The Effects of Lipophilicity of Propofol Derivatives on Lung Cancer Cells

Jason Richard Miller

This thesis is submitted in partial fulfillment of the requirements of the Research Honors Program in the Department of Chemistry and Biochemistry

Marietta College

Marietta, Ohio

April 27, 2018 Miller 2

This Research Honors thesis has been approved for the Department of Chemistry and Biochemistry and the Honors and Investigative Studies Committee by

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Table of Contents Acknowledgments...... 4 Abstract ...... 5 Introduction ...... 6 Anesthesia ...... 6 Lipid Model of Anesthesia...... 7 Figure 1: Mechanism of action for the lipid model ...... 7 Propofol...... 8 Figure 2: Structure of compounds tested ...... 8 Role of Lipophilicity ...... 9 Octanol:Water Partition Coefficient ...... 10 Toxicological Analysis ...... 12 Figure 3: Example of a dose response curve ...... 13 Rationale ...... 13 Cell Death ...... 14 Methods...... 16 Cell Culture ...... 16 Preparation of Diprivan...... 16 Cell Treatment with Compounds ...... 17 Figure 4: Diagram of the treatment plan ...... 17 Analysis of Cell Proliferation ...... 18 Results ...... 19 2,6-diisopropylphenol ...... 20 Phenol ...... 21 2,6-dimethylphenol ...... 22 2,6-ditertbutylphenol ...... 23 Trends ...... 24 Table 1: Calculated LC20 and ANOVA statistical analysis ...... 24 Figure 9: Correlation between lipophilicity and the concentration in which the number living of cells decreased by 20%...... 25 Discussion ...... 26 Interpreting Dose Response Curve ...... 26 Sources of Error ...... 27 Why No Trend?...... 28 Competing Models Lipid vs. Protein ...... 29 Alternative Explanations ...... 30 Future Research ...... 31 Literature Cited ...... 32

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Acknowledgments

I would like to express my gratitude for being able to be a part of the Dr. Kimberly

Suzanne George Parsons Biochemistry Research Lab since the second semester of my freshman year at Marietta College. Additionally, I would like to thank the Marietta College Department of

Chemistry and Biochemistry, Marietta College Honors Thesis Committee, Marietta College

Honors Program, and the Marietta College Investigative Studies Summer Fellowship Program for making it possible for me to carry out this research at Marietta College. I am appreciative that

Dr. Shiyong Wu at Ohio University provided H-1299 human lung cancer cells for my research.

Finally, I would like to thank my family and peers for offering their support along my journey in this research project.

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Abstract

Propofol (2,6-diisopropylphenol) is a commonly utilized general anesthetic that has been shown to induce apoptosis. Propofol derivatives of varying lipophilicity were used to treat H-

1299 human lung cancer cells. Lipophilicity is a significant characteristic that effects whether a compound can enter a cell by passing through the membrane. Clonogenic assays were used to evaluate the effect of each of the compounds on the number of cells attached to the plate after a

24-hour incubation. Dose-response curves were created for phenol, 2,6-dimethylphenol, propofol, and 2,6-di-t-butylphenol. Trend lines for the linear portion of the data were used to estimate LC20 values. The LC20 values for the compounds tested were compared, and no definitive trend was found relating lipophilicity to the number of living cells remaining on the plate after treatment. This could be a result of cell death being secondary to the anesthetic action of propofol. The calculated LC20 values were: 206 µM for phenol, 16.3 µM for 2,6- dimethylphenol, 9.33 µM for propofol, and 101 µM for 2,6-di-t-butylphenol. ANOVA testing found statistical significance between the various concentrations tested for phenol and propofol.

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Introduction Anesthesia

Although surgery predated anesthesiology by at least two centuries, people were fearful of the great pain endured as a result of being conscious for the entire operation; however, this changed with the use of anesthesia. An article published in an 1846 edition of the People’s

Journal of Medicine declared, “We have conquered pain.”1 This gave surgeons a more controlled environment in the operating room, ultimately allowing procedures to become much more invasive without needing to distract the patient from the pain.1

The first chemical anesthetic agent experimented with was carbon dioxide. This induced partial asphyxiation which resulted in unconsciousness in animal studies. Unfortunately, the risk of general hypoxia outweighed the benefits of inducing unconsciousness for this method.2

Another one of the earliest compounds implemented for general anesthesia was diethyl ether. By the early 1900s, ether was replaced by chloroform due to the compound acting more quickly.

