INTRODUCTION Understanding the pharmacokinetic

CHAPTER ONE: INTRODUCTION Understanding the pharmacokinetic (PK) properties of a biologically active compound is the critical step in the drug discovery process. Despite the potential that these biologically active compounds have as a therapeutic entity, their poor pharmacokinetic properties have hindered the drug development process (Nestorov, 2003). During drug development, the physical and chemical properties of any potent therapeutic compound are screened to better understand absorption, distribution, metabolism, and excretion (ADME) in the body (Jones et al., 2009; Lu et al., 2016). Different tools with increased computing power have gained importance in the last decade for pharmacokinetic modeling which helps eliminate pseudo-active biological compounds with fewer efficacies. History of PBPK Modeling In 1937, the concept of the multi-compartments was first used by Teorell and involved parameters necessary for simulating pharmacokinetic data (Teorell, 1937). Similarly, in 1971, a huge turnaround was made when Bischoff developed and proposed the application of physiologically based pharmacokinetic (PBPK) modeling. However, the mathematical complexity and requirement of large pharmacokinetic inputs for the model development was the prime reason the technique did not thrive well enough to be practiced in pharmaceutical companies (Bischoff et al., 1971). With the in-vivo, in-vitro and in-silico data necessary in predicting a tissue to plasma partition coefficient (an important parameter for drug development), the application of this model has become more widespread (Poulin, 2002; Berezhkovskiy, 1

2004). PBPK modeling is now an important tool in the drug development step. Currently, some of the large research-based companies have launched fully functional software that helps in modeling and simulation. This software predicts ADME properties, pharmacodynamics, and drug-drug interaction. The most common software is SimcypSimulator, GastroPlus, PKSIM, PK-Sim, PK-Quest, Bio-DMET (Yeo et al., 2011).

Physiologically-based Pharmacokinetic (PBPK) Model The model that helps in characterizing and predicting the pharmacokinetics of an active biological compound across different tissues in the body is called the PBPK Model (Clewell et al., 2007). The PBPK model is composed of several compartments. Different organs and tissues are linked by the blood flow through the circulatory system, which describes the exposure, toxicity, biotransformation, and clearance processes (Clewell et al., 2007). In the PBPK model, the organ system involving the ADME properties of any particular drug is taken as a major consideration. Most important organ system s include skin, lungs, heart, adipose tissues, gut, liver, pancreas, stomach, intestine, kidney, and bone. Arterial and venous blood vessels channel the flow of blood through different organ compartments in the body.

These compartments are

differentiated on a basis of tissue volume, partition coefficient, blood-plasma ratio, blood flow rate, and permeability. Each individual tissue is assumed to be either perfusion or permeability-rate-limited. Generally, for smaller and lipophilic compounds the perfusion-rate-limited-kinetic applies. Whereas, in larger and hydrophilic compounds permeability-rate-kinetic is assumed. 2

Figure 1: Structural network of a typical PBPK model (Q: Blood flow, CLint: Intrinsic clearance)

Steps in PBPK Modeling PBPK modeling processes can be employed as follows (Reddy et al., 2013; Clewell et al, 2007; Nestorov, 2003): 1. Compound Identification 2. Literature Evaluation •

Understanding toxicity

3

•

Knowing biochemical metabolic pathways

•

Physiological parameters

3. Model Formulation 4. Simulation 5. Model Validation 6. Model Application Classical PK-model versus PBPK model In a classical PK model, the plasma is considered as the central compartment and is connected to at least two peripheral compartments using different parameters, i.e. clearance rate and distribution volume, both having an important role in understanding the half-life of any drug in the body. However, the PBPK model is a multi-compartment

model

without

the

existence

of

central

or

peripheral

compartments. Most importantly, PBPK modeling technique simultaneously describes concentration-time profiles in the blood (serum or plasma) as well as different tissues in the body and fluids (feces, urine), which was a shortcoming of the “classical” PK model (Nestorov, 2003). The PBPK model’s structure is predetermined and is independent of any particular compound. This makes PBPK models extremely powerful as they rely on the anatomical and the physiological structure of the desired organism rather than relying on any evidence generated directly from drug-related findings.

