Quality by design case study 1: Design of 5-fluorouracil loaded lipid nanoparticles by the W/O/W double emulsion - Solvent evaporation method

A comparison between the effects of two liposome-encapsulated bevacizumab formulations on ocular neovascularization inhibition

Bevacizumab (BVZ), an anti-VEGF antibody, has demonstrated reliable outcomes in the treatment of irritating ocular neovascularization. Frequent intravitreal injections are necessitated due to rapid clearance and short local accessibility. We recruited liposome as a highly prevailing drug delivery system to enhance drug availability. Two liposome formulations were characterized and their in vitro stability was analyzed. The toxicity of the formulations on some ocular cell lines was also evaluated. In addition, the anti-angiogenic effects of formulations were examined. Drug permeation was measured across ARPE-19 and HCE cell lines as in vitro cellular barrier models. Results revealed that NLP-DOPE-BVZ acquired high stability at 4 °C, 24 °C, and 37 °C for 45 days. It also showed more capacity to entrap BVZ in NLP-DOPE-BVZ (DEE% 69.1 ± 1.4 and DLE% 55.66 ± 1.15) as compared to NLP-BVZ (DEE% 43.57 ± 14.64, and DLE% 37.72 ± 12.01). Although both formulations inhibited the migration and proliferation of HUVECs, NLP-DOPE-BVZ was more effective at inhibiting angiogenesis. Furthermore, NLP-DOPE-BVZ better crossed our established barrier cellular models. Based on the findings, the inclusion of DOPE in NLPs has significantly enhanced the features of drug carriers. This makes them a potential candidate for treating ocular neovascularization and other related ailments.

Integrating Artificial Intelligence with Quality by Design in the Formulation of Lecithin/Chitosan Nanoparticles of a Poorly Water-Soluble Drug

The aim of the current study is to explore the potential of artificial intelligence (AI) when integrated with Quality by Design (QbD) approach in the formulation of a poorly water-soluble drug, for its potential use in carcinoma. Silymarin is used as a model drug for its potential effectiveness in liver cancer. A detailed QbD approach was applied. The effect of the critical process parameters was studied on each of the particle size, size distribution, and entrapment efficiency. Response surface designs were applied in the screening and optimization of lecithin/chitosan nanoparticles, to obtain an optimized formula. The release rate was tested, where artificial neural network models were used to predict the % release of the drug from the optimized formula at different time intervals. The optimized formula was tested for its cytotoxicity. A design space was established, with an optimized formula having a molar ratio of 18.33:1 lecithin:chitosan and 38.35 mg silymarin. This resulted in nanoparticles with a size of 161 nm, a polydispersity index of 0.2, and an entrapment efficiency of 97%. The optimized formula showed a zeta potential of +38 mV, with well-developed spherical particles. AI successfully showed high prediction ability of the drug's release rate. The optimized formula showed an enhancement in the cytotoxic effect of silymarin with a decreased IC50 compared to standard silymarin. Lecithin/chitosan nanoparticles were successfully formulated, with deep process and product understanding. Several tools were used as AI which could shift pharmaceutical formulations from experience-dependent studies to data-driven methodologies in the future.

Design of experiment (DoE) as a quality by design (QbD) tool to optimise formulations of lipid nanoparticles for nose-to-brain drug delivery

INTRODUCTION

The nose-to-brain route has been widely investigated to improve drug targeting to the central nervous system (CNS), where lipid nanoparticles (solid lipid nanoparticles - SLN and nanostructured lipid carriers - NLC) seem promising, although they should meet specific criteria of particle size (PS) <200 nm, polydispersity index (PDI) <0.3, zeta potential (ZP) ~|20| mV and encapsulation efficiency (EE) >80%. To optimize SLN and NLC formulations, design of experiment (DoE) has been recommended as a quality by design (QbD) tool.

AREAS COVERED

This review presents recently published work on the optimization of SLN and NLC formulations for nose-to-brain drug delivery. The impact of different factors (or independent variables) on responses (or dependent variables) is critically analyzed.

EXPERT OPINION

Different DoEs have been used to optimize SLN and NLC formulations for nose-brain drug delivery, and the independent variables lipid and surfactant concentration and sonication time had the greatest impact on the dependent variables PS, EE, and PDI. Exploring different DoE approaches is important to gain a deeper understanding of the factors that affect successful optimization of SLN and NLC and to facilitate future work improving machine learning techniques.

Enhanced Cytotoxic Activity of 6-Mercaptopurine-Loaded Solid Lipid Nanoparticles in Hepatic Cancer Treatment

