FINAL PROJECT(1)

STUDY OF

CYTOTOXICITY EFFECTS OF CISPLATIN AND GOLD DRUGS

ON HUMAN OVARIAN CANCER CELL LINES

WITH ATR-FTIR SPECTROSCOPY TECHNIQUES

A dissertation submitted to the University of Manchester for the degree of

Master of Science (Biotechnology) in the Faculty of Engineering and Physical

Sciences

2010

NIDHI KAPIL

School of Chemical Engineering and Analytical Science

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CONTENTS

DESCRIPTION PAGE

NO.

TABLE OF CONTENTS 2

LIST OF FIGURES 3

LIST OF TABLES 5

NOMENCLATURE 6

ABSTRACT 7

DECLARATION 8

COPYRIGHT STATEMENT 9

ACKNOWLEDGEMENTS 10

CHAPTER 1: INTRODUCTION 11

CHAPTER 2: LITERATURE REVIEW 14

2.1 Cancer 14

2.2 Ovarian Cancer 15

2.3 Treatment of Ovarian Cancer 16

2.4 IR Spectroscopy 22

CHAPTER 3: EXPERIMENTAL TECHNIQUES AND

METHODOLOGY

40

3.1 A2780 cell line culture 40

3.2 Cell Fixation 40

3.3 ATR- FTIR Measurement 40

3.4 FTIR Measurement 43

CHAPTER 4: DATA ANALYSIS AND RESULTS 46

4.1 ATR-FTIR Data Results 46

4.2 FTIR Data Results 58

CHAPTER 5: CONCLUSIONS 64

REFERENCES 65

WORD COUNT 19,805

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List of Figures

Figure

No.

Description Page

No.

1 Schematic diagram showing normal and cancerous cells 14

2 Schematic diagram showing cancer stages 15

3 Cisplatin 18

4 Taxol 20

5 Methotrexate 20

6 Fluorouracil 21

7 Olaparib 21

8 Chart of characteristics vibrations 22

9 Schematic diagram showing stretching & bending modes of CH2

Group

23

10 Schematic diagram showing different absorption bands 24

11 Schematic diagram showing Spectrometer Layout 25

12 Schematic diagram showing working of FTIR spectroscopy 26

13 Schematic diagram showing evescent wave 27

14 Schematic diagram showing working of ATR-FTIR spectroscopy 28

15 FTIR-ATR experimental set up 42

16a ATR Germanium crystal base 42

16b ATR Germanium crystal top 42

17 - 20 ATR spectra of CIS & KF(IC-30) and PCA scores Plots,

(950-3800 cm-1 )

52

21-22 ATR spectra of CIS & KF(IC-50), (950-3800 cm-1

) 52

23-34 ATR spectra of CIS & KF(IC-50), PCA Score Plots & loading Plots

(950-3800 cm-1 )

53-54

35-38 ATR spectra of CIS & KF(IC-70), PCA Score Plots (950-3800 cm-1

) 55

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59-60

Figure60-61

No.65

Description Page

No.

39-44 ATR spectra of CIS (IC-30), (IC-50). & (IC-70) and PCA Score Plots

(950-3800 cm-1

)

55-56

45-52 ATR spectra of KF (IC-30), (IC-50). & (IC-70), PCA Score Plots and

loading Plots.

56-57


(Lipid Region )

58-59


(Protein Region )

59-60


(Carbohydrate & Nucleic acid Region )

60-61

77-80 FTIR spectra of CIS & KF(IC-30), PCA Score Plots and RMeiS

(Iteration 1 & 8)

65


(Iteration 1 & 8)

65-66


(Iteration 1 & 8)

66

89-92 FTIR spectra of CIS(IC-30, IC-50 & IC-70 ), PCA Score Plots and

RMeiS (Iteration 1 & 8)

67

93-96 FTIR spectra of KF(IC-30, IC-50 & IC-70 ), PCA Score Plots and

RMeiS (Iteration 1 & 8)

67-68

97-98 FTIR spectra of CIS(IC-30, IC-50 & IC-70 ), PCA Score Plots (950-

4000 cm-1

and RMeiS (Iteration 8)

68

99-100 FTIR spectra of KF(IC-30, IC-50 & IC-70 ), PCA Score Plots (950-

4000 cm-1

and RMeiS (Iteration 8)

68

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List of Tables

Table

No.

Description Page No.

1 Properties of infrared transmitting materials for ATR-IR

spectroscopy.

28

2 Details of samples and their spectrum 41

3 Details of Observed Peaks and their assignment in Cisplatin and

KF01-01 treated samples in Nucleic Acid & Carbohydrate and

Lipid Region.

50

4 Details of Observed Peaks and their assignment in Cisplatin and

KF01-01 treated samples in Protein Region.

51

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NOMENCLATURE/ABBREVIATIONS

SYMBOL/ABBREVIATION DESCRIPTION

FTIR Fourier Transform Infrared

ATR Attenuated Total Reflectance

KF Gold complexes drug

(KF01-01)

CIS Cisplatin drug

IR Infrared

DNA DeoxyriboNucleic Acid

CO2 Carbon Dioxide

& And

PCA Principal Component

Analysis

PARP Poly ADP Ribose

Polymerase

UV Ultraviolet

MRI Magnetic Resonance

Imaging

PBS Phosphate Buffer Saline

RMieS Resonant Mie Scattering

EMSC Extended Multiplicative

Signal Correction

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ABSTRACT

Attenuated Total Reflectance (ATR) and Fourier Transform Infrared (FTIR)

microspectroscopy has been used for studying the effect of cisplatin and KF01-01 drugs

on A2780 ovarian cancer cell lines as a function of concentrations . The A2780 cells were

exposed to KF01-01 and cisplatin drugs at different concentrations (IC-30, IC-50 and

IC-70) for 24 hrs. The spectra of these drug treated cells were acquired using ATR &

FTIR techniques. Further principle component analysis (PCA) was used for studying the

finer details of spectrums for evaluation of changes occurred in cell composition due to

these drugs at different concentrations..

Results from PCA analysis of ATR & FTIR data revealed that there are remarkable

differences in the spectra at all concentrations of KF01-01 and cisplatin. PCA analysis of

ATR and FTIR spectral data of samples treated with Cisplatin and KF01-01 drugs

revealed that drug samples at IC-50 concentration are clearly separable from the samples

of IC-30 and IC-70. Analysis of spectral data samples of both drugs corresponding to

lipid, protein and carbohydrate & nucleic acid regions revealed different clusters of score

points pertaining to these drugs . It is observed that clusters are more clear in

carbohydrate & nucleic acid region in comparison to lipid and protein region. These results

show that both drugs are inducing completely different chemical changes at different bands

indicating different working mechanism of both drugs. The analysis of spectra of KF01-01

& cisplatin treated cells at each concentration suggest that all the concentrations of KF01-

01 and cisplatin have an anticancer effect on A2780 cells . These results indicate similar

cytotoxic profile of KF01-01 and cisplatin at concentrations corresponding to IC-30 and

IC-70 and these produces important cell killing effect on A2780 cells at IC-50. Further

these results also indicate that cytotoxic effect of both drugs is non linear and IC-50

appears optimum dose.

It is observed that results of both ATR and FTIR techniques are in agreement . However

the spectrums measured with ATR revealed more details in comparison to spectral data

measured with FTIR system. FTIR spectral data had distortions and RMieS corrections

were applied for removal of distortions before PCA analysis. These correction techniques

sometimes can remove the important bands from the spectra. Accordingly ATR

spectroscopy appears better than FTIR spectroscopy.

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DECLARATION

No portion of the work referred in the dissertation has been submitted in support of

an application for another degree or qualification of this or any other University or

other Institute of Learning.

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COPYRIGHT STATEMENT

1. Copyright in text of this thesis rests with the author. Copies (by any process) either

in full, or of extracts, may be made only in accordance with instructions given by

the author and lodged in the John Ryland University Library of Manchester. Details

may be obtained from the Librarian. This page must for part of any copies made.

Further copies (by any process) of copies made in accordance with such

instructions may not be made without the permission (in writing) of the author.

2. The ownership of any intellectual property rights which may be described in this

thesis is vested in the University of Manchester, subject to any prior agreement to

the contrary, and may not be made available for use by third parties without the

written permission of the University, which will prescribe the terms and conditions

of any such agreement.

3. Further information on the conditions under which disclosures and exploitation

may take place is available from the Head of School of Chemical Engineering and

Analytical Science.

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AKNOWLEDGEMENTS

A journey is easier when you travel together and Interdependence is certainly more

valuable than Independence. This project is the result of three months hard work whereby I

was accompanied & guided by many great people. Now I have the opportunity to express

my gratitude to all who helped me during this project.

First of all I would like to express my deepest thanks to Dr. Peter Gardner, Senior Lecturer,

CEAS for providing me this great opportunity of working in his lab in Manchester

Interdisciplinary Biocentre in the University Of Manchester.

I would also like to thank the PhD students Paul Bassan, Konrad Dorling, Intisar Khalifa

and Geraldine Monjardez who helped me throughout the project. They were always there

to listen and to give advice for doing the project well. I am also very thankful to them for

their immense help in planning and executing the work and as well as for the

encouragement, generosity and complete guidance to complete my project on time.

Last, but not the least I am thankful to my parents who have supported me throughout the

project and helped me in facing the challenges related to the project work. Honesty,

sincerity, and ambition with commitment in one's continued persistence towards the goals

are priceless values instilled in me by my parents. These insights and parables always

kept me enthusiastic and in inspired state through this project.

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CHAPTER 1

1. INTRODUCTION

Despite of many advances in the medical sciences, cancer is continued to be a major health

problem. Cancer is becoming one of the common disease in today‟s scenario.Cancer is a

cell disease and it occurs due to alterations in the normal properties of the cell. These

alterations cause changes in nucleic acid, protein, lipid and carbohydrate quantities and / or

conformations (Mahadevan et al. 1997). These changes result in a faulty set of instructions

of cell function and cells do not undergo programmed cell death and they continue to

divide in an uncontrolled manner. These extra cells can form a mass of tissue called a

growth or tumor. The genes and proteins that control cell cycle and apoptosis are key in

understanding the mechanism of cancer and in development of anticancer drugs (Grey

2005). Most of anticancer drugs developed so far cause DNA damage through various

processes including distribution of cell cycle check points, growth factor and signal

transduction and ultimately cell death occurs by apoptosis . The design of anticancer drug

is a complicated issue and it should cover the inhibitory properties of drug alongwith its

delivery dosage and residence time in vivo. Finding the target of anticancer drug is

preliminary work of the discovery of its anticancer mechanism. Generally DNA is the

typical target for many anticancer drugs. Interaction between cancer cells and anticancer

drug is of prime importance in discovery of new drugs. The non invaging techniques

capable of providing molecular level information for investigation of functional groups,

bonding types and molecular conformations are very helpful in detection of cancer and for

development of anticancer drugs.

In recent years infrared spectroscopy has become a powerful tool for identifying

vibrational modes of various biomolecules of the cell which gives characteristic IR

spectrum and provides information about the structural and functional aspects ( Jackson

and Mantsch 1996). These IR vibrations can be correlated directly to the biochemical

composition . It has been found that IR spectrum alters in cancer disease and therefore IR

spectroscopy can detect and monitor characteristic changes in molecular composition and

structure that accompany transformation from normal to cancerous state. IR features of

various forms of cancer has been reported (Wood and et al. 2004). Furthermore IR

spectroscopy can be useful tool for monitoring the metastasis process and in cancer

grading ( Anderus et al. 1998). An interesting application of IR spectroscopy in biomedical

research is assessment of effects of drugs on cancer cells by monitoring biochemical

changes in cell before and after treatment with drugs. Sensitivity or resistance to drugs can

also be investigated by IR. Apoptosis or programmed cell death is of great

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importance in understanding progression and treatment of cancer . As an active process,

apoptosis is accompanied by biochemical changes to three essential cellular components

i.e. DNA, protein and lipid. IR spectroscopy capable of analysing theses three cellular

components can provide unique insights into apoptosis processes, including analyses of the

effects of anticancer drugs ( Gaspari and Muzio ) 2003.

Spectroscopy has emerged as one of the major tools in biomedical applications and

significant development has taken place in this field related to cancer detection and

anticancer drug development. The development in IR instrumentation and in data

processing techniques in the past decade have demonstrated its potential in disease

diagnosis and treatment. Among the different spectroscopy methods that have been

evaluated for distinction between normal and neo-plastic tissues, Fourier Transform

Infrared (FTIR) spectroscopy and ATR-FTIR have shown huge potential in diagnosis of

normal and malignant cells (Sahu & Mordechai 2005). These vibration spectroscopic

techniques are relatively simple, reproducible and non destructive to the tissue. These

techniques provide molecular level information allowing investigation of functional

groups, bonding types and molecular conformations. The spectral bands in the spectrum

are molecule specific & provide direct information about the biological composition.

In FTIR spectroscopy time domain measurement of an interference pattern in the infrared

is utilized to form a frequency domain plot, showing the magnitude of each wavelength in

a given range. FTIR spectrums are generally distorted and do not appear as clear

absorbance spectrum due to Mie scattering. These distortions are corrected through various

processing techniques. Due to these problems special interest has arisen in ATR-FTIR

methods based on evanescent wave absorbance.

