药学学报

Technology	Particle structure	Sample	MD/%	PVP K30/%	NH₄HCO₃/%	Nozzle diameter/mm
/	Non-porous MD	MD	10	/	/	2.0
MD-P0	10	/	/	2.0
MD-P0'	10	/	/	2.0
Template agent method	Porous MD	MD-P1	10	1	/	2.0
MD-P3	10	3	/	2.0
MD-P5	10	5	/	2.0
Pore-forming agent	Porous MD	MD-N1	10	/	1	2.0
MD-N3	10	/	3	2.0
MD-N5	10	/	5	2.0
MD-N5 (1.0)	10	/	5	1.0
MD-N5 (1.5)	10	/	5	1.5

Technology

Particle structure

Sample

MD/%

PVP K30/%

NH₄HCO₃/%

Nozzle diameter/mm

Non-porous MD

2.0

MD-P0

2.0

MD-P0'

2.0

Template agent method

Porous MD

MD-P1

2.0

MD-P3

2.0

MD-P5

2.0

Pore-forming agent

Porous MD

MD-N1

2.0

MD-N3

2.0

MD-N5

2.0

MD-N5 (1.0)

1.0

MD-N5 (1.5)

1.5

Technology	Particle structure	Sample	MD/%	PVP K30/%	NH₄HCO₃/%	Nozzle diameter/mm
/	Non-porous MD	MD	10	/	/	2.0
MD-P0	10	/	/	2.0
MD-P0'	10	/	/	2.0
Template agent method	Porous MD	MD-P1	10	1	/	2.0
MD-P3	10	3	/	2.0
MD-P5	10	5	/	2.0
Pore-forming agent	Porous MD	MD-N1	10	/	1	2.0
MD-N3	10	/	3	2.0
MD-N5	10	/	5	2.0
MD-N5 (1.0)	10	/	5	1.0
MD-N5 (1.5)	10	/	5	1.5

Technology

Particle structure

Sample

MD/%

PVP K30/%

NH₄HCO₃/%

Nozzle diameter/mm

Non-porous MD

2.0

MD-P0

2.0

MD-P0'

2.0

Template agent method

Porous MD

MD-P1

2.0

MD-P3

2.0

MD-P5

2.0

Pore-forming agent

Porous MD

MD-N1

2.0

MD-N3

2.0

MD-N5

2.0

MD-N5 (1.0)

1.0

MD-N5 (1.5)

1.5

Sample	Yield /%	MC/%	AR/°	d_0.5/μm	Span	Uniformity	CI	HR	ρ_b/g·mL^-1	ρ_t/g·mL^-1	ρ_ture/g·mL^-1	SA-BET /m²·g^-1	SA-BJH/ m²·g^-1	PV /cm³·g^-1	PD/nm
MD	/	0.62 ± 0.03	43.02 ± 0.7	49.678 ± 0.31	1.441 ± 0.003	0.442 ± 0.002	15.33 ± 1.15	1.18 ± 0.02	0.49 ± 0.00	0.58 ± 0.01	1.578 1 ± 0.001 1	0.101 3	0.019 0	0.000 371	2.094 5
MD-P0	77.0	0.43 ± 0.03	47.89 ± 0.6	5.433 ± 0.20	1.382 ± 0.010	0.429 ± 0.003	37.33 ± 1.16	1.60 ± 0.03	0.30 ± 0.01	0.48 ± 0.01	1.482 2 ± 0.002 1	1.1353	0.339 2	0.003 258	12.002 9
MD-P0'	77.0	0.52 ± 0.03	45.00 ± 0.0	5.762 ± 0.04	1.453 ± 0.034	0.476 ± 0.051	36.67 ± 1.15	1.58 ± 0.03	0.32 ± 0.00	0.50 ± 0.00	1.489 6 ± 0.002 3	1.187 6	0.477 0	0.003 369	11.346 1
MD-P1	80.4	0.62 ± 0.03	44.22 ± 0.7	8.492 ± 0.14	1.614 ± 0.023	0.508 ± 0.007	41.33 ± 1.15	1.71 ± 0.03	0.26 ± 0.01	0.45 ± 0.01	1.403 2 ± 0.001 5	1.597 4	1.272 6	0.004 092	28.059 6
MD-P3	81.0	0.57 ± 0.03	44.61 ± 0.7	11.495 ± 0.68	2.213 ± 0.281	0.703 ± 0.096	40.67 ± 1.16	1.69 ± 0.03	0.24 ± 0.00	0.41 ± 0.00	1.401 2 ± 0.001 5	3.037 6	2.902 3	0.011 843	16.321 7
MD-P5	80.5	0.45 ± 0.00	51.47 ± 0.5	12.615 ± 0.12	4.180 ± 0.330	1.203 ± 0.081	38.67 ± 1.15	1.63 ± 0.03	0.23 ± 0.00	0.38 ± 0.00	1.378 2 ± 0.003 5	3.670 2	3.435 7	0.013 123	15.278 5
MD-N1	80.1	0.38 ± 0.03	45.75 ± 0.7	8.538 ± 0.21	1.877 ± 0.031	0.581 ± 0.008	40.67 ± 1.16	1.69 ± 0.03	0.21 ± 0.00	0.35 ± 0.00	0.976 8 ± 0.000 7	0.709 9	0.138 7	0.001 571	45.306 5
MD-N3	78.3	0.47 ± 0.03	46.84 ± 0.6	12.277 ± 0.31	1.545 ± 0.007	0.475 ± 0.001	43.33 ± 1.16	1.77 ± 0.04	0.18 ± 0.00	0.32 ± 0.00	0.911 8 ± 0.013 0	2.410 1	0.506 6	0.004 669	36.863 5
MD-N5	77.7	0.47 ± 0.03	48.91 ± 0.6	19.603 ± 0.76	1.769 ± 0.073	0.544 ± 0.020	46.00 ± 0.00	1.85 ± 0.00	0.11 ± 0.00	0.20 ± 0.01	0.745 6 ± 0.002 0	6.449 4	2.859 2	0.023 449	32.8042
MD-N5 (1.0)	78.1	0.57 ± 0.03	48.57 ± 0.6	15.516 ± 0.67	1.625 ± 0.027	0.504 ± 0.010	47.36 ± 1.15	1.90 ± 0.04	0.10 ± 0.00	0.19 ± 0.01	0.783 3 ± 0.005 3	8.262 5	5.487 0	0.027 887	20.329 1
MD-N5 (1.5)	81.0	0.48 ± 0.03	48.91 ± 0.6	17.828 ± 0.17	1.687 ± 0.152	0.522 ± 0.086	47.34 ± 1.18	1.90 ± 0.04	0.10 ± 0.00	0.19 ± 0.00	0.776 8 ± 0.011 8	7.526 1	4.555 8	0.028 088	24.661 6

Sample

Yield /%

MC/%

AR/°

d_0.5/μm

Span

Uniformity

ρ_b/g·mL^-1

ρ_t/g·mL^-1

ρ_ture/g·mL^-1

SA-BET /m²·g^-1

SA-BJH/ m²·g^-1

PV /cm³·g^-1

PD/nm

0.62 ± 0.03

43.02 ± 0.7

49.678 ± 0.31

1.441 ± 0.003

0.442 ± 0.002

15.33 ± 1.15

1.18 ± 0.02

0.49 ± 0.00

0.58 ± 0.01

1.578 1 ± 0.001 1

0.101 3

0.019 0

0.000 371

2.094 5

MD-P0

77.0

0.43 ± 0.03

47.89 ± 0.6

5.433 ± 0.20

1.382 ± 0.010

0.429 ± 0.003

37.33 ± 1.16

1.60 ± 0.03

0.30 ± 0.01

0.48 ± 0.01

1.482 2 ± 0.002 1

1.1353

0.339 2

0.003 258

12.002 9

MD-P0'

77.0

0.52 ± 0.03

45.00 ± 0.0

5.762 ± 0.04

1.453 ± 0.034

0.476 ± 0.051

36.67 ± 1.15

1.58 ± 0.03

0.32 ± 0.00

0.50 ± 0.00

1.489 6 ± 0.002 3

1.187 6

0.477 0

0.003 369

11.346 1

MD-P1

80.4

0.62 ± 0.03

44.22 ± 0.7

8.492 ± 0.14

1.614 ± 0.023

0.508 ± 0.007

41.33 ± 1.15

1.71 ± 0.03

0.26 ± 0.01

0.45 ± 0.01

1.403 2 ± 0.001 5

1.597 4

1.272 6

0.004 092

28.059 6

MD-P3

81.0

0.57 ± 0.03

44.61 ± 0.7

11.495 ± 0.68

2.213 ± 0.281

0.703 ± 0.096

40.67 ± 1.16

1.69 ± 0.03

0.24 ± 0.00

0.41 ± 0.00

1.401 2 ± 0.001 5

3.037 6

2.902 3

0.011 843

16.321 7

MD-P5

80.5

0.45 ± 0.00

51.47 ± 0.5

12.615 ± 0.12

4.180 ± 0.330

1.203 ± 0.081

38.67 ± 1.15

1.63 ± 0.03

0.23 ± 0.00

0.38 ± 0.00

1.378 2 ± 0.003 5

3.670 2

3.435 7

0.013 123

15.278 5

MD-N1

80.1

0.38 ± 0.03

45.75 ± 0.7

8.538 ± 0.21

1.877 ± 0.031

0.581 ± 0.008

40.67 ± 1.16

1.69 ± 0.03

0.21 ± 0.00

0.35 ± 0.00

0.976 8 ± 0.000 7

0.709 9

0.138 7

0.001 571

45.306 5

MD-N3

78.3

0.47 ± 0.03

46.84 ± 0.6

12.277 ± 0.31

1.545 ± 0.007

0.475 ± 0.001

43.33 ± 1.16

1.77 ± 0.04

0.18 ± 0.00

0.32 ± 0.00

0.911 8 ± 0.013 0

2.410 1

0.506 6

0.004 669

36.863 5

MD-N5

77.7

0.47 ± 0.03

48.91 ± 0.6

19.603 ± 0.76

1.769 ± 0.073

0.544 ± 0.020

46.00 ± 0.00

1.85 ± 0.00

0.11 ± 0.00

0.20 ± 0.01

0.745 6 ± 0.002 0

6.449 4

2.859 2

0.023 449

32.8042

MD-N5
(1.0)

78.1

0.57 ± 0.03

48.57 ± 0.6

15.516 ± 0.67

1.625 ± 0.027

0.504 ± 0.010

47.36 ± 1.15

1.90 ± 0.04

0.10 ± 0.00

0.19 ± 0.01

0.783 3 ± 0.005 3

8.262 5

5.487 0

0.027 887

20.329 1

MD-N5
(1.5)

81.0

0.48 ± 0.03

48.91 ± 0.6

17.828 ± 0.17

1.687 ± 0.152

0.522 ± 0.086

47.34 ± 1.18

1.90 ± 0.04

0.10 ± 0.00

0.19 ± 0.00

0.776 8 ± 0.011 8

7.526 1

4.555 8

0.028 088

24.661 6

Sample	Yield /%	MC/%	AR/°	d_0.5/μm	Span	Uniformity	CI	HR	ρ_b/g·mL^-1	ρ_t/g·mL^-1	ρ_ture/g·mL^-1	SA-BET /m²·g^-1	SA-BJH/ m²·g^-1	PV /cm³·g^-1	PD/nm
MD	/	0.62 ± 0.03	43.02 ± 0.7	49.678 ± 0.31	1.441 ± 0.003	0.442 ± 0.002	15.33 ± 1.15	1.18 ± 0.02	0.49 ± 0.00	0.58 ± 0.01	1.578 1 ± 0.001 1	0.101 3	0.019 0	0.000 371	2.094 5
MD-P0	77.0	0.43 ± 0.03	47.89 ± 0.6	5.433 ± 0.20	1.382 ± 0.010	0.429 ± 0.003	37.33 ± 1.16	1.60 ± 0.03	0.30 ± 0.01	0.48 ± 0.01	1.482 2 ± 0.002 1	1.1353	0.339 2	0.003 258	12.002 9
MD-P0'	77.0	0.52 ± 0.03	45.00 ± 0.0	5.762 ± 0.04	1.453 ± 0.034	0.476 ± 0.051	36.67 ± 1.15	1.58 ± 0.03	0.32 ± 0.00	0.50 ± 0.00	1.489 6 ± 0.002 3	1.187 6	0.477 0	0.003 369	11.346 1
MD-P1	80.4	0.62 ± 0.03	44.22 ± 0.7	8.492 ± 0.14	1.614 ± 0.023	0.508 ± 0.007	41.33 ± 1.15	1.71 ± 0.03	0.26 ± 0.01	0.45 ± 0.01	1.403 2 ± 0.001 5	1.597 4	1.272 6	0.004 092	28.059 6
MD-P3	81.0	0.57 ± 0.03	44.61 ± 0.7	11.495 ± 0.68	2.213 ± 0.281	0.703 ± 0.096	40.67 ± 1.16	1.69 ± 0.03	0.24 ± 0.00	0.41 ± 0.00	1.401 2 ± 0.001 5	3.037 6	2.902 3	0.011 843	16.321 7
MD-P5	80.5	0.45 ± 0.00	51.47 ± 0.5	12.615 ± 0.12	4.180 ± 0.330	1.203 ± 0.081	38.67 ± 1.15	1.63 ± 0.03	0.23 ± 0.00	0.38 ± 0.00	1.378 2 ± 0.003 5	3.670 2	3.435 7	0.013 123	15.278 5
MD-N1	80.1	0.38 ± 0.03	45.75 ± 0.7	8.538 ± 0.21	1.877 ± 0.031	0.581 ± 0.008	40.67 ± 1.16	1.69 ± 0.03	0.21 ± 0.00	0.35 ± 0.00	0.976 8 ± 0.000 7	0.709 9	0.138 7	0.001 571	45.306 5
MD-N3	78.3	0.47 ± 0.03	46.84 ± 0.6	12.277 ± 0.31	1.545 ± 0.007	0.475 ± 0.001	43.33 ± 1.16	1.77 ± 0.04	0.18 ± 0.00	0.32 ± 0.00	0.911 8 ± 0.013 0	2.410 1	0.506 6	0.004 669	36.863 5
MD-N5	77.7	0.47 ± 0.03	48.91 ± 0.6	19.603 ± 0.76	1.769 ± 0.073	0.544 ± 0.020	46.00 ± 0.00	1.85 ± 0.00	0.11 ± 0.00	0.20 ± 0.01	0.745 6 ± 0.002 0	6.449 4	2.859 2	0.023 449	32.8042
MD-N5 (1.0)	78.1	0.57 ± 0.03	48.57 ± 0.6	15.516 ± 0.67	1.625 ± 0.027	0.504 ± 0.010	47.36 ± 1.15	1.90 ± 0.04	0.10 ± 0.00	0.19 ± 0.01	0.783 3 ± 0.005 3	8.262 5	5.487 0	0.027 887	20.329 1
MD-N5 (1.5)	81.0	0.48 ± 0.03	48.91 ± 0.6	17.828 ± 0.17	1.687 ± 0.152	0.522 ± 0.086	47.34 ± 1.18	1.90 ± 0.04	0.10 ± 0.00	0.19 ± 0.00	0.776 8 ± 0.011 8	7.526 1	4.555 8	0.028 088	24.661 6

Sample

Yield /%

MC/%

AR/°

d_0.5/μm

Span

Uniformity

ρ_b/g·mL^-1

ρ_t/g·mL^-1

ρ_ture/g·mL^-1

SA-BET /m²·g^-1

SA-BJH/ m²·g^-1

PV /cm³·g^-1

PD/nm

0.62 ± 0.03

43.02 ± 0.7

49.678 ± 0.31

1.441 ± 0.003

0.442 ± 0.002

15.33 ± 1.15

1.18 ± 0.02

0.49 ± 0.00

0.58 ± 0.01

1.578 1 ± 0.001 1

0.101 3

0.019 0

0.000 371

2.094 5

MD-P0

77.0

0.43 ± 0.03

47.89 ± 0.6

5.433 ± 0.20

1.382 ± 0.010

0.429 ± 0.003

37.33 ± 1.16

1.60 ± 0.03

0.30 ± 0.01

0.48 ± 0.01

1.482 2 ± 0.002 1

1.1353

0.339 2

0.003 258

12.002 9

MD-P0'

77.0

0.52 ± 0.03

45.00 ± 0.0

5.762 ± 0.04

1.453 ± 0.034

0.476 ± 0.051

36.67 ± 1.15

1.58 ± 0.03

0.32 ± 0.00

0.50 ± 0.00

1.489 6 ± 0.002 3

1.187 6

0.477 0

0.003 369

11.346 1

MD-P1

80.4

0.62 ± 0.03

44.22 ± 0.7

8.492 ± 0.14

1.614 ± 0.023

0.508 ± 0.007

41.33 ± 1.15

1.71 ± 0.03

0.26 ± 0.01

0.45 ± 0.01

1.403 2 ± 0.001 5

1.597 4

1.272 6

0.004 092

28.059 6

MD-P3

81.0

0.57 ± 0.03

44.61 ± 0.7

11.495 ± 0.68

2.213 ± 0.281

0.703 ± 0.096

40.67 ± 1.16

1.69 ± 0.03

0.24 ± 0.00

0.41 ± 0.00

1.401 2 ± 0.001 5

3.037 6

2.902 3

0.011 843

16.321 7

MD-P5

80.5

0.45 ± 0.00

51.47 ± 0.5

12.615 ± 0.12

4.180 ± 0.330

1.203 ± 0.081

38.67 ± 1.15

1.63 ± 0.03

0.23 ± 0.00

0.38 ± 0.00

1.378 2 ± 0.003 5

3.670 2

3.435 7

0.013 123

15.278 5

MD-N1

80.1

0.38 ± 0.03

45.75 ± 0.7

8.538 ± 0.21

1.877 ± 0.031

0.581 ± 0.008

40.67 ± 1.16

1.69 ± 0.03

0.21 ± 0.00

0.35 ± 0.00

0.976 8 ± 0.000 7

0.709 9

0.138 7

0.001 571

45.306 5

MD-N3

78.3

0.47 ± 0.03

46.84 ± 0.6

12.277 ± 0.31

1.545 ± 0.007

0.475 ± 0.001

43.33 ± 1.16

1.77 ± 0.04

0.18 ± 0.00

0.32 ± 0.00

0.911 8 ± 0.013 0

2.410 1

0.506 6

0.004 669

36.863 5

MD-N5

77.7

0.47 ± 0.03

48.91 ± 0.6

19.603 ± 0.76

1.769 ± 0.073

0.544 ± 0.020

46.00 ± 0.00

1.85 ± 0.00

0.11 ± 0.00

0.20 ± 0.01

0.745 6 ± 0.002 0

6.449 4

2.859 2

0.023 449

32.8042

MD-N5
(1.0)

78.1

0.57 ± 0.03

48.57 ± 0.6

15.516 ± 0.67

1.625 ± 0.027

0.504 ± 0.010

47.36 ± 1.15

1.90 ± 0.04

0.10 ± 0.00

0.19 ± 0.01

0.783 3 ± 0.005 3

8.262 5

5.487 0

0.027 887

20.329 1

MD-N5
(1.5)

