Methodical Challenges and a Possible Resolution in the Assessment of Receptor Reserve for Adenosine, an Agonist with Short Half-Life
<p>Ex vivo biological (panel <b>A</b>) and in silico simulated (panel <b>B</b>) models showing concentration-response (E/c) curves of two agonists with short (square symbols) and long (circle symbols) half-lives, acting in systems with unaffected (filled symbols) and reduced (open symbols) receptor number. The <span class="html-italic">x</span>-axis shows the common logarithm of the molar concentration of agonists (in the bathing medium), and the <span class="html-italic">y</span>-axis indicates the effect. The continuous lines denote the fitted Hill equation. On the panel <b>A</b>, symbols show mean ± SEM. Ado: adenosine (the endogenous A<sub>1</sub> adenosine receptor agonist with a short half-life); CPA: <span class="html-italic">N</span><sup>6</sup>-cyclopentyladenosine (a synthetic A<sub>1</sub> adenosine receptor agonist with a long half-life); FSCPX: a prior treatment with 8-cyclopentyl-<span class="html-italic">N</span><sup>3</sup>-[3-(4-(fluorosulfonyl)benzoyloxy)propyl]-<span class="html-italic">N</span><sup>1</sup>-propylxanthine (an irreversible A<sub>1</sub> adenosine receptor antagonist); A: agonist A (simulating adenosine); B: agonist B (simulating CPA); IA: a prior treatment with an irreversible antagonist (simulating an FSCPX pretreatment); CF: contractile force. Data of panel <b>A</b> are redrawn from [<a href="#B31-molecules-22-00839" class="html-bibr">31</a>].</p> "> Figure 2
<p>Ex vivo biological (panel <b>A</b>) and in silico simulated (panel <b>B</b>) models displaying E/c curves of an agonist with a short half-life, in the absence and presence of an agonist transport inhibitor, acting in systems with unaffected (filled symbols) and reduced (open symbols) receptor number. The real and the simulated agonist used to generate the E/c curves are both identical with the endogenous agonist of the given model that agonist is extensively transported and then eliminated. The <span class="html-italic">x</span>-axis denotes the common logarithm of the molar concentration of agonists (in the bathing medium), and the <span class="html-italic">y</span>-axis indicates the effect. The continuous lines represent the fitted Hill equation. On the panel <b>A</b>, symbols show mean ± SEM. Ado: adenosine; NBTI: a treatment with S-(2-hydroxy-5-nitrobenzyl)-6-thioinosine (an inhibitor of the nucleoside transporter type ENT1); FSCPX: a prior treatment with 8-cyclopentyl-<span class="html-italic">N</span><sup>3</sup>-[3-(4-(fluorosulfonyl)benzoyloxy)propyl]-<span class="html-italic">N</span><sup>1</sup>-propylxanthine (an irreversible A<sub>1</sub> adenosine receptor antagonist); A: agonist A (simulating adenosine); TI: a treatment with an inhibitor of agonist A transport (simulating the presence of NBTI); IA: a prior treatment with an irreversible antagonist (simulating an FSCPX pretreatment); CF: contractile force. Data of panel <b>A</b> are redrawn from [<a href="#B31-molecules-22-00839" class="html-bibr">31</a>].</p> "> Figure 3
<p>Ex vivo biological (panel <b>A</b>) and in silico simulated (panel <b>B</b>) models exhibiting E/c curves of a synthetic agonist with a long half-life, in the absence and presence of an agonist transport inhibitor, acting in a system with naïve receptor population. The transport inhibition do not affect the fate of the agonist used for the E/c curves, only the transport of the endogenous agonist (activating the same receptor as the synthetic one) was inhibited in both models. The <span class="html-italic">x</span>-axis indicates the common logarithm of the molar concentration of agonists (in the bathing medium), and the <span class="html-italic">y</span>-axis denotes the effect. The continuous lines represent the fitted Hill equation, while the dotted lines show the fitted equation of RRM (receptorial responsiveness method). On the panel <b>A</b>, symbols show the mean ± SEM. CPA: <span class="html-italic">N</span><sup>6</sup>-cyclopentyladenosine; NBTI: a treatment with S-(2-hydroxy-5-nitrobenzyl)-6-thioinosine; B: agonist B (simulating CPA); TI: a treatment with an inhibitor of the transport of agonist A but not B (simulating the presence of NBTI); CF: contractile force. Data of panel <b>A</b> are redrawn from [<a href="#B31-molecules-22-00839" class="html-bibr">31</a>].</p> "> Figure 4
