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Discovery of potent inhibitors of 11#-hydroxysteroid dehydrogenase type 1 using a novel growth-based protocol of in silico screening and optimization in CONTOUR® Zhijie Liu, Suresh B Singh, Yajun Zheng, Peter Lindblom, Colin Tice, Chengguo Dong, Linghang Zhuang, Yi Zhao, Barbara A Kruk, Deepak Lala, David A. Claremon, Gerard M McGeehan, Richard D Gregg, and Robert Cain J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.9b00198 • Publication Date (Web): 29 Jul 2019 Downloaded from pubs.acs.org on July 29, 2019

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Journal of Chemical Information and Modeling

Discovery of Potent Inhibitors of

Dehydrogenase Type 1 Using a Novel

Growth-Based Protocol of In Silico Screening and Optimization in CONTOUR®

Zhijie Liu1,2#*, Suresh B Singh2, Yajun Zheng1,2, Peter Lindblom2, Colin Tice2, Chengguo Dong1,2, Linghang Zhuang2, Yi Zhao1,2, Barbara A. Kruk2, Deepak Lala2, David A. Claremon2, Gerard M. McGeehan2, Richard D. Gregg2, Robert Cain1

1 Allergan Plc. 2525 Dupont Drive, Irvine, California 92612, United States 2 Vitae Pharmaceuticals, Inc. 502 West Office Center Drive, Fort Washington, Pennsylvania 19034, United States

# Present address: Janssen Pharmaceutical Companies of Johnson & Johnson, 1400 Mckean Road, Spring House, Pennsylvania 19477, United States

* Corresponding author. Email: [email protected]

ACS Paragon Plus Environment

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Abstract With the continuous progress in ultra-large virtual libraries which are readily accessible, it is of great interest to explore this large chemical space for hit identification and lead optimization using reliable structure-based approaches. In this work, a novel growth-based screening protocol has been designed and implemented in the structure-based design platform CONTOUR®. The protocol was used to screen the ZINC database in silico and optimize hits to discover

E@; ! inhibitors.

In contrast to molecular docking, the virtual screening process makes significant improvements in computational efficiency without losing chemical equities through partitioning 1.8 million ZINC compounds into fragments, docking fragments to form key hydrogen bonds with anchor residues, reorganizing molecules into molecular fragment trees using matched fragments and common substructures, and then regrowing molecules with the help of developed intelligent growth features inside the protein binding site to find hits. The growth-base screening approach is validated by the high hit rate. Total 50 compounds have been selected for testing, of these, 15 hits having diverse scaffolds are found to inhibit

E@; ! with IC50 values of less than 1 M in a biochemical

enzyme assay. The best hit which exhibits an enzyme IC50 of 33nM is further developed to a novel series of bicyclic

E@; ! inhibitors with the best inhibition of enzyme IC50 of 3.1nM. The final

lead candidate exhibits IC50s of 7.2nM and 21nM in enzyme and adipocyte assays, respectively, displayed greater than 1000-fold of selectivity over

E@; !

and two other related

hydroxysteroid dehydrogenases, and can serve as good starting points for further optimization to develop clinical candidates.


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Introduction As a member of the short chain dehydrogenase reductase (SDR) superfamily hydroxysteroid dehydrogenase type 1 H

[1,2],

E@

E@; ! I is a NADPH-dependent microsomal enzyme

responsible for the local conversion of inactive cortisone to the active glucocorticoid cortisol [3]. Cortisol binds to and activates the glucocorticoid receptor, driving gluconeogenesis in the liver and adipogenesis in adipose tissue. Too much cortisol can lead to central obesity and is associated with insulin resistant type 2 diabetes.

E@; ! can effectively amplify the glucocorticoid action

in key metabolic tissues [4]. Therefore, the inhibition of

E@; ! has been pursued as a potential

therapy of metabolic disorders such as type 2 diabetes [5]. Extensive efforts have been pursued to develop

E@; ! inhibitors as potential treatment of type

2 diabetes from multiple groups [6]. Phase I results were reported for 1 PF-00915275 [7], 2 HSD016 [8], 3 HSD-621 [9], 4 AMG221 [10] and 5 AZD8329 [11]. Phase II trials were conducted for

O

O

O

F3C

S H2 N

N

O

O N

N H

O S

S

S

F3C

HO

CF3

N

O

N

HO

N

CF3

N

O

S

N H

NH2 CN

PF-00915275 1

HSD-016 2

F

HSD-621 3

F

AMG221 4

O N O

Cl

N

N

O

N

N

N NH

COOH N F

AZD8329 5

Figure 1 Examples of

MK-0916 6

E@; !

OH

O

BI 135585 7

inhibitors enter into clinical trials. These structures have

hydrogen bonding acceptors at the center position (in solid red circles), with aromatic or hydrophobic groups at both left-hand and right-hand sides (in dashed blue circles).



INCB13739 (structure not disclosed) [12] and 6 MK-0916 [13]. At Vitae, we have developed a series of oxazinanone based study

[14,15,16,17].

E@; ! inhibitors and compound 7 BI 135585 has entered the Phase II

A simple structural analysis shows that these molecules share some common

pharmacophore features (Figure 1), including the hydrogen bonding acceptors at the center position and aromatic or hydrophobic groups at both left-hand and right-hand sides. These pharmacophoric features illustrate the basic binding mode of

E@; ! inhibitors and have

guided our exploration to find new chemotypes using in silico screening and optimization in this work. In silico screening serves as a cost-effective and time-efficient approach complementing highthroughput screening (HTS) to identify initial hits in drug discovery process [18]. The earliest in silico screening methods were based on 2D similarity searching using molecular fingerprints, descriptors or sub-structure [19,20,21]. Non-receptor based molecular searching has made significant progress as a virtual screening tool by including 3D properties such as electrostatic field, molecular shape and/or pharmacophores [22,23,24,25]. With the increasing number of high-resolution crystal structures of protein targets and complexes, the structure-based screening approaches, especially molecular docking, have become more common in hit identification. The power of these methods is their ability to characterize the binding behavior of ligands inside the binding site of a target protein [26,27,28,29,30,31,32]. Advances in molecular docking methods and scoring functions have led to many successful applications of virtual screening

[33].