81.0

0.48 ± 0.03

48.91 ± 0.6

17.828 ± 0.17

1.687 ± 0.152

0.522 ± 0.086

47.34 ± 1.18

1.90 ± 0.04

0.10 ± 0.00

0.19 ± 0.00

0.776 8 ± 0.011 8

7.526 1

4.555 8

0.028 088

24.661 6

Sample	Curcumin
MD	6.95 ± 0.98	2.85 ± 0.00	12.17 ± 0.43	5.99 ± 0.24
MD-P0	9.55 ± 0.75	4.63 ± 0.19	15.64 ± 0.45	6.42 ± 0.25
MD-P0'	9.87 ± 1.83	5.29 ± 0.27	15.16 ± 1.05	7.49 ± 0.46
MD-P1	10.71 ± 1.61	5.33 ± 0.32	22.64 ± 0.99	11.30 ± 0.17
MD-P3	17.05 ± 2.93	7.43 ± 0.00	26.67 ± 4.55	12.96 ± 0.00
MD-P5	17.77 ± 1.90	8.52 ± 0.14	29.92 ± 1.47	13.90 ± 0.16
MD-N1	17.14 ± 1.45	5.19 ± 0.14	22.44 ± 2.57	8.56 ± 0.16
MD-N3	20.37 ± 1.57	7.91 ± 0.14	26.18 ± 1.60	10.80 ± 0.17
MD-N5	23.89 ± 0.53	8.06 ± 0.21	35.87 ± 1.33	12.24 ± 0.29
MD-N5 (1.0)	23.86 ± 0.38	8.02 ± 0.17	36.31 ± 1.01	12.47 ± 0.00
MD-N5 (1.5)	23.90 ± 0.60	8.01 ± 0.00	36.34 ± 1.32	12.39 ± 0.00

Sample

Curcumin

Ibuprofen

Drug loading/%

Adsorption efficiency/%

Drug loading/%

Adsorption efficiency/%

6.95 ± 0.98

2.85 ± 0.00

12.17 ± 0.43

5.99 ± 0.24

MD-P0

9.55 ± 0.75

4.63 ± 0.19

15.64 ± 0.45

6.42 ± 0.25

MD-P0'

9.87 ± 1.83

5.29 ± 0.27

15.16 ± 1.05

7.49 ± 0.46

MD-P1

10.71 ± 1.61

5.33 ± 0.32

22.64 ± 0.99

11.30 ± 0.17

MD-P3

17.05 ± 2.93

7.43 ± 0.00

26.67 ± 4.55

12.96 ± 0.00

MD-P5

17.77 ± 1.90

8.52 ± 0.14

29.92 ± 1.47

13.90 ± 0.16

MD-N1

17.14 ± 1.45

5.19 ± 0.14

22.44 ± 2.57

8.56 ± 0.16

MD-N3

20.37 ± 1.57

7.91 ± 0.14

26.18 ± 1.60

10.80 ± 0.17

MD-N5

23.89 ± 0.53

8.06 ± 0.21

35.87 ± 1.33

12.24 ± 0.29

MD-N5 (1.0)

23.86 ± 0.38

8.02 ± 0.17

36.31 ± 1.01

12.47 ± 0.00

MD-N5 (1.5)

23.90 ± 0.60

8.01 ± 0.00

36.34 ± 1.32

12.39 ± 0.00

Sample	Curcumin
MD	6.95 ± 0.98	2.85 ± 0.00	12.17 ± 0.43	5.99 ± 0.24
MD-P0	9.55 ± 0.75	4.63 ± 0.19	15.64 ± 0.45	6.42 ± 0.25
MD-P0'	9.87 ± 1.83	5.29 ± 0.27	15.16 ± 1.05	7.49 ± 0.46
MD-P1	10.71 ± 1.61	5.33 ± 0.32	22.64 ± 0.99	11.30 ± 0.17
MD-P3	17.05 ± 2.93	7.43 ± 0.00	26.67 ± 4.55	12.96 ± 0.00
MD-P5	17.77 ± 1.90	8.52 ± 0.14	29.92 ± 1.47	13.90 ± 0.16
MD-N1	17.14 ± 1.45	5.19 ± 0.14	22.44 ± 2.57	8.56 ± 0.16
MD-N3	20.37 ± 1.57	7.91 ± 0.14	26.18 ± 1.60	10.80 ± 0.17
MD-N5	23.89 ± 0.53	8.06 ± 0.21	35.87 ± 1.33	12.24 ± 0.29
MD-N5 (1.0)	23.86 ± 0.38	8.02 ± 0.17	36.31 ± 1.01	12.47 ± 0.00
MD-N5 (1.5)	23.90 ± 0.60	8.01 ± 0.00	36.34 ± 1.32	12.39 ± 0.00

Sample

Curcumin

Ibuprofen

Drug loading/%

Adsorption efficiency/%

Drug loading/%

Adsorption efficiency/%

6.95 ± 0.98

2.85 ± 0.00

12.17 ± 0.43

5.99 ± 0.24

MD-P0

9.55 ± 0.75

4.63 ± 0.19

15.64 ± 0.45

6.42 ± 0.25

MD-P0'

9.87 ± 1.83

5.29 ± 0.27

15.16 ± 1.05

7.49 ± 0.46

MD-P1

10.71 ± 1.61

5.33 ± 0.32

22.64 ± 0.99

11.30 ± 0.17

MD-P3

17.05 ± 2.93

7.43 ± 0.00

26.67 ± 4.55

12.96 ± 0.00

MD-P5

17.77 ± 1.90

8.52 ± 0.14

29.92 ± 1.47

13.90 ± 0.16

MD-N1

17.14 ± 1.45

5.19 ± 0.14

22.44 ± 2.57

8.56 ± 0.16

MD-N3

20.37 ± 1.57

7.91 ± 0.14

26.18 ± 1.60

10.80 ± 0.17

MD-N5

23.89 ± 0.53

8.06 ± 0.21

35.87 ± 1.33

12.24 ± 0.29

MD-N5 (1.0)

23.86 ± 0.38

8.02 ± 0.17

36.31 ± 1.01

12.47 ± 0.00

MD-N5 (1.5)

23.90 ± 0.60

8.01 ± 0.00

36.34 ± 1.32

12.39 ± 0.00

Adsorption kinetics model	Sample	Template-agent method
Quasi-first order	MD	In (0.010 2-Q_t) = -0.005 5t - 4.585 4	0.738 7		In (1.999 6-Q_t) = -0.025 7t + 0.692 9	0.943 0
MD-P0	In (0.017 2-Q_t) = -0.007 5t - 4.062 8	0.867 1		In (2.142 8-Q_t) = -0.018 3t + 0.762 1	0.977 8
MD-P0'	In (0.020 4-Q_t) = -0.015 5t - 3.892 2	0.984 4		In (2.713 3-Q_t) = -0.026 0t + 0.998 2	0.946 7
MD-P1	In (0.027 2-Q_t) = -0.007 2t - 3.604 5	0.989 9		In (4.712 3-Q_t) = -0.006 5t + 1.550 1	0.996 9
MD-P3	In (0.037 6-Q_t) = -0.007 0t - 3.280 8	0.993 8		In (5.283 5-Q_t) = -0.007 0t + 1.664 6	0.989 7
MD-P5	In (0.040 8-Q_t) = -0.009 0t - 3.199 1	0.993 9		In (6.141 8-Q_t) = -0.006 5t + 1.815 1	0.995 1
Quasi-second order	MD	t/Q_t = 92.417t + 1 491.2	0.984 9		t/Q_t = 0.480 1t + 9.086	0.993 8
MD-P0	t/Q_t = 54.187t + 1 644.7	0.990 3		t/Q_t = 0.443 7t + 10.550	0.992 4
MD-P0'	t/Q_t = 44.697t + 2 036.4	0.978 0		t/Q_t = 0.353 5t + 6.364	0.994 3
MD-P1	t/Q_t = 32.962t + 2 125.4	0.980 5		t/Q_t = 0.172 2t + 20.684	0.929 4
MD-P3	t/Q_t = 23.386t + 1 770.3	0.973 6		t/Q_t = 0.161 0t + 14.581	0.960 0
MD-P5	t/Q_t = 22.049t + 1 294.3	0.982 4		t/Q_t = 0.136 8t + 13.557	0.954 3

Adsorption kinetics model	Sample	Pore-forming agent method
Curcumin		Ibuprofen
Regression equation	R²		Regression equation	R²
Quasi-first order	MD-N1	In (0.025 0-Q_t) = -0.006 3t - 3.688 9	0.988 1		In (4.428 6-Q_t) = -0.007 2t + 1.488 1	0.955 4
MD-N3	In (0.036 2-Q_t) = -0.008 0t - 3.318 7	0.991 2		In (5.142 0-Q_t) = -0.006 0t + 1.637 4	0.970 3
MD-N5	In (0.039 8-Q_t) = -0.006 6t - 3.223 4	0.994 6		In (6.142 8-Q_t) = -0.005 4t + 1.815 3	0.957 2
Quasi-second order	MD-N1	t/Q_t = 40.000t + 3 424.2	0.952 1		t/Q_t = 0.225 8t + 15.078	0.971 5
MD-N3	t/Q_t = 27.624t + 2 368.5	0.939 1		t/Q_t = 0.194 5t + 12.222	0.975 3
MD-N5	t/Q_t = 25.126t + 1 982.0	0.960 6		t/Q_t = 0.162 8t + 10.851	0.969 5

Adsorption kinetics model

Sample

Template-agent method

Curcumin

Ibuprofen

Regression equation

R²

Regression equation

R²

Quasi-first order

In (0.010 2-Q_t) = -0.005 5t - 4.585 4

0.738 7

In (1.999 6-Q_t) = -0.025 7t + 0.692 9

0.943 0

MD-P0

In (0.017 2-Q_t) = -0.007 5t - 4.062 8

0.867 1

In (2.142 8-Q_t) = -0.018 3t + 0.762 1

0.977 8

MD-P0'

In (0.020 4-Q_t) = -0.015 5t - 3.892 2

0.984 4

In (2.713 3-Q_t) = -0.026 0t + 0.998 2

0.946 7

MD-P1

In (0.027 2-Q_t) = -0.007 2t - 3.604 5

0.989 9

In (4.712 3-Q_t) = -0.006 5t + 1.550 1

0.996 9

MD-P3

In (0.037 6-Q_t) = -0.007 0t - 3.280 8

0.993 8

In (5.283 5-Q_t) = -0.007 0t + 1.664 6

0.989 7

MD-P5

In (0.040 8-Q_t) = -0.009 0t - 3.199 1

0.993 9

In (6.141 8-Q_t) = -0.006 5t + 1.815 1

0.995 1

Quasi-second order

t/Q_t = 92.417t + 1 491.2

0.984 9

t/Q_t = 0.480 1t + 9.086

0.993 8

MD-P0

t/Q_t = 54.187t + 1 644.7

0.990 3

t/Q_t = 0.443 7t + 10.550

0.992 4

MD-P0'

t/Q_t = 44.697t + 2 036.4

0.978 0

t/Q_t = 0.353 5t + 6.364

0.994 3

MD-P1

t/Q_t = 32.962t + 2 125.4

0.980 5

t/Q_t = 0.172 2t + 20.684

0.929 4

MD-P3

t/Q_t = 23.386t + 1 770.3

0.973 6

t/Q_t = 0.161 0t + 14.581

0.960 0

MD-P5

t/Q_t = 22.049t + 1 294.3

0.982 4

t/Q_t = 0.136 8t + 13.557

0.954 3

Adsorption kinetics model

Sample

Pore-forming agent method

Curcumin

Ibuprofen

Regression equation

R²

Regression equation

R²

Quasi-first order

MD-N1

In (0.025 0-Q_t) = -0.006 3t - 3.688 9

0.988 1

In (4.428 6-Q_t) = -0.007 2t + 1.488 1

0.955 4

MD-N3

In (0.036 2-Q_t) = -0.008 0t - 3.318 7

0.991 2

In (5.142 0-Q_t) = -0.006 0t + 1.637 4

0.970 3

MD-N5

In (0.039 8-Q_t) = -0.006 6t - 3.223 4

0.994 6

In (6.142 8-Q_t) = -0.005 4t + 1.815 3

0.957 2

Quasi-second order

MD-N1

t/Q_t = 40.000t + 3 424.2

0.952 1

t/Q_t = 0.225 8t + 15.078

0.971 5

MD-N3

t/Q_t = 27.624t + 2 368.5

0.939 1

t/Q_t = 0.194 5t + 12.222

0.975 3

MD-N5

t/Q_t = 25.126t + 1 982.0

0.960 6

t/Q_t = 0.162 8t + 10.851

0.969 5

Adsorption kinetics model	Sample	Template-agent method
Quasi-first order	MD	In (0.010 2-Q_t) = -0.005 5t - 4.585 4	0.738 7		In (1.999 6-Q_t) = -0.025 7t + 0.692 9	0.943 0
MD-P0	In (0.017 2-Q_t) = -0.007 5t - 4.062 8	0.867 1		In (2.142 8-Q_t) = -0.018 3t + 0.762 1	0.977 8
MD-P0'	In (0.020 4-Q_t) = -0.015 5t - 3.892 2	0.984 4		In (2.713 3-Q_t) = -0.026 0t + 0.998 2	0.946 7
MD-P1	In (0.027 2-Q_t) = -0.007 2t - 3.604 5	0.989 9		In (4.712 3-Q_t) = -0.006 5t + 1.550 1	0.996 9
MD-P3	In (0.037 6-Q_t) = -0.007 0t - 3.280 8	0.993 8		In (5.283 5-Q_t) = -0.007 0t + 1.664 6	0.989 7
MD-P5	In (0.040 8-Q_t) = -0.009 0t - 3.199 1	0.993 9		In (6.141 8-Q_t) = -0.006 5t + 1.815 1	0.995 1
Quasi-second order	MD	t/Q_t = 92.417t + 1 491.2	0.984 9		t/Q_t = 0.480 1t + 9.086	0.993 8
MD-P0	t/Q_t = 54.187t + 1 644.7	0.990 3		t/Q_t = 0.443 7t + 10.550	0.992 4
MD-P0'	t/Q_t = 44.697t + 2 036.4	0.978 0		t/Q_t = 0.353 5t + 6.364	0.994 3
MD-P1	t/Q_t = 32.962t + 2 125.4	0.980 5		t/Q_t = 0.172 2t + 20.684	0.929 4
MD-P3	t/Q_t = 23.386t + 1 770.3	0.973 6		t/Q_t = 0.161 0t + 14.581	0.960 0
MD-P5	t/Q_t = 22.049t + 1 294.3	0.982 4		t/Q_t = 0.136 8t + 13.557	0.954 3

Adsorption kinetics model	Sample	Pore-forming agent method
Curcumin		Ibuprofen
Regression equation	R²		Regression equation	R²
Quasi-first order	MD-N1	In (0.025 0-Q_t) = -0.006 3t - 3.688 9	0.988 1		In (4.428 6-Q_t) = -0.007 2t + 1.488 1	0.955 4
MD-N3	In (0.036 2-Q_t) = -0.008 0t - 3.318 7	0.991 2		In (5.142 0-Q_t) = -0.006 0t + 1.637 4	0.970 3
MD-N5	In (0.039 8-Q_t) = -0.006 6t - 3.223 4	0.994 6		In (6.142 8-Q_t) = -0.005 4t + 1.815 3	0.957 2
Quasi-second order	MD-N1	t/Q_t = 40.000t + 3 424.2	0.952 1		t/Q_t = 0.225 8t + 15.078	0.971 5
MD-N3	t/Q_t = 27.624t + 2 368.5	0.939 1		t/Q_t = 0.194 5t + 12.222	0.975 3
MD-N5	t/Q_t = 25.126t + 1 982.0	0.960 6		t/Q_t = 0.162 8t + 10.851	0.969 5

Adsorption kinetics model

Sample

Template-agent method

Curcumin

Ibuprofen

Regression equation

R²

Regression equation

R²

Quasi-first order

In (0.010 2-Q_t) = -0.005 5t - 4.585 4

0.738 7

In (1.999 6-Q_t) = -0.025 7t + 0.692 9

0.943 0

MD-P0

In (0.017 2-Q_t) = -0.007 5t - 4.062 8

0.867 1

In (2.142 8-Q_t) = -0.018 3t + 0.762 1

0.977 8

MD-P0'

In (0.020 4-Q_t) = -0.015 5t - 3.892 2

0.984 4

In (2.713 3-Q_t) = -0.026 0t + 0.998 2

0.946 7

MD-P1

In (0.027 2-Q_t) = -0.007 2t - 3.604 5

0.989 9

In (4.712 3-Q_t) = -0.006 5t + 1.550 1

0.996 9

MD-P3

In (0.037 6-Q_t) = -0.007 0t - 3.280 8

0.993 8

In (5.283 5-Q_t) = -0.007 0t + 1.664 6

0.989 7

MD-P5

In (0.040 8-Q_t) = -0.009 0t - 3.199 1

0.993 9

In (6.141 8-Q_t) = -0.006 5t + 1.815 1

0.995 1

Quasi-second order

t/Q_t = 92.417t + 1 491.2

0.984 9

t/Q_t = 0.480 1t + 9.086

0.993 8

MD-P0

t/Q_t = 54.187t + 1 644.7

0.990 3

t/Q_t = 0.443 7t + 10.550

0.992 4

MD-P0'

