MaltSci 麦伴科研

Appearance

Curated Papers > Highly Cited Authoritative Literature on "AI Drug Discovery"

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. ​

Literature Information ​

DOI10.3390/pharmaceutics15071916
PMID37514102
JournalPharmaceutics
Impact Factor5.5
JCR QuartileQ1
Publication Year2023
Times Cited154
KeywordsPBPK, QSAR, artificial intelligence (AI), dosage form testing, drug discovery
Literature TypeJournal Article, Review
ISSN1999-4923
Issue15(7)
AuthorsLalitkumar K Vora, Amol D Gholap, Keshava Jetha, Raghu Raj Singh Thakur, Hetvi K Solanki, Vivek P Chavda

TL;DR ​

This review discusses the transformative potential of artificial intelligence (AI) in drug discovery and development, emphasizing its ability to analyze biological data for identifying disease targets and predicting drug interactions, which enhances efficiency and reduces costs. By optimizing research processes and facilitating personalized medicine, AI presents significant opportunities for improving treatment outcomes and advancing pharmaceutical technology, despite some challenges.

Search for more papers on MaltSci.com ​

PBPK · QSAR · artificial intelligence (AI) · dosage form testing · drug discovery

Abstract ​

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

MaltSci.com AI Research Service ​

Intelligent ReadingAnswer any question about the paper and explain complex charts and formulas
Locate StatementsFind traces of a specific claim within the paper
Add to KBasePerform data extraction, report drafting, and advanced knowledge mining

Primary Questions Addressed ​

  1. How can AI algorithms specifically enhance the accuracy of drug interaction predictions in pharmaceutical research?
  2. What are the potential ethical implications of using AI in personalized medicine approaches within the pharmaceutical industry?
  3. In what ways can machine learning improve the efficiency of clinical trials in drug development?
  4. How does the integration of AI in drug formulation processes impact the overall cost and time required for drug development?
  5. What challenges do researchers face when implementing AI technologies in pharmacokinetics and pharmacodynamics studies?

Key Findings ​

Research Background and Objectives ​

Artificial Intelligence (AI) has rapidly emerged as a transformative force in the pharmaceutical industry, addressing critical challenges in drug discovery, formulation, and testing. This review aims to provide a comprehensive overview of AI applications in pharmaceutical technology, focusing on its impact on drug delivery systems, process optimization, and pharmacokinetics/pharmacodynamics (PK/PD) studies.

Main Methods/Materials/Experimental Design ​

The review discusses various AI methodologies, particularly machine learning (ML) techniques, including supervised and unsupervised learning. Key AI models used in pharmaceutical applications are summarized in the following flowchart:

Mermaid diagram

Key Results and Findings ​

  1. Drug Discovery and Design: AI algorithms enhance the identification of drug targets, optimize lead compounds, and predict drug interactions, significantly improving the efficiency of drug development.
  2. Process Optimization: AI aids in the optimization of drug formulations, predicting release kinetics, and ensuring quality control during manufacturing processes.
  3. Clinical Trials: AI technologies facilitate patient recruitment, trial design, and real-time data analysis, thus improving trial efficiency and reducing costs.
  4. Pharmacokinetics and Pharmacodynamics: AI models predict ADME (absorption, distribution, metabolism, and excretion) parameters, offering insights into drug behavior and safety.

Main Conclusions/Significance/Innovativeness ​

AI represents a paradigm shift in pharmaceutical development, enabling faster, more accurate, and cost-effective drug discovery and delivery processes. The integration of AI into pharmacokinetics and pharmacodynamics enhances the understanding of drug actions and interactions, paving the way for personalized medicine approaches. The review emphasizes the potential of AI to address existing challenges in the pharmaceutical sector, ultimately leading to improved patient outcomes.

Research Limitations and Future Directions ​

  1. Data Limitations: The effectiveness of AI models is contingent on the quality and availability of training data, which can be limited for rare diseases or specific populations.
  2. Bias and Interpretability: AI models can exhibit biases based on the training data and may lack transparency, making it challenging to interpret results and gain regulatory approval.
  3. Need for Collaboration: Future advancements in AI applications in pharmaceuticals will require collaboration between researchers, regulatory bodies, and healthcare professionals to ensure ethical considerations and data integrity.

Future research should focus on improving data quality, enhancing model interpretability, and integrating AI with traditional methodologies to validate predictions and enhance the reliability of AI-driven drug development processes. The review highlights the need for continued investment in AI technologies to unlock their full potential in revolutionizing the pharmaceutical industry.

