Tumor Growth Inhibition In Models Research Articles

A Minimal Model of Tumor Growth Inhibition in Combination Regimens Under the Hypothesis of No Interaction Between Drugs

One important issue in the preclinical development of an anticancer drug is the assessment of the compound under investigation when administered in combination with other drugs. Several experiments are routinely conducted in xenograft mice to evaluate if drugs interact or not. Experimental data are generally qualitatively analyzed on empirical basis. The ability of deriving from single drug experiments a reference response to the joint administrations, assuming no interaction, and comparing it to real responses would be key to recognize synergic and antagonist compounds. Therefore, in this paper, the minimal model of tumor growth inhibition (TGI), previously developed for a single drug, is reformulated to account for the effects of noninteracting drugs and simulate, under this hypothesis, combination regimens. The model is derived from a minimal set of basic assumptions that include and extend those formulated at cellular level for the single drug administration. The tumor growth dynamics is well approximated by the deterministic evolution of its expected value that is obtained through the solution of an ordinary and several partial differential equations. Under suitable assumptions on the cell death process, the model reduces to a lumped parameter model that represents the extension of the very popular Simeoni TGI model to the combined administration of noninteracting drugs.

Abstract 3777: Applying mechanistic pharmacokinetic-pharmacodynamic models (PKPD) to describe the growth and inhibition of xenograft tumors in rats and mice by targeted anticancer agents

Abstract Introduction: M. Simeoni et al., Cancer Res 64, 1094-1101 (2004) published a mathematical model, which is now widely used in pre-clinical experimentation to describe the growth and inhibition by anti-cancer agents of mouse and rat xenograft models. It models drug-concentration induced cell damage, and uses a series of transit-compartments to introduce delay into the system and empirically describe populations of cells undergoing subsequent stages of cell damage and death. The model involves a number of simplifying assumptions: All healthy cells are equally susceptible to drug treatment at all times; drug action is linearly related to drug concentration; drug action causes cell damage and death with a single rate of progress through a series of empirical transit compartments; DNA damage repair mechanisms are assumed not to exist. B. Ribba et al. Eur. J. Cancer, 47, 479-490 (2011) recently demonstrated the utility of a mechanistic model that characterizes the tumor xenograft in terms of non-hypoxic, hypoxic, and necrotic cells and the drug action on these sub-populations of cells. In this poster we will illustrate several modeling approaches that have been utilized when the simplifying assumptions in the Simeoni model are invalid. We will also extend the model presented by Ribba et al., to incorporate the spatial features of a tumor in an attempt to better describe the tumor micro-environment. Methods: We have adapted the Simeoni and Ribba models to be more mechanistic by incorporating features that describe: (1) the utility of biomarkers proximal to the target in cell signaling pathways as a driver for growth inhibition; (2) multiple mechanisms of drug action on sub-populations of cells that drive tumor growth. Pharmacokinetic, biomarker and tumor growth data for example compounds have been used to demonstrate the utility of these mechanistic models. Results: Incorporating non-linearity between drug exposure, biomarker response, and tumor growth inhibition allows observed differences between dosing schedules to be explained. Replacing drug exposure with a biomarker as the driver for tumor growth inhibition provides a more meaningful surrogate for pharmacological action, particularly in the situation where biomarker response to drug is significantly delayed compared to the pharmacokinetics. Modeling multiple mechanisms of action on sub-populations of cells can allow an accurate representation of the drug effect on the disease biology. Conclusions: We demonstrate that adding mechanistic features to a descriptive model of drug-induced tumor growth inhibition makes it more representative of the disease biology and drug action. This offers the opportunity for modeling targeted drug action, which was not possible with the original model. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3777. doi:1538-7445.AM2012-3777

Abstract 3770: PK/PD/Efficacy characterization of AZ285, a selective CK2 kinase inhibitor

Abstract Casein Kinase 2 (CK2) is an abundant, highly conserved protein serine/threonine kinase with numerous physiological targets and participates in the maintenance of cell viability with a role in cell growth and proliferation. While the expression level of CK2 in normal cells is tightly regulated, there is an increasing body of evidence demonstrating it's over expression in cancerous cells, thereby making inhibition a viable chemotherapeutic target. Here we will describe a mathematical tumor growth inhibition model describing the relationship between systemic exposure to a potent inhibitor of CK2 (AZ285) and efficacy in the HCT116 xenograft model. Furthermore, a PK/PD model describing the onset, intensity, and duration of the pAKT and cleaved caspase 3 response as function of concentration and time is described and linked to pre-clinical xenograft efficacy outcomes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3770. doi:1538-7445.AM2012-3770