An impressive success was recently

reported from docking an ultra-large virtual library containing hundreds of millions of molecules, which can be easily synthesized with well-defined reactions [34]. Molecular docking also facilitates lead optimization to modify the initial screening hits to lead candidates. Together with other structure-based optimization methods such as scaffold hopping,


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fragment growing and fragment linking, structure-based drug design has been applied successfully in drug discovery

[35,36,37].

In recent years, with the progress of artificial intelligence, machine

learning accelerates the single task and multitask studies and has made impressive progress in compound profiling prediction using molecular bioactivity fingerprints

[38,39,40].

Meanwhile, it

boosts the structure-based virtual screening as well [41,42]. In drug discovery, it is of great interest to explore the largest possible chemical space for hit identification and lead optimization. Fragment-based de novo design approaches including fragment growth and scaffold replacement have been developed to achieve more chemical diversity. Similarly, significant progress has been made in designing ultra-large virtual libraries, such as the Enamine REAL library (https://enamine.net/library-synthesis/real-compounds/realcompound-libraries) [34], developed from 70,000+ reagent fragments with 130+ reactions to build hundreds of millions to billions of virtual molecules of which 85% can reliably be synthesized. Such large virtual libraries can be combined with 2D or 3D search methods for the identification of new chemical moieties. Considering that fragment is a common underlying component in these efforts, our perspective is that development of a fragment-based screening approach can be readily integrated with the construction of virtual libraries. This fragment-based method can provide a rational route to build up the library and identify hits simultaneously. At Vitae and Allergan, we have developed a structure-based drug design platform called CONTOUR to design drug-like molecules using a programmable growth engine guided by a unique empirical scoring function based on molecular orbital theory [43,44,45,46]. Within a given protein binding site, CONTOUR can grow molecules through assembling context sensitive fragments together that best complement the binding pocket. CONTOUR has been successfully applied in de novo design and lead optimization processes for multiple projects. In this work, we will apply CONTOUR to screen



compounds from ZINC database against 11 -HSD1 and then optimize hits resulting in a new series of lead candidates with novel scaffold [47,48]. The CONTOUR growth algorithm uses a novel growthbased screening protocol to identify hits, in a process that is different from regular structure-based docking. The protocol dynamically groups ZINC molecules having common substructures together into trees and grows them simultaneously inside the binding site guided by these trees. Using defined hydrogen bonding constraints and our intelligent growth algorithm, the growthbased screening protocol has found multiple classes of molecules that exhibit inhibitory activities against 11 -HSD1. The most potent hit from ZINC has been further optimized with CONTOUR growth and a novel single-digit nanomolar lead candidate has been achieved with good selectivity and metabolic profile. Methods 1. Structure A publicly available X-ray structure of 11 -HSD1 complexed with an adamantane ether inhibitor 8 (PDB: 2IRW

[49])

is employed through the modeling processes of virtual screening and lead

optimization [50]. The complex structure is in a homodimeric form. The ligand shares the same pharmacophore groups as aforementioned clinical inhibitors (Figure 2). Two residues in the structure, Ser170 and Tyr183 form key hydrogen


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(a) Tyr183

Ser170 O

Tyr177

O

Tyr 280 *

N H

NADP

O

Tyr 284 * H 2N

Val180

O

Met 286 *

Ile121

Leu126 8

Val 231

Met 179

(b) Figure 2 The interaction between 11 -HSD1 and adamantane ether inhibitor in 2IRW. Residues Tyr183 and Ser170 form hydrogen bonds with the carbonyl group at the center position of ligand. The pocket is surrounded by hydrophobic residues, interacting with left-hand adamantane and



right-hand phenyl groups. NADP sits at the top-left edge of the pocket interacting with polar amide group through favorable electrostatic interaction. The right exit (in black dotted curve) is partially covered by the second monomer providing additional interactions. (a) 3D binding pocket with ligand and key residues. (b) 2D ligand interaction diagram with center hydrogen bonding acceptor in solid red circle, aromatic and hydrophobic groups in dashed blue circles. * refers to the second monomer.

bonds with the carbonyl group at the center position of the ligand and have been validated to be critical for the ligand binding [14,16,50]. The binding site is surrounded by hydrophobic residues including Ile121, Leu126, Val180, Val231, Met179 and Tyr177, which interact with adamantane and phenyl groups from the left-hand to the right-hand of the ligand. NADP group is located at the top left edge of the binding pocket and provides opportunity of favorable electrostatic interactions with polar groups from the ligand. The right side of the pocket is partly occluded in the homodimeric structure. The second monomer provides additional interactions through residues such as Tyr280, Tyr284 and Met286. 11 -HSD1 is active as a homodimer and exists as a homodimer in the assay conditions [51]. 2. Compounds for screening The ZINC8 database has been searched to find 11 -HSD1 inhibitors through CONTOUR growthbased virtual screening [47,48]. The lead-like subset was selected and further filtered for molecular weight < 350 and rotatable bonds < 9, resulting in a database of 1,884,245 compounds. This subset of ZINC8 were virtually screened with the growth-based algorithm and protocol described in the section 3 and 4.


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3. CONTOUR growth algorithm for structure-based design To help understand the virtual screening protocol in the section 4, here we first provide a brief introduction of CONTOUR and relevant details. As shown in Figure 3,

Figure 3 Illustration of the molecule assembly process in the context of the protein binding site using the CONTOUR growth algorithm with the enhancements from the intelligent growth features including dynamic vector selection (DVS), dynamic fragment selection and dynamic conformation selection (DFS & DCS).

CONTOUR designs novel drug-like molecules by filling binding pockets through assembling fragments step by step using a user-directed growth which is assisted by programmable



instructions described in subsection a)

[43,44,45,46].

