Model-based drug development applied to oncology

Integration of Pharmacokinetics, Pharmacodynamics, Safety, and Efficacy into Model-Informed Dose Selection in Oncology First-in-Human Study: A Case of Roblitinib (FGF401)

Model-informed dose selection has been drawing increasing interest in oncology early clinical development. The current paper describes the example of FGF401, a selective fibroblast growth factor receptor 4 (FGFR4) inhibitor, in which a comprehensive modeling and simulation (M&S) framework, using both pharmacometrics and statistical methods, was established during its first-in-human clinical development using the totality of pharmacokinetics (PK), pharmacodynamic (PD) biomarkers, and safety and efficacy data in patients with cancer. These M&S results were used to inform FGF401 dose selection for future development. A two-compartment population PK (PopPK) model with a delayed 0-order absorption and linear elimination adequately described FGF401 PK. Indirect PopPK/PD models including a precursor compartment were independently established for two biomarkers: circulating FGF19 and 7α-hydroxy-4-cholesten-3-one (C4). Model simulations indicated a close-to-maximal PD effect achieved at the clinical exposure range. Time-to-progression was analyzed by Kaplan-Meier method which favored a trough concentration (C_trough )-driven efficacy requiring C_trough above a threshold close to the drug concentration producing 90% inhibition of phospho-FGFR4. Clinical tumor growth inhibition was described by a PopPK/PD model that reproduced the dose-dependent effect on tumor growth. Exposure-safety analyses on the expected on-target adverse events, including elevation of aspartate aminotransferase and diarrhea, indicated a lack of clinically relevant relationship with FGF401 exposure. Simulations from an indirect PopPK/PD model established for alanine aminotransferase, including a chain of three precursor compartments, further supported that maximal target inhibition was achieved and there was a lack of safety-exposure relationship. This M&S framework supported a dose selection of 120 mg once daily fasted or with a low-fat meal and provides a practical example that might be applied broadly in oncology early clinical development.

Fabrication of doxorubicin conjugated methoxy poly(ethylene glycol)-block-poly(ε-caprolactone) nanoparticles and study on their in vitro antitumor activities

The purpose of this study was to develop a novel drug-polymer conjugation (mPEG-b-PCL-DOX) and study on its toxicity, bio-safety, and in vitro antitumor activity of mPEG-b-PCL-DOX. The polymer methoxy poly(ethylene glycol)-block-poly(ε-caprolactone) (mPEG-b-PCL) was prepared by ring-opening polymerization. Then, succinic anhydride was reacted with mPEG-b-PCL via esterification reaction to produce mPEG-b-PCL-COOH. Finally, the polymer mPEG-b-PCL-DOX was obtained by conjugating DOX to mPEG-b-PCL-COOH by amidation. The Fourier transform infrared spectroscopy (FTIR) and ¹H nuclear magnetic resonance (¹H NMR) spectra were used to study the structures of obtained polymers. Transmission electron microscope (TEM) and Dynamic laser scattering (DLS) were employed to monitor the morphology and size distribution of mPEG-b-PCL-DOX nanoparticles (NPs). The mPEG-b-PCL-DOX NPs were administrated to KM rats by intraperitoneal injection to study the bio-safety of final NPs. The cell uptake and in vitro anti-tumor activity of final NPs were carried out with HCT116 cells as models. FTIR and ¹H NMR spectra confirmed the obtaining of mPEG-b-PCL-DOX. The fabricated NPs were in round shapes with an average diameter of 300 nm. These NPs did not induce hemolysis and physiological or pathological changes in rats's organs. Finally, cell teats showed that these NPs could be endocytosed by HCT 116 cells, and they had better anti-tumor effects than free DOX did. Therefore, the mPEG-b-PCL-DOX NPs had a potential application in anti-cancer therapy.

