Fixed Bed Modeling of Nonsteroidal Anti-Inflammatory Drug Removal

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Fixed bed modeling of nonsteroidal anti-inflammatory drug removal by ion-exchange in synthetic urine: Mass removal or toxicity reduction? Kelly Ann Landry, and Treavor H Boyer Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02273 • Publication Date (Web): 21 Jul 2017 Downloaded from http://pubs.acs.org on July 24, 2017

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Environmental Science & Technology

Fixed bed modeling of nonsteroidal anti-inflammatory drug removal by ion-exchange in synthetic urine: Mass removal or toxicity reduction?

Kelly A. Landry*,a and Treavor H. Boyerb a

Department of Environmental Engineering Sciences Engineering School of Sustainable Infrastructure & Environment University of Florida P.O. Box 116450 Gainesville, Florida 32611-6450, USA b

School of Sustainable Engineering and the Built Environment Arizona State University PO Box 873005, Tempe, AZ 85287-3005, USA

*Corresponding author. Tel.: 1-352-514-9129; fax: 1-352-392-3076. E-mail addresses: [email protected] (K.A. Landry); [email protected] (T.H. Boyer). Submitted to


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TOC

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ABSTRACT

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Ion-exchange removal of nonsteroidal anti-inflammatory drugs (NSAIDs) in synthetic urine can

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selectively remove pharmaceuticals with minimal co-removal of nutrients to enhance nutrient

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recovery efforts. However, the effect of endogenous metabolites in urine on ion-exchange

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removal, and the corresponding reduction in ecotoxicity potential of pharmaceuticals in treated

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urine entering the environment, is unknown. To assess treatment efficacy, this work paired

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predicted breakthrough curves determined by the homogenous surface diffusion model to an in

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vitro bioassay to evaluate COX-1 inhibition. The presence of endogenous metabolites in urine

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significantly impacted pharmaceutical removal, by competing for ion-exchange sites on the resin

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and reducing the resin capacity for pharmaceuticals. This indicates ion-exchange would be

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ineffective at removing NSAIDs and other negatively charged compounds in urine. Due to

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hydrolysis of pharmaceutical metabolites back to the parent compound, treatment systems should

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be designed based on the ultimate pharmaceutical concentration in ureolyzed urine. Mass

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removal and COX-1 inhibition followed a nonlinear correlation and mixture toxicity followed

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the generalized concentration addition model. This work demonstrates the importance of

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evaluating removal of contaminants of emerging concern, such as pharmaceuticals, using a risk-

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based approach to ecotoxicity endpoints in conjunction with mass removal. 1 ACS Paragon Plus Environment

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INTRODUCTION

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Urine source separation is a method to separate pharmaceuticals primarily excreted in urine from

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the general waste stream where it may be more effectively treated as opposed to centralized

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wastewater treatment where urine is diluted by a factor of 100.1, 2 Approximately 50–100% of a

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consumed dose of nonsteroidal anti-inflammatory drugs (NSAIDs) are excreted in urine as the

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parent compound and metabolites, with NSAIDs representing one of the most widely consumed

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classes of pharmaceuticals in the world.3-7 Conventional wastewater treatment is ineffective at

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removing these compounds, and is considered a major point source of pharmaceutical discharge

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in the environment.8, 9 Furthermore, ibuprofen, diclofenac, and their metabolites have been

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identified as having the highest potential ecotoxicological risk out of 42 pharmaceuticals from 27

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therapeutic groups.3 Urine is also high in nitrogen and phosphorus which may be utilized as an

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alternative fertilizer.10 Therefore, effective separation of pharmaceuticals from nutrients is

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necessary to produce a “contaminant free” fertilizer product to reduce potential pharmaceutical

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risk to ecological and human health.11

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From previous research, ion-exchange treatment of synthetic urine can selectively

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remove NSAIDs with minimal co-removal of nutrients.12, 13 However, the work by Landry and

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Boyer13 and Landry et al.12 focused on equilibrium ion-exchange of parent compounds, and no

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research has been done evaluating pharmaceutical metabolite removal. This is important because

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pharmaceuticals are primarily excreted as metabolites, some of which may induce a response, or

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may be converted back to the parent compound.14, 15 Although isotherms provide information

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describing how contaminants interact with sorbent materials (e.g., sorption mechanisms, surface

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properties, selectivity), these experiments are performed under batch conditions at equilibrium.16

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Predictive column modeling, such as the homogenous surface diffusion model (HSDM) is

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commonly used to describe practical treatment performance which is influenced by equilibrium

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isotherms, and individual transport processes in the column and sorbent.17

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Although column modeling may provide insight into the practical application of ion-

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exchange technologies, evaluating mass removal alone is inadequate at evaluating

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pharmaceutical risk, and quantification of pharmaceuticals below detection limits does not

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provide an assessment of endpoint ecotoxicity, nor the contribution to mixture toxicity.18

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Recently, there has been a paradigm shift in toxicity (i.e., human toxicity and ecotoxicity) testing

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towards in vitro cell-based and cell-free bioassays to rapidly assess water quality treatment

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processes.19, 20 The benefit of using cell-based bioassays is that they evaluate the potential for

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adverse effect. Cellular response is one aspect of taking a systems-level approach to assess whole

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organism and population response.21 Escher et al.20 evaluated pharmaceutical ecotoxicity in

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source separated urine using bioassays after urine was treated using various advanced processes

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including nanofiltration and ozonation; however, no research has been done evaluating

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pharmaceutical ecotoxicity reduction using ion-exchange in source separated urine. Furthermore,

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Escher et al.19 evaluated 103 in vitro bioassays to benchmark organic micropollutants in water,

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wastewater, and reclaimed water. It was determined that a no single battery of assays should be

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applied universally but rather tailored to fit the specific needs of the application.19 For example,

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Nishi et al.22 evaluated NSAID ecotoxicity in surface water and wastewater using an in vitro

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cyclooxygenase (COX) inhibition bioassay, which is the primary mode of action of NSAIDs, and

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a dose-response relationship was observed between COX inhibition and NSAID concentration.

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However, this study did not evaluate the specific activity of pharmaceutical metabolites.

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This study combined predictive column modeling with in vitro bioassays to provide a proof-of-concept assessment of fixed-bed ion-exchange removal of NSAIDs to reduce


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ecotoxicity potential. Inhibition of the COX-1 enzyme, which is associated with normal cellular

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homeostasis, has been attributed to aquatic toxicity.23, 24 For this reason, inhibition of COX-1 was

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the mode of action evaluated in this study. The goal of this research was to develop a systematic

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approach to evaluate the ion-exchange removal of pharmaceutical parent compounds and

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pharmaceutical metabolites in urine and evaluate the corresponding reduction in ecotoxicity

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utilizing the entire dose-response curve through three main objectives: (1) compare COX-1

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inhibition and mass removal for individual compounds, (2) compare COX-1 inhibition and mass

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removal for a pharmaceutical mixture, and (3) compare the effect of urine matrices on

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pharmaceutical ion-exchange removal.

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MATERIALS AND METHODS

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Materials.

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Pharmaceuticals and Pharmaceutical Metabolites.

