In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling

A new workflow for the effective curation of membrane permeability data from open ADME information

Membrane permeability is an in vitro parameter that represents the apparent permeability (Papp) of a compound, and is a key absorption, distribution, metabolism, and excretion parameter in drug development. Although the Caco-2 cell lines are the most used cell lines to measure Papp, other cell lines, such as the Madin-Darby Canine Kidney (MDCK), LLC-Pig Kidney 1 (LLC-PK1), and Ralph Russ Canine Kidney (RRCK) cell lines, can also be used to estimate Papp. Therefore, constructing in silico models for Papp estimation using the MDCK, LLC-PK1, and RRCK cell lines requires collecting extensive amounts of in vitro Papp data. An open database offers extensive measurements of various compounds covering a vast chemical space; however, concerns were reported on the use of data published in open databases without the appropriate accuracy and quality checks. Ensuring the quality of datasets for training in silico models is critical because artificial intelligence (AI, including deep learning) was used to develop models to predict various pharmacokinetic properties, and data quality affects the performance of these models. Hence, careful curation of the collected data is imperative. Herein, we developed a new workflow that supports automatic curation of Papp data measured in the MDCK, LLC-PK1, and RRCK cell lines collected from ChEMBL using KNIME. The workflow consisted of four main phases. Data were extracted from ChEMBL and filtered to identify the target protocols. A total of 1661 high-quality entries were retained after checking 436 articles. The workflow is freely available, can be updated, and has high reusability. Our study provides a novel approach for data quality analysis and accelerates the development of helpful in silico models for effective drug discovery. Scientific Contribution: The cost of building highly accurate predictive models can be significantly reduced by automating the collection of reliable measurement data. Our tool reduces the time and effort required for data collection and will enable researchers to focus on constructing high-performance in silico models for other types of analysis. To the best of our knowledge, no such tool is available in the literature.

3-styrylcoumarin scaffold-based derivatives as a new approach for leishmaniasis intervention: biological and molecular modeling studies

Seven 3-styrylcoumarins were tested for antileishmanial activity against Leishmania (Viannia) panamensis amastigotes. Cytotoxic activity was also evaluated against mammalian U-937 cells. The 3-methoxy-4-hydroxy coumarin derivative 6 was the most active with an IC50 of 40.5 µM, and did not reveal any conspicuous toxicity toward mammalian U-937 cells. Therefore, it may have potential to be considered as candidate for antileishmanial drug development. Further, among several druggable Leishmania targets, molecular docking studies revealed that compound 6 had docking preference by the N-myristoyltransferase (Lp-NMT) of Leishmania panamensis, showing a higher docking score of - 10.1 kcal mol-1 than positive controls and making this protein as a presumably druggable target for this compound. On the other hand, molecular dynamics simulations affirm the docking hypothesis, showing a conformational stability of the 6/Lp-NMT complex throughout 100 ns simulation. Moreover, the molecular mechanics/Poisson-Boltzmann surface area method also support the docking findings, revealing a total free energy of binding of - 47.26 ± 0.08 kcal mol-1, and identifying through energy decomposition analysis that those key aminoacids are contributing strongly to ligand binding. Finally, an optimal pharmacokinetic profile was also estimated for 6. Altogether, coumarin 6 could be addressed as starting point for further pharmacological studies concerning the therapeutic leishmaniasis intervention.