Pharmacokinetic/Pharmacodynamic Modeling of Crizotinib for Anaplastic Lymphoma Kinase Inhibition and Antitumor Efficacy in Human Tumor Xenograft Mouse Models

Crizotinib [Xalkori; PF02341066; (R)-3-[1-(2,6-dichloro-3-fluoro-phenyl)-ethoxy]-5-(1-piperidin-4-yl-1H-pyrazol-4-yl)-pyridin-2-ylamine] is an orally available dual inhibitor of anaplastic lymphoma kinase (ALK) and hepatocyte growth factor receptor. The objectives of the present studies were to characterize: 1) the pharmacokinetic/pharmacodynamic relationship of crizotinib plasma concentrations to the inhibition of ALK phosphorylation in tumors, and 2) the relationship of ALK inhibition to antitumor efficacy in human tumor xenograft models. Crizotinib was orally administered to athymic nu/nu mice implanted with H3122 non-small-cell lung carcinomas or severe combined immunodeficient/beige mice implanted with Karpas299 anaplastic large-cell lymphomas. Plasma concentration-time courses of crizotinib were adequately described by a one-compartment pharmacokinetic model. A pharmacodynamic link model reasonably fit the time courses of ALK inhibition in both H3122 and Karpas299 models with EC(50) values of 233 and 666 ng/ml, respectively. A tumor growth inhibition model also reasonably fit the time course of individual tumor growth curves with EC(50) values of 255 and 875 ng/ml, respectively. Thus, the EC(50) for ALK inhibition approximately corresponded to the EC(50) for tumor growth inhibition in both xenograft models, suggesting that >50% ALK inhibition would be required for significant antitumor efficacy (>50%). Furthermore, based on the observed clinical pharmacokinetic data coupled with the pharmacodynamic parameters obtained from the present nonclinical xenograft mouse model, >70% ALK inhibition was projected in patients with non-small-cell lung cancer who were administered the clinically recommended dosage of crizotinib, twice-daily doses of 250 mg (500 mg/day). The result suggests that crizotinib could sufficiently inhibit ALK phosphorylation for significant antitumor efficacy in patients.

TGI-Simulator: A visual tool to support the preclinical phase of the drug discovery process by assessing in silico the effect of an anticancer drug

This paper presents TGI-Simulator, a software tool designed to show, through a 2-D graphical animation, the simulated time effect of an anticancer drug on a tumor mass by exploiting the well-known Tumor Growth Inhibition (TGI) model published by Simeoni et al. [1]. Simeoni TGI model is a mathematical model routinely used by pharma companies and researchers during the drug development process. The application is based on a Java graphical user interface (GUI) including a self installing differential equation solver implemented in Matlab together with an optimization algorithm that performs model identification via Weighted Least Squares (WLS). However, it can graphically show also the simulation results obtained within other scientific software tools, if they are preventively stored into a suitable ASCII file. The tool would be a valid support also for researchers with no specific skills in scientific calculations and in pharmacokinetic and pharmacodynamic modeling but daily involved in pharma companies drug development processes at different levels. The availability of a movie with a temporal varying 2-D iconographic representation is an original instrument to communicate results and learn Simeoni TGI model and its potential application in preclinical studies.

Pharmacokinetic and pathophysiologic covariates influencing treatment outcomes with T-DM1 in patients with HER2-positive metastatic breast cancer (MBC).

633 Background: T-DM1 is a HER2-directed antibody-drug conjugate (ADC) in development for the treatment of HER2-positive MBC. T-DM1 is a unique ADC in that it provides intracellular delivery of the highly potent cytotoxic agent DM1 (derivative of maytansine) in addition to the antitumor properties of trastuzumab. Tumor growth inhibition (TGI) models have been developed for oncology therapies and can be a useful tool for predicting outcomes. We developed a model to predict the effect of T-DM1 exposure and baseline pathophysiologic covariates on TGI. Methods: The TGI model was based on data from one phase I (TDM3569g) and two phase II studies (TDM4258g, TDM4374g) (N=274) of single agent T-DM1 (3.6 mg/kg q3w) in heavily pretreated HER2-positive MBC. Objective response rates (ORR) in clinical studies were according to RECIST; tumor size for the model was based on the Sum of Longest Diameters (SLD) of target lesions. Tumor size is described as a function of time and drug exposure and the model accounts for natural tumor growth rate, antitumor drug effect (cell kill rate) and drug resistance. Baseline patient-specific and pathophysiological characteristics (e.g., time after last trastuzumab dose, total trastuzumab levels, baseline SLD, shed HER2 extracellular domain and albumin level) were evaluated as covariates. Results: The model-predicted TGI rate (SLD reduced by ³30%) with T-DM1 was 38% (90% prediction interval 31%–45%) based on the nominal dosing intensity. ORR ranged from 26% to 44% in the 3 trials. A correlative trend was observed between Week 6 and Week 12 T-DM1 AUC and TGI. Variability in tumor response was high and not explained by variability in exposure. No strong correlation was observed between T-DM1 exposure (AUC, Cmax, and Cmin) and TGI rate. None of the baseline covariates tested appeared to influence TGI. Conclusions: The model-predicted TGI rate appears to correlate with observed ORR. Further work including a drug-disease simulation framework is being developed to predict antitumor response and overall survival with T-DM1 and may be used to simulate untested dosing regimens and support dosing decisions during clinical development.

