Pursuing the complexity of alzheimer’s disease: Discovery of fluoren-9-amines as selective butyrylcholinesterase inhibitors and n-methyl-d-aspartate receptor antagonists

biomolecules

Article

Pursuing the Complexity of Alzheimer’s Disease: Discovery of

Fluoren-9-Amines as Selective Butyrylcholinesterase Inhibitors

and N-Methyl-D-Aspartate Receptor Antagonists

Jan Konecny ^1,2,†, Anna Misiachna ^3,4,5,†, Martina Hrabinova ^1,2, Lenka Pulkrabkova ^1,2, Marketa Benkova ²,

Lukas Prchal ², Tomas Kucera ^1,2, Tereza Kobrlova ², Vladimir Finger ^2,6, Marharyta Kolcheva ^3,4

,

Stepan Kortus ^3,4, Daniel Jun ^1,2, Marian Valko ⁷, Martin Horak ^3,4, Ondrej Soukup ^1,2,

*

and

Jan Korabecny ^1,2,

*

1

2

3

Department of Toxicology and Military Pharmacy, Faculty of Military Health Sciences, Trebesska 1575,

500 01 Hradec Kralove, Czech Republic; jan.konecny@unob.cz (J.K.); martina.hrabinova@unob.cz (M.H.);

lenka.pulkrabkova@fnhk.cz (L.P.); tomas.kucera2@unob.cz (T.K.); daniel.jun@unob.cz (D.J.)

Biomedical Research Centre, University Hospital Hradec Kralove, Sokolska 581,

500 05 Hradec Kralove, Czech Republic; Marketa.Benkova@fnhk.cz (M.B.); lukas.prchal@fnhk.cz (L.P.);

tereza.kobrlova@fnhk.cz (T.K.); ﬁngerv@faf.cuni.cz (V.F.)

Institute of Experimental Medicine of the Czech Academy of Sciences, Videnska 1083,

142 20 Prague, Czech Republic; anna.misiachna@iem.cas.cz (A.M.); marharyta.kolcheva@iem.cas.cz (M.K.);

stepan.kortus@iem.cas.cz (S.K.); martin.horak@iem.cas.cz (M.H.)

Institute of Physiology of the Czech Academy of Sciences, Videnska 1083, 142 20 Prague, Czech Republic

Department of Physiology, Faculty of Science, Charles University in Prague, Albertov 6,

128 43 Prague, Czech Republic

Department of Organic and Bioorganic Chemistry, Faculty of Pharmacy in Hradec Kralove, Charles

University, Akademika Heyrovskeho 1203, 500 05 Hradec Kralove, Czech Republic

Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinskeho 9,

812 37 Bratislava, Slovakia; marian.valko@stuba.sk

4

5

6

7

ꢀꢁꢂꢀꢃꢄꢅꢆꢇ

ꢀꢁꢂꢃꢄꢅꢆ

Citation: Konecny, J.; Misiachna, A.;

Hrabinova, M.; Pulkrabkova, L.;

Benkova, M.; Prchal, L.; Kucera, T.;

Kobrlova, T.; Finger, V.; Kolcheva, M.;

Kortus, S.; et al. Pursuing the

*

Correspondence: ondrej.soukup@fnhk.cz (O.S.); jan.korabecny@fnhk.cz (J.K.);

Tel.: +420-495-833-447 (O.S. & J.K.)

These authors contributed equally to this paper.

†

Complexity of Alzheimer’s Disease:

Discovery of Fluoren-9-Amines as

Selective Butyrylcholinesterase

Inhibitors and N-Methyl-D-Aspartate

Receptor Antagonists. Biomolecules

2021, 11, 3. https://dx.doi.org/

10.3390/biom11010003

Abstract: Alzheimer’s disease (AD) is a complex disorder with unknown etiology. Currently, only

symptomatic therapy of AD is available, comprising cholinesterase inhibitors and N-methyl-D-

aspartate (NMDA) receptor antagonists. Drugs targeting only one pathological condition have

generated only limited efﬁcacy. Thus, combining two or more therapeutic interventions into one

molecule is believed to provide higher beneﬁt for the treatment of AD. In the presented study, we

designed, synthesized, and biologically evaluated 15 novel ﬂuoren-9-amine derivatives. The in silico

prediction suggested both the oral availability and permeation through the blood–brain barrier (BBB).

An initial assessment of the biological proﬁle included determination of the cholinesterase inhibition

and NMDA receptor antagonism at the GluN1/GluN2A and GluN1/GluN2B subunits, along with

a low cytotoxicity proﬁle in the CHO-K1 cell line. Interestingly, compounds revealed a selective

butyrylcholinesterase (BChE) inhibition pattern with antagonistic activity on the NMDARs. Their

interaction with butyrylcholinesterase was elucidated by studying enzyme kinetics for compound 3c

in tandem with the in silico docking simulation. The docking study showed the interaction of the

tricyclic core of new derivatives with Trp82 within the anionic site of the enzyme in a similar way as

the template drug tacrine. From the kinetic analysis, it is apparent that 3c is a competitive inhibitor

of BChE.

Received: 25 November 2020

Accepted: 18 December 2020

Published: 22 December 2020

Publisher’s Note: MDPI stays neu-

tral with regard to jurisdictional claims

in published maps and institutional

afﬁliations.

censee MDPI, Basel, Switzerland. This

article is an open access article distributed

under the terms and conditions of the

Creative Commons Attribution (CC BY)

license (https://creativecommons.org/

licenses/by/4.0/).

Keywords: acetylcholinesterase; Alzheimer’s disease; butyrylcholinesterase; ﬂuorene; in vitro; in

silico; multi-target directed ligands; N-methyl-D-aspartate receptor

Biomolecules 2021, 11, 3. https://dx.doi.org/10.3390/biom11010003

https://ww w .mdpi.com/journal/biomolecules

Biomolecules 2021, 11, 3

2 of 23

1. Introduction

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that mani-

fests as progressive memory loss leading to dementia [ ]. AD not only represents a

serious health burden, but it also imposes social and economic issues [ ]. Worldwide,

nearly 50 million people have developed AD, and it is expected that the number will

double within the next 20 years [ ]. An effective cure able to halt or slow down the dis-

1,2

3–6

7

ease progression still does not exist, mainly because of our limited knowledge about AD

pathophysiology. However, mechanisms resulting in the clinical symptoms of AD are

well-established [

as major contributors to AD, forming extracellular aggregates [10

neuroﬁbrillary tangles, respectively [12 14]. So far, the therapy is only palliative, either

enhancing the cholinergic neurotransmission or modulating the synaptic excitotoxicity

via N-methyl-D-aspartate (NMDA) receptors [15 18]. Oxidative stress [19 20], metal ion

8,9

]. Among them, amyloid-

β

(A

β) and tau proteins are considered

,11] and intracellular

–

,

imbalance [21], or neuroinﬂammation [22] are other crucial players in AD pathophysiology.

Currently, AD is treated by acetylcholinesterase (AChE, E.C. 3.1.1.7) inhibitors to

restore the physiological levels of acetylcholine (ACh) and the NMDA receptor antagonist,

which reduce the excitotoxicity by mitigating the excessive glutamate stimulation of the

receptors [23,24]. Donepezil, galantamine, and rivastigmine represent the marketed drugs

from the group of AChE inhibitors; memantine acts as a noncompetitive NMDA receptor

antagonist, blocking the overstimulation of the respective receptors. Since AChE inhibitors

are administered in mild-to-moderate stages of AD, memantine is indicated for severe

stages of the disease [25]. It is worth mentioning that the combination of donepezil

and memantine into one capsule, known as Namzaric, was approved in 2014. Such a

combination offers an improved efﬁcacy compared to single-agent administration with a

good pharmacokinetic proﬁle, safety, and tolerability [26,27].

Given the complexity of AD and the high failure rate of single-targeted drug can-

didates from clinical trials in the last few years, the idea of multitarget directed ligands

(MTDLs) has emerged as a new approach to tackle the disease [28

design of these molecules is oriented towards the modulation of different pathological

pathways simultaneously [29 32]. Recently, we have critically pointed out that not all the

–31]. Accordingly, the

,

combined effects are suitable to be amalgamated into a single molecule to achieve the

highest therapeutic effect [29]. Indeed, AD is a long-term condition with progressive brain

changes occurring 20 years before the AD outbreak into the symptomatic phase [33]. With

respect to the combination of therapeutic targets of interest and to achieve maximum syn-

ergy, the ideal drugs should be designed by following the timescale for speciﬁc pathological

cascades. Within this study, we have focused on the symptomatic stage of AD, in which

A

β

plaques and neuroﬁbrillary tangles of paired helical ﬁlaments are ubiquitous, and their

clearance has no effect on improving cognitive functions. This is well-documented, for

instance, by the insufﬁcient efﬁcacy of -secretase inhibitors in phase III clinical trials [34].

β

With this in mind, we turned our attention to impaired neurotransmission, which is the

most critically affected at the later stages of AD, thus pursuing the two most pronounced

systems, namely cholinergic and glutamatergic ones [35,36].

Tacrine (THA), an AChE inhibitor, was the ﬁrst drug approved for AD treatment in

1993. Hepatotoxicity and gastrointestinal discomfort are the culprits responsible for its

withdrawal in 2013 [37]. THA acts not only as AChE inhibitor but, also, via the antagonism

of NMDA receptors, which might contribute to its pharmacological effects [38]. Indeed,

THA inhibits NMDA receptor responses with high speciﬁcity in a concentration-dependent

manner with an IC₅₀= 20

potentials [39]. Mechanistically, the THA action at NMDA receptors can be classiﬁed as

reversible, blocking the channel’s open state with binding in the proximity of the channel

µ

M at

−

60 mV and much higher values at positive membrane

entrance [40

a potential applicability for AD treatment due to the pharmacological behavior of both

cholinesterases and the NMDA receptor [39 42]. Based on this, our goal was to design

,41]. Likewise, other THA derivatives emerged as interesting drugs with

,

Biomolecules 2021, 11, 3

3 of 23

a series of novel multipotent agents, which, in one molecule, shows a representative

cholinesterase inhibition together with NMDA antagonist activity.

In a recent work, we developed novel molecules building on a fluorene moiety. The

compounds are structurally related to other tricyclic congeners like THA or carbazole, known

for their cholinesterase inhibition and/or neuroprotective properties (Figure 1

took advantage of the above-mentioned and designed novel molecules substituted at the C9

position by various primary or secondary amines, generating 15 novel compounds (3a–o;

) [43 –45 ]. We

Figure 1 ) as potential cholinesterase inhibitors and NMDA receptor antagonists. Importantly,

since several techniques on how to construct MTDLs exist, we applied the so-called merging

approach in this study. This approach best corresponds to the drug-likeness of the final

molecule compared to the linking or fusing strategies [28 ,29 ,32 ]. Furthermore, the design of

the novel family was envisaged in parallel with their physicochemical properties, presuming

both oral and central bioavailability. The biological profile includes the assessment of AChE,

butyrylcholinesterase (BChE; E.C. 3.1.1.8), and NMDA receptor affinities, with the cytotoxicity

and BBB permeation being evaluated as well.

Figure 1. Design of a novel 9-aminoﬂuorene overlapping the tacrine (THA) and carbazole moieties.

NMDA: N-methyl-D-aspartate.

