A systems pharmacology model for inflammatory bowel disease

Motivation The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets. Results In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s

Systems Pharmacology in Modelling Complex Scenarios: Opportunities and Challenges

The treatment of complex diseases represents currently a major challenge. In this context systems pharmacology (SP) is an emergent discipline that provides an opportunity to get deeper insights in this type of diseases by integrating different areas of knowledge including biology, pharmacology, pharmacometrics, statistics, and computational modelling. Nowadays, SP has relevance throughout the entire process of drug development, since it has been able to show that systems computational models allow increasing the understanding of different mechanisms of action and regulatory processes, demonstrating their usefulness for organizing large biological data sets and extracting significant information. These models are useful for (i) the identification and validation of new therapeutic targets, (ii) the discovery of new biomarkers, (iii) patient stratification, (iv) dose individualization, (v) the identification of new sources of variability and (vi) the prediction of toxicity and adverse effects. In this thesis, different types of mechanistic models were explored showing its capabilities and drawbacks. The Introduction section provides a brief description and uses of systems pharmacology models. Chapter 1 presents a systems pharmacology model for Systemic Lupus Erythematosus. This model, based in Boolean equations, allows identifying different patient subpopulations according to their molecular alterations, predicting the variability in the progression of the disease and designing individualized drug therapies with a high likelihood of success. In Chapter 2 two systems pharmacology models for coagulation cascade published in the literature are implemented and reproduced. Then, experimental data obtained from the literature was incorporated in both models to reproduce coagulation tests. Finally, a semi-mechanistic pharmacokinetic/pharmacodynamic (PKPD) model was built to fit this experimental data. Chapter 1 and Chapter 2 provide an overview of the characteristics of the disease or biological system and their therapeutic alternatives as well as the description of the information and methodology used to develop the SP models, together with the corresponding results. On the other hand, Chapter 3 discusses the impact of considering exposure at the target site with regard to systemic concentrations, a piece of information that usually remains forgotten in mechanistic modelling. The General Discussion highlights the most relevant aspects of the three chapters, followed by the Conclusions section, which summarizes the main findings of this thesis. Finally, in the Annex, an article of a systems pharmacology model developed for inflammatory bowled disease, recently published in PLOS ONE journal is enclosed

Systems Pharmacology in Modelling Complex Scenarios: Opportunities and Challenges

The treatment of complex diseases represents currently a major challenge. In this context systems pharmacology (SP) is an emergent discipline that provides an opportunity to get deeper insights in this type of diseases by integrating different areas of knowledge including biology, pharmacology, pharmacometrics, statistics, and computational modelling. Nowadays, SP has relevance throughout the entire process of drug development, since it has been able to show that systems computational models allow increasing the understanding of different mechanisms of action and regulatory processes, demonstrating their usefulness for organizing large biological data sets and extracting significant information. These models are useful for (i) the identification and validation of new therapeutic targets, (ii) the discovery of new biomarkers, (iii) patient stratification, (iv) dose individualization, (v) the identification of new sources of variability and (vi) the prediction of toxicity and adverse effects. In this thesis, different types of mechanistic models were explored showing its capabilities and drawbacks. The Introduction section provides a brief description and uses of systems pharmacology models. Chapter 1 presents a systems pharmacology model for Systemic Lupus Erythematosus. This model, based in Boolean equations, allows identifying different patient subpopulations according to their molecular alterations, predicting the variability in the progression of the disease and designing individualized drug therapies with a high likelihood of success. In Chapter 2 two systems pharmacology models for coagulation cascade published in the literature are implemented and reproduced. Then, experimental data obtained from the literature was incorporated in both models to reproduce coagulation tests. Finally, a semi-mechanistic pharmacokinetic/pharmacodynamic (PKPD) model was built to fit this experimental data. Chapter 1 and Chapter 2 provide an overview of the characteristics of the disease or biological system and their therapeutic alternatives as well as the description of the information and methodology used to develop the SP models, together with the corresponding results. On the other hand, Chapter 3 discusses the impact of considering exposure at the target site with regard to systemic concentrations, a piece of information that usually remains forgotten in mechanistic modelling. The General Discussion highlights the most relevant aspects of the three chapters, followed by the Conclusions section, which summarizes the main findings of this thesis. Finally, in the Annex, an article of a systems pharmacology model developed for inflammatory bowled disease, recently published in PLOS ONE journal is enclosed

Boolean functions (BF) of the IBD model for the internal and the output nodes.

<p>Boolean functions (BF) of the IBD model for the internal and the output nodes.</p

Comparison of MMPs expression after the simulation in IBD simulated individuals of different therapies.

<p>Simulated therapies: Anti-TNFα, GMA therapy (equivalent of knock out our MACR node), anti-IL17, human recombinant IL10 (rhulL-10), anti-IFNγ, anti-IL2 and anti-IL12-IL23. Comparing with untreated simulation, we can see a 30.7%, a 27.1%, a 31.9% and a 4.1% decrease in the MMPs expression simulating anti-TNFα, GMA therapy, anti-IL2 and anti-IL12-IL23 respectively. There is no major change in MMPs expression for the two which failed in clinical trials anti-IL17 (a 6.5% decrease) and human recombinant IL10 (a 3.2% decrease). Otherwise, anti-IFNγ therapy simulation shows an increase in MMPs expression of 16.0% compared to Untreated.</p

Graphical representation of IBD model.

<p>Nodes represent cells, proteins, bacterial antigens, receptors or ligands. Bacterial antigens trigger the IBD immune response through activation of different pattern recognition receptors (TLR2, TLR4 and NOD2) starting the innate and adaptive immune response. Reprinted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192949#pone.0192949.ref036" target="_blank">36</a>] under a CC BY license, with permission from the organizers of the 2016 International Conference on Systems Biology, original copyright 2016.</p

Boolean functions (BF) of the IBD model to simulate the initial conditions.

<p>Boolean functions (BF) of the IBD model to simulate the initial conditions.</p

IBD network perturbation analysis and clustering.

<p>The heatmap indicates the effect of single blockage of each node (columns) in every network node (rows). The colour in each cell corresponds to the Perturbation Index (PI) of the nodes. When there is no change in the expression of the node, the cells of the heatmap would be black, having a value between 0.8 and 1.25 in their PIs. Otherwise, when the perturbation causes an overexpression in a node, the cell in the heatmap would be orange coloured, with PIs values greater than 1.25. On the contrary, a value of 0.8 or smaller, blue colour, indicates that the perturbation causes a downregulation of the node. The numeric scale in the legend represents different values of the nodes PI under different perturbations. Nodes that induce similar alterations are hierarchically clustered.</p

A systems pharmacology model for inflammatory bowel disease

Motivation The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets. Results In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s

Expression of network nodes in IBD patients.

<p>Expression of network nodes in IBD patients.</p