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The PBPK model is physiologically relevant for drug designing, as the parameters crucial for in vitro-in vivo extrapolation (IVIVE) can be scaled quantitatively. Hence, rendering a logistic approach for predicting plasma and concentration-time profiles for newer drugs. The Classical PK model is inefficient at using data from healthy individual to extrapolate the data dose necessary for diseased individuals and illustrates why the PBPK method of modeling was necessary. For example, knowing the data for reduced P450 (CYP) expression from an individual with renal impairment and incorporating those data obtained in a PBPK model, has been a beneficial step in making adjustments for exact dosage in any drug, which was an approach limited while using the classical PK model (Yeo et al., 2011). Application of PBPK Model PBPK models can be applied for inter-species, inter-tissue, inter-route, and interdrug extrapolation (Nestorov, 2003). More importantly, inter-species extrapolation using PBPK was well documented when a dataset from the animal model was extrapolated onto human pharmacokinetics, and showed higher predictive values before compounds were entered into a human subject (Jones et al., 2006). The mechanistic basis of the PBPK model has a potential in determining whether the outcomes of different examinations show deviation, and what helps in exploiting the factor responsible for such inconsistency. PBPK modeling anticipates pharmacokinetic properties of the specified drugs in animals. By combining the physiochemical result obtained from PBPK modeling and invitro analysis, the unnecessary time spent on animal testing can be prevented (Germani et

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al., 2007). PBPK modeling approaches were first utilized in the field of oncology, in which the cancer tissues were divided into the separate compartments so that it became easy to describe the pharmacokinetics as well as the pharmacodynamics (Baxter et al., 1995). Also, a PBPK model has been used to determine drug distribution through the placenta and breast milk of the mother into the fetus and infant, respectively (Fisher et al., 1997). Other studies include modeling tissue distribution of antibiotics cyclosporine, and serve as an example of one of the most successful PBPK models developed thus far (Nakijima et al., 2000). PBPK Modeling in Pediatrics PBPK modeling has wide applications in explaining the age-dependent pharmacokinetic change in the pediatric model as it suggests efficient clinical dosing and sampling times of drugs (Grass and Sinko, 2002). An example of an existing PBPK model in adults reflecting age-specific pharmacokinetic changes from newborn up to 18 years clearly shows the effectiveness of the known pharmacokinetics of the drug (Edginton et al., 2006). The curves for five drugs (paracetamol, theophylline, levofloxacin, alfentanil, and morphine) with a known concentration-time profile, after simulation in adults, matched with the predicted pediatric clearance values. Thus, the age-modified model (changes in physiological parameters according to age) and the predicted pediatric clearance value help to predict pediatric plasma concentration (Edginton et al., 2006). A study on 2 year old children was performed to understand if midazolam pharmacokinetics changes upon the addition of interacting drugs. The deliberate addition

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of interacting drugs CYP 3A4 inducer-rifamipicin; CYP 3A4 inhibitor-rifamipicin and fluconazole; and CYP 3A4 inhibitor rifamipicin, fluconazole, and clarithromycin on midazolam (CYP 3A4 substrate) showed an effect on the plasma concentration and, thus, showed a potential for PBPK modeling for complex drug-drug interaction (Johnson and Rostami-Hodjegan,2011). PBPK Modeling for Lead Optimization PBPK modeling has made pronounced advances in early stage and clinical development stages(Jones and Rowland-Yeo, 2013). In a study about the potential of a candidate compound for treating drug addiction: YQA-14-selective dopamine D3 receptor antagonist, showed the remarkable application of PBPK modeling. The major problem with YQA-14 in humans was its poor oral bioavailability because of metabolism by aldehyde oxidase (AO). However, the compound stability in rats and dogs (both in vivo and in vitro) supported the concept of validating and simulating its use in humans. First, the physiochemical parameters obtained from the in vitro study were generated to build a PBPK model. Secondly, the model was validated by in vivo pharmacokinetic profiles of both animals. The model validation (oral bioavailability: rat-15.6%; dog-45.9%) and physiochemical parameters from in vitro human data helped to create a simulation of plasma concentration vs. time-profile at 287mg QD (oral bioavailability: 16.9%), but upon decreasing the oral dose (57.4 mg), bioavailability increased to 35.1%. These showed pharmacokinetic properties such as solubility and clearance rate playing a major role in making YQA-14 an efficacious drug. So, YQA-14 with improved pharmacokinetic properties could then be used as a potent dopamine antagonist. This