6-Mercaptopurine (6-MCP) is an antiproliferative purine analog used in acute lymphoblastic leukemia, non-Hodgkin lymphoma, and inflammatory bowel disease (Crohn's disease, ulcerative colitis). Although 6-MCP has the great therapeutic potential for cancer and immunosuppressant-related diseases, 6-MCP is not readily soluble in water, presents a high first-pass effect, short half-life (0.5-1.5 h), and implies a low bioavailability (16%). On the contrary, solid lipid nanoparticles (SLNs) are prepared from solid lipids at room temperature and body temperature. In this study, SLNs were prepared w/o/w double emulsion-solvent evaporation method using Precirol ATO5 as matrix lipid. In the emulsion stabilization, surfactant (Tween 80) and polymeric stabilizer (polyvinyl alcohol [PVA]) were used. Two group formulations using Tween 80 and PVA were compared in terms of particle size, polydispersity index, zeta potential encapsulation efficiency%, and process yield%. Differential calorimetric analysis and release properties were examined for optimum formulation, and release kinetics were calculated. According to studies, sustained release was obtained with SLNs by the Korsmayer-Peppas kinetic model. The in vitro cytotoxicity studies were performed on the hepatocarcinoma (HEP3G) cell line. According to the results, successful SLN formulations were produced, and PVA was found best stabilizer. Optimum formulation exhibited significantly higher cytotoxic effects on HEP3G than on pure 6-MCP. These results demonstrated that solid lipid nanodrug delivery systems have great potential for formulation of 6-MCP.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Advances in nanoparticle mediated targeting of RNA binding protein for cancer

RNA binding proteins (RBPs) enact a very crucial part in the RNA directive processes. Atypical expression of these RBPs affects many steps of RNA metabolism, majorly altering its expression. Altered expression and dysfunction of RNA binding proteins lead to cancer progression and other diseases. We enumerate various available interventions, and recent findings focused on targeting RBPs for cancer therapy and diagnosis. The treatment, sensitization, chemoprevention, gene-mediated, and virus mediated interventions were studied to treat and diagnose cancer. The application of passively and actively targeted lipidic nanoparticles, polymeric nanoparticles, virus-based particles, and vaccine-based immunotherapy for the delivery of therapeutic agent/s against cancer are discussed. We also discuss the formulation aspect of nanoparticles for achieving delivery at the site of action and ongoing clinical trials targeting RBPs.

Artificial intelligence to bring nanomedicine to life

The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.

Nanostructured lipid carriers: a promising drug carrier for targeting brain tumours

Background

In recent years, the field of nanotechnology and nanomedicine has transformed the pharmaceutical industry with the development of novel drug delivery systems that overcome the shortcomings of traditional drug delivery systems. Nanostructured lipid carriers (NLCs), also known as the second-generation lipid nanocarriers, are one such efficient and targeted drug delivery system that has gained immense attention all across due to their myriad advantages and applications. Scientific advancements have revolutionized our health system, but still, brain diseases like brain tumour have remained formidable owing to poor prognosis and the challenging drug delivery to the brain tissue. In this review, we highlighted the application and potential of NLCs in brain-specific delivery of chemotherapeutic agents.

Main body

NLCs are lipid-based formulations with a solid matrix at room temperature and offer advantages like enhanced stability, low toxicity, increased shelf life, improved drug loading capacity, and biocompatibility over other conventional lipid-based nanocarriers such as nanoemulsions and solid lipid nanoparticles. This review meticulously articulates the structure, classification, components, and various methods of preparation exemplified with various research studies along with their advantages and disadvantages. The concept of drug loading and release has been discussed followed by a brief about stability and strategies to improve stability of NLCs. The review also summarizes various in vitro and in vivo research studies on NLCs encapsulated with cytotoxic drugs and their potential application in brain-specific drug delivery.

Conclusion

NLCs are employed as an important carrier for the delivery of food, cosmetics, and medicines and recently have been used in brain targeting, cancer, and gene therapy. However, in this review, the applications and importance of NLCs in targeting brain tumour have been discussed in detail stating examples of various research studies conducted in recent years. In addition, to shed light on the promising role of NLCs, the current clinical status of NLCs has also been summarized.

Graphical Abstract

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

Effect of Formulation Variables for the Production of WGA-Grafted, Levodopa-Loaded PLGA Nanoparticles

Levodopa is used for the treatment of Parkinson’s disease (PD) for the last few decades. However, adverse reactions such as dyskinesia, somnolence, nausea, itching, rash, as well as the need for frequent dosing and low bioavailability problems affect the success of the treatment. To prevent side effects caused by conventional therapy, a nanoparticular drug delivery system has been developed, in which receptors are constantly stimulated, and the frequency of dosing is reduced. In this study, levodopa was loaded in Poly lactic-co-glycolic acid (PLGA) nanoparticles (NP) which modified with Wheat Germ Agglutinin (WGA) To increase the effectiveness of levodopa, reduce its side effects and apply to the nasal area which is an alternative way for brain targeting with lower doses. To obtain the optimum levodopa loaded PLGA nanoparticles, the effect of some formulation variables such as polyvinyl alcohol (PVA) concentration, homogenization speed, polymer amount and molecular weight, and levodopa content on the entrapment efficiency (EE) and particle size of the nanoparticles were investigated. Besides these variables, the effect of different parameters on the WGA binding constant was also searched. In addition to in vitro release studies, Differential Scanning Calorimetry (DSC) and Fourier Transform Infrared Spectrophotometer (FT-IR), and Transmission electron microscopy (TEM) analysis were used in the characterization of nanoparticles. Among all formulations, A2 and A8a which was produced with different molcular weights of PLGA, different added levodopa amounts and with different homogenization speeds were chosen as optimum formulations due to their sustained release properties and the ability to release 80 % of their drug content.WGA binding constant was found 78.20 % for A8a-1 and 95 % for A2-1. In this study, we aimed to determine the effect of different formulation parameters on the development of levodopa loaded and WGA grafted PLGA nanoparticles and on the quality characteristics of nanoparticle formulations such as particle size, zeta potential, and EE. In this paper, our results are demonstrated for a better understanding of the effect of process parameters on the development of nanoparticle-based drug delivery systems by using the double-emulsion solvent evaporation technique and on WGA binding of drug-loaded PLGA nanoparticles.

