Repurposing de novo designed entities reveals phosphodiesterase 3B and cathepsin L modulators

Article abstract of DOI:10.1039/c5cc01376c

Using computational bioactivity prediction models we identified phosphodiesterase 3B (PDE3B) and cathepsin L as macromolecular targets of de novo designed compounds. By disclosing the most potent cathepsin L activator known to date, small molecule repurposing by target panel prediction represents a feasible route towards innovative leads for chemical biology and molecular medicine. This journal is

Full text of DOI:10.1039/c5cc01376c

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Repurposing de novo designed entities reveals
phosphodiesterase 3B and cathepsin L
modulators†
Cite this:DOI: 10.1039/c5cc01376c
Received 13th February 2015,
Accepted 26th March 2015
Tiago Rodrigues,a Yen-Chu Lin,a Markus Hartenfeller,ab Steffen Renner,b Yi Fan Lima
and Gisbert Schneider*a
DOI: 10.1039/c5cc01376c
www.rsc.org/chemcomm
Using computational bioactivity prediction models we identified two of our ligand-based software tools (DOGS18,19 and its extension
phosphodiesterase 3B (PDE3B) and cathepsin L as macromolecular libDOGS)20 to generate libraries of potential matrix metallo-
targets of de novo designed compounds. By disclosing the most potent proteinase (MMP) and human immunodeficiency virus-1 (HIV-1)
cathepsin L activator known to date, small molecule repurposing by protease inhibitors. Structurally diverse non-zinc chelating small
target panel prediction represents a feasible route towards innovative molecules21–24 served as template structures for prototyping inno-
leads for chemical biology and molecular medicine.
vative MMP inhibitors (ESI†). We selected compounds 1–4 for
synthesis (Fig. 1) and biochemical profiling against MMP-1, -3, -8,
Computer-assisted de novo molecular design is a maturing technol- -12 and -13, but no significant inhibition was observable at a
ogy for generating new chemical entities (NCEs) in chemical biology concentration of 100 mM. Similarly, compounds 5–6 (Fig. 1), which
and molecular medicine.1–5 At the same time, the computational had been designed as mimetics of amprenavir and obtained
prediction of macromolecular target engagement by small molecules through Mitsunobu chemistry, were profiled against HIV-1 protease
may offer a platform for expeditious profiling of drug-like mole- with a negative outcome. Although bioactivity against MMPs and
cules.6–9 Here we combine both concepts and demonstrate that HIV-1 protease was not obtained as intended, the low structural
predictive quantitative bioaffinity models8,10 are suited for the similarity of 1–6 to the design templates (Tanimoto similarity o 0.50;
identification of the macromolecular targets of de novo designed Table 1) corroborates the scaffold hopping capabilities of the
drug-like molecules. We report tool compounds for gauging the design algorithm and designates these compounds as poten-
effects of phosphodiesterase 3B (PDE3B) inhibition on lipolysis, tially valuable chemical agents.
and disclose a pioneering and best-in-class cathepsin L activator.
Having drug-like compounds 1–6 in hand we hypothesized
The prediction of target profiles for NCEs is of current interest in that their macromolecular targets could be discovered with our
drug discovery.11,12 Although it is widely accepted that promiscuity is GP models constructed from annotated ChEMBL data.25 The
ubiquitous across drug-like chemical space,13 only few robust models predict pAffinity values using CATS226 pharmacophores
computational tools are presently available for the in silico qualitative and Morgan substructure fingerprints. To ensure non-trivial
projection of polypharmacology.6,9,14–17 Importantly, only one of predictions the software calculates the Mahalanobis distance
these tools (SPiDER) was constructed for explicit use with de novo (MD) of the predicted values to predictions made for a collec-
designed molecules.6 We have recently reported a machine-learning tion of randomly selected molecules.10 We nominated potential
approach (Gaussian process regression, GP) for quantitative drug targets based on pAffinity 46 and/or MD 44 values (ESI†).
structure–activity relationship (SAR) modeling for a large panel
of human drug targets, including a confidence estimation.8,10
We here present the application of this quantitative approach
to de novo designed NCEs that had failed to engage the originally
intended on-targets. As part of our de novo design efforts we used
a Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied
¨
Biosciences, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland.
E-mail: gisbert.schneider@pharma.ethz.ch
b Novartis Pharma AG, Postfach, 4002 Basel, Switzerland
† Electronic supplementary information (ESI) available: Computational details,
synthesis and biochemical testing. See DOI: 10.1039/c5cc01376c
Fig. 1 De novo designed entities 1–6 and their retrosyntheses.
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Table 1 Computationally designed molecules 1–6 and their nearest neighbors from the template set
De novo designed entities
Templates
LEa
Functional activity
Nearest neighbors
Tc
b
Cpd
Structure
EC50/mM
1
38 ꢀ 1%c
o0.22
PDE3B inhibitor
0.17
0.19
2
47 ꢀ 10%c
B0.27
PDE3B inhibitor
3
4
11 ꢀ 1
6 ꢀ 1
0.41
0.32
PDE3B inhibitor
PDE3B inhibitor
0.15
0.18
5
38 ꢀ 6
6 ꢀ 2
0.24
0.27
Cathepsin L inhibitor
0.45
0.45
6
Cathepsin L activator
a
b
Ligand efficiency (LE) = ꢁ1.4 ꢂ log EC50/HA; HA = non-hydrogen atoms. Tanimoto coefficient (Tc) calculated with Morgan substructure
c
fingerprints (radius = 2, nbits = 2048, pathlength = 1–7). Percentage of inhibition at a ligand concentration of 50 mM.
For 1–4 our prediction suggested PDE3B, which is involved in the
1, 2 and 4 add new SAR information that can be used for
metabolic regulation of lipolysis, and its inhibitors may find applic- future optimization of PDE3B inhibitors featuring the phenyl-
