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Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method's flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions., Competing Interests: Additional information Competing interests: The authors declare that they have no conflict of interest.

Across a broad range of disciplines, agent-based models (ABMs) are increasingly utilized for replicating, predicting, and understanding complex systems and their emergent behavior. In the biological and biomedical sciences, researchers employ ABMs to elucidate complex cellular and molecular interactions across multiple scales under varying conditions. Data generated at these multiple scales, however, presents a computational challenge for robust analysis with ABMs. Indeed, calibrating ABMs remains an open topic of research due to their own high-dimensional parameter spaces. In response to these challenges, we extend and validate our novel methodology, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), arriving at a computationally efficient framework for connecting high dimensional ABM parameter spaces with multidimensional data. Specifically, we modify SMoRe ParS to initially confine high dimensional ABM parameter spaces using unidimensional data, namely, single time-course information of in vitro cancer cell growth assays. Subsequently, we broaden the scope of our approach to encompass more complex ABMs and constrain parameter spaces using multidimensional data. We explore this extension with in vitro cancer cell inhibition assays involving the chemotherapeutic agent oxaliplatin. For each scenario, we validate and evaluate the effectiveness of our approach by comparing how well ABM simulations match the experimental data when using SMoRe ParS-inferred parameters versus parameters inferred by a commonly used direct method. In so doing, we show that our approach of using an explicitly formulated surrogate model as an interlocutor between the ABM and the experimental data effectively calibrates the ABM parameter space to multidimensional data. Our method thus provides a robust and scalable strategy for leveraging multidimensional data to inform multiscale ABMs and explore the uncertainty in their parameters., (© 2023. The Author(s).)

Multiscale systems biology is having an increasingly powerful impact on our understanding of the interconnected molecular, cellular, and microenvironmental drivers of tumor growth and the effects of novel drugs and drug combinations for cancer therapy. Agent-based models (ABMs) that treat cells as autonomous decision-makers, each with their own intrinsic characteristics, are a natural platform for capturing intratumoral heterogeneity. Agent-based models are also useful for integrating the multiple time and spatial scales associated with vascular tumor growth and response to treatment. Despite all their benefits, the computational costs of solving agent-based models escalate and become prohibitive when simulating millions of cells, making parameter exploration and model parameterization from experimental data very challenging. Moreover, such data are typically limited, coarse-grained and may lack any spatial resolution, compounding these challenges. We address these issues by developing a first-of-its-kind method that leverages explicitly formulated surrogate models (SMs) to bridge the current computational divide between agent-based models and experimental data. In our approach, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), we quantify the uncertainty in the relationship between agent-based model inputs and surrogate model parameters, and between surrogate model parameters and experimental data. In this way, surrogate model parameters serve as intermediaries between agent-based model input and data, making it possible to use them for calibration and uncertainty quantification of agent-based model parameters that map directly onto an experimental data set. We illustrate the functionality and novelty of Surrogate Modeling for Reconstructing Parameter Surfaces by applying it to an agent-based model of 3D vascular tumor growth, and experimental data in the form of tumor volume time-courses. Our method is broadly applicable to situations where preserving underlying mechanistic information is of interest, and where computational complexity and sparse, noisy calibration data hinder model parameterization., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Jain, Norton, Prado and Jackson.)

We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells., (© 2022. The Author(s).)

Sipuleucel-T (Provenge) is the first live cell vaccine approved for advanced, hormonally refractive prostate cancer. However, survival benefit is modest and the optimal combination or schedule of sipuleucel-T with androgen depletion remains unknown. We employ a nonlinear dynamical systems approach to modeling the response of hormonally refractive prostate cancer to sipuleucel-T. Our mechanistic model incorporates the immune response to the cancer elicited by vaccination, and the effect of androgen depletion therapy. Because only a fraction of patients benefit from sipuleucel-T treatment, inter-individual heterogeneity is clearly crucial. Therefore, we introduce our novel approach, Standing Variations Modeling, which exploits inestimability of model parameters to capture heterogeneity in a deterministic model. We use data from mouse xenograft experiments to infer distributions on parameters critical to tumor growth and to the resultant immune response. Sampling model parameters from these distributions allows us to represent heterogeneity, both at the level of the tumor cells and the individual (mouse) being treated. Our model simulations explain the limited success of sipuleucel-T observed in practice, and predict an optimal combination regime that maximizes predicted efficacy. This approach will generalize to a range of emerging cancer immunotherapies.