The minute changes encoded in the large spectral data of FTIR, ATR-FTIR are generally

difficult to visualize directly. Statistical methods are applied to extract important

information from the collected spectrum. Principle component analysis (PCA) is well-

known data compression method where large spectral data are reduced into small number

of independent variables. The principle components are linear combinations of variables

that describe the major source of variance between the spectra. The first principle

component (PC1) accounts for the most variance, and subsequent PCs account for

decreasing amounts of different source of variance. PCA has been utilized for pattern

recognition in the spectral data obtained through FTIR and ATR-FTIR measurements done

on various samples.

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Cisplatin and its various analogues are widely used in treatment of ovarian cancer.

Cisplatin develops resistance to cancerous cells during course of treatment and it has

serious side effects. Extensive research is going on for discovering the new drugs for

cancer treatment with minimum side effects. Gold complexes compound based drugs have

shown great potential in cancer treatment and these have less side effects in comparison to

cisplatin.

It may be mentioned that cells treated with potential drugs will experience modifications

correlated with their cellular mode of action. Because infrared spectrum of cells yield a

precise image of all the chemical bonds present in the sample, different drug actions are

likely to yield a unique fingerprint characteristic of the mode of action of the drugs .

Accordingly the present work is undertaken to examine the application of FTIR and

Attenuated Total Reflectance (ATR) spectroscopy in studying the efficacy and working

mechanism of cisplatin and KF01-01 used in treatment of ovarian cancer . The A2780

ovarian cancer cells treated with cisplatin and KF01-01 drugs at different concentrations

i.e. IC30, IC50 and IC70 were utilized for FTIR & FTIR-ATR measurements. In all seven

samples of cisplatin and eight samples of KF01-01 treated with different concentrations

were analyzed by both techniques by taking ten spectra of each sample . The resulted

spectra reflected changes in biochemistry of A2780 ovarian cancer cells induced by

cisplatin and KF01-01 drugs. However these changes are difficult to detect in FTIR and

ATR-FTIR spectra. Therefore Principle component analysis (PCA) was applied for

extracting the hidden information from the collected spectra. ATR-FTIR spectral data

provided clean spectrum without any distortion and data was analyzed directly with PCA.

However FTIR spectral data reflected some distortions and therefore Resonant Mie

Scattering (RMieS) corrections were applied before principal component analysis. PCA

was performed for several combinations of spectral data of both drugs collected with

different concentrations.

The PCA analysis of these data indicates that chemical changes in A2780 cells induced by

cisplatin and KF01-01 drugs are different indicating that both drugs act differently. These

results suggest that all the concentrations of KF01-01 and cisplatin have anticancer effect

on A2780 cells . It is further inferred that both cisplatin and KF01-01 produces maximum

cancer cells killing effect at IC-50 in comparison to effect at IC-30 and IC-70

concentrations. Therefore the cytotoxic effect of both drugs is not linear at different

concentrations.

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CHAPTER 2

2. LITERATURE REVIEW

2.1 Cancer

Cancer is a disease of the cell cycle. Some of the body‟s cells divide uncontrollably and

forms tumors. There are several factors that regulate the cell cycle and assure a cell divides

correctly. Chemical signals tell a cell when to start and stop dividing. Neighboring cells

communicate with dividing cells to regulate their growth. Normally, cells grow and divide

to form new cells as the body needs them. Before a cell divides, the DNA is checked to

make sure it has replicated correctly. If DNA does not copy itself correctly, a gene

mutation occurs and this DNA mutations disrupt the cell cycle. Due to this disruption in

cell cycle new cells grow when the body does not need them and old cells do not die when

they should. These extra cells can form a mass of tissue called a growth or tumor. Tumors

can be benign or malignant. Benign tumors are not cancer. Generally, benign tumors can

be removed and they do not grow back. Cells from benign tumors do not spread to other

parts of the body and they do not invade the tissues around them. On the other hand

Malignant tumors are cancer .

Malignant tumors also can be removed. But sometimes they grow back. Malignant tumors

cells invade other organs and form new tumors that damage these organs. The spread of

cancer is called metastasis. The occurrence of cancer depends on variety of factors such as-

due to abnormalities caused in the genetic material of the transformed cells, or by some

type of carcinogens like tobacco, smoke, radiations etc. The two classes of genes that are

affected by the genetic abnormality are- oncogenes and tumor suppressor genes. The

oncogenes are activated in the cancer cells which helps the cells to have programmed cell

death, growth and division. Whereas the tumor suppressor genes are inactivated in the

Figure 1. Schematic diagram showing normal and cancerous cells (Ref. 69 )

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cancer cells which losses the normal functioning of those cells such as DNA replication

and control over the cell cycle.

The type of cancer is resembled by the type of the cell that resembles the tumor. The

different tumor includes:

Carcinoma : These are the tumors derived from the epithelial cells and include

breast cancer, prostate, lung and colon cancer.

Sarcoma : These are the tumors derived from the connective tissue.

Lymphoma and Leukemia : These are the tumors derived from the blood forming

cells.

Germ Cell Tumor : These are the tumors derived from the totipotent cells (the cells

that are able to divide and produce an organism). This category includes the testicle

cancer or the ovary cancer.

2.2 Ovarian Cancer

Ovarian cancer is a type of cancer that occurs in the females. This cancer usually occurs in

the ovaries of a women. Ovaries are the reproductive glands in which ova or eggs are

formed. When there is division and rapid growth of cells in one or both the ovaries of a

women then ovarian cancer occurs. The loss of the growth control of the cells leads to this

type of cancer as the uncontrolled growth leads to the formation of tumor.

The three types of cells which forms ovarian cancer are:-

Epithelial cancer : This is the most common category of cancer which arises from

Figure 2. Schematic diagram showing cancer stages (Ref. 70 )

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the cells that are covering the ovaries.

Germ cell cancer : This cancer starts from the cells that are designed to form eggs

in the ovaries.

Sex cord cancer : This type of cancer begins in the cells that are used to hold the

ovaries together and produce the female hormones.

Epithelial tumor is present on the surface of epithelial of the ovaries and accounts for about

90% of all the ovarian cancers. This tumor is subdivided into four different tumors which

include- serous, endometrioid, mucinous and clear cell tumors.

Most common ovarian cancers are ovarian epithelial carcinomas. It begins in the tissue

that covers ovaries. Ovarian cancer can invade, shed, or spread to other organs. When

cancer spreads from its original place to another part of the body, the new tumor has the

same kind of abnormal cells and the same name as the original tumor. If ovarian cancer

spreads to the liver, the cancer cells in the liver are actually ovarian cancer cells and it is

treated as ovarian cancer, not a liver cancer. Doctors call this new tumor “distant” or

metastatic disease.

Following are the stages of ovarian cancer:

Stage I: Cancer cells are found in one or both ovaries. Cancer cells may be found on

the surface of the ovaries or in fluid collected from the abdomen.

Stage II: Cancer cells have spread from one or both ovaries to other tissues in the

pelvis. Cancer cells are found on the fallopian tubes, the uterus, or other tissues in the

pelvis. Cancer cells may be found in fluid collected from the abdomen.

Stage III: Cancer cells have spread to tissues outside the pelvis or to the regional lymph

nodes. Cancer cells may be found on the outside of the liver.

Stage IV: Cancer cells have spread to tissues outside the abdomen and pelvis. Cancer

cells may be found inside the liver, in the lungs, or in other organs.

2.3 Treatment of Ovarian Cancer

Following are various ways of treating the ovarian cancer disease.

Surgery : This is the most common treatment for cancer. In this a particular cancer

tumor is removed. Sometimes the nearby lymph nodes are also removed so as to

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control the spread of cancer.

Radiation Therapy :This is also known as radiotherapy treatment and can be applied

either before the surgery or sometimes after the surgery. The radiation rays are given to

the area where cancer is present. This helps in control of division and the growth of

the cells . This is done before the surgery. But if person has undergone the surgery then

the rays can be given for killing the remaining cells which can form cancer.

Chemotherapy : In this anti cancer drugs are used for the treatment of the cancer. This

is used when the cancer is spread in large area. These drugs are either given orally, or

through injection in the vein or muscle. These drugs reach the cancer cells and kills

them. Most of anticancer drugs used in chemotherapy cause DNA damage through

various processes and ultimately cell death occurs by apoptosis. Most anti-cancer

drugs used include agents that target the cell cycle in order to inhibit the hyper

proliferation state of cancer cells and subsequently to induce apoptosis . Based on their

mode of action chemotherapeutic drugs can be classified into distinct groups ( i) drugs

that interferes with DNA synthesis (e.g. cisplatin) (ii) drugs that introduce DNA

damage (e.g. Fluorouracil, Methotrexate) and (iii) drugs that inhibit the function of

mitotic spindle (e.g. Taxol).

The most popular anti cancer drugs that are used for treating ovarian cancer are-

Cisplatin and its various analogues like carboplatin

Gold complexes

Taxol

Methotrexate

Fluorouracil

Olaparib

These drugs are different in nature and act differently in the body of the patient suffering

from ovarian cancer.

2.3.1 Cisplatin

Metals are essential for the normal functioning of living organisms and these occupy an

important role in current medical application (Bruijnincx & Sadler 2008). One of the major

medical breakthroughs for metal–based drugs was the accidental discovery of the

anticancer properties of cisplatin and its clinical introduction in the 1970. Cisplatin and

its analogues are widely used in treatment of ovarian cancer. The success of platinum

complexes in killing cancer cells mainly results from their ability to form various types of

adducts on DNA and this triggers apoptosis process that lead to cell death.

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Later on it was observed that some tumors develop resistance during the course of therapy.

It is demonstrated that resistance might be mediated through two mechanisms : first, a

failure of a sufficient amount of platinum to reach the target DNA; and second, a failure to

achieve cell death after platinum–DNA adduct formation (Messori and Marcon 2004).

However the successes of the effectiveness of cisplatin against cancer further opened new

way of research for development of new drugs on other metal-based compounds

(Kartalon and Essigmann 2007).

Cisplatin is the most commonly used drug in chemotherapy for treatment of cancers of

the head, neck, bladder, ovary & testis. It is a platinum based drug and can be used for

treating different types of cancer. The compound cis-PtCl2(NH3)2 (Fig.3) was first

described by M. Peyrone in 1845 and was known for a long time as Peyrone's salt.

Cisplatin is colourless fluid in nature. Usually this drug is given through drip (infusion) to

the patient.

Chemotherapy is done in several sessions or cycles over a period of time. This drug is

given alongwith some other chemotherapy drugs for the treatment. Cisplatin acts as

alkylating agent inside the body and it replaces the hydrogen by the alkyl group in a

molecule necessary for tumor growth.

Fig. 3: Cisplatin (Ref. 71 )

This drug kills the cancer cells by binding to the DNA and interfering in the cell

mechanism thereby causing the cell death. The binding of cisplatin to DNA changes the

secondary structure of DNA & consequently the metabolism of cell. Unfortunately many

cancer cells are resistant to cisplatin treatment. The only way to overcome acquired

resistance to cisplatin in cancer cells is by increasing dosage, which results in higher

toxicity in normal body cells. Resistance to cisplatin treatment can be due to lack of the

p53gene, by the activation of cell survival genes such as nuclear factor KB and by the

reduced cellular concentration to cisplatin. Recently (Haitao et al. 2010) has reported

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that Kaempferol enhances cisplatin‟s effect on ovarian cancer cells through promoting

apoptosis caused by down regulation of cMyc. Kaempferol is a dietry flavonoid that is

widely distributed in fruits & vegetables. Epidemiology studies have revealed a protective

effect of Kaempferol against ovarian cancer risk. It has been reported that Kaempteral is

effective in reducing vascular endothelial growth factor (VEGF) expression in ovarian

cancer cells.

2.3.2 Gold Complexes

Gold (I) and gold (III) complexes are potentially attractive as anticancer agents (Messori

and Marcon 2004). Both Gold(I) and Gold(III) complexes are isoelectric and isostructural

with platinum Pt(II) complex and are potentially attractive as anti cancer drug. The

category of the gold complexes which reacts well with the cancer is square planar gold(III)

complexes. These complexes have been allowed for in vitro pharmacological testing under

the physiological conditions. The gold (III) complexes constitutes five different gold

complexes-Au(en)2, Au(dien), Au(cyclam), Au(phen) and Au(terpy). A series of

organogold (III) complexes were also screened for the anti cancer activity with having

positive results in the case of human ovarian cancer cell line A2780.

Another category of gold complexes which showed cytotoxicity properties to the human

ovarian cancer cell line A2780 was dinucclear oxo gold (III) compounds. The design of an

effective anticancer drug is a complicated issue that must cover not only the drug's inherent

inhibitory properties but also its delivery, dosage, and residence time in vivo. Gold (I) and

gold (III) complexes overcome some of these challenges by forming strong covalent

attachments to targets. The gold(III) compounds have emerged as potential metal drugs

and this has resulted in synthesis of number of gold (III) compounds . These compounds

produce effect at molecular level such as direct DNA damage, modification of the cell

cycle, alterations of mitochondrial functions and induction of apoptosis. On the basis of

some new experimental evidence it is suggested that gold based compounds promote

apoptosis to a greater extent than cisplatin (Barnard et al. 2007).

2.3.3 Taxol

Taxol is used for curing different types of cancers (Fig. 4). This drug interferes with the

cancer cells and slow down their growth in the body. This drug can be used for curing

breast cancer, ovarian cancer and also lung cancer. It is present as a white powder and

when prepared for treatment it becomes a colourless and clear liquid. It is given

intravenously.

