Christopher J Knutson1, Nicholas C Pflug2, Wyanna Yeung3, Matthew Grobstein3, Eric V Patterson3, David M Cwiertny4, James B Gloer1. 1. Department of Chemistry, University of Iowa, Iowa City, Iowa 52242, United States. 2. Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland. 3. Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States. 4. Department of Civil and Environmental Engineering, University of Iowa, Iowa City, Iowa 52242, United States.
Abstract
There is growing interest in the fate and effects of transformation products generated from emerging pollutant classes, and new tools that help predict the products most likely to form will aid in risk assessment. Here, using a family of structurally related steroids (enones, dienones, and trienones), we evaluate the use of density functional theory to help predict products from reaction with chlorine, a common chemical disinfectant. For steroidal dienones (e.g., dienogest) and trienones (e.g., 17β-trenbolone), computational data support that reactions proceed through spontaneous C4 chlorination to yield 4-chloro derivatives for trienones and, after further reaction, 9,10-epoxide structures for dienones. For testosterone, a simple steroidal enone, in silico predictions suggest that C4 chlorination is still most likely, but slow at environmentally relevant conditions. Predictions were then assessed through laboratory chlorination reactions (0.5-5 mg Cl2/L) with product characterization via HRMS and NMR, which confirmed near exclusive 4-chloro and 9,10-epoxide products for most trienones and all dienones, respectively. Also consistent with computational expectations, testosterone was effectively unreactive at these same chlorine levels, although products consistent with in silico predictions were observed at higher concentrations (in excess of 500 mg Cl2/L). Although slight deviations from in silico predictions were observed for steroids with electron-rich substituents (e.g., C17 allyl-substituted altrenogest), this work highlights the potential for computational approaches to improve our understanding of transformation products generated from emerging pollutant classes.
There is growing interest in the fate and effects of transformation products generated from emerging pollutant classes, and new tools that help predict the products most likely to form will aid in risk assessment. Here, using a family of structurally related steroids (enones, dienones, and trienones), we evaluate the use of density functional theory to help predict products from reaction with chlorine, a common chemical disinfectant. For steroidal dienones (e.g., dienogest) and trienones (e.g., 17β-trenbolone), computational data support that reactions proceed through spontaneous C4 chlorination to yield 4-chloro derivatives for trienones and, after further reaction, 9,10-epoxide structures for dienones. For testosterone, a simple steroidal enone, in silico predictions suggest that C4 chlorination is still most likely, but slow at environmentally relevant conditions. Predictions were then assessed through laboratory chlorination reactions (0.5-5 mg Cl2/L) with product characterization via HRMS and NMR, which confirmed near exclusive 4-chloro and 9,10-epoxide products for most trienones and all dienones, respectively. Also consistent with computational expectations, testosterone was effectively unreactive at these same chlorine levels, although products consistent with in silico predictions were observed at higher concentrations (in excess of 500 mg Cl2/L). Although slight deviations from in silico predictions were observed for steroids with electron-rich substituents (e.g., C17 allyl-substituted altrenogest), this work highlights the potential for computational approaches to improve our understanding of transformation products generated from emerging pollutant classes.
Entities:
Keywords:
Chlorination; Computational Chemistry; Prediction; Steroids; Water Treatment
Although
hazards associated with chemical contaminants are often
assumed to be mitigated through their environmental transformation,
bioactive and potentially harmful reaction products often persist
and can complicate risk assessment for some emerging pollutant classes.[1−5] A good example are synthetic steroids widely used in medicine and
agriculture, for which we have previously demonstrated retained and/or
altered bioactivity in products generated from environmental reactions
including photolysis in sunlit surface waters and chemical disinfection
with free chlorine.[3,4] Nevertheless, a major challenge
associated with bioactive transformation products is that environmental
reactions can have a multiplicative effect on the sheer number of
species that need to be evaluated for ecological and human health
impacts, including many that lack readily available analytical standards.
As such, tools that can help predict likely products generated from
environmental processes without extensive laboratory investigation
will be critical to more comprehensive risk assessment of high priority
chemical contaminants.Given this need, recent attention has
focused on whether computational
tools can facilitate accurate prediction of environmental transformation
products. A popular approach has been the use of rules-based prediction
systems based on transformation pathways reported in the literature.