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Starting with an anchor fragment derived from

either a complexed ligand in a crystal structure or a fragment docked to a defined functional interaction site in the binding cavity, the program assembles fragments in a sequential fashion to grow molecules. Fragments are attached using their open valences, referred to as growth vectors. The attachment of each fragment is followed by a systematic or random search of low energy rotamers and conformational optimization using a novel deterministic optimization algorithm. The optimization algorithm derives a minimization vector and decomposes the vector onto each system freedom such as translation, rotation or dihedral to optimize hydrogen bonds, electrostatic interactions, and sterics leading to low energy conformations in the binding site, while rejecting the sterically disallowed conformers. This process of attaching fragments coupled with conformational search and optimization recursively continues until preset combinations of fragments are assembled or a defined limit is achieved. Final molecules are ranked and selected using an empirical scoring function based on the molecular orbital theory [45]. The scoring function has been designed to capture essential physical features of molecular interactions and desolvation effect in protein-ligand binding

[45].

On the basis of atomic

hybridization and polarization states, each atom is modeled by molecular orbital representations including electron lone pairs, p-orbitals, and polar and non-polar hydrogens. The protein–ligand association energy is computed as a discrete set of linear sum of pair-wise interactions and desolvation terms of these orbital representations. The pair-wise interaction energy captures shortrange electrostatic interactions via hydrogen bonds, electrostatic repulsions of like charges, nonpolar attractions and repulsions. The desolvation energy is estimated by calculating the energy required to desolvate interaction surfaces of the protein and the ligand in the complex. Each energy term has weight and energetic contribution derived from experimental affinity data for protein-


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ligand complexes with high resolutions crystal structures. As shown in equation 1, where

Wpl (ti , d i ) is a weight derived from the training set for the protein-ligand interaction i with the interaction type of

ti at the distance of d i ; Fi is the directional factor calculated from interaction

geometry;

is the calculated desolvation factor caused by the screening effect from the

interaction i as a result of complex formation and

( ) is the desolvation weight derived for the

interaction i with the type . The calculated score is correlated to pKi, a score of 9.0 means the predicted Ki is 1.0 nM and the binding energy is -12.3kcal/mol.

E

E Interaction

Wpl (t i , d i ) Fi

E Solvation i

Ws (t i ) f i

screened

(1)

i

a) Programmable growth directives encoding molecules CONTOUR growth is programmed with growth directives, instructions defining each “growth step” and organizing growth steps into tree structures to grow molecules. In this work, the ZINC molecules are reconstructed as a series of growth directives stating from a small number of fragments. The basic growth step is “fragment”, each fragment can be described by the formulism as “[vectors attaching to proceeding steps] ”. Here these growth vectors are represented by hydrogen IDs of open valences in the fragment though CONTOUR can include much complex vectors for ring fusion. Depending on the choice of anchor fragment, any molecule can be written in different directives. For example, compound 1 can be described as a sequential directive like “cyano (1) – (1) phenyl (4) – (1) phenyl (4) – (1) sulfone (2) – (1) amine (2) – (1) pyridine (5) – (1) amine (-1)” if using the cyano group as the anchor fragment. The molecule also can be translated to a branched directive like “sulfone (1)(2) {(1) amine (2) – (1) pyridine (5) – (1) amine (-1)} {(1) phenyl (4) – (1) phenyl (4) – (1)



cyano (-1)}”, if using the sulfone group as the anchor fragment. More often a growth step is defined as a collection of fragments such as “sequence” or “list”. A basic sequence specifies a sequence of fragments to be grown such as the sequential directive of compound 1. A basic list specifies a list of alternative fragments will be tried one by one but not together. From the above definitions, it is obvious to see that the complex growth steps such as the sequence and the list are growth steps consisting multiple basic growth steps such as the fragments. More sophisticated cases are highdimensional growth steps, in which the lists and the sequences can be nested into each other to create the list of lists, the list of sequences, the sequence of sequences or the sequence of lists. In this way, growth directives can be customized as desired patterns ranging from very simple ones such as assembling fragments sequentially to sophisticated tree implementations and grow molecules with varying degrees of complexity and flexibility. b) Dynamic vector selection (DVS) CONTOUR selects growth directions to fill the binding pocket with fragments in an exhaustive, random and/or customized fashion. To make growth more efficient, an intelligent growth feature called dynamic vector selection (DVS) has been developed to add fragments onto desired vectors selected with physical characteristics of the local binding sites. Figure 4 gives a simple illustration how DVS works. When the growth is going to add new fragments to


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Figure 4 Dynamic vector selection (DVS) mode in growth. DVS attaches linear poly-acetylene probe to each growth vector (pink arrow), detects the nearest neighboring protein atoms (solid color circles), calculates the descriptors of local pockets (solid ovals) and rank-orders growth vectors (order number in red) to select the best growth directions to speed up molecular design.

extend the molecule, DVS will attach a linear poly-acetylene probe to each vector of the existing molecular piece at first. The probe can reach up to10 Å to detect nearest neighboring protein atoms and calculate the descriptors of the local pocket. DVS rank-orders growth vectors based on steric space available for further growth using the shape and the size of the corresponding local pocket with optional consideration of the key hydrogen bonding contributions. DVS will select the best growth vectors which may have highest scores with prospective contributions to the binding affinity and ignore the directions open to the solvent or bumping against the pocket wall. DVS is dynamically activated at every growth step to speed up molecular design than exhaustive search.



c) Dynamic fragment selection and dynamic conformation selection (DFS&DCS) To significantly speed up the samplings of chemical space and conformational space, CONTOUR has implemented another novel intelligent growth feature called dynamic fragment selection and dynamic conformation selection (DFS&DCS) using the physical characteristics of the binding site to select complementary fragments with preferred binding conformations to generate molecules. In the DFS&DCS mode (Figure 5), from the probe of selected growth vector,

Figure 5 Dynamic fragment selection and dynamic conformation selection (DFS&DCS) mode in growth. From the growth vector of ligand (yellow arrow in A), multiple vectors are radiated (B)


