Dive into the intersection of Artificial Intelligence (AI), Robotics, and Computational Fluid Dynamics (CFD) in pharmaceutical sciences. This presentation provides a comprehensive overview, from the foundational principles to advanced applications in pharmaceutical automation. Explore the transformative impact of AI and robotics on drug discovery, manufacturing, and delivery, alongside CFD's role in optimizing processes. Delve into the advantages and disadvantages of integrating these technologies, uncover current challenges, and envision future directions shaping the future of pharmaceutical innovation.
This presentation will explore the intersection of artificial intelligence, robotics, and computational fluid dynamics in the context of pharmaceutical automation. We will provide an overview of these technologies, discuss their applications in the pharmaceutical industry, highlight the advantages and disadvantages of their use, and examine current challenges and future directions.
The integration of artificial intelligence, robotics, and computational fluid dynamics in pharmaceutical automation has the potential to revolutionize the industry, improving efficiency, safety, and quality control. However, challenges related to data management, standardization, workforce adaptation, and regulatory compliance must be addressed. The future of pharmaceutical automation lies in the continued development and integration of these technologies, leading to more efficient, reliable, and innovative drug manufacturing processes.
AI in Pharmaceutical Industry
Pharmaceutical Automation
Robotics in Pharma
Computational Fluid Dynamics (CFD)
Drug Discovery
Pharmaceutical Manufacturing
Pharmaceutical Applications
Advantages of AI and Robotics
Disadvantages of AI and Robotics
Challenges in Pharmaceutical Automation
Future of AI and Robotics in Pharma
Artificial Intelligence
Robotics
Computational Fluid Dynamics
Pharmaceutical Automation
Drug Discovery
Manufacturing Optimization
AI in Healthcare
Robotics in Pharmaceuticals
CFD Applications
Pharmaceutical Industry
Advantages of AI
Disadvantages of Robotics
Challenges in CFD
Future of AI in Pharma
Automation Trends
Machine learning in health data analytics and pharmacovigilance
Machine learning and data analytics can help improve pharmacovigilance in several ways:
1) Machine learning algorithms can automatically extract adverse drug reactions from biomedical literature and FDA drug labels, helping pharmacovigilance teams more efficiently identify all potential ADRs.
2) Large healthcare datasets and sophisticated algorithms can help pharmaceutical companies with drug discovery, clinical trials, personalized treatment, and epidemic outbreak prediction.
3) Advances in machine learning are reshaping healthcare and have the potential to cut clinical trial costs, improve quality, speed up trials, and facilitate tasks like reviewing literature, recruiting patients, and making diagnoses.
AI and machine learning are becoming increasingly influential in healthcare, enabling tools for disease diagnosis and drug discovery. Deep learning uses artificial neural networks to perform computations on large datasets to solve medical problems. AI can help with tasks like robotic surgery, targeted drug delivery, and automating data entry to free up physician time. While having potential benefits, AI also faces limitations such as data set size and quality that need to be addressed. Overall, AI shows promise as a tool to enhance healthcare and identify accurate diagnoses.
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This document discusses artificial intelligence and its applications in the pharmaceutical industry. It begins with definitions of artificial intelligence and its goal of simulating human logic and reasoning. It then describes several applications of AI in pharmaceuticals, including disease identification, personalized treatment, drug discovery/manufacturing, clinical trial research, radiology/radiotherapy, and electronic health records. Challenges and the future of AI are also mentioned. In conclusion, the author states that AI has great potential to guide humanity if developed responsibly.
It is no secret that artificial intelligence is shaping new business landscapes in every industries. As one of emerging convergence technologies, Artificial Intelligence (AI) creates new products and services, finally innovating business models. Especially, it has been noted by industry experts and researchers that healthcare sector has the biggest potential of AI convergence. In fact, major technology companies including Google, Microsoft and IBM have invested in AI in healthcare sector. Thousands of AI startups are active launching innovative services related to healthcare.
