Frances Brightman

Differential feedback regulation of the MAPK cascade underlies the quantitative differences in EGF and NGF signalling in PC12 cells

Although epidermal growth factor (EGF) induces transient activation of Ras and the mitogen‐activated protein kinase (MAPK) cascade in PC12 cells, whereas nerve growth factor (NGF) stimulates sustained activation, the basis for these contrasting responses is not known. We have developed a computer simulation of EGF‐induced MAPK cascade activation, which provides quantitative evidence that feedback inhibition of the MAPK cascade is the most important factor in determining the duration of cascade activation. Hence, we propose that the observed quantitative differences in EGF and NGF signalling can be accounted for by differential feedback regulation of the MAPK cascade.

APPLICATION OF A GENERIC PHYSIOLOGICALLY BASED PHARMACOKINETIC MODEL TO THE ESTIMATION OF XENOBIOTIC LEVELS IN RAT PLASMA

The routine assessment of xenobiotic in vivo kinetic behavior is currently dependent upon data obtained through animal experimentation, although in vitro surrogates for determining key absorption, distribution, metabolism, and elimination properties are available. Here we present a unique, generic, physiologically based pharmacokinetic (PBPK) model and demonstrate its application to the estimation of rat plasma pharmacokinetics, following intravenous dosing, from in vitro data alone. The model was parameterized through an optimization process, using a training set of in vivo data taken from the literature and validated using a separate test set of in vivo discovery compound data. On average, the vertical divergence of the predicted plasma concentrations from the observed data, on a semilog concentration-time plot, was approximately 0.5 log unit. Around 70% of all the predicted values of a standardized measure of area under the concentration-time curve (AUC) were within 3-fold of the observed values, as were over 90% of the training set t1/2 predictions and 60% of those for the test set; however, there was a tendency to overpredict t1/2 for the test set compounds. The capability of the model to rank compounds according to a given criterion was also assessed: of the 25% of the test set compounds ranked by the model as having the largest values for AUC, 61% were correctly identified. These validation results lead us to conclude that the generic PBPK model is potentially a powerful and cost-effective tool for predicting the mammalian pharmacokinetics of a wide range of organic compounds, from readily available in vitro inputs only.

Simulation Modelling of Human Intestinal Absorption using Caco-2 Permeability and Kinetic Solubility Data for Early Drug Discovery

Measurement of permeation across a monolayer of the human adenocarcinoma cell line, Caco-2, is a popular surrogate for a compounds permeation across the human intestinal epithelium. Taken alone, however, Caco-2 permeability has certain limitations in the prediction of the extent of absorption of an orally-administered compound, because it does not take into account confounding factors such as solubility and dissolution in the gastrointestinal (GI) tract fluids. A simulation model is described that uses Caco-2 permeability measured in the apical to basolateral direction plus kinetic solubility in buffered solution (both measured at pH 7.4) to predict human intestinal absorption. The model features novel treatment of time-varying fluid volume in the GI tract, as a consequence of secretions into, and absorption of fluid from, the upper part of the GI tract. The model has been trained and cross-validated with data for 120 combinations of compound and dose. It has superior predictive power to recently published simulation and quantitative structure property relationship models, and is suitable for high-throughput screening during lead identification and lead optimisation in drug discovery.

Abstract 852: Modeling the emergence of resistance to chemotherapeutics with virtual tumor