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shows how in vivo, in vitro and in-silico results leading to pharmacokinetic data can refine and regenerate PBPK model (Liu et al, 2014); (Zhuang and Lu, 2016). PBPK Modeling for Renal Impairments PBPK modeling has shown success in choosing the effective doses for patients with renal impairment (RI). In the patient with renal impairment, the exposure of the drug orteronel is increased because of impaired function of the urinary system. So, the orteronel biotransformation decreases because of the minor role of cytochrome P450 isoenzyme located in the liver. The clinical data from the patient was used to construct a model to predict the pharmacokinetic of orteronel for two different case scenarios: moderate RI patient with glomerular filtration rate 30-60 ml/min, and severe RI patients with glomerular filtration rate greater than 30ml/min. Based on PBPK model constructed the orteronel (220mg) for the severe RI patients would achieve similar exposures when compared to control group (400mg). Thus concluding both having the similar orteronel plasma concentration and clearly depicting how dose selection among different group can yield good results; a step forward in drug designing (Suri et al, 2015); (Zhuang and Lu, 2016). Limitation of PBPK Modeling Despite the wide application of PBPK modeling for human, animal and environmental studies there still exists a gap which has to be addressed. Constructing a PBPK modeling requires an abundant amount of data on toxicity associated, biochemical and metabolic pathways, physiological and physiochemical process. For these necessary

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data one source is never sufficient, but using multiple sources can lead to inconsistency and confusion for model construction (Khalil and Laer, 2011). Despite the lack of system parameters such as abundant proteins, enzymes, and transporters, an effort is made on refining in-vitro data to predict ADME properties of the drug (Jones and Rowland-Yeo, 2013). However, this is valid only for low clearance compounds. For metabolically stable compounds, transporters are being incorporated in the PBPK model but the issues exist for in-vitro systems as it is demanding for physically mimicking the in-vivo system. Moreover, poor PBPK modeling and simulation can produce a biased model, if the experimental data not supporting simulation results (Khalil and Laer, 2011). Thus, even with the wide application in drug research and development, judgment and interpretations errors and some uncertainties prevents PBPK modeling to be used as a generic predictive tool in clinical practice. Depression and Diazepam Anxiety and depression remain highly prevalent in the United States and affects 6.7% of adults each year (Kessler et al., 2005). Loss of appetite, suicidal feelings, disrupted sleeping patterns etc. are typically observed in patients with depression (Pittman, 2014; Fonseka et al., 2016). There is no well-established mechanism underlying the pathophysiology associated with anxiety and depression, but there is some basic understanding of a few major neurotransmitters responsible for depression. Even with the few neurotransmitters associated with depression, research has shown that there is likely more than a single neurotransmitter responsible making the study of this disorder very complex. As depression is an association with complex neural connections it is, therefore,

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appropriate to name it as a “system level spectrum disorder” (Pittman, 2014). However, a possible mechanistic approach might be to quantify each neurotransmitter responsible for this disorder, which possibly could further reproduce a novel understanding of the functional aspect of diazepam. Diazepam falls under the family of benzodiazepines and is typically used for treating anxiety. Benzodiazepines are associated with influencing the hypothalamic– pituitary–adrenal axis (HPA) by reducing adenocorticotropic hormone (ACTH) and subsequently cortisol release (Abreu et al., 2014). Studies have shown that diazepam acts as an allosteric modulator by increasing the amount of the neurotransmitter gammaamino butyric acid (GABA) which is primarily responsible for its anxiolytic activity (Kay et al., 1970; Levin, 2011). Likewise, diazepam has an anxiolytic effect in depression-induced zebrafish, showing a reduction in bottom-dwelling behavior when a novel tank diving test is performed (Bencan et al., 2009; Gebauer et at., 2011; Levin, 2011). A recent study constructed a PBPK model for diazepam employing twelve tissue compartments and two blood compartments in rats. Based on the standard allometric scaling methods, the model was then validated for humans (Gueorguieva et al., 2004; Gueorguieva et al., 2005). In the same study, each tissue was assumed to have perfusion-rate limited distribution, blood flows were also assumed to be unaffected by the distribution of the drugs, and tissue compartments were presented as independent of one another. Similar assumptions will be taken into consideration in this thesis to develop a PBPK model for diazepam.

