Unstructured Formulation Data Analysis for the Optimization of Lipid Nanoparticle Drug Delivery Vehicles

Charge-switchable cell-penetrating peptides for rerouting nanoparticles to glioblastoma treatment

Glioblastoma (GB) is one of the most lethal types of neoplasms with unique anatomic, physiologic, and pathologic features that usually persist after exposure to standard therapeutic modalities. It is biologically aggressive, and the existence of the blood-brain barrier (BBB) limits the efficacy of standard therapies. In this work, we hypothesize the potential of surface-functionalized ultra-small nanostructured lipid carriers (usNLCs) with charge-switchable cell-penetrating peptides (CPPs) to overcome this biological barrier and improve targeted delivery to brain tumor tissues. The big question is: what is the potential of CPPs in directing nanoparticles toward brain tumor tissue? To answer this question, the usNLCs were functionalized with distinct biomolecules [five CPPs, c(RGDfK) and transferrin, Tf] through electrostatic interaction and its ability as a targeting approach to BBB (HBMEC) and glioma cells (U87 cells) evaluated in terms of physicochemical properties, cellular uptake, permeability in a 2D-BBB model, and tumor growth inhibition. Monte Carlo simulations elucidated CPP adsorption patterns. The permeability studies revealed that targeted usNLCs, especially usNLCsTf and usNLCsCPP4, exhibited an increased permeability coefficient compared to the non-targeted usNLCs. Functionalized usNLCs evidenced enhanced uptake in BBB cells, with smaller CPPs showing higher internalization (CPP1 and CPP2). Similarly, functionalized usNLCs exhibited more significant cytotoxicity in glioma cells, with specific CPPs promoting favorable internalization. Analysis of the endocytic pathway indicated that usNLCsCPPs were mainly internalized by direct translocation and caveolae-mediated endocytosis. Optimal usNLCs with dual targeting capabilities to both BBB and GB cells provide a promising therapeutic strategy for GB.

A two-pronged approach against glioblastoma: drug repurposing and nanoformulation design for in situ-controlled release

Glioblastoma (GB) is one of the most lethal types of neoplasms. Its biologically aggressive nature and the presence of the blood-brain barrier (BBB) limit the efficacy of standard therapies. Several strategies are currently being developed to both overcome the BBB and deliver drugs site specifically to tumor cells. This work hypothesizes a two-pronged approach to tackle GB: drug repurposing with celecoxib (CXB) and a nanoformulation using ultra-small nanostructured lipid carriers (usNLCs). CXB antitumor druggable activity was inspected bioinformatically and screened in four glioma cell lines aiming at the comparison with temozolomide (TMZ), as standard of care. Delving into formulation design, it was tailored aiming at (i) improving the drug solubility/loading properties, (ii) assigning a thermal-triggerable drug release based on a lipid matrix with a low melting point, and (iii) enhancing the cytotoxic effect by selecting a template targetable to tumor cells. For this purpose, an integrated analysis of the critical material attributes (CMAs), critical process parameters (CPPs), and critical quality attributes (CQAs) was conducted under the umbrella of a quality by design approach. CMAs that demonstrate a high-risk level for the final quality and performance of the usNLCs include the drug solubility in lipids (solid and liquid), the lipid composition (envisioning a thermoresponsive approach), the ratio between lipids (solid vs. liquid), and the surfactant type and concentration. Particle size was shown to be governed by the interaction lipid-surfactant followed by surfactant type. The drug encapsulation did not influence colloidal characteristics, making it a promising carrier for lipophilic drugs. In general, usNLCs exhibited a controlled drug release during the 72 h at 37 °C with a final release of ca. 25%, while at 45 °C this was doubled. The in vitro cellular performance depended on the surfactant type and lipid composition, with the formulations containing a sole solid lipid (Suppocire® NB) and Kolliphor® RH40 as surfactant being the most cytotoxic. usNLCs with an average diameter of ca. 70 nm and a narrow size distribution (PdI lower than 0.2) were yielded, exhibiting high stability, drug protection, sustained and thermo-sensitive release properties, and high cytotoxicity to glioma cells, meeting the suitable CQAs for parenteral administration. This formulation may pave the way to a multi-addressable purpose to improve GB treatment.

