Insights into Inhalation Drug Disposition: The Roles of Pulmonary Drug-Metabolizing Enzymes and Transporters.

The past five decades have witnessed remarkable advancements in the field of inhaled medicines targeting the lungs for respiratory disease treatment. As a non-invasive drug delivery route, inhalation therapy offers numerous benefits to respiratory patients, including rapid and targeted exposure at specific sites, quick onset of action, bypassing first-pass metabolism, and beyond. Understanding the characteristics of pulmonary drug transporters and metabolizing enzymes is crucial for comprehending efficient drug exposure and clearance processes within the lungs. These processes are intricately linked to both local and systemic pharmacokinetics and pharmacodynamics of drugs. This review aims to provide a comprehensive overview of the literature on lung transporters and metabolizing enzymes while exploring their roles in exogenous and endogenous substance disposition. Additionally, we identify and discuss the principal challenges in this area of research, providing a foundation for future investigations aimed at optimizing inhaled drug administration. Moving forward, it is imperative that future research endeavors to focus on refining and validating in vitro and ex vivo models to more accurately mimic the human respiratory system. Such advancements will enhance our understanding of drug processing in different pathological states and facilitate the discovery of novel approaches for investigating lung-specific drug transporters and metabolizing enzymes. This deeper insight will be crucial in developing more effective and targeted therapies for respiratory diseases, ultimately leading to improved patient outcomes.

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.

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

Revisiting drug-protein interaction prediction: a novel global-local perspective.

MOTIVATION: Accurate inference of potential drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance. RESULTS: We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multilayer perceptrons to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach is expected to offer valuable insights for furthering drug repurposing and personalized medicine research. AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.

Towards Greener and More Cost-efficient Biosynthesis of Pharmaceuticals and Fragrance Molecules.

Enzymes are natural catalysts which are gaining momentum in chemical synthesis due to their exquisiteselectivity and their biodegradability. However, the cost-efficiency and the sustainability of the overall biocatalytic process must be enhanced to unlock completely the potential of enzymes for industrial applications. To reach this goal, enzyme immobilization and the integration into continuous flow reactors have been the cornerstone of our research. We showed key examples of the advantages of those tools for the biosynthesis of antivirals, anticancer drugs, and valuable fragrance molecules. By combining new strategies to immobilize biocatalysts, innovative bioengineering approaches, and process development, the performance of the reactions could be boosted up to 100-fold.

Anaerobic co-metabolic biodegradation of pharmaceuticals and personal care products driven by glycerol fermentation.

Anaerobic digestion in two sequential phases, acidogenesis and methanogenesis, has been shown to be beneficial for enhancing the biomethane generation from wastewater. In this work, the application of glycerol (GOH) as a fermentation co-substrate during the wastewater treatment was evaluated on the biodegradation of different pharmaceuticals and personal care products (PPCPs). GOH co-digestion during acidogenesis led to a significant increase in the biodegradation of acetaminophen (from 78 to 89%), ciprofloxacin (from 25 to 46%), naproxen (from 73 to 86%), diclofenac (from 36 to 48%), ibuprofen (from 65 to 88%), metoprolol (from 45 to 59%), methylparaben (from 64 to 78%) and propylparaben (from 68 to 74%). The heterotrophic co-metabolism of PPCPs driven by glycerol was confirmed by the biodegradation kinetics, in which kbio (biodegradation kinetics constant) values increased from 0.18 to 2.11 to 0.27-3.60 L g-1-VSS d-1, for the operational phases without and with GOH, respectively. The assessment of metabolic pathways in each phase revealed that the prevalence of aromatic compounds degradation, metabolism of xenobiotics by cytochrome P450, and benzoate degradation routes during acidogenesis are key factors for the enzymatic mechanisms linked to the PPCPs co-metabolism. The phase separation of anaerobic digestion was effective in the PPCPs biodegradation, and the co-fermentation of glycerol provided an increase in the generation potential of biomethane in the system (energetic potential of 5.0 and 6.3 kJ g-1-CODremoved, without and with GOH, respectively). This study showed evidence that glycerol co-fermentation can exert a synergistic effect on the PPCPs removal during anaerobic digestion mediated by heterotrophic co-metabolism.

Potential impact of underlying diseases influencing ADME in nonclinical safety assessment.