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Fig 4: Taxol (Ref. 72)

2.3.4 Methotrexate

This drug is used for curing cancer disease and also some autoimmune diseases (Fig. 5). It

is also known as amethopterin. This drug is used for treatment of breast cancer, skin

cancer, lung cancer and ovarian cancer. It acts by inhibiting the metabolism of the folic

acid.

Fig 5: Methotrexate (Ref. 73 )

2.3.5 Fluorouracil

It is a pyrimidine drug used for curing cancer. This drug comes under the category of

antimetabolites. This is used in treatment of colon cancer, breast cancer, ovarian

cancer, stomach cancer and the pancreas cancer. This drug interferes with the growth

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of cancer cells thereby leading to decrease the growth of the cancer cells in the body.

Fig 6: Fluorouracil (Ref. 74 )

2.3.6 Olaparib

Recently the first of a totally new type of drug for cancer treatment has been clinically

tested and results were published in the lancet. The trials were led by Dr. Andrew Tutt,

consultant clinical oncologist and director of the Breakthrough Breast Cancer Research

unit at King‟s College London.The drug investigated in the trials, olaparib, is a new class

of drug called PARP inhibitors and comes in pill form (Fig. 7) . It targets cancer cells

caused by faulty BRCA1 or BRCA2 genes.

Olaparib uses the synthetic lethality approach to kill cancer cells with faulty BRCA1 or

BRCA2 gene. It does this by blocking a protein called PARP and is therefore known as a

PARP inhibitor. Olaparib causes cancer cell with a BRCA fault to lose control of their

DNA stability. This causes the cancer cell to die and means that the tumour should either

stop growing or get smaller. Due to the drug working in a targeted way, it kills cancer

cells while leaving healthy cells relatively unaffected, which means fewer side effects for

patients.

Fig 7: Olaparib (Ref. 75 )

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The results are for two separate Phase II international, multi-centre clinical trials – one for

breast cancer and one for ovarian cancer patients. The ovarian cancer trial looked at a

group of 57 women with advanced ovarian cancer who had already received

chemotherapy. Twenty four patients took 100 mg doses of olaparib while another 33 took

400 mg closes. Over 33 per cent of tumours in the higher dose group reduced significantly

in size and tumours were prevented from progressing for an average of six months. The

patients had only relatively minor side effects, such a fatigue and nausea.

2.4. IR SPECTROSCOPY

Infrared (IR) spectroscopy is one of the most common spectroscopic techniques used in

biology. Simply, it is the absorption measurement of different IR frequencies by a sample

positioned in the path of an IR beam. The main goal of IR spectroscopic analysis is to

determine the chemical functional groups in the sample. Different functional groups absorb

characteristic frequencies of IR radiation (Fig.8).

Fig. 8 Chart of characteristics vibrations (Ref. 76 )

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IR radiations does not have enough energy to induce electronic transition as seen with UV.

Absorption of IR is restricted to compounds with small energy differences in the possible

vibrational states. For a molecule to absorb IR, the vibration within a molecule must cause

a net change in the dipole moment of the molecule. If the frequency of the radiation

matches with the vibrational frequency of the molecule, radiation will be absorbed causing

a change in the amplitude of molecular vibration.

IR radiation promotes vibration of the covalent bonds of molecules within the samples that

absorbs it. The wave length of the IR radiation that is absorbed depends upon the nature of

the covalent bond & the strength of any intermolecular interactions. Various bimolecular

components give a characteristic IR spectrum that is akin to a bio chemical fingerprint of

that tissue. This helps in measurement of complex molecular vibration modes which

contain valuable information on changes occurring due to disease. The position of atoms

in molecules are not fixed and they are subject to a number of different vibrations falls into

two main categories of stretching and bending. Stretching occurs due to change in inter

atomic distance along bond axis. These can be two types symmetric and asymmetric.

Bending occurs due to change in angle between two bonds. These are four types : Rocking,

Scissoring, Wagging & Twisting (Fig.9) .

Figure 9. Schematic diagram showing stretching &

bending modes of CH2 Group (Ref. 77 )

- 24 -

Therefore, the spectrum of each compound is unique similar to fingerprint. Intensities at

many wave numbers are simultaneously measured, but only a few of these wave numbers

of spectral reasons show diagnostic potential. In general the vibration frequency decreases

with increase in atomic weight and it increases with increase of bond energy. Specific

wave numbers for regions of absorbance in the IR spectra of cells / tissue are most affected

due to disease. Spectral bands in the spectra are molecule specific and provide direct

information about the bio-chemical composition (Fig.10). FTIR peaks are relevantly

narrow and in many cases can be associated with the vibration of a particular chemical

bond in a molecule. The physical effect of infrared is created by absorption and mainly

influences the dipole and Ionic bond.

2.4.1 Fourier Transform Infrared Spectroscopy (FTIR)

FTIR stands for Fourier Transform InfraRed, the preferred method of infrared

spectroscopy. In infrared spectroscopy, IR radiation is passed through a sample. Some of

the infrared radiation is absorbed by the sample and some of it is passed through . The

resulting spectrum represents the molecular absorption and transmission, creating a

molecular fingerprint of the sample. Like a fingerprint no two unique molecular structures

produce the same infrared spectrum. This makes infrared spectroscopy useful for several

types of analysis.

FTIR spectroscopy is the study of the interaction of IR radiation with matter. Earlier

instruments were dispersive and thus intensity at each wave number was measured

separately and this method was time consuming. On the other hand FTIR spectroscopy

system contains no monochromaters but an optical element composed of an

Figure 10. Schematic diagram showing different absorption bands (Ref.78

)

- 25 -

intrerferometer, allowing simultaneous measurements of complete region of wave numbers

in a short time (Fig. 11) . The Fourier transform computerized methodology transfers the

time domain spectrum frequency domain. This helps reduction in noise levels and make

data collection more rapid.

FTIR spectroscopy is typically used for viewing the IR radiations from 3µm to 15µm

wavelength. It is now used extensively in biological sciences and in many fields including

chemistry, physics, astronomy, semiconductor processing & even in forensics. It can be

applied to solids, liquids or gases. In last decades infrared spectroscopy has demonstrated

its potential as a novel technology for diagnosis and discovering new drugs for cancer.

The prominent areas where FTIR spectroscopy may be used in cancer diagnosis in the

future are ( Sahu and Mordechai 2005)

Differentiation of normal and diseased tissues in organs – breast, colon, liver,

ovarian and cervix ; detection of early stages of malignancy.

Monitoring abnormal cell growth and proliferation in tissue sections – colonic

Figure 11. Schematic diagram showing Spectrometer Layout

(Ref. 79)

- 26 -

crypts cervical epithelium.

Distinguishing between normal and abnormal cell-cell scrapings (cervix), biopsies

from smaller organs like the prostate , thyroid and body fluids.

Differentiation between cancer and other pathologic conditions with similar clinical

manifestation – IBD and colon cancer.

Monitor the effect of anticancer therapy – chemotherapy in leukemia and tumor

grading - lymphoid tumors.

2.4.2 FTIR Instrumentation

Fourier transform spectrometers now almost replaced dispersive instruments for most

applications due to their superior speed and sensitivity. They have greatly extended the

capabilities of infrared spectroscopy and have been applied to many areas that are very

difficult or nearly impossible to analyze by dispersive instruments. Instead of viewing each

component frequency sequentially, as in a dispersive IR spectrometer, all frequencies are

examined simultaneously in Fourier transform infrared (FTIR) spectroscopy (Fig. 12).

There are three basic spectrometer components in an FTIR system : radiation source,

interferometer, and detector. In FTIR an interferometer is used to differentiate and measure

the absorption component frequencies. The interferometer divides radiant beams and

generates an optical path difference between the beams. Further it recombines them for

producing repetitive interference signals measured as a function of optical path difference

by a detector. The interferometer produces interference signals, which contain infrared

spectral information generated after passing through a sample. When IR beam is directed

through the sample , the amplitude of set of waves are reduced by absorption if the

Figure 12. Schematic diagram showing working of FTIR

spectroscopy (Ref. 80)

- 27 -

frequency of set of waves is same as one of the characteristics frequencies of the sample.

The interferogram is the record of the interference signals recorded in time domain. It

contains information over the entire IR region to which the detector is responsive. A

mathematical operation known as Fourier transformation converts the interferogram to the

frequency domain spectrum showing intensity versus frequency or wave number.

During FTIR measurement Mie scattering significantly distort the spectrum and these

spectrums are can not be used directly for any meaningful interpretation. Various

processing techniques are applied for correcting these distortions for extracting the

information corresponding to various absorption bands.

2.4.3 Attenuated total reflectance ( ATR) - FTIR

In view of the distortion due to Mie scattering in FTIR rapid developments have taken

place in ATR based spectroscopy in which measurements are free from these scattering

effects . In ATR, special processing techniques are not required and measured spectra can

be used directly for interpretation of various absorption bands.

Attenuated total reflectance (ATR) is especially useful for obtaining IR spectra of difficult

samples that cannot be readily examined by the normal transmission method. It is suitable

for studying thick or highly absorbing solid and liquid materials. ATR requires little or no

sample preparation for most samples and is one of the most versatile techniques.

ATR occurs when a beam of radiation enters from a more-dense (with a higher refractive

index) into a less-dense medium (with a lower refractive index). The fraction of the

Figure 13. Schematic diagram showing evescent wave (Ref. 81 )

- 28 -

incident beam reflected increases when the angle of incidence increases. All incident

radiation is completely reflected at the interface when the angle of incidence is greater than

the critical angle. The beam penetrates a very short distance beyond the interface and into

the less-dense medium before the complete reflection occurs. This penetration is called the

evanescent wave (Fig. 13) and typically is at a depth of a few micrometers (0.5µm to

5µm). Its intensity is attenuated by the sample in regions of the IR spectrum where the

sample absorbs.

The sample is normally placed in close contact with a more-dense, high-refractive-index

crystal such as zinc selenide, thallium bromide–thallium iodide (KRS-5), or germanium.

The IR beam is directed onto the beveled edge of the ATR crystal and internally reflected

through the crystal with a single or multiple reflections. Both the number of reflections and

the penetration depth decrease with increasing angle of incidence.

Figure 14. Schematic diagram showing working of ATR-FT IR spectroscopy

(Ref. 82 )

- 29 -

Table 1

Properties of infrared transmitting materials for ATR-IR spectroscopy

Material Comments Short

Wavelength

(cm-1

)

Long

Wavelength

(cm-1

)

Refractive

Index

pH Range

ZnSe Most

common

15000 461 2.40 5-9

Diamond Very costly 30000 <2 2.40 1-14

Ge Brittle 5500 432 4.0 1-14

Thallium

Bromide /

Thallium

Iodide

Very toxic 17900 204 2.37 5-8

The attenuated energy of the evanescent wave from the sample is passed back to the IR

beam, which then exits the opposite end of the crystal and is passed to the detector in the

IR spectrometer for generation of infrared spectrum. The resulting ATR-IR spectrum

resembles the conventional IR spectrum, but with some differences: The absorption band

positions are identical in the two spectra, but the relative intensities of corresponding bands

are different.

2.4.4 Advantages of FTIR –ATR Spectroscopy

These techniques are reagent free and can rapidly & non-invasively detect changes

in the biochemical composition of cells & tissues at the molecular level.

In contrast to UV, X-Rays & Gamma rays, IR rays are non destructive to biological

samples and therefore the samples can be analyzed without any destruction and

measurement can be repeated if required.

Compared to MRI & positron emission tomography (PET) these requires smaller

amount of samples.

Faster than Raman spectroscopy for ex-vivo analysis.

These are economic and simple to operate .

Sample preparation does not require exclusive & special treatment which helps in

rapid diagnosis.

Can be used in analysis of tissues, fluids and cells.

Can help in identification of suitable common biomarkers for different types of

malignancies, irrespective of their nature & origin.

- 30 -

2.4.5 Application of FTIR and ATR-FTIR in cancer diagnosis and development of

anticancer drug

Cancer cell has defective cell cycle control and does not undergo programmed cell death.

Subsequently they continue to divide in an uncontrolled manner compared to normal cells.

These cells are characterized by increased nuclear material, an increased nuclear to

cytoplasmic ratio, increased mitotic activity, abnormal chromatin distribution and

decreased differentiation. These result in specific change in nucleic acid, protein, lipid and

carbohydrate quantities. The DNA protein interaction is also disturbed in malignant

transformations resulting in repeated duplication and amplification of DNA sequence

(Mahadevan 1997). Genes and proteins that control cell cycle and apoptosis are key in

understanding cancer and for development of potential anticancer drugs (Grey et al.

2005). The design of an effective anticancer drug is a complicated issue that must cover

not only the drug's inherent inhibitory properties but also its delivery, dosage, and

residence time in vivo.

DNA is the typical target for many anti-cancer drug. Different anti cancer drug interact

with cancer cells in different ways. Understanding the mechanism of anti cancer drugs

with cancer cells is becoming one of the focus in development and screening of new

anticancer drugs. Development of advance level IR instruments and computerized data

processing tools have accelerated the research for discovering new drugs for cancer

treatment.

In recent years, infrared spectroscopy has become a powerful tool for identifying

vibrational modes of various biomolecules of the cell which give a characteristic IR

spectrum (Jackson & Mantsch 1996). These infrared vibrations can then correlated

directly to the biochemical species and resultant IR spectrum can then be described as

biomarker. It has been found that the IR spectrum is altered in disease such as cancer.

