• Non ci sono risultati.

Ecotoxicity interspecies QAAR models from daphnia toxicity of Pharmaceuticals and Personal Care Products

N/A
N/A
Protected

Academic year: 2021

Condividi "Ecotoxicity interspecies QAAR models from daphnia toxicity of Pharmaceuticals and Personal Care Products "

Copied!
14
0
0

Testo completo

(1)

Supporting information of

Ecotoxicity interspecies QAAR models from daphnia toxicity of Pharmaceuticals and Personal Care Products

Alessandro Sangion* and Paola Gramatica

QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese,

Italy

ͲŵĂŝů͗ĂůĞƐƐĂŶоƌŽ͘ƐĂŶŐŝŽŶΛƵŶŝŶƐƵďƌŝĂ͘ŝƚ͖ŚƚƚƉ͗ͬͬǁǁǁ͘ƋƐĂƌ͘ŝƚ͖dĞů͗нϯϵͲϬϯϯϮͲϰϮϭϱϳϯ



SI-1

Content of Sheets:

Case 1: D.magna-O.mykiss: Type, CAS, SMILES, Experimental Toxicities, Predicted response (Simple Linear Regression with/without outliers and Multiple Linear Regression), Hat, Molecular descriptors and Splitting schemes

Case 2: D.manga-P.promelas: Type, CAS, SMILES, Experimental Toxicities, Predicted response (Simple Linear Regression with/without outliers and Multiple Linear Regression), Hat, Molecular descriptors and Splitting schemes

Case 3: P.promelas-O.mykiss: Type, CAS, SMILES, Experimental Toxicities, Predicted response (Simple Linear Regression with/without outliers and Multiple Linear Regression), Hat, Molecular descriptors and Splitting schemes

Case 4: O.mykiss-P.promelas: Type, CAS, SMILES, Experimental Toxicities, Predicted response (Simple Linear Regression with/without outliers and Multiple Linear Regression) , Hat, Molecular descriptors and Splitting schemes



Available at: http://dx.doi.org/10.1080/1062936X.2016.1233139

(2)

6833257,1*,1)250$7,212)

(FRWR[LFLW\LQWHUVSHFLHV4$$5PRGHOVIURPGDSKQLDWR[LFLW\RI

3KDUPDFHXWLFDOVDQG3HUVRQDO&DUH3URGXFWV

$OHVVDQGUR6DQJLRQ DQG3DROD*UDPDWLFD

46$55HVHDUFK8QLWLQ(QYLURQPHQWDO&KHPLVWU\DQG(FRWR[LFRORJ\'HSDUWPHQWRI7KHRUHWLFDODQG

$SSOLHG6FLHQFHV8QLYHUVLW\RI,QVXEULD9DUHVH,WDO\

(PDLODOHVVDQGURVDQJLRQ#XQLQVXEULDLWKWWSZZZTVDULW7HO

6,

&RQWHQWV

1. 6LPSOH5HJUHVVLRQV

2. 0RGHOVJUDSKVDQGHTXDWLRQV

2.1. &DVH 'DSKQLDPDJQD2QFRUK\QFKXVP\NLVV 2.2. &DVH 'DSKQLDPDJQD3LPHSKDOHVSURPHODV 2.3. &DVH 3LPHSKDOHVSURPHODV±2QFRUK\QFKXVP\NLVV 2.4. &DVH 2QFRUK\QFKXVP\NLVV±3LPHSKDOHVSURPHODV

(3)

1. 6LPSOH5HJUHVVLRQV

)LJXUH66LPSOHUHJUHVVLRQIRUWKHFDVHV

(4)

2. 0RGHOVJUDSKVDQGHTXDWLRQV

2.1. &DVH 'DSKQLDPDJQD±2QFRUK\QFKXVP\NLVV

26WVSOLWWLQJ

 

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH26WPRGHO

pLC50 (96h)O.mykiss OSt = 1.29 + 0.79pEC50D.magna -1.38GATS1e

25HVSOLWWLQJ



)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH25HPRGHO

(5)

pLC50 (96h)O.mykiss ORe = 1.40 + 0.74pEC50D.magna -1.5GATS1e

5QGVSOLWWLQJ

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH5QGPRGHO

pLC50 (96h)O.mykiss Rnd= 1.74 + 0.78pEC50D.magna -1.8GATS1e

)XOOPRGHO

 

