A population pharmacodynamic model for lactate dehydrogenase and neuron specific enolase to predict tumor progression in small cell lung cancer patients

Optimal starting dosing regimen of intravenous oxytocin for labor induction based on the population kinetic-pharmacodynamic model of uterine contraction frequency

BACKGROUND

Intravenous oxytocin is commonly used for labor induction. However, a consensus on the initial dosing regimen is lac with conflicting research findings and varying guidelines. This study aimed to develop a population kinetic-pharmacodynamic (K-PD) model for oxytocin-induced uterine contractions considering real-world data and relevant influencing factors to establish an optimal starting dosing regimen for intravenous oxytocin.

METHODS

This retrospective study included pregnant women who underwent labor induction with intravenous oxytocin at Peking University Third Hospital in 2020. A  population K-PD model was developed to depict the time course of uterine contraction frequency (UCF), and covariate screening identified significant factors affecting the pharmacokinetics and pharmacodynamics of oxytocin. Model-based simulations were used to optimize the current starting regimen based on specific guidelines.

RESULTS

Data from 77 pregnant women with 1095 UCF observations were described well by the K-PD model. Parity, cervical dilation, and membrane integrity are significant factors influencing the effectiveness of oxytocin. Based on the model-based simulations, the current regimens showed prolonged onset times and high infusion rates. This study proposed a revised approach, beginning with a rapid infusion followed by a reduced infusion rate, enabling most women to achieve the target UCF within approximately 30 min with the lowest possible infusion rate.

CONCLUSION

The K-PD model of oxytocin effectively described the changes in UCF during labor induction. Furthermore, it revealed that parity, cervical dilation, and membrane integrity are key factors that influence the effectiveness of oxytocin. The optimal starting dosing regimens obtained through model simulations provide valuable clinical references for oxytocin treatment.

C-Reactive Protein as an Early Predictor of Efficacy in Advanced Non-Small-Cell Lung Cancer Patients: A Tumor Dynamics-Biomarker Modeling Framework

In oncology, longitudinal biomarkers reflecting the patient's status and disease evolution can offer reliable predictions of the patient's response to treatment and prognosis. By leveraging clinical data in patients with advanced non-small-cell lung cancer receiving first-line chemotherapy, we aimed to develop a framework combining anticancer drug exposure, tumor dynamics (RECIST criteria), and C-reactive protein (CRP) concentrations, using nonlinear mixed-effects models, to evaluate and quantify by means of parametric time-to-event models the significance of early longitudinal predictors of progression-free survival (PFS) and overall survival (OS). Tumor dynamics was characterized by a tumor size (TS) model accounting for anticancer drug exposure and development of drug resistance. CRP concentrations over time were characterized by a turnover model. An x-fold change in TS from baseline linearly affected CRP production. CRP concentration at treatment cycle 3 (day 42) and the difference between CRP concentration at treatment cycles 3 and 2 were the strongest predictors of PFS and OS. Measuring longitudinal CRP allows for the monitoring of inflammatory levels and, along with its reduction across treatment cycles, presents a promising prognostic marker. This framework could be applied to other treatment modalities such as immunotherapies or targeted therapies allowing the timely identification of patients at risk of early progression and/or short survival to spare them unnecessary toxicities and provide alternative treatment decisions.

Prognostic value of neuron specific enolase in patients with advanced and metastatic non-neuroendocrine non-small cell lung cancer

Background: Increased serum neuron-specific enolase (NSE) level was found in a substantial proportion (30–69%) of patients with non-small-cell lung cancer (NSCLC), but little was known about the clinical properties of NSE in NSCLC.

Objective: We aimed to assess the level of serum NSE to predict prognosis and treatment response in patients with advanced or metastatic non-neuroendocrine NSCLC.

Methods: We retrospectively analyzed 363 patients with advanced and metastatic NSCLC between January 2011 and October 2016. The serum NSE level was measured before initiation of treatment.

