Supplementary information

Supplementary Information

Large-scale exploration and analysis of drug combinations

Peng Li

§

, Chao Huang

§

, Yingxue Fu

§

, Jinan Wang

§

, Ziyin Wu, Jinlong Ru, Chunli Zheng, Zihu

Guo, Xuetong Chen, Wei Zhou, Wenjuan Zhang, Yan Li, Jianxin Chen , Aiping Lu, Yonghua

Wang*

§

Contributing equally to this work

*Corresponding author: Yonghua Wang; E-mail: yh_wang@nwsuaf.edu.cn

Supplementary Figures ........................................................................................................................... 1

Supplementary Figure 1 ........................................................................................................................................... 1

Supplementary Figure 2. .......................................................................................................................................... 1

Supplementary Figure 3 ........................................................................................................................................... 2

Supplementary Tables ............................................................................................................................. 2

Supplementary Table 1 ............................................................................................................................................. 2









Supplementary Table 10 ......................................................................................................................................... 10

Supplementary Table 11 ......................................................................................................................................... 11

Supplementary Text ............................................................................................................................... 11

Materials ................................................................................................................................................................. 11

Database building and evaluation .......................................................................................................................... 11

Disease distribution analysis .................................................................................................................................. 12

Association of diseases for drug combinations ...................................................................................................... 13

In vitro experimental validation ............................................................................................................................. 13

References .............................................................................................................................................................. 16

Supplementary Figures

Supplementary Figure 1. The drug –drug similarity between components of each drug combination. The drug

–drug similarity of known effective drug combinations were separately measured for six molecular/pharmacological descriptors, i.e., (a) Anatomical Therapeutic and

Chemical (ATC) classification system, (b) chemical structure, (c) target protein sequence, (d) distance in protein-protein interaction (PPI) network, (e) Gene Ontology (GO) and (f) drug side effects. The distribution of similarities for all the six descriptors are shifted toward higher values compared with that of random control ( P < 0.01). For instance, the distribution of ATC similarity shows that 70% of combinations belong to the same therapeutic category (from the first to fifth level of the ATC code) compared with 20% expected by chance ( P ≪ 0.01), indicating that drugs in combinations often have therapeutic effects in common. The most obvious example is the combination of anticancer drugs.

Supplementary Figure 2.

Database evaluation.

Comparison of the (a) chemical diversity and

(b) disease diversity of PreDC (blue line) with DrugBank (green line).

1

Supplementary Figure 3. The red line corresponds to the disease similarity between our prediction results and the benchmark datasets (mean = 0.56). The green line corresponds to random similarity distribution (mean = 0.16).

Supplementary Tables

Supplementary Table 1 . The Pearson correlation coefficients of six features between each other effective drug combinations (EDCs).

PCC (P value) Structure ATC Side Effect GO PPI Sequence

Structure

1 0.48 0.32 0.4 0.25 0.35

ATC

0.48 1 0.23 0.39 0.18 0.38

Side Effect

0.32 0.23 1 0.16 0.12 0.06

GO

0.4 0.39 0.16 1 0.29 0.44

PPI

0.25 0.18 0.12 0.29 1 0.4

Sequence

Structure

0.35 0.38 0.06 0.44 0.4 1

Supplementary Table 2 . The Pearson correlation coefficients of six features between each other undesirable drug-drug interactions (UDDIs).

PCC (P value) Structure ATC Side Effect GO PPI Sequence

1 0.33 0.15 0.3 0.24 0.29

ATC

0.33 1 0.1 0.35 0.23 0.36

Side Effect

0.15 0.1 1 0.04 0.16 0.05

GO

0.3 0.35 0.04 1 0.37 0.51

2

PPI

0.24 0.23 0.16 0.37 1 0.43

Sequence

0.29 0.36 0.05 0.51 0.43 1

Supplementary Table 3 .

Performance of PEA model by dividing drugs into ‘new drugs’ and ‘known drugs’.

AUC of

(ROC)

Both known drugs One known and one new drug

Both new drugs

EDCs

UDDIs

0.88

0.92

0.836

0.86

0.76

0.71

Supplementary Table 4 . Distribution of experimentally validated drug combinations in the binary diagram

Quadrant 1' 1 2 4

Anti-infection 11 (8) 27 (23)

Anti-cancer 33 (27) 48 (34)

Total 44 (35) 75 (57)

1 (1)

1 (0)

2 (1)

19 (14)

6 (5)

25 (19)

Supplementary Table 5 . Benchmark datasets of drug combinations derived from the literature

Supplementary Table 5 can be downloaded as additional excel fileat our website http://sm.nwsuaf.edu.cn/lsp/predc.php.