Graphical abstract

Multitask learning for predicting pulmonary absorption of chemicals

Pulmonary absorption is an important route for drug delivery and chemical exposure. To streamline the chemical assessment process for the reduction of animal experiments, several animal-free models were developed for pulmonary absorption research. While Calu-3 and Caco-2 cells and their derived computational models were used in estimating pulmonary permeability, the ex vivo isolated perfused lung (IPL) models are considered more clinically relevant measurements. However, the IPL experiments are resource-consuming making it infeasible for the large-scale screening of potential inhaled toxicants and drugs. In silico models are desirable for estimating pulmonary absorption. This study presented a novel machine learning method that employed an extratrees-based multitask learning approach to predict the IPL absorption rate constant (kaIPL) of various chemicals. The shared permeability knowledge was extracted by simultaneously learning three relevant tasks of Caco-2 and Calu-3 cell permeability and IPL absorption rate. Seven informative physicochemical descriptors were identified. A rigorous evaluation of the developed prediction model showed good performance with a high correlation between predictions and observations (r = 0.84) in the independent test dataset. Two case studies of inhalation drugs and respiratory sensitizers revealed the potential application of this model, which may serve as a valuable tool for predicting pulmonary absorption of chemicals.

Synthesis, molecular docking analysis and in vitro evaluation of new heterocyclic hybrids of 4-aza-podophyllotoxin as potent cytotoxic agents

Two different synthetic approaches to novel heterocyclic hybrid compounds of 4-azapodophyllotoxin were investigated. The obtained products were characterized by infrared spectroscopy, nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry. MTT protocol was then performed to examine the cytotoxic activity of these products against KB, HepG2, A549, MCF7, and Hek-293 cell lines. The cytotoxic assessment indicated that all products displayed moderate to high cytotoxicity against all tested cancer cell lines. The most active compound 13k containing the 2-methoxypyridin-4-yl group exhibited selective cytotoxicity against KB, A549, and HepG2 cell lines with the IC50 values ranging from 0.23 to 0.27 μM, which were between 5- to 10-fold more potent than the positive control ellipticine. Compounds 13a (HetAr = thiophen-3-yl) and 13d (HetAr = 5-bromofuran-2-yl) displayed high cytotoxic selectivity for A549 and HepG2 cancer cell lines when compared to the other cancer cell lines and low toxicity to the normal Hek-293 cell line. Molecular docking study was conducted to evaluate the interaction of new synthesized compounds with the colchicine-binding-site of tubulin. Besides that, physicochemical and pharmacokinetic properties of the most active compounds 13h,k were predicted.

Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficiency of early-stage drug discovery. This review aims to present a comprehensive overview of the utilization of AI methods in the field of small drug design, with a specific focus on four key areas: protein structure prediction, molecular virtual screening, molecular design, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Additionally, the role and limitations of AI in drug development are explored, and the impact of AI on decision-making processes is studied. It is important to note that while AI can bring numerous benefits to the early stage of drug development, the direction and quality of decision-making should still be emphasized, as AI should be considered as a tool rather than a decisive factor.

Paeoniflorin-6'-O-benzene sulfonate protected the intestinal epithelial barrier by restoring the inhibitory effect of GRK2 and β-arrestin 2 on ERK1/2-NF-κB

Peoniflorin-6'-O-benzene sulfonate (CP-25) inhibited the activity of GRK2 to exert anti-inflammatory and immunomodulatory effects. This study aimed to investigate the effect of CP-25 the intestinal epithelial barrier and the mechanism. CaCO-2 cell monolayer and dextran sulfate salt (DSS)-induced colitis mouse model was used to evaluate intestinal epithelial barrier function in vitro and in vivo, respectively. Results showed that CP-25 prevented dysfunction of the intestinal epithelial barrier and inhibited NF-κB p65 activation in TNF-α-induced CaCO-2 cells. The colon structure destroyed in DSS-induced colitis mice was improved by CP-25. CP-25 has a role in inhibition membrane translocation of GRK2-β-arrestin 2 complex, stabilization of the binding of GRK2 and β-arrestin 2 to ERK1/2 in cytoplasm. Subsequently down-regulated the nuclear transcription and transactivation of NF-κB p65 via inhibiting its phosphorylation of Ser536, and Ser276, respectively and restored the epithelial barrier function. In conclusion, CP-25 inhibited ERK1/2-NF-κB activation and thereby protected the intestinal epithelial barrier, which was associated with restoring the inhibition of GRK2 and β-arrestin 2 on ERK1/2.

