Multivariate modeling of encapsulation and release of an ionizable drug from polymer microspheres

Preparation, Characterization, and Biological Evaluation of a Hydrophilic Peptide Loaded on PEG-PLGA Nanoparticles

The encapsulation of peptides and proteins in nanosystems has been extensively investigated for masking unfavorable biopharmaceutical properties, including short half-life and poor permeation through biological membranes. Therefore, the aim of this work was to encapsulate a small antimicrobial hydrophilic peptide (H-Ser-Pro-Trp-Thr-NH2, FS10) in PEG-PLGA (polyethylene glycol-poly lactic acid-co-glycolic acid) nanoparticles (Nps) and thereby overcome the common limitations of hydrophilic drugs, which because they facilitate water absorption suffer from rapid degradation. FS10 is structurally related to the well-known RNAIII inhibiting peptide (RIP) and inhibits S. aureus biofilm formation. Various parameters, including different method (double emulsion and nanoprecipitation), pH of the aqueous phase and polymeric composition, were investigated to load FS10 into PEG-PLGA nanoparticles. The combination of different strategies resulted in an encapsulation efficiency of around 25% for both the double emulsion and the nanoprecipitation method. It was found that the most influential parameters were the pH—which tailors the peptides charge—and the polymeric composition. FS10-PEG-PLGA nanoparticles, obtained under optimized parameters, showed size lower than 180 nm with zeta potential values ranging from −11 to −21 mV. In vitro release studies showed that the Nps had an initial burst release of 48−63%, followed by a continuous drug release up to 21 h, probably caused by the porous character of the Nps. Furthermore, transmission electron microscopy (TEM) analysis revealed particles with a spherical morphology and size of around 100 nm. Antimicrobial assay showed that the minimum inhibitory concentration (MIC) of the FS10-loaded Nps, against S. aureus strains, was lower (>128 µg/mL) than that of the free FS10 (>256 µg/mL). The main goal of this work was to develop polymeric drug delivery systems aiming at protecting the peptide from a fast degradation, thus improving its accumulation in the target site and increasing the drug-bacterial membrane interactions.

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.

Potential of Raman spectroscopy in facilitating pharmaceutical formulations development - An AI perspective

Drug development is time-consuming and inherently possesses a high failure rate. Pharmaceutical formulation development is the bridge that links a new chemical entity (NCE) to pre-clinical and clinical trials, and has a high impact on the efficacy and safety of the final drug product. Further, the time required for this process is escalating as formulation techniques are becoming more complicated due to the rising demands for drug products with better efficacy and patient compliance, as well as the inherent difficulties of addressing the unfavorable properties of NCEs such as low water solubility. The advent of artificial intelligence (AI) provides possibilities to accelerate the drug development process. In this review, we first examine applications of AI methods in different types of pharmaceutical formulations and formulation techniques. Moreover, as availability of data is the engine for the advancement of AI, we then suggest a potential way (i.e. applying Raman spectroscopy) for faster high-quality data gathering from formulations. Raman techniques have the capability of analyzing the composition and distribution of components and the physicochemical properties thereof within formulations, which are prominent factors governing drug dissolution profiles and subsequently bioavailability. Thus, useful information can be obtained bridging formulation development to the final product quality.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Developing micro-/nanoparticulate drug delivery systems using "design of experiments"

Of late, micro and nanoparticluate drug delivery systems have been gaining immense importance primarily attributed to their improved drug release controlling and targeting efficiencies. Also, the small particle size and desirable surface charge associated with these delivery systems render them suitable for specific applications like lymphatic uptake, pulmonary uptake, tumor targeting, brain targeting, etc. For decades, micro and nanoparticulate systems have been prepared by the conventional "trial and error" approach of changing One Variable at a Time (OVAT). Using this methodology, the solution of a specific problematic formulation characteristic can certainly be achieved, but attainment of the true optimal composition is never guaranteed. Thus, the present manuscript provides an updated account of the systematic approach "Design of Experiments (DoE)" as applicable to formulation development of microparticles and nanostructured systems. Besides providing a bird's eye view of the various experimental designs and optimization techniques employed for DoE optimization of such systems, the present manuscript also presents a copilation of the major micro/nano-structuctred systems optimized through DoE till date. In a nutshell, the article will act both as a ready reckoner of DoE optimization of micro/nano drug delivery systems and a catalyst in providing an impetus to young pharmaceutical "nano & micro" researchers to venture into the rewarding field of systematic DoE optimization.