Preclinical pharmacokinetic/pharmacodynamic models to predict synergistic effects of co-administered anti-cancer agents

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology

BACKGROUND

To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data.

METHODS

We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves.

RESULTS

The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication.

CONCLUSIONS

A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

Optimized scaling of translational factors in oncology: from xenografts to RECIST

Purpose

Tumor growth inhibition (TGI) models are regularly used to quantify the PK–PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response.

Method

To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data.

Results

The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of − 0.25.

Conclusions

We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04458-8.

Modeling long-term tumor growth and kill after combinations of radiation and radiosensitizing agents

PURPOSE

Radiation therapy, whether given alone or in combination with chemical agents, is one of the cornerstones of oncology. We develop a quantitative model that describes tumor growth during and after treatment with radiation and radiosensitizing agents. The model also describes long-term treatment effects including tumor regrowth and eradication.

METHODS

We challenge the model with data from a xenograft study using a clinically relevant administration schedule and use a mixed-effects approach for model-fitting. We use the calibrated model to predict exposure combinations that result in tumor eradication using Tumor Static Exposure (TSE).

RESULTS

The model is able to adequately describe data from all treatment groups, with the parameter estimates taking biologically reasonable values. Using TSE, we predict the total radiation dose necessary for tumor eradication to be 110 Gy, which is reduced to 80 or 30 Gy with co-administration of 25 or 100 mg kg^-1 of a radiosensitizer. TSE is also explored via a heat map of different growth and shrinkage rates. Finally, we discuss the translational potential of the model and TSE concept to humans.

CONCLUSIONS

The new model is capable of describing different tumor dynamics including tumor eradication and tumor regrowth with different rates, and can be calibrated using data from standard xenograft experiments. TSE and related concepts can be used to predict tumor shrinkage and eradication, and have the potential to guide new experiments and support translations from animals to humans.

Acetyl-macrocalin B suppresses tumor growth in esophageal squamous cell carcinoma and exhibits synergistic anti-cancer effects with the Chk1/2 inhibitor AZD7762

Natural products derived from herbal medicines have become a major focus of anti-cancer drug discovery studies. Acetyl-macrocalin B (A-macB) is an ent-diterpenoid isolated from Isodon silvatica. This study aimed to examine the effect and molecular action of A-macB in esophageal squamous cell carcinoma (ESCC) and explore possible drug synergistic modalities. A-macB induced cellular reactive oxygen species (ROS) generation, initiated the p38 mitogen-activated protein kinase (MAPK) signaling pathway, and triggered the caspase-9-dependent apoptosis cascade in ESCC cells. The ROS scavenger N-acetylcysteine (NAC) and the specific p38 inhibitor SB203580 reversed the effects of A-macB on the p38 network and thus rescued ESCC cells from apoptosis. The cellular ROS increase was at least partially due to the suppression of glutathione-S-transferase P1 (GSTP1) by A-macB. A-macB also upregulated the Chk1/Chk2-Cdc25C/Cdc2/Cyclin B1 axis to induce G2/M phase arrest. The cell growth inhibition induced by A-macB was further enhanced by AZD7762, a specific Chk1/Chk2 inhibitor, with a combination index (CI) of <1. Moreover, A-macB efficiently suppressed xenograft growth without inducing significant toxicity, and AZD7762 potentiated the effects of A-macB in the suppression of tumor growth in vivo. Taken together, A-macB is a promising lead compound for ESCC and exerts synergistic anti-cancer effects with AZD7762.

Synergistic Anticancer Effect of a Combination of Paclitaxel and 5-Demethylnobiletin Against Lung Cancer Cell Line In Vitro and In Vivo

Lung cancer remains a highly prevalent disease and a leading cause of cancer-related deaths worldwide. Currently, exploring antitumor drugs derived from herbs used in traditional Chinese medicine is increasingly becoming an attractive area of research. Paclitaxel (PTX), a highly effective chemotherapeutic drug, is widely used for treating different cancers; however, the clinical use of PTX is dose limited because of its adverse side effects. Chemotherapeutic agents are being developed to enhance the anticancer activity of PTX, particularly for use in combination therapy. 5-Demethylnobiletin (5-DMN), a natural, active compound isolated from orange peel, has been reported to induce apoptosis in several cancer cell lines. In this study, we tested the synergistic anticancer antiproliferative effects of combinations of PTX and 5-DMN on CL1-5 lung cancer cells through the MTT and propidium iodide assays. After low-dose combination treatments (PTX and 5-DMN), a reduction in cell viability and a concomitant increase in apoptosis were observed in the CL1-5 cells. We propose that 5-DMN cooperates with PTX to induce apoptosis via the caspase pathway (by modulating caspase-3, caspase-8, and caspase-9 activities). Furthermore, we observed that the combination treatment significantly suppressed tumor growth in the nude mouse xenograft model. The results suggest that the synergistic effects of PTX and 5-DMN in lung cancer cells deserve particular attention and indicate the possibility of developing additional new strategies for treating lung cancer.