A Stepwise Framework for the Systematic Development of Lipid Nanoparticles

A properly designed nanosystem aims to deliver an optimized concentration of the active pharmaceutical ingredient (API) at the site of action, resulting in a therapeutic response with reduced adverse effects. Due to the vast availability of lipids and surfactants, producing stable lipid dispersions is a double-edged sword: on the one hand, the versatility of composition allows for a refined design and tuning of properties; on the other hand, the complexity of the materials and their physical interactions often result in laborious and time-consuming pre-formulation studies. However, how can they be tailored, and which premises are required for a “right at first time” development? Here, a stepwise framework encompassing the sequential stages of nanoparticle production for disulfiram delivery is presented. Drug in lipid solubility analysis leads to the selection of the most suitable liquid lipids. As for the solid lipid, drug partitioning studies point out the lipids with increased capacity for solubilizing and entrapping disulfiram. The microscopical evaluation of the physical compatibility between liquid and solid lipids further indicates the most promising core compositions. The impact of the outer surfactant layer on the colloidal properties of the nanosystems is evaluated recurring to machine learning algorithms, in particular, hierarchical clustering, principal component analysis, and partial least squares regression. Overall, this work represents a comprehensive systematic approach to nanoparticle formulation studies that serves as a basis for selecting the most suitable excipients that comprise solid lipid nanoparticles and nanostructured lipid carriers.

Quality-by-design in pharmaceutical development: From current perspectives to practical applications

Current pharmaceutical research directions tend to follow a systematic approach in the field of applied research and development. The concept of quality-by-design (QbD) has been the focus of the current progress of pharmaceutical sciences. It is based on, but not limited, to risk assessment, design of experiments and other computational methods and process analytical technology. These tools offer a well-organized methodology, both to identify and analyse the hazards that should be handled as critical, and are therefore applicable in the control strategy. Once implemented, the QbD approach will augment the comprehension of experts concerning the developed analytical technique or manufacturing process. The main activities are oriented towards the identification of the quality target product profiles, along with the critical quality attributes, the risk management of these and their analysis through in silico aided methods. This review aims to offer an overview of the current standpoints and general applications of QbD methods in pharmaceutical development.

Sorting hidden patterns in nanoparticle performance for glioblastoma using machine learning algorithms

Cationic compounds have been described to readily penetrate cell membranes. Assigning positive charge to nanosystems, e.g. lipid nanoparticles, has been identified as a key feature to promote electrostatic binding and design ligand-based constructs for tumour targeting. However, their intrinsic high cytotoxicity has hampered their biomedical application. This paper seeks to establish which cationic compounds and properties are compelling for interface modulation, in order to improve the design of tumour targeted nanoparticles against glioblastoma. How can intrinsic features (e.g. nature, structure, conformation) shape efficacy outcomes? In the quest for safer alternative cationic compounds, we evaluate the effects of two novel glycerol-based lipids, GLY1 and GLY2, on the architecture and performance of nanostructured lipid carriers (NLCs). These two molecules, composed of two alkylated chains and a glycerol backbone, differ only in their polar head and proved to be efficient in reversing the zeta potential of the nanosystems to positive values. The use of unsupervised and supervised machine learning (ML) techniques unraveled their structural similarities: in spite of their common backbone, GLY1 exhibited a better performance in increasing zeta potential and cytotoxicity, while decreasing particle size. Furthermore, NLCs containing GLY1 showed a favorable hemocompatible profile, as well as an improved uptake by tumour cells. Summing-up, GLY1 circumvents the intrinsic cytotoxicity of a common surfactant, CTAB, is effective at increasing glioblastoma uptake, and exhibits encouraging anticancer activity. Moreover, the use of ML is strongly incited for formulation design and optimization.

Diving into Batch-to-Batch Variability of Topical Products-a Regulatory Bottleneck

PURPOSE

Following the recent European Medicine Agency (EMA) draft guideline on quality and equivalence of topical products, a modular framework for bioequivalence assessment is proposed, wherein the qualitative, quantitative, microstructure and product performance sameness is demanded to support generic applications. Strict regulatory limits are now imposed, but, the suitability of these limits has been subject of intense debate. In this context, this paper aims to address these issues by characterizing a panel of 8 reference blockbuster semisolid topical products.

