Pharmaceutical Compounds Research Articles

Flavin‐Dependent Nitroreductases: Privileged Enzymes for Chemical and Photochemical Synthesis

Nitroreductases, present within both prokaryotes and eukaryotes, form a group of flavin‐dependent enzymes capable of reducing nitro compounds using NAD(P)H as reducing agent. These enzymes have been widely studied due to their diverse roles in bioremediation, cancer therapy, cell ablation, and antimicrobial resistance. In recent times, the versatility of nitroreductases has been expanded towards the synthesis of highly valuable compounds such as aromatic and aliphatic amines, azoxy‐ and azobenzenes, as well as N‐heterocycles. This review examines the biological role and diversity of flavin‐dependent nitroreductases, and highlights their current and potential future application as biocatalysts for the sustainable synthesis of nitrogen‐containing pharmaceutical compounds and bulk chemicals.

Removal of pharmaceutical compounds from sewage effluent by the nanofiltration membrane

Membrane is a promising technology for water treatment, and its commercial production has emerged globally. However, commercial membranes' effectiveness is still uncertain due to the new types of contaminants, such as pharmaceutical compounds (PC). Thus, in the current work, the performance of two commercial membranes (NFX and GC) has been evaluated using feed water from the effluent of sewage treatment plants (STP) and hospital wastewater (HWW). The spiked feed water contains PC classes of analgesics/anti-inflammatory, anti-hypertensives, beta-blockers, and psychiatric/antidepressants. The percentage difference between feed and permeate was measured. While the fouling properties of the membranes are also evaluated using STP and HWW as feed, along with the pure water flux in a variable-pressure setting, the results showed that NFX exhibits excellent, consistent rejection for the targeted PCs (>80 %). GC showed a broader range of rejection (10 % to 90 %) and presented a better flux flow than NFX. The main rejection factor is due to the membrane's pore size variation and the layer construction, which refer to the absorption, size exclusion, and diffusion of the PCs instead of the type of feed water and the flux. Current work provides scientific data on the diverse fabrication and market-available types of membranes, which would pose various efficiencies towards the type of targeted pollution.

Analytical method for predicting interference in ion mobility spectrometric detection of drug compounds with phthalates

In ion mobility spectrometry (IMS), ions are distinguished by their mobility through a gas, which depends on their size, shape, and charge. Ions with very similar reduced ion mobility (Ko) values are not separated. This technique is widely used for the onsite detection of drug compounds. However, the presence of phthalates can interfere with IMS-based detection of drug compounds because some of them may have Ko values similar to those of drug compounds. In this study, the interference of phthalates in the IMS detection of drug compounds was investigated using drug compounds and phthalates in the molecular weight range of 134–335 g mol−1. The relationships between Ko and the m/z value, collision cross section (CCS), maximum cross section (CSmax), and minimum cross section (CSmin) of the product ions were examined. The CSmax and CSmin values were calculated from the energy-minimized structures of the ions. It was found that the m/z, CCS, and CSmax values were not closely related to the Ko value. However, the CSmin values showed a strong correlation with the Ko values, exhibiting excellent linear relationships with R2 values of 0.997 for the drug compounds and 0.990 for the phthalates. By comparing the CSmin values of drug compounds and phthalates, the probability of their peaks overlapping in the IMS spectra can be determined. For chemicals that are difficult to identify, the overlap of the peaks with the target chemicals can be determined using their CSmin values.

Silver Nanoparticles and L-Cysteine Composite Redresses Carbon Tetrachloride-Induced Hepatotoxicity in Swiss Albino Rats.