Results: Patients with high NSE level (≥26.1 ng/ml) showed significantly shorter progression-free survival (PFS) (5.69 vs 8.09 months; P=0.02) and significantly shorter overall survival (OS) than patients with low NSE level (11.41 vs 24.31 months; P=0.01).

NSE level was an independent prognostic factor for short PFS (univariate analysis, hazard ratio [HR] = 2.40 (1.71–3.38), P<0.001; multivariate analysis, [HR] = 1.81 (1.28–2.56), P=0.001) and OS (univariate analysis, [HR] = 2.40 (1.71–3.37), P<0.001; multivariate analysis, [HR] = 1.76 (1.24–2.50), P=0.002).

Conclusion: The survival of NSCLC patients with high serum NSE level was shorter than that of NSCLC patients with low serum NSE levels. Serum NSE level was a predictor of treatment response and an independent prognostic factor.

Drug Exposure to Establish Pharmacokinetic-Response Relationships in Oncology

In the oncology field, understanding the relationship between the dose administered and the exerted effect is particularly important because of the narrow therapeutic index associated with anti-cancer drugs and the high interpatient variability. Therefore, in this review, we provide a critical perspective of the different methods of characterising treatment exposure in the oncology setting. The increasing number of modelling applications in oncology reflects the applicability and the impact of pharmacometrics on all phases of the drug development process and patient management as well. Pharmacometric modelling is a worthy component within the current paradigm of model-based drug development, but pharmacometric modelling techniques are also accessible for the clinician in the optimisation of current oncology therapies. Consequently, the application of population models in a hospital setting by generating close collaborations between physicians and pharmacometricians is highly recommended, providing a systematic means of developing and assessing model-based metrics as 'drivers' for various responses to treatments, which can then be evaluated as predictors for treatment success. Characterising the key determinants of variability in exposure is of particular importance for anticancer agents, as efficacy and toxicity are associated with exposure. We present the different strategies to describe and predict drug exposure that can be applied depending on the data available, with the objective of obtaining the most useful information in the patients' favour throughout the full drug cycle. Therefore, the objective of the present article is to review the different approaches used to characterise a patient's exposure to oncology drugs, which will result in a better understanding of the time course of the response and the magnitude of interpatient variability.

The role of oxygen intake and liver enzyme on the dynamics of damaged hepatocytes: Implications to ischaemic liver injury via a mathematical model

Ischaemic Hepatitis (IH) or Hypoxic Hepatitis (HH) also known as centrilobular liver cell necrosis is an acute liver injury characterized by a rapid increase in serum aminotransferase. The liver injury typically results from different underlying medical conditions such as cardiac failure, respiratory failure and septic shock in which the liver becomes damaged due to deprivation of either blood or oxygen. IH is a potentially lethal condition that is often preventable if diagnosed timely. The role of mechanisms that cause IH is often not well understood, making it difficult to diagnose or accurately quantify the patterns of related biomarkers. In most patients, currently, the only way to determine a case of IH is to rule out all other possible conditions for liver injuries. A better understanding of the liver's response to IH is necessary to aid in its diagnosis, measurement, and improve outcomes. The goal of this study is to identify mechanisms that can alter associated biomarkers for reducing the density of damaged hepatocytes, and thus reduce the chances of IH. We develop a mathematical model capturing dynamics of hepatocytes in the liver through the rise and fall of associated liver enzymes aspartate transaminase (AST), alanine transaminase (ALT) and lactate dehydrogenase (LDH) related to the condition of IH. The model analysis provides a novel approach to predict the level of biomarkers given variations in the systemic oxygen in the body. Using IH patient data in the US, novel model parameters are described and then estimated for the first time to capture real-time dynamics of hepatocytes in the presence and absence of IH condition. The results may allow physicians to estimate the extent of liver damage in an IH patient based on their enzyme levels and receive faster treatment on a real-time basis.

