Simulating Senses - McGill University · 2019. 2. 27. · 6 Cosmetics • Cosmetics market is...

1

Simulating Senses

Dr. Beena Rai

Principal Scientist & Head

Physical Sciences Research

Tata Consultancy Services, Pune

[email protected]

mailto:[email protected]

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TCS Research and InnovationEngineering Technology

centers

Engineeringservice

providers

ISV Softwarepartners

Tier 1 Suppliers

Captive Centers

Customer Eco-System

21+ TCS

Innovation Labs(Research Area/Programs)

Center of Excellence

Co-Innovation Network

- 29 Start-ups &

6 VC Partners

20+ Standard

Setting Bodies

Industry solutions, ConnectedPlatforms, Social, Big Data

30+ Academic

Institutions

40+ ISV,

Semiconductor Platform Providers

30+ Industrial Alliances

3

Physical SciencesEngaged in cutting-edge

materials research leveraging

enormous opportunity unleashed

by digital revolution impacting

the complete life cycle of

materials (Discovery-

Development-Deployment-

Recycle)

Focus Areas

Materials

Informatics

Biological System: in-

silico Model/Digital Twin Chemical

Formulations

Battery

Materials

Photovoltaic

materials E-waste

Recycling

Materials & Manufacturing

Processes Modeling &

Simulation

Domain Expertise

Design &

Optimization

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Today’s Agenda

Touch Taste Sight HearSmell

Senses

Organs

Applications

Transdermal Drug delivery

Personal care formulations

Artificial skin/robo skin

Flavouring additives

Molecular mechanism of taste perception

Quantifying taste

AI/ML models for food quality evaluation

Monitoring food freshness

Sensing enabled IoTsolutions

Design of fragrances

Discovery, design and testing of fragrances

Understanding odour perception

Ultrasonic sensors for non destructive estimation of food firmness

Image Source: https://www.visiblebody.com/learn/nervous/five-senses

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Human Skin Digital Twin


Senses

Organs

Applications






Quantifying taste








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Cosmetics• Cosmetics market is expected to

garner $429.8 B by 2022#

• Global skin care is ~$ 120 B USD

Pharma• Transdermal drug delivery ~$35 B • CAGR of 9.3% over the period of 2017-2023*• 1/3 drugs involved in clinical trial are for

delivery through skin

Wound1/Burn2 CareMarket worth

$22.01 B by 20221

$ 2.23 B by 20212

Issues & Challenges

• Differential penetration is required across skin• EU regulation (76/768/EEC,Feb,2003)

prohibits the use of animal or animal derived substances for the development and testing of cosmetics and pharmaceutical ingredients

• Avoidance of pain• Compliance issues related to injections• Possibility to control drug release over long time• Avoiding the first-pas metabolism, bypassing the

GI tract (oral route)• Improved bio-availability

Benefits

• Skin barrier (Stratum Corneum)• Inter and intra personal variability • Very few transdermal drug formulations

have been approved by FDA

Issues & Challenges

• Skin grafting is time challenging• Slow wound healing• Infection and pain• Hypertrophic scarring

Issues & Challenges

• Help to perfume, clean, change theappearance, protect, keep in good conditionand correct body odours

• New age in skincare preparations –the age ofCosmeceuticals

Benefits

Digital intervention for in-silico design & testing Rapid development of drugs and cosmetics

Reduced time & cost to market

*Business wire, Jan 2018#Allied Market Research, July 20161Marketandmarket, 2016

Skin Care Overview

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Skin Functions & Complexity

J. van Smeden et al. Biochimica et Biophysica Acta. 2014 Norlen et al. 2012, J. of Investigative DermatologyRoberts et. Al. 2013. Advanced Drug Delivery Reviews https://www.medicalnewstoday.com/articles/262881.php

