Translation of anticancer efficacy from nonclinical models to the clinic

Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development

Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.

Multiscale systems pharmacological analysis of everolimus action in hepatocellular carcinoma

Dysregulation of mTOR pathway is common in hepatocellular carcinoma (HCC). A translational quantitative systems pharmacology (QSP), pharmacokinetic (PK), and pharmacodynamic (PD) model dissecting the circuitry of this pathway was developed to predict HCC patients' response to everolimus, an mTOR inhibitor. The time course of key signaling proteins in the mTOR pathway, HCC cells viability, tumor volume (TV) and everolimus plasma and tumor concentrations in xenograft mice, clinical PK of everolimus and progression free survival (PFS) in placebo and everolimus-treated patients were extracted from literature. A comprehensive and multiscale QSP/PK/PD model was developed, qualified, and translated to clinical settings. Model fittings and simulations were performed using Monolix software. The S6-kinase protein was identified as critical in the mTOR signaling pathway for describing everolimus lack of efficacy in HCC patients. The net growth rate constant (kg) of HCC cells was estimated at 0.02 h^-1 (2.88%RSE). The partition coefficient of everolimus into the tumor (kp) was determined at 0.06 (12.98%RSE). The kg in patients was calculated from the doubling time of TV in naturally progressing HCC patients, and was determined at 0.004 day^-1. Model-predicted and observed PFS were in good agreement for placebo and everolimus-treated patients. In conclusion, a multiscale QSP/PK/PD model elucidating everolimus lack of efficacy in HCC patients was successfully developed and predicted PFS reasonably well compared to observed clinical findings. This model may provide insights into clinical response to everolimus-based therapy and serve as a valuable tool for the clinical translation of efficacy for novel mTOR inhibitors.

Current mathematical models for cancer drug discovery

INTRODUCTION

Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development. Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application. Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.

Translational Modeling to Guide Study Design and Dose Choice in Obesity Exemplified by AZD1979, a Melanin-concentrating Hormone Receptor 1 Antagonist

In this study, we present the translational modeling used in the discovery of AZD1979, a melanin‐concentrating hormone receptor 1 (MCHr1) antagonist aimed for treatment of obesity. The model quantitatively connects the relevant biomarkers and thereby closes the scaling path from rodent to man, as well as from dose to effect level. The complexity of individual modeling steps depends on the quality and quantity of data as well as the prior information; from semimechanistic body‐composition models to standard linear regression. Key predictions are obtained by standard forward simulation (e.g., predicting effect from exposure), as well as non‐parametric input estimation (e.g., predicting energy intake from longitudinal body‐weight data), across species. The work illustrates how modeling integrates data from several species, fills critical gaps between biomarkers, and supports experimental design and human dose‐prediction. We believe this approach can be of general interest for translation in the obesity field, and might inspire translational reasoning more broadly.

Translational modeling and simulation approaches for molecularly targeted small molecule anticancer agents from bench to bedside

INTRODUCTION

Recent advances in molecular biology have enabled personalized cancer therapies with molecularly targeted agents (MTAs), which offer a promising future for cancer therapy. Dynamic modeling and simulation (M&S) is a powerful mathematical approach linking drug exposures to pharmacological responses, providing a quantitative assessment of in vivo drug potency. Accordingly, a growing emphasis is being placed upon M&S to quantitatively understand therapeutic exposure-response relationships of MTAs in nonclinical models.

AREAS COVERED

An overview of M&S approaches for MTAs in nonclinical models is presented with discussion about mechanistic extrapolation of antitumor efficacy from bench to bedside. Emphasis is placed upon recent advances in M&S approaches linking drug exposures, biomarker responses (e.g. target modulation) and pharmacological outcomes (e.g. antitumor efficacy).

EXPERT OPINION

For successful personalized cancer therapies with MTAs, it is critical to mechanistically and quantitatively understand their exposure-response relationships in nonclinical models, and to logically and properly apply such knowledge to the clinic. Particularly, M&S approaches to predict pharmacologically active concentrations of MTAs in patients based upon nonclinical data would be highly valuable in guiding the design and execution of clinical trials. Proactive approaches to understand their exposure-response relationships could substantially increase probability of achieving a positive proof-of-concept in the clinic.