For example, the US EPA’s Chemical Transformation Simulator
(CTS) generates viable transformation pathways based on user-specified
reaction conditions and a library of known pathways (e.g., hydrolysis
and reduction) for reactive functional groups.[6] Yuan et al. also recently extended the capability of CTS by demonstrating
that its cheminformatics approach can also predict transformation
products (with 40% accuracy) of direct photolysis based on reported
reaction pathways.[7] A similar approach
was developed by Wackett et al.[8−15] for biotransformation pathways. The Biocatalysis/Biodegradation
Database, now maintained by the Swiss Federal Institute of Aquatic
Science and Technology (EAWAG), offers a rules-based pathway prediction
system for microbial degradation of chemical compounds.[11]A promising alternative to rules-based
cheminformatics prediction
systems is the use of theory to identify most probable reaction pathways
and products based on thermodynamic considerations. For example, Barr
et al. used electronic theory to model the biotransformation of 17α-ethinylestradiol
(EE2), finding good agreement between frontier electron density modeling
and measured metabolites of EE2 under aerobic conditions in mixed
culture.[16] We have similarly used complementary
experimental and theoretical approaches to better understand formation
of products generated during direct photolysis of trenbolone acetate
(TBA) metabolites (i.e., 17α- and 17β-trenbolone). Using
a combination of density functional theory (DFT) and multireference
molecular orbital theory, we were able to rationalize the formation
and stability of experimentally observed phototransformation products
across a range of reaction conditions (e.g., from acidic to basic
pH).[17] Such theory-based approaches for
transformation product prediction may be valuable when there is insufficient
information available for a particular process or specific functional
groups to develop a rules-based pathway prediction system.In
this study, we explored the use of in silico modeling for identifying
the most probable environmental reaction products generated during
the reaction of free chlorine with a family of structurally related
steroidal trienones (i.e., 17α-trenbolone, 17β-trenbolone,
methyltrenbolone, gestrinone, and altrenogest), dienones (i.e., dienogest,
dienedione, and methyldienolone), and enones (i.e., testosterone)
(see structures in Figure S1 of the Supporting Information, SI). We focused on chlorination reactions due to the widespread use
of free chlorine in chemical disinfection of water and wastewater,[18−20] the well-recognized ability of chlorination of the steroid ring
structure to amplify anabolic activity,[21] and the limited amount of existing work using computational tools
to predict chlorination products from emerging pollutant classes,
including aromatic hydrocarbons, various amines, and naproxen.[22−26] Although other steroid classes (e.g., estrogens[27] and glucocorticoids[4]) have been
extensively investigated, far less is known about the reaction of
free chlorine with steroidal enones. Further, based on recent work
with α,β-unsaturated carbonyls,[28] we anticipated that most of these steroidal enones would be reactive
toward free chlorine under conditions representative of water treatment
and distribution.As a proof of concept, we independently predicted
the chlorination
products of three of these steroids (shown in Figure ), testosterone, dienogest, and 17β-trenbolone,
in silico. In complementary laboratory experiments, we examined the
rate and extent of steroid transformation through kinetic batch studies
via reaction with chlorine, while also using semipreparative high
performance liquid chromatography (HPLC), high-resolution electrospray
ionization mass spectrometry (HRESIMS), high-resolution electron ionization
mass spectrometry (HREIMS), ultraviolet–visible spectroscopy
(UV–vis), and 1D and 2D nuclear magnetic resonance (NMR) techniques
complemented by electronic circular dichroism (ECD), where necessary,
to identify major transformation products, including stereochemistry,
when appropriate. This approach for verifying the in silico results
provides us the highest degree of certainty among existing approaches[29] with regard to the structure elucidation of
novel transformation products. It also allows factors such as stereochemistry,
which can have a dramatic effect on bioactivity,[30] to be explicitly addressed. Outcomes of this work should
improve predictions of the environmental fate of potent synthetic
steroids, especially those with structural features common to our
analyte suite (e.g., α,β-unsaturated ketones), while also
guiding future occurrence studies for novel or bioactive steroidal
transformation products.
Figure 1
Representative structures of an enone, dienone,
and trienone along
with associated chlorination products that were fully characterized
during this study.
Representative structures of an enone, dienone,
and trienone along
with associated chlorination products that were fully characterized
during this study.
Experimental and Theoretical
Methods
Reagents
A full list of reagents can be found in the SI.
Chlorination Reactions
Steroid chlorination
reactions
followed protocols outlined in our earlier work.[4] Briefly, chlorination experiments were generally conducted
in batch systems with 25 μM (6.8–7.8 mg/L) initial steroid
concentration. Reactors were dosed between 0.5 to 5 mg (7–70
μM) Cl2/L at pH 7 and allowed to react for 5 h. For
testosterone, reactors contained 1 mM testosterone and were dosed
with 0.5 to 5.9 g (7–83 mM) Cl2/L at pH 7 and allowed
to react while stirring for 20 h. Further details regarding the reaction
conditions and reactor sampling can be found in the SI.
