Development of non-invasive biosensors for clinical analysis

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DEVELOPMENT OF NON INVASIVE BIOSENSORS FOR CLINICAL ANALYSIS

ANURADHA SONI

CENTRE FOR BIOMEDICAL ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY DELHI

JUNE 2018

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DEVELOPMENT OF NON INVASIVE BIOSENSORS FOR CLINICAL ANALYSIS

by

ANURADHA SONI

CENTRE FOR BIOMEDICAL ENGINEERING

Submitted

in fulfilment of the requirements of the degree of Doctor of Philosophy to the

INDIAN INSTITUTE OF TECHNOLOGY DELHI

JUNE 2018

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Dedicated to

My family and friends

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“Difficulties in your life do not come to destroy you, but to help you realize your hidden potential and power, let difficulties know you too are difficult”

- Dr. A.P.J. Abdul Kalam

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CERTIFICATE

This is to certify that the thesis entitled ‘Development of non-invasive biosensors for clinical analysis’ being submitted by Ms. Anuradha Soni to the Indian Institute of Technology Delhi for the award of Doctor of Philosophy is a record of bonafide research work carried out by her. Ms. Anuradha Soni has worked under my guidance and supervision and has fulfilled the requirements for the submission of this thesis, which to my knowledge has reached the requisite standard.

The results obtained in this thesis are original and have not been submitted, in part or full, to any other University or Institute for the award of any other degree or diploma.

Dr. Sandeep Kumar Jha

Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi – 110016 India.

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ACKNOWLEDGEMENTS

Successful completion of a well cherished task in scientific research has its own reward. At the same time remembering the kind guidance of caring teachers and everlasting encouragement from all the dear ones is an experience of its own kind. Without their continuous guidance and support, the study would not have seen light of the day. I am extremely grateful to all those persons and organizations that have kindly extended help in different ways during the course of my study. Acknowledging them for their noble deed is a matter of great pleasure for me.

I would like to express my sincere thanks to my doctoral supervisor Dr. Sandeep Kumar Jha for his valuable guidance, encouragement and support at all stages of research as well as writing work. Working under his supervision has truly been an enriching experience and I learnt and mastered many techniques in his laboratory under guidance. It was my privilege and matter of pride to be a IIT Delhi student and having Dr. Sandeep Jha as my PhD supervisor.

I would also like to thank my SRC members, Prof. Veena Koul, Prof. Sneh Anand and Dr. Shalini Gupta for their insightful suggestions and comments. I extend my sincere thanks to all faculty members and staff members of the Centre for Biomedical Engineering for their guidance and generous support.

I am extremely thankful to Indian Institute of Technology Delhi for providing all the facilities to carry out my research work. I am also thankful to Indian Council of Medical Research (ICMR) for providing me research assistantship to carry out my research work.

I am also thankful to the pathology lab staff of Indian Institute of Technology Delhi Hospital who helped me carry out clinical studies. I would also like to express my sincere gratitude to all the volunteers who participated in this study.

It is my immense pleasure to thank all my colleagues at CBME and all my lab members Appan Roychoudhury, Amit Kumar Singh, Tanu Bhardwaj, Rishi Raj, Marieshwaran &

Avinash Kaur for their love, help, moral support and friendly atmosphere throughout the research work.

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I would also like to owe my deep sense of gratitude for my parents for their tremendous support, constant encouragement, blessings, love and great guidance which always lighten up my task and my life.

Above all I thank the master of ceremonies, the all powerful Almighty God who gave me the confidence, power, blessings, skill, energy and courage to accomplish this work.

Anuradha Soni

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ABSTRACT

Advances in healthcare technology have led to the development of handheld biosensors for easy and daily monitoring of several disorders such as diabetes. Yet, most of them are blood- based which cause pain and discomfort to subjects who have to monitor their body parameters on a regular basis. In recent years, non-invasive biosensors using alternate body fluids such as saliva, tears, sweat etc. have emerged as better alternatives to invasive ones.

Still, they suffer from various drawbacks such as poor correlation among body fluids, interferences from surrounding molecules, high cost, inaccuracy etc. because of which they fail to create an impact in the world market. Hence, development of a pain-free, affordable, easy to operate and reliable non-invasive biosensor holds great commercial potential as well as can gain huge popularity among common people especially in developing countries.