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The chemical characteristics of the pharmaceutical parent compounds and respective metabolites

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investigated in this work are listed in Table S1. Diclofenac sodium (DCF) (CAS 15307-79-6),

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ibuprofen sodium (IBP) (CAS 31121-93-4), ketoprofen (KTP) (CAS 22071-15-4), and naproxen

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sodium (NPX) (CAS 26159-54-2) are all weakly acidic pharmaceuticals from the NSAID class.

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A primary metabolite of each parent compound was also investigated. 4’-OH-diclofenac (OH-

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DCF) (CAS 64118-84-9), hydroxy ibuprofen (OH-IBP) (CAS 53949-53-4), ketoprofen acyl

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glucuronide (KTP-gluc) (CAS 76690-94-3), and O-desmethylnaproxen (Odm-NPX) (CAS

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52079-10-4). All metabolites were purchased from Toronto Research Chemicals and all

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pharmaceutical parent compounds were purchased from Sigma Aldrich. Separate stock solutions

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were made by dissolving each compound in methanol. Pharmaceutical parent compound and

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metabolite concentrations in urine were estimated in urine following a previously described



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method.12 Detailed methodology is described in the Supporting Information (SI). Table 1 lists the

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excretion rates and estimated parent compound and metabolite concentrations in urine.

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Synthetic and Real Urine.

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Synthetic ureolyzed urine was made according to a previously described method and adjusted to

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include the six major endogenous metabolites found in human urine (i.e., citrate, creatinine,

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glycine, hippurate, L-cysteine, and taurine) (Table S3).12, 25 Pharmaceutical parent compounds

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and metabolites were spiked individually in urine at an initial concentration of 1,000 µg/L.

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Pharmaceuticals were spiked in urine at 1,000 µg/L to make comparison between pharmaceutical

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isotherms, and so that expected pharmaceutical concentrations in urine (e.g., 0.547 µmol/L

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diclofenac) fall within the isotherm to estimate resin capacity. The same bulk solution of urine

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was used for both the kinetic test and equilibrium test of the respective compounds. Real urine

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was collected from one male and one female, and stored to allow urea hydrolysis to occur (i.e.,

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conversion of urea to ammonia and bicarbonate), which is catalyzed by the naturally occurring

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urease enzyme.26

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Anion Exchange Resin.

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A commercially available anion-exchange resin (AER), Dowex 22 (DOW Chemical Company)

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was used for all batch kinetic and equilibrium experiments. This resin is a strong-base,

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polystyrene, macroporous AER functionalized with dimethylethanolamine functional groups.

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The AER was preconditioned using NaCl, and dried following a previously described method.13

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Details regarding the AER properties are listed in Table S4.

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Experimental Methods.

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Experimental Approach.


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An in vitro bioassay was used to develop COX-1 inhibition dose-response curves for individual

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pharmaceuticals, pharmaceutical metabolites, and a pharmaceutical mixture. Compounds that did

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not inhibit the COX-1 enzyme were not evaluated in the batch studies. The batch equilibrium and

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kinetic tests were used to develop predicted breakthrough curves using the HSDM. The

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normalized mass breakthrough curve (C/C0) was converted to effluent concentration values

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(µmol/L) using the predicted pharmaceutical and pharmaceutical metabolite concentrations in

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urine listed in Table 1. The Hill equation coefficients determined from the dose-response curves

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were used to convert the y-axis of the breakthrough curve for each individual compound from

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effluent concentration to effluent % COX-1 inhibition. For the pharmaceutical mixture, the

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effluent concentration was assumed to follow the individual breakthrough curves for DCF, KTP,

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NPX, and Odm-NPX. The generalized concentration addition (GCA) was used to estimate the

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effluent COX-1 inhibition of the pharmaceutical mixture.

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Toxicity Bioassays.

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The inhibiting activity of the COX-1 enzyme was measured using a COX Colorimetric Inhibitor

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Screening Assay Kit (Cayman Chemical Co.) according to the protocol provided by Cayman

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Chemical Co. The COX-1 enzyme was incubated with each inhibitor for 30 min prior to plate

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development. Each compound was evaluated for COX-1 inhibition at five concentration points

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and performed in triplicate. Pharmaceutical stock solutions were diluted in methanol for the

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bioassays to evaluate COX-1 inhibition from the pharmaceutical parent compounds and

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pharmaceutical metabolites only, and to avoid interference from the high concentrations of

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nutrients, salts, and endogenous metabolites in synthetic urine. The concentration points were

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made by serial dilution and corresponded to a 10-log concentration factor (i.e., 0.01×, 0.1×, 1×,

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10×, 100×), where 1× corresponds to the realistic concentration found in urine (Table 1). Effect



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concentrations for single compound dose-response curves is listed in Table S5. Dose-response

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curves were modeled to the classic Hill equation (Eq. 1) using a 3-parametric logistic regression

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developed by Cardillo 27 in MATLAB (8.2.0.701 R2013b).28 (𝐼𝐼𝑚𝑚𝑚𝑚𝑚𝑚 −𝐼𝐼0 )

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𝐼𝐼 = 𝐼𝐼0 +

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Where I is the observed inhibition, I0 is the minimum observed inhibition, Imax is the

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maximum observed inhibition, IC50 is concentration at which 50% of the COX-1 enzyme is

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inhibited (µmol/L), C is the inhibitor concentration (µmol/L), and H is the Hill slope. One

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experiment was conducted as a mixture of DCF, KTP, KTP-gluc, NPX, and Odm-NPX. Mixture

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toxicity was evaluated using the generalized concentration addition model (Eq. 3).29

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𝐼𝐼𝑚𝑚𝑚𝑚𝑚𝑚 =

𝐼𝐼𝐼𝐼 1+� 𝐶𝐶50 �

(1)

𝐻𝐻

𝐼𝐼𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝐶𝐶𝐴𝐴 ⁄𝐼𝐼𝐶𝐶50𝐴𝐴 +𝐼𝐼𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝐶𝐶𝐵𝐵 ⁄𝐸𝐸𝐶𝐶50𝐵𝐵 +⋯ 1+𝐶𝐶𝐴𝐴 ⁄𝐸𝐸𝐶𝐶50𝐴𝐴 +𝐶𝐶𝐵𝐵 ⁄𝐸𝐸𝐶𝐶50𝐵𝐵 +⋯

(2)

Where Imix is the effect of the mixture at a specific concentration, ImaxA is the maximum

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inhibition of chemical A, IC50A is the IC50 of chemical A, and CA is the concentration of

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chemical A in the mixture, and so-forth for chemical B, etc. Inhibition concentrations for the

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pharmaceutical mixture dose-response curves is listed in Table S6. Data from the bioassays were

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the mean value of triplicate samples.

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Fixed-Bed Column Modeling.

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Batch kinetic and equilibrium tests were performed following a previously described method

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using synthetic and real urine.13 Individual batch tests were conducted for each pharmaceutical

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(i.e., DCF, KTP, NPX, Odm-NPX) spiked in synthetic urine with metabolites and one

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equilibrium batch test was conducted with real urine spiked with DCF. All pharmaceuticals were

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present at an initial pharmaceutical parent compound or metabolite concentration of 1,000 µg/L.

