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Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.
Advanced Healthcare Materials ( IF 10.0 ) Pub Date : 2020-07-06 , DOI: 10.1002/adhm.201901862
Ajay Vikram Singh 1 , Mohammad Hasan Dad Ansari 2, 3 , Daniel Rosenkranz 1 , Romi Singh Maharjan 1 , Fabian L Kriegel 1 , Kaustubh Gandhi 4 , Anurag Kanase 5 , Rishabh Singh 6 , Peter Laux 1 , Andreas Luch 1
Affiliation  

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial‐based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)‐based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure–activity relationships at nanoscale (nano‐QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

中文翻译:

计算纳米毒理学中的人工智能和机器学习:纳米医学的发展和赋能。

纳米医学的进步,加上在纳米尺度上制造先进材料的新颖方法,为医疗保健和医疗产品的发展开辟了新的前景。必须特别注意基于纳米材料的产品的安全设计方法。最近,人工智能(AI)和机器学习(ML)赋予了计算工具以增强和改善纳米毒理学和纳米治疗学的仿真和建模过程的能力。特别地,体外产生的药代动力学和药效学与体内应用场景之间的相关性是朝着开发安全的纳米药物产品迈出的重要一步。这篇评论描绘了如何在计算机模型中使用体外和体内数据集来解锁和授权纳米医学。主要描述了基于生理学的药代动力学(PBPK)建模和基于吸收,分布,代谢和排泄(ADME)的计算机模拟方法,以及作为纳米医学关注领域的剂量学模型。回顾了计算OMICS,胶体颗粒测定和建立吸入毒理学剂量学的算法,以及在纳米级(nano-QSAR)的定量构效关系。在这个计算主导的时代,纳米毒理学盲点所面临的挑战和机遇被凸显为加速纳米医学临床翻译的未来。并建立了用于确定吸入毒理学的剂量学的算法,并重新研究了纳米级(nano-QSAR)的定量构效关系。纳米毒理学在计算领域占主导地位的时代所面临的挑战和机遇,被作为未来加速纳米医学临床翻译的重点。并建立了用于确定吸入毒理学的剂量学的算法,并重新研究了纳米级(nano-QSAR)的定量构效关系。纳米毒理学在计算领域占主导地位的时代所面临的挑战和机遇,被作为未来加速纳米医学临床翻译的重点。
更新日期:2020-09-10
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