Molecular docking and molecular dynamics approach to identify potential compounds in Huperzia squarrosa for treating Alzheimer's disease

OBJECTIVES

Alzheimer's disease (AD) is a lingering progressive neurodegenerative disorder that causes patients to lose cognitive function. The enzyme Acetylcholinesterase (AChE), Butyrylcholinesterase (BuChE), Monoamine oxidase A (MAO A), Beta-secretase cleavage enzyme (BACE 1) and N-methyl-D-aspartate (NMDA) receptors play an important role in the pathogenesis of Alzheimer's disease. Therefore, inhibiting enzymes is an effective method to treat Alzheimer disease. In this study, we evaluated in silico inhibitory effects of AChE, BuChE, MAO A, BACE 1 and NMDA enzyme of Huperzia squarrosa's compounds.

METHODS

The three-dimensional (3D) of N-methyl-D-aspartate receptor (PDB ID: 1PBQ), enzyme β-secretase 1 (PDB ID: 4X7I), enzyme monoamine oxidase A (PDB ID: 2Z5X), enzyme butyrylcholinesterase (PDB ID: 4BDS) and enzyme acetylcholinesterase (PDB ID: 1EVE) were retrieved from the Protein Data Bank RCSB. Molecular docking was done by Autodock vina software and molecular dynamics (MD) simulation of the ligand-protein complex with the least binding energy pose was perfomed by MOE. Lipinski Rule of Five is used to compare compounds with drug-like and non-drug-like properties. Pharmacokinetic parameters of potential compounds were evaluated using the pkCSM tool.

RESULTS

Based on previous publication of Huperzia squarrosa, we have collected 15 compounds. In these compounds, huperzine B, huperzinine, lycoposerramine U N-oxide, 12-epilycodine N-oxide showed strongly inhibit the five AChE, BuChE, MAO A, BACE 1 and NMDA targets for Alzheimer's treatment. Lipinski rule of five and ADMET predict have shown that four above compounds have drug-likeness properties, good absorption ability and cross the blood-brain barrier, which have the most potential to become drugs for the treatment of Alzheimer's in the future. Furthermore, MD study showed that huperzine B and huperzinine have stability of the docking pose with NMDA target.

CONCLUSIONS

In this study, we found two natural compounds in Huperzia squarrosa including Huperzine B and Huperzinine have drug-likeness properties, good absorption ability and cross the blood-brain barrier, which have potential to become drugs for the treatment of Alzheimer's in the future.

Computational prediction of Calu-3-based in vitro pulmonary permeability of chemicals

Pulmonary is a potential route for drug delivery and exposure to toxic chemicals. The human bronchial epithelial cell line Calu-3 is generally considered to be a useful in vitro model of pulmonary permeability by calculating the apparent permeability coefficient (Papp) values. Since in vitro experiments are time-consuming and labor-intensive, computational models for pulmonary permeability are desirable for accelerating drug design and toxic chemical assessment. This study presents the first attempt for developing quantitative structure-activity relationship (QSAR) models for addressing this goal. A total of 57 chemicals with Papp values based on Calu-3 experiments was first curated from literature for model development and testing. Subsequently, eleven descriptors were identified by a sequential forward feature selection algorithm to maximize the cross-validation performance of a voting regression model integrating linear regression and nonlinear random forest algorithms. With applicability domain adjustment, the developed model achieved high performance with correlation coefficient values of 0.935 and 0.824 for cross-validation and independent test, respectively. The preliminary results showed that computational models could be helpful for predicting Calu-3-based in vitro Pulmonary Permeability of Chemicals. Future works include the collection of more data for further validating and improving the model.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43–0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

Synthesis and biological activity, and molecular modelling studies of potent cytotoxic podophyllotoxin-naphthoquinone compounds

A new approach for the synthesis of podophyllotoxin-naphthoquinone compounds using microwave-assisted three-component reactions is reported in this study. Novel podophyllotoxin-naphthoquinone derivatives with modification on ring E were synthesized. All the synthetic compounds were assessed in terms of their cytotoxicity profile against four cancer cell lines (KB, HepG2, A549, and MCF7), and noncancerous Hek-293 cell lines. Notably, treatment of SK-LU-1 cells with compounds 5a and 5b resulted in G2/M phase arrest of the cell cycle, caspase-3/7 activation, and apoptosis. Additionally, molecular docking studies were performed and showed important interaction of two compounds against residues in the colchicine-binding-site of tubulin as well. Taken together, compounds 5a and 5b were identified as potent anticancer agents.

