Given the range and complex interplay between factors that modify drug action, conventional approaches are usually unlikely to describe the variation adequately to achieve clinical translation. Systems pharmacology holds potential for a deeper parsing of interindividual drug response variability, through harnessing both rapidly advancing drug‐centred systems biology and pharmacometrics in an integrated and iterative manner. In doing so, systems pharmacology offers a holistic approach for further identification, characterization and quantitative integration of factors associated with drug perturbation.

Pharmacologically‐orientated Systems Biology

Pharmacologically‐orientated systems approaches are developing along four main interconnected fronts: collation of increasingly large well‐characterized patient samples, implementation of novel omics technologies, amassment of Big Data from multiple sources into publically available databases in conjunction with network‐based analysis, and development toward structural systems biology and pharmacology. Firstly collaborations, such as the international Serious Adverse Event Consortium (iSAEC), alongside phenotype standardization initiatives,76 are facilitating international patient recruitment to increase sample sizes and collect blood samples for biobank storage whilst concomitantly ensuring high quality phenotypic data is captured using consistent definitions to reduce intra‐ and inter‐study heterogeneity. For ADRs that occur along a spectrum, such as carbamazepine drug‐induced skin injury and statin‐induced myotoxicity, phenotype standardization is especially important.

Secondly, besides traditional GWAS, novel omics technologies and bioinformatics methods are being increasingly implemented in clinical pharmacology studies. For example, next‐generation sequencing facilitates investigation of rare as well as common variation. In a study of drug‐associated torsades de pointes, 23% Caucasian patients carried a highly conserved rare nonsynonymous variant compared to 1.7% of population controls (p = 0.0027) within 22 congenital arrhythmia genes.77 High‐throughput omics technologies are increasingly being used to measure the dynamic intermediate biological levels to further characterize drug exposure phenotype and facilitate identification of novel drug response associations. For example, pharmacometabolomics identified higher postaspirin levels of inosine and adenosine in individuals classified as poor responders to aspirin compared to good responders, determined by ex vivo aspirin‐induced platelet reactivity, and pharmacometabolomics‐informed pharmacogenomics identified rs16931294 (14820A > G) in adenosine kinase to be strongly associated with differential aspirin response.39 Similarly, metabolomics determined that elevated pretreatment plasma glycine levels may be a risk marker for decreased response to escitalopram in patients with major depressive disorder; subsequent genotyping identified rs10975641 in glycine dehydrogenase to be associated with treatment outcome phenotypes.78 Multi‐omics in vitro approaches also facilitate novel insight into ADR pathogenicity. For example, single and integrative transcriptomic, proteomic and metabolomics analyses of mouse hepatocytes, following exposure to the prototypical hepatotoxicant cyclosporin A, revealed mechanisms underlying cyclosporin A cholestasis including endoplasmic reticulum stress.79

Thirdly, a transition into an era of Big Data is rapidly occurring with data storage in large, often publically available online repositories. These data are being amassed from multiple sources including the increasing use of omics technologies to analyze patient‐derived biological samples, systematic cell line transcriptomic profiling after genetic or pharmacological perturbation, systematic investigations of biological molecule interactions (e.g., gene‐regulatory and protein–protein interactions) and increasing drug‐centric data (e.g., phenotype and physiochemical data). Table 2 provides examples of publically available databases relevant to studying drug variability. Network description and analysis has emerged as a powerful tool to intelligently combine, visualize, and interrogate heterogeneous Big Data.13 One function of network analysis is prediction of new drug targets. For example, construction of a single node type drug–drug relation network based on phenotypic side effect similarity of 746 marketed drugs uncovered 261 unexpected drug‐drug relations formed of chemically dissimilar drugs from different therapeutic indications that share a common side effect.80 Complementary in vitro experimentation confirmed binding activity to at least one predicted target for 13 of 20 tested unexpected but network‐predicted drug pairs. For example, the proton pump inhibitor rabeprazole was newly confirmed to bind the DRD3 and HTR1D receptors, which are known targets of pergolide.80

