How Beyond Rule of 5 Drugs and Clinical Candidates Bind to Their

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How Beyond Rule of 5 Drugs and Clinical Candidates Bind to Their Targets Bradley Croy Doak, Jie Zheng, Doreen Dobritzsch, and Jan Kihlberg J. Med. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jmedchem.5b01286 • Publication Date (Web): 12 Oct 2015 Downloaded from http://pubs.acs.org on October 14, 2015

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Journal of Medicinal Chemistry

How Beyond Rule of 5 Drugs and Clinical Candidates Bind to Their Targets Bradley C. Doak, Jie Zheng, Doreen Dobritzsch and Jan Kihlberg* Department of Chemistry - BMC, Uppsala University, Box 576, SE-751 23 Uppsala, Sweden

ABSTRACT

To improve discovery of drugs for difficult targets the opportunities of chemical space beyond the rule of 5 (bRo5) was examined by retrospective analysis of a comprehensive set of structures for complexes between drugs and clinical candidates and their targets. The analysis illustrates the potential of compounds far beyond rule of 5 space to modulate novel and difficult target classes that have large, flat and groove-shaped binding sites. However, ligand efficiencies are significantly reduced for flatand groove-shape binding sites, suggesting that adjustments of how to use such metrics are required. Ligands bRo5 appear to benefit from an appropriate balance between rigidity and flexibility to bind with sufficient affinity to their targets, with macrocycles and non-macrocycles being found to have similar flexibility. However, macrocycles were more disc and sphere-like which may contribute to their superior binding to flat sites, while rigidification of non-macrocycles lead to rod-like ligands that

1 ACS Paragon Plus Environment


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bind well to groove-shaped binding sites. These insights should contribute to altering perceptions of what targets are considered "druggable" and provide support for drug design in beyond rule of 5 space.

1. INTRODUCTION Drug discovery is at a crossroads where ground-breaking advances in our understanding of how diseases develop are now made at an unprecedented pace. However, efficiency of drug discovery has continued to decline as the number of new drugs approved each year has essentially been constant during the last 30 years, while the costs of pharmaceutical development have increased dramatically.1-3 This decline has been attributed to a few fundamental issues, including a need to deliver first-in-class treatments for complex diseases, while at the same time meeting increased demands for safety and efficacy.4 As a result there is high attrition in phase II and III clinical trials, mainly due to lack of efficacy and safety issues.2, 5, 6 Therefore, it has been emphasised that improved selection of targets that are associated with diseases is the single most important factor required to increase efficacy and deliver innovative medicines.2

During the last two decades the human genome and various other genomes have been mapped,7 and significant progress has been made towards mapping the human proteome.8, 9 These rapid advances have made an increased number of potential drug targets accessible that belong to both established and novel target classes. Despite the advances in target identification less than a quarter of recently approved drugs are directed against novel targets, and the majority of these drugs target established classes of G-protein coupled receptors (GPCRs), transporters or enzymes.1, 10 A limiting factor may be that approximately 3,000 of the genes in the human genome have been estimated to be related to


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disease. Out of these only 600-1500 have been considered amenable for manipulation with "traditional" small molecule drugs,11 i.e. drugs that comply with the rule of 5 (Ro5) guidelines and are highly likely to be cell permeable and orally bioavailable. Still, it has been pointed out that large portions of well-established target classes, such as ion channels, GPCRs and nuclear receptors remain unexplored.10 However, an even larger number of targets from less explored and novel classes which are "difficult-to-drug" using Ro5 compliant compounds could provide significant, additional opportunities for drug discovery. For example, the human proteome8, 9 is estimated to have 100,000 to 1,000,000 binary protein-protein interactions (PPIs)12, 13 and may constitute one of the most important sources of novel targets for drug discovery. However, the proportions of the proteome and its massive number of PPIs that are involved in pathogenic mechanisms remains to be established. Even with that caveat the recent and rapid developments in target identification urgently need to be matched by innovative approaches for modulating non-traditional target classes, such as PPIs.14, 15

Targets currently classified as "difficult-to-drug" with Ro5 compliant ligands characteristically have binding sites that are large, highly lipophilic or highly polar, flexible, flat or featureless (i.e. contain few opportunities for molecular interactions such as hydrogen bond donors and acceptors).16-19 In addition, the perceived lack of oral bioavailability outside of Ro5 space has led many to abandon these targets and classify them as "undruggable". Thus, what initially appears as vast opportunities of novel targets emerging from advances in genomics and proteomics will, to a large extent, require small molecule drug discovery to move outside of Ro5 space into what has been termed beyond Ro5 (bRo5) or "middle space".20 Interestingly, recent analysis of drugs and clinical candidates that fall outside of Ro5 space has shown that this space offers significant possibilities for discovery of orally bioavailable and cell permeable compounds, possibly more than previously thought.21 It can therefore be argued



