In Silico screening of nonsteroidal anti-inflammatory drugs and their combined action on Prostaglandin H Synthase-1

The detailed kinetic model of Prostaglandin H Synthase-1 (PGHS-1) was applied to in silico screening of dose-dependencies for the different types of nonsteroidal anti-inflammatory drugs (NSAIDs), such as: reversible/irreversible, nonselective/selective to PGHS-1/PGHS-2 and time dependent/independent inhibitors (aspirin, ibuprofen, celecoxib, etc.) The computational screening has shown a significant variability in the IC50s of the same drug, depending on different in vitro and in vivo experimental conditions. To study this high heterogeneity in the inhibitory effects of NSAIDs, we have developed an in silico approach to evaluate NSAID action on targets under different PGHS-1 microenvironmental conditions, such as arachidonic acid, reducing cofactor, and peroxide concentrations. The designed technique permits translating the drug IC50, obtained in one experimental setting to another, and predicts in vivo inhibitory effects based on the relevant in vitro data. For the aspirin case, we elucidated the mechanism underlying the enhancement and reduction (aspirin resistance) of its efficacy, depending on PGHS-1 microenvironment in in vitro/in vivo experimental settings. We also present the results of the in silico screening of the combined action of sets of two NSAIDs (aspirin with ibuprofen, aspirin with celecoxib), and study the mechanism of the experimentally observed effect of the suppression of aspirin-mediated PGHS-1 inhibition by selective and nonselective NSAIDs. Furthermore, we discuss the applications of the obtained results to the problems of standardization of NSAID test assay, dependence of the NSAID efficacy on cellular environment of PGHS-1, drug resistance, and NSAID combination therapy

Customizing the therapeutic response of signaling networks to promote antitumor responses by drug combinations

Drug resistance, de novo and acquired, pervades cellular signaling networks (SNs) from one signaling motif to another as a result of cancer progression and/or drug intervention. This resistance is one of the key determinants of efficacy in targeted anti-cancer drug therapy. Although poorly understood, drug resistance is already being addressed in combination therapy by selecting drug targets where SN sensitivity increases due to combination components or as a result of de novo or acquired mutations. Additionally, successive drug combinations have shown low resistance potential. To promote a rational, systematic development of combination therapies, it is necessary to establish the underlying mechanisms that drive the advantages of combination therapies, and design methods to determine drug targets for combination regimens. Based on a joint systems analysis of cellular SN response and its sensitivity to drug action and oncogenic mutations, we describe an in silico method to analyze the targets of drug combinations. Our method explores mechanisms of sensitizing the SN through a combination of two drugs targeting vertical signaling pathways. We propose a paradigm of SN response customization by one drug to both maximize the effect of another drug in combination and promote a robust therapeutic response against oncogenic mutations. The method was applied to customize the response of the ErbB/PI3K/PTEN/AKT pathway by combination of drugs targeting HER2 receptors and proteins in the down-stream pathway. The results of a computational experiment showed that the modification of the SN response from hyperbolic to smooth sigmoid response by manipulation of two drugs in combination leads to greater robustness in therapeutic response against oncogenic mutations determining cancer heterogeneity. The application of this method in drug combination co-development suggests a combined evaluation of inhibition effects together with the capability of drug combinations to suppress resistance mechanisms before they become clinically manifest

Kinetic modelling of in vitro data of PI3K, mTOR1, PTEN enzymes and on-target inhibitors Rapamycin, BEZ235, and LY294002