Lipid Nanocarriers for Hyperproliferative Skin Diseases

Simple Summary

Different drugs, including antiproliferative and corticosteroids in general, are recommended for the treatment of hyperproliferative skin diseases (HSD). The effectiveness of many of these drugs is limited due to their low solubility in water and low penetration in the skin. The loading of these drugs in lipid nanocarriers, such as lipid nanoparticles and liposomes, has been considered as a successful solution to improve the drug bioavailability through the skin, to control their release kinetics and thus reduce the risk of potential side effects. In this work, we discuss the use of lipid nanocarriers loading drugs against HSD.

Abstract

Hyperproliferative skin diseases (HSD) are a group of diseases that include cancers, pre-cancerous lesions and diseases of unknown etiology that present different skin manifestations in terms of the degree and distribution of the injuries. Anti-proliferative agents used to treat these diseases are so diverse, including 5-aminolevulinic acid, 5-fluorouracil, imiquimod, methotrexate, paclitaxel, podophyllotoxin, realgar, and corticosteroids in general. These drugs usually have low aqueous solubility, which consequently decreases skin permeation. Thus, their incorporation in lipid nanocarriers has been proposed with the main objective to increase the effectiveness of topical treatment and reduce side effects. This manuscript aims to describe the advantages of using lipid nanoparticles and liposomes that can be used to load diversity of chemically different drugs for the treatment of HSD.

Optimization of nanovesicular carriers of a poorly soluble drug using factorial design methodology and artificial neural network by applying quality by design approach

The current work aims to utilize a quality by design (QbD) approach to develop and optimize nanovesicular carriers of a hydrophobic drug. Rosuvastatin calcium was used as a model drug, which suffers poor bioavailability. Several tools were used in the risk assessment study as Ishikawa diagrams. The critical process parameters (CPP) were found to be the particle size, polydispersity index, zeta potential, and entrapment efficiency. A factorial design was used in risk analysis, which was complemented with an artificial neural network (ANN); to assure its accuracy. A design space was established, with an optimized nanostructured lipid carrier formula containing 3.2% total lipid content, 0.139% surfactant, and 0.1197 mg % drug. The optimized formula showed a sustained drug release up to 72 h. It successfully lowered each of the total cholesterol, low-density lipoprotein, and triglycerides and elevated the high-density lipoprotein levels, as compared to the standard drug. Thus, the concurrent use of the factorial design with ANN using the QbD approach permitted the exploration of the experimental regions for a successful nanovesicular carrier formulation and could be used as a reference for many nanostructured drug delivery studies during their pharmaceutical development and product manufacturing.

QbD enabled optimization of solvent shifting method for fabrication of PLGA-based nanoparticles for promising delivery of Capecitabine for antitumor activity

The key objective of the current research was to fabricate and optimize Capecitabine (Cap)-loaded [poly(lactic-co-glycolic acid)] PLGA-based nanoparticles (NPs) by enabling quality by design (QbD) approach for enhancing antitumor activity by promising delivery of the drug at the colonic site. The current research was based on fabricating PLGA-based nanoparticles along with Eudragit S100 as enteric polymer employing solvent shifting method followed by optimization using QbD approach. This approach was found to be useful for understanding the multiple factors and their interaction influencing the product by utilizing Design of Experiment (DOE). Box-Behnken design (BBD) was adopted to achieve the required critical quality attributes (CQAs), i.e., minimizing particle size, maximizing entrapment efficiency, and minimizing PDI value. The optimized nanoparticles were lyophilized and characterized by FT-IR, DSC, TEM, DLS, MTT assay using HT-29 cell lines, and in vivo pharmacokinetic studies. The optimized PLGA-based nanoparticles were found to possess average particle size, PDI, zeta potential, and entrapment efficiency of 195 nm, 0.214, -6.65 mV, and 65%, respectively. TEM analysis revealed the spherical nature of nanoparticles. The FT-IR and DSC studies revealed no interaction. The bioavailability of Cap-loaded nanoparticles was found to be two fold increased than the pure drug, and also, it exhibited significantly more cytotoxic to tumor cells as compared to pure drug as confirmed by MTT assay. The optimized PLGA-based nanoparticles found to possess enhanced bioavailability and significantly more cytotoxic potential as compared to pure drug.

Machine learning directed drug formulation development

Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.

