Antibiotics in the WWTP environment Heike Schmitt, Andrew C. Singer.

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

mC

eph

alo

spo

rin

Mac

rolid

e

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

me

Ce

fota

xim

e

Am

oxi

cilli

n

Ery

thro

myc

in

Cla

rith

rom

ycin

Le

voflo

xaci

n

Cla

vula

na

te

Mo

xiflo

xaci

n

Do

xycy

clin

e

Ta

mifl

u

Za

na

miv

ir

Do

se

(m

g d

-1)

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

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

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




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)


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

Antibiotics in the WWTP environment Heike Schmitt, Andrew C. Singer.

Documents

Transcript of Antibiotics in the WWTP environment Heike Schmitt, Andrew C. Singer.