List out the challenges of ml ai for delivering clinical impact - Pubrica
Pubrica explores the main challenges and limitations of AI in healthcare and considers the steps required to translate these potentially transformative technologies from research to clinical practice.
Continue Reading: https://bit.ly/3o4hjPT
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
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Artificial intelligence has the potential to accelerate drug discovery by generating new molecular structures, automatically designing drug candidates, and using historical data to identify treatments. Companies are using AI techniques like deep learning and generative adversarial networks to analyze vast amounts of data to propose new drug candidates. Robots are also being used in pharmaceutical laboratories and manufacturing to perform repetitive and precise tasks, allowing researchers to focus on higher-level work. Computational fluid dynamics is another tool being used to analyze and optimize pharmaceutical processes.
Week 5 power point slide -3-case study 3- designing drug virtually
Computers play several important roles in the drug discovery process:
(1) They help analyze molecular structures, organize data on molecules and compounds in databases, and visualize and model molecules.
(2) They help identify potential drug candidates through virtual screening and modeling how well candidates bind to target proteins.
(3) They help optimize drug candidates' properties like absorption, distribution, metabolism, excretion and toxicity to develop drug leads.
(4) Various software tools are used throughout the drug design process, from target identification to clinical trials, accelerating discovery.
Week 5 power point slide -3-case study 3- designing drug virtually
Computers play several important roles in the drug discovery process:
(1) They help analyze molecular structures, organize data on molecules and compounds in databases, and visualize and model molecules.
(2) They help identify potential drug candidates through virtual screening of large libraries of compounds.
(3) They help optimize drug candidates' binding to target proteins and properties like absorption, distribution, metabolism and toxicity.
(4) Drug design software is used for structure-based and ligand-based drug design to model proteins, design novel drugs and dock ligands to targets virtually.
Artificial intelligence ,robotics and cfd by sneha gaurkar
The document discusses artificial intelligence, robotics, and computational fluid dynamics. It provides introductions and definitions for each topic, as well as descriptions of their applications in areas like pharmaceutical manufacturing and drug discovery. It also outlines some advantages and challenges of adopting AI technologies in the pharmaceutical industry, such as reducing errors but also challenges around data quality and changing traditional practices. The document takes an overview approach to these emerging fields.
1) AI systems like Adam and Eve have discovered new scientific knowledge by autonomously generating and testing hypotheses about yeast genes using public databases and laboratory experiments.
2) AI is being applied throughout the drug development process, including target identification, compound design and synthesis, clinical trial optimization, and drug repurposing.
3) Partnerships between pharmaceutical companies and AI firms are exploring applications like generating new immuno-oncology treatments, metabolic disease therapies, and cancer treatments through large-scale data analysis.
Artificial intelligence in Drug discovery and delivery.pptx
This document summarizes a seminar on integrating artificial intelligence in drug discovery and delivery. It begins with an introduction to AI, defining it as using machine learning to emulate human cognitive tasks. It then reviews literature on using AI in various pharmaceutical applications and discusses types of AI like deep learning and machine learning. The document outlines several uses of AI in drug discovery for tasks like target identification, toxicity prediction, and drug design. It also discusses using AI to model drug delivery systems like solid dispersions and emulsions. Finally, it acknowledges challenges of AI integration like data quality but emphasizes the benefits of combining AI and human expertise to enhance the drug development process.
‘Integration of Artificial Intelligence in Drug Discovery and Delivery A Comp...
This process comprises obtaining data, developing efficient systems for the uses of obtained data, illustrating definite or approximate conclusions and self-corrections / adjustments.
In general, AI is used for analyzing the machine learning to imitate thecognitive tasks of individual.
AI technology is exercised to perform more accurate analyses as well as to attain useful interpretation.
It can handle large volumes of data with enhanced automation.
