General overview, Pharmaceutical Automation, Pharmaceutical applications, Advantages and Disadvantages, Current Challenges and Future Directions

1. ARTIFICIAL INTELLIGENCE (AI),
ROBOTICS AND COMPUTATIONAL
FLUID DYNAMICS (CFD)
Presented By:
Ms. Prajakta Sawant
First Year M.Pharm (Roll No. 5)
(Dept. of Pharmaceutics)
Sub: COMPUTER AIDED DRUG
DEVELOPMENT (Sem-II)
Alard College Of Pharmacy,
Pune.
Under the Guidance of:
Dr. Nalanda Borkar
Head of Department
(Dept. of Pharmaceutics)
Alard College Of Pharmacy,
Pune.
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2. CONTENTS
 General overview
 Pharmaceutical Automation
 Pharmaceutical applications
 Advantages and Disadvantages
 Current Challenges and Future Directions
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3. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
(AI)
 According to the father of Artificial Intelligence, John McCarthy, it is
“The science and engineering of making intelligent machines,
especially intelligent computer programs”.
 Also, intelligence distinguish us from everything in the world. As it
has the ability to understand, apply knowledge.
 Also, improve skills that played a significant role in our evolution. We
can define AI as the area of computer science.
 Further, they deal with the ways in which computers can be made. As
they made to perform cognitive functions ascribed to humans.
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5.  Artificial Intelligence (AI) refers to the ability of a computer or
a computer- enabled robotic system to process information and
produce outcomes in a manner similar to the thought process of
humans in learning, decision making and solving problems.
 By extension, the goal of AI systems is to develop systems
capable of tacking complex problems in ways similar to human
logic and reasoning.
 Artificial intelligence – AI – is getting increasingly
sophisticated at doing what humans do, albeit more efficiently,
more quickly, and more cheaply.
 While AI and robotics are becoming a natural part of our
everyday lives, their potential within healthcare is vast.
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6.  Artificial Intelligence is a new electronic machine that stores large amount
of information and process it at very high speed.
 The computer is interrogated by a human via a teletype It passes if the
human cannot tell if there is a computer or human at the other end.
 The ability to solve the problems.
 It is the science and engineering of making intelligent machines, especially
intelligent computer programs.
 It is related to the similar task of using computers to understand human
intelligence.
 Brief history of AI:
 1941- First electronic computer (technology finally available)
 1956- Term artificial intellience introduced.
 1960s- Checkers –playing program that was able to play with opponents.
 1980s- Quality control system.
 2000- First sophisticated walking robot.
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7. OVERVIEW OF AI :
 Since the invention of computers or machines, their capability to
perform various tasks went on growing exponentially.
 Humans have developed the power of computer systems in terms of
their diverse working domains, their increasing speed, and reducing
size with respect to time.
 A branch of Computer Science named Artificial Intelligence pursues
creating the computers or machines as intelligent as human beings.
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8. PHILOSOPHY OF AI
 While exploiting the power of the computer systems, the curiosity of
human, lead him to wonder, “Can a machine think and behave like
humans do?”
 Thus, the development of AI started with the intention of creating
similar intelligence in machines that we find and regard high in
humans.
Goals of AI
 To Create Expert Systems.
 To Implement Human Intelligence in Machines.
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10. PROGRAMMING WITHOUT AND WITH AI
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11. WHAT IS AI TECHNIQUE?
In the real world, the knowledge has some unwelcomed properties:
 Its volume is huge, next to unimaginable.
 It is not well-organized or well-formatted.
 It keeps changing constantly.
 AI Technique is a manner to organize and use the knowledge efficiently
in such a way that: It should be perceivable by the people who provide
it.
 It should be easily modifiable to correct errors.
 It should be useful in many situations though it is incomplete or
inaccurate.
 AI techniques elevate the speed of execution of the complex program it
is equipped with.
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13. ADVANTAGES OF AI :
a. Error Reduction:
 We use artificial intelligence in most of the cases. As this helps us in
reducing the risk.
 Also, increases the chance of reaching accuracy with the greater
degree of precision.
b. Difficult Exploration:
 In mining, we use artificial intelligence and science of robotics. Also,
other fuel exploration processes.