The first intravenous (IV) anesthetics were barbiturates. These compounds, such as thiopentone, were particularly useful for inducing anesthesia due to being both short-acting and quick to take effect. Thiopentone was phased out in the 1980s, due to the development of a better pharmaceutical – the current standard of propofol, which is particularly beneficial due to its antiemetic (nausea preventing) properties.2 Throughout the years, the compounds utilized as anesthetics have changed, but there have been commonalities in the presence of both hydrophilic portions of the molecule, which are polar or capable of hydrogen bonding, and hydrophobic parts that are carbon chains or rings.

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Lipid Model of Anesthesia

The exact mechanism of how anesthesia works is not known; however, there are two leading theories. The “lipid theory” hypothesizes that the drug dissolves into the membrane of a cell interacting with membrane proteins which results in anesthetic effects. Alternatively, the

“protein theory” claims that the anesthetic can react directly with the membrane proteins without first embedding itself within the cell membrane.3

The lipid model is supported by the fact that anesthetic molecules have been found to cause changes in membranes.3 This indicates that the molecule can insert itself into the membrane. However, further study is needed to determine if the lipid model is the leading contributor to anesthetic effect.3

Hydrophilic exterior

Hydrophobic core

Hydrophilic interior

Figure 1: Mechanism of action for the lipid model. Anesthetic molecules (green) embed in the hydrophobic core of the cell membrane (blue). Interaction occurs with the membrane proteins (orange) causing the anesthetic effect.

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Propofol

Propofol is a commonly used general anesthetic which has a chemical structure (Figure

2C) that consists of a phenol with two isopropyl substituents in the 2 and 6 position on the phenol. Other R groups tested include hydrogen, methyl, and t-butyl substituents. These are in the 2 and 6 positions on the ring as indicated by a R in Figure 2E. Despite being relatively reactive, it is not very potent.4 Thus, it is used clinically in higher dosages than inhaled anesthetics. During anesthesia, propofol levels are typically maintained around 25-130 micromolar in the blood.4

A B C

D E

Figure 2: Structure of compounds tested: phenol (A); 2,6-dimethylphenol (B); 2,6- diisopropylphenol/propofol (C); and 2,6-di-t-butylylphenol (D). These compounds are all based on phenol with substituents changing in the R group (E). The R groups are in the 2 and 6 position of the phenol ring. These compounds were used to treat the cells in this experiment to test the effects of lipophilicity on change in proliferation.

In a study by Tsuchiya et al., propofol was found to induce apoptosis. The authors of this paper believe that this is due to stimulation of protein kinase C.4 They used various experimental techniques, such as DNA fragmentation and western blot, to find that apoptosis was induced.4

Immunological analysis verified that apoptosis was occurring by detecting caspase activity as well as the release of cytochrome c from the mitochondria. As the concentration of propofol Miller 9 increased, there was an increase in the number of cells that experienced DNA fragmentation, which indicated that cellular death had occurred.4

Role of Lipophilicity

Lipophilicity is one of the most important properties of a molecule affecting whether it can enter the cell and cause an effect on the body.5 Thus, it is a crucial concept in medicinal chemistry. A set of rules was created to aid in drug development to make it easier to determine if the compound would be able to cross the cell’s membrane. The Rule of Five requires that the molecule must have: a molecular weight less than 500; log of the partition coefficient less than 5; the number of hydrogen bond donor groups less than 5; and the number of hydrogen bond acceptor groups less than 10.5 This rule describes the chemical aspects of drugs that can be readily determined early in the drug development process. The Rule of Five is named due to the four parameters, which all include numerical values that are multiples of five. It is useful, because it has a confidence level of at least ninety percent for determining if a compound will be able to be absorbed by cells.6 For the most part, this rule is adhered to during the developmental phase of new pharmaceuticals. Although all of these factors are important, over the past few years, the drugs that have made it to market, as well as the ones used in primary literature studies, have had very similar octanol:water partition coefficients. This means that the lipophilicity of the compound is one of the most crucial factors for a drug to be successful.7