Therefore, IR spectroscopy can detect and monitor characteristic changes in molecular

composition and structure in transformation of cell from normal to cancerous state.

FTIR and ATR-FTIR spectroscopy has been utilized successfully in the following

medical area

Analysis of normal and cancerous tissues of esophagus cancer

Fixation protocols for sub cellular imaging by FTIR

FTIR used for prostate cancer diagnosis

FTIR used for observing the biochemical changes in the Pancreatic cancer

- 31 -

FTIR used in cancer detection

Cancer histopathology by using FTIR

FTIR studies on normal and oncogenes

Vibrational spectroscopy used in cancer diagnosis

Study of prostrate cell line using synchrotron based FTIR

FTIR used in characterization of stem like cells

FTIR used in discrimination of the factors influencing prostrate cell line

FTIR and Raman spectroscopy used for Ovarian tissues

Study of cisplatin resistance for the ovarian cancer cell line

FTIR spectroscopy for biological tissues

The IR features of various forms of cancer have been widely reported (Salman et al. 2001,

Faolain et al. 2005). Infrared based techniques in biomedicine have become a reality with

a large amount of information accumulated from clinical studies. Among the different

spectroscopic methods that have been used , Fourier transform (FTIR) spectroscopy has

shown huge potential in biomedicine application (Wood et al. 2004) . FTIR spectroscopy

studies suggest that progression of normal cell to the metastasis state involves structural

modification in DNA. Moris et al. (1995) studied samples that contained cell collected

from cervical canals using FTIR and observed changes in the spectra at various stages of

cervical intraepithelial neoplasia that were of diagnostic potential . Another report dealt

with the distinction of normal and dysplastic cells in cervical samples using FTIR and

reported the similarity of the dyplastic cell spectra with the HeLa cells spectra (Wood et al.

1995). The important observation made utilizing principal component analysis was that

the glycogen absorbance was a prominent diagnostic feature. The development of

advanced computational methods to analyze the spectra further enhanced the use of FTIR

in distinction of normal and cancerous tissues (Cohenford et al. 1997, Romeo et al. 1998).

Sahu & Mordechai (2005) studied the application of FTIR in cancer detection . Wood et

al. ( 1998) reported FTIR spectroscopy as a biodiagnostic tool for cervical cancer. They

carried out an FTIR microscopic investigation of cell types and potential confounding

variables in screening for cervical malignancies. The aim of the study was to determine the

effectiveness of infrared spectroscopy in the diagnosis of cervical cancer and dysplasia. It

was found that leukocytes and in particular lymphocytes have spectral features in the

phosphodiester region ( 1300-900 cm-1

).

Wang et al. (1997) focused on the microscopic FTIR studies of lung cancer cells in pleural

fluid. The results demonstrate significant spectral differences between normal, lung cancer

- 32 -

and tuberculous cells.

Binoy and others ( 2004) used the FTIR in studying the anticancer drug. Sensitivity or

resistance to drugs can be also investigated by the FTIR. Liu et al. (2007) examined the IR

spectral changes corresponding to protein, lipid and nucleic acid and demonstrated there is

a difference in spectra corresponding to drug resistant and drug sensitive leukemia cell

line. Understanding apoptosis or programmed cell death, has great importance in

progression of cancer and its treatment. As an active process, apoptosis is accompanied by

profound biochemical changes to three essential cellular components i.e. DNA, protein and

lipid. IR spectroscopy is capable of analysing these three essential cellular components

and simultaneously can provide unique insights into apoptosis processes, including

analyses of the effects of anticancer drugs (Gasparri and Muzio 2003).

Baker et al. (2009) used FTIR and histological pathology to compare the results of the

prostate tissue to know about the disease severity. Prostate cancer is the second most

common cancer of men. The biochemical changes involved with prostate cancer were also

evaluated using FTIR technique. The morphological features of the prostate cancer are

named according to the score with Gleason Grading System. This helps in dividing the

prostate cancer into five different grades. FTIR along with PC-DFA was done on the

formalin fixed prostate cancer tissue . These results were compared with those assigned to

the gleason grading. This grade system is divided into three categories depending upon the

stage of cancer-

GD<7 least aggressive

GS=7 intermediate potential

GS>7 likely to progress

The tissue biopsies were differentiated into the above three categories. After this division

MATLAB along with PCA was done so as to differentiate between them. The analysis was

performed on the basis of three models-

Model A- spectra vector normalized

Model B- spectra vector normalized followed by first derivative

Model C- spectra vector normalized followed by second derivative

Model B achieved highest overall sensitivity and Model A achieved highest specificity. It

was inferred that FTIR can not identify the specific protein, lipid or carbohydrate but can

help in estimating the concentrations of these molecules in the given sample.

- 33 -

Bhargava (2007) presented an excellent review on FTIR technology with reference to

prostate cancer. He has observed that it is critical to understand that data acquisition,

classification and its validation for translation of FTIR results to clinical pathology. He

also compared the manual clinical testing methods with FTIR and discussed advantage and

disadvantage of both techniques.

Gazi et al. (2009) used FTIR spectroscopy technique for the classification and

discrimination of the prostate cancer cell lines. In this study the FTIR spectra of the fixed

cancer cell line and also the primary epithelial cells were obtained from benign prostatic

hyperplasia(BPH). The analysis which gave the positive results in discrimination and

classifying the cell lines of the prostate cancer is called multivariate chemometric analysis.

This analysis gave the sensitivity and selectivity values of >94% and >98%. Alongwith

this examination two other results were also concluded. The effect of media on the cell line

and also the differences in nucleus to cytoplasm (N/C) was also examined. The major

contributing factor for the classification and discrimination of cell line was the biochemical

differences between the cell lines.

Gazi et al. (2008) used FTIR spectroscopy for the identification of clinically aggressive

prostate cancer. Alongwith the FTIR another process called principal component

discriminant analysis (PC-DFA) was applied for grading of the given tissues of prostate

cancer. This algorithm increased the sensitivity and selectivity of the gleason grades. The

main aim of this research was to determine the possible biochemical changes associated

with the progression of Prostate cancer. The gleason grading of the prostate cancer biopsies

is subject to both inter and intra variabililty, which reduces the value arising from the

grading system (Latouf and Saad, 2002).

Jacob et al. (2001) used FTIR for measuring the IR spectra of the normal genes and the H-

Ras gene of mouse fibroblasts. Both the genes have different results in the form of

spectrum. The absorption of the normal gene was higher than the malignant ones. The

spectrum was obtained in between 600- 3200 cm-1

. Also the carbohydrates and phosphate

contents were higher in the normal cells as compared to the H-Ras genes. Moreover the

RNA/DNA content was high in the transfected cells which also concludes that

transcriptional activity increases in the case of cancerous cells.

The Ras oncogene are the first nonviral oncogenes to be recognized. The over expression

of Ras gene and Ras mutations is seen in lung carcinoma, head and neck carcinoma and

pancreatic carcinoma. This study was taken up by Lohr et al. (2000) and Schull et al.

- 34 -

(2000). The IR spectrum of the normal fibroblasts was obtained and two significant peaks

were seen in the spectrum. The first peak was at 1648 cm-1

which was amide I and the

other was at 1544 cm-1

corresponding amide II. The amide I band arises due to C=O

stretching and amide II band arises due to C-N stretching. The peaks at 1241 cm-1

and

1086 cm-1

were due to absorption of phosphate groups. The amount of absorption was

larger in the case of normal cells as compared to the cancerous cells. The band area of

phosphate was larger in the normal cells as compared to the Ras transfected cells. Also it

was seen that the DNA content of normal fibroblasts was higher than the Ras transfected

cells. After these studies it was concluded that FTIR absorption range from 600 cm-1

to

3200 cm-1

was low as compared to the tumorogenic Ras cells. Moreover the amount of

carbohydrate and phosphate content was high in normal cells as compared to the Ras cells.

Catherine et al. (2009) did work in the area of clinical diagnostics using both FTIR and

Raman spectroscopy. Raman Mapping and also IR imaging helped in identification and

classification of biochemical changes that are related to carcinogenesis. FTIR and Raman

were also used for the diagnosis of cancer and also for getting the information about the

changes that usually occur before cancer. FTIR is also used as a powerful tool for probing

the structure of lipids, nucleic acids, carbohydrates and also primary and secondary

structure of proteins (Jackson et al. 1991, Wang et al. 2008) . FTIR has been used in

variety of different fields which includes- probing the tissue, biological fluids etc and in

distinguishing between the different phases of the cell life cycle and also differenciating

between cancerous and normal cells (Deleris and Petibois 2003). The multivariate spectral

models showed better results of the different stages of cancer with high accuracy levels.

Raman and FTIR were used extensively for studying the different cancers like-

gastrointestinal, lung cancer, head and neck cancer etc.

Casson et al. (2009) used FTIR for characterization of stem like cell population in human

esophageal normal and adenocarcinoma cell lines. They worked in differentiating between

the normal and adenocarcinoma cell lines and also in evaluating spheroids. These are stem

like cell population which are derived from the parent cell line when grown in the serum

free media. The cell lines were discriminated using the IR spectrum. The stem like cells

were differenciated easily as compared to the adenocarcioma cells as they show a

absorbance spectrum at the range of 1000–1200 cm-1

. FTIR and Raman spectroscopy has

been used in the identification of CSC(cancer stem cell) in human malignancy (Wang and

Dick 2005, Leedham 2008). The work by Martin et al. (2001) also indicated the possibility

of using infrared absorption band of the symmetric –PO2 stretching mode at 1080 cm-1

.

This was assigned to DNA as a specific marker of the cancer stem cells.

- 35 -

The amide bands and the amount of protein played a important role in differenciating

among the cancerous samples and normal ones. The amide I and amide II were the two

significant bands in the case of breast carcinoma (Meurens et al.1996). The amide II region

was also used for differenciating between the colorectal cancer from the non cancerous

tissue (Fabian et al.2006). Major spectral changes were also seen in cervical cancer tissue

predominantly at the amide I region (Bamberry et al. 2006).

Chen et al. (2006) used FTIR for observing the changes in the biochemical imaging of the

pancreatic cancer. In this study the biochemical imaging changes of lipids, proteins and

nucleic acid in the pancreatic cancer tissue samples were inferred using imaging and line

scan technique. The intensities and frequencies of the absorption bands of the IR spectrum

was reduced at the amide bands of the proteins and also the stretching of the lipids. The

cancerous tissue contained large amount of protein and the distribution of lipids and DNA

was low. These substances are the high molecular mass glycoproteins which are produced

by the tumor cells. IR microspectroscopies techniques are also used for analysing and

detection of lipids, proteins and also detection of polytene chromosomes in cells and

tissues.

Wang et al. (2003) applied FTIR for analysis of normal and cancerous tissues of esophagus

and significant differences between normal and malignant tissues were observed. For the

bands of protein , amide bands I and II, amide band II at about 1550 cm-1

was weak and

broad in malignant tissues and sharp in normal tissue. It was shown that peak at 1080 cm-1

was stronger and higher in malignant tissues than the normal tissue indicating that DNA

content in malignant tissues is significantly higher than in normal tissues.

Jian et al. (2003) used FTIR for investigating and finding out the differences in the

cancerous and non cancerous tissues of the esophageal cancer. In this experiment 27

samples of the esophageal cancer were taken. Each tissue was divided into two parts and

both were stored separately. One was fixed in 10% formalin and the other was frozen with

liquid nitrogen. The spectra of the samples was recorded on Nicolet Magna 750 FTIR

spectrometer with mercury cadmium telluride (MCT) detector (Argov et al. 2002). Total

64 scans were recorded in the region from 900 to 4000 cm-1

. With the help of FTIR

spectrum different areas with different feature was diagnosed. The proteins bands amide I

and amide II were seen in the cancerous and non cancerous samples of the esophageus.

- 36 -

The amide II band was weak in the cancerous tissues and was strong and sharp in the

normal tissues. Also the lipid content was less in the cancerous tissues as compared to the

normal tissues. Moreover the amount of DNA was more in cancerous tissues due to

increased DNA replication as compared to the normal tissues.

Gazi et al. (2005) worked Synchroton based Fourier transform infrared for studying the

cellular events. In the process imaging mode was used to generate biospectroscopic

chemical maps of formalin fixed cells of Prostate cancer. The imaging mode of FTIR is

usually applied to single cells at spatial resolution. This helps in studying the amount of

lipid, protein and carbohydrate in the cell. The cells that are fixed on formalin at low

concentration of 4% give better signals of IR spectrum as compared to the unfixed cells.

The experiment took place in two different events – cytokinesis and cell locomotion.

Cytokinesis is the process in which the cell goes cell division. It means the cell divides into

daughter cells. The images obtained revealed that there is heterogeneous distribution of

lipids. The experiment resulted in the conclusion that the lipid intensity is higher due to

process of ruffling and lateral membrane flow which are the consequences of cell

migration.

Kohler et al. (2009) used synchrotron infrared microscopy for studying the single, isolated

cancer cell. The Mie scattering was used for getting a corrected spectrum of the given

cancer cell line. The FTIR was used for getting the spectrum having good signal to noise

ratio. The spectrum obtained of the nuclei from synchrotron based FTIR showed changes

in the lipids, DNA and proteins as compared to the spectrum obtained of the lung cancer

cells. The optical and chemical properties of the single cells and the nuclei was compared

using the Mie scattering technique.