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Details regarding the experimental method are provided in the SI. Data from the equilibrium

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tests were the mean value of triplicate samples, respectively. The unsteady-state adsorption of 7 ACS Paragon Plus Environment

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pharmaceutical parent compounds and metabolites in a fixed-bed column were predicted by the

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HSDM using the Fixed-bed Adsorption Simulation Tool (Fast 2.1beta).30 Details regarding the

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HSDM are- in the SI.

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Analytical Methods.

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Details regarding the analytical instruments and methods used for analyzing COX-1 inhibition,

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pharmaceutical concentrations, total organic carbon (TOC) content, and urine conductivity are

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listed in the SI.

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Data Analysis.

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An analysis of covariance (ANOCOVA) was conducted using MATLAB (8.2.0.701 R2013b) to

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determine if there was a significant difference (α = 0.05) between the slopes of the log-log

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transformed ion-exchange isotherms.28 The null hypothesis states that there was not a significant

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difference between the slopes (p > 0.05) and the alternative hypothesis states that there was a

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significant difference between the slopes (p < 0.05).

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RESULTS AND DISCUSSION

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COX-1 Inhibition for Individual Compounds.

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The HSDM was selected to predict fixed-bed ion-exchange removal of DCF, KTP, NPX, and

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Odm-NPX (Figure 1). As shown in Figure S1 and Figure S2d, DCF, KTP, KTP-gluc, NPX, and

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Odm-NPX inhibited the COX-1 enzyme to different extents. However, IBP, OH-DCF, and OH-

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IBP did not inhibit COX-1 enzyme at any pharmaceutical dose (Figure S2a, S2b, and S2c). For

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brevity, results and discussion of this paper will focus on pharmaceuticals that inhibit COX-1

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enzyme. The Freundlich isotherm parameters used for model calibration are listed in Table S8.

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The HSDM model was also fit to existing fixed-bed column data from previously published

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research and newly generated data to confirm model validity.12, 31 The R2 and sum of squares



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error (SSE) was 0.98 and 1.22, respectively, for DCF ion-exchange removal in synthetic urine

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without metabolites (Figure S3). In general, a poor model fit was observed for the experimental

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data from Landry et al.12 and Landry and Boyer31 (Figure S4). These bench-scale column

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experiments were performed as a pharmaceutical mixture in synthetic urine with and without

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metabolites. It is possible the poor fit may be attributed to competitive effects due to the presence

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of multiple pharmaceuticals in synthetic urine, as opposed to the single compound (DCF)

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evaluated in Figure S3.32 The HSDM was chosen in this study because it has successfully

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described organics removal by activated carbon and macroporous resins, such as the AER (i.e.,

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Dowex 22) used in Figure S4 and this current study.33-35 Similarly, the HSDM is frequently used

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to describe surface diffusion for gel-type resins,36 such as the AER (i.e., Dowex 11) used in

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Figure S3. However, Li and Sengupta37 determined that surface diffusion and pore diffusion

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were the rate limiting steps for gel-type and macroporous resins, respectively. Furthermore,

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broad tailing in the experimental data, particularly for DCF, may also be attributed to flow non-

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idealities such as column channeling.38 Nevertheless, although the HSDM may not be suitable

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for evaluating pharmaceutical mixtures, the HSDM was used to evaluate the individual

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compound breakthrough curves to pursue the objective of coupling ecotoxicity reduction with

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ion-exchange removal. The mass breakthrough curves in Figure 1 exhibit a broad trailing edge

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possibly due to slow intraparticle diffusion within the AER pore space.38 Furthermore, the

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Freundlich isotherm parameters influence the breakthrough curve profile.39 In general, increasing

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selectivity (i.e., decreasing 1/n) or increasing AER capacity (i.e., KF) increases the volume

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treated until breakthrough, and decreases the intraparticle mass transfer rate (DS) resulting in a

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broad trailing edge. Conversely, decreasing selectivity (i.e., increasing 1/n) or decreasing AER

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capacity (i.e., decreasing KF) decreases the volume treated until breakthrough, and increases the


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intraparticle mass transfer rate (DS) resulting in a sharper trailing edge. Although improvements

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are needed to successfully model fixed-bed breakthrough performance of pharmaceutical

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mixtures, the benefit of predicting breakthrough curves is that column parameters may be

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optimized, and material requirements and costs may be estimated prior to pilot or full-scale

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implementation.40

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As stated previously, the premise of this research is that both mass removal and

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ecotoxicity potential are needed to evaluate pharmaceutical risk. To address this, an alternative

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approach to evaluating the fixed-bed breakthrough was taken by converting the commonly

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depicted normalized effluent concentration (i.e., C/C0) to percent COX-1 inhibition. By

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evaluating breakthrough curves as a function of COX-1 inhibition, ion-exchange performance

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may be compared to the entire dose-response curve and used to help establish treatment

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objectives. The absolute effluent concentrations (i.e., µmol/L) from the breakthrough curves for

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DCF, KTP, NPX, and Odm-NPX were transformed to COX-1 inhibition using the Hill

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parameters from the dose-response curves (Table S13, Figure S1). Figure 1 shows the

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simultaneous normalized effluent concentration (C/C0) predicted from the HSDM and COX-1

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inhibition as a function of treated bed volumes (BV) of urine. The expected COX-1 inhibition of

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untreated urine, based on the predicted pharmaceutical concentrations in urine (Table 1),

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followed a decreasing trend of DCF (74%) > KTP (51%) > NPX (26%) > Odm-NPX (20%)

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(Figure 1). Using the IC10 (i.e., pharmaceutical concentration corresponding to 10% COX-1

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inhibition) as an arbitrary treatment criterion (i.e., breakthrough), 616 and 209 BV of synthetic

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urine containing DCF and KTP, respectively, may be treated before reaching breakthrough.

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Although DCF was more active (i.e., induced greater COX-1 inhibition) than KTP (see IC50

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values in Table S13), the AER had a greater capacity for DCF compared with KTP so a larger



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volume of urine may be treated before COX-1 inhibition by DCF is detected in the effluent. This

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demonstrates that although a pharmaceutical may not be as active, less effective mass removal

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may induce greater ecotoxicity potential. Conversely, although, a breakthrough curve for KTP-

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gluc was not developed, it is expected that it would be less selective for the resin, due to its

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greater hydrophilicity,12 and the mass breakthrough curve (C/C0) would reach saturation before

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KTP. However, KTP-gluc inhibited COX-1 by 2% in untreated urine which suggests that

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removing this compound would be ineffective at reducing ecotoxicity. Furthermore, as shown in

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Figure S5, there was a nonlinear correlation between % mass removal and % COX-1 inhibition

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for DCF and KTP. This suggests that evaluating for mass removal alone may not be an adequate

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surrogate for evaluating ecotoxicity reduction. However, only a slight correlation was observed

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for NPX and Odm-NPX (Figure S5) because although complete removal (i.e., C/C0 ≈ 0) this may

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be attributed to the dose-response curves which had I0 values of 20% and 13%, respectively,

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which suggests that targeting these compounds for removal may not significantly improve urine

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quality with respect to COX-1 inhibition. However, the minimum and maximum response did

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not reach 0% or 100%, respectively, for NPX and Odm-NPX, therefore it is difficult to

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accurately predict the dose-response behavior outside of the range of available data.