A new approach for the synthesis of podophyllotoxin-naphthoquinone compounds using microwave-assisted three-component reactions is reported in this study.

Assessment of the Intestinal Absorption of Higher Olefins by the Everted Gut Sac Model in Combination with In Silico New Approach Methodologies

To reduce the number of animals and studies needed to fulfill the information requirements as required by Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) (EC no. 1907/2006), a read-across approach was used to support approximately 30 higher olefins. This study aimed to assess the absorption potential of higher olefins through the gut wall as the experimentally determined bioavailability which would strengthen the read-across hypothesis and justification, reducing the need for toxicity studies on all of the higher olefins. The absorption potential of a series of higher olefins (carbon range from 6 to 28, with five configurations of the double bond) was determined in the in vitro everted rat small intestinal sac model and subsequently ranked. In addition, in silico approaches were applied to predict the reactivity, lipophilicity, and permeability of higher olefins. In the in vitro model, everted sacs were incubated in “fed-state simulated small intestinal fluid” saturated with individual higher olefins. The sac contents were then collected, extracted, and analyzed for olefin content using gas chromatography with a flame ionization detector. The C6 to C10 molecules were readily absorbed into the intestinal sacs. Marked inter-compound differences were observed, with the amount of absorption generally decreasing with the increase in carbon number. Higher olefins with ≥C14 carbons were either not absorbed or very poorly absorbed. In the reactivity simulation study, the reactivity is well described by the position of the double bond rather than the number of carbon atoms. In the lipophilicity and permeability analysis, both parameter descriptors depend mainly on the number of carbon atoms and less on the position of the double bond. In conclusion, these new approach methodologies provide supporting information on any trends or breakpoints in intestinal uptake and a hazard matrix based on carbon number and position of the double bond. This matrix will further assist in the selection of substances for inclusion in the mammalian toxicity testing programme.

Best practices in current models mimicking drug permeability in the gastrointestinal tract - an UNGAP review

The absorption of orally administered drug products is a complex, dynamic process, dependent on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but in vitro and ex vivo tools provide initial screening approaches are important tools for direct assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.

Essential oils of Uvaria boniana - chemical composition, in vitro bioactivity, docking, and in silico ADMET profiling of selective major compounds

Phytochemical investigation applying GC (gas chromatography)-MS (mass spectrometry)/GC-FID (flame ionization detection) on the hydro-distilled essential oils of the Vietnamese medicinal plant Uvaria boniana leaf and twig lead to the detection of 35 constituents (97.36%) in the leaf oil and 52 constituents (98.75%) in the twig oil. Monoterpenes, monoterpenoids, sesquiterpenes, and sesquiterpenoids were characteristic of U. boniana essential oils. The leaf oil was represented by major components (E)-caryophyllene (16.90%), bicyclogermacrene (15.95%), α-humulene (14.96%), and linalool (12.40%), whereas four compounds α-cadinol (16.16%), epi-α-muurolol (10.19%), α-pinene (11.01%), and β-pinene (8.08%) were the main ones in the twig oil. As compared with the leaf oil, the twig oil was better in antimicrobial activity. With the same MIC value of 40 mg/mL, the twig oil successfully controlled the growth of Gram (+) bacterium Bacillus subtilis, Gram (-) bacterium Escherichia coli, fungus Aspergillus niger, and yeast Saccharomyces cerevisiae. In addition, both two oil samples have induced antiinflammatory activity with the IC₅₀ values of 223.7-240.6 mg/mL in NO productive inhibition when BV2 cells had been stimulated by LPS. Docking simulations of four major compounds of U. boniana twig oil on eight relevant antibacterial targets revealed that epi-α-muurolol and α-cadinol are moderate inhibitors of E. coli DNA gyrase subunit B, penicillin binding protein 2X and penicillin binding protein 3 of Pseudomonas aeruginosa with similar free binding energies of -30.1, -29.3, and -29.3 kJ/mol, respectively. Furthermore, in silico ADMET studies indicated that all four docked compounds have acceptable oral absorption, low metabolism, and appropriated toxicological profile to be considered further as drug candidates.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Prediction of permeability across intestinal cell monolayers for 219 disparate chemicals using in vitro experimental coefficients in a pH gradient system and in silico analyses by trivariate linear regressions and machine learning

