Lipid Based Nano Carriers in Cancer Therapy

Abstract

Cancer remains a leading cause of mortality worldwide, and conventional therapies often face limitations such as non-specificity, systemic toxicity, and multidrug resistance. Lipid-based nanocarriers have emerged as a promising strategy for targeted cancer therapy due to their biocompatibility, ability to encapsulate both hydrophilic and hydrophobic drugs, and potential for surface modification to enhance targeting. This review highlights various types of lipid-based nanocarriers, including liposomes, solid lipid nanoparticles, and nanostructured lipid carriers, focusing on their design, mechanisms of targeting, and therapeutic applications. Emphasis is placed on advances in active and passive targeting approaches, current clinical status, and the challenges that need to be addressed for successful translation into clinical practice. The integration of lipid-based nanocarriers with emerging technologies such as stimuli-responsive systems and personalized medicine holds great potential to revolutionize cancer treatment.

Introduction

Cancer is a complex and deadly disease, with current chemotherapy treatments limited by poor targeting, toxicity, and drug resistance. To improve therapy, novel drug delivery systems are needed that selectively target tumors while minimizing side effects.

Nanotechnology, especially lipid-based nanocarriers like liposomes, solid lipid nanoparticles, and nanostructured lipid carriers, offers promising solutions due to their biocompatibility, ability to carry diverse drugs, and mimicry of biological membranes. These carriers enable both passive targeting—exploiting the enhanced permeability and retention (EPR) effect in tumors—and active targeting through ligand modification that binds specific tumor markers.

Stimuli-responsive lipid nanocarriers, which release drugs in response to tumor-specific conditions such as pH, temperature, enzymes, or redox changes, enhance specificity and therapeutic efficacy while reducing toxicity. Techniques for drug loading include passive incorporation and active loading using transmembrane gradients, impacting drug stability and release.

Clinical examples like Doxil® and DaunoXome® validate the therapeutic potential of these systems. Preclinical animal models and advanced imaging assess biodistribution, pharmacokinetics, and safety, showing reduced toxicity compared to free drugs.

Despite significant progress, challenges remain in formulation stability, scale-up manufacturing, regulatory approval, and commercialization. However, lipid-based nanocarriers represent a key frontier in personalized and precision cancer therapy, combining improved targeting with controlled drug release for better patient outcomes.

Conclusion

A. Integration with Immunotherapy and Gene Therapy Lipid nanocarriers can co-deliver anticancer drugs + siRNA/CRISPR constructs or immune modulators to stimulate anti-tumor immunity and overcome resistance. B. AI and Machine Learning in Design ML models are increasingly used to predict: • Nanocarrier behavior based on composition • Optimal particle size and surface charge • Drug loading efficiencies • Patient-specific response predictions This enables data-driven formulation design and personalized delivery strategies. C. Personalized Nanomedicine Combining genomic profiling of patients with customizable lipid nanocarriers allows for tailored drug combinations, improving outcomes in precision oncology. D. Combination Therapies Co-delivery of multiple drugs (e.g., chemotherapy + anti-angiogenics) in one carrier can synergize action, reduce resistance, and simplify dosing regimens. E. Smart and Programmable Nanocarriers Future systems may feature: • Self-regulating release systems responsive to multi-stimuli • Logic-gated drug release (e.g., “AND” gates activated only under two conditions) • Real-time feedback systems integrated with wearables or biosensors13,14,15

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Copyright

Copyright © 2025 Kalyani Nerkar, Vedant Rane, Mohini Chaudhari, Prof. Rahul S. Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.