Prediction Of Bioavailability Research Articles

Take a Swipe at Actinide Bioavailability: Application of a New In Vitro Method.

Filter swipe tests are used for routine analyses of actinides in nuclear industrial, research, and weapon facilities as well as following accidental release. Actinide physicochemical properties will determine in part bioavailability and internal contamination levels. The aim of this work was to develop and validate a new approach to predict actinide bioavailability recovered by filter swipe tests. As proof of concept and to simulate a routine or an accidental situation, filter swipes were obtained from a nuclear research facility glove box. A recently-developed biomimetic assay for prediction of actinide bioavailability was adapted for bioavailability measurements using material obtained from these filter swipes. In addition, the efficacy of the clinically-used chelator, diethylenetriamine pentaacetate (Ca-DTPA), to enhance transportability was determined. This report shows that it is possible to evaluate physicochemical properties and to predict bioavailability of filter swipe-associated actinides.

Predicting Human Bioavailability of Subcutaneously Administered Fusion Proteins and Monoclonal Antibodies Using Human Intravenous Clearance or Antibody Isoelectric Point

There has been an increasing trend towards subcutaneous (SC) delivery of fusion proteins and monoclonal antibodies (mAbs) in recent years versus intravenous (IV) administration. The prediction of bioavailability is one of the major barriers in clinical translation of SC-administered therapeutic proteins due to a lack of reliable in vitro and preclinical in vivo predictive models. In this study, we explored the relationships between human SC bioavailability and physicochemical or pharmacokinetic properties of 19 Fc- or albumin-fusion proteins and 98 monoclonal antibodies. An inverse linear correlation was observed between human SC bioavailability and intravenous clearance (CL) or isoelectric point (pI). Multivariate regression models were developed using intravenous CL and pI of a training set (N = 59) as independent variables. The predictive models of mAbs were validated with an independent test set (N = 33). Two linear regression models resulted in 24 (73%) and 27 (82%) among 33 predictions within 0.8- to 1.2-fold deviations. Due to the small sample size of dataset, regression model validation was not conducted for fusion proteins. Overall, this study demonstrated that CL- and pI-based multivariate regression models could be used to predict human SC bioavailability of mAbs.

Prediction of cadmium bioavailability in the rice-soil system on a county scale based on the multi-surface speciation model

Relative to total cadmium (Cd) content, bioavailable Cd in paddy soil is regarded as a more reasonable indicator for the risk of Cd bioaccumulation in rice. However, there is still a lack of approach to accurately predict the content of bioavailable Cd in paddy soil due to its heterogeneity and complexity. Here, multi-surface speciation model (MSM) was employed to predict the bioavailable Cd and Cd immobilization effect. Moreover, a precise remediation strategy was designed based on screening and scenario simulation of the sensitive factors with MSM. The results demonstrated that MSM can well predict Cd bioaccumulation risk in rice. The contribution of pH to Cd bioavailability was quantified under three analysis scenarios, accounting for 87.51% of the total variance of bioavailable Cd. In addition, the pH alert value (6.31 ± 0.52) for Cd risk was acquired for each rice field on a county scale. A precise map for the application amount of lime materials was constructed by taking CaCO3 (3.38–15.75 t ha−1) as a recommended economical and green immobilization agent. This study provides a potentially effective approach for risk assessment of Cd contamination in rice and important reference for precise Cd remediation in paddy soil.

Development of physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability_suppl

Development of physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability_suppl

Development of physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability.

