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. 2017 Dec 19;7(1):17803.
doi: 10.1038/s41598-017-17378-y.

Connecting Neuronal Cell Protective Pathways and Drug Combinations in a Huntington's Disease Model Through the Application of Quantitative Systems Pharmacology

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Free PMC article

Connecting Neuronal Cell Protective Pathways and Drug Combinations in a Huntington's Disease Model Through the Application of Quantitative Systems Pharmacology

Fen Pei et al. Sci Rep. .
Free PMC article

Abstract

Quantitative Systems Pharmacology (QSP) is a drug discovery approach that integrates computational and experimental methods in an iterative way to gain a comprehensive, unbiased understanding of disease processes to inform effective therapeutic strategies. We report the implementation of QSP to Huntington's Disease, with the application of a chemogenomics platform to identify strategies to protect neuronal cells from mutant huntingtin induced death. Using the STHdh Q111 cell model, we investigated the protective effects of small molecule probes having diverse canonical modes-of-action to infer pathways of neuronal cell protection connected to drug mechanism. Several mechanistically diverse protective probes were identified, most of which showed less than 50% efficacy. Specific combinations of these probes were synergistic in enhancing efficacy. Computational analysis of these probes revealed a convergence of pathways indicating activation of PKA. Analysis of phospho-PKA levels showed lower cytoplasmic levels in STHdh Q111 cells compared to wild type STHdh Q7 cells, and these levels were increased by several of the protective compounds. Pharmacological inhibition of PKA activity reduced protection supporting the hypothesis that protection may be working, in part, through activation of the PKA network. The systems-level studies described here can be broadly applied to any discovery strategy involving small molecule modulation of disease phenotype.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Chemogenomics component of the QSP platform. (a) Libraries of mechanism annotated probe compounds are screened in a clinically relevant phenotypic assay to identify phenotype modulating probes. (b) Targets for the active probes are identified from various drug-target databases and then are associated with biological pathways using information from protein-pathway databases. (c) Using a systems level analysis of all pathways identified, computational analysis is performed to predict the optimal modulating pathways/networks based on the activity of the respective probes (i.e., activation or inhibition of pathways in relation to the known effects of the pathway on the phenotype). (d) Predicted pathway/network hypotheses are tested in phenotypic assays by i) testing additional compounds known to modulate the pathways, ii) testing compounds predicted by advanced machine learning methods that will modulated the pathway, iii) modulate pathways by knock-down and knock-in approaches, and/or iv) evaluate probes in pathway specific phenotypic assays. If pathways are not confirmed, then the hypothesis is refined with the new information gained from the testing, additional probes are identified, and the new hypothesis is tested. If pathways are confirmed, then the active probes are advanced to in vivo testing. (e) At the initial screening analysis stage, the heterogeneity of phenotype modulating response is assessed. If no heterogeneity is detected, then proceed as above. However, if heterogeneity is detected, then hypotheses are developed and tested to characterize the basis of the heterogeneity (e.g., effects of combinations of different compounds). The information gained from the heterogeneity analysis is used to inform the prediction of the phenotype modulating pathways/networks. (f) The outputs of this strategy are i) a systems level understanding of the pathways/networks involved in the clinically relevant phenotype which enables the design of optimal therapeutic strategies, and ii) probes/drugs that can be advanced to in vivo and clinical testing.
Figure 2
Figure 2
Compounds with confirmed neuroprotective activity in the STHdh Q111 model. Compound titrations were tested for protective activity in the 384-well PI assay. Compounds representing a diverse set of canonical mechanisms show only partial efficacy in protecting STHdh Q111 cells from mHTT induced cell death. (a) Compounds reported in the literature to be associated with central nervous system (CNS) activity: 1) 3-tropanyl-indole-3-carboxylate hydrochloride; 2) Benztropine mesylate; 3) Cyproheptadine hydrochloride; 4) Domperidone; 5) Isoetarine mesylate; 6) JWH-015; 7) Loxapine succinate; 8) Meclizine; 9) Mianserin hydrochloride; 10) PD 168,077 maleate; 11) Quipazine, N-methyl-,dimaleate; 12) Ruthenium red; 13) SB 203186; 14) Triprolidine hydrochloride; 15) Vinpocetine. (b) Compounds reported to be associated with non-CNS activity: 16) (Z)-Gugglesterone; 17) Beclomethasone; 18) Betamethasone; 19) Budesonide; 20) Ethoxzolamide; 21) Flutamide; 22) Hydrocortisone; 23) Lansoprazole; 24) Lonidamine; 25) m-Iodobenzylguanidine hemisulfate; 26) Papaverine hydrochloride; 27) Prednisolone; 28) Sodium Nitroprusside; 29) Vorinostat; 30) Tetradecylthioacetic acid; 31) Triamcinolone; 32) U-83836 dihydrochloride. Results are from triplicate samples run in at least two independent experiments (Error bars are +/−SE).
Figure 3
Figure 3
