Abstract
Cancer of the liver is the second most common cause of death from cancer in the world in men after lung cancer and the sixth most common cause of death from cancer in females. Molecular docking virtual screening was performed to virtually screen and identify potential lead compounds, DFT calculations was performed to determine reactivity of the studied compound and the ADMET and drug likeness properties were evaluated using swissADME and PKCSM online web tools.
Based on the molecular docking virtual screening performed, compound 12 with the highest mole dock score of -155.236 kcal/mole for series 1, compound 8 with the highest mole dock score of -188.528 kcal/mole for series 2, compound 9 with the highest mole dock score of -163.985 kcal/mole for series 3 and compound 14 with the highest mole dock score of -157.08 kcal/mole for series 4, were identified as the best lead compound in this study.The drug likeness and ADMET properties prediction performed showed the studied compounds including the best lead compounds were drug like in nature with good pharmacokinetics profile and they all have bioavailability of 0.55 respectively. Furthermore, based on the DFT calculations compound 12 for series 1 with the energy gap of 3.5ev, compound 8 for series 2 with the energy gap of 4.5ev, compound 9 for series 3 with energy gap of 5.ev, and compound 14 for series 4 with the energy gap of 3.8ev were identified in this study as the most reactive.Based on this research, the lead compounds identified can serve as potential drug for cancer of the liver. After the pre-clinic trials.
Table of Contents
Cover Page i
Fly Leaf Page ii
Tittle Page iii
Declaration iv
Certification v
Acknowledgment vi
Dedication vii
Abstract viii
Table of Contents ix
Lists of Tables xii
Lists Of Figures xiv
List Of Abbreviations xvi
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study 1
1.2 Resistance to Current Drugs 2
1.3 Disease Treatment Strategies 2
1.4 Development of Inhibitors for CYP1A1 3
1.5 Mechanisms of Action of Liver Cancer Drug 4
1.6 Rational Drug Design 5
1.6.1 Structure-based drug design 7
1.6.2 Ligand based drug design 8
1.7 Statement of The Research Problem 8
1.8 Research justification 9
1.9 Research Questions 9
1.10 Research Hypotheses 9
1.11 Aim and Objectives 10
1.12 Scope and Limitations 11
CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Liver Cancer 12
2.2 Liver Cancer Mortality 13
2.3 Pathology of Liver Cancer 13
2.4 Screening of Liver Cancer 14
2.5 Symptoms of Liver Cancer 15
2.6 Staging of Liver Cancer 15
2.7 Treatment of Liver Cancer 15
2.7.1 Curative treatment 15
2.7.2 Palliative treatment 16
2.7.3 Supportive care 17
2.8 Risk factor 17
2.8.1 Infection 18
2.8.2 Alcohol 18
2.8.3 Smoking 19
2.8.4 Diabetes mellitus 20
2.8.5 Non-alcoholic fatter liver disease (NAFLD) 20
2.8.6 Obesity 21
2.8.7 Aflatoxins 21
2.8.8 Hereditary hematochromatosis 22
CHAPTER THREE
3.0. MATERIALS AND METHODS
3.1. Materials 24
3.1.1 Computer Specifications: 24
3.1.2 Software 24
3.2 Methodology 24
3.2.1 Dataset 24
3.2.2 Molecular structure generation using chemdraw ultra V12.0 25
3.2.3 Geometry optimization with Spartan 14 V1.1.0 38
3.2.4 Method of Retrieving Target Receptor 38
3.2.5 Ligand and Receptor preparation 38
3.2.6 Receptor-ligand docking using MVD software 38
3.2.7 Procedure in Viewing Interaction Residues of the Complex with Discovery Studio 39
3.2.8 Pharmacokinetics Properties Prediction Method 39
3.2.9 DFT Method 39
CHAPTER FOUR
4.0. RESULT AND DISCUSSION
4.1 Molecular Docking of Series 1 Compounds 42
4.2 The Drug Likeness of Series 1 Compounds. 48
4.3 ADMET Properties of Series 1 Compounds 49
4.4 Density Functional Theory (DFT) 50
4.5 Mode Of Binding Interaction Between Series 2 Compounds. 57
4.6 The drug likeness of series 2 compounds 63
4.7 ADMET Properties of Series 2 Compounds 65
