Exploring Potential of Nano-formulations in the Treatment of Alzheimer's Disease through Nasal Route.

OBJECTIVE: Alzheimer's disease, a progressive neurodegenerative disorder, severely impacts cognitive function and daily living. The current treatment provides only symptomatic relief, and thus, disease-modifying therapies targeting underlying causes are needed. Although several potential therapies are in various stages of clinical trials, bringing a new Alzheimer's drug to market remains challenging. Hence, researchers are also exploring monoclonal antibodies, tau protein inhibitors, and anti-inflammatory drugs as treatment options. Conventionally designed dosage forms come with limitations like poor absorption, first-pass metabolism, and low bioavailability. They also cause systemic adverse effects because these designed systems do not provide target- specific drug delivery. Thus, in this review, the authors highlighted the current advancements in the development of intranasal nanoformulations for the treatment of Alzheimer's disease. This strategy of delivering anti-Alzheimer drugs through the nasal route may help to target the drug exactly to the brain, achieve rapid onset of action, avoid first-pass metabolism, and reduce the side effects and dose required for administration. CONCLUSION: Delivering drugs to the brain through the nasal route for treating Alzheimer's disease is crucial due to the limited efficacy of existing treatments and the profound impact of the disease on patients and their families. Thus, by exploring innovative approaches such as nose-to-brain drug delivery, it is possible to improve the quality of life for individuals living with Alzheimer's and alleviate its societal burden.

Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects.

Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug formulation and development is no exception. This review article aims to provide an overview of the applications of AI in drug formulation and development and explore its future prospects. The article begins by introducing the fundamental concepts of AI, including machine learning, deep learning, and artificial neural networks and their relevance in the pharmaceutical industry. Furthermore, the article discusses the network and tools of AI and its applications in the pharmaceutical development process, including various areas, such as drug discovery, manufacturing, quality control, clinical trial management, and drug delivery. The utilization of AI in various conventional as well as modified dosage forms has been compiled. It also highlights the challenges and limitations associated with the implementation of AI in this field, including data availability, model interpretability, and regulatory considerations. Finally, the article presents the future prospects of AI in drug formulation and development, emphasizing the potential for personalized medicine, precision drug targeting, and rapid formulation optimization. It also discusses the ethical implications of AI in this context, including issues of privacy, bias, and accountability.

Solidification and oral delivery of biologics to the colon- A review.

The oral delivery of biologics such as therapeutic proteins, peptides and oligonucleotides for the treatment of colon related diseases has been the focus of increasing attention over the last years. However, the major disadvantage of these macromolecules is their degradation propensity in liquid state which can lead to the undesirable and complete loss of function. Therefore, to increase the stability of the biologic and reduce their degradation propensity, formulation techniques such as solidification can be performed to obtain a stable solid dosage form for oral administration. Due to their fragility, stress exerted on the biologic during solidification has to be reduced with the incorporation of stabilizing excipients into the formulation. This review focuses on the state-of-the-art solidification techniques required to obtain a solid dosage form for the oral delivery of biologics to the colon and the use of suitable excipients for adequate stabilization upon solidification. The solidifying processes discussed within this review are spray drying, freeze drying, bead coating and also other techniques such as spray freeze drying, electro spraying, vacuum- and supercritical fluid drying. Further, the colon as site of absorption in both healthy and diseased state is critically reviewed and possible oral delivery systems for biologics are discussed.

Current State and Opportunities with Long-acting Injectables: Industry Perspectives from the Innovation and Quality Consortium "Long-Acting Injectables" Working Group.

Long-acting injectable (LAI) formulations can provide several advantages over the more traditional oral formulation as drug product opportunities. LAI formulations can achieve sustained drug release for extended periods of time, which results in less frequent dosing requirements leading to higher patient adherence and more optimal therapeutic outcomes. This review article will provide an industry perspective on the development and associated challenges of long-acting injectable formulations. The LAIs described herein include polymer-based formulations, oil-based formulations, and crystalline drug suspensions. The review discusses manufacturing processes, including quality controls, considerations of the Active Pharmaceutical Ingredient (API), biopharmaceutical properties and clinical requirements pertaining to LAI technology selection, and characterization of LAIs through in vitro, in vivo and in silico approaches. Lastly, the article includes a discussion around the current lack of suitable compendial and biorelevant in vitro models for the evaluation of LAIs and its subsequent impact on LAI product development and approval.