t/Q_t = 44.697t + 2 036.4

0.978 0

t/Q_t = 0.353 5t + 6.364

0.994 3

MD-P1

t/Q_t = 32.962t + 2 125.4

0.980 5

t/Q_t = 0.172 2t + 20.684

0.929 4

MD-P3

t/Q_t = 23.386t + 1 770.3

0.973 6

t/Q_t = 0.161 0t + 14.581

0.960 0

MD-P5

t/Q_t = 22.049t + 1 294.3

0.982 4

t/Q_t = 0.136 8t + 13.557

0.954 3

Adsorption kinetics model

Sample

Pore-forming agent method

Curcumin

Ibuprofen

Regression equation

R²

Regression equation

R²

Quasi-first order

MD-N1

In (0.025 0-Q_t) = -0.006 3t - 3.688 9

0.988 1

In (4.428 6-Q_t) = -0.007 2t + 1.488 1

0.955 4

MD-N3

In (0.036 2-Q_t) = -0.008 0t - 3.318 7

0.991 2

In (5.142 0-Q_t) = -0.006 0t + 1.637 4

0.970 3

MD-N5

In (0.039 8-Q_t) = -0.006 6t - 3.223 4

0.994 6

In (6.142 8-Q_t) = -0.005 4t + 1.815 3

0.957 2

Quasi-second order

MD-N1

t/Q_t = 40.000t + 3 424.2

0.952 1

t/Q_t = 0.225 8t + 15.078

0.971 5

MD-N3

t/Q_t = 27.624t + 2 368.5

0.939 1

t/Q_t = 0.194 5t + 12.222

0.975 3

MD-N5

t/Q_t = 25.126t + 1 982.0

0.960 6

t/Q_t = 0.162 8t + 10.851

0.969 5

Method	Sample	Mathematical
Template agent method	MD-Cur	Q = 0.050 8t+19.387 2, R²=0.577 7	100-Q = 100exp^{(-0.002 6t)}, R² = -6.517 5	Q = 1.052 3t^1/2 + 15.245 2, R² = 0.760 7	Q = 12.276 1t^{0.166 3}, R² = 0.912 2
	MD-P0-Cur	Q = 0.041 9t + 24.105 1, R² = 0.852 0	100-Q = 100exp^{(-0.003 2t)}, R² = -15.657 7	Q = 0.863 8t^1/2 + 20.716 1, R² = 0.760 3	Q = 14.465 7t^{0.117 1}, R² = 0.927 1
	MD-P0'-Cur	Q = 0.038 6t + 24.147 3, R² = 0.620 4	100-Q = 100exp^{(-0.003 1t)}, R² = -20.097 4	Q = 0.787 8t^1/2 + 21.079 8, R² = 0.803 1	Q = 18.040 4t^{0.106 7}, R² = 0.942 8
	MD-P1-Cur	Q = 0.216 1t + 40.785 3, R²=0.644 3	100-Q = 100exp^{(-0.019 7t)}, R² = 0.576 6	Q = 4.378 4t^1/2 + 23.864 6, R² = 0.822 7	Q = 19.485 7t^{0.275 4}, R² = 0.913 0
	MD-P3-Cur	Q = 0.168 8t + 52.265 2, R² = 0.675 1	100-Q = 100exp^{(-0.030 8t)}, R² = -0.314 3	Q = 3.384 1t^1/2 + 39.308 8, R² = 0.847 7	Q = 31.376 2t^{0.188 9}, R² = 0.949 2
	MD-P5-Cur	Q = 0.125 3t + 62.292 7, R² = 0.562 9	100-Q = 100exp^{(-0.062 9t)}, R² = -1.053 4	Q = 2.589 2t^1/2 + 52.118 5, R² = 0.734 2	Q = 43.154 7t^{0.133 3}, R² = 0.902 2
	MD-Ibu	Q = 0.078 6t + 15.416 5, R² = 0.493 1	100-Q = 100exp^{(-0.002 6t)}, R² = -1.080 4	Q = 1.631 2t^1/2 + 8.982 2, R² = 0.732 0	Q = 7.417 3t^{0.271 7}, R² = 0.854 7
	MD-P0-Ibu	Q = 0.085 8t + 17.217 2, R² = 0.473 1	100-Q = 100exp^{(-0.003 0t)}, R² = -1.035 6	Q = 1.799 9t^1/2 + 10.053 3, R² = 0.723 2	Q = 8.287 3t^{0.270 2}, R² = 0.852 0
	MD-P0'-Ibu	Q=0.070 6t + 18.247 0, R² = 0.453 3	100-Q = 100exp^{(-0.002 9t)}, R² = -2.209 7	Q = 1.485 4t^1/2 + 12.321 4, R² = 0.700 1	Q = 9.774 7t^{0.227 8}, R² = 0.851 5
	MD-P1-Ibu	Q = 0.252 4t + 35.141 1, R² = 0.593 8	100-Q = 100exp^{(-0.017 5t)}, R² = 0.824 5	Q = 5.113 6t^1/2 + 15.380 9, R² = 0.822 9	Q = 14.567 8t^{0.334 0}, R² = 0.895 2
	MD-P3-Ibu	Q = 0.226 2t + 41.911 1, R² = 0.656 8	100-Q = 100exp^{(-0.020 9t)}, R² = 0.581 0	Q = 4.497 6t^1/2 + 24.815 8, R² = 0.867 3	Q = 20.158 4t^{0.274 7}, R² = 0.941 9
	MD-P5-Ibu	Q = 0.210 5t + 45.898 3, R² = 0.646 6	100-Q = 100exp^{(-0.024 3t)}, R² = 0.429 5	Q = 4.200 1t^1/2 + 29.886 8, R² = 0.860 8	Q = 23.756 5t^{0.245 7}, R² = 0.944 0
Pore-forming agent	MD-N1-Cur	Q = 0.180 1t + 42.287 0, R² = 0.572 5	100-Q = 100exp^{(-0.019 4t)}, R² = 0.197 7	Q = 3.742 4t^1/2 + 27.506 4, R² = 0.790 0	Q = 21.930 5t^{0.241 3}, R² = 0.890 8
	MD-N3-Cur	Q = 0.183 8t + 42.655 1, R² = 0.587 0	100-Q = 100exp^{(-0.019 9t)}, R² = 0.253 9	Q = 3.799 1t^1/2 + 27.719 4, R² = 0.801 0	Q = 22.094 0t^{0.242 2}, R² = 0.896 7
	MD-N5-Cur	Q = 0.180 6t + 44.841 0, R² = 0.544 9	100-Q = 100exp^{(-0.022 3t)}, R² = 0.274 8	Q = 3.785 3t^1/2 + 29.786 2, R² = 0.764 7	Q = 23.660 9t^{0.233 8}, R² = 0.872 0
	MD-N5 (1.0)-Cur	Q = 0.177 6t + 42.752 7, R² = 0.549 6	100-Q = 100exp^{(-0.019 8t)}, R² = 0.201 6	Q = 3.712 3t^1/2 + 28.023 1, R² = 0.766 9	Q = 22.289 0t^{0.238 5}, R² = 0.866 9
	MD-N5 (1.5)-Cur	Q = 0.181 4t + 43.162 3, R² = 0.550 6	100-Q = 100exp^{(-0.020 5t)}, R² = 0.266 8	Q = 3.791 6t^1/2 + 28.117 2, R² = 0.768 4	Q = 22.393 8t^{0.240 4}, R² = 0.868 8
	MD-N1-Ibu	Q = 0.228 1t + 33.794 7, R² = 0.550 6	100-Q = 100exp^{(-0.016 0t)}, R² = 0.711 0	Q = 0.476 0t^1/2 + 14.818 9, R² = 0.770 9	Q = 14.275 4t^{0.325 4}, R² = 0.838 7
	MD-N3-Ibu	Q = 0.203 0t + 38.797 9, R² = 0.543 1	100-Q = 100exp^{(-0.018 3t)}, R² = 0.560 2	Q = 4.268 0t^1/2 + 21.777 3, R² = 0.767 2	Q = 18.200 9t^{0.279 9}, R² = 0.856 3
	MD-N5-Ibu	Q = 0.205 5t + 40.677 7, R² = 0.555 3	100-Q = 100exp^{(-0.020 1t)}, R² = 0.581 2	Q = 4.296 5t^1/2 + 23.624 6, R² = 0.775 5	Q = 19.434 4t^{0.272 8}, R² = 0.863 3
	MD-N5 (1.0)-Ibu	Q = 0.204 5t + 39.173 0, R² = 0.543 6	100-Q = 100exp^{(-0.018 7t)}, R² = 0.571 1	Q = 4.288 0t^1/2 + 22.111 3, R² = 0.763 7	Q = 18.404 9t^{0.279 3}, R² = 0.849 1
	MD-N5 (1.5)-Ibu	Q =0.212 3t + 38.140 8, R² = 0.577 1	100-Q = 100exp^{(-0.018 0t)}, R² = 0.613 7	Q = 4.400 4t^1/2 + 20.799 1, R² = 0.792 2	Q = 17.535 9t^{0.288 9}, R² = 0.868 3

Method

Sample

Mathematical

Zero-order

First-order

Higuchi

Peppas

Template agent method

MD-Cur

Q = 0.050 8t+19.387 2, R²=0.577 7

100-Q = 100exp^{(-0.002 6t)}, R² = -6.517 5

Q = 1.052 3t^1/2 + 15.245 2, R² = 0.760 7

Q = 12.276 1t^{0.166 3}, R² = 0.912 2

MD-P0-Cur

Q = 0.041 9t + 24.105 1, R² = 0.852 0

100-Q = 100exp^{(-0.003 2t)}, R² = -15.657 7

Q = 0.863 8t^1/2 + 20.716 1, R² = 0.760 3

Q = 14.465 7t^{0.117 1}, R² = 0.927 1

MD-P0'-Cur

Q = 0.038 6t + 24.147 3, R² = 0.620 4

100-Q = 100exp^{(-0.003 1t)}, R² = -20.097 4

Q = 0.787 8t^1/2 + 21.079 8, R² = 0.803 1

Q = 18.040 4t^{0.106 7}, R² = 0.942 8

MD-P1-Cur

Q = 0.216 1t + 40.785 3, R²=0.644 3

100-Q = 100exp^{(-0.019 7t)}, R² = 0.576 6

Q = 4.378 4t^1/2 + 23.864 6, R² = 0.822 7

Q = 19.485 7t^{0.275 4}, R² = 0.913 0

MD-P3-Cur

Q = 0.168 8t + 52.265 2, R² = 0.675 1

100-Q = 100exp^{(-0.030 8t)}, R² = -0.314 3

Q = 3.384 1t^1/2 + 39.308 8, R² = 0.847 7

Q = 31.376 2t^{0.188 9}, R² = 0.949 2

MD-P5-Cur

Q = 0.125 3t + 62.292 7, R² = 0.562 9

100-Q = 100exp^{(-0.062 9t)}, R² = -1.053 4

Q = 2.589 2t^1/2 + 52.118 5, R² = 0.734 2

Q = 43.154 7t^{0.133 3}, R² = 0.902 2

MD-Ibu

Q = 0.078 6t + 15.416 5, R² = 0.493 1

100-Q = 100exp^{(-0.002 6t)}, R² = -1.080 4

Q = 1.631 2t^1/2 + 8.982 2, R² = 0.732 0

Q = 7.417 3t^{0.271 7}, R² = 0.854 7

MD-P0-Ibu

Q = 0.085 8t + 17.217 2, R² = 0.473 1

100-Q = 100exp^{(-0.003 0t)}, R² = -1.035 6

Q = 1.799 9t^1/2 + 10.053 3, R² = 0.723 2

Q = 8.287 3t^{0.270 2}, R² = 0.852 0

MD-P0'-Ibu

Q=0.070 6t + 18.247 0, R² = 0.453 3

100-Q = 100exp^{(-0.002 9t)}, R² = -2.209 7

Q = 1.485 4t^1/2 + 12.321 4, R² = 0.700 1

Q = 9.774 7t^{0.227 8}, R² = 0.851 5

MD-P1-Ibu

Q = 0.252 4t + 35.141 1, R² = 0.593 8

100-Q = 100exp^{(-0.017 5t)}, R² = 0.824 5

Q = 5.113 6t^1/2 + 15.380 9, R² = 0.822 9

Q = 14.567 8t^{0.334 0}, R² = 0.895 2

MD-P3-Ibu

Q = 0.226 2t + 41.911 1, R² = 0.656 8

100-Q = 100exp^{(-0.020 9t)}, R² = 0.581 0

Q = 4.497 6t^1/2 + 24.815 8, R² = 0.867 3

Q = 20.158 4t^{0.274 7}, R² = 0.941 9

MD-P5-Ibu

Q = 0.210 5t + 45.898 3, R² = 0.646 6

100-Q = 100exp^{(-0.024 3t)}, R² = 0.429 5

Q = 4.200 1t^1/2 + 29.886 8, R² = 0.860 8

Q = 23.756 5t^{0.245 7}, R² = 0.944 0

Pore-forming agent

MD-N1-Cur

Q = 0.180 1t + 42.287 0, R² = 0.572 5

100-Q = 100exp^{(-0.019 4t)}, R² = 0.197 7

Q = 3.742 4t^1/2 + 27.506 4, R² = 0.790 0

Q = 21.930 5t^{0.241 3}, R² = 0.890 8

MD-N3-Cur

Q = 0.183 8t + 42.655 1, R² = 0.587 0

100-Q = 100exp^{(-0.019 9t)}, R² = 0.253 9

Q = 3.799 1t^1/2 + 27.719 4, R² = 0.801 0

Q = 22.094 0t^{0.242 2}, R² = 0.896 7

MD-N5-Cur

Q = 0.180 6t + 44.841 0, R² = 0.544 9

100-Q = 100exp^{(-0.022 3t)}, R² = 0.274 8

Q = 3.785 3t^1/2 + 29.786 2, R² = 0.764 7

Q = 23.660 9t^{0.233 8}, R² = 0.872 0

MD-N5 (1.0)-Cur

Q = 0.177 6t + 42.752 7, R² = 0.549 6

100-Q = 100exp^{(-0.019 8t)}, R² = 0.201 6

Q = 3.712 3t^1/2 + 28.023 1, R² = 0.766 9

Q = 22.289 0t^{0.238 5}, R² = 0.866 9

MD-N5 (1.5)-Cur

Q = 0.181 4t + 43.162 3, R² = 0.550 6

100-Q = 100exp^{(-0.020 5t)}, R² = 0.266 8

Q = 3.791 6t^1/2 + 28.117 2, R² = 0.768 4

Q = 22.393 8t^{0.240 4}, R² = 0.868 8

MD-N1-Ibu

Q = 0.228 1t + 33.794 7, R² = 0.550 6

100-Q = 100exp^{(-0.016 0t)}, R² = 0.711 0

Q = 0.476 0t^1/2 + 14.818 9, R² = 0.770 9

Q = 14.275 4t^{0.325 4}, R² = 0.838 7

MD-N3-Ibu

Q = 0.203 0t + 38.797 9, R² = 0.543 1

100-Q = 100exp^{(-0.018 3t)}, R² = 0.560 2

Q = 4.268 0t^1/2 + 21.777 3, R² = 0.767 2

Q = 18.200 9t^{0.279 9}, R² = 0.856 3

MD-N5-Ibu

Q = 0.205 5t + 40.677 7, R² = 0.555 3

100-Q = 100exp^{(-0.020 1t)}, R² = 0.581 2

Q = 4.296 5t^1/2 + 23.624 6, R² = 0.775 5

Q = 19.434 4t^{0.272 8}, R² = 0.863 3

MD-N5 (1.0)-Ibu

Q = 0.204 5t + 39.173 0, R² = 0.543 6

100-Q = 100exp^{(-0.018 7t)}, R² = 0.571 1

Q = 4.288 0t^1/2 + 22.111 3, R² = 0.763 7

Q = 18.404 9t^{0.279 3}, R² = 0.849 1

MD-N5 (1.5)-Ibu

Q =0.212 3t + 38.140 8, R² = 0.577 1

100-Q = 100exp^{(-0.018 0t)}, R² = 0.613 7

Q = 4.400 4t^1/2 + 20.799 1, R² = 0.792 2

Q = 17.535 9t^{0.288 9}, R² = 0.868 3

Method	Sample	Mathematical
Template agent method	MD-Cur	Q = 0.050 8t+19.387 2, R²=0.577 7	100-Q = 100exp^{(-0.002 6t)}, R² = -6.517 5	Q = 1.052 3t^1/2 + 15.245 2, R² = 0.760 7	Q = 12.276 1t^{0.166 3}, R² = 0.912 2
	MD-P0-Cur	Q = 0.041 9t + 24.105 1, R² = 0.852 0	100-Q = 100exp^{(-0.003 2t)}, R² = -15.657 7	Q = 0.863 8t^1/2 + 20.716 1, R² = 0.760 3	Q = 14.465 7t^{0.117 1}, R² = 0.927 1
	MD-P0'-Cur	Q = 0.038 6t + 24.147 3, R² = 0.620 4	100-Q = 100exp^{(-0.003 1t)}, R² = -20.097 4	Q = 0.787 8t^1/2 + 21.079 8, R² = 0.803 1	Q = 18.040 4t^{0.106 7}, R² = 0.942 8
	MD-P1-Cur	Q = 0.216 1t + 40.785 3, R²=0.644 3	100-Q = 100exp^{(-0.019 7t)}, R² = 0.576 6	Q = 4.378 4t^1/2 + 23.864 6, R² = 0.822 7	Q = 19.485 7t^{0.275 4}, R² = 0.913 0
	MD-P3-Cur	Q = 0.168 8t + 52.265 2, R² = 0.675 1	100-Q = 100exp^{(-0.030 8t)}, R² = -0.314 3	Q = 3.384 1t^1/2 + 39.308 8, R² = 0.847 7	Q = 31.376 2t^{0.188 9}, R² = 0.949 2
	MD-P5-Cur	Q = 0.125 3t + 62.292 7, R² = 0.562 9	100-Q = 100exp^{(-0.062 9t)}, R² = -1.053 4	Q = 2.589 2t^1/2 + 52.118 5, R² = 0.734 2	Q = 43.154 7t^{0.133 3}, R² = 0.902 2
	MD-Ibu	Q = 0.078 6t + 15.416 5, R² = 0.493 1	100-Q = 100exp^{(-0.002 6t)}, R² = -1.080 4	Q = 1.631 2t^1/2 + 8.982 2, R² = 0.732 0	Q = 7.417 3t^{0.271 7}, R² = 0.854 7
	MD-P0-Ibu	Q = 0.085 8t + 17.217 2, R² = 0.473 1	100-Q = 100exp^{(-0.003 0t)}, R² = -1.035 6	Q = 1.799 9t^1/2 + 10.053 3, R² = 0.723 2	Q = 8.287 3t^{0.270 2}, R² = 0.852 0
	MD-P0'-Ibu	Q=0.070 6t + 18.247 0, R² = 0.453 3	100-Q = 100exp^{(-0.002 9t)}, R² = -2.209 7	Q = 1.485 4t^1/2 + 12.321 4, R² = 0.700 1	Q = 9.774 7t^{0.227 8}, R² = 0.851 5
	MD-P1-Ibu	Q = 0.252 4t + 35.141 1, R² = 0.593 8	100-Q = 100exp^{(-0.017 5t)}, R² = 0.824 5	Q = 5.113 6t^1/2 + 15.380 9, R² = 0.822 9	Q = 14.567 8t^{0.334 0}, R² = 0.895 2
	MD-P3-Ibu	Q = 0.226 2t + 41.911 1, R² = 0.656 8	100-Q = 100exp^{(-0.020 9t)}, R² = 0.581 0	Q = 4.497 6t^1/2 + 24.815 8, R² = 0.867 3	Q = 20.158 4t^{0.274 7}, R² = 0.941 9
	MD-P5-Ibu	Q = 0.210 5t + 45.898 3, R² = 0.646 6	100-Q = 100exp^{(-0.024 3t)}, R² = 0.429 5	Q = 4.200 1t^1/2 + 29.886 8, R² = 0.860 8	Q = 23.756 5t^{0.245 7}, R² = 0.944 0
Pore-forming agent	MD-N1-Cur	Q = 0.180 1t + 42.287 0, R² = 0.572 5	100-Q = 100exp^{(-0.019 4t)}, R² = 0.197 7	Q = 3.742 4t^1/2 + 27.506 4, R² = 0.790 0	Q = 21.930 5t^{0.241 3}, R² = 0.890 8
	MD-N3-Cur	Q = 0.183 8t + 42.655 1, R² = 0.587 0	100-Q = 100exp^{(-0.019 9t)}, R² = 0.253 9	Q = 3.799 1t^1/2 + 27.719 4, R² = 0.801 0	Q = 22.094 0t^{0.242 2}, R² = 0.896 7
	MD-N5-Cur	Q = 0.180 6t + 44.841 0, R² = 0.544 9	100-Q = 100exp^{(-0.022 3t)}, R² = 0.274 8	Q = 3.785 3t^1/2 + 29.786 2, R² = 0.764 7	Q = 23.660 9t^{0.233 8}, R² = 0.872 0
	MD-N5 (1.0)-Cur	Q = 0.177 6t + 42.752 7, R² = 0.549 6	100-Q = 100exp^{(-0.019 8t)}, R² = 0.201 6	Q = 3.712 3t^1/2 + 28.023 1, R² = 0.766 9	Q = 22.289 0t^{0.238 5}, R² = 0.866 9
	MD-N5 (1.5)-Cur	Q = 0.181 4t + 43.162 3, R² = 0.550 6	100-Q = 100exp^{(-0.020 5t)}, R² = 0.266 8	Q = 3.791 6t^1/2 + 28.117 2, R² = 0.768 4	Q = 22.393 8t^{0.240 4}, R² = 0.868 8
	MD-N1-Ibu	Q = 0.228 1t + 33.794 7, R² = 0.550 6	100-Q = 100exp^{(-0.016 0t)}, R² = 0.711 0	Q = 0.476 0t^1/2 + 14.818 9, R² = 0.770 9	Q = 14.275 4t^{0.325 4}, R² = 0.838 7
	MD-N3-Ibu	Q = 0.203 0t + 38.797 9, R² = 0.543 1	100-Q = 100exp^{(-0.018 3t)}, R² = 0.560 2	Q = 4.268 0t^1/2 + 21.777 3, R² = 0.767 2	Q = 18.200 9t^{0.279 9}, R² = 0.856 3
	MD-N5-Ibu	Q = 0.205 5t + 40.677 7, R² = 0.555 3	100-Q = 100exp^{(-0.020 1t)}, R² = 0.581 2	Q = 4.296 5t^1/2 + 23.624 6, R² = 0.775 5	Q = 19.434 4t^{0.272 8}, R² = 0.863 3
	MD-N5 (1.0)-Ibu	Q = 0.204 5t + 39.173 0, R² = 0.543 6	100-Q = 100exp^{(-0.018 7t)}, R² = 0.571 1	Q = 4.288 0t^1/2 + 22.111 3, R² = 0.763 7	Q = 18.404 9t^{0.279 3}, R² = 0.849 1
	MD-N5 (1.5)-Ibu	Q =0.212 3t + 38.140 8, R² = 0.577 1	100-Q = 100exp^{(-0.018 0t)}, R² = 0.613 7	Q = 4.400 4t^1/2 + 20.799 1, R² = 0.792 2	Q = 17.535 9t^{0.288 9}, R² = 0.868 3