2. Materials and Methods

2.1. Chemistry

All chemical solvents and reagents were used in the highest available purity with-

out further puriﬁcation, and they were purchased from Sigma-Aldrich (Prague, Czech

Republic) or FluoroChem (Hadﬁeld, UK). The reactions were monitored by thin-layer

chromatography (TLC) on silica gel plates (60 F254, Merck, Prague, Czech Republic), and

the spots were visualized by ultraviolet light (254 nm). Puriﬁcation of crude products was

carried out using a PuriFlash Gen5 column, 5.250 (Interchim, Montluçon, France) (silica

gel 100, 60 Å, 230–400-mesh ASTM, Sigma-Aldrich, Prague, Czech Republic). NMR spec-

tra were recorded in deuterated chloroform (CDCl3) and deuterated dimethyl sulfoxide

(DMSO-d6) on a Bruker Avance NEO 500 MHz spectrometer (499.87 MHz for 1H-NMR

and 125.71 MHz for 13C-NMR; Vienna, Austria). Chemical shifts are reported in parts per

millions (ppm), and spin multiplicities are given as broad singlet (bs), doublet (d), doublet

Biomolecules 2021, 11, 3

4 of 23

of doublet (dd), triplet (t), doublet of triplet (dt), quartet (q), pentet (p), or multiplet (m).

Coupling constants (J) are reported in Hz. Recorded NMR spectra are available in the

Supplementary Materials. Melting points were measured using an automated melting

point recorder M-565 (Büchi, Flawil, Switzerland). The synthesized compounds were

analyzed by an LC-MS system consisting of UHLPC Dionex Ultimate 3000 RS coupled

with a Q Exactive Plus orbitrap mass spectrometer to obtain high-resolution mass spectra

(Thermo Fisher Scientiﬁc, Bremen, Germany) (see Supplementary Materials). The samples

were dissolved in DMSO/methanol 50/50 (v/v). Reverse-phase C18 column Kinetex EVO

(Phenomenex, Torrance, CA, USA) was used as a stationary phase, and puriﬁed water with

0.1% formic acid (mobile phase A) and LC-MS grade acetonitrile with 0.1% formic acid

(mobile phase B) were used as the mobile phases. Gradient elution was used to determine

purities and mass spectra. Method started with 5% B for 0.3 min, then the gradient rose

to 100% B in the third min and was at 100% B for 0.7 min and then went back to 5% B

and was equilibrated for 3.5 min. Total run time of the method was 7.5 min. Column

was tempered to 27 ^◦C, the ﬂow of the mobile phase was 0.5 mL/min, and the injection

volume was 1 µL. Gradient LC analysis with UV detection (254 nm) conﬁrmed a >97%

purity. High-resolution mass spectra were collected from the total ion current in the scan

range 105–1000 m/z, with the resolution set to 140,000.

2.2. General Procedure for the Preparation of 9-Bromoﬂuorene hydrochlorides (3a–o)

Appropriate primary or secondary amine (Scheme 1)_◦was dissolved in 10 mL of dry

MeCN under argon atmosphere and stirred vigorously at 40 C for 15 min. 9-Bromoﬂuorene

(Scheme 1) was dissolved in 5 mL of dry MeCN and added dropwise to the reaction mixture.

The reaction was maintained with stirring at 40 ^◦C for 3 h. After cooling, the solvent was

evaporated under reduced pressure, and the crude material was puriﬁed on a FlashChrom

column (eluent dichloromethane/methanol (DCM/MeOH) with 1% of NH₃, gradient

95:5

→

90:10) to get the appropriate products as a free base. These were converted into

hydrochloride salts by the treatment with 1 mL of concentrated aqueous solution of HCl

(35%) in 5 mL of MeOH, starting from 0 ^◦C to room temperature for 1 h. The solvent was

removed in vacuo, and the residual water was distilled via azeotropic distillation with

absolute EtOH three times. The solid product was washed by ice-cold acetone, resulting in

hydrochloride salt as a white solid.

Scheme 1. Synthetic procedure for the preparation of substituted ﬂuoren-9-amines 3a–o. Reaction conditions: (i) MeCN,

◦

40 C, 3 h and (ii) HCl (aq.), MeOH, RT, 1 h.

Biomolecules 2021, 11, 3

5 of 23

2.2.1. N-Propan-2-yl-9H-ﬂuoren-9-amine hydrochloride (3a)

Yield 43% as a white solid. Melting point (m.p.): decomposed at 238.5 ^◦C. ¹H-NMR

(500 MHz, DMSO-d₆) 10.17 (bs, J = 5.6 Hz, 2H), 8.12 (dd, J = 7.6, 1.0 Hz, 2H), 7.95 (dt,

J = 7.6, 1.0 Hz, 2H), 7.54 (td, J = 7.6, 1.0 Hz, 2H), 7.42 (td, J = 7.6, 1.0 Hz, 2H), 5.61–5.57 (m,

δ

1H), 3.27–3.17 (m, 1H), 1.18 (d, J = 6.5 Hz, 6H). ¹³C-NMR (126 MHz, dmso)

δ 141.41, 138.60,

130.43, 128.33, 127.35, 121.21, 58.25, 48.44, 20.18. HRMS (ESI⁺): [M + H]⁺: calculated for

C₁₆H₁₈N⁺(m/z): 224.14392; found: 224.14343. LC-MS purity 99%.

2.2.2. N-Methyl-9H-ﬂuoren-9-amine hydrochloride (3b)

Yield 13% as a white solid. M.p.: decomposed at 205.9 C. ¹H-NMR (500 MHz, DMSO-

◦

d₆)

δ

10.52 (bs, 2H), 8.07 (dd, J = 7.6, 1.0 Hz, 2H), 7.94 (dd, J = 7.6, 1.0 Hz, 2H), 7.53 (td, J = 7.6,

1.0 Hz, 2H), 7.42 (td, J = 7.6, 1.0 Hz, 2H), 5.64 (s, 1H), 2.12 (s, 3H). ¹³C-NMR (126 MHz,

dmso)

δ

141.42, 137.56, 130.25, 128.20, 126.62, 120.94, 59.75, 27.06. HRMS (ESI⁺): [M + H]⁺:

calculated for C₁₄H₁₄N⁺(m/z): 196.11262; found: 196.11229. LC-MS purity 100%.

2.2.3. N-Cyclohexyl-9H-ﬂuoren-9-amine hydrochloride (3c)

Yield 43% as a white solid. M.p. 267.3–269.6 ^◦C. ¹H-NMR (500 MHz, DMSO-d₆)

δ

10.17 (bs, 2H), 8.10 (dd, J = 7.6, 1.0 Hz, 2H), 7.95 (dd, J = 7.6, 1.0 Hz, 2H), 7.54 (td, J = 7.6,

1.0 Hz, 2H), 7.42 (td, J = 7.6, 1.0 Hz, 2H), 5.60 (s, 1H), 2.84 (t, J = 11.5 Hz, 1H), 1.81 – 1.71

(m, 2H), 1.68–1.59 (m, 2H), 1.50 (m, J = 15.0, 5.3, 4.4 Hz, 3H), 1.13–1.00 (m, 3H). ¹³C-NMR

(126 MHz, dmso) δ 141.09, 138.37, 130.15, 128.09, 127.00, 120.94, 57.68, 54.59, 29.72, 24.68,

23.99. HRMS (ESI⁺): [M + H]⁺: calculated for C₁₉H₂₂N⁺(m/z): 264.17522; found: 264.17465.

LC-MS purity 99%.

2.2.4. N-Cyclopropyl-9H-ﬂuoren-9-amine hydrochloride (3d)

◦

Yield 57% as a white solid. M.p.: decomposed at 233.1 C. ¹H-NMR (500 MHz, DMSO-

d₆)

δ

10.63 (bs, 2H), 8.13 (dd, J = 7.6, 1.0 Hz, 2H), 7.94 (dd, J = 7.6, 1.0 Hz, 2H), 7.53 (td,

J = 7.6, 1.0 Hz, 2H), 7.41 (td, J = 7.6, 1.0 Hz, 2H), 5.63 (s, 1H), 2.25–2.15 (m, 1H), 0.85–0.76

(m, 2H), 0.55–0.46 (m, 2H). ¹³C-NMR (126 MHz, dmso)

δ 141.38, 138.07, 130.14, 127.95,

127.08, 120.82, 60.17, 26.24, 3.50. HRMS (ESI⁺): [M + H]⁺: calculated for C₁₆H₁₆N⁺(m/z):

222.12827; found: 222.12776. LC-MS purity 99%.

2.2.5. N-Cyclobutyl-9H-ﬂuoren-9-amine hydrochloride (3e)

◦

Yield 51% as a white solid. M.p.: decomposed at 271.6 C. ¹H-NMR (500 MHz, DMSO-

d₆)

δ

10.77 (bs, 2H), 8.10 (dd, J = 7.6, 1.0 Hz, 2H), 7.92 (dd, J = 7.6, 1.0 Hz, 2H), 7.52 (td,

J = 7.6, 1.0 Hz, 2H), 7.40 (td, J = 7.6, 1.0 Hz, 2H), 5.57 (s, 1H), 3.12 (p, J = 9.1, 8.6 Hz, 1H),

2.22–2.10 (m, 2H), 1.62–1.41 (m, 4H). ¹³C-NMR (126 MHz, dmso)

δ 141.23, 137.87, 130.16,

127.97, 126.96, 120.84, 58.50, 47.72, 27.67, 15.64. HRMS (ESI⁺): [M + H]⁺: calculated for

C₁₇H₁₈N⁺(m/z): 236.14392; found: 236.14333. LC-MS purity 99%.

2.2.6. 1-(9H-ﬂuoren-9-yl)piperidine hydrochloride (3f)

Yield 45% as a white solid. M.p.: decomposed at 251.4 C. ¹H-NMR (500 MHz, DMSO-

◦

d₆)

δ

11.79 (bs, 1H), 8.24 (dd, J = 7.6, 1.0 Hz, 2H), 7.94 (dd, J = 7.6, 1.0 Hz, 2H), 7.55 (td,

J = 7.6, 1.0 Hz, 2H), 7.42 (td, J = 7.6, 1.0 Hz, 2H), 5.72 (s, 1H), 3.23 (d, J = 11.7 Hz, 2H), 2.79

(q, J = 11.7 Hz, 2H), 2.07 (q, J = 12.6, 11.7 Hz, 2H), 1.67 (d, J = 12.6 Hz, 3H), 1.34–1.20 (m,

1H). ¹³C-NMR (126 MHz, dmso)

δ 141.83, 135.94, 130.66, 128.29, 128.17, 120.91, 67.23, 49.46,

22.29, 21.67. HRMS (ESI⁺): [M + H]⁺: calculated for C₁₈H₂₀N⁺(m/z): 250.15957; found:

250.15875. LC-MS purity 98%.

2.2.7. N-(2-methoxyethyl)-9H-ﬂuoren-9-amine hydrochloride (3g)

Yield 11% as a white solid. M.p.: 183.4–185.1 ^◦C. ¹H-NMR (500 MHz, DMSO-d₆)

δ

10.60 (bs, 2H), 8.15 (dd, J = 7.6, 1.0 Hz, 2H), 7.93 (dd, J = 7.6, 1.0 Hz, 2H), 7.53 (td, J = 7.6,

1.0 Hz, 2H), 7.42 (td, J = 7.6, 1.0 Hz, 2H), 5.59 (s, 1H), 3.47 (t, J = 5.2 Hz, 2H), 3.19 (s,

3H), 2.47–2.40 (m, 2H). ¹³C-NMR (126 MHz, dmso)

δ 141.42, 137.56, 130.27, 128.26, 126.69,

Biomolecules 2021, 11, 3

6 of 23

120.93, 67.03, 59.32, 58.16, 40.87. HRMS (ESI⁺): [M + H]⁺: calculated for C₁₆H₁₈NO⁺(m/z):

240.13884; found: 240.13831. LC-MS purity 99%.

