==== Front PLoS Comput BiolPLoS Comput. BiolplosploscompPLoS Computational Biology1553-734X1553-7358Public Library of Science San Francisco, USA (539)465-7973-PLCB-RA-2141R210.1371/journal.pcbi.1000938Research ArticleBiochemistry/Biomacromolecule-Ligand InteractionsCardiovascular Disorders/Cardiovascular PharmacologyCardiovascular Disorders/HypertensionComputational Biology/Metabolic NetworksComputational Biology/Systems BiologyGenetics and Genomics/BioinformaticsGenetics and Genomics/Gene ExpressionGenetics and Genomics/Genetics of DiseasePharmacology/Adverse ReactionsPharmacology/Drug DevelopmentPharmacology/Personalized MedicinePhysiology/Renal, Fluid, and Electrolyte PhysiologyDrug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model Modeling Drug Off-Target EffectsChang Roger L. 1 Xie Li 2 Xie Lei 3 Bourne Philip E. 2 3 Palsson Bernhard Ø. 1 * 1 Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America2 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America3 San Diego Supercomputer Center, University of California San Diego, La Jolla, California, United States of AmericaDunbrack Roland L. EditorFox Chase Cancer Center, United States of America* E-mail: hernandezanthony@example.net and designed the experiments: Roger L. Chang, Philip E. Bourne. Performed the experiments: Roger L. Chang. Analyzed the data: Roger L. Chang, Li Xie. Contributed reagents/materials/analysis tools: Philip E. Bourne, Bernhard Ø. Pallson. Wrote the paper: Roger L. Chang, Li Xie, Lei Xie, Philip E. Bourne, Bernhard Ø. Pallson. Provided conceptual content to the study: Bernhard Ø. Pallson, Li Xie. 9 2010 23 9 2010 6 9 e100093823 4 2010 23 8 2010 Chang et al.2010This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine. Author Summary Pharmaceutical science is only beginning to scratch the surface on the exact mechanisms of drug action that lead to a drug's breadth of patient responses, both intended and side effects. Decades of clinical trials, molecular studies, and more recent computational analysis have sought to characterize the interactions between a drug and the cell's molecular machinery. We have devised an integrated computational approach to assess how a drug may affect a particular system, in our study the metabolism of the human kidney, and its capacity for filtration of the contents of the blood. We applied this approach to retrospectively investigate potential causal drug targets leading to increased blood pressure in participants of clinical trials for the drug torcetrapib in an effort to display how our approach could be directly useful in the drug development process. Our results suggest specific metabolic enzymes that may be directly responsible for the side effect. The drug screening framework we have developed could be used to link adverse side effects to particular drug targets, discover new uses for old drugs, identify biomarkers for metabolic disease and drug response, and suggest genetic or dietary risk factors to help guide personalized patient care. ==== Body Introduction Despite the advantages gained from drug therapy in medicine, drug development has historically presented an expensive and frequently perplexing challenge for researchers. Identifying useful drug targets for treating disease and matching them to chemical compounds that can elicit the desired effect through drug-target interaction has been the paradigm for the drug development process in the era of molecular medicine. However, this approach has yielded many failed drug treatments and an incomplete understanding of the consequences of treatments for human health, even with drugs that have made it to market and been prescribed for decades. Two major contributing factors that confound individual molecular target-based drug discovery are drug off-target binding and the lack of systems-level understanding of drug response [1]. Adopting a new, systems-based approach to drug development is therefore a desirable goal in the era of systems medicine. The growing wealth of omics data offers a valuable opportunity for novel approaches in systems medicine but also presents significant challenges for data integration [2]. Increasingly sophisticated computational approaches are being developed to analyze and manipulate omics data in order to gain a greater understanding of complex biological systems. An algorithm for identifying and comparing ligand binding sites on protein structures [3] was recently employed to predict drug off-target binding sites across the proteome [4]. Such a tool offers unique capabilities for drug development by providing a comprehensive survey of uncharacterized drug targets that may participate directly in drug response, which is likely to be important as polypharmacology interactions suggest that drug promiscuity is a predominant property of existing drugs [5]. Biological systems exhibit redundant pathways and synergistic effects conferring a robustness of phenotype when confronted with external stimuli. As a result, multi-target drugs are generally more clinically efficacious than single-target drugs. These facts highlight the critical importance of studying polypharmacology in a systems level context [6]. The increasing use of genome-scale metabolic network models for a variety of applications [7], [8] has established this research platform as a promising means for studying the emergent properties of complex systems. The published applications of metabolic models for drug development have thus far focused on identifying drug targets for antibacterial treatment in such pathogens as M. tuberculosis [9], [10], S. aureus [11], [10], H. pylori, and E. coli [10]. However, the human metabolic network reconstruction (Recon 1) [12] and developed context-specific metabolic modeling algorithms [13], [14] permit human-centered in silico drug studies. Integrating these structural bioinformatics and human system modeling techniques for application in drug development represents a first computational step into the era of systems medicine. As an example of this integrative approach, the results of protein off-target prediction for the drug torcetrapib [4], a cholesteryl ester transfer protein (CETP) inhibitor, were evaluated in the context of a model of renal metabolism. CETP inhibitors are intended to treat patients at risk for atherosclerosis and other cardiovascular diseases by raising high-density lipoprotein cholesterol (HDL-C) and lowering low-density lipoprotein cholesterol (LDL-C) [15]. Torcetrapib was withdrawn from phase III clinical trials after a substantial investment of labor and capital due to its observed side effect of fatal hypertension in some patients [16]. It has since been of great interest to elucidate the cause of this side effect in order to avert such failures in the future and to better define the potential of CETP inhibitors for treatment [17]. Subsequent