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that a too strict implementation of the Ro5 may have hampered the pharmaceutical industry from seizing opportunities involving novel but more difficult targets.21-25

We and others have hypothesised the benefits of using bRo5 drugs for difficult targets,14, 15, 21, 26, 27 and examples and case studies have been reported in the literature. Here we present a comprehensive analysis of bRo5 drugs and clinical candidates that highlight their ability to modulate difficult targets, thereby expanding the number of targets for which we can design oral and parenteral drugs. First, we assessed what target classes current drugs and clinical candidates outside Ro5 space are directed towards in comparison to Ro5 compliant drugs. Analysis then focused on how drugs and clinical candidates outside Ro5 space bind to their targets based on crystal structures of 130 clinically relevant complexes, which were compared to drug-target complexes in Ro5 space. This allowed us to define to what extent binding site and ligand characteristics such as size, shape, molecular interactions, affinity and ligand efficiencies differ between different drug spaces. The influence of conformational flexibility of the ligand and its shape was also investigated for compounds in beyond Ro5 space. The results are then discussed to provide guidance for design of bioactive small molecule drugs outside of Ro5 space for difficult targets.

2. THE DRUGS AND CLINICAL CANDIDATES DATASETS To facilitate this in-depth analysis of how drugs and clinical candidates that do not comply with the Ro5 bind to their targets a comprehensive dataset of 475 drugs and clinical candidates with MW >500 Da was classified by the compounds calculated physicochemical properties. They were then divided into two datasets where intuitive and natural divisions in the ligand property distributions appeared as previously reported,21 each representing different chemical spaces (Figure 1a). Two datasets of Ro5


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compliant drugs were also compiled from ChEMBL28 and the recent literature10 for comparison during analysis (Figure 1, 2 & 3). In this analysis compounds in rule of 5 space adhere to all of Lipinski's guidelines, whereas compounds that break one Ro5 guideline (MW 500-700 Da) and also have other properties which may extend a short distance outside strict Ro5 space were classified as being in extended Ro5 space (eRo5).21 Finally, compounds in beyond Ro5 space (bRo5) all have MW >500 Da and in addition have one or more properties outside the eRo5 ranges They are thus far beyond Ro5 space, but with an upper MW limit of 3000 Da set to exclude biologics such as insulin. The classification into eRo5 and bRo5 space is useful to completely separate compounds in Ro5 space from those that reside far away in bRo5 space and do not conform to its trends. Thus, eRo5 space may be thought of as a buffer zone between Ro5 and bRo5 space, representing the natural tail of the distribution of Ro5 drugs, in line with the original report of Lipinski,29 and the beginning of bRo5 space. The rational for the eRo5 and bRo5 classification is further highlighted by the two datasets having mean quantitative estimates of drug-likeness (QED) scores of 0.31 and 0.16, respectively, providing a single measure of the distance from traditional rule of 5 space. Both of these QED scores are significantly below 0.67 and 0.49 which is the mean value identified by medicinal chemists for compounds being "attractive" and "unattractive" for drug development, respectively.21, 30 Our dataset of drugs and clinical candidates was obtained by searching different databases for compounds with MW ranging from 500 to 3000 followed by filtering to remove contrast agents, veterinarian products etc.21 Therefore some drugs and clinical candidates outside of our strict definition of Ro5 space, i.e. those with MW 5, HBA >10 or ClogP >5 or 5, HBA >10, PSA >200 Å2 NRotB >20

eRo5, N =195 71% oral

bRo5, N =280 30% oral

b)

Figure 1. (a) Classification of 475 drugs and clinical candidates that have MW >500 Da into extended rule of 5 (blue) and beyond rule of 5 (green) chemical space based on calculated physicochemical 6 ACS Paragon Plus Environment

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properties.

(b)

Development

pipeline

by

chemical

space

and

chemical

class

showing

peptides/peptidomimetics (green), natural products and derivatives (blue) and de novo designed drugs and clinical candidates (red) by phase. Orals are in dark and parenterals in light colors, respectively.

The dataset of 475 drugs and clinical candidates in eRo5 and bRo5 space that make up the current dataset was previously curated and used to investigate oral bioavailability in eRo5 and bRo5 space.21 It was also classified with regards to chemical class, route of administration and phase of development.21 This allows discussion of trends in drug development and demonstrates that de novo designed compounds

are

in

majority

(43%),

with

equal

numbers

of

natural

products

and

peptides/peptiodomimetics (26% each) across the full dataset.21 The majority of de novo designed compounds are oral (64%), whereas natural products and in particular peptides/peptidomimetics are mainly parenteral (59 and 80%, respectively). Analysing the dataset by phase of development, chemical space and chemical class demonstrates that de novo designed compounds dominate strongly in all clinical phases in eRo5 space, and that the majority of them are intended for oral administration (Figure 1b). In bRo5 space peptides constitute the largest group across clinical candidates with proportions of de novo designed compounds and natural products are only somewhat lower. In phase II, III and approved, bRo5 natural products and peptides are mainly for parenteral administration, whereas the proportion of orals is >45% for de novo designed compounds. In summary, the drug discovery industry is focusing on development of de novo designed drug candidates for oral administration in eRo5 space, while relying on all three chemical classes and more on parenteral delivery in bRo5 space. It is noteworthy that a significant number of compounds (161 in total) from all three chemical classes in bRo5 space are in clinical development, indicating a willingness to venture outside of the Ro5. This is further supported by the emergence of a number of biotech companies that