4.8 Density Functional Theory (DFT) 66
4.9 Mode of Binding Interaction Between Series 3 Compounds. 71
4.10 The drug likeness of series 3 compounds 77
4.11 ADMET Properties of Series 3 Compounds 79
4.12 Density Functional Theory (DFT) 80
4.13 Mode of Binding Interaction Between Series 4 Compounds. 86
4.14 The Drug Likeness of Series 4 Compounds 94
4.15 ADMET Properties of Series 4 Compounds 96
4.16Density Functional Theory (DFT) 98
CHAPTER FIVE
5.0 SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 SUMMARY 103
5.2 CONCLUSION 103
5.3 RECOMMENDATION 104
Reference 105
Lists of Tables
Table 3.1:Series 1 dataset………………………………………………….…………………….25
Table 3.2: Series 2 data set……………………………………………………………………….27
Table 3.3: Series 3 data set………………………………………............................................30
Table 3.4: Series 4 data set……………………………………………….…………………….32
Table 4.1: Series 1 docking results……………………………………….………………………42
Table 4.2: Series 1drug likeness result….…………………………………………...………..…48
Table 4.3: Summary of violation of filtering criteria…………………………..…………………49
Table 4.4: Series 1 ADMET (Pharmacokinetic) result…..…………………..….………………..50
Table 4.5: Series 1 DFT Calculations……………………….………..…………………………..50
Table 4.6: Series 2 docking results…………………..……………………….…………………..57
Table 4.7:Series 2 drug likeness result………...……..………………………………………….63
Table 4.8: Summary of violation of the filtering criteria…...…………………..……….............64
Table 4.9: Series 2 ADMET (Pharmacokinetic) result…………………………………………...65
Table 4.10: Series 2 DFT Calculations……………………………………………..…………….66
Table 4.11: Series 3 docking result…………………………………………………..…………...71
Table 4.12: Series 3 drug likeness result………………………………………………................78
Table 4.13: Summary of violation of the filtering criteria…………….………………………….78
Table 4.14: Series 3 ADMET (Pharmacokinetic) result………………………………………….79
Table 4.15: Series 3 DFT Calculations…………………………………………………………...80
Table 4.16: Series 4 docking result……………………………………………………………….86
Table 4.17: Series 4 drug likeness result.…………………………………...……………………94
Table 4.18: Summary of violation of the filtering criteria…………….………………………..96
Table 4.19: Series 4 ADMET (Pharmacokinetic) result…………………………………............97
Table 4.20: Series 4 DFT Calculations…………………………………………………………...98
Lists Of Figures
Figure 3.1: Schematic flow chart of the methodology…………………………..……………….41
Figure 4.1: 2D structure of compound 12 in a complex with the receptor………………….…..45
Figure 4.2: 2D structure of compound 4 in a complex with the receptor…………......................45
Figure 4.3: 2D structure of compound 1 in a complex with the receptor………………………...46
Figure 4.4: 2D structure of compound 9 in a complex with the receptor………………………...47
Figure 4.5: 2D structure of compound 11 in a complex with the receptor……………………….47
Figure 4.6: Showing (a) lumo, homo and (b) electrostatic potential of compound 1.……………52
Figure 4.7: Showing (a) lumo, homo and (b) electrostatic potential of compound 5…………….53
Figure 4.8:Showing (a) lumo, homo and (b) electrostatic potential of compound 11…………...54
Figure 4.9: Showing (a) lumo, homo and (b) electrostatic potential of compound 2….…………55
Figure 4.10: Showing (a) lumo, homo and (b) electrostatic potential of compound 4…………...56
Figure 4.11: 2D structure of compound 8 in a complex with the receptor ………………………60
Figure 4.12: 2D structure of compound 10 in a complex with the receptor ……………………..61
Figure 4.13: 2D structure of compound 4 in a complex with the receptor ………………………61
Figure 4.14: 2D structure of compound 5 in a complex with the receptor ………………………62
Figure 4.15: 2D structure of compound 11 in a complex with the receptor ………………..……63
Figure 4.16: Showing (a) lumo, homo and (b) electrostatic potential of compound 3…….……..67
Figure 4.17: Showing (a) lumo, homo and (b) electrostatic potential of compound 10……..…..67