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Zebrafish and Neurotransmitter System Zebrafish have become a model organism of increased interest and use especially in the field of neuroscience (Norton and Bally-Cuif, 2010; Kalueff et al., 2013). Because of its homology of structural and functional aspects that it shares with mammals, zebrafish are considered a translational model organism (Panula et al., 2006). Zebrafish are a powerful model because they possess a balance of system complexity and practical simplicity for understanding structural-functional relationships in humans (Dooley, 2000). Also, zebrafish independently reproduce the endophenotypes which are similar to humans and are considered a biomarker to develop pharmacological tests (Hasler et al., 2004). Similar to mammals, zebrafish have three subtypes of GABA binding receptors (GABAA, GABAB, and GABAC) which act as a binding site for the benzodiazepine (Oggier et al., 2010). Studies on zebrafish have gained importance for the development of psychiatric drugs, as zebrafish in the past have been used as a model for understanding cognitive, social and behavioral responses against different classes of drugs (Sackerman et al., 2010; Ton, Lin and Willett, 2006). Zebrafish share 70% homology with mammals making them a good model for carrying out preclinical research (Barbazuk et al., 2000). Compared with other vertebrates, zebrafish reproduce easily, are low in cost, easy to maintain, small in size, easy to handle, and have evolutionarily conserved traits that make them highly useful as a model organism (Pittman, 2014; Fonseka et al., 2016). Zebrafish and human neurotransmitter systems exhibit structural and functional analogy, thus, making zebrafish an important model for psychopharmacological studies (Norton and Bally-Cuif, 2010).

The

monoaminergic

(MAO)

system, 11

which

involves

serotonin

(5-

hydroxytryptamine), norepinephrine, and dopamine are considered to have a major role in anxiety and depression (Ressler and Nemeroff, 2000; Panula et al., 2006). The overall aim of this study was to understand behavioral responses in zebrafish and quantify neurotransmitters (serotonin, norepinephrine, dopamine, and cortisol) for different concentrations of diazepam (15mg/l, 25mg/l, and 35mg/l). And, lastly to construct a PBPK model for zebrafish based on the obtained results from quantification and on the basis of allometric scaling (body weight and mass modulation) using BioDMET software.

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CHAPTER TWO: MATERIALS AND METHODS Animal and Housing A mixed-sex population of 50 adult zebrafish was acquired online from liveaquaria.com (Rhinelander, Wisconsin). The fish were housed in 1L tanks (Thoren Aquatics: recirculation high-density rack system specifically designed for zebrafish) in groups of seven fish per tank. The water of these tanks was filtered using mechanical (sponge), chemical (activated carbon), and biological filtration units. The tanks were filled with de-ionized water treated with Prime Freshwater® concentrated conditioner as recommended by the manufacturer. The water temperature was maintained at 25-27°C. The fish were kept on a 10/14 light-dark cycle. All fish were fed a mixture of ground flake food (Tetramin Tropical Flakes; Tetra USA, Blacksburg, VA) once every day. All the fish were maintained and the procedure was performed in accordance with the Institutional Animal Care and Use Committee of Troy University, Troy, AL, USA. Treatments All the fish were allowed at least seven days to adapt to the laboratory temperature. Three test groups, each having seven zebrafish, were separated. Depression was induced in each group of zebrafish by treatment of 3% ethanol for a two-week period. The test groups of zebrafish then were placed in three different beakers for drug treatment with 15mg/l, 25mg/l, and 35mg/l diazepam-water solution. A control group of zebrafish, with no drug, was exposed only to home tank treated water. Similarly, the positive control group with no diazepam was administered 3% ethanol immediately followed by two weeks of fluoxetine exposure. 13

Behavioral Analysis Stress response and anxiety studies followed a similar methodology introduced by Levin et al’s study (2007) (Figure 1). After two weeks of diazepam exposure, the behavioral study was conducted. The repeated measure was conducted during a fiveminute period. The choice of position (top, middle, and bottom) was considered as the index of anxiety. In this method, five different groups: diazepam treated (15mg/l, 25mg/l, and 35mg/l), fluoxetine and a control were evaluated to understand the behavioral response of the zebrafish. In this method, depressed zebrafish typically spend more time in the bottom of the tank in a novel environment (Levin and Cerutti, 2009). Data were presented in the mean±S.D form.

Figure2.1: Response of fish to stress and anxiety test. Fish that dwell in the deeper sections of the tank indicate correspondingly higher levels of stress.

Sample Preparation To measure the concentrations of various neurotransmitters in the both brain and body portions separately, the zebrafish were sacrificed using 0.1% ice-cold Tricaine. The 0.1% Tricaine was placed in a watch glass and the watch glass was kept on top of ice