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The document discusses how artificial intelligence can be applied in clinical trials to improve efficiency and outcomes. It provides examples of how AI is currently used across different stages of drug development, from data aggregation and analysis to patient recruitment and monitoring. The use of AI and machine learning applied to real-world data is highlighted as a way to better understand diseases, select appropriate patients and sites, and design more effective clinical trial processes and studies. Case studies are presented showing how several companies are already using AI to match patients to suitable trials, analyze cancer patient data to identify eligibility, and create more personalized treatments.
This document discusses the use of artificial intelligence in clinical trials. It begins with definitions of artificial intelligence and examples of AI technologies like machine learning, deep learning, and natural language processing. It then provides examples of how AI can be used across different stages of drug development from data aggregation to clinical trial design, patient recruitment, monitoring and analysis. Specific companies applying AI in clinical trials are highlighted, such as Antidote using machine learning to match patients to trials and Mendel.ai analyzing cancer patient data to identify eligible patients. The document concludes that while clinical trials will still use the gold standard of randomized controlled trials, AI has potential to transform trials by improving success rates, reducing costs and accelerating drug development through applications like enhanced patient selection
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AI is a program designed to produce outcome in a manner similar to human intelligence,logic and reasoning.This can be used in field of Pharmacy for betterment of humankind, to save lives,money and time
Machine learning in health data analytics and pharmacovigilanceRevathi Boyina
Machine learning and data analytics can help improve pharmacovigilance in several ways:
1) Machine learning algorithms can automatically extract adverse drug reactions from biomedical literature and FDA drug labels, helping pharmacovigilance teams more efficiently identify all potential ADRs.
2) Large healthcare datasets and sophisticated algorithms can help pharmaceutical companies with drug discovery, clinical trials, personalized treatment, and epidemic outbreak prediction.
3) Advances in machine learning are reshaping healthcare and have the potential to cut clinical trial costs, improve quality, speed up trials, and facilitate tasks like reviewing literature, recruiting patients, and making diagnoses.
AI and machine learning are becoming increasingly influential in healthcare, enabling tools for disease diagnosis and drug discovery. Deep learning uses artificial neural networks to perform computations on large datasets to solve medical problems. AI can help with tasks like robotic surgery, targeted drug delivery, and automating data entry to free up physician time. While having potential benefits, AI also faces limitations such as data set size and quality that need to be addressed. Overall, AI shows promise as a tool to enhance healthcare and identify accurate diagnoses.
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This document discusses artificial intelligence and its applications in the pharmaceutical industry. It begins with definitions of artificial intelligence and its goal of simulating human logic and reasoning. It then describes several applications of AI in pharmaceuticals, including disease identification, personalized treatment, drug discovery/manufacturing, clinical trial research, radiology/radiotherapy, and electronic health records. Challenges and the future of AI are also mentioned. In conclusion, the author states that AI has great potential to guide humanity if developed responsibly.
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List out the challenges of ml ai for delivering clinical impact - PubricaPubrica
Pubrica explores the main challenges and limitations of AI in healthcare and considers the steps required to translate these potentially transformative technologies from research to clinical practice.
Continue Reading: https://bit.ly/3o4hjPT
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica?
When you order our services, Plagiarism free|on Time|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Artificial intelligence has the potential to accelerate drug discovery by generating new molecular structures, automatically designing drug candidates, and using historical data to identify treatments. Companies are using AI techniques like deep learning and generative adversarial networks to analyze vast amounts of data to propose new drug candidates. Robots are also being used in pharmaceutical laboratories and manufacturing to perform repetitive and precise tasks, allowing researchers to focus on higher-level work. Computational fluid dynamics is another tool being used to analyze and optimize pharmaceutical processes.
Week 5 power point slide -3-case study 3- designing drug virtuallyZulkifflee Sofee
Computers play several important roles in the drug discovery process:
(1) They help analyze molecular structures, organize data on molecules and compounds in databases, and visualize and model molecules.
(2) They help identify potential drug candidates through virtual screening and modeling how well candidates bind to target proteins.
