Identification of organophosphorus simulants for the development of next-generation detection technologies

Article abstract of DOI:10.1039/d0ob02523b

Organophosphorus (OP) chemical warfare agents (CWAs) represent an ongoing threat but the understandable widespread prohibition of their use places limitations on the development of technologies to counter the effects of any OP CWA release. Herein, we describe new, accessible methods for the identification of appropriate molecular simulants to mimic the hydrogen bond accepting capacity of the PO moiety, common to every member of this class of CWAs. Using the predictive methodologies developed herein, we have identified OP CWA hydrogen bond acceptor simulants for soman and sarin. It is hoped that the effective use of these physical property specific simulants will aid future countermeasure developments.

Full text of DOI:10.1039/d0ob02523b

Organic &
Biomolecular Chemistry
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PAPER
Identification of organophosphorus simulants for
the development of next-generation detection
technologies†
Cite this: Org. Biomol. Chem., 2021,
19, 2008
Rebecca J. Ellaby,a Ewan R. Clark,*a Nyasha Allen,b Faith R. Taylor,a
Kendrick K. L. Ng,a Milan Dimitrovski,a Dominique F. Chu,c Daniel P. Mulvihillb and
a
Jennifer R. Hiscock
*
Organophosphorus (OP) chemical warfare agents (CWAs) represent an ongoing threat but the understand-
able widespread prohibition of their use places limitations on the development of technologies to counter
the effects of any OP CWA release. Herein, we describe new, accessible methods for the identification of
appropriate molecular simulants to mimic the hydrogen bond accepting capacity of the PvO moiety,
common to every member of this class of CWAs. Using the predictive methodologies developed herein, we
have identified OP CWA hydrogen bond acceptor simulants for soman and sarin. It is hoped that the
effective use of these physical property specific simulants will aid future countermeasure developments.
Received 17th December 2020,
Accepted 9th February 2021
DOI: 10.1039/d0ob02523b
rsc.li/obc
materials.15–17 Additionally, Cragg and co-workers have devel-
oped a crown-(thio)urea based receptor capable of sensing the
Introduction
Organophosphorus (OP) compounds have been used on a presence of sarin and soman hydrolysis breakdown products.18
global scale as pesticides and chemical warfare agents (CWAs) Here the resultant fluoride ion binds to the thio(urea) moiety,
for almost a century.1 The uncontrolled release of these highly while the phosphate group coordinates to the crown ether,
toxic compounds presents a continuing global risk as evi- resulting in a colourless to orange colorimetric change.
denced by recent small-scale events within Germany (2020),2 Finally, a body of work by Ward and co-workers has shown
the UK (2018)3 and Malaysia (2017),4 combined with larger- that self-assembled cages can bind CWA simulants inducing a
scale events in Syria (2013)5 and Japan (1995).6 The develop- fluorescence response,19 and act as catalysts for the hydrolysis
ment of novel detection technologies is therefore of high of phospho-ester species.20
global importance.
OP simulants are typically the only available option for devel-
Recently, supramolecular systems have been effectively tar- oping novel approaches to combat OP CWA release due to the
geted to fulfil this need.7 This has included the development highly toxic nature of, and legal restrictions placed upon, the
of fluorescent8 and luminescent sensors,9–11 and a body of live agents themselves. However, no single simulant can simul-
work from Gale and co-workers in which hydrogen bond taneously mimic all the chemical properties of an OP CWA
donating compounds have been shown to: selectively form OP without also inheriting undesirable molecular traits. It is there-
CWA hydrogen bonded complexes;12,13 act as OP CWA hydro- fore vital to consider the appropriate properties of any simulant
lysis organocatalysts;14 produce supramolecular organogels chosen to aid in the development of novel OP CWA detection,
that act both as OP CWA sensory and decontamination decontamination, or remediation methodologies.21–24 Recently,
work undertaken by Snurr and Mendonca, has shown that
density functional theory (DFT) can be used to study the mecha-
nism of OP hydrolysis, which has resulted in the development
of a quantitative structure activity relationship (QSAR) model
aSchool of Physical Sciences, University of Kent, Park Wood Road, Canterbury, Kent,
CT2 7NH, UK. E-mail: J.R.Hiscock@Kent.ac.uk, E.R.Clark@Kent.ac.uk;
Tel: +44(0) 1227 816467, +44(0) 1227 816152
bSchool of Biosciences, University of Kent, Park Wood Road, Canterbury, Kent,
CT2 7NH, UK
that enables the identification of appropriate OP CWA simulants
for decontamination purposes.25
cSchool of Computing, University of Kent, Darwin Road, Canterbury, Kent, CT2 7NZ,
UK
However, when considering supramolecular technologies,
the moiety target is often selective coordination of the polar
PvO functionality. This chemical group, common to all OP
CWAs, plays a significant role in both molecular surface
coordination properties and reactivity. Herein, we extend our
†Electronic supplementary information (ESI) available: Experimental details,
mass spectrometry, NMR, crystallography‡ and computational modelling data.