<p>Ex vivo biological (panel <b>A</b>) and in silico simulated (panel <b>B</b>) models showing corrected E/c curves of an agonist with a short half-life, in the absence and presence of an agonist transport inhibitor, acting in systems with unaffected (filled symbols) and reduced (open symbols) receptor number (while symbols of the built-in in silico control curves labelled as “unbiased” are simply x and asterisk). The <span class="html-italic">x</span>-axis denotes the common logarithm of the molar concentration of agonists (in the bathing medium), and the <span class="html-italic">y</span>-axis indicates the effect. The dotted lines between symbols only connect them, while the dotted lines without symbols represent the Hill equation fitted to data of the control adenosine E/c curve (panel A) and the simple unbiased E/c curve of agonist A generated upon naïve receptor population (panel B). Ado: adenosine; NBTI: a treatment with S-(2-hydroxy-5-nitrobenzyl)-6-thioinosine; FSCPX: a prior treatment with 8-cyclopentyl-<span class="html-italic">N</span><sup>3</sup>-[3-(4-(fluorosulfonyl)benzoyloxy)propyl]-<span class="html-italic">N</span><sup>1</sup>-propylxanthine; A: agonist A (simulating adenosine); TI: a treatment with an inhibitor of agonist A transport (simulating the presence of NBTI); IA: a prior treatment with an irreversible antagonist (simulating an FSCPX pretreatment); <span class="html-italic">unbiased</span>: unbiased E/c curves of agonist A (control functions for the corresponding corrected E/c curves); <span class="html-italic">corrected</span>: E/c curves corrected with our method; CF: contractile force. Data of panel <b>A</b> are redrawn from [<a href="#B31-molecules-22-00839" class="html-bibr">31</a>].</p> ">
Abstract
:1. Introduction
2. Results
2.1. Features of the Simple Unbiased E/c Curves of Agonists A and B
2.2. The Effect of a Treatment with TI Alone and a Co-Treatment with IA and TI on the E/c Curve of Agonist A (Biased E/c Curves of Agonist A)
2.3. The Effect of a TI Treatment on the E/c Curve of Agonist B (Biased E/c Curve of Agonist B)
2.4. The Corrected E/c Curves of Agonist A (Derived from the Biased Ones)
3. Discussion
4. Materials and Methods
4.1. Properties of the Biological Model to Be Simulated
4.2. Applied Mathematical Tools
- To generate unbiased E/c curves, the operational model of agonism, a comprehensive receptor function model forming a hybrid between empirical and mechanistic models [39], was applied. Two equations of this model was used, one determining the effect of one agonist [36], and another one described for the co-action of two (different or the same) agonists [44]. In this latter equation, one agonist was present always at a single concentration, while the other one was applied in a range of concentrations. Functions provided by the equation for one agonist’s action represented simple unbiased E/c curves, while those, yielded by the equation for the co-action of two agonists, simulated unbiased E/c curves that could be easily biased by ignoring the agonist concentration used with a single value (see below). This ignored agonist concentration (defined arbitrarily for the simulation) served as a model for the surplus interstitial concentration of endogenous adenosine produced by NBTI, while increasing concentrations of the other agonist simulated the concentrations administered for the E/c curve.
- To geometrically characterize functions via curve fitting or to calculate effect values from concentrations and best-fit values obtained by curve fitting, the Hill equation, the most widely used empirical receptor function model [38], was applied.
- Adenosine and CPA were modelled with an agonist A and B, respectively. Therefore, a continuous extracellular production, intracellular elimination and transmembrane transport of agonist A were considered. Thus, “exogenous” agonist A concentrations (simulating the administration of agonist A for an E/c curve) were manipulated, when calculating their effect, to model the function of nucleoside transporters (see paragraph 5). Furthermore, a surplus “endogenous” agonist A concentration was designated as a response to the inhibition of nucleoside transport (for simplicity, only ENT1, i.e., an inhabitable carrier, was built into the present model). In contrast, agonist B concentrations were considered to be constant after their administration (see paragraph 5).
- NBTI and FSCPX were represented by a so-called transport inhibitor (TI) and irreversible antagonist (IA), respectively.
