Multi-objective optimization of tumor response to drug release from vasculature-bound nanoparticles

A multi-objective optimization framework through genetic algorithm for hyperthermia-mediated drug delivery

This study presents an approach to the multi-objective optimization of hyperthermia-mediated drug delivery using thermo-sensitive liposomes (TSLs) for the treatment of hepatocellular carcinoma. The research focuses on addressing the non-optimal coupling methods that combine thermal treatments and chemotherapy by employing a Multi-Objective Genetic Algorithm (MOGA) optimization process, in order to identify the right combination of design variables to achieve better treatment outcomes. The proposed model integrates Computational Fluid Dynamics (CFD) analysis using the Pennes' Bioheat equation for tissue heating and a convection-diffusion model for drug delivery. The goal is to maximize the fraction of killed cancer cells through the pharmaceutical treatment while minimizing thermal damage to the tissue, aiming to not hinder the drug feeding from the vascular system. The optimization considers several design variables, including heating power, timing, and the number of antenna slots for the microwave heating. Simulations results suggest that a two-slots antenna configuration with a specific heating schedule yields optimal therapeutic outcomes by maximizing drug concentration in the tumor while limiting damage to healthy tissue. The results of the CFD analysis also show a significant improvement in the treatment outcomes compared to non-optimized results proposed previously in the literature, leading to an increase from the 10 % up to the 33 % for the fraction of killed cells function. The proposed optimization through Genetic Algorithm framework could significantly improve patient-specific treatment planning for hyperthermia-mediated drug delivery.

Quercetin-functionalized nanomaterials: Innovative therapeutic avenues for Alzheimer's disease management

Alzheimer's Disease (AD) is a major global health challenge, largely due to its complex pathology and the limited effectiveness of existing treatments. Quercetin, a bioactive compound belonging to the flavonoid class, its promising antioxidant, anti-inflammatory, and neuroprotective effects in addressing AD. However, its therapeutic potential is hindered by challenges such as low bioavailability, instability, and restricted permeability across the blood-brain barrier (BBB). Advances in nanotechnology have paved the way for quercetin-functionalized nanomaterials, offering solutions to these challenges. These nanostructures enhance quercetin's solubility, stability, and targeted brain delivery, thereby augmenting its therapeutic potential. In this review, nanocarriers (like liposomes, polymeric nanoparticles, and metal-based nanosystems) are explored for their potential application in optimizing quercetin delivery in AD management. It discusses the mechanisms by which these nanostructures enhance BBB penetration and exert neuroprotective effects. Furthermore, the review examines the outcomes of preclinical and in vitro studies, while addressing the challenges of scaling these approaches for clinical application. By merging the fields of nanotechnology and neurotherapeutics, the importance of quercetin-functionalized nanomaterials in advancing AD management strategies is underscored in this review.

The MOEO algorithm for multi-objective optimization of the cancer immuno-chemotherapy

In cancer treatment, chemotherapy has the disadvantage of killing both healthy and cancerous cells. Hence, the mixed-treatment of cancer such as chemo-immunotherapy is recommended. However, deriving the optimal dosage of each drug is a challenging issue. Although metaheuristic algorithms have received more attention in solving engineering problems due to their simplicity and flexibility, they have not consistently produced the best results for every problem. Thus, the need to introduce novel algorithms or extend the previous ones is felt for important optimization problems. Hence, in this paper, the multi-objective Equilibrium Optimizer algorithm, as an extension of the single-objective Equilibrium Optimizer algorithm, is recommended for cancer treatment problems. Then, the performance of the proposed algorithm is considered in both chemotherapy and mixed chemo-immunotherapy of cancer, considering the constraints of the tumor-immune dynamic system and the health level of the patients. For this purpose, two different patients with real clinical data are considered. The Pareto front obtained from the multi-objective optimization algorithm shows the points that can be selected for treatment under different criteria. Using the proposed multi-objective algorithm has reduced the total chemo-drug dose to 138.92 and 5.84 in the first patient, and 16.9 and 0.4384 in the second patient, for chemotherapy and chemo-immunotherapy, respectively. Comparing the results with previous studies demonstrates MOEO's superior performance in both chemotherapy and chemo-immunotherapy. However, it is shown that the proposed algorithm is more suitable for mixed-treatment approaches.