Nonclinical studies involve in vitro, in silico, and in vivo experiments to assess the toxicokinetics, toxicology, and safety pharmacology of drugs according to regulatory requirements by a national or international authority. In this review, we summarize the potential effects of various underlying diseases governing the absorption, distribution, metabolism, and excretion (ADME) of drugs to consider the use of animal models of diseases in nonclinical trials. Obesity models showed alterations in hepatic metabolizing enzymes, transporters, and renal pathophysiology, which increase the risk of drug-induced toxicity. Diabetes models displayed changes in hepatic metabolizing enzymes, transporters, and glomerular filtration rates (GFR), leading to variability in drug responses and susceptibility to toxicity. Animal models of advanced age exhibited impairment of drug metabolism and kidney function, thereby reducing the drug-metabolizing capacity and clearance. Along with changes in hepatic metabolic enzymes, animal models of metabolic syndrome-related hypertension showed renal dysfunction, resulting in a reduced GFR and urinary excretion of drugs. Taken together, underlying diseases can induce dysfunction of organs involved in the ADME of drugs, ultimately affecting toxicity. Therefore, the use of animal models of representative underlying diseases in nonclinical toxicity studies can be considered to improve the predictability of drug side effects before clinical trials.

Validity conditions of approximations for a target-mediated drug disposition model: A novel first-order approximation and its comparison to other approximations.

Target-mediated drug disposition (TMDD) is a phenomenon characterized by a drug's high-affinity binding to a target molecule, which significantly influences its pharmacokinetic profile within an organism. The comprehensive TMDD model delineates this interaction, yet it may become overly complex and computationally demanding in the absence of specific concentration data for the target or its complexes. Consequently, simplified TMDD models employing quasi-steady state approximations (QSSAs) have been introduced; however, the precise conditions under which these models yield accurate results require further elucidation. Here, we establish the validity of three simplified TMDD models: the Michaelis-Menten model reduced with the standard QSSA (mTMDD), the QSS model reduced with the total QSSA (qTMDD), and a first-order approximation of the total QSSA (pTMDD). Specifically, we find that mTMDD is applicable only when initial drug concentrations substantially exceed total target concentrations, while qTMDD can be used for all drug concentrations. Notably, pTMDD offers a simpler and faster alternative to qTMDD, with broader applicability than mTMDD. These findings are confirmed with antibody-drug conjugate real-world data. Our findings provide a framework for selecting appropriate simplified TMDD models while ensuring accuracy, potentially enhancing drug development and facilitating safer, more personalized treatments.

Unveiling the influence of microaeration and sludge recirculation on enhancement of pharmaceutical removal and microbial community change of the novel anaerobic baffled biofilm - membrane bioreactor in treating building wastewater.

This research aims to conduct a comparative investigation of the role played by microaeration and sludge recirculation in the novel anaerobic baffled biofilm-membrane bioreactor (AnBB-MBR) for enhancing pharmaceutical removal from building wastewater. Three AnBB-MBRs - R1: AnBB-MBR, R2: AnBB-MBR with microaeration and R3: AnBB-MBR with microaeration and sludge recirculation - were operated simultaneously to remove Ciprofloxacin (CIP), Caffeine (CAF), Sulfamethoxazole (SMX) and Diclofenac (DCF) from real building wastewater at the hydraulic retention time (HRT) of 30 h for 115 days. From the removal profiles of the targeted pharmaceuticals in the AnBB-MBRs, it was found that the fixed-film compartment (C1) could significantly reduce the targeted pharmaceuticals. The remaining pharmaceuticals were further removed with the microaeration compartment. R2 exhibited the utmost removal efficiency for CIP (78.0 %) and DCF (40.8 %), while SMX was removed most successfully by R3 (microaeration with sludge recirculation) at 91.3 %, followed by microaeration in R2 (88.5 %). For CAF, it was easily removed by all AnBB-MBR systems (>90 %). The removal mechanisms indicate that the microaeration in R2 facilitated the adsorption of CIP onto microaerobic biomass, while the enhanced biodegradation of CAF, SMX and DCF was confirmed by batch biotransformation kinetics and the adsorption isotherms of the targeted pharmaceuticals. The microbial groups involved in biodegradation of the targeted compounds under microaeration were identified as nitrogen removal microbials (Nitrosomonas, Nitrospira, Thiobacillus, and Denitratisoma) and methanotrophs (Methylosarcina, Methylocaldum, and Methylocystis). Overall, explication of the integration of AnBB-MBR with microaeration (R2) confirmed it as a prospective technology for pharmaceutical removal from building wastewater due to its energy-efficient approach characterized by minimal aeration supply.

Validation of a Caco-2 microfluidic Chip model for predicting intestinal absorption of BCS Class I-IV drugs.