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focus on this chemical space,27 often in partnership with larger pharmaceutical companies. In the current analysis we aim to globally assess bRo5 ligand-target interactions to define if moving far from traditional Ro5 space is warranted in efforts to conquer difficult targets and for what target classes and types of binding sites compounds in bRo5 space provide advantages.

3. TARGET CLASS MODULATION BY CHEMICAL SPACE To analyse the target class preferences of drugs and clinical candidates in eRo5 and bRo5 space, the dataset of 475 drugs and clinical candidates with MW >500 Da21 was first classified using a similar taxonomy as employed to dissect trends and innovations in drug development for a large dataset of approved drugs (Figure 2a).10 A Ro5 target class reference dataset was obtained by filtering this large literature dataset of approved drugs10 by all of the Ro5 guidelines and selection of only one target per drug. Drugs in eRo5 and bRo5 space showed interesting differences in their target class preferences compared to Ro5 compliant drugs (Figure 2b). For instance, an increased proportion of eRo5 and bRo5 drugs and clinical candidates modulate protease and kinase targets, which have been increasingly explored only in the last decade.10 Similar to the approved Ro5 compliant kinase inhibitors in our dataset, tyrosine kinases were the largest subgroup targeted by eRo5 and bRo5 drugs and clinical candidates (42%). Kinase inhibitors currently in clinical trials originate from Ro5, as well as from eRo5 and bRo5 space, and no significant trends linking subgroups of kinases to a particular chemical space were apparent. Nevertheless it is clear that medicinal chemists are drawing from compounds outside traditional Ro5 space to target an expanding number of kinases. Structural and adhesion targets such as tubulin, as well as transferases and isomerases are also more prevalent for eRo5 and/or bRo5 drugs and clinical candidates. In our analysis there is also a higher prevalence of bRo5 and eRo5 drugs and clinical candidates at "other" targets; a class consisting of, antioxidants, vitamin and hormone


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replacements, orphan drugs and other unclassifiable molecular targets. Moreover, as compared to Ro5 drugs a smaller proportion of bRo5 drugs and clinical candidates bind to well-established target classes such as ion channels and nuclear receptors. Similar trends in target class preference are also observed for the oral only and approved only subsets of eRo5 and bRo5 drugs and clinical candidates (Supporting Information Figure S3). Importantly, a number of classes that are more frequently targeted by eRo5 and bRo5 drugs and clinical candidates, such as proteases, kinases and transferases are among those recently concluded to be underexplored in drug discovery.10

As different targets have been found to have different preferred ligand chemical spaces,31,

32

we

conclude that increased exploration of eRo5 and bRo5 space should be beneficial for future development of drugs for underexplored target classes. The discovery of protein kinase inhibitors, now commonly used in oncology, is an important example of how exploration of novel chemical space can expand what targets are considered "druggable".33 It should also be noted that eRo5 and bRo5 compounds also appear to be among the most suitable for modulating the increasing number of protein-protein interactions (PPI) that are emerging as therapeutic targets.14, 34

Rask-Andersen et al. dataset of approved drugs rule of 5, filtered by all of: MW ≤500 Da, ClogP 0-5, HBD ≤5, HBA ≤10 Primary target selected for each drug

a)

Ro5 drug-target pairs N=579 Approved

eRo5, N =195, 71 % oral 59 App., 32 PIII, 67 PII, 37 PI

bRo5, N =280, 30 % oral 119 App., 37 PIII, 88 PII, 36 PI

Target classification similar to Rask-Andersen et al., removal of unclassified compounds eRo5 drug-target pairs N=185, 71% Oral, 30% Approved

bRo5 drug-target pairs N =228, 31% Oral, 38% Approved



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b) Figure 2. (a) Creation of target class preference datasets. The dataset compiled by Rask-Andersen et al.10 was filtered by all the rule of 5 (Ro5) guidelines and the primary target reported in the literature was selected for each drug to give the Ro5 dataset. The primary targets reported in the literature for the full set of extended Ro5 (eRo5) and beyond Ro5 (bRo5) drugs and clinical candidates were classified using the same taxonomy. (b) Proportion of Ro5 drugs (red), eRo5 (blue) and bRo5 (green) drugs and clinical candidates modulating the indicated target classes. The proportion of compounds in each of the three chemical space datasets that modulates a specific target class (the number of compounds that modulate that target class divided by the total number in the dataset, N), is shown by the vertical bars. Proportions were calculated to show differences in target preferences as the number of compounds differ significantly between the three datasets. Compounds that are in phase (P) I, II or III or approved (App.) are shown by increasingly darker colour shadings of the datasets. Targets are arranged by Ro5 target class preference from highest (left) to lowest (right). Alternate plots that show only approved, clinical or orally bioavailable dugs and clinical candidates in eRo5 and bRo5 space, as well as the exact number of compounds in each category, are included in Supporting Information Figure S3.