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to form a cage around the local protein binding pocket (C). The vectors contact the nearest neighboring protein atoms (D) and shrink back to form a lattice of the local binding pocket (E and F). The lattice is converted to a 24-colum 2D matrix (top matrix in G) with value describing pocket size and color describing the polarity (polar in red and hydrophobic in green) along each vector. A similar matrix (bottom matrix in G) is precalculated for each vector of fragment to be grown, will be compared to the local pocket matrix with all configurations through shifting columns to find if the fragment with specific rotamer can fit. The fragment with specific rotamer (blue in H) which can match to the local protein binding pocket (yellow case in H) will be selected to grow a new molecule (I).

new vectors radiated from the probe carbons to contact the nearest neighboring protein atoms. There are 24 vectors radiated from each carbon atom every 15 degree on the plane perpendicular to the probe. These vectors stop at the surface of protein atoms and their ends form a lattice of local binding pocket. The vector lattice is converted to a 24-colum 2D matrix with each column corresponding to a specific rotamer angle. The value of each matrix cell is the vector length describing the local pocket size along the vector and the cell color describes if the vector contacting a polar protein atom. If a cell is assigned with a polar color, a hydrogen bonding vector will be derived and assigned as a donor or an acceptor for precise alignment with the polar vector from fragments to be grown, where a similar 24-column matrix is precalculated for each attaching vector of all fragments. Through shifting columns of fragment matrix, all fragment rotamers are evaluated against the pocket. For each rotamer of each fragment, the matrix values will be compared to see if the fragment shape can fit into the pocket and polar vectors will be matched to see if a hydrogen bonding vector of the fragment can be aligned complementarily without electrostatic repulsion.



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DFS&DCS is applied dynamically at each growth step, selecting the subset of fragments with specific configurations that best match the steric shape, electrostatic preferences, and hydrogen bonding features of a local pocket in the binding site. The selections are further validated through optimization in growth and assembled to molecules in a piece-wise fashion. In this way, DFS&DCS significantly reduces the vast chemical and configurational space, and the fragments and conformations can be sampled almost exhaustively by distributing the calculation over a dedicated number of processors. 4. Growth-based virtual screening protocol In contrast to regular structure-based virtual screening that docks each compound to the binding pocket individually, using CONTOUR growth algorithm described in section 3, a novel growthbased protocol has been designed to screen ZINC compounds for

E@; ! " The protocol

decomposes molecules into fragments and merges molecules into trees using topology mapping. Each tree can be converted to growth directives to grow contained molecules into the binding site together to identify hits. The protocol includes four key phases: a) Partition molecules into fragments; b) Dock fragments to the anchor position with defined residues forming key interactions; c) Merge molecules into trees; d) Grow molecules with trees. a) Partition molecules into fragments Nearly 1.9 million aforementioned lead-like compounds from ZINC8 database have been preprocessed using LigPrep with default parameters to generate 3D conformations with reasonable protonation, tautomerization and ring flexibility [52]. Each molecule is chopped at every rotatable bond to create fragments. With the RMSD threshold of 0.5 Å, a total of 55,845 fragment conformations are constructed into the fragment library which include 24,883 2D unique fragments. For each compound, the growth directives based on the connectivity of these fragment


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is generated to encode the reassembling instructions for the fragments to regrow the corresponding parent molecule. b) Dock fragment to the anchor position with defined residues forming key interactions A key step in CONTOUR growth and growth-based virtual screening is to place the anchor fragment with right binding conformation. Because the hydrogen bonding to Ser170 and Tyr183 is critical for the inhibition of 11 -HSD1, all fragments in the fragment library are docked to sidechain hydroxyls of Ser170 and Tyr183 in the catalytic site using PDB structure 2IRW. The fragments to be docked must have at least one hydrogen bonding acceptor whose lone pair is aligned initially to the hydroxyl group of Ser170 to form a hydrogen bond. Then the fragment is rotated around the aligned hydrogen bonding axis every 30 degree with 12 rotamers sampled and minimized at each orientation to remove steric clashes and electrostatic repulsion and optimize hydrogen bonds. Only the fragments with scores greater than 1.2 (about -1.6 kcal/mol) and hydrogen bonding to both residues of Ser170 and Tyr183 are selected. Total 6,857 docked poses of fragments were generated and used as anchor fragments in the growth and initial roots in the molecular trees described below to serve as the starting point for growing molecules containing these fragments. c) Merge molecules into trees The growth directive of each molecule can be easily converted to an equivalent tree using a fragment as a node and a bond connecting two fragments with specific growth vectors as an edge, vice versa. One molecule can be mapped to multiple growth directives using different anchor fragments as roots. Starting from a given root, molecules that have that root as a common substructure (even just a common functional group such as hydroxyl) are identified. These molecules could be merged together into a complex tree using the common substructure or one of its fragment pieces as the root. Please note one molecule can include multiple roots and appear in



multiple merged trees. Beyond the roots, for fragments to be attached at a single growth position, identical fragments will be merged to a single copy and non-identical fragments will be added to a growth list. The roots initially are just docked anchor fragments from step b), with the growth proceeding, the updated roots become larger growth sequences representing the common substructures shared in related molecules. The complex trees will be updated dynamically as the roots grow. In this way, a tree node is indeed a growth step which could include a growth fragment, a growth sequence, and most common a growth list. d) Grow molecules with trees Using combined trees constructed from molecules having a common substructure as the root, a growth directive can be derived to instruct CONTOUR to grow these molecules inside the protein binding site. Due to the steric constraints from the binding pocket, not all growth directions are capable of expansion and the dynamic vector selection (DVS) can be applied to each edge of the tree to trim those growth vectors leading to unfit molecules, speeding up the computation. At the same time, not all fragments to be grown can fit into the binding site and the dynamic fragment selection and dynamic conformation selection (DFS&DCS) can be applied to each node of the tree to select only those fragments that sterically fit and complement the given site. Especially for the node consisting of a list of fragments, DFS&DCS will accelerate the sampling of fragments and their conformations efficiently. For each fragment that is accepted by DFS&DCS and attached to the previous fragment, all the molecules containing that substructure will be retained for further growth. Logically the fragments that don’t fit into the site lead to the removal of all the corresponding molecules containing them. The growth process is designed as a recursive process with pruning of the tree based on sterics and chemical complementarity and allows the growth algorithm to assemble one fragment at a time from left to right and top to bottom as defined in the