However, lipophilicity is much more important than merely allowing a pharmaceutical to be absorbed and carry out its desired effect. If the compound is too lipophilic, it can disrupt membranes leading to nonspecific effects.8 The characteristics of molecules described in the

Rule of Five are important because the factors discussed play a major role in the ability of the Miller 10 compound to dissolve in organic and aqueous solvents, as well as pass through a membrane. As the molecular weight increases above 500, the size of the compound becomes so large that there are issues with it being able to cross through a membrane. As the number of hydrogen bond donors increase, the interactions with the aqueous environment increases. The same is true for an increasing number of hydrogen bond acceptors because the body is mostly an aqueous environment and water can act as both a hydrogen bond donor and acceptor.6 A hydrogen bond donor contains a hydrogen atom that is bound to an electronegative atom such as oxygen, nitrogen, or fluorine, while a hydrogen bond acceptor is an electronegative atom itself.9

Hydrogen bonds are essential to the anesthetic action of propofol. In another study, a derivative known as fropofol was created with fluorine replacing the hydroxyl, and the effects of anesthesia were not achieved.10 Removing the ability of the compound to act as a hydrogen bond donor eliminates its anesthetic effect. This is important to note, because it shows that substituting a hydrogen bond acceptor for a hydrogen bond donor can prevent the molecule from either taking part in a chemical reaction or fitting into a binding site of an enzyme. This means that hydrogen bond donors and acceptors need to be thought of separately, such as how they are separated in the Rule of Five.

Octanol:Water Partition Coefficient

Lipophilicity is important in determining if a compound can cross the cell membrane, but it needs to be quantified in a manner that can model the membrane. Thus, the octanol:water partition coefficient must be used. Despite the fact that the interior of the cell, as well as the extracellular fluid outside of the cell, are mostly water, the interior of the cell membrane is composed of fatty acids. This makes a hydrophobic, lipophilic environment which is not suitable Miller 11 for water-soluble compounds. To enter the cell, compounds must be able to cross through the hydrophobic core of the membrane.6 It is difficult to measure this directly, so an octanol:water partition coefficient is used. The octanol represents the hydrophobic interior of the cell membrane, while the water compares to the interior of the cell and the extracellular fluid surrounding the cell. The compound distributes itself between these layers of liquid, and the concentration in each of the layers is used to calculate a partition coefficient and predict the compound’s ability to cross the membrane.11

This type of test is important because like tends to dissolve like. Molecules can contain both polar and nonpolar functional groups, so determining the octanol:water partition coefficient can help to determine if a molecule is more polar or nonpolar. Water is a protic solvent, meaning that it can donate a proton through hydrogen bonding.9 These types of solvents tend to be polar, and water is certainly no exception. Thus, polar molecules will more readily dissolve in polar solvents. On the other hand, octanol is a non-polar solvent. Despite being an alcohol, the length of the carbon chain causes the overall dipole moment of the molecule to be small.9 Ultimately, nonpolar molecules will dissolve in the nonpolar solvent over the polar solvent.9

An article by Makovskaya, et al. noted that partition coefficient values are not as available as they could be, so the authors proposed a method of computer modeling to generate more values, focusing on phenols with various substituent groups.11 To experimentally collect the data, the compound of interest is dissolved in either the polar or nonpolar solvent. Then, the other solvent is added, and the two layers of solvent are allowed to interact with each other. This allows for the compound of interest to partition between the two layers. The resulting concentration of the compound in the two solvents is then determined so the partition constant can be calculated. These values are used to rank the lipophilicity of the compounds utilized in Miller 12 this study.11 The logarithm of the octanol:water partition coefficient according to this computer modeling is 1.5 for phenol, 2.5 for 2,6-dimethylphenol, 4.0 for propofol, and 5.1 for 2,6-di-t- butylphenol.11,12 These values match expectations, because the larger the alkane chain, the more readily the compound should dissolve in the organic (octanol) solvent compared to the aqueous

(water) solvent.