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COX-1 Inhibition Mixture Effects.

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Realistically, NSAIDs are present in urine as a mixture at varying concentrations, and with

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varying resin selectivity. The Hill parameters for individual COX-1 inhibition curves (Table

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S13) were used to evaluate COX-1 inhibition for a pharmaceutical mixture containing DCF,

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KTP, NPX, and Odm-NPX (Figure 2). The COX-1 inhibition of untreated urine (i.e., a

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summation of the predicted DCF, KTP, NPX, and Odm-NPX concentrations in urine (Table 1)

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for the pharmaceutical mixture was 63% based on the dose-response curve (Figure 2). The


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generalized concentration addition (GCA) model was used to predict the mixture effects using

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the IC50 and maximum COX-1 inhibition from the individual compound dose-response curves.

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The benefit of using the GCA model is that individual dose-response curves may be used to

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predict mixture response for multiple pharmaceuticals.29 The GCA model adequately predicted

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pharmaceutical mixture ecotoxicity, with an R2 of 0.98 and SSE of 0.33, although it

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overestimated the expected COX-1 inhibition in urine by 17% with a predicted inhibition of 75%

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(Figure 2). Total excretion for NSAIDs, including parent compounds and metabolites, range

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from 50%–100%. As many as five metabolites may be excreted, however only one metabolite

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with the highest excretion was evaluated in this study. For example, only 6.4% of KTP is

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excreted in urine unchanged and 52.8% is excreted as an acyl glucuronide (KTP-gluc) (Table 1).5

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However, acyl glucuronides have been shown to be highly unstable in urine and rapidly

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hydrolyze back to the parent compound at high pH and temperature, which are the conditions for

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stored urine.15, 41, 42 This suggests that the concentration of KTP in urine may be much greater

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than what was estimated in urine based on excretion. The expected COX-1 inhibition of KTP-

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gluc in urine was 2%. However, if KTP-gluc was completely hydrolyzed back to KTP in

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ureolyzed urine, the predicted COX-1 inhibition due to KTP would increase from 51% to 83%,

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and increase COX-1 inhibition for the pharmaceutical mixture from 75% to 91%. This is

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demonstrated by the shift in the GCA model in Figure 2.

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The influent concentration of NSAIDs at varying concentrations will influence both

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fixed-bed performance and expected COX-1 inhibition. Furthermore, the effluent concentration

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of each NSAID changes as a function of BV treated until the resin is fully saturated. The GCA

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model was used to predict COX-1 inhibition with increasing number of BV treated for a

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pharmaceutical mixture containing DCF, KTP, NPX, and Odm-NPX (Figure 3a). Approximately



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210 BV of urine may be treated before reaching breakthrough. It should be noted that the

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breakthrough curve was the summation of the individual compound breakthrough curves and it is

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expected that the capacity of the resin would decrease for each compound for the pharmaceutical

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mixture resulting in a smaller breakthrough volume.12 Furthermore, if KTP-gluc hydrolyzed back

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to KTP, increasing the initial concentration of KTP in urine, breakthrough would decrease to 800 in vitro

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endpoints for >1,800 chemicals, DCF and IBP induced a response in 48 and 17 bioassays,

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respectively, with biological endpoints ranging from cell death, regulation of gene expression,

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and receptor binding.43 Figure S6 depicts the AC50 (i.e., concentration that induces 50% affect)

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of the in vitro bioassays with various endpoints that induce a response from exposure to DCF and

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IBP. For DCF, the COX-1 bioassay may be considered a protective assay because it is more

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sensitive than other endpoints evaluated. The AC50 for most of the in vitro bioassays exceeded

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that of the expected DCF concentration in urine. However, linking in vitro assays to long-term in


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vivo outcomes is difficult due to the complex molecular, cellular, and tissue changes from the

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biological target to adverse outcomes.44 In general, in vitro COX-1 bioassays from literature

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were more sensitive than in vivo studies when exposed to DCF, with the exception of M.

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galloprovincialis larvae development when exposed to DCF.24, 45-48 On the other hand, IBP did

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not induce COX-1 inhibition, however alternative in vitro bioassays such as the nuclear receptor

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assay ATG_ERE_CIS_up may be utilized to evaluate the estrogen response.43 Furthermore,

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researchers have utilized ToxCast and the Toxicity Reference Database containing in vivo

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chronic ecotoxicity data to develop predictive ecotoxicity models.44, 49-51

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Comparison of Urine Matrices.

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To compare the effect of urine matrices on pharmaceutical ion-exchange removal, the

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breakthrough curve for DCF removal in synthetic urine was compared with real urine. As shown

306

in Figure 1a and Figure S7, approximately 3.4× more synthetic urine than real urine may be

307

treated before DCF reaches breakthrough (i.e., IC10), this suggests that the presence of

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endogenous metabolites in real urine may be interfering with DCF ion-exchange. To further

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evaluate the impact of endogenous metabolites, the equilibrium data for pharmaceutical removal

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in synthetic urine with metabolites was compared with previously conducted equilibrium

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experiments in synthetic urine in the absence of metabolites.12 As shown in Figure 4 and Figure

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S8, the presence of endogenous metabolites in synthetic urine significantly reduced the ion-

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exchange capacity and removal efficacy. An analysis of covariance determined that there was a

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significant difference between the slopes (p < 0.05) for diclofenac removal in synthetic urine

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with metabolites and real urine, however there was not a significant difference between the

316

slopes (p > 0.05) for diclofenac removal in synthetic urine with and without metabolites. At a

317

resin dose of 2 mL/L, DCF removal decreased from 89% in synthetic urine without metabolites



318

to 74% in synthetic urine with metabolites, and further decreased to 32% in real urine. A similar

319

trend was observed at the 4 mL/L resin dose, however at the 8 mL/L resin dose, diclofenac

320

removal was 95%, 91%, and 97% in synthetic urine with and without metabolites, and real urine,

321

respectively. A reduction in color and discoloration of the AER was observed with increasing

322

AER dose. It was speculated that endogenous metabolites were competing for ion-exchange sites

323

on the AER. To confirm this, DCF samples from experiments using synthetic urine with

324

metabolites and real human urine were analyzed for total organic carbon (TOC) to estimate

325

endogenous metabolite removal. As shown in Figure 5, the mass of TOC (mg as C) removed

326

from synthetic and real urine increased with increasing resin dose. Furthermore, the TOC content

327

due to endogenous metabolites was 3,200× and 27,000× greater than the pharmaceutical content

328

in synthetic and real urine, respectively. The metabolites present in real urine was 2.7× greater

329

than the concentration (mg C/L) in synthetic urine (Table S2). Similar competition was observed

330

for micropollutant adsorption in the presence of natural organic matter during drinking water

331

treatment, which is present at much higher concentrations than micropollutants.52 Ion-exchange