In this regard, the present doctoral work consisting of six chapters aims to develop handheld, non-invasive biosensors using a simple strategy involving the key techniques of immobilization and miniaturization. Most of the work presented in this thesis deals with non- invasive glucose biosensor development using saliva sample and a smartphone on a strip based format and finally the developed system has been tested for detection of other analytes such as urea in saliva. The first chapter of the thesis deals with extensive literature review on non-invasive biosensor development and the last chapter discusses work summary along with the future outcomes of the research work.

Second chapter deals with the feasibility analysis of developing a salivary glucose biosensor using a simple technique by immobilizing enzyme and pH sensitive dye on a filter paper strip and scanning color changes by RGB profiling using an office scanner and open source image processing software. Good correlation between salivary and blood glucose levels was found in case of diabetic subjects, which was reported for the first time in our study.

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Third chapter demonstrates the fabrication and development of sensor strip and standalone android application to replace the office scanner with a smartphone for on-site detection as well as improvement of sensor sensitivity. Sensitivity enhancement of the sensor was also carried out using various means such as change of dye, introduction of slope based method to calculate analyte concentration, use of combination of colors (R+G+B) against single color (R/G/B) etc.

Fourth chapter deals with the optimization of sensor measurement conditions under different interferences using finalized strip design and smartphone app. Clinical studies were carried out on real samples for further validation of the sensor where good correlation was obtained between blood and salivary glucose levels in diabetic subjects which was consistent to previous results. Thus, the sensor can be used for mass diagnosis of diabetes especially in resource limited countries due to cost-effectiveness and there should be no need of any dedicated instrument for analysis.

The fifth chapter explores the feasibility of the developed system for testing other analytes such as urea in saliva using the same strategy but with a strip having different enzyme (urease)-dye combination. The successful implementation of the proposed system for urea as well as glucose detection proves that our methodology of detecting analytes via smartphone is feasible and can be used for multi-analyte detection in a non-invasive manner in future, thus replacing many spectrophotometric based traditional methods.

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सार

स्वास्थ सम्फन्धी तकनीक भें प्रगतत के साथ ही हाथ भें लरए जा सकने वारे फामोसेंसयों का ववकास हुआहैजजनकेद्वाया भधुभेहजैसे अनेकववकायों काआसानएवॊतनमलभतरुऩसे तनयीऺणककमाजा

सकता है | ककन्तु, उनभें से फहुतसाये फामोसेंसय अबीबी यक्त-आधारयत हैंजो ककरोगों भें ददद एवॊ

ऩयेशानीकाकायणफनतेहैंखासकयककउनभयीजों भेंजजन्हेअऩनेशयीयकेभाऩदॊडोंकीतनमलभतरूऩ सेजाॊचकयनीहोतीहै | हारकेवषोंभें, वैकजपऩकतयरऩदाथदजैसे राय, आॊसू, ऩसीनाआददकाप्रमोग कयकेववकलसतककमेगए गैय-इनवेलसवफामोसेंसय इनवेलसवफामोसेंसयोंकीतुरनाभेंफेहतयववकपऩ के रूऩ भें उबये हैं | इसके उऩयाॊत बी इन ववकलसत ककमे गए फामोसेंसयों भें कई सायी खालभमाॊ

हैं जैसे कक शयीय के तयर ऩदाथों के फीच ख़याफ सम्फन्ध, आसऩास के अणुओॊ से अॊतयण, उच्च रागत, अशुद्धध आदद जजनके कायण वे ववश्व-फाजाय भें प्रबाव ऩैदा कयने भें ववपर यहे

हैं | इसलरए, एक ददद^-यदहत, सस्ता, सॊचालरत कयने भें आसान औय ववश्वसनीम गैय-इनवेलसव फामोसेंसय का ववकास भहान व्मावसातमक ऺभता के साथ-साथ ववकासशीर देशों भें आभ रोगों के फीच कापी रोकवप्रमता हालसर कय सकता है।

इसीसन्दबदभें, महशोधकामदजोककछहअध्मामोंभेंववबाजजतहैऔयइसकाउद्देश्म हाथभेंलरएजा

सकने वारे, गैय-इनवेलसव फामोसेंसयों का आसान तयीकों जैसे कक इभोबफराईज़ेशन तथा रघुकयण ववधधमों से ववकास कयना है | इस शोधकामद भें ऩेश ककमा गमा अधधकतभ कामद राय के नभूनों