2.2.8. N-Ethyl-9H-ﬂuoren-9-amine hydrochloride (3h)

Yield 36% as a white solid. M.p.: decomposed at 264.8 C. ¹H-NMR (500 MHz, DMSO-

◦

d₆)

δ

10.46 (bs, J = 9.3 Hz, 2H), 8.12 (dd, J = 7.6, 1.0 Hz, 2H), 7.94 (dd, J = 7.6, 1.0 Hz, 2H),

7.53 (td, J = 7.6, 1.0 Hz, 2H), 7.42 (td, J = 7.6, 1.0 Hz, 2H), 5.61 (d, J = 3.5 Hz, 1H), 2.51–2.48

(m, 2H), 1.14 (t, J = 7.2 Hz, 3H). ¹³C-NMR (126 MHz, dmso)

δ 141.29, 137.89, 130.19, 128.18,

126.72, 120.91, 59.17, 37.35, 11.53. HRMS (ESI⁺): [M + H]⁺: calculated for C₁₅H₁₆N⁺(m/z):

210.12827; found: 210.12784. LC-MS purity 99%.

2.2.9. N,N-Diethyl-9H-ﬂuoren-9-amine hydrochloride (3i)

◦

Yield 7% as a white solid. M.p.: 166.2–167.9 C. ¹H-NMR (500 MHz, DMSO-d₆)

δ 11.66

(bs, 1H), 8.15 (dd, J = 7.6, 1.0 Hz, 2H), 7.98 (dd, J = 7.6, 1.0 Hz, 2H), 7.57 (td, J = 7.6, 1.0 Hz,

2H), 7.44 (td, J = 7.6, 1.0 Hz, 2H), 5.85 (s, 1H), 3.12–3.02 (m, 1H), 3.02–2.94 (m, 1H), 1.30 (t,

J = 7.2 Hz, 6H). ¹³C-NMR (126 MHz, dmso)

δ 141.62, 136.45, 130.54, 128.37, 127.49, 121.07,

63.15, 46.29, 10.32. HRMS (ESI⁺): [M + H]⁺: calculated for C₁₇H₂₀N⁺(m/z): 238.15957;

found: 238.15903. LC-MS purity 99%.

2.2.10. 1-(9H-ﬂuorene-9-yl)-4-methylpiperazine dihydrochloride (3j)

◦

Yield 7% as a white solid. M.p.: decomposed at 206.4 C. ¹H-NMR (500 MHz, DMSO-

d₆)

δ

10.80 (bs, 1H), 7.85 (dd, J = 7.6, 1.0 Hz, 2H), 7.59 (dd, J = 7.6, 1.0 Hz, 2H), 7.42 (td,

J = 7.6, 1.0 Hz, 2H), 7.34 (td, J = 7.6, 1.0 Hz, 2H), 5.03 (s, 1H), 3.31–2.71 (m, 8H), 2.68 (s,

3H). ¹³C-NMR (126 MHz, dmso)

δ 143.00, 140.64, 128.64, 127.51, 125.88, 120.36, 68.65, 53.41,

45.50, 42.34. HRMS (ESI⁺): [M + H]⁺: calculated for C₁₈H₂₁N₂⁺(m/z): 265.17047; found:

265.16974. LC-MS purity 99%.

2.2.11. 1-(9H-ﬂuorene-9-yl)-4-ethylpiperazine dihydrochloride (3k)

◦

Yield 25% as a white solid. M.p.: decomposed at 233.6 C. ¹H-NMR (500 MHz, DMSO-

d₆)

δ

10.75 (bs, 1H), 7.85 (dd, J = 7.6, 1.0 Hz, 2H), 7.59 (dd, J = 7.6, 1.0 Hz, 2H), 7.42 (td,

J = 7.6, 1.0 Hz, 2H), 7.33 (td, J = 7.6, 1.0 Hz, 2H), 5.03 (s, 1H), 3.02 (q, J = 7.3 Hz, 2H),

2.99–2.61 (m, 8H), 1.19 (t, J = 7.3 Hz, 3H). ¹³C-NMR (126 MHz, dmso)

δ 143.05, 140.63,

128.62, 127.49, 125.90, 120.34, 68.67, 51.28, 50.79, 45.45, 9.01. HRMS (ESI⁺): [M + H]⁺:

calculated for C₁₉H₂₄N₂⁺(m/z): 279.18612; found: 279.18555. LC-MS purity 99%.

2.2.12. 4-(9H-ﬂuorene-9-yl)morpholine hydrochloride (3l)

Yield 50% as a white solid. M.p.: decomposed at 239.8 C. ¹H-NMR (500 MHz, DMSO-

◦

d₆)

δ

12.66 (bs, 1H), 8.21 (dd, J = 7.6, 1.0 Hz, 2H), 7.96 (dd, J = 7.6, 1.0 Hz, 2H), 7.58 (td, J = 7.6,

1.0 Hz, 2H), 7.45 (td, J = 7.6, 1.0 Hz, 2H), 5.81 (s, 1H), 4.18–3.93 (m, 2H), 3.94–3.75 (m, 2H),

3.31–3.09 (m, 2H), 3.09–2.87 (m, 2H). ¹³C-NMR (126 MHz, DMSO)

δ 142.19, 131.09, 128.60,

128.46, 121.30, 67.38, 63.41, 48.58. HRMS (ESI⁺): [M + H]⁺: calculated for C₁₇H₁₈NO⁺(m/z):

252.13884; found: 252.13837. LC-MS purity 99%.

2.2.13. N-Butyl-9H-ﬂuoren-9-amine hydrochloride (3m)

Yield 12% as a white solid. M.p.: decomposed at 201.3 C. ¹H-NMR (500 MHz, DMSO-

◦

d₆)

δ

10.13 (bs, 2H), 8.10 (dd, J = 7.6, 1.0 Hz, 2H), 7.93 (dd, J = 7.6, 1.0 Hz, 2H), 7.52 (td,

J = 7.6, 1.0 Hz, 2H), 7.42 (td, J = 7.6, 1.0 Hz, 2H), 5.54 (s, 1H), 2.33 (t, 2H), 1.58–1.47 (m, 2H),

1.25–1.14 (m, 2H), 0.74 (t, J = 7.3 Hz, 3H). ¹³C-NMR (126 MHz, DMSO)

δ 141.52, 130.26,

128.40, 126.78, 121.12, 59.98, 41.80, 28.70, 19.70, 13.86. HRMS (ESI⁺): [M + H]⁺: calculated

for C₁₇H₂₀N⁺(m/z): 238.15957; found: 238.15912. LC-MS purity 97%.

2.2.14. N-(2-methylpropyl)-9H-ﬂuoren-9-amine hydrochloride (3n)

Yield 34% as a white solid. M.p.: 254.8–256.1 ^◦C. ¹H-NMR (500 MHz, DMSO-d₆)

10.52 (bs, 2H), 8.19 (dd, J = 7.6, 1.0 Hz, 2H), 7.94 (dd, J = 7.6, 1.0 Hz, 2H), 7.54 (td, J = 7.6,

δ

Biomolecules 2021, 11, 3

7 of 23

1.0 Hz, 2H), 7.44 (td, J = 7.6, 1.0 Hz, 2H), 5.62 (s, 1H), 2.13–2.03 (m, 2H), 1.98–1.85 (m, 1H),

0.82 (d, J = 6.7 Hz, 6H). ¹³C-NMR (126 MHz, DMSO)

δ 141.72, 137.87, 130.54, 128.56, 126.89,

121.22, 59.76, 48.58, 26.03, 20.52. HRMS (ESI⁺): [M + H]⁺: calculated for C₁₇H₂₀N⁺(m/z):

238.15957; found: 238.15903. LC-MS purity 97%.

2.2.15. 1-(9H-ﬂuoren-9-yl)pyrrolidine hydrochloride (3o)

Yield 30% as a white solid. M.p.: decomposed at 239.3 C. ¹H-NMR (500 MHz, DMSO-

◦

d₆)

δ

12.31 (bs, 1H), 8.14 (dd, J = 7.6, 1.0 Hz, 2H), 7.96 (dd, J = 7.6, 1.0 Hz, 2H), 7.56 (td, J = 7.6,

1.0 Hz, 2H), 7.43 (td, J = 7.6, 1.0 Hz, 2H), 5.84 (s, 1H), 3.49–3.27 (m, 2H; signal overlapped

with residual DMSO), 3.13–2.92 (m, 2H), 1.97–1.72 (m, 4H). ¹³C-NMR (126 MHz, DMSO)

δ

141.85, 130.84, 128.57, 127.78, 121.22, 64.49, 50.55, 23.96. HRMS (ESI⁺): [M + H]⁺: calculated

for C₁₇H₁₈N⁺(m/z): 236.14392; detected: 236.14328. LC-MS purity 99%.

2.3. In Vitro Anti-ChE Assay

The human AChE/human BChE (hAChE/hBChE) inhibitory activity of the tested

derivatives was determined using Ellman’s method [46–48] and is expressed as IC₅₀, i.e.,

a concentration that reduces the cholinesterase activity by 50%. The human recombinant

BChE and AChE were prepared at the Department of Toxicology and Military Pharmacy

(Faculty of Military Health Sciences, Hradec Kralove, Czech Republic). 5,5⁰-dithiobis(2-

nitrobenzoic acid) (Ellman’s reagent, DTNB), phosphate buffer (PB, pH 7.4), acetylthio-

choline (ATC), and butyrylthiocholine (BTC) were purchased from Sigma-Aldrich, Prague,

Czech Republic. For measuring purposes, polystyrene Nunc 96-well microplates with a

ﬂat bottom shape (Thermo Fisher Scientiﬁc, Waltham, MA, USA) were utilized. All the

assays were carried out in 0.1-M KH₂PO₄/K₂HPO₄buffer, pH 7.4. Enzyme solutions were

prepared at 2.0 units/mL in 2-mL aliquots. The assay medium (100

µ

L) consisted of 40

µ

L

of 0.1-M phosphate buffer (pH 7.4), 20 L of 0.01-M DTNB, 10 L of the enzyme, and 20

µ

of 0.01-M substrate (ATC/BTC iodide solution). Inhibitor solutions in the concentration

range 10⁻³–10⁻¹¹M were prepared, and IC₅₀values were calculated. Tested compounds

were preincubated for 5 min. The reaction was started by the immediate addition of 20 µL

of the substrate. The activity was determined by measuring the increase in absorbance at

412 for hAChE/hBChE at 37 ^◦C at 2 min intervals using a multimode microplate reader

Synergy 2 (BioTek Instruments, Inc., Winooski, VT, USA). Each concentration was assayed

in triplicate. Software GraphPad Prism 5 (San Diego, CA, USA) was used for the statistical

data evaluation.

2.4. Kinetic Study of hBChE Inhibition

The kinetic study of hBChE was performed by using the above-mentioned modiﬁed

Ellman’s method. The values of V_maxand K_mof the Michaelis-Menten kinetics, as well as

the value of K_i, were calculated by nonlinear regression from the substrate velocity curves.

Linear regression was used for calculation of the Cornish-Bowden plots. All calculations

were performed using GraphPad Prism software version 6.07 for Windows (San Diego,

CA, USA).

2.5. Antagonist Activity Towards the NMDA receptor

2.5.1. HEK293 Cell Culture and Transfection

Human embryonic kidney 293 (HEK293) cells were cultured in Opti-MEM I containing

5% fetal bovine serum (FBS; both from Thermo Fisher Scientiﬁc) [49]. The cells grown in a

24-well plate were put in Opti-MEM I media containing a mixture of 0.9 µL of MATra-A

Reagent (IBA) and 900 ng of DNA vectors carrying the human versions of the GluN1-1a

(GluN1), GluN2A, or GluN2B subunits and green ﬂuorescent protein (GFP; all vectors were

added in equal ratio) [39,50,51]. The cells were placed on a strong magnet plate for 30 min

and then trypsinized; resuspended in Opti-MEM I containing 1% FBS, 20-mM MgCl₂,

and 3-mM kynurenic acid (to inhibit the NMDAR-induced excitotoxicity); and plated on

Biomolecules 2021, 11, 3

8 of 23

poly-L-lysine-coated glass coverslips. The electrophysiological experiments were executed

24–48 h after transfection [52].

