1.0 Introduction
Skin sensitization is a commonly observed occupational health issue
which arises from an immunological allergic response. Skin sensitizers
are chemical substances that elicit an allergic response after exposure
to the skin, leading to allergic contact dermatitis
(ACD).[1] It has been reported that
between 15-20% of the general population will suffer sensitization over
the course of their lives. The disease is a significant regulatory
health concern and has resulted in European Union legislation in the
form of the Registration, Evaluation, Authorization and Restriction of
Chemicals (REACH). This legislation requires that the skin sensitization
potential of all chemical substances manufactured or imported at level
of one ton per annum must be assessed. A further goal of REACH is to
increase the use of nonanimal models for chemical
assessment.[2,
3]
Skin sensitization arises from the reaction of chemical sensitizer with
skin proteins triggering an immune
response.[4,
5] A range of different techniques are
available to assess the skin sensitization of chemicals. These including
in-vivo, in-vitro, in-chemico and in-silico methods have been
developed.[6-13] As a result of
ethical standards set by REACH legislation there has been an increasing
push away from in vivo models such as the gold standard in
vivo murine Local Lymph Node Assay
(LLNA),[7] towards in vitromethods, such as KeratinoSensTM
assay,[14] and in chemicoalternatives, such as peptide depletion
assays.[13] Hoffman et al.analyzed 128 compounds with a range of sensitization endpoints find that
the LLNA assay shows ~75% concordance between human
results and the LLNA assay, while that for the
KeratinoSensTM assay was roughly
comparable.[15] Natsch et
al. [8] reported that the latterin vitro approach showed a 60% concordance with the LLNA methods
for a set of 312 chemicals.
Hoffman[16] and
others[8,
17] report that the most effective
strategy to predict skinsensitization potential is to employ a multiple
non-animal methods. The former reports that incorporation of essentially
orthogonal test strategies comprising in vitro , in chemicoand in silico inputs demonstrated the best overall performance,
equivalent or superior to the LLNA assay on their curated set of 128
datapoints.[16]
In silico methods are desirable alternatives to in vivomodels since a prediction on an unknown chemical can be made from its
chemical structure alone. While this means the methods generally cost
resource and time efficient, they are generally of lower accuracy than
their experimental alternatives. In silico models can range from
similarity or substructural methods[5,
18, 19]
that allow the identification of like-molecules with experimental data
(read-across) or statistical models that can relate 2D chemical
descriptors to a qualitative or quantitative prediction of activity.
[20-22] Methods TIMES, Toxtree, Derek
Nexus etc. [17,
23, 24]
have proved useful in compound assessment in their own
right,[21,
25] and as part of multi-tiered testing
strategies with in silico models as the first tier
approach.[21,
26]
A number of different statistical models that relate chemical properties
to the degree of sensitization have been reported in the literature.
Guidelines that all in silico models must meet are: (a) a defined
endpoint, (b) an unambiguous QSAR model, which is (c), mechanistically
interpretable. In addition the model must have (d) predictivity that is
fit for purpose and (e) a defined domain of
applicability.[5,
27] Notable models include the relative
alkylation index (RAI) of Roberts et al.[28], models built on individual
chemical domain basis[12,
29-32] or global
basis.[21,
22, 33,
34] Global models are generally
desirable due to their greater applicability
domain,[35] however in many cases
focusing a QSAR model on individual chemical classes (i.e. Schiff bases,
Michael acceptors, SN1/SN2,
SNAr, Acyl,etc. )[12] we can obtain better
“local” performance.[36] These
mechanistically interpretable models can offer increased confidence over
black box models which may be important in a regulatory situation.
There has been a general trend towards more information rich 3D
specific, or use of quantum chemical descriptors in QSAR modelling
studies associated with ligand
bioactivity.[37-45] This includes the
incorporation of dynamical effects via descriptors derived from
MD simulations[46-48] and interaction
energies and conformational energies via quantum mechanics
(QM).[49-53] and chemical
reactivity.[54,
55] Indeed, these trends towards more
information rich descriptors have been observed in studies related to
skin sensitization prediction given that chemical reactivity can be
encoded much effectively with quantum chemical derived descriptors than
those that are empirical derived. For example, Miller et
al. [34] have used semi-empirical
HOMO-LUMO energies for their QSAR studies, Enoch et al. used
density functional theory energies of key reaction intermediate as
surrogates for the rate determining barriers of Michael acceptors
[55] while Promkatkaew et al.fully profiled all intermediates and transition states in the reaction
mechanism of SNAr
chemicals.[54] Additional efforts
have been spent investigating ligand conformational effects on
sensitization - given that molecules are not perfectly described by a
single conformation. Yu et al. have used 4D fingerprints in their
studies[33] while Kostal et
al. have incorporated Monte Carlo conformational sampling in their
hybrid QSAR models with good
results.[56]
In this work we apply quantum chemical methods to rationalize the
sensitization potential of chemicals in the Schiff base (SB) domain
(Scheme 1). Roberts et al. have previously reported a
quantitative mechanistic model to predict the LLNA pEC3 using the Taft
σ* values and logP.[32] We were
interested in expanding on this work by (a) employing a more diverse
datasets to cover a wider range of SB functional groups as well as (b)
investigate whether QM derived estimates of chemical reactivity could
prove useful. In our previous work we showed that DFT derived barrier
estimates did indeed perform comparably well for the
SNAr domain.[54] A
key advantage of such methods are that prediction can be made for
functional groups where the experimental Taft σ* values are not readily
available. To this end we have collected 22 SB base chemicals covering
aliphatic and aromatic aldehydes and ketones, 1,2 diones and 1,3 diones,
expanding considerably the domain of applicability of the model over the
previous study (11 out of 16 were aliphatic aldehydes). The full
reaction energy profile leading to the formation of the 30 possible SB
products for the 22 compounds, including 8 chemicals where more than one
product is possible. The relationship between the rate determining (RDS)
barrier to reaction and the LLNA pEC3 was then assessed. Finally we
constructed a two parameter quantitative molecular model (QMM) using
only the RDS barrier and the computed logP, the latter being another
property identified as being important for skin sensitization of
SBs.[32]