More than 300 types of CER and FFA are present in human skin

Transport Barrier

Healthy Collagen

Denatured Collagen

Mechanical Strength

Epidermis – Chemical barrier

Dermis – Mechanical support

Hypodermis – Thermal barrier

8 Gupta R and Rai B. J .Phys. Chem. B. 2015, 119, 11643−11655J. van Smeden et al. Biochimica et Biophysica Acta 1841 (2014) 295–313

Skin Barrier Function

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Transport Model Development

Gupta R and Rai B. J .Phys. Chem. B. 2015, 119, 11643−11655

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Phase Transition temperature : 370 K – 380 K

Moore, D. J. Phys. Chem. B. 1997, 101, 8933-8940Shah et al. J. Lipid Res. 1995, 36, 1936-1944 & 1945-1955. Gupta R and Rai B.J .Phys. Chem. B. 2015 119 (35), pp 11643–11655

CER chains in hexagonal packing

Model Validation: Structural properties of lipid layer

• Below the phase transition temperature CHOL reduces the order parameter and gives fluidity to bilayer

• CHOL Increases the order parameter above the phase transition temperature

Notman et al. Biophys. J. 2007, 93, 2056–2068Dmitry et al. (BBA)- Biomembranes. 2004, 1666, 142-157

Ratio in figures stands for molar ratio of CER:CHOL:FFA

11Alwarawrah et al. J Phys Chem B. 2010, 114(22), 7516-7523Marrink et al. J. Phys. Chem. B. 2001, 105, 6122-6127

Chol. sits in between CER and FFA

Gupta R and Rai B.J .Phys. Chem. B. 2015 119 (35), pp 11643–11655

CHOL gives rigidity to layer and FFAprovides the flexibility

Model Validation: Structural properties of lipid layer

Ratio in figures stands for molar ratio of CER:CHOL:FFA

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Model Validation: Variation in ceramide constituents

Gupta R and Rai B.J .Phys. Chem. B. 2015 119 (35), pp 11643–11655

Alwarawrah et al. J Phys Chem B. 2010, 114(22), 7516-7523

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Multiscale Model: Molecular to Macro

Kishore Gajula, Rakesh Gupta , D B Sridhar and Beena Rai, Journal of Chemical Information and Modeling. 2017.

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Bringing in Complexity: Personalized Models

Rogers et al. Archives of dermatological research. 1996

• Model of various ceramides are developed and tested with experimental data

• The model can capture demographic variation

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Case StudiesFormulation Design (Safety and Efficacy)

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1. In-silico design of safe formulations

Rakesh Gupta, Beena Rai and Samir Mitragotri. ACS JPC B. 2019 (in review)

• Ethanol extracts the skin lipids • Extent of extraction depends upon its

concentration• The optimum ethanol concentration could

be obtained using in silico model • Both structural and thermodynamics

properties are calculated to quantify the extent of extraction

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2. In-silico Screening of Permeation Enhancers

a) geraniol, b) geranic acid, c) isopropyl palmitate, d) monoolein, e) limonene, f) n-octyl

pyrrolidone, g) palmitic acid, h) oleic acid and i) undecanoic acid. Rakesh G, D Sridhar, Beena R and Samir M. Nature Sci. Rep. 2018

ER (enhancement ratio): It is a

measure of the extent of the SC

disruption or (permeability)

enhancement. Quantitatively, ER is

defined as skin conductivity at the end

of 24 h, when incubated with a

particular formulation, normalized to

conductivity at time 0s.

The experimental parameter (ER) and simulation parameter reverse

of order parameter (1/<s>) are compared for 10 permeation

enhancers

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3. Optimum Concentration of Active/s in Formulation

Gupta R and Rai B. RSC Nano Scale. 2017Matins et al. Engineering in Life Sciences. 2017

• The optimum concentration of fullerene in aqueous formulation is determined using in-silico model.

• The simulation predictions were confirmed by experimental study on pig skin by Matins et al.

Gupta R and Rai B. RSC Nano Scale. 2017

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4. Nanoparticles Design and Testing Framework

Rakesh Gupta and Beena Rai. Nanoscale, 2018, 1-12.