CCDC 1855728. For ESI and crystallographic data in CIF or other electronic
format see DOI: 10.1039/d0ob02523b
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prior work, which showed that simple, low level computational
modelling may be used to predict hydrogen bond mediated
aggregation events26,27 and antimicrobial activity,28 to identify
appropriate OP CWA simulants for the development of supra-
molecular detection technologies, complementing the recent
advances made in this area for hydrolysis siumulants.25
Here, we hypothesised that neutral hydrogen bond donating
receptors 1–4 (Fig. 1) would form hydrogen bonded complexes
with potential OP CWA simulants (5–26) and, due to the struc-
tural simplicity of 1–4, the strength of the complex formed
would depend on the properties associated with the principal
hydrogen bond acceptor (HBA) group. The association constants
(Kass) relating to these hydrogen bonded complexation processes
should therefore provide information relating to the simulants’
principal HBA site, which corresponds to the moiety mimicking
the PvO group of a specific OP CWA. These experimental data
are then correlated with parameters derived from accessible
computational modelling methods, to enable the identification
of an appropriate simulant. Compounds 11–15 are commer-
cially available species commonly used to simulate OP CWAs.
The structures of simulants 5–10 and 16–26 were designed to
fulfil the following criteria: charge neutrality; not readily
ionised under experimental conditions; containing a single elec-
trophilic site (S or P); and containing a single HBA centre
(either a PvO (5–15), or OvSvO (16–26)).
Synthesis
The syntheses of 1–4, 7–9, 17–21, and 23–26 have previously
been reported.29–40 Compounds 5, 6 and 10 were obtained as
colourless oils in 65%, 90%, and 85% yields respectively, from
the reaction of diethyl chlorophosphate with the respective
alcohol in chloroform. Compound 16 was obtained as a white
solid in a 59% yield from the reaction of 4-(trifluoromethyl)
phenol with p-toluene sulfonylchloride in chloroform.
Compound 22 was obtained as a pale-yellow solid in a 91%
yield from the reaction of 4-(trifluoromethyl)phenol with
methylsulfonyl chloride in chloroform.
Fig. 1 Chemical structure of 1–26, sarin and soman.
Results and discussion
Association constant determination
Here, only the most acidic receptor (1) was found to produce
association constants that span the ≥two orders of magnitude
necessary to validate our hypothesis. Therefore, further data
analysis utilises results obtained with 1 only. These initial
results were then expanded to include all simulants (5–26);
this final data set is given in Table 2.
Experimental association constants for receptor : simulant
complexes were determined in CD3CN using H NMR titration
1
techniques, following the downfield change in chemical shift
of the thiourea/pyrrole NH resonances upon increasing con-
centration of the simulant with respect to the appropriate
receptor (1–4). In all cases the experimental data were fitted to
1 : 1, 2 : 1, and 1 : 2 host : guest binding isotherms using
Bindfit v0.5.41 The errors produced from fitting these data
were compared and found to support the formation of 1 : 1
receptor : simulant complexes. Job plot experiments were also
performed but did not give conclusive results.42
A single crystal X-ray structure,‡ obtained for a 1 : 1 complex
of 1 and DMSO (Fig. 2), shows the formation of one hydrogen
‡A suitable crystal of 1 with DMSO was selected and mounted on a Rigaku
Oxford Diffraction Supernova diffractometer. Data were collected using Cu Kα
radiation at 100 K. Structures were solved with ShelXT55 structure solution pro-
grammes via direct methods and refined with ShelXL56 on least squares mini-
A series of initial association studies were conducted to
identify a lead receptor; the results are summarised in Table 1. misation. Olex257 was used as an interface to all ShelX programmes.
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Table 1 Kass (M−1) calculated for 1–4 with potential simulants 5, 6, 8,
10, 16, 18 and 20 in a CD3CN solution at 298 K, obtained through the
fitting of 1H NMR titration data to a 1 : 1 host : guest binding isotherm
using Bindfit v0.541
No.