Figure 4.18: Showing (a) lumo, homo and (b) electrostatic potential of compound 1…………...68
Figure 4.19Showing (a) lumo, homo and (b) electrostatic potential of compound 12……….....69
Figure 4.20: Showing (a) lumo, homo and (b) electrostatic potential of compound 2……..…….70
Figure 4.21: 2D structure of compound 9 in a complex with the receptor ………………………74
Figure 4.22: 2D structure of compound 8 in a complex with the receptor ………..……………..75
Figure 4.23: 2D structure of compound 10 in a complex with the receptor ………………….....76
Figure 4.24: 2D structure of compound 7 in a complex with the receptor ………………….…...76
Figure 4.25: 2D structure of compound 5 in a complex with the receptor ………………………77
Figure 4.26: Showing (a) lumo, homo and (b) electrostatic potential of compound 6…………...81
Figure 4.27: Showing (a) lumo, homo and (b) electrostatic potential of compound 8……….......82
Figure 4.28: Showing (a) lumo, homo and (b) electrostatic potential of compound 9…………...83
Figure 4.29: Showing (a) lumo, homo and (b) electrostatic potential of compound 10…………84
Figure 4.30: Showing (a) lumo, homo and (b) electrostatic potential of compound 1………......85
Figure 4.31: 2D structure of compound 14 in a complex with the receptor…………………......91
Figure 4.32: 2D structure of compound 13 in a complex with the receptor………………….…..92
Figure 4.33: 2D structure of compound 17 in a complex with the receptor……………………..92
Figure 4.34: 2D structure of compound 18 in a complex with the receptor……………………..93
Figure 4.35: 2D structure of compound 11 in a complex with the receptor……………………...94
Figure 4.36: Showing (a) lumo, homo and (b) electrostatic potential of compound 3………...…99
Figure 4.37: Showing (a) lumo, homo and (b) electrostatic potential of compound 1…..……..100
Figure 4.38: Showing (a) lumo, homo and (b) electrostatic potential of compound 2………….100
Figure 4.39: Showing (a) lumo, homo and (b) electrostatic potential of compound 11...………101
Figure 4.40: Showing (a) lumo, homo and (b) electrostatic potential of compound 13..…….…102
List Of Abbreviations
AFP- Alpha-fetoprotein
AHH- Aryl hydrocarbon hydroxylase
ASR- Automated Speech Recognition
BaP- Benzo(a)pyrene
BBB- Blood-brain barrier
BCLC- Barcelona Clinic Liver Cancer
CADD- Computer-aided drug design
CYP1A1- Cytochrome P450
C-Kit- Proto-oncogene receptor tyrosine kinase
CNS- Central Nervous System
CL- Confidence level
CPU- Central processing unit
DFT- Density functional theory
EH- Epoxide hydrolase
GB- Gigabyte
HFE- Humochromatosis Factor
HCC- Hepatocellular carcinoma
HBV- Hepatitis B virus
HCV- Hepatitis C virus
HR- Human Resources
LBDD- Ligand-based drug design
MVD- Molegro virtual docker
MW- Molecular weight
NAFLD- Nonalcoholic fatty liver disease
NASH- Nonalcoholic steatohepatitis
NMR- Nuclear magnetic resonance
PLC- Primary liver cancer
PDB- Protein Data Bank
PDGFR- Platelet-derived growth factor receptor
RAM- Random access memory
RET- Rearranged during Transfection
SBDD- Sructure-based drug design
TACE- Transarterial chemoembolization
UK- United Kingdom
VEGFR-Vascular endothelial growth factor receptor
WHO- World Health Organization
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Cancer is the second leading cause of death worldwide. According to data from World Health Organization (WHO), it was responsible for 8.8 million deaths in 2015 out of which 788,000 deaths were caused by liver cancer. The American Cancer Society has predicted that about 40,710 new cases (29,200 in men and 11,510 in women) will be diagnosed and 28,920 people (19,610 men and 9,310 women) will die of primary liver cancer. They further added that Liver cancer are more common in countries in sub-Saharan Africa and Southeast Asia accounting for more than 600,000 deaths each year. Liver is the second largest organ in the human body located at the right side of belly weighing about 3 pounds.