Chronic administration of cryptolepine nanoparticle formulation alleviates seizures in a neurocysticercosis model

Worldwide, neurocysticercosis remains an important cause of acquired epilepsy. We therefore seek to investigate the effectiveness of the nanoparticle formulation of cryptolepine in alleviating seizures in a neurocysticercosis model. A solid-lipid nanoparticle formulation of extracted cryptolepine was prepared. The parasites were maintained in T. crassiceps metacestode (ORF strain) - infected female BALB/c mice. Cryp (5 ​mg/kg), SLN-CRYP (5 ​mg/kg), ABZ (50 ​mg/kg) DXM (0.5 ​mg/kg), and PHE (30 ​mg/kg). were assessed for in vitro cysticidal, in vivo cysticidal and/or antiseizure activity in 70 mice that had developed seizures from infection with T. crassiceps. General pathologic processes were studied in the host tissue and inflammatory mediators were quantified from isolated mice brains. All treatments (CRYP, SLN-CRYP and ABZ) caused significantly reduced viability of T. crassiceps cysts. Treatment with SLN-CRYP significantly shrunk cysticerci and resolved ventricular expansion and deviation similar to albendazole on examination of encephala. SLN-CRYP inhibited hyperemia but was more effective against microgliosis, calcification, edema and meningitis. Mean seizure score was significantly reduced in models administered with SLN-CRYP (p ​< ​0.0001); as were frequency (p ​< ​0.0001) and duration (p ​< ​0.0001) of seizures. SLN-CRYP significantly reduced brain homogenate levels of IL-10 (p ​= ​0.0016) and IFN-γ (p ​< ​0.0001). Our study shows that the chronic administration of the nanoparticle formulation of cryptolepine is effective in alleviating seizures associated with neurocysticercosis in a mouse model.

Quality by design prospects of pharmaceuticals application of double emulsion method for PLGA loaded nanoparticles

Abstract

QbD approach empowers the pharma researchers to minimize the number of experimental trials and time. It helps identify the significant, influential factors such as critical material attributes, critical formulation variables, and critical process parameters, which may significantly impact the quality of the products. Poly lactic-co-glycolic acid (PLGA), a biocompatible and biodegradable polymer, has gained an immense potential and wide range of applications as a carrier for manufacturing of polymeric nanoparticle drug delivery systems as per US-FDA and European Medicine Agency for drug delivery. The double emulsion method for preparing PLGA nanoparticles to encapsulate hydrophilic drugs has attracted interest in manufacturing processes. The double emulsion is a two-step process consisting of two different emulsification, making the process more complicated. The stability of nanoparticles obtained by a double emulsion method remains questionable due to the many formulations and process attributes. Currently, PLGA based nanoparticles prepared by a double emulsion technique are an alternative pharmaceutical manufacturing operation for getting the quality product by employing the Quality by Design approach. This present review has discussed the QbD elements to elucidate the effect of material attributes, formulation, and process variables on the critical quality attributes of the drug product, such as particle size distribution, encapsulation efficiency, etc. The components of a double emulsion, characteristics of drugs, polymers, and stabilizers used have been discussed in detail in this review.

Graphic abstract

In vitro evaluation of mucoadhesive in situ nanogel of celecoxib for buccal delivery

OBJECTIVE

The research aims for finding the possibility of buccal delivery of celecoxib from an in situ mucoadhesive nanogel, and in vitro evaluation of the gel to evidence the enhanced permeation of drug from buccal mucosa.

MATERIALS AND METHODS

Six formulations of celecoxib loaded NLCs were prepared using stearic acid and oleic acid in different ratio in aqueous solution of poloxamer by emulsion solvent evaporation technique. The physicochemical evaluations of the celecoxib loaded NLC (CeL-NLC) were carried out. The formulation F4 was selected as the best and subjected for FTIR, DSC, PXRD, and surface morphology study. In situ mucoadhesive gel was prepared with F4 in HPC, HPMC and Carbopol 934 gelling systems. The gels were evaluated for their mechanical and rheological properties and in vitro permeation studies through rabbit oral mucosa.

RESULTS

The selected process at high shear homogenization could yield nanoparticles of desired physiochemical properties. The drug and excipients were compatible as disseminated from FTIR study. Transformation of the crystal form to amorphous form of celecoxib was revealed by the solid-state characterization studies. The AFM study unfold the formation of discrete asymmetric nano particles. All mucoadhesive gels found to have good mucoadhesion and rheological property with good in vitro permeation of drug. A comparative study and statistical analysis unveiled that gel containing Carbopol 934 was found to be the best mucoadhesive in situ gel of nano particles of celecoxib with enhanced permeation parameters.

CONCLUSION

Therefore, the above in vitro evaluation of in situ mucoadhesive nano gel proved the potential of the formulation as a promising buccal delivery of celecoxib.