Table 2. Key Publically Available Data Resources that Can Be Utilized and Integrated for Systems Pharmacology Analyses Resource Description URL Biological General Repository for Interaction Datasets (BioGRID) Genetic and protein interaction data for different species including humans http://thebiogrid.org/ Cancer Target Discovery and Development (CTD 6 Cell line fitness following genetic or drug perturbation, and data from judicious animal model testing https://ocg.cancer.gov/programs/ctd2 ChEMBL Database of small molecule bioactivities https://www.ebi.ac.uk/chembl/ Connectivity Map (CMAP) and Library of Integrated Network‐based Cellular Signatures (LINCS L1000) Human mostly cancer cell line gene expression signatures following drug or endogenous ligand perturbation http://lincs.hms.harvard.edu/explore_/canvasbrowser/ DrugBank Drug (chemical, pharmacological and pharmaceutical) and drug target information http://www.drugbank.ca/ Encyclopedia of DNA Elements (ENCODE) Genomic map of gene regulatory elements, including transcription factor and histone modification binding sites https://www.encodeproject.org/ US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) Adverse events and medication errors submitted to the FDA http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ Gene Expression Omnibus (GEO) Gene expression signatures from cell lines and tissues following genetic or drug perturbation http://www.ncbi.nlm.nih.gov/geo/ Gene Ontology (GO) Species‐independent functional annotation of gene products by associated biological processes, cellular components and molecular functions http://geneontology.org/ Genomics of Drug Sensitivity in Cancer Fitness of multiple cancer cell lines to drug perturbation, correlated to cell line genomic and expression data http://www.cancerrxgene.org/ Genotype‐Tissue Expression Project (GTEx) Expression quantitative trait loci (eQTL), derived from expression signatures of multiple human tissues http://www.gtexportal.org/home/ Interactome3D Protein‐protein interaction network with structural annotations http://interactome3d.irbbarcelona.org/ International Mouse Phenotype Consortium (IMPC) Systematic determination of gene knockout‐mouse phenotype associations https://www.mousephenotype.org/ Kyoto Encyclopedia of Genes and Genomes (KEGG) Biological molecular interaction pathways/systems, genomic, chemical and drug related information http://www.genome.jp/kegg/ The miRNA Pharmacogenomics Database (PharmacomiR) Literature‐derived miRNA pharmacogenomic data http://www.pharmaco‐mir.org/ Online Mendelian Inheritance in Man (OMIM) Compendium of all known mendelian disorders and multifactorial diseases with a genetic component, focusing on genotype‐phenotype associations http://www.omim.org/ The Pharmacogenomics Knowledgebase (PharmGKB) Clinical drug information, gene‐drug and genotype‐phenotype associations https://www.pharmgkb.org/ Protein Data Bank (PDB) Three‐dimensional structural information of large biological molecules, predominantly proteins, from multiple species http://www.rcsb.org/pdb/home/home.do Roadmap Epigenomics Development toward reference epigenomes for a range of human cells http://www.roadmapepigenomics.org/ Side Effect Resource (SIDER) Recorded ADRs of marketed drugs http://sideeffects.embl.de/ Therapeutic Targets Database (TTD) Established and exploratory drug target data, corresponding drug data and links to associated targeted pathways and diseases http://bidd.nus.edu.sg/group/cjttd/

Distinguishing real signals from noise within empirical (especially omics) data using conventional statistics is challenging. However, network‐based analyses can filter empirically derived data to gain nonintuitive insight, uncover novel associations and prioritize further research by defining the biological context of targets involved in therapeutic and adverse actions.81 One method is creating a seed list from existing knowledge and using it as an input for network‐building computational algorithms. For example, recently, 167 rhabdomyolysis‐inducing drugs (RIDs) served as the seed list for construction of a bipartite pharmacological network with edges to 272 known protein targets.82 The drug–protein target interaction data was sourced from DrugBank, Therapeutic Targets Database, and PharmGKB databases.82 This network was extended through inclusion of ‘intermediate’ proteins that interact with any of the known drug targets by either protein‐protein or genetic interactions according to the Biological General Repository for Interaction Datasets (BioGRID) database. Subsequent enrichment analysis identified 78 novel intermediate proteins significantly associated with the rhabdomyolysis network compared to random drug sets.82 However, the target space of existing drug–target and protein–protein interaction databases is incomplete. Therefore, a complementary analysis of Connectivity Map empirical gene expression drug perturbation data, for 75 RIDs where this data was available, was undertaken. Of the 9899 genes whose expression was altered by at least 1 RID, CPT2 (carnitine palmitoyltransferase II) was in the top 1% of most commonly perturbed genes by this group of drugs.82 CPT2 mutations have previously been associated with lipid lowering therapy‐associated myopathy,83 but this finding highlights its potential importance, prioritizing it for further study.