Analytical Methods
A combination
of techniques including
HPLC, NMR, HRESIMS, HREIMS, UV–vis, and ECD were used to identify
products. Analytical details regarding product isolation and characterization,
including instrumentation, can be found in the SI.
Theoretical Methods
All stationary
points were located
using the MN15[31] density functional in
conjunction with the 6-31+G(d,p) basis set in the presence of SMD
(solvent model density)[32] water. Vibrational
frequencies were determined to characterize each stationary point
as a minimum or a transition state (one imaginary vibrational mode)
and to provide thermodynamic corrections. Intrinsic reaction coordinate
(IRC) calculations were performed to confirm the minima connected
to each transition state. Energies discussed are Gibbs free energies
at standard conditions. Calculations were performed using Gaussian
16.[33]On the basis of a preliminary
screening with skeletal steroid structures (discussed below), full
reactions were modeled between testosterone, dienogest, and 17β-trenbolone
with HOCl, focusing on initial chlorination at C4 in all cases. HOCl
was chosen as the chlorinating agent because [HOCl] greatly exceeds
[Cl2] at all pH values of relevance to wastewater treatment.
Several explicit water molecules were used to stabilize the nascent
hydroxide ion formed following electrophilic addition of chlorine
to the steroid, giving more accurate energetics and allowing for various
proton transfers necessary for subsequent mechanistic steps. The utility
of this approach has been demonstrated for the chlorination of amines
by HOCl.[22−24] All atoms were present in all calculations (i.e.,
the reactant species are reaction complexes rather than separated
reactants and so on; see the SI for complete
structures).
Results and Discussion
Preliminary Calculations
on Model Steroid Structures
Computational studies first examined
all possible monochlorinated
cationic intermediates of bare enone, dienone, and trienone skeletal
structures (e.g., with no substituents, Figure ). These structures are all β-carbocations
rather than bridged chloronium ions as noted by Olah.[34] Unsurprisingly, structure E4 in Figure is the only intermediate observed
in chlorination of the model enone. Any attempt to place the chlorine
at C5 results in a shift to give E4. Therefore, electrophilic
chlorination at C4 of enones is the only pathway to consider.
Figure 2
Model cationic
intermediate structures considered for bare enone,
dienone, and trienone skeletal structures.
Model cationic
intermediate structures considered for bare enone,
dienone, and trienone skeletal structures.In the dienone model system, it was found that D4 is
10 kcal/mol more stable than D9. This is attributable
to two causes. First, as evident in Figure , even though cation D9 is resonance-stabilized,
some positive charge ends up adjacent to the strongly electron-withdrawing
carbonyl group. This is a much less electronically favorable situation
than is seen in D4 where the charge is insulated from
the carbonyl. Second, the C9 position is more sterically crowded than
C4. We can therefore predict that C4 chlorination will be the dominant
pathway for dienones.While the situation is less clear-cut
for the model trienone, intermediate T4 remains the most
favorable. Intermediate T9 is 23 kcal/mol less stable
than T4 due to the same
considerations as for D9, but also due to loss of conjugation
compared to T4. Intermediate T10 is 9 kcal/mol
less stable than T4 due to loss of conjugation and steric
issues. Intermediate T12 lies only 3 kcal/mol higher
than T4. Intermediate T12 does not suffer
from steric issues and the conjugation extends through the carbonyl,
making it more conjugated than T4. However, conjugation
of a carbocation with a carbonyl is a destabilizing feature. Therefore, T4 remains the most likely site for chlorination even in the
trienone model, albeit by a smaller margin.
Steroid Chlorination Experiments
Consistent with prior
reports, testosterone was resistant to transformation unless very
high concentrations of chlorine (i.e., 500–5900 mg Cl2/L at pH 7) were used.[35,36] We observed greater
reactivity among all the trienone and dienone steroids examined, with
near-complete transformation in the 5 mg Cl2/L systems
over 5 h (Figure S2). Our experimental
approach, which did not include collection of samples over time under
conditions of excess (or ∼constant) chlorine, precludes us
from determining second order rate constants for this reaction that
could be used to model trienone and dienone fate in treatment systems.
We can say definitively that compared to glucocorticoids, which we
previously estimated[4] would be at most
only partially degraded (∼10%) based on their rate of reaction
with chlorine, trienones and dienones are far more reactive. This
is consistent with the work of Mash, who found trenbolone to be highly
reactive and oxidized at rates comparable to those previously reported
for estrogens including 17β-estradiol, estrone, and ethynyl
estradiol.[37] In fact, in our work with
glucocorticoids,[4] we conducted preliminary
experiments that showed 17β-trenbolone and estrone to be degraded
at comparable rates and over much shorter time scales than glucocorticoids.