Non-biological Complex Drugs (NBCDs): Complex Pharmaceuticals in Need of Individual Robust Clinical Assessment Before Any Therapeutic Equivalence Decision

Non-Biological Complex Drugs (NBCDs) are complex non-biological drugs comprised of large high molecular weight molecules and, often, nanoparticular structures (including liposomes and block-copolymer micelles). In the case of NBCDs, the entire complex is the active pharmaceutical ingredient and its properties cannot be fully characterized by physicochemical analysis. Moreover, the manufacturing process is fundamental in creating the correct originator product. The same is true for generic versions of the product. A recent appraisal of approval procedures for NBCDs "follow-on products" approved in Europe shows a diversity of regulatory pathways. In fact, three different abridged application procedures, under European legislation, were used: the generic application procedure of Article 10(1), the hybrid application procedure of Article 10(3), and the biosimilar application procedure of Article 10(4). Three informed consent applications via Article 10(c) from innovator companies of glatiramer acetate and sevelamer carbonate were submitted shortly after the approval of the first follow-on products. Furthermore, a number of "well-established use" applications [via Article 10(a)] were approved for iron sucrose and iron dextran complexes. In order to protect patients from the increased risks of NBCD products and NBCD follow-on products, two complementary approaches should be considered: (i) improving the regulatory procedures and their guidance documents within the pre-registration phase, and (ii) not considering interchangeability whenever clinical data is not available. With regards to the latter, the need for adequate safety and efficacy data might also include risk management programmes within post-approval pharmacovigilance actions. This, however, would depend on a risk appraisal that must be considered for individual medicinal products, based on the nature of the submitted relevant set of safety/efficacy data.

Development of a Curcumin-Loaded Polymeric Microparticulate Oral Drug Delivery System for Colon Targeting by Quality-by-Design Approach

The purpose of this study was to apply the quality-by-design (QbD) approach for the development of colon-targeted curcumin-loaded polymeric microparticles (Col-CUR-MPs). The proportion of the enterosoluble polymer (Eudragit^® FS) in the polymeric matrix, curcumin concentration, and the concentration of the polymer mixture (Eudragit^® FS-polycaprolactone) were identified as potential risk factors for the quality of the final product following risk assessment. The influence of these variables on the critical quality attributes (CQAs) of Col-CUR-MPs was investigated. Therefore, a central composite face experimental design was used in order to determine the functional relationships between variables and product CQAs. The obtained regression model and contour plots were used to establish the design space. Finally, the model was validated by preparing two microparticulate formulations, one corresponding to the robust setpoint from within the design space and one outside the established design space, and calculating the percentage bias between the experimental and predicted values. The in vivo study, which was conducted on a fluorescein-loaded formulation that corresponded to the robust setpoint determined by QbD and that contained a mixture of polycaprolactone and Eudragit^® FS (60:40, w/w), confirmed the colon-targeting qualities of this formulation.

Hyaluronic acid-decorated liposomal nanoparticles for targeted delivery of 5-fluorouracil into HT-29 colorectal cancer cells

The use of liposomes as drug carriers improves the therapeutic efficacy of anticancer drugs, while at the same time reducing side effects. Hyaluronic acid (HA) is recognized by the CD44 receptor, which is overexpressed in many cancer cells. In this study, we developed HA-modified liposomes encapsulating 5-fluorouracil (5-FU) and tested them against a CD44 expressing colorectal cell line (HT29) and a non-CD44 expressing hepatoma cell line. The average size of 5-FU-lipo and 5-FU-lipo-HA nanoparticles were 112 ± 28 and 144 ± 77 nm, respectively. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) assay showed selective cancer cell death depending on the CD44 expression in a time-dependent manner. Apoptosis assays and cell-cycle analysis indicated that G0/G1 arrest occurred. The colony formation study revealed that cells treated with 5-FU-lipo and 5-FU-lipo-HA had reduced colony formation. Quantitative reverse-transcription polymerase chain reaction study showed that the oncogenic messenger RNA and microRNA levels were significantly reduced in the 5-FU-lipo-HA-treated group, while tumor suppressors were increased in that group. We suggest that optimal targeted delivery and release of 5-FU into colorectal cancer cells, renders them susceptible to apoptosis, cell-cycle arrest, and decreased colony formation.

Development and Statistical Optimization of Solid Lipid Nanoparticle Formulations of Fluticasone Propionate

OBJECTIVES

The aim of this study was to develop fluticasone propionate (FP)-loaded solid lipid nanoparticle (SLN) formulations by using factorial design approach.

MATERIALS AND METHODS

Tristearin percentages (X1) (1%, 2%, and 4%) and homogenization cycles (X2) (2, 4, and 8 cycles) were selected as independent variables in the factorial design. SLN formulations were optimized by multiple linear regression (MLR) to evaluate the influence of the selected process and formulation independent variables on SLNs' characteristics, namely as encapsulation efficiency (Q1) and particle size (Q2). The polydispersity index and surface charge of the SLNs were also evaluated in this research. Moreover, transmission electron microscopy, differential scanning calorimetry, and in vitro drug release studies were carried out on the optimum SLN formulation.

RESULTS

The MLR analysis indicated that as the homogenization cycle (X2) increased in the production process, the mean particle size decreased.

CONCLUSION

This research showed that FP-encapsulated SLNs with desired characteristics can be produced by varying the production and content variables of the formulations.