Toxicological Analysis

To evaluate the changes in cell proliferation as a result of the propofol derivative treatments, the toxicity of a compound was analyzed through the creation of a dose-response curve. One must have a large number of organisms for the sample, such as many cells growing on a plate to quantify the percentage of cellular death.13 The percent of the organisms or cells that die are plotted at different concentrations. The linear portion of the curve is used to estimate an LC20 – the LC stands for lethal concentration, while the subscript represents the amount of decrease in live cells present. Thus, LC20 means the dosage that causes a twenty percent decrease of the living cells. The lower the LC20, the more toxic the compound, because a smaller dose causes the same amount of death. Figure 3 shows an example of a dose-response curve. As the dose increases, the effect is also expected to increase. However, this is not a linear relationship.

At low doses, there is not much of an impact from the treatment. Then, the threshold, or the lowest dosage with an observable effect, is reached. Next, the effect has a seemingly linear relationship with dose. This is the portion that is used to estimate the LC20. Finally, the curve levels off at higher concentrations where a maximum effect is achieved.13 Another aspect of the dose-response curve that needs to be determined is if the treatment groups at various concentrations for a particular compound are significantly different from each other. Analysis of Miller 13 variance (ANOVA) testing can be utilized to determine if there is a statistical difference between the responses to the treatment conditions. A p-value of less than 0.05 indicates that there is a statistically significant difference between the treatment conditions.14

Figure 3: Example of a dose response curve.13

Rationale

In previous research, it was found that cellular death resulted from increasing propofol concentration.4 Propofol is a commonly utilized anesthetic; however, it is a relatively simple molecule. It is a phenol with two identical substituents on each side of the hydroxyl group. By using molecules that have substituents of increasing alkane size, the role of lipophilicity on cellular death can be studied with similar phenols. A proposed mechanism of anesthesia, the lipid model, involves the anesthetic needing to embed in the cell membrane to cause an effect, so Miller 14 altering the lipophilicity of the molecules could provide a relevant analysis. Since propofol has been found to induce cellular death, the propofol derivatives were analyzed to determine the relationship between lipophilicity and the ability to cause changes in cellular proliferation.

Cell Death

As mentioned earlier, propofol can cause cellular death, and literature suggests that it induces apoptosis.4 If the propofol derivatives induce cellular death, this means that the compound has entered and caused an effect on the cell. This study is focusing on how the lipophilicity of the compound can affect the amount of cellular death it induces. Apoptosis is a naturally occurring method for cells to die, thus it is considered a healthy mechanism of cellular death.15 This mechanism of cell death is controlled due to the dead cells being packaged into membrane bound vesicles for elimination.15 Apoptosis is especially important in development as well as aging to remove old cells from the body as new cells replace them.15 In addition to being a regular part of a cell’s biochemical pathways, apoptosis can be used as a defense mechanism to cause cellular death if a cell has been damaged. DNA damage is one of the triggers for an apoptotic pathway.15

As apoptosis begins, cells shrink, which is visible via light microscopy.15 However, there are more precise ways of indicating that apoptosis, or programmed cell death, is occurring, such as caspase proteins being activated.4 A fundamental difference for necrosis versus apoptosis as a manner of cellular death is that the cell swells, ruptures, and releases its cytoplasm into the extracellular space, as opposed to the cell shrinking with the membrane intact and the cytoplasm remaining in apoptotic bodies. Necrosis is potentially bad for the organism as damaged cells release their contents into the rest of the body, causing inflammation. Thus, this would be an issue if a pharmaceutical product caused this form of cellular death. Therefore, apoptosis is the Miller 15 preferred method of cell death. Apoptosis is similar to necrosis in terms of the first biochemical pathways through which it occurs.15 Early in the process of cellular death, apoptosis cannot be distinguished from necrosis.

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Methods Cell Culture

Human lung cells (H-1299) were grown in an incubator at 37 degrees Celsius, 5 percent carbon dioxide. There was a pan of water in the incubator to provide a humid environment. They were cultured under sterile conditions. Corning Cellgro RPMI 1640 (Ross Park Memorial

Institute) medium (for cell growth) with 10 percent HyClone fetal bovine serum (FBS) which provides growth factors for the cells was used in the media. Cells were cultured in 100 mm plates once culture was established.

Preparation of Diprivan

The solvent for the propofol was a recreation of the pharmaceutical Diprivan. This solution was prepared by combining 100 mg/mL of soybean oil (Crisco pure vegetable oil) to dissolve the compound of interest (phenol, 2,6-dimethylphenol, propofol, or 2,6-di-t- butylphenol), 22.5 mg/mL of Acros organics glycerol to adjust the tonicity, 12 mg/mL of egg lecithin as an emulsifier, and 0.005% Boston Bioproducts EDTA as an antimicrobial agent.