332

removal of NSAIDs in urine is due to the electrostatic interactions between the negatively

333

charged functional group of the pharmaceutical and positively charged quaternary ammonium

334

functional group of the AER, and van der Waals interactions between the aromatic ring structure

335

between the pharmaceutical and AER.12 In addition to being primarily negatively charged or

336

neutral, endogenous metabolites have an aliphatic or aromatic organic structure.25 Thus, it is

337

reasonable to expect that negatively charged endogenous metabolites with an aromatic ring

338

structure (e.g., hippurate) would compete with pharmaceuticals for ion-exchange sites on the

339

resin due to favorable van de Waals interactions between the metabolites and AER. The

340

competitive effects of endogenous metabolites demonstrate that ion-exchange would not


Page 16 of 29

Page 17 of 29


341

effectively remove NSAIDs and other negatively charged pharmaceuticals. However, removal of

342

positively charged pharmaceuticals by a cation exchange resin, such as citalopram, may

343

experience less competition for ion-exchange sites on the resin. It is expected that because the

344

majority of endogenous metabolites are negatively charged, they will exhibit unfavorable

345

electrostatic interactions with the negatively charged surface of the cation exchange resin.53

346

Synthetic urine has been used in several urine source separation studies for nutrient

347

recovery and pharmaceutical removal. Tarpeh et al. 54 observed no significant difference between

348

ammonium adsorption by cliniptilolite zeolite, a polyacrylic cation exchange resin, or a

349

polystyrene cation exchange resin in synthetic and real urine. Minimal impact between urine

350

compositions may be because most endogenous metabolites are negatively charged or neutral in

351

urine, thus lack the necessary electrostatic interactions for cation exchange removal.25

352

Precipitation processes, such as struvite, are driven by supersaturation of the respective inorganic

353

compounds (e.g., Mg+2, PO4–3, and NH4+) which is dependent on their concentration in urine.55

354

During the nucleation step, organic compounds can adsorb to the crystals and inhibit further

355

precipitation.56, 57 The presence of endogenous metabolites in urine slightly reduced the amount

356

of struvite precipitated but decreased the rate of precipitation by a factor of 4.26, 55 Conversely,

357

Pronk et al. 58 found an increase in pharmaceutical retention during nanofiltration of real urine

358

compared with synthetic due to complexation of pharmaceuticals with endogenous metabolites,

359

changes in surface charge and/or membrane fouling due to endogenous metabolites. The varied

360

effect of endogenous metabolites on pharmaceutical removal and nutrient recovery processes

361

suggests that synthetic urine is not an adequate proxy for evaluating all urine source separation

362

processes. In general, the presence of endogenous metabolites appears to least impact nitrogen

363

cation exchange and slightly impact struvite precipitation and pharmaceutical removal by



364

membrane processes. However, favorable interactions between endogenous metabolites and

365

AER significantly impacted removal of negatively charged pharmaceuticals indicates that

366

synthetic urine is not an appropriate proxy for evaluating the efficacy of pharmaceutical removal.

367

The competitive effects of organic metabolites on ion-exchange removal of NSAIDs

368

highlights the need to evaluate alternative sorbents that have higher selectivity or capacity. The

369

AER used in this study is a commercially available material, however, sorbent material designed

370

to selectively remove target compounds may improve pharmaceutical removal in urine. For

371

example, molecularly imprinted polymers (MIPs) have been used extensively as extraction

372

methods for sample analysis,59 including selective extraction of naproxen in urine.60 Studies have

373

also shown that MIP adsorption may be used to selectively remove >90% of NSAIDs from

374

surface water.61 Alternatively, an adsorbent with much higher capacity and similar selectivity

375

would increase pharmaceutical removal in the presence of endogenous metabolites.

376

This study utilized a high-throughput in vitro bioassay to evaluate the ion-exchange

377

removal of NSAIDs in source separated urine and corresponding reduction in ecotoxicity

378

potential. Evaluating breakthrough curves as a function of ecotoxicity as opposed to C/C0

379

provides a better understanding of treatment objectives for emerging contaminants, such as

380

pharmaceuticals. For example, increasing mass removal of naproxen and O-desmethylnaproxen

381

did not necessarily reduce ecotoxicity potential due to the dose-response behavior. Furthermore,

382

human urine contains a mixture of endogenous metabolites that compete for ion-exchange sites

383

on the AER. More selective or higher capacity resins may improve the efficacy of using sorption

384

technologies to remove pharmaceuticals from urine.

385

Evaluating treatment efficacy in terms of COX-1 inhibition for the pharmaceutical

386

mixture synthesizes the concurrent relationships between varying pharmaceutical concentrations


Page 18 of 29

Page 19 of 29


387

in urine, pharmaceutical mixture ecotoxicity, and resin-pharmaceutical interactions. The

388

generalized concentration addition model may be used to predict COX-1 inhibition as a function

389

of BV treated for a pharmaceutical mixture with varying concentrations and mass removal

390

efficacy. Pharmaceutical metabolite instability (i.e., ketoprofen glucuronide hydrolysis) in urine

391

provides insight into the practical application and design of sorption technology in urine source

392

separation systems (e.g., treatment of fresh vs. ureolyzed urine). Due to a lack of regulatory

393

framework for pharmaceutical treatment guidelines, treatment efficacy for emerging

394

contaminants should include risk based approaches to ecotoxicity endpoints as well as mass

395

removal. This work established an approach to evaluate pharmaceutical adsorption in source

396

separated urine by combining kinetic and equilibrium tests, and high-throughput bioassays.

397

Utilizing kinetic and equilibrium tests to predict fixed-bed breakthrough is a rapid way to

398

generate data for various adsorbents and pharmaceuticals from diverse therapeutic classes.

399

Furthermore, high-throughput in vitro bioassays provide a unique opportunity to compare

400

treatment performance to various ecotoxicity endpoints.

401

SUPPORTING INFORMATION

402

Information regarding the experimental methods, and additional supporting tables and figures.

403

This material is available free of charge via the Internet at http://pubs.acs.org.

404

ACKNOWLEDGEMENTS

405

The authors would like to thank Dr. Nancy Denslow and Kevin Kroll at the University of Florida

406

for their insights into ecotoxicity testing, and Dr. Guenther Hochhaus at the University of Florida

407

for his assistance with sample analysis. This material is based upon work supported by the

408

National Science Foundation Graduate Research Fellowship under Grant No. DGE-1315138 and

409

the NSF CAREER grant CBET-1150790. Any opinions, findings, conclusions or



410

recommendations expressed in this publication are those of the authors and do not necessarily

411

reflect the views of NSF. This manuscript was improved by the thoughtful comments of three

412

anonymous reviewers.

