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Hamis, S. J.

Publications and source records attributed to Hamis, S. J..

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Combining Hypoxia-Activated Prodrugs and Radiotherapy in silico: Impact of Treatment Scheduling and the Intra-Tumoural Oxygen Landscape

Hypoxia-activated prodrugs (HAPs) present a conceptually elegant approach to not only overcome, but better yet, exploit intra-tumoural hypoxia. Despite being successful in vitro and in vivo, HAPs are yet to achieve successful results in clinical settings. It has been hypothesised that this lack of clinical success can, in part, be explained by the insufficiently stringent clinical screening selection of determining which tumours are suitable for HAP treatments. Taking a mathematical modelling approach, we investigate how tumour properties and HAP-radiation scheduling influence treatment outcomes in simulated tumours. The following key results are demonstrated in silico: (i) HAP and ionising radiation (IR) monotherapies may attack tumours in dissimilar, and complementary, ways. (ii) HAP-IR scheduling may impact treatment efficacy. (iii) HAPs may function as IR treatment intensifiers. (iv) The spatio-temporal intra-tumoural oxygen landscape may impact HAP efficacy. Our in silico framework is based on an on-lattice, hybrid, multiscale cellular automaton spanning three spatial dimensions. The mathematical model for tumour spheroid growth is parameterised by multicellular tumour spheroid (MCTS) data. Author SummaryWhen cancer patients present with solid tumours, the tumours often contain regions that are oxygen-deprived or, in other words, hypoxic. Hypoxic cancer cells are more resistant to conventional anti-cancer therapies, such as chemotherapy and radiotherapy, and therefore tumour hypoxia may complicate treatments. Hypoxia-activated prodrugs constitute a conceptually elegant approach to not only overcome, but better yet, exploit tumour hypoxia. Hypoxia-activated prodrugs are drugs that act as Trojan horses, they are theoretically harmless vehicles that are converted into warheads when they reach their targets: hypoxic tumour cells. Despite being conceptually clever and successful in experimental settings, hypoxia-activated prodrugs are yet to achieve successful results in clinical trials. It has been hypothesised that this lack of clinical success can, in part, be explained by an insufficiently stringent clinical screening selection of determining which tumours are suitable for hypoxia-activated prodrug treatments. In this article, we investigate how simulated tumours with different oxygen landscapes respond to anti-cancer treatments that include hypoxia-activated prodrugs, either alone or in combination with radiotherapy. Our simulation framework is based on a mathematical model that describes how individual cancer cells in a tumour divide and respond to treatments. We demonstrate that the efficacy of hypoxia-activated prodrugs depends on both the treatment scheduling, and on the oxygen landscape of the specific, simulated tumour.

cancer biology

Targeting Cellular DNA Damage Responses: Predicting in vivo treatment responses using an in vitro-calibrated agent-based mathematical model.

We combine a systems pharmacology approach with an agent-based modelling approach to simulate LoVo cells subjected to AZD6738, an ATR (ataxia telangiectasia mutated and rad3-related kinase) inhibiting anti-cancer drug that can hinder tumour proliferation by targeting cellular DNA damage responses. The agent-based model used in this study is governed by a set of empirically observable rules. By adjusting only the rules when moving between monolayer and multi-cellular tumour spheroid simulations, whilst keeping the fundamental mathematical model and parameters intact, the agent-based model is first parameterised by monolayer in vitro data and is thereafter used to simulate treatment responses in in vitro tumour spheroids subjected to dynamic drug delivery. Spheroid simulations are subsequently compared to in vivo data from xenografts in mice. The spheroid simulations are able to capture the dynamics of in vivo tumour growth and regression for approximately eight days post tumour injection. Translating quantitative information between in vitro and in vivo research remains a scientifically and financially challenging step in preclinical drug development processes. However, well-developed in silico tools can be used to facilitate this in vitro to in vivo translation, and in this article we exemplify how data-driven, agent-based models can be used to bridge the gap between in vitro and in vivo research. We further highlight how agent-based models, that are currently underutilised in pharmaceutical contexts, can be used in preclinical drug development.

cancer biology