Absorption in the gastrointestinal tract is a key factor determining the bioavailability of chemicals after oral exposure but is frequently assumed to have a conservative value of 100% for environmental chemicals, particularly in the context of high-throughput toxicokinetics for in vitro-to-in vivo extrapolation (IVIVE). For pharmaceutical compounds, the physiologically based advanced compartmental absorption and transit (ACAT) model has been used extensively to predict gut absorption but has not generally been applied to environmental chemicals. Here we develop a probabilistic environmental compart­mental absorption and transit (PECAT) model, adapting the ACAT model to environmental chemicals. We calibrated the model parameters to human in vivo, ex vivo, and in vitro datasets of drug permeability and fractional absorption by con­sidering two key factors: (1) differences between permeability in Caco-2 cells and in vivo permeability in the jejunum, and (2) differences in in vivo permeability across different gut segments. Incorporating these factors probabilistically, we found that given Caco-2 permeability measurements, predictions of the PECAT model are consistent with the (limited) available gut absorption data for environmental chemicals. However, the substantial chemical-to-chemical variability observed in the cal­ibration data often led to wide probabilistic confidence bounds in the predicted fraction absorbed and resulting steady state blood concentration. Thus, while the PECAT model provides a statistically rigorous, physiologically based approach for incor­porating in vitro data on gut absorption into toxicokinetic modeling and IVIVE, it also highlights the need for more accurate in vitro models and data for measuring gut segment-specific in vivo permeability of environmental chemicals.

Network Pharmacology Approach to Investigate the Mechanism of Danggui-Shaoyao-San against Diabetic Kidney Disease.

Danggui-Shaoyao-San (DSS) is a traditional Chinese medicine formula that has been widely used to treat a variety of disorders, including renal diseases. Despite being well-established in clinical practice, the mechanisms behind the therapeutic effects of DSS on diabetic nephropathy (DN) remain elusive. To explore the therapeutic mechanism, we explored the action mechanism of DSS on DN using network pharmacology strategies. All ingredients were selected from the relevant databases, and active ingredients were chosen on the basis of their oral bioavailability prediction and drug-likeness evaluation. The putative proteins of DSS were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, whereas the potential genes of DN were obtained from the GeneCards and OMIM databases. Enrichment analysis using gene ontology (GO) and the Kyoto encyclopedia of genes and genomes (KEGG) was performed to discover possible hub targets and gene-related pathways. Afterwards, the underlying molecular mechanisms of DSS against DN were validated experimentally in vivo against db/db mice. We identified 91 phytochemicals using the comprehensive network pharmacology technique, 51 of which were chosen as bioactive components. There were 40 proteins and 20 pathways in the target-pathway network. The experimental validation results demonstrated that DSS may reduce the expression of TNF-α, IL-6, and ICAM-1, as well as extracellular matrix deposition, by blocking the JNK pathway activation, which protects against kidney injury. This study discovered the putative molecular mechanisms of action of DSS against diabetic kidney damage through a network pharmacology approach and experimental validation.

Unveiling Acacia farnesiana phytochemicals as Angiotensin Converting Enzyme (ACE) inhibitors via in-silico drug design, molecular docking, and bioavailability predictions: An illustration against SARS-CoV-2 spike protein

Angiotensin-converting enzyme 2 (ACE2) is a transmembrane protein that functions as a receptor for coronavirus spike protein. When spike protein fragments as the ligand binds with ACE2 protein, this ACE2 protein functions as a virus receptor, participating in the biological process known as the viral particle entry in the host cell. Hence, an in-silico study was carried out since it is faster and less expensive than trial and error methods based on experimental investigations. To study the effect of Acacia farnesiana phytochemicals on spike protein, molecular docking analyses were carried out. In this study, twelve phytochemicals from Acacia farnesiana have been selected as small molecules based on their ACEI and anti- inflammatory nature to evaluate molecular interaction between spike protein of SARS-CoV2 with ACE2 of the human complex molecule. Gallic acid, methyl gallate, kaempferol, Rhamnocitrin, naringenin, apigenin, ellagic acid, ferulic acid, myricetin, Diosmetin, Caffeic acid, and Quercetin were chosen as competent natural compounds from Acacia farnesiana as potent small molecules against COVID-19 and further ADME analysis were carried out. The result indicated that due to the presence of ACEIs and anti-inflammatory phytochemicals in Acacia farnesiana, the bound structure of ACE2 and spike protein becomes unstable. Therefore, these natural compounds can show antiviral activity by destabilizing spike protein binding with the human host ACE2 receptor.

Characteristics and DGT Based Bioavailability of Cadmium in the Soil-Crop Systems from the East Edge of the Dongting Lake, China.

