Research | The Shamay Lab

Research Interests

#1 The Role of Automation and Nanoinformatics in Advancing Nanomedicine and Drug Delivery

The digital age has revolutionized research through automated data analysis, machine learning, and data mining, particularly in drug delivery and nanomedicine. Nanoinformatics applies data and information science to optimize and understand the synthesis, characterization, and biological effects of nanomaterials. Automation accelerates nanomedicine discovery by enabling high-throughput screening, precise formulation, and reproducibility, replacing slow and inconsistent manual processes. Advanced systems integrating robotics, microfluidics, dynamic light scattering, and real-time imaging, combined with AI, provide continuous feedback, enhancing efficiency and enabling complex therapeutic strategies that would be impractical otherwise.

some of previous publications:

Y. Shamay, “Mastering the complexities of cancer nanomedicine with text mining, AI and automation,” J. Controlled Release, vol. 379, pp. 906–919, Mar. 2025, doi: 10.1016/j.jconrel.2025.01.057. Link

Y. Harris, H. Sason, D. Niezni, and Y. Shamay, “Automated discovery of nanomaterials via drug aggregation induced emission,” Biomaterials, vol. 289, p. 121800, Oct. 2022, doi: 10.1016/j.biomaterials.2022.121800. Link

Excitingly, this article is being featured in the Israeli news! click here to check it out.

#2 Personalized Cancer Nanomedicine and High Complexity Therapy

Precision and personalized medicines target specific molecular pathways in cellular signaling but can be limited by adaptive signaling in treated cells. To address this, nanomedicine and combination therapy are explored. The goal for personalized cancer nanomedicine is to design patient-specific therapies using AI, automation, and knowledge integration. High complexity therapy involves creating optimized drug combinations, potentially with five or more drugs, tailored to the patient. This approach considers factors like pharmacokinetics, toxicity, delivery, and timing. A computational workflow was developed that builds personalized treatment plans by adding monotherapies based on evidence and synergy scoring.

some of previous publications:

Y. Shamay, “Mastering the complexities of cancer nanomedicine with text mining, AI and automation,” J. Controlled Release, vol. 379, pp. 906–919, Mar. 2025, doi: 10.1016/j.jconrel.2025.01.057. Link

D. M. Azagury, B. F. Gluck, Y. Harris, Y. Avrutin, D. Niezni, H. Sason, and Y. Shamay, “Prediction of cancer nanomedicines self-assembled from meta-synergistic drug pairs,” Journal of Controlled Release, vol. 360, pp. 418–432, Aug. 2023, doi: 10.1016/j.jconrel.2023.06.040. Link
Excitingly, this article is being featured in the Hebrew news! click here to check it out.

Y. Shamay et al., “Quantitative self-assembly prediction yields targeted nanomedicines,” Nat. Mater., vol. 17, no. 4, pp. 361–368, Apr. 2018, doi: 10.1038/s41563-017-0007-z. Link

#3 Literature Data Mining and Knowledge Base Construction

Literature data mining is presented as a powerful tool for extracting insights from the growing body of biomedical literature. By using advanced extractive search tools and structuring hypotheses around modular templates (e.g., drug-biomaterial-target-cancer relationships), researchers can generate, validate, and prioritize hypotheses that would otherwise be difficult to explore systematically. This process revealed that only 1% of potential drug delivery hypotheses have been investigated, indicating a vast amount of unexplored therapeutic options. With the rapid expansion of scientific and clinical data, navigating and organizing this information to understand known and unknown aspects of cancer complexity is becoming increasingly crucial. The goal is to develop methods for predicting personalized combination therapies based on this vast knowledge base.

some of previous publications:

Y. Shamay, “Mastering the complexities of cancer nanomedicine with text mining, AI and automation,” J. Controlled Release, vol. 379, pp. 906–919, Mar. 2025, doi: 10.1016/j.jconrel.2025.01.057. Link

D. M. Azagury, B. F. Gluck, Y. Harris, Y. Avrutin, D. Niezni, H. Sason, and Y. Shamay, “Prediction of cancer nanomedicines self-assembled from meta-synergistic drug pairs,” Journal of Controlled Release, vol. 360, pp. 418–432, Aug. 2023, doi: 10.1016/j.jconrel.2023.06.040. Link
Excitingly, this article is being featured in the Hebrew news! click here to check it out.

