Numerical simulation of vascular tumour growth under antiangiogenic treatment: addressing the paradigm of single-agent bevacizumab therapy with the use of experimental data

Background: Antiangiogenic agents have been recently added to the oncological armamentarium with bevacizumab probably being the most popular representative in current clinical practice. The elucidation of the mode of action of these agents is a prerequisite for personalized prediction of antiangiogenic treatment response and selection of patients who may benefit from this kind of therapy. To this end, having used as a basis a preexisting continuous vascular tumour growth model which addresses the targeted nature of antiangiogenic treatment, we present a paper characterized by the following three features. First, the integration of a two-compartmental bevacizumab specific pharmacokinetic module into the core of the aforementioned preexisting model. Second, its mathematical modification in order to reproduce the asymptotic behaviour of tumour volume in the theoretical case of a total destruction of tumour neovasculature. Third, the exploitation of a range of published animal datasets pertaining to antitumour efficacy of bevacizumab on various tumour types (breast, lung, head and neck, colon).Results: Results for both the unperturbed growth and the treatment module reveal qualitative similarities with experimental observations establishing the biologically acceptable behaviour of the model. The dynamics of the untreated tumour has been studied via a parameter analysis, revealing the role of each relevant input parameter to tumour evolution. The combined effect of endogenous proangiogenic and antiangiogenic factors on the angiogenic potential of a tumour is also studied, in order to capture the dynamics of molecular competition between the two key-players of tumoural angiogenesis. The adopted methodology also allows accounting for the newly recognized direct antitumour effect of the specific agent.Conclusions: Interesting observations have been made, suggesting a potential size-dependent tumour response to different treatment modalities and determining the relative timing of cytotoxic versus antiangiogenic agents administration. Insight into the comparative effectiveness of different antiangiogenic treatment strategies is revealed. The results of a series of in vivo experiments in mice bearing diverse types of tumours (breast, lung, head and neck, colon) and treated with bevacizumab are successfully reproduced, supporting thus the validity of the underlying model.Reviewers: This article was reviewed by L. Hanin, T. Radivoyevitch and L. Edler

Modeling long-term longitudinal HIV dynamics with application to an AIDS clinical study

A virologic marker, the number of HIV RNA copies or viral load, is currently used to evaluate antiretroviral (ARV) therapies in AIDS clinical trials. This marker can be used to assess the ARV potency of therapies, but is easily affected by drug exposures, drug resistance and other factors during the long-term treatment evaluation process. HIV dynamic studies have significantly contributed to the understanding of HIV pathogenesis and ARV treatment strategies. However, the models of these studies are used to quantify short-term HIV dynamics ($<$ 1 month), and are not applicable to describe long-term virological response to ARV treatment due to the difficulty of establishing a relationship of antiviral response with multiple treatment factors such as drug exposure and drug susceptibility during long-term treatment. Long-term therapy with ARV agents in HIV-infected patients often results in failure to suppress the viral load. Pharmacokinetics (PK), drug resistance and imperfect adherence to prescribed antiviral drugs are important factors explaining the resurgence of virus. To better understand the factors responsible for the virological failure, this paper develops the mechanism-based nonlinear differential equation models for characterizing long-term viral dynamics with ARV therapy. The models directly incorporate drug concentration, adherence and drug susceptibility into a function of treatment efficacy and, hence, fully integrate virologic, PK, drug adherence and resistance from an AIDS clinical trial into the analysis. A Bayesian nonlinear mixed-effects modeling approach in conjunction with the rescaled version of dynamic differential equations is investigated to estimate dynamic parameters and make inference. In addition, the correlations of baseline factors with estimated dynamic parameters are explored and some biologically meaningful correlation results are presented. Further, the estimated dynamic parameters in patients with virologic success were compared to those in patients with virologic failure and significantly important findings were summarized. These results suggest that viral dynamic parameters may play an important role in understanding HIV pathogenesis, designing new treatment strategies for long-term care of AIDS patients.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS192 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Hybrid Modeling of Cancer Drug Resistance Mechanisms