The liver has two lobes: Right and Left, and is in contact with gallbladder, pancreas and intestines. As number of organs are in contact with liver, cancer in liver can be both primary (originating from various cell that make up the liver) and secondary or liver metastases (caused due to cancerous cell from other organs). Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer among all liver cancers (Baloghet al., 2016).
Cytochrome P450, family 1, subfamily A, polypeptide 1 is a protein that in humans is encoded by the CYP1A1 gene(k Kawajiri, 1999; Kaname Kawajiri, 1999).The protein is a member of the cytochrome P450 superfamily of enzymes. Also added that CYP1A1 is involved in phase I xenobiotic and drug metabolism (one substrate of it is theophylline(Kaname Kawajiri, 1999). (Smith, Stubbins, Harries, & Wolf, 1998) It is inhibited by hesperetin (a flavonoid found in lime, sweet orange) (Briguglio & al, 2018; M. Briguglio et al., 2018)reported that fluoroquinolones and macrolides and induced by aromatic hydrocarbons. According to(A.P Beresford, 1993; Alan P Beresford, 1993),CYP1A1 is also known as AHH (arylhydrocarbon hydroxylase). It is involved in the metabolic activation of aromatic hydrocarbons (polycyclic aromatic hydrocarbons, PAH), for example, benzo[a]pyrene (BaP), by transforming it to an epoxide. CYP1A1 metabolism of various foreign agents to carcinogens has been implicated in the formation of various types of human cancer(Badal & Delgoda, 2014a, 2014b; GO, Hwang, & Choi, 2015).
Here are some examples of liver cancer drugs that work in different ways:
• Sorafenib:Sorafenib is a tyrosine kinase inhibitor that targets a number of different tyrosine kinases, including VEGFR, PDGFR, c-Kit, and RET.
• Avastin:Avastin is an angiogenesis inhibitor that blocks the growth of new blood vessels.
• Nivolumab:Nivolumab is an immunotherapy that helps the immune system fight cancer.
These are just a few examples of liver cancer drugs that work in different ways. There are many other drugs that are available or in development, and the specific drug that is right for a particular patient will depend on the individual patient's circumstances.
1.2 Resistance to Current Drugs
With long-term use, chemotherapeutic drugs, such as sorafenib and doxorubicin, have additional issues such as toxicity and/or drug inefficacy. As a result, neither current ablation therapies nor chemotherapy is appreciably effective in improving outcomes of this devastating disease. Further research to find better methods for treating liver cancer are necessaryBray et al. (2018); (Mohanty, Lobo, & Cheng, 2023; World, 2023).
