Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems

Approaches to increase the efficiency in developing drugs and diagnostics tools, including new drug delivery and diagnostic technologies, are needed for improved diagnosis and treatment of major diseases and health problems such as cancer, inflammatory diseases, chronic wounds, and antibiotic resistance. Development within several areas of research ranging from computational sciences, material sciences, bioengineering to biomedical sciences and bioimaging is needed to realize innovative drug development and diagnostic (DDD) approaches. Here, an overview of recent progresses within key areas that can provide customizable solutions to improve processes and the approaches taken within DDD is provided. Due to the broadness of the area, unfortunately all relevant aspects such as pharmacokinetics of bioactive molecules and delivery systems cannot be covered. Tailored approaches within (i) bioinformatics and computer-aided drug design, (ii) nanotechnology, (iii) novel materials and technologies for drug delivery and diagnostic systems, and (iv) disease models to predict safety and efficacy of medicines under development are focused on. Current developments and challenges ahead are discussed. The broad scope reflects the multidisciplinary nature of the field of DDD and aims to highlight the convergence of biological, pharmaceutical, and medical disciplines needed to meet the societal challenges of the 21st century

VAP-1 as drug target in inflammation : structural features determining ligand interactions

Vascular adhesion protein-1 (VAP-1), which belongs to the copper amine oxidases (CAOs), is a validated drug target in inflammatory diseases. Inhibition of VAP-1 blocks the leukocyte trafficking to sites of inflammation and alleviates inflammatory reactions. In this study, a novel set of potent pyridazinone inhibitors is presented together with their X-ray structure complexes with VAP-1. The crystal structure of serum VAP-1 (sVAP-1) revealed an imidazole binding site in the active site channel and, analogously, the pyridazinone inhibitors were designed to bind into the channel. This is the first time human VAP-1 has been crystallized with a reversible inhibitor and the structures reveal detailed information of the binding mode on the atomic level. Similarly to some earlier studied inhibitors of human VAP-1, the designed pyridazinone inhibitors bind rodent VAP-1 with a lower affinity than human VAP-1. Therefore, we made homology models of rodent VAP-1 and compared human and rodent enzymes to determine differences that might affect the inhibitor binding. The comparison of the crystal structures of the human VAP-1 and the mouse VAP-1 homology model revealed key differences important for the species specific binding properties. In general, the channel in mouse VAP-1 is more narrow and polar than the channel in human VAP-1, which is wider and more hydrophobic. The differences are located in the channel leading to the active site, as well as, in the entrance to the active site channel. The information obtained from these studies is of great importance for the development and design of drugs blocking the activity of human VAP-1, as rodents are often used for in vivo testing of candidate drugs. In order to gain more insight into the selective binding properties of the different CAOs in one species a comprehensive evolutionary study of mammalian CAOs was performed. We found that CAOs can be classified into sub-families according to the residues X1 and X2 of the Thr/Ser-X1-X2-Asn-Tyr-Asp active site motif. In the phylogenetic tree, CAOs group into diamine oxidase, retina specific amine oxidase and VAP-1/serum amine oxidase clades based on the residue in the position X2. We also found that VAP-1 and SAO can be further differentiated based on the residue in the position X1. This is the first large-scale comparison of CAO sequences, which explains some of the reasons for the unique substrate specificities within the CAO family

Multivalent Interactions of Human Primary Amine Oxidase with the V and C2₂ Domains of Sialic Acid-Binding Immunoglobulin-Like Lectin-9 Regulate Its Binding and Amine Oxidase Activity