एवॊ स्भार्दपोन की सहामता से ऩट्र्ी (जस्िऩ) रूऩी प्रारूऩ ऩय ग्रूकोस फामोसेंसय के ववकास ऩय आधारयत है औय अॊत भें ववकलसत प्रणारी को राय भें भौजूद दूसये ऩदाथद जैसे कक मूरयमा

की ऩहचान के लरए ककमा गमा है | ऩहरे अध्माम भें गैय-इनवेलसव फामोसेंसय के ववकास ऩय शास्र ऩुनववदरोकन शालभर है तथा अॊततभ अध्माम शोधकामद के सायाॊश एवॊ ऩरयणाभों का

वववयण है |

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दूसयेअध्मामराय द्वायाग्रूकोस फामोसेंसयकेववकास केव्मवहामदताववश्रेषण ऩयआधारयत हैजहाॉ

सयरववधधसे किपर्यकागज़ ऩयएॊजाइभतथा ऩीएचसम्वेदनशीरडाई के जस्थयीकयण सेफामोसेंसय का ववकास ककमा गमा है तथा ऑकपस स्कैनय तथा ओऩन सोसद इभेज प्रोसेलसॊग सॉफ्र्वेमय की

सहामता से आय जी फी प्रोपाइलरॊग द्वाया यॊग-ऩरयवतदन को भाऩा गमा है | भधुभेह के भाभरों भें

राय औय यक्त शकदया के स्तय के फीच अच्छे सॊफॊध ऩाए गए, जो हभाये अध्ममन भें ऩहरी

फाय सूधचत ककमा गमा है |

तीसये अध्माम भें सेंसय जस्िऩ तथा स्र्ैंडअरोन एॊड्राइड एप्रीकेशन के तनभादण एवॊ ववकास को

दशादमा गमा है जजसभे ऑकपस स्कैनय को फदरकय स्भार्दपोन का प्रमोग ककमा गमा है

जजससे की ओन-साइर् जाॊच के साथ सेंसय सॊवेदनशीरता भें सुधाय बी होता है | सेंसय की

सॊवेदनशीरता भें वृद्धध के लरए ववलबन्न तयीकों का उऩमोग ककमा गमा है जैसे की दूसयी

डाई का प्रमोग, स्रोऩ आधारयत ऩद्धतत का ऩदाथद की भारा जाॊचने भें प्रमोग, अकेरे एक यॊग (आय/जी/फी) की जगह यॊग सॊमोजन (आय+जी+फी) का प्रमोग इत्मादद |

चौथा अध्माम अॊततभ ऩट्र्ी (जस्िऩ) डडजाइन औय स्भार्दपोन ऐऩ का उऩमोग कयके ववलबन्न अॊतयणों के तहत सेंसय भाऩ शतों के अनुकूरन से सम्फॊधधत है | सेंसय को कपय से नैदातनक सत्माऩन के लरए वास्तववक नभूनों ऩय अध्ममन ककमा गमा जजसभे वऩछरे अध्ममन के

अनुरूऩ ही भधुभेह योधगमों के यक्त तथा राय भें भौजूद शकदया के स्तय के फीच अच्छा

सम्फन्ध ऩामा गमा | इस प्रकाय, सेंसय को सॊसाधन सीलभत देशो भें फड़े ऩैभाने ऩय भधुभेह की जाॊच के लरए उऩमोग भें रामा जा सकता है तथा इसभें कोई बी सभवऩदत उऩकयण की

आवश्मकता बी नहीॊ है|

ऩाॊचवें अध्माम भें इसी यणनीतत का उऩमोग कयते हुए राय भें ऩाए जाने वारे अन्म ऩदाथों

जैसे की मूरयमा की जाॉच वही जस्िऩ ऩयन्तु ववलबन्न एॊजाइभ (मुरयएस)-डाई सॊमोजन के द्वाया

की गमी है | प्रस्ताववत प्रणारी के सपर कामादन्वमन से ऩता चरता है कक स्भार्दिोन के

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भाध्मभ से ऩदाथों का ऩता रगाने की हभायी ऩद्धतत व्मावहारयक है तथा बववष्म भें गैय- इनवेलसव तयीकों से फहु-ववश्रेषकों की ऩहचान के लरए अन्म स्ऩेक्िोपोर्ोभेदिक ऩद्धततमों के

