Role of Artificial Intelligence in Pharmacy

1. Department of Pharmacy, IEC College of Engineering & Technology,
IEC Group of Institutions, Greater Noida, Uttar Pradesh, India
Organizing a Webinar on
“Artificial Intelligence : Innovative Approach for
Pharmacy and Healthcare Development in India”
Time & Date : 2-3 pm on September 13, 2020 (Sunday)
by
Prof. (Dr.) Bhanu P. S. Sagar
Professor & Director DOP, IECGI,
Greater Noida, Uttar Pradesh, INDIA

2. Innovations in Medical and Biological Engineering
• 1950s and earlier
• Artificial Kidney
• X ray
• Electrocardiogram
• Cardiac Pacemaker
• Cardiopulmonary bypass
• Antibiotic Production
technology
• Defibrillator
• 1960s
• Heart valve replacement
• Intraocular lens
• Ultrasound
• Vascular grafts
• Blood analysis and processing
• 1970s
– Computer assisted
tomography
– Artificial hip and knee
replacements
– Balloon catheter
– Endoscopy
– Biological plant food
engineering
• 1980s
– Magnetic resonance imaging
– Laser surgery
– Vascular grafts
– Recombinant therapeutics
• Present day
• Genomic sequencing and
microarrays
• Positron Emission
tomography
• Image guided surgery

3. New generations of medical technology products are
Combination of different technologies

4.  Any technique which enables computers to mimic human brain.
 As per McCarthy, it is “The science and engineering of making
intelligent machines”.
 Artificial Intelligence is defined as a field that deals with the
design and application of algorithms for analysis, learning &
interpreting data, “Use of a computer to model intelligent
behaviour with minimal human intervention”.
 Machines & computer programs are capable of problem solving
and learning, like a human brain.
 Goals of AI system is to develop system capable of taking
complex problems in ways similar to human logic and reasoning.
What is Artificial Intelligence ?

5. Artificial Intelligence
AI encompasses many branches of statistical and machine learning, pattern
recognition, clustering, similarity-based methods, logics and probability theory,
as well as biologically motivated approaches, such as neural networks and fuzzy
modeling.
Natural Language Processing (“NLP”) and translation,
Pattern recognition, Visual perception Decision making.
Machine Learning (“ML”), one of the most exciting areas for Development of
computational approaches to automatically make sense of data
Advantage of Machine
Can retain information ; Becomes smarter over time ;
Machine is not susceptible to Sleep deprivation; distractions; information
overload and short-term memory loss.

6. ARTIFICIAL INTELLIGENCE
 Artificial intelligence (AI) is the study of complex information which processes
problems that have their roles in some aspect of biological information processing.
 The main aim of the subject is to identify useful information processing.
 Pharmaceutical drug manufacturing, from formulation development to finished
product, is very complex. This process includes multivariate interactions between
raw materials and process conditions. These interactions are very important for the
process ability and quality of the finished product.
 The use of artificial intelligence in pharmaceutical technology has increased over the
years, and the use of technology can save time and money while providing a better
understanding of the relationships between different formulation and process
parameters.

7. CT Participant Identifier
Connected Machines
Dosage error Detection
Fraud detection Adm.
workflow Assistance
Virtual Nurshing…
Robot-assisted Surgery
estimated potential
Artificial Intellegence 7
annual benefit for each
application by 2026(in
billon USD)
0 10 20 30 40 50
Fig: Estimated potential annual benefit for
each application by 2026(in billon USD)
Source: Accenture Analysis
Total= $150 Billions
Cybersecurity
Advance Image Diagnosis
Preliminary Diagnosis

8. Problems of AI/ Challenges
Reasoning, Problem Solving
Knowledge representation
Planning
Learning
Natural language processing
Perception
Motion manipulation
Social Intelligence
Creativity
General Intelligence
Approaches
Cybernetics
Symbolic
Statistical
Integrating the approaches
Applications
Healthcare and Medicines
Automotive
Finance and economic
Video Games
Heavy Industries
Robotics

9. Three Steps
Three elements of AI
Computers and programs
Massive amount of data
Sophisticated algorithms The Turing test
High performance parallel
processors
The Darmont Conference
Intelligence of machines and the branch of computer science which
aims to create it.
“Machines will be capable, within 50 years, of doing any work a
man can do.” –Herbert Simon, 1965 (AI innovator)

10. Artificial Intelligence Representation: Machines can retain data or
information and come to be keener in the future as like human beings.