The phosphatidylinositide 3-kinases (PI3K) and mammalian target of rapamycin-1 (mTOR1) are two key targets for anti-cancer therapy. Predicting the response of the PI3K/AKT/mTOR1 signalling pathway to targeted therapy is made difficult because of network complexities. Systems biology models can help explore those complexities but the value of such models is dependent on accurate parameterisation. Motivated by a need to increase accuracy in kinetic parameter estimation, and therefore the predictive power of the model, we present a framework to integrate kinetic data from enzyme assays into a unified enzyme kinetic model. We present exemplar kinetic models of PI3K and mTOR1, calibrated on in vitro enzyme data and founded on Michaelis-Menten (MM) approximation. We describe the effects of an allosteric mTOR1 inhibitor (Rapamycin) and ATP-competitive inhibitors (BEZ2235 and LY294002) that show dual inhibition of mTOR1 and PI3K. We also model the kinetics of phosphatase and tensin homolog (PTEN), which modulates sensitivity of the PI3K/AKT/mTOR1 pathway to these drugs. Model validation with independent data sets allows investigation of enzyme function and drug dose dependencies in a wide range of experimental conditions. Modelling of the mTOR1 kinetics showed that Rapamycin has an IC50 independent of ATP concentration and that it is a selective inhibitor of mTOR1 substrates S6K1 and 4EBP1: it retains 40% of mTOR1 activity relative to 4EBP1 phosphorylation and inhibits completely S6K1 activity. For the dual ATP-competitive inhibitors of mTOR1 and PI3K, LY294002 and BEZ235, we derived the dependence of the IC50 on ATP concentration that allows prediction of the IC50 at different ATP concentrations in enzyme and cellular assays. Comparison of the drug effectiveness in enzyme and cellular assays showed that some features of these drugs arise from signalling modulation beyond the on-target action and MM approximation and require a systems-level consideration of the whole PI3K/PTEN/AKT/mTOR1 network in order to understand mechanisms of drug sensitivity and resistance in different cancer cell lines. We suggest that using these models in systems biology investigation of the PI3K/AKT/mTOR1 signalling in cancer cells can bridge the gap between direct drug target action and the therapeutic response to these drugs and their combinations

Compensatory effects in the PI3K/PTEN/AKT signaling network following receptor tyrosine kinase inhibition

Overcoming de novo and acquired resistance to anticancer drugs that target signaling networks is a formidable challenge for drug design and effective cancer therapy. Understanding the mechanisms by which this resistance arises may offer a route to addressing the insensitivity of signaling networks to drug intervention and restore the efficacy of anticancer therapy. Extending our recent work identifying PTEN as a key regulator of Herceptin sensitivity, we present an integrated theoretical and experimental approach to study the compensatory mechanisms within the PI3K/PTEN/AKT signaling network that afford resistance to receptor tyrosine kinase (RTK) inhibition by anti-HER2 monoclonal antibodies. In a computational model representing the dynamics of the signaling network, we define a single control parameter that encapsulates the balance of activities of the enzymes involved in the PI3K/PTEN/AKT cycle. By varying this control parameter we are able to demonstrate both distinct dynamic regimes of behavior of the signaling network and the transitions between those regimes. We demonstrate resistance, sensitivity, and suppression of RTK signals by the signaling network. Through model analysis we link the sensitivity-to-resistance transition to specific compensatory mechanisms within the signaling network. We study this transition in detail theoretically by variation of activities of PTEN, PI3K, AKT enzymes, and use the results to inform experiments that perturb the signaling network using combinatorial inhibition of RTK, PTEN, and PI3K enzymes in human ovarian carcinoma cell lines. We find good alignment between theoretical predictions and experimental results. We discuss the application of the results to the challenges of hypersensitivity of the signaling network to RTK signals, suppression of drug resistance, and efficacy of drug combinations in anticancer therapy

Advocating the need of a systems biology approach for personalised prognosis and treatment of B-CLL patients

The clinical course of B-CLL is heterogeneous. This heterogeneity leads to a clinical dilemma: can we identify those patients who will benefit from early treatment and predict the survival? In recent years, mathematical modelling has contributed significantly in understanding the complexity of diseases. In order to build a mathematical model for determining prognosis of B-CLL one has to identify, characterise and quantify key molecules involved in the disease. Here we discuss the need and role of mathematical modelling in predicting B-CLL disease pathogenesis and suggest a new systems biology approach for a personalised therapy of B-CLL patients

