ObjectivesTo determine the ability of 11 sildenafil analogues to discriminate between cyclic nucleotide phosphodiesterases (cnPDEs) and to characterise their inhibitory potencies (K i values) of PDE5A1‐dependent guanosine cyclic monophosphate (cGMP) hydrolysis.MethodsSildenafil analogues were identified by virtual ligand screening (VLS) and screened for their ability to inhibit adenosine cyclic monophosphate (cAMP) hydrolysis by PDE1A1, PDE1B1, PDE2A1, PDE3A, PDE10A1 and PDE10A2, and cGMP hydrolysis by PDE5A, PDE6C, PDE9A2 for a low (1 nm) and high concentration (10 μm). Complete IC 50 plots for all analogues were performed for PDE5A‐dependent cGMP hydrolysis. Docking studies and scoring were made using the ICM molecular modelling software.Key findingsThe analogues in a low concentration showed no or low inhibition of PDE1A1, PDE1B1, PDE2A1, PDE3A, PDE10A1 and PDE10A2. In contrast, PDE5A and PDE6C were markedly inhibited to a similar extent by the analogues in a low concentration, whereas PDE9A2 was much less inhibited. The analogues showed a relative narrow range of K i values for PDE5A inhibition (1.2–14 nm). The sildenafil molecule was docked in the structure of PDE5A1 co‐crystallised with sildenafil. All the analogues had similar binding poses as sildenafil.ConclusionsSildenafil analogues that inhibit cellular cGMP efflux are potent inhibitors of PDE5A and PDE6C.

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In this paper we have presented basic principles of analysis of quantitative data in two paired samples. Examples of normality testing, calculating paired t-test both manually and using the STATA software have been given. We have also considered assumptions for using paired t-test as well as the main principles of presentation of the results in scientific publications. The article has given only basic information on the use of t-test in research and it does not substitute reading specialized literature.

In this paper we present basic principles of analysis of quantitative data in three or more paired samples. Examples of normality testing, both manual calculations of repeated measurements analysis of variance (RM-ANOVA) and calculations using STATA software are described. We also discuss assumptions for using RM-ANOVA as well as the main principles of presentation of the results in scientific publications. The article gives only basic information on the use of RM-ANOVA in biomedical research and it does not substitute reading specialized literature.