6 research outputs found

    QSPcc reduces bottlenecks in computational model simulations

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    Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances

    A Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat

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    Gaucher's disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first-line SRT approved for GD1, on treatment-na\uefve or patients with ERT-stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype-phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population

    Effect of barrier microbes on organ-based inflammation

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    The prevalence and incidence of chronic inflammatory disorders, including allergies and asthma, as well as inflammatory bowel disease, remain on the increase. Microbes are among the environmental factors that play an important role in shaping normal and pathologic immune responses. Several concepts have been put forward to explain the effect of microbes on the development of these conditions, including the hygiene hypothesis and the microbiota hypothesis. Recently, the dynamics of the development of (intestinal) microbial colonization, its effect on innate and adaptive immune responses (homeostasis), and the role of environmental factors, such as nutrition and others, have been extensively investigated. Furthermore, there is now increasing evidence that a qualitative and quantitative disturbance in colonization (dysbiosis) is associated with dysfunction of immune responses and development of various chronic inflammatory disorders. In this article the recent epidemiologic, clinical, and experimental evidence for this interaction is discussed
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