l-cysteine is a versatile amino acid that plays a pivotal role in synthesizing critical molecules, enzymatic catalysis, regulation, and electron transport. It also has tremendous potential to act as an adjuvant for enhancing the biological efficacy of various nanoparticles in vivo. The current study is aimed to evaluate the protective efficacy of silver nanoparticles (AgNPs) decorated with l-cysteine in carbon tetrachloride (CCl4)-induced hepatotoxicity in the Swiss albino rats as an animal model. The rats were divided into four treatment groups: Group 1 (control without any treatments), Group 2 treated with AgNPs and l-cysteine composite (5 mg/kg body weight on every third day), Group 3 (single dose of 1 mL/kg CCl4), and Group 4 treated with AgNPs-l-cysteine composite in the rats pre-administered with CCl4. After treatment for a month, the rats were killed, and their liver and blood samples were subjected to biochemical analysis and histological examination.: Group 2 showed all the parameters comparable to control Group 1. On the contrary, CCl4-treated, Group 3 rats showed abnormally raised liver function markers (AST and ALT) and liver toxicity markers (GGT, LDH, and total bilirubin) concomitant with disturbed oxidative stress parameters (GSH and MDA) compared to the control. However, Group 4 rats demonstrated a significant recovery from CCl4-induced biochemical alteration in the animals as compared to Group 3. In addition, the biochemical measurements were harmonious with the histological analysis of the liver sections of the treated rats. Hence, the proposed AgNPs-l-cysteine composite is a potent hepato-protecting agent in vivo that can be employed in regulating CCl4-induced hepatotoxicity or any drug or potential pharmaceutical compound exerting similar toxicity.

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

Small-Scale Aqueous Suspension Preparation using Dual Centrifugation: The Effect of Process Parameters on the Sizes of Drug Particles

The dual centrifugation approach has in the recent years emerged as a powerful milling tool to prepare pharmaceutical suspensions in submicron range with a fast-milling capacity by milling 40 samples simultaneously in 2 mL vials. While there is some standardized milling conditions described in the literature when preparing aqueous suspensions with dual centrifugation, a more systematic and experimental understanding of the milling process to evaluate the impact of different process variables in the dual centrifuge on the final sizes of the suspended drug particles independent of the drug compound used. Overall, the present study demonstrated the applicability of the dual centrifuge for small-scale screening purposes and showed the impact of process parameters on the physical attributes of prepared suspensions. In the present work, the rate of size reduction on three different model compounds, i.e., cinnarizine, haloperidol, and indomethacin, was found to be mostly influenced by the milling speed, size of milling beads, and the bead loading during milling, whereas the rotor temperature did not affect the particle size profiles when stabilized with polysorbate 20 during milling with dual centrifugation. Smaller particle sizes were in general obtained at the highest milling intensity, i.e., 1500 rpm, smallest bead size, i.e., 0.2 mm, and higher bead loadings (42%, 56%, and 83%). The grinding limit of approximately 0.50 µm, 0.70 µm, and 0.35 µm for cinnarizine, haloperidol, and indomethacin, respectively, was achieved relatively fast, i.e., 30 minutes of milling at the specified conditions, compared to when suspensions were milled with larger bead sizes (i.e., 1.0 mm), lower milling intensities (e.g., 1000 rpm), and lower bead loadings (e.g., 14 or 28%). The study further confirmed that a higher milling intensity was necessary during milling of haloperidol suspensions probably due to the compounds predominantly plastic properties. Sizes of indomethacin particles increased with longer milling runs up to 240 minutes and also higher bead loadings of 56% and 83%. These observations were further supported by the color conversion from white to yellow of indomethacin suspensions which indicated generation of small quantities of amorphic material after milling with a high milling intensity. Upscale investigations showed comparable particle size profiles for all three model compounds while milling at 1500 rpm for five minutes.

Strategic innovation and competitive advantage: A comparative analysis of different industries

This study offers a full comprehension of the correlation between strategic innovation and competitive advantage within a range of businesses. The primary objective of this study is to provide a comprehensive analysis that compares various tactics, methods, and best practices utilized by firms in order to acquire and maintain a competitive edge within the current dynamic business environment. The study used a qualitative comparative case study methodology. The primary data collection method involved the utilization of semi-structured interviews with key informants who hold significant positions inside firms operating in the chosen industries. Those interviewed consisted of senior executives, innovation leaders, and experts who possess the knowledge and expertise necessary to offer valuable insights into strategic innovation efforts and their influence on competitive advantage. Findings suggest that pharmaceutical businesses allocate substantial resources towards research and development endeavors to explore and advance novel pharmaceutical compounds and therapeutic interventions. The strategic innovation strategy of the pharmaceutical business emphasizes the significance of continuous investment in research and development, adept handling of regulatory obstacles, and the strategic administration of intellectual property. Retailers who successfully adapt to the digital era are able to enhance their competitive advantage by providing customers with convenient, personalized, and efficient purchasing experiences.