D. Niezni, H. Taub-Tabib, Y. Harris, H. Sason, Y. Amrusi, D. Meron-Azagury, M. Avrashami, S. Launer-Wachs, J. Borchardt, M. Kusold, A. Tiktinsky, T. Hope, Y. Goldberg, and Y. Shamay, “Extending the boundaries of cancer therapeutic complexity with literature text mining,” Artificial Intelligence in Medicine, vol. 145, p. 102681, 2023, doi: 10.1016/j.artmed.2023.102681. Link

C. Chen, Z. Yaari, E. Apfelbaum, P. Grodzinski, Y. Shamay, and D. A. Heller, “Merging data curation and machine learning to improve nanomedicines,” Adv. Drug Deliv. Rev., vol. 183, p. 114172, Apr. 2022, doi: 10.1016/j.addr.2022.114172. Link

#4 Nano-Combination Therapy and Meta-Synergy

Our understanding of optimal drug combinations for diseases remains limited, and formulating these combinations into nanoparticles is even more challenging. To address this, we use computer science, data mining, and machine learning to identify the best drug combinations in nanomedicine. We introduced the concept of Meta-Synergy, which goes beyond simple drug pairing by considering multiple types of cooperation between drugs, such as pharmacodynamic, pharmacokinetic, chemical, and delivery-based interactions. An example is the co-assembly of bortezomib and cabozantinib into a nanoparticle, which enhances tumor targeting and reduces toxicity. Meta-synergistic systems often require computational tools and AI due to the complex, multi-dimensional interactions they involve.

some of previous publications:

Y. Shamay, “Mastering the complexities of cancer nanomedicine with text mining, AI and automation,” J. Controlled Release, vol. 379, pp. 906–919, Mar. 2025, doi: 10.1016/j.jconrel.2025.01.057. Link

D. M. Azagury, B. F. Gluck, Y. Harris, Y. Avrutin, D. Niezni, H. Sason, and Y. Shamay, “Prediction of cancer nanomedicines self-assembled from meta-synergistic drug pairs,” Journal of Controlled Release, vol. 360, pp. 418–432, Aug. 2023, doi: 10.1016/j.jconrel.2023.06.040. Link
Excitingly, this article is being featured in the Israeli news! click here to check it out.

#5 Crossing challenging barriers

A core challenge in cancer nanomedicine is overcoming physical and biological barriers like poor tumor penetration, immune clearance, and drug resistance, i.e how multi-synergistic drug delivery systems, which combine agents that reduce interstitial pressure, prime the immune system, or modulate the tumor microenvironment, can enhance therapeutic efficacy and penetration. These strategies are carefully designed using data-driven tools to ensure a balance between delivery success and safety, often requiring tailored or sequential administration protocols. Additionally, a powerful in vitro tool has been developed to identify and characterize matrix penetration processes, offering improved sensitivity to detect subtle dynamic changes in dense cellular environments.

some of previous publications:

S. Launer-Wachs, H. Taub-Tabib, J. T. Madem, O. Bar-Natan, Y. Goldberg, and Y. Shamay, “From centralized to ad-hoc knowledge base construction for hypotheses generation,” J. Biomed. Inform., vol. 142, p. 104383, Jun. 2023, doi: 10.1016/j.jbi.2023.104383. Link

D. M. Azagury, B. F. Gluck, Y. Harris, Y. Avrutin, D. Niezni, H. Sason, and Y. Shamay, “Prediction of cancer nanomedicines self-assembled from meta-synergistic drug pairs,” Journal of Controlled Release, vol. 360, pp. 418–432, Aug. 2023, doi: 10.1016/j.jconrel.2023.06.040. Link

Excitingly, this article is being featured in the Hebrew news! click here to check it out.

H. Kodama, Y. Shamay, Y. Kimura, J. Shah, S. B. Solomon, D. Heller, and S. Srimathveeravalli, “Electroporation-induced changes in tumor vasculature and microenvironment can promote the delivery and increase the efficacy of sorafenib nanoparticles,” Bioelectrochemistry, vol. 130, p. 107328, Dec. 2019, doi: 10.1016/j.bioelechem.2019.107328. Link

P. V. Jena, Y. Shamay, J. Shah, D. Roxbury, N. Paknejad, and D. A. Heller, “Photoluminescent carbon nanotubes interrogate the permeability of multicellular tumor spheroids,” Carbon, vol. 97, pp. 99–109, Feb. 2016, doi: 10.1016/j.carbon.2015.08.024. Link