Pharmaceutical formulation and manufacturing using particle/powder technology for personalized medicines

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Through machine learning (ML), deep learning, natural language processing (NLP), and robotic automation, AI has enhanced efficiency, accuracy, and innovation in the field. The purpose of this review is to shed light on the practical applications and potential of AI in various pharmaceutical fields. These fields include medicinal chemistry, pharmaceutics, pharmacology and toxicology, clinical pharmacy, pharmaceutical biotechnology, pharmaceutical nanotechnology, pharmacognosy, and pharmaceutical management and economics. By leveraging AI technologies such as ML, deep learning, NLP, and robotic automation, this review delves into the role of AI in enhancing drug discovery, development processes, and personalized patient care. It analyzes AI's impact in specific areas such as drug synthesis planning, formulation development, toxicology predictions, pharmacy automation, and market analysis. Artificial intelligence integration into pharmaceutical sciences has significantly improved medicinal chemistry, drug discovery, and synthesis planning. In pharmaceutics, AI has advanced personalized medicine and formulation development. In pharmacology and toxicology, AI offers predictive capabilities for drug mechanisms and toxic effects. In clinical pharmacy, AI has facilitated automation and enhanced patient care. Additionally, AI has contributed to protein engineering, gene therapy, nanocarrier design, discovery of natural product therapeutics, and pharmaceutical management and economics, including marketing research and clinical trials management. Artificial intelligence has transformed pharmaceuticals, improving efficiency, accuracy, and innovation. This review highlights AI's role in drug development and personalized care, serving as a reference for professionals. The future promises a revolutionized field with AI-driven methodologies.

Smart nanoparticles for cancer therapy

Smart nanoparticles, which can respond to biological cues or be guided by them, are emerging as a promising drug delivery platform for precise cancer treatment. The field of oncology, nanotechnology, and biomedicine has witnessed rapid progress, leading to innovative developments in smart nanoparticles for safer and more effective cancer therapy. In this review, we will highlight recent advancements in smart nanoparticles, including polymeric nanoparticles, dendrimers, micelles, liposomes, protein nanoparticles, cell membrane nanoparticles, mesoporous silica nanoparticles, gold nanoparticles, iron oxide nanoparticles, quantum dots, carbon nanotubes, black phosphorus, MOF nanoparticles, and others. We will focus on their classification, structures, synthesis, and intelligent features. These smart nanoparticles possess the ability to respond to various external and internal stimuli, such as enzymes, pH, temperature, optics, and magnetism, making them intelligent systems. Additionally, this review will explore the latest studies on tumor targeting by functionalizing the surfaces of smart nanoparticles with tumor-specific ligands like antibodies, peptides, transferrin, and folic acid. We will also summarize different types of drug delivery options, including small molecules, peptides, proteins, nucleic acids, and even living cells, for their potential use in cancer therapy. While the potential of smart nanoparticles is promising, we will also acknowledge the challenges and clinical prospects associated with their use. Finally, we will propose a blueprint that involves the use of artificial intelligence-powered nanoparticles in cancer treatment applications. By harnessing the potential of smart nanoparticles, this review aims to usher in a new era of precise and personalized cancer therapy, providing patients with individualized treatment options.

Focusing on powder processing in dry powder inhalation product development, manufacturing and performance

Dry powder inhalers (DPI) are well established products for the delivery of actives via the pulmonary route. Various DPI products are marketed or developed for the treatment of local lung diseases such as chronic obstructive pulmonary disease (COPD), asthma or cystic fibrosis as well as systemic diseases targeted through inhaled delivery (i.e. Diabetes Mellitus). One of the key prerequisites of DPI formulations is that the aerodynamic size of the drug particles needs to be below 5 µm to enter deeply into the respiratory tract. These inherently cohesive inhalable size particles are either formulated as adhesive mixture with coarse carrier particles like lactose called carrier-based DPI or are formulated as free-flowing carrier-free particles (e.g. soft agglomerates, large hollow particles). In either case, it is common practice that drug and/or excipient particles of DPI formulations are obtained by processing API and API/excipients. The DPI manufacturing process heavily involves several particle and powder technologies such as micronization of the API, dry blending, powder filling and other particle engineering processes such as spray drying, crystallization etc. In this context, it is essential to thoroughly understand the impact of powder/particle properties and processing on the quality and performance of the DPI formulations. This will enable prediction of the processability of the DPI formulations and controlling the manufacturing process so that meticulously designed formulations are able to be finally developed as the finished DPI dosage form. This article is intended to provide a concise account of various aspects of DPI powder processing, including the process understanding and material properties that are important to achieve the desired DPI product quality. Various endeavors of model informed formulation/process design and development for DPI powder and PAT enabled process monitoring and control are also discussed.

Oral mucus-penetrating PEGylated liposomes to improve drug absorption: Differences in the interaction mechanisms of a mucoadhesive liposome

We investigated the feasibility of densely polyethylene glycol (PEG2000)-modified liposomes as mucus-penetrating particles (MPPs) for oral delivery of systemically absorbed peptides. The oral absorption of MPPs and mucoadhesive liposomes modified with glycol chitosan (GCS) was compared. In an in vitro artificial mucus model, the densely PEGylated liposomes showed mucus permeability. Intracellular uptake of liposomes was evaluated in a Caco-2 and mucus-secreting Caco-2/HT29 co-culture. Intracellular uptake of MPPs was unaffected by mucus in the co-culture system, whereas the cellular uptake of GCS-liposomes was lower with a mucus layer than in Caco-2 alone. Rat in vivo oral absorption of liposomes was evaluated by using fluorescein isothiocyanate dextran (FD) as a model peptide drug. Oral absorption was higher for densely PEGylated than for unmodified liposomes and was PEG-concentration dependent, but excessive PEGylation decreased FD blood concentration. PEGylated liposomes incorporating spermine (SPM) as an absorption enhancer were then designed and showed the highest in vivo absorption of FD of all tested formulations. The pharmacological effects of the oral liposomes were evaluated by using elcatonin and did not correlate with FD oral absorption. The non-PEGylated SPM liposomes showed the highest pharmacological effect, suggesting the need for drug-specific optimization of liposomal components and surface modifiers.