- Development of agonist concentrations was considered in two compartments. For all agonists, concentration values in the bathing medium were defined to be independent from any kind of simulated treatments. When constructing E/c curves, effect values were always plotted against the bathing medium concentrations (simulating the condition that usually these concentrations are known during ex vivo experiments). However, effect values were always computed from near-receptor concentrations defined as follows (after testing several value combinations). The bathing medium concentration was designated as a concentration at the receptors for agonist B (in all circumstances) and for agonist A under TI treatment and without IA treatment (simulating complete ENT1 inhibition). Furthermore, the bathing medium concentration divided by 400 was designated as near-receptor concentration for agonist A without IA and TI treatment (simulating the presence of intact ENT1). Finally, the bathing medium concentration divided by 3 was designated as near-receptor concentration for agonist A in the presence of TI and after an IA treatment (simulating the incomplete inhibition of ENT1). These simple operations simulated the effect of ENT1 on the concentration of adenosine, but not CPA, in the vicinity of the cell-surface A1 receptors, producing a parallel leftward shift of the treatment-naïve E/c curve of agonist A as compared to that of agonist B. This is in accordance with the fact that adenosine and CPA, two A1 receptor agonists, have practically the same Emax but very different EC50 [25].
- Consistently, the effect of TI was simulated by omitting the division by 400, when computing the near-receptor concentration of the exogenous agonist A from the bathing medium one (see the previous paragraph), and also by taking a surplus near-receptor concentration of endogenous agonist A into account (see paragraphs 3 and 7).
- The effect of IA treatment was simulated with a division of the total receptor concentration ([R0]) by 5 (see below). Thus, according to our previous results with FSCPX [13], it was assumed that 20% of the A1 receptors remained intact after IA treatment. In addition, in the case of IA and TI co-treatment, bathing medium concentrations of exogenous agonist A were divided by 3 to compute its near-receptor levels. Furthermore, a third of the value of surplus near-receptor concentration of endogenous agonist A, which was designated to simulate the treatment with TI alone, was taken into account (see paragraphs 3 and 6).
- For the correction of the biased effect values, RRM was applied as described previously [27]. Briefly, the biased E/c curves of agonist B were fitted to the equation of RRM to provide information about the neglected surplus near-receptor concentration of agonist A. Using this information along with Hill parameters of the simple unbiased E/c curves of agonist B, the biased effect values were corrected by means of a rearranged form of the equation used for the biasing transformation (see paragraph 8).
4.3. First Step: Construction and Analysis of Simple Unbiased E/c Curves
4.4. Second Step: Computation of Unbiased Effect Values for a Subsequent Biasing Transformation
4.5. Third Step: Construction and Analysis of Biased E/c Curves
4.6. Fourth Step: Construction of Corrected E/c Curves from the Biased E/c Curves of Agonist A
4.7. Computer Simulation and Data Analysis
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Ruffolo, R.R., Jr. Review important concepts of receptor theory. J. Auton. Pharmacol. 1982, 2, 277–295. [Google Scholar] [CrossRef] [PubMed]
- Giraldo, J.; Vivas, N.M.; Vila, E.; Badia, A. Assessing the (a)symmetry of concentration-effect curves: Empirical versus mechanistic models. Pharmacol. Ther. 2002, 95, 21–45. [Google Scholar] [CrossRef]
- Dhalla, A.K.; Shryock, J.C.; Shreeniwas, R.; Belardinelli, L. Pharmacology and therapeutic applications of A1 adenosine receptor ligands. Curr. Top. Med. Chem. 2003, 3, 369–385. [Google Scholar] [CrossRef] [PubMed]
- Kenakin, T.P. A Pharmacology Primer. Techniques for More Effective and Strategic Drug Discovery; Academic Press: Amsterdam, The Netherland, 2014; ISBN 978-0-12-407663-1. [Google Scholar]