3

Supplementary Table 6 . The full list of the verified proportion and q-values between or within drug classes

Drug class Drug class

Number of all possible drug combinations

Number of predicted drug combinations

Number of drug combinations in gold-standard dataset

Number of drug combinations in benchmark dataset a b

Verified proportion

P -value q-value

(<0.05)

L01B

D06A

D06A

D10A

D10A

H02A

L01B

M01A

R03D

G01A

D07A

A01A

J05A

D07X

D10A

H02A

L01B

D10A

J01D

A07A

L01C

A01A

R01A

L01C

H02A

J01X

M01A

R03A

S02A

L01D

R05D

R06A

S03B

R03A

R03A

J05A

R03A

L04A

R03A

L04A

G01A

J01X

J01X

L01X

J01X

R03A

72

143

90

105

52

80

111

117

64

93

216

96

336

122

387

167

135

300

48

180

64

194

153

12

16

10

12

15

28

13

25

14

10

14

10

14

18

20

11

22

45

15

10

15

12

14

2

1

3

1

3

2

7

12

2

6

3

1

11

9

44

6

5

16

0

2

0

5

2

6

7

13

6

6

8

5

13

7

5

7

5

8

10

11

6

12

29

9

6

9

7

8

0.52

0.50

0.50

0.50

0.50

0.50

0.50

0.50

0.50

0.47

0.46

0.46

0.64

0.60

0.60

0.60

0.58

0.57

0.57

0.56

0.55

0.55

0.55

1.0E-11 7.1E-09

3.7E-03 2.4E-02

9.0E-06 5.8E-04

5.5E-04 8.3E-03

1.1E-05 6.4E-04

7.3E-06 5.8E-04

9.5E-07 1.1E-04

4.8E-04 7.3E-03

4.7E-05 1.6E-03

1.5E-04 3.5E-03

3.4E-05 1.4E-03

2.7E-03 2.0E-02

6.3E-03 3.1E-02

7.7E-03 3.4E-02

8.2E-06 5.8E-04

8.9E-04 1.1E-02

6.0E-03 3.0E-02

4.1E-05 1.5E-03

1.2E-02 4.1E-02

6.0E-04 8.4E-03

1.7E-02 4.8E-02

8.0E-03 3.4E-02

4.5E-04 7.1E-03

4

D10A

R01A

R03B

D07X

G01A

A01A

J01F

S01B

D10A

J01C

J01G

L01B

A07E

D10A

L01D

A07E

A01A

R03A

G01A

A01A

D07A

L01X

A01A

A01A

M02A

R01A

C05A

A01A

A02B

D06A

J01G

R03C

S03A

G01A

H02A

S02A

S01A

S03A

S01A

J01X

S02A

L01X

S03A

M02A

L01X

R03A

G01A

S01B

S02B

S03A

R03C

L01X

D10A

J02A

N02A

S03A

G01A

A07A

R06A

S01B

96

101

56

78

104

120

177

96

357

216

69

559

120

135

130

148

210

96

109

60

903

167

151

64

120

473

64

167

96

65

10

10

15

23

23

13

13

16

27

11

11

102

14

17

23

29

21

21

12

12

58

15

10

11

11

33

16

23

21

19

5

3

3

6

2

2

2

0

4

7

4

1

37

6

1

3

5

1

2

6

0

55

2

7

2

1

18

2

4

0

2

9

9

5

5

4

4

6

6

10

4

4

37

10

7

7

5

6

8

5

5

24

6

4

5

5

15

7

10

9

8

1.2E-02 4.3E-02

5.6E-04 8.3E-03

1.5E-08 3.6E-06

1.4E-02 4.5E-02

6.3E-05 1.8E-03

6.5E-04 8.5E-03

1.8E-02 4.9E-02

9.7E-03 3.7E-02

9.0E-03 3.6E-02

2.6E-10 9.2E-08

2.2E-04 4.5E-03

7.5E-03 3.4E-02

6.6E-03 3.2E-02

5.6E-03 3.0E-02

1.5E-02 4.5E-02

1.8E-02 4.9E-02

3.3E-03 2.3E-02

9.6E-03 3.7E-02

4.0E-04 6.7E-03

1.1E-02 4.0E-02

2.0E-06 2.0E-04

1.2E-03 1.2E-02

1.8E-02 4.9E-02

1.3E-07 2.3E-05

9.8E-03 3.7E-02

8.5E-04 1.1E-02

1.3E-03 1.2E-02

2.7E-03 2.0E-02

7.5E-05 1.9E-03

1.4E-02 4.4E-02

0.40

0.40

0.40

0.39

0.39

0.38

0.38

0.38

0.37

0.36

0.36

0.36

0.36

0.35

0.35

0.34

0.33

0.33

0.45

0.45

0.45

0.44

0.43

0.43

0.42

0.42

0.42

0.41

0.40

0.40

D11A

S01A

S01A

C05A

A01A

S01A

A07A

D07X

D10A

D11A

H02A

R01A

S01B

S02A

S03A

R03A

D06A

S03B

G01A

S01A

N03A

R01A

S01A

S02A

180

252

102

240

153

71

234

204

80

109

120

137

50

10

10

50

20

12

18

18

19

16

29

13

6

2

0

6

1

0

6

4

1

3

4

4

15

3

3

15

6

4

6

6

6

5

9

4

0.33

0.33

0.33

0.32

0.31

0.31

0.31

0.30

0.30

0.30

0.30

0.30

1.3E-02 4.4E-02

6.1E-04 8.4E-03

5.8E-04 8.4E-03

1.7E-02 4.8E-02

1.3E-02 4.3E-02

1.0E-02 3.8E-02

1.2E-02 4.1E-02

2.6E-03 2.0E-02

2.0E-03 1.7E-02

9.1E-03 3.6E-02

1.8E-04 3.9E-03

1.2E-03 1.2E-02

6

Supplementary Table 7 . Combination effects of predicted drug pairs on the growth of the human non-small cell lung cancer A549 cells.