Oral delivery is considered the most patient preferred route of drug administration, however, the drug must be sufficiently soluble and permeable to successfully formulate an oral formulation. There have been advancements in the development of more predictive solubility and dissolution tools, but the tools that has been developed for permeability assays have not been validated as extensively as the gold-standard Caco-2 Transwell assay. Here, we evaluated Caco-2 intestinal permeability assay in Transwells and a commercially available microfluidic Chip using 19 representative Biopharmaceutics Classification System (BCS) Class I-IV compounds. For each selected compound, we performed a comprehensive viability test, quantified its apparent permeability (Papp), and established an in vitro in vivo correlation (IVIVC) to the human fraction absorbed (fa) in both culture conditions. Permeability differences were observed across the models as demonstrated by antipyrine (Transwell Papp: 38.5 ± 6.1 × 10-8 cm/s vs Chip Papp: 32.9 ± 11.3 × 10-8 cm/s) and nadolol (Transwell Papp: 0.6 ± 0.1 × 10-7 cm/s vs Chip Papp: 3 ± 1.2 × 10-7 cm/s). The in vitro in vivo correlation (IVIVC; Papp vs. fa) of the Transwell model (r2 = 0.59-0.83) was similar to the Chip model (r2 = 0.41-0.79), highlighting similar levels of predictivity. Comparing to historical data, our Chip Papp data was more closely aligned to native tissues assessed in Ussing chambers. This is the first study to comprehensively validate a commercial Gut-on-a-Chip model as a predictive tool for assessing oral absorption to further reduce our reliance on animal models.

Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods.

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

Biotransformation of fluorinated drugs and xenobiotics by the model fungus Cunninghamella elegans.

Some species of the genus Cunninghamella (C. elegans, C. echinulata and C. blaskesleeana) produce the same phase I and phase II metabolites when incubated with xenobiotics as mammals, and thus are considered microbial models of mammalian metabolism. This had made these fungi attractive for metabolism studies with drugs, pesticides and environmental pollutants. As a substantial proportion of pharmaceuticals and agrochemicals are fluorinated, their biotransformation has been studied in Cunninghamella fungi and C. elegans in particular. This article details the methods employed for cultivating the fungi in planktonic and biofilm cultures, and extraction and analysis of fluorinated metabolites. Furthermore, protocols for the heterologous expression of Cunninghamella cytochromes P450 (CYPs), which are the enzymes associated with phase I metabolism, are described.

Sorting drug conformers in enzyme active sites: the XTB way.

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles.

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Parental exposure to antidepressants has lasting effects on offspring? A case study with zebrafish.

Fish have common neurotransmitter pathways with humans, exhibiting a significant degree of conservation and homology. Thus, exposure to fluoxetine makes fish potentially susceptible to biochemical and physiological changes, similarly to what is observed in humans. Over the years, several studies demonstrated the potential effects of fluoxetine on different fish species and at different levels of biological organization. However, the effects of parental exposure to unexposed offspring remain largely unknown. The consequences of 15-day parental exposure to relevant concentrations of fluoxetine (100 and 1000 ng/L) were assessed on offspring using zebrafish as a model organism. Parental exposure resulted in offspring early hatching, non-inflation of the swimming bladder, increased malformation frequency, decreased heart rate and blood flow, and reduced growth. Additionally, a significant behavioral impairment was also found (reduced startle response, basal locomotor activity, and altered non-associative learning during early stages and a negative geotaxis and scototaxis, reduced thigmotaxis, and anti-social behavior at later life stages). These behavior alterations are consistent with decreased anxiety, a significant increase in the expression of the monoaminergic genes slc6a4a (sert), slc6a3 (dat), slc18a2 (vmat2), mao, tph1a, and th2, and altered levels of monoaminergic neurotransmitters. Alterations in behavior, expression of monoaminergic genes, and neurotransmitter levels persisted until offspring adulthood. Given the high conservation of neuronal pathways between fish and humans, data show the possibility of potential transgenerational and multigenerational effects of pharmaceuticals' exposure. These results reinforce the need for transgenerational and multigenerational studies in fish, under realistic scenarios, to provide realistic insights into the impact of these pharmaceuticals.

From molecules to visuals: Empowering drug discovery and development with mass spectrometry imaging.

Over the past three decades, mass spectrometry imaging (MSI) has emerged as a valuable tool for the spatial localization of drugs and metabolites directly from tissue surfaces without the need for labels. MSI offers molecular specificity, making it increasingly popular in the pharmaceutical industry compared to conventional imaging techniques like quantitative whole-body autoradiography (QWBA) and immunohistochemistry, which are unable to distinguish parent drugs from metabolites. Across the industry, there has been a consistent uptake in the utilization of MSI to investigate drug and metabolite distribution patterns, and the integration of MSI with omics technologies in preclinical investigations. To continue the further adoption of MSI in drug discovery and development, we believe there are two key areas that need to be addressed. First, there is a need for accurate quantification of analytes from MSI distribution studies. Second, there is a need for increased interactions with regulatory agencies for guidance on the utility and incorporation of MSI techniques in regulatory filings. Ongoing efforts are being made to address these areas, and it is hoped that MSI will gain broader utilization within the industry, thereby becoming a critical ingredient in driving drug discovery and development.