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH)XOOPRGHO

pLC50 (96h)O.mykiss = 1.14 + 0.8pEC50D.magna -1.21GATS1e

2.2. &DVH 'DSKQLDPDJQD±3LPHSKDOHVSURPHODV

(6)

26WVSOLWWLQJ

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW  IRUWKH&DVH26WPRGHO

pLC50 (96h)P.promelas OSt= -0.41 + 0.91pEC50D.magna 0.002ATS4s

25HVSOLWWLQJ

 

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH25HPRGHO

pLC50 (96h)P.promelas ORe= -0.36 + 0.85pEC50D.magna 0.003ATS4s

(7)

5QGVSOLWWLQJ



)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH5QGPRGHO

pLC50 (96h)P.promelas Rnd= -0.39 + 0.9pEC50D.magna 0.002ATS4s

)XOOPRGHO

 

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH)XOOPRGHO

pLC50 (96h)P.promelas = -0.33 + 0.88pEC50D.magna 0.002ATS4s

(8)

2.3. &DVH 3LPHSKDOHVSURPHODV±2QFRUK\QFKXVP\NLVV

26WVSOLWWLQJ



)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH26WPRGHO

pLC50 (96h)O.mykiss = -2.86 + 0.85pEC50P.promelas + 0.06AATSC0v

25HVSOLWWLQJ

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH25HPRGHO

pLC50 (96h)O.mykiss = -1.86 + 0.89pEC50P.promelas + 0.04AATSC0v

(9)

5QGVSOLWWLQJ



)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH5QGPRGHO

pLC50 (96h)O.mykiss = -2.85 + 0.88pEC50P.promelas + 0.06AATSC0v

)XOOPRGHO

 

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH)XOOPRGHO

pLC50 (96h)O.mykiss = -2.21 + 0.88pEC50P.promelas + 0.05AATSC0v

(10)

2.4. &DVH 2QFRUK\QFKXVP\NLVV±3LPHSKDOHVSURPHODV

26WVSOLWWLQJ

 

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH26WPRGHO

pLC50 (96h)P.promelas OSt = -0.22 + 0.9pEC50O.mykiss + 0.38AATS7p

5QGVSOLWWLQJ

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH5QGPRGHO

pLC50 (96h)P.promelas Rnd = -2.8 + 0.9pEC50O.mykiss + 0.5AATS7p

(11)

25HVSOLWWLQJ

 

)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH25HPRGHO

pLC50 (96h)P.promelas ORe = -0.38 + 0.96pEC50O.mykiss + 0.51AATS7p

)XOOPRGHO



)LJXUH6*UDSKRIH[SHULPHQWDOYVSUHGLFWHGYDOXHV RQWKHOHIW DQG:LOOLDPV3ORW RQWKHULJKW IRUWKH

&DVH)XOOPRGHO

pLC50 (96h)P.promelas = -0.18 + 0.9pEC50O.mykiss + 0.4AATS7p

(12)



(13)























 

Paper II

Paper IV

(14)





Riferimenti

Documenti correlati

[r]

From the above results, we see that the iterative LR approach is better than both the classical LR models and the Box-Jenkins model in both fitting and forecasting.. 3.2

12-4 PREDICTION OF NEW OBSERVATIONS 12-5 MODEL ADEQUACY CHECKING 12-5.1 Residual Analysis 12-5.2 Influential Observations 12-6 ASPECTS OF MULTIPLE REGRESSION MODELING 12-6.1

Keywords: RND efflux pumps, multidrug transporter, Pseudomonas aeruginosa, antibiotic resistance, molecular dynamics, molecular

Proposal to the stores The store-level budget is a simple measure of the gap between the current stock levels and the future expected sales potential, it gives store managers

The aim of this work is to provide the broadband (550-1350 nm) characterization of elastin absorption, as a first step towards the in vivo quantification in biological tissues

An analysis of few collapse case studies like that one of Annone overpass, or that one on SP10 crossing the highway A14 near Ancona, as well as a discussion on the

Conversely, if the problem of justice is meant in the sense of optimizing the limited resources of transplant- ation, then the choice may not be unsound: the patient is not