Discovering Anti-platelet Drug Combinations with an Integrated Model of Activator-Inhibitor Relationships, Activator-Activator Synergies and Inhibitor-Inhibitor Synergies

<div><p>Identifying effective therapeutic drug combinations that modulate complex signaling pathways in platelets is central to the advancement of effective anti-thrombotic therapies. However, there is no systems model of the platelet that predicts responses to different inhibitor combinations. We developed an approach which goes beyond current inhibitor-inhibitor combination screening to efficiently consider other signaling aspects that may give insights into the behaviour of the platelet as a system. We investigated combinations of platelet inhibitors and activators. We evaluated three distinct strands of information, namely: activator-inhibitor combination screens (testing a panel of inhibitors against a panel of activators); inhibitor-inhibitor synergy screens; and activator-activator synergy screens. We demonstrated how these analyses may be efficiently performed, both experimentally and computationally, to identify particular combinations of most interest. Robust tests of activator-activator synergy and of inhibitor-inhibitor synergy required combinations to show significant excesses over the double doses of each component. Modeling identified multiple effects of an inhibitor of the P2Y12 ADP receptor, and complementarity between inhibitor-inhibitor synergy effects and activator-inhibitor combination effects. This approach accelerates the mapping of combination effects of compounds to develop combinations that may be therapeutically beneficial. We integrated the three information sources into a unified model that predicted the benefits of a triple drug combination targeting ADP, thromboxane and thrombin signaling.</p></div

Integrated modelling and validation of synergy and activator-inhibitor combination effects.

<p><b>(A)</b> a schematic of the integrated model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004119#pcbi.1004119.s010" target="_blank">S4 Table</a>), investigating the influence of five activators (green dots) and five inhibitors (red dots) on platelet activation. Each solid line (10 black main effects, 4 purple activator-inhibitor combination effects, 3 red inhibitor-inhibitor synergy effects, 2 green activator-activator synergy effects) represents a parameter within the multiple regression model predicting platelet activation. The five receptors and the kinase shown in the model are not explicitly modelled since there is no direct data on their activation states. The predictions of this model were used to assess the impact of all possible three way combinations of inhibitors on platelets activated by a cocktail of five activators (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004119#pcbi.1004119.s011" target="_blank">S5 Table</a>). <b>(B)</b> testing the most strongly predicted inhibitor triple combination. This shows that the most strongly predicted three-way combination of <i>Xi</i>, <i>Ai</i>, <i>Ti</i> had a clearly stronger effect than the alternative <i>Xi</i>, <i>Ai</i>, <i>Pi</i> combination which was ranked more weakly by the predictive model (p = 0.0003).</p

Interpreting the integrated model in the context of platelet signaling pathways.

<p>As in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004119#pcbi.1004119.g007" target="_blank">Fig. 7</a>, activatory synergies are represented by green lines, inhibitory synergies by red lines. Activators U46619 (Xa), TRAP (Ta), Epinephrine (Ea) ADP (Aa) and CRP (Ca) are indicated extracellularly, acting on their receptors, namely the thromboxane receptor (TXA2R), the thrombin receptor (PAR1), the Epinephrine receptor (α2AR), the ADP receptors (P2Y12, P2Y1 and P2X), and the collagen receptor (GPVI).</p

Identification of activator-activator synergy, inhibitor-inhibitor synergy, and activator-inhibitor combination effects.