स्थान ऩय प्रमोग भें रामी जा सकती है |

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List of Figures and Illustrations

Figure number

Figure Title Page

number

Fig. 1.1 Dextrostix test strips along with color chart for quantification of blood glucose

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Fig. 1.2 Ames reflectance meter: The first electronic meter for glucose determination

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Fig. 1.3 Mouthguard based non-invasive biosensor for lactate determination in saliva

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Fig. 1.4 Fig. 1.5

Fig. 1.6 Fig. 1.7 Fig. 1.8 Fig. 1.9 Fig. 1.10 Fig. 1.11 Fig. 1.12 Fig. 1.13 Fig. 1.14 Fig. 1.15 Fig. 1.16 Fig. 1.17 Fig. 1.18 Fig. 2.1

Orsense non-invasive blood glucometer

Glucotrack ear clip and main unit displaying glucose level iquickit saliva analyzer

iBGStar glucose monitor Noviosense tear glucose sensor Google smart contact lens

C8 Medisensors non-invasive glucose monitoring system Cnoga‟s non-invasive “no-blood” glucometer

Glucoband CGM device Dexcom G4 Platinum

Glucovista continuous non-invasive glucometer The AbStats AGIS biosensor

Pendra non-invasive glucose monitoring device Hypo-sense non-invasive night time monitor BACtrackSkyn wearable alcohol biosensor

Schematics of salivary glucose detection using paper strip based biosensor

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Fig. 2.3

Fig. 2.4

Fig. 2.5

Fig. 2.6 Fig. 2.7 Fig. 2.8

Fig. 2.9

Fig. 2.10

Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5

Fig. 3.6

Color of the strip at the time of sample application (1) and after 45 seconds (2) with 250 mg/dL glucose

Color change in the biosensor strips with increase in glucose concentration in the range of 0.5-500 mM

(A) Sensor response using methyl red indicator in terms of R, G and B pixel intensity, (B) sensor response in terms of H, S and I for 500 mg/dL glucose.

(A) Calibration curve of paper strip based biosensor in terms of concentration (mg/dL) vs. red (-□-)/green(-○-)/blue(-■-) pixel intensity, (B) linearized calibration curve for blue pixels formed by taking double log (log10) on both the axes.

Interference of ascorbic acid on sensor response.

Interference of lactic acid on sensor response.

Correlation between salivary (SGL) and blood glucose levels (BGL) in clinical patients and healthy individuals: the correlations for (A) non-diabetic (-■-) (0.64), (B) Diabetic (-○-) (0.95), (C) before breakfast (fasting glucose) (-∆-) (0.78) and (D) post breakfast (PP) (-▲-) (0.96) samples were calculated.

Comparison between BGL and SGL of same individual (to maintain homogeneity of sample) using DNS reagent (SGL), biosensor (SGL) and commercial blood glucose (BGL) (Accuchek Active, Roche Inc.) methods. Sample no. 1 was obtained 2 h post meal and no. 2 was pre breakfast sample.

Sample no. 3 was pre breakfast sample from same healthy individual obtained on second day. This comparison is shown to illustrate day to day and pre/post meal variation of SGL/BGL in same individual.

Reproducibility of biosensor (analysis was performed in triplicates).

Strip design 1.

Strip design 2.

Front and back view of the strip (strip design 3).

Front and back view of credit card sized strip (strip design 4).

(A & B) Front and back view of the strip (strip design 5), (C) layered structure of the strip.

(A & B). Front view of portable handheld device to measure

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xi Fig. 3.7

Fig. 3.8 Fig. 3.9 Fig. 3.10

Fig. 3.11

Fig. 3.12

Fig. 3.13

Fig. 3.14

Fig. 3.15

Fig. 3.16

glucose concentration as well as the methodology help menu for app‟s usage, (C) front view of portable handheld device,

showing glucose level in the sample taken.

Flowchart of the developed application “Glucosensor” showing steps in glucose detection.

Screenshots of the app showing steps of glucose detection.

Flowchart of the app showing steps in glucose detection.