2.5.2. Electrophysiology

Whole-cell patch-clamp recordings were conducted at room temperature on the GFP-

positive HEK293 cells using an Axopatch 200B ampliﬁer (Molecular Devices, San Jose,

CA, USA); the series resistance compensation was >80% for all cells [39]. The recorded

currents were ﬁltered at 2 kHz with an eight-pole low-pass Bessel ﬁlter and digitized at

5 kHz with Digidata 1322A and pClamp 10 software (Molecular Devices). The WAS02

application system, which can reach the solution exchange time around the recorded cell

of 10–20 ms, was used to apply the extracellular solution (ECS) [53]. The ECS contained

(in mM): 160 NaCl, 2.5 KCl, 10 HEPES, 10 glucose, 0.2 EDTA, and 0.7 CaCl₂(pH adjusted to

7.3 with NaOH) and was supplemented with the saturating concentrations of co-agonists

glycine (50 µM) and agonist glutamate (1 mM; both from Merck). The stock solutions

of 3a–o (10 mM) were prepared freshly before each experiment in dimethylsulfoxide

(DMSO; Merck), and the background concentration of DMSO was kept equal in each

ECS. The borosilicate glass pipettes with a tip resistance ~4-7 M

Ω were made using a

P-1000 micropipette puller (Sutter Instrument Co., Novato, CA, USA) and were ﬁlled

with an intracellular solution (in mM: 125 gluconic acid, 15 CsCl, 5 EGTA, 10 HEPES, 3

MgCl₂, 0.5 CaCl₂, and 2 ATP-Mg salt (pH adjusted to 7.2 with CsOH). Data were analyzed

using SigmaPlot 14.0 (Systat Software, Inc., Chicago, IL, USA), and the dose-response

curves were built using Equation (1): I = 1/(1 + ([compound]/IC₅₀)^h), where IC₅₀is the

concentration of the compound that produces a 50% inhibition of the glutamate-evoked

current, [compound] is the concentration of the studied compound, and h is the apparent

Hill coefﬁcient [54].

2.6. In Vitro Cell Viability Assessment

Standard MTT assay (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide;

Sigma Aldrich, Prague, Czech Republic) was used according to the manufacturer’s pro-

tocols on the CHO-K1 (Chinese hamster ovary, ECACC, Salisbury, UK) and hCMEC/D3

(Sigma Aldrich, St. Louis, MO, USA). The cells were cultured according to recommended

conditions and seeded in a density of 8000 cells per well, as described previously [55].

Brieﬂy, the tested compounds were dissolved in dimethyl sulfoxide (DMSO; Sigma Aldrich,

St. Louis, MO, USA) and subsequently diluted in the nutrient mixture F-12 ham growth

medium (Sigma Aldrich, St. Louis, MO, USA), supplemented with 10% fetal bovine serum

and 1% penicillin-streptomycin (both Sigma Aldrich, St. Louis, MO, USA), so that the

ﬁnal concentration of DMSO did not exceed 0.5% (v/v). CHO-K1 cells were exposed to

serially diluted tested compounds for 24 h. Then, the medium was replaced by a fresh

medium containing 0.5 mg/mL of MTT, and the cells were allowed to produce formazan

for another approximately 3 h under surveillance. Thereafter, the medium with MTT

was removed, and crystals of formazan were dissolved in DMSO (100 µL/well; PENTA

s.r.o., Prague, Czech Republic). Cell viability was assessed spectrophotometrically by the

amount of formazan produced. The absorbance was measured at 570 nm with a 650-nm

reference wavelength on Synergy HT (BioTek, Winooski, VT, USA). The IC₅₀value (half-

maximal inhibitory concentration) was calculated from the control-subtracted triplicates

using nonlinear regression (four parameters) by GraphPad Prism 7.03 software (GraphPad

Software Inc., San Diego, CA, USA) for the CHO-K1 cell line, and for hCMEC/D3 cells,

the cell viability was expressed as the % relative to the untreated control. Final IC₅₀and

SEM (standard error of the mean) values were obtained as a mean of three independent

measurements.

2.7. Determination of In Vitro BBB Permeation

The D3 assay evaluates the ability of compounds to diffuse from the donor compart-

ment through the D3/hCMEC cell membrane into the acceptor compartment. The D3 cells

Biomolecules 2021, 11, 3

9 of 23

were seeded on a PET membrane (area 1.12 cm²) with 3-

µm pores of the 12-well plates

with 12-mm inserts. The tested compounds were dissolved in DMSO and then diluted

with Opti-MEM to reach ﬁnal a concentration of 30 M for 3m and 50 M for others; the

concentration of DMSO did not exceed 0.5% (v/v). The donor solution (750 L) was added

µ

to the donor compartment (insert), and the same volume of Opti-MEM was added into the

acceptor. The concentration of the drug in both compartments was measured by UV-VIS

spectrophotometry in 5, 15, 30, and 60 min of incubation in triplicates. The apparent

permeability coefﬁcient (P_app) was calculated from the concentration ratios. Tightness of

the D3 monolayer was conducted by the ﬂuorescein isothiocyanate (FITC) test after each

experiment. The accepted values of FITC (ﬂuorescein isothiocyanate) in 0.4 mg/mL in the

acceptor compartment after 30 min of incubation must not exceed 1.5% of the initial donor

concentration.

ꢀ

ꢁ

dC

dt

V_r

Papp =

×

(1)

(A × C₀)

A: area of the well/cell monolayer,

dC/dt: amount in the receiver compartment in given time,

V_r: volume of the receiver compartment, and

C₀: the initial concentration of tested compounds.

2.8. Molecular Modeling Studies

The model of hBChE (Protein Data Bank (PDB) ID: 4BDS, resolution: 2.10 Å) was

downloaded from the RCSB Protein Data Bank (https://rcsb.org) and prepared for ﬂexible

molecular docking by MGL Tools [56,57]. The preparation of these receptors involved

removal of the inhibitor, water molecules and nonbonded co-crystalized compounds, and

the addition of polar hydrogens. Default Gasteiger charges were assigned to all atoms.

Flexible parts of the enzymes were set based on previous experiences [58,59]. The rotatable

bonds in the ﬂexible residues were detected automatically by AutoDock Tools 1.5.4. The

ﬂexible receptor parts contained 39 residues. The studied ligands were ﬁrstly drawn

in HyperChem 8.0, then manually protonated, as suggested by MarvinSketch 18.24.0.

software (http://ww w .chemaxon.com), geometrically optimized by software Avogadro,

and stored as pdb ﬁles. The structures of the ligands were processed for docking by

the AutoDock Tools 1.5.4 program. Molecular docking was carried out by the software

AutoDock Vina 1.1.2 utilizing computer resources of the Czech National Grid Infrastructure

MetaCentrum. The search algorithm of AutoDock Vina efﬁciently combines a Markov

chain Monte Carlo-like method for the global search and a Broyden-Fletcher-Goldfarb-

Shano gradient approach for the local search [60]. It is a type of memetic algorithm based

on interleaving stochastic and deterministic calculations [61]. Each docking task was

repeated 20 times with the exhaustiveness parameter set to 8, employing 8 CPUs in parallel

multithreading. From the obtained results, the solutions reaching the minimum docking

score were taken as the top-scoring modes. The graphical representations of the docked

poses were shown in PyMOL (The PyMOL Molecular Graphics System, Version 2.0.6.

Schrödinger, LLC, New York, NY, USA). 2D diagrams were generated using Maestro 12.3

(Schrödinger Release, Schrödinger, LLC, New York, NY, USA).

2.9. In Silico Pharmacokinetics and Drug-Likeness Prediction

SwissADME, web tool was used to predict gastrointestinal absorption, BBB perme-

ation and bioavailability [62]. Physicochemical properties (pK_a, ClogP, HBA, HBD, and

TPSA) were predicted by MarvinSketch 20.4.0, ChemAxon Ltd. (Budapest, Hungary; see

Table S1, Supplementary Materials). BBB scores were calculated by the BBB calculator

available at the ACS website [63].

Biomolecules 2021, 11, 3

10 of 23

3. Results and Discussion

3.1. In Silico Prediction of the CNS and Oral Availability

All the newly designed compounds were initially screened in silico to predict their

peroral and CNS availability. Indeed, both of these features are essential in the drug

discovery process of small molecules as potential anti-AD therapeutics and should be

estimated before synthesis. To this end, the in silico pharmacokinetics; drug-likeness; and

ADME (absorption, distribution, metabolism, and excretion) prediction was applied to

compounds 3a–o using a web-based tool SwissADME [62,64,65]. THA and memantine

were used as references with known CNS statuses and pharmacokinetic proﬁles [42

Data are presented in Figures 2 and 3 and Table 1 and Table S1.

,66,67].

Table 1. Summary of the in silico absorption, distribution, metabolism, and excretion (ADME)

and drug-likeness of derivatives 3a–o with tacrine (THA) and memantine using various prediction

models.

BBB ^b

Lipinski ^d

Compound

GIA ^a

BBB Score ^c

Bio. Score ^e

3a

3b

3c

3d

3e

3f

3g

3h

3i

3j

3k

3l

3m

3n

3o

High

Yes

5.63

5.59

5.64

5.93

5.15

5.68

5.63

5.17

5.67

5.86

5.55

5.62

5.72

Yes

0.55

THA

Memantine

High

Yes

5.38

4.61

Yes

c

0.55

a

b

GIA = gastrointestinal absorption, BBB = blood–brain barrier permeation, Reference [63],

Reference [68], and ^eBio. Score = bioavailability score [69].

d

The pink middle area (Figures 2 and 3) represents the optimum range for bioavail-

ability. It is a sum of the lipophilicity (LIPO), size, polarity (POLAR), solubility (INSOLU),

saturation (INSATU), and ﬂexibility (FLEX). The red lines show the predicted state for the

designed compounds [62,64,65]. The calculated parameters for all the compounds, except

for 3b and 3h, are situated inside the pink area, suggesting their high oral bioavailabil-

ity [69]. A positive correlation for the bioavailability was also supported by the BOILED-

Egg method [70], proposing high gastrointestinal adsorption and penetration through the

BBB [70]. The BBB score, a new predictive model for CNS availability, was also calculated

for all the derivatives ﬁtting the range between 5 and 6, indicating a high probability to

permeate through the BBB (estimated physicochemical parameters are shown in Table S1,

Supplementary Materials) [63]. Besides, all compounds also fulﬁll the drug-likeness rules

of pharmacological models deﬁned by pharmaceutical companies like Veber’s (GSK) [71],

Lipinski’s (Pﬁzer) [72], Egan’s (Pharmacopeia) [73], Ghose’s (Amgen) [74], and Muegge’s

(Bayer) [75] (Table S2, Supplementary Materials). In summary, derivatives 3a–o unam-

biguously show high predictive peroral bioavailability, BBB permeation, and acceptable

pharmacokinetic proﬁles, making the compounds prospective candidates for the treatment

of CNS disorders, including AD.

Biomolecules 2021, 11, 3

11 of 23

Figure 2. The bioavailability radar chart of the designed ﬂuorene-9-amines 3a–o.

Biomolecules 2021, 11, 3

12 of 23

Figure 3. The bioavailability radar chart for the standards THA and memantine (please see detailed

description in the text).

3.2. Synthesis

Fluoren-9-amines were prepared in a one-step procedure from commercially avail-

able 9-bromoﬂuorene (

1) with an excess of various primary or secondary amines (2a–o)

in dry acetonitrile (Scheme 1). This reaction afforded the formation of ﬂuoren-9-amines

(3a–o) in high yields (80–95%). The free bases were then converted to the respective hy-

drochloride salts. The ﬁnal products were characterized by ¹H, ¹³C-NMR spectra, melting

points, and HRMS analysis. LC analysis conﬁrmed the purity for all the derivatives > 97%

(Supplementary Materials).