Tumor Static Concentration Curves in Combination Therapy

Combination therapies are widely accepted as a cornerstone for treatment of different cancer types. A tumor growth inhibition (TGI) model is developed for combinations of cetuximab and cisplatin obtained from xenograft mice. Unlike traditional TGI models, both natural cell growth and cell death are considered explicitly. The growth rate was estimated to 0.006 h^-1 and the natural cell death to 0.0039 h^-1 resulting in a tumor doubling time of 14 days. The tumor static concentrations (TSC) are predicted for each individual compound. When the compounds are given as single-agents, the required concentrations were computed to be 506 μg · mL^-1 and 56 ng · mL^-1 for cetuximab and cisplatin, respectively. A TSC curve is constructed for different combinations of the two drugs, which separates concentration combinations into regions of tumor shrinkage and tumor growth. The more concave the TSC curve is, the lower is the total exposure to test compounds necessary to achieve tumor regression. The TSC curve for cetuximab and cisplatin showed weak concavity. TSC values and TSC curves were estimated that predict tumor regression for 95% of the population by taking between-subject variability into account. The TSC concept is further discussed for different concentration-effect relationships and for combinations of three or more compounds.

Assessment of non-linear combination effect terms for drug-drug interactions

Drugs interact with their targets in different ways. A diversity of modeling approaches exists to describe the combination effects of two drugs. We investigate several combination effect terms (CET) regarding their underlying mechanism based on drug-receptor binding kinetics, empirical and statistical summation principles and indirect response models. A list with properties is provided and the interrelationship of the CETs is analyzed. A method is presented to calculate the optimal drug concentration pair to produce the half-maximal combination effect. This work provides a comprehensive overview of typically applied CETs and should shed light into the question as to which CET is appropriate for application in pharmacokinetic/pharmacodynamic models to describe a specific drug-drug interaction mechanism.

Pharmacodynamic modeling of combined chemotherapeutic effects predicts synergistic activity of gemcitabine and trabectedin in pancreatic cancer cells

PURPOSE

This study investigates the combined effects of gemcitabine and trabectedin (ecteinascidin 743) in two pancreatic cancer cell lines and proposes a pharmacodynamic (PD) model to quantify their pharmacological interactions.

METHODS

Effects of gemcitabine and trabectedin upon the pancreatic cancer cell lines MiaPaCa-2 and BxPC-3 were investigated using cell proliferation assays. Cells were exposed to a range of concentrations of the two drugs, alone and in combination. Viable cell numbers were obtained daily over 5 days. A model incorporating nonlinear cytotoxicity, transit compartments, and an interaction parameter ψ was used to quantify the effects of the individual drugs and combinations.

RESULTS

Simultaneous fitting of temporal cell growth profiles for all drug concentrations provided reasonable cytotoxicity parameter estimates (the cell killing rate constant K max and the sensitivity constant KC50) for each drug. The interaction parameter ψ was estimated as 0.806 for MiaPaCa-2 and 0.843 for BxPC-3 cells, suggesting that the two drugs exert modestly synergistic effects.

CONCLUSIONS

The proposed PD model enables quantification of the temporal profiles of drug combinations over a range of concentrations with drug-specific parameters. Based upon these in vitro studies, trabectedin may have augmented benefit in combination with gemcitabine. The PD model may have general relevance for the study of other cytotoxic drug combinations.

Modeling of human tumor xenografts and dose rationale in oncology

Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.