Principle and schematics for glucose estimation using open source android app “Colorpicker”

Color change in strips with increase in glucose concentration for strip design 1

(A) Response curve obtained for R, G and B pixels with respect to time using bromocresol purple indicator and 500 mg/dL glucose. Pixel intensity for red showed maximum change as compared to Blue and Green pixels. (B) Response curve obtained for H, S and V with respect to time using bromocresol purple indicator. Response in this case varied only slightly as compared to R, G and B

(A) Response curve obtained for changes in red, green and blue pixel intensities for strip design 1. Higher sensor response was obtained with red pixels as compared to blue and green pixel intensities. (B) Calibration curve for strip design 1 with a response time of 3 minutes.

Color change in the strips with respect to glucose concentration for strip design 2. (Concentrations used were 0, 1, 5, 10, 25, 50, 100, 250, 500 and 750 mg/dL respectively).

Response curve comparison for R, G and B pixels obtained using two different indicators (methyl red and bromocresol purple). Methyl red indicator (A) showed a decreasing trend for all the pixels with maximum sensitivity for blue pixels. On the other hand, bromocresol purple (B) showed an increasing trend with maximum sensitivity for red pixels as compared to blue and green pixels.

(A) Calibration curve for glucose biosensor (strip design 2) plotted against change in red pixel intensity with respect to glucose concentration within a response time of 2 minutes. (B) Linearized calibration curve for glucose biosensor (strip design 2) obtained after taking log₁₀ on both the axes.

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xii Fig. 3.17

Fig. 3.18

Fig. 3.19

Fig. 3.20

Fig. 3.21

Fig. 3.22

Fig. 3.23

Fig. 3.24

Fig. 3.25

Fig. 4.1 Fig. 4.2 Fig. 4.3

Correlation between blood glucose level (BGL) and salivary glucose level (SGL) among healthy and diabetic subjects for strip design 2 using “Colorpicker” app.

(A) Calibration curve for strip design 3 using the application

“Glucosensor” within a response time of 30 seconds with an enzyme loading of 10U/strip. (B) Linearized calibration curve by taking double log on both axes.

(A) Response curve obtained for 100 mg/dL synthetic glucose under different modes of sensing, (B) calibration curves obtained with different enzyme loading per strip under attached mode.

(A) Calibration curve with 10 U of enzyme loading per strip in attached mode (flashlight on), (B) linearized calibration curve formed by taking double log on both the axes.

(A) Calibration curve for strip design 3 in attached mode using slope method (Slope=δRed pixels/time), (B) linearized calibration curve obtained after taking double log on both axes.

(A) Calibration curve obtained for strip design 4 under ambient light conditions using slope method. (B) Linearized calibration curve obtained by taking double log on both axes.

(A) Calibration curve obtained for strip design 4 (immobilized using entrapment method) by slope method using application

“Biosense”, (B) linearized calibration curve for red pixels obtained using slope method.

Calibration curve for strip design 4 in borate buffer using

“Biosense” app working on slope method. Response time was 70 seconds which was too high from end user point of view.

(A) Calibration curve plotted between glucose concentration versus slope for R, G, B and (R+G+B), (B) calibration curve between slope (R+G+B) and glucose concentration using slope method for strip design 5 (response time was 20 seconds), (C) linearized calibration curve for slope (R+G+B) by taking double log on both axes.

Shelf life of the biosensor for a period of 30 days.

Study of batch variation on glucosensor strips.

Variation in sample volume applied on the strip using a commercial ear bud. Average volume of saliva transferred to the strip was 5.2 µL (n=6).

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xiii Fig. 4.4

Fig. 4.5 Fig. 4.6

Fig. 4.7

Fig. 4.8

Fig. 4.9

Fig. 4.10

Fig. 4.11

Differences in calibration curves obtained using a pipette and an ear bud. Calibration curve obtained using a bud gave more response as compared to a pipette due to uniformity in sample distribution on the strip.

Effect of various dyes on sensor response.

(A) Calibration curve plotted against glucose concentration v/s change in slope for R (-□-), G (-○-) and B pixels(-Δ-), (B) calibration curve plotted with respect to glucose concentration v/s change in slope for (R+G) (-■-), (G+B) (-●-), (R+B) (-▲-) as well as (R+G+B) (-▼-) pixels, (C) final calibration curve plotted against glucose concentration with respect to slope (R+G+B) pixels, (D) linearized calibration curve formed by taking double log (log₁₀) on both the axes for slope (R+G+B) plot.