A multivariate formulation and process development platform for direct compression.

The efficient development of robust tableting processes is challenging due to the lack of mechanistic understanding on the impact of raw material properties and process parameters on tablet quality. The experimental determination of the effect of process and formulation parameters on tablet properties and subsequent optimization is labor-intensive, expensive and time-consuming. The combined use of an extensive raw material property database, process simulation tools and multivariate modeling allows more efficient and more optimized development of the direct compression (DC) process. In this study, key material attributes and in-process mechanical properties with a potential effect on tablet processability and tablet properties were identified. In a first step, an extensive characterization of 55 raw materials (over 100 material descriptors) (Van Snick et al., 2018) and 26 formulation blends (31 material descriptors) (Dhondt et al., 2022) was performed. These blends were subsequently compacted on a compaction simulator under multiple process conditions through a design of experiments (DoE) approach. A T-shaped partial least squares (T-PLS) model was established which correlates tablet quality attributes with process settings, raw material properties and blend ratios. During future development of the DC formulation and process for a new active pharmaceutical ingredient (API), this model can then be used to provide a preliminary formulation and compaction process settings as starting point to be further optimized during development trials based on well-defined raw material characteristics and compaction tests. This study hence contributes to a better understanding on the impact of raw material properties and process settings on a DC process and final properties of the produced tablets; and provides a platform allowing a more efficient and more optimized development of a robust tableting process.

A multivariate methodology for material sparing characterization and blend design in drug product development.

This study developed a material and time saving method for powder characterization. Building on an earlier developed raw material property database for use towards development of pharmaceutical dry powder processes, blends were selected in an efficient way to include maximal variability of the underlying raw material dataset. For both raw materials and blends, powder characterization methods were kept to a minimum by selecting the testing methods that described the highest amount of variability in physical powder properties based on principal component analysis (PCA). This method selection was made by identifying the overarching properties described by the principal components of the PCA model. Ring shear testing, powder bed compressibility, bulk/tapped density, helium pycnometry, loss on drying and aeration were identified as the most discriminating characterization techniques from this dataset to detect differences in physical powder properties. This ensured a workload reduction while most of the powder variability that could be detected was still included. The methodology proposed in this paper could be used as a material-saving alternative to the current "Design of Experiment" approach, which will be investigated further for applicability to speed up the development of formulations and processes for new drug products and building an end-to-end predictive platform.

Stability evaluation of [18F]FDG: literature study, stability studies from two different PET centres and future recommendations.

BACKGROUND: The need for a stability evaluation of [18F]FDG is evident. The main purpose of this study was to make recommendations for determining the shelf life based on the available stability literature and our own two-centre stability studies. RESULTS: We performed a non-systematic literature study to find the most relevant stability data for [18F]FDG. The amount of radioactivity, radio-stabilizers, choice of synthesis, dilution, pH, temperature, storage and the choice of stability tests and acceptance criteria were the most important factors to evaluate for the implementation of good manufacturing practice. Moreover, we discuss some limitations of the study, especially the choice of synthesis, photostability, the environment, temperature and storage. Based on these data, we designed our own two-centre stability studies. All the defined acceptance criteria were met. CONCLUSIONS: We have made recommendations for future stability evaluations based on our findings. The most important findings were that the amount of the radio-stabilizer ethanol should be > 0.1 % ethanol for activities up to 4 GBq/mL and > 0.2 % ethanol for activities up to 22.7 GBq/mL to keep [18F]FDG stable.

Formulation of spray dried enzymes for dry powder inhalers: An integrated methodology.