Gazi et al. (2004) gave the different fixation protocols for subcellular imaging by using

FTIR. Synchroton based Fourier Transform Infrared spectroscopy was used for the

analysis of lipids, proteins, carbohydrates and some phosphorylated molecules of the cells

is called. This technique is used in the imaging mode and it gives the results in the form of

biospectroscopic maps which is different from the spectrum that is obtained from FTIR.

The experiment was done so as to differenciate between the results obtained from the cells

which were fixed by formalin to those cells which were not fixed. The vibrations that are

obtained during the process are due to the presence of lipids, proteins and carbohydrates.

The spectrum obtained is in the form of peaks which shows the chemical bonding. The

maps of single cell is generated by reducing the aperture size to the size of the cell. The

perfect size of the aperture to get good signals is 5×5 µm. The cells were fixed with

formalin so as to preserve the structural and biochemical constituents of the cell.

- 37 -

Formalin is also used to preserve lipids by reaction of the hydrated formalin with double

bonds of unsaturated hydrocarbon chains (Steward 1973). During the process, calcium

fluoride disks are used when FTIR is done in the transmission mode and if it is done in the

reflectance mode than we use MirrIR slides. MirrIR plates are thick and have large thermal

mass. The cells which were fixed on 4% formalin on to the MirrIR slides were washed

with deionized water for the removal of residual PBS from the surface of the cells. The

trypan blue stain was used to distinguish between the cells and also to obtain the intergrity

of the formalin fixed cells. The lipid localization in the formalin fixed cells was more in

the cytoplasm as compared to the nuclei. Formalin fixation is a simple and less time

consuming process for the sample preparation and it gives highly resolved

biospectroscopic images.

Fabian et al. ( 1995) made a comparative study on human breast tumors, human breast

tumor cell lines and xenografted human tumor cells and has been demonstrated that

significant difference exists in spectrums.

Ventao et al. (2006) used FTIR and Raman microspectroscopy to study normal, benign and

malignant formalin fixed ovarian tissues. FTIR and Raman spectroscopy was done so as to

investigate and understand the biochemical changes occurring in ovarian cancer. The

Raman and FTIR spectra of normal and benign showed lot of similarities as compared to

malignant tissue. Raman spectra in the range of 700-1700 cm -1

gave two groups

corresponding to one group of normal and benign and the second group of malignant.

These two groups were obtained after applying first derivative to the given data. Ovarian

cancers can also be discriminated on the basis of tissue autofluoroscence pattern (Malins et

al. 1998). Diagnosis by the proteomics method also been used for ovarian cancer (Krishna

et al. 2005). The tissues are formalin fixed so as to store for histopathology and these

tissues are studied using vibrational spectroscopies. The spectra obtained showed some

kind of spectral contamination due to formalin. This contamination was removed by

washing the tissue samples with saline (Haung et al. 2003). The normal and benign tissues

were easily studied and discriminated as compared to malignant sample. Normal tissue

spectra was characterized by higher protein content and on other hand more amount of

DNA and lipid was obtained in malignant tissues.

Godwin et al. (1993) worked for studying the cisplatin drug on ovarian cancer cell lines.

The study of Sukuta and Bruch (1995) was on factor analysis of cancer FTIR evanescent

wave fibroptical (FTIR-FEW) spectra. It has been demonstrated that FTIR-FEW technique

and chemical factor analysis has a potential of a clinical diagnostic tool.

- 38 -

Wong et al. ( 1995 ) carried out research on exfoliated cells and tissues from human

endocervix and ectocervix by FTIR and ATR/FTIR spectroscopy and it has been

demonstrated that ATR/FTIR is more desirable method than the transmission method to

obtain meaningful and good quality infrared spectra of tissues.

Clarke et al. (2009) did work in this removal of dispersion artefacts of the FTIR spectra

that was obtained from the single biological cells. The FTIR spectra of the single

biological cells in the transflection mode gives the outcome in the form of dispersion

artefacts (Lasch et al. 2002). The spectrum that is obtained can have a weighted sum of the

sample reflection and transmission and the dominance of the reflection spectrum in

optically dense regions can account for dispersion artefacts.

Attenuated total reflection is a promising tool used for the biochemical origin of the given

disease. In the case of the cells, the cytoplasm is relatively sparse whereas the nucleus is

dense in terms of biochemical content. The dispersion artefact can be corrected by back

transforming the spectra and extracting the real and the imaginary components of the

spectrum resulting in decreased dispersive components. The contribution of the reflection

spectrum can become dominant in regions of high optical density giving rise to “dispersion

artefacts”. In the transflection geometry the effect is real and there is no dispersion artefact

present.

2.5 Principal Component Analysis

Principal component analysis is a variable reduction procedure. It is useful when data is

taken on number of variables and some of the variables are correlated with one another.

Because of this correlation it is possible to reduce the observed variables into a smaller

number of principal components that will account for most of the variance in the observed

variables.

A principal component can be defined as a linear combination of optimally-weighted

observed variables. The first component extracted in a principal component analysis

accounts for a maximum amount of total variance in the observed variables and will be

correlated with at least some of the observed variables. Further the second component

extracted will have two important characteristics. First, this component will account for a

maximal amount of variance in the data set that was not accounted for by the first

component. The second component will be correlated with some of the observed variables

that did not display strong correlations with component 1.The second characteristic of the

second component is that it will be uncorrelated with the first component. This means that

- 39 -

the correlation between components 1 and 2 would be zero. The remaining components

that are extracted in the analysis display the same two characteristics: each component

accounts for a maximal amount of variance in the observed variables that was not

accounted for by the preceding components, and is uncorrelated with all of the preceding

components. A principal component analysis proceeds in this fashion, with each new

component accounting for progressively smaller and smaller amounts of variance.

PCA is used abundantly in biological sciences because it is a simple, non-parametric

method of extracting relevant information from confusing data sets. PCA is helpful in

identifying patterns in data for highlighting their similarities and differences . PCA

provides a roadmap to reduce a complex data set to a lower dimension and this helps in

identifying the hidden information in the data.

The first principle component (PC1) accounts for the most variance, and subsequent PCs

account for decreasing amounts of different source of variance. The data may be analyzed

and viewed in for clustering when viewed in a particular direction and other aspect of the

information that is derived from PCA is indication of the important of particular variable

for each PC. These properties can be displayed on loading plot and each PC loading plot

provide useful information about the possible important variables and the contribution of

these variables to each PC responsible for the patterns observed on a PC score plot.

Therefore loading plot can be used to identify the molecular factor that underlies the

grouping or discrimination of the original data (Heri et al. 2003, Walsh et al. 2008).

- 40 -

CHAPTER 3

3. EXPERIMENTAL TECHNIQUES AND METHODOLOGY

The aim of the experiment was to study the effect of cisplatin and KF01-01 drug on

A2780 human ovarian cancer cell lines using FTIR and ATR-FTIR spectroscopy. Total

15 samples consisting seven samples treated with cisplatin and eight samples treated with

KF01-01 were taken for the data analysis. These samples were treated with different

concentration of IC-30, IC-50 and IC-70 . The details of samples are given in Table no. 2 .

3.1 A2780 cell line culture

A2780 human ovarian cancer cell line was obtained from the section of ovarian tissue of

the patient. The cultured cells were washed with 4ml of phosphate buffer saline (PBS)

solution several times. After washing the cells were allowed to harvest after treating with

trypsin in 0.5ml solution. After this step the cells were allowed to grow on the MirrIR

slides till the volume of the cells became 70% onto the slide.

3.2 Cell Fixation :-

The cells were fixed onto the MirrIR slides. The MirrIR slides are used because they

reflect about 95% of mid IR radiation without any interfering absorption. The cells which

were grown on the MirrIR slides were exposed to different concentration ( IC-30, IC-50

and IC-70) of cisplatin and KF01-01 for about 24 hours . After the exposure these slides

were washed with PBS, so as to remove any extra culture media or dead cells from the

slide. For the fixation of the cells 4ml of formaldehyde was added on cells for about 40

minutes. After washing with PBS the fixed slides were washed with distilled water and

then allowed for drying.

3.3 ATR- FTIR Measurement

FTIR stands for Fourier Transform Infrared Spectroscopy and the results are directly

obtained in the frequency domain (Fig.15). ATR stands for Attenuated Total Reflectance

and in this system in addition to FTIR system, ATR crystal is also used for measuring the

spectrum. The measured spectrums are distortion free. In this technique the main

component is the crystal. There are different varieties of crystals that are used for the

experiment. The refractive index of the crystal should always be high as compared to the

refractive index of the sample that is to be measured . In the present measurement

Germanium crystal (Fig.16a) has been used for taking the spectra of all the

- 41 -

samples. The crystal was inserted into the microscope for taking the spectrum. ATR was

done in the reflection mode because the slides used were the MirriR slides. The measured

spectrum data was uploaded into Matlab software for PCA analysis for extraction of

hidden information from the spectra of A2780 ovarian cancer cell lines samples treated

with cisplatin and KF01-01 drugs. Details of the measured spectra of A2780 cell lines

treated with cisplatin and KF01-01 corresponding to their concentrations are given in

Table No. 2

Table No. 2 : Details of samples and their spectrum

S.

No.

Type Of Drug Sample No./

Concentration

No.Of Spectrums

ATR-FTIR FTIR

1 Cisplatin (CIS) Sample 1 - IC 30 10 10







8 Gold (KF0101) Sample 8 - IC 30 10 10

9 Gold (KF0101) Sample 9 - IC 30 10 10

10 Gold (KF0101) Sample 10 - IC 50 10 10

11 Gold (KF0101) Sample 11 - IC 50 10 10

12 Gold (KF0101) Sample 12 - IC 50 10 10

13 Gold (KF0101) Sample 13 - IC 70 10 10

14 Gold (KF0101) Sample 14 - IC 70 10 10

15 Gold (KF0101) Sample 15 - IC 70 10 10

- 42 -

Fig15: ATR-FTIR experimental set up (Ref. 83 )

The crystals that have high refractive index as compared to the refractive index of the

sample are used for measurement. The light is allowed to pass through crystal which has a

high refractive index of 4 as compared to the refractive index of the given sample. The

crystal is pressed against the sample to take the measurements.

Fig16a : ATR Germanium crystal base Fig16b : ATR Germanium crystal top

(Ref. 84 )

- 43 -

3.3.1 Measurement Procedure for taking ATR-FTIR Spectrum

The Germanium crystal is taken and adjusted just above the stage into the microscope.

Then a clear MirrIR slide without any sample is taken and adjusted onto the stage. The

background scans of 2056 were taken by touching the crystal on to the slide. After taking

the background measurement the crystal is removed and cleaned with the ethanol to

remove any particles from the crystal which can disturb the spectra. After cleaning the

crystal it is again placed into the microscope and sample slide is loaded on to the stage.

Aperture size adjustment is not required in the ATR experiment. Now with the help of joy

stick the particular area of cells is selected and that area is made clear with the help of

adjusting screw placed near the stage of microscope. This process is done without the

crystal. The moment a particular area is selected than the crystal is inserted in to the

microscope. Now the pre processing beam is checked on to the computer screen. Now

with the help of fine tune screw present on the joy stick we make the crystal to touch one

of the cells. The moment the crystal touches one of the cells a peak of amide I appears on

the screen of computer in the pre processing beam. Once that peak appears the spectrum of

the cell is measured. The total of 10 spectrum with 1056 scans for each sample were taken

at the resolution of 4cm-1

. Everytime after measuring the spectrum the crystal was cleaned

with the help of ethanol and then inserted again in the microscope. The cleaning is done so

as to remove any dust or sample from the crystal. The presence of sample or dust might

cause disturbances in the spectrum of the sample. After the spectrum is obtained than the

file is converted from „bph‟ to the form of „csv‟ file. This file format is accepted by

Matlab software. The files are loaded in the Matlab software for further analysis. The data

is processed for extraction of hidden information by applying first or second derivative.

This is done so as to reduce the noise and for getting better results. After applying

derivative to the raw spectrum data , normalisation is done and then the data is passed for

the PCA analysis. PCA stands for principal component analysis and it reduces the data to

scores. These scores are present according to the components like PC1 and PC2. Both the

components have different characteristics according to which the particular data is

converted into scores. After PCA plot, the loading plot is plotted to get the exact location

of the peaks obtained in the spectrum.

3.4 FTIR Measurement

Spectra of all the samples was also taken with FTIR. The Infrared spectral data for the

A2780 human ovarian cancer cell lines was obtained using FTIR system. In this system the

- 44 -

microscope contains a detector known as liquid nitrogen cooled Mercury Cadmium

Telluride (MCT) detector. The spectrometer was purged using dry nitrogen for removing

any presence of water vapour and carbon dioxide (CO2). The spectrums were obtained in

reflection mode and then PCA (Principal Component Analysis) was done on the

spectrums. Before doing the PCA, spectrums were corrected with RMieS (Resonance Mie

Scattering) so as to remove the dispersion artefacts that usually occurs in the FTIR

experimental data. Dispersion Artefacts are observed as a sharp decrease in the intensity of

high wave number side of the absorption bands. This is seen in the Amide I band case at

1655cm-1

, causing a downward shift of the real peak. The other algorithm that is used at

the place where the Mie scattering is weak and no strong distortion is seen in the Amide I

band region is called Extended Multiplicative Signal Correction (EMSC) algorithm. It has

been shown that the principal origin of dispersion artefacts is due to the process called as

Resonance Mie scattering and this is present in both FTIR transmission and transflection

mode. Dispersion artefacts affects both shape and the position of the Amide I band. After

applying RMieS on the measured data, PCA was done for getting the better separation of

the scores based on the two different components. Details of the measured spectra of

A2780 cell lines treated with cisplatin and KF01-01 corresponding to their concentration

are given in Table No. 2. The measured spectrum data was uploaded into Matlab software

for PCA analysis for extraction of hidden information from the spectra of A2780 ovarian

cancer cell lines samples treated with cisplatin and KF01-01 drugs.