PK/PD Mediated Dose Optimization of Emactuzumab, a CSF1R Inhibitor, in Patients With Advanced Solid Tumors and Diffuse-Type Tenosynovial Giant Cell Tumor

Targeted biological therapies may achieve maximal therapeutic efficacy at doses below the maximum tolerated dose (MTD); therefore, the search for the MTD in clinical studies may not be ideal for these agents. Emactuzumab is an investigational monoclonal antibody that binds to and inhibits the activation of the cell surface colony‐stimulating factor‐1 receptor. Here, we show how modeling target‐mediated drug disposition coupled with pharmacodynamic end points was used to optimize the dose of emactuzumab without defining an MTD. The model could be used to recommend doses across different disease indications. The approach recommended an optimal biological dose of emactuzumab for dosing every 2 weeks (q2w) ≥ 900 mg, approximately three‐fold lower than the highest dose tested clinically. The model predicted that emactuzumab doses ≥ 900 mg q2w would achieve target saturation in excess of 90% over the entire dosing cycle. Subsequently, a dose of 1,000 mg q2w was used in the extension phase of a phase I study of emactuzumab in patients with advanced solid tumors or diffuse‐type tenosynovial giant cell tumor. Clinical data from this study were consistent with model predictions. The model was also used to predict the optimum dose of emactuzumab for use with dosing every 3 weeks, enabling dosing flexibility with respect to comedications. In summary, this work demonstrates the value of quantitative clinical pharmacology approaches to dose selection in oncology as opposed to traditional MTD methods.

Pharmacokinetic/Pharmacodynamic Modeling for Drug Development in Oncology

High drug attrition rates remain a critical issue in oncology drug development. A series of steps during drug development must be addressed to better understand the pharmacokinetic (PK) and pharmacodynamic (PD) properties of novel agents and, thus, increase their probability of success. As available data continues to expand in both volume and complexity, comprehensive integration of PK and PD information into a robust mathematical model represents a very useful tool throughout all stages of drug development. During the discovery phase, PK/PD models can be used to identify and select the best drug candidates, which helps characterize the mechanism of action and disease behavior of a given drug, to predict clinical response in humans, and to facilitate a better understanding about the potential clinical relevance of preclinical efficacy data. During early drug development, PK/PD modeling can optimize the design of clinical trials, guide the dose and regimen that should be tested further, help evaluate proof of mechanism in humans, anticipate the effect in certain subpopulations, and better predict drug-drug interactions; all of these effects could lead to a more efficient drug development process. Because of certain peculiarities of immunotherapies, such as PK and PD characteristics, PK/PD modeling could be particularly relevant and thus have an important impact on decision making during the development of these agents.

Accelerating drug development by efficiently using emerging PK/PD data from an adaptable entry-into-human trial: example of lumretuzumab

PURPOSE

This study aimed at evaluating if pharmacokinetic and pharmacodynamic data from the first few patients treated with an investigational monoclonal antibody in a dose-escalation study can be used to guide the early initiation of potentially more efficacious combination regimens.

METHODS

Emerging pharmacokinetic and pharmacodynamic data from the first nine patients treated with lumretuzumab (a glycoengineered anti-HER3 monoclonal antibody) monotherapy at doses from 100 to 400 mg q2w were used along with a pharmacokinetic model that incorporated target-mediated drug disposition to guide the selection of the starting dose for use in combination regimens.

RESULTS

The dose-escalation study investigated lumretuzumab doses up to 2000 mg q2w and a maximum tolerated dose was not reached. However, the model described in this report predicted linear lumretuzumab pharmacokinetics and >95% target saturation at doses ≥400 mg q2w. These data, along with safety data, contributed to the decision to begin dose-escalation studies in combination with cetuximab and erlotinib using a starting dose of 400 mg lumretuzumab. Pharmacokinetic data from patients treated with lumretuzumab 400-2000 mg q2w in combination regimens were consistent with the model predictions.