(3) They help optimize drug candidates' properties like absorption, distribution, metabolism, excretion and toxicity to develop drug leads.
(4) Various software tools are used throughout the drug design process, from target identification to clinical trials, accelerating discovery.
Week 5 power point slide -3-case study 3- designing drug virtuallyZulkifflee Sofee
Computers play several important roles in the drug discovery process:
(1) They help analyze molecular structures, organize data on molecules and compounds in databases, and visualize and model molecules.
(2) They help identify potential drug candidates through virtual screening of large libraries of compounds.
(3) They help optimize drug candidates' binding to target proteins and properties like absorption, distribution, metabolism and toxicity.
(4) Drug design software is used for structure-based and ligand-based drug design to model proteins, design novel drugs and dock ligands to targets virtually.
Artificial intelligence ,robotics and cfd by sneha gaurkar Sneha Gaurkar
The document discusses artificial intelligence, robotics, and computational fluid dynamics. It provides introductions and definitions for each topic, as well as descriptions of their applications in areas like pharmaceutical manufacturing and drug discovery. It also outlines some advantages and challenges of adopting AI technologies in the pharmaceutical industry, such as reducing errors but also challenges around data quality and changing traditional practices. The document takes an overview approach to these emerging fields.
1) AI systems like Adam and Eve have discovered new scientific knowledge by autonomously generating and testing hypotheses about yeast genes using public databases and laboratory experiments.
2) AI is being applied throughout the drug development process, including target identification, compound design and synthesis, clinical trial optimization, and drug repurposing.
3) Partnerships between pharmaceutical companies and AI firms are exploring applications like generating new immuno-oncology treatments, metabolic disease therapies, and cancer treatments through large-scale data analysis.
Artificial intelligence in Drug discovery and delivery.pptxManjusha Bandi
This document summarizes a seminar on integrating artificial intelligence in drug discovery and delivery. It begins with an introduction to AI, defining it as using machine learning to emulate human cognitive tasks. It then reviews literature on using AI in various pharmaceutical applications and discusses types of AI like deep learning and machine learning. The document outlines several uses of AI in drug discovery for tasks like target identification, toxicity prediction, and drug design. It also discusses using AI to model drug delivery systems like solid dispersions and emulsions. Finally, it acknowledges challenges of AI integration like data quality but emphasizes the benefits of combining AI and human expertise to enhance the drug development process.
This process comprises obtaining data, developing efficient systems for the uses of obtained data, illustrating definite or approximate conclusions and self-corrections / adjustments.
In general, AI is used for analyzing the machine learning to imitate thecognitive tasks of individual.
AI technology is exercised to perform more accurate analyses as well as to attain useful interpretation.
It can handle large volumes of data with enhanced automation.
Similar to Artificial Intelligence (AI), Robotics and Computational fluid dynamics (20)
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Artificial Intelligence (AI), Robotics and Computational fluid dynamics
1. Department of Pharmaceutics
B. K. Mody Government Pharmacy College, Rajkot
Artificial intelligence (AI), robotics and
Computational fluid dynamics
Prepared by – Chintan S Kalsariya
M.pharm (sem - II)
1
2. Contents
2
No Title
1 AI and its pharmaceutical automation
2 Robotics
3 Computational fluid dynamics
4 Partnerships of AI establishments with pharmaceutical firms
5 AI-Aided Computational Tools for Facilitating Drug Discovery
4. 4
Artificial intelligence, or AI, is the field of computer science
that focuses on developing intelligent machines capable of
performing tasks that typically require human intelligence.
These tasks include things like understanding natural
language, recognizing images, making decisions, and
learning from experience.
AI systems use techniques like machine learning, deep
learning, and neural networks to process and analyze data,
allowing them to learn and adapt over time.
The ultimate goal of AI research is to create machines that
can think, reason, and act in ways that are clear from human
beings.
5. 5
Artificial intelligence-AI- is getting increasingly sophisticated at doing what humans
do, more efficiently, more cheaply.
