Modeling tumor growth kinetics after treatment with pazopanib or placebo in patients with renal cell carcinoma

A meta-analysis of tumour response and relapse kinetics based on 34,881 patients: A question of cancer type, treatment and line of treatment

PURPOSE

Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design.

EXPERIMENTAL DESIGN

Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA.

RESULTS

Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported.

CONCLUSIONS

Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used.

Early predictions of response and survival from a tumor dynamics model in patients with recurrent, metastatic head and neck squamous cell carcinoma treated with immunotherapy

We developed and evaluated a method for making early predictions of best overall response (BOR) and overall survival at 6 months (OS6) in patients with cancer treated with immunotherapy. This method combines machine learning with modeling of longitudinal tumor size data. We applied our method to data from durvalumab‐exposed patients with recurrent/metastatic head and neck cancer. A fivefold cross‐validation was used for model selection. Independent trial data, with various degrees of data truncation, were used for model validation. Mean classification error rates (90% confidence intervals [CIs]) from cross‐validation were 5.99% (90% CI 2.98%–7.50%) for BOR and 19.8% (90% CI 15.8%–39.3%) for OS6. During model validation, the area under the receiver operating characteristic curves was preserved for BOR (0.97, 0.97, and 0.94) and OS6 (0.85, 0.84, and 0.82) at 24, 18, and 12 weeks, respectively. These results suggest our method predicts trial outcomes accurately from early data and could be used to aid drug development.

Comparison of Various Phase I Combination Therapy Designs in Oncology for Evaluation of Early Tumor Shrinkage Using Simulations

There is still a lack of efficient designs for identifying the dose response in oncology combination therapies in early clinical trials. The concentration response relationship can be identified using the early tumor shrinkage time course, which has been shown to be a good early response marker of clinical efficacy. The performance of various designs using an exposure–tumor growth inhibition model was explored using simulations. Different combination effects of new drug M and cetuximab (reference therapy) were explored first assuming no effect of M on cetuximab (to investigate the type I error (α)), and subsequently assuming additivity or synergy between cetuximab and M. One‐arm, two‐arm, and four‐arm designs were evaluated. In the one‐arm design, 60 patients received cetuximab + M. In the two‐arm design, 30 patients received cetuximab and 30 received cetuximab + M. In the four‐arm design, in addition to cetuximab and cetuximab + M as standard doses, combination arms with lower doses of cetuximab were evaluated (15 patients/arm). Model‐based predictions or “simulated observations” of early tumor shrinkage at week 8 (ETS8) were compared between the different arms. With the same number of individuals, the one‐arm design showed better statistical power than other designs but led to strong inflation of α in case of misestimated reference for ETS8 value. The two‐arm design protected against this misestimation and, with the same total number of subjects, would provide higher statistical power than a four‐arm design. However, a four‐arm design would be helpful for exploring more doses of cetuximab in combination with M to better understand the interaction.

A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors

Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.

Clinical Pharmacokinetics and Pharmacodynamics of Pazopanib: Towards Optimized Dosing

Pazopanib is an inhibitor of the vascular endothelial growth factor receptor, platelet-derived growth factor receptor, fibroblast growth factor receptor and stem cell receptor c-Kit, and has been approved for the treatment of renal cell carcinoma and soft tissue sarcoma. The pharmacokinetics of pazopanib are complex and are characterized by pH-dependent solubility, large interpatient variability and low, non-linear and time-dependent bioavailability. Exposure to pazopanib is increased by both food and coadministration of ketoconazole, but drastically reduced by proton pump inhibitors. Studies have demonstrated relationships between systemic exposure to pazopanib and toxicity, such as hypertension. Furthermore, a strong relationship between pazopanib trough level ≥20 mg/L and both tumor shrinkage and progression-free survival has been established. At the currently approved daily dose of 800 mg, approximately 20% of patients do not reach this threshold and may be at risk of suboptimal treatment. As a result of this, clinical trials have explored individualized pazopanib dosing, which demonstrate the safety and feasibility of individualized pazopanib dosing based on trough levels. In summary, we provide an overview of the complex pharmacokinetic and pharmacodynamic profiles of pazopanib and, based on the available data, we propose optimized dosing strategies.