Photo-crosslinkable polyester microneedles as sustained drug release systems toward hypertrophic scar treatment.

Burn injuries can result in a significant inflammatory response, often leading to hypertrophic scarring (HTS). Local drug therapies e.g. corticoid injections are advised to treat HTS, although they are invasive, operator-dependent, extremely painful and do not permit extended drug release. Polymer-based microneedle (MN) arrays can offer a viable alternative to standard care, while allowing for direct, painless dermal drug delivery with tailorable drug release profile. In the current study, we synthesized photo-crosslinkable, acrylate-endcapped urethane-based poly(ε-caprolactone) (AUP-PCL) toward the fabrication of MNs. Physico-chemical characterization (1H-NMR, evaluation of swelling, gel fraction) of the developed polymer was performed and confirmed successful acrylation of PCL-diol. Subsequently, AUP-PCL, and commercially available PCL-based microneedle arrays were fabricated for comparative evaluation of the constructs. Hydrocortisone was chosen as model drug. To enhance the drug release efficiency of the MNs, Brij®35, a nonionic surfactant was exploited. The thermal properties of the MNs were evaluated via differential scanning calorimetry. Compression testing of the arrays confirmed that the MNs stay intact upon applying a load of 7 N, which correlates to the standard dermal insertion force of MNs. The drug release profile of the arrays was evaluated, suggesting that the developed PCL arrays can offer efficient drug delivery for up to two days, while the AUP-PCL arrays can provide a release up to three weeks. Finally, the insertion of MN arrays into skin samples was performed, followed by histological analysis demonstrating the AUP-PCL MNs outperforming the PCL arrays upon providing pyramidical-shaped perforations through the epidermal layer of the skin.

AUP-PCL MN arrays provide long-term transdermal drug delivery of hydrocortisoneAUP-PCL-based MN arrays provide superior drug release profiles compared to PCL MNsEffective skin penetration AUP-PCL-based MNs on skin was achieved.

Intranasal Nose-to-Brain Drug Delivery via the Olfactory Region in Mice: Two In-Depth Protocols for Region-Specific Intranasal Application of Antibodies and for Expression Analysis of Fc Receptors via In Situ Hybridization in the Nasal Mucosa.

A region-specific catheter-based intranasal administration method was successfully developed, established, and validated as reported previously. By using this method, drugs can be applicated specifically to the olfactory region. Thereby, intranasally administered drugs could be delivered via neuronal connections to the central nervous system. Here, we present a detailed protocol with a step-by-step procedure for nose-to-brain delivery via the olfactory mucosa.Fc receptors such as the neonatal Fc receptor (FcRn) and potentially Fcγ receptor IIb (FcγRIIb) are involved in the uptake and transport of antibodies via the olfactory nasal mucosa. To better characterize their expression levels and their role in CNS drug delivery via the nose, an in situ hybridization (ISH) protocol was adapted for nasal mucosa samples and described in abundant details.

Antarctic marine sediment as a source of filamentous fungi-derived antimicrobial and antitumor compounds of pharmaceutical interest.

Antarctica harbors a microbial diversity still poorly explored and of inestimable biotechnological value. Cold-adapted microorganisms can produce a diverse range of metabolites stable at low temperatures, making these compounds industrially interesting for biotechnological use. The present work investigated the biotechnological potential for antimicrobial and antitumor activity of filamentous fungi and bacteria isolated from marine sediment samples collected at Deception Island, Antarctica. A total of 89 microbial isolates were recovered from marine sediments and submitted to an initial screening for L-glutaminase with antitumoral activity and for antimicrobial metabolites. The isolates Pseudogymnoascus sp. FDG01, Pseudogymnoascus sp. FDG02, and Penicillium sp. FAD33 showed potential antiproliferative action against human pancreatic carcinoma cells while showing no toxic effect on non-tumor cells. The microbial extracts from unidentified three bacteria and four filamentous fungi showed antibacterial activity against at least one tested pathogenic bacterial strain. The isolate FDG01 inhibited four bacterial species, while the isolate FDG01 was active against Micrococcus luteus in the minimal inhibitory concentration of 0.015625 µg mL -1. The results pave the way for further optimization of enzyme production and characterization of enzymes and metabolites found and reaffirm Antarctic marine environments as a wealthy source of compounds potentially applicable in the healthcare and pharmaceutical industry.