We extend an agent-based multiscale model of vascular tumour growth and angiogenesis to describe transarterial chemoembolisation (TACE) therapies. The model accounts for tumour and normal cells that are both nested in a vascular system that changes its structure according to tumour-related growth factors. Oxygen promotes nutrients to the tissue and determines cell proliferation or death rates. Within the extended model TACE is included as a two-step process: First, the purely mechanical influence of the embolisation therapy is modelled by a local occlusion of the tumour vasculature. There we distinguish between partial and complete responders, where parts of the vascular system are occluded for the first and the whole tumour vasculature is destroyed for the latter. In the second part of the model, drug eluding beads (DEBs) carrying the chemotherapeutic drug doxorubicin are located at destroyed vascular locations, releasing the drug over a certain time-window. Simulation results are parameterised to qualitatively reproduce clinical observations. Patients that undergo a TACE-treatment are categorised in partial and complete responders one day after the treatment. Another 90 days later reoccurance or complete response are detected by volume perfusion computer tomography (VPCT). Our simulations reveal that directly after a TACE- treatment an unstable tumour state can be observed, where regrowth and total tumour death have the same likeliness. It is argued that this short time-window is favorable for another therapeutical intervention with a less radical therapy. This procedure can shift the outcome to more effectiveness. Simulation results with an oxygen therapy within the unstable time-window demonstrate a potentially positive manipulated outcome. Finally, we conclude that our TACE model can motivate new therapeutical strategies and help clinicians analyse the intertwined relations and cross-links in tumours.

Glioblastomas are among the most lethal cancers, with a 5 year survival rate below 25%. Temozolomide is typically used in glioblastoma treatment; however, the enzymes alkylpurine-DNA- N -glycosylase (APNG) and methylguanine-DNA-methyltransferase (MGMT) efficiently mediate the repair of DNA damage caused by temozolomide, reducing treatment efficacy. Consequently, APNG and MGMT inhibition has been proposed as a way of overcoming chemotherapy resistance. Here, we develop a mechanistic mathematical model that explicitly incorporates the effects of chemotherapy on tumour cells, including the processes of DNA damage induction, cell arrest and DNA repair. Our model is carefully parametrized and validated, and then used to virtually recreate the response of heteroclonal glioblastomas to dual treatment with temozolomide and inhibitors of APNG/MGMT. Using our mechanistic model, we identify four combination treatment strategies optimized by tumour cell phenotype, and isolate the strategy most likely to succeed in a pre-clinical and clinical setting. If confirmed in clinical trials, these strategies have the potential to offset chemotherapy resistance in patients with glioblastoma and improve overall survival.