studies have provided evidence in favor of the hypothesis that the cause of this side effect was not due directly to the mechanism of HDL-C and LDL-C regulation via CETP inhibition [18]. Instead, it has been suggested that the hypertensive side effect may result from uncharacterized drug off-target effects [17]. Two other CETP inhibitors are now under clinical trial, anacetrapib [18] and JTT-705 [19]. Thus far, studies have not indicated the same risk of hypertension associated with the latter two drugs; however, these studies have been limited to relatively small patient groups lacking in diversity and over relatively short-term treatment. Even if these alternative CETP inhibitors do not carry the same adverse side effects, it is still of value to future drug development to determine the exact mechanism of torcetrapib's adverse action. It has been suggested that off-target effects of torcetrapib lead to increased activity of the renin-angiotensin-aldosterone-system (RAAS) and thereby hypertension [4], [20], but a recent review of the published CETP inhibitor clinical studies [21] concludes that the effect on blood pressure is most likely independent of the increase in aldosterone. Currently the exact cause of the hypertensive side effect of torcetrapib remains to be unambiguously identified. The predicted torcetrapib off-targets include many metabolic enzymes and metabolite transport proteins. Although there are several mechanisms involved in regulating blood pressure that may be responsible for the hypertensive side effect, one possible mode is the renal regulation of blood pressure via metabolite reabsorption and secretion. The kidneys are the primary organs that filter the blood and therefore are strong contributors to maintaining a normotensive state even independent of RAAS function. Thus a model of renal metabolism was developed as the system context in which to analyze torcetrapib off-targets and predict drug response phenotypes. The two best-supported causal off-targets predicted in this study are prostaglandin I2 synthase (PTGIS), due to decreased capacity for renal prostaglandin I2 (PGI2) secretion, and acyl-CoA oxidase 1 (ACOX1), due to decreased capacity for renal citrate and amino acid reabsorption. Four other predicted off-targets are also predicted to impact amino acid, glucose, citrate, or bicarbonate reabsorption. As well, the model predicts no effect on renal reabsorption or secretion for a number of other predicted off-target metabolic proteins. The goal of this study is not only to provide new insight into the torcetrapib problem but also to reveal the theoretical implications that this computational systems medicine platform has for drug development and personalized medicine. Characterizing the influence that genetic variation has in determining drug response phenotypes has been recognized as a crucial goal for the future of drug development [22]. To this end, the renal model was also used to analyze metabolic disorders resulting from genetic deficiencies and to identify those deficiencies that may pose additional risks for drug treatment in select individuals. Although many of the predictions generated by this approach are supported by clinical and other experimental evidence that describe the impact of loss of function for predicted causal off-targets and genetic deficiencies, the full set of exact metabolic mechanisms of drug action predicted by our model remain to be completely validated. While this is seen as a limitation of this study, it also offers a number of opportunities to experimentally evaluate promising hypotheses that, if validated, will lead to significant advancements in developing CETP inhibitors for treatment and novel insight into certain renal disorders. Results Renal Metabolic Model The approach for context-specific organ modeling proposed in this study (see Materials and Methods and Figure 1) yielded a renal metabolic model capturing functions of the kidney for reabsorption and secretion (Table 1). Many components of the renal objective function are factors known to be relevant determinants of blood pressure. However, there is currently incomplete knowledge about the exact role that some of these components play in blood pressure regulation. Calcium reabsorption, for example, leads to vasoconstriction in kidney glomeruli through the action of L-type and N-type calcium ion channels [23] suggesting a resulting increase in blood pressure if this mechanism applies across all vascular tissues. Calcium reabsorption also leads to an inhibition of renal sodium reabsorption in the proximal tubule [24] suggesting a blood pressure lowering effect consistent with the observation that increased dietary calcium also lowers blood pressure [25]. This highlights the complexity of the effect certain renal reasborptions have on blood pressure. Nevertheless, the many components accounted for in the renal objective function enabled explicit predictions about how system perturbations such as drug treatment and genetic deficiencies affect the kidney's ability to regulate the small molecule content of the blood. 10.1371/journal.pcbi.1000938.g001Figure 1 Context-specific organ metabolic modeling. Preliminary constraints were imposed upon metabolite exchange fluxes of the full metabolic network based on coordinated experimental detection of transportable metabolites both in the organ tissue and the biofluids processed by the organ. Metabolites detected in both biofluid and organ were assumed freely exchangeable in the model, and the remainder of the metabolite exchanges were tentatively constrained to zero. Organ physiology literature was reviewed to compile an objective function consisting of the metabolic functions of the organ. Each function was tested for compatibility with the preliminary model. Metabolite exchange, transport, and demand reactions required to achieve some functions were added to the network, and exchange fluxes for objective metabolites were directionally constrained in accordance with the literature. Functions not compatible with the model were removed from the overall objective function. The objective function was then integrated with gene expression data obtained from an organ tissue sample to derive a net, context-specific metabolic organ model representing the metabolic exchange between the organ and the rest of the body and the metabolic reactions that take place within the organ to achieve this exchange. 