Collectively, therefore, our results suggest that trienone steroids
such as 17β-trenbolone, 17α-trenbolone, methyltrenbolone,
gestrinone, and altrenogest, along with the dienone steroids dienogest,
dienedione, and methyldienolone, are susceptible to transformation
and likely to be at least partially degraded, if not fully so, under
typical chlorine concentrations and residence times used for wastewater
treatment and during drinking water treatment and distribution.Initial HPLC assessment of reaction mixtures of testosterone revealed
the formation of two new, less polar, products at high chlorine concentrations
(Figure A). Testosterone
products 1 and 2 were produced in a ratio
that was ultimately dependent on chlorine concentration. At lower
chlorine concentrations, evidence of additional products was observed,
but these were not generated in sufficient quantities to allow isolation
for structural identification.
Figure 3
Reversed phase HPLC chromatograms of starting
materials and the
product mixtures obtained upon chlorination of testosterone, dienogest,
and 17β-trenbolone are shown in panels A, C, and E, respectively.
UV spectra of the products and parent compounds are displayed in panels
B, D, and F. Individual chromatograms were monitored at different
wavelengths suggested by their UV spectra. Testosterone was monitored
at 250 nm, and its chlorination products 1 and 2, which eluted at 9.3 and 9.9 min, were monitored at 210
nm. Dienogest was monitored at 300 nm, and its product 3 (eluting at 6.2 min) was monitored at 240 nm. 17β-Trenbolone
and its reaction product 4 (eluting at 9.8 min) were
both monitored at 350 nm.
Reversed phase HPLC chromatograms of starting
materials and the
product mixtures obtained upon chlorination of testosterone, dienogest,
and 17β-trenbolone are shown in panels A, C, and E, respectively.
UV spectra of the products and parent compounds are displayed in panels
B, D, and F. Individual chromatograms were monitored at different
wavelengths suggested by their UV spectra. Testosterone was monitored
at 250 nm, and its chlorination products 1 and 2, which eluted at 9.3 and 9.9 min, were monitored at 210
nm. Dienogest was monitored at 300 nm, and its product 3 (eluting at 6.2 min) was monitored at 240 nm. 17β-Trenbolone
and its reaction product 4 (eluting at 9.8 min) were
both monitored at 350 nm.In all dienone (dienogest, dienedione, and methyldienolone) and
most trienone systems (17α-trenbolone, 17β-trenbolone,
methyltrenbolone, and gestrinone), we observed exclusive transformation
to a single product across all ranges of chlorine doses tested (0.5–5
mg Cl2/L). For example, chlorination of dienogest produced
a single more polar product 3 (Figure C). Reaction of 17β-trenbolone (Figure E) formed a new less
polar product 4. UV spectra of the products (Figure B,D,F) showed changes
in the chromophores consistent with disruption or modification of
the conjugated systems, most notably for the products of testosterone
and dienogest. Thus, our preliminary assessment suggested clear differences
in the end products generated from the reactions of dienones and trienones
with free chlorine.Altrenogest was the only trienone to demonstrate
a more complex
product formation pathway. Like other trienones, altrenogest was highly
reactive across all concentrations investigated (from 0.1 to 5.0 mg/L
as Cl2), but several peaks were observed using LC-DAD suggesting
that a mixture of products was formed. Presumably, the electron-rich
allyl substituent at C17 enables additional, alternative reaction
pathways that influence the product distribution upon electrophilic
chlorination.
Product Structure Elucidation
Guided
by outputs from
our preliminary computational studies, structural identification focused
on presumed changes in the A-ring of all steroids. In all cases below,
the compounds that were isolated and characterized were the major
products of the process, as judged both chromatographically and by
the yields of material obtained.
Testosterone
We observed formation
of two products
(1 and 2) at high free chlorine concentrations.
HREIMS produced a molecular ion M+• for product 1 at m/z 372.1246, indicating a formula of
C19H26O3Cl2, and for product 2 at m/z 370.1117, indicating a formula of
C19H24O3Cl2.NMR
signal assignments for testosterone were based on those previously
published in the literature.[38] The structures
of products 1 and 2 were independently determined
mainly by analysis of heteronuclear single quantum coherence spectroscopy
(HSQC) and heteronuclear multiple-bond correlation spectroscopy (HMBC)
data. These two-dimensional NMR techniques provide much more detailed
structural information than standard 1H or 13C NMR experiments, as they allow independent determination of single
and multiple carbon–hydrogen bond relationships based on heteronuclear
(JCH) couplings. Analysis of these data
led to assignment of the structure of product 1 as 2,2-dichloro-4β,5β-epoxy-17β-hydroxyandrost-3-one
and that of product 2 as 2,2-dichloro-4β,5β-epoxyandrostan-3,17-dione.