Design, Synthesis and Biological Evaluation of 4,6-Substituted 2-Styrylquinoline Derivatives: Potential Antimicrobial and Anticancer Agents

A series of novel 4,6-substituted 2-styrylquinoline derivatives (3a-h) was designed and synthesized based on the substitution in quinoline scaffolds (with Br, H, NO2 at C6 position) as well as the substitution in the styryl moeity (at C4' position). The target compounds were investigated for their antimicrobial activity against Gram-positive Staphylococcus aureus (ATCC 25923), Enterococcus faecalis (ATCC 29212) and Gram-negative bacterium Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853) and Candida albicans (ATCC 10231) and anticancer activity against human colon adenocarcinoma HT29 cells. Compared to standard drug ciprofloxacin, compounds 3e, 3b and 3c showed significant antibacterial activity against Staphylococcus aureus, Enterococcus faecalis and Escherichia coli. Additionally, synthesized compound 3e had significant activity against Candida albicans with MIC value of 8 µg/mL. In anticancer study, the maximum cell death rate was found in compounds 3e, 3b and 3c at the dose of 100 µM i.e., 72.9%., 44.01% and 35.46%, respectively and the measured IC50 were 58.84, 114.53 and 130 µM. The cytotoxic potential of compound 3e was confirmed by AO-EtBr dual staining assay in which red fluorescence of necrotic cells lost their membrane integrity due to toxicity induced by compound 3e. This growth inhibition and apoptotic activity of compound 3e was dose-dependent when the colour changed from green to red. Furthermore, in silico studies were conducted on the synthesized compounds to elucidate the potential mechanisms of action against dihydrofolate reductase (DHFR) from E. coli, DNA gyrase from P. aeruginosa, DHFR from S. aureus, acetolactate synthase from K. pneumoniae and DHFR from C. albicans. The derivatives also exhibited the strongest ligand-receptor binding affinity. Additionally, ADME analysis was performed to predict the pharmacokinetic profiles of the compounds, indicating good oral drug-like properties.

A Review on Medicinal Importance of Tridax Procumbens

Nature has provided plentiful plant sources which provides various medicinal properties. The plant of Tridax Procumbens is belongs to the family Asteraceae and it is also known as Jakham judi, Kambarmodi, Tantani, Gaddi chemanthi etc. These medicinal plants are the rich sources of various natural remedies for the treatment of pathogenic and other diseases. It Consists of Various secondary metabolites in the plants are Alkaloids, Flavonoids, saponins, Tannins, Anthocynins, phenol steroids, proteins, amino acids and carbohydrates have been used for important pharmaceutical compounds .This review paper is attempt to understand the phytochemical constitution of T. Procumbens and it’s antilithiatic action. Kidney stones are the major life style disorder in present era and there is wider need scope for the research of better Lithotripsic and antilithiatic drug alternatives. It gives various pharmacological action like antilithiatic, anti-inflammatory, antihypertensive, antimicrobial, antifungal, wound healing, insects repellent activity, hepatoprotective, anticancer, antidiabetic. T. Procumbens known as JAYANTI VEDA in ayurvedic pharmacopoeia is commonly called as coat button

Pharmaceutical Active Compounds Contamination and Seasonal-Based Ecotoxicity Assessment of the Yamuna River for the Delhi stretch