14

cubes. After immersion in the watch glass, the sacrificed zebrafish was taken out and placed on a slide where the head section was removed by cutting from the gill region. Obtained head and body sections of the zebrafish from all the five different groups were kept separately in Eppendorf tubes where it was stored with 1 mL of ice-cold 1 × phosphate buffer saline (PBS). Head/ Body Neurotransmitter Analysis Depressed zebrafish from the novel tank test were used for measuring neurotransmitters (serotonin, dopamine, cortisol, and norepinephrine). The obtained head and body sections previously placed in the PBS were grounded separately using a polytron and were used for Enzyme-Linked Immunosorbent Assay (ELISA) analysis. A standard ELISA kit (Salimetrics LLC, State College, PA) was used for evaluating cortisol concentration. Furthermore, concentrations of serotonin, dopamine, and norepinephrine were evaluated using Labor Diagnostika Nord (LDN) (ELISA fast-track). Model Development Bio-DMET, a physiologically based pharmacokinetic (PBPK) modeling software package was used to construct a model for diazepam in the mouse. The Bio-DMET tool was chosen for diazepam modeling, as non-critical compartments shown in Figure 2.2 (Graf et al., 2012). It was easy to alter the model to allow for both the reduction and addition of compartments as needed to imitate the desired tissue systems. Keeping in mind the parameters, such as body mass, tissue volume, and flow rates, unique to each species the data for mice were calibrated and extrapolated for a zebrafish whole-body PBPK model. As shown in Figure 2.3(Graf et al., 2012), the tissues are divided into 15

vasculature space, interstitial space, and intracellular space. Thus, the software specifically shows its efficiency to understand changes in distribution and flow rates at the cellular level. Bio-DMET was used for calculating the flow rates, the mass and tissue volume across different tissues. As the drug of interest, diazepam was selected for the model development. The value for the different physical and chemical properties was collected from the literature and other published sources and was incorporated in the software. Based on these necessary data, as shown in Table2.1 the biodistribution simulation was run and the distribution of diazepam across different tissue system became better understood. As shown in Figure 2.4 and 2.5, the biodistribution inputs, such as number of administration, drug dose, time of administration, and sampling time, were entered into the Bio-DMET software. The biodistribution simulation yielded the desired flow rates, tissue volumes and mass of the tissue necessary for model development.

Figure 2.2: Tissue structure with the types of transport processes modeled in Bio-DMET.

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Figure 2.3: Tissue and cell structure with types of transport processes modeled in BioDMET Parameters

Values

Molecular weight (Daltons)

284.74

Hydrodynamic Diameter

0.967

Plasma protein binding (%)

98.5

Liver Microsome Clearance Rate (ml/min/mg 0.266 protein) Log P

3.076

Membrane Permeability

0.201

Table 2.1: PBPK parameters of diazepam used for creating a new host model.

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Figure 2.4: Biodistribution inputs (number of administration, drug dose and time of administration) for Bio-DMET software based on experimental data.

Figure 2.5: Biodistribution input (sampling time) for Bio-DMET software based on experimental data. 18

CHAPTER THREE: RESULTS Behavioral Analysis Based on the two week study for different concentrations of diazepam and fluoxetine, the percentage of time spent by the zebrafish in the upper half of the novel diving tank was recorded and is shown in Figure 3.1. The data presented are in mean±S.D. The percentage of time spent by zebrafish treated with 15mg/l, 25mg/l and 35mg/l diazepam was 54.6%, 70.4% and 75.8% respectively. Similarly, the time spent by the positive control group (fluoxetine treated) was 71.8%, and the control (no drug treatment) was 51.4%.

% Time

% Time spent in Upper Half of Tank 90 80 70 60 50 40 30 20 10 0

71.8

70.4 54.6

51.4

Control

75.8

Fluoxetine

15mg/l

25mg/l

35mg/l

Figure 3.1: Behavioral effects of exposure to 3% ethanol daily for two weeks followed by exposure to three different concentrations (15mg/l, 25mg/l, and 35mg/l) of diazepam (n=7). Data are presented as mean±S.D.

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Head/ Body Neurotransmitter Analysis ELISA was carried out for evaluating four different neurotransmitters and the results are presented in Figure 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, and 3.9. An analysis of understanding changes in neurotransmitters using three different concentrations of drugs (15mg/l, 25mg/l, and 35mg/l diazepam) are presented for head and body sections separately. Figure 3.2 and 3.3 depicts dopamine level after drug treatment and compares the outcome of the group with fluoxetine and the control group. A similar trend is followed for all other neurotransmitters (serotonin, norepinephrine, and cortisol), which are shown in figure below. The concentration in the head portion for dopamine among three groups was highest for a group treated with 25mg/l diazepam (0.91ng/gm) followed by 15mg/l diazepam (0.676ng/gm) and 35mg/l diazepam (0.63ng/gm). Similarly, the body portion of dopamine concentration for the group treated with 15mg/l diazepam was 0.539ng/gm. The value recorded for the group treated with 25mg/l was highest (0.755ng/gm). However, for the group treated with 35mg/l diazepam the dopamine concentration was lowest (0.537ng/gm). The values for serotonin in the head portion (Figure 3.4) for the given treatments were: control (0.486ng/gm), fluoxetine (0.727ng/gm), 15mg/l diazepam (0.365ng/gm), 25mg/l diazepam (0.699ng/gm), and 35mg/l diazepam (0.582ng/gm). Similarly, serotonin concentration in body portion (Figure 3.5) for the given treatments was: control (0.40ng/gm), fluoxetine (0.487ng/gm), 15mg/l diazepam (0.313ng/gm), 25mg/l diazepam (0.528ng/gm), and 35mg/l diazepam (0.402ng/gm).