10.1371/journal.pcbi.1000938.t001Table 1 Renal objective function. Exchange Class Metabolite Abbreviation Relation to Blood Pressure Reference secretion hormones Prostaglandin I2 PGI2 vasodilation [60], [61] Prostaglandin D2 PGD2 vasodilation [61] Calcitriol - lowers blood pressure, Ca2+ reabsorption [62]–[65] urea Urea - water/ion counter current system regulating osmolality [66] cyclic amp Cyclic AMP cAMP important for vaso-dilation/constriction [67] urate Urate - uknown, but secreted [68] tryptamine Tryptamine - uknown, but secreted [67] absorption water Water H2O determinant of blood pressure, ion absorption [66], [69], [70] ions/electrolytes Phosphate - determinant of blood pressure [71], [72] Sodium Na+ determinant of blood pressure [70] Calcium Ca2+ determinant of blood pressure [23]–[25], [63], [73], [74] Chloride Cl- determinant of blood pressure [75]–[77] Protium H+ determinant of blood pressure [78], [79] Potassium K+ determinant of blood pressure [25], [75] Bicarbonate HCO3- determinant of blood pressure [78] carboxylates Acetate - unknown, but reabsorbed [80] Citrate - effects sodium reabsorption [81] Oxalate - effects sodium reabsorption [82] glucose D-Glucose - effects sodium reabsorption [80], [83]–[85] amino acids L-Alanine Ala associated reduction of hypertension/vasodilation [80], [85], [86] L-Arginine Arg associated reduction of hypertension/vasodilation [80], [85], [86] L-Asparagine Asn associated reduction of hypertension/vasodilation [80], [85], [86] L-Aspartate Asp associated reduction of hypertension/vasodilation [80], [85], [86] L-Cysteine Cys associated reduction of hypertension/vasodilation [80], [85], [86] L-Glutamine Gln associated reduction of hypertension/vasodilation [80], [85], [86] L-Glutamate Glu associated reduction of hypertension/vasodilation [80], [85], [86] Glycine Gly associated reduction of hypertension/vasodilation [80], [85], [86] L-Histidine His associated reduction of hypertension/vasodilation [80], [85], [86] L-Isoleucine Ile associated reduction of hypertension/vasodilation [80], [85], [86] L-Leucine Leu associated reduction of hypertension/vasodilation [80], [85], [86] L-Lysine Lys associated reduction of hypertension/vasodilation [80], [85], [86] L-Methionine Met associated reduction of hypertension/vasodilation [80], [85], [86] L-Phenylalanine Phe associated reduction of hypertension/vasodilation [80], [85], [86] L-Proline Pro associated reduction of hypertension/vasodilation [80], [85], [86] L-Serine Ser associated reduction of hypertension/vasodilation [80], [85], [86] L-Threonine Thr associated reduction of hypertension/vasodilation [80], [85], [86] L-Tryptophan Trp associated reduction of hypertension/vasodilation [80], [85], [86] L-Tyrosine Tyr associated reduction of hypertension/vasodilation [80], [85], [86] L-Valine Val associated reduction of hypertension/vasodilation [80], [85], [86] oligopeptides L-Carnosine - unknown, but reabsorbed [66], [87], [88] Glutathione GSH unknown, but reabsorbed [54] The kidney model included 336 explicitly predicted active metabolic genes (Table S1) that met criteria for activity as summarized in Figure 2. The majority, 243 genes, satisfied the gene expression significance threshold (see Materials and Methods), although the activity of 58 genes was predicted despite expression values below the threshold. These genes were activated by the GIMME algorithm [13] to optimally achieve the renal objectives while remaining minimally inconsistent with gene expression data and may represent post-transcriptionally upregulated genes. The other 35 genes were predicted to be active without penalty since no corresponding probesets existed on the microarray upon which the transcriptomic data was obtained. Since many of these genes participated in optimal pathways for achieving renal objectives, it is projected that experimental measurement would confirm their expression if performed. 10.1371/journal.pcbi.1000938.g002Figure 2 Summary of gene activity predictions in the full kidney model. The pie chart at bottom represents the Recon1 gene activity predictions resulting from deriving the kidney model. Genes predicted inactive are those genes with no associated active reaction fluxes in the kidney model. Genes for which no activity prediction was made are those associated with active reaction fluxes in the kidney model but either are not represented in the gene expression data or were not determined as the gene whose expression level is most limiting for any associated reaction through evaluation of GPR Boolean rules with respect to gene expression data. The slice at top represents genes predicted active in the kidney model. The active reactions in the model reflect both the possible pathways by which the kidney can achieve the specified renal objectives as well as other functions supported by the gene expression data. The model included 1587 active reactions (Table S2), excluding model-based reactions such as objective functions, exchanges, and demands. Of these active reactions, 333 comprised a single connected sub-model accounting for all pathways which could possibly support the specified renal objectives. We refer to this sub-model as the reduced kidney model (see Table S1 and Table S2 for the contents of the reduced model and Dataset S1 for the actual model in SBML format). It should be noted that because the reduced model included all reactions that can carry flux in support of the renal objectives, it had the exact same effective flux state solution space as the full renal model. The reduced kidney model reactions spanned a broad range of metabolic subsystems (Figure 3). The largest subsystem consisted of plasma membrane-spanning transport reactions, which is expected given that this model captured renal filtration and secretion functions. The second largest subsystem represented intracellular transport, signifying the importance of interaction among sub-cellular compartments in renal function including the cytosol, endoplasimic reticulum, Golgi apparatus, and mitochondria. A significant proportion of the other active subsystems in the reduced kidney model were involved directly in the metabolism of components of the renal objective function including carbohydrate, amino acid, vitamin, lipid, carboxylate, and glutathione metabolism as well as the urea cycle. These permitted the indirect reabsorption of metabolites as well as the required synthetic pathways for renal secretions. 