Yamuna River is the only river in Delhi. This results in heavy reliance on Yamuna to meet Delhi Water demands. This has prompted several research works covering physicochemical, biological, heavy metal concentration, emerging pollutant occurrence, and their risk assessment. This study investigated occurrence, seasonal variation (pre-monsoon, monsoon, and post-monsoon), and risk assessment posed by 7 PhACs at five sampling locations along a 22 km stretch of Yamuna River in Delhi. The samples were collected and analysed in pre-monsoon, monsoon, and post-monsoon seasons. The seven PhACs, comprised of 2 antibiotics (Ciprofloxacin; CIP, sulfamethoxazole; SMZ), 2 NSAIDs (Paracetamol; PCM, Ketoprofen; KPF), 1 anxiety control (Lorazepam, LOR), 1 anticonvulsant (Carbamazepine; CBZ) and 1 statin (Fluvastatin; FUT). The PhACs range of occurrence across three seasons was PCM 75-589 ng L-1, KPF 31-238 ng L-1, CBZ 11-192 ng L-1, LOR 62-462 ng L-1, CIP 48-192 ng L-1, SMZ 192-1534 ng L-1, and FUT 0-421 ng L-1. The seasonal occurrence was in the order of post-monsoon> pre-monsoon> monsoon. PCM, CBZ FUT posed a negligible ecotoxicological risk, LOR posed a low-medium risk, and ketoprofen, ciprofloxacin, and sulfamethoxazole posed high risks. Based on the risk index, all seasons have high ecological risk at every sample point. Discharges of untreated sewage and insufficient and inefficient treated wastewater are the primary contributors of PhACs in the Yamuna River. This study concludes that existing WWTPs need drastic upgrades. Policies and measures should also be developed to prevent untreated wastewater from reaching the Yamuna River. This necessitates further studies to investigate processes suitable for installing and treating wastewater along the longitudinal section of the drain and assess their technical feasibility.

Effects of effluents from the Villa Victoria Reservoir (Mexico) on the development of Danio rerio at early life stages through apoptotic response and oxidative-induced state

As one of Mexico's most crucial water storage facilities, the Villa Victoria Reservoir (VVR) supplies water to over six million people residing in the Mexico City Metropolitan Area. In recent years, this water resource has been subjected to significant risks due to several factors, including human population growth, alterations in global climate patterns, excessive resource utilization, and insufficient protective regulations, thereby endangering not only the biocenosis itself, but also the water supply for numerous inhabitants. This study aimed to evaluate the current state of the reservoir through the determination of conventional and emerging pollutants present in the sampling points, as well as embryotoxicity and oxidative damage in Danio rerio embryos exposed to effluents from the VVR. Embryotoxicity was quantified using the General Morphology Score (GMS) and teratogenic index, whereas oxidative damage was assessed based on lipid peroxidation, hydroperoxide content, oxidized proteins, antioxidant enzyme activity, and gene expression. These results revealed the presence of heavy metals, diverse pharmaceutical compounds, and pesticides. In addition, elevated lipid, hydroperoxide, and protein oxidation accompanied by alterations in superoxide dismutase (SOD) and catalase (CAT) enzymatic activity were observed during exposure. GMS resulted in impaired embryo development and teratogenic effects, including pericardial, axial, and skeletal edema. Furthermore, the upregulation of genes associated with apoptotic processes and antioxidant defense reflects a comprehensive response to oxidative stress. The study concluded that pollutants in VVR water induced oxidative damage, modified antioxidant activity, elicited embryotoxicity, and upregulated oxidative damage-related genes. The findings underscore the necessity of undertaking restoration efforts for water sources, as pollution can potentially endanger aquatic organisms and human well-being.