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Thus, the trend shown existed for both the head and body portions, and those treated with 25mg/l diazepam showed the highest serotonin concentration. The norepinephrine concentrations in the head portion (Figure 3.6) for the given treatments were: control (0.865ng/gm), fluoxetine (1.056ng/gm), 15mg/l diazepam (0.754ng/gm), 25mg/l diazepam (0.954ng/gm), and 35mg/l diazepam (0.824ng/gm). While the norepinephrine concentrations in the body portion (Figure 3.7) for the given treatments were: control (0.523ng/gm), fluoxetine (0.657ng/gm), 15mg/l diazepam (0.518ng/gm), 25mg/l diazepam (0.771ng/gm), and 35mg/l diazepam (0.545ng/gm). The cortisol concentrations in the head portion (Figure 3.8) for the given treatments were:

control (0.509ng/gm), fluoxetine (0.65ng/gm), 15mg/l diazepam

(0.389ng/gm), 25mg/l diazepam (0.532ng/gm), and 35mg/l diazepam (0.44ng/gm). The cortisol concentration in body portion (Figure 3.9) for the given treatments was: control (0.387ng/gm), fluoxetine (0.516ng/gm), 15mg/l diazepam (0.348ng/gm), 25mg/l diazepam (0.478ng/gm), and 35mg/l diazepam (0.38ng/gm). Like the other three, cortisol concentration was highest for the group treated with 25mg/l diazepam.

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Concentration (ng/gm)

Dopamine (Head) 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0.91 0.808 0.702

Control

0.676

Fluoxetine

Dopamine 15mg/l

0.63

Dopamine 25mg/l

Dopamine 35mg/l

Figure 3.2: ELISA analysis to evaluate dopamine levels at three different concentrations of diazepam in the head portion of zebrafish.

Concentration (ng/gm)

Dopamine (Body) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0.776 0.545

Control

0.755 0.607

0.539

Fluoxetine

Dopamine 15mg/l

Dopamine 25mg/l

Dopamine 35mg/l

Figure 3.3: ELISA analysis to evaluate dopamine levels at three different concentrations of diazepam in the body portion of zebrafish.

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Concentration (ng/gm)

Serotonin (Head) 0.8

0.727

0.699

0.7 0.6 0.5

0.582 0.486 0.365

0.4 0.3

0.2 0.1 0 Control

Fluoxetine

Serotonin 15mg/l

Serotonin 25mg/l

Serotonin 35mg/l

Figure 3.4: ELISA analysis to evaluate serotonin levels at three different concentrations of diazepam in the head portion of zebrafish.

Serotonin (Body) Concentration (ng/gm)

0.6

0.528

0.487

0.5

0.402

0.4 0.4

0.313

0.3 0.2 0.1 0 Control

Fluoxetine

Serotonin 15mg/l

Serotonin 25mg/l

Serotonin 35mg/l

Figure 3.5: ELISA analysis to evaluate serotonin levels at three different concentrations of diazepam in the body portion of zebrafish. 23

Norephinephrine (Head) Concentration (ng/gm)

1.2 1

1.056 0.954 0.865

0.824

0.754

0.8 0.6 0.4 0.2 0 Control

Fluoxetine

NE 15mg/l

NE 25mg/l

NE 35mg/l

Figure 3.6: ELISA analysis to evaluate norepinephrine levels at three different concentrations of diazepam in the head portion of zebrafish.

Norephinephrine (Body) Concentration (ng/mL)

0.9 0.771

0.8 0.657

0.7 0.6

0.523

0.545

0.518

0.5 0.4 0.3

0.2 0.1 0 Control

Fluoxetine

NE 15mg/l

NE 25mg/l

NE 35mg/l

Figure 3.7: ELISA analysis to evaluate norepinephrine levels at three different concentrations of diazepam in the body portion of zebrafish.