ability for treating obesity.27 In full agreement with the prediction, - dihydropyridazinone scaffold. These results suggest that our
1–4 showed potent functional inhibition of PDE3B in a cell-based GP models provide a feasible and adequate solution for target
assay (Table 1, Fig. 2A). Publically available data further suggests that identification of de novo designed small molecules.
insertion of a methyl group in the dihydropyridazinone moiety
Significantly, 3 and 4 did not show measurable colloidal
increases activity.28,29 While compound 3 had previously been aggregation at 50 mM, under dynamic light scattering, that could
disclosed as a PDE3 inhibitor28 and pimobendan30 presents a have resulted in unspecific binding to PDE3B and a false-positive
related scaffold, compounds 1, 2 and 4 remained unreported.
readout. Moreover, screening of 4 against a PDE panel revealed a
Fig. 2 (A) EC50 curves of compounds 3 and 4 against phosphodiesterase 3B (PDE3B) in a cell-based assay; control: milrinone (IC50 = 0.67 mM, Hill
slope = ꢁ0.8). (B) and (C) EC50 curves of compounds 5, NID-1 (B), and 6 (C) against cathepsin L; control: E6431 (IC50 = 8.6 nM, Hill slope = ꢁ1.3).
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range of weak to potent inhibitory effects at 50 mM, in line with specificities.43–45 In a preliminary focused screening campaign we
pAffinity (ESI†) and supporting specific binding to PDE3B. While further probed selectivity by 5 and 6 for cathepsin L. Using the
ligand-efficient entities were identified, e.g. 3 and 4, their same assay technology, 5 and 6 were confirmed inactive against
similarity to compounds in the training data is apparent (ESI†). cathepsin S up to a concentration of 100 mM (ESI†), advocating
Still, low structural fingerprint similarity (Tc o 0.2) between specific cathepsin L binding and engagement. This result is
these de novo designed entities and their respective nearest noteworthy as it may provide introductory evidence of a positive
neighbors in ChEMBL suggests, in this case, that straightforward allosteric effect of 6 on cathepsin L, considering the similar
similarity searching would likely have failed in their retrieval. In substrate specificities between the protease subtypes. Taking
fact, when using the SEA32 software for target prediction, PDE3B into account the paramount role of cathepsin L for presenting
is not confidently predicted for 1, 2 and 4. On the other hand, the peptides on MHC class II molecules,46,47 protein processing in the
SPiDER6 model confidently predicts PDE3B for 1–4, which is in heart,48 and as a putative tumor suppressor in the K14-HPV16
line with our GP affinity predictions.
Then we set out to identify drug targets for 5 and 6, which had here may find multiple areas of application.
mouse model of epidermal cancer,49 the tool compound unveiled
been designed as orthosteric site inhibitors of HIV-1 protease – a
While the exact molecular mechanism of recognition of 6 by
validated drug target for acquired immunodeficiency syndrome cathepsin L and its biological implications are currently unclear,
therapy.33 Poor modulation of HIV-1 protease by 5 and 6 at a our re-purposing approach delivered a pioneering tool compound
concentration of 100 mM can be partly explained by the inexistence to leverage studies on the role of cathepsin L activation in
of a hydroxyl group capable of displacing a catalytic water molecule (patho)physiological events.
from the active site. Accordingly, our regression models did not
We identified PDE3B inhibitors and the best-in-class cathepsin L
suggest inhibition of HIV-1 protease, unlike SEA and SPiDER, activator as a tool compound for mechanistic elucidation of cathepsin
but predicted cathepsin L, a lysosomal cysteine protease involved L-related biological effects. The results of this study corroborate the
in several pathophysiological events including cancer,34–36 as a suitability of GP models for quantitatively predicting the target
potential target of 5 and 6. Biochemical evaluation revealed affinities of computationally designed NCEs, thereby complementing
moderate cathepsin L inhibition by (30R)-5 (IC50 = 38 ꢀ 6 mM; publically available prediction software. Our GP approach and similar
Table 1, Fig. 2B); in line with the predicted pAffinity value, and in silico ‘‘polypharmacology’’ platforms may find multiple uses
with similar potency to the nearest neighbor in ChEMBL (ESI†). in chemical biology and pharmaceutical research by unveiling
Remarkably, the diastereomer (30S)-6 presented the opposite ligand–target associations that allow to identify and study bio-
functional effect (Fig. 2C), potently activating cathepsin L (EC50
=
chemical pathways and processes.
6 ꢀ 2 mM, Table 1). We ruled out artifact readouts by re-screening 6
ETH Zu¨rich, Novartis Pharma and the OPO Foundation
against different cathepsin L batches, and by measuring auto- funded this research. The Chemical Computing Group provided
fluorescence as well as colloidal aggregation up to 100 mM. We an MOE software license.
then compared 6 to NID-1 (Fig. 2B), which has also been reported
as a cathepsin L activator.37 Surprisingly, we were unable to
reproduce activation of cathepsin L at approximately 16 mM as
Notes and references
reported by Varma et al.37 Our data rather suggest concentration-
dependent enzyme inhibition by NID-1 (IC50 = 43 ꢀ 4 mM, Fig. 2B)
and possible ‘‘frequent hitter’’34 pan-assay interference according
to PAINS38,39 and REOS40 filters. Conversely, our tool compound 6
does not feature potentially problematic moieties according to
PAINS and REOS, further supporting its appropriateness as a
chemical probe. Nevertheless, in vitro cytotoxicity against MCF7
cells corroborates the previously identified cell death promotion
by NID-1 (ESI†). In the same assay system, both 5 and 6 showed
negligible cytotoxicity.
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