Interference studies conducted using lactic and ascorbic acid.

(A) Effect of lactic acid concentration on sensor response. It was observed that in the clinically relevant range of lactic acid found in mouth (0-1mM), saliva was found to act as a buffer to some extent while with higher concentrations of lactic acid, sensor response increased with decrease in pH. (B) Study of buffering action of saliva upon addition of lactic acid. (C) Effect of ascorbic acid concentration on sensor response. Similar behaviour has been noted as in the case of lactic acid with increasing concentration. (D) Study of buffering capacity of saliva upon addition of ascorbic acid. (E) Effect of lactose concentration on sensor response.

(A) Response curves obtained for 100 mg/dL synthetic glucose in different modes of measurement, (B) effect of heightwise variations on sensor response with red, green and blue pixels.

Effect of different smartphone brands on sensor response (A) inside a dark box and (B) under ambient light. It was observed that inside a dark box there was only slight change in pixel intensity with respect to smartphone models while in ambient light conditions, difference in camera position and intensity of flash lead to variations.

Correlation between blood and salivary glucose levels in case of (A) diabetic (-□-), (B) pre diabetic (-○-) and (C) healthy (-Δ-) subjects. Correlation curves obtained between BGL and SGL for (D) pre breakfast diabetic (-◄-), (E) pre breakfast healthy (-▼-) and (F) post breakfast samples (-◊-).

Gender-wise correlation between BGL and SGL in healthy, prediabetic and diabetic subjects.

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xiv Fig. 4.12

Fig. 4.13

Fig. 4.14 Fig. 4.15

Fig. 4.16

Fig. 4.17

Fig. 5.1

Fig. 5.2

Fig. 5.3

Fig. 5.4

Age-wise correlation between BGL and SGL in healthy, prediabetic and diabetic subjects.

Correlation between BGL and SGL in healthy and diabetic subjects with respect to gender as well as age group.

Age and diabetic statuswise correlation between BGL and SGL.

Comparison between three methods of glucose detection i.e.

Blood Glucose (BGL) using Accuchek active glucometer, Salivary Glucose (SGL) using DNS method and Salivary Glucose (SGL) estimated using our biosensor.

Reproducibility of the biosensor as estimated for five different samples on three different days using „Biosense‟ app.

Clarke error grid showing correlation between the reference method for glucose estimation (BGL by Glucometer) and predicted glucose concentration (SGL through our developed biosensor). The readings coincided within the zones A and B of the error grid, thus proving that the developed biosensor should be medically acceptable as per the accuracy standards set for glucometers by ISO.

Shelf life study on the biosensor strips carried out for a period of 60 days. The graph shows an exponentially decaying trend with around 50% loss in activity after 30 days.

(A) Reaction principle involved in the biosensor, (B) schematics of the biosensor showing steps for urea detection and (C) color change in the strips with increase in urea concentration (0,5,10,20,30,50,100,150,200, 250,500 & 1000 mg/dL respectively).

(A) Calibration curve plotted against urea concentration v/s change in slope for R (-□-), G (-○-) and B pixels(-Δ-) in the broad range (10-1000 mg/dL) and (B) in the clinically relevant range of 10-260 mg/dL. Maximum sensitivity has been reported for slope (G) within this range.

Interference studies using lactic and ascorbic acid. (A) Effect of ascorbic acid concentration on sensor response. It has been observed that in the clinically relevant range of ascorbic acid found in mouth, components in saliva acted as a buffer to some extent while with higher concentrations of ascorbic acid, sensor response increased with a net decrease in pH. (B) Study of buffering action of saliva upon addition of ascorbic acid. (C) Effect of lactic acid concentration on sensor response. Similar

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xv Fig. 5.5

Fig. 5.6

trend has been noted as in the case of ascorbic acid. (D) Study of buffering capacity of saliva upon addition of lactic acid.

Correlation between urea concentration obtained using phenol- hypochlorite method with salivary urea level deduced using our developed biosensor. Studies were carried out by spiking saliva samples obtained from 3 healthy subjects with synthetic urea solutions prepared in phosphate buffer (1mM, pH 7.0).