<div><p>Sialic acid-binding immunoglobulin-like lectin-9 (Siglec-9) on leukocyte surface is a counter-receptor for endothelial cell surface adhesin, human primary amine oxidase (hAOC3), a target protein for anti-inflammatory agents. This interaction can be used to detect inflammation and cancer <i>in vivo</i>, since the labeled peptides derived from the second C2 domain (C2₂) of Siglec-9 specifically bind to the inflammation-inducible hAOC3. As limited knowledge on the interaction between Siglec-9 and hAOC3 has hampered both hAOC3-targeted drug design and <i>in vivo</i> imaging applications, we have now produced and purified the extracellular region of Siglec-9 (Siglec-9-EC) consisting of the V, C2₁ and C2₂ domains, modeled its 3D structure and characterized the hAOC3–Siglec-9 interactions using biophysical methods and activity/inhibition assays. Our results assign individual, previously unknown roles for the V and C2₂ domains. The V domain is responsible for the unusually tight Siglec-9–hAOC3 interactions whereas the intact C2₂ domain of Siglec-9 is required for modulating the enzymatic activity of hAOC3, crucial for the hAOC3-mediated leukocyte trafficking. By characterizing the Siglec-9-EC mutants, we could conclude that R120 in the V domain likely interacts with the terminal sialic acids of hAOC3 attached glycans whereas residues R284 and R290 in C2₂ are involved in the interactions with the active site channel of hAOC3. Furthermore, the C2₂ domain binding enhances the enzymatic activity of hAOC3 although the sialic acid-binding capacity of the V domain of Siglec-9 is abolished by the R120S mutation. To conclude, our results prove that the V and C2₂ domains of Siglec-9-EC interact with hAOC3 in a multifaceted and unique way, forming both glycan-mediated and direct protein-protein interactions, respectively. The reported results on the mechanism of the Siglec-9–hAOC3 interaction are valuable for the development of hAOC3-targeted therapeutics and diagnostic tools.</p></div

The role of sugars, sialic acid binding domain (ΔV) and the mutations R284S, R290S on the binding of Siglec-9-EC to hAOC3.

<p><b>(A)</b> Sialic acid addition decreases the binding of Siglec-9-EC (WT) to hAOC3 by SPR. <b>(B)</b> The effect of the disialyl lactotetraosylceramide (DSLc4) sugar on hAOC3–Siglec-9-EC interaction. Each experiment was performed as duplicate. <b>(C)</b> Relative binding of Siglec-9-EC (WT), -R284S, -R290S, -ΔV, -R120S, R120S/R284 and–R120S/R290S mutants at concentration of 0.5 μM to immobilized hAOC3 (means of two (R120S based mutants)-three experiments are shown ± SEM). *: p<0.05, **:p<0.01 and ***:p<0.001.</p

Characterization of Siglec-9-EC with the fluorescence-based thermal shift assay.

<p><b>(A)</b> The effect of different buffer pH values on the stability of Siglec-9-EC. The tested buffers were: Citric_pH4 = 100 mM citrate buffer pH 4.0, NaAcetate_pH5 = 100 mM Sodium acetate buffer pH 5.0, MES_pH6 = 100 mM MES buffer pH 6.0, HEPES_pH7 = 100 mM HEPES buffer pH 7.0, Tris-HCl_pH8 = 100 mM Tris-HCl buffer pH 8.0, ImidMaleic_pH 8.5 = 100 mM N-imidazolyl maleamic acid pH 8.5, CHES_pH 9.5 = 100 mM CHES buffer pH 9.5. All of them contained 125 mM NaCl. <b>(B)</b> Thermal shift assay of Siglec-9-EC in 20 mM HEPES buffer, 150mM NaCl, pH 7.4 in the presence of different additives. <b>(C)</b> Thermal shift assay of Siglec-9-EC and the mutant Siglec-9-EC proteins to check the effect of the mutations on the stability of the protein.</p

Purification of Siglec-9-EC from the culture medium.

<p><b>(A)</b> Size exclusion chromatogram of the final purification step for Siglec-9-EC. The fractions from the peak region, marked with double arrow, were collected. The Siglec-9-EC mutants were purified using the same protocol and gave similar curves (data not shown). The calibration curve of the standard proteins, bovine thyroglobulin (670 kDa), bovine γ-globulin (158 kDa), chicken ovalbumin (44 kDa) and horse myoglobulin (17 kDa), is shown aside the chromatogram. <b>(B)</b> SDS-PAGE analysis of the purified wild-type (WT) sample that was collected from the peak region shown in Fig 2A.</p

Binding constants of Siglec-9-EC proteins on hAOC3.

<p><i>K</i>_<i>d</i> of Siglec-9-EC/R284S differed significantly from <i>K</i>_<i>d</i> of Siglec-9-EC (Z = -2.61, <i>p</i> = .027), but <i>K</i>_<i>d</i>(Siglec-9-EC/R290S) did not (Z = -1.57, <i>p</i> = .302).</p

The interplay between the binding of Siglec-9 to hAOC3 and the enzymatic activity of hAOC3.