For medicines, the apparent membrane permeability coefficients (P_app) across human colorectal carcinoma cell line (Caco-2) monolayers under a pH gradient generally correlate with the fraction absorbed after oral intake. Furthermore, the in vitro P_app values of 29 industrial chemicals were found to have an inverse association with their reported no-observed effect levels for hepatotoxicity in rats. In the current study, we expanded our influx permeability predictions for the 90 previously investigated chemicals to both influx and efflux permeability predictions for 207 diverse primary compounds, along with those for 23 secondary compounds. Trivariate linear regression analysis found that the observed influx and efflux logP_app values determined by in vitro experiments significantly correlated with molecular weights and the octanol-water distribution coefficients at apical and basal pH levels (pH 6.0 and 7.4, respectively) (apical to basal, r = 0.76, n = 198; and basal to apical, r = 0.77, n = 202); the distribution coefficients were estimated in silico. Further, prediction accuracy was enhanced by applying a light gradient boosting machine learning system (LightGBM) to estimate influx and efflux logP_app values that incorporated 17 and 19 in silico chemical descriptors (r = 0.83-0.84, p < 0.001). The determination in vitro and/or prediction in silico of permeability coefficients across intestinal cell monolayers of a diverse range of industrial chemicals/food components/medicines could contribute to the safety evaluations of oral intakes of general chemicals in humans. Such new alternative methods could also reduce the need for animal testing during toxicity assessment.

Oral delivery of peptide therapeutics in infants: Challenges and opportunities

The vast majority of drugs are not designed or developed for pediatric and infant populations. Peptide drugs, which have become increasingly relevant in the past several decades, are no exception. Unfortunately, nearly all of the 60+ approved peptide drugs are formulated for injection, a particularly unfriendly mode of administration for infants. Although three peptide drugs were recently approved for oral formulations, this major advance in peptide drug delivery is available only for adults. In this review, we consider the current challenges and opportunities for the oral formulation of peptide therapeutics, specifically for infant populations. We describe the strategies that enable oral protein delivery and the potential impact of infant physiology on those strategies. We also detail the limited but encouraging progress towards 1) adapting conventional drug development and delivery approaches to infants and 2) designing novel infant-centric formulations. Together, these efforts underscore the feasibility of oral peptide delivery in infants and provide motivation to increase attention paid to this underserved area of drug delivery and formulation.

Discovery of novel isoform-selective histone deacetylases 5 and 9 inhibitors through combined ligand-based pharmacophore modeling, molecular mocking, and molecular dynamics simulations for cancer treatment

Class IIa histone deacetylases (HDACs) 5 and 9 play crucial roles in several human disorders such as cancer, making them important targets for drug design. Continuous research is pursed to overcome the cytotoxicity side effect that comes with the currently available broad-spectrum HDACs inhibitors. Herein, common features of active HDACs inhibitors in clinical trials and use have been calculated to generate the best pharmacophore hypothesis. Guner-Henry scoring system was used to validate the generated hypotheses. Hypo1 of HDAC5 and Hypo2 of HDAC9 exhibited the most statistically significance hypotheses. Compounds with fit value of 3 and more were examined by QuickVina 2 docking tool to calculate their binding affinity toward all class IIa HDACs. A total of 6 potential selective compounds were subjected to 100 molecular dynamics (MD) simulation to examine their binding modes. The free binding energy calculations were computed according to the MM-PBSA method. Proposed selective compounds displayed good stability with their targets and thus they may offer potent leads for the designing of HDAC5 and HDAC9 isoform selective inhibitors.