Drug resistance is a major cause of treatment failure in cancer, and understanding and overcoming mechanisms of resistance is a key challenge in advancing cancer therapy. Although the progression from cytotoxic chemotherapy to drugs aimed at specific molecular targets has improved response rates and reduced adverse effects, in the majority of cases there is still no effective treatment for metastatic disease; resistance constrains the effectiveness of both conventional chemotherapy and targeted agents. Resistance arises from mutations in the genome of cancer cells and/or epigenetic changes. The problem is compounded by considerable intra- and inter-tumour genetic heterogeneity, dictated by the genetic background and history of each cancer cell. It is therefore not surprising that patients with apparently the same cancer can respond differently to the same treatment, and it is becoming increasingly clear that cancer should be managed through personalized medicine. Some steps have already been taken in this direction, such as the development of CancerDR (Cancer Drug Resistance Database), but the approach requires large-scale genomic profiling, which is unlikely to be widespread in clinical practice in the immediate future. In the interim, recent studies have shown that the emergence of drug-resistant disease can at least be delayed through treatment with novel dosing regimens. Physiomics has developed a ‘Virtual Tumour’ (VT) technology that can predict how a tumor will respond to drug exposure. This integrated PK/PD simulation platform can be used to optimize drug dosing and scheduling, and to design new combination therapies. The VT technology integrates pharmacokinetic and pharmacodynamic effects, and models the way individual cells behave within a tumor population. These agent-based methods are particularly suitable for modeling multiple cell populations, and representing the heterogeneity of a clinical tumor. Given the significance of cancer drug resistance, and the form that future cancer therapy is likely to take, Physiomics is actively engaged in developing personalized medicine solutions. As a first step, we have incorporated chemotherapeutic resistance into our VT platform. The VT has been extended by the addition of a resistance module, which has been developed and calibrated using data taken from the literature. This module captures the fundamental mechanism by which resistance arises. Through a case study also derived from the literature, we demonstrate that the extended VT can be applied to model the emergence of resistance in patient-derived xenografts. Furthermore, we show that the VT can be used to identify and optimize therapeutic strategies for delaying the emergence of drug resistance. Our enhanced VT capability represents the first step towards a ground-breaking tool for developing personalized treatment, which is set to revolutionize cancer therapy in the near future, especially for patients with resistant disease. Citation Format: Frances A. Brightman, Eric Fernandez, David Orrell, Christophe Chassagnole. Modeling the emergence of resistance to chemotherapeutics with virtual tumor. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 852.

Abstract 366: Translational modeling of docetaxel-thalidomide combination treatment in metastatic, castrate-resistant prostate cancer: predicting clinical response using preclinical data

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA A major cause of drug failure in the clinic is that preclinical studies do not predict with sufficient certainty what will happen in clinical practice. Accurately translating information from animal studies to the clinic would have a major impact on attrition rate. We have developed a mathematical model of a tumor cell population called the Virtual Tumour, which has been used extensively to predict the efficacy of single drug or drug combination treatment in preclinical studies. We have now extended and adapted our preclinical model to predict efficacy in the clinic, thus creating the “Virtual Tumour Clinical.” Here we show a comparative study of the preclinical Virtual Tumour calibrated model for prostate tumor xenografts in mice, with a Virtual Tumour Clinical version calibrated with a clinical data set comprising 53 prostate cancer patients treated with thalidomide, 25 treated with docetaxel and 50 treated with a docetaxel and thalidomide combination. PSA measurements were used as proxy for tumor size. We analysed the consistency, the capability and the limitations of the models in translating the effect of the drug combination from the preclinical situation to the clinic. Citation Format: Eric Fernandez, Hitesh Mistry, Frances Brightman, David Orrell, William L. Dahut, William D. Figg, Wilfried D. Stein, Christophe Chassagnole. Translational modeling of docetaxel-thalidomide combination treatment in metastatic, castrate-resistant prostate cancer: predicting clinical response using preclinical data. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 366. doi:10.1158/1538-7445.AM2014-366

Abstract 5147: drugCARD: a database of anticancer treatment regimens and drug combinations.

Physiomics and Pharmacometrics have collaborated to design a new database of anti-cancer drugs and therapeutic treatment information. The objective is to provide a database of anti-neoplastic agents, regimens and combinations for use by clinicians and researchers in oncology. The drugCARD database, accessible through the web, offers data on more than 130 anti-cancer drugs (small molecules and biologics) used in research and in the clinic. It contains information on drug combinations as well as several hundreds of cancer chemotherapy regimens used routinely in the clinic. The data are classified according to tumour type, species and experimental system (in vitro or in vivo). This database will be regularly expanded and curated with the most current information. Individual drug information contained within the database comprises pharmacokinetic profiles, mechanisms of action and resistance, dose-response effect, dosing limits, therapeutic index and immunosuppression data. Drug combinations are also referenced. The database covers synergy or antagonism, and includes the combination therapeutic index and cross-resistance information. Drug combinations where the level of synergy is dependent upon the drug schedule, drug sequence or administration timing are also referenced and thoroughly discussed. The user can browse and compare chemotherapeutic regimens, and analyse the overall drug dose over a course of treatment, by tumour type, in animal and clinical models. Moreover, the database enables users to design new combinations and regimens that obey dosing constraints (such as MLD and MTD), and can be used to determine drug candidates that could be combined with a new chemical or biological entity, given the respective mechanisms of action and other PK/PD data. Data can be exported for analysis in spreadsheets, modelling software or simulation packages. Advanced functions will include the ability to carry out statistical analysis on drug usage and dosing in various contexts. Finally, the database allows the expression and nomenclature of chemotherapy regimens to be standardized, which is of paramount importance in improving efficacy, as well as reducing medication errors (Kohler et al 1998). Citation Format: Eric Fernandez, Jianxiong Pang, Chris Snell, Cathy Derow, Frances Brightman, Christophe Chassagnole, Robert Jackson. drugCARD: a database of anticancer treatment regimens and drug combinations. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5147. doi:10.1158/1538-7445.AM2013-5147

Abstract 5233: Modeling ionizing radiation exposure in vitro and in vivo using the Virtual Tumour.