Then, sodium hydroxide was added to adjust the pH to 8. The solutions were prepared with approximately 20 mg of the compound of interest dissolved in 250 mL of the Diprivan solvent.

Propofol was a liquid, so its density was used as a conversion factor to add a volume of the liquid. After the solution was made, it was autoclaved to sterilize the solution. This solution was prepared without the compound of interest to be used as the solvent for diluting to lower concentrations for the treatment groups.

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Cell Treatment with Compounds

Approximately equal numbers of cells were placed into each well in two 12 well plates for treatment, as seen in Figure 4. This was done in triplicate. Two of the four columns received different concentrations of propofol or its derivative. A control with no propofol or solvent was utilized in one of the four columns, while another control with only solvent was used in the other column. The second plate had four different concentration treatment groups. The various concentrations were prepared by diluting the prepared solution with the solvent (inactive

Diprivan – sterilized water, soybean oil, glycerol, EDTA, lecithin, without the compound of interest). This dilution scheme is shown in Figure 4. As the study progressed, the concentration of the compounds was adjusted to determine which concentration caused similar declines in the number of live cells. The LC20 concentration was determined by plotting the results and fitting a trend line to the linear portion of the data. The y-value of the trendline equation was set to 20%, then the equation was solved for x (the concentration at which 20% of the cells died).

µL treatment solution 0 0 250 500 750 1000 1250 1500 µL inactive solvent 0 1500 1250 1000 750 500 250 0

Plate 1 Plate 2

Figure 4: Diagram of the treatment plan for each column of a twelve-well plate divided into a 4x3 grid with each column having the same conditions performed in triplicate. Overall, there were two controls and six different concentrations tested. Miller 18

Analysis of Cell Proliferation

Phosphate-buffered saline was used to wash the dead cells off the plate after a 24-hour incubation with the treatment conditions. Then, 0.05% crystal violet, prepared using 20% methanol in water, was applied for five minutes at room temperature on a shaker to dye the remaining cells a dark purple color. Next, the excess crystal violet was washed off the plate so it could be read by the BioTek Epoch plate reader at 510 nm. The average and standard deviations were calculated. The solvent only treatment control group was standardized to 100 percent cell viability. The cells treated with propofol at varying concentrations were compared to that of the

100 percent viability control. This was repeated for three replicates in order to achieve statistical significance.

The resulting data was plotted to create a dose-response curve to determine the LC20. The

LC20 was used to compare the toxicity of the propofol derivatives. The LC20 was plotted against the partition coefficient to determine if a trend existed between the lipophilicity of the compound and the number of live cells remaining on the plates that were treated with the compounds.

ANOVA testing was performed on the data from various concentrations for a particular compound using Microsoft Excel. All of the individual wells that were standardized to 100% viability for the control were grouped for each treatment condition, then a single factor ANOVA was calculated by the computer, generating a p-value to test for statistical significance.

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Results

Propofol (2,6-diisopropylphenol) treatment at various concentrations resulted in a linear trend for the toxicity curve. However, the other compounds tested did not result in as clear of a linear trend. Phenol and 2,6-dimethylphenol both experienced an increase in the number of live cells once the decline rose to about thirty percent (250 µM for phenol and 20 µM for 2,6- dimethylphenol). 2,6-di-t-butylphenol had data points with no decline in the number of live cells when standardized for the control. However, the linear portion of a positive slope was used to generate a trend line and was used to estimate the LC20 for the compounds tested. These calculated LC20 values are shown in Table 1.

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2,6-diisopropylphenol

Propofol (2,6-diisopropylphenol) was the basis of this study. It has two isopropyl substituents in the 2 and 6 position on the phenol ring. As shown in Figure 5, treatment with propofol resulted in a linear relationship for all concentrations tested. This trendline was used to calculate the LC20 value to be 9.33 µM. Additionally, the error bars for this compound were smaller than the other compounds tested.

Figure 5: The dose response curve for H-1299 cells treated with propofol (2,6- diisopropylphenol). The trend line to predict the LC20 was based on all data points. At the end of a 24-hour incubation period with propofol treatment, the plate was washed to remove the dead cells. The remaining attached living cells were stained with crystal violet. The absorbance of the stained cells was recorded in a plate reader.