Applying Quality by Design Principles in the Development and Preparation of a New Radiopharmaceutical: Technetium-99m-Imatinib Mesylate

The clinical impact and accessibility of ^99mTc tracers for cancer diagnosis would be greatly enhanced by the availability of a new, simple, and easy labeling process and radiopharmaceuticals. In this study, Technetium-99m-imatinib mesylate ([^99mTc]TcIMT) was developed and prepared as a new radiopharmaceutical for breast cancer diagnosis. The effect of critical process parameters on the product quality and stability of [^99mTc]TcIMT was investigated using the quality by design concept of the ICH Q8 (Pharmaceutical Development) guideline. [^99mTc]TcIMT was subjected to in vitro cell binding studies to determine healthy and cancer cell affinity using HaCaT and MCF-7 cells, respectively. The optimal radiolabeling procedure with 1 mg of IMT, 500 μg of stannous chloride, 0.1 mg of ascorbic acid, and ^1mCi ^99mTc radioactivity was obtained for [^99mTc]TcIMT. The pH of the reaction mixture was adjusted to 10 and allowed to react for 15 min at room temperature. The radiochemical purity of [^99mTc]TcIMT was found to be higher than 90% at room temperature up to 6 h. Chromatography analysis revealed >85% [^99mTc]TcIMT complex formation with promising stability in saline, cell medium, and serum up to 6 h. The radiolabeled complex showed a higher cell-binding ratio to MCF-7 cells (88.90% ± 3.12) than HaCaT cells (45.64 ± 4.72) when compared to ^99mTc. Our findings show that the developed preparation method for [^99mTc]TcIMT falls well within the proven acceptable ranges. Applying quality by design (QbD) principles is feasible and worthwhile for the preparation of [^99mTc]TcIMT. In conclusion, radiochemical purity, stability, and in vitro cell binding evaluation of the [^99mTc]TcIMT complex indicate that the agent can be utilized for imaging of breast cancer cells.

Nanotechnology approaches in the current therapy of skin cancer

Skin cancer is a high burden disease with a high impact on global health. Conventional therapies have several drawbacks; thus, the development of effective therapies is required. In this context, nanotechnology approaches are an attractive strategy for cancer therapy because they enable the efficient delivery of drugs and other bioactive molecules to target tissues with low toxic effects. In this review, nanotechnological tools for skin cancer will be summarized and discussed. First, pathology and conventional therapies will be presented, followed by the challenges of skin cancer therapy. Then, the main features of developing efficient nanosystems will be discussed, and next, the most commonly used nanoparticles (NPs) described in the literature for skin cancer therapy will be presented. Subsequently, the use of NPs to deliver chemotherapeutics, immune and vaccine molecules and nucleic acids will be reviewed and discussed as will the combination of physical methods and NPs. Finally, multifunctional delivery systems to codeliver anticancer therapeutic agents containing or not surface functionalization will be summarized.

Lipid Nanocarriers for Enhanced Delivery of Temozolomide to the Brain

Brain disorders, a diverse range of conditions comprising of neurological and psychiatric conditions, are the leading cause of disability, severely affect the quality of life, and in many cases lead to mortality. The prime challenge in treatment of brain disorders is to deliver therapeutics by overcoming the blood-brain barrier (BBB), a unique anatomical and physiological barrier which restricts the passage of a number of molecules, proteins, and cells from the bloodstream. Lipid nanoparticles have emerged as promising drug delivery systems primarily because of biodegradability, low toxicity potential, and the ability to cross physiological barriers especially the BBB even without surface modifications.In this chapter we discuss the preparation and characterization of nanostructured lipid carriers of temozolomide, a chemotherapeutic drug. Evaluation of pharmacokinetics and biodistribution of the nanocarrier system in rats revealed improved delivery of the chemotherapeutic agent to the brain with the potential of lesser side effects.

Development of Topical Nanocarriers for Skin Cancer Treatment Using Quality by Design Approach

BACKGROUND

One of the most compelling medical challenges of this century is the treatment of cancer and among them, skin cancer is the most common type. Thus, current treatments need to be renewed continuously to handle this challenge.

OBJECTIVE

This review presents considerations which can be employed during the development of nanosized formulations dedicated to the topical treatment of skin cancer. We aimed to collect and organize literature data on the treatment options for skin cancer in order to determine the required quality attributes of an effective dermal anticancer formulation.

METHOD

With the consideration of the Quality by Design (QbD) approach related to the development of new pharmaceutical formulations, a cost-saving process ensuring a high-quality product taking into account patient expectations, industrial and regulatory aspects can be achieved. Furthermore, this concept is highly recommended by regulatory agencies.

RESULTS

Our work discusses the current therapies, active agents, drug carrier systems, and evaluation methods in connection with the treatment of skin cancer and outlines Critical Quality Attributes which need to be considered during the development of a nanosized dermal anticancer formulation.

CONCLUSION

The first part of this review summarizes the most important topical treatment therapies for skin cancer and highlights the future therapeutic perspectives, focusing on the benefits of nanotechnology and dermal administration. The second part outlines the critical points of nanosized dermal anticancer formulation development in the view of QbD approach. Our research emphasizes the application of QbD method for a rationalized and more effective anticancer formulation development process.

