Navigating Skin Delivery Horizon: An Innovative Approach in Pioneering Surface Modification of Ultradeformable Vesicles.

In the dynamic landscape of pharmaceutical advancements, the strategic application of active pharmaceutical ingredients to the skin through topical and transdermal routes has emerged as a compelling avenue for therapeutic interventions. This non-invasive approach has garnered considerable attention in recent decades, with numerous attempts yielding approaches and demonstrating substantial clinical potential. However, the formidable barrier function of the skin, mainly the confinement of drugs on the upper layers of the stratum corneum, poses a substantial hurdle, impeding successful drug delivery via this route. Ultradeformable vesicles/carriers (UDVs), positioned within the expansive realm of nanomedicine, have emerged as a promising tool for developing advanced dermal and transdermal therapies. The current review focuses on improving the passive dermal and transdermal targeting capacity by integrating functionalization groups by strategic surface modification of drug-loaded UDV nanocarriers. The present review discusses the details of case studies of different surface-modified UDVs with their bonding strategies and covers the recent patents and clinical trials. The design of surface modifications holds promise for overcoming existing challenges in drug delivery by marking a significant leap forward in the field of pharmaceutical sciences.

Artificial Intelligence in Drug Identification and Validation: A Scoping Review.

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.

Esofagitis disecante supeEsofagitis disecante superficial: dos casos de una misma entidad poco frecuente / Esophagitis dissecans superficialis: two cases of the same rare entity

La esofagitis disecante superficial (EDS) es una entidad infrecuente que se caracteriza endoscópicamente por el desprendimiento de las capas superficiales del epitelio esofágico e, histológicamente, por el aspecto bitonal del epitelio escamoso esofágico secundario a la necrosis de los estratos más superficiales. La etiología es desconocida, aunque se ha asociado con la ingesta de determinados fármacos, enfermedades autoinmunes, estasis esofágica y procedimientos endoscópicos. Presentamos dos casos: uno de ellos acontece en una mujer tras un episodio de disfagia abrupta y el segundo en un varón con comorbilidades y clínica de dolor epigástrico. La EDS es una patología que hay que considerar en su adecuado contexto clínico y endoscópico, ya que su curso es autolimitado en comparación con otras entidades de evolución tórpida o que precisan un tratamiento específico. (AU)

Esophagitis dissecans superficialis (EDS) is a rare disease characterized by sloughing of the superficial esophageal mucosa and, histologically, by the bitonal appearance of the squamous epithelium secondary to necrosis of the most superficial layers. Etiology is uncertain, however, it has been associated with some medications, autoimmune diseases, esophageal stasis and endoscopic procedures. Here, two cases are presented, one of them which appeared in a woman after an episode of dysphagia and another one which occurred to a man with comorbidities and epigastric pain. This entity should be considered due to its self-limiting clinical course, compared to other entities with a more torpid evolution or that require more specific treatment. (AU)

Impact of Obesity on Hepatic Drug Clearance: What are the Influential Variables?

Drug clearance in obese subjects varies widely among different drugs and across subjects with different severity of obesity. This study investigates correlations between plasma clearance (CLp) and drug- and patient-related characteristics in obese subjects, and evaluates the systematic accuracy of common weight-based dosing methods. A physiologically-based pharmacokinetic (PBPK) modeling approach that uses recent information on obesity-related changes in physiology was used to simulate CLp for a normal-weight subject (body mass index [BMI] = 20) and subjects with various severities of obesity (BMI 25-60) for hypothetical hepatically cleared drugs with a wide range of properties. Influential variables for CLp change were investigated. For each drug and obese subject, the exponent that yields perfect allometric scaling of CLp from normal-weight subjects was assessed. Among all variables, BMI and relative changes in enzyme activity resulting from obesity proved highly correlated with obesity-related CLp changes. Drugs bound to α1-acid glycoprotein (AAG) had lower CLp changes compared to drugs bound to human serum albumin (HSA). Lower extraction ratios (ER) corresponded to higher CLp changes compared to higher ER. The allometric exponent for perfect scaling ranged from -3.84 to 3.34 illustrating that none of the scaling methods performed well in all situations. While all three dosing methods are generally systematically accurate for drugs with unchanged or up to 50% increased enzyme activity in subjects with a BMI below 30 kg/m2, in any of the other cases, information on the different drug properties and severity of obesity is required to select an appropriate dosing method for individuals with obesity.