Brain tumor growth and tumor-induced edema result in increased intracranial pressure (ICP), which, in turn, is responsible for conditions as benign as headaches and vomiting or as severe as seizures, neurological damage, or even death. Therefore, it has been hypothesized that tracking ICP dynamics may offer improved prognostic potential in terms of early detection of brain cancer and better delimitation of the tumor boundary. However, translating such theory into clinical practice remains a challenge, in part because of an incomplete understanding of how ICP correlates with tumor grade. Here, we propose a multiphase mixture model that describes the biomechanical response of healthy brain tissue-in terms of changes in ICP and edema-to a growing tumor. The model captures ICP dynamics within the diseased brain and accounts for the ability/inability of healthy tissue to compensate for this pressure. We propose parameter regimes that distinguish brain tumors by grade, thereby providing critical insight into how ICP dynamics vary by severity of disease. In particular, we offer an explanation for clinically observed phenomena, such as a lack of symptoms in low-grade glioma patients versus a rapid onset of symptoms in those with malignant tumors. Our model also takes into account the effects tumor-derived proteases may have on ICP levels and the extent of tumor invasion. This work represents an important first step toward understanding the mechanisms that underlie the onset of edema and ICP in cancer-afflicted brains. Continued modeling effort in this direction has the potential to make an impact in the field of brain cancer diagnostics., (Copyright © 2019 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Hepatitis C virus (HCV) infection has reached epidemic proportions worldwide. Individuals with chronic HCV infection and without access to treatment are at high risk for developing hepatocellular carcinoma (HCC), a liver cancer that is rapidly fatal after diagnosis. A number of factors have been identified that contribute to HCV-driven carcinogenesis such as scarring of the liver, and chronic inflammation. Recent evidence indicates a direct role for HCV-encoded proteins themselves in oncogenesis of infected hepatocytes. The viral protein HCV core has been shown to interact directly with the host tumor suppressor protein p53, and to modulate p53-activity in a biphasic manner. Here, biochemically-motivated mathematical models of HCV-p53 interactions are developed to elucidate the mechanisms underlying this phenomenon. We show that by itself, direct interaction between HCV core and p53 is insufficient to recapitulate the experimental data. We postulate the existence of an additional factor, activated by HCV core that inhibits p53 function. We present experimental evidence in support of this hypothesis. The model including this additional factor reproduces the experimental results, validating our assumptions. Finally, we investigate what effect HCV core-p53 interactions could have on the capacity of an infected hepatocyte to repair damage to its DNA. Integrating our model with an existing model of the oscillatory response of p53 to DNA damage predicts a biphasic relationship between HCV core and the transformative potential of infected hepatocytes. In addition to providing mechanistic insights, these results suggest a potential biomarker that could help in identifying those HCV patients most at risk of progression to HCC., (Copyright © 2018 Elsevier Inc. All rights reserved.)

A rapid and cost-effective transient transfection method for mammalian cells is essential for screening biopharmaceuticals in early stages of development. A library of 25 amphipathic trans-acting oligodeoxythymidine phosphorothioate triester (dTtaPS) transfection reagents, carrying positively charged and lipophilic groups, has been constructed for this purpose. High-throughput screening of the library, using an imaging cytometer and an automated microbioreactor system, has led to the identification of dTtaPS 10+ as a potent transfection reagent. This reagent efficiently delivers a plasmid encoding enhanced green fluorescent protein in adherent HeLa cells while exhibiting low cytotoxicity. The microbioreactor system has been particularly useful for assessing the ability of dTtaPS 10+ to deliver a plasmid encoding immunoglobulin IgG1 in a fed-batch serum-free suspension CHO cell culture; dTtaPS 10+ -mediated transfection resulted in the production of IgG1 in yields comparable to or better than those obtained with commercial lipid-based transfection reagents under similar conditions. The ability of dTtaPS 10+ to deliver plasmids is essentially unaffected by the presence of a silicone-based antifoaming reagent, which is commonly used in bioreactor cell cultures. The transfection efficiency of lyophilized dTtaPS 10+ -plasmid complexes has been significantly restored upon aqueous reconstitution when compared to that achieved while using commercial transfection reagent complexes under similar conditions. The results of all experiments underscore the potential of dTtaPS 10+ for transient transfection of plasmids into adherent cells and fed-batch serum-free suspension CHO cells and rapid screening of reagents in a microbioreactor system., (© 2017 International Union of Biochemistry and Molecular Biology, Inc.)