Found in Translation: Maximizing the Clinical Relevance of Nonclinical Oncology Studies

Purpose: The translation of nonclinical oncology studies is a subject of continuous debate. We propose that translational oncology studies need to optimize both pharmacokinetic (drug exposure) and pharmacodynamic (xenograft model) aspects. While improvements in pharmacodynamic translatability can be obtained by choosing cell lines or patient-derived xenograft models closer to the clinical indication, significant ambiguity and variability exists when optimizing the pharmacokinetic translation of small molecule and biotherapeutic agents.Experimental Design and Results: In this work, we propose a pharmacokinetic-based strategy to select nonclinical doses for approved drug molecules. We define a clinically relevant dose (CRD) as the dosing regimen in mice that most closely approximates the relevant pharmacokinetic metric in humans. Such metrics include area under the time-concentration curve and maximal or minimal concentrations within the dosing interval. The methodology is applied to six drugs, including targeted agents and chemotherapeutics, small and large molecules (erlotinib, dasatinib, vismodegib, trastuzumab, irinotecan, and capecitabine). The resulting efficacy response at the CRD is compared with clinical responses.Conclusions: We conclude that nonclinical studies designed with the appropriate CRDs of approved drug molecules will maximize the translatability of efficacy results, which is critical when testing approved and investigational agents in combination. Clin Cancer Res; 23(4); 1080-90. ©2016 AACR.

Translational Pharmacokinetic-Pharmacodynamic Modeling and Simulation: Optimizing 5-Fluorouracil Dosing in Children With Pediatric Ependymoma

We previously investigated novel therapies for pediatric ependymoma and found 5‐fluorouracil (5‐FU) i.v. bolus increased survival in a representative mouse model. However, without a quantitative framework to derive clinical dosing recommendations, we devised a translational pharmacokinetic‐pharmacodynamic (PK‐PD) modeling and simulation approach. Results from our preclinical PK‐PD model suggested tumor concentrations exceeded the 1‐hour target exposure (in vitro IC₉₀), leading to tumor growth delay and increased survival. Using an adult population PK model, we scaled our preclinical PK‐PD model to children. To select a 5‐FU dosage for our clinical trial in children with ependymoma, we simulated various 5‐FU dosages for tumor exposures and tumor growth inhibition, as well as considering tolerability to bolus 5‐FU administration. We developed a pediatric population PK model of bolus 5‐FU and simulated tumor exposures for our patients. Simulations for tumor concentrations indicated that all patients would be above the 1‐hour target exposure for antitumor effect.

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.

Oxygen-Driven Tumour Growth Model: A Pathology-Relevant Mathematical Approach

Xenografts -as simplified animal models of cancer- differ substantially in vasculature and stromal architecture when compared to clinical tumours. This makes mathematical model-based predictions of clinical outcome challenging. Our objective is to further understand differences in tumour progression and physiology between animal models and the clinic.

To achieve that, we propose a mathematical model based upon tumour pathophysiology, where oxygen -as a surrogate for endocrine delivery- is our main focus. The Oxygen-Driven Model (ODM), using oxygen diffusion equations, describes tumour growth, hypoxia and necrosis. The ODM describes two key physiological parameters. Apparent oxygen uptake rate (kR′) represents the amount of oxygen cells seem to need to proliferate. The more oxygen they appear to need, the more the oxygen transport. kR′ gathers variability from the vasculature, stroma and tumour morphology. Proliferating rate (k_p) deals with cell line specific factors to promote growth. The K_H,K_N describe the switch of hypoxia and necrosis. Retrospectively, using archived data, we looked at longitudinal tumour volume datasets for 38 xenografted cell lines and 5 patient-derived xenograft-like models.

Exploration of the parameter space allows us to distinguish 2 groups of parameters. Group 1 of cell lines shows a spread in values of kR′ and lower k_p, indicating that tumours are poorly perfused and slow growing. Group 2 share the value of the oxygen uptake rate (kR′) and vary greatly in k_p, which we interpret as having similar oxygen transport, but more tumour intrinsic variability in growth.

However, the ODM has some limitations when tested in explant-like animal models, whose complex tumour-stromal morphology may not be captured in the current version of the model. Incorporation of stroma in the ODM will help explain these discrepancies. We have provided an example. The ODM is a very simple -and versatile- model suitable for the design of preclinical experiments, which can be modified and enhanced whilst maintaining confidence in its predictions.

Tumour-bearing animal models of cancer are needed to discover new drugs to treat cancer. We aim in this article to capture—through mathematics- some underlying phenomena of tumour growth in animals. We propose a set of equations that, despite being very simple, describe tumour growth, hypoxia and necrosis. Cells under low oxygen levels change into a stress state called “hypoxia”, which will ultimately lead to tissue death, also known as “necrosis” and “apoptosis”. Hypoxic cells undergo a variety of changes which impact tumour growth, development, metastasis and -most importantly- response to therapy. Hence, oxygen distribution is important. We simulate oxygen profiles to locate hypoxic and necrotic tumour regions. Finally, this mathematical model allows us to compare and classify animal models from a grounded and physiological perspective, rather than a more convenient and empirical one. This will help us understand how well (or poorly) animal tumours mimic tumours in patients. The simplicity of our mathematical model allows us to obtain more information out of the same animal sets without any further experiments, hopefully saving time, money and animal usage.