10.1371/journal.pcbi.1000938.g003Figure 3 Reduced kidney model subsystem distribution. The distribution of metabolic reactions predicted to be active in the reduced kidney model with respect to broad metabolic subsystem categories is shown. The distribution excludes objective function, exchange, and demand reactions used to perform simulations in the model. Causal Drug Off-Targets The integrative framework adopted for predicting causal drug targets associated with response phenotypes employed both structural bioinformatics tools as well as modeling techniques of systems biology (see Materials and Methods and Figure 4). The workflow begins with screening of the entire human structural proteome, with each subsequent step in the process narrowing the list of proteins ultimately into a set of targets for which a response phenotype was predicted upon functional inhibition. The first step of this process identified putative off-target drug binding sites using a ligand-binding site structural alignment algorithm (see Materials and Methods). The 41 predicted metabolic protein off-targets were the focus of this study (see Table S3), 28 of which had predicted drug binding sites overlapping with their functional sites. Simulated inhibition of these targets in the reduced kidney model (see Materials and Methods) predicted response phenotypes for 6 of the off-target proteins with respect to renal function (Figure 5). The results of all analysis steps for these 6 off-targets are summarized in Table 2. The expression of all of these targets was determined to be the most limiting for their associated metabolic reactions included in the reduced kidney model (see Materials and Methods), providing additional evidence supporting that inhibition of these targets would be expected to have at least some deleterious impact on those reactions. 10.1371/journal.pcbi.1000938.g004Figure 4 Identifying causal genes for drug response phenotypes and metabolic disorders. First, the human proteome was screened to identify off-target drug-binding sites. The resulting list of putative off-targets was filtered to focus on just metabolic proteins. Then, for each predicted metabolic off-target, the endogenous functional sites were compared to the predicted drug-binding site to identify overlap. Off-target proteins for which overlapping binding sites were identified were considered to be competitively inhibitable by the drug at the overlapping endogenous functional sites. The functional consequences of such inhibitions were then tested in an appropriate context-specific metabolic model. All possible individual gene knockouts were also simulated to predict genetic disorders that lead to functional deficiencies either alone or in combination with drug treatment. Those off-targets whose inhibition impacted model function represent causal off-targets predicted to be associated with the drug response phenotype, and the gene knockouts that impacted model function represent genetic risk factors for metabolic disorders, which may lead to amplification of the drug response phenotype. 10.1371/journal.pcbi.1000938.g005Figure 5 CETP inhibitor renal response phenotypes. Elements of the color matrix represent the percent of the maximum normal, untreated renal objective flux achievable by the CETP-inhibitor-treated normal kidney model. The x-axis corresponds to individual renal objective functions, and the y-axis corresponds to the predicted drug off-targets. Metabolite abbreviations are defined in Table 1. Only the subset of renal objective functions for which a drug response phenotype was predicted is displayed. 10.1371/journal.pcbi.1000938.t002Table 2 Drug side effect causal off-targets. Official Symbol PDB ID Gene ID SMAP Prediction Functional Site Overlap Reduced Model Reactions Limited by Expression Impacts Renal Function in Simulation Stronger Drug Binding Affinity Cryptic Genetic Risk Factors References Supporting Causal Drug Target Prediction PTGIS 2IAG 5740 × × × × × [26], [27] ACOX1 1IS2a 51 × × × × × AK3L1 2BBW 205 × × × × HAO2 1LTDa 51179 × × × × SLC3A1;SLC7A9;SLC7A10;ABCC1 [28], [29] MT-COI 1V54a 4512 × × × × CYP27B1;ABCC1 UQCRC1 1PP9a 7384 × × × × CYP27B1;ABCC1 a Non-human protein structures were mapped to human genes via bi-directional BLAST against the human proteome and choosing the top hit only if it had E-value <10−50. The renal response phenotypes for inhibition of two of the predicted drug off-targets were supported by existing scientific literature. Simulated PTGIS inhibition completely precluded PGI2 secretion. Based on the relation of renal PGI2 secretion to blood pressure (see Table 1), this inhibition would be expected to have a hypertensive effect. Experimental studies confirmed that PTGIS is associated with essential hypertension in humans [26] and that transgenic rats highly expressing human PTGIS exhibited decreased mean pulmonary arterial pressure despite treatment with monocrotaline to induce hypertension [27]. Inhibition of hydroxyacid oxidase 2 (HAO2) in the reduced kidney model led to reduced glutamate, glycine, and serine reabsorption suggesting a possible role for HAO2 in the hypertensive side effect following CETP inhibitor treatment based on the association of amino acid reabsorption with vasodilation and hypertension (see Table 1). HAO2 is highly expressed in human kidney [28] and was identified as a candidate quantitative trait locus for blood pressure in rat kidney in a study comparing normal to hypertensive rats [29]. Two predicted causal CETP inhibitor off-targets, PTGIS and ACOX1, exhibited notable binding affinity differences when comparing docking results for their endogenous substrates to those for the three CETP inhibitors (Figure 6). The mean predicted binding affinity of PTGIS for its endogenous substrate prostaglandin H2 was weaker than for all three CETP inhibitors (Figure 6A). Anacetrapib was predicted to have the strongest mean binding affinity of all four tested molecules for PTGIS and JTT-705 the weakest of the three drugs. The predicted mean binding affinity of ACOX1 for its endogenous substrate palmitoyl-CoA was weaker than for torcetrapib and anacetrapib but stronger than the affinity of the protein for JTT-705 (Figure 6B). These results supported potential competitive inhibition of PTGIS and ACOX1 by torcetrapib and anacetrapib, but the predictions suggested a lesser effect of JTT-705 on ACOX1. 10.1371/journal.pcbi.1000938.g006Figure 6 Differential causal off-target ligand and drug binding affinities. (A) Binding affinities of the prostaglandin I2 (prostacyclin) synthase protein for CETP inhibitors and prostaglandin H2, the endogenous substrate. (B) Binding affinities of the acyl-Coenzyme A oxidase 1, palmitoyl protein for CETP inhibitors and palmitoyl-CoA, the endogenous substrate. Each bar shows the mean binding energy predicted from docking trials. The standard error is indicated for each bar along with the number of predicted binding poses. Renal Disorders and Drug Treatment Similar to the use of the model to test inhibitory effects on drug targets, the model was also used to predict genetic deficiencies that lead to renal disorders and drug off-targets that act synergistically with genetic deficiencies. Simulated gene knockouts predicted to impact renal objective functions are displayed in Figure S1, Figure S2 and Table S4. The 118 deficient genes predicted to cause disorders impacted a variety of renal secretions and absorptions to varying degrees. Thirteen of these deficiencies predicted total loss of at least one renal function (see Figure S2). Some renal disorders were only predicted in the gene-deficient models in combination with drug treatment, not in the untreated gene-deficient