Ionizing radiation exposure induces DNA double strand breaks in mammalian cells. In response, cells activate repair mechanisms which involve cell cycle arrest at discrete transition points of the cell cycle. The results in an accumulation of cells in S and G2 phase in an irradiated cell population over a period of time following exposure. Using the Virtual Tumour, a cell population simulator of cancer cells and tissue, we have built a model of irradiation of tumour cell culture in vitro as well as tumour tissue in vivo for various cancer cell types. We show how cell cycle timings and radiosensitivity can accurately reproduce both in vivo and in vitro response to irradiation-induced DNA damage. We also study how the radiosensitization effect of gemcitabine is time-dependent. This demonstrates how the Virtual Tumour can help maximise therapeutic efficacy of combinations involving irradiation. Citation Format: Eric Fernandez, David Orrell, Frances Brightman, David Fell, Christophe Chassagnole. Modeling ionizing radiation exposure in vitro and in vivo using the Virtual Tumour. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5233. doi:10.1158/1538-7445.AM2013-5233

Abstract 4942: Can three-dimensional cell cultures be used to predict in vivo drug response and synergistic combinations

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL The discovery of synergistic combinations of standard-of-care drugs or new chemical or biological entities shows much promise in the treatment of cancer, and there is much interest in this strategy. However, the increasing number of possible combinations makes the task of selecting the best regimens particularly difficult. As a response to this problem, we developed a computerized PK-PD model of a growing tumor, called the Virtual Tumor™. We demonstrated that this platform can successfully simulate the outcome of various drug combination schedules in xenografts, as well as predict optimal drug schedules and combinations. Although xenografts represent a convenient and relatively inexpensive approach to assessing the likely efficacy of proposed dosing regimens in vivo, the number of permutations that can be tested is still limited by practical considerations. We have therefore explored the use of three-dimensional tumor cell cultures (microtissues) as a more cost-effective alternative to xenografts for validating Virtual Tumor™ predictions. Using the Virtual Tumor™, we previously predicted that the efficacy of a gemcitabine-docetaxel combination could vary greatly depending on the scheduling of the drug administration, and verified these findings in vivo in MX-1 xenograft mouse model. We have now conducted a comparable study using MX-1 microtissues, in which the cultures were treated with these same two drugs in isolation or in combination, according to various regimens. Here we show how the microtissue Virtual Tumor™ model can be employed to simulate microtissue growth and response to drug treatment, and the capability of this model to predict drug synergy in vivo in xenografts. Furthermore, we discuss whether microtissues can be used as a surrogate for xenografts, in conjunction with the Virtual Tumor™, for designing new drug regimens, testing possible schedules for combinations of different drugs and prioritizing the most effective drug combinations. 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 4942. doi:1538-7445.AM2012-4942

Abstract A191: The application of three-dimensional cell cultures in combination with the Virtual Tumor for designing optimal drug dosing schedules.

There is currently a great deal of interest in determining synergistic drug combinations, however, it is not easy to determine which schedules should be tested, since the number of different possible schedules increases combinatorially when more than one drug is considered. We have therefore developed a predictive PK-PD “Virtual Tumor” model that allows rational design of schedules for drug combinations. We previously built a Virtual Tumor that was capable of successfully simulating the outcome of various drug combination schedules in xenografts; using this model we were also able to propose new optimal administration schemas. Although xenografts represent a convenient and relatively inexpensive approach to assessing the likely efficacy of proposed dosing regimens in vivo, the number of permutations that can be tested is still limited by practical considerations. We have therefore explored the use of three-dimensional tumor cell cultures (microtissues) as a more cost-effective alternative to xenografts for validating Virtual Tumor predictions. Here we present a microtissue Virtual Tumor that is analogous to our xenograft model, and a comparison of the utility of each in simulating and optimising drug dosing schedules. The application of microtissues in combination with the Virtual Tumor technology can be used to design new regimens with proprietary compounds as well as standards of care, small molecules or biotherapeutical agents; help test possible schedules for combinations of different drugs that would be effectively impossible to investigate experimentally; and allow prioritisation of the most effective drug combinations. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A191.