Singlet oxygen and hydroxyl radical in the transformation of acetaminophen in surface water: The mechanism on the degradation pathways based on DFT calculations

Pharmaceuticals compounds are continuously released into the aquatic environment due to the widely used therapeutically in livestock and humans. These pharmaceuticals and their degradation products accumulate in fish and have potential impacts on human and ecosystems. In this study, acetaminophen (ACE), a pharmaceutical widely used in COVID-19 epidemic, was selected as the research object to study its microscopic transformation mechanism and ecotoxicity using density functional theory and computational toxicology. The research demonstrated that ACE could be degraded by 1O2 in three pathways (2,2-cycloaddition, Ene reaction and 2,4-cycloaddition). The 2,4-cycloaddition mechanism at para-site plays a dominant role in the 1O2 oxidation of ACE. The total rate constant of ACE for •OH-initiated reaction was 1.78×109 M−1s−1 at 298 K. Furthermore, the half-life of ACE was determined as 143 and 3.5 days at ambient temperatures, given the concentration of •OH and 1O2 as 10−16.5 and 10−13 M, respectively. Although the aquatic toxicity of most degradation by-products appears to be not harmful level, some products were identified as very toxic and have developmental toxicity, especially hydroquinone and 1,4-benzoquinone. The systematic mechanism study provides a pathway to understand the transformation and environmental impact of pharmaceutical pollutants containing paracetamol in natural water environment at the molecular level.

Application of Fluorescence- and Bioluminescence-Based Biosensors in Cancer Drug Discovery

Recent advances in drug discovery have established biosensors as indispensable tools, particularly valued for their precision, sensitivity, and real-time monitoring capabilities. The review begins with a brief overview of cancer drug discovery, underscoring the pivotal role of biosensors in advancing cancer research. Various types of biosensors employed in cancer drug discovery are then explored, with particular emphasis on fluorescence- and bioluminescence-based technologies such as FRET, TR-FRET, BRET, NanoBRET, and NanoBiT. These biosensors have enabled breakthrough discoveries, including the identification of Celastrol as a novel YAP-TEAD inhibitor through NanoBiT-based screening, and the development of TR-FRET assays that successfully identified Ro-31-8220 as a SMAD4R361H/SMAD3 interaction inducer. The integration of biosensors in high throughput screening and validation for cancer drug compounds is examined, highlighting successful applications such as the development of LATS biosensors that revealed VEGFR as an upstream regulator of the Hippo signaling pathway. Real-time monitoring of cellular responses through biosensors has yielded invaluable insights into cancer cell signaling pathways, as demonstrated by NanoBRET assays detecting RAF dimerization and HiBiT systems monitoring protein degradation dynamics. The review addresses challenges linked to biosensor applications, such as maintaining stability in complex tumor microenvironments and achieving consistent sensitivity in HTS applications. Emerging trends are discussed, including integrating artificial intelligence and advanced nanomaterials for enhanced biosensor performance. In conclusion, this review offers a comprehensive analysis of fluorescence- and bioluminescence-based biosensor applications in the dynamic cancer drug discovery field, presenting quantitative evidence of their impact and highlighting their potential to revolutionize targeted cancer treatments.

Synthetic versatility: the C-P bond odyssey.

C-P bond formation reactions have garnered significant attention due to the widespread presence of organophosphorus compounds in pharmaceuticals, phosphine-containing ligands, pesticides, and materials science. Consequently, various efficient methodologies have been established in recent decades for constructing C-P bonds. This review article traces the historical evolution of C-P bond research and explores the prospects of C-P bond formation. It contrasts biotechnological approaches with chemical synthesis, emphasizing the critical importance of precision and innovation in developing novel C-P structures. A forward-looking perspective is provided on the role of computational tools and machine learning in optimizing C-P bond synthesis and discovering new compounds. The article explores prospective avenues for reactions that form C-P bonds and advocates for enhanced interdisciplinary collaboration to propel scientific and technological advancements.

GATNM: Graph with Attention Neural Network Model for Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds

Mycobacterium tuberculosis cell wall has complexity and unusual organization. These conditions make the nutrients and antibiotics difficult to penetrate this wall which affects the low activity of several antimycobacterial drugs in mycobacteria cells. Based on this information, the cell wall permeability prediction in some compounds becomes important and would help develop novel antitubercular drugs. Recently, there have been many predictions helped by computational technology using the Simplified Molecular Input Line Entry System (SMILES) input drug compounds. In this study, we applied computational technology to predict the permeability of cell walls to some compounds or drugs. We evaluated several common machine learning models for their ability to predict cell wall permeability. However, none of these models achieved satisfactory performance. We investigated a Graph with Attention Neural Network (GATNN) model to address this challenge. In the case of permeability detection, to the best of our knowledge, the GATNN model is considered a new approach to improve the prediction performance of the penetration ability of some compounds to the cell wall of the mycobacterial. Additionally, we optimized the accuracy value to get the best hyperparameter and the best model by Optuna. After getting the optimal model, by using the benchmark dataset, this model has slightly increased the performance over the previous model in accuracy and specificity to 78.9% and 81.5%. As a complementary, we also provided an ensemble model and generated the interpretability of the model. The code and materials of all experiments in this paper can be accessed freely at this link: https://github.com/asw1982/MTbPrediction.

A novel approach of catechol production assisted by plasma process: Lab-scale experiment and modeling, commercial-scale design and economic evaluation

In this work, a novel process was proposed for the production of catechol, an important organic intermediate in the pharmaceutical compound synthesis. The study was realized on a laboratory pilot and extrapolated to an industrial scale, using a modeling approach.The process focused on the hydroxylation of phenol in aqueous solution by a plasma gliding arc discharge system and a sequence of conventional process. The pilot-scale and the experimental study were carried out by a new plasma-plug reactor, operated by the Venturi effect.Experimental results were employed to predict by-products constant rates, using COMSOL software. The modeling approach was also utilized for the first time to simulate non-thermal plasma (NTP) processes on an industrial scale, and to estimate some physicochemical properties of plasma active species, using Aspen HYSYS software. Furthermore, an economic study was performed using Aspen Process Economic Analyzer (APEA) software to estimate the process revenues.The study has confirmed the ability of the proposed process to ensure a massive catechol production, with a conversion rate of phenol to catechol close to 46 % and a selectivity ratio of 97 %, after 45 min of reacting time. In addition, the economics study shows that this process can reach a maximum annual income of 167 million USD year−1 for plasma gas and phenol solution flowrates of 10 and 8 m3/h, respectively.The use of air as plasma gas-vector and water as solvent instead of organic compounds, permits to confirm that our process is green, safe and economically attractive for the pharmaceutical industry.

Rapid Determination of Methamphetamine, Methylenedioxymethamphetamine, Methadone, Ketamine, Cocaine, and New Psychoactive Substances in Urine Samples Using Comprehensive Two-Dimensional Gas Chromatography.

Background/Objectives: This study evaluates the applicability of a comprehensive two-dimensional gas chromatography-flame ionisation detection (GC×GC-FID) approach for the simultaneous determination of 12 underivatised psychoactive drugs, including new psychoactive substances, that comprised of amphetamine, methamphetamine, mephedrone, 3,4-methylenedioxyamphetamine, 3,4-methylenedioxymethamphetamine, α-pyrrolidinovalerophenone, n-ethylpentylone (ephylone), norketamine, ketamine, 3,4-methylenedioxypyrovalerone, methadone, and cocaine. Methods: Separation was effected using a non-polar first dimension (1D) and a polar second dimension (2D) column, demonstrating an improved separation of drug compounds compared to a polar/non-polar column configuration. Interference-free baseline separation of all psychoactive compounds in a urine matrix was achieved within 8 min. The GC×GC-FID method was validated according to the guidelines defined by Standard Practices for Method Validation in Forensic Toxicology. Results: The calibration curves for the 12 psychoactive drugs were well correlated (r2 > 0.99) within the concentration ranges of 50-1500 ng mL-1. Detection limits of 10-20 ng mL-1 were obtained, and good repeatability and reproducibility (CV < 11.4%) were attained for retention times and peak areas. Method recoveries for the small-scale solvent extraction procedure ranged from 96.9 to 114.5%, and bias was between -3.1% and 14.5%. Conclusions: The validated approach was successfully applied for the determination of these illicit compounds in spiked urine samples of different concentrations, highlighting its potential for rapid forensic drug screening.