CONCLUSION

PK/PD modelling of emerging clinical data might accelerate development programs by enabling additional parts of a trial to commence before completion of the monotherapy part. The dose and schedule of lumretuzumab were optimised for concomitant therapy at doses substantially below the highest dose investigated.

Comparative Effects of CT Imaging Measurement on RECIST End Points and Tumor Growth Kinetics Modeling

Quantitative assessments of tumor burden and modeling of longitudinal growth could improve phase II oncology trials. To identify obstacles to wider use of quantitative measures we obtained recorded linear tumor measurements from three published lung cancer trials. Model-based parameters of tumor burden change were estimated and compared with similarly sized samples from separate trials. Time-to-tumor growth (TTG) was computed from measurements recorded on case report forms and a second radiologist blinded to the form data. Response Evaluation Criteria in Solid Tumors (RECIST)-based progression-free survival (PFS) measures were perfectly concordant between the original forms data and the blinded radiologist re-evaluation (intraclass correlation coefficient = 1), but these routine interrater differences in the identification and measurement of target lesions were associated with an average 18-week delay (range, -20 to 55 weeks) in TTG (intraclass correlation coefficient = 0.32). To exploit computational metrics for improving statistical power in small clinical trials will require increased precision of tumor burden assessments.

Modeling NSCLC progression: recent advances and opportunities available

Non-small cell lung cancer (NSCLC) is one of the leading causes of death around the world with an estimated 5-year relative survival rate of 16% at diagnosis. Development of drugs treating NSCLC is not easy, and the success rate for an anticancer treatment to pass through the whole clinical development process is as low as 5%. Modeling and simulation lend themselves as tools which can potentially streamline drug development. A critical component of the models developed is a description of how the disease progresses over time and how a treatment would affect its trajectory. Our aim was to review the literature to present the models and growth functions which have been used for describing NSCLC dynamics, and how anticancer treatments can affect such dynamics, both in animals and in humans. Only a limited set of models were identified for such a purpose. Most of the models which have been used were descriptive of tumor growth, yet there were attempts to account for the underlying processes, especially in animals where it is more feasible to collect data needed for developing such models. Moreover, we discuss how modeling and simulation can aid in decision making across the different stages of drug development. Based on some encouraging results from trials of other cancer types where modeling tumor dynamics has played an important role, we propose further exploration of NSCLC using model-based techniques and further use of these techniques in designing and evaluating NSCLC trials.

Mathematical modeling of tumor growth and tumor growth inhibition in oncology drug development

INTRODUCTION

Approaches aiming to model the time course of tumor growth and tumor growth inhibition following a therapeutic intervention have recently been proposed for supporting decision making in oncology drug development. When considered in a comprehensive model-based approach, tumor growth can be included in the cascade of quantitative and causally related markers that lead to the prediction of survival, the final clinical response.

AREAS COVERED

The authors examine articles dealing with the modeling of tumor growth and tumor growth inhibition in both preclinical and clinical settings. In addition, the authors review models describing how pharmacological markers can be used to predict tumor growth and models describing how tumor growth can be linked to survival endpoints.

EXPERT OPINION

Approaches and success stories of application of model-based drug development centered on tumor growth modeling are growing. It is also apparent that these approaches can answer practical questions on drug development more effectively than that in the past. For modeling purposes, some improvements are still needed related to study design and data quality. Further efforts are needed to encourage the mind shift from a simple description of data to the prediction of untested conditions that modeling approaches allow.

The use of clinical utility assessments in early clinical development

A quickly realizable benefit of model-based drug development is in reducing uncertainty in risk/benefit, comprising individually of safety and effectiveness, two key attributes of a product evaluated for regulatory approval, marketing, and use. In this review, we investigate gaps and opportunities in using fundamental decision analytic principles in drug development and present a quantitative clinical pharmacology framework for the application of such aids for early clinical development decision making. We anticipate that implementation of such emerging tools will enable sufficient scientific understanding of the two attributes to facilitate the early termination of compounds with less than desirable risk/benefit profiles and continuance of compounds with acceptable risk/benefit profiles.