4. CHARACTERIZATION OF DRUG-TARGET COMPLEXES BY CHEMICAL SPACE


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4.1. Generating drug-target structure datasets. To probe how drugs outside of Ro5 space bind to their targets structural data for all available complexes of eRo5 and bRo5 drugs and clinical candidates with their targets were extracted by cross-referencing the 475 drugs and clinical candidates with the Protein Data Bank (PDB). In total, 93 drugs had crystal structures of relevant drug-target complexes that fulfilled the chosen quality requirements (20% of the dataset, Figure 3). A reference set of 37 crystal structures of relevant Ro5 drug-target complexes was also obtained after clustering a Ro5 filtered ChEMBL drugs dataset according to their physicochemical properties (Figure 3). In order to remove bias towards highly explored targets or drug classes and ensure that conclusions reflect the true variation between chemical spaces, redundant complexes between a target and other members of the same drug class were excluded. For example, the erythromycin A-ribosome complex 1JZY was used as representative of all erythronolide-ribosome complexes. This produced three non-redundant datasets of crystal structures of Ro5 drugs (N=29), eRo5 (N=26) and bRo5 (N=22) drugs and clinical candidates bound to their targets (Table 1). An extensive set of additional annotated figures for both the all structures and the non-redundant datasets can be found in the supplementary information along with statistical analysis.



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ChEMBL drugs dataset N=10,460 rule of 5 filtered by all of: MW ≤500 Da, ClogP 0-5 HBD ≤5, HBA ≤10 Clustered by physicochemical properties

eRo5, N =195, 71 % oral 59 App., 32 PIII, 67 PII, 37 PI

bRo5, N =280, 30 % oral 119 App., 37 PIII, 88 PII, 36 PI

Cross-referencing with the PDB and selection of crystal structures: Resolution 0-3.8 Å, good density at binding site interface, clinically relevant drug-target complex all representative Ro5 drugtarget structures N =37 86% oral, all approved

all eRo5 drug-target structures N =47, 72 % oral 21 App., 5 PIII, 11 PII, 10 PI

all bRo5 drug-target structures N =46, 65% oral 32 App., 5 PIII, 8 PII, 1 PI

Results in Supporting Information

Filtering to remove redundant drug class-target binding sites. e.g. Erythromycin A was selected to represent all erythronolide-ribosome complexes non-redundant Ro5 structures N =29, 86% oral all approved

non-redundant eRo5 structures N =26, 58 % oral 11 App., 4 PIII, 7 PII, 4 PI

non-redundant bRo5 structures N =22, 48 % oral 17 App., 1 PIII, 3 PII, 1 PI

Results in paper & Supporting Information

Figure 3. Cross referencing the extended Ro5, beyond Ro5 datasets and a representative set of ChEMBL28 Ro5 drugs with the Protein Data Bank (PDB) and filtering by quality constraints gave three datasets of relevant Ro5, eRo5 and bRo5 drug-target structures. To avoid bias towards highly explored drug classes further filtering to remove redundant drug class-target structures gave unbiased, non-redundant datasets which contain only one compound per drug-target class (e.g. one erythronolide, one azole anti-infective etc.). The number of compounds approved (App.) and in Phases (P) III, II and I development are shown. Results of the analysis of the complete datasets can be found in the supporting information for comparison, while the non-redundant datasets are analysed within the paper as well as in the supporting information.


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Table 1. Analysed non-redundant drug-target complexes in extended and beyond Ro5 chemical space Compound name

Beyond Ro5 (N=22) Argatroban β-Acarbose Birinapant Capremycin Cyclosporine A Dactinomycin Doxorubicin Eptifibatide Erythromycin A Etoposide Eritoran Itraconazole Ivermectin 22,23dihydro B1a Navitoclax Ouabain Paclitaxel PF-03715455 Quinurpistin Rapamycin Rifampicin Simeprevir Thiosptrepton Extended Ro5 (N=26) Aliskiren AMG-131 Atorvastatin BGJ-398 BMS-777607 BMS-791325 Ceritinib Cobimetinib Dalfopristin EPZ-5676 Ergotamine Fedratinib Homoharringtonine Intedanib Ispinesib Lapatinib Lonafarnib Mometasone furoate Nilotinib Pictilisib

Macromolecule (Target)