1.3 Disease Treatment Strategies
The prognosis of liver cancer is poor. Only 5% to 15% of the patients are eligible for surgical removal, which is suitable only for early-stage patients and due to diminished hepatic regenerative capacity(C. Cleveland, 2023; E. R. Cleveland et al., 2019). Treatment options for more advanced stages include the following:
(a) Trans-arterial chemoembolization (TACE), which leads to a 23% improvement in the 2-year survival in comparison to conservative therapy for intermediate stage HCC patients(Y. Chang, Jeong, Young Jang, & Jae Kim, 2020; Journal, 2019)
(b) Oral dosing with sorafenib, a kinase inhibitor and the most accepted option for late-stage cases. However, fewer than one-third of patients benefit from the treatment, and drug resistance is evident within six months of initiating the regimen(Keating, 2017; Sorafenib, 2017).
1.4 Development of Inhibitors for CYP1A1
The development of inhibitors for CYP1A1 is an active area of research, as these compounds have potential applications in a variety of fields, including cancer prevention, drug development, and environmental toxicology(BMC, 2016a; O. o. D. M. Expert, Toxicology, 2013; Gawain A Heckley, Jarl, Asamoah, & G-Gerdtham, 2011).
There are a number of different approaches to the development of CYP1A1 inhibitors. One approach is to target the active site of the enzyme. This can be done by designing compounds that resemble the natural substrates of CYP1A1 and that can compete for binding to the active site. Another approach is to target allosteric sites on the enzyme. These are sites that are not directly involved in catalysis, but that can modulate the activity of the enzyme(BMC, 2016b; Gawain A Heckley et al., 2011).
A number of different compounds have been identified as potential inhibitors of CYP1A1. Some of these compounds are natural products, such as resveratrol and quercetin. Others are synthetic compounds, such as aminoglutethimide and omeprazole(Discover, 2023; Kopec, Bozyczko-Coyne, & Williams, 2005).
The development of CYP1A1 inhibitors is still in its early stages, but there is a growing body of evidence that these compounds have potential therapeutic applications. Further research is needed to optimize the design of CYP1A1 inhibitors and to evaluate their safety and efficacy in humans(BMC, 2016c; Gawain A Heckley et al., 2011).
Here are some of the potential benefits of developing CYP1A1 inhibitors:
• Cancer prevention: CYP1A1 is involved in the metabolic activation of a number of carcinogens, including benzo[a]pyrene. By inhibiting CYP1A1, it may be possible to reduce the risk of cancer.
• Drug development: CYP1A1 can metabolize a number of drugs, including some that are used to treat cancer. By inhibiting CYP1A1, it may be possible to improve the efficacy of these drugs(Alan P Beresford, 1993; O. Expert, on Drug, Metabolism, Toxicology, 2012).
• Environmental toxicology: CYP1A1 is involved in the metabolism of environmental pollutants, such as polycyclic aromatic hydrocarbons (PAHs). By inhibiting CYP1A1, it may be possible to reduce the toxicity of these pollutants(O. o. D. M. Expert, Toxicology, 2013; Mrema et al., 2013).
The development of CYP1A1 inhibitors is a promising area of research with the potential to improve the treatment of liver cancer, drug development, and environmental toxicology (O. o. D. M. Expert, Toxicology, 2013; Mrema et al., 2013).
1.5 Mechanisms of Action of Liver Cancer Drug
There are a number of different liver cancer drugs that work in different ways. Some of the most common mechanisms of action include:
• Tyrosine kinase inhibition: Tyrosine kinases are proteins that play a role in cell growth and division. By blocking tyrosine kinases, it is possible to inhibit the growth and spread of cancer cells(R. C. O. Nature, 2023).
• Angiogenesis inhibition: Angiogenesis is the growth of new blood vessels. Cancer cells need a blood supply to grow and spread, so by targeting angiogenesis, it is possible to starve cancer cells of the blood they need(R. Nature, Clinical Oncology, 2017).
• Immunomodulation: Immunomodulation is the process of altering the immune system. By modulating the immune system, it is possible to help the immune system fight cancer(Cancer, 2019).