11. Focus areas of AI in an organization

12. Advantages of Artificial Intelligence Technology:
AI is complex in nature and use. It is a combination of dense mixture of mathematics,
computer science and other sciences. Helps the machines to reproduce the cognitive
abilities of human beings. Advantages of AI are:
Error Reduction - AI helps to reduce error and increases the accuracy with more
precision. For Example, Intelligent robots.
Difficult Exploration - Used in mining and fuel exploration sectors. Robots can perform
more hard work and laborious work easily without exhaust.
Daily Application - AI is useful for the daily application purpose. For example GPS
system (helpful in long drives), corrects the errors in spelling. For example, Lady SIRI
and Cortana robots. When anyone is posting photographs on social media' like twitter,
face-book, the AI program identifies and tags the person's face.
Digital Assistants - AI systems ‘avatar’ which are models of digital assistants are used
by advanced organizations to reduce the need for human resources.

13. Advantages of Artificial Intelligence Technology:
Repetitive Jobs - Humans can do only one task at a time. Machines can perform multi-
tasking and can think faster than human beings.
No Breaks / Limitless functions - Humans can work 8 hours per day with 2 or 3 breaks.
Machines can work continuously with constant output. Machines do everything better
than humans
No risk of harm – For Example working at the fire stations mishap causes harm to the
personnel. While machines, they don’t feel and have emotions. Also, If machines are
broken, it is possible to mantle the parts.
Medical Applications - Nowadays, the physicians are assessing the patients and
analyzing the health risks with the help of AI.
Increase technological Growth Rate - AI technology helps to find new chemical
compounds and entities. For example – CADD, QSAR.
Act as aids - AI technology can be used to serve children with disability or the elders
on a 24/7 basis. (source for teaching and learning; security alerts in fires, robbery and
in difficult conditions).

14. Types of Artificial Intelligence Technology:
AI is a wide-ranging concept and can be classified into a number of ways.
Based upon their calibre, AI system is classified as follows:
1. Weak intelligence or Artificial narrow intelligence (ANI) – This system is designed
and trained to perform a narrow task, such as facial recognition, driving a car,
playing chess, traffic signalling. E.g.: Apple SIRI virtual personal assistance, tagging
in social media.
2. Artificial General Intelligence (AGI) or Strong AI - It is also called as Human Level
AI. It has the ability to simplify human intellectual abilities. Due to this, when it
exposed to an unfamiliar task, it has the ability to find the solution. AGI can perform
all the things as humans.
3. Artificial Super intelligence (ASI): It is a brain power, which is more active than
smart humans drawing, mathematics, space, etc; in each and every field from
science to art. It ranges from the computer just little than the human to trillion times
smarter than humans.

15. Types of Artificial Intelligence Technology:
Arend Hintze, AI scientist classified the AI technology based upon their
presence. They are as follows:
1. Type 1: This type of AI system is called as Reactive machines. E.g. Deep
Blue, aIBM chess program which hit the chess champion Garry Kasparov
in the 1990s. Another example is Google's AlphaGo.
2. Type 2: This type of AI systems is called as “Limited memory systems”
which has ability to use past experiences for present & future problems.
3. Type 3: This type of AI system is called as “theory of mind”. It means
that all the humans have their own thinking, intentions and desires which
impact the decisions they make.
4. Type 4: These are called as self-awareness. The AI systems having the
sense of self and consciousness. If the machine has self-awareness, it
understands the condition and uses the ideas present in other's brain.

16. AI in field of Pharmacy
• It is one of the top technologies shaping the future of pharmacy.
• Pharma industries has been developing cure & treatment for centuries. Traditionally
the design & manufacturing of drug requires several years, lengthy clinical trials &
huge costs.
• With the rise of 21st century technologies, this has been changing.
• In future we will see completely different drug designs, manufacture & clinical trials.