- Morey, T.E.; Belardinelli, L.; Dennis, D.M. Validation of Furchgott’s method to determine agonist-dependent A1-adenosine receptor reserve in guinea-pig atrium. Br. J. Pharmacol. 1998, 123, 1425–1433. [Google Scholar] [CrossRef] [PubMed]
- Brown, J.H.; Goldstein, D. Differences in muscarinic receptor reserve for inhibition of adenylate cyclase and stimulation of phosphoinositide hydrolysis in chick heart cells. Mol. Pharmacol. 1986, 30, 566–570. [Google Scholar] [PubMed]
- Srinivas, M.; Shryock, J.C.; Dennis, D.M.; Baker, S.P.; Belardinelli, L. Differential A1 adenosine receptor reserve for two actions of adenosine on guinea pig atrial myocytes. Mol. Pharmacol. 1997, 52, 683–691. [Google Scholar] [CrossRef] [PubMed]
- Albrecht-Küpper, B.E.; Leineweber, K.; Nell, P.G. Partial adenosine A1 receptor agonists for cardiovascular therapies. Purinergic Signal. 2012, 105, 91–99. [Google Scholar] [CrossRef] [PubMed]
- Greene, S.J.; Sabbah, H.N.; Butler, J.; Voors, A.A.; Albrecht-Küpper, B.E.; Düngen, H.D.; Dinh, W.; Gheorghiade, M. Partial adenosine A1 receptor agonism: A potential new therapeutic strategy for heart failure. Heart Fail. Rev. 2016, 21, 95–102. [Google Scholar] [CrossRef] [PubMed]
- Seemann, W.K.; Wenzel, D.; Schrage, R.; Etscheid, J.; Bödefeld, T.; Bartol, A.; Warnken, M.; Sasse, P.; Klöckner, J.; Holzgrabe, U.; et al. Engineered Context-Sensitive Agonism: Tissue-Selective Drug Signaling through a G Protein-Coupled Receptor. J. Pharmacol. Exp. Ther. 2017, 360, 289–299. [Google Scholar] [CrossRef] [PubMed]
- Peleli, M.; Fredholm, B.B.; Sobrevia, L.; Carlström, M. Pharmacological targeting of adenosine receptor signaling. Mol. Asp. Med. 2017. [Google Scholar] [CrossRef] [PubMed]
- Kenakin, T.P. Pharmacologic Analysis of Drug-Receptor Interaction; Lippincott-Raven: New York, NY, USA, 1997; ISBN 0-397-51815-3. [Google Scholar]
- Gesztelyi, R.; Kiss, Z.; Wachal, Z.; Juhasz, B.; Bombicz, M.; Csepanyi, E.; Pak, K.; Zsuga, J.; Papp, C.; Galajda, Z.; et al. The surmountable effect of FSCPX, an irreversible A(1) adenosine receptor antagonist, on the negative inotropic action of A(1) adenosine receptor full agonists in isolated guinea pig left atria. Arch. Pharm. Res. 2013, 36, 293–305. [Google Scholar] [CrossRef] [PubMed]
- IJzerman, A.P.; Fredholm, B.B.; Frenguelli, B.G.; Jacobson, K.A.; Klotz, K.N.; Linden, J.; Mueller, C.E.; Schwabe, U.; Stiles, G.L.; Hills, R. Adenosine Receptors, Introduction. IUPHAR/BPS Guide to Pharmacology, 2015. Available online: http://www.guidetopharmacology.org/GRAC/FamilyIntroductionForward?familyId=3 (accessed on 28 March 2017).
- Fredholm, B.B.; IJzerman, A.P.; Jacobson, K.A.; Klotz, K.N.; Linden, J. International Union of Pharmacology. XXV. Nomenclature and classification of adenosine receptors. Pharmacol. Rev. 2001, 53, 527–552. [Google Scholar] [PubMed]
- Fredholm, B.B.; IJzerman, A.P.; Jacobson, K.A.; Linden, J.; Müller, C.E. International Union of Basic and Clinical Pharmacology. LXXXI. Nomenclature and classification of adenosine receptors—An update. Pharmacol. Rev. 2011, 63, 1–34. [Google Scholar] [CrossRef] [PubMed]
- Burnstock, G.; Pelleg, A. Cardiac purinergic signalling in health and disease. Purinergic Signal. 2015, 11, 1–46. [Google Scholar] [CrossRef] [PubMed]
- Headrick, J.P.; Hack, B.; Ashton, K.J. Acute adenosinergic cardioprotection in ischemic-reperfused hearts. Am. J. Physiol. 2003, 285, H1797–H1818. [Google Scholar] [CrossRef] [PubMed]
- Headrick, J.P.; Peart, J.N.; Reichelt, M.E.; Haseler, L.J. Adenosine and its receptors in the heart: Regulation, retaliation and adaptation. Biochim. Biophys. Acta 2011, 1808, 1413–1428. [Google Scholar] [CrossRef] [PubMed]
- Headrick, J.P.; Ashton, K.J.; Rose’meyer, R.B.; Peart, J.N. Cardiovascular adenosine receptors: Expression, actions and interactions. Pharmacol. Ther. 2013, 140, 92–111. [Google Scholar] [CrossRef] [PubMed]
- IJzerman, A.P.; Fredholm, B.B.; Jacobson, K.A.; Linden, J.; Mueller, C.E. Adenosine Receptors: A1 Receptor. IUPHAR/BPS Guide to Pharmacology, 2017. Available online: http://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=18 (accessed on 28 March 2017).