 Moreover, we use complex machines for exploring the ocean. Hence,
overcoming the ocean limitation.
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14. c. Daily Application:
 As we know that computed methods and learning have become
commonplace in daily life.
 Financial institutions and banking institutions are widely using AI. That is
to organize and manage data.
 Also, AI is used in the detection of fraud users in a smart card based
system.
d. Digital Assistants:
 “Avatars” are used by highly advanced organizations. That are digital
assistants.
 Also, they can interact with the users. Hence. They are saving human
needs of resources.
 As we can say that the emotions are associated with mood.
 That they can cloud judgment and affect human efficiency. Moreover,
completely ruled out for machine intelligence.
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15. e. No breaks:
 Machines do not require frequent breaks and refreshments for humans. As
machines are programmed for long hours.
 Also, they can continuously perform without getting bored.
f. Increase Work Efficiency:
 For a particular repetitive task, AI-powered machines are great with
amazing efficiency.
 Best is they remove human errors from their tasks to achieve accurate
results.
g. Reduce cost of training and operation:
 Deep Learning and neural networks algorithms used in AI to learn new
things like humans do.
 Also, this way they eliminate the need to write new code every time.
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16. DISADVANTAGES OF ARTIFICIAL
INTELLIGENCE:
a. High Cost:
 Its creation requires huge costs as they are very complex machines.
 Also, repair and maintenance require huge costs.
b. No Replicating Humans:
 As intelligence is believed to be a gift of nature.
 An ethical argument continues, whether human intelligence is to be
replicated or not.
c. Lesser Jobs:
 As we are aware that machines do routine and repeatable tasks much
better than humans.
 Moreover, machines are used of instead of humans. As to increase
their profitability in businesses.
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17. d. Lack of Personal Connections:
 We can’t rely too much on these machines for educational oversights.
 That hurt learners more than help.
e. Addiction:
 As we rely on machines to make everyday tasks more efficient we use
machines.
f. Efficient Decision Making:
 As we know computers are getting smarter every day.
 Also, they are demonstrating not only an ability to learn but to teach
other computers.
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18. APPLICATIONS OF AI IN
PHARMACEUTICALS
AI have various applications in health care and pharmacy which are as
follows:
 Disease Identification
 Personalize treartment
 Drug Discovery/Manufacturing
 Clinical Trial Research
 Radiology and Radiotherapy
 Smart electronic health record
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19.  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.
 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(human not machines).
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20.  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: FormRules) Designing of the processes.
 CLINICAL TRIAL RESEARCH:
 Machine learning- to shape, direct clinical trials.
 Advanced predictive analysis in identifying candidates for clinical trials.
 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.
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21.  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.
 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.
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22. AI technique use in Drug Discovery:
 Deep learning technique known as a generative adversarial network (GAN)
by Baltimore based company, Insilico Medicine.
 GPU (graphics processing unit)-accelerated deep learning to target cancer
and age-related illnesses by above organization.
 Benevolent Bio’s deep learning software, powered by the NVIDIA DGX-1
AI supercomputer (it ingests & analyzes the information to find connections
and propose drug candidates).
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23. CURRENT CHALLENGES/ FUTURE
ASPECT
 Many big Pharmaceutical companies began investing in AI
in order to develop better diagnostics or biomarkers, to
identify drug targets and to design new drugs and products.
 Merck partnership with Numerate generating novel small
molecule cardiovascular disease target. in March 2012
focusing on drug leads for unnamed
 In December, 2016 Pfizer and IBM announced partnership to
accelerate drug discovery in immunooncology.
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24. INTRODUCTION TO ROBOTICS
 Robotics is defined to include intelligent machines and systems used in
space, exploration, human services or manufacturing where as
automation includes the use of automated methods in various
applications, for example, system factory, office, home, or
transportations to improve performance and productivity.
 Objective: Robots are aimed at manipulating the objects by perceiving,
picking, moving, modifying the physical properties of object, destroying
it, or to have an effect thereby freeing manpower from doing repetitive
functions without getting bored, distracted, or exhausted.