Method

Sample

Mathematical

Zero-order

First-order

Higuchi

Peppas

Template agent method

MD-Cur

Q = 0.050 8t+19.387 2, R²=0.577 7

100-Q = 100exp^{(-0.002 6t)}, R² = -6.517 5

Q = 1.052 3t^1/2 + 15.245 2, R² = 0.760 7

Q = 12.276 1t^{0.166 3}, R² = 0.912 2

MD-P0-Cur

Q = 0.041 9t + 24.105 1, R² = 0.852 0

100-Q = 100exp^{(-0.003 2t)}, R² = -15.657 7

Q = 0.863 8t^1/2 + 20.716 1, R² = 0.760 3

Q = 14.465 7t^{0.117 1}, R² = 0.927 1

MD-P0'-Cur

Q = 0.038 6t + 24.147 3, R² = 0.620 4

100-Q = 100exp^{(-0.003 1t)}, R² = -20.097 4

Q = 0.787 8t^1/2 + 21.079 8, R² = 0.803 1

Q = 18.040 4t^{0.106 7}, R² = 0.942 8

MD-P1-Cur

Q = 0.216 1t + 40.785 3, R²=0.644 3

100-Q = 100exp^{(-0.019 7t)}, R² = 0.576 6

Q = 4.378 4t^1/2 + 23.864 6, R² = 0.822 7

Q = 19.485 7t^{0.275 4}, R² = 0.913 0

MD-P3-Cur

Q = 0.168 8t + 52.265 2, R² = 0.675 1

100-Q = 100exp^{(-0.030 8t)}, R² = -0.314 3

Q = 3.384 1t^1/2 + 39.308 8, R² = 0.847 7

Q = 31.376 2t^{0.188 9}, R² = 0.949 2

MD-P5-Cur

Q = 0.125 3t + 62.292 7, R² = 0.562 9

100-Q = 100exp^{(-0.062 9t)}, R² = -1.053 4

Q = 2.589 2t^1/2 + 52.118 5, R² = 0.734 2

Q = 43.154 7t^{0.133 3}, R² = 0.902 2

MD-Ibu

Q = 0.078 6t + 15.416 5, R² = 0.493 1

100-Q = 100exp^{(-0.002 6t)}, R² = -1.080 4

Q = 1.631 2t^1/2 + 8.982 2, R² = 0.732 0

Q = 7.417 3t^{0.271 7}, R² = 0.854 7

MD-P0-Ibu

Q = 0.085 8t + 17.217 2, R² = 0.473 1

100-Q = 100exp^{(-0.003 0t)}, R² = -1.035 6

Q = 1.799 9t^1/2 + 10.053 3, R² = 0.723 2

Q = 8.287 3t^{0.270 2}, R² = 0.852 0

MD-P0'-Ibu

Q=0.070 6t + 18.247 0, R² = 0.453 3

100-Q = 100exp^{(-0.002 9t)}, R² = -2.209 7

Q = 1.485 4t^1/2 + 12.321 4, R² = 0.700 1

Q = 9.774 7t^{0.227 8}, R² = 0.851 5

MD-P1-Ibu

Q = 0.252 4t + 35.141 1, R² = 0.593 8

100-Q = 100exp^{(-0.017 5t)}, R² = 0.824 5

Q = 5.113 6t^1/2 + 15.380 9, R² = 0.822 9

Q = 14.567 8t^{0.334 0}, R² = 0.895 2

MD-P3-Ibu

Q = 0.226 2t + 41.911 1, R² = 0.656 8

100-Q = 100exp^{(-0.020 9t)}, R² = 0.581 0

Q = 4.497 6t^1/2 + 24.815 8, R² = 0.867 3

Q = 20.158 4t^{0.274 7}, R² = 0.941 9

MD-P5-Ibu

Q = 0.210 5t + 45.898 3, R² = 0.646 6

100-Q = 100exp^{(-0.024 3t)}, R² = 0.429 5

Q = 4.200 1t^1/2 + 29.886 8, R² = 0.860 8

Q = 23.756 5t^{0.245 7}, R² = 0.944 0

Pore-forming agent

MD-N1-Cur

Q = 0.180 1t + 42.287 0, R² = 0.572 5

100-Q = 100exp^{(-0.019 4t)}, R² = 0.197 7

Q = 3.742 4t^1/2 + 27.506 4, R² = 0.790 0

Q = 21.930 5t^{0.241 3}, R² = 0.890 8

MD-N3-Cur

Q = 0.183 8t + 42.655 1, R² = 0.587 0

100-Q = 100exp^{(-0.019 9t)}, R² = 0.253 9

Q = 3.799 1t^1/2 + 27.719 4, R² = 0.801 0

Q = 22.094 0t^{0.242 2}, R² = 0.896 7

MD-N5-Cur

Q = 0.180 6t + 44.841 0, R² = 0.544 9

100-Q = 100exp^{(-0.022 3t)}, R² = 0.274 8

Q = 3.785 3t^1/2 + 29.786 2, R² = 0.764 7

Q = 23.660 9t^{0.233 8}, R² = 0.872 0

MD-N5 (1.0)-Cur

Q = 0.177 6t + 42.752 7, R² = 0.549 6

100-Q = 100exp^{(-0.019 8t)}, R² = 0.201 6

Q = 3.712 3t^1/2 + 28.023 1, R² = 0.766 9

Q = 22.289 0t^{0.238 5}, R² = 0.866 9

MD-N5 (1.5)-Cur

Q = 0.181 4t + 43.162 3, R² = 0.550 6

100-Q = 100exp^{(-0.020 5t)}, R² = 0.266 8

Q = 3.791 6t^1/2 + 28.117 2, R² = 0.768 4

Q = 22.393 8t^{0.240 4}, R² = 0.868 8

MD-N1-Ibu

Q = 0.228 1t + 33.794 7, R² = 0.550 6

100-Q = 100exp^{(-0.016 0t)}, R² = 0.711 0

Q = 0.476 0t^1/2 + 14.818 9, R² = 0.770 9

Q = 14.275 4t^{0.325 4}, R² = 0.838 7

MD-N3-Ibu

Q = 0.203 0t + 38.797 9, R² = 0.543 1

100-Q = 100exp^{(-0.018 3t)}, R² = 0.560 2

Q = 4.268 0t^1/2 + 21.777 3, R² = 0.767 2

Q = 18.200 9t^{0.279 9}, R² = 0.856 3

MD-N5-Ibu

Q = 0.205 5t + 40.677 7, R² = 0.555 3

100-Q = 100exp^{(-0.020 1t)}, R² = 0.581 2

Q = 4.296 5t^1/2 + 23.624 6, R² = 0.775 5

Q = 19.434 4t^{0.272 8}, R² = 0.863 3

MD-N5 (1.0)-Ibu

Q = 0.204 5t + 39.173 0, R² = 0.543 6

100-Q = 100exp^{(-0.018 7t)}, R² = 0.571 1

Q = 4.288 0t^1/2 + 22.111 3, R² = 0.763 7

Q = 18.404 9t^{0.279 3}, R² = 0.849 1

MD-N5 (1.5)-Ibu

Q =0.212 3t + 38.140 8, R² = 0.577 1

100-Q = 100exp^{(-0.018 0t)}, R² = 0.613 7

Q = 4.400 4t^1/2 + 20.799 1, R² = 0.792 2

Q = 17.535 9t^{0.288 9}, R² = 0.868 3

模板剂法与致孔剂法对多孔麦芽糊精结构及药物递送影响研究

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李哲 , 罗晓岁 , 朱卫丰 , 李琼 , 管咏梅 , 金正吉 , 陈丽华 , 明良山 ^*

药学学报 | 研究论文 2024,59(8): 2381-2395

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药学学报 | 研究论文 2024, 59(8): 2381-2395

模板剂法与致孔剂法对多孔麦芽糊精结构及药物递送影响研究

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李哲, 罗晓岁, 朱卫丰, 李琼, 管咏梅, 金正吉, 陈丽华, 明良山^*

作者信息

江西中医药大学, 现代中药制剂教育部重点实验室, 高等研究院, 江西南昌 330004

通讯作者:

^*明良山，Tel: 15970770163, E-mail: jazmaster@163.com

Effects of template and pore-forming agent method on the structure and drug delivery of porous maltodextrin

Zhe LI, Xiao-sui LUO, Wei-feng ZHU, Qiong LI, Yong-mei GUAN, Zheng-ji JIN, Li-hua CHEN, Liang-shan MING^*

Affiliations

State Key Laboratory of Modern Preparation of TCM, Ministry of Education, Institute for Advanced Study, Jiangxi University of Chinese Medicine, Nanchang 330004, China

出版时间: 2024-08-12 doi: 10.16438/j.0513-4870.2024-0080

文章导航

摘要

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本研究以麦芽糊精为原料、1%~5%聚乙烯比咯烷酮K30为模板剂、1%~5%碳酸氢铵为致孔剂、姜黄素和布洛芬为模型药物, 分别采用模板剂法和致孔剂法制备多孔麦芽糊精, 对不同工艺制备多孔麦芽糊精结构和药物递送行为进行全面表征。结果显示, 致孔剂法制备多孔麦芽糊精具有更大比表面积(6.449 4 m²·g^-1) 和孔径(32.804 2 nm), 明显优于模板剂法(3.670 2 m²·g^-1, 15.278 5 nm)。致孔剂法制备多孔麦芽糊精吸附姜黄素过程符合准一级吸附动力学模型, 吸附布洛芬过程则符合准二级吸附动力学模型, 而模板剂法制备多孔麦芽糊精吸附两种模型药物过程均符合准一级吸附动力学模型。溶出行为表明两种工艺制备多孔麦芽糊精均可显著改善难溶性药物溶出行为, 且药物释放均符合Peppas释放动力学模型和扩散机制, 但模板剂法制备多孔麦芽糊精具有更快释药速率。喷嘴口径改变对多孔麦芽糊精吸附模型药物过程和药物释放行为均无明显影响。综上, 两种不同工艺制备多孔麦芽糊精均有利于难溶性药物的递送, 其中以模板剂法制备多孔麦芽糊精对难溶性药物的递送效果最优。该研究可为多孔粒子的制备提供理论依据, 促进多孔粒子在难溶性药物中的应用, 提高难溶性药物的生物利用度。

关键词

麦芽糊精 / 多孔结构 / 模板剂法 / 致孔剂法 / 吸附 / 药物递送

Abstract

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This study using maltodextrin as raw material, 1%-5% polyvinylpyrrolidone K30 as template agent, 1%-5% ammonium bicarbonate as pore-forming agent, curcumin and ibuprofen as model drugs. Porous maltodextrin was prepared by template and pore-forming agent methods, respectively. The structure and drug delivery behavior of porous maltodextrin prepared by different technologies were comprehensively characterized. The results showed that the porous maltodextrin prepared by pore-forming agent method had larger specific surface area (6.449 4 m²·g^-1) and pore size (32.804 2 nm), which was significantly better than that by template agent method (3.670 2 m²·g^-1, 15.278 5 nm). The adsorption kinetics between porous maltodextrin prepared by pore-forming agent method and curcumin were suitable for quasi-first order adsorption kinetic model, and that between porous maltodextrin and ibuprofen were suitable for quasi-second order adsorption kinetic model. While the adsorption kinetics between porous maltodextrin prepared by template agent method and two model drugs were both suitable for the quasi-first order adsorption kinetic model. In addition, the dissolution behavior analysis showed that the porous maltodextrin prepared by the two technologies can significantly improve the dissolution behavior of insoluble drugs, and the drug release was both carried out by diffusion mechanism, which suitable for the Peppas kinetic release model, but the porous maltodextrin prepared by template agent method had a faster release rate. The change of nozzle diameter had no significant effect on the adsorption process and drug release behavior of porous maltodextrin. In conclusion, the porous maltodextrins prepared by two different technologies were both beneficial to the delivery of insoluble drugs, and the template agent method was the best for delivery of insoluble drugs. This study can provide theoretical basis for the preparation of porous particles, promote the application of porous particles in insoluble drugs, and improve the bioavailability of insoluble drugs.

Key words

maltodextrin / porous structure / template agent method / pore-forming agent method / adsorption / drug delivery

引用本文

李哲, 罗晓岁, 朱卫丰, 李琼, 管咏梅, 金正吉, 陈丽华, 明良山. 模板剂法与致孔剂法对多孔麦芽糊精结构及药物递送影响研究. 药学学报, 2024 , 59 (8) : 2381 -2395 . DOI: 10.16438/j.0513-4870.2024-0080

Zhe LI, Xiao-sui LUO, Wei-feng ZHU, Qiong LI, Yong-mei GUAN, Zheng-ji JIN, Li-hua CHEN, Liang-shan MING. Effects of template and pore-forming agent method on the structure and drug delivery of porous maltodextrin[J]. Acta Pharmaceutica Sinica, 2024 , 59 (8) : 2381 -2395 . DOI: 10.16438/j.0513-4870.2024-0080

正文

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药物的口服吸收主要受药物在胃肠道溶解度、溶出速度和跨膜渗透能力的影响^[1]。口服吸收作为最常用的给药方法, 可以最大程度避免药物毒性和依赖性, 并且可以降低交叉感染的风险; 但大多数治疗药物的口服生物利用度有限, 其主要受药物在水中的低溶解度、低渗透性和较差的稳定性的影响, 所有这些都对其治疗效果构成了严重的挑战^[2]。溶解性是决定药物吸收和临床疗效的关键指标。据统计, 目前已上市的药物中存在40%以上的难溶性药物, 且新开发的药物中90%存在水溶性差的问题, 严重影响药物的吸收和生物利用度, 制约了药物疗效的发挥^[3,4]。因此, 提高难溶性药物的溶解度已成为目前亟需解决的难题。

目前, 各种配方技术被广泛应用于提高难溶性药物的溶解性能, 其中将药物分散在多孔材料中是提高难溶性药物溶解性的有效策略^[5-8]。在过去的几十年里, 多孔材料由于其较大的孔隙和较高的比表面积, 广泛应用于制药领域^[9,10]; 常用的制备方法主要包括模板剂法、致孔剂法、利用自身基质成孔和化学合成法^[11-14]。

麦芽糊精(maltodextrin, MD) 是一种由淀粉经部分酶解而产生的多糖, MD因易溶于水, 且易分散的特性在食品、化学和制药工业中应用广泛^[15,16], 尤其在药用固体制剂中应用较广^[17]。本研究以MD为例, 制备多孔MD, 以改善难溶性药物的溶出行为, 提高生物利用度。

本研究以MD为原材料, 姜黄素(curcumin, Cur) 和布洛芬(ibuprofen, Ibu) 为模型药物, 聚乙烯吡咯烷酮K30 (polyvinylpyrrolidone K30, PVP K30) 为模板剂, 碳酸氢铵(ammonium bicarbonate, NH₄HCO₃) 为致孔剂进行多孔MD的制备, 随后以表面张力和黏度等指标评价多孔MD的液体性质^[18], 采用扫描电镜(scanning electron microscope, SEM)、比表面积(specific surface area, SA)、孔体积(pore volume, PV)、孔直径(pore diameter, PD)、密度、流动性、傅里叶变换红外光谱(fourier transform infrared spectroscopy, FTIR)、X射线衍射(X-ray diffraction, XRD) 和差式扫描量热法(differential scanning calorimetry, DSC) 等方法对样品进行表征^[19,20], 最后进行吸附动力学和载药粉末的体外溶出研究, 探讨模板剂法和致孔剂法对多孔麦芽糊精的结构和药物递送的影响, 为多孔粒子的制备提供理论依据, 促进多孔粒子在难溶性药物中的应用, 提高难溶性药物的生物利用度。

材料与方法

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仪器小型喷雾干燥仪(B290 Buchi, 瑞士BUCHI有限公司); 电子天平(京制00000246号, 北京赛多利斯科学仪器有限公司); 智能磁力搅拌器(ZNCL-BS, 西安安泰仪器科技有限公司); 水分测定仪(HE53/02, 美国梅特勒-托利多仪器上海有限公司); 扫描电子显微镜(Quanta 250, 美国FEI公司); 马尔文激光粒度仪(MS2000, 英国马尔文公司); 真密度测定仪(Accupyc)、全自动比表面及孔隙度分析仪(APAP 2460) (美国麦克默瑞提克仪器公司); X射线衍射仪(D8Advance, 德国布鲁克公司); 全自动表/界面张力仪(BZY-1, 上海衡平仪器仪表厂); 黏度计(IKA ROTAVISC lo-vi, 广州市怡华新电子仪器有限公司); 粉体特性测定仪(BT-1000, Bettersize仪器上海有限公司); 差示扫描量热分析仪(DSC 8000, 美国PerkinElmer公司); 紫外可见分光光度仪(UV-VIS 2550, 日本岛津公司); 智能溶出仪(ZRS-8G, 天大天发科技有限公司)。

试剂 MD原粉(批号: 210112)、PVP K30 (批号: 180814)、NH₄HCO₃ (批号: 20002760) (安徽山河药用辅料有限公司); Cur (Lot#: C14362695)、Ibu (Lot#: C14552947)、溴化钾(Lot#: C2329459) (上海麦克林生化科技有限公司)。

多孔麦芽糊精的制备 将MD原粉溶于一定量的纯水中(10%, w/w), 通过改变模板剂PVP K30的用量(1%、3%、5%, w/w) 制备不同的样品溶液, 分别标记为MD-P1、MD-P3和MD-P5。所有样品溶液混合后均在室温下磁力搅拌30 min, 得到澄清透明的溶液进行喷雾干燥, 喷雾干燥过程参数如下: 进风温度为180 ℃, 雾化压力为0.2 bar, 进料速度为15 (蠕动泵设置值), 喷嘴口径为2.0 mm。随后将喷雾后的干粉与乙醇按1∶10的比例混合, 在室温下进行两次磁力搅拌, 转速为300 r·min^-1, 每次12 h, 除去上清液, 并移至烘箱中50 ℃干燥12 h。最后, 过三号筛, 收集干燥粉末。

称取一定量的MD原粉溶于纯水中(10%, w/w), 通过改变致孔剂NH₄HCO₃水溶液用量(1%、3%、5%, w/w) 制备多孔MD, 喷嘴口径与喷雾干燥条件同上, 样品分别标记为MD-N1、MD-N3和MD-N5。另选定NH₄HCO₃水溶液用量(5%, w/w), 通过改变喷嘴口径进行不同多孔MD制备, 样品分别标记为MD-N5 (1.0) 和MD-N5 (1.5)。所有样品溶液均在室温下磁力搅拌30 min, 得到澄清透明溶液进行喷雾干燥, 喷雾干燥条件同上。最后, 过三号筛, 收集喷雾干燥粉末。

根据公式(1)^[21]计算样品收率(Yield, %):

(1)

$ \mathrm{Y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}=\frac{{m}_{1}}{{m}_{0}}\times \mathrm{ }100 $

其中, m₁为喷雾干燥后的产品质量, m₀为喷雾干燥前的MD原料和模板剂/致孔剂的质量和。

本研究也对MD原粉进行了研究。未用模板剂/致孔剂和乙醇洗脱处理的未致孔MD标记为MD-P0 (MD-N0); 未用模板剂/致孔剂, 但用乙醇洗脱处理的未致孔MD标记为MD-P0'。两种未致孔MD制备条件与模板剂法制备多孔MD相同。实验中各样品的处方比例、制备工艺及喷嘴口径如表 1所示。

载药粉末的制备 分别将100.00 mg Cur和100.00 mg Ibu溶于30.0 mL乙醇中得到药物溶液, 与5.0 g上述样品混合, 以300 r·min^-1的转速磁力搅拌7 h, 以5 000 r·min^-1的转速进行离心(此时离心力为2 599 ×g), 除去上清液, 50 ℃干燥12 h即得载药粉末。

液体性质考察 采用全自动表/界面张力仪对喷雾干燥样品溶液的表面张力进行表征。

采用黏度计对喷雾干燥前样品溶液的黏度进行表征。黏度模式为VOL-SP-6.7, 测量范围为10~2 000 mPa·s, 转速为10 r·min^-1[21]。

粉体学基本性质考察 采用水分测定仪对样品的含水量(moisture content, MC) 进行表征^[22]。

采用全自动比表面及孔隙度分析仪表征样品在77 K液氮条件下的氮吸附等温线, 比表面积(SA)、孔体积(PV) 和孔直径(PD) 分别由Brunauser-Emmet-Teller (BET) 方程和Barrett-Joyner-Halenda (BJH) 方程计算得到^[23]。

采用马尔文激光粒度仪表征样品的粒径(d_0.5)、粒径分布(span) 和均一性(uniformity)。

采用粉体特性测定仪表征样品的松散密度(bulk density, ρ_b) 和振实密度(tap density, ρ_t), 并根据公式(2) 和(3) 计算其卡尔指数(Carrs's index, CI) 和豪纳斯比(Hausner ratio, HR)。采用真密度测定仪表征样品的真密度(true density, ρ_true)。采用固定漏斗法表征样品的休止角(angle of repose, AR)^[22]。

(2)

$ \mathrm{C}\mathrm{I}=\frac{{\rho }_{t}-{\rho }_{b}}{{\rho }_{t}} $

(3)

$ \mathrm{H}\mathrm{R}=\frac{{\rho }_{t}}{{\rho }_{b}} $

SEM 取少量样品, 经喷金处理后利用SEM表征样品结构。实验加速电压为10 kV, 放大倍率为3 000 X和6 000 X^[24]。

FTIR 采用FTIR经溴化钾压片法对样品进行表征, 扫描范围为4 000~400 cm^-1, 分辨率为4.0 cm^-1, 扫描次数16次^[25]。

XRD 采用X射线衍射仪, 在40 mA、40 kV的Cu Kα1辐射下表征MD、MD喷干粉及PVP K30和NH₄HCO₃的晶体形态, 扫描范围为5~60°, 扫描速度为每步0.1 s, 步长为0.02°。

DSC 取样品约10 mg至密封坩埚中, 采用DSC进行表征。升温速率为10 ℃·min^-1, N₂吹扫速率为20 mL·min^{-1 [26]}。

吸附动力学 称取适量的Cur和Ibu, 分别溶于100 mL无水乙醇中, 然后以自制喷干粉作为吸附剂, 分别称取0.500 0 g加入到Cur和Ibu的乙醇溶液中, 观察吸附情况。室温下以300 r·min^-1的速度搅拌, 于不同时间取样, 过滤, 取续滤液, 紫外可见分光光度仪测定Cur和Ibu的吸附量, 波长分别为425和264 nm。Cur和Ibu的吸附量与准一级、准二级吸附动力学方程如下式^[27-29]:

(4)

$ {Q}_{e}=({C}_{0}-{C}_{e})\times V/m $

(5)

$ In\left({Q}_{e}-{Q}_{t}\right)=In{Q}_{e}-{k}_{1}\times t $

(6)

$ t/{Q}_{t}=1/({k}_{2}\times {Q}_{e}^{2})+t/{Q}_{e} $

式中, C₀、C_e分别为模型药物的初始质量浓度、平衡质量浓度, 单位为mg·L^-1; m表示吸附剂的质量, 单位为g; V表示模型药物乙醇溶液的体积; Q_e、Q_t分别为任意时刻样品的吸附量和平衡吸附量, 单位为mg·g^-1; k₁、k₂分别为准一级和准二级吸附动力学的速率常数。

溶出行为 采用智能溶出仪表征负载Cur和Ibu的样品的溶出行为。将含有5% Tween 80的pH = 6.8磷酸盐缓冲盐溶液作为溶出介质(200 mL), 温度为37 ± 0.5 ℃, 转速为100 r·min^-1。分别称取负载Cur的载药粉末200 mg和负载Ibu的载药粉末500 mg, 置于200 mL溶出介质中, 分别在5、10、15、30、45、60、90、120和240 min时取样, 并及时补给相同体积的溶出介质。取出的溶出液需通过0.45 μm有机滤膜过滤, 取续滤液, 分别在425和264 nm处进行紫外分析。绘制溶出曲线, 并通过对零级、一级、Higuchi和Peppas模型的拟合, 以确定释药模型。

统计学方法 采用SPSS 21.0数据分析软件对所得数据进行处理, 经正态性、方差齐性检验后进行方差分析(ANOVA), 并使用LSD检验作多重比较。

结果与讨论

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1 液体性质分析

喷雾干燥过程及其产物的性质会受到样品溶液表面张力、黏度的影响^[30]。由图 1可知, 混合溶液的表面张力与模板剂的加入量成反比, 而混合溶液的黏度与模板剂的加入量成正比, 即随着PVP K30用量的增加, 混合溶液的表面张力逐渐减小(图 1A), 而黏度逐渐增加(图 1B)。致孔剂的加入量使样品溶液的表面张力和黏度的变化规律与模板剂法相同, 即随着NH₄HCO₃用量的增加, 混合溶液的表面张力逐渐减小(图 1C), 而黏度逐渐增加(图 1D), 但两者均比未添加PVP K30/NH₄HCO₃溶液的表面张力和黏度小。与10% MD溶液相比, MD-P1、MD-P3和MD-P5的表面张力分别降低了23.9%、27.3%和36.0%, 黏度分别降低了43.4%、13.4%和8.8%, 随着PVP K30用量的增加, 溶液的表面张力降低, 黏度增加; 其次, 与10% MD溶液相比, MD-N1、MD-N3和MD-N5的表面张力分别降低9.2%、22.3%和26.4%, 黏度分别降低48.0%、44.5%和41.6%, 随着NH₄HCO₃用量的增加, 溶液表面张力降低, 黏度增加。结果表明, 模板剂和致孔剂均会影响混合样品溶液的表面张力和黏度, 随着模板剂/致孔剂用量增加, 混合溶液的表面张力降低, 黏度增加。

2 粒子的表面形态分析

SEM表征结果证实模板剂法和致孔剂法均成功制备了多孔MD (图 2)。与MD原粉和未致孔MD相比, 多孔MD表现出明显孔隙结构。MD原粉(图 2A) 呈现表面皱缩的元宝状结构, 而经过喷雾干燥处理后的MD (图 2B~K) 为表面光滑或皱缩的球形, 其中多孔MD (图 2D~K) 均具有明显孔隙结构。通过对比可以发现, 经喷雾干燥、但未用模板剂/致孔剂处理的未致孔MD (图 2B、C) 均为表面光滑的球形, 表明乙醇洗脱不会破坏粉体结构; 致孔剂法制备的多孔MD (图 2G~K) 具有较光滑的表面形貌, 而模板剂法制备的多孔MD (图 2D~F) 表现出皱缩的表面形貌。随着模板剂/致孔剂用量的增加, 各多孔MD的孔隙结构更加明显, 均呈现出与未致孔MD (图 2B、C) 不同的表面形貌，表明两种工艺均可成功制备多孔MD。图 2I~K展示了致孔剂用量相同, 喷嘴口径不同时制备的多孔MD的SEM结果, 通过对比, 发现喷嘴口径的改变不会对多孔MD的表面形貌产生影响, 但会改变多孔MD的粒子大小。SEM结果表明, 模板剂法和致孔剂法均能成功制备多孔MD, 但随着模板剂/致孔剂用量和喷嘴口径的变化, 粒子的多孔结构发生改变。

3 粉体学基本性质分析

为考察模板剂法和致孔剂法对多孔MD结构的影响, 对其进行粉体学基本性质表征(表 2)。结果表明, 由不同工艺制备的多孔MD具有相似的结构, 制备工艺对多孔MD的粉体学基本性质无影响; 当制备工艺相同时, 处方比例和喷嘴口径的不同会导致多孔MD的结构与基本性质发生改变。具体表现为模板剂法与致孔剂法制备的多孔MD均呈现出较好的蓬松性, 较差的流动性和较小的粒径, 且多孔MD具有较低的密度值和较高的SA、PD; 其次, 随着处方中模板剂/致孔剂用量的增加, 多孔MD的流动性更差, 粒径变大、密度值降低, SA值增加、PD降低; 当喷嘴口径最大时, 多孔MD具有最大的粒径和PD。

对模板剂法制备的多孔MD, PVP K30的加入使样品收率明显提高, 与MD-P0'相比, MD-P1、MD-P3和MD-P5的收率分别增加了4.42%、5.19%和4.55%。样品的MC较低, 范围为0.43%~0.62%, 对实验影响可忽略不计。与MD原粉和未致孔MD (MD-P0, MD-P0') 相比, 多孔MD表现出更小的粒径。研究表明, 材料粒径分布与d_0.5, span和uniformity相关, span和uniformity值越大, 粒径分布越不均匀, d_0.5越小, 粒径分布越不均匀^[31,32]。与MD原粉相比, 多孔MD的d_0.5较低, 分别降低了82.9%、76.9%和74.6%; 而span和uniformity逐渐增加, 多孔MD的span分别比MD增加了12.0%、53.6%和190.1%, uniformity分别增加14.9%、59.0%和172.2%。随着PVP K30用量增加, 多孔MD的粒径增加, 这可能因喷雾干燥过程中PVP K30用量增加导致混合样品溶液表面张力降低、黏度增加(图 1)。样品溶液表面张力越小, 喷雾干燥产物粒径越小^[33]; 而溶液黏度是决定雾化颗粒粒径另一关键指标, 两者之间正相关, 即溶液黏度越大, 雾化颗粒粒径越大^[34]。粒径结果表明, 溶液黏度比表面张力对粒径的影响更明显。AR、CI和HR通常被用来表征材料的流动性, 材料流动性与上述3个参数成反比, 即值越小, 材料流动性越好^[35]。与MD原粉相比, 多孔MD的AR分别增加2.8%、3.7%和19.6%; CI分别增加169.6%、165.3%和152.3%; HR分别增加44.9%、43.2%和38.1%。较高的AR、CI和HR表明多孔MD较差的流动性。小颗粒往往导致较差的流动性, 这一结论与多孔MD粒径结果相对应。另外, 与MD原粉相比, 多孔MD的ρ_b、ρ_t和ρ_true均较低, 分别降低46.9%~53.1%、22.4%~34.5%和11.1%~12.7%, 较低密度值表明多孔MD蓬松性较好, 具有较好填充和药物负载能力; 且多孔MD具有较高SA和较大PD, 多孔MD的SA分别比MD原粉增加14.8、29.0和35.2倍; PD分别增加12.4、6.8和6.3倍。与未致孔MD相比(MD-P0'), 多孔MD同样具有较大的SA和PD。多孔MD的SA分别比MD-P0'增加了34.5%、155.8%和209.0%, PD分别增加了147.3%、43.9%和34.7%。

其次, 致孔剂法制备的多孔MD: ①当喷嘴口径相同, 致孔剂用量不同时, 与MD原粉相比, 多孔MD的AR较大, 且随着NH₄HCO₃用量增加而增加; 多孔MD的CI和HR表现出与AR相同增长趋势, 表明与MD原粉相比, 经过喷雾干燥处理后多孔MD流动性较差, 这一结果与模板剂法制备多孔MD的结果相同。与MD原粉相比, 多孔MD粒径较小, 其中多孔MD d_0.5值分别降低82.8%、75.3%和60.5%, 但span和uniformity值分别增加30.3%、7.2%、22.8%和31.4%、7.5%、23.1%, 可能是因多孔MD中NH₄HCO₃用量增加导致溶液黏度增加, 溶液黏度是导致粒径变化的主要因素。这一发现与模板剂法制备多孔MD的粒径结果相同。与MD原粉相比, 较低密度值表明制备多孔MD具有较好蓬松性; 且多孔MD的SA-BET分别比MD原粉增加6.0、22.8和62.7倍; SA-BJH分别增加6.3、25.7和149.5倍; PV分别增加3.2、11.6和5.3倍; PD分别增加20.6、16.6和14.7倍。随着致孔剂用量增加, 多孔MD的SA呈递增趋势, 而PD呈下降趋势(表 2), 这可能是因致孔剂用量增加, 多孔MD表面具有更复杂孔隙结构, 孔隙的数量和大小发生改变, 粒子表面粗糙程度不同。②当处方中致孔剂用量相同, 喷嘴口径不同时, 多孔MD粒径大小随着喷嘴口径的增加而增加, 且由小口径喷嘴(1.0 mm) 制备的多孔MD具有略大的CI和HR, MD-N5 (1.0) 的CI和HR分别比MD-N5增加3.0%和2.7%, 流动性变差。这一发现与不同喷嘴直径会影响粉末均匀性的结论一致^[36]。另外, 多孔MD的SA和PD也随喷嘴口径增加而变化, 多孔MD的SA逐渐增加, 而PD逐渐降低, 这一发现与前文讨论的结果一致。综上, 对于同种原料, 制备工艺对喷雾干燥样品的结构和基本性质无影响, 但处方比例和喷嘴口径对喷雾干燥样品的结构和基本性质的影响较大。

4 FTIR分析

为验证多孔MD是否有模板剂、致孔剂残留及以多孔MD为载体对模型药物是否成功负载, 分别对模板剂法和致孔剂法制备样品进行FTIR分析(图 3)。结果表明, 与MD原粉比较, 两种不同工艺制备多孔MD的FTIR中均未有PVP K30和NH₄HCO₃特征峰, 表明两种工艺均成功制备出无杂质的多孔MD, 且随着模板剂/致孔剂用量增加曲线均未发生明显变化, 表明模板剂/致孔剂的用量对多孔MD的FTIR结果无影响; 以两种不同工艺制备多孔MD为载体时, 载药前后样品红外曲线相同, 表明以两种不同工艺制备的多孔MD为载体均能实现药物的负载。

图 3A、B展示了模板剂法制备多孔MD载药前后FTIR图, 其中图 3A为载药前各MD样品FTIR图, MD在3 500 cm^-1附近有较强的O-H伸缩振动峰; 在2 900 cm^-1附近有尖锐吸收峰, 为C-H伸缩振动峰; 另外MD在1 600 cm^-1附近吸收峰为C=O伸缩振动、1 290~1 022 cm^-1内吸收峰为C-O-H弯曲振动和C-O伸缩振动, 其中在1 022 cm^-1吸收峰强度高, 与文献^[37]中报道的结果一致。研究发现, PVP的羰基伸缩振动峰最为明显, 通常出现在1 658 cm^-1附近^[38]; 而以PVP为模板剂制备多孔MD (MD-P1、MD-P3和MD-P5) 不存在该吸收峰, 且随着PVP K30用量的增加, 多孔MD曲线无明显变化, 表明多孔MD的模板剂均已被洗脱干净, 且模板剂用量对红外结果无影响。载药后MD样品红外曲线如图 3B。Cur在3 510 cm^-1处有一明显吸收峰, 对应于Cur中酚羟基O-H伸缩振动, 在1 632 cm^-1处吸收峰对应Cur羰基^[39]。而负载Cur的MD-P0'和MD-P5没有Cur特征峰, 且1 632 cm^-1处吸收峰消失, 提示Cur的羰基与MD羟基结合形成氢键, 表明Cur被成功负载。对于Ibu, 其在3 120~2 950 cm^-1内吸收峰对应于芳香族C-H伸缩振动, 2 950~2 866 cm^-1吸收峰对应于脂肪族C-H伸缩振动, 其余在1 718、1 230和778 cm^-1处吸收峰分别为羧基拉伸振动、C-C键拉伸振动和CH₂摇摆振动^[40]。负载Ibu的MD-P0'和MD-P5同样未有Ibu的特征峰, 表明Ibu被成功负载。

图 3C、D为致孔剂法制备MD样品载药前后FTIR结果。图 3C为载药前各样品红外曲线, MD、MD-N0和多孔MD具有类似图谱(图 3C), 表明喷雾干燥过程不会改变样品结构信息。图中3 114 cm^-1处宽峰为NH₄⁺特征峰, 1 398~834 cm^-1波段为碳酸盐振动峰^[41], 而以NH₄HCO₃为致孔剂制备多孔MD不存在这些特征峰, 且随着NH₄HCO₃用量增加, 各多孔MD (MD-N1、MD-N3和MD-N5) 红外曲线无明显变化, 表明无致孔剂存在。通过多孔MD载药前后对比(图 3D), 发现致孔剂法制备多孔MD载药后红外曲线与载药前相同, 且均不包含药物特征峰, 表明药物可被成功负载。这一结论与模板剂法制备载药粉末结果相同。

FTIR结果进一步证实多孔MD的成功制备, 表明模板剂法和致孔剂法均可成功制备多孔粒子, 且处方中模板剂/致孔剂用量改变不会影响样品的FTIR结果, 不同工艺制备多孔MD作为载体均可实现药物成功负载。

5 XRD分析

XRD作为测定晶体材料基本结构信息快速、直接的方法被广泛应用。通常, 结晶材料呈现尖锐峰, 而无定形材料呈现扩散峰(宽峰)。XRD结果表明, 基于宽峰存在, 所有MD样品均呈现无定形结构(图 4), 且随着处方中模板剂/致孔剂用量增加, 多孔MD的XRD曲线无明显变化, 表明制备工艺和处方比例改变不会影响材料XRD结果。

图 4A显示MD原粉、MD喷雾干燥样品及PVP K30的XRD图, MD原粉在2θ = 19°处出现明显的宽峰, 表明MD无定型状态; 而MD喷干粉在2θ = 19°处出现同样宽峰, 表明喷雾干燥过程不会影响材料结晶特性, 这与文献^[42]中报道喷雾干燥过程不会影响MD材料特性结果一致。另外, PVP在2θ = 11.6°和2θ = 22.1°处出现两宽峰, 表明PVP无定形结构^[43]。对比多孔MD (MD-P1、MD-P3和MD-P5) 和PVP的XRD谱图可以发现, 多孔MD中不存在PVP扩散峰, 进一步表明模板剂已被洗脱干净, 与前文讨论的模板剂法制备多孔MD的FTIR结果一致。

图 4B展示致孔剂法制备的MD样品XRD结果, 经喷雾干燥处理后未致孔MD (MD-N0) 和多孔MD (MD-N1, MD-N3和MD-N5) 具有相同XRD曲线, 且这些样品XRD曲线均呈现出宽峰, 表明经过喷雾干燥处理后样品呈现无定形状态; 而MD原粉也表现为无定形状态, 表明喷雾干燥过程不会改变MD原有形态。这一发现与模板剂法制备多孔MD的XRD结果一致。图中NH₄HCO₃在2θ值在17.1°、23.8°、30.4°和42.5°为尖锐的单峰, 表明NH₄HCO₃以结晶态存在, 这一发现与Han等^[44]关于NH₄HCO₃以结晶态形式存在的结果一致。而多孔MD的XRD曲线中不存在NH₄HCO₃相应2θ峰, 表明致孔剂法制备多孔MD中无NH₄HCO₃残留。

6 DSC分析

DSC验证模型药物与MD喷雾干燥样品包合状态, 探讨由模板剂法和致孔剂法制备多孔MD对药物负载的影响(图 5)。结果显示, 与载药前样品相比, 载药后样品DSC曲线均有相应药物熔融峰, 表明两种不同工艺制备多孔MD均能实现药物负载。

根据DSC曲线可知所有样品均表现出熔融峰, 其中Cur的熔融峰位于183 ℃ (图 5A), 与文献^[45]报道Cur的DSC结果相似。图 5B展示模板剂法制备MD样品载药前后DSC结果, 负载Cur的MD-P0'和MD-P5分别在186和189 ℃处有一个尖峰, 可能是Cur的熔融峰, 但该峰与纯Cur熔融峰相比发生了偏移, 可能是由于Cur与MD结合形成氢键, 这一结论与前面载药粉末的FTIR结果一致。Ibu熔融峰位于79 ℃^[46], 负载Ibu的MD-P0'和MD-P5分别位于81和85 ℃ (图 5B), 与纯Ibu熔融峰相比, 载药粉末中药物熔融峰也发生偏移。另外载药前后MD-P0'和MD-P5的DSC曲线对比表明药物被成功负载, 这与载药粉末FTIR结果相对应。

图 5C为致孔剂法制备MD样品载药前后DSC结果, 负载Cur载药粉末在184 ℃附近出现一个尖锐单峰, 为Cur熔融峰; 同样, 负载Ibu载药粉末在80 ℃附近出现一个小峰, 为Ibu的熔融峰, 这一结论与模板剂法制备多孔MD的DSC结果一致, 表明药物可被成功负载。DSC结果表明, 不同工艺制备MD样品均能实现药物负载。

7 吸附动力学分析

本研究分别以模板剂法、致孔剂法和不同喷嘴口径制备多孔MD作为吸附剂对模型药物Cur和Ibu进行吸附动力学拟合(图 6~8)。结果表明, 模板剂法和致孔剂法制备多孔MD对两种模型药物载药量和吸附率均大于MD原粉和未致孔MD, 且随处方中模板剂/致孔剂用量增加而增加, 当模板剂/致孔剂用量最大(5%), 多孔MD具有最大吸附率; 以模板剂法制备多孔MD对模型药物吸附率更大(表 3)。研究表明, 吸附动力学相关系数(R²) 通常与吸附模型选择密切相关^[47]。根据R²可知, 不同工艺制备多孔MD样品与模型药物之间存在不同吸附模型。当处方致孔剂用量和制备工艺相同, 而喷嘴口径不同时(图 8), 多孔MD对模型药物吸附量差异较小, 由最大喷嘴口径(2.0 mm) 与最小喷嘴口径(1.0 mm) 制备多孔MD对模型药物吸附量误差范围分别为 < 0.005 mg·g^-1和 < 0.5 mg·g^-1, 值较小, 可忽略不计, 表明喷嘴口径改变对药物吸附量无显著影响。吸附动力学结果表明, 不同制备工艺和处方中模板剂/致孔剂用量均会影响药物吸附量, 而喷嘴口径对吸附量影响可忽略不计。

以模板剂法制备MD样品作为吸附剂对模型药物吸附拟合曲线如图 6所示。当吸附剂相同时, 模型药物吸附量随时间增加而增加, 最终在6 h达到吸附平衡; 相同条件下, Ibu吸附量明显大于Cur吸附量(图 6A、B)。可能是药物分子大小和形状、MD大小与排列差异导致药物分子在多孔结构分散不同^[6]。对两种模型药物而言, 当处方中模板剂用量逐渐增加时, 多孔MD对药物吸附量也随之增加; 当模板剂用量为5%时, 多孔MD对模型药物存在最大吸附量, 分别为0.041和6.3 mg·g^-1。由图 6C~F所示, 模型药物准一级和准二级吸附动力学拟合曲线均存在线性关系, 拟合方程见表 4。对于两种模型药物, 以多孔MD为吸附剂, 准一级吸附动力学R²大于准二级吸附动力学, 因此, 当采用模板剂法制备多孔MD时, 准一级吸附动力学模型更符合其对Cur和Ibu吸附过程。吸附动力学结果表明, 处方中模板剂用量是造成药物吸附量存在差异原因之一, 当模板剂用量最大时(5%), 多孔材料对模型药物吸附量最大。

以致孔剂法制备的多孔MD为吸附剂对模型药物的吸附拟合曲线如图 7所示, 随着吸附时间的增加, 各吸附剂在6 h时也达到吸附平衡, 且在吸附剂相同的条件下, Ibu的吸附量大于Cur的吸附量(图 7A、B), 这一发现与模板剂法结果相同。对于两种模型药物而言, 随着致孔剂用量的增加, 吸附量逐渐增加, 当致孔剂用量为5%时, 多孔MD对模型药物存在最大吸附量, 分别为0.039和6.1 mg·g^-1, 与模板剂法制备多孔MD吸附量差异小。Cur和Ibu准一级和准二级拟合曲线均存在线性关系(表 4)。对于模型药物Cur而言, 当以多孔MD为吸附剂时, 准一级吸附动力学R²比准二级吸附动力学大, 表明准一级吸附动力学更适合其与Cur吸附过程; 而对于Ibu, 准二级吸附动力学R²比准一级吸附动力学大, 表明准二级吸附动力学更适合其与Ibu吸附过程, 这一结论有别于模板剂法。

当制备工艺和处方中致孔剂用量相同时, 通过改变喷嘴口径制备得到多孔MD对模型药物吸附量无明显差异(图 8), 由最大喷嘴口径(2.0 mm) 与最小喷嘴口径(1.0 mm) 制备多孔MD对模型药物吸附量误差范围较小, 可忽略不计, 表明当制备工艺和处方中致孔剂用量相同时, 喷嘴口径改变不会影响药物吸附量。吸附动力学结果表明, 制备工艺和处方中模板剂/致孔剂比例变化对模型药物吸附动力学过程均会产生影响, 而喷嘴口径不同对吸附动力学结果无明显影响。

8 溶出行为分析

为验证多孔辅料对难溶性药物溶出行为改善效果进行载药粉末体外溶出研究。本研究分别以模板剂法和致孔剂法及改变喷嘴口径制备多孔MD为载体对模型药物进行体外溶出行为研究(图 9)。结果显示, 制备工艺和处方中模板剂/致孔剂用量均会对载药粉末累积释放率产生影响, 其中以模板剂法制备多孔MD为载体具有较高累积释放率, 表明其对难溶性药物溶出行为改善效果最好; 其次, 当制备工艺相同, 处方中模板剂/致孔剂用量不同时, 模型药物累积释放率存在差异, 即模型药物累积释放率随着处方中模板剂/致孔剂用量增加而增加, 模板剂/致孔剂用量为5%时, 模型药物具有最大累积释放率, 溶出行为改善效果最佳。当制备工艺和处方中致孔剂用量相同, 喷嘴口径改变时, 难溶性药物溶出行为无明显变化。因此, 多孔MD制备工艺和处方比例均会影响药物释放。

为进一步探索药物的释放动力学, 使用4种常见的数学模型^[48,49], 即零级、一级、Higuchi和Peppas动力学模型对多孔MD载药粉末体外释放动力学进行拟合, 拟合结果见表 5。零级、一级和Higuchi动力学模型均不符合模型药物从多孔MD释放, 而Peppas是解释药物释放机制最佳模型; 其次, 模板剂法和致孔剂法制备多孔MD载药粉末Peppas释放动力学R²最高, 进一步验证药物最佳释放模型。研究表明, Peppas模型中n表示释放曲线, n ≤ 0.5为扩散释放, n ≥ 1为侵蚀释放机制, 0.5 ≤ n ≤ 1为扩散和侵蚀组合机制 ^[50,51]。在本研究中, Peppas模型的n值均低于0.5, 表明药物释放主要是以扩散机制。

以模板剂法制备MD样品为载体时, 对模型药物Cur (图 9A), 其多孔载药粉末在前15 min内快速释放, 在120 min时达到溶出平衡, 此后溶出介质中药物浓度无明显变化, 平衡时各多孔载药粉末累积释放率分别为78%、82%和84%。对于模型药物Ibu (图 9B), 其多孔载药粉末在前15 min内释放速度较快, 在120 min时趋于平衡状态, 此时, 各多孔载药粉末累积释放率分别为78%、82%和83%。结合数学模型发现(表 5), Peppas动力学模型更符合药物体外释放, 且药物释放表现为扩散机制。对比图 9A、B可知, 当以MD-P5为载体进行药物负载时, 其溶出行为表现最好, 累积释放率最高。结果表明, 当制备工艺相同时, 处方中模板剂用量增加对难溶性药物累积释放率影响显著, 其中当模板剂用量为5%时, 对难溶性药物溶出行为改善效果最好。

以致孔剂法制备MD样品为载体, 体外溶出曲线如图 9C、D所示。结果表明, 随着时间进行, 各样品在120 min时也达到溶出平衡, 此时各多孔载药粉末累积释放率分别为71%、73%、75%和71%、72%、74%, 明显低于模板剂法, 但药物释放机制与模板剂法相同。对比图 9A~D发现, 制备工艺显著影响难溶性药物溶出行为, 其中以模板剂法制备多孔MD对难溶性药物改善效果最好。处方比例是影响药物溶出行为另一重要因素, 当处方中致孔剂用量最大时(5%), 难溶性药物累积释放率最大, 溶出行为改善效果最佳。当制备工艺和处方中致孔剂用量相同, 喷嘴口径不同时进行多孔MD制备, 并将其作为载体进行药物负载, 其体外溶出曲线如图 9E、F所示, 各样品在120 min均达到溶出平衡, 在平衡时各样品累积释放率无明显差异, 表明喷嘴口径改变对难溶性药物溶出行为无显著影响。

综上所述, 制备工艺和处方中模板剂/致孔剂比例均会影响难溶性药物溶出, 其中以模板剂法制备多孔MD为载体, 且处方组成中模板剂用量为5%时, 难溶性药物溶出行为改善效果最好; 而当制备工艺和处方中致孔剂用量均相同时, 喷嘴口径改变对难溶性药物溶出行为无明显影响。结合药物释放动力学模型发现, 两种不同工艺制备载药粉末药物释放均符合Peppas动力学模型, 且模型药物主要通过扩散机制进行释放。但本研究中使用模型药物Cur和Ibu均为BCS2类药物, 难以包含所有难溶性药物; 其次, 本研究虽然成功筛选出最佳制备工艺、处方比例和喷嘴口径, 但缺乏致孔剂对提高口服吸收生物利用度影响分析。因此, 未来将基于本研究, 增加BCS4类的药物, 如紫杉醇、冬凌草甲素等^[52,53]以验证技术的通用性; 并根据筛选出的最佳处方, 选择模型药物, 研究致孔剂对口服吸收生物利用度的影响。

结论

收起

本研究采用模板剂法和致孔剂法进行多孔MD的制备, 考察不同制备工艺、处方中模板剂/致孔剂比例和喷嘴口径对多孔MD结构影响, 探讨不同工艺对难溶性药物(Cur和Ibu) 溶出行为改善效果, 同时对药物释放机制进行讨论。结果表明, 模板剂法和致孔剂法均可成功制备具有较大SA和PD多孔MD, 其中模板剂法制备多孔MD的SA和PD最大。吸附动力学结果表明, 模板剂法制备多孔MD与模型药物间吸附均符合准一级吸附动力学模型, 而致孔剂法制备多孔MD与模型药物间吸附表现出不同吸附模型。两种不同工艺制备多孔MD吸附量均随着改性剂比例增加而增加, 但模板剂法制备多孔MD对模型药物吸附量略大。体外溶出结果表明, 两种工艺制备多孔MD均能有效改善难溶性药物溶出行为, 且药物释放为扩散机制。随着处方中模板剂/致孔剂比例的增加, 模型药物累积释放率逐渐增加, 其中当处方中模板剂/致孔剂的比例最大时(5%), 模型药物具有最大累积释放率; 但由模板剂法制备多孔MD释药速率快, 且药物累积释放率最高, 对难溶性药物溶出行为改善效果最佳。喷嘴口径改变对模型药物吸附动力学和难溶性药物溶出行为改善均无明显影响。本研究通过两种制备工艺对多孔MD结构和药物递送影响分析, 为多孔粒子选择最佳制备工艺和处方比例提供参考, 促进多孔粒子在难溶性药物中应用, 提高难溶性药物生物利用度。

作者贡献: 李哲负责实验和论文框架设计、论文撰写与修改; 罗晓岁负责查阅文献、实验实施、数据处理与论文撰写; 朱卫丰提供研究思路; 李琼、管咏梅、金正吉和陈丽华参与文献资料的调研、文本修改; 明良山负责实验设计、全文的审阅与修改。

利益冲突: 本文所有作者声明不存在利益冲突关系。

基金

收起

国家自然科学基金资助项目(82360777)
江西省自然科学基金资助项目(20232ACB216015)
江西中医药大学博士科研启动基金项目资助(2021BSZR015)
江西中医药大学博士科研启动基金项目资助(2022BSZR003)
江西中医药大学校级研究生创新专项资金项目(JZYC23S70)

参考文献

收起

文献

收起

参考文献引证文献

排序方式：

[1]

Zhang Y, Cui T, Li SX, et al. Improving oral absorption of BCS Ⅱ drugs by increasing solubility: frequently overlooked permeability [J]. Acta Pharm Sin (药学学报), 2019, 54: 1-7.

[2]

Laffleur F, Keckeis V. Advances in drug delivery systems: work in progress still needed? [J]. Int J Pharm, 2020, 590: 119912.

[3]

Loftsson T, Brewster ME. Pharmaceutical applications of cyclodextrins: basic science and product development [J]. J Pharm Pharmacol, 2010, 62: 1607-1621.

[4]

Bhakay A, Rahman M, Dave RN, et al. Bioavailability enhancement of poorly water-soluble drugs via nanocomposites: formulation-processing aspects and challenges [J]. Pharmaceutics, 2018, 10: 86.

[5]

Liu W, Wang TT, Tang XL, et al. Porous lactose as a novel ingredient carrier for the improvement of quercetin solubility in vitro [J]. Bioinorg Chem Appl, 2021, 2021: 2586990.

[6]

Saffari M, Ebrahimi A, Langrish T. A novel formulation for solubility and content uniformity enhancement of poorly water-soluble drugs using highly-porous mannitol [J]. Eur J Pharm Sci, 2016, 83: 52-61.

[7]

Tan SW, Ebrahimi A, Liu XY, et al. Hollow flower-like lactose particles as potential drug carriers: effect of particle size and feed concentration [J]. Powder Technol, 2017, 320: 1-6.

[8]

Ebrahimi A, Saffari M, Langrish T. Improving the dissolution rate of hydrophobic drugs through encapsulation in porous lactose as a new biocompatible porous carrier [J]. Int J Pharm, 2017, 521: 204-213.

[9]

Sun Y, Chu YL, Wu WB, et al. Nanocellulose-based lightweight porous materials: a review [J]. Carbohydr Polym, 2021, 255: 117489.

[10]

Maksoud FJ, Velazquez de la Paz MF, Hann AJ, et al. Porous biomaterials for tissue engineering: a review [J]. J Mater Chem B, 2022, 10: 8111-8165.

[11]

Zhou MM, Shen L, Lin X, et al. Design and pharmaceutical applications of porous particles [J]. RSC Adv, 2017, 7: 39490-39501.

[12]

Ebrahimi A, Saffari M, Langrish T. Spray drying and post-processing production of highly-porous lactose particles using sugars as templating agents [J]. Powder Technol, 2015, 283: 171-177.

[13]

Tan SW, Ebrahimi A, Liu XY, et al. Role of templating agents in the spray drying and postcrystallization of lactose for the production of highly porous powders [J]. Drying Technol, 2018, 36: 1882-1891.

[14]

Wang XH, Wang G, Marchetti A, et al. Preparation of porous hydroxyapatite and its application in Pb ions effective removal [J]. AIP Adv, 2019, 9: 025123.

[15]

Aakash P, Siddharth A, Kirtesh R. A review on applications of maltodextrin in pharmaceutical industry [J]. Int J Pharm Bio Sci, 2014, 4: 67-74.

[16]

Pashminehazar R, Ahmed SJ, Kharaghani A, et al. Spatial morphology of maltodextrin agglomerates from X-ray microtomographic data: real structure evaluation vs. spherical primary particle model [J]. Powder Technol, 2018, 331: 204-217.

[17]

Nugraheni RW, Setyawan D, Yusuf H. Physical characteristics of liposomal formulation dispersed in HPMC matrix and freeze-dried using maltodextrin and mannitol as lyoprotectant [J]. Pharm Sci-Iran, 2017, 23: 285-292.

[18]

Carles P, Popinet S. The effect of viscosity, surface tension and non-linearity on Richtmyer–Meshkov instability [J]. Eur J Mech B Fluids, 2002, 21: 511-526.

[19]

Tian JJ, Yu YT, Zhao LJ, et al. Study on the chemical properties of volatile oil and its powder solidified by mesoporous silica [J]. Acta Pharm Sin (药学学报), 2019, 54: 1493-1501.

[20]

Zhou JY, Zhang L, Mao SR. Research progress of protein and peptide drug dry powder inhalants [J]. Acta Pharm Sin (药学学报), 2015, 50: 814-823.

[21]

Ouyang SL, Zhang MZ, Hu QC, et al. Raman spectroscopy study on surface tension, viscosity and hydrogen bond of dimethyl sulfoxide solution [J]. Spectrosc Spectr Anal (光谱学与光谱分析), 2018, 38: 2778-2781.

[22]

Yu YT, Zhao LJ, Xu JJ, et al. Study on the effect of microcrystalline cellulose on the direct pressing properties of the extract powder of wind-dissipating detoxification formula [J]. Chin Tradit Herb Drugs (中草药), 2019, 50: 4041-4050.

[23]

Zhu WF, Zhu L, Li Z, et al. The novel use of PVP K30 as templating agent in production of porous lactose [J]. Pharmaceutics, 2021, 13: 814.

[24]

Ming LS, Li Z, Wu F, et al. A two-step approach for fluidized bed granulation in pharmaceutical processing: assessing different models for design and control [J]. PLoS One, 2017, 12: e0180209.

[25]

Li Z, Jiang XX, Huang H, et al. Chitosan/zein films incorporated with essential oil nanoparticles and nanoemulsions: similarities and differences [J]. Int J Biol Macromol, 2022, 208: 983-994.

[26]

Li Z, Jiang XX, Yao ZH, et al. Chitosan functionalized cellulose nanocrystals for stabilizing Pickering emulsion: fabrication, characterization and stability evaluation [J]. Colloid Surface A, 2022, 632: 127769.

[27]

Ziyat H, Naciri Bennani M, Hajjaj H, et al. Adsorption of thymol onto natural clays of morocco: kinetic and isotherm studies [J]. J Chem, 2020, 2020: 1-10.

[28]

Rizzi V, Gubitosa J, Fini P, et al. Commercial bentonite clay as low-cost and recyclable "natural" adsorbent for the Carbendazim removal/recover from water: overview on the adsorption process and preliminary photodegradation considerations [J]. Colloids Surf A Physicochem Eng Asp, 2020, 602: 125060.

[29]

Wang LP, She XH, Chen Z, et al. Preparation and characterization of a chiral molecularly imprinted polymer with a novel functional monomer for controlled release of s-sulpiride [J]. Int J Pharm, 2021, 601: 120526.

[30]

Li JX. Effect of Physicochemical Properties of Water Extract of Traditional Chinese Medicine on Spray Drying Process (中药水提液理化特性对喷雾干燥过程的影响研究) [D]. Shanghai: Shanghai University of Traditional Chinese Medicine, 2019.

[31]

Li Z, Lin X, Shen L, et al. Composite particles based on particle engineering for direct compaction [J]. Int J Pharm, 2017, 519: 272-286.

[32]

Li Z, Xian JC, Wu F, et al. Development of TCM-based composite particles for direct compaction by particle design [J]. Powder Technol, 2018, 338: 481-492.

[33]

Almansour K, Ali R, Alheibshy F, et al. Particle engineering by nano spray drying: optimization of process parameters with hydroethanolic versus aqueous solutions [J]. Pharmaceutics, 2022, 14: 800.

[34]

Xu QZ, Cai J, Li W. Effect of viscosity on particle size of ultrasonic atomized solution [J]. Aeronautical Manuf Tech (航空制造技术), 2014, 13: 74-77.

[35]

Zeng RG, Jiang QY, Liao ZG, et al. Study on improvement of fluidity and hygroscopicity of Chinese herbal extract powder by surface coating modification [J]. China J Chin Mater Med (中国中药杂志), 2016, 41: 2245-2249.

[36]

Stranzinger S, Faulhammer E, Calzolari V, et al. The effect of material attributes and process parameters on the powder bed uniformity during a low-dose dosator capsule filling process [J]. Int J Pharm, 2017, 516: 9-20.

[37]

Kang YR, Lee YK, Kim YJ, et al. Characterization and storage stability of chlorophylls microencapsulated in different combination of gum Arabic and maltodextrin [J]. Food Chem, 2019, 272: 337-346.

[38]

Yang QQ, Cai WQ, Wang ZX, et al. Structural characteristics, binding behaviors, and stability of ternary nanocomplexes of lecithin, polyvinylpyrrolidone, and curcumin [J]. Lwt-Food Sci Technol, 2023, 175: 114489.

[39]

Nikolić L, Urošević M, Nikolić V, et al. The formulation of curcumin: 2-hydroxypropyl-beta-cyclodextrin complex with smart hydrogel for prolonged release of curcumin [J]. Pharmaceutics, 2023, 15: 382.

[40]

Kamari Y, Ghiaci M. Preparation and characterization of ibuprofen/modified chitosan/TiO₂ hybrid composite as a controlled drug-delivery system [J]. Micropor Mesopor Mat, 2016, 234: 361-369.

[41]

Jiao Y, Lu YP, Xiao GY, et al. Preparation and characterization of hollow hydroxyapatite microspheres by the centrifugal spray drying method [J]. Powder Technol, 2012, 217: 581-584.

[42]

Botrel DA, de Barros Fernandes RV, Borges SV, et al. Influence of wall matrix systems on the properties of spray-dried microparticles containing fish oil [J]. Food Res Int, 2014, 62: 344-352.

[43]

Wang B, Wang DD, Zhao S, et al. Evaluate the ability of PVP to inhibit crystallization of amorphous solid dispersions by density functional theory and experimental verify [J]. Eur J Pharm Sci, 2017, 96: 45-52.

[44]

Han RJ, Wang L, Chen KZ, et al. Photoluminescence properties of Y₃Al₅O₁₂: Eu nanocrystallites prepared by co-precipitation method using a mixed precipitator of NH₄HCO₃ and NH₃·H₂O [J]. Mater Sci Eng B, 2010, 166: 41-45.

[45]

Jourghanian P, Ghaffari S, Ardjmand M, et al. Sustained release curcumin loaded solid lipid nanoparticles [J]. Adv Pharm Bull, 2016, 6: 17-21.

[46]

Oladiran GS, Batchelor HK. Determination of ibuprofen solubility in wax: a comparison of microscopic, thermal and release rate techniques [J]. Eur J Pharm Biopharm, 2007, 67: 106-111.

[47]

Ju J, Chen XQ, Xie YF, et al. Simple microencapsulation of plant essential oil in porous starch granules: adsorption kinetics and antibacterial activity evaluation [J]. J Food Process Pres, 2019, 43: e14156.

[48]

Jain A, Jain SK. In vitro release kinetics model fitting of liposomes: an insight [J]. Chem Phys Lipids, 2016, 201: 28-40.

[49]

Mariz M, Murta J, Gil MH, et al. An ocular insert with zero-order extended delivery: release kinetics and mathematical models [J]. Eur J Pharm Biopharm, 2022, 181: 79-87.

[50]

Bayan MF, Chandrasekaran B, Alyami MH. Development and characterization of econazole topical gel [J]. Gels, 2023, 9: 929.

[51]

Maleki M, Amani-Tehran M, Latifi M, et al. Drug release profile in core-shell nanofibrous structures: a study on Peppas equation and artificial neural network modeling [J]. Comput Methods Programs Biomed, 2014, 113: 92-100.

[52]

Xiao QQ, Li XT, Liu C, et al. Improving cancer immunotherapy via co-delivering checkpoint blockade and thrombospondin-1 downregulator [J]. Acta Pharm Sin B, 2023, 13: 3503-3517.

[53]

Li BB, Teng C, Yu HL, et al. Alleviating experimental pulmonary hypertension via co-delivering FoxO1 stimulus and apoptosis activator to hyperproliferating pulmonary arteries [J]. Acta Pharm Sin B, 2023, 13: 2369-2382.

2024年第59卷第8期

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文章信息

doi: 10.16438/j.0513-4870.2024-0080

接收时间：2024-01-25
首发时间：2025-11-26
出版时间：2024-08-12

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收稿日期：2024-01-25
修回日期：2024-04-11

基金

国家自然科学基金资助项目(82360777)

江西省自然科学基金资助项目(20232ACB216015)

江西中医药大学博士科研启动基金项目资助(2021BSZR015)

江西中医药大学博士科研启动基金项目资助(2022BSZR003)

江西中医药大学校级研究生创新专项资金项目(JZYC23S70)

作者信息

江西中医药大学, 现代中药制剂教育部重点实验室, 高等研究院, 江西南昌 330004

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^*明良山，Tel: 15970770163, E-mail: jazmaster@163.com

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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Technology	Particle structure	Sample	MD/%	PVP K30/%	NH₄HCO₃/%	Nozzle diameter/mm
/	Non-porous MD	MD	10	/	/	2.0
MD-P0	10	/	/	2.0
MD-P0'	10	/	/	2.0
Template agent method	Porous MD	MD-P1	10	1	/	2.0
MD-P3	10	3	/	2.0
MD-P5	10	5	/	2.0
Pore-forming agent	Porous MD	MD-N1	10	/	1	2.0
MD-N3	10	/	3	2.0
MD-N5	10	/	5	2.0
MD-N5 (1.0)	10	/	5	1.0
MD-N5 (1.5)	10	/	5	1.5

Technology

Particle structure

Sample

MD/%

PVP K30/%

NH₄HCO₃/%

Nozzle diameter/mm

Non-porous MD

2.0

MD-P0

2.0

MD-P0'

2.0

Template agent method

Porous MD

MD-P1

2.0

MD-P3

2.0

MD-P5

2.0

Pore-forming agent

Porous MD

MD-N1

2.0

MD-N3

2.0

MD-N5

2.0

MD-N5 (1.0)

1.0

MD-N5 (1.5)

1.5

Sample	Yield /%	MC/%	AR/°	d_0.5/μm	Span	Uniformity	CI	HR	ρ_b/g·mL^-1	ρ_t/g·mL^-1	ρ_ture/g·mL^-1	SA-BET /m²·g^-1	SA-BJH/ m²·g^-1	PV /cm³·g^-1	PD/nm
MD	/	0.62 ± 0.03	43.02 ± 0.7	49.678 ± 0.31	1.441 ± 0.003	0.442 ± 0.002	15.33 ± 1.15	1.18 ± 0.02	0.49 ± 0.00	0.58 ± 0.01	1.578 1 ± 0.001 1	0.101 3	0.019 0	0.000 371	2.094 5
MD-P0	77.0	0.43 ± 0.03	47.89 ± 0.6	5.433 ± 0.20	1.382 ± 0.010	0.429 ± 0.003	37.33 ± 1.16	1.60 ± 0.03	0.30 ± 0.01	0.48 ± 0.01	1.482 2 ± 0.002 1	1.1353	0.339 2	0.003 258	12.002 9
MD-P0'	77.0	0.52 ± 0.03	45.00 ± 0.0	5.762 ± 0.04	1.453 ± 0.034	0.476 ± 0.051	36.67 ± 1.15	1.58 ± 0.03	0.32 ± 0.00	0.50 ± 0.00	1.489 6 ± 0.002 3	1.187 6	0.477 0	0.003 369	11.346 1
MD-P1	80.4	0.62 ± 0.03	44.22 ± 0.7	8.492 ± 0.14	1.614 ± 0.023	0.508 ± 0.007	41.33 ± 1.15	1.71 ± 0.03	0.26 ± 0.01	0.45 ± 0.01	1.403 2 ± 0.001 5	1.597 4	1.272 6	0.004 092	28.059 6
MD-P3	81.0	0.57 ± 0.03	44.61 ± 0.7	11.495 ± 0.68	2.213 ± 0.281	0.703 ± 0.096	40.67 ± 1.16	1.69 ± 0.03	0.24 ± 0.00	0.41 ± 0.00	1.401 2 ± 0.001 5	3.037 6	2.902 3	0.011 843	16.321 7
MD-P5	80.5	0.45 ± 0.00	51.47 ± 0.5	12.615 ± 0.12	4.180 ± 0.330	1.203 ± 0.081	38.67 ± 1.15	1.63 ± 0.03	0.23 ± 0.00	0.38 ± 0.00	1.378 2 ± 0.003 5	3.670 2	3.435 7	0.013 123	15.278 5
MD-N1	80.1	0.38 ± 0.03	45.75 ± 0.7	8.538 ± 0.21	1.877 ± 0.031	0.581 ± 0.008	40.67 ± 1.16	1.69 ± 0.03	0.21 ± 0.00	0.35 ± 0.00	0.976 8 ± 0.000 7	0.709 9	0.138 7	0.001 571	45.306 5
MD-N3	78.3	0.47 ± 0.03	46.84 ± 0.6	12.277 ± 0.31	1.545 ± 0.007	0.475 ± 0.001	43.33 ± 1.16	1.77 ± 0.04	0.18 ± 0.00	0.32 ± 0.00	0.911 8 ± 0.013 0	2.410 1	0.506 6	0.004 669	36.863 5
MD-N5	77.7	0.47 ± 0.03	48.91 ± 0.6	19.603 ± 0.76	1.769 ± 0.073	0.544 ± 0.020	46.00 ± 0.00	1.85 ± 0.00	0.11 ± 0.00	0.20 ± 0.01	0.745 6 ± 0.002 0	6.449 4	2.859 2	0.023 449	32.8042
MD-N5 (1.0)	78.1	0.57 ± 0.03	48.57 ± 0.6	15.516 ± 0.67	1.625 ± 0.027	0.504 ± 0.010	47.36 ± 1.15	1.90 ± 0.04	0.10 ± 0.00	0.19 ± 0.01	0.783 3 ± 0.005 3	8.262 5	5.487 0	0.027 887	20.329 1
MD-N5 (1.5)	81.0	0.48 ± 0.03	48.91 ± 0.6	17.828 ± 0.17	1.687 ± 0.152	0.522 ± 0.086	47.34 ± 1.18	1.90 ± 0.04	0.10 ± 0.00	0.19 ± 0.00	0.776 8 ± 0.011 8	7.526 1	4.555 8	0.028 088	24.661 6

Sample

Yield /%

MC/%

AR/°

d_0.5/μm

Span

Uniformity

ρ_b/g·mL^-1

ρ_t/g·mL^-1

ρ_ture/g·mL^-1

SA-BET /m²·g^-1

SA-BJH/ m²·g^-1

PV /cm³·g^-1

PD/nm

0.62 ± 0.03

43.02 ± 0.7

49.678 ± 0.31

1.441 ± 0.003

0.442 ± 0.002

15.33 ± 1.15

1.18 ± 0.02

0.49 ± 0.00

0.58 ± 0.01

1.578 1 ± 0.001 1

0.101 3

0.019 0

0.000 371

2.094 5

MD-P0

77.0

0.43 ± 0.03

47.89 ± 0.6

5.433 ± 0.20

1.382 ± 0.010

0.429 ± 0.003

37.33 ± 1.16

1.60 ± 0.03

0.30 ± 0.01

0.48 ± 0.01

1.482 2 ± 0.002 1

1.1353

0.339 2

0.003 258

12.002 9

MD-P0'

77.0

0.52 ± 0.03

45.00 ± 0.0

5.762 ± 0.04

1.453 ± 0.034

0.476 ± 0.051

36.67 ± 1.15

1.58 ± 0.03

0.32 ± 0.00

0.50 ± 0.00

1.489 6 ± 0.002 3

1.187 6

0.477 0

0.003 369

11.346 1

MD-P1

80.4

0.62 ± 0.03

44.22 ± 0.7

8.492 ± 0.14

1.614 ± 0.023

0.508 ± 0.007

41.33 ± 1.15

1.71 ± 0.03

0.26 ± 0.01

0.45 ± 0.01

1.403 2 ± 0.001 5

1.597 4

1.272 6

0.004 092

28.059 6

MD-P3

81.0

0.57 ± 0.03

44.61 ± 0.7

11.495 ± 0.68

2.213 ± 0.281

0.703 ± 0.096

40.67 ± 1.16

1.69 ± 0.03

0.24 ± 0.00

0.41 ± 0.00

1.401 2 ± 0.001 5

3.037 6

2.902 3

0.011 843

16.321 7

MD-P5

80.5

0.45 ± 0.00

51.47 ± 0.5

12.615 ± 0.12

4.180 ± 0.330

1.203 ± 0.081

38.67 ± 1.15

1.63 ± 0.03

0.23 ± 0.00

0.38 ± 0.00

1.378 2 ± 0.003 5

3.670 2

3.435 7

0.013 123

15.278 5

MD-N1

80.1

0.38 ± 0.03

45.75 ± 0.7

8.538 ± 0.21

1.877 ± 0.031

0.581 ± 0.008

40.67 ± 1.16

1.69 ± 0.03

0.21 ± 0.00

0.35 ± 0.00

0.976 8 ± 0.000 7

0.709 9

0.138 7

0.001 571

45.306 5

MD-N3

78.3

0.47 ± 0.03

46.84 ± 0.6

12.277 ± 0.31

1.545 ± 0.007

0.475 ± 0.001

43.33 ± 1.16

1.77 ± 0.04

0.18 ± 0.00

0.32 ± 0.00

0.911 8 ± 0.013 0

2.410 1

0.506 6

0.004 669

36.863 5

MD-N5

77.7

0.47 ± 0.03

48.91 ± 0.6

19.603 ± 0.76

1.769 ± 0.073

0.544 ± 0.020

46.00 ± 0.00

1.85 ± 0.00

0.11 ± 0.00

0.20 ± 0.01

0.745 6 ± 0.002 0

6.449 4

2.859 2

0.023 449

32.8042

MD-N5
(1.0)

78.1

0.57 ± 0.03

48.57 ± 0.6

15.516 ± 0.67

1.625 ± 0.027

0.504 ± 0.010

47.36 ± 1.15

1.90 ± 0.04

0.10 ± 0.00

0.19 ± 0.01

0.783 3 ± 0.005 3

8.262 5

5.487 0

0.027 887

20.329 1

MD-N5
(1.5)

81.0

0.48 ± 0.03

48.91 ± 0.6

17.828 ± 0.17

1.687 ± 0.152

0.522 ± 0.086

47.34 ± 1.18

1.90 ± 0.04

0.10 ± 0.00

0.19 ± 0.00

0.776 8 ± 0.011 8

7.526 1

4.555 8

0.028 088

24.661 6

Sample	Curcumin
MD	6.95 ± 0.98	2.85 ± 0.00	12.17 ± 0.43	5.99 ± 0.24
MD-P0	9.55 ± 0.75	4.63 ± 0.19	15.64 ± 0.45	6.42 ± 0.25
MD-P0'	9.87 ± 1.83	5.29 ± 0.27	15.16 ± 1.05	7.49 ± 0.46
MD-P1	10.71 ± 1.61	5.33 ± 0.32	22.64 ± 0.99	11.30 ± 0.17
MD-P3	17.05 ± 2.93	7.43 ± 0.00	26.67 ± 4.55	12.96 ± 0.00
MD-P5	17.77 ± 1.90	8.52 ± 0.14	29.92 ± 1.47	13.90 ± 0.16
MD-N1	17.14 ± 1.45	5.19 ± 0.14	22.44 ± 2.57	8.56 ± 0.16
MD-N3	20.37 ± 1.57	7.91 ± 0.14	26.18 ± 1.60	10.80 ± 0.17
MD-N5	23.89 ± 0.53	8.06 ± 0.21	35.87 ± 1.33	12.24 ± 0.29
MD-N5 (1.0)	23.86 ± 0.38	8.02 ± 0.17	36.31 ± 1.01	12.47 ± 0.00
MD-N5 (1.5)	23.90 ± 0.60	8.01 ± 0.00	36.34 ± 1.32	12.39 ± 0.00

Sample

Curcumin

Ibuprofen

Drug loading/%

Adsorption efficiency/%

Drug loading/%

Adsorption efficiency/%

6.95 ± 0.98

2.85 ± 0.00

12.17 ± 0.43

5.99 ± 0.24

MD-P0

9.55 ± 0.75

4.63 ± 0.19

15.64 ± 0.45

6.42 ± 0.25

MD-P0'

9.87 ± 1.83

5.29 ± 0.27

15.16 ± 1.05

7.49 ± 0.46

MD-P1

10.71 ± 1.61

5.33 ± 0.32

22.64 ± 0.99

11.30 ± 0.17

MD-P3

17.05 ± 2.93

7.43 ± 0.00

26.67 ± 4.55

12.96 ± 0.00

MD-P5

17.77 ± 1.90

8.52 ± 0.14

29.92 ± 1.47

13.90 ± 0.16

MD-N1

17.14 ± 1.45

5.19 ± 0.14

22.44 ± 2.57

8.56 ± 0.16

MD-N3

20.37 ± 1.57

7.91 ± 0.14

26.18 ± 1.60

10.80 ± 0.17

MD-N5

23.89 ± 0.53

8.06 ± 0.21

35.87 ± 1.33

12.24 ± 0.29

MD-N5 (1.0)

23.86 ± 0.38

8.02 ± 0.17

36.31 ± 1.01

12.47 ± 0.00

MD-N5 (1.5)

23.90 ± 0.60

8.01 ± 0.00

36.34 ± 1.32

12.39 ± 0.00

Adsorption kinetics model	Sample	Template-agent method
Quasi-first order	MD	In (0.010 2-Q_t) = -0.005 5t - 4.585 4	0.738 7		In (1.999 6-Q_t) = -0.025 7t + 0.692 9	0.943 0
MD-P0	In (0.017 2-Q_t) = -0.007 5t - 4.062 8	0.867 1		In (2.142 8-Q_t) = -0.018 3t + 0.762 1	0.977 8
MD-P0'	In (0.020 4-Q_t) = -0.015 5t - 3.892 2	0.984 4		In (2.713 3-Q_t) = -0.026 0t + 0.998 2	0.946 7
MD-P1	In (0.027 2-Q_t) = -0.007 2t - 3.604 5	0.989 9		In (4.712 3-Q_t) = -0.006 5t + 1.550 1	0.996 9
MD-P3	In (0.037 6-Q_t) = -0.007 0t - 3.280 8	0.993 8		In (5.283 5-Q_t) = -0.007 0t + 1.664 6	0.989 7
MD-P5	In (0.040 8-Q_t) = -0.009 0t - 3.199 1	0.993 9		In (6.141 8-Q_t) = -0.006 5t + 1.815 1	0.995 1
Quasi-second order	MD	t/Q_t = 92.417t + 1 491.2	0.984 9		t/Q_t = 0.480 1t + 9.086	0.993 8
MD-P0	t/Q_t = 54.187t + 1 644.7	0.990 3		t/Q_t = 0.443 7t + 10.550	0.992 4
MD-P0'	t/Q_t = 44.697t + 2 036.4	0.978 0		t/Q_t = 0.353 5t + 6.364	0.994 3
MD-P1	t/Q_t = 32.962t + 2 125.4	0.980 5		t/Q_t = 0.172 2t + 20.684	0.929 4
MD-P3	t/Q_t = 23.386t + 1 770.3	0.973 6		t/Q_t = 0.161 0t + 14.581	0.960 0
MD-P5	t/Q_t = 22.049t + 1 294.3	0.982 4		t/Q_t = 0.136 8t + 13.557	0.954 3