According to father of AI John McCarthy…
It is a “the science and engineering of making
intelligent machines, especially intelligent
computer programs”.
7. 7
Artificial Neural Networks (ANNs) :
An Artificial Neural Network (ANN) is a machine
learning model inspired by the human brain's
neural structure. It comprises interconnected nodes
(neurons) organized into layers. Data flows through
these nodes, adjusting the weights of connections
to learn patterns and make predictions.
they can create nonlinear input-output mappings,
optimize gradient conditions in chromatography,
analyze multivariate nonlinear relationships in
pharmaceutical research, design pre-formulations,
and predict the behavior of drugs.
9. 9
2. Dynamic neural networks:
It is also known as recurrent networks, utilize past
information to predict present and future system states.
They are valuable for modeling drug release from
controlled formulations due to their ability to make
predictions based on past data.
1. Static Neural Network :
Static networks like Multilayer Perceptron (MLP) use feedforward connections to
compute outputs. They consist of multiple fully connected layers, aiding in recognizing
specific elements.
MLPs find applications in designing controlled release formulations, predicting drug
dissolution profiles, and optimizing formulations due to their interconnected nature.
10. 10
B. Pharma automation :
Pharmaceutical automation, powered by robotics and AI, is
transforming drug development, manufacturing, quality
control, and distribution.
Robotics ensure precision and speed in tasks like handling,
dispensing, and packaging, improving productivity and
reducing contamination risks.
AI analyzes data, predicts outcomes, and learns patterns,
enhancing quality assurance and driving innovation in
pharmaceutical processes.
11. 11
C. Advantages of AI & Pharma automation :
Error Reduction
Daily Application
Digital Assistants
Repetitive Jobs
Medical Applications
Save time
Decrease in cost for the end user
Increasing accuracy & reproducibility
12. 12
D. Dis-advantages of AI & Pharma automation :
Data Privacy and Security Concerns
Job Losses Due to Automation
Ethical Considerations
Regulation and Compliance
Technical Limitations
Lack of Trust and Understanding
Lack of Creativity
High Cost
13. 13
D. APPLICATIONS OF AI IN PHARMACEUTICALS
1. Drug Discovery and Development
A. Peptide synthesis
B. Identifying novel antimycobacterial drugs
C. Predicting the effectiveness of drug dosing and delivery methods
D. Rapid identification of the bioactive agents and monitoring of drug release
E. Optimizing drug release from matrix tablets
2. Manufacturing and Supply Chain
3. Patient Care and Drug Delivery
4. Regulatory Compliance and Safety
5. Clinical Research
6. Disease Identification
7. Radiology & Radiotherapy
14. 14
1. Drug Discovery and Development
A. Peptide synthesis
Biotechnology and peptide synthesis
advancements have enabled the exploration of
peptides' pharmacological effects.
Artificial Neural Networks (ANNs) are used to
evaluate organ-targeting peptides, accurately
predict peptide binding, and identify immune
response targets.
15. 15
B. Identifying novel antimycobacterial drugs
Virtual screening techniques and an
Artificial Neural Network (ANN) model
are used to identify new antimycobacterial
drugs and reduce multidrug-resistant
tuberculosis.
Cheminformatics tools and molecular
descriptors are employed to evaluate
antimycobacterial agents and identify
effective compounds against
Mycobacterium tuberculosis.
16. 16
C. Predicting the effectiveness of drug dosing and delivery methods:
A data-driven predictive system has been developed
using a machine learning framework capable of
modeling the dynamics between pathogens and drugs.
The system predicts the effectiveness of dosing
patterns and drug delivery methods.
The system achieved an accuracy of 85% in
performance evaluation.
17. 17
D. Rapid identification of the bioactive agents & monitoring of drug release
ANNs are used in various pharmaceutical applications, including
drug modeling, drug release prediction, and controlled drug
delivery optimization.