Targeting nuclear kinases in cancer: development of cell cycle kinase inhibitors

Cellular proliferation is a tightly controlled set of events that is regulated by numerous nuclear protein kinases. The proteins involved include checkpoint kinases (CHK), cyclin-dependent kinases (CDK), which regulate the cell cycle and aurora kinases (AURK) and polo-like kinases (PLK), which regulate mitosis. In cancer, these nuclear kinases are often dysregulated and cause uncontrolled cell proliferation and growth. Much work has gone into developing novel therapeutics that target each of these protein kinases in cancer but none have been approved in patients. In this review we provide an overview of the current compounds being developed clinically to target these nuclear kinases involved in regulating the cell cycle and mitosis.

A predictive pharmacokinetic-pharmacodynamic model of tumor growth kinetics in xenograft mice after administration of anticancer agents given in combination

Purpose

In clinical oncology, combination treatments are widely used and increasingly preferred over single drug administrations. A better characterization of the interaction between drug effects and the selection of synergistic combinations represent an open challenge in drug development process. To this aim, preclinical studies are routinely performed, even if they are only qualitatively analyzed due to the lack of generally applicable mathematical models.

Methods

This paper presents a new pharmacokinetic–pharmacodynamic model that, starting from the well-known single agent Simeoni TGI model, is able to describe tumor growth in xenograft mice after the co-administration of two anticancer agents. Due to the drug action, tumor cells are divided in two groups: damaged and not damaged ones. The damaging rate has two terms proportional to drug concentrations (as in the single drug administration model) and one interaction term proportional to their product. Six of the eight pharmacodynamic parameters assume the same value as in the corresponding single drug models. Only one parameter summarizes the interaction, and it can be used to compute two important indexes that are a clear way to score the synergistic/antagonistic interaction among drug effects.

Results

The model was successfully applied to four new compounds co-administered with four drugs already available on the market for the treatment of three different tumor cell lines. It also provided reliable predictions of different combination regimens in which the same drugs were administered at different doses/schedules.

Conclusions

A good and quantitative measurement of the intensity and nature of interaction between drug effects, as well as the capability to correctly predict new combination arms, suggest the use of this generally applicable model for supporting the experiment optimal design and the prioritization of different therapies.

Electronic supplementary material

The online version of this article (doi:10.1007/s00280-013-2208-8) contains supplementary material, which is available to authorized users.

Development of a preclinical PK/PD model to assess antitumor response of a sequential aflibercept and doxorubicin-dosing strategy in acute myeloid leukemia

Timing of the anti-angiogenic agent with respect to the chemotherapeutic agent may be crucial in determining the success of combination therapy in cancer. We investigated the effects of sequential therapy with the potent VEGF inhibitor, aflibercept, and doxorubicin (DOX) in preclinical acute myeloid leukemia (AML) models. Mice were engrafted with human HL-60 and HEL-luciferase leukemia cells via S.C. and/or I.V. injection and treated with two to three doses of aflibercept (5-25 mg/kg) up to 3-7 days prior to doxorubicin (30 mg/kg) administration. Leukemia growth was determined by local tumor measurements (days 0-16) and systemic bioluminescent imaging (days 0-28) in animals receiving DOX (3 mg/kg) with or without aflibercept. A PK/PD model was developed to characterize how prior administration of aflibercept altered intratumoral DOX uptake. DOX concentration-time profiles were described using a four-compartment PK model with linear elimination. We determined that intratumoral DOX concentrations were 6-fold higher in the aflibercept plus DOX treatment group versus DOX alone in association with increased drug uptake rates (from 0.125 to 0.471 ml/h/kg) into tumor without affecting drug efflux. PD modeling demonstrated that the observed growth retardation was mainly due to the combination of DOX plus TRAP group; 0.00794 vs. 0.0043 h(-1). This PK/PD modeling approach in leukemia enabled us to predict the effects of dosing frequency and sequence for the combination of anti-VEGF and cytotoxic agents on AML growth in both xenograft and marrow, and may be useful in the design of future rational combinatorial dosing regimens in hematological malignancies.

PK-PD modeling of combination efficacy effect from administration of the MEK inhibitor GDC-0973 and PI3K inhibitor GDC-0941 in A2058 xenografts

PURPOSE

Mutations and activations of the MEK and PI3K pathways are associated with the development of many cancers. GDC-0973 and GDC-0941 are inhibitors of MEK and PI3K, respectively, currently being evaluated clinically in combination as anti-cancer treatment. The objective of these studies was to characterize the relationship between the plasma concentrations of GDC-0973 and GDC-0941 administered in combination and efficacy in A2058 melanoma xenograft.