Re-discover the value of protein binding assessments in hepatic and renal impairment studies and its contributions in drug labels and dose decisions.

One of the key pharmacokinetic properties of most small molecule drugs is their ability to bind to serum proteins. Unbound or free drug is responsible for pharmacological activity while the balance between free and bound drug can impact drug distribution, elimination, and other safety parameters. In the hepatic impairment (HI) and renal impairment (RI) clinical studies, unbound drug concentration is often assessed; however, the relevance and impact of the protein binding (PB) results is largely limited. We analyzed published clinical safety and pharmacokinetic studies in subjects with HI or RI with PB assessment up to October 2022 and summarized the contribution of PB results on their label dose recommendations. Among drugs with HI publication, 32% (17/53) associated product labels include PB results in HI section. Of these, the majority (9/17, 53%) recommend dose adjustments consistent with observed PB change. Among drugs with RI publication, 27% (12/44) of associated product labels include PB results in RI section with the majority (7/12, 58%) recommending no dose adjustment, consistent with the reported absence of PB change. PB results were found to be consistent with a tailored dose recommendation in 53% and 58% of the approved labels for HI and RI section, respectively. We further discussed the interpretation challenges of PB results, explored treatment decision factors including total drug concentration, exposure-response relationships, and safety considerations in these case examples. Collectively, comprehending the alterations in free drug levels in HI and RI informs treatment decision through a risk-based approach.

Solid-phase microextraction - a future technique in pharmacology and coating trends.

Traditional sample preparation techniques based on liquid-liquid extraction (LLE) or solid-phase extraction (SPE) often suffer from a major error due to the matrix effects caused by significant co-extraction of matrix components. The implementation of a modern extraction technique such as solid-phase microextraction (SPME) was aimed at reducing analysis time and the use of organic solvents, as well as eliminating pre-analytical and analytical errors. Solid-phase microextraction (SPME) is an innovative technique for extracting low molecular weight compounds (less than 1500 Da) from highly complex matrices, including biological matrices. It has a wide range of applications in various types of analysis including pharmaceutical, clinical, metabolomics and proteomics. SPME has a number of advantages over other extraction techniques. Among the most important are low environmental impact, the ability to sample and preconcentrate analytes in one step, simple automation, and the ability to extract multiple analytes simultaneously. It is expected to become, in the future, another method for cell cycle research. Numerous available literature sources prove that solid-phase microextraction can be a future technique in many scientific fields, including pharmaceutical sciences. This paper provides a literature review of trends in SPME coatings and pharmacological applications.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery.

INTRODUCTION: For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs. AREAS COVERED: End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (koff and kon) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations. EXPERT OPINION: The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules.

INTRODUCTION: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED: This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION: ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

A culture-independent nucleic acid diagnostics method for use in the detection and quantification of Burkholderia cepacia complex contamination in aqueous finished pharmaceutical products.