A synthetic 8-mer, amphipathic, trans-acting poly-2'-O-methyluridylic thiophosphate triester RNA element (2'-OMeUtaPS) can be prepared using solid-phase synthesis protocols. 2'-OMeUtaPS efficiently mediates the delivery of uncharged polyA-tailed phosphorodiamidate morpholino (PMO) sequences in HeLa pLuc 705 cells, as evidenced by flow cytometry measurements. In this cell line, 2'-OMeUtaPS-mediated transfection of an antisense polyA-tailed PMO sequence induces alternative splicing of an aberrant luciferase pre-mRNA splice site, leading to restoration of functional luciferase, as quantitatively measured using a typical luciferase assay. 2'-OMeUtaPS is also potent at delivering an uncharged antisense polyA-tailed PMO sequence in muscle cells of the mdx mouse model of muscular dystrophy; targeting the polyA-tailed PMO sequence against a splice site of the pre-mRNA encoding mutated dystrophin triggers an alternate splicing event that results in excision of the mutated exon (exon 23) from the pre-mRNA and production of functional dystrophin, as demonstrated by agarose gel electrophoresis. © 2018 by John Wiley & Sons, Inc., (© 2018 John Wiley & Sons, Inc.)

Mathematical modeling has a long history in the field of cancer therapeutics, and there is increasing recognition that it can help uncover the mechanisms that underlie tumor response to treatment. However, making quantitative predictions with such models often requires parameter estimation from data, raising questions of parameter identifiability and estimability. Even in the case of structural (theoretical) identifiability, imperfect data and the resulting practical unidentifiability of model parameters can make it difficult to infer the desired information, and in some cases, to yield biologically correct inferences and predictions. Here, we examine parameter identifiability and estimability using a case study of two compartmental, ordinary differential equation models of cancer treatment with drugs that are cell cycle-specific (taxol) as well as non-specific (oxaliplatin). We proceed through model building, structural identifiability analysis, parameter estimation, practical identifiability analysis and its biological implications, as well as alternative data collection protocols and experimental designs that render the model identifiable. We use the differential algebra/input-output relationship approach for structural identifiability, and primarily the profile likelihood approach for practical identifiability. Despite the models being structurally identifiable, we show that without consideration of practical identifiability, incorrect cell cycle distributions can be inferred, that would result in suboptimal therapeutic choices. We illustrate the usefulness of estimating practically identifiable combinations (in addition to the more typically considered structurally identifiable combinations) in generating biologically meaningful insights. We also use simulated data to evaluate how the practical identifiability of the model would change under alternative experimental designs. These results highlight the importance of understanding the underlying mechanisms rather than purely using parsimony or information criteria/goodness-of-fit to decide model selection questions. The overall roadmap for identifiability testing laid out here can be used to help provide mechanistic insight into complex biological phenomena, reduce experimental costs, and optimize model-driven experimentation., (Copyright © 2017. Published by Elsevier Ltd.)

An efficient method for the delivery of uncharged polyA-tailed phosphorodiamidate morpholino sequences (PMO) in mammalian cells consists of employing a synthetic 8- mer amphipathic trans -acting poly-2'- O -methyluridylic thiophosphate triester element (2'-OMeUtaPS) as a transfection reagent. Unlike the dTtaPS DNA-based element, this RNA element is potent at delivering polyA-tailed PMO sequences to HeLa pLuc 705 cells or to myotube muscle cells. However, much like dTtaPS, the 2'-OMeUtaPS-mediated internalization of PMO sequences occurs through an energy-dependent mechanism; macropinocytosis appears to be the predominant endocytic pathway used for cellular uptake. The transfected PMO sequences induce alternate splicing of either the pre-mRNA encoding luciferase in HeLa pLuc 705 cells or the excision of exon 23 from the pre-mRNA encoding dystrophin in myotube muscle cells of the mdx mouse model of muscular dystrophy with an efficiency comparable to that of commercial cationic lipid reagents but without detrimental cytotoxicity., Competing Interests: Conflicts of interest There are no conflicts to declare.