Structure elucidation details, including epoxide stereochemical assignment,
can be found in the SI (Figures S3–S13 and Tables S1–S3).Attempts to characterize the minor products observed via
reaction
with testosterone at lower concentrations of free chlorine using LC-HRMS
were unsuccessful, presumably due to their limited abundance and/or
poor ability to ionize during analysis. Because these products were
not observed at high chlorine concentration, they are presumed to
be reactive and likely precursors to formation of products 1 and 2.
Dienogest
HRESIMS analysis of the
reaction product
from dienogest (3) gave an (M + H)+ ion at m/z 328.1867, corresponding to the formula
C20H25NO3, indicative of the addition
of an oxygen atom. Analysis of the spectroscopic data collected for
product 3 indicated epoxidation across the C-9/C-10 olefin
of the parent dienogest resulting in the product 9,10-epoxy-dienogest.Detailed characterization and the spectroscopic data gathered for
the structure elucidation of the dienogest product (3) can be found in the SI (Figures S14–S23 and Table S4).
17β-Trenbolone
For the trienone
steroid 17β-trenbolone,
HRESIMS analysis (Figure S25) of the chlorination
product gave an (M + H)+ ion at m/z 305.1312, corresponding to the formula C18H21ClO2 and indicative of replacement of a hydrogen
with a chlorine atom. Spectroscopic information collectively allowed
identification of product 4 as 4-chloro-17β-trenbolone.
NMR, HRESIMS, and a structure elucidation narrative can be found in
the SI (Figures S24–S27 and Table S5).
Additional
Dienones and Trienones
With the exception
of altrenogest, the other dienones and trienones we examined were
assumed to also yield exclusively 9α,10α-epoxidation and
4-chlorination products, respectively, based on HRESIMS, UV, and relative
retention time data (Figures S34–S43). In fact, 9α,10α-epoxy-methyldienolone is a known product
of reaction of methyldienolone with m-chloro-perbenzoic
acid, and the λmax (∼244 nm) previously reported
for this product matched that observed for the product in our chlorination
systems.[39,40]For altrenogest, complete characterization
of its more complex product distribution generated from the reaction
with free chlorine is beyond the scope of the current work. However,
we did isolate the two most abundant products (again judged on the
basis of both chromatographic prominence and yields obtained) with
HPLC and characterized them using NMR and HRESIMS. Analysis of the
gathered spectroscopic data for products 5 and 6 allowed them to be assigned as 4-chloro-altrenogest and
11,12-epoxy-altrenogest, respectively. Notably, although chlorination
at C4 is consistent with the results for other trienones, additional
products including an 11,12-epoxide were not observed for any other
species. Characterization details, including elucidation narrative,
NMR, and HRESIMS data, can be found in the SI (Figures S28–S33 and Table S6).
Modeling Complete Reactions
Having established that
electrophilic addition of chlorine to C4 is favored in enone, dienone,
and trienone steroids (from preliminary calculations with model steroid
structures; Figure and Table S7), we proceeded to model
complete reaction pathways for select steroids. Free energy diagrams
associated with the chlorination of testosterone, dienogest, and 17β-trenbolone
are shown in Figure . We considered the reaction under both neutral and alkaline pH conditions,
which are most representative of the use of free chlorine as hypochlorous
acid/hypochlorite during water treatment. In every case, we modeled
the initial electrophilic chlorination by adding HOCl to the bottom
face of each steroid at C4 (which we define as being anti to the C10 methyl group of testosterone or the C13 methyl group
of both dienogest and 17β-trenbolone). This attack was chosen
because it avoids steric conflict with the C10 methyl in testosterone,
and we wished to explore a consistent attack for all three steroids.
Regardless of steroid, this leads to a cationic intermediate structure
with Cl in a pseudoequatorial position at C4 and generates a free
hydroxide, which can then migrate via various proton transfers to
other reactive positions.
Figure 4
Reaction coordinates for testosterone (A), dienogest
(B), and 17β-trenbolone
(C), with key species (e.g., cationic intermediate, chlorohydrin/C4–Cl
product, and epoxide) noted. Note the reversible nature of the carbocationic
intermediate (point 3 on reaction coordinate) for testosterone.