The Burkholderia cepacia complex (Bcc) is the number one bacterial complex associated with contaminated Finished Pharmaceutical Products (FPPs). This has resulted in multiple healthcare related infection morbidity and mortality events in conjunction with significant FPP recalls globally. Current microbiological quality control of FPPs before release for distribution depends on lengthy, laborious, non-specific, traditional culture-dependent methods which lack sensitivity. Here, we present the development of a culture-independent Bcc Nucleic Acid Diagnostic (NAD) method for detecting Bcc contaminants associated with Over-The-Counter aqueous FPPs. The culture-independent Bcc NAD method was validated to be specific for detecting Bcc at different contamination levels from spiked aqueous FPPs. The accuracy in Bcc quantitative measurements was achieved by the high degree of Bcc recovery from aqueous FPPs. The low variation observed between several repeated Bcc quantitative measurements further demonstrated the precision of Bcc quantification in FPPs. The robustness of the culture-independent Bcc NAD method was determined when its accuracy and precision were not significantly affected during testing of numerous aqueous FPP types with different ingredient matrices, antimicrobial preservative components and routes of administration. The culture-independent Bcc NAD method showed an ability to detect Bcc in spiked aqueous FPPs at a concentration of 20 Bcc CFU/mL. The rapid (≤ 4 hours from sample in to result out), robust, culture-independent Bcc NAD method presented provides rigorous test specificity, accuracy, precision, and sensitivity. This method, validated with equivalence to ISO standard ISO/TS 12869:2019, can be a valuable diagnostic tool in supporting microbiological quality control procedures to aid the pharmaceutical industry in preventing Bcc contamination of aqueous FPPs for consumer safety.

Removal of pharmaceutical compounds and toxicology study in wastewater using Malaysian fungal Ganoderma lucidum.

Elevated usage of pharmaceutical products leads to the accumulation of emerging contaminants in sewage. In the current work, Ganoderma lucidum (GL) was used to remove pharmaceutical compounds (PCs), proposed as a tertiary method in sewage treatment plants (STPs). The PCs consisted of a group of painkillers (ketoprofen, diclofenac, and dexamethasone), psychiatrists (carbamazepine, venlafaxine, and citalopram), beta-blockers (atenolol, metoprolol, and propranolol), and anti-hypertensives (losartan and valsartan). The performance of 800 mL of synthetic water, effluent STP, and hospital wastewater (HWW) was evaluated. Parameters, including treatment time, inoculum volume, and mechanical agitation speed, have been tested. The toxicity of the GL after treatment is being studied based on exposure levels to zebrafish embryos (ZFET) and the morphology of the GL has been observed via Field Emission Scanning Electron Microscopy (FESEM). The findings conclude that GL can reduce PCs from <10% to >90%. Diclofenac and valsartan are the highest (>90%) in the synthetic model, while citalopram and propranolol (>80%) are in the real wastewater. GL effectively removed pollutants in 48 h, 1% of the inoculum volume, and 50 rpm. The ZFET showed GL is non-toxic (LC50 is 209.95 mg/mL). In the morphology observation, pellets GL do not show major differences after treatment, showing potential to be used for a longer treatment time and to be re-useable in the system. GL offers advantages to removing PCs in water due to their non-specific extracellular enzymes that allow for the biodegradation of PCs and indicates a good potential in real-world applications as a favourable alternative treatment.

Pharmaceutically active compounds removal from aqueous solutions by MIL-101(Cr)-NH2: A molecular dynamics study.

Discharging pharmaceutically active drugs into water and wastewater has become a significant environmental threat. Traditional methods are unable to effectively remove these compounds from wastewater, so it is necessary to search for more effective methods. This study investigates the potential of MIL-101(Cr)-NH2 as a preferable and more effective adsorbent for the adsorption and removal of pharmaceutically active compounds from aqueous solutions. By utilizing its large porosity, high specific surface area, and high stability, the structural and transport properties of three pharmaceutically active compounds naproxen (NAP), diclofenac (DIC) and sulfamethoxazole (SMX)) studied using molecular dynamics simulation. The results indicate that the MIL-101(Cr)-NH2 adsorbent is suitable for removing drug molecules from aqueous solutions, with maximum adsorption capacities of 697.75 mg/g for naproxen, 704.99 mg/g for diclofenac, and 725.51 mg/g for sulfamethoxazole.

BindingSiteDTI: differential-scale binding site modelling for drug-target interaction prediction.