Drug

Combinations

IMA + DOX

GEF + DOX

5FU + DOX

EPI + SUN

IMA + TET

CIS + TET

DOX + EPI

FLU + ETO

EPI + CAR

TET + DOX

DOC + FLU

EPI + SOR

DOX + CAR

ETO + CAR

EPI + IMA

CAR + FLU

BOR + 5FU

TET + BOR

5FU + FLU

GEF + 5FU

BOR + CAR

5FU + IMA

5FU + CAR

5FU + ETO

5FU + SUN

5FU + EPI

BOR + EPI

FLU + TET

DOX + SUN

ETO + DOX

EPI + CIS

CIS + SOR

BOR + FLU

DOX + FLU

GEF + SOR

FLU + CIS

EPI + FLU

CAR + SUN

ETO + SOR

FLU + SOR

FLU + VIN

VIN + CIS

CI Values at:

IC50 IC75 IC90 IC95

0.331 0.141 0.065 0.040

0.351 0.152 0.086 0.063

0.544 0.264 0.130 0.081

0.502 0.301 0.188 0.140

0.386 0.250 0.217 0.214

0.626 0.309 0.194 0.164

0.481 0.370 0.315 0.296

0.669 0.453 0.336 0.287

0.679 0.473 0.366 0.324

0.672 0.461 0.392 0.380

0.663 0.495 0.452 0.451

0.817 0.604 0.451 0.371

0.774 0.596 0.458 0.383

0.448 0.486 0.528 0.558

0.558 0.538 0.524 0.517

0.542 0.541 0.540 0.540

0.640 0.592 0.551 0.526

0.782 0.630 0.550 0.518

0.830 0.683 0.563 0.494

0.453 0.530 0.622 0.695

0.693 0.689 0.686 0.683

0.640 0.674 0.711 0.738

0.473 0.572 0.718 0.851

0.926 0.805 0.702 0.641

0.698 0.701 0.750 0.809

0.991 0.887 0.795 0.739

0.821 0.803 0.808 0.824

0.952 0.885 0.823 0.784

0.856 0.847 0.839 0.834

1.222 0.993 0.812 0.711

1.434 1.044 0.786 0.658

0.805 0.815 0.850 0.885

0.997 0.897 0.850 0.842

0.947 0.903 0.888 0.889

1.000 0.924 0.898 0.900

1.175 1.025 0.904 0.833

1.113 0.991 0.911 0.873

1.167 1.027 0.913 0.848

1.813 1.125 0.881 0.777

0.427 0.676 1.087 1.517

1.472 1.268 1.096 0.995

1.928 1.414 1.077 0.913

0.985 0.845

0.988 0

0.950 0.001

0.946 0.030

0.323 0.381

0.914 0.290

0.976 0.002

0.985 0.002

0.831 0

0.972 0.347

0.983 0.001

0.987 0.030

0.946 0.241

0.865 0

0.978 0.568

0.984 0

0.965 0

0.959 0.260

0.967 0

0.971 0

0.849 0

0.984 0.044

0.967 0

0.970 0

0.992 0

0.832 0

0.989 0

0.976 0.001

0.992 0.296

0.988 0.001

0.987 0.008

0.994 0.450

0.982 0.001

0.976 0.008

0.999 0

0.985 0.032

0.976 0.001

0.994 0.008

0.985 0.008

0.988 0.010

0.827 0.440

0.935 0.720

1'

1

4

1

1'

4

1

1'

1'

4

1

1'

1'

1

1'

1'

1'

1'

1

1'

1'

1'

1'

4

4

1'

1'

1'

1

1'

1'

1'

1'

1'

1

1'

1'

1'

1'

1'

4

1

Weighted

Average CI

Values

0.721

0.758

0.811

0.815

0.833

0.840

0.849

0.851

0.540

0.558

0.577

0.586

0.616

0.686

0.708

0.717

0.373

0.402

0.429

0.481

0.486

0.487

0.524

0.527

0.097

0.117

0.178

0.223

0.239

0.248

0.335

0.852

0.871

0.897

0.914

0.927

0.932

0.935

0.981

1.111

1.127

1.164

P

1

P

2

Quadrant

7

ETO + SUN

BOR + IMA

PAC + VIN

SOR + IMA

FLU + SUN

EPI + VIN

GEF + VIN

DOC + VIN

PAC + FLU

VIN + SOR

DOC + SOR

GEF + FLU

DOC + PAC

0.751 0.980 1.279 1.533

0.832 0.725 1.153 2.076

2.719 1.913 1.395 1.146

2.640 2.020 1.558 1.313

0.905 1.362 2.255 3.301

1.885 2.114 2.396 2.624

1.154 1.729 2.697 3.740

3.039 2.789 2.713 2.729

1.322 1.868 2.734 3.604

0.829 1.537 2.855 4.352

1.664 2.235 3.010 3.691

2.707 2.996 3.377 3.695

1.489 3.100 6.671 11.386

1.268

1.405

1.532

1.661

2.360

2.380

2.766

2.767

2.768

2.988

2.993

3.361

7.325

0.983 0.008

0.988 0.487

0.984 0.440

0.997 0.754

0.985 0.007

0.938 0.394

0.980 0.654

0.984 0.482

0.983 0

0.921 0.957

1.000 0.403

0.985 0.001

0.957 0

Supplementary Table 8 . Combination effects of predicted drug pairs on the growth of E. coli.