Single-Step Synthesis of Highly Tunable Multifunctional Nanoliposomes for Synergistic Cancer Therapy

Cancer is still one of the major diseases that humans have not conquered yet. Nanotechnology has promoted the development of multifunctional nanoparticles, which integrate diagnostic and treatment abilities for tumor imaging and therapy. However, its preparation methods usually require complicated unit operations, which result in large batch-to-batch differences, poor reproducibility, high production costs, and difficulty in clinical transformation. Furthermore, precisely manufacturing nanoliposomes with different tunable features (e.g., size, surface charge, targeting ligands, and so forth) remains a challenge, limiting effective nanoliposome optimization for tumor therapy. Due to the accurate control of the synthesis process and continuous operation mode, microfluidic technology becomes an emerging approach for the manufacturing of nanoliposomes. However, there are few reports on the single-step preparation of complex nanoliposomes by precise tuning of the physical properties, while investigating the influence of anti-cancer efficiency. Herein, we have prepared multifunctional nanoliposomes with accurate tuning properties through a microfluidic device in a single step, with synergistic photodynamic and chemodynamic effects for targeted tumor therapy. The preparation method provides an effective way for the one-step preparation of multifunctional nanoparticles with controllable particle sizes and surface properties.

Using Parallel Coordinates in Optimization of Nano-Particle Drug Delivery

Nanoparticle drug delivery better targets neoplastic lesions than free drugs and thus has emerged as a safer form of cancer therapy. Nanoparticle design variables are important determinants of efficacy as they influence the drug biodistribution and pharmacokinetics. Previously, we determined optimal designs through mechanistic modeling and optimization. However, the numerical nature of the tumor model and numerous candidate nanoparticle designs hinder hypothesis generation and treatment personalization. In this paper, we utilize the parallel coordinates technique to visualize high-dimensional optimal solutions and extract correlations between nanoparticle design and treatment outcomes. We found that at optimality, two major design variables are dependent, and thus the optimization problem can be reduced. In addition, we obtained an analytical relationship between optimal nanoparticle sizes and optimal distribution, which could facilitate the utilization of tumors models in preclinical studies. Our approach has simplified the results of the previously integrated modeling and optimization framework developed for nanotherapy and enhanced the interpretation and utilization of findings. Integrated mathematical frameworks are increasing in the medical field, and our method can be applied outside nanotherapy to facilitate the clinical translation of computational methods.

Eradication of large established tumors by drug-loaded bacterial particles via a neutrophil-mediated mechanism

The treatment of large established tumors remains a significant challenge and is generally hampered by poor drug penetration and intrinsic drug resistance of tumor cells in the central tumor region. In the present study, we developed bacterial particles (BactPs) to deliver chemotherapeutics into the tumor mass by hijacking neutrophils as natural cell-based carriers. BactPs loaded with doxorubicin, 5-fluorosuracil, or paclitaxel induced significantly greater tumor regression than unconjugated drugs. This effect was mediated by the ability of BactPs to incorporate chemotherapeutics and serve as vascular disrupting agents that trigger innate host responses and recruit phagocytic neutrophils. Vascular disruption resulted in extensive cell death in the central areas of the tumor mass. Recruited neutrophils acted as natural cellular carriers to deliver engulfed BactPs, which ensured drug delivery into the tumor mass and cytotoxic effects in areas that are normally inaccessible to traditional chemotherapy. Thus, BactPs eradicate large established tumors by functioning as vascular disrupters and natural drug carriers for neutrophil-mediated chemotherapy.

Multi-objective optimization methods in novel drug design

Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.

Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis

The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.