References ​

  1. Deep learning in neural networks: an overview. - Jürgen Schmidhuber - Neural networks : the official journal of the International Neural Network Society (2015)
  2. Predictive modeling of clinical trial terminations using feature engineering and embedding learning. - Magdalyn E Elkin;Xingquan Zhu - Scientific reports (2021)
  3. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. - Hm Jones;K Rowland-Yeo - CPT: pharmacometrics & systems pharmacology (2013)
  4. QSAR, molecular docking and ADMET properties in silico studies of novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2-Yl derivatives derived from dimedone as potent anti-tumor agents through inhibition of C-Met receptor tyrosine kinase. - Ossama Daoui;Souad Elkhattabi;Samir Chtita;Rachida Elkhalabi;Hsaine Zgou;Adil Touimi Benjelloun - Heliyon (2021)
  5. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. - Yogesh Kumar;Apeksha Koul;Ruchi Singla;Muhammad Fazal Ijaz - Journal of ambient intelligence and humanized computing (2023)
  6. mRNA-Based Vaccines and Therapeutics for COVID-19 and Future Pandemics. - Vivek P Chavda;Shailvi Soni;Lalitkumar K Vora;Shruti Soni;Avinash Khadela;Jinal Ajabiya - Vaccines (2022)
  7. Machine Learning in Drug Discovery: A Review. - Suresh Dara;Swetha Dhamercherla;Surender Singh Jadav;Ch Madhu Babu;Mohamed Jawed Ahsan - Artificial intelligence review (2022)
  8. Example-based support vector machine for drug concentration analysis. - Wenqi You;Nicolas Widmer;Giovanni De Micheli - Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference (2011)
  9. We Cannot Win the Access to Medicines Struggle Using the Same Thinking That Causes the Chronic Access Crisis. - Gaëlle Krikorian;Els Torreele - Health and human rights (2021)
  10. Machine learning predicts 3D printing performance of over 900 drug delivery systems. - Brais Muñiz Castro;Moe Elbadawi;Jun Jie Ong;Thomas Pollard;Zhe Song;Simon Gaisford;Gilberto Pérez;Abdul W Basit;Pedro Cabalar;Alvaro Goyanes - Journal of controlled release : official journal of the Controlled Release Society (2021)

Literatures Citing This Work ​

  1. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. - Sarfaraz K Niazi - Drug design, development and therapy (2023)
  2. Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside. - K S Vidhya;Ayesha Sultana;Naveen Kumar M;Harish Rangareddy - Cureus (2023)
  3. Exploring the Potential of Artificial Intelligence for Hydrogel Development-A Short Review. - Irina Negut;Bogdan Bita - Gels (Basel, Switzerland) (2023)
  4. Development of Bioactive Scaffolds for Orthopedic Applications by Designing Additively Manufactured Titanium Porous Structures: A Critical Review. - Mikhail V Kiselevskiy;Natalia Yu Anisimova;Alexei V Kapustin;Alexander A Ryzhkin;Daria N Kuznetsova;Veronika V Polyakova;Nariman A Enikeev - Biomimetics (Basel, Switzerland) (2023)
  5. Applications of Artificial Intelligence in Microbial Diagnosis. - Yogendra P Shelke;Ankit K Badge;Nandkishor J Bankar - Cureus (2023)
  6. AI Models and Drug Discovery Within Pharmaceutical Drug Market. - Bridget Klaus - Delaware journal of public health (2023)
  7. Revolutionizing Patient Care: A Comprehensive Review of Artificial Intelligence Applications in Anesthesia. - Amol Singam - Cureus (2023)
  8. Genetic perspectives on childhood monogenic diabetes: Diagnosis, management, and future directions. - Hong-Yan Sun;Xiao-Yan Lin - World journal of diabetes (2023)
  9. Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin. - Md Ashrafur Rahman;Evangelos Victoros;Julianne Ernest;Rob Davis;Yeasna Shanjana;Md Rabiul Islam - Clinical pathology (Thousand Oaks, Ventura County, Calif.) (2024)
  10. Artificial Intelligence Technologies used for the Assessment of Pharmaceutical Excipients. - Ashutosh Kumar;Ghanshyam Das Gupta;Sarjana Raikwar - Current pharmaceutical design (2024)

... (144 more literatures)

© 2025 MaltSci - We reshape scientific research with AI technology