Indication

PDB code

Oral/ Parenterala

Clinical/ Approved

Thrombin α-Amylase E3 ubiquitin-protein ligase XIAP Ribosome Cyclophilin A DNA DNA Integrin alpha-IIB Ribosome (D. radiodurans) DNA topoisomerase-IIb Toll-like receptor 4 Lanosterol 14-α demethylase Glutamate-gated chloride channel

Haematology Endocrinology Oncology Infection Immunology Oncology Oncology Cardiovascular Infection Oncology Infection Infection Infection

1DWC 1PPI 4KMP 3KNL 1CWA 1I3W 1P20 2VDN 1JZY 3QX3 2Z65 4K0F 3RHW

parenteral parenteral parenteral parenteral oral parenteral parenteral parenteral oral oral parenteral oral oral

App App Phase II App App App App App Phase III App App App App

B cell lymphoma-2, Bcl-2 Na-K ATPase Tubulin α-chain Mitogen-activated protein kinase Ribosome (H. marismortui) FK560 binding protein DNA-directed RNA polymerase Hepatitis C virus NS3/4A protease Ribosome (D. radiodurans)

Oncology Cardiovascular Oncology Respiratory Infection Immunology Infection Infection Infection

4LVT 3A3Y 1JFF 2YIS 1YJW 4DRI 4KMU 3KEE 3CF5

oral oral oral parenteral parenteral oral oral oral parenteral

Phase II App Phase II Phase I App App App App App

Renin Peroxisome proliferator-activated receptor-γ HMG-CoA reductase Fibroblast growth factor 1 Hepatocyte growth factor receptor Hepatitis C virus NS5b subunit Anaplastic lymphoma kinase Mitogen-activated protein kinase kinase Ribosome (D. radiodurans) DOT1-like histone H3 methyltransferase Serotonin receptor 1B chimera Bromodomain BRD4 Ribosome (H. marismortui) Vascular endothelial growth factor receptor 2 Kinesin Eg5 Epidermal growth factor receptor Protein farnesyltransferase Glucocorticoid receptor Tyrosine-protein kinase ABL1 Phosphoinositide-3 kinase

Cardiovascular Cardiovascular

2V0Z 3FUR

oral oral

App Phase II

Cardiovascular Oncology Oncology Infection Oncology Oncology Infection Oncology Pain Oncology Infection Oncology

1HWK 3TT0 3F82 4NLD 4MKC 4AN2 1SM1 4HRA 4IAR 4OGJ 3G6E 3C7Q

oral oral oral oral oral oral parenteral parenteral parenteral oral parenteral parenteral

App Phase I Phase I Phase II Phase II Phase III App Phase I App Phase III App Phase III

Oncology Oncology Oncology Respiratory Oncology Oncology

4A5Y 1XKK 1O5M 4P6W 3CS9 3DBS

parenteral oral oral parenteral oral oral

Phase II App Phase II App App Phase II



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Pseudomonic acid A PU-H71 Saquinavir Taladegib Tubocurarane Volasertib a

Isoleucyl-tRNA synthetase Heat shock protein-90 HIV-1 protease Smoothened homolog Soluble acetylcholine receptor Polo-like kinase 1

Infection Oncology Infection Oncology Anaesthesiology Oncology

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1QU2 2FWZ 3OXC 4JKV 3PMZ 3FC2

parenteral parenteral oral oral parenteral parenteral

App Phase I App Phase II App Phase III

Route of administration used in the indicated phase of development.

4.2 Shape and size of binding sites. The binding site shape of drug-target complexes can be assessed manually or with the aid of descriptors calculated from binding sites identified by automated methods. Two such methods, the recently described Difference of Gaussian Site (DoGSite)35, 36 and MetaPocket 2.0,37 the latter of which is based on several algorithms, were used to analyse the three datasets of drug-target complexes. The threshold for successful identification of binding sites was set as >20% of the volume of the bound drug being covered by the calculated binding site. With this lenient cut-off only 54% and 43% of all bRo5 drug-target bindings sites were successfully identified by DoGSite and MetaPocket, respectively (Supporting Information Figure S4a). In addition, the successfully calculated bRo5 binding sites covered a significantly lower proportion of the bound drug than Ro5 drug binding sites (Mean 54% vs 94%, respectively; Supporting Information Figure S4). Hence, bindings site shape was also assessed manually by visual inspection and classification as being flat, groove, tunnel, pocket or internal. These correspond to the drug interacting with its target by a single face for a flat site, two or three faces for a groove and four faces with two non-interacting opposing faces for a tunnel-shaped site. Interactions of the drug through four or five faces, leaving one non-interactive face, characterises a pocket, and for an internal binding site the drug is completely buried inside the target (Supporting Information Figure S5). For binding sites that were successfully calculated with DoGSite, descriptors such as enclosure and depth corresponded well to the shape classifications and thereby support the manual classification (Supporting Information Figure S6). However, volume and sphericity descriptors failed to accurately describe the size and shape of flat14 ACS Paragon Plus Environment

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and groove-shaped binding sites as the calculated sites poorly covered the actual drug or clinical candidate binding site. The manual classification is also supported by the increase in the mean proportion of drug surface areas (SA) that become buried upon binding to the target from flat to internal binding sites (Supporting Information Figure S8). Due to the low success rate in binding site calculation, and the inability of descriptors to characterize those sites that were successfully calculated, the manual classification of binding site shapes was used throughout the current analysis.