<p>Combination experiments of activators and inhibitors. <b>(A)</b> Mean log₁₀ ADP release across platelets from 10 blood donors are shown, with green indicating platelet activation. Combinations of activator and inhibitors. Single and double doses (concentrations) of each activator alone are shown at the bottom; single and double doses of each inhibitor in the presence of a cocktail of all five activators are shown to the right; resting and cocktail are shown bottom right <b>(B)</b> Activator-activator combinations and inhibitor-inhibitor combinations, log(AAU) ADP release. Inhibitor-inhibitor data represents the inhibition of a cocktail of all five agonists. <b>(C)</b> To more easily visualize the data allowing for the differences in levels of activation among the five activators, a simple correction of the data is shown, with the values in panel A subtracted by the value of the single dose activator alone (thus, for CaXi the value is 4.90–4.97 = −0.07). Four significant activator-inhibitor combinations identified by statistical modeling (see text) are highlighted within a white box. Two of these lie on the diagonal, as expected <i>a priori</i>. <b>(D)</b> As for panel B, but calculated to display the difference of the activation or inhibition from the most effective double dose of either the first or the second agent within the combination (thus, for PiTi the value is 5.29–5.13 = 0.16). Positive synergy corresponds to more combined stimulation for the activator-activator pairs, indicated in magenta, and also to less combined stimulation for the inhibitor-inhibitor pairs, which are also indicated in magenta (i.e. magenta implies strong positive synergy of either activation, or of inhibition).</p

Identification of activator-activator synergy, inhibitor-inhibitor synergy, and activator-inhibitor combination effects.

<p>Combination experiments of activators and inhibitors. <b>(A)</b> Mean log₁₀ ADP release across platelets from 10 blood donors are shown, with green indicating platelet activation. Combinations of activator and inhibitors. Single and double doses (concentrations) of each activator alone are shown at the bottom; single and double doses of each inhibitor in the presence of a cocktail of all five activators are shown to the right; resting and cocktail are shown bottom right <b>(B)</b> Activator-activator combinations and inhibitor-inhibitor combinations, log(AAU) ADP release. Inhibitor-inhibitor data represents the inhibition of a cocktail of all five agonists. <b>(C)</b> To more easily visualize the data allowing for the differences in levels of activation among the five activators, a simple correction of the data is shown, with the values in panel A subtracted by the value of the single dose activator alone (thus, for CaXi the value is 4.90–4.97 = −0.07). Four significant activator-inhibitor combinations identified by statistical modeling (see text) are highlighted within a white box. Two of these lie on the diagonal, as expected <i>a priori</i>. <b>(D)</b> As for panel B, but calculated to display the difference of the activation or inhibition from the most effective double dose of either the first or the second agent within the combination (thus, for PiTi the value is 5.29–5.13 = 0.16). Positive synergy corresponds to more combined stimulation for the activator-activator pairs, indicated in magenta, and also to less combined stimulation for the inhibitor-inhibitor pairs, which are also indicated in magenta (i.e. magenta implies strong positive synergy of either activation, or of inhibition).</p

Heatmap of platelet activation (log ADP release) in each donor for each reagent combination.

<p>Columns: 10 donors. Rows: different experimental conditions. Green: activated platelets with high ADP release, measured in log₁₀ Arbitrary Absorbance Units (AAU); red: non-activated platelets. White vertical line: actual value of log₁₀ (AAU). The white vertical dashed lines across each column represent the middle value between the maximum and minimum values observed for the entire dataset. Data were grouped by hierarchical clustering. Any technically replicated results were represented by their means. The five activators used were used at doses typically corresponding to their EC50 (see text): 0.025 μM Epinephrine (Ea), 0.5 μM U46619 (Xa), 1 μg/ml CRP (Ca), 4 μM TRAP (Ta), and 10 μM ADP (Aa), respectively intended to activate the epinephrine, thromboxane, collagen, thrombin and ADP receptors; K represents a cocktail comprising all five activators combined at a dilution corresponding to their combined EC50 (the individual concentrations shown, multiplied by 0.1636). The five inhibitors used at their IC50 values were 1uM Yohimbine (Ei), 68.39 nM SQ29548 (Xi), 16.5 nM Wortmannin (Pi), 2.85 uM BMS200261 (Ti), and 36.77 uM MRS2395 (Ai), respectively intended to inhibit the epinephrine receptor, thromboxane receptor, PI3K, thrombin receptor and ADP receptor. For comparison purposes, the double doses of individual activators and inhibitors were included, which are shown preceded by the number “2”; EC90 and IC90 doses (see text) were also included for comparison, with the prefix “90”.</p

Robust tests of synergy for activator-activator and inhibitor-inhibitor combinations.