models or in the normal drug-treated model, and are referred to in this study as cryptic genetic risk factors. Five such gene deficiencies were predicted (see Table S4). A deficiency in CYP27B1, which impacted vitamin D secretion alone, also exhibited defects in proline reabsorption when combined with drug treatment in simulation. Defects in three amino acid transport proteins (SLC7A10, SLC3A1, and SLC7A9) were predicted to decrease renal glycine reabsorption in combination with drug treatment along with the disorders predicted in the absence of drug treatment. The model deficient in the ATP-binding cassette sub-family C member 1 gene (ABCC1) was predicted to exhibit a cryptic deficiency in renal phosphate reabsorption under drug treatment. These predictions are of special importance because they suggest that these renal phenotypes would only surface in gene-deficient individuals under certain conditions, such as when treated with CETP inhibitors. Model Evaluation and Validation Multiple evaluations were performed to analyze and validate the content of the reduced kidney model. The reduced kidney model effectively predicted activity of significantly expressed metabolic genes. The ability of our modeling approach to correctly and robustly predict activity of highly expressing genes was evaluated by a five-fold cross validation (see Materials and Methods). Our approach showed significant recall of the 20% most highly expressed metabolic genes, p-value = 4.57×10−22. This observation is especially notable since the reduced kidney model was not a global model of kidney metabolism, and the result suggests the relative importance of the renal functions captured by our model within the context of total kidney gene activity. We compared the metabolic gene activity predictions from the reduced kidney model to the set of significantly expressed genes as well as to a proteomic dataset derived from normal, healthy human kidney glomerulus tissue [30] (Figure 7A). A total of 164 genes active in the reduced kidney model, 72% of the predicted activities, were supported by either significantly expressed mRNA levels, high-confidence protein detection, or both (see Table S1 for a detailed list). The remaining 64 gene activities accounted for in the model include 23 genes with no corresponding microarray probesets, and therefore not experimentally measured mRNA, and 41 genes that were determined to express more marginally below the established significance threshold. Despite a strong overlap between the transcriptomic and proteomic datasets, there were also large proportions of both which are unique. This disagreement may be due to tissue samples being taken from different kidney sub-tissues in each experiment, absent probesets on the microarray, or the propensity of mass spectrometry proteomic experiments to produce false negatives. All of the counted activities in Figure 7A were included in the full human metabolic network, signifying that the reduced kidney model was not a global kidney model and that there is potential for expansion to account for more metabolic functions than those of concern in this study. 10.1371/journal.pcbi.1000938.g007Figure 7 Comparative reduced kidney model evaluation. (A) Overlap of gene activity predictions with genes expressing above the significance threshold. Regions of the diagram are approximately proportional to their associated set sizes. The magenta circle represents the set of genes predicted active in the reduced kidney model. The cyan circle represents the set of Recon1-associated genes with expression levels above the significance threshold in the kidney tissue data. The yellow circle represents the set of genes encoding proteins that were detected in normal human kidney glomerulus tissue. (B) Renal metabolic objectives supported by predicted reaction flux states. The orange circle represents renal metabolic objectives supported both by the kidney model developed in this study and a kidney model derived from the reaction activity predictions of Shlomi et al. The red circle represents renal metabolic objectives supported only by the kidney model from this study. Metabolite abbreviations are defined in Table 1. The literature-curated renal functions achievable by the kidney model were also compared to those achievable by a model derived from the predictions of Shlomi et al (Figure 7B). While the kidney model developed in this study was compatible with all 41 curated renal functions, the predictions of Shlomi et al were only compatible with 25 functions. This difference in functionality was due to false negative inactivity predictions made by Shlomi et al such as inactive urea transport, prostaglandin synthesis, and ATP synthesis. These results underscore the need to manually curate automatically generated metabolic network reconstructions and the advantage of integrating objective functions with context-specific modeling. Next, the model was functionally validated by comparing the gene deficiencies predicted to cause renal disorder to disease phenotypes in the OMIM database collected from clinical studies. Twenty known gene deficiencies leading to specific disease phenotypes were accurately predicted using the model (see Table S4). Loss of function mutations in the gene encoding 25-hydroxyvitamin D3-1-alpha hydroxylase (CYP27B1) have been linked to vitamin D-dependent rickets type I in both human patients [31] and pigs [32] consistent with the predicted inability of the gene-deficient model to secrete calcitriol. Hypouricosuria, low urinary excretion of urate, is a symptom of xanthinuria that is caused by xanthine dehydrogenase (XDH) deficiency [33], which is consistent with the deficient model's inability to excrete urate. Similarly, hypouricemia, low blood serum urate, is a consequence of nucleoside phosphorylase (NP) deficiency [34] also predicted in the model. Deficiency of aromatic L-amino acid decarboxylase (DDC) leads to increased urinary excretion of 5-hydroxytryptophan [35], which is consistent with the decreased ability to reabsorb tryptophan and secrete tryptamine predicted through simulation. Mutations in the mitochondrial cytochrome c oxidase gene (COX6B1) lead to de Toni-Fanconi-Debre renal syndrome, whose symptoms include a deficiency in the renal reabsorption of glucose, amino acids, and bicarbonate [36], [37], all of which were predicted in the model. Deficiencies in seven NADH dehydrogenase genes all lead to hypoglycemia, confirmed in simulation, and a decreased ability to oxidize citrate and glutamate [38], reactions important for indirect renal reabsorption of citrate and glutamate in the model. Proline dehydrogenase (PRODH) deficiency causes an inability to oxidize proline in kidney and other tissues leading to hyperprolinemia that includes increased urinary excretion of proline as a symptom [39]–[41], which is also consistent