases (4/9) and 60.0% of HER2-type cases (3/5). Hematological and non-hematological toxicity was reversible and manageable. No patients withdrew from treatment, and favorable compliance was achieved. The present study demonstrated that neoadjuvant FEC followed by weekly administration of paclitaxel and/or trastuzumab induces a high pathological response and a high pCR rate in patients with hormone receptor-negative breast cancer. Due to the high clinical benefit rate and acceptable safety profile, this regimen should be considered an acceptable neoadjuvant treatment option for hormone receptor-negative breast cancer.

A Six-Stage Workflow for Robust Application of Systems Pharmacology

Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

Drug development (and academic medicine): charting the course forward

Drug development is both scientifically and economically driven. Past efforts to support the process have had great success, but increasing economic and regulatory pressures again threaten continued progress. The path from discovery to clinical use is in need of reevaluation with regard to substantive changes to reenergize the process. Such reevaluation includes clinical pharmacology input from all in our academia, industry, and regulatory sectors.