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Phenol

Phenol was tested for its toxicity. Phenol has hydrogen in the 2 and 6 position on the ring.

As seen in Figure 6, low concentrations had little effect on the number of live cells. However, after the threshold was reached, there was a steady decline in the number of live cells for the concentrations tested between 100 and 250 micromolar phenol. This portion of the graph was utilized to create a linear trend line to estimate the LC20 value of 206 µM. The two highest concentrations tested exhibited less of an effect from the treatment than the data point for ~250

µM.

Figure 6: The dose response curve for human lung cancer cells treated with phenol. The orange data points were used to predict the LC20 value. At the end of a 24-hour incubation period, the dead cells were washed off the plate, and the living attached cells were stained with crystal violet. The absorbance of the stained cells was recorded in a plate reader. Miller 22

2,6-dimethylphenol

2,6-Dimethylphenol (methyl substituents in the 2 and 6 position) had a decline in the number of live cells over the lowest concentrations tested. This section was used to create a trend line to predict the LC20 value (16.3 µM), as seen in Figure 7. However, the higher concentrations tested resulted in a less of a decline in the number of live cells.

Figure 7: The dose response curve for human lung cells treated with 2,6-dimethylphenol. The orange data points at the beginning were used to predict the LC20 value. After a 24-hour exposure, the dead cells were washed off the plate, and the living cells that were still attached were stained with crystal violet. Then, the absorbance was recorded in a plate reader.

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2,6-ditertbutylphenol

2,6-di-t-Butylphenol had the most inconsistent results of the compounds tested. There was an increase in cell proliferation (no decrease in living cells for the lower concentrations tested). However, the linear portion from about ten to thirty micromolar 2,6-di-t-butylphenol was used to create a trend line to estimate the LC20 of 101 µM as seen in Figure 8.

Figure 8: Data for the 2,6-di-t-butylphenol dose response curve for H-1299 cells. The negative decline in the number of live cells was relative to the solvent only control. Cells were exposed for 24 hours. To measure the number of living cells, the dead cells were washed off the plate before crystal violet staining was performed. A plate reader recorded the absorbance to quantify the number of cells. Miller 24

Trends

The trend lines generated in the plots of each compound tested were utilized to calculate the LC20 values. From top to bottom, the compounds are listed in order of increasing predicted lipophilicity based on computer modeling data. There was no consistent increase or decrease among the LC20 values for the change in lipophilicity. The p-values listed were from ANOVA testing to determine if the decreased number of living cells were significantly different for the concentrations tested for each individual compound. Phenol and propofol had statistical significance for the percentages of decline in the number of live cells for the various concentrations tested with a p-value of less than 0.05, while the other compounds did not cause a significant change in the number of cells relative to the control.

Table 1: Calculated LC20 and ANOVA statistical analysis.

LC20 (µM) p-value phenol 206±116 0.0130 2,6-dimethylphenol 16.3±14.0 0.113 propofol 9.33±1.17 8.30 x 10-6 2,6-di-t-butylphenol 101±15.4 0.612

The LC20 data shown in Table 1 are plotted in Figure 9 against the logarithm of the partition coefficient. This allows for the correlation, or lack thereof, between lipophilicity and the number of living cells to be visualized. To further evaluate the correlation, a trendline was created for the data points for phenol, 2,6-dimethylphenol and propofol. The trendline that includes all four compounds was omitted from the figure due to having an R2 value of less than

0.2.

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LC

Figure 9: Correlation between lipophilicity and the concentration in which the number living of cells decreased by 20%. The logarithm of the octanol:water partition coefficient was used to rank the lipophilicity of the compounds tested. The data point from 2,6-di-t-butylphenol was excluded from this trendline.

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Discussion

Interpreting Dose Response Curve

Propofol had a strong linear trend over seven data points, so the prediction of the LC20 should be reliable. Phenol did not have an increasing decline in the number of living cells at the higher concentrations tested, and there was a large amount of error, so its prediction may not be reliable. 2,6-Dimethylphenol had the same issue as phenol at the higher concentrations tested, but there was still error, so its predicted LC20 could have issues with being reliable. 2,6-di-t-

Butylphenol had too much error and did not approach a 20% decline in the number of live cells to produce a reliable LC20 prediction.