Reaction coordinates for testosterone (A), dienogest
(B), and 17β-trenbolone
(C), with key species (e.g., cationic intermediate, chlorohydrin/C4–Cl
product, and epoxide) noted. Note the reversible nature of the carbocationic
intermediate (point 3 on reaction coordinate) for testosterone.
Initial Chlorination of Testosterone, Dienogest,
and 17β-Trenbolone
The computational data support a
slow and endergonic initial chlorination
event via HOCl at C4 in testosterone. The same chlorination event
is predicted to have a more modest barrier and be exergonic with dienogest,
and be quite rapid and significantly exergonic with 17β-trenbolone
(Table S8). These predictions are consistent
with our current and prior experimental observations that testosterone
is difficult to chlorinate,[35] especially
relative to the high reactivity of dienones and trienones. This trend
in the ease of initial electrophilic chlorine addition is easily understood
from physical organic principles; the cationic intermediate formed
from addition to testosterone is a secondary carbocation with no other
stabilizing features. As such, the electrophilic addition is predicted
to be unfavorable and reversible, resulting in a slow reaction. In
contrast, the intermediate for dienogest is an allylic cation, which
benefits from resonance stabilization, whereas for 17β-trenbolone
there is additional resonance leading to even greater stability of
the intermediate. For both conjugated cases, the electrophilic addition
is favorable, and rapid reaction is expected.
Subsequent Reaction Steps
for Testosterone
We probed
the additional chemistry of the carbocationic intermediate formed
from electrophilic chlorination at C4 in testosterone by exploring
several reasonable possibilities. Despite multiple attempts, no satisfactory
stationary points could be located corresponding to a transition state
for deprotonation. However, a pathway was found for C4–C5 chlorohydrin
formation. The transition state for chlorohydrin formation lies 15
kcal/mol above reactants, placing it just a few kcal/mol lower in
energy than the initial chlorination transition state. While the remainder
of the pathway is favorable (Figure and Table S9), it is evident
that the first step is slow and at least partially reversible. We
note that the C4–C5 chlorohydrin appears to be isoenergetic
and in rapid equilibrium with the corresponding epoxide under these
conditions. This equilibrium will be greatly affected by actual reaction
conditions, and we treat computational evidence for the chlorohydrin
as sufficient evidence for formation of the epoxide.Collectively,
our predicted reaction pathway for testosterone is in good agreement
with experimental observations. No evidence for C4 deprotonation is
seen in either case, while a C4–C5 epoxide product is isolated.
Presumably, some of the products observed to form at low chlorine
equivalents in experimental systems correspond to the C4–C5
epoxide product, which we suspect subsequently chlorinates at C2 to
yield the dichlorinated derivatives identified via NMR. Indeed, we
suspect that C2 chlorination is possible for all of the steroidal
enones investigated herein at high free chlorine concentrations like
those used to generate sufficient product mass for isolation in testosterone
systems.
Subsequent Reaction Steps for Dienogest
We located
two forward pathways from the dienogest carbocationic intermediate,
one for deprotonation to form the C4–Cl product and one for
C9–C10 epoxide formation via the C4–C9 chlorohydrin
(Figure and Table S9). Under the conditions modeled, the
base/nucleophile is the nascent hydroxide formed from HOCl. The deprotonation
step is found to proceed with no apparent additional barrier. The
final C4–Cl product lies well below reactants.Due to
the stereochemical constraints, the anti C4–C5
and syn C4–C9 chlorohydrins are the two most
reasonable products that can form from the initial cationic intermediate.
The C4–C9 is considerably more stable than the C4–C5,
so that is the only pathway we pursued in detail. There is a noticeable
barrier of 7 kcal/mol to formation of the syn C4–C9
chlorohydrin, but the overall reaction is highly exergonic. In contrast
to the deprotonation step, chlorohydrin formation requires very little
reorganization of the steroid ring structure. The importance of this
will be discussed below. Closure to the C9–C10 epoxide requires
a modest barrier, but is also very exergonic. The overall pathway
from reactants to epoxide shows that the initial chlorination is the
rate-determining step with each ensuing mechanistic step being effectively
spontaneous.Since both deprotonation and chlorohydrin formation
are predicted
to be highly exergonic and irreversible, any bifurcation between the
two is determined by the highest barrier along each pathway subsequent
to the common intermediate. Such an analysis shows that the deprotonation
pathway is the dominant pathway (i.e., the difference in activation barriers between the two pathways is 7 kcal/mol in
favor of deprotonation). This should lead to an overwhelming preponderance
of C4–Cl dienogest product and negligible epoxide product,
in direct contradiction to our experimental result.To address
this contradiction, we note that the use of hydroxide
as the base/nucleophile in our model is necessitated to maintain mass
balance in the computational system. This implies an alkaline environment.