Drug

Combinations

CI Values at:

IC50 IC75 IC90 IC95

IMI + TRI

CIS + TRI

1.008 0.843 0.711 0.590

1.240 0.953 0.758 0.661

STR + TRI 1.176 0.963 0.792 0.695

CEF + ERY 1.138 0.962 0.823 0.697

CEF + AMO 0.982 0.899 0.827 0.782

ERY + SUL 1.004 0.930 0.861 0.818

TET + RIF 0.962 0.932 0.932 0.882

PEF + SUL 1.117 1.003 0.922 0.881

PEF + AMO 1.034 0.985 0.943 0.917

IMI + PEF 1.069 1.010 0.956 0.920

PEF + ERY 1.114 1.023 0.953 0.915

IMI + AMO 1.280 1.113 0.968 0.880

RIF + SUL 1.175 1.081 1.012 0.977

CIS + PEF 1.014 1.021 1.033 1.042

TET + PEF 1.122 1.081 1.049 1.031

IMI + SUL 1.354 1.167 1.025 0.948

CEF + STR 1.262 1.149 1.047 0.983

CEF + TRI 1.645 1.260 1.006 0.887

IMI + STR 1.095 1.095 1.095 1.096

TET + TRI 1.108 1.100 1.105 1.116

IMI + CEF 1.496 1.287 1.110 0.937

TET + CEF 0.991 1.052 1.122 1.175

PEF + STR 1.241 1.181 1.124 1.087

TET + SUL 1.354 1.228 1.115 1.046

TET + ERY 1.248 1.171 1.128 1.112

TET + AMO 1.174 1.161 1.148 1.139

ERY + TRI 1.385 1.253 1.136 1.064

RIF + TRI 1.291 1.221 1.159 1.119

STR + SUL 0.994 1.110 1.266 1.398

Weighted

Average CI

Values

0.964

0.968

0.993

1.028

1.032

1.055

1.056

1.063

1.073

1.095

0.718

0.806

0.826

0.832

0.839

0.872

0.915

0.941

0.950

1.109

1.115

1.116

1.133

1.134

1.142

1.149

1.155

1.169

1.260

P

1

P

2

Quadrant

0.761 0.001

0.892 0.001

0.836 0

0.997 0.504

0.990 0.001

0.934 0.062

1.000 0.918

0.876 0.011

1.000 0.118

0.989 0.012

0.939 0.364

0.999 0.122

0.572 0.286

0.837 0.016

0.970 0.475

0.561 0.004

0.997 0.162

0.677 0

0.958 0.457

0.935 0

0.975 0.006

0.986 0.737

0.996 0.020

0.745 0.045

0.935 0.002

0.973 0.756

0.939 0.002

0.736 0.187

0.636 0

1

4

1

4

1

1'

1

1

4

4

1'

1'

2

4

1

4

4

4

1

1'

1

1'

4

4

1'

1'

1

1

4

8

1'

2

1

1'

1'

1

1'

1

1

1

1'

1

1

TET + STR 1.394 1.342 1.294 1.263

AMO + SUL 1.221 1.250 1.310 1.367

RIF + IMI 1.291 1.305 1.321 1.332

ERY + STR 1.500 1.410 1.351 1.324

PEF + TRI 1.425 1.385 1.361 1.352

CEF + SUL 1.717 1.560 1.467 1.429

AMO + TRI 1.494 1.537 1.600 1.657

RIF + PEF 1.817 1.699 1.595 1.532

IMI + ERY 1.474 1.599 1.774 1.923

RIF + STR 1.265 1.528 1.847 2.100

RIF + AMO 1.399 1.684 2.027 2.300

RIF + CEF 1.655 1.887 2.171 2.402

AMO + STR 1.944 2.088 2.247 2.365

TET + IMI 2.094 2.335 2.605 2.808

ERY + AMO 2.242 2.461 2.783 3.316

1.301

1.312

1.319

1.367

1.369

1.495

1.600

1.613

1.769

1.826

2.005

2.155

2.232

2.581

2.878

0.945 0

0.610 0.012

0.783 0.351

0.924 0

0.984 0

0.628 0.003

0.650 0

0.730 0.481

0.995 0.532

0.987 0.730

0.681 0.350

0.725 0.351

0.994 0.208

0.991 0.808

0.996 0.412

4

1

1

4

4

1

1

1

1'

4

4

1'

1'

4

4

Supplementary Table 9 . Combination effects of predicted drug pairs on the growth of S. aureus.