with the predicted decrease in renal proline reabsorption. Deficiencies in two genes that take part in the ubiquinol-cytochrome c reductase complex III (UQCRQ and UQCRB) lead to proximal tubulopathy, including an inability to reabsorb amino acids [42]; the gene-deficient model exhibited reduced renal reabsorption of alanine, glutamate, and proline. Fumarate hydratase (FH) deficiency leads to defects in glutamate oxidation in kidney and other tissues [43], [44], which is also consistent with the decreased indirect renal reabsorption of glutamate predicted by the model. Renal glucosuria, recapitulated in the model, results from deficiency in a sodium-glucose transporter (SLC5A2) [45]. Dicarboxylicamino aciduria [46] exhibits impaired renal glutamate and aspartate reabsorption and hypoglycemia resulting from a deficient glutamate transporter (SLC1A1), all symptoms predicted by the model. Severe dehydration is one symptom resulting from another deficient transporter (SLC5A1) [47], confirmed through decreased reabsorption of water in the model. These results qualitatively describe the ability of our modeling approach to predict perturbed phenotypic states. To more rigorously quantify the predictive ability of our model simulation approach, we performed area under receiver operating characteristic (AROC) analysis based on not only the abovementioned clinical validations of our gene-deficient phenotype predictions but based on the entire set of such known clinical phenotypes that could potentially have been investigated using our model (see Figure S3 and Materials and Methods). The sharp declines in rates with increasingly stringent classifier ratio thresholds (see Figure S3) reflect the likely low coverage of actual disorder phenotypes by existing clinical studies. Nevertheless, our approach performed very well based on this analysis, with an AROC of 0.7565. Permutation trials resulted in a mean AROC of 0.5112, in close agreement with the expected theoretical randomly achievable AROC of 0.5. Our approach achieved a significantly greater AROC than could be expected by chance, p-value = 8.71×10−70. Given the relatively low number of actual clinical negatives available (see Table S5), we also assessed the significance of our prediction results based purely on the true positive rates determined through the AROC analysis. The mean true positive rate of our results in this analysis was 0.2859, significantly greater than the 0.0215 mean true positive rate obtained randomly, p-value = 3.29×10−127. These analysis results illustrate that our approach for predicting perturbation phenotypes exhibits both favorable sensitivity and specificity based on actual clinical data and should hold not only for predicting genetic deficiency phenotypes but also enzyme inhibition by drugs, which exhibits a similarly deleterious phenotypic effect. Parameter Sensitivity Analysis In order to assess the effects of some of the critical assumptions made in the model development and simulation procedures, we performed sensitivity analysis with respect to the predicted renal disorder phenotypes. First, we compared the predictive capability of our reduced kidney model to that of the original, unconstrained human Recon1 metabolic network. The same approach to simulating renal disorder states was employed using both models (see Materials and Methods). We simulated all single gene knock outs in both models and assessed the renal disorder phenotypes with respect to each individual component of the renal objective function based on the ratio of maximum objective flux in the perturbed state to maximum objective flux in the unperturbed state. Comparing the results achieved by each model (Figure 8), it is apparent that although there are a few cases where both models predict an equal degree of renal disorder given the same genetic perturbation, the vast majority of disorder phenotypes are more apparent in the reduced kidney model than in Recon1 alone. In fact, 427 out of the 608 (71%) disorder phenotypes predicted by the reduced kidney model showed no degree of disorder relative to the unperturbed state in Recon1, including 36 of the most severe phenotypes for which a total loss of renal function was predicted by the reduced kidney model. These observations display the predictive ability gained through integration of the gene expression data via the GIMME algorithm, incorporating metabolomics data to set exchange constraints, and the addition of six key membrane transport reactions during the limited function-enabling manual curation of the model. These reactions involve the transport of prostaglandins I2 and H2, calcitriol, and carnosine. It should be noted that the 7 disorders for which Recon1 predicted a more severe phenotype than the kidney model result directly from the addition of these transporters in that these transporters have enabled additional pathways in the kidney model that are absent in Recon1. All but one of the predictions concerning CETP inhibitors showed a clearer phenotype in the kidney model as well; this off-target is PTGIS for which both models predict a complete loss of function when fully inhibited. Finally, 28 out of the 33 clinically validated phenotypes are predicted more noticeably by the kidney model, 17 of these showing no disorder phenotype in Recon1. Overall, this comparison establishes the relative contribution of context-specific modeling in studying disorder and drug response phenotypes. 10.1371/journal.pcbi.1000938.g008Figure 8 Predictive ability gained by modeling. The dotted black line is the line y = x for ease of visual comparison. Red marks represent predictions resulting from inhibition of a predicted CETP inhibitor off-target. Blue marks represent predictions resulting from non-drug-target gene inhibition. Pluses represent predictions validated in the OMIM database. There are 608 marks in total plotted and exact and partial overlap of some marks precludes complete visual resolution. Second, we investigated the sensitivity of our drug off-target response phenotype predictions to the variability of two important parameters used in our simulations, the system boundary flux constraint, set as equal fractions of the upper bound on renal objective fluxes (see Materials and Methods), and the degree of enzymatic activity inhibition assumed to result from drug treatment. The system boundary flux constraint was imposed upon demand and exchange reactions other than those optimized during a given simulation. By default we set this constraint assuming that all allowed boundary fluxes can carry an equal fraction of the potential maximum renal objective flux. This assumption was made to allow all pathways that could possibly contribute to the objective to be used simultaneously in the optimal flux state, providing the most flexible state while maintaining maximum sensitivity of our model