The distribution of binding site shapes showed striking differences between the three sets of drugtarget complexes (Figure 4a), with higher proportions of bRo5 drugs and clinical candidates binding to the "difficult" open, flat and groove binding sites compared to Ro5 drugs. In contrast, Ro5 drugs display a preference for pocket and internal binding sites, which conforms well with the view of such sites as being highly "druggable" with Ro5 compliant compounds. Binding site shapes observed for eRo5 drugs and clinical candidates are more evenly distributed between groove, tunnel, pocket and internal sites, revealing an ability of compounds residing just outside Ro5 chemical space to target a wide range of sites, with the exception of flat binding sites. It should be noted, that compounds in eRo5 space also bind to pocket shaped and internal sites, which are then larger than those that have Ro5 compliant ligands (Supporting Information Figure S8). Too few ligands in bRo5 space bind to pockets to draw any statistically significant conclusions regarding binding site size. The shape of the ligands, in their target bound conformations, were also assessed using normalised principle moment of inertia (nPMI) plots, which characterize ligands by their similarity to rod, disc and sphere shapes (Figure 4b). In agreement with previous analyses based on calculated 3D conformations,38 Ro5 compliant drugs were predominantly rod-like, while those in eRo5 and particularly in bRo5 space were more disc- and sphere-like. Flat and groove binding sites were also found to have ligands that were



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significantly more disc- and sphere-like compared to ligands for pocket and internal binding site shapes (Supporting Information Figure S9-S10).

a)

c)

b)

d)

e)

Figure 4. (a) Distribution of binding site shapes, (b) ligand normalised principle moment of inertia (nPMI) shape plot, (c) buried ligand surface area (SA, Å2) as a function of total ligand SA (Å2), (d) box plot of buried ligand SA (Å2) and (e) box plot of proportion of buried ligand SA. Each figure presents data for rule of 5 drugs (Ro5, red), extended Ro5 (eRo5, blue) and beyond Ro5 (bRo5, green) drugs and clinical candidates in complex with their respective targets. Protein-protein interaction (PPI) 16 ACS Paragon Plus Environment

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interface SA data were extracted from Luo et al.39 and reanalysed. Box plots show minimum and maximum values as whiskers, the 25th, 50th and 75th percentiles as boxes and means as crosses. Horizontal lines indicate unpaired and unequal variance t-test of compared datasets with their p-values.

In addition to binding site shape, the ligand surface area (SA) that is buried upon binding and its proportion to the total ligand SA also provides useful information about the nature of the binding sites. While the buried SA indicates the size of the binding site, the proportion of buried ligand SA can indicate how open or exposed the binding site is. The plot of buried ligand SA against total ligand SA shows that eRo5 and bRo5 drugs have larger SAs buried in complexes with their targets than Ro5 drugs (Figure 4c, d). The proportion of buried ligand SA is, however, lower for drugs in eRo5 and bRo5 space compared to Ro5 space (Figure 4c, e, Supporting Information Figure S8), which is consistent with the preference of eRo5 and bRo5 drugs for flat and groove binding sites. Although the buried SA of eRo5 and bRo5 drugs is larger than that of Ro5 drugs, it is still slightly smaller than interface areas in weak protein-protein interactions (PPIs with Kd values >1 µM) and significantly smaller than those in strong protein-protein interactions (PPIs with Kd values 500 Da also reveals that orally bioavailable, as



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well as parenteral, macrocycles are significantly enriched in bRo5 space compared to non-macrocycles (Figure 8a), a finding that has been highlighted before.21, 25, 27 Though the reason for this enrichment has not been conclusively identified, it is known that conformational constraints imposed by the macrocyclic structure can convey improved pharmacokinetics, but also higher potency and better selectivity as compared to related acyclic analogues at similar binding sites.26, 27, 48, 49 Conformational restriction has therefore been postulated as a general principle leading to macrocycle enrichment in bRo5 space26, 27, 48, 49 and is consistent with studies showing that increasing the number of rotatable bonds has a negative effect on the oral bioavailability of drugs, independent of their chemical class.50, 51

Generally increasing the conformational flexibility of drug candidates is expected to reduce both the

affinity and selectivity of target binding,48 although the correlation between flexibility and promiscuity has been questioned.52, 53 As the influence of conformational restriction through macrocyclization on target binding still remains unclear, at least in some cases,54 we also investigated flexibility and shape of macrocycles and non-macrocycles for our dataset of bRo5 drugs and clinical candidates to obtain an overview of flexibility in bRo5 space.

