A robust computational pipeline for model-based and data-driven phenotype clustering.

MOTIVATION: Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease. RESULTS: We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show. AVAILABILITY AND IMPLEMENTATION: The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Compartmentalized PDE4A5 Signaling Impairs Hippocampal Synaptic Plasticity and Long-Term Memory.

UNLABELLED: Alterations in cAMP signaling are thought to contribute to neurocognitive and neuropsychiatric disorders. Members of the cAMP-specific phosphodiesterase 4 (PDE4) family, which contains >25 different isoforms, play a key role in determining spatial cAMP degradation so as to orchestrate compartmentalized cAMP signaling in cells. Each isoform binds to a different set of protein complexes through its unique N-terminal domain, thereby leading to targeted degradation of cAMP in specific intracellular compartments. However, the functional role of specific compartmentalized PDE4 isoforms has not been examined in vivo Here, we show that increasing protein levels of the PDE4A5 isoform in mouse hippocampal excitatory neurons impairs a long-lasting form of hippocampal synaptic plasticity and attenuates hippocampus-dependent long-term memories without affecting anxiety. In contrast, viral expression of a truncated version of PDE4A5, which lacks the unique N-terminal targeting domain, does not affect long-term memory. Further, overexpression of the PDE4A1 isoform, which targets a different subset of signalosomes, leaves memory undisturbed. Fluorescence resonance energy transfer sensor-based cAMP measurements reveal that the full-length PDE4A5, in contrast to the truncated form, hampers forskolin-mediated increases in neuronal cAMP levels. Our study indicates that the unique N-terminal localization domain of PDE4A5 is essential for the targeting of specific cAMP-dependent signaling underlying synaptic plasticity and memory. The development of compounds to disrupt the compartmentalization of individual PDE4 isoforms by targeting their unique N-terminal domains may provide a fruitful approach to prevent cognitive deficits in neuropsychiatric and neurocognitive disorders that are associated with alterations in cAMP signaling. SIGNIFICANCE STATEMENT: Neurons exhibit localized signaling processes that enable biochemical cascades to be activated selectively in specific subcellular compartments. The phosphodiesterase 4 (PDE4) family coordinates the degradation of cAMP, leading to the local attenuation of cAMP-dependent signaling pathways. Sleep deprivation leads to increased hippocampal expression of the PDE4A5 isoform. Here, we explored whether PDE4A5 overexpression mimics behavioral and synaptic plasticity phenotypes associated with sleep deprivation. Viral expression of PDE4A5 in hippocampal neurons impairs long-term potentiation and attenuates the formation of hippocampus-dependent long-term memories. Our findings suggest that PDE4A5 is a molecular constraint on cognitive processes and may contribute to the development of novel therapeutic approaches to prevent cognitive deficits in neuropsychiatric and neurocognitive disorders that are associated with alterations in cAMP signaling.

Cross-regulation of Phosphodiesterase 1 and Phosphodiesterase 2 Activities Controls Dopamine-mediated Striatal α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic Acid (AMPA) Receptor Trafficking.

Dopamine, a key striatal neuromodulator, increases synaptic strength by promoting surface insertion and/or retention of AMPA receptors (AMPARs). This process is mediated by the phosphorylation of the GluA1 subunit of AMPAR by cyclic nucleotide-dependent kinases, making cyclic nucleotide phosphodiesterases (PDEs) potential regulators of synaptic strength. In this study, we examined the role of phosphodiesterase 2 (PDE2), a medium spiny neuron-enriched and cGMP-activated PDE, in AMPAR trafficking. We found that inhibiting PDE2 resulted in enhancement of dopamine-induced surface GluA1 expression in dopamine receptor 1-expressing medium spiny neurons. Using pharmacological and genetic approaches, we found that inhibition of PDE1 resulted in a decrease in surface AMPAR levels because of the allosteric activation of PDE2. The cross-regulation of PDE1 and PDE2 activities results in counterintuitive control of surface AMPAR expression, making it possible to regulate the directionality and magnitude of AMPAR trafficking.

Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis.

Lysosomal storage diseases (LSDs) are characterized by the abnormal accumulation of substrates in tissues due to the deficiency of lysosomal proteins. Among the numerous clinical manifestations, chronic inflammation has been consistently reported for several LSDs. However, the molecular mechanisms involved in the inflammatory response are still not completely understood. In this study, we performed text-mining and systems biology analyses to investigate the inflammatory signals in three LSDs characterized by sphingolipid accumulation: Gaucher disease, Acid Sphingomyelinase Deficiency (ASMD), and Fabry Disease. We first identified the cytokines linked to the LSDs, and then built on the extracted knowledge to investigate the inflammatory signals. We found numerous transcription factors that are putative regulators of cytokine expression in a cell-specific context, such as the signaling axes controlled by STAT2, JUN, and NR4A2 as candidate regulators of the monocyte Gaucher disease cytokine network. Overall, our results suggest the presence of a complex inflammatory signaling in LSDs involving many cellular and molecular players that could be further investigated as putative targets of anti-inflammatory therapies.