MOTIVATION: Enhanced by contemporary computational advances, the prediction of drug-target interactions (DTIs) has become crucial in developing de novo and effective drugs. Existing deep learning approaches to DTI prediction are frequently beleaguered by a tendency to overfit specific molecular representations, which significantly impedes their predictive reliability and utility in novel drug discovery contexts. Furthermore, existing DTI networks often disregard the molecular size variance between macro molecules (targets) and micro molecules (drugs) by treating them at an equivalent scale that undermines the accurate elucidation of their interaction. RESULTS: We propose a novel DTI network with a differential-scale scheme to model the binding site for enhancing DTI prediction, which is named as BindingSiteDTI. It explicitly extracts multiscale substructures from targets with different scales of molecular size and fixed-scale substructures from drugs, facilitating the identification of structurally similar substructural tokens, and models the concealed relationships at the substructural level to construct interaction feature. Experiments conducted on popular benchmarks, including DUD-E, human, and BindingDB, shown that BindingSiteDTI contains significant improvements compared with recent DTI prediction methods. AVAILABILITY AND IMPLEMENTATION: The source code of BindingSiteDTI can be accessed at https://github.com/MagicPF/BindingSiteDTI.

Drugs in Human Milk Part 1: Practical and Analytical Considerations in Measuring Drugs and Metabolites in Human Milk.

Human milk is a remarkable biofluid that provides essential nutrients and immune protection to newborns. Breastfeeding women consuming medications could pass the drug through their milk to neonates. Drugs can be transferred to human milk by passive diffusion or active transport. The physicochemical properties of the drug largely impact the extent of drug transfer into human milk. A comprehensive understanding of the physiology of human milk formation, composition of milk, mechanisms of drug transfer, and factors influencing drug transfer into human milk is critical for appropriate selection and use of medications in lactating women. Quantification of drugs in the milk is essential for assessing the safety of pharmacotherapy during lactation. This can be achieved by developing specific, sensitive, and reproducible analytical methods using techniques such as liquid chromatography coupled with mass spectrometry. The present review briefly discusses the physiology of human milk formation, composition of human milk, mechanisms of drug transfer into human milk, and factors influencing transfer of drugs from blood to milk. We further expand upon and critically evaluate the existing analytical approaches/assays used for the quantification of drugs in human milk.

PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction.

Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the understanding of disease pathogenesis and lead to more effective drug targets. Additionally, it can aid in determining drug parameters, ensuring proper absorption, distribution, and metabolism within the body. Due to incomplete feature representation or the model's inadequate adaptation to protein-ligand complexes, the existing methodologies suffer from suboptimal predictive accuracy. To address these pitfalls, in this study, we designed a new deep learning method based on transformer and GCN. We first utilized the transformer network to grasp crucial information of the original protein sequences within the smile sequences and connected them to prevent falling into a local optimum. Furthermore, a series of dilation convolutions are performed to obtain the pocket features and smile features, subsequently subjected to graphical convolution to optimize the connections. The combined representations are fed into the proposed model for classification prediction. Experiments conducted on various protein-ligand binding prediction methods prove the effectiveness of our proposed method. It is expected that the PfgPDI can contribute to drug prediction and accelerate the development of new drugs, while also serving as a valuable partner for drug testing and Research and Development engineers.

Predictive computational models for assessing the impact of co-milling on drug dissolution.

Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.

Occurrence of pharmaceutically active compounds in groundwater and their effects to the human health.

Groundwater contamination by pharmaceutically active compounds (PhACs) has been considered a public health concern worldwide. Alongside the potential toxicological risk of these organic substances, many countries still rely on groundwater for drinking water supply. Thus, this study identified a priority list of seven licit PhACs, comprising acetaminophen (ACT), tramadol (TRA), carbamazepine (CBZ), erythromycin (ERY), sulfamethoxazole (SMX), metformin (MET), and oxazepam (OXZ). Consumption, concentration, and human toxicity in silico results were collected from open access databases. These three indicators were analyzed separately and grouped through a general risk index. The consumption index (data from the USA and Brazil) indicated that ACT, TRA, and MET are the most consumed. Monitoring samples from the USA and Europe (n = 816) indicated that OXZ and ERY stand out as the higher occurrence index considering both regions, but the ranking for each region showed considerable differences. When assessing toxicological risk, an index ≥ 0.5 was attributed to CBZ, MET, OXZ, SMX, and TRA. The general risk indicated the need to be attentive to MET, OXZ, and TRA as they presented ≥ 0.5 index values for at least two indicators.

