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Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. ​

文献信息 ​

DOI10.3390/pharmaceutics15071916
PMID37514102
期刊Pharmaceutics
影响因子5.5
JCR 分区Q1
发表年份2023
被引次数154
关键词PBPK, 定量构效关系（QSAR）, 人工智能（AI）, 剂型测试, 药物发现
文献类型Journal Article, Review
ISSN1999-4923
期号15(7)
作者Lalitkumar K Vora, Amol D Gholap, Keshava Jetha, Raghu Raj Singh Thakur, Hetvi K Solanki, Vivek P Chavda

一句话小结 ​

本综述探讨了人工智能在药物发现、递送设计和过程优化中的应用，强调其通过分析生物数据提高药物开发效率和降低成本的潜力。研究表明，AI技术能够识别疾病靶点、预测药物相互作用，并促进个性化医学，展现了改善药物开发流程和患者治疗效果的前景。

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PBPK · 定量构效关系（QSAR） · 人工智能（AI） · 剂型测试 · 药物发现

摘要 ​

人工智能（AI）已成为一种强大的工具，它利用类人知识并为复杂挑战提供快速解决方案。在药物发现、制剂和药物剂型测试方面，AI技术和机器学习的显著进展为转型机会带来了新的可能性。通过利用分析广泛生物数据（包括基因组学和蛋白质组学）的AI算法，研究人员能够识别与疾病相关的靶点，并预测它们与潜在药物候选者的相互作用。这使得药物发现过程更加高效和针对性，从而提高成功获得药物批准的可能性。此外，AI还可以通过优化研发流程来降低开发成本。机器学习算法有助于实验设计，并能够预测药物候选者的药代动力学和毒性。这一能力使得优先考虑和优化领先化合物成为可能，从而减少大量昂贵的动物实验需求。个性化医学的方法可以通过分析现实世界患者数据的AI算法得到促进，从而实现更有效的治疗效果和改善患者依从性。本综述探讨了AI在药物发现、药物递送剂型设计、过程优化、测试以及药代动力学/药效学（PK/PD）研究中的广泛应用。本综述概述了在制药技术中采用的各种基于AI的方法，突显其优缺点。然而，制药行业对AI的持续投资和探索为改善药物开发流程和患者护理提供了令人振奋的前景。

英文摘要 ​

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.

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主要研究问题 ​

  1. 在药物发现过程中，人工智能如何提高药物靶点的识别效率？
  2. 机器学习在药物毒性预测中的具体应用有哪些？如何提高预测的准确性？
  3. 人工智能在个性化医学中的角色如何演变？未来可能带来哪些新机遇？
  4. AI算法在优化药物研发成本方面的具体案例有哪些？它们的成效如何评估？
  5. 在药物递送系统设计中，人工智能可以解决哪些具体挑战？有哪些成功的应用实例？

核心洞察 ​

研究背景和目的 ​

随着人工智能（AI）技术的快速发展，药物发现和药物递送领域正经历着显著的变革。AI的应用可以加速药物的发现、优化制剂设计和提高临床试验的效率。本综述旨在探讨AI在药物技术和药物递送设计中的多种应用，评估其潜在优势与局限性，并展望未来的研究方向。

主要方法/材料/实验设计 ​

本研究通过分析大量的生物数据（如基因组学和蛋白质组学），结合机器学习算法，识别与疾病相关的靶点，并预测其与潜在药物候选物的相互作用。以下是技术路线的流程图：

Mermaid diagram

关键结果和发现 ​

  1. 药物发现：AI算法在药物发现过程中能有效识别潜在的药物靶点，进行虚拟筛选，加速新药候选物的确定。
  2. 药物优化：通过建立结构-活性关系（SAR）模型，AI可以优化药物分子的化学结构，提高其生物活性和药代动力学特性。
  3. 个性化医疗：AI分析患者的真实世界数据，促进个性化治疗方案的制定，提高患者的依从性和治疗效果。
  4. 临床试验优化：AI在患者招募、试验设计和实时数据分析方面展现出巨大的潜力，能够加快临床试验的进程。

主要结论/意义/创新性 ​

AI的引入为药物开发带来了新的视角和工具，使得药物发现和开发过程更加高效、精准。通过利用AI技术，制药行业可以降低研发成本、缩短时间并提高药物的成功率。此外，AI在个性化医疗和药物递送系统中的应用将进一步改善患者的治疗体验和结果。

研究局限性和未来方向 ​

尽管AI在药物开发中展现出巨大潜力，但仍存在一些局限性：

  • 数据依赖性：AI模型需要大量高质量的数据进行训练，数据不足或偏差可能影响预测结果的准确性。
  • 可解释性：许多AI模型的“黑箱”特性使得其预测结果难以解释，可能影响临床应用的信任度。
  • 更新困难：随着新数据的不断出现，更新AI模型可能需要额外的时间和资源。

未来的研究应聚焦于提高AI模型的可解释性、减少数据偏差和开发更灵活的模型更新机制，以促进AI在药物开发中的广泛应用。

参考文献 ​

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  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)

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