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25.  Robots in laboratory, life science and pharmaceutical applications
perform tasks at rates beyond human capability.
 These robots function in potentially hazardous settings in proximity to
biological dangers, the threat of radioactive contamination and toxic
chemotherapy compounds.
 Robotics is called upon to assemble and package a variety of medical
devices and implants as well as preparing prescriptions for mail-order
pharmacies or hospitals.
 Robots are doing assay analysis and automating the movement of test
tubes in research laboratories.
 Because of the high number of samples that need analysis and the
amount of data collection required, the process and costs are easily
validated with robotics.
 In pharmaceutical applications, hospitals use robots to mix potentially
hazardous cancer drugs and those associated with radiation.
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27. TYPES OF AUTOMATION TYPES OF
AUTOMATION
 1.Feedback control
 2.Sequential control & logical sequence control
 3. Computer control
ROBOTS USED IN PHARMACEUTICAL INDUSTRY
 Pharmaceutical Container Replacement Robot
 Cylindrical Robot for High Throughput Screening
 Six-Axis Robots suit Class 1 Clean Room Applications
 Space Saving Ceiling Mounted Robot
 Metal Detector Targets Pharmaceutical Industry
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28. APPLICATIONS OF ROBOTS
PHARMACEUTICAL INDUSTRY
 Research and Development (R&D)
 Control Systems
 Sterilization and Clean Rooms
 Packaging Operations
 Flexible Feeding 3
 Vision Systems
 Grinding Applications
 Sterile Syringe Filling
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29. LABORATORY ROBOT IN OPERATIONAL
RESEARCH
 Laboratory robotics is the act of using robots in biology or chemistry
labs.
 For example, pharmaceutical companies employ robots to move
biological or chemical samples around to synthesize novel chemical
entities or to test pharmaceutical value of existing chemical matter.
 Advanced laboratory robotics can be used to completely automate the
process of science, as in the Robot Scientist project.
 Laboratory processes are suited for robotic automation as the processes
are composed of repetitive movements (e.g. pick/place, liquid & solid
additions, heating/cooling, mixing, shaking and testing).
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30. COMBINATORIAL LIBRARY SYNTHESIS
 Robotics has applications with Combinatorial Chemistry which has great
impact on the pharmaceutical industry.
 The use of robotics has allowed for the use of much smaller reagent
quantities and mass expansion of chemical libraries.
 The "parallel synthesis" method can be improved upon with automation.
 The main disadvantage to "parallel-synthesis" is the amount of time it
takes to develop a library; automation is typically applied to make this
process more efficient.
 The main types of automation are classified by the type of solid- phase
substrates, the methods for adding and removing reagents, and design of
reaction chambers.
 Polymer resins may be used as a substrate for solid-phase.
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32. PURIFICATION
 Simulated distillation, a type of gas chromatography testing method used in
the petroleum, can be automated via robotics.
 An older method used a system called ORCA (Optimized Robot for
Chemical Analysis) was used for the analysis of petroleum samples by
simulated distillation (SIMDIS).
 ORCA has allowed for shorter analysis times and has reduced maximum
temperature needed to elute compounds.
 One major advantage of automating purification is the scale at which
separations can be done.
 Using microprocessors, ion exchange separation can be conducted on a
Nano liter scale in a short period of time.
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33.  Robotics has been implemented in liquid-liquid extraction (LLE) to
streamline the process of preparing biological samples using 96-well
plates.
 This is an alternative method to solid phase extraction methods and
protein precipitation, which has the advantage of being more
reproducible and robotic assistance has made LLE comparable in
speed to solid phase extraction.
 The robotics used for LLE can perform an entire extraction with
quantities in the micro liter scale and performing the extraction in as
little as ten minutes.
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34. ADVANTAGES OF ROBOTICS
 One of the advantages to automation faster processing, but it is not
necessarily faster than a human operator.
 Repeatability and reproducibility are improved as automated systems
as less likely to have variances in reagent quantities and less likely to
have variances in reaction conditions.
 Typically productivity is increased since human constraints, such as
time constraints, are no longer a factor.
 Efficiency is generally improved as robots can work continuously and
reduce the amount of reagents used to perform a reaction. Also there is
a reduction in material waste.