Cancer is a multi-scale disease and its overwhelming complexity depends upon the multiple interwind events occurring at both molecular and cellular levels, making it very difficult for therapeutic advancements in cancer research. The resistance to cancer drugs is a significant challenge faced by scientists nowadays. The roots of the problem reside not only at the molecular level, due to multiple type of mutations in a single tumor, but also at the cellular level of drug interactions with the tumor. Tumor heterogeneity is the term used by oncologists for the involvement of multiple mutations in the development of a tumor at the sub-cellular level. The mechanisms for tumor heterogeneity are rigorously being explored as a reason for drug resistance in cancer patients. It is important to observe cell interactions not only at intra-tumoral level, but it is also essential to study the drug and tumor cell interactions at cellular level to have a complete picture of the mechanisms underlying drug resistance. The multi-scale nature of cancer drug resistance problem require modeling approaches that can capture all the multiple sub-cellular and cellular interaction factors with respect to dierent scales for time and space. Hybrid modeling offers a way to integrate both discrete and continuous dynamics to overcome this challenge. This research work is focused on the development of hybrid models to understand the drug resistance behaviors in colorectal and lung cancers. The common thing about the two types of cancer is that they both have dierent mutations at epidermal growth factor receptors (EGFRs) and they are normally treated with anti-EGFR drugs, to which they develop resistances with the passage of time. The acquiring of resistance is the sign of relapse in both kind of tumors. The most challenging task in colorectal cancer research nowadays is to understand the development of acquired resistance to anti-EGFR drugs. The key reason for this problem is the KRAS mutations appearance after the treatment with monoclonal antibodies (moAb). A hybrid model is proposed for the analysis of KRAS mutations behavior in colorectal cancer with respect to moAb treatments. The colorectal tumor hybrid model is represented as a single state automata, which shows tumor progression and evolution by means of mathematical equations for tumor sub-populations, immune system components and drugs for the treatment. The drug introduction is managed as a discrete step in this model. To evaluate the drug performance on a tumor, equations for two types of tumors cells are developed, i.e KRAS mutated and KRAS wild-type. Both tumor cell populations were treated with a combination of moAb and chemotherapy drugs. It is observed that even a minimal initial concentration of KRAS mutated cells before the treatment has the ability to make the tumor refractory to the treatment. Moreover, a small population of KRAS mutated cells has a strong influence on a large number of wild-type cells by making them resistant to chemotherapy. Patient's immune responses are specifically taken into considerations and it is found that, in case of KRAS mutations, the immune strength does not affect medication efficacy. Finally, cetuximab (moAb) and irinotecan (chemotherapy) drugs are analyzed as first-line treatment of colorectal cancer with few KRAS mutated cells. Results show that this combined treatment could be only effective for patients with high immune strengths and it should not be recommended as first-line therapy for patients with moderate immune strengths or weak immune systems because of a potential risk of relapse, with KRAS mutant cells acquired resistance involved with them. Lung cancer is more complicated then colorectal cancer because of acquiring of multiple resistances to anti-EGFR drugs. The appearance of EGFR T790M and KRAS mutations makes tumor resistant to a geftinib and AZD9291 drugs, respectively. The hybrid model for lung cancer consists of two non-resistant and resistant states of tumor. The non-resistant state is treated with geftinib drug until resistance to this drug makes tumor regrowth leading towards the resistant state. The resistant state is treated with AZD9291 drug for recovery. In this model the complete resistant state due to KRAS mutations is ignored because of the unavailability of parameter information and patient data. Each tumor state is evaluated by mathematical differential equations for tumor growth and progression. The tumor model consists of four tumor sub-population equations depending upon the type of mutations. The drug administration in this model is also managed as a discrete step for exact scheduling and dosages. The parameter values for the model are obtained by experiments performed in the laboratory. The experimental data is only available for the tumor progression along with the geftinib drug. The model is then fine tuned for obtaining the exact tumor growth patterns as observed in clinic, only for the geftinib drug. The growth rate for EGFR T790M tumor sub-population is changed to obtain the same tumor progression patterns as observed in real patients. The growth rate of mutations largely depends upon the immune system strength and by manipulating the growth rates for different tumor populations, it is possible to capture the factor of immune strength of the patient. The fine tuned model is then used to analyze the effect of AZD9291 drug on geftinib resistant state of the tumor. It is observed that AZD9291 could be the best candidate for the treatment of the EGFR T790M tumor sub-population. Hybrid modeling helps to understand the tumor drug resistance along with tumor progression due to multiple mutations, in a more realistic way and it also provides a way for personalized therapy by managing the drug administration in a strict pattern that avoid the growth of resistant sub-populations as well as target other populations at the same time. The only key to avoid relapse in cancer is the personalized therapy and the proposed hybrid models promises to do that

Combined tumour treatment by coupling conventional radiotherapy to an additional dose contribution from thermal neutrons