Drug

Combinations

CI Values at:

IC50 IC75 IC90

TET + RIF 0.595 0.461

RIF + STR 0.385 0.412

RIF + TRI

IMI + TRI

0.543

0.561

0.460

0.538

PEF + AMO 0.551 0.595

RIF + IMI 0.722 0.677

PEF + STR 0.569 0.612

TET + ERY 0.693 0.672

IMI + AMO 0.625 0.634

RIF + PEF 0.577 0.629

RIF + AMO 0.642 0.664

TET + IMI 0.874 0.765

RIF + SUL 0.844 0.702

CEF + STR 0.663 0.703

TET + STR 0.537 0.640

TET + CEF 0.864 0.800

AMO + STR 0.677 0.725

CEF + TRI 0.669 0.716

IMI + CEF 1.032 0.940

IMI + SUL

EPI + TRI

EPI + TET

1.220

1.418

0.884

1.026

1.095

0.891

IMI + ERY 1.453 1.113

TET + AMO 0.662 0.777

EPI + CEF 1.151 1.058

TET + TRI 0.697 0.797

CEF + SUL 0.833 0.924

IC95

0.727

0.716

0.643

0.748

0.785

0.864

0.725

0.820

0.830

0.805

0.308

0.463

0.426

0.521

0.682

0.607

0.694

0.646

0.691

0.772

0.714

0.924

0.713

1.037

0.926

1.172

1.169

0.686

0.692

0.685

0.696

0.749

0.764

0.751

0.779

0.778

0.857

0.361

0.442

0.426

0.524

0.644

0.634

0.660

0.655

0.661

0.865

0.848

0.908

0.854

0.920

0.976

0.983

1.056

Weighted

Average CI

Values

0.680

0.691

0.703

0.733

0.746

0.757

0.762

0.774

0.776

0.870

0.383

0.439

0.444

0.529

0.640

0.641

0.655

0.659

0.664

0.895

0.901

0.909

0.909

0.912

0.990

0.993

1.053

P

1

P

2

1.000 0.918

0.987 0.730

0.736 0.187

0.761 0.001

1.000 0.118

0.783 0.351

0.996 0.020

0.935 0.002

0.999 0.122

0.730 0.481

0.681 0.350

0.991 0.808

0.572 0.286

0.997 0.162

0.945 0

0.986 0.737

0.994 0.208

0.677 0.000

0.975 0.006

0.561 0.004

0.855 0

0.943 0.511

0.995 0.532

0.973 0.756

0.930 0.000

0.935 0.000

0.628 0.003

Quadrant

1'

1

1

4

1'

4

4

1

4

1

1

4

1

1'

1

2

1

4

4

4

4

1

1

1

1'

1'

4

9

TET + PEF 0.996 1.005

IMI + PEF 1.102 1.121

ERY + AMO 1.242 1.177

AMO + TRI 0.812 0.969

ERY + TRI 1.168 1.211

ERY + SUL 1.452 1.381

PEF + ERY 1.466 1.469

TET + SUL 0.910 1.090

CEF + AMO 0.700 1.081

STR + SUL 1.580 1.494

IMI + STR 1.044 1.378

STR + TRI 2.187 2.040

RIF + CEF 1.116 1.561

PEF + TRI 2.038 2.262

ERY + STR 1.867 2.969

2.185

2.561

4.781

PEF + SUL 8.239 7.438 6.890

AMO + SUL 3.595 10.705 32.737

CEF + ERY 1.372 2.597 4.916

1.056

1.144

1.146

1.174

1.302

1.444

1.488

1.478

1.684

1.692

1.819

1.916

1.112

1.162

1.141

1.346

1.397

1.555

1.509

1.901

2.288

1.945

2.199

1.843

2.746

2.806

6.652

6.588

70.656

7.586

1.062

1.142

1.160

1.166

1.308

1.477

1.490

1.513

1.707

1.742

1.805

1.939

2.178

2.547

4.876

7.014

40.584

5.166

0.970 0.475

0.989 0.012

0.996 0.412

0.650 0

0.939 0.002

0.934 0.062

0.939 0.364

0.745 0.045

0.990 0.001

0.636 0

0.958 0.457

0.836 0

0.725 0.351

0.984 0

0.924 0

0.876 0.011

0.610 0.012

0.997 0.504

Supplementary Table 10 . Table list of all drugs used in this study, their abbreviation, therapeutic class and mechanism of action.