An innovative approach to the delivery of uncharged peptide nucleic acids (PNAs) and phosphorodiamidate morpholino (PMO) oligomers in mammalian cells is described and consists of extending the sequence of those oligomers with a short PNA-polyA or PMO-polyA tail. Recognition of the polyA-tailed PNA or PMO oligomers by an amphipathic trans-acting polythymidylic thiophosphate triester element (dTtaPS) results in efficient internalization of those oligomers in several cell lines. The authors' findings indicate that cellular uptake of the oligomers occurs through an energy-dependent mechanism and macropinocytosis appears to be the predominant endocytic pathway used for internalization. The functionality of the internalized oligomers is demonstrated by alternate splicing of the pre-mRNA encoding luciferase in HeLa pLuc 705 cells. Amphipathic phosphorothioate DNA elements may represent a unique class of cellular transporters for robust delivery of uncharged nucleic acid sequences in live mammalian cells. © 2016 by John Wiley & Sons, Inc., (Copyright © 2016 John Wiley & Sons, Inc.)

Platinum drug-resistance in ovarian cancers mediated by anti-apoptotic proteins such as Bcl-xL is a major factor contributing to the chemotherapeutic resistance of recurrent disease. Consequently, concurrent inhibition of Bcl-xL in combination with chemotherapy may improve treatment outcomes for patients. Here, we develop a mathematical model to investigate the potential of combination therapy with ABT-737, a small molecule inhibitor of Bcl-xL, and carboplatin, a platinum-based drug, on a simulated tumor xenograft. The model is calibrated against in vivo experimental data, wherein xenografts established in mice were treated with ABT-737 and/or carboplatin on a fixed periodic schedule. The validated model is used to predict the minimum drug load that will achieve a predetermined level of tumor growth inhibition, thereby maximizing the synergy between the two drugs. Our simulations suggest that the infusion-duration of each carboplatin dose is a critical parameter, with an 8-hour infusion of carboplatin given weekly combined with a daily bolus dose of ABT-737 predicted to minimize residual disease. The potential of combination therapy to prevent or delay the onset of carboplatin-resistance is also investigated. When resistance is acquired as a result of aberrant DNA-damage repair in cells treated with carboplatin, drug delivery schedules that induce tumor remission with even low doses of combination therapy can be identified. Intrinsic resistance due to pre-existing cohorts of resistant cells precludes tumor regression, but dosing strategies that extend disease-free survival periods can still be identified. These results highlight the potential of our model to accelerate the development of novel therapeutics such as BH3 mimetics.

The bioactivity of a CpG-containing phosphorothioate DNA oligonucleotide with thermolytic 2-(N-formyl-N-methylamino)ethyl (fma) thiophosphate groups in mice led us to investigate the parameters affecting the internalization of these thermosensitive DNA prodrugs in various cell lines. Flow cytometry and confocal microscopy analyses indicate that 5'-fluoresceinated fma-phosphorothioate DNA sequences are poorly internalized in Vero, HeLa and GC-2 cells. However, when four fma-thiophosphate groups of a 15-nucleotide long oligothymidylate prodrug are replaced with 3-(N,N-dimethylamino)prop-1-yl thiophosphate functions, internalization of the positively charged prodrug, under physiological conditions, increased fourfold in HeLa and 40-fold in Vero or GC-2 cells. No cytotoxic effects are observed in Vero cells even at an extracellular prodrug concentration of 50 μM over a period of 72 h. Confocal microscopy studies show that internalization of the positively charged oligothymidylate prodrug in Vero cells is time-dependent with early trafficking of the DNA sequence through endosomal vesicles and, eventually, to the nucleus of the cells. Thus, the incorporation of four 3-(N,N-dimethylamino)prop-1-yl thiophosphate groups into thermosentive fma-phosphorothioate DNA prodrugs is an attractive strategy for efficient cellular internalization of these nucleic acid-based drugs for potential therapeutic indications., (Published by Elsevier Ltd.)

Biochemically failing metastatic prostate cancer is typically treated with androgen ablation. However, due to the emergence of castration-resistant cells that can survive in low androgen concentrations, such therapy eventually fails. Here, we develop a partial differential equation model of the growth and response to treatment of prostate cancer that has metastasized to the bone. Existence and uniqueness results are derived for the resulting free boundary problem. In particular, existence and uniqueness of solutions for all time are proven for the radially symmetric case. Finally, numerical simulations of a tumor growing in 2-dimensions with radial symmetry are carried in order to evaluate the therapeutic potential of different treatment strategies. These simulations are able to reproduce a variety of clinically observed responses to treatment, and suggest treatment strategies that may result in tumor remission, underscoring our model's potential to make a significant contribution in the field of prostate cancer therapeutics.