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tree instead of growing the whole molecules. The partially grown structures get used as the new roots to rebuild the trees and reenter the growth recursive cycle. In this way, the growth process would guarantee that the common structures of grown molecules

O

O N

N

F

N

N

F

O

N O

N

O

O O

N O

(a)


N


(b)


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(c)



(d)


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O

H

H

O

O H

H

N

N

N

O

O N

O N

N

N

N

F N

O

O

O

O

O N

N

O N

N N

O

N

F

(e) Figure 6 Illustration of growth-based screening process using a few molecular examples. (a) Five molecules having a common substructure of carbonyl group. (b) Molecules decomposed into fragments at every single rotatable bond and merged together with the common root of carbonyl group. (c) The initial tree representation of merged molecules for generating the growth directive, DVS applied to growth vectors represented by solid lines as tree edges, and DFS&DCS applied to each growth step represented by dashed faded circles as tree nodes. (d) Roots generated from updated common substructures during recursive growth. (e) Grown substructures or molecules corresponding to the roots.



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would be generated only once, which improves the computational efficiency of sampling of fragments and their conformations if compared with docking molecules individually with regular docking tools. Here we use molecules in Figure 6 as an example to illustrate the growth-based screening process. Five molecules in Figure 6a are decomposed into fragments at every rotatable single bond in the first step. The fragments having hydrogen bonding acceptors are docked to key residues Ser170 and Tyr183, only the carbonyl group is able to make hydrogen bonds to both residues with a CONTOUR score greater than 1.2 and is kept as an initial root to merge molecules into a tree (Figure 6b). Intuitively the tree in Figure 6c has fragments represented as solid nodes (circles with solid lines) and growth vectors of fragments represented as solid edges (solid lines). When molecules are merged together, multiple fragments with defined attaching vectors could be combined and connected at a same growth vector of a proceeding common substructure. For example, phenyl (A1), ether (A2), benzofuran (A3) and methyl (A4) can be grown to the left vector of carbonyl group (R), piperazine (B1) from molecules

and piperidine (B2) from molecules

can be

grown to the right of carbonyl with only one copy is retained. Then the intermediate nodes (faded circle with dashed lines) are introduced to combine these downstream fragments together to growth steps, which are growth lists, with the intermediate edges corresponding to the attaching vectors of these fragments (dashed lines). After converting the tree to the growth directive, CONTOUR growth is executed to grow one fragment per round. The generated substructures or molecules are used as new roots to regenerate the tree and regrow recursively, in this way, the roots gradually grow down as in Figure 6d, building up screened molecules or partial structures able to fit into the binding pocket (Figure 6e).


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Results and Discussions Fragments interacting with key residues of Ser170 and Tyr183 in

#

catalytic site

Designing molecules that contain functional groups directly hydrogen bonded to the residues Ser170 and Tyr183 is a reasonable and efficient way to obtain

E@; ! inhibition. 6,857

fragment conformations from total 55,845 fragments can be successfully docked into the 11 HSD1 binding site. Many of these fragments contain a carbonyl group which uses two polar lone pairs interacting with hydroxyl groups of the two residues in the hydrogen bonding geometry that is seen in the 11 -HSD1/inhibitor crystal structures. While these derivates include simple ketone, amide, ester, urea, and carbamate compounds, the majority places the carbonyl-containing functional group in a ring, such as lactam and lactone. Most of these fragments include variations such as hetero substitutions inside the rings, bi-cyclic and tri-cyclic ring systems, and even spiro ring fusion (Figure 7). Besides the carbonyl-containing fragments, quite

O

O

O

O

S O

O

O

O

O NH

HN

O O

O

HN

O N H

S

O

O NH

NH

HN S

S

O

NH N

HN

HN

NH

O

O

N

N

S O O N

O O H

N

N

N S


N N N

S


Figure 7. Anchor fragments from final selected screening compounds which interact with key residues Ser170 and Tyr183 in

E@; ! catalytic sites through hydrogen bonds.

a few other kind fragments such as sulfone, triazole, isoxazole, and thiadiazole are identified, which can form two hydrogen bonds and are close analogues to the anchor fragments in aforementioned

E@; ! inhibitors from literature. Figure 7 lists the examples of a few anchor

fragments from the final selected screening compounds. These collected fragments comprise rich resources to explore the novelty in drug discovery. For example, through structure superimposition, it could be possible to quickly select some new fragments to replace the anchor fragments in existing series to get new scaffolds; through substructure similarity search, novel series of compounds could be identified for structure-based screening and design. This hydrogen-bonding constraint-based design methodology retains critical interactions for binding potency while improves the design efficiency, it does not only work for unique hydrogen-bonding constraint as in enzyme, it could be applied to any protein targets having hydrogen bonding positions where all positions could be tried alternately to find the optimal fragment pieces for screening and design. Moreover, this constraint-based approach can be easily applied for designing covalent inhibitors to gain enhanced potency and selectivity [53]. Identified

#

inhibitors from ZINC database using growth-based virtual screening

From nearly 1.9 million ZINC compounds, a total of 91,818 docked molecular conformations have been obtained with score >4.3 (about Ki=50 M), hydrogen bonding score >1.2, MW 0.015 (defined as the quotient of the score divided by the molecular weight). These compounds are filtered to select 2,000 molecules through clustering and considering top


Page 26 of 61

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scores, molecular polarity, occupancy of hydrophobic pockets on both left-hand and right-hand sides using developed Pipeline Pilot protocols. At last a visual inspection is used to reject molecules based on ligand conformational strain, electrostatic repulsion, and polar atom in nonpolar environments. This process leads to a set of 50 molecules with diverse scaffolds and predicted scores ranging from 4.3 to 8.7 (~Ki from by the enzyme assay

