Massively parallel pooled screening reveals genomic determinants of nanoparticle-cell interactions

Boehnke, Natalie; Jp, Straehla; Hc, Safford; Kocak, Mustafa; Mg, Rees; Ronan, Melissa; Adelmann, Charles H.; Rr, Chivukula; Jh, Cheah; H, Li; Dm, Sabatini; Ja, Roth; An, Koehler; Pt, Hammond

doi:10.1101/2021.04.05.438521

Cited by 6 publications

(6 citation statements)

References 78 publications

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“…Using this approach, the authors were able to construct genomic and protein−protein interaction networks to identify several cellular biomarkers for NP uptake and subcellular trafficking. 844 While further work is still needed to improve the training and validation of these models, these early results are key steps to improving nanomedicine development. Importantly, however, nanomedicines interact with many biological systems, including the immune system, and thus must be evaluated using multiple tools and end points.…”

Section: Nsaf Applications and Outlookmentioning

confidence: 99%

A Nanomedicine Structure–Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

et al. 2022

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Despite their clinical success in drug delivery applications, the potential of theranostic nanomedicines is hampered by mechanistic uncertainty and a lack of scienceinformed regulatory guidance. Both the therapeutic efficacy and the toxicity of nanoformulations are tightly controlled by the complex interplay of the nanoparticle's physicochemical properties and the individual patient/tumor biology; however, it can be difficult to correlate such information with observed outcomes. Additionally, as nanomedicine research attempts to gradually move away from large-scale animal testing, the need for computer-assisted solutions for evaluation will increase. Such models will depend on a clear understanding of structure− activity relationships. This review provides a comprehensive overview of the field of cancer nanomedicine and provides a knowledge framework and foundational interaction maps that can facilitate future research, assessments, and regulation. By forming three complementary maps profiling nanobio interactions and pathways at different levels of biological complexity, a clear picture of a nanoparticle's journey through the body and the therapeutic and adverse consequences of each potential interaction are presented.

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Section: Nsaf Applications and Outlookmentioning

confidence: 99%

A Nanomedicine Structure–Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

et al. 2022

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“…We noticed the pioneering work to explore the influence of biological heterogeneity on NP-cell interaction through the lens of multi-omics from other groups. [27] The high-throughput screening used in that study is indeed a powerful tool to identify potential biomarkers for the development of precision nanomedicine. However, the delivery of NPs in vivo is dependent on many other factors beyond the NP-cell interactions in vitro.…”

Section: Discussionmentioning

confidence: 99%

Interpretable XGBoost-SHAP model predicts the nanoparticles delivery and reveals its interaction with tumor genomic profiles

Wang

Tang

2022

Preprint

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Understanding the complex interaction between nanoparticles (NPs) and tumors in vivo and how it dominates the delivery efficacy of NPs is critical for the translation of nanomedicine. Herein, we proposed an interpretable XGBoost-SHAP model by integrating the information of NPs physicochemical properties and tumor genomic profile to predict the delivery efficacy. The correlation coefficients were > 0.99 for all training sets, and 0.830, 0.839, and 0.741 for the prediction of maximum delivery efficacy (DEmax), delivery efficacy at 24 h (DE24), and delivery efficacy at 168 (DE168) for test sets. The analysis of the feature importance revealed that the tumor genomic mutations and their interaction with NPs properties played an important role in the delivery of NPs. The functional profile of the NP-delivery-related genes was further explored through gene ontology enrichment analysis. Our work provides a method to accurately predict the delivery efficacy of NPs to heterogeneous tumors and highlights the power of simultaneously using omics data and interpretable machine learning algorithms for discovering the interaction between NPs and tumors, which is important for the development of precision nanomedicine.

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“…Hammond and co-workers engineered massively parallel pooled cell screens to investigate how cancer cells take up nanoparticle formulations of diverse compositions, sizes, and charges and identified a suite of biomarkers predictive of nanoparticle−cell interactions. 175 They did not restrict their focus to nanomaterial features but also paid equal consideration to intracellular processes and gene expression to construct "interaction networks" using random forest algorithms. 175 A nanoparticle library with systematically varied composition (PLGA, polystyrene, and liposomes), sizes, surface modifications, and ζ-potential, was screened using pooled cell lines containing DNA barcodes.…”

Section: High-throughput Evaluation Of Polymeric Gene Delivery In Cel...mentioning

confidence: 99%

“…175 They did not restrict their focus to nanomaterial features but also paid equal consideration to intracellular processes and gene expression to construct "interaction networks" using random forest algorithms. 175 A nanoparticle library with systematically varied composition (PLGA, polystyrene, and liposomes), sizes, surface modifications, and ζ-potential, was screened using pooled cell lines containing DNA barcodes. Cell lines were sourced from hundreds of cancer types (ovarian, breast, bone, etc.)…”

Section: High-throughput Evaluation Of Polymeric Gene Delivery In Cel...mentioning

confidence: 99%

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Kumar

2022

ACS Appl. Bio Mater.

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Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic−hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

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Massively parallel pooled screening reveals genomic determinants of nanoparticle-cell interactions

Cited by 6 publications

References 78 publications

A Nanomedicine Structure–Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

A Nanomedicine Structure–Activity Framework for Research, Development, and Regulation of Future Cancer Therapies

Interpretable XGBoost-SHAP model predicts the nanoparticles delivery and reveals its interaction with tumor genomic profiles

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

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