Drug Name Abbreviation Class Mechanism of action

4

1'

4

1

4

1

1'

1

4

1'

1'

1

4

4

1

4

1'

1'

Docetaxel

Paclitaxel

Cisplatin

Carboplatin

Fludarabine

Epirubicin

Etoposide

DOC

PAC

CIS

CAR

FLU

EPI

ETO

Antimicrotubule agent

Antimicrotubule agent

Cross-linking reagent

Cross-linking reagent

DNA synthesis inhibitor

Inhibits the microtubule network

Inhibits the microtubule network

Inhibits DNA synthesis

Inhibits DNA synthesis

Inhibits DNA polymerase alpha, ribonucleotide reductase and DNA primase

Inhibits activity of topoisomerase II and

DNA helicase

Inhibits DNA topoisomerase II

Doxorubicin

5-Fluorouracil

Bortezomib

Gefitinib

Sorafenib

Imatinib

Sunitinib

Vinblastine

Ceftazidime

DOX

5FU

BOR

GEF

SOR

IMA

SUN

VIN

CEF

Nucleic acid and protein synthesis inhibitor

Nucleic acid synthesis inhibitor



Proteasome inhibitor

Protein kinase inhibitor




Tubulin modulator

Inhibits DNA topoisomerase II

Inhibits the thymidylate synthetase

Inhibits the 26S proteasome

Inhibits EGFR tyrosine kinase

Interacts with multiple intracellular and cell surface kinases

Inhibits the Bcr-Abl tyrosine kinase

Inhibits multiple RTKs binds to the microtubular proteins of the mitotic spindle

Inhibits penicillin binding protein 3

Imipenem IMI

Cell wall synthesis inhibitor

Cell wall synthesis Inhibits pencillin binding proteins

10

Amoxicillin

Trimethoprim

Sulfamethoxazole SUL

Pefloxacin

Rifampin

Tetracycline

Erythromycin

Streptomycin

AMO

TRI

PEF

RIF

TET

ERY

STR inhibitor

Cell wall synthesis inhibitor

Folic acid synthesis

Inhibits penicillin-binding protein 1A

Inhibits dihydrofolate reductase inhibitor

Folic acid synthesis inhibitor


Inhibits dihydrofolate synthetase

Inhibits activity of DNA gyrase and topoisomerase IV


Inhibits RNA polymerase activity

Protein synthesis inhibitor Inhibits binding of aminoacyl t-RNA

Protein synthesis inhibitor Inhibits translocation of peptidyl t-RNA

Protein synthesis inhibitor Inhibits binding of formylmethionyl t-RNA

Supplementary Table 11 . In vitro experimental validation

Model Effective Ineffective Prediction accuracy

Anti-bacterial ( S. Aureus, E. coli ) 38 9 0.81 (38/47)

Anti-cancer (A549)

Total

39

77

16

25

0.71 (39/55)

0.75

Supplementary Text

Materials

The 2D chemical structures of the drugs were downloaded in Mol file format from DrugBank (Knox, et al.,

2011). Drug targets were obtained from the DCDB (Liu, et al., 2010) and DrugBank databases. Drug side effects were downloaded from the SIDER database (Kuhn, et al., 2010). Human protein-protein interaction

(PPI) data were assembled from multiple sources. including Biomolecular Interaction Network Database (BIND)

(Bader, et al., 2003), Biological General Repository for Interaction Dataset (BioGRID) (Stark, et al., 2006),

Molecular INTeraction database (MINT) (Zanzoni, et al., 2002), Mammalian Protein-Protein Interaction

Database (Pagel, et al., 2005), Protein –Protein Interaction Database for PDZ-domains (PDZBase) (Beuming, et al., 2005) and Reactome (Vastrik, et al., 2007) databases. Protein sequences and Gene Ontology (GO) annotations (Ashburner, et al., 2000) were parsed from Uniprot (Jain, et al., 2009). Anatomical Therapeutic

Chemical (ATC) codes of drugs were extracted from DrugBank (Knox, et al., 2011).

Database building and evaluation

To systematically identify effective drug pairs, we combined drug combination data from multiple sources

(Jia, et al., 2009; Liu, et al., 2010; Zhu, et al., 2012), resulting 1571 known effective drug combinations (EDCs).

Importantly, we included information about drug side effects, target proteins and associated diseases, as this

11

information is often critical for determining whether a drug combination is practical or not. In addition, we also

collected 1536 undesirable drug-drug interactions (UDDIs) from DrugBank version 3(Knox, et al., 2011). The

UDDIs are defined as those interactions in which two drugs can interact to cause an adverse effect, such as the increase of toxicity or the decrease of effect. To our knowledge, PreDC database is currently the world's largest database of drug combinations either approved by FDA or validated by in vitro or in vivo experiments.

To evaluate the database quality, the chemical and disease diversities were calculated and further

compared with those of DrugBank, the largest drug database(Knox, et al., 2011). It is shown that the coverage

of chemical space of PreDC database is not statistically significantly different from Drugbank (Supplementary

Figure 2a, p = 0, Kolmogorov –Smirnov test), which indicates that the PreDC database is sufficiently large enough to cover diverse chemical spaces. In addition, the radar chart analysis shows that the diseases in

PreDC concentrate on four major classes: i.e., C04 - neoplasms, C10 - nervous system diseases, C01 - bacterial infections and mycoses, and C14 - cardiovascular diseases (Supplementary Figure 2b, blue line), which are very similar to Drugbank (Supplementary Figure 2b, green line). All these indicate that PreDC database is chemically and pharmacologically diverse, so as to ensure the generation of predictive models with reliable performance and generalization ability.