[17].

to 2nM) for purchase and being tested

Among 50 compounds, 3 compounds have measured IC50s less than

100nM, 15 compounds less than 1

, and 25 and 28 compounds less than 5

respectively. With the threshold of IC50=1

and 10

, the hit rate of the virtual screening is 30% with the

best hit 9 exhibiting the IC50 of 33nM (Table 1). These 15 compounds include eight different scaffolds with consistent pharmacophore features to the known 11 -HSD1 inhibitors in Figure 1. The predicted CONTOUR scores are compared with pIC50s from the experimental binding data. The average absolute error of total 37 active compounds is 1.1 log unit, most of these compounds have absolute errors falling in the range of 2.0 log units, a good level of accuracy for an empirical scoring function and sufficient to guide virtual screening (Figure 8). The 9 compounds with larger absolute errors greater than 1.5 and 13 inactive compounds are further examined. Compound 17 is the only compound which is underestimated about 1.6 log unit, it has a rigid structure providing hydrophobic complementarity and perfect hydrogen bonds to gain high binding efficiency. All other compounds’ IC50s are overestimated with at least one possible reasons described below leading to over-prediction. The binding pocket of

E@; ! is quite hydrophobic except the

catalytic residues Ser170 and Tyr183. The principal reason for scoring function over-estimating the activity of compounds is that these compounds



Correlation between CONTOUR scores and pIC50s of active screened hits 10 9 8

CONTOUR Score

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

7 6 5 4 3 2 2

3

4

5

6

7

8

9

10

Enzyme pIC50

Figure 8 Correlation between CONTOUR scores and pIC50s of 37 active screened hits.

have polar atoms in a buried hydrophobic binding environment which desolvation penalty is underestimated, such as the indole NH in compound 22, piperazine in compound 29, 31, 33, 39, urea NH in compound 36, 57, the extra carbonyl of pyrrolidine-2,5-dione in compound 58, pyrimidin4(3H)-one in compound 55, 56, and 1,4-diazepan-5-one in compound 46, 47, 48, 49, 50, 51. Of the 7 compounds 20, 22, 29, 31, 33, 36, 58 selected because they have scores greater than 7.5, six of them are over-predicted for this reason. For instance, most of 1,4-diazepan-5-one compounds only fill half of the binding pocket with a rigid core. When these molecules are grown, high steric tolerance is assigned and the interaction between protein and ligand are compromised to


Page 28 of 61

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compensate the protein flexibility. The obtained structures have high steric energy which prevents the molecules to bind, such as compounds 44, 54, 56. A few compounds including 22, 29, 36, 52, 55 have long flexible sidechains, compared to their free states in solvation, they have to pay penalty to distort some dihedral angles to fit into the binding pocket. These conformational strains such as gauche-gauche interaction happened in the ligand growing process is not well considered by the scoring function. Compound 43 and 53 include the isoxazole group, which has weaker hydrogen bonding ability due to the relatively lower basicity. For inhibition of enzymes, the right acidity or basicity of the functional group are validated to be very important for activity [54].

Table 1 Final 50 compounds selected from virtual screening with the experimental binding data and predicted scores Compound

Structure

9

ZINC8 ID or CAS Number 478247-17-5

10

ZINC00458144


CONTOUR Error

Enzyme IC50(nM)

pIC50

33.1

7.5

6.2

-1.3

74.9

7.1

5.7

-1.4

Score


11

ZINC01403647

12

ZINC12406554

13

ZINC12406548

14

ZINC00292642

15

ZINC01056264


Page 30 of 61

98.3

7.0

5.7

-1.3

100.4

7.0

6.7

-0.3

128.5

6.9

5.4

-1.5

161.6

6.8

5.7

-1.1

180.9

6.7

5.8

-0.9

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16

ZINC01292412

17

ZINC00209924

18

ZINC01402074

19

ZINC04057952

20

ZINC04937953


242.4

6.6

7.1

0.5

313.8

6.5

4.9

-1.6

349.8

6.5

5.5

-1.0

514.0

6.3

5.9

-0.4

670.6

6.2

7.6

1.4


21

ZINC01260941

22

ZINC03452526

23

ZINC06818781

24

ZINC01402075

25

ZINC00035195


Page 32 of 61

799.3

6.1

7.0

0.9

829.4

6.1

7.9

1.8

843.7

6.1

5.7

-0.4

1,022

6.0

5.5

-0.5

1,089

6.0

5.5

-0.5

Page 33 of 61 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


26

ZINC04891152

27

ZINC00122834

28

ZINC00162301

29

ZINC00052792

30

ZINC01402076


1,791

5.7

6.1

1.4

2,102

5.7

6.7

1.0

2,170

5.7

6.2

0.5

2,513

5.6

8.2

2.6

2,990

5.5

6.0

0.5


31

ZINC00376396

32

ZINC00038933

33

ZINC00102051

34

ZINC04060971

35

Page 34 of 61

3,169

5.5

8.0

2.5

3,363

5.5

5.3

-0.2

3,713

5.4

7.9

2.5

5,856

5.2

5.0

-0.2

5,869

5.2

6.2

1.0

ZINC00228126


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36

ZINC04543101

37

ZINC06557592

38

ZINC00369635

39

ZINC01428541

40

ZINC01401013


7,726

5.1

8.0

2.9

10,500

5.0

5.4

0.4

11,368

4.9

4.5

-0.4

15,100

4.8

6.8

2.0

26,200

4.6

4.9

0.3


41

ZINC00098093

42

1008492-24-7

43

ZINC01392399

44

ZINC00385841

45

1008256-95-8


Page 36 of 61

37,500

4.4

5.4

1.0

45,800

4.3

5.7

1.4

50,000

4.3

6.3

2.0

69,800

4.2

6.2

2.0

85,300

4.1

4.8

0.7

Page 37 of 61 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


46

ZINC12934490

47

ZINC04993013

48

ZINC01802348

49

ZINC01463595

50

ZINC04988415


>1,000

1,000

1,000

1,000

1,000

1,000

50,000

50,000

100,000

100,000

100,000

100,000

100,000

100

66

>100

ring, with an extra methyl substituent at the methyl linker which is borrowed from compound 7, improves the potency nearly 10-fold and the compound 61 achieves the comparable binding affinity with IC50 of 73nm to the best hit 9 (Table 2b).


