Integrated PK-PD and agent-based modeling in oncology

Wang, Zhihui; Butner, Joseph D.; Cristini, Vittorio; Deisboeck, Thomas S.

doi:10.1007/s10928-015-9403-7

Cited by 55 publications

(35 citation statements)

References 93 publications

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“…Recent reviews note that ABM is now widely used, for example, in health [296] and more specifically oncology [297]; epidemiology [298]; chronic [299] and noncommunicable diseases [300]. Other reviews look at their use in organizational science [301]; emergency response [302]; land use [303,304]; manufacturing [305]; ecosystem management [306]; and marketing [228,307].…”

Section: Agent-based Modellingmentioning

confidence: 99%

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Hoekstra

Steinbuch

Verbong³

2017

Complexity

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The energy domain is still dominated by equilibrium models that underestimate both the dangers and opportunities related to climate change. In reality, climate and energy systems contain tipping points, feedback loops, and exponential developments. This paper describes how to create realistic energy transition management models: quantitative models that can discover profitable pathways from fossil fuels to renewable energy. We review the literature regarding agent-based economics, disruptive innovation, and transition management and determine the following requirements. Actors must be detailed, heterogeneous, interacting, learning, and strategizing. Technology should be represented as a detailed and heterogeneous portfolio that can develop in a bottom-up manner, using endogenous feedback loops. Assumptions about discount rates and the social cost of carbon should be configurable. The model should contain interactions between the global, national, local, and individual level. A review of modelling techniques shows that equilibrium models are unsuitable and that system dynamics and discrete event simulation are too limited. The agent-based approach is found to be uniquely suited for the complex adaptive sociotechnical systems that must be modelled. But the choice for agent-based models does not mean a rejection of other approaches because they can be accommodated within the agent-based framework. We conclude with practical guidelines.

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Section: Agent-based Modellingmentioning

confidence: 99%

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Hoekstra

Steinbuch

Verbong³

2017

Complexity

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“…Nanotherapeutic response can be simulated by describing tumors as physical objects subject to physical laws, such as conservation of mass and momentum. Extensive modeling work studying tumor growth as well as angiogenesis has been published in recent years, as reviewed in [119–137] and associated references, with a subset of studies focusing on nanotherapy [24,27,138–148]. For example, the interaction between NP vascular extravasation, uptake, and distribution with heterogeneous tumor interstitial, vascular, and lymphatic conditions was studied in [140], finding that an elevated interstitial hydraulic conductivity leads to low NP accumulation regardless of NP capacity for vascular extravasation.…”

Section: Evaluation Of Nanotherapy Using Computational Modelingmentioning

confidence: 99%

Evaluation of uptake and distribution of gold nanoparticles in solid tumors

2015

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Although nanotherapeutics offer a targeted and potentially less toxic alternative to systemic chemotherapy in cancer treatment, nanotherapeutic transport is typically hindered by abnormal characteristics of tumor tissue. Once nanoparticles targeted to tumor cells arrive in the circulation of tumor vasculature, they must extravasate from irregular vessels and diffuse through the tissue to ideally reach all malignant cells in cytotoxic concentrations. The enhanced permeability and retention effect can be leveraged to promote extravasation of appropriately sized particles from tumor vasculature; however, therapeutic success remains elusive partly due to inadequate intra-tumoral transport promoting heterogeneous nanoparticle uptake and distribution. Irregular tumor vasculature not only hinders particle transport but also sustains hypoxic tissue kregions with quiescent cells, which may be unaffected by cycle-dependent chemotherapeutics released from nanoparticles and thus regrow tumor tissue following nanotherapy. Furthermore, a large proportion of systemically injected nanoparticles may become sequestered by the reticuloendothelial system, resulting in overall diminished efficacy. We review recent work evaluating the uptake and distribution of gold nanoparticles in pre-clinical tumor models, with the goal to help improve nanotherapy outcomes. We also examine the potential role of novel layered gold nanoparticles designed to address some of these critical issues, assessing their uptake and transport in cancerous tissue.

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“…7,14,53 Computational modeling and systems biology approaches can help integrate large sets of data and provide additional biological insights, e.g. by using models to predict optimal conditions or to control model behavior.…”

Section: Introductionmentioning

confidence: 99%

Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens

et al. 2017

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Introduction Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Here we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens. Methods We define two different single-antibiotic treatments to compare the efficiency and accuracy in predicting optimal treatment regimens of two optimization algorithms: genetic algorithms (GA) and surrogate-assisted optimization through radial basis function (RBF) networks. We also illustrate the use of RBF networks to optimize double-antibiotic treatments. Results We found that while GAs can locate optimal treatment regimens more accurately, RBF networks provide a more practical strategy to TB treatment optimization with fewer simulations, and successfully estimated optimal double-antibiotic treatment regimens. Conclusions Our results indicate surrogate-assisted optimization can locate optimal TB treatment regimens from a larger set of antibiotics, doses and schedules, and could be applied to solve optimization problems in other areas of research using systems biology approaches. Our findings have important implications for the treatment of diseases like TB that have lengthy protocols or for any disease that requires multiple drugs.

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Integrated PK-PD and agent-based modeling in oncology

Cited by 55 publications

References 93 publications

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

Evaluation of uptake and distribution of gold nanoparticles in solid tumors

Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens

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