Variable response of CNS hemangioblastomas to Pazopanib in a single patient with von Hippel-Lindau disease: Case report

Von Hippel-Lindau (VHL) disease is a multisystem genetic disease, the cardinal manifestations of which include central nervous system hemangioblastomas (CNS HB), renal cell carcinomas (RCC), and pheochromocytoma. Tumorigenesis in VHL of both RCC and CNS HB occurs secondary to downstream effects of a mutated or absent VHL protein. Treatment of RCCs with tyrosine kinase inhibitors (TKIs) such as Pazopanib is now first line therapy, but their effect on VHL-associated CNS HBs remains unknown. We report the use of Pazopanib in a patient with VHL disease for treatment of RCC who also harbored multiple CNS HBs. Following initiation of treatment, a large cervical and a lumbar spinal HB regressed in size while the remaining CNS HBs exhibited stable or progressive disease. These findings highlight the multiplicity of factors contributing to hemangioblastoma development, even among tumors with a common germline mutation, and the potential limitations of TKIs, but additionally this report supports the conservative management of asymptomatic VHL patients with spinal HBs whereby tumor response to TKI treatment may alleviate or postpone the need for surgery.

The role of pazopanib on tumour angiogenesis and in the management of cancers: A review

Pazopanib is a relatively new compound to be introduced into the chemotherapy field. It is thought to have decent anti-angiogenic properties, which gives an additional hope for the treatment of certain types of cancers. A systematic review solely discussing about pazopanib and its anti-angiogenic effect is yet to be published to date, despite several relevant clinical trials being conducted over the recent years. In this review, we aim to investigate the mechanism of pazopanib's anti-angiogenic effect and its effectiveness in treating several cancers. We have included, in this study, findings from electronically searchable data from randomized clinical trials, clinical studies, cohort studies and other relevant articles. A total of 352 studies were included in this review. From the studies, the effect of pazopanib in various cancers or models was observed and recorded. Study quality is indefinite, with a few decent quality articles. The most elaborately studied cancers include renal cell carcinoma, solid tumors, advanced solid tumors, soft tissue sarcoma, breast cancer and gynecological cancers. In addition, several less commonly studied cancers are included in the studies as well. Pazopanib had demonstrated its anti-angiogenic effect based on favorable results observed in cancers, which are caused by angiogenesis-related mechanisms, such as renal cell carcinoma, solid tumors, advanced solid tumors and soft tissue sarcoma. This review was conducted to study, analyze and review the anti-angiogenic properties of pazopanib in various cancers. The results obtained can provide a decent reference when considering treatment options for angiogenesis-related malignancies. Furthermore, the definite observations of the anti-angiogenic effects of pazopanib could provide newer insights leading to the future development of drugs of the same mechanism with increased efficiency and reduced adverse effects.

A Review of Modeling Approaches to Predict Drug Response in Clinical Oncology

Model-based approaches have emerged as important tools for quantitatively understanding temporal relationships between drug dose, concentration, and effect over the course of treatment, and have now become central to optimal drug development and tailored drug treatment. In oncology, the therapeutic index of a chemotherapeutic drug is typically narrow and a full dose-response relationship is not available, often because of treatment failure. Noting the benefits of model-based approaches and the low therapeutic index of oncology drugs, in recent years, modeling approaches have been increasingly used to streamline oncologic drug development through early identification and quantification of dose-response relationships. With this background, this report reviews publications that used model-based approaches to evaluate drug treatment outcome variables in oncology therapeutics, ranging from tumor size dynamics to tumor/biomarker time courses and survival response.

Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors

The objective of this work was to establish the quantitative relationship between Lanreotide Autogel® (LAN) on serum chromogranin A (CgA) and progression-free survival (PFS) in patients with nonfunctioning gastroenteropancreatic neuroendocrine tumors (GEP-NETs) through an integrated pharmacokinetic/pharmacodynamic (PK/PD) model. In CLARINET, a phase III, randomized, double-blind, placebo-controlled study, 204 patients received deep subcutaneous injections of LAN 120 mg (n = 101) or placebo (n = 103) every 4 weeks for 96 weeks. Data for 810 LAN and 1298 CgA serum samples (n = 632 placebo and n = 666 LAN) were used to develop a parametric time-to-event model to relate CgA levels and PFS (76 patients experienced disease progression: n = 49 placebo and n = 27 LAN). LAN serum profiles were described by a one-compartment disposition model. Absorption was characterized by two parallel pathways following first- and zero-order kinetics. As PFS data were considered informative dropouts, CgA and PFS responses were modeled jointly. The LAN-induced decrease in CgA levels was described by an inhibitory E MAX model. Patient age and target lesions at baseline were associated with an increment in baseline CgA. Weibull model distribution showed that decreases in CgA from baseline reduced the hazard of disease progression significantly (P < 0.001). Covariates of tumor location in the pancreas and tumor hepatic tumor load were associated with worse prognosis (P < 0.001). We established a semimechanistic PK/PD model to better understand the effect of LAN on a surrogate endpoint (serum CgA) and ultimately the clinical endpoint (PFS) in treatment-naive patients with nonfunctioning GEP-NETs.

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

The objective was to leverage tumor size data from preclinical experiments to propose a model of tumor growth and angiogenesis inhibition for the analysis of pazopanib efficacy in renal cell carcinoma (RCC) patients. We analyzed tumor data in mice with RCC CAKI‐2 cell line treated with pazopanib. Clinical tumor size data obtained in a subset of patients with RCC were also analyzed. A model accounting for the processes of tumor growth, angiogenesis, and drug effect was developed. The final tumor model was composed of two variables: the tumor and its vasculature. Our results show that, both in mice and in humans, pazopanib exhibits a dual mechanism of action, and parameter estimation values highlight the inherent difference between mice and humans on the time scale of tumor size response. We developed a semimechanistic tumor growth inhibition model that takes into account tumor angiogenesis in order to describe the effects of pazopanib in mice. Analyzing rich preclinical data with a semimechanistic model may be a relevant approach to facilitate the description of sparse clinical longitudinal tumor size data and to provide insights for the understanding of the drug mechanisms of action in patients.

A Joint Model for the Kinetics of CTC Count and PSA Concentration During Treatment in Metastatic Castration-Resistant Prostate Cancer

Assessment of treatment efficacy in metastatic castration-resistant prostate cancer (mCRPC) is limited by frequent nonmeasurable bone metastases. The count of circulating tumor cells (CTCs) is a promising surrogate marker that may replace the widely used prostate-specific antigen (PSA). The purpose of this study was to quantify the dynamic relationships between the longitudinal kinetics of these markers during treatment in patients with mCRPC. Data from 223 patients with mCRPC treated by chemotherapy and/or hormonotherapy were analyzed for up to 6 months of treatment. A semimechanistic model was built, combining the following several pharmacometric advanced features: (1) Kinetic-Pharmacodynamic (K-PD) compartments for treatments (chemotherapy and hormonotherapy); (2) a latent variable linking both marker kinetics; (3) modeling of CTC kinetics with a cell lifespan model; and (4) a negative binomial distribution for the CTC random sampling. Linked with survival, this model would potentially be useful for predicting treatment efficacy during drug development or for therapeutic adjustment in treated patients.

A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis

Population modeling of tumor size dynamics has recently emerged as an important tool in pharmacometric research. A series of new mixed-effects models have been reported recently, and we present herein a synthetic view of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment. This selection of models will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology.