Mixture Design Response Surface Methodology Analysis of

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Mixture Design Response Surface Methodology Analysis of Seven Mixture Design Response Surface Methodology Analysis of Seven

Natural Bioactive Compounds to Treat Prostate Cancer Natural Bioactive Compounds to Treat Prostate Cancer

Ian Geddes Berlin Brigham Young University

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Mixture Design Response Surface Methodology Analysis of

Seven Natural Bioactive Compounds

to Treat Prostate Cancer

Ian Geddes Berlin

A thesis submitted to the faculty of Brigham Young University

in partial fulfillment for the requirements of the degree of

Master of Science

Jason Kenealey, Chair Merrill Christensen

Chad Hancock

Department of Nutrition, Dietetics, and Food Science

Brigham Young University

Copyright © 2021 Ian Geddes Berlin

All Rights Reserved

ABSTRACT

Mixture Design Response Surface Methodology Analysis of Seven Natural Bioactive Compounds

to Treat Prostate Cancer

Ian Geddes Berlin Department of Nutrition, Dietetics, and Food Science, BYU

Master of Science Natural bioactive compounds have drawn the interest of many researchers worldwide in their effort to find novel treatments, including prostate cancer (PC) treatment which is estimated to be 13.1% of all new cancer cases in the U.S. in 2021. Many of these bioactive compounds have been identified from treatments in traditional Chinese medicine (TCM), that often have multiple bioactive compounds present. However, in vitro studies frequently focus on the compounds in isolation, or in simple combinations of two compounds. We used mixture design response surface methodology (MDRSM) to assess changes in PC cell viability after 48 hours of treatment to identify the optimal mixture of all 35 three-compound combinations of seven bioactive compounds from TCM. We used Berberine, Wogonin, Shikonin, Curcumin, Triptolide, Emodin, and Silybin to treat PC-3, DU145, and LNCaP human PC cells, and a drug-resistant PC-3 cell line. Berberine and Wogonin most frequently contributed to the optimal combination to reduce cell viability in PC-3 and LNCaP cells; DU145 cells more frequently responded best to a single compound. Keywords: prostate cancer, mixture design response surface methodology (MDRSM), berberine, wogonin, shikonin, curcumin, triptolide, emodin, silybin, traditional Chinese medicine (TCM)

ACKNOWLEDGEMENTS

I would like to express my gratitude to:

My mentor, Dr. Jason Kenealey, for his support, patience, and belief in me and my

potential to succeed as a graduate student and scientist;

My parents, John and Diane Berlin, for their lifelong sacrifices to help me succeed and

overcome challenges both academic and personal;

My siblings and siblings-in-law, Jake and LeAnne Berlin, Spencer and Hannah Moore,

and Seth, Ben, Isaac, and Anders Berlin, for their encouragement;

Lab members, Spencer Shin and Charity Jennings, who helped run many experiments.

Through association with them and the other Kenealey lab members, the long hours in the lab

were much more enjoyable.

iv

TABLE OF CONTENTS TITLE PAGE ................................................................................................................................................ i

ABSTRACT ................................................................................................................................................. ii

ACKNOWLEDGEMENTS .......................................................................................................................iii

TABLE OF CONTENTS ........................................................................................................................... iv

LIST OF TABLES ....................................................................................................................................... vi

LIST OF FIGURES ................................................................................................................................... vii

MANUSCRIPT ........................................................................................................................................... 1

1. Introduction ........................................................................................................................................ 1

2. Results .................................................................................................................................................. 5

2.1. IC50 calculations ......................................................................................................................... 5

2.2. Mixture design response surface methodology (MDRSM) ................................................... 8

3. Discussion ......................................................................................................................................... 16

4. Materials and Methods .................................................................................................................... 21

4.1 Cell Lines ..................................................................................................................................... 21

4.2 Compounds ................................................................................................................................. 21

4.3 Cell Viability ............................................................................................................................... 21

4.4 IC50 Value Calculation .............................................................................................................. 22

4.5 Mixture Design Response Surface Methodology .................................................................. 22

References ............................................................................................................................................. 24

APPENDIX A ............................................................................................................................................ 32

Supplemental Figure Legends ........................................................................................................... 33

APPENDIX B ............................................................................................................................................ 48

Proposed Research ............................................................................................................................... 49

Introduction and statement of the problem ................................................................................. 49

Proposed Hypothesis & Aims ........................................................................................................ 50

Alternate ............................................................................................................................................ 51

REVIEW OF LITERATURE .................................................................................................................... 52

Introduction and Statement of the Problem ..................................................................................... 52

Development of Castration Resistance ......................................................................................... 52

Development of Drug Resistance .................................................................................................. 53

Link Between Castration and Drug Resistance ........................................................................... 54

v

Combination Therapy ......................................................................................................................... 55

Combination of Bioactive Compounds in Traditional Chinese Medicine (TCM) .................. 56

Mixture Design Response Surface Methodology (MDRSM) ..................................................... 57

Mechanism of Action of the Natural Compounds .......................................................................... 58

Berberine ........................................................................................................................................... 59

Curcumin........................................................................................................................................... 59

Emodin .............................................................................................................................................. 60

Wogonin ............................................................................................................................................ 61

Shikonin ............................................................................................................................................. 61

Triptolide ........................................................................................................................................... 62

Silybin ................................................................................................................................................ 63

Conclusion ............................................................................................................................................ 63

References ............................................................................................................................................. 65

vi

LIST OF TABLES

Table 1. Summary of IC50 values of each compound. .......................................................................... 8

Table 2. All 3-way combinations in PC-3 cells ....................................................................................... 9

Table 3. All 3-way combinations in DU145 cells ................................................................................. 10

Table 4. All 3-way combinations in LNCaP cells ................................................................................. 12

Table 5. Summary of the number of optimal treatments each compound contributed ................. 15

Table 6. Summary select MDRSM Cell Viability Results in DR PC-3 Cells ..................................... 16

vii

LIST OF FIGURES

Figure 1. ....................................................................................................................................................... 4

Figure 2. ....................................................................................................................................................... 7

Supplemental Figure 1 ............................................................................................................................ 35



Supplementary Figure 4 .......................................................................................................................... 47

1

MANUSCRIPT 1. Introduction

Prostate cancer (PC) is the most commonly diagnosed cancer in men in the United

States and worldwide estimates range from 1 in 10 men to as high as 1 in 5 men will be

diagnosed with PC. Data from the United States shows the overall 5-year survival rate for PC

patients is 97.8%, in part due to the effectiveness of androgen deprivation therapy (ADT) [1].

ADT is effective when PC is less advanced and still dependent on androgens to proliferate.

This statistic, however, masks the lethality of metastatic and androgen-independent or

castration-resistant PC (mCRPC) which has a 5-year survival rate of just 30.6%, and causes

34,000 deaths annually in the US [1]. On average, patients diagnosed with mCRPC pass away

9-13 months after diagnosis because current treatments have limited effectiveness against

mCRPC [1-4]. There is a substantial need to improve treatments against lethal metastatic and

treatment-resistant PC while keeping side effects in noncancerous cells to a minimum.

Treatments for PC include surgery (i.e. prostatectomy and removal of regional lymph

nodes), radiotherapy, ADT, and chemotherapy. Prostatectomy and radiotherapy are used for

localized PC and at times are used with ADT or chemotherapy. Chemotherapy and ADT are

the most common treatment options for metastatic PC and chemotherapy is often used after

the PC begins to resist ADT [4]. Resistance to chemotherapy can also develop, but combining

chemotherapies can lead to improved treatment response and limited development of drug

resistance [5]. Bioactive compounds that have reported chemotherapeutic properties are being

2

studied for their novel chemotherapeutic properties to improve PC treatments and possibly

limit drug resistance. One recent study, for example, showed docetaxel, a common

chemotherapeutic used to treat PC, in combination with vitamin E led to a significant decrease

in cell viability of PC-3 cells compared to docetaxel alone [6]. A major source of these bioactive

compounds is traditional Chinese medicine (TCM).

TCM has garnered increased research attention in the effort to discover improved

treatments for mCRPC [7]. In part, this is because the bioactive compounds from TCM have

been used in humans for hundreds and in some cases thousands of years and because they

have novel mechanisms of action. Many studies have sought to pinpoint the mechanism of

action of these compounds which is proving difficult because of the compounds' small size

and promiscuous binding patterns. Other studies have looked into simple two-compound

combinations or the effectiveness of commonly used combination treatments, however, there

is a paucity of research that investigates the statistical contribution of each compound in

combinations of three or more and not just the overall effect of the combination.

Understanding the effects of each compound in combination is an important step to determine

how TCM compounds can be used with western chemotherapeutics to treat mCRPC.

To identify how effective combinations were at reducing cell viability and to see which

combinations merit greater attention, we designed our study to explore combinations of seven

bioactive compounds from TCM to treat PC that have reported chemotherapeutic effects.

Specifically, we tested all three-way combinations of seven bioactive compounds with

3

reported chemotherapeutic effects. The compounds we chose included Berberine (BB) [8,9],

Curcumin (Cur) [10-12], Emodin (Em) [13-15], Wogonin (Wo) [16], Shikonin (Shk) [17,18],

Triptolide (Ttd) [19,20], and Silybin (Sy) (silibinin) [21-24]. These compounds have a range of

effects including upregulating anti-cancer pathways and down-regulating oncogenic

pathways, cell cycle inhibition and inducing ROS and apoptosis [8-24].

One of the most common statistical methods used to measure drug combinations is the

Chou-Talalay method because of its ability to distinguish between synergistic, antagonistic, or

additive interactions [25]. However, this method is limited because it can only test

combinations of two compounds. Since we planned on testing three-compound combinations

based on their IC50 values, we needed to find a different combination model. We chose to use

each compound’s IC50 value because it is a common pharmacological property used to

describe the effectiveness of bioactive compounds or drugs, also by using mixtures from the

IC50 value we were using smaller doses of each compound which likely will cause fewer side

effects than higher doses of each compound. Response surface methodologies (RSM) are

another type of combination statistical model. RSM are used to identify the combinations that

maximize the desired effect. Examples include Box-Behnken, fractional factorial, and Plackett-

Burman, and mixture design response surface methodology (MDRSM) [26]. Response surface

methodologies are often used in food science, engineering, and manufacturing, but used less

frequently in biomedical research. MDRSM builds a response surface with an axis for each

compound tested. Because we used three compounds, MDRSM built a ternary plot with three

4

axes [Figure 1A] that had ten mixtures of the three compounds [Figure 1B]. Point one is 100%

of compound 1 just as point two is 100% of compound 2 and point three is 100% of compound

3. We used the IC50 value of each compound as the 100% point, thus maxing the IC50 value

the highest concentrations used. The points within the ternary plot are proportions of the full

dose of each compound used at the vertices; for example, point four is 50% of compounds 1

and 2, point seven is 66% of compound 1 and 16% of compounds 2 and 3 and point 10 is 33%

of each compound. MDRSM uses the experimental data from these ten points to build a

statistical model that predicts the optimal combination of the chosen compounds.

Figure 1. (A) combination points on a response surface ternary plot. (B) MDRSM combination ratios for each of the 10 corresponding points as shown in the ternary plot in figure1A. The IC50 value was used for the proportion of 1 and fractions of the IC50 value make up the other points as shown in figure 1B.

We chose to use MDRSM because it can measure the effects of three or more

compounds and allowed us to use each compound’s IC50 value for the 100% treatment

independent of the concentrations chosen for the other compounds. It also required fewer

00.10.20.30.40.50.60.70.80.91

Compound 1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

11

2

3

4 5

6

7

8

910

A Combination Ratios

PointCompound

1Compound

2Compound

3

1 1 0 0

2 0 1 0

3 0 0 1

4 0.5 0.5 0

5 0.5 0 0.5

6 0 0.5 0.5

7 0.6667 0.1667 0.1667

8 0.1667 0.6667 0.1667

9 0.1667 0.1667 0.6667

10 0.3333 0.3333 0.3333

B

5

experimental runs than other response surface statistical methods and has previously been

used to measure three-way chemotherapy combinations against PC in vitro [26,27].

In this study, we tested all 35 three-compound combinations possible from the seven

bioactive compounds to identify the most effective mixtures as determined by MDRSM. Each

combination was tested in biological triplicate in three different PC cell lines (PC-3, DU145,

LNCaP) [28-30]. All three cell lines are derived from metastatic PC. PC-3 and DU145 cells are

castration-resistant (CR), and thus model more advanced and difficult to treat mCRPC than

the androgen-dependent PC the LNCaP cells model [28-30]. We show that Berberine and

Wogonin frequently were in combinations that resulted in high predicted cell death. Emodin

also contributed to a large number of optimal combinations, though more frequently alone

than in combination compared to Berberine and Wogonin [Table 2, 3 & 4]. This pattern was

seen in PC-3 and LNCaP cells, whereas DU145 cells were most effectively treated by single

compounds. Contrary to what we expected there were no predicted optimal treatments that

included all three compounds from in the MDRSM models.

2. Results

2.1. IC50 calculations

Using cell viability, we determined the IC50 values for each of the compounds against

PC-3 cells. We identified the IC50 values to determine the concentrations to use in the

MDRSM models because IC50 values are a commonly reported pharmacological property and

6

provide a straightforward insight into the compounds’ effect on cell viability. Also, by using

the IC50 values as the 100% dose in the MDRSM we had room to identify combinations that

were either more or less effective than the 50% reduction in cell viability the IC50 represents.

We used IC50 values from the PC-3 cells for the MDRSM model for all cell lines. This allowed

us to directly compare the different responses from the cell lines at the same concentration

and compare the docetaxel-resistant PC-3 cells (DR PC-3) used at the end of the study to

regular PC-3 cells.

The most potent compounds were Tripolide with an IC50 value of 0.01818 uM (95%

CI: 0.01094, 0.03662) and Shikonin at 0.6002 uM (95% CI: 0.4191, 1.515) with both of their IC50

concentrations in the nanomolar range [Figure 2 & Table 1]. The rest of the compounds’ IC50

values were in the micromolar range as follows Curcumin at 20.83 uM (95% CI: 17.37, 26.60),

Emodin at 57.38 uM (95% CI: 49.96, 65,38), Wogoinin 97.87 uM (95% CI: 88.06, 110.5),

Berberine at 101.4 uM (95% CI: 87.37, 125.4), and Sylibin at 106.2 uM (95% CI: 89.13, 156.6)

[Figure 2 & Table 1].

7

Figure 2. (A-G) IC50 graphs for each of the compounds. (A) Berberine (BB), (B) Silybin (Sy), (C) Wogonin (Wo) (D) Curcumin* (Cur), (E) Emodin* (Em) (F) Shikonin* (Shk), (G) Triptolide* (Ttd). (*Some points used to calculate the IC50 are not shown in the graph for improved clarity of the curvature for the graphical figure.)

A B C

D E F

G

8

Table 1. Summary of IC50 values of each compound.

IC50 Value

Compounds IC50 Value 95% confidence

interval R

Squared Berberine (BB) 101.4 uM 87.37, 125.4 0.9344

Silybin (Sy) 106.2uM 89.13, 156.5 0.7192

Wogonin (Wo) 97.87 uM 88.06, 110.5 0.9324

Emodin (Em) 57.38 uM 49.96, 65.38 0.8707

Curcumin (Cur) 20.83 uM 17.37, 26.60 0.8806

Shikonin (Shk) 0.6002 uM 0.4191, 1.515 0.8759

Triptolide (Ttd) 0.01818 uM 0.01094, 0.03662 0.9167

Table 1: Each compound with their abbreviation and IC50 value with its associated 95% confidence interval and R squared. The IC50 values were obtained by biological triplicate.

2.2. Mixture design response surface methodology (MDRSM)

We tested all 35 three-compound combinations in triplicate in PC-3, DU145, and

LNCaP cells using MDRSM to predict the optimal treatment combination by fitting the data to

a response surface [Figure 1A]. This resulted in 105 unique combinations. The ternary plots

for each combination A-Ii are shown in Supplemental Figures 1, 2, and 3 for PC-3, DU145 and

LNCaP cells respectively. Of the 105 combinations, 46 of them had their predicted optimal

treatment to be a mixture of two compounds, and the remaining 59 combinations had an

optimal treatment of just a single compound at its IC50 value. No optimal treatment predicted

a combination of all three compounds in any of the cell lines [Tables 2, 3, and 4].

Tables 2-4 report the results of the MDRSM in PC-3, DU145, and LNCaP cells

respectively. The combinations are organized in the tables by descending order based on the

predicted decrease in percent cell viability. The tables include the compounds used to

9

calculate the response surface, the proportion of their IC50 value, the resulting molarity of

each compound at the predicted optimal treatment, the 95% confidence interval for the

predicted maximum decrease in cell viability, and the model’s lack of fit and desirability. The

lack of fit statistic checks to make sure the experimental data can create a statistical model

with a high level of confidence. Accordingly, if the p-value for the lack of fit is statistically

significant that means the data and statistical model do not fit well and all the predicted

statistics from the model should be rejected. Models with p-values lower than 0.05 were

identified as having a lack of fit. Only three combinations, all of which were in the LNCaP cell

lines, had statistical lack of fit and were not used for further studies.

PC-3 cells had four optimal treatments which predicted more than 50% reduction of

cell viability, of which three were mixtures of Berberine and Wogonin. While the most

effective treatment appeared to be just Shikonin alone this trend was not repeated. Berberine

and Wogonin consistently appeared to be part of optimal treatments contributing to 47.06% of

the optimal treatments against PC-3 cells, whereas Shikonin only contributed to 7.84% of the

optimal treatments [Table 5]. A total of 16 optimal treatments were combinations and 19

optimal treatments were just a single compound. The optimal treatments made up of just a

single compound frequently predicted a smaller decrease in PC-3 cell viability than those with

combinations.

DU145 and LNCaP cells were treated with the same concentrations of compounds

based on the IC50 values of each of the compounds determined in PC-3 cells. The

10

combinations with the highest predicted decrease of DU145 cell viability were more

frequently a single compound rather than a combination compared to PC-3 and LNCaP cells.

The top six most effective treatments against DU145 cells were a single compound and the

seventh combination was barely a combination with just a small fraction of Silybin combined

with an overwhelming majority of Curcumin [Table 2 (row G)]. The next three optimal

treatments were also a single compound making the top ten predicted treatments essentially

just a single compound. In contrast six and five out of the top ten optimal treatments for PC-3

and LNCaP cells were combinations (three of the top ten combinations for LNCaP cells had

significant lack of fit and were excluded). Curcumin or Emodin alone were most frequently

the optimal treatment against DU145 cells. Curcumin and Emodin contributed to 16.33% and

20.41% of all DU145 optimal treatments respectively [Table 5].

LNCaP cells responded most frequently to Berberine, Wogonin, and Emodin

especially in the most effective combinations this stands true even when excluding the top

two optimal treatments, both of which contained Berberine and Wogonin but had significant

lack of fit [Table 4 (row A&B)]. Berberine, Wogonin, and Emodin either alone or in

combination contributed to 76.47% of all optimal treatments against LNCaP cells.

The results indicate that PC-3, DU145, and LNCaP cells all responded differently to the

compounds with similarities mentioned above. The compounds that contributed to the top

optimal treatments in combination were Berberine and Wogonin, both contributing to 24.50%

and 19.88% of all optimal treatments respectively. Emodin also contributed to the optimal

11

treatments notably at 22.51% of all combinations, but more frequently alone rather than in

combination (23 times as a single compound compared to 11 times in combination). Emodin is

also more frequently in the optimal combination against DU145 and LNCaP cells than in PC-3

cells. [Table 4].

9

Table 2. All 3-way combinations in PC-3 cells

PC-3

Compounds

Proportion of compounds' IC50

concentrations

Molarity (uM) of 1st compound

Molarity (uM) of

2nd compound

Molarity (uM) of

3rd compound

Predicted decrease in % cell viability

95% confidence

interval

Lack of fit

Prob>F Desirability A Shk, BB, Wo 1, 0, 0 0.6002 0 0 59.16 38.89, 79.43 0.5369 0.6479 B Cur, BB, Wo 0, .520, .480 0 52.728 46.9776 56.78 40.41, 73.14 0.3191 0.7299 C BB, Em, Wo .627, 0, .373 63.5778 0 36.50551 53.69 46.17, 61.21 0.459 0.6161 D Sy, BB, Wo 0, .448, .552 0 45.4272 54.02424 50.09 38.27, 61.91 0.924 0.844 E Ttd, BB, Wo 0, .627, .373 0 63.5778 36.50551 48.26 38.15, 58.38 0.4736 0.7736 F Cur, Shk, Ttd 1,0,0 20.83 0 0 48.12 38.40, 57.84 0.217 0.8164 G Cur, Em, BB 0, .506, .494 0 29.03428 50.0916 47.14 34.88, 59.40 0.5391 0.7726 H Cur, Wo, Shk 0, .745, .255 0 72.91315 0.153051 47.06 34.32, 59.81 0.8051 0.6897 I BB, Cur, Shk 1,0,0 101.4 0 0 44.73 28.30, 61.15 0.4379 0.7689 J BB, Cur, Ttd 1, 0, 0 101.4 0 0 44.52 26.59, 62.45 0.8526 0.7931 K BB, Em, Sy .707, 0, .293 71.6898 0 31.1166 44.12 27.02, 61.22 0.1848 0.8412 L Cur, Wo, Sy 0, 1, 0 0 57.38 0 43.67 27.40, 59.93 0.9404 0.6609 M Em, Shk, Sy .514, 0, .486 29.49332 0 51.6132 42.93 34.61, 51.24 0.883 0.7438 N Cur, Em, Sy 0, .871, .129 0 46.87946 13.6998 41.55 32.25, 50.84 0.791 0.6818 O BB, Em, Ttd 0, 1, 0 0 57.38 0 41.44 29.98, 52.91 0.7975 0.5856 P Em, Ttd, Sy .597, 0, .403 34.25586 0 42.7986 41.43 37.01, 45.86 0.1998 0.8953 Q Cur, Wo, Ttd .484, .516, 0 10.08172 50.50092 0 40.17 31.36, 48.98 0.4764 0.7901 R BB, Em, Shk 1, 0, 0 101.4 0 0 39.51 24.38, 54.65 0.9579 0.5886 S BB, Ttd, Sy .578, 0, .422 58.6092 0 44.8164 38.53 29.79, 47.27 0.363 0.758 T BB, Shk, Sy .627, 0, .373 63.5778 0 39.6126 38.48 27.34, 49.62 0.8598 0.8215

10

U Em, Ttd, Shk 1, 0, 0 57.38 0 0 37.97 28.78, 47.15 0.9885 0.8449 V BB, Cur, Sy .578, 0, .422 58.6092 0 44.8164 37.94 22.81, 53.07 0.5204 0.7127 W Cur, Em, Shk 0, 1, 0 0 57.38 0 37.44 22.11, 52.77 0.841 0.6159 X Cur, Em, Ttd 0, 0, 1 0 0 0.01819 35.57 18.81, 52.33 0.9274 0.6097 Y Cur, Em, Wo 0, 1, 0 0 57.38 0 35.21 19.31, 51.11 0.9428 0.6369 Z Wo, Em, Shk 0, 0, 1 0 0 0.6002 33.87 13.00, 54.73 0.9918 0.5596

Aa Ttd, Shk, Sy 1, 0, 0 0.01819 0 0 33.31 23.64, 42.98 0.5774 0.4248 Bb Wo, Em, Ttd 1, 0, 0 97.87 0 0 32.67 19.19, 46.16 0.5866 0.7000 Cc Wo, Em, Sy 1, 0, 0 57.38 0 0 30.34 17.83, 42.85 0.5587 0.7216 Dd Cur, Ttd, Sy 1, 0, 0 20.83 0 0 30.31 13.32, 47.30 0.8853 0.6207 Ee Wo, Ttd, Sy .596, .404, 0 58.13478 0.00734876 0 28.41 11.63, 45.19 0.5538 0.7477 Ff Wo, Shk, Sy .557, .443, 0 31.96066 0.2658886 0 26.96 10.40, 43.51 0.811 0.7319 Gg BB, Shk, Ttd 1, 0, 0 101.4 0 0 25.52 14.86, 36.18 0.7203 0.873 Hh Wo, Ttd, Shk 0, 1, 0 0 0.01819 0 24.36 10.57, 38.14 0.9556 0.7637 Ii Cur, Shk, Sy 1, 0, 0 20.83 0 0 15.69 5.495, 25.89 0.5211 0.7801

The combinations are ordered according to the predicted decrease in % cell viability. Cur, Curcumin; Shk, Shikonin; BB, Berberine; Wo, Wogonin; Sy, Silybin; Em, Emodin; Ttd, Triptolide

Table 3. All 3-way combinations in DU145 cells

DU-145

Compounds


concentrations


Molarity (uM) of

2nd compound

Molarity (uM) of

3rd compound


95% confidence

interval

Lack of fit

Prob>F Desirability

A Cur, Shk, Ttd 1, 0, 0 20.83 0 0 77.49 59.40, 95.53 0.0712 0.8463 B BB, Cur, Ttd 0, 1, 0 0 20.83 0 71.33 56.36, 86.29 0.1536 0.8765 C Wo, Em, Sy 1, 0, 0 97.87 0 0 71.22 56.97, 85.46 0.8862 0.7986

11

D Cur, Wo, Sy 1, 0, 0 20.83 0 0 70.04 52.15, 87.94 0.984 0.7654 E Wo, Em, Shk 1, 0, 0 97.87 0 0 69.55 48.92, 90.18 0.9789 0.7323 F Cur, Em, Ttd 1, 0, 0 20.83 0 0 67.24 46.40, 88.07 0.9579 0.8264 G Cur, Ttd, Sy .958, 0, .042 19.95514 0 4.4604 66.88 51.74, 82.01 0.313 0.8541 H Cur, Wo, Ttd 1, 0, 0 20.83 0 0 65.99 50.33, 81.65 0.5925 0.8111 I Cur, Em, BB 0, 1, 0 0 57.38 0 65.61 40.92, 90.29 0.9099 0.6101 J BB, Em, Wo 0, 1, 0 0 57.38 0 64.03 43.93, 84.13 0.1972 0.6653 K Sy, BB, Wo 0, .474, .526 0 48.0636 51.47962 63.64 42.48, 84.79 0.9685 0.8224 L Wo, Em, Ttd 0, 1, 0 0 57.38 0 63.33 47.48, 79.19 0.9379 0.7494 M Cur, Em, Wo 0, 1, 0 0 57.38 0 63.15 33.71, 92.59 0.9994 0.6553 N Cur, BB, Wo 0, .445, .555 0 45.123 54.31785 62.96 50.79, 75.13 0.0833 0.8677 O Cur, Wo, Shk 1, 0, 0 20.83 0 0 62.48 49.00, 75.96 0.6593 0.76809 P Em, Shk, Sy 1, 0, 0 57.38 0 0 61.09 45.79, 76.40 0.6701 0.7512 Q Em, Ttd, Sy 1, 0, 0 57.38 0 0 57.3 51.55, 63.06 0.2144 0.805 R Wo, Shk, Sy 1, 0, 0 97.87 0 0 55.1 41.29, 68.90 0.5601 0.8204 S BB, Cur, Sy 0, .824, .176 0 17.16392 18.6912 55.08 41.04, 69.11 0.9696 0.678 T Ttd, BB, Wo 0, .482, .518 0 48.8748 50.69666 54.43 42.08, 66.77 0.5511 0.8916 U Cur, Em, Sy 0, 1, 0 0 57.38 0 54.26 39.12, 69.40 0.7412 0.8113 V Em, Ttd, Shk 1, 0, 0 57.38 0 0 53.23 33.93, 72.54 0.9745 0.748 W Shk, BB, Wo 0, .500, .500 0 50.7 48.935 53.12 41.82, 64.43 0.3417 0.8864 X Cur, Shk, Sy 1, 0, 0 20.83 0 0 52.45 33.98, 70.92 0.2852 0.7916 Y BB, Em, Shk .194, .806, 0 19.6716 0.4837612 0 52.11 38.75, 65.48 0.7572 0.8539 Z BB, Cur, Shk 0, 1, 0 0 20.83 0 49.69 28.98, 70.40 0.9064 0.6536

Aa Wo, Ttd, Sy .679, .321, 0 66.45373 0.00583899 0 48.7 40.10, 57.31 0.8279 0.7509 Bb BB, Ttd, Sy .609, 0, .391 61.7526 0 41.5242 48.55 37.20, 59.89 0.5255 0.6829 Cc BB, Shk, Sy .600, 0, .400 60.84 0 42.48 47.03 33.11, 60.96 0.923 0.7017 Dd Cur, Em, Shk 0, 1, 0 0 57.38 0 44.49 28.20, 60.79 0.928 0.636 Ee BB, Em, Ttd .254, .746, 0 25.7556 42.80548 0 44.44 30.13, 58.76 0.7285 0.7047 Ff Wo, Ttd, Shk .768, .232, 0 75.16416 0.00422008 0 43.67 35.41, 51.94 0.985 0.7827

12

Gg BB, Em, Sy 0, 1, 0 0 57.38 0 38.93 28.02, 49.84 0.6622 0.7248 Hh Ttd, Shk, Sy .586, 0, .414 0.01065934 0 43.9668 30.47 17.33, 43.60 0.437 0.6644 Ii BB, Shk, Ttd .684, 0, .316 69.3576 0 0.00574804 19.36 8.877, 29.84 0.8222 0.581


Table 4. All 3-way combinations in LNCaP cells

LNCaP

Compounds


concentrations


Molarity (uM) of

2nd compound

Molarity (uM) of

3rd compound


95% confidence

interval

Lack of fit

Prob>F* Desirability

A Shk, BB, Wo 0, .406, .594 0 41.1684 58.13478 102.02 89.56, 114.48 <.0001* 0.9908 B Ttd, BB, Wo 0, .402, .598 0 40.7628 58.52626 98.78 88.79, 108.76 <.0001* 0.965 C BB, Em, Ttd .331, .669, 0 33.5634 38.38722 0 93.95 85.21, 102.70 0.0681 0.9086 D Sy, BB, Wo 0, .265, .735 0 26.871 71.93445 93.91 82.17, 105.66 0.6715 0.9081 E BB, Em, Shk .324, .676, 0 32.8536 38.78888 0 92.88 83.29, 102.48 0.5864 0.9192 F Cur, Em, BB 0, .749, .251 0 42.97762 25.4514 92.65 87.46, 97.84 0.5957 0.9314 G Cur, Em, Sy 0, 1, 0 0 57.38 0 90.81 77.23, 104.40 0.6309 0.8702 H BB, Ttd, Sy .548, 0, .452 55.5672 0 48.0024 89.54 74.15, 104.93 0.0244* 0.9764 I Wo, Em, Sy 0, 1, 0 0 57.38 0 88.71 77.11, 100.30 0.5019 0.8247 J Cur, Em, Shk 0, 1, 0 0 57.38 0 88.65 72.57, 104.72 0.6057 0.9688 K Cur, Em, Ttd 0, 1, 0 0 57.38 0 88.31 70.81, 105.80 0.3734 0.9637 L Wo, Em, Shk 0, 1, 0 0 57.38 0 88.13 75.20, 101.06 0.6651 0.8664 M BB, Em, Sy .483, 0, .517 48.9762 0 54.9054 87.77 57.80, 117.74 0.9821 0.9027 N Em, Ttd, Shk 1, 0, 0 57.38 0 0 87.71 73.61, 101.81 0.326 0.9542 O Wo, Em, Ttd 0, 1, 0 0 57.38 0 87.26 78.35, 96.17 0.4243 0.9377

13

P Em, Shk, Sy 1, 0, 0 57.38 0 0 86.22 69.57, 102.88 0.0246 0.9487 Q BB, Cur, Sy .493, 0, .507 49.9902 0 53.8434 85.73 60.03, 111.43 0.8623 0.8429 R Cur, BB, Wo 0, .459, .541 0 46.5426 52.94767 85.49 69.87, 101.10 0.9735 0.7795 S Cur, Em, Wo 0, 1, 0 0 57.38 0 85.04 67.85, 102.23 0.0483* 0.9218 T BB, Shk, Sy .511, 0, .489 51.8154 0 51.9318 85 67.29, 102.72 0.3582 0.9335 U BB, Em, Wo .251, .749, 0 25.4514 42.97762 0 83.8 70.84, 96.75 0.286 0.8814 V Wo, Ttd, Sy 1, 0, 0 97.87 0 0 80.32 66.53, 94.10 0.3648 0.8641 W Wo, Ttd, Shk 1, 0, 0 97.87 0 0 79.86 62.94, 96.77 0.3791 0.8833 X Em, Ttd, Sy .798, 0, .202 45.78924 0 21.4524 77.17 56.49, 97.86 0.9296 0.7963 Y Cur, Wo, Shk 0, 1, 0 0 97.87 0 72.45 55.00, 89.89 0.6122 0.8902 Z Cur, Wo, Ttd 0, 1, 0 0 97.87 0 70.02 53.13, 86.91 0.2592 0.8047

Aa Cur, Wo, Sy 0, 1, 0 0 97.87 0 66.19 51.29, 81.09 0.495 0.7572 Bb Cur, Shk, Sy 1, 0, 0 20.83 0 0 63.42 32.84, 94.01 0.8414 0.8474 Cc Wo, Shk, Sy 1, 0, 0 97.87 0 0 62.81 37.15, 88.46 0.7403 0.8423 Dd Cur, Ttd, Sy 1, 0, 0 20.83 0 0 55.99 21.69, 90.29 0.4261 0.6752 Ee BB, Cur, Ttd .428, .572, 0 43.3992 11.91476 0 55.89 31.97, 79.80 0.8575 0.6131 Ff BB, Cur, Shk .372, .628, 0 37.7208 0.3769256 0 55.78 31.63, 79.94 0.775 0.6484 Gg Ttd, Shk, Sy 0, 0, 1 0 0 106.2 53.95 41.09, 66.80 0.295 0.7861 Hh BB, Shk, Ttd .915, 0, .085 92.781 0 0.00154615 41.58 30.07, 53.09 0.9637 0.7571 Ii Cur, Shk, Ttd 1, 0, 0 20.83 0 0 37.34 21.44, 53.24 0.8755 0.8449


* Lack of fit statistic is less than 0.05 indicating the model and experimental data do not fit well which means the other statistics predicted for this combination cannot be accepted.

14

The mean predicted decrease in cell viability for all combinations against PC-3 cells was

40.04% (95% CI: 37.06, 43.02%) with a standard deviation of 8.538%. DU145 cells responded

intermediately with a mean of 57.33% (95% CI: 53.64, 61.01%) and a standard deviation of

10.55%. LNCaP cells were most affected by the compounds with the mean decrease of cell

viability of 77.02% (95% CI: 71.30, 82.75) and a standard deviation of 15.88%. Combination II for

both PC-3 and DU145 cells was a statistical outlier and was not included in the means above.

However, including combination II only lowered the mean by roughly 1%, and the standard

deviation was 1% larger in both cell lines. Combination II was included in calculating the

LNCaP mean decrease in cell viability, but combinations A, B, and H were excluded because

their models had a poor fit as indicated by statistically significant lack of fit.

To further explore the trends of how each cell type responded to the seven compounds

and their combinations we summarized the number of times each compound appeared as part

of the optimal treatment in Tables 2-4 in Table 5. The total number of compounds in all the

optimal treatments is 145 and comes from counting every time a compound is a part of the

predicted optimal treatment. There were 43 two-compound optimal treatments and 59 single

compound optimal treatments which results in 145 compounds participating as part of the

optimal treatments. This simplified table shows how frequently any given compound

contributed to the optimal treatment and this gave us an estimate of how important a role the

compound played in effectively treating the PC cell lines. Berberine and Wogonin together

contribute to 42.76% of all possible optimal treatments either alone or in a combination. Emodin

also contributed to 23.45% of the total combinations and showed it was particularly effective

15

against DU145 and LNCaP cells. Berberine and Wogonin were frequently combined for the

optimal treatment, and their combination merits further study.

Table 5. Summary of the number of optimal treatments each compound contributed

Summary of Compounds Associations in Optimal Treatments

Cell line Berberine Wogonin Silybin Curcumin Emodin Shikonin Triptolide

BB + Wo + Sy + Cur + Em + Shk + Ttd +

PC-3 4 9 3 8 0 7 3 1 4 4 2 2 3 1

DU145 0 9 3 6 0 5 8 2 10 2 0 0 0 4

LNCaP 0 12 6 2 1 4 3 2 9 5 0 0 0 1 Total (145)* 4 30 12 16 1 16 14 5 23 11 2 2 3 6

% of total 2.76 20.69 8.28 11.03 0.69 11.03 9.66 3.44 15.86 7.59 1.38 1.38 2.07 4.14

The compound abbreviation column represents the number of times the compound was the optimal treatment alone. The (+) column is for the number of times the compound contributed to an ideal treatment. *Of the 105 unique treatments 43 optimal treatments were combinations, each only using 2 compounds, the other 59 ideal treatments are a single compound leaving (43*2)+59=145 total.

Lastly, we tested the MDRSM combination of Shikonin, Berberine, and Wogonin against

docetaxel-resistant (DR) PC-3 cells. To produce the DR PC-3 cells, we grew PC-3 cells with

increasing concentrations of docetaxel until they proliferated regularly at 50nM docetaxel.

Docetaxel was maintained throughout the treatment process. Unlike the PC-3 and LNCaP

results, DR PC-3 cells' optimal treatment was Wogonin alone, and the predicted decrease in cell

viability was 47.83% (95% CI: 40.16, 55.50) compared to the 59.16% (95% CI: 38.89, 79.43)

predicted against regular PC-3 cells [Table 6]. The MDRSM combination of Shikonin, Berberine,

and Wogonin show the compounds are less effective against the DR PC-3 cells and that the

optimal treatment is just Wogonin with no Berberine.

16

Table 6. Summary select MDRSM Cell Viability Results in DR PC-3 Cells

DR PC-3

Compounds

Proportion of compounds'

IC50 concentrations

Molarity (uM) of

1st compound

Molarity (uM) of

2nd compound

Molarity (uM) of

3rd compound


95% confidence

interval

Lack of fit

Prob>F* Desirability

Shk, BB, Wo 0,0,1 0 0 97.87 47.83 40.16, 55.50

0.4805 0.8337

The MDRSM combination of Shikonin (Shk), Berberine (BB) & Wogonin (Wo) was used against docetaxel-resistant PC-3 cells (DR PC-3). The results come from 4 biological runs.

3. Discussion

Combination therapies have been used in TCM for thousands of years and are being

studied in Western medicine to treat many complex diseases including cancer [31,32]. TCM

often uses herbal treatments that contain multiple bioactive compounds, and because TCM

treatments were used long before scientific advancements provided substantial insight into the

mechanistic pathways of treatments, the complex interactions and benefits have yet to be

defined. In contrast, western medicine strives to have a detailed understanding of the

chemotherapy’s mechanistic effects before using it to treat patients. However, both western and

TCM recognize the benefit of combination treatments. Combination therapies that are used

against PC include concurrent or sequential treatment of different types of therapy such as

surgery, radiation, hormone, and chemotherapy. Docetaxel used earlier with ADT has been

shown to extend life expectancy and improve patient outcomes [5], and in the 2016 STAMPEDE

study, James et al. proposed that the standard of care for metastatic castration-sensitive PC

should use docetaxel along with long term ADT [33]. Combination chemotherapies are of

special interest to treat mCRPC because this aggressive cancer is often lethal. This has led

researchers to study in more depth the combinations of bioactive compounds used in TCM.

17

TCM combinations occur either by using multiple herbs or because multiple bioactive

compounds are present in the same plant such as Scutellaria baicalensis (Chinese skullcap) which

is the source of wogonin, wogoniside, baicalein, and baicalin all of which are reported to have

medicinal properties [34]. The bioactive components of TCM have been identified and their

mechanisms of action are an area of increasing research interest. Each of the compounds we

tested has multiple biological targets reported in the literature which made conclusively

determining the mechanism of action for each compound and especially for all 105

combinations we tested difficult and beyond the scope of this study. Additionally, relatively

few studies have looked at the combinations of three or more bioactive compounds which

further limited us from relying on previous studies to determine the mechanism of action of

each combination. Instead, we took a pragmatic approach of using the endpoint measurement

of cell viability to identify which combinations warrant further mechanistic studies.

We tested the assumption that the combination of three compounds would be more

effective than a single compound alone. Contrary to what we hypothesized, none of the

combinations’ MDRSM analyses predicted an optimal treatment that included all three

compounds. This may have been caused by using the IC50 values as the 100% dose (points 1-2

in Figure 1) because MDRSM calls for fractions of the 100% dose to be used for the mixtures

(points 4-10 in Figure 1) to fill in the response surface. For a simple three-compound

combination the fractions for the ternary plot are 50%, 33.33%, or 16.67% of the 100% dose

[Figure 1]. These mixtures have much lower concentrations of the compounds, and these lower

concentrations may not be biologically active. This problem is more likely to occur with the

compounds that have a stepwise shape to their IC50 curve [Figure 2]. We recommend choosing

18

a high enough 100% dose for each compound or drug tested that ensures the concentrations

used in the subsequent mixture points (points 4-10 in Figure 1A) are biologically relevant for

subsequent studies, as used by Asay et al. Furthermore, the MDRSM model can be improved by

adding additional points to augment the simplex model and improve the fit and allow for a

more exact predicted optimal treatment [6].

Despite these possible limitations, we demonstrated that both Berberine and Wogonin

had a clear tendency to interact well with other compounds and especially with each other.

Emodin also stood out as an effective chemotherapeutic compound, but more frequently alone.

[Table 4]. These compounds stand out as good candidates to study their mechanistic pathways

and in combination with more traditional chemotherapeutics. As small molecules, all three

compounds likely bind multiple cellular targets which contribute to the broad range of effects

reported in the literature. Berberine is known to modulate inflammatory response by inhibiting

NEK7-NLRP3 interaction, IL-1β, IL-6, and NF-kB expression. Decrease androgen receptor

expression, prostate-specific antigen, and COX-2, increase caspase-3, and induce apoptosis

[9,35-37]. Berberine has also been noted for additional anti-cancer effects in many in vivo studies

against various cancer types and for inducing apoptosis through increasing ROS [8,37].

Wogonin has been shown to increase p53, PUMA, Bax, and cytochrome C release from the

mitochondria leading to apoptosis. [16]. Wogonin also has been reported to modulate several

signal transduction pathways including inhibiting the Akt pathway to suppress tumor growth

[16]. Emodin has been reported to inhibit TNF-alpha activation of NF-kB, dysregulate

mitochondrial membrane potentials, cause glutathione depletion, and generate ROS [14,15,39].

19

A possible explanation for the effectiveness of Berberine, Wogonin, and Emodin might

be due to the more linear dose-response curve demonstrated by the IC50 calculation of

Berberine, Wogonin, and Emodin [Figure 2]. They did not have a steep stepwise shape that

contains most of their biological activity, and Berberine and Wogonin appear to have biological

activity even near the lowest doses tested for their IC50 values. Additionally, due to the length

of the study, the compounds’ stability may have become a factor. However, to prevent changes

from freeze-thaw cycling the treatments were aliquoted out so only the required amount of the

stock solution was thawed for each treatment and the excess was discarded. Even considering

these alternative explanations there remains a clear trend that Berberine and Wogonin

interacted well together, and that Emodin is effective.

The differences between the three cell lines’ responses to the compounds indicate the

need for additional research about the differences between the various PC cell line models. PC-3

cells and DU145 cells are androgen-independent, and therefore considered more advanced than

LNCaP cells. However, there remains a substantial need to identify differences in gene

expression, redox state, and metabolism of these cell lines which would help elucidate why

particular combinations or single compounds had different responses in the various PC cell

lines [40]. DU145 cells appeared to respond uniquely to the treatments as they had fewer similar

responses with PC-3 and LNCaP cells. This may be in part because DU145 cells have been

shown to have a more reducing potential environment compared to PC-3 cells and curcumin

which has antioxidant properties may have a greater ability to disrupt DU145 cell growth than

that of PC-3 and LNCaP cells [41]. Differences in the redox states and response to redox

modulating drugs have also been shown between PC-3 and LNCaP cells [42,43]. Due to the

20

different targets of the compounds and the differences in cellular environments, additional

studies should be performed to further confirm their efficacy in combination especially in in

vivo models. Further work should aim to identify the pathways Berberine, Wogonin and

Emodin are working through and how they are working together to reduce PC cell growth.

In conclusion, the results of our study demonstrate that MDRSM is a useful statistical

tool to quantify the contributions of bioactive compounds to treat PC. It required 5-10 fewer

experimental runs compared to Central Composite Design, Cross-Correlation Function, and

Box-Behnken Design, though additional points could be added to improve the resolution of the

model [26]. Biological samples tend to have more variation than other areas of study, but only

three out of the 105 combinations had statistically significant lack of fit which shows it is a

viable statistical method for cell viability assays. Also due to the different responses between

the cell lines, particularly the unique response of DU145 cells compared to PC-3 and LNCaP

cells highlights the need for additional studies to categorize the differences between cell-line

models from the same cancer. Our study identified Berberine and Wogonin as complementary

bioactive compounds. When Berberine and Wogonin were tested against DR PC-3 cells the

optimal treatment was Wogonin alone, and while it had a smaller decrease in cell viability in

the DR PC-3 cells than in the regular PC-3 cells is still caused a 47.83% (95% CI: 40.16, 55.50)

decrease in the DR PC-3 cell viability with treatment doses at the IC50 values from regular PC-3

cells [Table 5]. Berberine and Wogonin contributed the most to the optimal treatments,

especially combination treatments, and merit further study with other bioactive compounds

and with established chemotherapeutic drugs.

21

4. Materials and Methods

4.1 Cell Lines

We obtained human prostate cancer PC-3, DU145, and LNCaP cells from ATCC

(Rockville, MD, USA). The PC-3 cells were incubated in F-12K, 1X (Ham's F-12K Nutrient

Mixture, Kaighn's Mod.) with L-glutamine purchased from Corning Incorporated (Oneonta,

NY, USA). The DU145 cells were incubated in Eagle’s Minimum Essential Medium purchased

from ATCC. The LNCaP cells were grown in RPMI-16 media purchased from ATCC. For each

cell line, we added 10% fetal bovine serum and 1% antibiotic (streptomycin and penicillin) and

kept them in a 37°C humidified 5% CO2 incubator. Cells were used between passages 4-30. DR

PC-3 cells were previously prepared in the lab as reported by Asay 2020 [6].

4.2 Compounds

We purchased the bioactive compounds from Cayman Chemical (Ann Arbor, Michigan,

USA). Their product numbers are listed after each compound Berberine (10006427), Curcumin

(81025), Emodin (13109), Triptolide (11973), Wogonin (14248), Shikonin (14751), and Silybin

(10006211)*. The dry powder compounds were dissolved in DMSO to make 100mM stock

solutions and aliquoted out into microcentrifuge tubes to minimize freeze-thaw cycling.

*Cayman lists product number 10006211 as Silybin, however from PubChem it appears the

compound may have been a stereoisomer of Silybin called Silibinin [44,45]

4.3 Cell Viability

To assess cell viability, we used the alamarBlue cell viability assay. PC-3, DU145, and

LNCaP cells were grown to confluence, trypsinized, and plated in Greiner bio-one Cellstar 96

well plates at 5,000-10,000 cells per well with 100µL of medium. We allowed each cell line to

22

adhere to the bottom of the 96-well plate for a 24h period before treatment. Each 96-well plate

used its own control using DMSO vehicle control and was set to 100% viability and the other

treatments were normalized to this control. After 48h of incubation 10µL of alamarBlue

(Thermofisher and BioRad) were added to each well and the 96-well plate was returned to the

incubator for an additional 5-6 h. Cell viability was analyzed via relative fluorescence as read by

BMG LABTECH FLOUstar OPTIMA at 544nm excitation and 612nm emission, BioTek

(Winooski, VT, USA) using fluorescence measurement at 540/35nm excitation filter and a

590/20nm emission filter, Victor Nivo (PerkinElmer Inc. Waltham, MA, USA) 530/30nm

excitation and 595/10nm emission, and Spectra Max iD3 540nm excitation and 590nm emission

(Molecular Devices San Jose, CA, USA).

4.4 IC50 Value Calculation

We used GraphPad Prism 8 (Graphpad Software, San Diego, CA, USA) to calculate the

IC50 values for each of the seven compounds based on increasing treatment concentrations and

following the cell ability method stated in section 4.1. Using concentrations above and below

the IC50 concentration a variable slope non-linear regression model was fit to the experimental

results (r2 > 0.95). The concentration which resulted in a 50% reduction of cell viability was set

as the IC50 for each compound based on at least 3 biological runs.

4.5 Mixture Design Response Surface Methodology

We analyzed the compound combinations using JMP Pro15 software (SAS Institute,

Cary, NC, USA). ABCD mixture design was used for factor analysis and generation of the

ternary plots. The methods followed Oblad et al methods. We used the same simplex lattice

augmented with four additional points resulting in the ten experimental points and by using

23

the least-squares method coefficients were estimated for use in the quadratic mixture model.

Each 96-well plate had the ten mixture treatments and was normalized to a DMSO vehicle

control which was set at 100% viability.

Supplementary Materials: See Appendix A.

Author Contributions: Conceptualization, I.G.B., and J.D.K.; methodology, I.G.B., and J.D.K..;

investigation, I.G.B., S.S., and C.C.; data curation, I.G.B.; writing—original draft preparation,

I.G.B.; writing—review and editing, J.D.K and I.G.B..; visualization, J.D.K., I.G.B.; supervision,

J.D.K.; project administration, J.D.K.; funding acquisition, J.D.K. All authors have read and

agreed to the published version of the manuscript.

Funding: This research has been supported by a generous donation from Bryant Adams.

Acknowledgments: Bryant Adams, McKay Miller, and Simmons Center for Cancer Research.

Conflicts of Interest: The authors declare no conflict of interest.

24

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32

APPENDIX A

33

Supplemental Figure Legends Supplementary Figure 1. All ternary plots of PC-3 cells arranged from the most effective

combinations that caused the greatest predicted decrease in cell viability to the combinations

with the smallest decrease in cell viability. Each graph uses its own color key that is optimized

to show different levels of significance. All points within the same shade of red have no

statistical difference, although for each ternary graph a single ideal point is predicted. The

corresponding data for combinations A-Ii are presented in Table 2.

Supplementary Figure 2. All ternary plots of DU145 cells arranged from the most effective





corresponding data for combinations A-Ii are presented in Table 3.

Supplementary Figure 3. All ternary plots of LNCaP cells arranged from the most effective





corresponding data for combinations A-Ii are presented in Table 4

34

Supplementary Figure 4. Ternary plot of the combination from Shikonin, Berberine, and

Wogonin. The associated statistics are in Table 6.

35

Supplemental Figure 1

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death 2

15

23.75

32.5

41.25

50

F

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

35

40

45

50

55

C

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

15

26.25

37.5

48.75

60

B

00.10.20.30.40.50.60.70.80.91

Shikonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

30

37.5

45

52.5

60

A

00.10.20.30.40.50.60.70.80.91

Triptolide

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

15

23.75

32.5

41.25

50

E

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

0

11.25

22.5

33.75

45

H

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

15

23.75

32.5

41.25

50

G

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

10

18.75

27.5

36.25

45

I

00.10.20.30.40.50.60.70.80.91

Silybin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

20

30

40

45

50

D

36

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

10

18.75

27.5

36.25

45

K

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

20

25.625

31.25

36.875

42.5

Q

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

10

18.75

27.5

36.25

45

L

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

17.5

23.75

30

36.25

42.5

N

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

10

18.75

27.5

36.25

45

J

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

10

18.75

27.5

36.25

45

O

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

0

10

20

30

40

R

00.10.20.30.40.50.60.70.80.91

Emodin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

10

18.75

27.5

36.25

45

P

00.10.20.30.40.50.60.70.80.91

Emodin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

5

15

25

35

45

M

37

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

15

20

25

30

35

Y

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

15

20.625

26.25

31.875

37.5

X

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

15

20.625

26.25

31.875

37.5

W

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

0

10

20

30

40

V

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

22

25

28

31

34

Z

00.10.20.30.40.50.60.70.80.91

Emodin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

5

13.5

20

30

40

U

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

10

17.5

25

32.5

40

S

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

0

10

20

30

40

T

00.10.20.30.40.50.60.70.80.91

Triptolide

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

15

19.375

23.75

28.125

32.5

Aa

38

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

-5

5

15

25

35

Dd

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

-5

0.625

6.25

11.875

17.5

Ii

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

5

12.5

20

27.5

35

Bb

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

10

15.625

21.25

26.875

32.5

Cc

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

-2.5

4.375

11.25

18.125

25

Hh

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

2.5

9.375

16.25

23.125

30

Ee

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

0

7.5

15

22.5

30

Ff

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% PC-3 death

2.5

8.75

15

21.25

27.5

Gg

39


00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

20

35

50

65

80

A

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

10

25

40

55

70

F

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

25

36.25

47.5

58.75

70

H

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

30

41.25

52.5

63.75

75

D

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

42.5

48.75

55

61.25

67.5

I

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

20

35

50

65

80

B

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

0

10

30

50

70

G

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

35

43.75

52.5

61.25

70

E

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

0

20

40

60

80

C

40

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

15

27.5

40

52.5

65

O

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

47.5

51.875

56.25

60.625

65

J

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

30

38.75

47.5

56.25

65

M

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

20

31.25

42.5

53.75

65

N

00.10.20.30.40.50.60.70.80.91

Silybin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

10

25

40

55

70

K

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

25

35

45

55

65

L

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

0

10

25

45

60

R

00.10.20.30.40.50.60.70.80.91

Emodin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

0

15

30

45

60

Q

00.10.20.30.40.50.60.70.80.91

Emodin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

10

25

40

55

70

P

41

00.10.20.30.40.50.60.70.80.91

Shikonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

5

17.5

30

42.5

55

W

00.10.20.30.40.50.60.70.80.91

Triptolide

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

5

17.5

30

42.5

55

T

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

-10

7.5

25

42.5

60

U

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

25

31.25

37.5

43.75

50

Z

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

28

38

48

53

55

S

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

0

15

30

45

60

X

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

0

10

25

40

50

Aa

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

5

16

28

38

50

Y

00.10.20.30.40.50.60.70.80.91

Emodin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

10

21.25

32.5

43.75

55

V

42

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

0

10

20

30

40

Gg

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

0

11.25

22.5

33.75

45

Dd

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

-5

7.5

20

35

45

Ff

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

10

20

30

35

45

Ee

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

0

5

10

15

20

Ii

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

10

20

30

40

50

Bb

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

10

20

30

40

50

Cc

00.10.20.30.40.50.60.70.80.91

Triptolide

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DU145 death

10

15.625

21.25

26.875

32.5

Hh

43

00.10.20.30.40.50.60.70.80.91

Silybin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

55

75

85

90

95

D

00.10.20.30.40.50.60.70.80.91

Shikonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

0

30

60

90

110

A

00.10.20.30.40.50.60.70.80.91

Triptolide

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

30

50

70

90

100

B

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

65

75

80

87

95

F

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

50

60

72

82

95

G

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

50

60

70

80

90

I

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

45

60

75

85

100

C

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

20

35

60

85

100

E

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

45

55

65

80

90

H


44

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

35

48.75

62.5

76.25

90

M

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

10

30

50

70

90

K

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

55

63.75

72.5

81.25

90

R

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

-10

15

40

65

90

J

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

50

60

70

80

90

Q

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

50

60

70

80

90

O

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

30

50

70

80

90

L

00.10.20.30.40.50.60.70.80.91

Emodin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

10

30

50

70

90

N

00.10.20.30.40.50.60.70.80.91

Emodin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

5

22

45

70

90

P

45

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

20

35

55

65

80

W

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

45

55

65

75

85

U

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

40

52.5

65

77.5

90

S

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

10

30

45

60

80

Z

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

30

40

50

65

80

Y

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

40

50

56

62

66

Aa

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

30

40

60

70

80

V

00.10.20.30.40.50.60.70.80.91

Emodin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

20

30

45

65

80

X

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

10

25

50

70

90

T

46

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

0

5

15

30

40

Ii

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

30

35

45

52

56

Ee

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

5

20

35

50

60

Ff

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

25

30

40

50

60

Dd

00.10.20.30.40.50.60.70.80.91

Curcumin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

15

30

40

55

65

Bb

00.10.20.30.40.50.60.70.80.91

Wogonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

0

10

30

50

70

Cc

00.10.20.30.40.50.60.70.80.91

Berberine

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

0

10

25

40

42

Hh

00.10.20.30.40.50.60.70.80.91

Triptolide

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% LNCaP death

5

17.5

30

42.5

55

Gg

47

Supplementary Figure 4

00.10.20.30.40.50.60.70.80.91

Shikonin

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% DR PC-3 death

10

20

30

40

44

48

APPENDIX B

49

Proposed Research Introduction and statement of the problem

Prostate cancer (PC) is the most common cancer in men in the United States,

contributing to 5.5% of all cancer deaths. The 5-year survival rate is high for prostate cancer at

97.8%. Two common treatments for PC include androgen deprivation therapy (ADT) and

chemotherapy (Huggins and Hodges 1941; NCI 2020). These treatments help extend patient life

expectancy but are limited due to side effects especially if higher dosages and longer treatment

times are required. Additionally, PC can develop resistance to these treatment regimens which

ultimately limits their effectiveness. PC survival rate drops drastically to just 30.2% when

prostate cancer becomes metastatic (NCI 2020). Metastatic PC can begin to exhibit castration-

and drug-resistance after exposure to either treatment. Castration- and drug-resistant PC causes

most of these deaths because currently there are not any effective treatments against these

resistant cancers. (PDQ® Adult Treatment Editorial Board, NCI 2020).

The low 5-year survival rate and lack of effective treatments highlight the need to

develop treatment regimens that are more difficult for PC to become resistant to and to develop

treatments that effectively treat PC which has already become resistant to current treatments.

Both castration- and drug-resistance in PC are related to PC’s rapid mutation rate and ability to

selectively evolve to evade and survive treatment. The surviving PC cells then act as progenitor

cells for subsequent tumor growth which inherits resistance to standard treatments (Horning et

al., 2017; Bhangal et al., 2000; Longley & Johnston, 2005; Crawford et al., 2017). Combining both

ADT and chemotherapy has been shown to increase life expectancy and improve treatment

50

outcomes (Sweeney et al., 2015; Andrews et al., 2020). These results indicate that a mixed

treatment approach is likely superior to a single treatment method or a sequential approach.

One powerful and underutilized tool for understanding the contribution of multiple

compounds or drugs for cancer treatment is mixture design response surface methodology

(MDRSM). Mixture design techniques have been used frequently in food science and

engineering to optimize a product or procedure that has multiple variables. In consideration of

the need to discover more effective treatment options for PC and the promise of combination

therapy, I will screen the effectiveness of seven natural compounds in all 35 combinations of

three by using MDRSM.

Proposed Hypothesis & Aims

The overarching hypothesis is that combination treatments, which are effective against three

non-drug-resistant PC cell lines, will also be effective against docetaxel-resistant PC-3 cells, yet

be less cytotoxic in non-tumorigenic RWPE-1 human prostate epithelial cells. In addition to the

hypothesis, this research also will provide extensive support for the use of mixture design

response surface methodology (MDRSM) in cell-based research. The condensed aims of the

research project are listed below.

• Use MDRSM to screen all 35 possible 3-compound combinations of the seven natural

compounds for combinations that induce more cell death than the compounds alone in

LNCaP, DU145, and PC-3 human PC cells. The seven natural compounds are Berberine,

Curcumin, Emodin, Wogonin, Shikonin, Triptolide, and Silybin (silibinin).

51

• Test the most effective combinations (50% or greater reduction of cell viability) against

docetaxel-resistant PC-3 cells and check for cytotoxic side effects in RWPE-1 human

prostate epithelial cells.

• Identify unique responses to the combinations between LNCaP, DU145, and PC-3

human PC cell lines in response to combination treatments will help identify differences

between the cell lines and to note mixtures that could be the starting point for future

research projects.

Alternate

If the compounds fail to produce effective combinations that result in at least 50%

reduction of cell viability, the alternate plan is to test the compounds at their IC50 values in

combination with docetaxel to see if the compounds in combination with docetaxel help

sensitize the prostate cancer cell lines to the chemotherapeutic effect of docetaxel.

52

REVIEW OF LITERATURE Introduction and Statement of the Problem

Prostate cancer (PC) is the most common cancer in men in the United States, causing

34,000 deaths annually in the United States. The 5-year survival rate for PC patients is high

(97.8%) largely because PC generally grows slowly and can be treated by surgery, radiation,

hormone therapies, often called androgen deprivation therapy (ADT), and chemotherapy.

However, survival drops drastically to just 30.2% when PC becomes metastatic and begins to

exhibit resistance to ADT, which is called castration-resistance (SEER Cancer Stat Facts: Prostate

Cancer. National Cancer Institute., 2020). Chemotherapy is the last line of treatment against

metastatic and castration-resistant PC (mCRPC). Resistance to ADT and eventually resistance to

chemotherapy occur as some PC cells manage to survive the treatment-induced cell death due

to advantageous mutations and these cells continue to propagate despite the applied

treatments. Often these unique progenitor cells exhibit stem-cell-like behaviors and allow them

to adapt more rapidly and survive treatment methods (Horning, et al., 2017; Longley &

Johnston, 2005; Bhangal, et al., 2000; Crawford, Petrylak, & Sartor, 2017). Currently, there are no

effective treatments against mCRPC that has also begun to develop resistance to chemotherapy

(PDQ® Adult Treatment Editorial Board. PDQ Prostate Cancer Treatment. Bethesda, MD:

National Cancer Institute, 2020).

Development of Castration Resistance

Normal prostate epithelial cells require androgens i.e., steroid hormones such as

testosterone, to replicate and grow properly. Accordingly, early-stage PC responds well to ADT

because it has not yet mutated enough to grow independent of androgens (Crook et al., 2012).

53

ADT has been relied on heavily since 1941 when Huggins and Hodges (1941) discovered that

they could inhibit PC growth by decreasing androgen levels in patients. Both the initial

effectiveness of ADT and PC’s typical slow growth contribute to the high 5-year survival rate

for PC patients (NCI 2015). However, as PC progresses, the number of mutations within the

cancerous cells increases, and the PC selectively evolves towards advantageous mutations.

Advantageous mutations often include mutations in androgen receptor pathways, growth

checkpoints and factors, multidrug resistance genes, DNA repair genes, and apoptosis

pathways (Galletti et al. 2017). The cells that accumulate advantageous mutations most quickly

act as progenitor cells that lead to the development of tumors that grow independent of

androgen levels which is called castration resistance (CR). CRPC becomes more resistant to

controlled cell death which ultimately increases the malignancy of PC (Horning, et al., 2017).

The ability to grow independent of androgens aligns with the hallmark of cancer to grow

independent of external signals for growth, as identified by Hanahan and Weinberg (2011). The

development of CRPC necessitates the use of additional treatment methods, such as

chemotherapy.

Development of Drug Resistance

Taxanes are the primary class of cytotoxic chemotherapy used for PC and docetaxel is

the primary taxane used for PC (Galletti et al. 2017). Docetaxel acts by binding to tubulin, a

component of microtubules, and by binding tubulin docetaxel promotes the stabilization of

microtubules which prevents mitosis (PubChem. Docetaxel). However, just as PC can develop

resistance to ADT, PC can also develop resistance to docetaxel and other chemotherapeutics.

Resistance to docetaxel develops similarly to how castration resistance develops. Some of the

54

PC cells survive the initial chemotherapy treatments and then act as progenitor cells for a new

population of PC cells that inherit resistance to the mechanism of the chemotherapeutic drug

used. Several different genetic profiles contribute to a PC cell’s ability to resist the effects of

various drugs. In 2000 Bhangal et al. highlighted elevated expression of the multidrug

resistance gene (MDR1) in human PC. MDR1, also known as P-glycoprotein 1, acts by pumping

foreign substances out of the cell. PC cells often increase their expression of MDR1, making the

chemotherapeutics less effective. Bergers & Hanahan (2008) list other general mechanisms of

drug resistance, including alterations to the cell cycle, increased repair of DNA damage,

reduced programmed cell death via new apoptotic pathways, altered drug metabolism, and

activation or increased up-regulation of alternative proangiogenic pathways. In addition to

these evasive changes in PC, internal mutations to the pathways that chemotherapeutics are

designed to regulate, such as the deletion of proapoptotic genes, can lead to PC cells failing to

respond to chemotherapeutic treatments as drug resistance increases (Longley & Johnston,

2005)

Link Between Castration and Drug Resistance

Horning et al. (2017) suggested that castration and drug resistance both relate to a subset

of mutant PC cells that survive the initial treatments and then act as progenitor cells for

resistant tumors. To inhibit the development of resistant tumors Sweeney et al. (2015) and

Andrews et al. (2020) both found the concurrent early use of ADT and docetaxel, improved life

expectancy, and treatment outcomes compared to using a single treatment alone or sequential

treatment plans. These results indicate that a mixed treatment approach is likely superior to a

single treatment method or a sequential approach.

55

Combination Therapy

When PC no longer responds to ADT, the next line of defense is chemotherapy.

Docetaxel is commonly used against mCRPC (Andrews et al., 2020). Two recent meta-analyses

recommended that docetaxel should be used initially in standard care for PC before PC

develops CR or advances to be highly metastatic (Vale et al. 2016 & Andrews et al. 2020).

Supporting the results previously seen in an intervention study by Sweeney et al. in 2015 which

showed concurrent treatment of docetaxel with ADT early on led to a 13.6-month increase in the

overall median survival rate compared to ADT alone. These studies indicate that combination

treatments warrant further consideration because they improve treatment outcomes. A recent

review suggests there may also be additional benefits from combining chemotherapies to treat

CRPC, however, the best combinations or order for treatments remains to be determined

(Galletti et al. 2017). The premise behind combination therapy is to overwhelm the PC through a

variety of mechanisms and prevent it from successfully developing resistance to each drug’s

mechanism of action and without leading to unintended resistance to secondary treatment

options (Andrews et al., 2020). More research is required to identify the best treatment

combinations that do not interfere with each other and still leave the PC vulnerable to

secondary treatment options in case of reoccurrence. To identify novel chemotherapeutic

treatment options against mCRPC many studies are investigating bioactive compounds with

putative chemotherapeutic properties from traditional Chinese medicine (TCM).

56

Combination of Bioactive Compounds in Traditional Chinese Medicine (TCM)

TCM is one of the oldest medical practices worldwide and has notes dating back three

centuries about treating tumor growths (Li P., 2003; Liu et al. 2015). TCM has multiple treatment

methods including acupuncture, Ti Ci, and herbal TCM. Throughout this review, TCM will

refer to herbal TCM treatments (Xiang et al. 2019). The herbs and combinations of herbs used in

TCM treatments frequently contain multiple bioactive compounds, thus making the bioactive

compounds from TCM of particular interest for potential beneficial combination therapies. The

bioactive compounds have multiple targets including gene regulation, epigenetic modification,

apoptosis induction, ROS generation, and cell cycle arrest (Xiang et al. 2019; Huynh et al. 2017).

Because the compounds act through a variety of pathways and are used in combination and

that may interact, it has made identifying their exact mechanisms of action difficult (Li et al.,

2013; Hsiao and Liu, 2010). For example, Kumazoe et al., (2015) saw that the combination of two

compounds found in green tea, epigallocatechin-3-O-gallate (EGCG) and eriodictyol, was more

effective than either alone. Eriodictyol potentiated the apoptotic effects of EGCG and made

EGCG over five times as potent. Deep et al. (2006) found a similar trend with silymarin and

silibinin (silybin), both isolated from milkweed thistle, which had greater effectiveness in

combination, as compared to treatment with either compound alone Despite these difficulties,

promising and popular compounds are the topic for researched to understand their mechanism

of action. Additionally, effective compounds from TCM are studied in combination with

standard western chemotherapeutics in an effort to improve treatment outcomes against

mCRPC. To study combinations various statistical methods are employed because they allow

for the systematic comparison of the included drugs. Some models include Chou Talalay, Box

57

Behnken design, Central Composite design, and mixture design response surface methodology

(MDRSM).

Mixture Design Response Surface Methodology (MDRSM)

MDRSM is a powerful statistical model to identify the contributions of three or more

effectors, or in the case of my study the effect of three natural compounds. MDRSM does not

require that the concentrations of each compound to be in constant ratios of each other which is

one of the restraints of the Chou Talalay method. Chou Talalay also is limited to comparing

only two compounds. MDRSM requires fewer experimental runs than Box-Behnken or Central

Composite design to assess the contribution of three compounds in combination (5.3.3.6.3.

Comparisons of response surface designs). The researchers decide what the 100% dose

treatment of each compound will be (i.e. the IC50 value or any other concentration) in MDRSM

and the subsequent seven internal points of the simplex surface are mixtures of the compounds

in proportion to the 100% dose treatment chosen previously by the researcher (i.e. 50%, 33%,

16%. of the IC50 value) (See Figure 1 of the manuscript.). The experimental data is then used to

create a statistical model that can then be used to maximize the desired effect, i.e. cell death, and

this predicts the best percentage of each compound’s 100% dose treatment required to obtain

the optimal treatment combination. This optimization reduces the dose of each compound used

in combination and this may result in fewer side effects because lower doses of the compounds

are used to obtain the maximal reduction in cell viability.

Engineering and food science have regularly used mixture design methods and response

surfaces methodologies to identify optimal combinations that maximize the desired effect.

Biological and medical researchers have not used MDRSM as frequently despite the benefits of

58

fewer required experiments and the ability to assess three or more factors together (Azcarate et

al. 2020). Possible disadvantages to MDRSM include the possibility of poor fit of the model

either leading to large confidence intervals or possibly statistically significant lack of fit. The

lack of fit statistic identifies whether the experimental data produces a statistical model that fits

the data well. If the lack of fit is significant this means the model does not fit well and the test is

rejected. In vitro and in vivo experiments may be susceptible to poor fit because of the

complexity of the experimental systems involved and naturally occurring random error

common to such experimental systems. This may be compensated by additional data points

within the simplex design to improve the precision of the model. However, if the random error

and background noise of the experiment cannot be properly reduced MDRSM may not be the

best statistical tool. Even with possible limitations the MDRSM method still has many

advantages over other combination methods. MDRSM has been used effectively to determine

the optimal combination of various drugs and bioactive compounds previously in in vitro PC

models (Oblad et al. 2018; Asay et al. 2020).

Mechanism of Action of the Natural Compounds

This next section will briefly review some of the proposed mechanisms of action of

seven natural putative chemotherapeutic compounds. Some compounds have been studied in

greater depth and others have fewer studies focused on their mechanism of action, however,

even for the highly studied compounds, it appears that most compounds act in multiple

pathways making it difficult to tease apart their exact mechanism of action. The proposed

mechanisms of Berberine, Curcumin, Emodin, Wogonin, Shikonin, Triptolide, and Silybin

(Silibinin) will be discussed below.

59

Berberine

Meerana et al. (2008) reported that Berberine acted to induce cell death by causing an

increase in reactive oxygen species (ROS) in PC-3 cells. A cascade of ROS indicates cellular

damage and induces apoptosis. Berberine appears to act in concert with xanthine oxidase, an

enzyme that produces ROS, because adding a xanthine oxidase inhibitor (allopurinol) Meerana

et al. inhibited the effects of berberine. Increased levels of ROS are frequently observed to

initiate apoptosis. Berberine has also been reported to modulate the inflammatory response by

inhibiting NEK7-NLRP3 interaction, IL-1β, IL-6, and NF-kB, expression as well as decreasing

androgen receptor expression, prostate-specific antigen, and COX-2 while increasing caspase-3

and inducing apoptosis (Liu et al. 2019; Zeng et al. 2021; Tian et al. 2016; Li et al. 2017).

Berberine has been noted for anti-cancer effects in several in vivo studies against various cancer

types (Meerana et al. 2008; Li et al. 2017).

Curcumin

Curcumin is a known antioxidant and anti-inflammatory compound (Wahab 2020). Lv

et al. (2014) found that curcumin led to decreased expression of Bcl-2 an anti-apoptotic protein,

and increased expression of Bax, a pro-apoptotic protein in MDA-MB-231 and MCF-7 breast

cancer cells. The overall effect was an increase of the Bax/Bcl-2 ratio leading to increased levels

of apoptosis in the breast cancer cell lines the authors studied. Banerjee et al. (2017) reported the

same mechanism of action for Curcumin in PC-3 and DU145 PC cell lines. Banerjee et al. (2017)

also compared the effects of Curcumin alone and docetaxel alone to the mixture of both

docetaxel and curcumin and found that the mixture of the Curcumin and docetaxel was more

60

effective. This outcome was observed even when the mixture used lower concentrations than

the dosage used in the single treatments.

Emodin

Deng et al. (2015) reported that Emodin caused G2/M phase arrest and led to roughly a

20% increase in apoptosis when applied to PC-3 cells at a 60 uM concentration. In the same

study they saw that Emodin also increased the expression of Notch1 while decreasing the

expression of Jagged1, vascular endothelial growth factor (VEGF), and basic fibroblast growth

factor (bFGF) at both the mRNA level and the protein level. Notch1 plays a role in cell growth

and division, differentiation, and apoptosis. The authors found that Notch1 receptors moved to

the nuclear membrane and nucleus as the concentration of Emodin increased. VEGF signals for

the growth and development of blood vessels, so decreased expression helps reduce the blood

supply to the tumor and this can slow the growth of tumors. Li et al. (2014) reported that this

effect could be compounded by the addition of Triptolide which also decreases angiogenesis.

bFGF plays a role in cell growth, morphogenesis, tissue repair, and is also associated with

tumor growth and invasion. The increase of Notch1 and the decrease of Jagged1, VEGF, and

bFGF indicate the mechanism by which Emodin effectively decreases PC growth and

development. Yim et al. (1999) found that Emodin also acted by inhibiting casein kinase II,

which is a serine/threonine (Ser/Thr) kinase. Emodin also acts on other Ser/Thr kinases,

including cAMP-dependent protein kinase (PKA), protein kinase C (PKC), cdc2, and casein

kinase I (CKI). This effectively suppresses PC growth as kinases mutations are common in

cancer and Cdk1 phosphorylation of the androgen has been used as a biochemical marker to

predict shorter relapse time (Willder, et al., 2013).

61

Wogonin

Huynh et al., (2017) reviewed Wogonin’s effects on two PC cell lines including PC-3, an

aggressive castration-resistant metastatic tumor, and LNCaP which is an androgen-sensitive

metastatic tumor. Wogonin increased p53, PUMA (another pro-apoptotic protein), and Bax, an

apoptotic activator which is controlled in part by p53 expression and has similar effects to those

of Curcumin (Lv et al. 2014; Banerjee et al. 2017). Huynh et al. (2017) also saw Wogonin

modulate cytochrome C release from the mitochondria. An increase in released cytochrome C in

the cytoplasm induces the release of Ca2+ of the endoplasmic reticulum which in turn signals

apoptosis. Huynh et al. (2017) also reported Wogonin acted to decrease MDM2, which is a

proto-oncogene and decreased MDM2 leads to decreased tumor growth.

Shikonin

Chen et al., (2014) showed that Shikonin potently suppressed PC-3 and DU145 cell

growth by cell cycle arrest in the G2 phase. They also reported decreased invasion of PC-3 and

DU145 cells. Invasion is an in vitro method to approximate the metastatic nature of the cancer

cells. However, invasion methods often only poorly distinguish between metastatic ability and

growth because they measure how quickly the cells grow into a new area after being physically

removed. The researchers did try to support their earlier claims by looking at the activity of

matrix metalloproteinases (MMP) which play an important role in the development of the

extracellular matrix. MMP-2 and MMP-9 both significantly decreased following treatment with

2uM of Shikonin for 48h. They also saw increases in Bax and decreases in Bcl-2, which changes

are both associated with increased apoptosis as reported for Curcumin and Wogonin. The Bax

and Bcl-2 pathway can induce apoptosis by activating p53. However, if p53 has mutated and no

62

longer works properly, then Shikonin’s activation of the Bax/Bcl-2 pathway may fail to induce

apoptosis. Levine (1997) reported that roughly 50 percent of cancers have mutations to p53,

making it the most common mutation in cancer cells. The prevalence of this mutation would

decrease the effectiveness of drugs or compounds that treat cancer by acting through the p53

pathway.

Triptolide

Li et al. (2014) studied the effects of Triptolide on primary human breast cancer cells

(BCC) and breast cancer stem cells (BCSC). Triptolide was more effective against BCC than

BCSC but did reduce cell viability and cause a significant increase of apoptosis in both cell lines

in a time and dose-dependent manner. Breast cancer and PC have similarities in that both

cancers often begin as hormone-dependent cancers. Recently, another study showed that

Triptolide inhibited breast cancer growth and invasion by decreasing matrix metalloproteinase-

9 (MMP-9) expression through NF-kB and AP-1 signaling (Hong et al. 2021). MMP-9 has been

recognized to play a role in tumor progression in many cancer types including PC (Farina &

Mackay 2014). Increased expression of MMP-9 is associated with increased inflammation,

angiogenesis, and epithelial to mesenchymal transition of tumor (Farina & Mackay 2014). Yuan

et al. in 2016 found that triptolide also inhibited MMP-9 in PC-3 cells which they believed

decreased the invasion of PC-3 cells. Triptolide appears to help prevent some of the hallmark

properties of cancer and greatly limits the size the tumor can grow by inhibiting angiogenesis,

and alteration of the microenvironment that allows PC to spread. Shikonin is also reported to

decrease PC invasion by decreasing the expression and activation of MMP-2/-9 (Chen et al.

2014). Emodin also decreases angiogenesis, however, through a different mechanism, so the

63

combination of Emodin and Triptolide may inhibit angiogenesis even greater in combination (Li

et al., 2014).

Silybin

Deep et al., (2006) showed that Silybin (Silibinin) modulates the ATM–Chk1/2–Cdc25–

Cdc2—cyclin B1 pathway leading to G2–M arrest in PC3 cells. Silybin decreased levels of Cyclin

D1 in the cytoplasm and within the nucleus. Silybin also decreased CDK2 and increased Chk2

levels within the nucleus which acts to inhibit cell cycle progression through the G2-M phase.

Silybin increases phosphorylation of ATM, which plays an important role in recognizing DNA

damage and regulating nuclear protein expression along with cell cycle progression.

Additionally, Roy et al. (2007) used DU-145 and 22Rv1 cell lines to show the effects of Silybin.

They reported that Silybin acted primarily as a G1-S inhibitor and only as a mild G2-M

inhibitor, which finding contradicts Deep et al. (2006). However, this difference in results might

partly be explained by the slight differences between PC-3 and DU-145 PC cell lines. The

contradiction is further confounded by the different endpoints of the two studies, 12h and 24h

for Roy et al., compared to 24h and 48h for Deep et al. Silybin increased p21, a cyclin-dependent

kinase inhibitor (CKI), which blocks cyclin-CDK2, -CDK1, and -CDK4/6 complexes and thus

regulates G1-S phase transition. The data showed Silybin increased p21 and p27 (another CKI

regulating G1-S phase transition) for up to 36 hours. Both studies indicate that treatment with

Silybin led to G1 or G2 cell cycle arrest.

Conclusion

In conclusion, potential combination treatments using natural bioactive compounds,

ADT, and chemotherapeutics such as docetaxel, could act through many pathways to overcome

64

the evasive nature of PC, leading to more successful treatment and decreased risk of

reoccurrence. The efficacy of the treatment is strengthened by the over-lapping mechanisms of

some of the compounds, such as the action of Curcumin, Wogonin, and Shikonin on the

Bax/Blc-2 pathway to induce apoptosis and increase the natural anti-cancer effects of p53, and

the role played by both Emodin and Silybin in cell cycle inhibition through reduced cell

division. Additionally, Emodin and Triptolide both decrease angiogenesis, which helps reduce

tumor growth. The combined effect these compounds have through multiple unique pathways

is much greater than a single mechanism alone. One compound may sensitize the PC to the

effects of another, leading to greater effects with lower doses of either compound (Banerjee,

Singh, Chowdhury, Lillard, & Singh, 2017). If reoccurrence does occur, the progenitor cells

likely would have experienced lower levels of exposure to any one drug, which may reduce the

risk of developing resistance to specific compounds and allow them to be more responsive to

subsequent treatment regimes. However, this potential outcome must be further studied

because another possibility is that the recurrent PC could develop resistance to each of the

unique mechanisms of action and thus be much harder to treat if the PC returns. The

application of combination therapy is a promising treatment for PC, but the ideal sequence and

combinations require more research to be clearly defined. Evaluating combinations using an

MDRSM statistical design can be an effective and efficient approach to identify the best

combinations to treat PC.

65

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