1
2
3
4
5
6
8
10
16
18
20
36 ( 6%)
17 ( 1%)
<10
<10
25 ( 7%)
<10
<10
20 ( 5%)
23 ( 4%)
19 ( 11%)
27 ( 9%)
<10
<10
b
b
a
a
a
a
32 ( 7%)
<10
a
a
a
a
19 ( 6%)
a
106 ( 8%)
a Data could not be fitted to either a 1 : 1, 2 : 1 or 1 : 2 binding iso-
therm. b Peak overlap prevented binding constant determination.
Fig. 3 Possible binding modes for the formation of 1 : 1 complexes of 1
with (a) 16–26, (b) 16–26, (c) 5–15 and (d) 5–15. Hydrogen bonding
angles are shown in red.
Table 2 Kass (M−1) calculated for 1 with 5–26 in a CD3CN solution at
298 K, obtained through the fitting of 1H NMR titration data to a 1 :
1 host : guest binding isotherm using Bindfit v0.541
Simulant
Kass
Simulant
Kass
the sulfonate group, the formation of hydrogen bonds may
be permitted to one (Fig. 3a, O1′) or both of these atoms
(Fig. 3b, O2′).
5
6
7
8
36 ( 6%)
17 ( 1%)
<10
<10
<10
16
17
18
19
20
21
22
23
24
25
26
25 ( 7%)
<10
<10
48 ( 11%)
106 ( 8%)
41 ( 8%)
64 ( 5%)
46 ( 9%)
76 ( 6%)
<10
In-silico modelling
9
10
11
12
13
14
15
<10
As it is feasible that 16–26 could adopt one of two 1 : 1 binding
modes with 1 (Fig. 3b and c), the potential for one of these
binding modes prevailing within the solution state was further
explored computationally (M06-2X/6-311 g(d,p)), (PCM = aceto-
nitrile, Gaussian16).43–45
11 ( 1%)
52 ( 6%)
<10
<10
12 ( 2%)
<10
The geometries of 6, 9, 10, 11, 16–20, 25, 26, sarin and
soman with receptor 1, were optimised individually and as
interacting pairs in both O1′ and O2′ configurations.† These
calculations confirmed the viability of both O1′ and O2′
binding modes for the sulfonyl species. However, as
expected no stable O2′ binding modes were found for the
phosphonyl species (Fig. 3d). The energies of binding are
significant (−41.2 to −84.6 kJ mol−1) for both sulfonyl and
phosphonyl species, with no clear preference for O1′ over
O2′ found for the sulfonyls, although significant substituent
dependence was observed. This is attributed to the variable
changes in steric profile on moving the simulant between
O1′ and O2′ bonding and the resultant effect on secondary
interactions.
Fig. 2 Single crystal X-ray structure showing 1 binding a single DMSO
solvent molecule through the formation of two hydrogen bonds.
Disorder associated with the DMSO molecule and CF3 moieties have
been omitted for clarity. Grey = carbon; white = hydrogen; green =
fluorine; blue = nitrogen; yellow = sulphur. Red dashed line indicates
hydrogen bonding.
Having confirmed the binding modes, we then sought to
develop an accessible predictive model for association
strength. DFT (M06-2X/6-311 g(d,p)) is a good computational
model for systems such as these as it accounts for non-
covalent interactions but is computationally expensive.46 We
bond from each of the thiourea NH groups to the SvO HBA have previously shown that low-level calculations can supply
group of the DMSO solvent molecule. It is therefore reasonable useful guest-only parameters for predicting trends in associ-
to hypothesise that the analogous hydrogen bonded complexes ation constant without significant computational overhead.
may be formed between 1 and simulants 5–15, which contain Reducing the size of the calculations requires far less compu-
a single oxygen atom acting as the principal HBA (Fig. 3c). tational time; additionally, Spartan ‘16 runs on a convention-
However, when considering simulants 16–26, the possibility al desktop making this approach accessible to a wider chemi-
exists that, because of the two HBA oxygen atoms within cal audience. The range of parameters output by default for
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PM6 calculations is sparse, and so to expand the scope of
searchable parameter space, DFT (B3LYP/6-3G1*) calculations
were also performed using Spartan ‘16.47–49 The analogous
parameters obtained were consistent with those values pro-
duced from PM6 and M06-2X/6-311 g(d,p) calculations.†
In total, a list of 19 computationally derived parameters (P),
summarised in Table 3, were produced for each potential
simulant/OP CWA alone or when in a 1 : 1 complex with 1.
This list of parameters are easily accessible, unfiltered by
assumption of their relevance to avoid prejudicing the
parametric search. These parameters were then used along-
side the experimentally derived association constant data
(Table 2) to produce initial predictive association constant
models.