5.1 Flexibility of macrocycles and non-macrocycles. We first investigated whether macrocyclic drugs and clinical candidates are more rigid than non-macrocyclic ones in bRo5 chemical space. Due to the well-known difficulty in predicting the conformations of bRo5 compounds using computational methods,49, 55 we analysed all available experimental conformers from crystal structures in the Protein Databank (PDB) and Cambridge Structural Database (CSD) for our dataset of bRo5 drugs and clinical candidates. Arguably such an analysis is limited by the size of the dataset and the data that is available for each compound, and possibly biased by crystal packing artefacts and poorly refined geometries,56, 57

but it has the advantage of being based on experimental data. We found a total of 24 drugs and


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clinical candidates in bRo5 space, with 12 non-redundant drug classes that displayed multiple conformations in the crystalline state. In spite of its somewhat limited size, this dataset consists of the largest number of bRo5 drugs for which experimentally determined conformations have been analysed, and allowed us to reach some interesting, but potentially preliminary conclusions.

All conformers observed for a given drug or clinical candidate were clustered to identify a set of representative conformers that showed >1 Å root mean square deviation (RMSD) of all heavy atoms between different conformers. This ensured that the subsequent analysis was not biased by multiple crystal structures of the same conformation. The representative conformers were then compared using average RMSD, where a high value indicates that the compound is flexible and can adopt one or more significantly different conformations. In addition to the RMSD values for all atoms, RMSD values for the macrocyclic core atoms subset and core plus single heavy atoms attached directly to the core (peripheral atoms) subset were also calculated for macrocyclic drugs and clinical candidates.

We found that RMSD values of the macrocyclic core atoms are similar to those of the core plus peripheral atom subset, but that RMSDs are significantly higher when all atoms in the macrocycle drugs are taken into account (Figure 8b, Supporting Information Figure S20). This indicates that the side chains are commonly the most dynamic regions of macrocyclic drugs. Furthermore, the all atom RMSD of macrocycles is similar to that of non-macrocyclic drugs and clinical candidates in bRo5 space, suggesting that both chemical classes have a similar degree of overall flexibility. Although RMSD values are useful for comparison of conformations, they give little insight into the source of conformational flexibility. Therefore an analysis of the location of bonds that rotate to give the different conformations was conducted. For macrocyclic drugs in bRo5 space, bonds from all regions,



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i.e. the macrocyclic core, the side chains as well as bonds linking the two, were rotated to form the different conformers (Figure 8c, Supporting Information Figure S21). However, most bonds throughout all regions of the macrocycle display only modest or limited rotational freedom. Nonmacrocyclic drugs in bRo5 space also demonstrate a similar distribution of rotational freedom about bonds (Figure 8d, Supporting Information Figure S21). Conformationally constrained regions of nonmacrocyclic drugs arise from aromatic rings, other π-systems such as amides, and substituted aliphatic rings that occur in higher proportions than for macrocyclic drugs (Supporting Information Figure S20e & f). The flexibility of non-macrocycles mainly originates from rotation around bonds that connect these rigid elements. Overall, this analysis of conformational flexibility and its origins indicates that macrocycles are not more rigid than non-macrocyclic drugs in bRo5 space across different drug classes. Instead, macrocyclisation could be considered as a complementary strategy to other, more traditional approaches for introducing rigidity into drugs. Hence decreased flexibility may not be the primary reason for the enrichment of oral macrocycles in bRo5 space.


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a)

b) OH O O N

OH

O

O

O

O

O HO

HO OH

c)

H N

N O N S

O

O

OH N H

H N

O O S N

d) Figure 8. (a) Distribution of parenterals (triangles) and orals (circles) for macrocycles (right) in beyond Ro5 (bRo5 MC, green) and extended Ro5 space (eRo5 MC, blue), as well as non-macrocycles (left) in the two chemical spaces (bRo5 non-MC, red and eRo5 non-MC, orange). The dashed black box shows the eRo5 space limits for ClogP and MW. (b) Comparison of average root mean squared deviation (RMSD, Å) values of the representative conformers of bRo5 macrocyclic drugs (N=8, mean number of representative conformers = 3.5) and bRo5 non-macrocyclic drugs (N=4, mean number of representative conformers = 2.5). For the macrocycles, RMSD values for core atoms (atoms in the macrocycle ring), core plus periphery atoms (the core atoms plus all single heavy atoms attached to the 29 ACS Paragon Plus Environment


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core) as well as all atoms are compared. Box plots show minimum and maximum values as whiskers, the 25th, 50th and 75th percentiles as boxes and means as crosses. Horizontal lines indicate unpaired and unequal variance t-test of compared datasets with their p-values. (c) and (d) Chemical structure, all observed crystal structures and the most representative structure coloured by the circular standard deviation of each bond of erythromycin A and ritonavir, respectively. For erythromycin A core atoms are in purple, periphery atoms in orange and side chain atoms in black. The superimpositions show all experimentally determined structures from the Cambridge Structural Database and Protein Databank for the two drugs with heavy atoms coloured by chemical element (green for carbon, blue for nitrogen, red for oxygen and yellow for sulfur). Flexible bonds giving rise to different conformers of each drug were identified from the circular standard deviation of the dihedral angles and are colour-coded from white (0) to red (1) representing rigid to flexible bonds, respectively.