3.4.1 Measurement Procedure for FTIR data

The MirrIR slides in which the sample was allowed to grow was taken and placed on the

stage of the microscope. Sample slide was kept near to the clean MirrIR slide without any

sample on it. In the measurement of FTIR spectrums a particular aperture size is adjusted.

For this experiment an aperture size of 100µm×100µm was adjusted in the microscope.

Then the background scans of 2056 were taken for the clean MirrIR slide. After taking the

background the spectrums of the given sample slide were taken. For each of the sample 10

spectrums of 1056 scans were taken at the resolution of 4cm-1

. The range of the spectrum

which was selected for taking the spectrums was from 700-4000cm-1

and the parameters

were controlled by the Varian Software. The area was selected with the help of joy stick

which was controlled by the computer and making that particular area having cells clear

with the help of the adjusting screw that was present near the microscope stage. The

experiment was conducted in the reflection mode of the microscope for the given MirrIR

slides. Once the spectrums are obtained then the spectrum file is converted from „bph‟ into

- 45 -

the file „csv‟ that is accepted by the Matlab software. This software uploads the data and

converts into simple data in the form of scores. The data is converted into scores in the

PCA software. PCA converts the data into scores that gives the result according to two

different components. Before applying PCA the data is run through RMieS (Resonance

Mie Scattering) for the reduction of noise and the scattering that is obtained during the

process of taking spectrum. Once the data is run through this command then it is allowed

to proceed for PCA analysis. In the PCA plot we get the scores that are present according

to the two components (PC1 and PC2). The PCA plot is then titled according to the data

and also the name of the samples are given in the legend box. After the PCA plot is

obtained than the loading plot is also plotted. The results are finally obtained for the

analysis and for concluding the data.

- 46 -

CHAPTER 4

4. DATA ANALYSIS AND RESULTS

The present work has been carried out for studying the effect of Cisplatin and KF01-01

drugs on A2780 ovarian cancer cell lines as a function of concentration utilizing the ATR-

FTIR and FTIR spectroscopy. The used drugs concentration of IC-30, IC-50 & IC-70

cover the cytotoxic range with 30%, 50% & 70% of cell growth inhibition respectively.

The spectrums obtained after ATR-FTIR and FTIR measurements only provide very

limited information regarding biochemical composition of the sample. Therefore all ATR-

FTIR and FTIR corrected spectra of the A2780 cells have been statistically analyzed.

Principal component analysis (PCA) was preformed through MATLAB software for

classification of cells based on their response to drugs. Initially these data have been pre-

treated to enhance the spectral features and to eliminate unnecessary artefacts generated

due to variations in sample thickness or due to some other impurities. Pre-treatment

process includes taking first derivative or second derivative, mean centring and vector

normalization respectively.

4.1 ATR-FTIR Data Results

In case of ATR-FTIR initially PCA was performed on spectral data from 950- 3800 cm-1

range. Cisplatin and KF01-01 combined sample data of (IC-30, IC-50 and IC70)

concentration was analyzed separately. PCA was done by taking raw data , first derivative

and second derivative for reducing the noise and for enhancement of signal. Cisplatin

combined data of concentrations (IC-30, IC-50 and IC70) was analyzed with PCA.

KF01-01 data of (IC-30, IC-50 and IC70) concentrations was also analyzed similarly with

PCA. Further cisplatin and KF01-01 data at ((IC-30, IC-50 and IC70) was analyzed

separately for different regions i.e. lipid region (2800- 3000 cm-1

), protein region (1500-

1600 cm-1

) and carbohydrate & nucleic acid region (850 -1300cm-1

). In PCA analysis first

two components give maximum variation and therefore, only first two PCs were selected

for comparing observed patterns. Loading plot of scores for each PC were also plotted for

score plot. The loading plots allowed to identify the specific spectral features with

wavenumber location on each PC. Utilising the PCA score plots and loading plots of ATR

data of all Cisplatin and KF01-01 drug treated sample data all observed peaks at different

wave nos. were identified. The comical bonds or functions assigned to these waves nos.

are give in table no. 3 and 4. The assignment of bands of these waves nos. have been taken

- 47 -

from the database compiled by Monasaghi et al. (2008).

4.1.1 Cisplatin and KF01-01 drug treated samples at IC-30, IC-50 and IC-70

The spectra, PCA score plots and loading plots corresponding to all samples are given in

figure 17 to 76. The spectrum data of cisplatin and KF01-01 drug treated samples at

different concentrations (IC-30, IC-50 & IC-70) was combined and analyzed using

principal component analysis technique for discrimination of different patterns. The ATR

spectra of cisplatin and KF01-01 samples treated at IC-30 alongwith their PCA scores

corresponding to raw data and normalized second derivative data are given in Fig. 17- 20.

In the PCA score plots no clear pattern is observed and score points data is highly

scattered. Scattering in samples treated with cisplatin is more in comparisons to samples

treated with KF01-01. These results indicate that drug treated cells are in their different

apoptosis stages.

The ATR spectra and PCA scores of samples treated with cisplatin and KF01-01 at IC-50

corresponding to raw data and normalized second derivative data are given in Fig. 21- 34.

The spectra for cisplatin and KF01-01 at IC-50 are shown in Fig 21 and 22 respectively.

The absorbance intensity in KF01-01 treated samples is high in comparison to cisplatin

samples. In the raw data PCA score plot shown in Fig. 24 clear pattern is observed and

data corresponding to cisplatin scores and KF01-01 scores is lying in different clusters .

Scattering of scores points is also less. In the loading plots ( Fig. 25 and 26) Amide -I

and Amide II bands corresponding to KF01-01 are appearing at 1643 and 1517

respectively. Strong peak is observed at 1137 cm-1

corresponding to cisplatin. This band

is assigned to Oligosaccharide C-OH stretching band 2 – methylmannoside. These results

indicates that drug treated cells are in their similar apoptosis stages and mode of action for

both drugs is different. It is further inferred that IC-50 concentration appears optimum for

both drugs. In second derivative data (fig. 31 & 32) also a similar pattern is observed.

However some points are overlapping.

The ATR spectra of samples treated with cisplatin and KF01-01 at IC-70 alongwith their

PCA scores corresponding to raw data and normalized second derivative data are given

in Fig. 35- 38. In the PCA score plots no clear pattern is observed and score points are

highly scattered. These results indicate that drug treated cells are in their different

apoptosis stages. The score points corresponding to cisplatin and KF01-01 at IC-50

concentration are clearly observed in different clusters (fig.24). These results show that

both drugs are inducing chemical changes at different bands indicating different working

mechanism of both drugs.

- 48 -

PCA was performed on combined spectrum data of all samples treated with cisplatin at

different concentrations (IC-30, IC-50 & IC-70). The spectrum and PCA score plots are

shown in Fig. 39-44. It is observed that score points corresponding to IC-50 are in one

cluster with less scattering score points corresponding to IC-30 and IC-70 are overlapping

and highly scattered. Loading plots of score points are shown in figure (47-48). Peaks are

observed at 1124, 1130, 1246, 1545, 1537, 1643 & at 2918cm-1

. These results further

indicate that IC-50 appears optimum dose and drugs effect is not linear as per

concentrations.

PCA was also performed on combined spectral data of all corresponding to samples treated

with KF01-01 drug. The spectrum, PCA scores and loading plots are shown in fig. (45 -

52 ). In the score plots it is observed that all points, corresponding to IC-70 are in one

cluster with overlapping of few points. Score points, corresponding to IC-30 & IC – 50 do

not form any cluster. These results indicate that KF01-01 inducing its cytotoxic effect at

all concentrations. In the loading plots peak are observed at 1050, 1137, 1517 , 1537, 1637

& 1657. These peaks are different in comparison to peaks observed on cisplatin samples.

Most of the peaks are observed in carbohydrate & nucleic acid region (950-1300 cm-1

) &

in protein region (1500-1600 cm-1

).

4.1.2. Cisplatin and KF01-01 (IC-50) treated samples data analysis for Lipid, Protein ,

Carbohydrates & Nucleic acid Region.

The ATR spectra of combined samples of cisplatin and KF01-1 was analyzed for lipid,

protein , and carbohydrates and nucleic acid at IC-50. The PCA was performed separately

for each region.

Lipid Region

Spectra , score plot and loading plots for lipid region are shown in Fig. 53-60. PCA score

plot for raw data and second derivative are shown in Fig. 54 & 58 respectively. Two

different clusters of PCA scores are observed with overlapping of some data points. Points

corresponding to cisplatin and KF01-1 are clearly separable. In the loading plots ( Fig. 55

and 56) C-H stretching band is appearing at 2918, 2928, 2925 & at 2930. Other peaks

are observed at 2838 and at 2853.

Protein Region

Spectra , score plot and and loading plots for protein region are shown in Fig. 51-58. PCA

score plot for raw data and second derivative are shown in Fig. 62 & 66 respectively. Two

different clusters of PCA scores are observed with overlapping of some data

- 49 -

points. In the loading plots ( Fig. 63 and 64) peaks are observed at 1571cm-1

and 1537

cm-1

with respect to cisplatin . Other peak observed at 1589 cm-1

assigned to ring C-C

stretch of phenyl correspondence to KF01-01. These results indicate that mode of action of

both drugs is different in this region.

Carbohydrate an Nucleic Acid Region

Spectra , score plot and and loading plots for protein region are shown in Fig. 59-66. PCA

score plot for raw data and second derivative are shown in Fig. 70 & 74 respectively.

Two different clear clusters of PCA scores are observed with few overlapping points.

In the loading plots ( Fig. 71 and 72) peak observed at 1137 cm-1

corresponds to cisplatin

pertaining to C-OH band. Amide III and C-O stretching peaks corresponding to KF01-01

are appearing at 1235 cm-1

and at 1050 cm-1

respectively.

In this region separation of cisplatin and KF01-01 on the basis of score points is much

clear in comparison to the data of liquid and protein region. These results indicate that

mode of action of both drugs is clearly different in this region.

- 50 -

Table No. 3.

Details of Observed Peaks and their assignment in Cisplatin and KF01-01 treated

Samples in Nucleic Acid & Carbohydrate and Lipid Region

S.

No.

Peak/ Wave

number in cm-1

Assignment Region Observed

in Sample

1 1050 C-O stretching coupled with C-O

bending of C-OH of carbohydrate

Nucleic acid &

carbohydrates

KF01-01

2 1056 Stretching C-O deoxyribose -do- KF01-01

3 1137 Oligosaccharide C-OH stretching

band 2- Methylmannoside

-do- KF01-01

&

Cisplatin

4 1180 Amide III band region -do- KF01-01

5 1188 Deoxyribose -do- KF01-01

6 1235 Composed of Amide III as well as

phosphate vibration of nucleic acid

-do- KF01-01

7 1236 Amide III and asymmetric

phosphodister stretching mode

mainly from nucleic acids

-do- KF01-01

8 1246 PO- 2 asymmetric -do- Cisplatin

9 2838 Stretching C-H Lipid region KF01-01

10 2846 Symmetric stretching of methoxy

(4)

-do- KF01-01

11 2853 CH2 of lipids -do- KF01-01

12 2860 Stretching C-H -do- KF01-01

13 2916 Cholestrol, phospholipids and

creatine (higher in normal tissue)

-do- KF01-01

14 2918 Stretching C-H -do- Cisplatin

15 2925 C-H stretching bend in normal

tissue

-do- KF01-01

16 2928 Stretching C-H -do- KF01-01

17 2930 C-H stretching -do- KF01-01

- 51 -

Table No. 4.

Details of Observed Peaks and their assignment in Cisplatin

and KF01-01 treated Samples in Protein Region

S.

No.

Peak/ Wave

number in cm-1

Assignment Region Observed

in Sample

1 1517 Amide II Protein

region

KF01-01

2 1532 Stretching C=N, C=C Protein

region

Cisplatin

3 1537 Stretching C=N, C=C Protein

region

Cisplatin

4 1541 Amide II absorption ( primarily an

N-H bending coupled to a C-N

stretching vibrational mode)

-do- KF01-01 &

Cisplatin

5 1545 Protein band Amide II or Peptide

amide II

-do- KF01-01 &

Cisplatin

6 1549 Amide II -do- KF01-01

7 1552 Ring base -do- KF01-01

8 1571 C=N, Adenine -do- Cisplatin

9 1589 Ring C-C stretch of Phenyl -do- KF01-01

10 1635 β- sheet structure of amide I -do- KF01-01

11 1637 C=C uracil, C=O -do- KF01-01

12 1643 Amide I band of protein and H-O-

H deformation of water

-do- KF01-01

13 1653 C=O, C=N , N-H of adenine ,

thymine, guanine, cytosine

-do- KF01-01

14 1657 α helical structure of Amide I -do- KF01-01

- 52 -

10001500200025003000350040000

0.02

0.04

0.06

0.08

0.1

Wavenumber / cm-1

1000150020002500300035004000-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Wavenumber / cm-1

Fig 21: ATR spectra of A2780 cells treated with

CIS(IC-50).