 Automation can also establish safer working environments since
hazardous compounds do not have to be handled.
 Additionally automation allows staff to focus on other tasks that are
not repetitive.
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35. DISADVANTAGES OF ROBOTICS
 Typically the costs of a single synthesis or sample assessment are
expensive to set up and startup cost for automation can be expensive.
 Many techniques have not been developed for automation yet.
 Additionally there is difficultly automating instances where visual
analysis, recognition, or comparison is required such as color changes.
 This also leads to the analysis being limited by available sensory
inputs.
 One potential disadvantage is an increases job shortage as automation
may replace staff members who do tasks easily replicated by a robot.
 Some systems require the use of programming languages such as C++
or Visual Basic to run more complicated tasks.
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36. CURRENT CHALLENGES/ FUTURE
ASPECT
 In medical device manufacturing, robotics plays an active role in assembly. The
manufacturing process is highly regulated and must be approved by the Food
and Drug Administration (FDA). Manufactures use robotics to reduce cost.
Robotics performs important tasks in surgical procedures.
 Robots are used for delivery of radiation and for proton therapy. The goal is to
administer the smallest dose of radiation as possible to the precise location.
Robots are very precise, positioning equipment and patients accurately in three-
dimensional space.
 Robots are loading and unloading injection moulding machines, assembling
medical devices and polishing implants. In pharmaceutical production, robots
handle bottles in the cell culture process, loading and unloading autoclaves and
packaging machines, as well as de- nesting syringe tubs.
 Robotics has a certain future in laboratory, life science and pharmaceutical
applications. There is increased activity for bench-toproboticsperforming
various protocols. These stations are reprogrammable and many are complex.
Robotics is essential to modern scientific .
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37. COMPUTATIONAL FLUID DYNAMIC
(CFD)
 Computational fluid dynamics can be a viable tool to analyze and
troubleshoot various process equipment used in the pharmaceutical industry.
Because typical unit operations process large amounts of fluid, even small
improvements in efficiency and performance may increase revenue and
decrease costs.
 The integration of CFD methods can lead to shortened product-process
development cycles, optimization of existing processes, reduced energy
requirements, efficient design of new products and processes, and reduced
time to market.
 Unit operations in the pharmaceutical industry typically handle large amounts
of fluid. As a result, small increments in efficiency may generate large
increments in product cost savings.
 Thus, research and development staffs as well as plant and production
managers should understand the benefits of CFD so that it can be integrated
into the development process.
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40. APPLICATION OF CFD IN
PHARMACEUTICS
The application of CFD to a few key unit operations and processes in
the pharmaceutical industry was described as follows:
 CFD for mixing.
 CFD for solids handling.
 CFD for separation.
 CFD for dryers.
 CFD for packaging.
 CFD for energy generation and energy-transfer devices.
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41.  CFD FOR MIXING:
 CFD methods can be applied to examine the performance of static mixers
and to predict the degree of mixing achieved, thus indicating whether more
mixing elements are required shows surface mesh and blade orientation for a
Kinecs mixer depicts the mass fraction concentration of the two species
being mixed.
 The degree of mixing is shown as the color proceeds from distinct inlet
streams (red and blue) to the fully mixed outlet stream (green).
 A CFD solution can be used to derive the pressure drop, hence the power
required.
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42.  CFD FOR SOLIDS HANDLING:
 CFD techniques can be applied to analyze such flows and minimize or
eliminate the risk of erosion.
 CFD also can be applied to analyze the unsteady and chaotic flow
behavior in fluidized beds.
 Simulation of such a flow field requires unsteady flow calculations and
small time increments.
 As a result, performing calculations can take an extensive amount of
time.
 Simulations of gas–solid flows in complex three-dimensional reactors
can take months of computational time and are not practically feasible
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43.  CFD FOR SEPARATION:
 CFD techniques are used for analyzing separation devices such as cyclones and
scrubbers.
 The following example incorporates CFD methods to optimize and predict
performance of an existing cyclone design.
 CFD solutions depict particle paths for various particle sizes.
 In this example, CFD techniques were used to perform what-if analysis for
optimization of the design.