However, hydroxide will be found at very low concentrations in the
experimental system at pH 7, and water is the more probable base/nucleophile.
We would expect water to be much less efficient for deprotonation,
but perhaps similarly efficient for nucleophilic attack on the chlorohydrin.
To probe this possibility, we performed a limited set of calculations
replacing hydroxide with chloride, thus maintaining charge balance,
but forcing water to be the base/nucleophile. We computed only the
two transition states for deprotonation and chlorohydrin formation
as well as the 4-Cl and C4–C9 chlorohydrin products. Indeed,
we saw that deprotonation is now less favored by
5 kcal/mol, and the chlorohydrin to epoxide pathway is now dominant,
as seen in the experiment. Irreversibility of both pathways is retained
(Table S10). This suggests a strong pH
dependence on product formation in dienogest, which was subsequently
confirmed experimentally; we observed a change in product formation,
including evidence of new products, for chlorination reactions with
dienogest under alkaline conditions (pH 9) (Figure S44).
Subsequent Reaction Steps for 17β-Trenbolone
By analogy to the studies of dienogest, we explored both deprotonation
and chlorohydrin formation pathways via hydroxide reaction with the
17β-trenbolone cationic intermediate (Figure and Table S9).
Preliminary calculations revealed that the syn C4–C12
chlorohydrin is the most stable possible chlorohydrin, so that was
the only one examined in detail.The overall features of the
deprotonation pathway are comparable to those seen with dienogest.
While there is a modest barrier to deprotonation, the electrophilic
addition is still rate-determining by a large margin, and the overall
reaction is highly exergonic and irreversible. However, we note a
distinct contrast between the chlorohydrin pathways of these two steroids.
The forward and reverse barriers for C4–C12 chlorohydrin formation
in 17β-trenbolone are similar and low enough to be rapid at
room temperature, and chlorohydrin formation is predicted to be reversible.
This suggests that thermodynamic equilibrium plays a role in the chlorination
chemistry of 17β-trenbolone that is not seen in dienogest.We also examined the effect of pH on the process with 17β-trenbolone,
as we did with dienogest. With hydroxide as the base/nucleophile,
deprotonation is favored by 9 kcal/mol. When water is the base/nucleophile,
chlorohydrin formation is very slightly favored. However, deprotonation
is irreversible under both pH conditions and chlorohydrin formation
is reversible under both pH conditions (Table S10). Therefore, even at pH 7 where there is close kinetic
competition between the two pathways, the thermodynamic product (i.e.,
the 4-Cl product) will come to dominate the equilibrium mixture, as
was observed in our experimental systems.
Environmental Implications
Although continued development
and demonstration is necessary, this work illustrates the promise
of using computational tools to aid prediction and identification
of products generated via the environmental transformation of emerging
contaminant classes. In nearly all cases, the computational predictions
of relative reactivity and major products of steroid chlorination
were confirmed with experimental evidence, and instances where theory
and experiment were at odds could be easily rationalized. Several
generalizable best practices derived from this work may help advance
the use of computational tools for predicting environmental transformation
products by establishing a framework for computational prescreening
that could ultimately minimize the amount of confirmatory lab work
required. These are as follows:Start by screening the initial
attack using simple model systems. Because there are likely
to be multiple locations on a compound that are susceptible to reaction,
using simple structures with minimal functionalities (like those in Figure ) can help to identify
the most reactive sites. For steroidal trienones, dienones, and enones,
this helped identify the C4 locations as a primary reactive point
during chlorination.Next, screen for potential
pH effects. Herein, deviations between computational predictions
and experimental results could be rationalized when pH effects were
considered. For example, observation of epoxide rather than C4 chlorination
products for steroidal dienones could be rationalized with water (neutral
pH), rather than hydroxide (alkaline pH), as the probable nucleophile.
For chlorination, another pH effect to probe is how the pH-dependent
equilibrium between HOCl and OCl– affects the mechanism.