Contamination of heavy metals (including the cadmium, Cd) in agricultural soils has become an increased issue, posing a threat to the crop safety and human health. In order to evaluate the contamination characteristics and bioavailability of Cd in the soil−crop systems from the East edge of the Dongting Lake, four kinds of agricultural products for typical crops (rice, peanut, sweet potato, and corn) and corresponding rhizosphere soils were collected and analyzed for the Cd concentrations. The technique of diffusive gradients in thin-films (DGT) was applied to evaluate the Cd bioavailability in the rhizosphere soils. Concentrations of Cd ranged from 0.04 to 2.95 mg/kg (average 0.24 mg/kg) with 73.9% sites above the background levels, especially for paddy soils. Cd concentrations in the agricultural products ranged from 0.01 to 2.19 mg/kg (average 0.18 mg/kg), with Cd enrichment observed in the peanut samples. No obvious correlations (R2 < 0.25) were observed between the Cd concentrations in the agricultural products and total Cd concentrations in the rhizosphere soils, this indicated that the total Cd concentrations in the soils cannot predict the concentrations in the agricultural products of crops. While the DGT measured Cd concentrations showed good correlations (R2 = 0.64−0.90) with the concentrations in the most agricultural products of crops, which may be used to evaluate the safety of the soil and further safety of the agricultural products of crops. Overall, DGT showed a good potential for prediction of heavy metal bioavailability in soil since the DGT technique can simulate the sustained supply of heavy metals from solid to liquid in the soils.

Ethnomedicinal bioprospecting of Rhizophora apiculata leaves through in silico and in vitro approaches as antioxidant, ?-glucosidase inhibitor and anticancer

Abstract. Sibero MT, Pribadi R, Ambariyanto A, Haryanti D, Kharisma VD, Dewi AS, Patantis G, Zilda DS, Murwani R. 2022. Ethnomedicinal bioprospecting of Rhizophora apiculata leaves through in silico and in vitro approaches as antioxidant, a-glucosidase inhibitor and anticancer. Biodiversitas 23: 6437-6447. Investigating marine natural products for biopharmaceutical development leads to a massive study of mangrove metabolites. Rhizophora apiculata is utilized as a traditional medicine by the local community in Indonesia. However, only a few studies reported the lead compounds. The aim of this study was to discover the biological properties of R. apiculata metabolites from the leaves as an antioxidant, a-glucosidase inhibitor, and anticancer agent through in vitro and in silico approaches. The leaves of R. apiculata were extracted and then fractionated using the silica-OCC method. All fractions were screened for antioxidant, a-glucosidase inhibitors, and cytotoxicity assays. Then the bioactive compounds in prospective fractions were identified using LC-HRMS. The selected compounds with ppm error &lt;10 were applied for in silico analysis. The result of biological properties screening indicated Fr. 5 and Fr. 6 as the most potent sources of antioxidants, a-glucosidase inhibitors, and cytotoxic compounds. In total, 19 compounds were selected from two prospective fractions. The drug-likeness and bioavailability prediction results indicated that all selected compounds act as drug-like molecules. In addition, 17 were predicted to have antioxidant activity, and 15 compounds had antidiabetic activity. Moreover, 15 compounds had a more negative affinity binding than cytarabine. Molecular docking analysis showed that the mechanism of cytotoxicity against P388 Murine Leukaemia Cells was through the interaction of apigenin with protein tyrosine kinase (c-Kit) with a stronger inhibitory activity than the control drug (Cytarabine) and had the same binding site as the control (Cytarabine).

Mechanisms and Extent of Enhanced Passive Permeation by Colloidal Drug Particles.

