Antibiotics in the WWTP environment

Heike Schmitt, Andrew C. Singer

Swine Flu: Netherlands

How to model antibiotics at a sewage treatment plant and watershed during a pandemic?

• Which antibiotics would be used during a pandemic?

• How much is excreted in the active form?

Amoxicillin

Doxycycline

Moxifloxacin

Clarithromycin

Levofloxacin

Erythromycin

Cefotaxime

Clavulanic acid

Cefuroxime

β-l

acta

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alo

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rin

Mac

rolid

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Tetracycline

Qu

ino

lon

e

How much will be given to a patient?

Lim (2007) Thorax

Antivirals

Severely sick

Moderately sick

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000C

efu

roxi

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Am

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Cla

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CURB 0-2

CURB 3-5

How to model the ecotoxicological effects?

• Bacteria form the functional unit of sewage works and are key for ecosystem services in the river (and greater environment).

• Bacteria are also the target organisms of antibiotics

bacterial toxicity investigated

• Very little information on sensitivity of sewage sludge bacteria

use of MIC values of human pathogens as surrogate (EUCAST database, sensitive wild-types)

How to deal with MIC values? – amoxicillin

0.002 0.004 0.008 0.016 0.032 0.064 0.125 0.25 0.5 1 2 4 8 16 32Campylobacter coli 0 0 0 0 0 0 0 7 37 120 204 170 153 7 10Campylobacter jejuni 0 0 0 0 0 0 0 1 1 17 27 89 135 19 40Citrobacter spp 0 0 0 0 0 0 0 0 0 1 1 3 5 20 10Enterobacter aerogenes 0 0 0 0 0 0 0 0 0 0 1 0 1 3 4Enterobacter agglomerans 0 0 0 0 0 0 0 0 0 1 3 38 23 12 9Enterobacter cloacae 0 0 0 0 0 0 0 0 0 1 2 10 9 19 48Enterobacter dissolvens 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Enterobacter spp 0 0 0 0 0 0 0 0 0 4 3 17 30 1 5Enterococcus faecium 0 0 0 0 0 16 55 148 292 258 993 453 28 7 14Escherichia coli 0 0 0 0 0 0 0 0 9 96 737 1670 669 42 11Haemophilus influenzae 0 0 0 0 9 36 296 2614 6522 1593 717 279 275 430 550Haemophilus parainfluenzae 0 0 0 0 0 0 63 148 133 21 8 3 5 7 28Klebsiella oxytoca 0 0 0 0 0 0 0 0 0 1 0 1 0 6 15Klebsiella pneumoniae 0 0 0 0 0 0 0 0 0 1 2 4 1 2 45Legionella pneumophila 0 0 0 0 0 0 1 3 18 20 25 16 12 2 3Mannheimia haemolytica 0 0 0 3 1 8 22 26 3 0 0 0 3 9 24Moraxella catarrhalis 2 0 21 67 10 15 29 5 15 25 38 100 225 318 267Morganella morganii 0 0 0 0 0 0 0 0 2 1 3 1 2 5 8Pasteurella multocida 0 0 0 5 10 23 38 25 2 0 0 1 1 1 0Proteus mirabilis 0 0 0 0 0 0 0 2 93 17 2 0 0 1 10Salmonella spp 0 0 0 0 0 0 0 6 2418 5791 207 6 4 2 14Serratia liquefaciens 0 0 0 0 0 0 0 0 0 0 1 2 6 6 4Serratia spp 0 0 0 0 0 0 0 0 0 0 1 5 8 9 7Streptococcus agalactiae 0 0 0 9 54 228 15 0 0 0 0 0 0 0 0Streptococcus anginosus 0 0 0 0 1 8 10 4 0 0 0 0 0 0 0Streptococcus anginosus 0 0 0 2 3 17 17 4 0 1 0 0 0 0 0Streptococcus bovis 0 0 0 0 3 21 14 1 1 0 0 0 0 0 0Streptococcus gordonii 0 0 0 0 0 9 1 0 0 0 0 0 1 0 0Streptococcus group G 0 0 32 58 3 1 0 0 0 0 0 0 0 0 0Streptococcus intermedius 0 0 0 0 2 7 8 0 0 0 0 0 0 0 0Streptococcus mitis 0 0 0 0 15 5 2 4 0 0 0 0 0 0 0Streptococcus mutans 0 0 0 0 1 3 1 1 0 0 0 0 0 0 0Streptococcus oralis 0 0 4 41 62 26 23 12 9 6 5 5 0 3 0Streptococcus parasanguis 0 0 0 0 3 1 4 3 7 2 1 4 1 1 0Streptococcus pneumoniae 2 150 1471 2528 272 122 118 53 117 289 47 14 5 0 0Streptococcus pyogenes 0 3 130 224 7 6 0 0 0 0 0 0 0 0 0Streptococcus salivarius 0 0 0 0 4 2 0 0 0 0 1 0 0 0 0Streptococcus sanguis 0 0 0 0 1 2 11 9 9 2 2 0 1 0 0Streptococcus vestibularis 0 0 0 0 1 5 2 3 2 0 0 1 0 0 0Streptococcus viridans 0 0 4 26 54 81 81 38 21 17 10 2 0 1 0