- Kiesman, W.F.; Elzein, E.; Zablocki, J. A1 adenosine receptor antagonists, agonists, and allosteric enhancers. Handb. Exp. Pharmacol. 2009, 193, 25–58. [Google Scholar] [CrossRef]
- Schenone, S.; Brullo, C.; Musumeci, F.; Bruno, O.; Botta, M. A1 receptors ligands: Past, present and future trends. Curr. Top. Med. Chem. 2010, 10, 878–901. [Google Scholar] [CrossRef] [PubMed]
- Szentmiklosi, A.J.; Cseppento, A.; Harmati, G.; Nanasi, P.P. Novel trends in the treatment of cardiovascular disorders: Site- and event- selective adenosinergic drugs. Curr. Med. Chem. 2011, 18, 1164–1187. [Google Scholar] [CrossRef] [PubMed]
- Karsai, D.; Zsuga, J.; Juhász, B.; Dér, P.; Szentmiklósi, A.J.; Tósaki, A.; Gesztelyi, R. Effect of nucleoside transport blockade on the interstitial adenosine level characterized by a novel method in guinea pig atria. J. Cardiovasc. Pharmacol. 2006, 47, 103–109. [Google Scholar] [CrossRef] [PubMed]
- Ramakers, B.P.; Pickkers, P.; Deussen, A.; Rongen, G.A.; van den Broek, P.; van der Hoeven, J.G.; Smits, P.; Riksen, N.P. Measurement of the endogenous adenosine concentration in humans in vivo: Methodological considerations. Curr. Drug Metab. 2008, 9, 679–685. [Google Scholar] [CrossRef] [PubMed]
- Kiss, Z.; Pak, K.; Zsuga, J.; Juhasz, B.; Varga, B.; Szentmiklosi, A.J.; Haines, D.D.; Tosaki, A.; Gesztelyi, R. The guinea pig atrial A1 adenosine receptor reserve for the direct negative inotropic effect of adenosine. Gen. Physiol. Biophys. 2013, 32, 325–335. [Google Scholar] [CrossRef] [PubMed]
- Gesztelyi, R.; Zsuga, J.; Juhász, B.; Dér, P.; Vecsernyés, M.; Szentmiklósi, A.J. Concentration estimation via curve fitting: Quantification of negative inotropic agents by using a simple mathematical method in guinea pig atria. Bull. Math. Biol. 2004, 66, 1439–1453. [Google Scholar] [CrossRef] [PubMed]
- Grenczer, M.; Pinter, A.; Zsuga, J.; Kemeny-Beke, A.; Juhasz, B.; Szodoray, P.; Tosaki, A.; Gesztelyi, R. The influence of affinity, efficacy, and slope factor on the estimates obtained by the receptorial responsiveness method (RRM): A computer simulation study. Can. J. Physiol. Pharmacol. 2010, 88, 1061–1073. [Google Scholar] [CrossRef] [PubMed]
- Grenczer, M.; Zsuga, J.; Majoros, L.; Pinter, A.; Kemeny-Beke, A.; Juhasz, B.; Tosaki, A.; Gesztelyi, R. Effect of asymmetry of concentration-response curves on the results obtained by the receptorial responsiveness method (RRM): An in silico study. Can. J. Physiol. Pharmacol. 2010, 88, 1074–1083. [Google Scholar] [CrossRef] [PubMed]
- Pak, K.; Papp, C.; Galajda, Z.; Szerafin, T.; Varga, B.; Juhasz, B.; Haines, D.; Szentmiklosi, A.J.; Tosaki, A.; Gesztelyi, R. Approximation of A1 adenosine receptor reserve appertaining to the direct negative inotropic effect of adenosine in hyperthyroid guinea pig left atria. Gen. Physiol. Biophys. 2014, 33, 177–188. [Google Scholar] [CrossRef] [PubMed]
- Pak, K.; Zsuga, J.; Kepes, Z.; Erdei, T.; Varga, B.; Juhasz, B.; Szentmiklosi, A.J.; Gesztelyi, R. The effect of adenosine deaminase inhibition on the A1 adenosinergic and M2 muscarinergic control of contractility in eu- and hyperthyroid guinea pig atria. Naunyn Schmiedebergs Arch. Pharmacol. 2015, 388, 853–868. [Google Scholar] [CrossRef] [PubMed]
- Leff, P. The two-state model of receptor activation. Trends Pharmacol. Sci. 1995, 16, 89–97. [Google Scholar] [CrossRef]
- Colquhoun, D. Binding, gating, affinity and efficacy: The interpretation of structure-activity relationships for agonists and of the effects of mutating receptors. Br. J. Pharmacol. 1998, 125, 924–947. [Google Scholar] [CrossRef] [PubMed]
- Bindslev, N. Drug-Acceptor Interactions. 2008. Available online: http://journals.sfu.ca/coactionbks/index.php/Bindslev/index (accessed on 9 March 2017).