METHODS

GDC-0973 and GDC-0941 were administered to A2058 tumor-bearing mice daily (QD) or every third day (Q3D) either as single agents or in combination. A semi-mechanistic population anti-cancer model was developed to simultaneously describe the tumor growth following QD/Q3D single-agent and QD combination treatments. The interaction terms ψ included in the model were used to assess whether the combination was additive. Using this model, data from the Q3D combination regimen were simulated and compared with the observed tumor volumes.

RESULTS

The model consisting of saturable tumor growth provided the best fit of the data. The estimates for ψ were not significantly different from 1, suggesting an additive effect of GDC-0973 and GDC-0941 on tumor growth inhibition. The population rate constants associated with tumor growth inhibition for GDC-0973 and GDC-0941 were 0.00102 and 0000651 μM(-1) h(-1), respectively. Using the model based on single-agent and QD combination efficacy data, simulations adequately described the tumor growth from the Q3D combination regimen.

CONCLUSIONS

These findings suggest that, based on minimal data, it is possible to predict the effects of various combinations preclinically and also assess the potential clinical efficacy of combinations using human pharmacokinetic inputs.

Preclinical disposition of GDC-0973 and prospective and retrospective analysis of human dose and efficacy predictions

[3,4-Difluoro-2-(2-fluoro-4-iodo-phenylamino)-phenyl]-((S)-3-hydroxy-3-piperidin-2-yl-azetidin-1-yl)-methanone (GDC-0973) is a potent and highly selective inhibitor of mitogen-activated protein kinase(MAPK)/extracellular signal-regulated kinase (ERK) 1/2 (MEK1/2), a MAPK kinase that activates ERK1/2. The objectives of these studies were to characterize the disposition of GDC-0973 in preclinical species and to determine the relationship of GDC-0973 plasma concentrations to efficacy in Colo205 mouse xenograft models. The clearance (CL) of GDC-0973 was moderate in mouse (33.5 ml · min(-1) · kg(-1)), rat (37.9 ± 7.2 ml · min(-1) · kg(-1)), and monkey (29.6 ± 8.5 ml · min(-1) · kg(-1)). CL in dog was low (5.5 ± 0.3 ml · min(-1) · kg(-1)). The volume of distribution across species was large, 6-fold to 15-fold body water; half-lives ranged from 4 to 13 h. Protein binding in mouse, rat, dog, monkey, and human was high, with percentage unbound, 1 to 6%. GDC-0973-related radioactivity was rapidly and extensively distributed to tissues; however, low concentrations were observed in the brain. In rats and dogs, [(14)C]GDC-0973 was well absorbed (fraction absorbed, 70-80%). The majority of [(14)C]GDC-0973-related radioactivity was recovered in the bile of rat (74-81%) and dog (65%). The CL and volume of distribution of GDC-0973 in human, predicted by allometry, was 2.9 ml · min(-1) · kg(-1) and 9.9 l/kg, respectively. The predicted half-life was 39 h. To characterize the relationship between plasma concentration of GDC-0973 and tumor growth inhibition, pharmacokinetic-pharmacodynamic modeling was applied using an indirect response model. The KC(50) value for tumor growth inhibition in Colo205 xenografts was estimated to be 0.389 μM, and the predicted clinical efficacious dose was ∼10 mg. Taken together, these data are useful in assessing the disposition of GDC-0973, and where available, comparisons with human data were made.

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.

Preclinical study of treatment response in HCT-116 cells and xenografts with (1) H-decoupled (31) P MRS