Multifunctional neuron-specific enolase: its role in lung diseases

Neuron-specific enolase (NSE), also known as gamma (γ) enolase or enolase-2 (Eno2), is a form of glycolytic enolase isozyme and is considered a multifunctional protein. NSE is mainly expressed in the cytoplasm of neurons and neuroendocrine cells, especially in those of the amine precursor uptake and decarboxylation (APUD) lineage such as pituitary, thyroid, pancreas, intestine and lung. In addition to its well-established glycolysis function in the cytoplasm, changes in cell localization and differential expression of NSE are also associated with several pathologies such as infection, inflammation, autoimmune diseases and cancer. This article mainly discusses the role and diagnostic potential of NSE in some lung diseases.

Predicting circulating biomarker response and its impact on the survival of advanced melanoma patients treated with adjuvant therapy

Advanced melanoma remains a disease with poor prognosis. Several serologic markers have been investigated to help monitoring and prognostication, but to date only lactate dehydrogenase (LDH) has been validated as a standard prognostic factor biomarker for this disease by the American Joint Committee on Cancer. In this work, we built a semi-mechanistic model to explore the relationship between the time course of several circulating biomarkers and overall or progression free survival in advanced melanoma patients treated with adjuvant high-dose interferon-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\boldsymbol{\alpha }}{\bf{2}}{\bf{b}}$$\end{document}α2b. Additionally, due to the adverse interferon tolerability, a semi-mechanistic model describing the side effects of the treatment in the absolute neutrophil counts is proposed in order to simultaneously analyze the benefits and toxic effects of this treatment. The results of our analysis suggest that the relative change from baseline of LDH was the most significant predictor of the overall survival of the patients. Unfortunately, there was no significant difference in the proportion of patients with elevated serum biomarkers between the patients who recurred and those who remained free of disease. Still, we believe that the modelling framework presented in this work of circulating biomarkers and adverse effects could constitute an additional strategy for disease monitoring in advance melanoma patients.

Knockdown of neuron-specific enolase suppresses the proliferation and migration of NCI-H209 cells

Neuron-specific enolase (NSE) is generally considered as a marker for diagnosis and evaluation of the response to therapy in small cell lung cancer (SCLC). However, the role of NSE in the progression of SCLC remains to be elucidated. In the present study, the functions of NSE in SCLC, in addition to the potential mechanisms, were investigated using a loss-of-function approach with NSE-targeting small interfering (si)RNA. The knockdown of NSE markedly decreased the proliferation of NCI-H209 cells, as indicated by MTT assay (P<0.05). Furthermore, the silencing of NSE resulted in the formation of smaller and fewer colonies compared with that in the control group (P<0.001). Flow cytometric analysis indicated that the silencing of NSE resulted in a decreased S-phase population among NCI-H209 cells (P<0.05). Transwell assay demonstrated that the silencing of NSE suppressed the migration of NCI-H209 cells (P<0.001). NCI-H209 cells transfected with NSE siRNA-1 or negative control were collected and the protein levels of metastasis-associated genes were detected using western blot analysis. The results indicated that the knockdown of NSE led to downregulation of the pro-metastatic gene vascular endothelial growth factor (VEGF; P<0.05) and the upregulation of metastasis suppressor genes NM23 and E-cadherin (P<0.05). Taken together, the results of the present study demonstrated that the silencing of NSE suppressed the migration, proliferation and colony formation ability of SCLC cells and decreased the S-phase population. In addition, the knockdown of NSE resulted in the upregulation of E-cadherin and NM23 and the downregulation of VEGF. Collectively, these results indicated that intracellular NSE may have an important role in the progression of SCLC.

Systemic Exposure of Rituximab Increased by Ibrutinib: Pharmacokinetic Results and Modeling Based on the HELIOS Trial

INTRODUCTION

In the HELIOS trial, bendamustine/rituximab (BR) plus ibrutinib (BR-I) improved disease outcomes versus BR plus placebo in previously treated chronic lymphocytic leukemia/small lymphocytic lymphoma. Here, we describe the pharmacokinetic (PK) observations, along with modeling to further explore the interaction between ibrutinib and rituximab.