Vascular endothelial growth factor (VEGF) is the most studied family of soluble, secreted mediators of endothelial cell migration, survival, and proliferation. VEGF exerts its function by binding to specific tyrosine kinase receptors on the cell surface and transducing the effect through downstream signaling. In order to study the influence of VEGF binding on endothelial cell motion, we develop a hybrid model of VEGF-induced angiogenesis, based on the theory of reinforced random walks. The model includes the chemotactic response of endothelial cells to angiogenic factors bound to cell-surface receptors, rather than approximating this as a function of extracellular chemical concentrations. This allows us to capture biologically observed phenomena such as activation and polarization of endothelial cells in response to VEGF gradients across their lengths, as opposed to extracellular gradients throughout the tissue. We also propose a novel and more biologically reasonable functional form for the chemotactic sensitivity of endothelial cells, which is also governed by activated cell-surface receptors. This model is able to predict the threshold level of VEGF required to activate a cell to move in a directed fashion as well as an optimal VEGF concentration for motion. Model validation is achieved by comparison of simulation results directly with experimental data.

A one-step method for the synthesis of cyclic pronucleotide (cProTide) derivatives of 5-fluoro-2'-deoxyuridine (FdUrd), utilizing a novel phosphoramidating reagent, is described. Stereochemistry at phosphorus was established by NMR studies and modeling. Cytotoxicity data of representative cProTide derivatives of FdUrd are presented. The observed cell-to-cell variations in activity suggests that it is feasible to screen for structural variations in the cProTide moiety favoring metabolic activation in cancer cells, which may lead to an increase in the therapeutic effectiveness of FdUrd. The method described is applicable to all anticancer and antiviral nucleoside analogs having both the 5'- and the 3'-OH groups available for modification, forming cProTide derivatives capable of delivering the 5'-monophosphates to cells., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Tissue engineering constructs and other solid implants with biomedical applications, such as drug delivery devices or bioartificial organs, need oxygen (O(2)) to function properly. To understand better the vascular integration of such devices, we recently developed a novel model sensor containing O(2)-sensitive crystals, consisting of a polymeric capsule limited by a nanoporous filter. The sensor was implanted in mice with hydrogel alone (control) or hydrogel embedded with mouse CD117/c-kit+ bone marrow progenitor cells in order to stimulate peri-implant neovascularization. The sensor provided local partial O(2) pressure (pO(2)) using noninvasive electron paramagnetic resonance signal measurements. A consistently higher level of peri-implant oxygenation was observed in the cell-treatment case than in the control over a 10-week period. To provide a mechanistic explanation of these experimental observations, we present in this article a mathematical model, formulated as a system of coupled partial differential equations, that simulates peri-implant vascularization. In the control case, vascularization is considered to be the result of a foreign body reaction, while in the cell-treatment case, adipogenesis in response to paracrine stimuli produced by the stem cells is assumed to induce neovascularization. The model is validated by fitting numerical predictions of local pO(2) to measurements from the implanted sensor. The model is then used to investigate further the potential for using stem cell treatment to enhance the vascular integration of biomedical implants. We thus demonstrate how mathematical modeling combined with experimentation can be used to infer how vasculature develops around biomedical implants in control and stem cell-treated cases.