Cross-species comparison of in vivo PK/PD relationships for second-generation antisense oligonucleotides targeting apolipoprotein B-100

The in vivo pharmacokinetics/pharmacodynamics of 2'-O-(2-methoxyethyl) (2'-MOE) modified antisense oligonucleotides (ASOs), targeting apolipoprotein B-100 (apoB-100), were characterized in multiple species. The species-specific apoB antisense inhibitors demonstrated target apoB mRNA reduction in a drug concentration and time-dependent fashion in mice, monkeys, and humans. Consistent with the concentration-dependent decreases in liver apoB mRNA, reductions in serum apoB, and LDL-C, and total cholesterol were concurrently observed in animal models and humans. Additionally, the long duration of effect after cessation of dosing correlated well with the elimination half-life of 2'-MOE modified apoB ASOs studied in mice (t(1/2) congruent with 20 days) and humans (t(1/2) congruent with 30 days) following parental administrations. The plasma concentrations of ISIS 301012, observed in the terminal elimination phase of both mice and monkeys were in equilibrium with liver. The partition ratios between liver and plasma were similar, approximately 6000:1, across species, and thus provide a surrogate for tissue exposure in humans. Using an inhibitory E(max) model, the ASO liver EC(50s) were 101+/-32, 119+/-15, and 300+/-191 microg/g of ASO in high-fat-fed (HF) mice, transgenic mice containing the human apoB transgene, and monkeys, respectively. The estimated liver EC(50) in man, extrapolated from trough plasma exposure, was 81+/-122 microg/g. Therefore, extraordinary consistency of the exposure-response relationship for the apoB antisense inhibitor was observed across species, including human. The cross-species PK/PD relationships provide confidence in the use of pharmacology animal models to predict human dosing for second-generation ASOs targeting the liver.

Applications of physiologically based absorption models in drug discovery and development

This article describes the use of physiologically based models of intestinal drug absorption to guide the research and development of new drugs. Applications range from lead optimization in the drug discovery phase through clinical candidate selection and extrapolation to human to phase 2 formulation development. Early simulations in preclinical species integrate multiple screening data and add value by transforming these individual properties into a prediction of in vivo absorption. Comparison of simulations to plasma levels measured after oral dosing in animals highlights unexpected behavior, and parameter sensitivity analysis can explore the impact of uncertainties in key properties, point toward factors which are limiting absorption and contribute to assessment of compound developability. Physiological models provide reliable prediction of human absorption and with refinement based on phase 1 data are useful guides to further market formulation development. Improvements in the accuracy of simulations are expected as better in vitro methods generate more in vivo relevant solubility and permeability data, and modeling will play a central role in the development of more predictive methods for transporter-related effects on drug absorption.

A SAS-based solution to evaluate study design efficiency of phase I pediatric oncology trials via discrete event simulation

Previous exploration of oncology study design efficiency has focused on Markov processes alone (probability-based events) without consideration for time dependencies. Barriers to study completion include time delays associated with patient accrual, inevaluability (IE), time to dose limiting toxicities (DLT) and administrative and review time. Discrete event simulation (DES) can incorporate probability-based assignment of DLT and IE frequency, correlated with cohort in the case of DLT, with time-based events defined by stochastic relationships. A SAS-based solution to examine study efficiency metrics and evaluate design modifications that would improve study efficiency is presented. Virtual patients are simulated with attributes defined from prior distributions of relevant patient characteristics. Study population datasets are read into SAS macros which select patients and enroll them into a study based on the specific design criteria if the study is open to enrollment. Waiting times, arrival times and time to study events are also sampled from prior distributions; post-processing of study simulations is provided within the decision macros and compared across designs in a separate post-processing algorithm. This solution is examined via comparison of the standard 3+3 decision rule relative to the "rolling 6" design, a newly proposed enrollment strategy for the phase I pediatric oncology setting.