Crystallization and Solvent Evaporation Ionization Mass Spectrometry (CSEI-MS) for Rapid Detection of Drugs in Complex Matrices.

Illegal addition of drugs is common but seriously threatens public health safety. Conventional mass spectrometry methods are difficult to realize direct analysis of drugs existing in some complex matrices such as seawater or soil due to the ion suppression effect and contamination to MS parts caused by nonvolatile salts. In this work, a novel crystallization and solvent evaporation ionization mass spectrometry (CSEI-MS) method was constructed and developed to achieve rapid desalting detection. CSEI only consists of a heated plate and a nebulizer and exhibits excellent desalting performance, enabling direct analysis of six drugs dissolved in eight kinds of salt solutions (up to 200 mmol/L) and three complex salty matrices. Under optimized conditions, CSEI-MS presents high sensitivity, accuracy, linearity, and intraday and interday precision. Finally, this method is applied to the quantitative analysis of drugs in seawater, hand cream, and soil. Furthermore, the highly sensitive detection of CSEI-MS is demonstrated to remain even if the detection processes are conducted within 5 s via common commercial tools.

Application of in vitro bioassays to monitor pharmaceuticals in water: A synthesis of chronological analysis, mode of action, and practical insights.

Pharmaceutical compounds in wastewater have emerged as a significant concern for the aquatic environment. The use of in vitro bioassays represents a sustainable and cost-effective approach for assessing the potential toxicological risks of these biologically active compounds in wastewater and aligns with ethical considerations in research. It facilitates high-throughput analysis, captures mixture effects, integrates impacts of both known and unknown chemicals, and reduces reliance on animal testing. The core aim of the current review was to explore the practical application of in vitro bioassays in evaluating the environmental impacts of pharmaceuticals in wastewater. This comprehensive review strives to achieve several key objectives. First, it provides a summary categorisation of pharmaceuticals based on their mode of action, providing a structured framework for understanding their ecological significance. Second, a chronological analysis of pharmaceutical research aims to document their prevalence and trends over time, shedding light on evolving environmental challenges. Third, the review critically analyses existing bioassay applications in wastewater, while also examining bioassay coverage of representative compounds within major pharmaceutical classes. Finally, it explores the potential for developing innovative bioassays tailored for water quality monitoring of pharmaceuticals, paving the way for more robust environmental monitoring and risk assessment. Overall, adopting effect-based methods for pharmaceutical monitoring in water holds significant promise. It encompasses a broad spectrum of biological impacts, promotes standardized protocols, and supports a bioassay test battery approach indicative of different endpoints, thereby enhancing the effectiveness of environmental risk assessment.

Advances in Nanotechnology for Enhancing the Solubility and Bioavailability of Poorly Soluble Drugs.

This manuscript offers a comprehensive overview of nanotechnology's impact on the solubility and bioavailability of poorly soluble drugs, with a focus on BCS Class II and IV drugs. We explore various nanoscale drug delivery systems (NDDSs), including lipid-based, polymer-based, nanoemulsions, nanogels, and inorganic carriers. These systems offer improved drug efficacy, targeting, and reduced side effects. Emphasizing the crucial role of nanoparticle size and surface modifications, the review discusses the advancements in NDDSs for enhanced therapeutic outcomes. Challenges such as production cost and safety are acknowledged, yet the potential of NDDSs in transforming drug delivery methods is highlighted. This contribution underscores the importance of nanotechnology in pharmaceutical engineering, suggesting it as a significant advancement for medical applications and patient care.