Regional Intestinal Drug Absorption: Biopharmaceutics and Drug Formulation

The gastrointestinal tract (GIT) can be broadly divided into several regions: the stomach, the small intestine (which is subdivided to duodenum, jejunum, and ileum), and the colon. The conditions and environment in each of these segments, and even within the segment, are dependent on many factors, e.g., the surrounding pH, fluid composition, transporters expression, metabolic enzymes activity, tight junction resistance, different morphology along the GIT, variable intestinal mucosal cell differentiation, changes in drug concentration (in cases of carrier-mediated transport), thickness and types of mucus, and resident microflora. Each of these variables, alone or in combination with others, can fundamentally alter the solubility/dissolution, the intestinal permeability, and the overall absorption of various drugs. This is the underlying mechanistic basis of regional-dependent intestinal drug absorption, which has led to many attempts to deliver drugs to specific regions throughout the GIT, aiming to optimize drug absorption, bioavailability, pharmacokinetics, and/or pharmacodynamics. In this Editorial we provide an overview of the Special Issue "Regional Intestinal Drug Absorption: Biopharmaceutics and Drug Formulation". The objective of this Special Issue is to highlight the current progress and to provide an overview of the latest developments in the field of regional-dependent intestinal drug absorption and delivery, as well as pointing out the unmet needs of the field.

Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability

Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.

Drug Disposition in the Lower Gastrointestinal Tract: Targeting and Monitoring

The increasing prevalence of colonic diseases calls for a better understanding of the various colonic drug absorption barriers of colon-targeted formulations, and for reliable in vitro tools that accurately predict local drug disposition. In vivo relevant incubation conditions have been shown to better capture the composition of the limited colonic fluid and have resulted in relevant degradation and dissolution kinetics of drugs and formulations. Furthermore, drug hurdles such as efflux transporters and metabolising enzymes, and the presence of mucus and microbiome are slowly integrated into drug stability- and permeation assays. Traditionally, the well characterized Caco-2 cell line and the Ussing chamber technique are used to assess the absorption characteristics of small drug molecules. Recently, various stem cell-derived intestinal systems have emerged, closely mimicking epithelial physiology. Models that can assess microbiome-mediated drug metabolism or enable coculturing of gut microbiome with epithelial cells are also increasingly explored. Here we provide a comprehensive overview of the colonic physiology in relation to drug absorption, and review colon-targeting formulation strategies and in vitro tools to characterize colonic drug disposition.

Uncaria tomentosa (cat's claw): a promising herbal medicine against SARS-CoV-2/ACE-2 junction and SARS-CoV-2 spike protein based on molecular modeling

COVID-19 is a novel severe acute respiratory syndrome coronavirus. Currently, there is no effective treatment and vaccines seem to be the solution in the future. Virtual screening of potential drugs against the S protein of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) has provided small molecular compounds with a high binding affinity. Unfortunately, most of these drugs do not attach with the binding interface of the receptor-binding domain (RBD)–angiotensin-converting enzyme-2 (ACE-2) complex in host cells. Molecular modeling was carried out to evaluate the potential antiviral properties of the components of the medicinal herb Uncaria tomentosa (cat’s claw) focusing on the binding interface of the RBD–ACE-2 and the viral spike protein. The in silico approach starts with protein–ligand docking of 26 Cat’s claw key components followed by molecular dynamics simulations and re-docked calculations. Finally, we carried out drug-likeness calculations for the most qualified cat’s claw components. The structural bioinformatics approaches led to the identification of several bioactive compounds of U. tomentosa with potential therapeutic effect by dual strong interaction with interface of the RBD–ACE-2 and the ACE-2 binding site on SARS-CoV-2 RBD viral spike. In addition, in silico drug-likeness indices for these components were calculated and showed good predicted therapeutic profiles of these phytochemicals found in U. tomentosa (cat’s claw). Our findings suggest the potential effectiveness of cat’s claw as complementary and/or alternative medicine for COVID-19 treatment.

