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Review
. 2022 Feb 18:4:799341.
doi: 10.3389/fdgth.2022.799341. eCollection 2022.

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Mônica Villa Nova  1 , Tzu Ping Lin  2 , Saeed Shanehsazzadeh  3 , Kinjal Jain  2 , Samuel Cheng Yong Ng  2 , Richard Wacker  4 , Karim Chichakly  5 , Matthias G Wacker  2
Affiliations
Review

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Mônica Villa Nova et al. Front Digit Health. .

Abstract

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, "big data" approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

Keywords: PBPK/PKPD modeling and simulations; artificial intelligence - AI; design of experiment - DoE; drug delivery; liposomes; machine learning - ML; nanomedicine; nanoparticles.

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Conflict of interest statement

RW was employed by YellowMap AG and KC was employed by isee systems. Both companies did not influence the content presented in this review article. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Machine learning and modeling in nanomedicine development include a wide variety of data sources that can be compiled by several algorithms. Machine learning and artificial neuronal networks often require larger data volumes than a conventional modeling approach. Created with www.Biorender.com.
Figure 2
Figure 2
Illustration of nanomedicine production of a drug-loaded extracellular vesicle preparation using a design of experiments approach. Potential CPPs, CMAs, and CQAs are highlighted. Created with www.Biorender.com.
Figure 3
Figure 3
Illustration of the evaluation of pharmacokinetic data using NLME and PBPK models as well as ANNs. Created with www.Biorender.com.
See this image and copyright information in PMC

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