Breakpoints (mg/L)

• In total, 8 antibiotics, 21-100 species per antibiotic, >1 mio MIC values

Species sensitivity distributions

• Show percent of species that is affected at a given concentration

• Potentially affected fraction (PAF)

• N. van Straalen, T. Traas, L. Postuma, T. Aldenberg

Complications..

• We have data on many different strains of one bacterium per antibiotic

0.002 0.004 0.008 0.016 0.032 0.064 0.125 0.25 0.5 1 2 4 8 16 32Campylobacter coli 0 0 0 0 0 0 0 7 37 120 204 170 153 7 10

Breakpoints (mg/L)

Evaluate different ways of setting up the SSD

• Cautious: the most sensitive strain (5% percentile)

• Easy: median of all MICs per strain

• Most precise: use the whole distribution of values

Complications II..

• All is easy if sensitivities are normally distributed

• Is this the case? No..

Evaluate different ways of curve fitting

• Normal distribution

• Weibull / logistic curve fit

Final model: PAF!

• Whole distribution of MIC per species

• curve fit

How to model the ecotoxicological effects of many antibiotics simultaneously?

• All 8 antibiotics are present at the same time

• Do they act independently or jointly?

• Independently: drinking alcohole and getting a flower pot on your head

• Jointly: drinking beer and whine

Apply models for mixture toxicity

Mixture toxicity models

• Calculate joint action based on either of two models

• Or of a combination of the models

• Erythromycin, clarithromycin: macrolides (joint action)

→ msPAF!

ssPAF)(=msPAF 11

msPAF = toxREF * Σ ( conc / toxsubstance)

Results: Sewage Works Toxicity

• Maximum toxicities: 20-30% PAF at R0=2.3

Results: Sewage Works Toxicity

• Maximum toxicities: 20-30% PAF at R0=2.3

Sewage Works Toxicity - parameter influence

• Toxicity model parameters: add 10% variation in toxicity

Sewage Works Toxicity – background antibiotics use

• Normal antibiotic use yields quite some predicted toxicity

• Reasons: difficult...• Bioavailability• Bacteria ‘used’ to

antibiotics• Sensitivity of WWTP

bacteria lower

Sewage Works Toxicity – background antibiotics use

• Total toxicity of pandemic and background increases background toxicity by 0.1-16%

Toxicity to river stretches

Toxicity to river stretches

Comparison with existing experimental data

• For shortest-term toxicity, PAF matches experimental data

compound PAF [%] Toxicity parameter

erythromycin 0.1 22 10-35

erythromycin 1 56 50-62

Concen-tration [mg/L]

Size of effect measured [%]