17. Why AI in Pharma is a good idea ?
• Pharmaceutical industry can accelerate innovation by using AI technologies.
• The recent technological advancement that comes to mind would be artificial
advancement such as visual perception, speech recognition, decision-making &
translation between languages.
• An estimate by IBM shows that entire healthcare domain has approx. 161 billion GB
of data as of 2011.
• With humongous data available in this domain, AI can help in analysing the data &
presenting results that would help out in decision making, saving human effort,
time, money & thus help save lives.

18. Imagine a Future where
• AI is able to design new drugs
• Find new drug combination
• Deliver clinical trials within minutes
• Drugs are not tested on real humans or animals, but on virtual model that are
engineered to mimic the physiology of organs.
• Robots help in the manufacturing of medication as well as their distribution
• Counterfeiting drugs become almost impossible.
• Block-chain technology secures the entire distribution channel.
• Local pharmacists 3D prints personalised drugs in any shape & desired doses.

26. Applications of AI
 Disease Identification
 Radiology And Radiotherapy
 Clinical Trial Research
 Drug Discovery
 Personalized Medicine & Rare Disease Identification

27. AI in Clinical Research & Clinical Trial Research
 Cutting costs (Predictive analysis in identifying candidates for clinical trials)
 Improving trial quality (Machine learning- to shape, direct clinical trials)
 Improving trial time by almost half
 Finding biomarkers and gene signatures that cause diseases
 Reading volumes of text and data in seconds
 Discovering involving new diagnostic tools and treatments for Alzimer’s disease,
cancer, and other chronic and terminal illness.
 Remote monitoring and real time data access for increased safety; biological and
other signals for any sign of harm or death to participants.
 Finding best sample sizes for increased efficiency; addressing and adapting to
differences in sites for patient recruitments; using electronic medical records to
reduce data errors.

28. Drug Discovery
 A study published by the Massachusetts Institute of Technology
(MIT) has found that only 13.8% of drugs successfully pass clinical
trials.
 Furthermore, a company can expect to pay between $161 million
to $2 billion for any drug to complete the entire clinical trials process
and get FDA approval.
 With this in mind, pharma businesses are using AI to increase the
success rates of new drugs while decreasing operational costs at
the same time.
 Ideally, this would also translate to lower drug costs for patients,
all while offering them more treatment choices.

29. Drug Discovery / Manufacturing
 From initial screening of drug compounds to predicted success rate
based on biological factors.
 R&D discovery technology; next-generation sequencing.
 Previous experiments are used to train the model
 Optimization softwares (example: Form Rules)
 Designing of the processes.

30. Disease Identification
 2015- Report by Pharmaceutical Research and Manufacturers of
America- more than 800 drugs and vaccines are in trial phase to treat
cancer.
 Google’s DeepMind Health, announced multiple partnerships including
some eye hospitals in which they are developing technology to address
macular degeneration in aging eyes.
 Oxford’s Pivital® Predicting Response to Depression Treatment
(PReDicT) project is aiming to produce commercially-available
emotional test battery for use in clinical setting.
 Berg (US biopharma company) is using AI to research and develop
diagnostics and therapeutics in the fields of oncology, endocrinology,
and neurology.

31. Epidemic Outbreak Prediction
 To predict malaria outbreaks, from data like temperature, average monthly rainfall,
total number of positive cases, etc.
 ProMED-mail is a internet based reporting program for monitoring emerging
diseases and providing outbreak reports.
Radiology and Radiotherapy
 Google’s DeepMind Health is working with University College London Hospital
(UCLH) to develop machine learning algorithms capable of detecting differences
in healthy and cancerous tissues.
 The goal is to improve the accuracy of radiotherapy planning while minimizing
damage to healthy organs at risk.

32. Smart Electronic Health Records
 AI to help diagnosis, clinical decisions, and personalized
treatment suggestions.
 Handwriting recognition and transforming cursive or other
sketched handwriting into digitized characters.