Fig 22: ATR spectra of A2780 cells treated with

KF(IC-50).

Ab

sorb

ance

Ab

sorb

ance

Wavenumber in cm-1

Wavenumber in cm-1

1000150020002500300035004000-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Wavenumber / cm-1 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

PC1 (74.0%)

PC

2 (1

1.6

%)

PCA RAW DATA Plot

CIS

KF

Fig 17: Normalised ATR spectra (raw data) of

A2780 cells treated with CIS & KF(IC-30). Fig 18: ATR spectra scores plot (raw data) of

A2780 cells treated with CIS & KF(IC-30).

Ab

sorb

ance

Wavenumber in cm-1

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

PC1 (33.2%)

PC

2 (2

0.6

%)

PCA Second derivative Plot

CIS 30

KF 30

Fig 19: Normalised Second derivative of A2780

cells treated with CIS & KF(IC-30). Fig 20: PCA plot of second derivative of A2780

cells treated with CIS & KF(IC-30).

15002000250030003500-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1Wavenumber in cm-1

Ab

sorb

ance

- 53 -

1000150020002500300035004000-0.1

-0.05

0

0.05

0.1

Wavenumber / cm-1

Loadings for PC 1

1000150020002500300035004000-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

Wavenumber / cm-1

Loadings for PC 2

Fig 25: Loading plot of PC1 (raw data) of

A2780 cells treated with CIS(IC-50) &

KF(IC-50)

Wavenumber in cm-1

Wavenumber in cm-1

Fig 26: Loading plot of PC2 (raw data) of


KF(IC-50)

1643 1517

1137

1137

1532 1653

1000150020002500300035004000-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Wavenumber / cm-1

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

PC1 (71.7%)

PC

2 (1

3.2

%)

PCA RAW DATA Plot

CIS

KF



KF(IC-50).

Wavenumber in cm-1

Ab

sorb

ance

Fig 24: ATR spectra scores plot (raw data) of


KF(IC-50).

15002000250030003500-0.3

-0.2

-0.1

0

0.1

0.2

Wavenumber / cm-1

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

PC1 (56.2%)

PC

2 (2

4.1

%)

PCA FIRST DERIVATIVE PLOT

CIS

KF

Ab

sorb

ance

Wavenumber in cm-1

Fig 27: Normalised first derivative plot of ATR

spectra of A2780 cells treated with CIS(IC-

50) & KF(IC-50).

Fig 28: PCA plot of first derivative of A2780 cells

treated with CIS(IC-50) & KF(IC-50).

- 54 -

15002000250030003500-0.3

-0.2

-0.1

0

0.1

0.2

Wavenumber / cm-1

Loadings for PC 1

15002000250030003500-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Wavenumber / cm-1

Loadings for PC 2

Wavenumber in cm-1

Wavenumber in cm-1

Fig 33: Loading plot of PC1 component of

second derivative.

Fig 34: Loading plot of PC2 component of

second derivative.

2860

1236 1637 2853

2925

2853 2918

1549

1246

1653

1517

15002000250030003500-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Wavenumber / cm-1

Loadings for PC 2

15002000250030003500-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Wavenumber / cm-1

Loadings for PC 1

2955

2930

2853 1657

Wavenumber in cm-1

Fig 29: Loading plot of PC1 for first derivative of


KF(IC-50).

Wavenumber in cm-1

Fig 30: Loading plot of PC2 for first derivative of


KF(IC-50).

2860

2930

2846 2916

1246

A

bso

rban

ce

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

PC1 (32.0%)

PC

2 (2

1.1

%)

PCA SECOND DERIVATIVE Plot

CIS 50

KF 50

Wavenumber in cm-1

Ab

sorb

ance

15002000250030003500-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

Fig 31: Normalised second derivative plot of


KF(IC-50).

Fig 32: PCA plot of second derivative data of


KF(IC-50).

- 55 -

Ab

sorb

ance

Wavenumber in cm-1 15002000250030003500

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

PC1 (40.3%)

PC

2 (2

3.5

%)


CIS 70

KF 70

Fig 37: Normalised second derivative of A2780

cells treated with CIS & KF(IC-70). Fig 38: PCA plot of second derivative of A2780

cells treated with CIS & KF(IC-70).

Wavenumber in cm-1

1000150020002500300035004000-0.05

0

0.05

0.1

0.15

Wavenumber / cm-1

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

PC1 (77.7%)

PC

2 (1

3.7

%)

PCA RAW DATA Plot

CIS 70

KF 70


A2780 cells treated with CIS & KF(IC-70). Fig 36: ATR spectra scores plot (raw data) of

A2780 cells treated with CIS & KF(IC-70).

Ab

sorb

ance

Wavenumber in cm-1

1000150020002500300035004000

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Wavenumber / cm-1

Ab

sorb

ance

Fig 39: Combined Normalised ATR spectra (raw

data) of A2780 cells treated with CIS (IC-

30, IC-50 & IC-70).

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

PC1 (71.1%)

PC

2 (1

3.1

%)

PCA CISPLATIN Plot

cis 30

cis 50

cis 70

Fig 40: Combined ATR spectra scores plot (raw

data) of A2780 cells treated with CIS (IC-

30, IC-50 & IC-70).

- 56 -

Fig 46: PCA score plot of A2780 cells treated with

KF(IC-30, IC-50, IC-70).

of A2780 cells treated with CIS & KF(IC-30).

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

PC1 (75.2%)

PC

2 (1

2.1

%)

PCA RAW Plot

KF 30

KF 50

KF 70

1000150020002500300035004000-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

Wavenumber / cm-1

Wavenumber in cm-1

Fig 45: Combined ATR spectra (raw data) of A2780

cells treated with KF(IC-30, IC-50, IC-70 ).

Ab

sorb

ance

Wavenumber in cm-1

Fig 43: Combined Normalised second derivative

of A2780 cells treated with CIS (IC-30,

IC-50 & IC-70).

Fig 44: Combined PCA plot of second derivative

of A2780 cells treated with CIS (IC-30,

IC-50 & IC-70).

15002000250030003500-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

PC1 (28.9%)

PC

2 (2

2.0

%)

PCA Second derivative Plot

CIS 30

CIS 50

CIS 70

Wavenumber in cm-1

Fig 41: Loading plot of PC1, (raw data) of A2780 cells

treated with CIS (IC-30, IC-50 & IC-70).

1000150020002500300035004000-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

Wavenumber / cm-1

Loadings for PC 1

1137

1537

1657

657

Wavenumber cm-1

Fig 42: Loading plot of PC2 (raw data) of A2780 cells

treated with CIS (IC-30, IC-50 & IC-70).

1000150020002500300035004000-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

Wavenumber / cm-1

Loadings for PC 2

1246

1545 1646 2918

- 57 -

15002000250030003500-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Wavenumber / cm-1

Loadings for PC 1

Wavenumber cm-1

Fig 51: PCA Loading plot of cells treated with (IC-30, IC-50 &

IC-70), second derivation

15002000250030003500-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

Loadings for PC 2

Fig 52: score plot of cells treated with (IC-30, IC-

50 & IC-70), Second derivative

Wavenumber cm-1

Fig 47: PC1 loading plot of cells treated with

KF (IC-30, IC-50, IC-70), Raw Data

1000150020002500300035004000-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

Wavenumber / cm-1

Loadings for PC 1

1137

1517

1637

Fig 48: PC2 loading plot of cells treated with

KF(IC-30, IC-50, IC-70), Raw Data

1000150020002500300035004000-0.06

-0.04

-0.02

0

0.02

0.04

0.06

Wavenumber / cm-1

Loadings for PC 2

1137

1050

1537

1657

Wavenumber cm-1

15002000250030003500-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

FIG49: Normalised Second Derivative Plot of cells treated

with KF (IC-30, IC-50 & IC-70)

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

PC1 (32.5%)

PC

2 (2

5.6

%)


KF 30

KF 50

KF 70

Fig 50: PCA score plot cells treated with KF (IC-30,

IC-50, IC-70), Second derivative

- 58 -

Ab

sorb

ance

Wavenumber in cm-1

285029002950-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

Ab

sorb

ance

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

PC1 (63.4%)

PC

2 (2

3.9

%)


CIS 50

KF 50

Wavenumber in cm-1

Fig 57: ATR spectra of A2780 cells

treated with CIS & KF (IC-50),

normalised second derivative (Lipid

Region)

Fig.58: PCA score plot of A2780 cells

treated with CIS & KF (IC-50), normalised

second derivative (Lipid Region)

Wavenumber in cm-1

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

PC1 (78.2%)

PC

2 (1

1.1

%)

PCA LIPID IC50 Plot

CIS 50

KF 50

Fig 54: PCA score plot of A2780 cells

treated with CIS & KF (IC-50), raw data

(Lipid Region)

Fig.53: ATR spectra of A2780 cells treated

with CIS & KF (IC-50), raw data (Lipid

Region)

285029002950-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

Fig.55: PC2 loading plot of A2780 cells


(lipid region)



(lipid region)

285029002950-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Wavenumber / cm-1

Loadings for PC 1

2980

2945

2920 2853

Wavenumber in cm-1

285029002950

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

Loadings for PC 2

2961

2925

2978 2947

2838

2853

- 59 -

-0.5 0 0.5 1-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

PC1 (61.4%)

PC

2 (2

6.8

%)

PCA PROTEIN IC50 Plot

CIS 50

KF 50



(Protein Region)

15201540156015801600-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Wavenumber / cm-1

Abso

rban

ce

Wavenumber in cm-1


with CIS & KF (IC-50), raw data (Protein

Region)


treated with CIS & KF (IC-50), normalized

second derivative (lipid region)


treated with CIS & KF (IC-50), normalized

second derivative (lipid region)

Wavenumber in cm-1

285029002950-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Wavenumber / cm-1

Loadings for PC 1

2925

2959 2853

Wavenumber in cm-1

285029002950-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Wavenumber / cm-1

Loadings for PC 2

2930

2860

2955

2846



(Protein Region)



(Protein Region)

Wavenumber in cm-1

Wavenumber in cm-1

15201540156015801600

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Wavenumber / cm-1

Loadings for PC 1

1541

1517

15201540156015801600-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

Loadings for PC 2

1545

- 60 -

15201540156015801600-0.6

-0.4

-0.2

0

0.2

0.4

Wavenumber / cm-1


with CIS & KF (IC-50), normalised second

derivative (Protein Region)

Ab

sorb

ance

Wavenumber in cm-1

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

PC1 (44.8%)

PC

2 (3

1.4

%)


CIS 50

KF 50



second derivative (Protein Region)







Wavenumber in cm-1

Wavenumber in cm-1

15201540156015801600

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

Loadings for PC 2

15201540156015801600-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

Loadings for PC 1

9001000110012001300-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Wavenumber / cm-1


with CIS & KF (IC-50), raw data

(Carbohydrate & nucleic acid Region)

Ab

sorb

ance

Wavenumber in cm-1

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

PC1 (49.8%)

PC

2 (4

0.2

%)

PCA CARBO & NA Plot

CIS 50

KF 50




- 61 -




Wavenumber in cm-1




Wavenumber in cm-1

9001000110012001300-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Wavenumber / cm-1

Loadings for PC 1

1235

1137

1050

9001000110012001300-0.1

-0.05

0

0.05

0.1

0.15

Wavenumber / cm-1

Loadings for PC 2

852

1222

9001000110012001300-0.5

0

0.5

Wavenumber / cm-1

Fig.73: ATR spectra of A2780 cells treated with

CIS & KF (IC-50), normalised second derivative


Abso

rban

ce

-0.5 0 0.5 1-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

PC1 (41.5%)

PC

2 (2

3.0

%)


CIS 50

KF 50

Fig.74: PCA score plot of A2780 cells treated

with CIS & KF (IC-50), normalised second

derivative (Carbohydrate & nucleic acid Region)

Wavenumber in cm-1

Fig.75: PC2 loading plot of A2780 cells treated

with CIS & KF (IC-50), normalized second


Fig.76: PC1 loading plot of A2780 cells treated

with CIS & KF (IC-50), normalized second


9001000110012001300-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

Wavenumber / cm-1

Loadings for PC 2

1246

1265

9001000110012001300-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

Wavenumber / cm-1

Loadings for PC 1

1236

1180

1137

1056

941

981 1050

- 62 -

4.2 FTIR Data Results

FTIR spectroscopy was also applied to investigate the effect of cisplatin and KF01-01 on

A2780 ovarian cell lines at varying concentrations. In FTIR data RMieS corrections

were applied for removal of distortions from all the measured spectra before applying the

PCA analysis. Different combinations of spectral data were tried for getting the relevant

information from the recorded spectra. PCA allowed the separation between spectra of

cisplatin and KF01-01 treated samples. Initially RMieS and PCA applied on spectral data

from 1000-2200 cm-1

range. Both cisplatin and KF01-01 treated combined sample data

was analyzed for different concentrations (IC-30, IC-50 and IC-70) separately by applying

iteration 1 to iteration 8. It is observed that at IC-50 both cisplatin and KF01-01 treated

samples are in distinct clusters. Further all cisplatin treated sample data at concentrations

(IC-30, IC-50 and IC-70) was combined and analyzed with PCA by applying iteration 1 to

iteration 8. The score plot data indicates that all samples treated at IC-50 are in one cluster

. KF01-01 data with concentrations (IC-30, IC-50 and IC-70) was also analyzed similarly

with RMies &PCA. In order to study the effect of drugs in the lipid area, cisplatin and

KF01-01 data at ((IC-30, IC-50 and IC-70) was also analyzed separately from 950 -4000

cm-1

range . The relevant spectrum and score plots of sample data given in fig. 77 to fig.