<p>These tests correspond to a particular case of Loewe additivity.</p

Combinations of inhibitors.

<p>Synergy is defined as occurring where the double dose of either of the two individual reagents result in significantly less inhibition than the combination of single doses together. Reagents are labelled as with the suffix “i” indicating inhibitor. Label without a number indicates the chosen (typically 50% activation) dose for the inhibitor. The prefix “2” indicates a doubling of this dose. The prefix “90” indicates the dose chosen to approximate 90% activation by the reagent. “Log response" on the horizontal axis refers to the log₁₀ luminescence of the measured arbitrary absorbance units (AAU). The cocktail of activators is included in each experiment with the indicated inhibitors (excluding the “Resting” control of unactivated platelets). Small *: significant difference from the double dose of the indicated inhibitor, by one-tailed Wilcoxon test P < 0.05. Large *represents where both tests are significant(<b>c, e</b> and <b>f</b>). Combinations are shown for the following inhibitor pairs (A) Ai and Ti (B) Ai and Pi (C) Ti and Pi (D) Xi and Ai (E) Xi and Ti (F) Xi and Pi (G) Xi and Ei (H) Ei and Ai (I) Ei and Ti (L) Ei and Pi.</p

Combinations of activators and inhibitors.

<p>Boxplot indicating the effects of the five inhibitors on the five activators. Activators are indicated on their own in single dose (see text) and in combination with inhibitors at single dose. The four significant effects highlighted in the statistical model (see text) and in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004119#pcbi.1004119.g003" target="_blank">Fig. 3C</a> are indicated by asterisks. The central grey box represents the 25%–75% percentile of each distribution.</p

Computational and experimental analysis of bioactive peptide linear motifs in the integrin adhesome

Therapeutic modulation of protein interactions is challenging, but short linear motifs (SLiMs) represent potential targets. Focal adhesions play a central role in adhesion by linking cells to the extracellular matrix. Integrins are central to this process, and many other intracellular proteins are components of the integrin adhesome. We applied a peptide network targeting approach to explore the intracellular modulation of integrin function in platelets. Firstly, we computed a platelet-relevant integrin adhesome, inferred via homology of known platelet proteins to adhesome components. We then computationally selected peptides from the set of platelet integrin adhesome cytoplasmic and membrane adjacent protein-protein interfaces. Motifs of interest in the intracellular component of the platelet integrin adhesome were identified using a predictor of SLiMs based on analysis of protein primary amino acid sequences (SLiMPred), a predictor of strongly conserved motifs within disordered protein regions (SLiMPrints), and information from the literature regarding protein interactions in the complex. We then synthesized peptides incorporating these motifs combined with cell penetrating factors (tat peptide and palmitylation for cytoplasmic and membrane proteins respectively). We tested for the platelet activating effects of the peptides, as well as their abilities to inhibit activation. Bioactivity testing revealed a number of peptides that modulated platelet function, including those derived from α-actinin (ACTN1) and syndecan (SDC4), binding to vinculin and syntenin respectively. Both chimeric peptide experiments and peptide combination experiments failed to identify strong effects, perhaps characterizing the adhesome as relatively robust against within-adhesome synergistic perturbation. We investigated in more detail peptides targeting vinculin. Combined experimental and computational evidence suggested a model in which the positively charged tat-derived cell penetrating part of the peptide contributes to bioactivity via stabilizing charge interactions with a region of the ACTN1 negatively charged surface. We conclude that some interactions in the integrin adhesome appear to be capable of modulation by short peptides, and may aid in the identification and characterization of target sites within the complex that may be useful for therapeutic modulation