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Artificial Intelligence in Formulation and Product Design of BCS Class I and II Drugs

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Micro-Electronics and Telecommunication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 617))

Abstract

The pharmaceutical product development process is a challenging process involving two significant steps, namely formulation development and product manufacturing. A formulation comprises of an API/drug (pharmacological active compound) and a group of inactive substances known as excipients. The process of selecting excipients and their proportion in an intended physical form of the drug for its administration (dosage form or pharmaceutical product) is known as the formulation. The selection of excipient(s) is a complex process that depends upon various factors associated with the drug, drug-excipient interaction, and the impact of the excipient on the product efficacy, i.e., its intended attributes like product stability, drug release, bioavailability, and many more. Thus, it involves extensive experimentation and hence is challenging in terms of carrying out these requisite trial runs. The application of artificial intelligence may help in reducing the time required to carry out trials and wastage of resources via providing limited and promising formulation designs based upon the evaluation and correlation of existing experimental data through various networking models. In this manuscript, we have represented the outcome of an AI-based pharmaceutical formulation design model which supports the active involvement of AI into fully automated computer-assisted pharmaceutical product development solution, leading to optimization of resource and overcoming the financial constraints via avoiding excessive wastages expected during product design trials.

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Acknowledgements

I would like to acknowledge the inputs given by the team members of University Institute of Computer Sciences and Engineering for their support in compiling and framing the algorithms and networking-related components of the project.

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Authors and Affiliations

  1. University Institute of Pharma Sciences (UIPS), Chandigarh University Gharuan, Mohali, Punjab, 140413, India

    Jayant Singh Goud, Sanjay Kumar Elisetti & Vimal Arora

  2. University Institute of Computer Sciences and Engineering Chandigarh University Gharuan, Mohali, Punjab, 140413, India

    Navneet Kaur, Ranjit Singh & Kamaljit Singh Saini

Authors
  1. Jayant Singh Goud
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  2. Sanjay Kumar Elisetti
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  3. Navneet Kaur
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  4. Ranjit Singh
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  5. Kamaljit Singh Saini
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  6. Vimal Arora
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Corresponding author

Correspondence to Vimal Arora .

Editor information

Editors and Affiliations

  1. SRM Institute of Science and Technology, Ghaziabad, India

    Devendra Kumar Sharma

  2. National Taipei University of Business, Hualien, Taiwan

    Sheng-Lung Peng

  3. SRM Institute of Science and Technology, Ghaziabad, Uttar Pradesh, India

    Rohit Sharma

  4. Department of Embedded Systems Engineering, College of Information Technology, Incheon National University, Incheon, Korea (Republic of)

    Gwanggil Jeon

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Goud, J.S., Elisetti, S.K., Kaur, N., Singh, R., Saini, K.S., Arora, V. (2023). Artificial Intelligence in Formulation and Product Design of BCS Class I and II Drugs. In: Sharma, D.K., Peng, SL., Sharma, R., Jeon, G. (eds) Micro-Electronics and Telecommunication Engineering . Lecture Notes in Networks and Systems, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-19-9512-5_44

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  • DOI: https://doi.org/10.1007/978-981-19-9512-5_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-9511-8

  • Online ISBN: 978-981-19-9512-5

  • eBook Packages: EngineeringEngineering (R0)

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