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Cortisol (Head) 0.65

Concentration (ng/gm)

0.7 0.6

0.532

0.509

0.5

0.44

0.389

0.4 0.3 0.2 0.1 0 Control

Fluoxetine

Cortisol 15mg/l

Cortisol 25mg/l

Cortisol 35mg/l

Figure 3.8: ELISA analysis to evaluate cortisol levels at three different concentrations of diazepam in the head portion of zebrafish.

Cortisol (Body) Concentration (ng/gm)

0.6

0.516

0.478

0.5 0.4

0.387

0.38

0.348

0.3 0.2 0.1 0 Control

Fluoxetine

Cortisol 15mg/l

Cortisol 25mg/l

Cortisol 35mg/l

Figure 3.9: ELISA analysis to evaluate cortisol levels at three different concentrations of diazepam in the body portion of zebrafish.

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Model Development Based on allometric scaling, the body weight for the newly constructed model was 7 grams. This was calculated as each group is comprised of seven fish each with a weight of 1 gram. The result of the biodistribution simulation from the Bio-DMET yielded the flow rate, tissue volume, and tissue mass parameters helpful in generating a new model. These results are shown in Table 3.1. Similarly, the result of the concentration-time profile for diazepam in the new model was also generated across different tissues, some of which are shown in Figure 3.10, 3.11, 3.12, 3.13, 3.14. Most of the tissues, such as the brain, liver, heart, lungs, muscle kidney, pancreas, intestine, etc., exhibited similar drug distribution patterns. However, for the bladder and urine a different trend was observed (Figure 3.15, 3.16).

Figure 3.10:Concentration-time profile ofdiazepam (mg/ml) in blood for 14 days.

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Figure 3.11: Concentration-time profile of diazepam (mg/ml) in brain for 14 days.

Figure 3.12: Concentration-time profile of diazepam (mg/ml) in bone for 14 days.

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Figure 3.13: Concentration-time profile of diazepam (mg/ml) in kidney for 14 days.

Figure 3.14: Concentration-time profile of diazepam (mg/ml) in liver for 14 days.

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Figure 3.15: Concentration-time profile of diazepam (mg/ml) in urine for 14 days.

Figure 3.16: Concentration-time profile of diazepam (mg/ml) in bladder for 14 days.

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Tissue

Mass (gm)

Tissue Volume Flow (mL)

Rate (ml/min)

Adipose

0.491

0.530

0.101

Liver

0.481

0.447

0.077

Kidney

0.131

0.125

0.519

Stomach

0.086

0.079

0.065

Skin

1.254

1.184

0.210

Bone

0.706

0.483

0.114

Brain

0.121

0.117

0.088

Heart

0.038

0.035

0.276

Muscle

2.829

2.645

0.652

Lung

0.219

0.631

2.825

Spleen

0.031

0.029

0.061

Pancreas

0.024

0.022

0.002

Table 3.1: Obtained pharmacokinetic parameters for the zebrafish model (7gm) obtained from Bio-DMET software.

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CHAPTER FOUR: DISCUSSION The result of the behavioral analysis showed that the 35mg/l diazepam group spent the most time in the upper half of the tank. For all four neurotransmitters the maximum concentration was obtained at a drug concentration of 25mg/l and tended to decrease at 35mg/l; hence, leading to a discussion on down-regulation of receptors with increasing dose response. Results of ELISA analysis showed that the effective concentration (25mg/l diazepam) for maximum secretion of all four neurotransmitters in the head and body sections interestingly had relatively similar behavior to fluoxetine (positive control). Also, a notable similarity in the behavior was seen among the 15mg/l diazepam treated group and the control group, for all four neurotransmitters across both head and body sections. Thus, concluding that the 15mg/l drug concentration has the least effectiveness while the 25mg/l has maximum effect, raising the concept of receptor desensitization at higher concentration of drug treatment. A previous study conducted on understanding the effects of chronic administration (20 days) of diazepam showed down-regulation of the adenosine receptor. This important receptor, which helps control spontaneous firing of neurons in the central nervous system, was adequately affected and consequently lowered the GABA binding affinity within the cortex (Hawkins et al., 1988). Thus, presenting conclusive evidence that continuous diazepam treatment down-regulates the adenosine receptor binding. A similar possible scenario might be present here. More research is needed however to definitively reach this conclusion.