One of the main challenges when developing a spray dried formulation of an inhalable enzyme is the generation of a safe and effective aerosol, able to reach the lungs, while preserving protein function and structural levels of the biologic. Hence, an appropriate excipient selection based on enzyme stabilization, inhalation precedence and spray drying (SD) process development is required to meet this balance. Herein, an integrated methodology is presented to expedite the selection of the best dry powder inhaler excipient system to formulate three model enzymes of increasing molecular mass and structural complexity belonging to the oxidoreductase class and often implicated in oxidative stress: superoxide dismutase, glucose oxidase and catalase. Three non-reducing sugars and four amino acids were screened using High Throughput Isothermal Denaturation Fluorimetry (HT-ITDF) for a stabilizing effect on the enzymes quaternary structure. For each tested enzyme, the sugar and amino acid showcasing a stabilizing effect, were spray dried together at fixed process conditions for three different ratios, to assess which formulation would then display the best aerodynamic performance. After SD, using the selected conditions, all powders displayed 65-85% of fine particle fraction (FPF) whilst each enzyme kept the oligomeric state. The present integrated methodology proved to be successful, allowing to narrow down 36 potential formulations (three sugars × four amino acids × three ratios) to only one for each enzyme, within few hours, while requiring a µg range of sample amount.

Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective.

This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area. This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.

Key considerations in formulation development for gene therapy products.

Gene therapy emerged as an important area of research and led to the success of multiple product approvals in the clinic. The number of clinical trials for this class of therapeutics is expected to grow over the next decade. Gene therapy products are complex and heterogeneous, employ different types of vectors and are susceptible to degradation. The product development process for commercially viable gene-based pharmaceuticals remains challenging. In this review, challenges, stability, and drug product formulation development strategies using viral or non-viral vectors, as well as accelerated regulatory approval pathways for gene therapy products are discussed.

Shared IVIVR for Five Commercial Enabling Formulations Using the BiPHa+ Biphasic Dissolution Assay.

The present study intended to confirm the in vivo relevance of the BiPHa+ biphasic dissolution assay using a single set of assay parameters. Herein, we evaluated five commercial drug products formulated by various enabling formulation principles under fasted conditions using the BiPHa+ assay. The in vitro partitioning profiles in the organic phase were compared with human pharmacokinetic data obtained from literature. In the first part, a meaningful in vitro dose of the formulations was assessed by determining the maximum drug concentration in the artificial absorption sink during dissolution (organic 1-decanol layer, Cdec,max). Then, the maximum concentration of the partitioned drug in the organic layer was correlated with the in vivo fraction absorbed, which was derived from published human pharmacokinetic data. Fraction absorbed represents the percentage, which is absorbed from the intestine without considering first pass. It was found that the maximum drug concentration in the organic phase obtained from an in vitro dose of ten milligrams, which is equivalent to 15-25 µmol of the respective drug, led to the highest congruency with the fraction absorbed in vivo. In the second part, the in vivo relevance of the BiPHa+ dissolution data was verified by establishing a shared in vitro/in vivo relationship including all formulations. Based on the in vitro kinetics of the BiPHa+ experiments human in vivo plasma profiles were predicted using convolutional modelling approach. Subsequently, the calculated pharmacokinetic profiles were compared with in vivo performance of the studied drug products to assess the predictive power of the BiPHa+ assay. The BiPHa+ assay demonstrated biorelevance for the investigated in vitro partitioning profiles using a single set of assay parameters, which was verified based on human pharmacokinetic data of the five drug products.

Harnessing machine learning for development of microbiome therapeutics.

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.

Biphasic Dissolution as an Exploratory Method During Early Drug Product Development.

Dissolution testing is a major tool used to assess a drug product's performance and as a quality control test for solid oral dosage forms. However, compendial equipment and methods may lack discriminatory power and the ability to simulate aspects of in vivo dissolution. Using low buffer capacity media combined with an absorptive phase (biphasic dissolution) increases the physiologic relevance of in vitro testing. The purpose of this study was to use non-compendial and compendial dissolution test conditions to evaluate the in vitro performance of different formulations. The United States Pharmacopeia (USP)-recommended dissolution method greatly lacked discriminatory power, whereas low buffer capacity media discriminated between manufacturing methods. The use of an absorptive phase in the biphasic dissolution test assisted in controlling the medium pH due to the drug removal from the aqueous medium. Hence, the applied non-compendial methods were more discriminative to drug formulation differences and manufacturing methods than conventional dissolution conditions. In this study, it was demonstrated how biphasic dissolution and a low buffer capacity can be used to assess in vitro drug product performance differences. This can be a valuable approach during the early stages of drug product development for investigating in vitro drug release with improved physiological relevance.