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4.1 Nanoparticle formulation stability

Neutral

Charged

D Sridhar, Rakesh Gupta and Beena Rai. RSC PCCP. 2018

a 10 % b 20 %c 40 % d 80 %e 100 %

• Based on PMF, stability of the nanoparticles is determined

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4.2 Nanoparticle Permeation: Effect of Size

Huang et.al. Biomaterials. 2010. Gupta R and Rai B. J. Phys. Chem.B.2016

Self-Healing • The skin permeability of gold nanoparticle decreaseswith increase in their size

• The gold nanoparticle create local defects in the skinlipid layer which are temporary and the layer gets self-healed once nanoparticle crosses the layer

t = 3µsNP PermeationNP Permeation

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Labouta et al. Nanoscale, 2011, 3, 4989–4999

Gupta R and Rai B. Nature Scientific Report 2017

4.3 Nanoparticle Permeation: Surface Chemistry

• The neutral nanoparticle easily cross the lipid layer whereas the charged nanoparticle adsorb on the skin layer

• The permeability of charged nanoparticle are much lower as compared to neutral one

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4.4 Nanoparticle Permeation: Surface Pattern

• The nanoparticle of various surface pattern (mixed ofhydrophobic and hydrophilic beads) are screened basedon free energy of permeation across the layer using insilico skin model

• The binding of protein on screened nanoparticle ischecked and further utilized for co-delivery application

• Nanoparticle having 2:1 (hydrophobic:hydrophilic)homogenous surface pattern was found to be the bestcandidate for co-delivery application

Rakesh Gupta and Beena Rai. Nanoscale. 2018

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5. Electroporation of Skin

Rakesh Gupta and Beena Rai. Langmuir 2018

Pore stabilization Pore Resealing Drug Permeation

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Skin Mechanical Model

(Nils Krueger et al, Skin Research and Technology, 2011)

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Hemlatha J et al. Skin Physiochemical model. J. Biomechanics. (In review)

Distensibility behavior of skin Collagen degradation leading to hysteretic behavior of skin

Skin Mechanical Model

PHYSIOCHEMICAL MODEL OF SKIN LAYER/S FOR RAPID DESIGN AND TESTING OF FORMULATIONS. 18/01/2019. Application No: 201921002217. India

Exp: (Yu et al, Nature Materials, 2016)

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Take Away & Way Forward

PHYSIOCHEMICAL MODEL OF SKIN LAYER/S FOR RAPID DESIGN AND TESTING OF FORMULATIONS. 18/01/2019. Application No: 201921002217. India

Deeper layer models Combining transport and mechanical model Extending mechanical model in 2D and 3D Capturing demographic variation in models Development of complex formulations and testing Understanding of transport of macromolecules

through skin

• Skin models at various length scale could be built based on applications and requirements

• The demographic variation in skin composition could be captured

• Given the structural properties of a drug molecule, its release profile from skin SC can be obtained

• The in-silico model can be personalized based on the skin composition

• The influence of drug/formulation on skin mechanical properties (related to wrinkle formation) could be captured

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Design of Tastants


Senses

Organs

Applications






Quantifying taste








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*https://www.marketsandmarkets.com/PressReleases/food-flavors.asp# https://www.businesswire.com/news/home/20170301005073/en/Global-Sugar-free-Food-Beverages-Market-Size-ReachImage source : https://www.flexibledietinglifestyle.com/artificial-sweeteners/https://www.newsclick.in/generic-prescribing-medicines-diktats-are-not-substitute-sound-public-policy

Flavour Industry: Market

The food flavours current market is estimated to be

USD 13.56 billion and is projected to reach USD

17.10 billion by 2023, at a CAGR of 4.8% during the

forecast period.

