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Harnessing Big Data for Systems Pharmacology Annual Review of Pharmacology and Toxicology

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Review

Harnessing Large Data for Systems Pharmacology

Lei Xie  et al. Annu Rev Pharmacol Toxicol. .

Free PMC article

Abstract

Systems pharmacology aims to holistically empathise mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing corporeality of information at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics information, besides as the tremendous advances in large data technologies, have already enabled biologists to generate novel hypotheses and gain new noesis through computational models of genome-broad, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and noesis integration. In this review, nosotros talk over several emergent bug in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic spider web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

Keywords: NIH Commons; cloud computing; computational modeling; data science; machine learning; semantic spider web; systems biology; systems pharmacology modeling.

Figures

Figure 1
Effigy 1

Scheme of a systems pharmacology model direction system that adheres to Eatables digital object compliance Fair (findable, accessible, interoperable, and reusable) principles. (a) Architecture of model management systems. Models are linked with associated information sets, metadata, and software that are wrapped within a container and accessed through a model Commons. The whole arrangement may be supported by a cloud computing environment. (b) Model metadata are built on ontologies, including information on the model itself, data, algorithms, and software. (c) The model Eatables may demand a recommendation arrangement to rank the relevant models based on user requests in addition to a model registry and user interface.

Figure 2
Figure 2

Model integration in systems pharmacology. Various models need to exist integrated across multiple methodologies, multiple heterogeneous information sets, organismal hierarchy, and species (transportability). Abbreviations: CNV, copy-number variation; GWAS, genome-broad association studies; SNP, single-nucleotide polymorphisms.

Figure 3
Effigy three

An example to integrate systems pharmacology modeling and the semantic spider web. The output of systems pharmacology models is translated into an RDF triple and associated with a knowledge base that is built on semantic web technology. The knowledge base of operations will support automatic model validation, reasoning, and decision making. Abbreviations: PPAR, peroxisome proliferator-activated receptor; RDF, resource description framework.

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