A correlation between lipophilicity and the decrease in the number of live cells is expected, because lipophilicity plays a role in a compound being able to get into cells. As the lipophilicity of the compounds increased, it was expected that cellular death or the inhibition of growth would have a corresponding increase. This would have been evidenced by lower LC20 values for the more lipophilic compounds. However, this trend was not found as evidenced by the LC20 values in Table 1. At first, one may consider that the data from 2,6-di-t-butylphenol should be excluded due to the great amount of error. Then, the three remaining compounds would have decreasing LC20 values (increased toxicity) with increasing lipophilicity as expected.

However, upon further analysis this is not the case. When the LC20 values are plotted against the logarithm of the octanol:water partition coefficient, there is not a strong linear trend. The R2 value for the linear regression is less than 0.7, which is a poor fit for a data set consisting of only three points.

The various concentrations had a statistically significant difference by ANOVA testing for phenol and propofol. ANOVA testing evaluates the variance in the data for each treatment Miller 27 group. A statistically significant difference means that the difference in the treatment group is most likely not due to chance or a random spread of the data. Each group has spread in the data, and this could overlap with data for another group. ANOVA testing allows for the normal spread of data to be distinguished from a true difference in the treatment groups.14

Sources of Error

These compounds did not easily dissolve in the solvent. The compounds themselves were dissolved in soybean oil. Therefore, to prepare this mostly water solution, a great amount of agitation was necessary. Once the solution was homogenous, it looked like milk, so one would not be able to visually determine if any of the compounds came out of the solution. Given there was a twenty-four-hour incubation period with no agitation, the higher concentrations may have had the compound coming out of solution during the treatment. Solubility values were not readily available for Diprivan as a solvent, but it is mostly water. The 2,6-di-t-butylphenol would not be able to dissolve in water alone, because its solubility is only 2.5 mg/L.16 The other components of the Diprivan solution, particularly the soybean oil, should have improved its solubility, but the degree to which solubility was improved is not known. Thus, the issues experienced with the 2,6-di-t-butylphenol may have been primarily due to solubility. If there was an issue with solubility, then the actual concentration that the cells were exposed to would be less than calculated. This would result in the calculated LC20 value being higher than it actually was. However, solubility does not explain the decline for the concentrations of phenol tested, because phenol is highly soluble in water. Additionally, the solubility of 2,6-dimethylphenol in water was greater than that of propofol, which should have been high enough for the compound to remain in solution for the concentrations tested.17 Miller 28

Another potential issue with the data for 2,6-di-t-butylphenol was that the solvent control had too high of an absorbance value. This could have been a result of either excess crystal violet or too many cells being initially plated into the wells for the control. However, if the issues were only in the control, then setting the lowest concentration tested to zero percent instead of the control should have been able to predict an LC20 value that was similar to that of propofol.

There was one difference in the preparation of the propofol treatment solution compared to the other solutions. At room temperature, propofol was a liquid, while the other compounds were solid at room temperature. The density of propofol used was a conversion factor so that a volume could be measured instead of a mass for the liquid compound. Although this should not have caused an issue, it could have led to the compound being more uniformly distributed throughout the Diprivan solution for the propofol than the other compounds. The solvent

Diprivan was especially made for propofol to be administered pharmaceutically.4 If the solution was not homogenous, then this would have resulted in a increased spreading of the data points, because each well would have slightly different concentrations.

Why No Trend?

Propofol is believed to function by preventing the inhibitory neurotransmitter GABA

(gamma-aminobutyric acid) from functioning properly.18 Receptors are very specific, so a small change in the structure of the propofol derivative molecule could lead to the molecule no longer fitting into the receptor. If the decreased amount of viable cells on the plate were a secondary effect of the anesthesia, then there would be no expectation for the other compounds to have the same or similar effect on cell proliferation.19 However, the lipid models of anesthesia should still be analyzed, because GABA receptors are transmembrane proteins, and the anesthetic action of Miller 29 propofol is diminished in cells that have a mutation altering the transmembrane domain of

GABA receptors.20 Thus, the anesthetic propofol, still embeds itself within the membrane and cause its agonistic effect on the GABA receptor causing an inhibition in the propagation of neuronal signaling through binding to the transmembrane domain of the protein.20

The Rule of Five can be used to explain issues with 2,6-di-t-butylphenol. Although the compound satisfies the molecular weight as well as number of hydrogen bond donors and acceptors, it violates the logarithm of the partition coefficient. The rule states that this value must be lower than five, and this compound has a value of 5.1.6 Although a difference of 0.1 seems negligible, it is the logarithm of the partition coefficient so the real difference is much greater.