- marketsandmarkets*

Global Sugar-free Food and Beverages Market Size

to Reach USD 72.37 Billion by 2021

- Technavio#

https://www.businesswire.com/news/home/20170301005073/en/Global-Sugar-free-Food-Beverages-Market-Size-Reach

https://www.flexibledietinglifestyle.com/artificial-sweeteners/

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https://myersdetox.com/complete-list-of-artificial-sweeteners/Gaudette, Nicole J., and Gary J. Pickering. "The efficacy of bitter blockers on health-relevant bitterants." Journal of Functional Foods 4.1 (2012): 177-184.https://www.prnewswire.com/news-releases/senomyx-announces-approval-of-bittermyx-bb68-and-sweetmyx-sr96-flavor-ingredients-in-china-300320418.htmlhttp://www.who.int/news-room/fact-sheets/detail/diabetes

Artificial sweeteners

• Acesulfame K

• Aspartame

• Cyclamate

• Saccharin

• Sucralose

• Alitame

Bitter blockers

• β-cyclodextrin (β-CYCLO)

• homoeriodictyol sodium salt (HED)

• zinc sulphate monohydrate (ZnSO4)

• magnesium sulphate (MgSO4)

• carboxymethylcellulose sodium salt

Diabetes has risen from 108 million in 1980 to 422 million in 2014

Search for new Tastants is on

https://myersdetox.com/complete-list-of-artificial-sweeteners/

https://www.prnewswire.com/news-releases/senomyx-announces-approval-of-bittermyx-bb68-and-sweetmyx-sr96-flavor-ingredients-in-china-300320418.html

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Taste Perception: Molecular Mechanism

https://www.ncbi.nlm.nih.gov/pubmedhealth/PMH0072592/ American Journal of Clinical Nutrition 90(3):747S-752S. 2009

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SYSTEM AND METHOD FOR DISCOVERY, DESIGN, CLASSIFICATION AND TESTING OF PLURALITY OF TASTANTS. Indian Patent Application. 201921004696

In-Silico Design of Tastants

Atomic, shape, topological, geometrical, electrotopological

QSAR models Genetic Function

Approximation Artificial Neural Network

Compounds + biological activity

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• Number of sweet molecules - 488

• Sweetness Index (SI) - 0.2 to 220000

Structure Building

• Structure stored in a SMILE

• Optimized using Universal forcefield

• RMSD – 0.1 Kcal/mol

Dataset Preparation

Data Compilation

Descriptor Generation

• 564 descriptors generated

• Descriptors – topological, constitutional,

geometric, electronic, etc.

Goel, A., Gajula, K., Gupta, R. and Rai, B., 2018.. Food chemistry, 253, pp.127-131.

QSAR Model of Sweetness

Data Pretreatment

• Removal of constant or nearly constant features

• Removal of features having correlation greater than 0.85

• Normalization of features

• Feature selection - ensemble technique (lasso, MLR, SVM,

RF) – 17 features

32 outliers detected

Number of molecules in refined dataset - 455

Statistical Analysis

GFA

ANN

R2=0.86Rtest

2=0.83

R2=0.889Rtest

2=0.831

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Way Forward

SYSTEM AND METHOD FOR DISCOVERY, DESIGN, CLASSIFICATION AND TESTING OF PLURALITY OF TASTANTS. Indian Patent Application. 201921004696

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Fragrance Design


Senses

Organs

Applications






Quantifying taste








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Understanding Structure Odor Relationships (SOR)

• Global fragrance market is expected to reach $70 billion by 2022*

• Fragrances are formulated for different applications• Soaps, detergents, cosmetics, toiletries etc.

• Design methods are empirical based on experience and knowledge of experts

• Challenges:• Olfactory perception is complex and subjective

• Structurally similar molecules can show distinct odor profiles

• Classification itself can be ambiguous

Fragrance Classification

Intensity

TOP

MIDDLE

BASE

Character

FRUITY

FLORAL

WOODY

https://www.thelibraryoffragrance.eu/create-your-own-scent/

https://www.perfumerflavorist.com/fragrance/trends/Global-Fragrance-Market-to-Hit-70-Billion--469990353.html*

Vision

Wavelength of light

Hearing

Frequency of sound

Olfactory perception

???

