Modeling of celiac disease immune response and the therapeutic effect of potential drugs

BACKGROUND: Celiac disease (CD) is an autoimmune disorder that occurs in genetically predisposed people and is caused by a reaction to the gluten protein found in wheat, which leads to intestinal villous atrophy. Currently there is no drug for treatment of CD. The only known treatment is lifelong gluten-free diet. The main aim of this work is to develop a mathematical model of the immune response in CD patients and to predict the efficacy of a transglutaminase-2 (TG-2) inhibitor as a potential drug for treatment of CD. RESULTS: A thorough analysis of the developed model provided the following results: 1. TG-2 inhibitor treatment leads to insignificant decrease in antibody levels, and hence remains higher than in healthy individuals. 2. TG-2 inhibitor treatment does not lead to any significant increase in villous area. 3. The model predicts that the most effective treatment of CD would be the use of gluten peptide analogs that antagonize the binding of immunogenic gluten peptides to APC. The model predicts that the treatment of CD by such gluten peptide analogs can lead to a decrease in antibody levels to those of normal healthy people, and to a significant increase in villous area. CONCLUSIONS: The developed mathematical model of immune response in CD allows prediction of the efficacy of TG-2 inhibitors and other possible drugs for the treatment of CD: their influence on the intestinal villous area and on the antibody levels. The model also allows to understand what processes in the immune response have the strongest influence on the efficacy of different drugs. This model could be applied in the pharmaceutical R&D arena for the design of drugs against autoimmune small intestine disorders and on the design of their corresponding clinical trials

Best practices to maximize the use and reuse of quantitative and systems pharmacology models: recommendations from the United Kingdom quantitative and systems pharmacology network

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment

Novel TOPP descriptors in 3D-QSAR analysis of apoptosis inducing 4-aryl-4H-chromenes: comparison versus other 2D- and 3D-descriptors

Novel 3D-descriptors using Triplets Of Pharmacophoric Points (TOPP) were evaluated in QSAR-studies on 80 apoptosis-inducing 4-aryl-4H-chromenes. A predictive QSAR model was obtained using PLS, confirmed by means of internal and external validations. Performance of the TOPP approach was compared with that of other 2D- and 3D-descriptors; statistical analysis indicates that TOPP descriptors perform best. A ranking of TOPP > GRIND > BCI 4096 = ECFP > FCFP > GRID-GOLPE >> DRAGON >>> MDL 166 was achieved. Finally, in a 'consensus' analysis predictions obtained using the single methods were compared with an average approach using six out of eight methods. The use of the average is statistically superior to the single methods. Beyond it, the use of several methods can help to easily investigate the presence/absence of outliers according to the 'consensus' of the predicted values: agreement among all the methods indicates a precise prediction, whereas large differences between predicted values (for the same compounds by different methods) would demand caution when using such predictions. (C) 2007 Elsevier Ltd. All rights reserved

Quantitative systems pharmacology and machine learning: a match made in heaven or hell?

As pharmaceutical development moves from early stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realising the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modelling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data is highlighted in informing the initial use of ML or mechanistic Quantitative Systems Pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies, to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. We consider when best to apply Machine Learning (ML) and mechanistic Quantitative Systems Pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline

Schematic illustration of the process of applying ASPASIA to an SBML model.

<p>Using an initial SBML model as an input, ASPASIA generates a set of values as specified in a settings file, and new SBML files with parameters set to different values are created. Each model is then solved for sufficient time for a steady state to be reached and the resulting baseline values for all species supplied to ASPASIA. Then a new set of SBML files is created, and the same intervention, represented by a discrete alteration of a parameter or initial species concentration, added to all of them. The resulting files are then solved to steady state again and the effects of the intervention across the whole set of parameters and parameter values can be analysed. Black boxes represent processes that are performed only once, and white boxes represent processes that must be performed once for each model generated in step 3.</p

<i>In silico</i> experimentation reveals the parameters that control receptor X expression before and after polarisation with cytokines.

<p>ASPASIA was used to explore the sensitivity of the level of expression of the hypothetical receptor X following exposure to different cytokines. (A-C) Partial rank correlation coefficients (PRCC) for the correlation between receptor X expression and all parameters involved in phenotype switching were calculated for all models where receptor X acted to promote T-bet expression. PRCCs were calculated before polarisation (A), following polarisation with C₁₇ (B) and after C_X had been introduced (C). (D) PRCC for the correlation between the time taken for the phenotype switch to occur and all parameters involved in phenotype switching for the models where a phenotype switch took place. Details and definitions of all parameters are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005351#pcbi.1005351.s004" target="_blank">S3 Fig</a>.</p

ASPASIA-generated model reflects observed biological behaviours of Th17-polarised CD4⁺ T cells.

<p>From 200 ASPASIA-generated models, a single model was selected that best captured biological behaviours. Shown are concentration of polarisating cytokines (left panels) and levels of transcription factor mRNA (right panels) in (A) the absence of type-1 polarising cytokines (C₁) and type-17 polarising cytokines (C₁₇), (B) following stimulation with C₁₇, and (C) following subsequent restimulation with C₁. Black lines represent C₁₇ (left panels) and ROR<i>γ</i>t (right panels), red dashed lines represent C₁ (left panels) and T-bet (right panels).</p

Phenotype switch is robust to changes in concentration of cytokine X.

<p>Models were polarised to a Th17 state as previously described before 10 varying concentrations of cytokine X were simulated in order to drive a phenotype switch. (A) Dynamics of cytokine X in each of the 10 models. (B) Dynamics of ROR<i>γ</i>t (black) and T-bet (red, dashed) in each of the 10 models.</p

Experimentation using ASPASIA suggests that hypothetical receptor X drives phenotype switching in Th17 cells by promoting T-bet.

<p>(A) Representative profile of C₁₇ (black), C₁ (red, dashed) and C_X (blue, dashed) used to drive polarisation and phenotype switching. (B) Left panel: ROR<i>γ</i>t (black) and T-bet (red, dashed) expression in one representative model where C_X acts by inhibiting ROR<i>γ</i>t. Right panel: Number of models that have switched, or not switched under these conditions. (C) Left panel: ROR<i>γ</i>t (black) and T-bet (red, dashed) expression in a model with the same parameters as shown in left panel of B but with C_X acting by promoting T-bet resulting in a transition through a double-positive phase to an ex-Th17 state. Right panel: Number of models that have switched, or not switched under these conditions.</p

Current practices for QSP model assessment: an IQ consortium survey.

Quantitative Systems Pharmacology (QSP) modeling is increasingly applied in the pharmaceutical industry to influence decision making across a wide range of stages from early discovery to clinical development to post-marketing activities. Development of standards for how these models are constructed, assessed, and communicated is of active interest to the modeling community and regulators but is complicated by the wide variability in the structures and intended uses of the underlying models and the diverse expertise of QSP modelers. With this in mind, the IQ Consortium conducted a survey across the pharmaceutical/biotech industry to understand current practices for QSP modeling. This article presents the survey results and provides insights into current practices and methods used by QSP practitioners based on model type and the intended use at various stages of drug development. The survey also highlights key areas for future development including better integration with statistical methods, standardization of approaches towards virtual populations, and increased use of QSP models for late-stage clinical development and regulatory submissions