Study of the use of compounding drugs in pediatric practice

Aim study of the use of compounding drugs in pediatric practice. Material and methods. The objects of the study were official websites, information from the prescription journal of a compounding pharmacy, and instructions for the medical use of drugs. Research methods: content analysis, systematization, generalization, grouping and comparison. Results. Based on outpatient medical prescriptions of extemporaneous drugs for pediatric patients, the main age categories were identified (children from 1 month to 1 year and from 10 to 12 years). Children from 1 month to 1 year were prescribed powders from ready-made drugs medicines of industrial production (tablets) containing adrenergic blockers in 48% of cases (mainly propranolol), diuretics in 31% (spironolactone), PDE5 inhibitors in 6% (sildenafil). Prescriptions for children aged 10 to 12 years contained in 97% of cases the prescription of eye drops with atropine sulfate from a pharmaceutical substance. Conclusion. The study of medical prescriptions confirms an individual approach to the treatment of pediatric patients and justifies the need for pharmaceutical compounding of drugs. Keywords: compounding drugs, children, prescription, dosage forms, dosage. Conflict of Interest: nothing to disclose.

Machine learning strengthened formulation design of pharmaceutical suspensions

Many different formulation strategies have been investigated to oppose suboptimal treatment of long-term or chronic conditions, one of which are the nano- and microsuspensions prepared as long-acting injectables to prolong the release of an active pharmaceutical compound for a defined period of time by regulating the size of particles by milling. Typically, surfactant and/or polymers are added in the dispersion medium of the suspension during processing for stabilization purposes. However, current formulation investigations with milling are heavily based on prior expertise and trial-and-error approaches. Various interacting parameters such as the milling bead size, stabilizer type and concentration have confounded the investigation of milling process. The present study systematically exploited statistical and machine learning (ML) strategies to understand the relationship between suspension characteristics and formulation parameters under full-factorial milling experiments. Stabilizer concentration was identified as a significant factor (p < 0.001) for median suspension diameter (D50). A formulation stability classification ML model with high prediction accuracy (0.91) and F1-score (0.91) under 10-fold cross-validation was constructed based on 72 formulation datapoints. Model interpretation through Shapley additive explanations (SHAP) revealed the prominent impact of stabilizer concentration and milling bead size on formulation stability. The present work demonstrated the potential to achieve a deeper understanding of the design and optimization of nano- and microsuspensions through explainable ML modelling on formulation screening data.

Evaluation of mesenchymal stem cells as an in vitro model for inherited retinal diseases.

Retinal pathologies are major causes of vision impairment and blindness in humans, and inherited retinal diseases (IRDs), such as retinitis pigmentosa, Leber congenital amaurosis, and Stargardt disease, greatly contribute to this problem. In vitro disease modeling can be used for understanding the development of pathology and for screening therapeutic pharmaceutical compounds. In the preclinical research phase, in vitro models complement in vivo models by reducing animal studies, decreasing costs, and shortening research timelines. Additionally, animal models may not always accurately replicate the human disease phenotype. This review examines the types of cells that can be used to create in vitro IRD models, including retina-specific cell lines, primary retinal cells, induced pluripotent stem cells (iPSCs), and more. Special attention is given to mesenchymal stem cells (MSCs), which are characterized by various isolation sources, relative ease of isolation, and straightforward differentiation. MSCs derived from bone marrow (BM), adipose tissue (AT), dental tissue (DT), umbilical cord (UC), and other sources can differentiate into retinal cells, including photoreceptor cells and retinal pigment epithelial (RPE) cells, dysfunction of which is most commonly associated with IRDs. Subsequent differentiation of MSCs into retinal cells can be carried out via various methods: culturing in induction media supplemented with certain growth factors, co-culturing with retinal cells or in their conditioned media, or regulating gene expression with viral vector-delivered transcription factors (TFs) or microRNAs (miRNAs). Compared to the popular iPSCs, for example, MSC-based models are significantly cheaper and faster to obtain, making them more feasible for large-scale drug screening. Nevertheless, the existing differentiation methods need further optimization for this promising platform to receive the success it deserves.