Biochemical network-based drug-target prediction

Klipp, Edda; Wade, Rebecca C.; Kummer, Ursula

doi:10.1016/j.copbio.2010.05.004

Cited by 65 publications

(48 citation statements)

References 48 publications

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“…Prior knowledge extracted from such databases can be introduced in mathematical models (Fig. 2) that are starting to provide useful predictions regarding potential sideeffects that can be tested in preclinical or early clinical phases of drug development 37,38 . However, this complex issue is still far from being solved.…”

Section: Drug Development and Combination Therapy In Predictive Modelmentioning

confidence: 99%

Signaling networks in MS: A systems-based approach to developing new pharmacological therapies

et al. 2014

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The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies. AbstractThe pathogenesis of MS involves alterations to multiple pathways and processes, which represent a significant challenge for developing more effective therapies. Systems biology approaches that study pathway dysregulation will offer benefits by integrating in molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NFKb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis / cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. This data serves as a scaffold upon which computational modeling can be performed to identify disease subgroups based in the contribution of different processes. Such an analysis, targeting these relevant MS signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.

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Section: Drug Development and Combination Therapy In Predictive Modelmentioning

confidence: 99%

Signaling networks in MS: A systems-based approach to developing new pharmacological therapies

et al. 2014

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“…of drugs). In particular, tools based on visual paradigms, such as CellDesigner or others (see [35][36][37] and references therein), allow users to draw a graphical description of the network without writing down mathematical formulas, but uniquely exploiting the biological knowledge of the bioscenario to be modeled. After this intuitive circuital design phase, the above-mentioned visual tools can delegate stochastic or deterministic simulations of the dynamics and of the steady state of the systems to other software components such as libRoadRunner [37].…”

Section: Discussionmentioning

confidence: 99%

The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms

Puszyński

Gandolfi

d’Onofrio

2016

J Pharmacokinet Pharmacodyn

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In this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose.

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“…In systems biology, methodologies and tools for mathematical analysis, integration, and interpretation of biological data as well as simulation and visualization methods are employed to build mathematical models of biological processes such as cellular interaction networks [37][38][39][40]. The present analytical tools need enlargement to address more complex questions and the integration of aspects not yet considered to be relevant, including environmental conditions and lifestyle data [41].…”

Section: Systems Medicine: a Paradigm Shift In Personalized Medicinementioning

confidence: 99%

Future of Medicine: Models in Predictive Diagnostics and Personalized Medicine

Regierer

Zazzu

Sudbrak

et al. 2013

Advances in Biochemical Engineering/Biotechnology

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Molecular medicine is undergoing fundamental changes driving the whole area towards a revolution in modern medicine. The breakthrough was generated the fast-developing technologies in molecular biology since the first draft sequence of the human genome was published. The technological advances enabled the analysis of biological samples from cells and organs to whole organisms in a depth that was not possible before. These technologies are increasingly implemented in the medical and health care system to study diseases and refine diagnostics. As a consequence, the understanding of diseases and the health status of an individual patient is now based on an enormous amount of data that can only be interpreted in the context of the body as a whole. Systems biology as a new field in the life sciences develops new approaches for data integration and interpretation. Systems medicine as a specialized aspect of systems biology combines in an interdisciplinary approach all expertise necessary to decipher the human body in all its complexity. This created new challenges in the area of information and communication technologies to provide the infrastructure and technology needed to cope with the data flood that will accompany the next generation of medicine. The new initiative 'IT Future of Medicine' aims at driving this development even further and integrates not only molecular data (especially genomic information), but also anatomical, physiological, environmental, and lifestyle data in a predictive model approach-the 'virtual patient'-that will allow the clinician or the general practitioner to predict and anticipate the optimal treatment for the individual patient. The application of the virtual patient model will allow truly personalized medicine.

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Biochemical network-based drug-target prediction

Cited by 65 publications

References 48 publications

Signaling networks in MS: A systems-based approach to developing new pharmacological therapies

Signaling networks in MS: A systems-based approach to developing new pharmacological therapies

The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms

Future of Medicine: Models in Predictive Diagnostics and Personalized Medicine

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