ReviewPharmaceutics2020
Combination Therapy and Nanoparticulate Systems: Smart Approaches for the Effective Treatment of Breast Cancer.
Review in Pharmaceutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 41 citations in OpenAlex.
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- Clinical advances and challenges of antibody-mediated targeted drug delivery in breast cancer therapeutics.Discover oncology · 2026Review
- A Complete Sojourn of Liposomes in Breast Cancer Therapy: Types, Preparation Techniques, Lipid Selection, and Critical Quality Attributes.Current drug research reviews · 2026Review
- A Comprehensive Review on Current Knowledge and Future Potential of Topical Therapies in Breast Cancer Treatment.European journal of breast health · 2025Article
- A Comprehensive Review of Nanostructured Lipid Carriers: Innovations and Applications in Breast Cancer Treatment.Recent advances in drug delivery and formulation · 2025Review
- Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review.Journal of cancer research and clinical oncology · 2024Review
- Evaluating a targeted Palbociclib-Trastuzumab loaded smart niosome platform for treating HER2 positive breast cancer cells.International journal of pharmaceutics: X · 2024Article
- Article
- Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer.British journal of cancer · 2023Review
- Tracking the Development of Cancer Care After 75 Years of Independence: India's Fight Against Cancer Since 1947.Indian journal of surgical oncology · 2022Review
- Margetuximab conjugated-PEG-PAMAM G4 nano-complex: a smart nano-device for suppression of breast cancer.Biomedical engineering letters · 2022Article
- Review
- Nano Drug Delivery System for Tumor Immunotherapy: Next-Generation Therapeutics.Frontiers in oncology · 2022Review
- Receptor-Mediated Targeted Delivery of Surface-ModifiedNanomedicine in Breast Cancer: Recent Update and Challenges.Pharmaceutics · 2021Review
- Development and preclinical evaluation of microneedle-assisted resveratrol loaded nanostructured lipid carriers for localized delivery to breast cancer therapy.International journal of pharmaceutics · 2021Article
- Recent Advances in the Treatment of Bone Metastases and Primary Bone Tumors: An Up-to-Date Review.Cancers · 2021Review
- Hippo/TEAD4 signaling pathway as a potential target for the treatment of breast cancer.Oncology letters · 2021Review
- Iron Oxide-Based Magneto-Optical Nanocomposites for In Vivo Biomedical Applications.Biomedicines · 2021Review
- Concept Design, Development and Preliminary Physical and Chemical Characterization of Tamoxifen-Guided-Mesoporous Silica Nanoparticles.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Breast cancer has become one of the biggest concerns for oncologists in the past few decades because of its unpredictable etiopathology and nonavailability of personalized translational medicine. The number of women getting affected by breast cancer has increased dramatically, owing to lifestyle and environmental changes. Besides, the development of multidrug resistance has become a challenge in the therapeutic management of breast cancer. Studies reveal that the use of monotherapy is not effective in the management of breast cancer due to high toxicity and the development of resistance. Combination therapies, such as radiation therapy with adjuvant therapy, endocrine therapy with chemotherapy, and targeted therapy with immunotherapy, are found to be effective. Thus, multimodal and combination treatments, along with nanomedicine, have emerged as a promising strategy with minimum side effects and drug resistance. In this review, we emphasize the multimodal approaches and recent advancements in breast cancer treatment modalities, giving importance to the current data on clinical trials. The novel treatment approach by targeted therapy, according to type, such as luminal, HER2 positive, and triple-negative breast cancer, are discussed. Further, passive and active targeting technologies, including nanoparticles, bioconjugate systems, stimuli-responsive, and nucleic acid delivery systems, including siRNA and aptamer, are explained. The recent research exploring the role of nanomedicine in combination therapy and the possible use of artificial intelligence in breast cancer therapy is also discussed herein. The complexity and dynamism of disease changes require the constant upgrading of knowledge, and innovation is essential for future drug development for treating breast cancer.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.