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All four neurotransmitters (dopamine, serotonin, norepinephrine, and cortisol) showed a bell-shaped dose-response curve suggesting the effects of diazepam is most efficient at the concentration 25mg/l, where high expression of these four neurotransmitters was observed for the head as well as body portions. A study has suggested a higher concentration of dopamine can decrease network activities and result in reduced excitatory postsynaptic potential (EPSP), a potential which helps post synaptic neurons to fire an action potential (Cools and Esposito, 2011). However, lower dopamine concentration spikes were observed in the EPSPs, which help in upregulation and which causes higher neuronal activity. Hence, providing evidence that dopamine exhibits the bell-shaped pattern for cognitive function and memory (Kroener et al, 2009). Another study about hormone-neurotransmitter behavior showed that higher dopamine (example: high estrogen) was linked with lower prefrontal cortex activation (low activity) while lower dopamine (example: low estrogen) was linked to higher prefrontal cortex activation (high activity). This is a characteristic observed in depressed individuals specifically in lateral, orbitofrontal, and ventromedial regions of the prefrontal cortex (Jacobs and Esposito, 2009). Thus, the following conclusion provides support for a dopamine bell-shaped pattern attributed in the current experiment. Studies on rat learned helplessness tests (Martin et al, 1992) and locomotors activity analysis (Ramamoorthy et al, 2008), specifically swimming & tail suspension analysis in rodent models, have shown that serotonin receptor (5-HT3) agonist and antagonist exerting maximum effects at lower doses and being ineffective once the 32

dose is higher (Faerber et al, 2007;Betry et al, 2011). Thus, showing a U-shaped doseresponse pattern which is similar to the results we have found. Previous studies on diazepam have also shown the inverted U-shaped dose-response in serotonin is the result of receptor desensitization, which is a process well described by receptor internalization at higher concentrations (Yakel and Jackson, 1988). This research showed diazepam inducing 5-HT3 receptor desensitization, which led to the inefficiency of serotonin at higher drug concentrations. Also, some studies hypothesize the concept of heterogeneous responses at various concentrations and most importantly the steric hindrances that occur at higher concentrations leading to the conclusion of a bell-shaped pattern for serotonin (Andrews and File, 1992; Betry et al, 2011). As shown in Table 3.1, the parameters across different tissues can be obtained clearly, and this illustrates that it does not take much effort in developing a model on how specific compounds distribute across tissues throughout the body. A most beneficial aspect of knowing a parameter such as blood flow, tissue volume, flow rate, etc., for any compound makes most of the tedious work easier rather than starting from scratch and figuring out every possible entity necessary for a drug disposition for different tissues. Also, the diazepam concentration-time profile across different tissues showed a similar pattern regardless of varying concentrations (15, 25 and 35 mg/l), which is a major shortcoming associated with Bio-DMET. The software efficiency was only questioned when it showed a lack in obtaining a steady state equilibrium even after using same drug-concentration for 14 days. Bio-DMET is a newly developed software 33

without a large body of validation studies, hence, its efficiency to differentiate between drugs concentrations was observed to be lacking, which is, a shortcoming acknowledged by the developer. As model development is a process with the necessity of constant refinements in data from time-to-time, there is always room to fill those gaps with newer findings. Model development is also important as it helps minimize animal use because of its dependency on the literature. Nevertheless, the software helped to address the blood flow rates, tissue volume, and tissue mass associated with a specific animal weight for this study. Hence, a valuable aspect of any future research and logistically limiting the vigorous literature search for each individual entity in order to construct a fullyfledged PBPK model in future.

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CHAPTER FIVE: CONCLUSION Dosing of diazepam yielded higher levels of neurotransmitters (serotonin, dopamine and norepinephrine) upon administration to the zebrafish. The data suggests an analogy between the actions of established drugs, such as fluoxetine, with the 25mg/l diazepam used for the current experimental study. However, increasing the drug dose surprisingly resulted in a reduction in the expression of neurotransmitters. Thus, illustrating the importance of using the correct dose necessary to achieve high drug efficacy. Overall, the results also added further support in the utilization of zebrafish as a model organism to understand anxiety and depression-like disorders in humans. However, it is unclear why neurotransmitter expression decreased with an increase in drug concentration and shows a necessity for further research on this aspect. Similarly, despite obtaining the parameters for PBPK modeling through Bio-DMET, its efficiency in determining biodistribution of a drug in different tissue systems with varying drug concentrations was lacking. This highlights an important flaw in this newly developed PBPK modeling software’s ability to validate the obtained experimental data.

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