Quantitative self-assembly prediction yields targeted nanomedicines

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan M.; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

doi:10.1038/s41563-017-0007-z

Cited by 155 publications

(152 citation statements)

References 42 publications

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“…Although not fully addressed in the current study, our system can detect and image several independent channels to map more complicated biological events simultaneously. As shown in Figure S7 in the Supporting Information, by adding IR‐783@BSA complex in the 900–1000 nm region, three‐color NIR‐II imaging was achieved in mapping tumor, lymph nodes, and blood vessels.…”

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confidence: 99%

Multiplexed NIR‐II Probes for Lymph Node‐Invaded Cancer Detection and Imaging‐Guided Surgery

et al. 2020

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Tumor‐lymph node (LN) metastasis is the dominant prognostic factor for tumor staging and therapeutic decision‐making. However, concurrently visualizing metastasis and performing imaging‐guided lymph node surgery is challenging. Here, a multiplexed‐near‐infrared‐II (NIR‐II) in vivo imaging system using nonoverlapping NIR‐II probes with markedly suppressed photon scattering and zero‐autofluorescence is reported, which enables visualization of the metastatic tumor and the tumor metastatic proximal LNs resection. A bright and tumor‐seeking donor–acceptor–donor (D‐A‐D) dye, IR‐FD, is screened for primary/metastatic tumor imaging in the NIR‐IIa (1100–1300 nm) window. This optimized D‐A‐D dye exhibits greatly improved quantum yield of organic D‐A‐D fluorophores in aqueous solutions (≈6.0%) and good in vivo performance. Ultrabright PbS/CdS core/shell quantum dots (QDs) with dense polymer coating are used to visualize cancer‐invaded sentinel LNs in the NIR‐IIb (>1500 nm) window. Compared to clinically used indocyanine green, the QDs show superior brightness and photostability (no obvious bleaching even after continuous laser irradiation for 5 h); thus, only a picomolar dose is required for sentinel LNs detection. This combination of dual‐NIR‐II image‐guided surgery can be performed under bright light, adding to its convenience and appeal in clinical use.

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confidence: 99%

Multiplexed NIR‐II Probes for Lymph Node‐Invaded Cancer Detection and Imaging‐Guided Surgery

et al. 2020

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“…Two PEG molecular weights (20 and 10 kDa) as well as two numbers of generation of dendrons (G3 and G2) were selected to synthesize four Bis‐MPA hyperbranched PEG–Ppa amphiphiles (G320P, G310P, G220P, and G210P). Molecular dynamics (MD) simulations were employed to assess the stability of the self‐assembled nanostructures from four amphiphiles . The results after optimizing the quantum mechanics (QM) model in Figure S1a–d of the Supporting Information revealed there were strong π–π stacking and T‐shape stacking interactions between Ppa, and the G3‐dendron structure had a larger surface area and a bigger volume than the G2‐dendron one.…”

Section: Methodsmentioning

confidence: 98%

Tunable Hydrophile–Lipophile Balance for Manipulating Structural Stability and Tumor Retention of Amphiphilic Nanoparticles

et al. 2019

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reported that the optimized size may also improve the delivery efficiency in tumors [4] and NPs with moderate rigidity could enhance the multibiological barrier penetrating capability for drug delivery in comparison with their soft and hard counterparts. [5] NPs with poly(ethylene glycol) (PEG) modification can effectively transport across various biological barriers. [6] The hydrophile-lipophile balance (HLB) is a key factor in the formation of self-assembled NPs and its morphology, [7] however, the role of HLB of NPs in drug delivery, particularly in penetration and retention in tumors, remains unclear.One of the challenges is to prepare NPs with a tunable ratio of hydrophilic to hydrophobic units, or a tunable HLB value, which governs the self-assembling behavior of the NPs. In our previous work, we have reported a linear-peptide dendritic copolymer and its conjugated hydrophobic drug (doxorubicin) could self-assemble into stable and tumor microenvironment-responsive NPs by nonbonding interactions, such as π-π stacking, hydrophobic interaction, and hydrogen bonding. [8] However, it is a challenge to obtain NPs from the linear dendritic copolymers with tunable HLB values.Herein, we employed 2,2-bis(hydroxymethyl)propionic acid (Bis-MPA) hyperbranched PEG-OH dendrimer as a hydrophilic unit and pyropheophorbide-a (Ppa) as a hydrophobic unit to Hydrophile-lipophile balance (HLB) has a great influence on the selfassembly and physicochemical properties of amphiphiles, thus affecting their biological effects. It is shown that amphiphilic nanoparticles (NPs) with a moderate HLB value display enhanced stability and highly efficient tumor retention. 2,2-Bis(hydroxymethyl)propionic acid hyperbranched poly(ethylene glycol) (PEG)-pyropheophorbide-a (Ppa) amphiphiles (G320P, G310P, G220P, and G210P) are synthesized with a tunable HLB value from 6.1 to 9.9 by manipulating the number of generation of dendrons (G2 or G3) and the molecular weight of PEG chains (10 or 20 kDa). Molecular dynamics simulations reveal that G320P and G210P with a moderate HLB value (8.0 and 7.8) self-assemble into very stable NPs with a small solvent accessible surface area and high nonbonding interactions. G320P with a moderate HLB value (8.0) and a long PEG chain excels against other NPs in prolonging the blood circulation time of Ppa (up to 13-fold), penetrating deeply into multicellular tumor spheroids and accumulating in tumors, and enhancing the PDT efficacy with a tumor growth inhibition of 96.0%. Rational design of NPs with a moderate HLB value may be implemented in these NP-derived nanomedicines to achieve high levels of retention in tumors. Hydrophile-Lipophile BalanceRational designs of nanomedicine aid in overcoming physiological barriers of solid tumors for achieving enhanced therapeutic efficacy, [1] including manipulating physical, chemical, and biological properties of nanoparticles. [2] Negatively charged nanoparticles (NPs) are very stable in the blood circulatory system, thereby allowing NPs accumulation in tumors through enha...

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“…The ability of AI algorithms to process large datasets and recognize complex patterns can be exploited for improved design of nanotechnologies for diagnostics and treatment. Prediction of nanoparticle interactions with the target drug, biological media, and cell membranes, in addition to drug encapsulation efficiency and release kinetics can help optimize nanomedicine formulations . Moreover, pattern recognition and classification algorithms can be used in order to differentiate between healthy and diseased patients and predict drug efficacy in patients .…”

Section: Introductionmentioning

confidence: 99%

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

et al. 2019

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Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine—tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient‐specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

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Quantitative self-assembly prediction yields targeted nanomedicines

Cited by 155 publications

References 42 publications

Multiplexed NIR‐II Probes for Lymph Node‐Invaded Cancer Detection and Imaging‐Guided Surgery

Multiplexed NIR‐II Probes for Lymph Node‐Invaded Cancer Detection and Imaging‐Guided Surgery

Tunable Hydrophile–Lipophile Balance for Manipulating Structural Stability and Tumor Retention of Amphiphilic Nanoparticles

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

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