Reproducibility of the biosensor as estimated for three different samples at three different time intervals.

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xvi

List of Tables

Table number

Title Page

number Table 1.1

Table 2.1

Table 2.2

Table 4.1 Table 4.2 Table 4.3

Table 4.4 Table 4.5

Table 6.1

Electrochemical non-invasive biosensors.

Statistical correlation (t-test) between SGL (biosensor method) and BGL (commercial glucometer method).

Statistical correlation (t-test) between three methods for glucose detection.

Correlation between BGL and SGL in clinical samples.

Genderwise correlation between BGL and SGL.

Agewise correlation between BGL and SGL in healthy, prediabetic and diabetic subjects.

Age and gender-wise correlation between BGL and SGL.

Age and diabetic statuswise correlation between BGL and SGL.

Analytical performances of some non-invasive glucose biosensors including commercializable ones developed so far in comparison to our developed sensor.

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63

127 129 132

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xvii

List of Abbreviations

AOD/HRP Alcohol oxidase/horseradish peroxidase BAF‟s Boronic acid containing fluorophores BGL Blood glucose level

BUN Blood Urea Nitrogen CKD Chronic Kidney Disease CL Chemiluminescence

COMP Cartilage Oligomeric Matrix Protein

CPST Computer Screen Photo-assisted Technique CT Computer Tomography

DNA Deoxyribonucleic acid DNS Dinitrosalicylic acid DTT Dithiothreitol

EG-ISFET Extended Gate- Ion Selective Field Effect Transistor ELISA Enzyme linked immunosorbent assay

ESRD End Stage Renal Disease FDA Food and Drug Administration GFR Glomerular Filtration Rate

GLAM Glass Laser Multiplexed Biosensor GOx Glucose oxidase

GOx/HRP Glucose oxidase/Horseradish peroxidase HAU Haemagglutinin units

(27)

xviii hCG Human Chorionic Gonadotropin HSI Hue Saturation Intensity

HSV Hue Saturation Value

IDDM Insulin dependent diabetes mellitus IR Infra Red

LFIA Lateral Flow Immunoassay LOD Limit of detection

MEMS Micro-electro-mechanical-systems MMP-8 Matrix metalloproteinase 8

MRI Magnetic Resonance Imaging NIH National Institute of Health OPD Outpatient Department PANI Polyaniline

PCR Polymerase Chain Reaction PDMS Polydimethylsiloxane PEC Personal Ear Clip

PET Polyethylene terephthalate PIB Polyisobutylene

PNA Peptide Nucleic Acid POCT Point of Care Technology PVA Polyvinyl alcohol

QCM/QMB Quartz Crystal Microbalance RGB Red Green Blue

(28)

xix

RT-PCR Real Time-Polymerase Chain Reaction SARS Severe Acute Respiratory Syndrome SAW Surface acoustic wave

SD Standard deviation SEF Suction Effusion Fluid

SELEX Systematic Evolution of Ligands by Exponential Enrichment SGL Salivary glucose level

SMBG Self-monitoring of Blood Glucose SOP Standard Operating Protocol SPR Surface Plasmon Resonance STD‟s Sexually Transmitted Diseases VOCs Volatile Organic Compounds

Development of non-invasive biosensors for clinical analysis

DEVELOPMENT OF NON INVASIVE BIOSENSORS FOR CLINICAL ANALYSIS

ANURADHA SONI

CENTRE FOR BIOMEDICAL ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY DELHI

JUNE 2018

DEVELOPMENT OF NON INVASIVE BIOSENSORS FOR CLINICAL ANALYSIS

by

ANURADHA SONI

CENTRE FOR BIOMEDICAL ENGINEERING

Submitted

in fulfilment of the requirements of the degree of Doctor of Philosophy to the

INDIAN INSTITUTE OF TECHNOLOGY DELHI

JUNE 2018

Dedicated to

My family and friends

“Difficulties in your life do not come to destroy you, but to help you realize your hidden potential and power, let difficulties know you too are difficult”

- Dr. A.P.J. Abdul Kalam

CERTIFICATE

ACKNOWLEDGEMENTS

ABSTRACT

सार

CONTENTS

List of Figures and Illustrations

List of Tables

List of Abbreviations