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We continue the optimization with the new scaffold using CONTOUR growth. The 4-fluoride phenyl is grown to explore the optimization potential at four directions labelled as left (corresponding to 2-thiazole), up, right and down (corresponding to ortho, para and meta positions on phenyl rings, respectively) as shown in Table 3. Predicted from the model in Figure 9 and validated with new models, the left and up directions are difficult to place a larger group like 4fluorophenyl while the right and down directions could fit the fragment well in the right-side pocket. The prediction is validated by the results from the enzyme assay, the compound 63 achieves the activity with IC50 of 3.1 nM from growing at para position of phenyl and compound 64 is potent too at the meta position, but other two compounds are not active. These results demonstrate that potent compounds can be generated through filling the right-hand side of the pocket using the para or meta position from phenyl group. Table 4 Exploration from the left-hand side to optimize molecular polarity

Compound 63 67 68 69 70 71

R group -Me -Br -C(Me)2OH -C(O)NH2 -C(O)NHCH3 -C(O)N(Me)2

IC50 (nM) 3.1 6.8 74 76 >100 >100

As NADP group is located at the left edge of the binding pocket of 11 -HSD1, before further exploration at the right-hand side, reagents including hetero rings and polar groups were modeled



using CONTOUR at the left-hand side to see if they could form electrostatic interactions with NADP and improve the molecular polarity. The space here is tight, only a few small polar groups were selected for synthesis. While compound 67 with bromide substitution is still potent with an IC50 of single-digit nM, some smaller polar groups that could fit result in a loss of activity more than 10-fold, as in compound 68 and 69. The other attempts fail (Table 4). Table 5 A few compounds synthesized from the optimization at the right-hand side.

Compounds 72

R group

IC50 (nM) 7.2

73

>100

74

>100

An extensive CONTOUR growth is executed from the para position of the phenyl group using a combination of rings with terminal functional groups. The right-hand side is supposed to open to the solvent but it partially interacts with residues from the other monomer, such as Tyr280*, Tyr284* and Met286* shown in Figure 2. After the comparison with other 11 -HSD1 complex structures, obvious protein flexibility is observed at this place, and heterocyclic rings and small polar substitutions could be tolerated, which is already validated by our phase-II compound 7.


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Page 46 of 61

Finally, after a few rounds of simple optimization, we obtain the most potent compound 63 with enzyme IC50 of 3.1nM and the lead compound 72 with enzyme IC50 of 7.2nM. Additional data have been generated for these two compounds and are listed in Table 6. Compound 63 is more potent in the enzyme assay but it has larger shift (>5X) in the adipocyte assay and has the selectivity issue with 3 -HSD2. With the introduction of more polar cyano group for fluoride in 63, compound 72 has similar adipocyte activity as 63 but have great selectivity (>1000X) over all other three hydroxysteroid dehydrogenases. Both compounds are clean for CYP3A4 and CYP2D6 inhibition. As for CYP2C9, 72 with a polar cyano group is slightly better than 63, As the hetero ring in compound 7 is tolerated, when more polar hetero rings are integrated into the position of the second phenyl at the right-hand, this problem could be fixed. In summary, using growth-based virtual screening and lead optimization, very potent hits with a novel scaffold are identified and a lead with good profile is developed. Table 6 Profiles of the most potent compound 63 and the lead compound 72. Compound 72

63 11 -HSD1 uSome IC50 (nM) 11 -HSD1 Adip IC50 (nM)

3.1 17.5

7.2 21

>10,000

>10,000

778

>10,000

17 -HSD1 IC50 ( M)

>10,000

>10,000

CYP3A4 IC50 ( M)

>10,000

>10,000

CYP2C9 IC50 ( M)

6,100

7,400

CYP2D6 IC50 ( M)

>10,000

>10,000

11 -HSD2 IC50 ( M) 3 -HSD2 IC50 ( M)


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Sampling completeness and computational efficiency of the growth-based virtual screening Though multi-layer filters were applied to molecules during virtual screening, the growth-based screening protocol still achieves extensive sampling of the chemical space and the conformational space while improving the computational efficiency in four aspects. 1) The constraint of hydrogen bonding to residues Ser170 and Tyr183 filters 88% fragments and left only 6,857 binding conformations from total 55,845 fragments, which in turn removes most inactive compounds. Because the hydrogen bonds are so critical, any compounds that cannot form these hydrogen bonds will not be potent and the filtering is unlikely to remove any good binders. At the same time, the protocol allows a compound to explore multiple distinct possible bound conformations by including multiple docked fragments as different anchor fragments for the molecule. 2) The molecules having a common substructure are organized into a tree and grown together with the common substructure tried only once. Considering there are always multiple derived compounds from a same scaffold in ZINC, the growth will speed up the virtual screening through taking advantage of the common substructure. At the same time, it is reasonable to remove all relevant compounds if their common substructure cannot fit into pocket. 3) Dynamic vector selection (DVS) speeds the search by filtering out nonmeaningful growth directions. Figure 11a displays how efficient DVS can grow molecules compared with exhaustive growth and random growth. At a given growth position, exhaustive growth tries all 5 growth vectors to grow all possible molecules using the longest computation. DVS can try just the 3 best vectors to grow 95% of the molecules, most time almost all the favorable molecules would be included. Random growth performs much worse than DVS with only half the number of the total



molecules grown in the same computation time as exhaustive growth. DVS could select much fewer growth vectors to double the computational speed. 4) Dynamic fragment selection and dynamic conformation selection (DFS&DCS) is the most significant factor in speeding up the screening efficiency. In practical virtual screening and de novo design, we always face a challenging question – how to get active and high-quality molecules from large libraries or huge chemical space more efficiently. Figure 11b shows how DFS&DCS