Prostate cancer progression depends in part on the complex interactions between testosterone, its active metabolite DHT, and androgen receptors. In a metastatic setting, the first line of treatment is the elimination of testosterone. However, such interventions are not curative because cancer cells evolve via multiple mechanisms to a castrate-resistant state, allowing progression to a lethal outcome. It is hypothesized that administration of antiandrogen therapy in an intermittent, as opposed to continuous, manner may bestow improved disease control with fewer treatment-related toxicities. The present study develops a biochemically motivated mathematical model of antiandrogen therapy that can be tested prospectively as a predictive tool. The model includes "personalized" parameters, which address the heterogeneity in the predicted course of the disease under various androgen-deprivation schedules. Model simulations are able to capture a variety of clinically observed outcomes for "average" patient data under different intermittent schedules. The model predicts that in the absence of a competitive advantage of androgen-dependent cancer cells over castration-resistant cancer cells, intermittent scheduling can lead to more rapid treatment failure as compared to continuous treatment. However, increasing a competitive advantage for hormone-sensitive cells swings the balance in favor of intermittent scheduling, delaying the acquisition of genetic or epigenetic alterations empowering androgen resistance. Given the near universal prevalence of antiandrogen treatment failure in the absence of competing mortality, such modeling has the potential of developing into a useful tool for incorporation into clinical research trials and ultimately as a prognostic tool for individual patients.

Resistance to standard chemotherapy (carboplatin + paclitaxel) is one of the leading causes of therapeutic failure in ovarian carcinomas. Emergence of chemoresistance has been shown to be mediated in part by members of the Bcl family of proteins including the antiapoptotic protein Bcl-x(L), whose expression is correlated with shorter disease-free intervals in recurrent disease. ABT-737 is an example of one of the first small-molecule inhibitors of Bcl-2/Bcl-x(L) that has been shown to increase the sensitivity of ovarian cancer cells to carboplatin. To exploit the therapeutic potential of these two drugs and predict optimal doses and dose scheduling, it is essential to understand the molecular basis of their synergistic action. Here, we build and calibrate a mathematical model of ABT-737 and carboplatin action on an ovarian cancer cell line (IGROV-1). The model suggests that carboplatin treatment primes cells for ABT-737 therapy because of an increased dependence of cells with DNA damage on Bcl-x(L) for survival. Numerical simulations predict the existence of a threshold of Bcl-x(L) below which these cells are unable to recover. Furthermore, co- plus posttreatment of ABT-737 with carboplatin is predicted to be the best strategy to maximize synergism between these two drugs. A critical challenge in chemotherapy is to strike a balance between maximizing cell-kill while minimizing patient drug load. We show that the model can be used to compute minimal doses required for any desired fraction of cell kill. These results underscore the potential of the modeling work presented here as a valuable quantitative tool to aid in the translation of novel drugs such as ABT-737 from the experimental to clinical setting and highlight the need for close collaboration between modelers and experimental scientists.

The design and synthesis of 5-fluoro-6-[(2-aminoimidazol-1-yl)methyl]uracil (AIFU), a potent inhibitor of thymidine phosphorylase (TP) with K(i)-values of 11nM (ecTP) and 17nM (hTP), are described. Kinetic studies established that the type of inhibition of TP by AIFU is uncompetitive with respect to inorganic phosphate (or arsenate). The results obtained suggest that AIFU and other zwitterionic thymine analog inhibitors of TP act as transition state analogs, mimicking the anionic thymine leaving group, consistent with an S(N)2-type catalytic mechanism, and anchored by their protonated side chains to the enzyme-bound phosphate by electrostatic and H-bonding interactions., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Proapoptotic and antiapoptotic proteins in the Bcl family are key regulators of programmed cell death. It is the interaction between these molecules that determines cellular response to apoptotic signals, making them attractive targets for therapeutic intervention. In recent experiments designed to study tumor angiogenesis, Bcl-2 upregulation in endothelial cells was shown to be a critical mediator of vascular development. In this article, we develop a mathematical model that explicitly incorporates the response of endothelial cells to variations in proapoptotic and antiapoptotic proteins in the Bcl family, as well as the administration of specific antiangiogenic therapies targeted against Bcl-2. The model is validated by comparing its predictions to in vitro experimental data that reports microvessel density prior to and following the administration of 0.05 to 5.0 micromol/L of BL193, a promising small molecule inhibitor of Bcl-2. Numerical simulations of in vivo treatment of tumors predict the existence of a threshold for the amount of therapy required for successful treatment and quantify how this threshold varies with the stage of tumor growth. Furthermore, the model shows how rapidly the least effective dosage of BL193 decreases if an even moderately better inhibitor of Bcl-2 is used and predicts that increasing cell wall permeability of endothelial cells to BL193 does not significantly affect this threshold. A critical challenge of experimental therapeutics for cancer is to decide which drugs are the best candidates for clinical trials. These results underscore the potential of mathematical modeling to guide the development of novel antiangiogenic therapies and to direct drug design.

