Rational nanoparticle design, stimuli-responsive release control, and release kinetics matching with changing tissue microenvironment are improving preclinical and clinical regenerative therapy results and clinical translation utilizing artificial intelligence and machine learning. This review is intended to explore the use of interdisciplinary strategies that integrate computational design and experimental validation to advance tissue-focused drug delivery systems. Using AI-enabled procedures to increase encapsulation efficiencies and tunability of release and target engagement has led to the acceleration of the inverse design and closed-loop optimization processes in numerous preclinical models. Unfortunately, sparse metadata and tiny heterogeneous datasets restrained repeatability. Incompetent benchmark repositories, inconsistent reporting, and data sharing pipelines. The regulatory paths for algorithm-informed device-drug products are new and require models, quantification of uncertainty, sterilization validation, and clinical-grade stability data. The pragmatic roadmap emphasizes short-term metadata standards, intermediate community-based benchmarks, and long-term adaptive clinical trials linking computational outcomes with biomarker-driven goals. A consolidated series of technical, translational, and governance recommendations is presented with an actionable checklist made for bridging computational design with clinical translation, such as a synthesis of governance and experiments checklist, which has never been seen before, for faster real-world implementation.
Environmental pollution has increased steadily over the years, posing significant challenges to both ecosystems and human health. Among the major contributors to this problem are the pharmaceutical and chemical industries, which extensively use hazardous chemicals and organic solvents that adversely affect air and water quality. Organic solvents are indispensable in pharmaceutical manufacturing, serving as reaction media during active pharmaceutical ingredient (API) synthesis, purification, analysis, and other processing steps. However, many conventional solvents are toxic, volatile, and environmentally persistent, making their replacement with greener alternatives an urgent priority. This review discusses commonly used hazardous organic solvents and highlights their most suitable green replacements based on their physicochemical properties, performance, and sustainability. Particular emphasis is placed on ionic liquids as promising alternatives to conventional organic solvents because of their negligible vapor pressure, recyclability, and ability to reduce chemical waste. The review also summarizes reactions that have traditionally been performed using hazardous solvents such as pyridine, toluene, and tetrahydrofuran, but have now been successfully carried out using greener solvent systems without compromising reaction efficiency. By providing a comprehensive overview of sustainable solvent alternatives, this review aims to support the adoption of environmentally benign practices in pharmaceutical and chemical research and to encourage the development of greener synthetic methodologies that contribute to environmental protection and sustainable chemistry.
This research aimed to examine the impact of the ethanolic extract of Achillea fragrantissima aerial parts (ACF) on the rat CNS. Forty mature Sprague–Dawley male rats were assigned to four groupings (n = 10): 1st C: (control), 2nd DIZ: (diazepam), 3rd ACF-H: (800 mg/kg extract high-dose extract), and 4th ACF-L: (400 mg/kg low-dose extract) gavaged daily for 14 days. The HPLC-identified components are naringenin, ferulic acid, catechin, chlorogenic acid, diadzein, syringic acid, rutin, hesperetin, kaempferol, caffeic acid, vanillin, and quercetin. The effect of ACF extract on rat CNS was measured using the Open field, Hole-cross, Rotarod, Negative geotaxis, and Wrist-grip tests. ACF extract administration substantially (P < 0.05) reduced locomotor and exploratory behaviors in the open-field and hole-cross tests. In the negative geotaxis test, rats administered ACF extract exhibited a substantial delay (P < 0.05) in the turning 180° to a head-up position. In the rotarod and wrist-grip tests, the fall-off time and the latency to fall were considerably reduced (P < 0.05). Administration of ACF extract significantly (P < 0.05) elevated the brain GABA levels, while the increase in dopamine concentrations was observed but should be interpreted cautiously because these findings require confirmation using more specific analytical methods. Additionally, the extract significantly increased the total antioxidant capacity (TAC), but reduced lipid peroxidation (MDA), 8-hydroxy-2-deoxyguanosine (8OH2dG), and protein carbonyl (PCO) levels. ACF oral administration significantly (P < 0.05) upregulated brain GABAA receptor-linked protein (GABARAP), activity-regulated cytoskeleton-linked protein (ARC), and brain-derived neurotrophic factor (BDNF), meanwhile, significantly downregulated the monoamine oxidase A (MAOA) brain levels. For the first time, the outcomes of the ongoing trial demonstrated the in vivo promising effects of A. fragrantissima aerial parts ethanolic extract on neurobehavioral and antioxidant status in an animal model.
CYP3A4 is the primary cytochrome P450 that plays a secondary role along with CYP2E1 in the oxidation of acetaminophen (APAP) to the toxic metabolite N-acetyl-p-benzoquinone imine (NAPQI). APAP overdosing activates c-Jun N-terminal kinase (JNK), which activates the mitochondrial membrane permeability transition (MPT). This cascade results in disruption of the hepatic homeostasis. Initially, 70 compounds were short-listed and considered for molecular docking with CYP3A4. Solanine, having shown the highest docking score (− 11.2), was considered as a potential drug, and the ethanolic extract of the fruits of Solanum tuberosum was prepared from pulp and peel. Cytotoxicity of the pulp and peel extracts on Hep-G2 cells was assessed by MTT assay, colony formation assay, and DAPI staining. In the next phase, 14-day-old pathogen-free, fertilised chicken eggs were procured and inoculated with 100μL of APAP (0.01 mg/ml concentration), 10
Levodopa (LD) and dopamine (DA) are essential for managing Parkinson’s disease (PD), a neurological disorder that can cause serious complications. Patients often require multiple medications and ongoing monitoring to effectively manage their symptoms. Developing chemometric-assisted analytical methodologies for determining these therapeutic drugs, along with other co-administered drugs, would certainly be beneficial in clinical practice and therapeutic drug monitoring. The literature has addressed challenges in analyzing LD and DA, as well as concomitant drugs, major metabolites, and impurities of levodopa due to their similar physicochemical properties, which could result in interfering signals. Combining chemometrics with various analytical techniques has offered a fast and highly predictive approach that can simultaneously determine target analytes in complex matrices. Data preprocessing is considered a critical step before applying multivariate data analysis to enhance model prediction. This review mainly focuses on the reported chemometric analytical methods for the qualitative or quantitative determination of levodopa and dopamine in different matrices. The three main perspectives being focused on are implementing chemometric models for quantitative determination, experimental optimization, and discriminant analysis. Spectrophotometry, spectrofluorimetry and voltammetry, coupled with chemometrics, were the primary analytical techniques used for analyzing the studied drugs. This review provides a summary of the reported chemometric-assisted analytical techniques and some relevant limitations, aiming to guide researchers in enhancing the robustness and predictive power of analytical methods coupled with chemometrics. A future recommendation is to develop chemometric-assisted methods to simultaneously determine levodopa and dopamine along with other co-administered drugs and related impurities in various biological matrices, such as cerebrospinal fluid and human serum.