• Targeted therapy: Targeted therapy is a type of treatment that targets specific molecules or pathways that are involved in the development or progression of cancer. By targeting these molecules or pathways, it is possible to inhibit the growth and spread of cancer cells(Liver, 2017; Sorafenib, 2017).
The specific mechanism of action of a liver cancer drug will depend on the individual drug. However, all of these mechanisms have the potential to inhibit the growth and spread of cancer cells, which can improve patient outcomes(Trends, 2018).
1.6 Rational Drug Design
Rational drug design is a drug discovery process that uses knowledge of the biological target to design new drugs that will interact with the target and inhibit its activity. This approach is in contrast to traditional drug discovery, which relies on trial and error to find drugs that are effective (Annual, 2013; R. Nature, Drug, Discovery, 2013).
Rational drug design typically involves the following steps:
a. Identifying the biological target: The first step is to identify the biological target that is responsible for the disease or condition that you want to treat. This can be done by studying the biology of the disease or by using high-throughput screening to identify proteins that are associated with the disease.
b. Understanding the structure and function of the biological target: Once the biological target has been identified, the next step is to understand its structure and function. This can be done by studying the protein's amino acid sequence, its three-dimensional structure, and its interactions with other molecules.
c. Designing the drug molecule: Once the structure and function of the biological target are understood, the next step is to design a drug molecule that will interact with the target and inhibit its activity. This can be done using a variety of computer-aided drug design (CADD) tools.
d. Testing the drug molecule: Once a drug molecule has been designed, it needs to be tested to see if it is effective in inhibiting the biological target and if it has any side effects. This can be done in vitro (in cell cultures) or in vivo (in animals).
Rational drug design is a promising approach to drug discovery, as it can help to identify drugs that are more likely to be effective and have fewer side effects(Annual, 2013; R. Nature, Drug, Discovery, 2013).One challenge is that it can be difficult to obtain the three-dimensional structure of the biological target. Another challenge is that it can be difficult to design a drug molecule that will bind to the target with high affinity and selectivity(Annual, 2013; R. Nature, Drug, Discovery, 2013). However, the challenges of rational drug design are being overcome, and rational drug design is becoming an increasingly important tool for drug discovery(Annual, 2013; R. Nature, Drug, Discovery, 2013). There are 2 different approach used in this drug design pipeline; the SBDD and the LBDD.
1.6.1 Structure-based drug design
Structure-based drug design (SBDD) is a type of drug design that uses the three-dimensional structure of a protein target to design new drugs that will bind to the target and inhibit its activity. SBDD is a powerful tool for drug discovery, as it can help to identify potential drug targets and to design drugs that are more likely to be effective and have fewer side effects(Annual, 2013).
The process of SBDD typically involves the following steps:
a. Identifying the protein target: The first step is to identify the protein target that is responsible for the disease or condition that you want to treat. This can be done by studying the biology of the disease or by using high-throughput screening to identify proteins that are associated with the disease(R. Nature, Drug, Discovery, 2022).
b. Obtaining the structure of the protein target: Once the protein target has been identified, the next step is to obtain its three-dimensional structure. This can be done by X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or computational modeling(M. Nature, 2011).
c. Designing the drug molecule: Once the structure of the protein target is known, the next step is to design a drug molecule that will bind to the target and inhibit its activity. This can be done using a variety of computer-aided drug design (CADD) tools(Nature, 2012).
d. Testing the drug molecule: Once a drug molecule has been designed, it needs to be tested to see if it is effective in inhibiting the protein target and if it has any side effects. This can be done in vitro (in cell cultures) or in vivo (in animals).
1.6.2 Ligand based drug design
Ligand-based drug design (LBDD) is a type of drug design that uses information about the structure and properties of known ligands to design new drugs that will bind to the same target. This approach is in contrast to structure-based drug design (SBDD), which uses the three-dimensional structure of the target protein to design new drugs(Drug, 2013).