to additional system perturbations such as gene deficiencies or drug effects. This approach was unbiased in that it did not favor any possible pathway over another in achieving a set objective without imposing additional constraints, which may not always reflect biological reality but was the most conservative assumption in the absence of additional experimental data required to more precisely set these flux constraints. In our sensitivity analysis, we varied this parameter between 0 and 1000 flux units, the absolute lower and upper magnitudes possible in our model, and repeated the simulations of drug off-target effects. The result of this analysis (Figure S4) was captured in the normalized sensitivity coefficient computed for each simulation (see Materials and Methods). The coefficient can vary between negative and positive unity and displays the deviation from a base result, the primary predictions we have presented in this study. The base result is indicated by a black star in Figure S4, and the parameter value in this case equals 13.5 flux units. It is clear from Figure S4 that PTGIS inhibition resulted in the same renal disorder phenotype regardless of the value of the system boundary flux constraint parameter. This was because there was only one pathway in the model by which prostaglandin I2 could be secreted. Most other disorder phenotype predictions begin to diverge from the base result around a parameter value of 200 flux units, a fairly permissive value, which shows that the predictions were fairly robust to variability of this parameter. The closer to 1000 flux units this parameter was set, the more completely alternative pathways could compensate for a loss of function in the simulations. If alternative pathways existed to achieve a renal function, it was guaranteed that the ability to predict a disorder phenotype with respect to that function would be completely lost at the maximum possible parameter value of 1000. We similarly analyzed the sensitivity of our predictions to changes in the degree of enzyme inhibition assumed to follow from drug treatment (Figure S5). For the primary results presented in this study, we assumed complete inhibition of activity by the drug, corresponding to a fraction of maximum enzymatic reaction flux equal to 0 in Figure S5. Similar to the default setting of our system boundary flux constraint, this default of complete inhibition was chosen in order to maximize the sensitivity of our model in detecting disorder phenotypes. Most of the phenotypes were still detectable to varying degrees with as much as 25% of the maximum activity of drug targets. The predicted phenotypes associated with PTGIS, ACOX1, and AK3L1 were especially robust to variation in degree of inhibition, still exhibiting a phenotype near 50% of maximum activity. Decreased glucose and bicarbonate reabsorption under drug-induced MT-COI and UQCRC1 inhibition exhibited the most sensitivity to variability in this parameter, although none of the predicted phenotypes required complete inhibition of the drug target in order to be detected. Discussion A novel approach for making functional predictions of drug response phenotypes has been introduced that integrates techniques of both structural bioinformatics and systems biology. Although the current study focused on a specific metabolic system, the general methodology excluding techniques particular to metabolic modeling are extensible to other systems such as signaling or transcriptional regulation. Non-metabolic protein drug off-targets are predictable using the same structural analysis tools, and many such off-targets have indeed been predicted as well for CETP inhibitors [4]. The context-specific organ metabolic modeling strategy employed in this study represents an improvement upon previous efforts in this realm. Model development algorithms such as GIMME [13] or that developed by Shlomi et al, when integrated with multiple omics datasets, can lead to more biologically realistic models. It is also of critical importance to include context-specific metabolic objective functions in the model development process in order to yield a fully functional and predictive model, as is evident from the functional comparisons of models performed in this study. As an early effort at modeling such a context-specific metabolic system it is important to discuss the limitations of our model. Although the functional validations presented here are compelling, currently available clinical data only permits the assessment of a subset of the predictions possible in the model. Also, the functional portion of the model, the reduced kidney model, does not and is not intended to represent a global model of kidney metabolism but only the specific renal functions studied in this work. As such, our model does not fully resolve of complexity of the human kidney. The human kidney fulfills a number of functions not studied here and is a spatially distributed system across multiple distinct tissue types. Here we have summarily replaced the various kidney sub-tissues with a single, net system model. Because we integrated expression data with curated renal functions that operate across multiple kidney tissues, it is likely that our model approximates a superset of the metabolic pathways supporting these functions. Although we have made several simplifying assumptions in the model development process, even the current level of model validation suggests that the gene and reaction content of the model is fairly accurate and that simulations in this model indeed hold predictive capability. The simulation approach taken, optimization of a linear objective function, does not fully capture the full physiological role of the kidney. The goal of these simulations was to determine drug-target effects that may limit the capacity of the kidney to move towards a homeostatic nominal state from a state of high blood pressure, thereby decreasing the capacity of the kidney to lower blood pressure. This strategy is appropriate for the goals of the current study but would not be appropriate to simulate all physiological states of interest in the kidney. On a related note, the choice to define a disorder state based on the ratio of perturbed to unperturbed maximum achievable renal objective flux demonstrates a difference in the capacity of the renal function and not necessarily a precise flux state. Therefore this strategy too is not appropriate for modeling all physiological states. The predictions made for CETP inhibitors in this study serve as illustrative examples of many important implications that this approach has for drug development and personalized medicine. Predicted causal off-targets for renal metabolic disorders related to blood pressure may be responsible in part or full for the clinically observed hypertensive side effect of torcetrapib. The evidence