Adsorption kinetics model	Sample	Pore-forming agent method
Curcumin		Ibuprofen
Regression equation	R²		Regression equation	R²
Quasi-first order	MD-N1	In (0.025 0-Q_t) = -0.006 3t - 3.688 9	0.988 1		In (4.428 6-Q_t) = -0.007 2t + 1.488 1	0.955 4
MD-N3	In (0.036 2-Q_t) = -0.008 0t - 3.318 7	0.991 2		In (5.142 0-Q_t) = -0.006 0t + 1.637 4	0.970 3
MD-N5	In (0.039 8-Q_t) = -0.006 6t - 3.223 4	0.994 6		In (6.142 8-Q_t) = -0.005 4t + 1.815 3	0.957 2
Quasi-second order	MD-N1	t/Q_t = 40.000t + 3 424.2	0.952 1		t/Q_t = 0.225 8t + 15.078	0.971 5
MD-N3	t/Q_t = 27.624t + 2 368.5	0.939 1		t/Q_t = 0.194 5t + 12.222	0.975 3
MD-N5	t/Q_t = 25.126t + 1 982.0	0.960 6		t/Q_t = 0.162 8t + 10.851	0.969 5

Adsorption kinetics model

Sample

Template-agent method

Curcumin

Ibuprofen

Regression equation

R²

Regression equation

R²

Quasi-first order

In (0.010 2-Q_t) = -0.005 5t - 4.585 4

0.738 7

In (1.999 6-Q_t) = -0.025 7t + 0.692 9

0.943 0

MD-P0

In (0.017 2-Q_t) = -0.007 5t - 4.062 8

0.867 1

In (2.142 8-Q_t) = -0.018 3t + 0.762 1

0.977 8

MD-P0'

In (0.020 4-Q_t) = -0.015 5t - 3.892 2

0.984 4

In (2.713 3-Q_t) = -0.026 0t + 0.998 2

0.946 7

MD-P1

In (0.027 2-Q_t) = -0.007 2t - 3.604 5

0.989 9

In (4.712 3-Q_t) = -0.006 5t + 1.550 1

0.996 9

MD-P3

In (0.037 6-Q_t) = -0.007 0t - 3.280 8

0.993 8

In (5.283 5-Q_t) = -0.007 0t + 1.664 6

0.989 7

MD-P5

In (0.040 8-Q_t) = -0.009 0t - 3.199 1

0.993 9

In (6.141 8-Q_t) = -0.006 5t + 1.815 1

0.995 1

Quasi-second order

t/Q_t = 92.417t + 1 491.2

0.984 9

t/Q_t = 0.480 1t + 9.086

0.993 8

MD-P0

t/Q_t = 54.187t + 1 644.7

0.990 3

t/Q_t = 0.443 7t + 10.550

0.992 4

MD-P0'

t/Q_t = 44.697t + 2 036.4

0.978 0

t/Q_t = 0.353 5t + 6.364

0.994 3

MD-P1

t/Q_t = 32.962t + 2 125.4

0.980 5

t/Q_t = 0.172 2t + 20.684

0.929 4

MD-P3

t/Q_t = 23.386t + 1 770.3

0.973 6

t/Q_t = 0.161 0t + 14.581

0.960 0

MD-P5

t/Q_t = 22.049t + 1 294.3

0.982 4

t/Q_t = 0.136 8t + 13.557

0.954 3

Adsorption kinetics model

Sample

Pore-forming agent method

Curcumin

Ibuprofen

Regression equation

R²

Regression equation

R²

Quasi-first order

MD-N1

In (0.025 0-Q_t) = -0.006 3t - 3.688 9

0.988 1

In (4.428 6-Q_t) = -0.007 2t + 1.488 1

0.955 4

MD-N3

In (0.036 2-Q_t) = -0.008 0t - 3.318 7

0.991 2

In (5.142 0-Q_t) = -0.006 0t + 1.637 4

0.970 3

MD-N5

In (0.039 8-Q_t) = -0.006 6t - 3.223 4

0.994 6

In (6.142 8-Q_t) = -0.005 4t + 1.815 3

0.957 2

Quasi-second order

MD-N1

t/Q_t = 40.000t + 3 424.2

0.952 1

t/Q_t = 0.225 8t + 15.078

0.971 5

MD-N3

t/Q_t = 27.624t + 2 368.5

0.939 1

t/Q_t = 0.194 5t + 12.222

0.975 3

MD-N5

t/Q_t = 25.126t + 1 982.0

0.960 6

t/Q_t = 0.162 8t + 10.851

0.969 5

Method	Sample	Mathematical
Template agent method	MD-Cur	Q = 0.050 8t+19.387 2, R²=0.577 7	100-Q = 100exp^{(-0.002 6t)}, R² = -6.517 5	Q = 1.052 3t^1/2 + 15.245 2, R² = 0.760 7	Q = 12.276 1t^{0.166 3}, R² = 0.912 2
	MD-P0-Cur	Q = 0.041 9t + 24.105 1, R² = 0.852 0	100-Q = 100exp^{(-0.003 2t)}, R² = -15.657 7	Q = 0.863 8t^1/2 + 20.716 1, R² = 0.760 3	Q = 14.465 7t^{0.117 1}, R² = 0.927 1
	MD-P0'-Cur	Q = 0.038 6t + 24.147 3, R² = 0.620 4	100-Q = 100exp^{(-0.003 1t)}, R² = -20.097 4	Q = 0.787 8t^1/2 + 21.079 8, R² = 0.803 1	Q = 18.040 4t^{0.106 7}, R² = 0.942 8
	MD-P1-Cur	Q = 0.216 1t + 40.785 3, R²=0.644 3	100-Q = 100exp^{(-0.019 7t)}, R² = 0.576 6	Q = 4.378 4t^1/2 + 23.864 6, R² = 0.822 7	Q = 19.485 7t^{0.275 4}, R² = 0.913 0
	MD-P3-Cur	Q = 0.168 8t + 52.265 2, R² = 0.675 1	100-Q = 100exp^{(-0.030 8t)}, R² = -0.314 3	Q = 3.384 1t^1/2 + 39.308 8, R² = 0.847 7	Q = 31.376 2t^{0.188 9}, R² = 0.949 2
	MD-P5-Cur	Q = 0.125 3t + 62.292 7, R² = 0.562 9	100-Q = 100exp^{(-0.062 9t)}, R² = -1.053 4	Q = 2.589 2t^1/2 + 52.118 5, R² = 0.734 2	Q = 43.154 7t^{0.133 3}, R² = 0.902 2
	MD-Ibu	Q = 0.078 6t + 15.416 5, R² = 0.493 1	100-Q = 100exp^{(-0.002 6t)}, R² = -1.080 4	Q = 1.631 2t^1/2 + 8.982 2, R² = 0.732 0	Q = 7.417 3t^{0.271 7}, R² = 0.854 7
	MD-P0-Ibu	Q = 0.085 8t + 17.217 2, R² = 0.473 1	100-Q = 100exp^{(-0.003 0t)}, R² = -1.035 6	Q = 1.799 9t^1/2 + 10.053 3, R² = 0.723 2	Q = 8.287 3t^{0.270 2}, R² = 0.852 0
	MD-P0'-Ibu	Q=0.070 6t + 18.247 0, R² = 0.453 3	100-Q = 100exp^{(-0.002 9t)}, R² = -2.209 7	Q = 1.485 4t^1/2 + 12.321 4, R² = 0.700 1	Q = 9.774 7t^{0.227 8}, R² = 0.851 5
	MD-P1-Ibu	Q = 0.252 4t + 35.141 1, R² = 0.593 8	100-Q = 100exp^{(-0.017 5t)}, R² = 0.824 5	Q = 5.113 6t^1/2 + 15.380 9, R² = 0.822 9	Q = 14.567 8t^{0.334 0}, R² = 0.895 2
	MD-P3-Ibu	Q = 0.226 2t + 41.911 1, R² = 0.656 8	100-Q = 100exp^{(-0.020 9t)}, R² = 0.581 0	Q = 4.497 6t^1/2 + 24.815 8, R² = 0.867 3	Q = 20.158 4t^{0.274 7}, R² = 0.941 9
	MD-P5-Ibu	Q = 0.210 5t + 45.898 3, R² = 0.646 6	100-Q = 100exp^{(-0.024 3t)}, R² = 0.429 5	Q = 4.200 1t^1/2 + 29.886 8, R² = 0.860 8	Q = 23.756 5t^{0.245 7}, R² = 0.944 0
Pore-forming agent	MD-N1-Cur	Q = 0.180 1t + 42.287 0, R² = 0.572 5	100-Q = 100exp^{(-0.019 4t)}, R² = 0.197 7	Q = 3.742 4t^1/2 + 27.506 4, R² = 0.790 0	Q = 21.930 5t^{0.241 3}, R² = 0.890 8
	MD-N3-Cur	Q = 0.183 8t + 42.655 1, R² = 0.587 0	100-Q = 100exp^{(-0.019 9t)}, R² = 0.253 9	Q = 3.799 1t^1/2 + 27.719 4, R² = 0.801 0	Q = 22.094 0t^{0.242 2}, R² = 0.896 7
	MD-N5-Cur	Q = 0.180 6t + 44.841 0, R² = 0.544 9	100-Q = 100exp^{(-0.022 3t)}, R² = 0.274 8	Q = 3.785 3t^1/2 + 29.786 2, R² = 0.764 7	Q = 23.660 9t^{0.233 8}, R² = 0.872 0
	MD-N5 (1.0)-Cur	Q = 0.177 6t + 42.752 7, R² = 0.549 6	100-Q = 100exp^{(-0.019 8t)}, R² = 0.201 6	Q = 3.712 3t^1/2 + 28.023 1, R² = 0.766 9	Q = 22.289 0t^{0.238 5}, R² = 0.866 9
	MD-N5 (1.5)-Cur	Q = 0.181 4t + 43.162 3, R² = 0.550 6	100-Q = 100exp^{(-0.020 5t)}, R² = 0.266 8	Q = 3.791 6t^1/2 + 28.117 2, R² = 0.768 4	Q = 22.393 8t^{0.240 4}, R² = 0.868 8
	MD-N1-Ibu	Q = 0.228 1t + 33.794 7, R² = 0.550 6	100-Q = 100exp^{(-0.016 0t)}, R² = 0.711 0	Q = 0.476 0t^1/2 + 14.818 9, R² = 0.770 9	Q = 14.275 4t^{0.325 4}, R² = 0.838 7
	MD-N3-Ibu	Q = 0.203 0t + 38.797 9, R² = 0.543 1	100-Q = 100exp^{(-0.018 3t)}, R² = 0.560 2	Q = 4.268 0t^1/2 + 21.777 3, R² = 0.767 2	Q = 18.200 9t^{0.279 9}, R² = 0.856 3
	MD-N5-Ibu	Q = 0.205 5t + 40.677 7, R² = 0.555 3	100-Q = 100exp^{(-0.020 1t)}, R² = 0.581 2	Q = 4.296 5t^1/2 + 23.624 6, R² = 0.775 5	Q = 19.434 4t^{0.272 8}, R² = 0.863 3
	MD-N5 (1.0)-Ibu	Q = 0.204 5t + 39.173 0, R² = 0.543 6	100-Q = 100exp^{(-0.018 7t)}, R² = 0.571 1	Q = 4.288 0t^1/2 + 22.111 3, R² = 0.763 7	Q = 18.404 9t^{0.279 3}, R² = 0.849 1
	MD-N5 (1.5)-Ibu	Q =0.212 3t + 38.140 8, R² = 0.577 1	100-Q = 100exp^{(-0.018 0t)}, R² = 0.613 7	Q = 4.400 4t^1/2 + 20.799 1, R² = 0.792 2	Q = 17.535 9t^{0.288 9}, R² = 0.868 3

Method

Sample

Mathematical

Zero-order

First-order

Higuchi

Peppas

Template agent method

MD-Cur

Q = 0.050 8t+19.387 2, R²=0.577 7

100-Q = 100exp^{(-0.002 6t)}, R² = -6.517 5

Q = 1.052 3t^1/2 + 15.245 2, R² = 0.760 7

Q = 12.276 1t^{0.166 3}, R² = 0.912 2

MD-P0-Cur

Q = 0.041 9t + 24.105 1, R² = 0.852 0

100-Q = 100exp^{(-0.003 2t)}, R² = -15.657 7

Q = 0.863 8t^1/2 + 20.716 1, R² = 0.760 3

Q = 14.465 7t^{0.117 1}, R² = 0.927 1

MD-P0'-Cur

Q = 0.038 6t + 24.147 3, R² = 0.620 4

100-Q = 100exp^{(-0.003 1t)}, R² = -20.097 4

Q = 0.787 8t^1/2 + 21.079 8, R² = 0.803 1

Q = 18.040 4t^{0.106 7}, R² = 0.942 8

MD-P1-Cur

Q = 0.216 1t + 40.785 3, R²=0.644 3

100-Q = 100exp^{(-0.019 7t)}, R² = 0.576 6

Q = 4.378 4t^1/2 + 23.864 6, R² = 0.822 7

Q = 19.485 7t^{0.275 4}, R² = 0.913 0

MD-P3-Cur

Q = 0.168 8t + 52.265 2, R² = 0.675 1

100-Q = 100exp^{(-0.030 8t)}, R² = -0.314 3

Q = 3.384 1t^1/2 + 39.308 8, R² = 0.847 7

Q = 31.376 2t^{0.188 9}, R² = 0.949 2

MD-P5-Cur

Q = 0.125 3t + 62.292 7, R² = 0.562 9

100-Q = 100exp^{(-0.062 9t)}, R² = -1.053 4

Q = 2.589 2t^1/2 + 52.118 5, R² = 0.734 2

Q = 43.154 7t^{0.133 3}, R² = 0.902 2

MD-Ibu

Q = 0.078 6t + 15.416 5, R² = 0.493 1

100-Q = 100exp^{(-0.002 6t)}, R² = -1.080 4

Q = 1.631 2t^1/2 + 8.982 2, R² = 0.732 0

Q = 7.417 3t^{0.271 7}, R² = 0.854 7

MD-P0-Ibu

Q = 0.085 8t + 17.217 2, R² = 0.473 1

100-Q = 100exp^{(-0.003 0t)}, R² = -1.035 6

Q = 1.799 9t^1/2 + 10.053 3, R² = 0.723 2

Q = 8.287 3t^{0.270 2}, R² = 0.852 0

MD-P0'-Ibu

Q=0.070 6t + 18.247 0, R² = 0.453 3

100-Q = 100exp^{(-0.002 9t)}, R² = -2.209 7

Q = 1.485 4t^1/2 + 12.321 4, R² = 0.700 1

Q = 9.774 7t^{0.227 8}, R² = 0.851 5

MD-P1-Ibu

Q = 0.252 4t + 35.141 1, R² = 0.593 8

100-Q = 100exp^{(-0.017 5t)}, R² = 0.824 5

Q = 5.113 6t^1/2 + 15.380 9, R² = 0.822 9

Q = 14.567 8t^{0.334 0}, R² = 0.895 2

MD-P3-Ibu

Q = 0.226 2t + 41.911 1, R² = 0.656 8

100-Q = 100exp^{(-0.020 9t)}, R² = 0.581 0

Q = 4.497 6t^1/2 + 24.815 8, R² = 0.867 3

Q = 20.158 4t^{0.274 7}, R² = 0.941 9

MD-P5-Ibu

Q = 0.210 5t + 45.898 3, R² = 0.646 6

100-Q = 100exp^{(-0.024 3t)}, R² = 0.429 5

Q = 4.200 1t^1/2 + 29.886 8, R² = 0.860 8

Q = 23.756 5t^{0.245 7}, R² = 0.944 0

Pore-forming agent

MD-N1-Cur

Q = 0.180 1t + 42.287 0, R² = 0.572 5

100-Q = 100exp^{(-0.019 4t)}, R² = 0.197 7

Q = 3.742 4t^1/2 + 27.506 4, R² = 0.790 0

Q = 21.930 5t^{0.241 3}, R² = 0.890 8

MD-N3-Cur

Q = 0.183 8t + 42.655 1, R² = 0.587 0

100-Q = 100exp^{(-0.019 9t)}, R² = 0.253 9

Q = 3.799 1t^1/2 + 27.719 4, R² = 0.801 0

Q = 22.094 0t^{0.242 2}, R² = 0.896 7

MD-N5-Cur

Q = 0.180 6t + 44.841 0, R² = 0.544 9

100-Q = 100exp^{(-0.022 3t)}, R² = 0.274 8

Q = 3.785 3t^1/2 + 29.786 2, R² = 0.764 7

Q = 23.660 9t^{0.233 8}, R² = 0.872 0

MD-N5 (1.0)-Cur

Q = 0.177 6t + 42.752 7, R² = 0.549 6

100-Q = 100exp^{(-0.019 8t)}, R² = 0.201 6

Q = 3.712 3t^1/2 + 28.023 1, R² = 0.766 9

Q = 22.289 0t^{0.238 5}, R² = 0.866 9

MD-N5 (1.5)-Cur

Q = 0.181 4t + 43.162 3, R² = 0.550 6

100-Q = 100exp^{(-0.020 5t)}, R² = 0.266 8

Q = 3.791 6t^1/2 + 28.117 2, R² = 0.768 4

Q = 22.393 8t^{0.240 4}, R² = 0.868 8

MD-N1-Ibu

Q = 0.228 1t + 33.794 7, R² = 0.550 6

100-Q = 100exp^{(-0.016 0t)}, R² = 0.711 0

Q = 0.476 0t^1/2 + 14.818 9, R² = 0.770 9

Q = 14.275 4t^{0.325 4}, R² = 0.838 7

MD-N3-Ibu

Q = 0.203 0t + 38.797 9, R² = 0.543 1

100-Q = 100exp^{(-0.018 3t)}, R² = 0.560 2

Q = 4.268 0t^1/2 + 21.777 3, R² = 0.767 2

Q = 18.200 9t^{0.279 9}, R² = 0.856 3

MD-N5-Ibu

Q = 0.205 5t + 40.677 7, R² = 0.555 3

100-Q = 100exp^{(-0.020 1t)}, R² = 0.581 2

Q = 4.296 5t^1/2 + 23.624 6, R² = 0.775 5

Q = 19.434 4t^{0.272 8}, R² = 0.863 3

MD-N5 (1.0)-Ibu

Q = 0.204 5t + 39.173 0, R² = 0.543 6

100-Q = 100exp^{(-0.018 7t)}, R² = 0.571 1

Q = 4.288 0t^1/2 + 22.111 3, R² = 0.763 7

Q = 18.404 9t^{0.279 3}, R² = 0.849 1

MD-N5 (1.5)-Ibu

Q =0.212 3t + 38.140 8, R² = 0.577 1

100-Q = 100exp^{(-0.018 0t)}, R² = 0.613 7

Q = 4.400 4t^1/2 + 20.799 1, R² = 0.792 2

Q = 17.535 9t^{0.288 9}, R² = 0.868 3