Formulations containing nanosized drug particles such as nanocrystals and nanosized amorphous drug aggregates recently came into light as promising strategies to improve the bioavailability of poorly soluble drugs. However, the increased solubility due to the reduction in particle size cannot adequately explain the enhanced bioavailability. In this study, the mechanisms and extent of enhanced passive permeation by drug particles were investigated using atazanavir, lopinavir, and clotrimazole as model drugs. Franz diffusion cells with lipid-infused membranes were utilized to evaluate transmembrane flux. The impact of stirring rate, receiver buffer condition, and particle size was investigated, and mass transport analyses were conducted to calculate transmembrane flux. Flux enhancement by particles was found to be dependent on particle size as well as the partitioning behavior of the drug between the receiver solution and the membrane, which is determined by both the drug and buffer used. A flux plateau was observed at high particle concentrations above amorphous solubility, confirming that mass transfer of amorphous drug particles from the aqueous solution to the membrane occurs only through the molecularly dissolved drug. Mass transport models were used to calculate flux enhancement by particles for various drugs at different conditions. Good agreements were obtained between experimental and predicted values. These results should contribute to improved bioavailability prediction of nanosized drug particles and better design of formulations containing colloidal drug particles.

5-Nitroindazole-based compounds: further studies for activity optimization as anti-Trypanosoma cruzi agents

In this study, a new series of eleven 5-nitroindazole derivatives (10−20) and a related 6-nitroquinazoline (21) was synthesized and tested in vitro against different forms of the kinetoplastid parasite Trypanosoma cruzi, etiological agent of Chagas disease. Among these compounds, derivatives 11−14 and 17 showed trypanocidal profiles on epimastigotes (IC50 = 1.00−8.75 µM) considerably better than that of the reference drug benznidazole, BZ (IC50 = 25.22 µM). Furthermore, the lack of cytotoxicity observed for compounds 11, 12, 14, 17 and 18 over L929 fibroblasts, led to a notable selectivity (SI) on the extracellular replicative form of the parasite: SIEPI > 12.41 to > 256 µM. Since these five derivatives overpassed the cut-off value established by BZ (SIEPI ≥ 10), they were moved to a more specific assay against the intracellular and replicative form of T. cruzi, i.e, amastigotes. These molecules were not as active as BZ (IC50 = 0.57 µM) against this parasite form; however, all of them showed remarkable IC50 values lower than 7 µM. Special mention deserve compounds 12 and 17, whose SIAMA were > 246.15 and > 188.23, respectively. The results compiled in the present work, point to a positive impact over the trypanocidal activity of the electron withdrawing substituents introduced at position 2 of the N-2 benzyl moiety of these compounds, especially fluorine, i.e., derivatives 12 and 17. These outcomes, supported by the in silico prediction of good oral bioavailability and suitable risk profile, reinforce the 5-nitroindazole scaffold as an adequate template for preparing potential antichagasic agents.

Incorporating Tenax into the in vitro method to improve the predictive capability of bioaccessibility of triazole fungicides in grape

In vitro bioaccessibility assays have been developed for high-throughput prediction of relative bioavailability (RBA). However, methods to reliably and efficiently assess pesticide residues remain limited, hindering the precise estimation of pesticide exposure risk. The inclusion of a sorption sink material to simulate intestinal sorption could be a promising approach to optimize in vitro bioaccessibility methods. The current study aimed to explore the feasibility of incorporating Tenax into the Rijksinstituut voor Volksgezondheid en Milieu (RIVM) method for accurate evaluation of the bioaccessibility of triazole fungicides. The use of 1.0 g of Tenax enabled the valid trapping of triazole fungicides released from grape, resulting in a significant increase of 23.59–38.51 % in the value of bioaccessibility. A strong in vivo–in vitro correlation was observed between pesticide RBA and bioaccessibility, suggesting that the Tenax-assisted RIVM method is a suitable replacement for time-consuming and laborious in vivo alternatives. In addition, the exposure assessment indicated that the hazard quotients for triazole fungicides in grape may be overestimated by 5.79–27.34 % without considering bioaccessibility based on the Tenax-assisted RIVM method. These results provide further insights into the assessment of bioaccessibility-based human exposure to pesticides as well as dietary exposure and related risk for human health.