Fig. 4 One parameter association constant (Kass) prediction. Red =
phosphoryl species; blue = toluene substituted sulfonyl species; black =
methyl substituted sulfonyl species; green = predicted OP CWAs; purple
Single parameter predictive models
Initial comparison identified correlations between the experi-
mentally derived association constants and host : guest
hydrogen bond angle (obtained from B3LYP-6-31G* calcu-
lations), Fig. 4. For ease of interpretation, the hydrogen bond
angles involved in complex formation are represented as the
sum of the difference from the optimal 180° hydrogen
bonding angle for those hydrogen bonds formed during the
potential simulant : receptor complexation processes,50,51
such as those exemplified in Fig. 3. Here, we identify three
trends, one for the phosphoryl species and two for the sulfo-
nyl species. The two trends identified for the sulfonyl
shaded section = Kass ≤ 10 M−1
.
species correspond to the two different classes of sulfonate
molecules present within the library of simulants studied,
those substituted with a toluene group (16–20) and those
substituted with a methyl group (21–26). As the limitation of
the experimental association constant determination meth-
odology is ≈10 M−1, we suggest that any values incorporating
≤10 M−1 may be considered inappropriate when included
into a model such as this and should be treated with
caution. We hypothesise that for a complex formed utilising
a single HBA atom, increasing hydrogen bonding strength
will result in an overall decrease in hydrogen bonding angle
away from 180°, as the host and guest move closer together,
resulting in a positive correlation as observed for the phos-
phoryl species (blue and black trends, Fig. 4). However, for
complexes formed using two HBA oxygen atoms, the hydro-
gen bonding angles will move towards 180°, resulting in a
negative correlation as observed for both sulfonate species
(blue and black trends, Fig. 4). The two different trends
observed for the methyl and toluene substituted sulphonyl
groups we believe are due to additional R-group specific
interactions.
Table 3 List of parameters (P) used to produce association constant
predictive models and computational method used to derive each para-
meter. A definition for each of these parameters can be found within the
ESI (Table S46†)
Derivation
method
B3LYP/
PM6 6-31G*
P
P description
SI unit
P1
P2
P3
P4
P5
P6
P7
P8
P9
Emin
Emax
kJ mol−1
×
×
kJ mol−1
Molecular volume
Molecular area
Solvent accessible area
Polar surface area
% Polar surface area
Polarizability
Å3
Å2
Å2
Å2
%
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
The OP CWA association constants were then predicted for
sarin and soman (Fig. 4, green), using the trend shown for the
phosphoryl species (Fig. 4, red), resulting in predicted associ-
ation constant values of 22 M−1 and 13 M−1 for sarin and
soman respectively. Using the three trends shown in Fig. 4,
compounds 6 and 16 were identified as the most appropriate
simulants for sarin, while 10 and 11 were identified as the
most appropriate simulants for soman.
C m−2
Å2
Å2
eV
eV
kJ
Steric weighting factor (SWF)
P10 Steric accessibility factor (SAF)
P11 HOMO
P12 LUMO
P13 Total energy
P14 Electrostatic charge
P15 Additive N⋯O bond length
P16 log P
P17 Dipole moment
P18 Additive NH⋯O bond length
P19 H-Bond angle difference from
optimal 180°
C
Å
N/A
D
Å
Exhaustive parameter search
The energetics of association do not depend solely on a single
molecular property, and we have previously shown that the use
of an exhaustive, high throughput parameter search allows the
rapid identification of predictive models.28 To enable the eluci-
dation of further association constant predictive models using
°
N/A = non-applicable. ‘×’ identifies the computational modeling
method used to generate the values for a particular parameter (P).
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Table 4 Number of times a single parameter appears within the top
10 models, as identified through comparative R2 analysis, obtained from
an exhaustive search of P1–P19 with eqn (1) and (2)
more than one computational parameter (Table 3), R data ana-
lysis software52 was used to complete an exhaustive search of
all potential parameter combinations for direct and inverse
correlations of up to three parameters (eqn (1) and (2)). These
searches included all relevant association constant data
(Table 2) where Kass > 10, to identify a single linear correlation.
P
eqn (1)
eqn (2)
Total
P1
P2
P3
P4
P5
P6
P7
P8
1
0
0
0
0
0
0
0
0
8
4
0
0
4
2
0
0
1
0
2
0
0
0
0
1
0
0
0
9
4
0
0
5
2
0
3
4
0
3
0
0
0
0
1
0
0
0
17
8
0
0
9
4
0
3
5
0
Kass ¼ Pxa ꢀ Pyb
jaj þ jbj ¼ 2
ð1Þ
Kass ¼ Pxa ꢀ Pyb ꢀ Pzc
jaj þ jbj þ jcj ¼ 3
ð2Þ
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
The form of these equations is suggested by the manner in
which factors contribute to the free energy of binding and
thus association constant.† The top 10 fits of these data to
each of these two equations as identified through R2 analysis
are detailed within the supplementary materials. Although this
approach did not result in the identification of any viable pre-
dictive models in this instance, it does offer an insight into
which factors contribute to binding. Examination of the top 10
fits identified through either two parameter or three parameter
searches shows that only some parameters exhibit any ener-
getic significance, these results are summarised in Table 3.