Molecules grown into pocket Exhaustive Molecules into pocket

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

DVS

Random

600 500 400 300 200 100 0 0

1

2 3 4 Number of used vectors

5

6

(a)


Page 48 of 61

Page 49 of 61

Growth efficiency - DFS&DCS vs Random growth DFS & DCS

Random

1.2

Growth efficiency (poses per minute)

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


1

0.8

0.6

0.4

0.2

0 4

4.5

5

5.5

6

6.5

7

7.5

8

8.5

9

CONTOUR score

(b) Figure 11 Computational efficiency of intelligent growth features (a) DVS compared with exhaustive and random growth, (b) DFS&DCS compared with random growth grows molecules more efficiently than random growth. Each method is used to generate 1,000 poses to explore a subpocket. With the defined score cutoff of 4.7 (predicted pKi, corresponding to Ki=20uM), most of poses from random growth fail while DFS&DCS produces a wealth of high scored poses, the acceptance rate across the score range (integral or area under than curve) in single minute is much higher than the random growth. In addition, both DVS and DFS&DCS could be applied together as shown in the virtual screening process for multiple fragment growth to accelerate the computational speed exponentially.



Page 50 of 61

Table 7 A simple comparison of computation speeds between CONTOUR with different enhancement features and exhaustive docking Study cases

Molecule numbers

Cpd-72 docking Warhead docking 1-fragment growth 2-fragment growth

1 1 297 551,826

CONTOUR docking (sec) 41 1.0 14 (base) 3.2 (base)

CONTOUR exhaustive growth - with Tree (sec) NA NA 16 69,492

CONTOUR growth - with tree, DVS, DFS&DCS (sec) NA NA 1.2 26

Glide docking (sec) 44 36 28,996 (est.) 24,280,344 (est.)

Here we use the compound 72 as an example to illustrate the computational efficiency through simple comparisons between CONTOUR and exhaustive docking. With an Intel Xeon E5-2687W 3.10GHz CPU, CONTOUR takes 41 second to dock the compound 72, which is comparable to 44 seconds required for Glide [29] to dock the same compound. CONTOUR would have any efficiency advantage over Glide, if all molecules were docked exhaustively. CONTOUR’s growth-based screening protocol makes a big difference. CONTOUR growth starts from docking warhead fragments to hydrogen bond anchors. To dock the warhead 5,6,7,8-tetrahydro-4H-thiazolo[4,5c]azepin-4-one of the compound 72, it takes only one second with CONTOUR but 36 seconds with Glide, 30-fold faster. Given the scaffold of the compound 72, the right benzonitrile can be substituted with 297 monocyclic group. After spending 14 seconds on docking the scaffold and organizing the molecules into the tree, it takes additional 16 second for CONTOUR to grow these molecules exhaustively and only 1.2 second when using the intelligent features DVS and DFS&DCS. It is estimated to take 28,996 seconds to dock these 297 compounds exhaustively by Glide, about 10,000-fold slower. Given a more radical case, where the scaffold of the compound 72 is kept and the right [1,1’-biphenyl]-4-carbonitrile is modified using 551,826 combinations of two sequential monocyclic groups, it takes 69,492 seconds for CONTOUR to grow these half-


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million molecules, but only take 26 seconds if applying DVS, DFS&DCS together, which is nearly 1,000,000-fold faster than the estimated time by Glide. Of course, the latter two cases are ideal cases. Real libraries tend to be more diverse and it is rare to have large number of compounds having the same scaffold with variations at a single position. Nevertheless, in practice these four intelligent growth features discussed here together can screen molecules much faster than exhaustive searching and make the growth-based protocol applicable for virtual screening. With the development of ultra-large virtual library including hundreds of millions to billions of molecules, CONTOUR fragment & growth-based protocol has a natural fit for these molecules which can easily synthesized by fragments with well-defined reactions. Summary We present our exploratory efforts in developing a novel efficient structure-based virtual screening protocol and discovering potent inhibitors of 11 - HSD1. Building on the CONTOUR growth protocol, a fully structure-based in silico screening approach driven by key hydrogen-bonding constraint is developed. Starting with a known complex structure, molecules are decomposed to fragments which were then filtered and anchored to form key hydrogen bonds with the target protein. Using these core fragments as structural starting points, all molecules are then mapped into trees based on common substructures, and all corresponding substructures and molecules are dynamically rebuilt and screened while growing within the binding pocket fragment-by-fragment. The most significant aspect the protocol is that hits can be easily enriched for focused screening without losing diversity, which is very important to explore huge chemical space to find novel chemical equities in a crowded IP landscape. One reason is that all molecules are organized in trees and the redundant sampling at common substructures will be



reduced compared with docking molecules one by one. The second reason is that various filters specifically designed for intelligent growth can be easily applied at each level to concentrate the search on molecules most likely to fit well. We hope the method can help the structure-based study in exploring the emerging ultra-large libraries.

The approach is applied in the rational design of novel 11 -HSD1 inhibitors from initial virtual screening to the lead optimization, including identifying the best hit 9 with the enzyme IC50 of 33nM and optimizing to a potent and selective lead 72 with a novel bicyclic scaffold. Compound 72 has IC50 of 7.2nM in the enzyme assay and displays desirable in vitro inhibition to 11 -HSD1 with IC50 of 21 nM in the human adipocytes. It exhibits greater than 1000-fold selectivity over 11 -HSD2 and two other related hydroxysteroid dehydrogenases, and has clean CYP metabolism profile. Compound 72 can serve as a good starting point for further optimization to develop a clinical candidate. Acknowledgement We sincerely appreciate our former colleague of Vitae Pharmaceuticals, Dr. Yuanjie Ye, for his modeling assistance in selecting compounds. The first author also would like to thank his current colleagues at Janssen Research and Development, Dr. Renee L DesJarlais, for her kind help on revising the manuscript.


Page 52 of 61

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Discovery of Potent Inhibitors of 11Î² ... - ACS Publications

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