413

REFERENCES

414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450

1. Lamichhane, K. M. Environmental consequences of adopting source separated sanitation system: First and third world perspectives. Ph.D. Dissertation, University of Hawai'i at Manoa, Ann Arbor, 2013. 2. Larsen, T.; Gujer, W., Separate management of anthropogenic nutrient solutions (human urine). Water Sci Technol 1996, 34, (3-4), 87-94. 3. Lienert, J.; Gudel, K.; Escher, B. I., Screening method for ecotoxicological hazard assessment of 42 pharmaceuticals considering human metabolism and excretory routes. Environ Sci Technol 2007, 41, (12), 4471-8. 4. Sawchuk, R. J.; Maloney, J. A.; Cartier, L. L.; Rackley, R. J.; Chan, K. K.; Lau, H. S., Analysis of diclofenac and four of its metabolites in human urine by HPLC. Pharm Res 1995, 12, (5), 756-62. 5. Houghton, G. W.; Dennis, M. J.; Rigler, E. D.; Parsons, R. L., Urinary pharmacokinetics of orally administered ketoprofen in man. Eur J Drug Metab Pharmacokinet 1984, 9, (3), 201-4. 6. Sugawara, Y.; Fujihara, M.; Miura, Y.; Hayashida, K.; Takahashi, T., Studies on the fate of naproxen. II. Metabolic fate in various animals and man. Chem Pharm Bull (Tokyo) 1978, 26, (11), 3312-21. 7. IMS Health Top 20 Therapeutic Classes, 2011; https://www.imshealth.com/en/aboutus/news/top-line-market-data, 01/2017. 8. Verlicchi, P.; Al Aukidy, M.; Zambello, E., Occurrence of pharmaceutical compounds in urban wastewater: removal, mass load and environmental risk after a secondary treatment--a review. Sci Total Environ 2012, 429, (0), 123-55. 9. Petrie, B.; Barden, R.; Kasprzyk-Hordern, B., A review on emerging contaminants in wastewaters and the environment: current knowledge, understudied areas and recommendations for future monitoring. Water Res 2015, 72, (0), 3-27. 10. Kirchmann, H.; Pettersson, S., Human urine - Chemical composition and fertilizer use efficiency. Fert Res 1995, 40, (2), 149-154. 11. Wilkinson, J. L.; Hooda, P. S.; Barker, J.; Barton, S.; Swinden, J., Ecotoxic pharmaceuticals, personal care products, and other emerging contaminants: A review of environmental, receptor-mediated, developmental, and epigenetic toxicity with discussion of proposed toxicity to humans. Crit Rev Environ Sci Technol 2015, 46, (4), 336-381. 12. Landry, K. A.; Sun, P.; Huang, C. H.; Boyer, T. H., Ion-exchange selectivity of diclofenac, ibuprofen, ketoprofen, and naproxen in ureolyzed human urine. Water Res 2015, 68, (0), 510-21. 13. Landry, K. A.; Boyer, T. H., Diclofenac removal in urine using strong-base anion exchange polymer resins. Water Res 2013, 47, (17), 6432-44. 14. Moser, P.; Sallmann, A.; Wiesenberg, I., Synthesis and quantitative structure-activity relationships of diclofenac analogs. J Med Chem 1990, 33, (9), 2358-2368.


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15. Upton, R. A.; Buskin, J. N.; Williams, R. L.; Holford, N. H.; Riegelman, S., Negligible excretion of unchanged ketoprofen, naproxen, and probenecid in urine. J Pharm Sci 1980, 69, (11), 1254-7. 16. Foo, K. Y.; Hameed, B. H., Insights into the modeling of adsorption isotherm systems. Chem Eng J 2010, 156, (1), 2-10. 17. Alberti, G.; Amendola, V.; Pesavento, M.; Biesuz, R., Beyond the synthesis of novel solid phases: Review on modelling of sorption phenomena. Coordin Chem Rev 2012, 256, (1-2), 28-45. 18. Silva, E.; Rajapakse, N.; Kortenkamp, A., Something from “Nothing” − Eight Weak Estrogenic Chemicals Combined at Concentrations below NOECs Produce Significant Mixture Effects. Environ Sci Technol 2002, 36, (8), 1751-1756. 19. Escher, B. I.; Allinson, M.; Altenburger, R.; Bain, P. A.; Balaguer, P.; Busch, W.; Crago, J.; Denslow, N. D.; Dopp, E.; Hilscherova, K.; Humpage, A. R.; Kumar, A.; Grimaldi, M.; Jayasinghe, B. S.; Jarosova, B.; Jia, A.; Makarov, S.; Maruya, K. A.; Medvedev, A.; Mehinto, A. C.; Mendez, J. E.; Poulsen, A.; Prochazka, E.; Richard, J.; Schifferli, A.; Schlenk, D.; Scholz, S.; Shiraishi, F.; Snyder, S.; Su, G.; Tang, J. Y.; van der Burg, B.; van der Linden, S. C.; Werner, I.; Westerheide, S. D.; Wong, C. K.; Yang, M.; Yeung, B. H.; Zhang, X.; Leusch, F. D., Benchmarking organic micropollutants in wastewater, recycled water and drinking water with in vitro bioassays. Environ Sci Technol 2014, 48, (3), 1940-56. 20. Escher, B. I.; Pronk, W.; Suter, M. J.; Maurer, M., Monitoring the removal efficiency of pharmaceuticals and hormones in different treatment processes of source-separated urine with bioassays. Environ Sci Technol 2006, 40, (16), 5095-101. 21. Julia, M. G.; Portier, C. J., The Forest for the Trees: A Systems Approach to Human Health Research. Environ Health Perspect 2007, 115, (9), 1261-1263. 22. Nishi, I.; Komuro, T.; Kawakami, T.; Onodera, S., In vitro cyclooxygenase inhibition assay for evaluating ecotoxicity of the surface water and domestic wastewater in the Tone Canal, Japan. Arch Environ Contam Toxicol 2010, 58, (3), 535-42. 23. Cha, Y. I.; Kim, S. H.; Solnica-Krezel, L.; Dubois, R. N., Cyclooxygenase-1 signaling is required for vascular tube formation during development. Dev Biol 2005, 282, (1), 274-83. 24. Lee, J.; Ji, K.; Lim Kho, Y.; Kim, P.; Choi, K., Chronic exposure to diclofenac on two freshwater cladocerans and Japanese medaka. Ecotoxicol Environ Saf 2011, 74, (5), 1216-25. 25. Bouatra, S.; Aziat, F.; Mandal, R.; Guo, A. C.; Wilson, M. R.; Knox, C.; Bjorndahl, T. C.; Krishnamurthy, R.; Saleem, F.; Liu, P.; Dame, Z. T.; Poelzer, J.; Huynh, J.; Yallou, F. S.; Psychogios, N.; Dong, E.; Bogumil, R.; Roehring, C.; Wishart, D. S., The human urine metabolome. Plos One 2013, 8, (9), e73076. 26. Udert, K. M.; Larsen, T. A.; Biebow, M.; Gujer, W., Urea hydrolysis and precipitation dynamics in a urine-collecting system. Water Res 2003, 37, (11), 2571-82. 27. Cardillo, G. Four parameters logistic regression - There and back again, 2012. 28. MathWorks MATLAB and Statistics Toolbox Release 2013b, 8.2.0.701; The MathWorks, Inc.: Natick, Massachusetts, 2013. 29. Howard, G. J.; Webster, T. F., Generalized concentration addition: a method for examining mixtures containing partial agonists. J Theor Biol 2009, 259, (3), 469-77. 30. Sperlich, A.; Schimmelpfennig, S.; Baumgarten, B.; Genz, A.; Amy, G.; Worch, E.; Jekel, M., Predicting anion breakthrough in granular ferric hydroxide (GFH) adsorption filters. Water Res 2008, 42, (8-9), 2073-82.