From this analysis, P10 (steric accessibility factor) and P14 (HBA
electrostatic charge) are the two parameters that appear most
commonly, with a total of 17 and 9 occurrences respectively;
P11 (HOMO energy) is the third most common parameter, with
8 occurrences. The prevalence of P14 is not surprising as the
electrostatic charge at the acceptor atoms would be expected to
contribute to the electrostatic component of hydrogen
bonding. Likewise, the HOMO energies, as associated with
lone pairs at oxygen, will be involved in the potential for
covalent contribution to the hydrogen bonding. More perplex-
ing is the dominance of P10, which describes the exposed elec-
trophilic site of the simulant and not the HBA oxygen at all.
However, the trends identified show an inverse correlation
with P10 – the larger the exposed electrophilic site, the smaller
the binding constant. This therefore corresponds to the steric
bulk about the P or S atom and the extent to which the substi-
tuents are folded away from the electrophilic site and towards
the HBA oxygens. We can therefore regard P10 as a proxy for
steric bulk about the HBA centres. Taken together, these indi-
cate that simple electrostatic models of hydrogen bonding are
not sufficient to describe these systems, and that consideration
of secondary bonding (and repulsive) interactions will be key
in properly mimicking OP CWAs in future work (Table 4).
Fig. 5 S. pombe toxicity screening % cellular growth (recorded as
maximum OD600) reached at stationary phase in the presence of 5–12,
16–26 (3.3 mM) relative to controls after 45 hours, supplied to the
microbial broth solution in a 5% ethanol solution. Data shown represents
an average of three experiments. Green – cell growth impeded by <16%
over the course of 45 hours; Amber – cell growth impeded by 16–50%
over the course of 45 hours; Red – cell growth impeded by >50% over
the course of 45 hours. Control = 5% aqueous ethanol solution. Error =
standard error of the mean.
a biological environment.53,54 The growth of this model yeast
system was monitored by optical density measurements at
600 nm (OD600) in the presence and absence of 5–12 and
Potential simulant toxicity
It is imperative that any simulants do not duplicate the toxicity 16–26, which were supplied in a 1 : 19 EtOH : H2O solution to
of OP CWAs. The toxicity of these potential simulants against aid simulant solubility. The results of these studies are sum-
the commonly used model species Schizosaccharomyces pombe marised in Fig. 5.
(S. pombe) was ascertained to gain some insight as to the
These preliminary results show that, at 3.3 mM, the simu-
potential toxicity of 5–12 and 16–26 towards eukaryotic cells in lants 5, 8, 10, 16–20, 22, 25 and 26 have no significant impact
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on cell growth therefore demonstrating little/no toxic effects,
while simulants 6, 7, 9, 11, 12, 21, 23 and 24 show a degree of
toxicity that warrants further investigation. The lead simulants
identified to mimic complex formation for soman and sarin
were shown to reduce the growth of S. pombe by 22.1%, 17.8%,
7.6%, 9.3%, 22.3% and 6.5% for 6, 8–11, and 16 respectively
over a period of 45 h relative to control samples. This
indicates simulants 8, 10 and 16 pose no significant toxicity
risk to eukaryotic organisms, while simulants 6, 9 and 11
would require further investigation. It is also important
to note that 17, 19 and 22 showed greater than 100% cell
growth and further investigations into this are underway.
Nevertheless, full pharmacodynamic and pharmacokinetic
studies should be undertaken before these simulants are con-
sidered safe to handle without the use of personal protective
equipment.
Notes and references
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Conclusions
We have produced a first-generation predictive model to aid in
OP CWA simulant selection for the development of defensive
technologies relating to OP CWA sensing through complexa-
tion, combining low level computational modelling and
exhaustive parameter searches. The lead simulants currently
identified using this model are 6 and 16 (for sarin), and 10
8 B. D. de Grenu, D. Moreno, T. Torroba, A. Berg, J. Gunnars,
T. Nilsson, R. Nyman, M. Persson, J. Pettersson, I. Eklind
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Conflicts of interest
There are no conflicts to declare.
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