Quantitative analysis of NRF2 pathway reveals key elements of the regulatory circuits underlying antioxidant response and proliferation of ovarian cancer cells

Cells are constantly exposed to Reactive Oxygen Species (ROS) produced both endogenously to meet physiological requirements and from exogenous sources. While endogenous ROS are considered as important signalling molecules, high uncontrollable ROS are detrimental. It is unclear how cells can achieve a balance between maintaining physiological redox homeostasis and robustly activate the antioxidant system to remove exogenous ROS. We have utilised a Systems Biology approach to understand how this robust adaptive system fulfils homeostatic requirements of maintaining steady-state ROS and growth rate, while undergoing rapid readjustment under challenged conditions. Using a panel of human ovarian and normal cell lines, we experimentally quantified and established interrelationships between key elements of ROS homeostasis. The basal levels of NRF2 and KEAP1 were cell line specific and maintained in tight correlation with their growth rates and ROS. Furthermore, perturbation of this balance triggered cell specific kinetics of NRF2 nuclear–cytoplasmic relocalisation and sequestration of exogenous ROS. Our experimental data were employed to parameterise a mathematical model of the NRF2 pathway that elucidated key response mechanisms of redox regulation and showed that the dynamics of NRF2-H2O2 regulation defines a relationship between half-life, total and nuclear NRF2 level and endogenous H2O2 that is cell line specific

Infrared laser pulse triggers increased singlet oxygen production in tumour cells

Photodynamic therapy (PDT) is a technique developed to treat the ever-increasing global incidence of cancer. This technique utilises singlet oxygen (1O2) generation via a laser excited photosensitiser (PS) to kill cancer cells. However, prolonged sensitivity to intensive light (6–8 weeks for lung cancer), relatively low tissue penetration by activating light (630 nm up to 4 mm), and the cost of PS administration can limit progressive PDT applications. The development of quantum-dot laser diodes emitting in the highest absorption region (1268 nm) of triplet oxygen (3O2) presents the possibility of inducing apoptosis in tumour cells through direct 3O2 → 1O2 transition. Here we demonstrate that a single laser pulse triggers dose-dependent 1O2 generation in both normal keratinocytes and tumour cells and show that tumour cells yield the highest 1O2 far beyond the initial laser pulse exposure. Our modelling and experimental results support the development of direct infrared (IR) laser-induced tumour treatment as a promising approach in tumour PDT

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualisation toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery

SiViT

The Signalling Visualisation Toolkit (SiViT), is a cancer cell signalling network visualisation tool that provides an intuitive games-based real-time interactive interface to models of cancer cell dynamics. SiViT provides a games-technology approach to unlocking the complexities of cancer cells to anti-cancer drugs. SiViT can load a wide range of biological models in the form of SBML, but the work focuses on the Abertay Cancer cell model. SiViT allows clinicians and biologists to directly interact with the cancer cell model, introducing drugs or mutations. SiViT animates the effects of these introductions on the internal elements of the cell pathways and nodes. Showing how the cell network responds to these introductions such as increased or decreased activity, or re-rerouting to bypass the effected region of the network. Indicating the effectiveness of drugs, drug resistance and suggesting area for further experimentation.Offering bi-directional interaction and explorations. With drug inserts updating the model in real-time. Timings of drug introduction or mutations is crucial and SiViT allows for changes to occur at specific timings and model the result. This is critical in the exploration of combination therapees. SiViT follows the visual guidelines from existing literature on cell networking and dynamics, with ongoing HCI work being conducted to ensure information is visualised accessibly. SiViT was part of the UKRI Main Exhibition Stand at the American Association for the Advancement of Science 2019 conference. SiViT was the catalyst for a new 4-year project led by Macmillan Cancer Support on optimising health and social care service provision through interactive network visualisation.<br/