The topoisomerase I inhibitor, irinotecan, and its active metabolite SN-38 have been shown to induce G(2) /M cell cycle arrest without significant cell death in human colon carcinoma cells (HCT-116). Subsequent treatment of these G(2) /M-arrested cells with the cyclin-dependent kinase inhibitor, flavopiridol, induced these cells to undergo apoptosis. The goal of this study was to develop a noninvasive metabolic biomarker for early tumor response and target inhibition of irinotecan followed by flavopiridol treatment in a longitudinal study. A total of eleven mice bearing HCT-116 xenografts were separated into two cohorts where one cohort was administered saline and the other treated with a sequential course of irinotecan followed by flavopiridol. Each mouse xenograft was longitudinally monitored with proton ((1) H)-decoupled phosphorus ((31) P) magnetic resonance spectroscopy (MRS) before and after treatment. A statistically significant decrease in phosphocholine (p = 0.0004) and inorganic phosphate (p = 0.0103) levels were observed in HCT-116 xenografts following treatment, which were evidenced within twenty-four hours of treatment completion. Also, a significant growth delay was found in treated xenografts. To discern the underlying mechanism for the treatment response of the xenografts, in vitro HCT-116 cell cultures were investigated with enzymatic assays, cell cycle analysis, and apoptotic assays. Flavopiridol had a direct effect on choline kinase as measured by a 67% reduction in the phosphorylation of choline to phosphocholine. Cells treated with SN-38 alone underwent 83 ± 5% G(2) /M cell cycle arrest compared to untreated cells. In cells, flavopiridol alone induced 5 ± 1% apoptosis while the sequential treatment (SN-38 then flavopiridol) resulted in 39 ± 10% apoptosis. In vivo (1) H-decoupled (31) P MRS indirectly measures choline kinase activity. The decrease in phosphocholine may be a potential indicator of early tumor response to the sequential treatment of irinotecan followed by flavopiridol in noninvasive and/or longitudinal studies.

Antiapoptotic effects of roscovitine on camptothecin-induced DNA damage in neuroblastoma cells

In the present study dopaminergic neuroblastoma B65 cells were exposed to Camptothecin (CPT) (0.5-10 μM), either alone or in the presence of roscovitine (ROSC). The results show that CPT induces apoptosis through the activation of ataxia telangiectasia mutated (ATM)-induced cell-cycle alteration in neuroblastoma B65 cells. The apoptotic process is mediated through the activation of cystein proteases, namely calpain/caspases. However, whereas a pan-caspase inhibitor, zVADfmk, inhibited CPT-mediated apoptosis, a calpain inhibitor, calpeptin, did not prevent cell death. Interestingly, CPT also induces CDK5 activation and ROSC (25 μM) blocked CDK5, ATM activation and apoptosis (as measured by caspase-3 activation). By contrast, selective inhibition of ATM, by KU55933, and non-selective inhibition, by caffeine, did not prevent CPT-mediated apoptosis. Thus, we conclude that CDK5 is activated in response to DNA damage and that CDK5 inhibition prevents ATM and p53ser15 activation. However, pharmacological inhibition of ATM using KU55933 and caffeine suggests that ATM inhibition by ROSC is not the only mechanism that might explain the anti-apoptotic effects of this drug in this apoptosis model. Our findings have a potential clinical implication, suggesting that combinatory drugs in the treatment of cancer activation should be administered with caution.

In vitro and in vivo radiation sensitization of human tumor cells by a novel checkpoint kinase inhibitor, AZD7762

PURPOSE

Inhibition of checkpoint kinase 1 has been shown to enhance the cytotoxicity of DNA-damaging targeted chemotherapy through cell cycle checkpoint abrogation and impaired DNA damage repair. A novel checkpoint kinase 1/2 inhibitor, AZD7762, was evaluated for potential enhancement of radiosensitivity for human tumor cells in vitro and in vivo xenografts.

EXPERIMENTAL DESIGN

Survival of both p53 wild-type and mutant human cell lines was evaluated by clonogenic assay. Dose modification factors (DMF) were determined from survival curves (ratio of radiation doses for control versus drug treated at 10% survival). Flow cytometry, Western blot, and radiation-induced tumor regrowth delay assays were conducted.

RESULTS

AZD7762 treatment enhanced the radiosensitivity of p53-mutated tumor cell lines (DMFs ranging from 1.6-1.7) to a greater extent than for p53 wild-type tumor lines (DMFs ranging from 1.1-1.2). AZD7762 treatment alone exhibited little cytotoxicity to any of the cell lines and did not enhance the radiosensitivity of normal human fibroblasts (1522). AZD7762 treatment abrogated radiation-induced G(2) delay, inhibited radiation damage repair (assessed by gamma-H2AX), and suppressed radiation-induced cyclin B expression. HT29 xenografts exposed to five daily radiation fractions and to two daily AZD7762 doses exhibited significant radiation enhancement compared with radiation alone.

CONCLUSIONS

AZD7762 effectively enhanced the radiosensitivity of mutated p53 tumor cell lines and HT29 xenografts and was without untoward toxicity when administered alone or in combination with radiation. The results of this study support combining AZD7762 with radiation in clinical trials.