QSPcc reduces bottlenecks in computational model simulations.

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.

History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications.

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.

A novel secreted-cAMP pathway inhibits pulmonary hypertension via a feed-forward mechanism.

AIMS: Cyclic adenosine monophosphate (cAMP) is the predominant intracellular second messenger that transduces signals from Gs-coupled receptors. Intriguingly, there is evidence from various cell types that an extracellular cAMP pathway is active in the extracellular space. Herein, we investigated the role of extracellular cAMP in the lung and examined whether it may act on pulmonary vascular cell proliferation and pulmonary vasculature remodelling in the pathogenesis of pulmonary hypertension (PH). METHODS AND RESULTS: The expression of cyclic AMP-metabolizing enzymes was increased in lungs from patients with PH as well as in rats treated with monocrotaline and mice exposed to Sugen/hypoxia. We report that inhibition of the endogenous extracellular cAMP pathway exacerbated Sugen/hypoxia-induced lung remodelling. We found that application of extracellular cAMP induced an increase in intracellular cAMP levels and inhibited proliferation and migration of pulmonary vascular cells in vitro. Extracellular cAMP infusion in two in vivo PH models prevented and reversed pulmonary and cardiac remodelling associated with PH. Using protein expression analysis along with luciferase assays, we found that extracellular cAMP acts via the A2R/PKA/CREB/p53/Cyclin D1 pathway. CONCLUSIONS: Taken together, our data reveal the presence of an extracellular cAMP pathway in pulmonary arteries that attempts to protect the lung during PH, and suggest targeting of the extracellular cAMP signalling pathway to limit pulmonary vascular remodelling and PH.

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

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-naïve 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.

Phosphodiesterase Diversity and Signal Processing Within cAMP Signaling Networks.

A large number of neuromodulators activate G-protein coupled receptors (GPCRs) and mediate their cellular actions via the regulation of intracellular cAMP, the small highly diffusible second messenger. In fact, in the same neuron several different GPCRs can regulate cAMP with seemingly identical timecourses that give rise to distinct signaling outcomes, suggesting that cAMP does not have equivalent access to all its downstream effectors and may exist within defined intracellular pools or domains. cAMP compartmentalization is the process that allows the neuron to differentially interpret these various intracellular cAMP signals into cellular response. The molecular mechanisms that give rise to cAMP compartmentalization are not fully understood, but it is thought that phosphodiesterases (PDEs), the enzymes that degrade cAMP, significantly contribute to this process. PDEs, as the sole mechanism of signal termination for cAMP, hold great promise as therapeutic targets for pathologies that are due to the dysregulation of intracellular cAMP signaling. Due to their diverse catalytic activity, regulation and localization each PDE subtype expressed in a given neuron may have a distinct role on downstream signaling.

Cell shape information is transduced through tension-independent mechanisms.

The shape of a cell within tissues can represent the history of chemical and physical signals that it encounters, but can information from cell shape regulate cellular phenotype independently? Using optimal control theory to constrain reaction-diffusion schemes that are dependent on different surface-to-volume relationships, we find that information from cell shape can be resolved from mechanical signals. We used microfabricated 3-D biomimetic chips to validate predictions that shape-sensing occurs in a tension-independent manner through integrin ß3 signaling pathway in human kidney podocytes and smooth muscle cells. Differential proteomics and functional ablation assays indicate that integrin ß3 is critical in transduction of shape signals through ezrin-radixin-moesin (ERM) family. We used experimentally determined diffusion coefficients and experimentally validated simulations to show that shape sensing is an emergent cellular property enabled by multiple molecular characteristics of integrin ß3. We conclude that 3-D cell shape information, transduced through tension-independent mechanisms, can regulate phenotype.

Development of computational models of cAMP signaling.

Despite the growing evidence defining the cAMP signaling network as a master regulator of cellular function in a number of tissues, regulatory feedback loops, signal compartmentalization, as well as cross-talk with other signaling pathways make understanding the emergent properties of cAMP cellular action a daunting task. Dynamical models of signaling that combine quantitative rigor with molecular details can contribute valuable mechanistic insight into the complexity of intracellular cAMP signaling by complementing and guiding experimental efforts. In this chapter, we review the development of cAMP computational models. We describe how features of the cAMP network can be represented and review the types of experimental data useful in modeling cAMP signaling. We also compile a list of published cAMP models that can aid in the development of novel dynamical models of cAMP signaling.