The lipophilicity of 2,6-di-t-butylphenol is about twenty-five percent greater than that of a logarithm of a partition coefficient of 5. This would potentially prevent the compound from being able to cross the membrane to enter the cytoplasm. Without this insertion into the membrane and the ability to be removed, the compound would not be able to cause an effect per the lipid model.

Competing Models Lipid vs. Protein

Centuries have passed and the question remains: What is the exact mechanism that causes anesthesia to work? The “lipid theory” hypothesizes that the drug dissolves into the membrane of a cell decreasing the functionality of membrane proteins affecting the brain, while the “protein theory” claims that the anesthetic reacts directly with the membrane proteins themselves, altering the function of the brain inducing anesthesia.3 Both arguments could contribute to what is occurring. However, if the lipid model was the primary contributor, then altering the anesthetic’s lipophilicity would cause a change in the amount of the compound needed to induce anesthesia. Miller 30

More lipophilic derivatives would require less of the compound to achieve the same effect.3

Cellular death is one of the potential unintended consequences of anesthesia, so altering a characteristic of the anesthetic molecule that could be related to anesthetic action could result in different amounts of cellular death. Thus, altering the lipophilicity of an anesthetic that can cause cellular death, should alter the compound’s toxicity.

Unfortunately, there is no straightforward way to indicate anesthetic effects in cell culture. Since literature indicated that propofol induced cellular death4 and it was a relatively simple molecule, it could be derivatized. Propofol was tested along with other phenols with substituents of varying alkane sizes and therefore different lipophilicities in the 2 and 6 position on the ring to determine the change in the number of living cells that the compounds induce.

Although cellular death or proliferation cannot be used to predict a molecule’s ability to function as an anesthetic, it can be used to see how a small change such as adding or removing a methyl group from a molecule can affect the cell. In other words, it can be used to evaluate how this change to the molecule affects its ability to interact with the cell. If any of these compounds were to ever be examined as a potential anesthetic, this study would be important, because a new pharmaceutical product should not induce any more cellular death than the drug it is replacing.

Alternative Explanations

One of the limitations with the toxicity curves created was that the measurement of the number of cells was only performed at the end of the treatment for the cells attached to the plate.

This means that the decrease in the number of cells could be due to either the cells dying or an inhibition of cell proliferation. Additionally, it is possible that the various compounds could have caused a decrease in the number of cells through different mechanisms. Phenol is thought to Miller 31 cause damage to cells through the conversion of the phenol to a quinone. Then, the quinone creates reactive oxygen species which causes damage to the cell through a radical mechanism.21

This could be the mechanism that results in cellular death for some of the phenols tested in this study, but it may not apply to all of them.

Future Research

The next step in this research project would be to determine if the decrease in the number of live cells was from an inhibition of growth or a result of cellular death. Then, the method of cellular death, apoptosis or necrosis, should be determined. This could be accomplished through a variety of techniques. One of the methods that could be utilized is western blot. The immunoblotting portion of this technique would be performed to identify the presence of proteins in apoptotic pathways. Poly (ADP-ribose) polymerase (PARP) is a protein that can be used to indicate apoptosis as the method of cellular death.22 PARP can bind to DNA to repair the double helix, but its cleavage indicates apoptosis.23 Caspase is another family of proteins that could be probed to indicate the type of cellular death. Caspase proteins are a series of tightly regulated molecules that trigger cell death.24 Once caspases are activated, cell death will most likely occur.15 For this reason, the detection of caspase activation allows a researcher to determine if cellular death is a result of apoptosis. Another method that could be utilized is DNA analysis. DNA fragmentation is an indication of cellular death. A smear is indicative of death by necrosis, while sharp fragments are a sign of apoptotic cellular death. Miller 32

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