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