Aim: To employ the thermal neutron background in conventional X-rays radiotherapy treatments in order to add a localized neutron dose boost to the patient, enhancing the treatment effectiveness. Background: Conventional linear accelerators for radiotherapy produce fast secondary neutrons with a mean energy of about 1 MeV due to (\u3b3, n) reaction. This neutron field, isotropically distributed, is considered as an extra unaccounted dose during the treatment. Moreover, considering the moderating effect of human body, a thermal neutron field is localized in the tumour area: this neutron background could be employed for Boron Neutron Capture Therapy (BNCT) by previously administering a boron (10B enriched) carrier to the patient, acting as a localized radiosensitizer. The thermal neutron absorption in the 10B enriched tissue will improve radiotherapy effectiveness. Materials and Methods: The feasibility of the proposed method was investigated by using simplified tissue-equivalent phantoms with cavities in correspondence of relevant tissues or organs, suited for dosimetric measurements. A 10 cm 7 10 cm square photon field with different energies was delivered to the phantoms. Additional exposures were implemented, using a compact neutron photo-converter-moderator assembly, with the purpose of modifying the mixed photon-neutron field in the treatment region. Doses due to photons and neutrons were both measured by using radiochromic films and superheated bubble detectors, respectively, and simulated with Monte Carlo codes. Results: For a 10 cm 7 10 cm square photon field with accelerating potentials 6 MV, 10 MV and 15 MV, the neutron dose equivalent in phantom was measured and its values was 0.07 mGy/Gy (neutron dose equivalent / photon absorbed dose at isocentre), 0.99 mGy/Gy and 2.22 mGy/Gy, respectively. For a 18 MV treatment, simulations and measurements quantified the thermal neutron field in the treatment zone in 1.55 7 107 cm 122 Gy 121. Assuming a BNCT- standard 10B concentration in tumour tissue, the calculated additional BNCT dose at 4 cm depth in phantom would be 1.5 mGy-eq/Gy. This ratio would reach 43 mGy- eq/Gy for an intensity modulated radiotherapy treatment (IMRT). When a specifically designed compact neutron photo-converter-moderator assembly is applied to the LINAC to enhance the thermal neutron field, the photon field is modified. Particularly, a 15 MV photon field produces a dose profile very similar to that would be produced by a 6 MV field in absence of the photo-converter-moderator assembly. As far as the thermal neutron field is concerned, more thermal neutrons are present, and thermal neutrons per photon increase of a factor 3 to 12 according to the depth in phantom and to different photoconverter geometries. By contrast, the photo-converter-moderator assembly was found to reduce fast neutrons of a factor 16 in the direction of the incident beam. Conclusions: The parasitic thermal neutron component during conventional high- energy radiotherapy could be exploited to produce additional therapeutic doses if the 10B-carrier was administered to the patient. This radiosensitization effect could be increased by modifying the treatment field by using the specifically designed neutron photo-converter-moderator assembly

Novel approaches for translational research in oncology : pharmacometric modeling of oncolytic virus dynamics and a new tyrosine kinase inhibitor

Despite many innovative anti-cancer drugs in the pipeline, the attrition rate for anti-tumor drugs is high due to a lack of predictability of efficacy and safety from in vitro settings to animal models and at the preclinical-clinical interphase. In this thesis pharmacometric modeling was applied to support early cancer drug development exemplary for the concepts of oncolytic viruses (OV) and the tyrosine kinase inhibitor (TKI) BI 893923. For OV therapy a understanding of the bi-directional tumor-virus interaction is essential. Thus, a generic viral dynamic model was developed based on in vitro data from Newcastle disease virus, reovirus and parvovirus for the treatment of U87 glioblastoma cells, which simultaneously describes tumor growth and virus kinetics. The model was used for a depiction of virus efficacy and selection of optimal dose regimens. BI 893923 is a novel TKI of the insulin-like growth factor 1 receptor (IGF1R) and insulin receptor (INSR) with promising anti-tumor efficacy. Since for other IGF1R/INSR inhibitors dose-limiting hyperglycemia was reported, a mouse PK/PD model was developed, relating BI 893923 plasma concentration to biomarker modification and tumor growth as well as blood glucose to balance anti-tumor efficacy with the risk of hyperglycemia. The model was scaled to human by allometric principles using data from mouse, rat, dog, minipig and monkey and a risk-benefit analysis was conducted to determine the optimal safe and efficient human dose.Trotz intensiver Forschung in der Onkologie ist die Zahl neuer Zulassungen gering, da die Übertragbarkeit von Wirksamkeit und Sicherheit von in vitro Tests auf Tiermodelle und an der präklinisch-klinischen Schnittstelle nur ungenügend ist. Um die Entwicklung zu unterstützen, wurden in dieser Arbeit am Beispiel von onkolytischen Viren (OV) und des Tyrosinkinaseinhibitors (TKI) BI 893923 pharmakometrische Modelle entwickelt. Für die Therapie mit OV ist ein Verständnis der wechselseitigen Tumor-Virus-Beziehung essentiell. Daher wurde basierend auf in vitro Newcastle disease-, Reo-und Parvovirusdaten zur Behandlung von U87 Glioblastomzellen ein generisches Virus-Dynamik-Modell entwickelt, welches simultan Tumorwachstum und Viruskinetik beschreibt. Das Modell wurde zur Bestimmung von Viruseffizienz und optimaler Dosierungsregime genutzt. BI 893923 ist ein neuer TKI des Insulin-like Growth Faktor 1 Rezeptors (IGF1R) und Insulin Rezeptors (INSR). Da für andere IGF1R/INSR TKIs dosislimitierende Hyperglykämien berichtet wurden, wurde ein Maus PK/PD Model entwickelt, welches die BI 893923 Plasmakonzentration, Biomarkermodifikation, das Tumorwachstum und die Blutglukose beschreibt, um die antitumoraleWirksamkeit gegen das Hyperglykämierisiko abzuwägen. Es wurden Daten von Maus, Ratte, Hund, Minischwein und Affe genutzt um das Modell allometrisch auf den Menschen zu skalieren und es wurde eine Risiko-Nutzen-Analyse durchgeführt, um eine sichere und wirksame humane Dosis zu bestimmen

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research

Phase I–II trial design for biologic agents using conditional auto‐regressive models for toxicity and efficacy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147824/1/rssc12314_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147824/2/rssc12314.pd