Towards integrative systems pharmacology models in oncology drug development

Quantitative systems pharmacology (QSP) modeling represents an emerging area of value to further streamline knowledge integration and to better inform decision making processes in drug development. QSP models reside at the interface between systems biology models and pharmacological models, yet their concrete implementation still needs to be established further. This review outlines key modeling techniques in both of these areas and to subsequently discuss challenges and opportunities for further integration, in oncology drug development.

Model-Based Design of a Decision Tree for Treating HER2+ Cancers Based on Genetic and Protein Biomarkers

Human cancers are incredibly diverse with regard to molecular aberrations, dependence on oncogenic signaling pathways, and responses to pharmacological intervention. We wished to assess how cellular dependence on the canonical PI3K vs. MAPK pathways within HER2+ cancers affects responses to combinations of targeted therapies, and biomarkers predictive of their activity. Through an integrative analysis of mechanistic model simulations and in vitro cell line profiling, we designed a six-arm decision tree to stratify treatment of HER2+ cancers using combinations of targeted agents. Activating mutations in the PI3K and MAPK pathways (PIK3CA and KRAS), and expression of the HER3 ligand heregulin determined sensitivity to combinations of inhibitors against HER2 (lapatinib), HER3 (MM-111), AKT (MK-2206), and MEK (GSK-1120212; trametinib), in addition to the standard of care trastuzumab (Herceptin). The strategy used to identify effective combinations and predictive biomarkers in HER2-expressing tumors may be more broadly extendable to other human cancers.

Systems pharmacology approaches for optimization of antiangiogenic therapies: challenges and opportunities

Targeted therapies have become an important therapeutic paradigm for multiple malignancies. The rapid development of resistance to these therapies impedes the successful management of advanced cancer. Due to the redundancy in angiogenic signaling, alternative proangiogenic factors are activated upon treatment with anti-VEGF agents. Higher doses of the agents lead to greater stimulation of compensatory proangiogenic pathways that limit the therapeutic efficacy of VEGF-targeted drugs and produce escape mechanisms for tumor. Evidence suggests that dose intensity and schedules affect the dynamics of the development of this resistance. Thus, an optimal dosing regimen is crucial to maximizing the therapeutic benefit of antiangiogenic agents and limiting treatment resistance. A systems pharmacology approach using multiscale computational modeling can facilitate a mechanistic understanding of these dynamics of angiogenic biomarkers and their impacts on tumor reduction and resistance. Herein, we discuss a systems pharmacology approach integrating the biology of VEGF-targeted therapy resistance, including circulating biomarkers, and pharmacodynamics to enable the optimization of antiangiogenic therapy for therapeutic gains.

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

Mechanistic understanding of translational pharmacokinetic-pharmacodynamic relationships in nonclinical tumor models: a case study of orally available novel inhibitors of anaplastic lymphoma kinase

The orally available novel small molecules PF06463922 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(metheno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclotetradecine-3-carbonitrile] and PF06471402 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(azeno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclo-tetradecine-3-carbonitrile] are second-generation anaplastic lymphoma kinase (ALK) inhibitors targeted to both naïve and resistant patients with non-small cell lung cancer (NSCLC) to the first-generation ALK inhibitor crizotinib. The objectives of the present study were to characterize and compare the pharmacokinetic-pharmacodynamic (PKPD) relationships of PF06463922 and PF06471402 for target modulation in tumor and antitumor efficacy in athymic mice implanted with H3122 NSCLC cells expressing a crizotinib-resistant echinoderm microtubule-associated protein-like 4 (EML4)-ALK mutation, EML4-ALK(L1196M). Furthermore, the PKPD relationships for these ALK inhibitors were evaluated and compared between oral administration and subcutaneous constant infusion (i.e., between different pharmacokinetic [PK] profiles). Oral and subcutaneous PK profiles of these ALK inhibitors were adequately described by a one-compartment PK model. An indirect response model extended with a modulator fit the time courses of PF06463922- and PF06471402-mediated target modulation (i.e., ALK phosphorylation) with an estimated unbound EC50,in vivo of 36 and 20 nM, respectively, for oral administration, and 100 and 69 nM, respectively, for subcutaneous infusion. A drug-disease model based on the turnover concept fit tumor growth curves inhibited by PF06463922 and PF06471402 with estimated unbound tumor stasis concentrations of 51 and 27 nM, respectively, for oral administration, and 116 and 70 nM, respectively, for subcutaneous infusion. Thus, the EC50,in vivo to EC60,in vivo estimates for ALK inhibition corresponded to the concentrations required tumor stasis in all cases, suggesting that the pharmacodynamic relationships of target modulation to antitumor efficacy were consistent among the ALK inhibitors, even when the PK profiles with different administration routes were considerably different.