QbD Approach for Novel Crosslinker-Free Ionotropic Gelation of Risedronate Sodium-Chitosan Nebulizable Microspheres: Optimization and Characterization

Risedronate sodium (RS) is a potent inhibitor of bone resorption, having an extreme poor permeability and limited oral bioavailability (0.62%). RS should be orally administered under fasting conditions while keeping in an upright posture for at least 30 min to diminish common gastroesophageal injuries. To surmount such limitations, novel risedronate-chitosan (RS-CS) crosslinker-free nebulizable microspheres were developed adopting the quality by design (QbD) approach and risk assessment (RA) thinking. RS:CS ratio, surfactant (Pluronic® F127) concentration, homogenization duration, speed, and temperature were identified using Ishikawa diagrams as the highest formulation and process risk factors affecting the critical quality attributes (CQAs), average particle size (PS), and entrapment efficiency (EE%). The risk factors were screened using the Plackett-Burman design, and the levels of the most significant factors were optimized using a multilevel factorial design to explore the optimized system with the least PS, maximum EE%, and a prolonged drug release profile. The optimized system (B6) was developed at a RS:CS ratio of 1:7, a surfactant concentration of 2% (w/v), and a homogenization speed of 14,000 rpm. It revealed good correlation with QbD theoretical prediction, where positively charged (47.9 ± 3.39 mV) discrete, spherical microspheres (3.47 ± 0.16 μm) having a high EE% (94.58 ± 0.19%) and prolonged RS release over 12 h (Q_{12 h}, 89.70 ± 0.64%) were achieved. In vivo lung deposition after intratracheal instillation of B6 confirmed the delivery of high RS percentage to rat lung tissues (87 ± 3.54%) and its persistence for 24 h. This investigation demonstrated the effectiveness of QbD philosophy in developing RS-CS crosslinker-free nebulizable microspheres.

Delineating the effects of hot-melt extrusion on the performance of a polymeric film using artificial neural networks and an evolutionary algorithm

The aim of this study was to utilize an artificial neural network (ANN) in conjunction with an evolutionary algorithm to investigate the relationship between hot melt extrusion (HME) process parameters and vaginal film performance. Investigated HME process parameters were: barrel temperature, screw speed, and feed rate. Investigated film performance attributes were: percent dissolution at 30 min, puncture strength, and drug content. An ANN model was successfully developed and validated with a root mean squared error of 0.043 and 0.098 for training and validation, respectively. Of all three assessed process parameters, the model revealed that barrel temperature has a significant impact on film performance. An increase in barrel temperature resulted in increased dissolution and punctures strength and decreased drug content. Additionally, a successful implementation of an evolutionary algorithm was carried out in order to demonstrate the potential applicability of the developed ANN model in film formulation optimization. In this analysis, the values predicted of film performance attributes were within 1% error of the experimental data. The findings of this study provide a quantitative framework to understand the relationship between HME parameters and film performance. This quantitative framework has the potential to be used for film formulation development and optimization.

QbD guided early pharmaceutical development study: Production of lipid nanoparticles by high pressure homogenization for skin cancer treatment

This research attempts to bring together the positive aspects of lipid nanoparticles and Quality by Design (QbD) approach for developing a novel drug delivery system for skin cancers and aktinic keratosis. Lipid nanoparticles which is one of the most efficacious options for topical treatment of skin diseases were prepared due to their ability to overcome the complex structure of skin barrier and to enhance the skin penetration. Since the formulation development contains complex variables of active ingredients, raw materials or production method; all the variables of the product should be elaborated. QbD approach which refers to design and develop formulations and manufacturing processes to maintain the prescribed product quality was also successfully adopted to achieve a time- and cost-saving process ensuring a high-quality product. 5-Fluorouracil (5-FU) loaded lipid nanoparticles, both solid lipid nanoparticles and nanostructured lipid carriers, were developed and characterized by following QbD steps. Optimal lipid nanoparticle formulation with guaranteed quality which was within the design space has been reached through the artificial neural networks. The optimal lipid nanoparticle formulation which is a NLC formulation with a mean particle size of 205,8 ± 9,34 nm, narrow size distribution (0.279 ± 0.01) and negative zeta potantial -30,20 ± 0,92 was produced by high pressure homogenization method. Cytotoxicity profiles of the optimal NLC was determined by cell culture studies on epidermoid carcinoma cells and human keratinocyte cells. Optimal NLC showed significantly higher anticancer effect on epidermoid carcinoma cells than free 5-FU and also less cytotoxicity towards human keratinocyte cells. Optimal NLC was formulated in hydrogel formulation for ease of application which has suitable occlusive and mechanical properties, viscocity and pH for patient complience. The cumulative amount of 5-FU in dermal tissues of rat skin was found 20.11 ± 2.14 μg/cm2 and 9.73 ± 0.87 μg/cm2 after application of NLC enriched hydrogel and 5-FU hydrogel respectively. In conclusion, this study showed that a time and cost saving process ensuring a high-quality product can be obtained by QbD guided formulation development study with the help of artificial neural networks. A novel semisolid dosage form enriched by NLC which is promising for topical treatment of skin cancers was developed.