Disease distribution analysis

Figure 3a shows the disease distribution changing with P

1

and P

2

. The results show that, with the increasing of P

2

, there's a clear downward trend for the numbers of the four major groups of diseases, corresponding to the numbers of drug combinations. Importantly, when P

2

is in a small range of 0.0~0.2, the anti-cancer drug combination gets the most votes that has demonstrated therapeutic benefit in clinical trials (about 68% in all anti-cancer combinations), followed by cardiovascular disease (52%), infections (37%) and nervous system disease (39%). While in the range of 0.8~1.0, the cancer treatment is the lowest in number (5%), and the infection disease treatment (22%) makes up the greatest percentage of all combinations. It is known that most

drugs in cancer therapeutics have strong cytotoxicity(Chari, 2007), which may increase the risk of producing

severe side effects when combined together (large P

2

). In this context, the identification of drugs which show weak negative interaction with a specific drug with strong cytotoxicity has been a high priority. One means to achieve this is to combine them with drugs of mild lethality such as the anti-infections drugs, although their P

2 is possibly high (with high tendency of producing adverse effects). It is found that nearly 77% of the diseases are distributed in the range of 0.8~1 of P

1

, which is consistent with the fact that the majority of existing drug combinations are located in the regions with high P

1

value. All these demonstrate that the two quantitative standards provided by PEA can clearly distinguish the effective drug combinations and undesirable drug-drug interactions.

12

Association of diseases for drug combinations

For novel drug combinations, we inferred their indications based on their similarity to the known drug combinations and the known drug combination –disease associations. The drug combination which has the largest LR value to the query drug pair is regarded as a reference and its indications are then assigned to the novel drug combination.

Looking specifically at the 3269 optimal drug combinations, we acquire 3614 drug combination –disease associations, among which the neoplasms (C04) are the most common class of diseases that are related to

712 drug pairs (21.8% of all drug pairs), followed by the bacterial infections and mycoses (C01) (648, 19.8%), nervous system diseases (C10) (428, 13.1%) and cardiovascular diseases (C14) (353, 10.8%) (Figure 4c).

This is in good agreement with the interest of current combination therapies for complex and chronic

diseases(Woodcock, et al., 2011).

To validate these new associations, we used an independent benchmark dataset as mentioned above to compute the disease similarity between our predictions and corresponding diseases derived from literature.

We first mapped all diseases to the relevant Medical Subject Heading (MeSH) concepts manually, and then

computed the semantic similarities of MeSH concepts using the Lin semantic similarity measure(Lin, 1998) in

Semantic Measures Library and ToolKit (SML-Toolkit)(Harispe, et al., 2014). We used 10

4 random same-size set of diseases to generate a background distribution for comparison. The significance of difference was computed using a Wilcoxon ranked sum test, which is equivalent to the Mann-Whitney U-test.

Compared to random similarity distribution (mean = 0.16), we found a much more significant similarity

(mean = 0.56) between our predictions and the benchmark datasets ( p = 8.6×10 -61 , Wilcoxon ranked sum test,

Supplementary Figure 3). In addition, the present experiment also shows that 81% predicted antibacterial drug pairs are effective against S. aureus or E. coli models, and 71% predicted to treat cancer are effective against the A549 cells. These results suggest that it is a feasible way to explore the indications of a novel drug pair by the extended PEA model. The full list of predicted indications for the 3269 optimal combinations is available in the PreDC database (http://sm.nwsuaf.edu.cn/lsp/predc.php).

In vitro experimental validation

15 antitumor drugs were obtained from Shanghai Biochempartner Co., Ltd. (Shanghai, China). 10 antibiotics were purchased from Nanjing Zelang Medical Technology Co., Ltd. (Nanjing, China). Supplementary Table 10 lists the detailed information for all 25 drugs including their therapeutic class and mode of action. Antibiotics stock solutions were stored in the dark at -20

℃

, and were thawed and diluted in sterilized broth for experimental use. All antitumor drugs were freshly prepared when used. All experiments were conducted with

13

the standard reference strains Gram-negative Escherichia coli ( E. coli ) ATCC 25922 and Gram-positive

Staphylococcus aureus ( S. aureus ) ATCC 29213 (for antibacterial experiments) and Non-small cell lung cancer (NSCLC) line A549/ATCC (for antitumor experiment). S. aureus , E. coli and A549 cell lines were kindly provided by Prof. Jinyou Duan from NWSUAF, Yangling, China.

The A549 cell lines were grown as monolayer cultures in RPMI medium 1640 (Hyclone) supplemented with

10% FBS, 100 U/mL penicillin, and 100 μg/mL streptomycin at 37°C in 5% CO2/95% air, and were harvested with trypsin/EDTA when they were in the logarithmic phase of growth. Cytotoxic effects of drugs on cells were determined by the MTT assay. Briefly, 100 μL cells were plated at a density of 2000-4000 cells per well in

96-well plates. Following 24h incubation, cells were treated with a serial fivefold dilution of drug in growth medium to give 8 concentrations and cultivated for 72h. After incubation for specified times at 37°C in a humidified incubator, 20 μL of MTT (5 mg∕mL in PBS) were added to each well, and cells were incubated for a fu rther 4 h. After removal of the medium, 100 μL DMSO was added to each well. The absorbance was recorded on a microplate reader (DNM-9602, Beijing Pulang new technology Co., LTD. Beijing, China) at the wavelength of 490 nm. Each experiment was performed in sextuplicate for each drug concentration and was independently performed 2 or 3 times.