3.3. Evaluation of Cholinesterase Inhibitory Activity

Fluoren-9-amines were tested for their inhibitory potential against human AChE

(hAChE) and human BChE (hBChE) enzymes using the modiﬁed spectrophotometric

method of Ellman et al. [46,48,76]. THA was used as a reference compound. The IC₅₀

values of all tested compounds and their selectivity indexes (SI) for hBChE are summarized

in Table 2. All tested derivatives showed a preferential inhibition of hBChE (usually in

the single-digit micromolar range), whereas only compounds 3c and 3m showed weak

inhibitory activity for hAChE at 13.98 and 49.91

ranged between 0.47–54.65 M. According to the hBChE inhibition potency, the top-ranked

derivative from all the derivatives under the study was 3c (IC₅₀= 0.47 M), being 20 times

µM, respectively. The hBChE inhibition

µ

less active compared to THA. However, the relatively unique selectivity to BChE is of

immense interest. Indeed, the selectivity proﬁle for the ChE drugs potentially useful

in AD treatment is extensively discussed [77]. The currently used ChE inhibitors are

AChE-selective (donepezil) or possess more or less nonselective patterns of inhibition

(rivastigmine and galantamine) [25]. It was proven that the levels of BChE in the brain of

AD patients are elevated with age, while the levels of AChE are suppressed. This ﬁnding

underlines the importance of selective-BChE inhibitors in the treatment of the later stages

of AD [77,78].

3.4. Kinetic Study of hBChE Inhibition

To determine the inhibition pattern of 3c, a kinetic study was performed to elucidate

the interaction mechanism of the compound with hBChE. The kinetics of the inhibition

were revealed from velocity curves measured at several concentrations of 3c and the hBChE

substrate butyrylthiocholine. The type of enzyme inhibition and appropriate afﬁnity

parameter (K_i) were determined using nonlinear regression analysis. The results for each

type of model of inhibition (competitive, noncompetitive, uncompetitive, and mixed) were

compared by the sum-of-squares F-test. A statistical analysis showed a competitive type

of inhibition for hBChE (p < 0.05) that was in line with the Cornish–Bowden plot used for

visualization of the obtained data (Figure 4).

Biomolecules 2021, 11, 3

13 of 23

Table 2. The inhibitory activities of compounds 3a

–

o

for human acetylcholinesterase (hAChE) and

human butyrylcholinesterase (hBChE).

IC₅₀± SEM (µM) ^a

SI for BChE ^b

Compound

hAChE

hBChE

3a

3b

3c

3d

3e

3f

3g

3h

3i

3j

3k

3l

3m

3n

3o

THA ^c

n.a.

2.95 ± 0,09

8.91 ± 0.31

0.47 ± 0.01

6.42 ± 0.22

2.03 ± 0.08

2.13 ± 0.06

2.56 ± 0.11

6.58 ± 0.19

5.38 ± 0.11

23.5 ± 0.8

12.8 ± 0.3

54.7 ± 1.9

1.02 ± 0.03

0.90 ± 0,02

1.80 ± 0.06

-

13.98 ± 0.35

29.7

-

n.a.

-

49.91 ± 3.05

48.9

-

n.a.

0.50 ± 0.10 ^c

0.023 ± 0.003 ^c

21.7

a

Results are expressed as the mean of at least three experiments

±

SEM. n.a. stands for not active at

b

the tested concentration scale. SI = selectivity index; the selectivity for hBChE is determined as a

ratio of the half-maximal inhibitory concentration (IC₅₀)(hAChE)/IC₅₀(hBChE). ^cData taken from

Reference [79].

Figure 4. Steady-state inhibition of the hBChE substrate (human butyrylthiocholine) hydrolysis by

compound 3c at different concentrations. Cornish Bowden plots of (S)/v against the (I) of the initial

−

velocity at increasing substrate concentrations (2.5–20.0 mM) are presented. Lines were derived from

the linear regression of the data points. Each point is the average of three determinations.

The parallel lines indicate a competitive inhibition of hBChE by 3c, which means a

reversible binding mode to the active site of the enzyme. With the increasing concentration

of the inhibitor, the apparent V_maxremained unchanged and K_mincreased. A K_ivalue of

173 ± 25 nM was determined for 3c on hBChE.

Biomolecules 2021, 11, 3

14 of 23

3.5. Evaluation of Antagonist Activity towards the NMDA Receptor

Further, we aimed to examine the effects of all synthesized compounds (3a

–o) at both

the GluN1/GluN2A and GluN1/GluN2B receptors. First, we held the transfected cells at

a membrane potential of 60 mV, and we assessed the relative inhibitory effect of each

compound at a 10- M concentration upon its application with the (co-)agonists (Figure 4).

−

µ

These experiments showed that all studied compounds exhibited an inhibitory effect rang-

ing from ~7% to ~52% at both studied NMDAR combinations (Table 3 ). Interestingly, we

revealed that 3e was the most potent compound at the GluN1/GluN2A receptors, while 3m

was at the GluN1/GluN2B receptors. Subsequently, we generated concentration-response

curves for 3e and 3m (1–300

tors at the negative ( 60 mV) and positive (40 mV) membrane potentials (Figure 5). Our

analysis showed that both compounds exhibited a more profound inhibitory effect at the

negative membrane potential (IC₅₀values ranged from ~9 M to ~15 M), while they were

much less potent at the positive membrane potential (IC₅₀values ranged from ~83

to ~221 M); the results are summarized in Table 4. Together, our data show that both 3e

µM) for both the GluN1/GluN2A and GluN1/GluN2B recep-

−

µ

µM

µ

and 3m act as potent voltage-dependent inhibitors of the NMDARs, suggesting that they

act as open-channel blockers of NMDARs. Interestingly, the IC₅₀values for both 3e and

3m at the GluN1/GluN2A and GluN1/GluN2B receptors were in a similar range to those

previously obtained with THA (Table 4) [39]. However, both 3e and 3m were less potent

than memantine under the studied conditions (Table 4).

3.6. In Vitro Cell Viability Assessment

The in vitro cytotoxicity evaluation on mammalian cells served as a preliminary

toxicity indicator for the newly developed compounds (Table 5). In our previous work, we

observed a correlation between the calculated logP (ClogP) values (MarvinSketch software,

v. 18.24.0; Table 5) and the toxicity; however, our results within the presented study showed

no clear relationship [80]. For instance, one of the most lipophilic compounds (3f) yielded

as the least toxic, whereas other more hydrophilic ﬂuoren-9-amines were regarded as

low-toxic (e.g., 3g as the most hydrophilic compound) agents as well. The least cytotoxic

compound can be classiﬁed as 3f with an attached piperidine moiety. At the pole separation,

the most cytotoxic agents were 3c, 3m, and 3e. Importantly, some of the new derivatives

were less cytotoxic than THA.

Table 3. The inhibitory effect of the derivatives 3a

–

o

at the GluN1/GluN2A and GluN1/GluN2B

receptors.

GluN1/GluN2A

Compound

GluN1/GluN2B

RI^a(%) ± SEM

n

3a

3b

3c

3d

3e

3f

3g

3h

3i

3j

3k

3l

3m

3n

3o

33.29 ± 1.66

36.16 ± 2.16

41.02 ± 3.56

26.56 ± 0.85

52.30 ± 4.18

33.01 ± 1.96

18.68 ± 1.98

26.90 ± 2.14

25.66 ± 1.92

29.15 ± 2.85

32.08 ± 1.01

14.89 ± 1.76

36.23 ± 3.73

37.87 ± 2.43

27.80 ± 1.74

4

5

4

6

5

25.00 ± 1.82

18.01 ± 0.78

17.24 ± 2.39

14.26 ± 0.35

44.79 ± 3.43

19.35 ± 2.65

6.76 ± 1.00

17.48 ± 2.46

20.12 ± 2.82

21.88 ± 2.10

19.12 ± 1.48

11.64 ± 0.52

51.22 ± 3.24

49.71 ± 1.72

32.39 ± 2.99

4

5

4

5

4

6

5

a

The relative inhibition (RI) was calculated as the ratio of the steady-state currents in the presence

and absence of 10 M of the compound (multiplied by 100) at the membrane potential of 60 mV (see

text for more details). Data are shown as mean ± SEM; n = number of recorded cells (see Figure 5).

µ

−

Biomolecules 2021, 11, 3

15 of 23

Table 4. The activity of derivatives 3e and 3m at the N-methyl-D-aspartate (NMDA) receptors at different membrane

potentials compared to THA and memantine (Mem).

GluN1/GluN2A ^a

GluN1/GluN2B ^a

n

IC₅₀

−60 mV

h

IC₅₀

40 mV

h

IC₅₀

−60 mV

h

IC₅₀

40 mV

h

(−60/

−60 mV

40 mV

−60 mV

40 mV

40 mV)

3e

8.85 ± 1.21

14.21 ± 1.60

9.1 ± 0.5

1.07 ± 0.05

1.19 ± 0.14

1.7 ± 0.0

82.62 ± 14.17

113.99 ± 26.63

84.6 ± 1.6

1.28 ± 0.12

1.23 ± 0.13

1.6 ± 0.1

8/5

5/4

8/4

6/7

15.27 ± 2.38

11.67 ± 1.72

19.7 ± 1.8

1.17 ± 0.05

1.12 ± 0.05

1.8 ± 0.1

156.40 ± 14.94

220.78 ± 12.24

168.8 ± 9.3

1.10 ± 0.08

0.97 ± 0.10

1.4 ± 0.1

12/7

9/6

5/6

3m

THA ^b

Mem ^b

1.34 ± 0.08

1.04 ± 0.06

27.04 ± 1.19

1.16 ± 0.05

0.78 ± 0.09

1.04 ± 0.03

10.57 ± 0.63

0.84 ± 0.04

a

The experimental data obtained using six concentrations of each compound (1, 3, 10, 30, 100, and 300

µM) were ﬁtted by Equation (1) (see

Section 2.5.2); the values of the IC₅₀(in

µ

M), Hill coefﬁcient (h), and numbers of analyzed cells (n) obtained from the membrane potentials

of 60 mV or 40 mV are shown as mean

−

±

SEM. Dose-response relationships of the inhibitory effects of 3e or 3m at the GluN1/GluN2A

b

and GluN1/GluN2B receptors (see Figure 6). THA values were published by our group recently [39]. Mem corresponds to memantine.

Figure 5. The examples of the most (3e and 3m) and least (3l and 3g) potent compounds from all tested ﬂuoren-9-amines

at the NMDARs. Representative whole-cell currents induced by 1 mM of glutamate (Glu) and 50

negative ( 60 mV) membrane potential with ( ) the least potent compounds: 3l measured at GluN1/GluN2A and 3g at

) the most potent compounds: 3e measured at GluN1/GluN2A and 3m at GluN1/GluN2B

µM of glycine at the

−

A

GluN1/GluN2B NMDARs and (

B

NMDA receptor (see Table 3).

Biomolecules 2021, 11, 3

16 of 23

Figure 6. The most potent inhibitory compounds 3e and 3m acting at the GluN1/GluN2A and GluN1/GluN2B receptors.

) Whole-cell representative currents of the GluN1/GluN2A receptors showing the inhibitory effects of 3e (1–300 M)

at membrane potentials 60 mV and +40 mV. ( ) Representative currents of the GluN1/GluN2B receptors inhibited by

3m (1–300 M) at membrane potentials 60 mV and +40 mV. ( ) Dose-response inhibitory curves at two membrane

potentials ( 60 and +40 mV) at the GluN1/GluN2A receptors for 3e ) and at the GluN1/GluN2B receptors for 3m ).

(

A

µ

−

C

µ

−

B,D

−

(

B

(D

The inhibition dose-response curves were ﬁtted by Equation (1) (see Section 2.5.2); the resulting values are shown in Table 4.