METHODS

For each product, three batches were selected and, whenever possible, batches retrieved from different manufacturing sites were considered. Product microstructure was evaluated in terms of globule size, pH, rheological attributes and, if required, the thermal behaviour was also assessed. Performance was evaluated through in vitro release testing (IVRT). Finally, an integrated multivariate analysis was performed to highlight the features that most contribute for product variability.

RESULTS

Marked differences were registered within reference products. Statistical analysis demonstrated that if EMA criteria are applied, none of the same product batches can be considered as equivalent. Rheological parameters as well as IVRT indicators account for the majority of batch-to-batch differences.

CONCLUSIONS

Semisolid dosage forms exhibit intrinsic variability. This calls for the attention to the need of establishing reasonable equivalence criteria applied to generic drug products. Graphical abstract.

Peptide-lipid nanoconstructs act site-specifically towards glioblastoma growth impairment

Ultra-small nanostructured lipid carriers (usNLCs) have been hypothesized to promote site-specific glioblastoma (GB) drug delivery. Envisioning a multitarget purpose towards tumor cells and microenvironment, a surface-bioconjugated usNLC prototype is herein presented. The comeback of co-delivery by repurposing atorvastatin and curcumin, as complementary therapy, was unveiled and characterized, considering colloidal properties, stability, and drug release behavior. Specifically, the impact of the surface modification of usNLCs with hyaluronic acid (HA) conjugates bearing the cRGDfK and H₇k(R₂)₂ peptides, and folic acid (FA) on GB cells was sequentially evaluated, in terms of cytotoxicity, internalization, uptake mechanism and hemolytic character. As proof-of-principle, the biodistribution, tolerability, and efficacy of the nanocarriers were assessed, the latter in GB-bearing mice through magnetic resonance imaging and spectroscopy. The hierarchical modification of the usNLCs promotes a preferential targeting behavior to the brain, while simultaneously sparing the elimination by clearance organs. Moreover, usNLCs were found to be well tolerated by mice and able to impair tumor growth in an orthotopic xenograft model, whereas for mice administered with the non-encapsulated therapeutic compounds, tumor growth exceeded 181% in the same period. Relevant biomarkers extracted from metabolic spectroscopy were ultimately identified as a potential tumor signature.

Data Analytics Approach for Rational Design of Nanomedicines with Programmable Drug Release

Drug delivery vehicles can improve the functional efficacy of existing antimicrobial therapies by improving biodistribution and targeting. A critical property of such nanomedicine formulations is their ability to control the release kinetics of their payloads. The combination of (and interactions among) polymer, drug, and nanoparticle properties gives rise to nonlinear behavioral relationships and large data space. These factors complicate both first-principles modeling and screening of nanomedicine formulations. Predictive analytics may offer a more efficient approach toward the rational design of nanomedicines by identifying key descriptors and correlating them to nanoparticle release behavior. In this work, antibiotic release kinetics data were generated from polyanhydride nanoparticle formulations with varying copolymer compositions, encapsulated drug type, and drug loading. Four antibiotics, doxycycline, rifampicin, chloramphenicol, and pyrazinamide, were used. Linear manifold learning methods were used to relate drug release properties with polymer, drug, and nanoparticle properties, and key descriptors were identified that are highly correlated with release properties. However, these linear methods could not predict release behavior. Nonlinear multivariate modeling based on graph theory was then used to deconvolute the governing relationships between these properties, and predictive models were generated to rapidly screen lead nanomedicine formulations with desirable release properties with minimal nanoparticle characterization. Release kinetics predictions of two drugs containing atoms not included in the model showed good agreement with experimental results, validating the model and indicating its potential to virtually explore new polymer and drug pairs not included in the training data set. The models were shown to be robust after the inclusion of these new formulations, in that the new inclusions did not significantly change model regression. This approach provides the first step toward the development of a framework that can be used to rationally design nanomedicine formulations by selecting the appropriate carrier for a drug payload to program desirable release kinetics.