- Black, J.W.; Leff, P. Operational models of pharmacological agonism. Proc. R. Soc. Lond. B Biol. Sci. 1983, 220, 141–162. [Google Scholar] [CrossRef] [PubMed]
- Stott, L.A.; Hall, D.A.; Holliday, N.D. Unravelling intrinsic efficacy and ligand bias at G protein coupled receptors: A practical guide to assessing functional data. Biochem. Pharmacol. 2016, 101, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Gesztelyi, R.; Zsuga, J.; Kemeny-Beke, A.; Varga, B.; Juhasz, B.; Tosaki, A. The Hill equation and the origin of quantitative pharmacology. Arch. Hist. Exact Sci. 2012, 66, 427–438. [Google Scholar] [CrossRef]
- Roche, D.; Gil, D.; Giraldo, J. Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? Adv. Exp. Med. Biol. 2014, 796, 159–181. [Google Scholar] [CrossRef] [PubMed]
- Deussen, A.; Stappert, M.; Schäfer, S.; Kelm, M. Quantification of extracellular and intracellular adenosine production: Understanding the transmembranous concentration gradient. Circulation 1999, 99, 2041–2047. [Google Scholar] [CrossRef] [PubMed]
- Dekanski, D.; Piperski, V.; Tasić, J.; Marković, I.D.; Jokanović, M.; Stukalov, P.; Mitrović, D.M. Transport of endogenous nucleosides in guinea pig heart. Can. J. Physiol. Pharmacol. 2004, 82, 1061–1067. [Google Scholar] [CrossRef] [PubMed]
- Deussen, A.; Weichsel, J.; Pexa, A. Features of adenosine metabolism of mouse heart. Purinergic Signal. 2006, 2, 663–668. [Google Scholar] [CrossRef] [PubMed]
- Karsai, D.; Gesztelyi, R.; Zsuga, J.; Jakab, A.; Szendrei, L.; Juhasz, B.; Bak, I.; Szabo, G.; Lekli, I.; Vecsernyes, M.; et al. Influence of hyperthyroidism on the effect of adenosine transport blockade assessed by a novel method in guinea pig atria. Cell Biochem. Biophys. 2007, 47, 45–52. [Google Scholar] [CrossRef]
- Leff, P.; Dougall, I.G.; Harper, D. Estimation of partial agonist affinity by interaction with a full agonist: A direct operational model-fitting approach. Br. J. Pharmacol. 1993, 110, 239–244. [Google Scholar] [CrossRef] [PubMed]
- GraphPad Software Inc. GraphPad Curve Fitting Guide; GraphPad Software Inc.: La Jolla, CA, USA, 2016. [Google Scholar]
Sample Availability: Not Available. |
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zsuga, J.; Erdei, T.; Szabó, K.; Lampe, N.; Papp, C.; Pinter, A.; Szentmiklosi, A.J.; Juhasz, B.; Szilvássy, Z.; Gesztelyi, R. Methodical Challenges and a Possible Resolution in the Assessment of Receptor Reserve for Adenosine, an Agonist with Short Half-Life. Molecules 2017, 22, 839. https://doi.org/10.3390/molecules22050839
Zsuga J, Erdei T, Szabó K, Lampe N, Papp C, Pinter A, Szentmiklosi AJ, Juhasz B, Szilvássy Z, Gesztelyi R. Methodical Challenges and a Possible Resolution in the Assessment of Receptor Reserve for Adenosine, an Agonist with Short Half-Life. Molecules. 2017; 22(5):839. https://doi.org/10.3390/molecules22050839
Chicago/Turabian StyleZsuga, Judit, Tamas Erdei, Katalin Szabó, Nora Lampe, Csaba Papp, Akos Pinter, Andras Jozsef Szentmiklosi, Bela Juhasz, Zoltán Szilvássy, and Rudolf Gesztelyi. 2017. "Methodical Challenges and a Possible Resolution in the Assessment of Receptor Reserve for Adenosine, an Agonist with Short Half-Life" Molecules 22, no. 5: 839. https://doi.org/10.3390/molecules22050839