Combinations of probes with different canonical mechanisms provide enhanced protection of STHdh Q111 cells. (a) Using domperidone and papaverine as an example, concentrations of compounds that were on the plateau of the activity curve were chosen for combination experiments. In this example, 6 μM domperidone and 25 μM papaverine were selected. (b) Compounds were combined and tested in the 384-well PI assay. The percent activity of the combination was compared with the activity of the single compounds run in parallel, and the ratio of the combined activity to that of the single compound with the highest activity is taken as the combination ratio. For domperidone and papaverine the combination ratio shown here is 1.74 (n = 3 independent experiments, error bars are +/−SE). The combination experiments in panel  b were run independently from the titration experiments in panel a.
Figure 4
Figure 4
Combinations of probes show synergistic protection in STHdh Q111 cells. (a) Active LOPAC probes were screened in combinations using a single concentration of each probe. Combination numbers refer to the combinations listed in Supplementary Table S4. Bliss Independence Model analysis indicated 61 combinations to be synergistic in the single concentration combination screen. The Bliss Independence Model compares the predicted activity of probe combinations to the experimentally observed activity of the combination. The Bliss Combination Index (BCI) is the ratio of the observed combination activity to the predicted combination activity based on the activity of the individual compounds. A BCI > 1 indicates synergy (green bars) and a BCI < 1 indicates antagonism (red bars), while BCI = 1 indicates additivity (blue bars). To accommodate additive BCI calculations not equaling 1 exactly, a cutoff of 0.99–1.01 was assigned to classify synergy and antagonism. (Results from at least 2 independent runs, error bars are the Median Absolute Deviation). (b) 20 probe pairs were selected and tested using 4 different concentrations, 2 each from the plateau and linear portions of the single compound concentrations curves. Curves were analyzed by the method of Chou and Talely, and the isobolograms are plotted. Points below the diagonal line represent synergistic activity of the two compounds (n = 2 independent runs). The panel numbers are the Combination Numbers for the combinations tested listed in Supplementary Table S4.
Figure 5
Figure 5
Neuroprotective pathway hypothesized using the canonical targets of compounds that showed synergistic activity (see text for description).
Figure 6
Figure 6
Protective compounds can induce cAMP. cAMP levels were determined in STHdh Q111 cells after incubation with benztropine (25 μM), domperidone (6 μM), isoetarine (50 μM), loxapine (6 μM), mianserin (25 μM), papaverine (25 μM), and sodium nitroprusside (66 μM) for 15, 30, and 120 minutes. Though isoetarine was the only compound to show a statistically significant change at 15 and 30 minutes, except for mianserin, the other compounds showed at least a two-fold increase in cAMP levels at 15 mins. Over time the induced levels of cAMP decreased back to the control levels. Forskolin significantly induced cAMP levels at 15 and 30 minutes with the highest levels seen at 15 minutes. The values are the average from three independent experiments (+/−S.E.) except papaverine where n = 2. All compounds except forskolin are plotted on the blue scale on the left, while forskolin is plotted on the grey scale on the right. The three panel rows are 15, 30, and 120 minutes. T-test was used to assess changes in cAMP levels relative to the STHdh Q111 cells treated with DMSO.
Figure 7
Figure 7
PKA inhibitor H89 inhibits the protective effects of several probes. (a) The protection of STHdh Q111 cells from mHTT induced cell death by domperidone (6 μM), isoetarine (50 μM), loxapine (12.5 μM), mianserin (50 μM), papaverine (50 μM), and sodium nitroprusside (200 μM) co-incubated with the PKA inhibitor H89 (10 μM) was assessed in the 384-well PI assay. Benztropine (50 μM) was also tested, however, combination with H89 resulted in increased toxicity over the cell death seen in the DMSO control. The concentrations used were chosen to be on plateau of their respective activity curves (see Fig. 2). DMSO is H89 alone which showed no significant protection or toxicity. Analysis is from triplicate samples run in four independent experiments (Error bars are +/−SE). T-test was used to assess changes in the percent recovery levels relative to the STHdh Q111 cells treated with compound without H89. While only papaverine showed a statistically significant decrease, the other compounds showed a trend for H89 inhibition of the protective effects. (b) The integrated intensity of the pCREB signal was measured in the nucleus of the STHdh Q111 cells treated as above. CREB is a substrate for PKA and is used here as a surrogate marker for PKA activity to demonstrate inhibition of PKA activity by H89. Analysis is from triplicate samples run in four independent experiments (Error bars are +/−SE). T-test was used to assess changes in the pCREB intensity relative to the STHdh Q111 cells treated with compound without H89.
Figure 8
Figure 8
Ethoxzolamide may not work through the canonical carbonic anhydrase mechanism. The methyl sulfonyl analog of ETX does not contain the sulfonamide group of ETX and it is not expected to inhibit carbonic anhydrase, though we did not test this directly. This analog is 7-fold more potent than ETX in protecting STHdh Q111 cells from stress induced cell death in the propidium iodide assay suggesting that the mechanism of protection of ETX is not through carbonic anhydrase inhibition. Acetazolamide, brinzolamide and dorzolamide, all reported carbonic anhydride inhibitors, did not protect STHdh Q111 cells (see Supplementary Figure S8) further supporting the idea that inhibition of carbonic anhydrase is not a protective mechanism. Interestingly, the methyl sulfonyl analog only protected ~50% of the STHdh Q111 cells consistent with the existence of distinct protection mechanisms in different subpopulations of cells.

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