100 are discussed below.

4.2.1 Cisplatin and KF01-01 treated samples at IC-30, IC-50 and IC-70

concentrations covering spectral range from 1000 to 2200 cm-1

.

The spectral data of cisplatin and KF01-01 drug treated samples at different

concentrations (IC-30, IC-50 & IC-70) covering spectral range from 1000 to 2200 cm-1

was combined and analyzed using principal component analysis after applying RMeiS

correction technique for pattern recognition. The FTIR spectral data of samples treated at

IC-30 is given in fig. 77 . Peaks recorded in the protein region are large and narrow. The

peaks observed in the nucleic acid & carbohydrate region are small and wide. The PCA

scores and spectra of these samples corresponding to iteration 1 and iteration 8 are given

in Fig. 78- 80. In the PCA score plots , points corresponding to both drugs are observed in

different clusters except overlapping of few points. Score plots corresponding to cisplatin

drug treated points are scattered. However score plots corresponding to KF01-01 are

confined in a very narrow cluster.

The FTIR spectral data of samples treated at IC-50 is given in fig. 81 . Peaks recorded in

- 63 -

the protein region are large and narrow. The peaks observed in the nucleic acid &

carbohydrate region are small and wide. The FTIR spectra of samples treated at IC-50 and

their PCA scores corresponding to iteration 1 and iteration 8 data are given in Fig. 82- 84.

In the PCA score plots there is a remarkable discrimination between samples treated with

KF01-01 and cisplatin and points corresponding to both drugs are observed in different

clusters indicating different drug action mechanism (fig. 82 & fig. 84). In case of KF01-

01 points are falling in a narrow band which indicates that all samples are in similar stage

of apoptosis . However points corresponding to cisplatin are scattered in a larger band

area. These results indicate that all samples treated with KF01-01 are almost in similar

stage of apoptosis .

The FTIR spectra of cisplatin and KF01-01 treated samples at IC-70 and their PCA

scores corresponding to iteration 1 and iteration 8 are given in Fig. 85- 88. In the PCA

score plots points corresponding to both drugs are again in different clusters except

overlapping of some points. These results indicate that both KF01-01 and cisplatin induce

different chemical changes at all concentrations suggesting different mode of action

induced by KF01-01 with respect to cisplatin.

PCA was also performed on combined spectrum data of all samples treated with cisplatin

at different concentrations (IC-30, IC-50 & IC-70). The spectrum and PCA score plots

corresponding to iteration 1 and iteration 8 data are given in Fig. 89- 92 . A clear cluster

of sample points corresponding to IC-50 is observed ( Fig. 92 ). Score plot points

corresponding to IC-30 and IC-70 are overlapping and scattered. Score points

corresponding to IC-50 are in one cluster in a narrow band.

PCA was also performed on combined spectrum data of all samples treated with KF01-01

at different concentrations (IC-30, IC-50 & IC-70). The spectrum and PCA score plots

corresponding to iteration 1 and iteration 8 data are given in Fig. 93- 96 . Score points

corresponding to IC-50 are in a very narrow and small cluster. Score points corresponding

to IC-30 and IC-70 are overlapping and scattered ( fig. 94) .

It is observed that score points corresponding to IC-50 are in one cluster and in a narrow

for both ciplatin and KF01-01 treated samples. In case of KF01-01 drug the score points

are in a small cluster . On the other hand points corresponding to cisplatin are in a bigger

cluster.

- 64 -

4.2.1 Cisplatin and KF01-01 at IC-30, IC-50 and IC-70 ( 950-4000 cm-1

)

In order to study the effect of drugs covering the lipid area also, combined cisplatin at

((IC-30, IC-50 and IC-70) and KF01-01 at ((IC-30, IC-50 and IC-70) was also analyzed

separately from 950 -4000 cm-1

range . The spectrum data of Cisplatin and KF01-01 drug

treated samples at different concentrations (IC-30, IC-50 & IC-70) from 950 to 4000 cm-

1 was combined and analyzed using principal component analysis after applying RMeiS

correction technique for discrimination of patterns. The FTIR spectra of samples treated

with cisplatin alongwith their PCA scores corresponding to iteration 8 data are given in

Fig. 97- 98. In the PCA score plots points corresponding to IC-50 concentration are

observed in one cluster. However score points corresponding to IC-30 & IC-70 are

scattered. Results of similar analysis corresponding to KF01-01 are shown in Fig. 99-100.

In score plot, points corresponding to IC-50 are observed in one cluster having a very

narrow band . Score points coreponding to IC-30 and IC-70 are also in different cluster

with so highly scattered. These results confirm that samples treated with KF01-01 at

different concentration are in different stage of apoptosis.

In all the results the samples treated with both drugs at IC-50 concentrations are clearly

separated in one cluster. The similar results also observed with spectrum data of ATR

study. These results indicate that that IC-50 appears optimum dose for both cisplatin and

KF01-01 drugs.

- 65 -

1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Wavenumber / cm-1

Wavenumber in cm-1

Ab

sorb

ance

Fig. 77: FTIR spectra of A2780 cells treated

with CIS & KF (IC-30), RMIES (itr1)

-0.06 -0.04 -0.02 0 0.02 0.04 0.06-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

PC1 (50.1%)

PC

2 (3

1.9

%)

PCA IC 30 Plot

CIS 30

KF 30

Fig. 78: PCA score plot of A2780 cells


RMIES (itr 1)

1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Wavenumber / cm-1

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

PC1 (50.0%)

PC

2 (2

5.4

%)

PCA IC30 IT8 Plot

CIS 30

KF 30



RMIES (itr 8)

Abso

rban

ce

Fig.69: FTIR spectra of A2780 cells treated


Wavenumber in cm-1

1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Wavenumber / cm-1

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

PC1 (72.7%)

PC

2 (1

9.0

%)

PCA IC 50 Plot

CIS 50

KF 50



Ab

sorb

ance

Wavenumber in cm-1



- 66 -

1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Wavenumber / cm-1

-0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2-0.15

-0.1

-0.05

0

0.05

0.1

PC1 (47.6%)

PC

2 (1

8.2

%)

PCA IC50 IT8 Plot

CIS 50

KF 50



Ab

sorb

ance

Wavenumber in cm-1



1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Wavenumber / cm-1

-0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

PC1 (80.0%)

PC

2 (1

0.8

%)

PCA IC70 IT1 Plot

CIS 70

KF 70



Abso

rban

ce

Wavenumber in cm-1


with CIS & KF (IC70), RMIES (itr1)

1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Wavenumber / cm-1

-0.35 -0.3 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15-0.1

-0.05

0

0.05

0.1

0.15

PC1 (55.9%)

PC

2 (1

4.4

%)

PCA IC70 IT8 Plot

CIS 70

KF 70



Ab

sorb

ance

Wavenumber in cm-1



- 67 -

1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Wavenumber / cm-1

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

PC1 (58.9%)

PC

2 (1

5.0

%)

PCA CISPLATIN IT8 Plot

CIS 30

CIS 50

CIS 70

Abso

rban

ce

Wavenumber in cm-1


with CISPLATIN (IC-30, IC-50 &

IC-70), RMIES (itr8)




-0.05 0 0.05 0.1 0.15 0.2 0.25 0.3-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

PC1 (75.6%)

PC

2 (9

.5%

)

PCA GOLD IT1 Plot

KF 30

KF 50

KF 70

1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Wavenumber / cm-1

Ab

sorb

ance

Wavenumber in cm-1


with KF (IC-30, IC-50 & IC-70),

RMIES (itr1)


with KF (IC-30, IC-50 & IC-70),

RMIES (itr1)

1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Wavenumber / cm-1




Ab

sorb

ance

Wavenumber in cm-1

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

PC1 (67.6%)

PC

2 (1

7.9

%)

PCA CISPLATIN FTIR Plot

CIS 30

CIS 50

CIS 70




- 68 -

1000150020002500300035004000-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Wavenumber / cm-1

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

PC1 (48.8%)

PC

2 (1

8.4

%)

PCA CIS IT8 Plot

CIS 30

CIS 50

CIS 70


with CIS (IC-30, IC-50 & IC-70)

(itr8), 950 - 4000

Abso

rban

ce

Wavenumber in cm-1


with CIS (IC-30, IC-50 & IC-70)

(itr8), 950 - 4000

1000150020002500300035004000-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Wavenumber / cm-1

-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

PC1 (61.5%)

PC

2 (1

5.9

%)

PCA GOLD IT8 Plot

KF 30

KF 50

KF 70



(itr8), 950 - 4000

Ab

sorb

ance

Wavenumber in cm-1



(itr8), 950 - 4000

1000120014001600180020002200-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Wavenumber / cm-1


with KF (IC-30, IC-50 & IC-70),

RMIES (itr8)

Ab

sorb

ance

Wavenumber in cm-1

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

PC1 (38.7%)

PC

2 (2

0.1

%)

PCA GOLD IT8 Plot

KF 30

KF 50

KF 70


with KF (IC-30, IC-50 & IC-70),

RMIES (itr8)

- 69 -

CHAPTER 5

5. CONCLUSIONS

Attenuated Total Reflectance (ATR) and FTIR spectroscopy has been applied to

investigate the effect of increasing concentration of cisplatin and KF01-01 drugs on

ovarian cancer cell lines as well as for studying the mechanism of these drugs. The A2780

ovarian cancer cells treated with cisplatin and KF01-01 drugs at different concentrations

i.e. IC-30, IC-50 and IC-70 were utilized for ATR & FTIR measurements. Total 15

samples of cisplatin and KF01-01 were used in the analysis and 150 spectra were

measured. ATR-FTIR measured data provided clean spectra without any distortions and

data was analyzed directly with PCA. However FTIR spectral data indicated some

distortions and therefore Resonant Mie Scattering (RMieS) corrections were applied

before principal component analysis. PCA was performed for both ATR and FIR by taking

several combinations of spectral data of both drugs .

PCA analysis of ATR data of combined full range spectrum of Cisplatin and KF01-01

drugs revealed that drug samples at IC-50 concentration are lying in different clusters than

the samples of IC-30 and IC-70. Further analysis of spectral data of both drugs

corresponding to lipid, protein and carbohydrate & nucleic acid regions indicate that

different clusters are seen pertaining to these drugs. The separation of clusters is more

clear in carbohydrate & nucleic acid region in comparison to lipid and protein region.

These results indicate that both drug induce different chemical changes in the samples.

This means that working mechanism of both drugs is different.

Combined data analysis of cisplatin samples taken at IC-30, IC-50 & IC-70 concentrations

revealed that samples corresponding to IC-50 are lying in separate cluster. Samples

corresponding to IC-30 and IC-70 do not fall in separate cluster and lot of scattering

observed in their score points. Data analysis of KF01-01 samples also revealed similar

results. These results suggest that both drugs induce different chemical changes at different

concentrations. This means that changes observed in A2780 cells as a result of drug action

are not linear with the concentration. Points corresponding to IC-50 are seen in separate

cluster in both drug treated samples. This indicates that IC-50 appears optimum dose for

both drugs.

- 70 -

PCA analysis of FTIR data done at concentrations (IC-30, IC-50 & IC-70) also revealed

the clear separation of score points of IC-50 sample data from the IC-30 & IC-70

concentrations corresponding to both cisplatin and KF01-1 treated samples. Combined

analysis of cisplatin and KF01-01 sample data corresponding to different concentrations

also revealed that sample data are clearly separable in case of sample data of IC-50

concentration. A clear separation is not observed in samples corresponding to IC-30 & IC-

70 concentrations. These results also indicate that working mechanism of both drugs is

different and drugs action is not linear with their concentrations.

These results of both ATR and FTIR are in agreement . However the spectrum measured

with ATR are clear and provide fine details in comparison to spectral data measured

with FTIR system. Moreover no corrections like RMieS were applied before PCA. In

FTIR spectral data distortions were observed and RMieS correction was applied before

PCA analysis. These correction techniques sometimes can remove the important bands

from the spectra. Accordingly ATR spectroscopy technique appears better than FTIR.

ATR & FTIR spectroscopy can distinguish between normal and cancerous tissues & also

between different grade of malignancies. It is possible to calculate the extent of spread of

cancer / pre-malignancy using biopsies at a regular distance from the foci of the cancer

utilizing these techniques . ATR & FTIR can be used for identification of pre-malignancy

and to predict relapse of cancer or on set of pre-malignancy. IR spectroscopy can be

applied successfully for assessment of effects of drugs on cancer cells by monitoring

biochemical changes in cells before and after treatment with drugs. Sensitivity or

resistance to drugs can also be investigated by these techniques. Apoptosis process induced

by anticancer drugs can also be studied by analyzing the DNA, protein and lipid and this

can be helpful in development of anticancer drugs.

- 71 -

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