S. aureus/E. coli cells were inoculated into trypticase-soy broth (TSB) and incubated to exponential growth at 37

℃

. The growth density was adjusted to match an optical density at 600 nm (OD

600

) of about 0.1 (1×10 8 cfu/ml). Afterwards, a 1:200 dilution was prepared in a fresh TSB and used as the inoculum (≈5×10 5 cfu/ml).

We then transferred 180 μL cells plus medium to 96-well plates and to each well we added 20 μL of drugs with the desired concentrations. In the presence of the drugs, we grew the cells for 6 h at 37°C. Antimicrobial activity was determined by measuring the absorbance at 600 nm using a microplate reader (DNM-9602,

Beijing Pulang new technology Co., LTD. Beijing, China).

Median IC

50

values of each drug were first obtained using the antitumor and antibacterial assays as described above. Next, each drug was added at a 1:1 ratio based on their IC

50

values to create a mixture of combination drugs. Each experiment was performed in sextuplicate for each drug concentration and was independently performed 2 or 3 times. Combination effects were evaluated by identifying the combination

index (CI) as described by Chou and Talalay(Chou and TaLaLay, 1981).

CI



(

D

D x

)

1

1



(

D

D x

)

2

2

(1)

In the denominator, ( D x

)

1 and ( D x

)

2 are the concentration of two agents that individually achieve x % inhibition of a system. In the numerators, ( D )

1

+( D )

2

“in combination” also inhibit x %. CI < 1, = 1, and > 1 indicate the synergistic, additive, or antagonistic effect, respectively. In this analysis, because the high degrees of effects

14

are more important to the chemotherapy than the low degrees of effects, the weighted CI value was designed as CI wt

= (CI

50

+ 2CI

75

+ 3CI

90

+ 4CI

95

)/10 to quantify the combination effects. For infection and cancer models, the additive and synergistic drug combinations are thought to be therapeutically effective on diseases.

In total, we examined 102 novel predicted drug pairs, resulting in the confirmation of 77 effective combinations (~75% of all tested drug pairs) (Supplementary Table 11).

In the cancer model, we examined 55 predicted drug pairs against the human non-small cell lung cancer

A549 cells. Resultantly, 39 of these pairs are found effective (~71% accuracy), among which 34 cases are synergistic and 5 are additive (Supplementary Table 7 and 11). The drug pair with the greatest synergy is bortezomib + 5-fluorouracil (quadrant 1'; CI = 0.097), followed by bortezomib + tetracycline (quadrant 1; CI =

0.12), 5-fluorouracil + Fludarabine (quadrant 1'; CI = 0.18), Gefitinib + 5-fluorouracil (quadrant 1'; CI = 0.22), etc. These potent combinations are demonstrated promising for treatment of the lung cancer. More interestingly, we find that some drugs, so called promiscuous drugs, are inherently more likely to participate in the synergistic combinations. One example is epirubicin, which partakes in the effective combination with all the tested drugs (except vinblastine) against cancer cells. Indeed, intrinsic promiscuity of combinational drugs

may be the dominant factor in drug synergy(Cokol, et al., 2011).

For anti-bacterial model, 47 drug pairs were tested by S. aureus and E. coli . We validate that 38 pairs (~81% of all 47 antibacterial pairs) are effective (Supplementary Table 8, 9 and 11). Among them, 9 drug pairs exhibit activities (synergy or additivity) against both bacterial species. Examples include imipenem + trimethoprim

(quadrant 4; CI = 0.53 for S. aureus and 0.72 for E. coli ) and tetracycline + rifampin (quadrant 2; CI = 0.38 for S. aureus and 0.92 for E. coli ). These combinations may be used as antibacterial combinations with a broad spectrum of antimicrobial activity. Remaining combinations exert effects exclusively on one particular species:

S. aureus and E. coli are uniquely susceptible to 17 and 8 drug pairs, respectively. For example, S. aureus exhibits unique synergistic susceptibilities to rifampin in combination with streptomycin (quadrant 1; CI = 0.43) and trimethoprim (quadrant 4; CI = 0.44), while E. coli . is susceptible to the combinations of streptomycin and trimethoprim (quadrant 4; CI = 0.83) and of ceftazidime and erythromycin (quadrant 1; CI = 0.83). These drug pairs can only treat either Gram-positive or Gram-negative infections.

It should be noted that these in vitro assessments do not consider the potential adverse effects of the drug combinations, especially those drug pairs with high P

2

values, which must be evaluated through further appropriate clinical trials. For example, the combination of rifampin and tetracycline has been proved to be active against S. aureus (quadrant 2; CI = 0.44) in our antimicrobial assays, but its concomitant therapy might be restricted because rifampin, which, as a potent inducer of the hepatic microsomal system, might reduce the serum concentrations of tetracycline in vivo .

Nevertheless, these results confirm that our systematic

15

computational screening has sufficient accuracy and sensitivity to provide a wealth of novel hypotheses that can drive discovery of drug combinations.

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19

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