Communicated by Ramaswamy H. Sarma

Detection of macromolecular inversion-induced structural changes in osteosarcoma cells by FTIR microspectroscopy

Fourier transform infrared (FTIR) microspectroscopy provides a biochemical fingerprint of the cells. In this study, chemical changes in 143B osteosarcoma cells were investigated using FTIR analysis of cancer cells after their treatment with polymeric invertible micellar assemblies (IMAs) and curcumin-loaded IMAs and compared with untreated osteosarcoma cells. A comprehensive principal component analysis (PCA) was applied to analyze the FTIR results and confirm noticeable changes in cell surface chemical structures in the fingerprint regions of 1480-900 cm^-1. The performed clustering shows visible differences for all investigated groups of cancer cells. It is demonstrated that a combination of FTIR microspectroscopy with PCA can be an efficient approach in determining interactions of osteosarcoma cells and drug-loaded polymer micellar assemblies. Graphical abstract.

Flavones as tyrosinase inhibitors: kinetic studies in vitro and in silico

INTRODUCTION

Tyrosinase is a multifunctional copper-containing oxidase enzyme that catalyses the first steps in the formation of melanin pigments. Identification of tyrosinase inhibitors is of value for applications in cosmetics, medicine and agriculture.

OBJECTIVE

To develop an analytical method that allows identification of drug-like natural products that can be further developed as tyrosinase inhibitors. Results of in vitro and in silico studies will be compared in order to gain a deeper insight into the mechanism of action of enzyme inhibition.

METHOD

Using an in vitro assay we tested tyrosinase inhibitor effects of five structurally related flavones, i.e. luteolin (1), eupafolin (2), genkwanin (3), nobiletin (4), and chrysosplenetin (5). The strongest inhibitors were further investigated in silico, using enzyme docking simulations.

RESULTS

All compounds tested showed modest tyrosinase inhibitory effect compared to the positive control, kojic acid. The polymethoxy flavones 4 and 5 exhibited the strongest tyrosinase inhibitory effect with the half maximal inhibitory concentration (IC₅₀ ) values of 131.92 ± 1.75 μM and 99.87 ± 2.38 μM respectively. According to kinetic analysis 2, 4 and 5 were competitive inhibitors, whereas 1 and 3 were non-competitive inhibitors of tyrosinase. Docking studies indicated that methoxy groups on 4 and 5 caused steric hindrance which prevented alternative binding modes in the tyrosinase; the methoxy groups on the B-ring of these flavones faced the catalytic site in the enzyme.

CONCLUSIONS

The docking simulations nicely complemented the in vitro kinetic studies, opening the way for the development of predictive models for use in drug design.

PerMM: A Web Tool and Database for Analysis of Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules

The PerMM web server and database were developed for quantitative analysis and visualization of passive translocation of bioactive molecules across lipid membranes. The server is the first physics-based web tool that calculates membrane binding energies and permeability coefficients of diverse molecules through artificial and natural membranes (phospholipid bilayers, PAMPA-DS, blood-brain barrier, and Caco-2/MDCK cell membranes). It also visualizes the transmembrane translocation pathway as a sequence of translational and rotational positions of a permeant as it moves across the lipid bilayer, along with the corresponding changes in solvation energy. The server can be applied for prediction of permeability coefficients of compounds with diverse chemical scaffolds to facilitate selection and optimization of potential drug leads. The complementary PerMM database allows comparison of computationally and experimentally determined permeability coefficients for more than 500 compounds in different membrane systems. The website and database are freely accessible at https://permm.phar.umich.edu/ .