They aid in identifying drug structures, predicting pharmacokinetic
parameters, and enhancing drug delivery systems.
E. Optimizing drug release from matrix tablets
ANNs are used in optimizing drug release from matrix tablets, predicting dissolution
profiles, developing sustained-release formulations, and controlling release in various
tablet types.
They outperform traditional methods and enable accurate predictions of formulations and
release profiles.
18. 18
2. Manufacturing and supply chain
AI-powered systems can optimize pharmaceutical
manufacturing processes, improve quality control, and predict
supply chain disruptions, leading to cost savings and increased
productivity.
3. Patient Care and Drug Delivery
AI can be used to personalize treatment plans, predict patient
responses to drugs, and improve drug delivery systems for
better patient outcomes.
19. 19
4. Regulatory Compliance and Safety
AI can assist in the analysis of adverse event reports to
identify potential safety issues with drugs and help in the
development of safer drugs.
Automation and AI are reshaping clinical labs, boosting
efficiency and accuracy.
Robotics handle tasks like sample management, while
AI analyzes data for diagnoses and treatment plans.
5. Clinical Research
20. 20
Machine learning aids in rare disease identification and treatment optimization.
This integration improves testing speed, accuracy, and patient outcomes while lowering
costs.
Expect further advancements as technology evolves, enhancing healthcare delivery.
21. 21
6. Disease Identification
Machine Learning – particularly Deep Learning algorithms – have recently made
huge advances in automatically diagnosing diseases, making diagnostics cheaper
and more accessible.
How machines learn to diagnose
ML algorithms can learn to see patterns similarly to the way doctors see them. A
key difference is that algorithms need a lot of concrete
So Machine Learning is particularly helpful in areas where the diagnostic
information, a doctor examines is already digitized
22. 22
Cedars-Sinai investigators can identify comprehensive cardiovascular risk from CT scans
obtained without contrast dye, which some patients cannot tolerate, through the use of AI
algorithms.
[Image by Cedars-Sinai]
Exa software - AlphaMissense
23. 23
7. Radiology & Radiotherapy
Various kinds of data are collected during radiotherapy, including clinical
information, biological samples, images, planning parameters, and machine data.
These data can be combined and related to events like accurate contouring, plan
passing rate, treatment response and injury through AI approaches.
Ex- Qure.AI
25. 25
Robots, as defined by ISO 8373, are versatile
manipulators used in industrial automation.
In labs and pharmaceutical settings, they
handle tasks surpassing human capacity,
often in hazardous environments.
They assemble medical devices, prepare
prescriptions, and automate assay analysis,
significantly reducing costs and time in
research and healthcare.
28. 28
Disadvantages
Expensive to set up and startup
Many techniques have not been developed for
automation yet.
There are difficultly automating instances where visual
analysis, recognition or comparison is required such as
color changes.
Systems require the use of programming languages such
as C++ or visual Basic to run more complicated tasks.
Increases job shortage as automation may replace staff
members who do tasks easily replicated by a robot
29. 29
Application of Robotics
i. Research & development (R&D)
ii. Laboratory robotics - Thermo Fisher Scientific Spinnaker
Microplate Mover
iii. Clean rooms (SO 14644-1)
iv. Packaging operation
v. Vial holding robot
vi. Robotic Prescription Dispensing Systems-
ex- ScriptPro’s (SP)
30. 30
i. Research & development (R&D)
Robots now also play an essential role in the
development of new drugs.
In high throughput screening (H.T.S.) for instance,
millions of compounds are tested to determine which
could become new drugs.
There is a need for the use of robotics to test these
millions of compounds.
The use of robotics can speed this process up
significantly, just as they can any other process where
a robot replaces a person completing any repetitive
task.
31. 31
ii. Laboratory robotics
Robots in laboratories revolutionize scientific processes with automation, precision,
and efficiency.
They aid in drug discovery, assembly, and packaging, ensuring safety in hazardous
environments.