Accordingly, limiting pH-dependent pathways can be predicted, and
cases can be identified where the product distribution is likely to
be influenced by pH.Analyze the data from these
two screening approaches to classify reactive and unreactive compounds. We found that theory predicts and experiments confirm that testosterone
is effectively unreactive toward chlorine under conditions representative
of chemical disinfection, while trienone and dienone steroids exhibit
much greater reactivity. Thus, computational approaches will hold
value in helping identify which emerging pollutants to prioritize
when considering environmental transformation processes. Initial screening
to probe potential reactivity can be useful in limiting the number
of chemicals that merit experimental investigation, even without proceeding
on to prediction of probable transformation products.Probe functional groups that
may perturb predictions and/or lead to additional reactions. In our work, altrenogest, with its unique allyl group at C17,
provides a useful counter example of where other products could be
anticipated (i.e., in cases where electron-rich substituents are also
present). Interestingly, the generalized trend in product formation
still held to some degree for altrenogest; although 4-chlorination
and similar epoxidation products were identified, these were accompanied
by other minor, as-yet unidentified products that we presume involve
reactions with the allyl substituent. Such products, as well as products
of secondary transformations (i.e., those formed from the subsequent
reaction of transformation products), would not be captured in our
approach, which was limited to modeling pathways localized around
a generally reactive carbon center (C4) across most steroidal enones,
dienones, and trienones.In applying
this framework, it is recognized that computational
chemistry cannot capture every nuance of complex environmental systems.
However, we find significant value in using computational chemistry
to screen model systems to produce a strong, baseline prediction for
relative reactivity and probable transformation products in such systems.Beyond providing a framework for using computational tools to predict
transformation products, this work also illustrates the pressing need
to carefully consider transformation products for potent synthetic
chemicals, including steroids used in medicine and agriculture. Observation
of the trienone chlorination products is noteworthy, as introduction
of a chlorine atom at position C-4 is known to increase anabolic potency
of androgenic steroids.[21] This is especially
pertinent for the highly potent trienones 17β-trenbolone and
methyl trenbolone, which have a 20-fold and 250-to-350-fold increased
anabolic effect over testosterone, respectively.[41−43] Several studies
have shown that fish exposed to low doses (ng/L) of androgens can
lead to male secondary sex characteristics in females, and also decreased
fecundity, plasma vitellogenin, and sex steroid concentrations.[44−46] Thus, an increase in anabolic activity for these already potent
androgens is expected to also increase ecological risk associated
with these contaminants.Observation of the dienone epoxidation
products is also environmentally
relevant. As mentioned above, 9,10-epoxy-methyldienolone is a known
product of methyldienolone reaction with m-chloro-perbenzoic
acid, although, to the best of our knowledge, its bioactivity has
not previously been reported.[40] However,
in the same study, the 9,10-epoxy-methyldienolone product was treated
with aqueous potassium hydroxide, resulting in epoxide ring-opening,
dehydration, and aromatization to yield an aromatic product (Aromatic
1, Figure S44).[40] To test this, the previously isolated 9,10-epoxy-dienogest product
was dissolved in water followed by subsequent addition of either aqueous
acid or base, which afforded the analogous aromatic dienogest derivative
(Aromatic 2, Figure S44). Observation of
this aromatic compound, which has been previously isolated,[47] is notable because it has been reported to exhibit
estrogenic activity at ∼30% that of the endogenous ligand 17β-estradiol.[48,49] This level of estrogenic activity is similar to that of estrone,[50] one of the most well-recognized steroidal transformation
products in aquatic environments. Likewise, the analogous aromatic
product (Aromatic 3, Figure S44) derived
from 9,10-epoxy-dienedione has been reported to possess estrogenic
activity at ∼50% that of estrone.[51]More broadly, this work provides yet another example where
a common
environmental transformation process (chemical disinfection with free
chlorine) does not entirely eliminate ecosystem risks associated with
emerging pollutant classes. Conserved, enhanced, or broader spectrum
receptor bioactivity through environmental transformation processes
challenges our current regulatory and risk assessment paradigms, while
also complicating prioritization of analytical targets for environmental
monitoring. Collectively, outcomes of this work may help to guide
future occurrence studies for persistent and bioactive steroid transformation
products, while also improving predictions of the environmental fate
of potent synthetic steroids.
Authors: Nicholas C Pflug; Christopher J Knutson; Dalma Martinović-Weigelt; Dale C Swenson; Kristine H Wammer; David M Cwiertny; James B Gloer Journal: Org Lett Date: 2019-04-25 Impact factor: 6.005
Authors: Nicholas C Pflug; Madeline K Hankard; Stephanie M Berg; Meghan O'Connor; James B Gloer; Edward P Kolodziej; David M Cwiertny; Kristine H Wammer Journal: Environ Sci Process Impacts Date: 2017-11-15 Impact factor: 4.238
Authors: Emily L Marron; Jean Van Buren; Amy A Cuthbertson; Emily Darby; Urs von Gunten; David L Sedlak Journal: Environ Sci Technol Date: 2021-02-10 Impact factor: 11.357