Recent experiments show that vascular endothelial growth factor (VEGF) is the crucial mediator of downstream events that ultimately lead to enhanced endothelial cell survival and increased vascular density within many tumors. The newly discovered pathway involves up-regulation of the anti-apoptotic protein Bcl-2, which in turn leads to increased production of interleukin-8 (CXCL8). The VEGF-Bcl-2-CXCL8 pathway suggests new targets for the development of anti-angiogenic strategies including short interfering RNA (siRNA) that silence the CXCL8 gene and small molecule inhibitors of Bcl-2. In this paper, we present and validate a mathematical model designed to predict the effect of the therapeutic blockage of VEGF, CXCL8, and Bcl-2 at different stages of tumor progression. In agreement with experimental observations, the model predicts that curtailing the production of CXCL8 early in development can result in a delay in tumor growth and vascular development; however, it has little effect when applied at late stages of tumor progression. Numerical simulations also show that blocking Bcl-2 up-regulation, either at early stages or after the tumor has fully developed, ensures that both microvascular and tumor cell density stabilize at low values representing growth control. These results provide insight into those aspects of the VEGF-Bcl-2-CXCL8 pathway, which independently and in combination, are crucial mediators of tumor growth and vascular development. Continued quantitative modeling in this direction may have profound implications for the development of novel therapies directed against specific proteins and chemokines to alter tumor progression.

Endothelial cell apoptosis plays a critical role in the disruption of blood vessels mediated by natural inhibitors of angiogenesis and by anti-vascular drugs. However, the proportion of endothelial cells required to mediate a significant decrease in microvessel density is unknown. A system based on an inducible caspase (iCaspase-9) offers a unique opportunity to address this question. The dimerizer drug AP20187 induces apoptosis of human dermal microvascular endothelial cells stably transduced with iCaspase-9 (HDMEC-iCaspase-9), but not control cells (HDMEC-LXSN). Here, we generated blood vessels containing several HDMEC-iCaspase-9:HDMEC-LXSN ratios, and developed a mathematical modeling involving a system of differential equations to evaluate experimentally inaccessible ratios. A significant decrease in capillary sprouts was observed when at least 17% of the endothelial cells underwent apoptosis in vitro. Exposure to vascular endothelial growth factor (VEGF(165)) did not prevent apoptosis of HDMEC-iCaspase-9, but increased the apoptotic requirement for sprout disruption. In vivo experiments showed the requirement of at least 22% apoptotic endothelial cells for a significant decrease in microvascular density. The combined use of biological experimentation with mathematical modeling allowed us to conclude that apoptosis of a relatively small proportion of endothelial cells is sufficient to mediate a significant decrease in microvessel density.

We report a case of AML-M1 with 5q aberration at diagnosis. The patient was treated with high-dose chemotherapy (HDCT). After remission induction, he received allogenic peripheral blood stem cell transplantation (PBSCT) from an HLA-match donor brother. The successive follow-up conventional cytogenetics investigations in remission after HDCT and PBSCT revealed cytogenetic remission. The most interesting observation in this case is that relapsed marrow revealed the emergence of an entirely new, highly aberrant, unrelated clone with unusual translocations t(6;17)(p23;p11.2),+8,der(8)dup inv(8)(q23qter), t(10;19)(q26;q13.3) 4½ months after PBSCT. Our findings suggest the possibility of a mutagenic effect of HDCT and myeloablative intense chemotherapy before PBSCT that could have induced a genetic lesion in the recipient's genetically unstable stem cells in an environment of immunosuppression. The highly complex nature of the clone and the rapid clonal evolution indicates the possibility of selective pressure with proliferative advantage.