5.2 Ligand efficiency and shapes of ligands and binding sites. To further investigate the differences and similarities of macrocyclic and non-macrocyclic drugs and clinical candidates in bRo5 space the affinity, ligand efficiency (LE), binding site shape and ligand shape were analysed. Affinities did not differ significantly between macrocyclic and non-macrocyclic drugs and clinical candidates in bRo5 space, but macrocycles were found to have a slightly lower LE compared to non-macrocycles (Figure 9a-b, Supporting Information Figure S22). This is consistent with the observations that drugs and clinical candidates binding to flat binding sites have lower LE and that only macrocyclic drugs in bRo5 space bind to flat binding sites for the non-redundant datasets (Figure 9c). In contrast, nonmacrocyclic drugs display a slight preference for groove and pocket shaped binding sites. Although the number of non-redundant drugs and clinical candidates in these two categories is low, the complete bRo5 drug-target structure dataset also retains a higher proportion of flat binding sites for macrocycles


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(Supporting Information Figure S23). In line with these findings, the nPMI shape of bRo5 drugs indicates that macrocycles have a trend to be more disc- and sphere-like than non-macrocycles (Figure 9d and Supporting Information Figure S23). As already discussed above recent investigations of macrocyclic natural products25 and of inhibitors of protein-protein interactions14 provide additional support for these trends. Thus, one may conclude that the shape of macrocycles in combination with suitable rigidity and conformational preferences make them well suited for binding to difficult flat binding sites with sufficient potency and selectivity. The more linear and often aromatic nonmacrocycles appear to be somewhat better adapted for groove- and pocket-shaped binding sites, although macrocycles also bind to these sites.



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a)

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b)

c)

d)


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Figure 9. (a) Affinities, (b) ligand efficiencies (LE), (c) binding site shapes and (d) normalised principle moment of inertia (nPMI) plot of bioactive conformations of drugs for beyond Ro5 macrocyclic (bRo5 MC, green) and beyond Ro5 non-macrocyclic (bRo5 non-MC, red) drugs and targets. As a rule of thumb, Ro5 drug candidates are optimised to have 10 nM affinities, corresponding to a LE of 0.30 for a compound with a molecular weight of 500 Da (heavy atom count, HAC ~36). This "guideline" for optimisation is marked with a grey line in (b). Box plots show minimum and maximum values as whiskers, the 25th, 50th and 75th percentiles as boxes and means as crosses.

5.3 Advantages of macrocycles in beyond rule of 5 space. As highlighted in the literature,26, 27, 48 and discussed above, macrocyclisation of a linear compound can improve both oral bioavailability and affinity at the binding site of a specific target. As macrocyclisation decreases flexibility, it has been suggested that decreased flexibility is the main reason for enrichment of macrocycles in bRo5 space.48 However, in the current dataset we examine drugs and clinical candidates acting at different targets giving a global picture of flexibility in bRo5 space. This indicates that both macrocyclic and nonmacrocyclic drugs in bRo5 space have similar flexibilities and affinities across different targets and binding sites. Interestingly, we also find that disc and sphere-like macrocycles bind more commonly to flat binding sites than rod-like non-macrocycles, which more frequently target groove-shaped binding sites. Hence, we conclude that the unique ability of macrocycles to adopt disk- and sphere-like shapes that are better suited for binding to flat and groove-shaped sites is an important reason for enrichment of macrocycles in bRo5 space. Improved permeability across membranes, which translates into higher oral bioavailability, has been demonstrated for a number of macrocycles and constitutes another reason.48 It should also be remembered that most macrocycles in bRo5 space are natural products that were discovered prior to target-based drug discovery and high throughput screening. Thus, their



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current enrichment as oral drugs in beyond Ro5 space also reflects the history of drug discovery for difficult targets, while at the same time providing inspiration and insight for future discovery of oral drugs for difficult targets.

6. PERSPECTIVE 6.1 Designing drugs beyond the rule of 5. A key conclusion from this analysis is that drugs and clinical candidates in bRo5 space are better suited to modulate "difficult" and emerging target classes compared to Ro5 compliant drugs, in particular when binding sites are large and flat or groove shaped (Table 2). In previous analyses we demonstrated that 93% of the current oral drugs and clinical candidates in eRo5 and bRo5 space fall within an "outer limit" of physicochemical space where there still remains a reasonable chance to design orally bioavailable drugs.21, 27 This space was delineated by MW

How Beyond Rule of 5 Drugs and Clinical Candidates Bind to Their

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