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31. Landry, K. A.; Boyer, T. H., Life cycle assessment and costing of urine source separation: Focus on nonsteroidal anti-inflammatory drug removal. Water Res 2016, 105, 487495. 32. Crittenden, J. C.; Trussell, R. R.; Hand, D. W.; Howe, K. J.; Tchobanoglous, G., MWH's Water Treatment: Principles and Design. 3rd Edition ed.; 2012. 33. Ahmad, R. T.; Nguyen, T. V.; Shim, W. G.; Vigneswaran, S.; Moon, H.; Kandasamy, J., Effluent organic matter removal by Purolite®A500PS: Experimental performance and mathematical model. Sep Purif Technol 2012, 98, 46-54. 34. Likozar, B.; Senica, D.; Pavko, A., Equilibrium and kinetics of vancomycin adsorption on polymeric adsorbent. AIChE J 2012, 58, (1), 99-106. 35. Zhang, Q.; Crittenden, J.; Hristovski, K.; Hand, D.; Westerhoff, P., User-oriented batch reactor solutions to the homogeneous surface diffusion model for different activated carbon dosages. Water Res 2009, 43, (7), 1859-66. 36. Koh, J.-H.; Wankat, P. C.; Wang, N. H. L., Pore and Surface Diffusion and Bulk-Phase Mass Transfer in Packed and Fluidized Beds. Ind Eng Chem Res 1998, 37, (1), 228-239. 37. Li, P.; SenGupta, A. K., Intraparticle Diffusion during Selective Sorption of Trace Contaminants: The Effect of Gel versus Macroporous Morphology. Environ Sci Technol 2000, 34, (24), 5193-5200. 38. Chu, K. H., Improved fixed bed models for metal biosorption. Chem Eng J 2004, 97, (23), 233-239. 39. Hand, D. W.; Crittenden, J. C.; Thacker, W. E., Simplified Models for Design of Fixed‐ Bed Adsorption Systems. J Environ Eng 1984, 110, (2), 440-456. 40. Crittenden, J. C.; Hand, D. W.; Arora, H.; Lykins, B. W., Design Considerations for Gac Treatment of Organic-Chemicals. J Am Water Works Assoc 1987, 79, (1), 74-82. 41. Faed, E. M., Properties of acyl glucuronides: implications for studies of the pharmacokinetics and metabolism of acidic drugs. Drug Metab Rev 1984, 15, (5-6), 1213-49. 42. Ishii, S. K.; Boyer, T. H., Life cycle comparison of centralized wastewater treatment and urine source separation with struvite precipitation: Focus on urine nutrient management. Water Res 2015, 79, 88-103. 43. U.S. EPA Toxicity Forecaster (ToxCast); http://epa.gov/ncct/toxcast/, 01/2017. 44. Liu, J.; Mansouri, K.; Judson, R. S.; Martin, M. T.; Hong, H.; Chen, M.; Xu, X.; Thomas, R. S.; Shah, I., Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. Chem Res Toxicol 2015, 28, (4), 738-51. 45. Fabbri, R.; Montagna, M.; Balbi, T.; Raffo, E.; Palumbo, F.; Canesi, L., Adaptation of the bivalve embryotoxicity assay for the high throughput screening of emerging contaminants in Mytilus galloprovincialis. Mar Environ Res 2014, 99, 1-8. 46. Memmert, U.; Peither, A.; Burri, R.; Weber, K.; Schmidt, T.; Sumpter, J. P.; Hartmann, A., Diclofenac: New data on chronic toxicity and bioconcentration in fish. Environ Toxicol Chem 2013, 32, (2), 442-52. 47. Kato, M.; Nishida, S.; Kitasato, H.; Sakata, N.; Kawai, S., Cyclooxygenase-1 and cyclooxygenase-2 selectivity of non-steroidal anti-inflammatory drugs: investigation using human peripheral monocytes. J Pharm Pharmacol 2001, 53, (12), 1679-1685. 48. Botting, R. M., Inhibitors of cyclooxygenases: mechanisms, selectivity and uses. J Physiol Pharmacol 2006, 57 Suppl 5, 113-24.


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49. Martin, M. T.; Knudsen, T. B.; Reif, D. M.; Houck, K. A.; Judson, R. S.; Kavlock, R. J.; Dix, D. J., Predictive model of rat reproductive toxicity from ToxCast high throughput screening. Biol Reprod 2011, 85, (2), 327-39. 50. Rotroff, D. M.; Martin, M. T.; Dix, D. J.; Filer, D. L.; Houck, K. A.; Knudsen, T. B.; Sipes, N. S.; Reif, D. M.; Xia, M.; Huang, R.; Judson, R. S., Predictive endocrine testing in the 21st century using in vitro assays of estrogen receptor signaling responses. Environ Sci Technol 2014, 48, (15), 8706-16. 51. Sipes, N. S.; Martin, M. T.; Reif, D. M.; Kleinstreuer, N. C.; Judson, R. S.; Singh, A. V.; Chandler, K. J.; Dix, D. J.; Kavlock, R. J.; Knudsen, T. B., Predictive models of prenatal developmental toxicity from ToxCast high-throughput screening data. Toxicol Sci 2011, 124, (1), 109-27. 52. Worch, E., Adsorption Technology in Water Treatment. 2012. 53. Solanki, A.; Boyer, T. H., Pharmaceutical removal in synthetic human urine using biochar. Environ Sci: Water Res Technol 2017, 3, (3), 553-565. 54. Tarpeh, W. A.; Udert, K. M.; Nelson, K. L., Comparing Ion Exchange Adsorbents for Nitrogen Recovery from Source-Separated Urine. Environ Sci Technol 2017, 51, (4), 2373-2381. 55. Udert, K. M.; Larsen, T. A.; Gujer, W., Estimating the precipitation potential in urinecollecting systems. Water Res 2003, 37, (11), 2667-77. 56. Lin, Y. P.; Singer, P. C.; Aiken, G. R., Inhibition of calcite precipitation by natural organic material: kinetics, mechanism, and thermodynamics. Environ Sci Technol 2005, 39, (17), 6420-8. 57. Sindelar, H. R.; Brown, M. T.; Boyer, T. H., Effects of natural organic matter on calcium and phosphorus co-precipitation. Chemosphere 2015, 138, 218-24. 58. Pronk, W.; Palmquist, H.; Biebow, M.; Boller, M., Nanofiltration for the separation of pharmaceuticals from nutrients in source-separated urine. Water Res 2006, 40, (7), 1405-12. 59. Beltran, A.; Borrull, F.; Marcé, R. M.; Cormack, P. A. G., Molecularly-imprinted polymers: useful sorbents for selective extractions. TrAC Trends in Analytical Chemistry 2010, 29, (11), 1363-1375. 60. Caro, E.; Marce, R. M.; Cormack, P. A.; Sherrington, D. C.; Borrull, F., A new molecularly imprinted polymer for the selective extraction of naproxen from urine samples by solid-phase extraction. J Chromatogr B Analyt Technol Biomed Life Sci 2004, 813, (1-2), 13743. 61. Dai, C.-M.; Zhang, J.; Zhang, Y.-L.; Zhou, X.-F.; Duan, Y.-P.; Liu, S.-G., Selective removal of acidic pharmaceuticals from contaminated lake water using multi-templates molecularly imprinted polymer. Chem Eng J 2012, 211-212, 302-309.