METHODS

578 subjects were randomized to ibrutinib or placebo with BR (6 cycles). Ibrutinib PK samples and tumor measurements were obtained from all subjects; a subset was evaluated for bendamustine and rituximab PK. Population rituximab PK was assessed using nonlinear mixed-effects modeling.

RESULTS

Dose-normalized plasma concentration-time bendamustine data were comparable between the arms. Systemic rituximab exposure was higher with BR-I versus BR; mean trough serum concentrations were 2- to 3-fold higher in the first three cycles and 1.2- to 1.7-fold higher subsequently. No relevant safety differences were observed. In the modeling, including treatment arm as a categorical covariate and tumor burden as a continuous time-varying covariate on overall rituximab clearance significantly improved fitting of the data.

CONCLUSIONS

BR-I led to higher dose-normalized systemic rituximab exposure versus BR and more rapid steady-state achievement. The modeling data suggest that rituximab disposition is, at least in part, target mediated. Determining the clinical significance of these findings requires further assessments.

TRIAL REGISTRATION

This study is registered at https://clinicaltrials.gov/ct2/show/NCT01611090 .

Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse

Background: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. Methods: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. Results: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56–0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. Conclusion: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations.

Application of computed tomography-based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first-line chemotherapy

The aim of the present study was to investigate the utility of a computed tomography (CT)-based radiomics signature for the early prediction of the tumor response of small cell lung cancer (SCLC) patients to chemotherapy. A dataset including 92 patients from a clinical trial was retrospectively assembled. All of the patients received the standard first-line regimen of etoposide and cisplatin. According to the Response Evaluation Criteria in Solid Tumors 1.1, the patients were divided into two groups: Response and no response groups. A total of 21 radiomics features were extracted from CT images prior to and after two cycles of chemotherapy and a radiomics signature was constructed via a binary logistic regression model. The area under the receiver operating characteristics curve (AUC) was determined to evaluate the performance of the radiomics signature to predict the response to chemotherapy. The clinicopathological factors associated with chemotherapy in patients with SCLC were also evaluated, and a predictive model was established using a binary logistic regression analysis. The 21 radiological features were used to establish a radiomics signature that was significantly associated with the efficacy of SCLC chemotherapy (P<0.05). The performance of the radiomics signature to predict the chemotherapy efficacy (AUC=0.797) was better than that of the model using clinicopathological parameters (AUC=0.670). Therefore, the present study demonstrated that radiomics features may be promising prognostic imaging biomarkers to predict the response of SCLC patients to chemotherapy and may thus be utilized to guide appropriate treatment planning.

Challenging the dose-response-time data approach: Analysis of a complex system

This study presents an extensive dose-response-time (DRT) meta-analysis of the nicotinic acid-induced inhibition of free fatty acids and insulin release. The purpose was to quantify the implications of lacking exposure data when analysing complex pharmacodynamic systems. The DRT model successfully characterised various response behaviours-including time-delays, rebound, feedback mechanisms, and adaptation-on both the individual and the population level. Comparing the fitted DRT model to an exposure-driven reference analysis showed that bias and uncertainty were introduced in the parameter estimates. However, most estimates were within one standard error from the reference. In both approaches, a few parameters suffered from practical identifiability issues, likely due to large differences in half-lives of the different rate processes. Moreover, the optimal dosing strategies predicted by the DRT model differed slightly from those of the exposure-driven analysis, having a lower optimal steady-state reduction of free fatty acids exposure.

Propofol Breath Monitoring as a Potential Tool to Improve the Prediction of Intraoperative Plasma Concentrations

INTRODUCTION

Monitoring of drug concentrations in breathing gas is routinely being used to individualize drug dosing for the inhalation anesthetics. For intravenous anesthetics however, no decisive evidence in favor of breath concentration monitoring has been presented up until now. At the same time, questions remain with respect to the performance of currently used plasma pharmacokinetic models implemented in target-controlled infusion systems. In this study, we investigate whether breath monitoring of propofol could improve the predictive performance of currently applied, target-controlled infusion models.

