Fascinating world of macrocycles

Macrocycles belong to the “Middle” chemical space with structural and physicochemical properties positioned between small molecules and biologicals. Although most of the time macrocycles do not follow Lipinski’s rule-of-5,they do fit into the druggable physicochemical property space.1 The structural complexity of macrocycles results in conformational richness that determines the physicochemical and biological properties in a way that differs from the small-molecule chemical space. The 3D character of macrocycles results in a population of bioactive geometries, conformational mimicry, and conformational adaptation to different environments resulting in non-obvious properties (like permeability and oral bioavailability) that could not be deduced from the 2D structure alone (Figure 1).

Figure 1. Dependence of physicochemical and biological properties of macrocyclic compounds on their 3D structure.

Therefore, approaches used for the investigation and design of macrocyclic compounds differ from the standard ones used for the Lipinski ‘druggable’ space. Standard computational tools often fail to properly sample conformational space and to predict bioactive conformation or interactions with biological targets. Quantitative structure–property relationship models of solubility, lipophilicity or permeability, developed for small molecules, are mostly based on fragment contributions and do not take into account conformational effects. Analytical approaches for structural elucidation and conformational studies are also more complex for macrocycles requiring additional expertise and validation of the computational methods. Therefore, working with macrocycles requires additional efforts, equipment and time, but can also be extremely inspiring and rewarding due to their high potential in the yet underexplored chemical ‘middle-space’ and for challenging biological targets. An illustration of the 3D richness of macrocycles in comparison with small molecules is presented by principal moment of inertia (PMI) plot as shown in Figure 2. In light grey are 200 centroids from DrugBank2 which represent both small molecules and existing macrocyclic drugs, in dark grey are known kinase inhibitors and macrocyclic molecules are displayed in red for the comparison. Kinase inhibitors mainly sit on the rod/disc axis, while macrocycles are spread in the middle of the triangle towards the circle area.

Figure 2. Significant 3D enrichment within the macrocyclic chemistry space described with normalised PMI (principal moments of inertia) ratios (pmi1/pmi3; pmi2/pmi3) (structures are from DrugBank2: light grey—200 centroids; dark grey—kinase inhibitors; red— 116 macrocycles).

Most of the recent reviews analyze current macrocyclic ‘druggable’ space in terms of 2D properties, generating ‘Lipinski-like’ macrocyclic rules and boundaries that are not able to identify chameleonic compounds such as cyclosporine. The importance of using 3D conformation dependent descriptors has been highlighted by a Pfizer group’sanalysis of a macrocyclic subset,3 and was recently further elaborated by researchers from Boston University.4 Furthermore, Villar et al.5 recently demonstrated that macrocyclic ring atoms and small substituents directly linked to the ring contribute around 50% to the binding surface area, while the other half come from substituents that are attached to the macrocyclic ring. These analyses emphasize importance of size, shape and conformation of the macrocycle as well as the ring decorations. Figure 3 illustrates the 3D diversity that can be achieved by: (a) the decoration of one scaffold with the same substituent attached at the different positions on the macrocyclic ring, (b) macrocyclic rings with different sizes and (c) chameleonic conformational properties. In the first case, the azithromycin scaffold was decorated with a lipophilic substituent attached directly on the macrocyclic ring or on the sugar moieties.

Figure 3. 3D diversity introduced by (A) decorations on azithromycin scaffold, (B) three macrocycles of different ring sizes and (C) three environment induced cyclosporine conformations.

According to Whitty et al.4 the difference between 2D PSA and 3D PSA is a good indication of the chameleonic properties and could consequently be utilized to determine whether improved passive permeability can be achieved.

Generating reliable 3D structure of macrocyclic compounds is very important for any macrocyclic project. At Fidelta, we use a workflow described on Figure 4 where NMR constrains are used to generate ensemble of macrocyclic conformations. Conformations that satisfy NMR NOEs are further used in the docking experiments and field-based alignments. Macrocyclic conformations are only slightly relaxed while the ring decorations are fully optimized. Epitope mapping by STD NMR also proved to be very useful since x-ray structures of macrocyclic interactions with PPI partners are frequently not available and/or the solution state conformation of the protein-macrocycle complex is expected to be different from those in the crystal structure.

Figure 4. Computational/NMR workflow used in the design of novel macrocyclic modulators of challenging biological targets.

Stay tuned, our next blog post on macrocycles will discuss how described properties of macrocycles and methodologies were used to design FideltaMacro platform and nM inhibitors of IL-17/IL-17A interactions.


  • Author
    Sanja Koštrun, PhD
    Head of CADD group
    Fidelta Ltd.

  • Literature:
    1. Alihodžić, S.; Bukvić, M.; Elenkov, I. J.; Hutinec, A.; Koštrun, S.; Pešić, D.; Saxty, G.; Tomašković, L.; Žiher, D. Current Trends in Macrocyclic Drug Discovery and beyond-Ro5, Prog. Med. Chem. 2018, 57(1), 113-233.
    2. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 2014;42:D1091–7.
    3. Guimara˜es CR, Mathiowetz AM, Shalaeva M, Goetz G, Liras S. Use of 3D properties to characterize beyond rule-of-five property space for passive permeation. J Chem Inf Model 2012;52:882–90.
    4. Whitty A, Zhong M, Viarengo L, Beglov D, Hall DR, Vajda S. Quantifying the chameleonic properties of macrocycles and other high-molecular-weight drugs. Drug Discov Today 2016;21:712–7.
    5. Villar EA, Beglov D, Chennamadhavuni S, Porco Jr JA, Kozakov D, Vajda S, et al. How proteins bind macrocycles. Nat Chem Biol 2014;10:723–31.