Engineering critical nanoscale design parameters (CNDPs): A strategy for developing effective nanomedicine therapies and assessing quantitative nanoscale structure-activity relationships (QNSARs)

Tomalia, Donald A.; Nixon, Linda S.; Hedstrand, David M.

doi:10.1016/b978-0-12-814527-2.00001-9

Cited by 6 publications

(6 citation statements)

References 148 publications

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“…It is notable that nearly a decade ago a major effort concerning the safe/effective use of nanoparticles in nanomedicine applications [178] had begun to focus on critical nanoparticle CNDP's implicated in bio-physico-chemical interactions occurring at all nanobiological interfaces. This activity appeared nearly simultaneously with similar nanoparticle CNDP concepts advanced by Tomalia et al [90][91][92][179][180][181].…”

Section: Engineering Critical Nanoscale Design Parameters: a Proven Smentioning

confidence: 57%

“…More specifically, Wang and Cheng [94] systematically engineered only one CNDP (i.e., surface chemistry) for G5, amine-terminated PAMAM dendrimers with twenty different common amino acids, as shown in Figure 43. It is now widely recognized [180,[183][184][185] that all physico-chemical properties of dendrimers and other well-defined nanoparticles are strictly controlled by six nanoscale design parameters (CNDPs); namely: (1) size, (2) shape, (3) surface chemistry, (4) flexibility/rigidity, (5) architecture and (6) elemental composition. These six CNDPs associated with all well-defined hard/soft nanoparticles may be used for the systematic design and engineering of new nanoparticle properties [186], as well as a premise for unifying nanoscience [90][91][92]184].…”

Section: Engineering Critical Nanoscale Design Parameters: a Proven Smentioning

confidence: 99%

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The Role of Branch Cell Symmetry and Other Critical Nanoscale Design Parameters in the Determination of Dendrimer Encapsulation Properties

Tomalia

Nixon²,

Hedstrand³

2020

Biomolecules

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This article reviews progress over the past three decades related to the role of dendrimer-based, branch cell symmetry in the development of advanced drug delivery systems, aqueous based compatibilizers/solubilizers/excipients and nano-metal cluster catalysts. Historically, it begins with early unreported work by the Tomalia Group (i.e., The Dow Chemical Co.) revealing that all known dendrimer family types may be divided into two major symmetry categories; namely: Category I: symmetrical branch cell dendrimers (e.g., Tomalia, Vögtle, Newkome-type dendrimers) possessing interior hollowness/porosity and Category II: asymmetrical branch cell dendrimers (e.g., Denkewalter-type) possessing no interior void space. These two branch cell symmetry features were shown to be pivotal in directing internal packing modes; thereby, differentiating key dendrimer properties such as densities, refractive indices and interior porosities. Furthermore, this discovery provided an explanation for unimolecular micelle encapsulation (UME) behavior observed exclusively for Category I, but not for Category II. This account surveys early experiments confirming the inextricable influence of dendrimer branch cell symmetry on interior packing properties, first examples of Category (I) based UME behavior, nuclear magnetic resonance (NMR) protocols for systematic encapsulation characterization, application of these principles to the solubilization of active approved drugs, engineering dendrimer critical nanoscale design parameters (CNDPs) for optimized properties and concluding with high optimism for the anticipated role of dendrimer-based solubilization principles in emerging new life science, drug delivery and nanomedical applications.

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Section: Engineering Critical Nanoscale Design Parameters: a Proven Smentioning

confidence: 57%

Section: Engineering Critical Nanoscale Design Parameters: a Proven Smentioning

confidence: 99%

The Role of Branch Cell Symmetry and Other Critical Nanoscale Design Parameters in the Determination of Dendrimer Encapsulation Properties

Tomalia

Nixon²,

Hedstrand³

2020

Biomolecules

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“…QSARs are mathematical models, which relate a pharmacological or biological activity with the physicochemical characteristics (termed molecular descriptors) of molecule sets. Indicative examples of QSAR applications are the study of enzyme activity [194], the minimum effective dose of a drug estimation [195], and toxicity prediction of nanostructures [196]. The main advantage of QSAR models lies with their ability to predict activities of a large number of compounds with little to no prior experimental data.…”

Section: Discussion and The Road Aheadmentioning

confidence: 99%

Machine Learning in Nano-Scale Biomedical Engineering

Boulogeorgos

Trevlakis

Tegos

et al. 2021

IEEE Trans. Mol. Biol. Multi-Scale Commun.

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Machine learning (ML) empowers biomedical systems with the capability to optimize their performance through modeling of the available data extremely well, without using strong assumptions about the modeled system. Especially in nanoscale biosystems, where the generated data sets are too vast and complex to mentally parse without computational assist, ML is instrumental in analyzing and extracting new insights, accelerating material and structure discoveries and designing experience as well as supporting nano-scale communications and networks. However, despite these efforts, the use of ML in nano-scale biomedical engineering remains still under-explored in certain areas and research challenges are still open in fields such as structure and material design and simulations, communications and signal processing, and bio-medicine applications. In this article, we review the existing research regarding the use of ML in nano-scale biomedical engineering. In more detail, we first identify and discuss the main challenges that can be formulated as ML problems. These challenges are classified in the three aforementioned main categories. Next, we discuss the state of the art ML methodologies that are used to countermeasure the aforementioned challenges. For each of the presented methodologies, special emphasis is given to its principles, applications and limitations. Finally, we conclude the article with insightful discussions, that reveals research gaps and highlights possible future research directions.

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“…These include the nanoscale size versus large surface area, the high catalytic activity, and the unique optical properties. [99][100] In addition to the agglomeration effects and the potential interference between ENMs and the currently available assay kits, all these have raised the need to adopt new…”

Section: The Challenges In Nanotoxicologymentioning

confidence: 99%

“…[101][102][103][104][105] Progress in the growth of nanotechnology and its translation to clinical practices requires comprehensive characterization of ENMs and detecting their toxicity based on feasible and cost-effective approaches include considering those characteristics as safe-by-design approaches. 70,75,93,100 In addition to a suitable detection method to assess their potential toxicity, effective standardized regulatory guidelines are needed. 42,44,75…”

mentioning

confidence: 99%