METHODS

Based on data from a healthy volunteer study, we developed an addition to the current state-of-the-art pharmacokinetic model for propofol, to accommodate breath concentration measurements. The potential of using this pharmacokinetic (PK) model in a Bayesian forecasting setting was studied using a simulation study. Finally, by introducing bispectral index monitor (BIS) measurements and the accompanying BIS models into our PK model, we investigated the relationship between BIS and predicted breath concentrations.

RESULTS AND DISCUSSION

We show that the current state-of-the-art pharmacokinetic model is easily extended to reliably describe propofol kinetics in exhaled breath. Furthermore, we show that the predictive performance of the a priori model is improved by Bayesian adaptation based on the measured breath concentrations, thereby allowing further treatment individualization and a more stringent control on the targeted plasma concentrations during general anesthesia. Finally, we demonstrated concordance between currently advocated BIS models, relying on predicted effect-site concentrations, and our new approach in which BIS measurements are derived from predicted breath concentrations.

Laparotomy Cryoablation in Rabbit VX2 Pancreatic Carcinoma

OBJECTIVE

The aim of this study was to establish a suitable rabbit model and select the optimal protocol for laparotomy cryoablation of pancreatic carcinoma.

METHODS

VX2 tumor tissues were inoculated into rabbit pancreases to build the pancreatic carcinoma model; then, the tumor-bearing rabbits were randomly divided into 4 groups: control, treatment A (the cryoablated-iceball diameter was bigger than the tumor), treatment B (iceball was as big as the tumor), and treatment C (iceball was smaller than the tumor). Related laboratory tests were conducted, and survival time was recorded.

RESULTS

The VX2 pancreatic carcinoma model was successfully established, and serum neuron-specific enolase levels increased continuously after inoculation. Compared with controls, rabbits in treatments A and C groups had no significant survival benefit (P > 0.05), but treatment B significantly prolonged the survival time (P < 0.01).

CONCLUSIONS

VX2 pancreatic cancer model was successfully established with neuron-specific enolase as biomarker. Treatment B may be the optimal protocol for pancreatic carcinoma and a new treatment option for patients with unresectable pancreatic cancer.

Latent variable modeling improves AKI risk factor identification and AKI prediction compared to traditional methods

Background

Acute kidney injury (AKI) is diagnosed based on postoperative serum creatinine change, but AKI models have not consistently performed well, in part due to the omission of clinically important but practically unmeasurable variables that affect creatinine. We hypothesized that a latent variable mixture model of postoperative serum creatinine change would partially account for these unmeasured factors and therefore increase power to identify risk factors of AKI and improve predictive accuracy.

Methods

We constructed a two-component latent variable mixture model and a linear model using data from a prospective, 653-subject randomized clinical trial of AKI following cardiac surgery (NCT00791648) and included established AKI risk factors and covariates known to affect serum creatinine. We compared model fit, discrimination, power to detect AKI risk factors, and ability to predict AKI between the latent variable mixture model and the linear model.

Results

The latent variable mixture model demonstrated superior fit (likelihood ratio of 6.68 × 10⁷¹) and enhanced discrimination (permutation test of Spearman’s correlation coefficients, p < 0.001) compared to the linear model. The latent variable mixture model was 94% (−13 to 1132%) more powerful (median [range]) at identifying risk factors than the linear model, and demonstrated increased ability to predict change in serum creatinine (relative mean square error reduction of 6.8%).

Conclusions

A latent variable mixture model better fit a clinical cohort of cardiac surgery patients than a linear model, thus providing better assessment of the associations between risk factors of AKI and serum creatinine change and more accurate prediction of AKI. Incorporation of latent variable mixture modeling into AKI research will allow clinicians and investigators to account for clinically meaningful patient heterogeneity resulting from unmeasured variables, and therefore provide improved ability to examine risk factors, measure mechanisms and mediators of kidney injury, and more accurately predict AKI in clinical cohorts.

Electronic supplementary material

The online version of this article (doi:10.1186/s12882-017-0465-1) contains supplementary material, which is available to authorized users.