Sol-gel Silica Nanoparticles in Medicine: A Natural Choice. Design, Synthesis and Products

Silica is one of the most abundant minerals in the Earth's crust, and over time it has been introduced first into human life and later into engineering. Silica is present in the food chain and in the human body. As a biomaterial, silica is widely used in dentistry, orthopedics, and dermatology. Recently amorphous sol-gel SiO₂ nanoparticles (NPs) have appeared as nanocarriers in a wide range of medical applications, namely in drug/gene target delivery and imaging diagnosis, where they stand out for their high biocompatibility, hydrophilicity, enormous flexibility for surface modification with a high payload capacity, and prolonged blood circulation time. The sol-gel process is an extremely versatile bottom-up methodology used in the synthesis of silica NPs, offering a great variety of chemical possibilities, such as high homogeneity and purity, along with full scale pH processing. By introducing organic functional groups or surfactants during the sol-gel process, ORMOSIL NPs or mesoporous NPs are produced. Colloidal route, biomimetic synthesis, solution route and template synthesis (the main sol-gel methods to produce monosized silica nanoparticles) are compared and discussed. This short review goes over some of the emerging approaches in the field of non-porous sol-gel silica NPs aiming at medical applications, centered on the syntheses processes used.

The use of 5-fluorouracil-loaded nanobubbles combined with low-frequency ultrasound to treat hepatocellular carcinoma in nude mice

OBJECTIVE

This study aimed to investigate the therapeutic effects of 5-fluorouracil (5-FU)-loaded nanobubbles irradiated with low-intensity, low-frequency ultrasound in nude mice with hepatocellular carcinoma (HCC).

METHODS

A transplanted tumor model of HCC in nude mice was established in 40 mice, which were then randomly divided equally into four groups: group A (saline), group B (5-FU-loaded nanobubbles), group C (5-FU-loaded nanobubbles with non-low-frequency ultrasound), and group D (5-FU-loaded nanobubbles with low-frequency ultrasound). The tumor size in each mouse was observed via ultrasound before and after the treatments. Inhibition of the tumor growth in each group was compared, and survival curves were generated. Tumor tissues were removed to determine the apoptotic index using the TUNEL method and quantitative analysis. Tumor tissues with CD34-positive microvessels were observed by immunohistochemistry, and the tumor microvessel densities were calculated.

RESULTS

The growth rate of the tumor volumes in group D was significantly slower than that in the other groups, while the tumor inhibition rates and apoptotic index in group D were significantly higher than those of the other groups. The number of microvessels staining positive for CD34 was decreased in group D. Therefore, group D presented the most significant inhibitory effects.

CONCLUSIONS

Therefore, 5-FU-loaded nanobubbles subjected to irradiation with low-frequency ultrasound could further improve drug targeting and effectively inhibit the growth of transplanted tumors, which is expected to become an ideal drug carrier and targeted drug delivery system for the treatment of HCC in the future.

A two-step approach for fluidized bed granulation in pharmaceutical processing: Assessing different models for design and control

Various modeling techniques were used to understand fluidized bed granulation using a two-step approach. First, Plackett-Burman design (PBD) was used to identify the high-risk factors. Then, Box-Behnken design (BBD) was used to analyze and optimize those high-risk factors. The relationship between the high-risk input variables (inlet air temperature X1, binder solution rate X3, and binder-to-powder ratio X5) and quality attributes (flowability Y1, temperature Y2, moisture content Y3, aggregation index Y4, and compactability Y5) of the process was investigated using response surface model (RSM), partial least squares method (PLS) and artificial neural network of multilayer perceptron (MLP). The morphological study of the granules was also investigated using a scanning electron microscope. The results showed that X1, X3, and X5 significantly affected the properties of granule. The RSM, PLS and MLP models were found to be useful statistical analysis tools for a better mechanistic understanding of granulation. The statistical analysis results showed that the RSM model had a better ability to fit the quality attributes of granules compared to the PLS and MLP models. Understanding the effect of process parameters on granule properties provides the basis for modulating the granulation parameters and optimizing the product performance at the early development stage of pharmaceutical products.

Development of antiproliferative long-circulating liposomes co-encapsulating doxorubicin and curcumin, through the use of a quality-by-design approach

The aim of this work was to use the quality-by-design (QbD) approach in the development of long-circulating liposomes co-loaded with curcumin (CUR) and doxorubicin (DOX) and to evaluate the cytotoxic potential of these liposomes in vitro using C26 murine colon carcinoma cell line. Based on a risk assessment, six parameters, namely the phospholipid, CUR and DOX concentrations, the phospholipid:cholesterol molar ratio, the temperature during the evaporation and hydration steps and the pH of the phosphate buffer, were identified as potential risk factors for the quality of the final product. The influence of these variables on the critical quality attributes of the co-loaded liposomal CUR and DOX was investigated: particle size, zeta potential, drug loading and entrapment efficiency. For this, a 2⁶⁻² factorial design was employed to establish a proper regression model and to generate the contour plots for the responses. The obtained data served to establish the design space for which different combinations of variables yielded liposomes with characteristics within predefined specifications. The validation of the model was carried out by preparing two liposomal formulations corresponding to the robust set point from within the design space and one outside the design space and calculating the percentage bias between the predicted and actual experimental results. The in vitro antiproliferative test showed that at higher CUR concentrations, the liposomes co-encapsulating CUR and DOX had a greater cytotoxic effect than DOX-loaded liposomes. Overall, this study showed that QbD is a useful instrument for controlling and optimizing the manufacturing process of liposomes co-loaded with CUR and DOX and that this nanoparticulate system possesses a great potential for use in colon cancer therapy.