<p><b>(A)</b> We detected no activity of CHO-hAOC3 cell lysates (black columns) on Siglec-9-EC (WT) when compared to inactive CHO-hAOC3-Y471F lysate (white columns). Typical activity of CHO-hAOC3 cells was detected towards positive control substrate benzylamine (BA) and it was significantly higher than for the control CHO-hAOC3-Y471F cells (Z = -2.72, <i>p</i> = .006). Means of six experiments ± SEM. <b>(B)</b> The relative activity of CHO-hAOC3 cells on labeled BA, when known inhibitor semicarbazide (SC) or 5 μM Siglec-9-EC (WT) was added before the substrate. Mean of three independent experiments ± SEM are shown. The effect of Siglec-9-EC on the activity of CHO-hAOC3 towards BA was significantly elevated when compared to the activity without added Siglec-9-EC (Z = -1.96, p = .05). <b>(C)</b> The relative activity of the CHO-hAOC3 cells on labeled BA, when BSA control, known inhibitor SC, 1 μM Siglec-9-EC (WT), -R120S, -R284S, -R120S/R284S or–R120S/R290S was added before the substrate. Mean of three independent experiments ± SEM are shown. SC significantly reduces the activity of hAOC3 cells (Z = -2.31, <i>p</i> = .021) and Siglec-9-EC increases the activity of hAOC3 cells, although the difference is not statistically significant (Z = -1.41, p = .157). The R120S mutant, however, does increase the activity significantly (Z = -2.12, <i>p</i> = .034) whereas the R284S mutant does not (Z = -1.06, <i>p</i> = .289). In addition, both of the double mutants (R120S/R284S: Z = -0.87, <i>p</i> = .386; R120S/R290S: Z = -1.77, <i>p</i> = .077) abolish the effect of Siglec-9-EC because they fail to increase the activity of hAOC3. *: p<0.05, **:p<0.01 and ***:p<0.001.</p

The 3D model for Siglec-9-EC.

<p>The two alternative models for Siglec-9-EC with a differently modeled flexible linker region (FL, between C2₁ and C2₂ domains) before and after the refinement by MD simulations. <b>(A)</b> Model 1. <b>(B)</b> Model 2. <b>(C)</b> Superimposition of the V-C2₁ domains of the two models shows a slightly different orientation for the C2₂ domain. Model 1 in magenta and model 2 in cyan. <b>(D)</b> The best model for Siglec-9-EC (model 1). Sialic acid (SA; in grey sticks) is modeled to interact with R120 in the V domain. The V domain is shown in green, C2₁ in blue and C2₂ in magenta. In <b>(C)</b> and <b>(D)</b>, R120 in the V domain and R284 and R290 in the C2₂ domain are shown as spheres and cysteines forming the disulphide bridges in yellow.</p

The multiple sequence alignments used in the 3D modeling of Siglec-9-EC.

<p>The sequence numbering (according to the Siglec-9 sequence) and the secondary structural elements (yellow and pink boxes denoting the beta sheets and the 3/10-alpha helices, respectively) in the Siglec-5 structure (PDB ID 2ZG2), which was used as a template in the modeling procedure, are shown above both of the alignments. The conserved residues have a cyan background. <b>(A)</b> Multiple sequence alignment used in the 3D modeling of the V-C2₁ domains of Siglec-9. In the sequence alignment of the V-C2₁ domains of Siglec-3, -5, -6, -7, -9 and -14, the six cysteine residues forming disulphide bonds are marked as ‘1’, ‘2’ and ‘3’ (in green) below the alignment. The key sialic acid binding residue, R120, is highlighted in blue background. The two light blue arrows below the alignment mark the V domain and the light brown arrows define the C2₁ domain. <b>(B)</b> Multiple sequence alignment of the C2 domains of CD-33-related Siglec sequences used in the 3D modeling of the C2₂ domain of Siglec-9. The sequence alignment includes the C2₁, C2₂ and C2₃ domains. The key arginine residues, R284 and R290, are highlighted with a blue background. The phage peptide sequence is shown in green under the sequence alignment. The Siglec-9 peptide sequence used in the PET study [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166935#pone.0166935.ref006" target="_blank">6</a>] is boxed in the alignment. The conserved cysteine residues forming the disulphide bond within the C2 domain are in bold letters.</p