Table 5. The effects of the tested compounds on the CHO-K1 cell viability.

Compound.

IC₅₀± SEM (µM) ^a

ClogP

3a

3b

3c

3d

3e

3f

3g

3h

3i

3j

3k

3l

3m

3n

3o

387 ± 32

427 ± 12

111 ± 10

353 ± 32

192 ± 3

872 ± 10

738 ± 1

417 ± 16

266 ± 3

259 ± 12

239 ± 35

>800 ^b

3.76

2.99

4.79

3.46

3.90

4.22

2.94

3.35

4.09

3.22

3.58

3.15

4.31

4.29

3.78

140 ± 15

236 ± 24

413 ± 15

THA

248 ± 11 ^c

2.63

a

b

Values are expressed as the mean of three independent measurements.

maximum solubility of the compound. Data taken from Reference [81]. ClogP: calculated logP.

Determined at the

c

3.7. In Vitro BBB Permeation

The top-ranked BChE inhibitors and NMDA receptor antagonists were selected (i.e.,

3e, 3n, 3c, and 3m) to evaluate their ability to cross the BBB. For this reason, we used the

human brain microvascular endothelial cell line hCMEC/D3 as a suitable model for the

Biomolecules 2021, 11, 3

17 of 23

normal human BBB [82]. Before that, we investigated whether the applied concentration

(50

Despite the relatively low toxicity of the CHO-K1 model, we investigated the effects on

the cell viability of the hCMEC/D3 line using MTT at 100 and 50 M (Table S3). We found

that all tested compounds at 50 M did not substantially reduce the cell viability (>90% of

viable cells), except 3m, which reduced the cell viability to approx. 82% at 50 M. Due to

µM) of the tested compound negatively inﬂuenced the cell viability in the monolayer.

µ

this fact, a concentration of 30 µM was used for the BBB permeation test for 3m.

To investigate the potential of the BBB permeation, we used a panel of reference drugs

for which the BBB permeation in vivo is known and correlated the P_appvalues to them.

Thus, our measurements predict that the compounds 3c, 3e, and 3n have a low potential to

passively pass the BBB. On the other hand, the P_appvalue for 3m corresponds to those of

standard drugs with high CNS permeability (Table 6).

Table 6. Prediction of the BBB penetration of drugs expressed as the apparent permeability coefﬁcient

(P_app) ± SEM.

Compound

P_app± SEM (×10 cm s

CNS (+/−) ^a

N ^b

−6

−1

)

3e

3n

3c

3m

THA

13.51 ± 1.54

14.61 ± 1.88

12.28 ± 2.33

18.48 ± 2.37

22.29 ± 2.01

24.19 ± 2.83

28.63 ± 4.64

23.06 ± 2.06

17.19 ± 1.82

13.89 ± 0.92

14.35 ± 0.52

CNS −

CNS +

CNS −

3

4

5

3

4

Donepezil

Rivastigmine

Propranolol

Antipyrine

Furosemide

Obidoxime

a

CNS+ and CNS

−

represent the ﬁnal predictions of the compound capability to cross or not to cross

b

the BBB, respectively. n stands for the number of independent experiments.

3.8. In Silico Studies

To better understand the inhibition mechanism between hBChE with 3c (Figure 7A,B)

and 3n (Figure 7C,D), respectively, as the top-ranked inhibitors of the respective enzyme,

we performed a molecular modeling simulation. The crystal structure of the THA-hBChE

complex (Figure 7E,F; PDB ID: 4BDS) was used for comparative purposes [56]. In line with

the compounds’ designs, both of the inspected ﬂuoren-9-amines 3c and 3n adopted a similar

arrangement to that of THA within the hBChE active site. The crucial observation is that

the tricyclic cores of both new molecules interact with Trp82 via parallel π-π stacking in the

anionic site of the enzyme. The protonated amino group enabled hydrogen bond formation

to carboxylic oxygen from Asp70 (3c and 3n) and the hydroxyl group from Tyr332 (3n). In

the case of THA, this type of interaction is replaced by two water bridges with the primary

amino group. From the catalytic triad residues (His438, Glu197, and Ser198), only His438

is involved in hydrophobic contact with 3c and 3n, whereas THA is involved in direct

hydrogen contact with this residue. The docking results also highlighted the importance of

the attached appendages. Both aliphatic parts, either the cyclohexyl (3c) or iso-butyl (3n

)

moiety, are engaged in hydrophobic contacts with the Tyr332, Phe329, and Ala328 residues.

The cyclohexyl moiety of 3c seems to deliver another hydrophobic contact to Pro285 and

Gly78 that presumably mediates a slightly higher inhibition potency. Contrary to THA

anchoring, missing cation-π contacts in the 3c- and 3n-hBChE complexes can be denoted as

the culprit responsible for the slightly decreased afﬁnity of these two agents.

Biomolecules 2021, 11, 3

18 of 23

Figure 7. Top-scored docking poses of 3c

site (Protein Data Bank (PDB) ID: 4BDS) [56]. Ligands are displayed in green (

THA, respectively. Important amino acid residues involved in the enzyme-ligand interaction are shown in dark-blue, and

the catalytic triad is rendered in yellow. Distances are colored as dashed lines, and the distance is measured in Å ( ). 3D

(A

,B

), 3n

(

C

,D

), and the crystal structure of THA (

E

,F) within the hBChE active

A

), salmon ( ), and brown (

C

E

) for 3c 3n, and

,

A,C,E

ﬁgures were created by using PyMOL 2.0.6 viewer. 2D diagrams were generated with Maestro 12.3 (Schrödinger Release,

Schrödinger, LLC., New York, NY, USA).

4. Conclusions

Since the cholinergic hypothesis was ﬁrst postulated [15], ﬁve decades of intensive

research have generated a few marketed drugs for symptomatic relief only. The proper

and in-depth elucidation of the disease biology imposes challenging tasks that remain

Biomolecules 2021, 11, 3

19 of 23

to be resolved by delivering the drugs in the right stage of the disorder [29]. So far, the

attempts to approve any drugs have continuously failed, with memantine receiving the

ﬁnal approval in 2003. A glimmer of hope emerged in 2019 when sodium oligomannate

(GV-971) was approved in China, acting in a completely different way to other marketed

drugs. Indeed, GV-971 suppresses gut dysbiosis, mitigates neuroinﬂammation, and re-

verses cognition impairment [83]. Designing MTDLs is an interesting approach that has

generated several preclinical candidates for the therapy of AD. However, most of these

newly developed agents suffer from limitations, as disclosed recently [29]. Our study

was inspired by the appealing combination of donepezil and memantine into one capsule

that resulted in a higher therapeutic beneﬁt than the single-agent administrations [27].

Herewith, pursuing the symptomatic stage of AD by modulating the cholinergic and gluta-

matergic systems, we developed a series of 15 molecules with ﬂuorene tricyclic scaffolds

substituted with secondary or tertiary amines at C9. Prior to the synthesis, we took the

compound’s predicted physicochemical parameters and calculated their oral and CNS

availability. Indeed, all the compounds adhered to these criteria. Under in vitro conditions,

we demonstrated that the compounds can interact with both NMDAR and BChE while

having only a marginal effect on AChE. We also showed that they have a relatively low

cytotoxic proﬁle, as determined using CHO-K1 cell lines, and for one compound (3m) out

of the four selected candidates, a BBB permeation ability was conﬁrmed. The obtained data

herein warrant future research with this class of compounds; however, we may state that

we found low-toxic BChE-selective compounds with antagonistic activity on the NMDARs

that potentially permeate the BBB.

Supplementary Materials: The following are available online at https://ww w .mdpi.com/2218-273

X/11/1/3/s1, Table S1. Physiochemical parameters of compounds 3a–o, THA and Memantine.; Table

S2. Drug-likeness of derivatives 3a–o, THA and memantine.; Table S3. Effect of tested compounds on

the cell viability of the hCMEC/D3 cells.; ¹H and ¹³C NMR spectra; HRMS, and HPLC records of

compounds.

Author Contributions: J.K. (Jan Konecny), chemical synthesis, data interpretation, and manuscript

writing; M.H. (Martina Hrabinova), in vitro measurement of cholinesterase activity; L.P. (Lenka

Pulkrabkova) and M.B., cytotoxicity evaluation and BBB permeation; L.P. (Lukas Prchal), HRMS

analysis; T.K. (Tereza Kobrlova), BBB permeation; T.K. (Tomas Kucera), in silico calculations; V.F.,

design of the compounds; D.J., enzyme kinetic analysis; A.M., S.K., M.K., and M.H. (Martin Horak),

NMDA receptor measurements; M.V.; manuscript writing; O.S., design of the study, BBB permeation,

and manuscript writing; and J.K. (Jan Korabecny), design of the study, data interpretation, docking

studies, and manuscript writing. All authors have read and agreed to the published version of the

manuscript.

Funding: This project was supported by the Czech Health Research Council (No. NU20-08-00296), by

the faculty of Military Health Sciences, University of Defence (SV project FVZ201803, Long-term de-

velopment plan), by MH CZ—DRO (University Hospital Hradec Kralove, No. 00179906), by the Euro-

pean Regional Development Fund: Project “PharmaBrain” (no. CZ.02.1.01/0.0/0.0/16_025/0007444),

and by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the program Projects

of Large Research, Development and Innovations Infrastructures. V.F. acknowledges the support of

Charles University (SVV 260 547).

Data Availability Statement: Data is contained within the article or Supplementary Materials.

Acknowledgments: The authors would like to acknowledge the skillful assistance of Barbora Hejt-

mankova during the BChE enzyme kinetic evaluation.

Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1.

Ferri, C.P.; Prince, M.; Brayne, C.; Brodaty, H.; Fratiglioni, L.; Ganguli, M.; Hall, K.; Hasegawa, K.; Hendrie, H.; Huang, Y.; et al.

Global Prevalence of Dementia: A Delphi Consensus Study. Lancet 2005, 366, 2112–2117. [CrossRef]

2.

Bondi, M.W.; Edmonds, E.C.; Salmon, D.P. Alzheimer’s Disease: Past, Present, and Future. J. Int. Neuropsychol. Soc. 2017

23, 818–831. [CrossRef] [PubMed]

,

Biomolecules 2021, 11, 3

20 of 23

3.

4.

Kirova, A.-M.; Bays, R.B.; Lagalwar, S. Working Memory and Executive Function Decline across Normal Aging, Mild Cognitive

Impairment, and Alzheimer’s Disease. Biomed. Res. Int. 2015, 2015, 748212. [CrossRef] [PubMed]

Lyketsos, C.G.; Carrillo, M.C.; Ryan, J.M.; Khachaturian, A.S.; Trzepacz, P.; Amatniek, J.; Cedarbaum, J.; Brashear, R.; Miller, D.S.

Neuropsychiatric Symptoms in Alzheimer’s Disease. Alzheimers Dement 2011, 7, 532–539. [CrossRef]

Weller, J.; Budson, A. Current Understanding of Alzheimer’s Disease Diagnosis and Treatment. F1000Res 2018, 7. [CrossRef]

Schachter, A.S.; Davis, K.L. Alzheimer’s Disease. Dialogues Clin. Neurosci. 2000, 2, 91–100. [CrossRef]

2020 Alzheimer’s Disease Facts and Figures. Alzheimer’s Dement. 2020, 16, 391–460. [CrossRef]

Vaz, M.; Silvestre, S. Alzheimer’s Disease: Recent Treatment Strategies. Eur. J. Pharm. 2020, 887, 173554. [CrossRef]

Abeysinghe, A.A.D.T.; Deshapriya, R.D.U.S.; Udawatte, C. Alzheimer’s Disease; a Review of the Pathophysiological Basis and

Therapeutic Interventions. Life Sci. 2020, 256, 117996. [CrossRef]

5.

6.

7.

8.

9.