LBDD typically involves the following steps:
a. Identifying known ligands: The first step is to identify known ligands that bind to the target. This can be done by searching databases of known ligands or by using high-throughput screening(Drug, 2013).
b. Characterizing known ligands: Once known ligands have been identified, the next step is to characterize them. This includes determining their structure, their binding affinity, and their selectivity(Drug, 2013).
c. Designing new ligands: Once known ligands have been characterized, the next step is to design new ligands that will have improved properties. This can be done using a variety of computational methods, such as quantitative structure-activity relationship (QSAR) and pharmacophore modeling(Drug, 2013).
d. Testing new ligands: Once new ligands have been designed, they need to be tested to see if they are effective in binding to the target and if they have any side effects. This can be done in vitro (in cell cultures) or in vivo (in animals)(R. Nature, Drug, Discovery, 2023).
1.7 Statement of The Research Problem
The incidence of liver cancer has been steadily increasing, making it a significant global health concern. Despite advancements in cancer treatment, the available therapeutic options for liver cancer are limited, and there is an urgent need for the development of novel and effective drugs.
1.8 Research justification
Liver cancer is a highly aggressive malignancy with limited treatment options, and its incidence has been steadily increasing over the years. The current standard of care for liver cancer includes surgical resection, transplantation, and chemotherapy, but these approaches often have limited efficacy and significant side effects. Therefore, there is a critical need for the development of novel drugs specifically targeting liver cancer to improve patient outcomes and survival rates.
This research is justified for several reasons. Firstly, by focusing on the design and synthesis of new compounds, we have the opportunity to explore innovative molecular structures that may exhibit enhanced efficacy and selectivity against liver cancer cells.
As such, computer-aid drug design has unquantifiable role to play by predicting the binding mode of the novel drug that are yet to undergo pre-clinical trial, also predict their drug-likeness and ADMET properties mode to save time and resources involve in the preclinical trials.
1.9 Research Questions
i. What is the binding mode of the studied compounds against their principle target CYP?
ii. Are the studied compounds drug like in nature and pharmacologically active?
iii. Are the studied compounds reactive or not?
1.10 Research Hypotheses
Alternate Hypothesis:
1. The studied compounds will show favorable interaction in the active site of their target.
2. The studied compounds will demonstrate improved pharmacokinetic properties, including enhanced bioavailability, distribution to the liver, metabolism, and excretion, compared to existing drugs.
3. The studied compounds will exhibit acceptable toxicity profiles, minimizing adverse effects on normal liver cell function and overall patient well-being.
4. The studied compounds will show promise as potential lead candidates for further preclinical and clinical development as liver cancer drugs, based on their efficacy, safety, and pharmacokinetic profiles.
Null Hypothesis:
1. The studied compounds will not show favorable interaction in the active site of their target.
2. The studied compounds will not demonstrate improved pharmacokinetic properties, including enhanced bioavailability, distribution to the liver, metabolism, and excretion, compared to existing drugs
3. The studied compounds will not exhibit acceptable toxicity profiles, minimizing adverse effects on normal liver cell function and overall patient well-being.
4. The studied compounds will not show promise as potential lead candidates for further preclinical and clinical development as liver cancer drugs, based on their efficacy, safety, and pharmacokinetic profiles.
1.11 Aim and Objectives
The specific objectives are to;
- Collect data from literature data set
- Draw the chemical structures using ChemDraw software.
- Perform geometry optimization of the molecules using Spartan 14 software to obtain their energetically stable conformations.
- Conduct molecular docking studies using Molegro Virtual Docker to predict the binding interactions of the compounds with the active sites of target proteins associated with liver cancer.
- To predict the drug-likeness and ADMET properties of the compounds
- Carry out DFT calculation on the compounds.
1.12 Scope and Limitations
The research will focus on the molecular docking virtual screening drug-likeness pharmacokinetic properties prediction and DFT calculations of compounds specifically targeting liver cancer.
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