In pharmaceutical R&D, they accelerate trials and innovation. In environmental
monitoring and space exploration, robots collect data and perform tests, expanding
scientific understanding.
Additionally, they automate DNA sequencing and high-throughput assays, reducing
errors and enhancing experiment efficiency.
Ex- Cartesian, SCARA, Anthropomorphic
32. 32
iii. Clean rooms robots
Cleanroom robots, built for controlled environments like
pharmaceutical manufacturing, prioritize cleanliness and particle
control.
They're made from materials that prevent contamination deposition
and are sealed to prevent liquid ingress.
Certified to meet ISO 14644-1 standards, they're vital in industries
like healthcare and electronics, ensuring safety and quality while
minimizing contamination risk.
Reliable and efficient, they handle tasks with precision, reducing
human intervention and error.
Ex - Stäubli TX2-60 Cleanroom Robot.
33. 33
iv. Vial holding robot
Vial holding robots are crucial in the pharmaceutical industry, handling vials with precision
for tasks like filling, labeling, capping, and packaging.
They improve efficiency, reduce contamination and human error, and can work continuously
35. 35
v. Robotic Prescription Dispensing Systems
Robotic Prescription Dispensing Systems improve
efficiency, safety, and precision in the pharmaceutical
industry by automating tasks such as medication
dispensing and inventory management.
They can process a high volume of prescriptions,
reducing workload and human error.
Companies like ScriptPro offer systems with high
accuracy and reliability, while robots are also used in
drug discovery and cleanroom environments.
36. 36
iv. Packaging operation
Packaging processes, like other pharmaceutical operations, benefit from the speed and
repeatability that automation brings.
Pharmaceutical packaging machines are often custom designed to handle specific
product configurations such as vials.
39. 39
CFD is science that uses digital computer make quantitative prediction about fluid
flow phenomena.
CFD based on the conversation law of fluid motion, such as conversation of mass,
momentum, energy.
CFD can be viable tool to analyse and troubleshoot in various process & equipment
used in pharmaceutical industry.
The integration of CFD methods leads to shortened product-process development
cycle, optimization of existing processes, reduce energy requirements, efficient
design of new product and processes and reduce to market.
Unit operation in pharma typically handle Large amount of fluid.
40. 40
As, a result small increment in efficiency may generate large increment in product
cost savings.
CFD is a powerful tool in the pharmaceutical industry, enabling various applications
such as optimizing drug delivery systems, enhancing equipment and process
characterization, and supporting upstream biopharmaceutical manufacturing
processes.
41. 41
CFD analysis involves three main steps:
I. Pre-processing:
Identifying the flow region
Geometrical representation of the flow region
Defining an appropriate mesh for the region
Application of fluid dynamics principles.
II. Solution:
Uses a trial-and-error strategy to compute the
solution
All steps have been computerized.
.
III. Post-processing:
Analyzes the results
The solution provides data of the problem,
represented on the flow field
Plots flow variables on the 3D region of
interest
Analyst plots.
42. 42
Advantage
Enhanced understanding of fluid dynamics
Cost and time savings
Safety and risk management
Design optimization
Ability to simulation of real condition - CFD provides the ability to
theoretical simulation of any physical condition.
43. 43
Disadvantages
Complex setup and high computational requirements
Modeling limitations
Potential inaccuracies
Very expensive.
Possibility of error from unknown sources
44. 44
Application of CFD:
i. CFD in Mixing Process:
Static mixers: used for high viscous fluids, with
CFD analyzing their efficiency and mixing capacity
Stirred mixers: large mixing tanks with various
sizes and impeller types, analyzing flow
characteristics, impeller influence, and shear stress
distribution
Shear stress distribution is crucial for dissolution,
dispersion, and emulsification properties
45. 45
II. CFD in Separation Process
Centrifugation techniques for thickening,
solid-liquid separation, and post-treatment,
with CFD studying centrifuge design and
performance.