Abstract 5467: Application of modeling and simulation to the development of MBLI87, a new BCRP inhibitor

Abstract Objectives: Breast Cancer Resistance Protein (BCRP) confers resistance to irinotecan (CPT11) and its active metabolite SN38. MBLI87, a new specific and non-toxic BCRP inhibitor, has proven its efficacy to reverse CPT11 BCRP-mediated resistance in xenografts. The present work aimed at: 1) Optimizing CPT11+MBLI87 combination in mice through a model-based approach; 2) Comparing MBLI87 activity with that of gefitinib, the BCRP reference inhibitor, when combined to CPT11; 3) Assessing how modeling and simulation can be applied to early drug development. Methods: Mice were xenografted with HEK-293 cells expressing BCRP. Based on very preliminary data from these xenografted mice treated with 2 chemotherapy cycles (CPT11 +/- MBLI87, gefitinib) during 8 weeks including a 2-week rest period, we developed a Non Linear Mixed Effects (NLME) tumor growth inhibition model. Because only a few numbers of individuals were in each treatment group, NLME model was used to analyze the data. This method uses information from the entire experiment to estimate model parameters, allowing the analysis of sparse data. We used a modified Simeoni et al. model: a Kinetic-PharmacoDynamic (K-PD) component was introduced to deal with the lack of pharmacokinetic data (a drug accumulation and a mono-exponential elimination were assumed according to the dosing schedule) and a pharmacodynamic interaction between CPT11 and MBLI87/gefitinib was considered. Simulations, based on the NLME model and parameter estimates, were conducted to find the optimized dosing regimen able to maximize CPT11+MBLI87 combination effect. A confirmatory study was then carried out on xenografted mice to assess the validity of this optimized regimen. Results: This analysis showed that MBLI87 had no cytotoxic effect when administered alone (potency = 10−2 mg.d−1). A synergistic effect was found between CPT11 and MBLI87 whereas none was found with gefitinib (5.5 vs 0.1 mg−1). Simulations predicted that maximal tumor growth inhibition was reached for CPT11 given 3 days a week at 30 mg.kg−1 and MBLI87 given 5 days a week at 2.4 mg.kg−1 with no rest period. In the confirmatory study, tumor growth rates and drug potency estimates were similar to those from the former study. The interaction parameter was less than previously estimated suggesting that MBLI87 could be more active if administered on an intermittent basis with rest periods. Conclusions: NLME approach allowed describing tumor growth dynamics, quantify MLBI87 action and generate new hypotheses on MBLI87 action to anticipate the next steps of its development. MBLI87 was able to reverse CPT11 BCRP-mediated resistance at a 20-fold lower dose than gefitinib. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5467. doi:10.1158/1538-7445.AM2011-5467

A Modeling and Simulation Framework to Support Early Clinical Drug Development Decisions in Oncology

A Modeling and Simulation Framework to Support Early Clinical Drug Development Decisions in Oncology

Development of a modeling framework to simulate efficacy endpoints for motesanib in patients with thyroid cancer

To develop a modeling framework that simulates clinical endpoints (objective response rate and progression-free survival) to support development of motesanib. The framework was evaluated using results from a phase 2 study of motesanib in thyroid cancer. Models of probability and duration of dose modifications and overall survival were developed using data from 93 patients with differentiated thyroid cancer and 91 patients with medullary thyroid cancer, who received motesanib 125 mg once daily. The models, combined with previously developed population pharmacokinetic and tumor growth inhibition models, were assessed in predicting dose intensity, tumor size over time, objective response rate, and progression-free survival. Dose-response simulations were performed in patients with differentiated thyroid cancer. The predicted objective response rate and median progression-free survival in patients with differentiated thyroid cancer was 15.0% (95% prediction interval, 7.5%-23.7%) and 40 weeks (95% prediction interval, 32-49 weeks), respectively, compared with the observed objective response rate of 14.0% and median progression-free survival of 40 weeks. The simulated median objective response rate increased with motesanib starting dose from 13.5% at 100 mg once daily to 38.0% at 250 mg once daily. However, simulated median progression-free survival was independent of starting dose, ranging from 40.5 weeks (95% prediction interval, 38.6-46.9 weeks) at 100 mg once daily to 40.0 weeks (95% prediction interval, 38.6-46.8 weeks) at 250 mg once daily. Dose-response simulations confirmed the appropriateness of 125-mg once-daily dosing; no clinically relevant improvement in progression-free survival would be obtained by dose intensification. This modeling framework represents an important tool to simulate clinical response and support clinical development decisions.