As generic in silico‐based networks are not tissue‐specific, a recent cardiac‐specific long QT syndrome (LQTS) proteomics network was developed experimentally via immunoprecipitation of five known LQTS proteins from cardiac mouse tissue; the proteomics network was constructed from these five seed proteins and interacting proteins identified in the precipitates.84 This network was integrated with results from a large recent GWAS that had associated common variant loci with QT interval duration. Importantly, after excluding the congenital LQTS proteins, 12 proteins from the network were encoded by genes within these loci, prioritizing candidate genes for further functional assessment to elucidate causal mechanisms underlying these unexplained loci. Secondly, the network was used to filter SNPs modestly associated (p < 10−3) with QT duration according to whether the SNPs were located near genes of network proteins. Selected SNPs were genotyped in a replication cohort and three reached genome‐wide significance when meta‐analyzed with the initial GWAS: rs10824026 (VCL), rs889807 (SRL), and rs7498491 (TUFM/EIF3C/EIF3CL).84 As QT‐prolongation is frequently multifactorial, these common variants plausibly contribute to an individual's risk of drug‐induced Torsades de Pointes. Therefore collectively, these examples illustrate the potential of network‐based analysis to integrate with, filter and augment complex empirical data. Furthermore, such networks (consisting of seed and interacting nodes associated with clinical drug response phenotypes) are neither overly simplistic nor prohibitively complex. Importantly, this compromise makes network construction and analysis a rational means of defining the suitable ‘molecular space’ for targeted in‐depth biochemical kinetics analyses, facilitating quantitative pharmacometric modelling.81

Lastly, it is increasingly recognized that a physiochemical molecular level understanding of protein–protein interactions and protein–drug interactions is essential for a deeper understanding of interindividual differences in system responses to drug perturbation. To illustrate, approximately 20% of clinically apparent drug resistance to imatinib develops through acquisition of the T315I Abl gatekeeper mutation in the imatinib oncoprotein on‐target, Bcr‐Abl, and the substitution from wild‐type threonine to isoleucine sterically blocks imatinib binding. This structural insight is facilitating development of new Bcr‐Abl inhibitors, such as ponatinib, which are capable of inhibiting T315I Bcr‐Abl.19 Furthermore ligand‐induced, specific protein conformational states can result in selective signalling (biased agonism), which is thought to modulate, for example, the downstream signalling selectivity of G‐protein coupled receptors, a major drug‐target class.85 Therefore, structural systems biology is developing from the systematic integration of structural data into biological networks (e.g., networks derived from experimentally identified protein–protein interactions), which when integrated with pharmacometric modelling approaches, is leading to the emergence of ‘structural systems pharmacology’. For example, antibacterial mechanisms of compounds in Escherichia coli K12 have been predicted using a structural‐based algorithm to predict antibacterial protein targets from a genome‐scale model of metabolism integrated with protein structures (GEM‐PRO) and expanded to incorporate multimeric metabolic enzyme structures.86 Although a comprehensive interactome‐wide structurally annotated resource does not yet exist, recent breakthroughs in membrane protein crystallography and the rapidly increasing number of protein‐protein, protein‐ligand and protein‐nucleic acid three‐dimensional complexes being deposited in the Protein Data Bank (PDB) are advancing structural systems pharmacology,87 with potential for advancing our understanding of structure‐based differential drug response.