Raspberry ketone: an emerging molecule for treating depression and associated symptoms - molecular docking and pharmacokinetics studies

After analyzing multiple pharmaceutical databases like ScienceDirect, PubMed, Google Scholar, and others, it was discovered that RK has yet to be linked to any anti-depressant action. The primary goal of this work was to use in-silico techniques to investigate the anti-depressant capability of RK against three major targets (monoamine oxidases, MAOs; human serotonin transporter, SERT; and serotonin receptors, 5HTs). Moreover, prediction of drug-likeliness, bioavailability, and pharmacokinetics of RK has been done using online computational tools (SwissADME, SwissTargetPrediction, and SwissBioisostere). Thein silico study indicated the emerging role of RK in the management of clinical depression. The pharmacokinetic studies, target prediction studies against 3 species, and biosisosteric-based studies also supported the possibilities of lead development. The study will open new avenues of application of this low-molecular-weight-ligand (LMWL) for the clinicians, chemists, and other scientific professionals in context to emerging depression challenges.

Modelling polycyclic aromatic hydrocarbon bioavailability in historically contaminated soils with six in-vitro chemical extractions and three earthworm ecotypes

Accurate prediction of organic contaminant bioavailability for risk assessment in ecological applications is hindered by limited validation on relevant bioassay species. Here, six in-vitro chemical extraction methods (butanol, non-buffered and buffered hydroxypropyl-β-cyclodextrin (HPCD, Buf-HPCD), Tenax, potassium persulfate oxidation, polyoxymethylene solid phase extraction (POM)) were tested for PAH bioaccumulation prediction in three earthworm ecotypes with dissimilar exposures, Amynthas sp., Eisenia fetida, and Lumbricus terrestris, in historically contaminated soils from manufactured gas plant (MGP) sites. Extractions were compared directly and modelled in a calculation approach using equilibrium partitioning theory (EqPT) with a novel combination of different organic carbon/octanol-water partitioning parameters (KOC and KOW). In the direct comparison approach Buf-HPCD showed the closest prediction of accumulation for burrowing Amynthas sp. and L. terrestris (within 1.5 and 3.1, respectively), but Tenax and POM showed the closest approximation for E. fetida (within 1.1 and 0.9, respectively). The optimum method for predicting PAH bioaccumulation in the calculation approach depended on earthworm species and the partitioning parameters used in equations of the four models, but overall POM, which was independent of KOC, showed the closest approximation of accumulation, within a factor of 2.5 across all species. This work effectively identifies the optimum in-vitro based approaches for PAH bioavailability prediction in earthworms as a model soil health indicator for ecological risk assessment within regulatory and remediation decision frameworks.

Effect Factors and Model Prediction of Soil Heavy Metal Bioaccessibility

The soil environmental pollution situation has been severe in recent years, but studies on evaluating with bioavailability testing and prediction models are lacking, which makes it difficult to accurately assess the ecological risks of contaminated soil. As an important indicator of bioavailability, the bioaccessibility of cadmium (Cd), arsenic (As), copper (Cu), zinc (Zn), and lead (Pb) in the soil was analyzed in this study. The bioaccessibility content and their corresponding soil property data were screened and systematically analyzed to explore the relationship between bioaccessibility content and soil properties. Furthermore, some testing methods for bioaccessibility were summarized to analyze the relationship between bioaccessibility content, test methods, and bioavailability content. Additionally, the bioaccessibility content prediction models were established. The results showed that there was a strong correlation between the bioaccessibility content and the total content of heavy metals (P<0.01) and a significant (P<0.05) correlation with the soil pH. Different test methods had obvious effects on bioavailability. The proportion of bioaccessibility content determined via various test methods was as follows:in vitro gastrointestinal tract simulation>chemical reagent extraction. The proportions of bioaccessibility content of Cd and Pb in natural soil were relatively high, with mean values of 42.12% and 37.33%, respectively, indicating that Cd and Pb had higher risks of being absorbed by soil organisms. Moreover, 30 bioaccessibility prediction models for five heavy metals were constructed, which involved the soil properties and test methods. The results of this study can provide scientific information and bioaccessibility prediction models that can help in accurately assessing the ecological risks of contaminated soil.

Identification of molecular mechanisms underlying the therapeutic effects of Xintong granule in coronary artery disease by a network pharmacology and molecular docking approach.