Model-Based Prediction of Phase III Overall Survival in Colorectal Cancer on the Basis of Phase II Tumor Dynamics

We developed a drug-disease simulation model to predict antitumor response and overall survival in phase III studies from longitudinal tumor size data in phase II trials. We developed a longitudinal exposure-response tumor-growth inhibition (TGI) model of drug effect (and resistance) using phase II data of capecitabine (n = 34) and historical phase III data of fluorouracil (FU; n = 252) in colorectal cancer (CRC); and we developed a parametric survival model that related change in tumor size and patient characteristics to survival time using historical phase III data (n = 245). The models were validated in simulation of antitumor response and survival in an independent phase III study (n = 1,000 replicates) of capecitabine versus FU in CRC. The TGI model provided a good fit of longitudinal tumor size data. A lognormal distribution best described the survival time, and baseline tumor size and change in tumor size from baseline at week 7 were predictors (P < .00001). Predicted change of tumor size and survival time distributions in the phase III study for both capecitabine and FU were consistent with observed values, for example, 431 days (90% prediction interval, 362 to 514 days) versus 401 days observed for survival in the capecitabine arm. A modest survival improvement of 39 days (90% prediction interval, -21 to 110 days) versus 35 days observed was predicted for capecitabine. The modeling framework successfully predicted survival in a phase III trial on the basis of capecitabine phase II data in CRC. It is a useful tool to support end-of-phase II decisions and design of phase III studies.

A Minimal Model of Tumor Growth Inhibition

The preclinical development of antitumor drugs greatly benefits from the availability of models capable of predicting tumor growth as a function of the drug administration schedule. For being of practical use, such models should be simple enough to be identifiable from standard experiments conducted on animals. In the present paper, a stochastic model is derived from a set of minimal assumptions formulated at cellular level. Tumor cells are divided in two groups: proliferating and nonproliferating. The probability that a proliferating cell generates a new cell is a function of the tumor weight. The probability that a proliferating cell becomes nonproliferating is a function of the plasma drug concentration. The time-to-death of a nonproliferating cell is a random variable whose distribution reflects the nondeterministic delay between drug action and cell death. The evolution of the expected value of tumor weight obeys two differential equations (an ordinary and a partial differential one), whereas the variance is negligible. Therefore, the tumor growth dynamics can be well approximated by the deterministic evolution of its expected value. The tumor growth inhibition model, which is a lumped parameter model that in the last few years has been successfully applied to several antitumor drugs, is shown to be a special case of the minimal model presented here.

A Minimal Model of Tumor Growth Inhibition

The preclinical development of antitumor drugs greatly benefits from the availability of models capable of predicting tumor growth as a function of the drug administration schedule. For being of practical use, such models should be simple enough to be identifiable from standard experiments conducted on animals. In the present paper, a stochastic model is derived from a set of minimal assumptions formulated at cellular level. Tumor cells are divided in two groups: proliferating and nonproliferating. The probability that a proliferating cell generates a new cell is a function of the tumor weight. The probability that a proliferating cell becomes nonproliferating is a function of the plasma drug concentration. The time-to-death of a nonproliferating cell is a random variable whose distribution reflects the nondeterministic delay between drug action and cell death. The evolution of the expected value of tumor weight obeys two differential equations (an ordinary and a partial differential one), whereas the variance is negligible. Therefore, the tumor growth dynamics can be well approximated by the deterministic evolution of its expected value. The tumor growth inhibition model, which is a lumped parameter model that in the last few years has been successfully applied to several antitumor drugs, is shown to be a special case of the minimal model presented here.

Indole amide hydroxamic acids as potent inhibitors of histone deacetylases.

A series of hydroxamic acid-based HDAC inhibitors with an indole amide residue at the terminus have been synthesized and evaluated. Compounds with a 2-indole amide moiety have been found as the most active inhibitors among the different regioisomers. Introduction of substituents on the indole ring further improved the potency and generated a series of very potent inhibitors with significant antiproliferative activity. A representative compound in the series, 7b, has been found to be orally active in tumor growth inhibition model.