Reduction in live bacteria in mixed liquor samples after 20-45 minutes

Reduction in live bacteria in mixed liquor samples after 25-45 minutes

Comparison with existing experimental data II

• For short-term toxicity, PAF matches experimental data

compound PAF [%] Toxicity parameter

erythromycin 0.004 0.3 13 / 31 / 0

erythromycin 0.1 22

erythromycin 0.1 22

erythromycin 0.5 46 62 / 44 / 29

erythromycin 1 56

erythromycin 1 56

erythromycin 5 75 32

erythromycin 10 80 46

erythromycin 10 80

erythromycin 10 80

Concen-tration [mg/L]

Size of effect measured [%]

Batch reactors with activated sludge fed raw waste water: decreased NH

4 reduction

after 40 / 65 / 90 h55 / 90 (activated sludge from two different STP)

Batch reactors with activated sludge fed raw waste water: inhibition of specific N-NH4 evolution rate after 4 h

6 / 89 (activated sludge from two different STP)

Batch reactors with activated sludge fed raw waste water: Inhibition of the specific COD evolution rate after 4 hBatch reactors with activated sludge fed raw waste water: decreased nitrification (nitrate evolution) after 40 / 65 / 90 h

36 / 92 (activated sludge from two different STP)

Batch reactors with activated sludge fed raw waste water: inhibition of specific N-NH4 evolution rate after 4 h

51 / 70 (activated sludge from two different STP)

Batch reactors with activated sludge fed raw waste water: Inhibition of the specific COD evolution rate after 4 hBatch reactors with activated sludge fed raw waste water: Inhibition of the initial ammonia uptake rate over 24 hBatch reactors with activated sludge fed raw waste water: Inhibition of nitrification over 48 h

79 (standard deviation: 34)

Batch reactors with activated sludge fed raw waste water: Inhibition of the specific COD evolution rate after 4 h

40 (standard deviation: 25)

Batch reactors with activated sludge fed raw waste water: inhibition of specific N-NH4 evolution rate after 4 h

Louvet 2010 Process Biochem, Env Poll

Comparison with existing experimental data III

• 100 ug/L erythromycin (PAF: 22%) in sequencing batch reactors fed synthetic wastewater for 180 days: no effects

• But: up to 80% decreased functional diversity (ammonium oxidizing bacteria, nitrite oxidizing bacteria)

• Also: still effects with acclimated sludge in short-term tests with higher concentrations

Fan 2009 Appl Microbiol Biotechnol

Comparison with existing experimental data IV

• Limnic bacterial communities (protein synthesis)

• EC50: around PAF doxycycline of 8-19%

Brosche 2010 ET&C

And what about antibiotic resistance?

• WWTP are already a hot-spot for antibiotic resistance (and its transfer)

• Antimicrobial treatment during pandemics will most likely lead to increased influx of resistant bacteria from human effluent

• Do antibiotic residues in WWTP also favour resistance maintenance or resistance transfer?

Schlüter 2008 J Biotech

Experimental evidence

• Resistance in WWTP of pharmaceutical production plants

• Concentrations: comparable (penicillin G – PAF of amoxicillin: 34%) / much higher (oxytetracycline)

• Highest MICs observed for the class of antibiotics produced

• Also: resistance to unrelated groups

Li 2009 Env Microbiol

General Conclusions: Ecotoxicity

• Low viral infectivity (Ro=1.65, R0=1.9): no ecotoxicity risk

• Medium viral infectivity (Ro=2.3) 20-30% inhibition of sewage works bacterial species,

• ~40% of river stretches with toxicities between 5 to 30%, when secondary infection rates is 15%.

• Effects under “shock conditions”?

• What if limnic communities are affected?

Resistance: General Conclusions

• Increase in resistance likely

• Human exposure to water-borne resistance?

What Next

• Experimental work to assess vulnerability of sewage works to pandemic quantities of pharmaceuticals needed

• Assess resistance development under shock concentrations

of antibiotics