Data analytics on raw material properties to accelerate pharmaceutical drug development.

Manufacturability of active pharmaceutical ingredients (APIs) is often evaluated by an empirical approach during development due to limited material availability. This brings challenges in designing flexible yet robust manufacturing processes under highly accelerated timelines. Hence, good utilisation of a limited material dataset is key to accelerate the delivery of high quality final drug product into the market at minimum cost and maximum process capacity. In this study, we present a data-driven method to investigate a raw materials database where the integration of multivariate analysis and machine learning modelling aids the selection of new incoming materials based on their manufacturability. The procedure was applied to an industrial representative database of thirty-four APIs and seven excipients where eight measurements relevant to flow properties for each of those forty-one materials were collected. The models identified four clusters of materials with different flow properties. These models can serve as a risk assessment tool for new API in early product development phases based on the nearest surrogate material which behave similarly, as well as to identify targeted and material sparring experiments to address key risks during secondary process selection.

A multivariate raw material property database to facilitate drug product development and enable in-silico design of pharmaceutical dry powder processes.

In current study a holistic material characterization approach was proposed and an extensive raw material property database was developed including a wide variety of APIs and excipients with different functionalities. In total 55 different materials were characterized and described by over 100 raw material descriptors related to particle size and shape distribution, specific surface area, bulk, tapped and true density, compressibility, electrostatic charge, moisture content, hygroscopicity, permeability, flowability and wall friction. Principal component analysis (PCA) was applied to reveal similarities and dissimilarities between materials and to identify overarching properties. The developed PCA model allows to rationalize the number of critical characterization techniques in routine characterization and to identify surrogates for use during early drug product development stages when limited amounts of active pharmaceutical ingredients are available. Additionally, the developed database will be the basis to build predictive models for in silico process and formulation development based on (a selection of) property descriptors.

Next Generation Biopharmaceuticals: Product Development.

Therapeutic proteins show a rapid market growth. The relatively young biotech industry already represents 20 % of the total global pharma market. The biotech industry environment has traditionally been fast-pasted and intellectually stimulated. Nowadays the top ten best selling drugs are dominated by monoclonal antibodies (mABs).Despite mABs being the biggest medical breakthrough in the last 25 years, technical innovation does not stand still.The goal remains to preserve the benefits of a conventional mAB (serum half-life and specificity) whilst further improving efficacy and safety and to open new and better avenues for treating patients, e.g., improving the potency of molecules, target binding, tissue penetration, tailored pharmacokinetics, and reduced adverse effects or immunogenicity.The next generation of biopharmaceuticals can pose specific chemistry, manufacturing, and control (CMC) challenges. In contrast to conventional proteins, next-generation biopharmaceuticals often require lyophilization of the final drug product to ensure storage stability over shelf-life time. In addition, next-generation biopharmaceuticals require analytical methods that cover different ways of possible degradation patterns and pathways, and product development is a long way from being straight forward. The element of "prior knowledge" does not exist equally for most novel formats compared to antibodies, and thus the assessment of critical quality attributes (CQAs) and the definition of CQA assessment criteria and specifications is difficult, especially in early-stage development.

In vivo methods for drug absorption - comparative physiologies, model selection, correlations with in vitro methods (IVIVC), and applications for formulation/API/excipient characterization including food effects.

This review summarizes the current knowledge on anatomy and physiology of the human gastrointestinal tract in comparison with that of common laboratory animals (dog, pig, rat and mouse) with emphasis on in vivo methods for testing and prediction of oral dosage form performance. A wide range of factors and methods are considered in addition, such as imaging methods, perfusion models, models for predicting segmental/regional absorption, in vitro in vivo correlations as well as models to investigate the effects of excipients and the role of food on drug absorption. One goal of the authors was to clearly identify the gaps in today's knowledge in order to stimulate further work on refining the existing in vivo models and demonstrate their usefulness in drug formulation and product performance testing.