Coronary artery disease (CAD) is a cardiovascular disease characterized by atherosclerosis, angiogenesis, thrombogenesis, inflammation, etc. Xintong granule (XTG) is considered a practical therapeutic strategy in China for CAD. Although its therapeutic role in CAD has been reported, the molecular mechanisms of XTG in CAD have not yet been explored.A network pharmacology approach including drug-likeness (DL) evaluation, oral bioavailability (OB) prediction, protein-protein interaction (PPI) network construction and analysis, and Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses was used to predict the active ingredients, potential targets, and molecular mechanisms of XTG associated with the treatment of CAD. Molecular docking analysis was performed to investigate the interactions between the active compounds and the underlying targets.Fifty-one active ingredients of XTG and 294 CAD-related targets were screened for analysis. Gene Ontology enrichment analysis showed that the therapeutic targets of XTG in CAD are mainly involved in blood circulation and vascular regulation. KEGG pathway analysis indicated that XTG intervenes in CAD mainly through the regulation of fluid shear stress and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, and the relaxin signaling pathway. Molecular docking analysis showed that each key active ingredient (quercetin, luteolin, kaempferol, stigmasterol, resveratrol, fisetin, gamma-sitosterol, and beta-sitosterol) of XTG can bind to the core targets of CAD (AKT1, JUN, RELA, MAPK8, NFKB1, EDN1, and NOS3).The present study revealed the CAD treatment-related active ingredients, underlying targets, and potential molecular mechanisms of XTG acting by regulating fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and relaxin signaling pathway.

Development and in vitro digestion analysis of kefir fortified with by-product extracts: Prediction of total antioxidant and phenolic compounds bioavailability

Development and in vitro digestion analysis of kefir fortified with by-product extracts: Prediction of total antioxidant and phenolic compounds bioavailability

Vanadium: A Review of Different Extraction Methods to Evaluate Bioavailability and Speciation

The excessive input of heavy metals such as vanadium (V) into the environment has been one of the consequences of global industrial development. Excessive exposure to V can pose a potential threat to ecological safety and human health. Due to the heterogeneous composition and reactivity of the various elements in soils and sediments, quantitative analysis of the chemical speciation of V in different environmental samples is very complicated. The analysis of V chemical speciation can further reveal the bioavailability of V and accurately quantify its ecotoxicity. This is essential for assessing for exposure and for controlling ecological risks of V. Although the current investigation technologies for the chemical speciation of V have grown rapidly, the lack of comprehensive comparisons and systematic analyses of these types of technologies impedes a more comprehensive understanding of ecosystem safety and human health risks. In this review, we studied the chemical and physical extraction methods for V from multiple perspectives, such as technological, principle-based, and efficiency-based, and their application to the evaluation of V bioavailability. By sorting out the advantages and disadvantages of the current technologies, the future demand for the in situ detection of trace heavy metals such as V can be met and the accuracy of heavy metal bioavailability prediction can be improved, which will be conducive to development in the fields of environmental protection policy and risk management.

Deep learning tools for advancing drug discovery and development.

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

Overview of authorized drug products for subcutaneous administration: Pharmaceutical, therapeutic, and physicochemical properties

There is a growing body of research about subcutaneously administered biologics, emphasizing the need for optimized bioavailability predictions. It is important to inform both translational and in silico models with properties of the drug products and compounds. However, the pharmaceutical, therapeutic and physicochemical properties of market authorized drug products for subcutaneous administration are currently not collated in the public domain. We provide an overview of subcutaneous administered drug products for humans and animals market authorized in EU, Canada, and the US.Data on the drug products were collected from the respective authorities, i.e. European Medicines Agency, Health Canada, and U.S. Food and Drug Administration. Physicochemical properties of active substances were gathered from DrugBank.Human drug products were often indicated for treatment of diabetes and anemia. EU veterinary drug products were often immunologicals. Canadian and US veterinary drug products often acted as antiinfectives for systemic use, on the genito-urinary system or as sex hormones. The final dataset with >1700 subcutaneous drug products is provided. In EU drug products, the majority of active substances were biologics. In the US, drug products most often contained small molecules. Solutions, emulsions and suspensions were the most common dosage forms. A minority of subcutaneous drug products were also registered for intramuscular or intravenous administration.The analysis presented here could aid further research, exploring formulation properties, prescription or sales of market authorized SC drug products and development of inclusive in silico models.