Separation devices like cyclones and
scrubbers analyzed for efficiency,
Ex - 90% of 10-μm particles separated in
cyclones.
46. 46
III. CFD in Drying Process:
Analyzes spray dryer performance and congregation changes during drying to
avoid unnecessary costs and risks
47. 47
IV. CFD in Packing Process
Used for liquid formulations like syrups, suspensions, and emulsions to optimize
filing lines and avoid issues like delayed filling, spilling, splashing, and frothing.
V. CFD in Designing of Inhalers
MDI and DPI are common, with Computational
Fluid Dynamics (CFD) improving efficiency and
reproducibility of drug products.
CFD technology traces drug particle trajectories
inside the lung.
48. 48
VI. Dissolution apparatus hydrodynamics
Dissolution testing is crucial in pharma for
optimization, stability, and more.
CFD simulates and analyzes dissolution apparatus
hydrodynamics.
The USP paddle apparatus, the most common,
shows small tablet position changes significantly
impact dissolution rates due to varying
hydrodynamics.
49. 49
VII. CFD for energy generation and energy transfer device
CFD techniques analyze thermal and flow fields in devices, including flame
characteristics.
Flame stability and burner efficiency are crucial for process heaters, power plants, and
furnaces.
Flame length, shape, and size affect the process; long flames may cause damage, while
short flames can wear out the burner tip.
52. 52
References:
Automation and artificial intelligence in the clinicalLaboratory by Christopher Naugler & Deirdre L.
Church https://doi.org/10.1080/10408363.2018.1561640
The journal of “ Advanced Drug Delivery Reviews “ The significance of artificial intelligence in
drug delivery system design by Parichehr Hassanzadeh ⁎, Fatemeh Atyabi, Rassoul Dinarvand
The Journal of Young Pharmacists Artificial Intelligence the Futuristic Technology in the Drug
Discovery Process: A Review Annammadevi Govardhini Sayam*, Maharnab Pradhan, Aman Kumar
Choudhury
International Journal of Molecular Sciences Review Artificial Intelligence and Machine Learning
Technology Driven Modern Drug Discovery and Development by Chayna Sarkar 1, Biswadeep Das
2,* , Vikram Singh Rawat 3 , Julie Birdie Wahlang 1, Arvind Nongpiur 4, Iadarilang Tiewsoh 5,
Nari M. Lyngdoh 6, Debasmita Das 7, Manjunath Bidarolli 2 and Hannah Theresa Sony 2.
53. 53
Tamanna Sharma, Abhinav Mankoo, Vivek Sood. Artificial intelligence in advanced pharmacy.
International Journal of Science and Research Archive. 2021;2(1):047-054.
doi:10.30574/ijsra.2021.2.1.0301
Panwar V, Vandrangi SK, Emani S. Artificial intelligence-based computational fluid dynamics
approaches. In: Hybrid Computational Intelligence. Elsevier; 2020:173-190. doi:10.1016/b978-0-12-
818699-2.00009-3.
Wang B, Wang J. Application of Artificial Intelligence in Computational Fluid Dynamics. Ind Eng
Chem Res. 2021;60(7):2772-2790. doi:10.1021/acs.iecr.0c05045.
1. https://www.pharmatutor.org/articles/pharmaceutical-industrial-applications-robots-current-scenario-
recent-review.
1. Ivana Masic, Jelena Parojcic, and Zorica Djuric, University of Belgrade “Computer- aided
applications in pharmaceutical technology”Published by Woodhead Publishing Limited, 2013,
(page no.233)
54. 54
G.T.U QUESTINS
1. Give merits and demerits of pharmaceutical automation.
2. Write a note on artificial intelligence.
3. Give general overview of robotics and computational fluid
dynamics
4. Discuss the application of AI in drug delivery.
5. Discuss the application of computational fluid dynamics in drug
delivery.
6. Discuss the role of robotic system in pharmaceutical industry.
7. What are the advantages and disadvantages of automation in
pharmaceutical industry.