10. Goedert, M.; Crowther, R.A. Amyloid Plaques, Neuroﬁbrillary Tangles and Their Relevance for the Study of Alzheimer’s Disease.

Neurobiol. Aging 1989, 10, 405–406; discussion 412–414. [CrossRef]

11. Lewis, D.A.; Higgins, G.A.; Young, W.G.; Goldgaber, D.; Gajdusek, D.C.; Wilson, M.C.; Morrison, J.H. Distribution of Precursor

Amyloid-Beta-Protein Messenger RNA in Human Cerebral Cortex: Relationship to Neuroﬁbrillary Tangles and Neuritic Plaques.

Proc. Natl. Acad. Sci. USA 1988, 85, 1691–1695. [CrossRef] [PubMed]

12. Mondragón-Rodríguez, S.; Basurto-Islas, G.; Santa-Maria, I.; Mena, R.; Binder, L.I.; Avila, J.; Smith, M.A.; Perry, G.; García-Sierra,

F. Cleavage and Conformational Changes of Tau Protein Follow Phosphorylation during Alzheimer’s Disease. Int. J. Exp. Pathol.

2008, 89, 81–90. [CrossRef] [PubMed]

13. Mondragón-Rodríguez, S.; Mena, R.; Binder, L.I.; Smith, M.A.; Perry, G.; García-Sierra, F. Conformational Changes and Cleavage

of Tau in Pick Bodies Parallel the Early Processing of Tau Found in Alzheimer Pathology. Neuropathol. Appl. Neurobiol. 2008

34, 62–75. [CrossRef] [PubMed]

,

14. Mondragón-Rodríguez, S.; Perry, G.; Luna-Muñoz, J.; Acevedo-Aquino, M.C.; Williams, S. Phosphorylation of Tau Protein at

Sites Ser(396-404) Is One of the Earliest Events in Alzheimer’s Disease and Down Syndrome. Neuropathol. Appl. Neurobiol. 2014,

40, 121–135. [CrossRef] [PubMed]

15. Davies, P.; Maloney, A.J. Selective Loss of Central Cholinergic Neurons in Alzheimer’s Disease. Lancet 1976, 2, 1403. [CrossRef]

16. Sarter, M.; Bruno, J.P. Cognitive Functions of Cortical Acetylcholine: Toward a Unifying Hypothesis. Brain Res. Brain Res. Rev.

1997, 23, 28–46. [CrossRef]

17. Hasselmo, M.E.; Anderson, B.P.; Bower, J.M. Cholinergic Modulation of Cortical Associative Memory Function. J. Neurophysiol.

1992, 67, 1230–1246. [CrossRef]

18. Wang, R.; Reddy, P.H. Role of Glutamate and NMDA Receptors in Alzheimer’s Disease. J. Alzheimers Dis. 2017, 57, 1041–1048.

[CrossRef]

19. Cheignon, C.; Tomas, M.; Bonnefont-Rousselot, D.; Faller, P.; Hureau, C.; Collin, F. Oxidative Stress and the Amyloid Beta Peptide

in Alzheimer’s Disease. Redox Biol. 2018, 14, 450–464. [CrossRef]

20. Butterﬁeld, D.A.; Lauderback, C.M. Lipid Peroxidation and Protein Oxidation in Alzheimer’s Disease Brain: Potential Causes

and Consequences Involving Amyloid Beta-Peptide-Associated Free Radical Oxidative Stress. Free Radic. Biol. Med. 2002

32, 1050–1060. [CrossRef]

,

21. Bush, A.I.; Pettingell, W.H.; Multhaup, G.; Paradis, M.d.; Vonsattel, J.P.; Gusella, J.F.; Beyreuther, K.; Masters, C.L.; Tanzi, R.E.

Rapid Induction of Alzheimer A Beta Amyloid Formation by Zinc. Science 1994, 265, 1464–1467. [CrossRef] [PubMed]

22. Bolós, M.; Perea, J.R.; Avila, J. Alzheimer’s Disease as an Inﬂammatory Disease. Biomol. Concepts 2017, 8, 37–43. [CrossRef]

[PubMed]

23. Cummings, J.L.; Tong, G.; Ballard, C. Treatment Combinations for Alzheimer’s Disease: Current and Future Pharmacotherapy

Options. J. Alzheimers Dis. 2019, 67, 779–794. [CrossRef] [PubMed]

24. Joe, E.; Ringman, J.M. Cognitive Symptoms of Alzheimer’s Disease: Clinical Management and Prevention. BMJ 2019, 367, l6217.

[CrossRef]

25. Zemek, F.; Drtinova, L.; Nepovimova, E.; Sepsova, V.; Korabecny, J.; Klimes, J.; Kuca, K. Outcomes of Alzheimer’s Disease

Therapy with Acetylcholinesterase Inhibitors and Memantine. Expert Opin. Drug Saf. 2014, 13, 759–774. [CrossRef]

26. Owen, R.T. Memantine and Donepezil: A Fixed Drug Combination for the Treatment of Moderate to Severe Alzheimer’s

Dementia. Drugs Today 2016, 52, 239–248. [CrossRef]

27. Calhoun, A.; King, C.; Khoury, R.; Grossberg, G.T. An Evaluation of Memantine ER + Donepezil for the Treatment of Alzheimer’s

Disease. Expert Opin. Pharm. 2018, 19, 1711–1717. [CrossRef]

28. Cavalli, A.; Bolognesi, M.L.; Minarini, A.; Rosini, M.; Tumiatti, V.; Recanatini, M.; Melchiorre, C. Multi-Target-Directed Ligands to

Combat Neurodegenerative Diseases. J. Med. Chem. 2008, 51, 347–372. [CrossRef]

29. Benek, O.; Korabecny, J.; Soukup, O. A Perspective on Multi-Target Drugs for Alzheimer’s Disease. Trends Pharmacol. Sci. 2020

41, 434–445. [CrossRef]

,

30. Cummings, J.L.; Morstorf, T.; Zhong, K. Alzheimer’s Disease Drug-Development Pipeline: Few Candidates, Frequent Failures.

Alzheimer’s Res. Ther. 2014, 6, 37. [CrossRef]

31. Morphy, R.; Kay, C.; Rankovic, Z. From Magic Bullets to Designed Multiple Ligands. Drug Discov. Today 2004, 9, 641–651.

[CrossRef]

Biomolecules 2021, 11, 3

21 of 23

32. Proschak, E.; Stark, H.; Merk, D. Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds.

J. Med. Chem. 2019, 62, 420–444. [CrossRef] [PubMed]

33. Jack, C.R.; Knopman, D.S.; Jagust, W.J.; Petersen, R.C.; Weiner, M.W.; Aisen, P.S.; Shaw, L.M.; Vemuri, P.; Wiste, H.J.; Weigand, S.D.;

et al. Tracking Pathophysiological Processes in Alzheimer’s Disease: An Updated Hypothetical Model of Dynamic Biomarkers.

Lancet Neurol. 2013, 12, 207–216. [CrossRef]

34. Panza, F.; Lozupone, M.; Watling, M.; Imbimbo, B.P. Do BACE Inhibitor Failures in Alzheimer Patients Challenge the Amyloid

Hypothesis of the Disease? Expert Rev. Neurother. 2019, 19, 599–602. [CrossRef]

35. Kandimalla, R.; Reddy, P.H. Therapeutics of Neurotransmitters in Alzheimer’s Disease. J. Alzheimers Dis. 2017, 57, 1049–1069.

[CrossRef]

36. Fitzpatrick-Lewis, D.; Warren, R.; Ali, M.U.; Sherifali, D.; Raina, P. Treatment for Mild Cognitive Impairment: A Systematic

Review and Meta-Analysis. CMAJ Open 2015, 3, E419–E427. [CrossRef]

37. Watkins, P.B.; Zimmerman, H.J.; Knapp, M.J.; Gracon, S.I.; Lewis, K.W. Hepatotoxic Effects of Tacrine Administration in Patients

With Alzheimer’s Disease. JAMA 1994, 271, 992–998. [CrossRef]

38. Horak, M.; Holubova, K.; Nepovimova, E.; Krusek, J.; Kaniakova, M.; Korabecny, J.; Vyklicky, L.; Kuca, K.; Stuchlik, A.; Ricny,

J.; et al. The Pharmacology of Tacrine at N-Methyl-d-Aspartate Receptors. Prog. Neuropsychopharmacol. Biol. Psychiatry 2017

75, 54–62. [CrossRef]

,

39. Kaniakova, M.; Kleteckova, L.; Lichnerova, K.; Holubova, K.; Skrenkova, K.; Korinek, M.; Krusek, J.; Smejkalova, T.; Ko-

rabecny, J.; Vales, K.; et al. 7-Methoxyderivative of Tacrine Is a “foot-in-the-Door” Open-Channel Blocker of GluN1/GluN2 and

GluN1/GluN3 NMDA Receptors with Neuroprotective Activity in Vivo. Neuropharmacology 2018, 140, 217–232. [CrossRef]

40. Vorobjev, V.S.; Sharonova, I.N. Tetrahydroaminoacridine Blocks and Prolongs NMDA Receptor-Mediated Responses in a Voltage-

Dependent Manner. Eur. J. Pharmacol. 1994, 253, 1–8. [CrossRef]

41. Nelson, M.E.; Albuquerque, E.X. 9-Aminoacridines Act at a Site Different from That for Mg2+ in Blockade of the N-Methyl-D-

Aspartate Receptor Channel. Mol. Pharmacol. 1994, 46, 151–160. [PubMed]

42. Soukup, O.; Jun, D.; Zdarova-Karasova, J.; Patocka, J.; Musilek, K.; Korabecny, J.; Krusek, J.; Kaniakova, M.; Sepsova, V.;

Mandikova, J.; et al. A Resurrection of 7-MEOTA: A Comparison with Tacrine. Curr. Alzheimer Res. 2013, 10, 893–906. [CrossRef]

[PubMed]

43. Choubdar, N.; Golshani, M.; Jalili-Baleh, L.; Nadri, H.; Küçükkilinç, T.T.; Ayazgök, B.; Moradi, A.; Moghadam, F.H.; Abdolahi,

Z.; Ameri, A.; et al. New Classes of Carbazoles as Potential Multi-Functional Anti-Alzheimer’s Agents. Bioorg. Chem. 2019

91, 103164. [CrossRef] [PubMed]

,

44. Zhu, D.; Chen, M.; Li, M.; Luo, B.; Zhao, Y.; Huang, P.; Xue, F.; Rapposelli, S.; Pi, R.; Wen, S. Discovery of Novel N-Substituted

Carbazoles as Neuroprotective Agents with Potent Anti-Oxidative Activity. Eur. J. Med. Chem. 2013, 68, 81–88. [CrossRef]

45. Crismon, M.L. Tacrine: First Drug Approved for Alzheimer’s Disease. Ann. Pharm. 1994, 28, 744–751. [CrossRef]

46. Ellman, G.L.; Courtney, K.D.; Andres, V.; Feather-Stone, R.M. A New and Rapid Colorimetric Determination of Acetyl-

cholinesterase Activity. Biochem. Pharm. 1961, 7, 88–95. [CrossRef]

47. Pohanka, M.; Jun, D.; Kuca, K. Improvement of Acetylcholinesterase-Based Assay for Organophosphates in Way of Identiﬁcation

by Reactivators. Talanta 2008, 77, 451–454. [CrossRef]

48. Sepsova, V.; Karasova, J.Z.; Korabecny, J.; Dolezal, R.; Zemek, F.; Bennion, B.J.; Kuca, K. Oximes: Inhibitors of Human Recombinant

Acetylcholinesterase. A Structure-Activity Relationship (SAR) Study. Int. J. Mol. Sci. 2013, 14, 16882–16900. [CrossRef]

49. Skrenkova, K.; Hemelikova, K.; Kolcheva, M.; Kortus, S.; Kaniakova, M.; Krausova, B.; Horak, M. Structural Features in the