 The performance computed with CFD closely
matched that observed in physical testing wherein 90%
of 10-m particles were removed, but only 10%
of 1-m particles were separated from the air stream.
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44.  CFD FOR DRYERS:
 We used CFD to analyze the
performance of an industrial spray
dryer before making major structural
changes to the dryer.
 This strategy minimizes the risk of
lost profit during changeover,
especially if the improvement does
not materialize.
 CFD was applied to examine
configuration changes, thus
minimizing risk and avoiding
unnecessary downtime during testing
shows the velocity distribution
(skewed flow).
 This flow is a result of uneven
pressure distribution in the air
dispersing head.
 CFD models were applied to
determine optimum equipment
configuration and process settings.
 CFD results provided the necessary
confidence that the proposed
modifications would work so capital
equipment would be ordered and
field- testing could be scheduled.
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45.  CFD FOR PACKAGING:
 CFD can be applied to conduct virtual experiments before changes are
made to the filling lines or to the package geometry.
 This method allows a wide range of conditions to be tested and leads
to an optimized filling process, depicts the filling of a container.
 The figures shown are typical of solution results that are used to
optimize filling processes to increase throughput and reduce foaming.
(a)filling process, liquid surface location, strong
splash; (b) filling process, liquid surface location,
no splash.
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46.  CFD FOR ENERGY GENERATION AND ENERGY- TRANSFER
DEVICES:
 CFD techniques can be applied to analyze thermal and flow fields within
such devices.
 CFD modeling methods also can be applied to gain insight into flame
characteristics.
 Maintaining flame stability and burner efficiency is very critical to the
proper functioning of a process heater, power plant, or furnace. Flame
length, shape, and size can influence the process.
 If the flame is too long, then it can impinge on critical regions of the
apparatus and cause thermal damage.
 If the flame is too short, then it may wear out the burner tip.
 Replacing the burner or associated apparatus results in downtime and loss
of product revenue.
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47. ADVANTAGES OF CFD
 A great time reduction and cost reduction in new designs.
 There is a possibility to analyze different problem whose experiments
are very difficult and dangerous.
 The CFD techniques offer the capacity of studying system under
conditions over its limits.
 The level of detail is practically unlimited.
 The product gets added value.
 The possibility to generate different graph permits to understand the
features of the result. This encourages buying a new product.
 Hi-Tech CFD is a computer aided engineering company which
provides total solutions to engineering problems in the field of
Computational Fluid Dynamics (CFD),Computational
Electromagnetic, Computational Structural Mechanics, Dynamics and
Controls.
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48. DISADVANTAGES OF CFD
 Accuracy in the result is doubted i.e. in certain situations we will not
obtain successful result.
 It is necessary to simplify mathematically the phenomenon to facilitate
calculus. If the simplification has been good the result will be more
accurate.
 There are several incomplete models to describe the turbulence,
multiphase phenomenon, and other difficult problems.
 Untrained user of CFD has the tendency to believe that the output of
the pc is always true
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49. CURRENT CHALLENGES/ FUTURE
ASPECT
 The integration of CFD methods can shorten product-process development
cycles, optimize existing processes, reduce energy requirements, and lead to the
efficient design of new products and processes.
 Unit operations in the pharmaceutical industry handle large amounts of fluid. As
a result, small increments in efficiency, such as those created by implementing
CFD solutions, can lead to significant product cost savings.
 Key processes in the pharmaceutical industry can be improved with CFD
techniques.
 The aerospace and automobile industries already have integrated CFD methods
into their design process. T
 he chemical process and the pharmaceutical industries now are beginning to
integrate this technology.
 The full potential for process improvements using CFD solutions is yet to be
realized.
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50. REFERENCES:
 https://www.slideshare.net/ChandrakantKharude/artificialintelligencer
oboticsandcomputationalfluiddynamics-190207065757 Assessed Date:
14/06/2021
 https://www.slideshare.net/PrasathP13/artificial-intelligence-
138582081 Assessed Date: 14/06/2021
 https://www.slideshare.net/PrashantChaurasiya5/artificial-intligence-
and-robotics-ppt Assessed Date: 14/06/2021
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