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TABLES Table 1. Estimated concentration and corresponding fraction of dose excreted in urine for the parent compound, selected metabolites, and parent compound and all metabolites. Pharmaceutical and metabolite concentrations were estimated based on excretion values in literature, and detected pharmaceutical loading in wastewater and urine as outlined in the SI. Compound Concentration in urine, µg/L (µmol/L) Fraction of dose excreted in urine Diclofenac 174±180 (0.547±0.567)a 0.06b 4’-OH-diclofenac 456 (1.46) 0.16b Parent and all metabolites (4.6) 0.51b a Ibuprofen 2,409±3,320 (10.6±14.5) 0.07b Hydroxy ibuprofen 5,697 (25.6) 0.17b Parent and all metabolites (157) 1.04b a Ketoprofen 342±386 (1.35±1.52) 0.064c Ketoprofen acyl glucuronide 4,777 (11.1) 0.528c Parent and all metabolites (12.4) 0.592c a Naproxen 758±676 (3.01±2.68) 0.013d O-Desmethylnaproxen 300 (1.39) 0.006d Parent and all metabolites (116) 0.50d a Pharmaceutical concentrations estimated in literature from Table S2, 𝑥𝑥̅ ± 𝑆𝑆𝑆𝑆 b Lienert, Gudel and Escher 3 c Houghton, Dennis, Rigler and Parsons 5 d Sugawara, Fujihara, Miura, Hayashida and Takahashi 6

582 583 584 585 586 587 588 589 590 591 592


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593


FIGURES 100%

100%

(a)

80%

80%

60%

60%

40%

40% C/C0 % Inhibition

20% 0%

(b)

C/C0 % Inhibition

20% 0%

0

100%

1000

2000 3000 Bed Volume

4000

0

100%

(c) C/C0 % Inhibition

80%

400 600 Bed Volume

60%

40%

40%

20%

20%

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1000

(d) C/C0 % Inhibition

80%

60%

0%

200

0% 0

200

400 600 Bed Volume

800

1000

0

1000 2000 Bed Volume

3000

Figure 1. Column breakthrough curves for (a) diclofenac (C0 = 0.55 µmol/L), (b) ketoprofen (C0 = 1.3 µmol/L), (c) naproxen (C0 = 3.0 µmol/L), and (d) O-desmethylnaproxen (C0 = 1.4 µmol/L) removal by Dowex 22 AER in synthetic ureolyzed urine. The treated effluent is shown as the normalized effluent concentration (C/C0) (solid line) predicted by the HSDM or transformed to % COX-1 inhibition (dashed line) using the individual compound dose-response curves. 594 595 596 597



Mix Hill GCA GCA, KTP-gluc hydrolyzed

% COX-1 Inhibition

100% 80% 60% 40% 20% 0% -20%

10–2 100 102 Concentration, µmol/L

104

Figure 2. COX-1 inhibition curve for a pharmaceutical mixture containing diclofenac, ketoprofen, ketoprofen glucuronide, naproxen, and o-desmethylnaproxen. The solid line represents the 3-parametric Hill equation, the dashed line represents the GCA model for the pharmaceutical mixture, the dotted line represents the GCA model for the pharmaceutical mixture assuming ketoprofen glucuronide completely hydrolyzed back to ketoprofen. The symbols are the mean triplicate samples with error bars showing one standard deviation. The red vertical and horizontal lines correspond to the expected concentration of the pharmaceutical mixture in urine and corresponding COX-1 inhibition according to the dose-response curve, GCA model, and GCA model assuming ketoprofen glucurondie hydrolyzed back to ketoprofen. 598 599 600 601 602 603 604 605 25 ACS Paragon Plus Environment

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(a)

100%

(b)

100%

80%

80%

60%

60% % Inhibition

40%

C/C0

20%

% Inhibition

40%

C/C0

20%

0%

0% 0

1000 2000 Bed Volumes

3000

0

1000 2000 Bed Volumes

3000

Figure 3. Column breakthrough curves for a pharmaceutical mixture containing (a) diclofenac (C0 = 0.55 µmol/L), ketoprofen (C0 = 1.3 µmol/L), naproxen (C0 = 3.0 µmol/L), and Odesmethylnaproxen (C0 = 1.4 µmol/L), and (b) diclofenac ((C0 = 0.55 µmol/L), ketoprofen (C0 = 12.4 µmol/L), naproxen (C0 = 3.0 µmol/L), and O-desmethylnaproxen (C0 = 1.4 µmol/L) removed by Dowex 22 AER in synthetic ureolyzed urine. In figure (b), ketoprofen glucuronide was assumed to be hydrolyzed back to ketoprofen. The mass removal curve is a summation of the molar mass removal normalized by the total molar concentration. The treated effluent is shown as the normalized effluent concentration (C/C0) (solid line) of the pharmaceutical mixture or transformed to % COX-1 inhibition (dashed line) using the generalized concentration addition model. 606 607 608 609 610 611 612 26 ACS Paragon Plus Environment


0.8

No metabolites, C0 = 3.0 µM (b) 1 Metabolites, C0 = 2.2 µM Real urine, C0 = 0.71 µM 0.8

0.6

0.6

C/C0

C/C0

(a) 1

0.4

0.4

0.2

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0 0

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No metabolites, C0 = 3.6 µM Metabolites, C0 = 3.2 µM

0

10

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No metabolites, C0 = 7.8 µM (d) 1 Metabolites, C0 = 3.0 µM 0.8

0.6

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C/C0

(c) 1

4 6 Resin dose, mL/L

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0.4

0.4

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0

2


8

10

No metabolites, C0 = 7.5 µM Metabolites, C0 = 3.7 µM

0 0

2


8

10

0

2


8

10

Figure 4. Ion-exchange removal in synthetic urine with and without metabolites of (a) diclofenac, (b) ibuprofen, (c) ketoprofen, and (d) naproxen, and real human urine (DCF only). Data without metabolites (i.e., triangle data points) reproduced from Landry et al.12 The symbols are the mean triplicate samples with error bars showing one standard deviation. 613 614 615 616 617 618 619 27 ACS Paragon Plus Environment

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Figure 5. Mass of endogenous metabolites (TOC) removed (mg C) during equilibrium experiments using Dowex 22 AER for synthetic urine with metabolites and real urine. The sample volume was 125 mL. The bars are the mean triplicate samples with error bars showing one standard deviation. 620 621 622 623 624 625 626 627 628 629 630 631 632 28 ACS Paragon Plus Environment

Fixed Bed Modeling of Nonsteroidal Anti-Inflammatory Drug Removal

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