33. Personalized Treatment
 Micro biosensors and devices, mobile apps with more sophisticated
health-measurement and remote monitoring capabilities; these data
can further be used for R&D.
 DermCheck; app available in Google play store in which images are
sent to dermatologists.
 Using AI, body scans can detect cancer and other diseases early, as
well as predict health issues people might face based on their genetics.
 IBM Watson for Oncology is currently the leader in AI for personalized
treatment decisions in the oncology space. It uses each patient’s
medical information and history to optimize the treatment decision-
making.

34. APPLICATION OF AI IN PHARMACEUTICAL RESEARCH:
In Formulation :
a) Controlled release tablets:
 The first work in the use of neural networks for modeling pharmaceutical
formulations was performed by Hussain and co-workers at the University of
Cincinnati (OH, USA).
 In various studies they modeled the in vitro release characteristics of a range of
drugs dispersed in matrices prepared from various hydrophilic polymers.
b) Immediate release tablets:
 The networks produced were used to prepare three-dimensional plots of massing
time, compression pressure and crushing strength, or drug release, massing time
and compression pressure in an attempt to maximize tablet strength or to select
the best lubricant.
 Comparable neural network models were generated and then optimized using
genetic algorithms.

35. In Product Development:
 The pharmaceutical product development process is a multivariate optimization
problem. It involves the optimization of formulation and process variables.
 One of the most useful properties of artificial neural networks is their ability to
generalize. These features make them suitable for solving problems in the area of
optimization of formulations in pharmaceutical product development.
 ANN models showed better fitting and predicting abilities in the development of
solid dosage forms in investigations of the effects of several factors (such as
formulation, compression parameters) on tablet properties (such as dissolution).
 ANNs provided a useful tool for the development of micro emulsion-based drug-
delivery systems.
 ANNs can also be used to simulate aerosol behavior, with a view to employing
this type of methodology in the evaluation and design of pulmonary drug-delivery
systems.

36. Risks & Disadvantage Associated with AI
• Prof. Stephen Hawking had said that human efforts to create
machines that can think are a huge threat to the existence of
human race. Development of complete human AI could mean
that the human race would come to an end in the future.
• High cost - AI needs huge costs as they are complex machines
• Unemployment - AI can cause unemployment.
• No Match For Human Brain Intelligence
• No Improvement With Experience
• No Original Creativity

37. AI Applications in Healthcare
Managing Medical Records and other data
Doing repetitive jobs
Treatment Design
Digital Consultation
Virtual Nurses
Medication Management
Drug Discovery
Precision Medicine
Healthcare Monitoring
Healthcare System Analysis

38. Artificial intelligence in medicine : The virtual branch
The virtual component is represented by Machine
Learning, (also called Deep Learning)-
mathematical algorithms that improve learning
through experience.
Three types of machine learning algorithms:
1. Unsupervised (ability to find patterns)
2. Supervised (classification and prediction
algorithms based on previous examples)
3. Reinforcement learning (use of sequences
of rewards and punishments to form a
strategy for operation in a specific problem
space)

39. Use of robots to deliver treatment..robotic surgery
Use of robots to monitor effectiveness of treatment

40. Growth drivers of AI in healthcare
 Increasing individual healthcare expenses
 Larger Geriatric population
 Imbalance between health workforce and patients
 Increasing Global Health care expenditure
 Continuous shortage of nursing and technician staff. The number of
vacancies for nurses will be 1.2 million by 2020
 AI is and will help medical practitioners efficiently achieve their
tasks with minimal human intervention, a critical factor in meeting
increasing patient demand.

41. Growth drivers of AI in healthcare

42. Conclusion
AI is a big thing for pharma and Companies that are more flexible
and adopt AI faster will likely gain a strategic advantage.
In fact, experts anticipate that implementing AI will soon be
necessary to compete in the industry.
However, the transformation will not happen overnight. Instead, it
will gradually occur over the next 10 or 20 years.
By then, AI is expected to be integrated into most Pharma R&D
operations and this will improve the drug development success
rate and streamline R&D efforts.