Glycine-Binding Sites of the GluN1 and GluN3A Subunits Regulate the Surface Delivery of NMDA Receptors. Sci. Rep. 2019

9, 12303. [CrossRef]

,

50. Kaniakova, M.; Lichnerova, K.; Vyklicky, L.; Horak, M. Single Amino Acid Residue in the M4 Domain of GluN1 Subunit Regulates

the Surface Delivery of NMDA Receptors. J. Neurochem. 2012, 123, 385–395. [CrossRef]

51. Vyklicky, V.; Krausova, B.; Cerny, J.; Ladislav, M.; Smejkalova, T.; Kysilov, B.; Korinek, M.; Danacikova, S.; Horak, M.; Chodounska,

H.; et al. Surface Expression, Function, and Pharmacology of Disease-Associated Mutations in the Membrane Domain of the

Human GluN2B Subunit. Front. Mol. Neurosci. 2018, 11, 110. [CrossRef] [PubMed]

52. Gazova, Z.; Soukup, O.; Sepsova, V.; Siposova, K.; Drtinova, L.; Jost, P.; Spilovska, K.; Korabecny, J.; Nepovimova, E.; Fedunova,

D.; et al. Multi-Target-Directed Therapeutic Potential of 7-Methoxytacrine-Adamantylamine Heterodimers in the Alzheimer’s

Disease Treatment. Biochim. Et Biophys. Acta (BBA) Mol. Basis Dis. 2017, 1863, 607–619. [CrossRef] [PubMed]

53. Hemelíková, K.; Kolcheva, M.; Skrenkova, K.; Kaniaková, M.; Horák, M. Lectins Modulate the Functional Properties of

GluN1/GluN3-Containing NMDA Receptors. Neuropharmacology 2019. [CrossRef] [PubMed]

54. Kaniakova, M.; Nepovimova, E.; Kleteckova, L.; Skrenkova, K.; Holubova, K.; Chrienova, Z.; Hepnarova, V.; Kucera, T.; Kobrlova,

T.; Vales, K.; et al. Combination of Memantine and 6-Chlorotacrine as Novel Multi-Target Compound against Alzheimer’s Disease.

Curr. Alzheimer Res. 2019, 16, 821–833. [CrossRef] [PubMed]

55. Soukup, O.; Benkova, M.; Dolezal, R.; Sleha, R.; Malinak, D.; Salajkova, S.; Markova, A.; Hympanova, M.; Prchal, L.; Ryskova,

L.; et al. The Wide-Spectrum Antimicrobial Effect of Novel N-Alkyl Monoquaternary Ammonium Salts and Their Mixtures; the

QSAR Study against Bacteria. Eur. J. Med. Chem. 2020, 206, 112584. [CrossRef]

Biomolecules 2021, 11, 3

22 of 23

56. Nachon, F.; Carletti, E.; Ronco, C.; Trovaslet, M.; Nicolet, Y.; Jean, L.; Renard, P.-Y. Crystal Structures of Human Cholinesterases

in Complex with Huprine W and Tacrine: Elements of Speciﬁcity for Anti-Alzheimer’s Drugs Targeting Acetyl- and Butyryl-

Cholinesterase. Biochem. J. 2013, 453, 393–399. [CrossRef]

57. Cheung, J.; Rudolph, M.J.; Burshteyn, F.; Cassidy, M.S.; Gary, E.N.; Love, J.; Franklin, M.C.; Height, J.J. Structures of Human

Acetylcholinesterase in Complex with Pharmacologically Important Ligands. J. Med. Chem. 2012, 55, 10282–10286. [CrossRef]

58. Spilovska, K.; Korabecny, J.; Sepsova, V.; Jun, D.; Hrabinova, M.; Jost, P.; Muckova, L.; Soukup, O.; Janockova, J.; Kucera, T.;

et al. Novel Tacrine-Scutellarin Hybrids as Multipotent Anti-Alzheimer’s Agents: Design, Synthesis and Biological Evaluation.

Molecules 2017, 22, 1006. [CrossRef]

59. Mezeiova, E.; Korabecny, J.; Sepsova, V.; Hrabinova, M.; Jost, P.; Muckova, L.; Kucera, T.; Dolezal, R.; Misik, J.; Spilovska, K.; et al.

Development of 2-Methoxyhuprine as Novel Lead for Alzheimer’s Disease Therapy. Molecules 2017, 22, 1265. [CrossRef]

60. Trott, O.; Olson, A.J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efﬁcient

Optimization and Multithreading. J. Comput. Chem. 2010, 31, 455–461. [CrossRef]

61. Liu, B.; Wang, L.; Jin, Y.-H. An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling. IEEE Trans. Syst. ManCybern.

Part B (Cybern.) 2007, 37, 18–27. [CrossRef] [PubMed]

62. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal

Chemistry Friendliness of Small Molecules. Sci. Rep. 2017, 7, 42717. [CrossRef] [PubMed]

63. Gupta, M.; Lee, H.J.; Barden, C.J.; Weaver, D.F. The Blood-Brain Barrier (BBB) Score. J. Med. Chem. 2019, 62, 9824–9836. [CrossRef]

[PubMed]

64. Daina, A.; Michielin, O.; Zoete, V. ILOGP: A Simple, Robust, and Efﬁcient Description of n-Octanol/Water Partition Coefﬁcient

for Drug Design Using the GB/SA Approach. J. Chem. Inf. Model. 2014, 54, 3284–3301. [CrossRef] [PubMed]

65. Naz, A.; Iqtadar, R.; Siddiqui, F.A.; Ul-Haq, Z. Degradation Kinetics of Fluvoxamine in Buffer Solutions: In Silico ADMET

Proﬁling and Identiﬁcation of Degradation Products by LC-MS/ESI. Arab. J. Chem. 2020, 13, 4134–4146. [CrossRef]

66. Madden, S.; Spaldin, V.; Park, B.K. Clinical Pharmacokinetics of Tacrine. Clin. Pharm. 1995, 28, 449–457. [CrossRef]

67. Liu, M.-Y.; Meng, S.-N.; Wu, H.-Z.; Wang, S.; Wei, M.-J. Pharmacokinetics of Single-Dose and Multiple-Dose Memantine in

Healthy Chinese Volunteers Using an Analytic Method of Liquid Chromatography-Tandem Mass Spectrometry. Clin. Ther. 2008

,

30, 641–653. [CrossRef]

68. Lipinski, C.A. Lead- and Drug-like Compounds: The Rule-of-Five Revolution. Drug Discov. Today Technol. 2004, 1, 337–341.

[CrossRef]

69. Martin, Y.C. A Bioavailability Score. J. Med. Chem. 2005, 48, 3164–3170. [CrossRef]

70. Daina, A.; Zoete, V. A BOILED-Egg to Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMed-

Chem 2016, 11, 1117–1121. [CrossRef]

71. Veber, D.F.; Johnson, S.R.; Cheng, H.-Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular Properties That Inﬂuence the Oral

Bioavailability of Drug Candidates. J. Med. Chem. 2002, 45, 2615–2623. [CrossRef] [PubMed]

72. Bade, R.; Chan, H.-F.; Reynisson, J. Characteristics of Known Drug Space. Natural Products, Their Derivatives and Synthetic

Drugs. Eur. J. Med. Chem. 2010, 45, 5646–5652. [CrossRef] [PubMed]

73. Egan, W.J.; Merz, K.M.; Baldwin, J.J. Prediction of Drug Absorption Using Multivariate Statistics. J. Med. Chem. 2000, 43, 3867–3877.

[CrossRef] [PubMed]

74. Ghose, A.K.; Viswanadhan, V.N.; Wendoloski, J.J. A Knowledge-Based Approach in Designing Combinatorial or Medicinal

Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases. J. Comb.

Chem. 1999, 1, 55–68. [CrossRef]

75. Muegge, I.; Heald, S.L.; Brittelli, D. Simple Selection Criteria for Drug-like Chemical Matter. J. Med. Chem. 2001, 44, 1841–1846.

[CrossRef]

76. Pohanka, M.; Karasova, J.Z.; Kuca, K.; Pikula, J.; Holas, O.; Korabecny, J.; Cabal, J. Colorimetric Dipstick for Assay of Organophos-

phate Pesticides and Nerve Agents Represented by Paraoxon, Sarin and VX. Talanta 2010, 81, 621–624. [CrossRef]

77. Nordberg, A.; Ballard, C.; Bullock, R.; Darreh-Shori, T.; Somogyi, M. A Review of Butyrylcholinesterase as a Therapeutic Target in

the Treatment of Alzheimer’s Disease. Prim. Care Companion CNS Disord. 2013, 15. [CrossRef]

78. Greig, N.H.; Utsuki, T.; Ingram, D.K.; Wang, Y.; Pepeu, G.; Scali, C.; Yu, Q.-S.; Mamczarz, J.; Holloway, H.W.; Giordano, T.; et al.

Selective Butyrylcholinesterase Inhibition Elevates Brain Acetylcholine, Augments Learning and Lowers Alzheimer Beta-Amyloid

Peptide in Rodent. Proc. Natl. Acad. Sci. USA 2005, 102, 17213–17218. [CrossRef]

79. Nepovimova, E.; Korabecny, J.; Dolezal, R.; Babkova, K.; Ondrejicek, A.; Jun, D.; Sepsova, V.; Horova, A.; Hrabinova, M.; Soukup,

O.; et al. Tacrine-Trolox Hybrids: A Novel Class of Centrally Active, Nonhepatotoxic Multi-Target-Directed Ligands Exerting

Anticholinesterase and Antioxidant Activities with Low In Vivo Toxicity. J. Med. Chem. 2015, 58, 8985–9003. [CrossRef]

80. Sobolova, K.; Hrabinova, M.; Hepnarova, V.; Kucera, T.; Kobrlova, T.; Benkova, M.; Janockova, J.; Dolezal, R.; Prchal, L.; Benek,

O.; et al. Discovery of Novel Berberine Derivatives with Balanced Cholinesterase and Prolyl Oligopeptidase Inhibition Proﬁle.

Eur. J. Med. Chem. 2020, 203, 112593. [CrossRef]

81. Chalupova, K.; Korabecny, J.; Bartolini, M.; Monti, B.; Lamba, D.; Caliandro, R.; Pesaresi, A.; Brazzolotto, X.; Gastellier, A.-J.;

Nachon, F.; et al. Novel Tacrine-Tryptophan Hybrids: Multi-Target Directed Ligands as Potential Treatment for Alzheimer’s

Disease. Eur. J. Med. Chem. 2019, 168, 491–514. [CrossRef] [PubMed]

Biomolecules 2021, 11, 3

23 of 23

82. Weksler, B.; Romero, I.A.; Couraud, P.-O. The HCMEC/D3 Cell Line as a Model of the Human Blood Brain Barrier. Fluids Barriers

CNS 2013, 10, 16. [CrossRef] [PubMed]

83. Wang, X.; Sun, G.; Feng, T.; Zhang, J.; Huang, X.; Wang, T.; Xie, Z.; Chu, X.; Yang, J.; Wang, H.; et al. Sodium Oligomannate

Therapeutically Remodels Gut Microbiota and Suppresses Gut Bacterial Amino Acids-Shaped Neuroinﬂammation to Inhibit

Alzheimer’s Disease Progression. Cell Res. 2019, 29, 787–803. [CrossRef] [PubMed]

Article Doi

Pursuing the complexity of alzheimer’s disease: Discovery of fluoren-9-amines as selective butyrylcholinesterase inhibitors and n-methyl-d-aspartate receptor antagonists

DOI: 10.3390/biom11010003

Source and publish data:

Authors:

Article abstract of DOI:10.3390/biom11010003

Full text of DOI:10.3390/biom11010003

Products guided by the article

R&D Labs maybe for 101728-98-7

Relevant to this article

Hot Product