Neuron-specific enolase and response to initial therapy are important prognostic factors in patients with small cell lung cancer

PURPOSE

The prognostic factors for the survival of small cell lung cancer (SCLC) patients are still widely debated. The aim of this study was to identify the clinical features and prognostic factors in SCLC patients.

METHODS

A retrospective study was conducted on SCLC patients who were treated in our hospital between July 2010 and July 2015. Comparison of overall survival (OS) was performed using the Kaplan-Meier method. Prognostic factors for OS were identified by multivariate Cox regression models.

RESULTS

A total of 523 patients with complete data and ECOG 0-2 were enrolled in our study. A total of 383 patients (73.2%) were diagnosed with ES-SCLC (extensive-stage SCLC) and 140 patients (26.8%) were diagnosed with LS-SCLC (limited-stage SCLC). In all patients, early disease stage, good ECOG, normal neuron-specific enolase (NSE), thoracic radiotherapy, ≥4 cycles of chemotherapy, prophylactic cranial irradiation, good response to initial therapy were independent favorable prognostic factors for OS, along with gender, age, CEA and CA125. In LS-SCLC patients, normal NSE, normal CEA, good response to initial therapy and surgery were independent favorable prognostic factors for OS. In ES-SCLC patients, good ECOG, normal NSE, thoracic radiotherapy, ≥4 cycles of chemotherapy, prophylactic cranial irradiation and good response to initial therapy were independent favorable prognostic factors for OS. Remarkably, NSE and response to initial therapy were independent prognostic factors for OS in all SCLC patients, LS-SCLC patients and ES-SCLC patients.

CONCLUSION

The normal NSE and good response to initial therapy predicted a better survival for SCLC patients, regardless of disease stage.

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.

Biomarkers of injury to neural tissue in veterinary medicine

There are numerous biomarkers of central and peripheral nervous system damage described in human and veterinary medicine. Many of these are already used as tools in the diagnosis of human neurological disorders, and many are investigated in regard to their use in small and large animal veterinary medicine. The following review presents the current knowledge about the application of cell-type (glial fibrillary acidic protein, neurofilament subunit NF-H, myelin basic protein) and central nervous system specific proteins (S100B, neuron specific enolase, tau protein, alpha II spectrin, ubiquitin carboxy-terminal hydrolase L1, creatine kinase BB) present in the cerebrospinal fluid and/or serum of animals in the diagnosis of central or peripheral nervous system damage in veterinary medicine.

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.

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications

Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians.

IMPLICATIONS FOR PRACTICE

The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

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.

Early Prediction of Disease Progression in Small Cell Lung Cancer: Toward Model-Based Personalized Medicine in Oncology

Predictive biomarkers can play a key role in individualized disease monitoring. Unfortunately, the use of biomarkers in clinical settings has thus far been limited. We have previously shown that mechanism-based pharmacokinetic/pharmacodynamic modeling enables integration of nonvalidated biomarker data to provide predictive model-based biomarkers for response classification. The biomarker model we developed incorporates an underlying latent variable (disease) representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment. Here, we show that by integrating CT scan data, the population model can be expanded to include patient outcome. Moreover, we show that in conjunction with routine medical monitoring data, the population model can support accurate individual predictions of outcome. Our combined model predicts that a change in disease of 29.2% (relative standard error 20%) between two consecutive CT scans (i.e., 6-8 weeks) gives a probability of disease progression of 50%. We apply this framework to an external dataset containing biomarker data from 22 small cell lung cancer patients (four patients progressing during follow-up). Using only data up until the end of treatment (a total of 137 lactate dehydrogenase and 77 neuron-specific enolase observations), the statistical framework prospectively identified 75% of the individuals as having a predictable outcome in follow-up visits. This included two of the four patients who eventually progressed. In all identified individuals, the model-predicted outcomes matched the observed outcomes. This framework allows at risk patients to be identified early and therapeutic intervention/monitoring to be adjusted individually, which may improve overall patient survival.