Artificial Intelligence (AI), Robotics and Computational fluid dynamics

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

1. Department of Pharmaceutics B. K. Mody Government Pharmacy College, Rajkot A rtificial 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

3. 1. ARTIFICIAL INTELLEGENCE (AI) 3

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”.

6. 6 How the AI works :

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.

8. 8 Ty pes of neural networks:

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 Drug Discovery and Development Peptide synthesis Identifying novel antimycobacterial drugs Predicting the effectiveness of drug dosing and delivery methods Rapid identification of the bioactive agents and monitoring of drug release 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 . Cheminformatic s 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 c hain 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 pl ans. 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

24. 24 2. ROBOTICS

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.

26. 26

27. 27 Advantages: Accuracy Tirelessness Reliability Return on investment Affordability Production Quality Speed Flexibility Safety Savings Reduced chances of contamination

28. 28 Disadvantages E xpensive 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. S ystems 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 Research & development (R&D) Laboratory robotics - Thermo Fisher Scientific Spinnaker Microplate Mover Clean rooms ( SO 14644-1) Packaging operation Vial holding robot Robotic Prescription Dispensing Systems- ex- ScriptPro’s (SP)

30. 30 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 , A nthropomorphic

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

34. 34

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.

37. 37

38. 38 3. Computational fluid dynamics

39. 39 CFD is science that uses digital computer make quantitative predictio n 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: Pre-processing: Identifying the flow region Geometrical representation of the flow region Defining an appropriate mesh for the region Application of fluid dynamics principles. 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: 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.

50. 50 Partnerships of AI establishments with pharmaceutical firms .

51. 51 AI-Aided Computational Tools for Facilitating Drug Discovery .

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. https://www.pharmatutor.org/articles/pharmaceutical-industrial-applications-robots-current-scenario-recent-review .   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 Give merits and demerits of pharmaceutical automation. Write a note on artificial intelligence. Give general overview of robotics and computational fluid dynamics Discuss the application of AI in drug delivery. Discuss the application of computational fluid dynamics in drug delivery. Discuss the role of robotic system in pharmaceutical industry. What are the advantages and disadvantages of automation in pharmaceutical industry.

55. 55 Thank you