resulting from this study suggests that PTGIS and ACOX1 are both potential causal torcetrapib off-targets, the inhibition of which may explain the side effect of hypertension. In addition, AK3L1, HAO2, MT-COI, and UQCRC1 may also play a role in this side effect as we have predicted, although our docking trials did not suggest that they are bound as strongly by torcetrapib. The specific predicted deficiencies in renal function associated with the drug off-targets can serve as biomarkers to be measured in patients participating in clinical trials. A positive correlation of these biomarkers with side effects would lend support to the predictions of this study and confirm these biomarkers as risk indicators in future patient treatment. It is important to note that although these predictions comprise the basis for a renal filtration and secretion-based hypothesis explaining the hypertensive side effect of torcetrapib, these results do not refute the hypothesis based on a RAAS-mediated mechanism. These two hypotheses are not mutually exclusive and could potentially contribute alternatively or synergistically to the clinically observed side effects. This possibility illustrates the major tenet for systems biology: studying a single protein or even a single pathway is not necessarily sufficient to explain complex biological phenomena. Aside from the confirmation that some of our predicted off-targets are known to be involved in renal disorders, we do not currently present direct experimental verification that torcetrapib binds and inhibits the predicted targets and that this inhibition leads to the predicted response phenotypes. Although this would be the obvious next step, a retrospective validation is currently hampered by the availability of the drug and the nature of the phenotypes both predicted and known. Ideally, relevant physiological studies would be carried out during actual clinical trials, when a method such as ours would be most useful, in preclinical and clinical phases of drug development. The extended structural analysis of causal drug off-targets to identify differential binding affinities for endogenous substrates and drug molecules suggests possible differences in drug response phenotypes across the CETP inhibitors tested. The results suggest that anacetrapib may potentially lead to a similar response phenotype to that of torcetrapib, while JTT-705 may not carry the same adverse effect, at least with respect to the off-targets detailed in this study. This particular type of analysis may aid in differentiating between likely response phenotypes expected for chemically and functionally similar drugs. Results of the computational pipeline for interaction prediction between proteins and CETP inhibitors employed in this study, SMAP and docking, have yet to be confirmed experimentally. Although we are currently unable to provide direct experimental evidence for the off-target interaction predictions for this class of drugs, multiple recent studies have shown experimental support for the general efficacy of this approach for interaction prediction [48], [49]. The predicted renal metabolic disorders with a genetic basis suggest classes of individuals in which treatment with CETP inhibitors may pose a higher risk for adverse side effects. These predictions suggest a likely relationship between participants in torcetrapib clinical trials exhibiting symptoms of these disorders and the observed adverse side effects. The concept of cryptic genetic risk factors for drug treatment introduced in this study suggests a novel approach to personalized medicine. Should polymorphisms within these genes be clinically linked to side effects of drug treatment, the result would comprise a basis for genetic screening to assess the risk of drug treatment for future patients. Given that these cryptic risk factors are not expected to elicit the predicted abnormal phenotypes in the absence of drug treatment, identification of causal polymorphisms through association studies could only occur during clinical phase when a sufficient number of patients could be observed to gain the statistical power needed to draw significant correlations. As illustrated above, this approach for in silico drug testing could become an indispensible tool during the pre-clinical and clinical phases of new drug development for studying the nature of adverse side effects. In addition, this platform holds obvious potential for analyzing drug efficacy in general and identification of potential beneficent drug side effects that may be useful for drug repositioning and could also be easily adapted for studying combinatorial drug treatment. For a failed drug like torcetrapib, results from this approach could reinitiate the drug development process, providing new insight to help target patients who could benefit from the treatment without the risk of serious adverse side effects. Materials and Methods Prediction of CETP Inhibitor Drug Off-Targets The binding site for CETP inhibitors on the CETP structure and the predicted off-target binding sites for this class of drug across the proteome were assumed to be as previously predicted using the SMAP program [4], which implements the Sequence Order Independent Profile-Profile Alignment (SOIPPA) algorithm to identify significant structural similarity to a given ligand-binding site [3]. The results contained proteins from all organisms represented in the PDB, not just human structures. Mapping Off-Target Proteins to the Metabolic Network In order to integrate the result of drug off-target predictions with the metabolic network, it was necessary to first map all PDB structures (http://www.pdb.org) corresponding to human metabolic proteins included in Recon1, downloaded from the BiGG database, to their respective gene identifiers as represented in Recon1. The BiGG database requires registration and a password, which can be requested by visiting (http://bigg.ucsd.edu/bigg/home.pl). The UniProt ID mapping tool (http://www.uniprot.org/) was used to map PDB structures corresponding to human proteins to gene identifiers linked to metabolic reactions in Recon1 accounting for all predicted human metabolic protein drug off-targets. All non-human predicted metabolic protein drug off-targets were mapped to their human orthologs using the Basic Local Alignment Search Tool (BLAST) [50] to perform a bi-directional BLAST with a mutual best hit criterion. BLAST was also used to resolve inconsistencies in functional annotation between Recon1 gene-protein-reaction associations (GPRs) and gene annotations from the Entrez Gene database (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene) with respect to predicted drug targets, leading to the reannotation of three Recon 1 GPRs. The overall result of this mapping was that 97 metabolic reactions in Recon1 were linked to
{'source': 'PubMedDataset'}