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Earlier days – perfumes entirely made of natural products

– Restricted to wealthy

– Personal use

Chanel No 5: first fine fragrance to use synthetic organic chemicals

Synthetic ingredients advantages:

– Cost, Availability

– Consistency, Stability

– Originality

New compound likely to be fragrant –

– What will it smell like?

– How intense will its smell be?

Chemical point of view

Volatile @ ambient temperature

M.W. < 400g/mol

Weak polarity

Lipophilicity

Bio-chemical point of view

Should vaporize

Ease of approach to Nasal cavity

Solubilized within the olfactory mucus

Be the agonist of an Olfactory Receptor

General requirements of an odorant

Need for Structure - Odor Relationships

Karen J. Rassiter, Chemical Reviews, 1996, 96, 3201-3240

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Psychophysical dataset for Olfactory Perception

• Aromatic Aldehydes (Group 11) are rated to be most familiar at the same time most pleasant on the basis of average ratings from 55 Subjects

Keller et al.BMC Neuroscience (2016)

• Aromatic Acids (Group 5) are rated to be least familiar and least pleasant, presenting a starting point to develop new fragrances

• Within Aromatic Aldehydes there are 2 odorants which are the most familiar and pleasant while being most intense

• Within Aromatics acids there is 1 Odorant which is the most pleasant but not so familiar while being less intense

Charts made using TCS Proprietary software

Charts made using TCS Proprietary software

Odor Familiarity

Od

or

Ple

asan

tnes

s

Odor Intensity

(Aliphatic, Aromatics)Acids (4,5)Alcohol (6,7)Ester (8,9)Aldehyde (10, 11)Ketone (12,13)Cyclic (3)Others (14,15,16)

11

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Food Quality & Freshness Monitoring

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Food Wastage: Global Scenario One third of the food produced (1.6 billion tonnes) in the

world for human consumption is lost every year– About $1.2 trillion are lost– Accounts for 8% of global greenhouse gas emission(Food and Agriculture Organization (FAO))

One in nine people do not have enough food (795 million people)

UN’s Sustainable Development Goals sets a target of halving the food loss and waste by 2030

0

50

100

150

200

250

300

350

South and

Southeast

Asia

Subsaharan

Africa

North

Africa, West

and Central

Asia

Latin

America

Europe North

AmericaFo

od

Lo

ss p

er

Ye

ar

pe

r C

ap

ita

(kg

)

Region

Production to retailing Consumer

0

50

100

150

200

250

300

350

400

Fo

od

Lo

ss p

er

Ye

ar

pe

r C

ap

ita

(kg

)

Country

-

10

20

30

40

50

60

70

80

90

100

Na

tio

na

l F

oo

d W

ast

e p

er

Ye

ar

(Millio

n t

on

ne

s)

Country

Food Loss in different countries

Food Loss in different regions

https://www.wfp.org/hunger/stats

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Framework for Monitoring Food Freshness/Shelf Life

Deterioration of quality during Transportation/storage in food supply chain(Environmental conditions and time spent at each stage)

Dynamic monitoring of quality (freshness) and shelf life computation (Dynamic Pricing)

Food container

Sensors

Invasive/

Non-invasive

Models

Physics based

Data based

Kinetics based

T H M G L

Farm production

Harvesting and

packagingGrading &

SortingCentralized

Storage

Consumer

Processing Centre

Food product storage

DistributorLocal supplier

Knowledge Base

FSSI Standards, Geography specific food cultivar and environmental data, Real time food pricing

data

Freshness Shelf life Price

Digital Twins of Food productsDyn

amic

co

ntr

ol

of

envi

ron

men

tal

con

dit

ion

s

Rea

l tim

e p

red

icti

on

T: Temperature

H: Relative humidity

M: Moisture

G: Gases (O2,C2H4, CO2)

L:Light

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Modular framework for multimodal sensing andsynchronized monitoring of food items

Novel framework for multimodal sensing and monitoring of perishable commodities, 201821040783 (Indian patent application)

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Case Studies1. Freshness of banana2. Shelf life estimation of potato

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1. Sensing & Monitoring – Fresh Banana

Food FreshnessModel

O2

Good

OK

Bad

System and method for monitoring and quality evaluation of perishable food items. Indian Patent application201921004783

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White sprouts Green sprouts - Solanine

Dark Light

Shrinkage

Hot conditionsHigh humidity

Fungal growth

High sugar in potato → Brown chips (Acrylamide – carcinogen)

Reducing sugar 2.3%Reducing sugar 0.5%

Effect of storage conditions on the food quality

Parameter

Weight loss

Compo-sition

ToxinSprouting

Colour

Shelf life defining attributes

Sugar, Starch, Protein, Fats, Vitamins

Flavour

Moisture

2. Shelf life estimation of potato

Long term storage in cold conditions

• Potato is the world's fourth-largest food crop, following maize, wheat and rice

• India is 3rd largest producer of potato in the world

- Yearly production of 25 million tonnes

- Low export (Only 0.5% of global potato market) and low processing

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Shelf life prediction based on the weight loss data

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66

14

22

31

41

0

20

40

60

80

100

10 15 20 25

Shel

f lif

e (d

ays)

Shelf life criteria (Weight loss (%) )

Inside Chamber Temp 10 °C & 90% RH

Outside Chamber Temp Avg 29.9 °C & 38.4% RH

Experimental data and kinetic model Shelf life prediction

*Potatos has previous storage history of 2 months

0

2

4

6

8

10

12

14

16

0 20 40 60 80 100

Cum

ula

tive

wei

ght

loss

(%

)

Number of days (#)

Potato Atlanta Avg Temp 29 °C & 60% RH

Predicted (Zero order (1-5 day) & First order (5day onwards)

Shelf life estimation of potato

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5

10

15

20

0

20

40

60

80

100

120

140

A B C D

Sh

elf

lif

e (

da

ys)

Storage conditions (Temp & RH)

5 10 15 20

Storage conditions A -10 °C (Freezer)

B 10 °C & 40 % RH (Freeze)

C 29 °C & 60 % RH (Ambient)

D 35 °C & 40 % RH (Incubator)

0 10 20 30 40 50 60

0

2

4

6

8

10

12

14

16

Time (days)

Weig

ht

loss

(%

)

25 C & RH 50%

25 C & RH 60%

25 C & RH 70%

25 C & RH 90%

Shelf life estimation of potato: effect of process parameters

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Shelf Life Prediction: Optimum Environmental Condition

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Way Forward – Farm to Market

Smart and efficient sensing technology in supply chain

Reduce food loss

Sustainable economy

Visibility & Accountability

Assisting local food producers

Ensuring and retaining nutritional value

Personalized food

Natural ripening of climacteric fruits Smart Packaging

Image Source: https://www.raconteur.net/sustainability/smart-packaging-brings-challenges-to-privacy-in-the-homeImage Source: https://www.raconteur.net/sustainability/smart-packaging-brings-challenges-to-privacy-in-the-home

Food Composition Assessment

Image Source: https://www.slideshare.net/CorVerdouw/20150624-tp-organics-req-smart-farming-food-security-v1

Smart Supply Chain

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AR/VR

Summary & Road Ahead

GSR

Electro-Chemical

Hyperspectral camera

Ultrasonic

Gases

Data

Touch

Taste

Sight

Sound

Smell

ScienceDigital Twin

Digital Thread

Image Source: https://www.inc.com/james-paine/how-blockchain-is-disrupting-supply-chain-management.html

https://www.networkworld.com/article/3280225/internet-of-things/

Simulating Senses - McGill University · 2019. 2. 27. · 6 Cosmetics • Cosmetics market is...

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