Physicochemical QSAR Analysis of Passive Permeability Across Caco-2 Monolayers

Comparison of in vitro membrane permeabilities of diverse environmental chemicals with in silico predictions

The parallel artificial membrane permeability assay (PAMPA) is widely used for estimating biomembrane permeabilities of experimental drugs in pharmaceutical research. However, there are few reports of studies using PAMPA to measure membrane permeabilities of chemicals of environmental concern (CECs) outside the pharmaceutical domain, many of which differ substantially from drugs in their physicochemical properties. We applied PAMPA methods simulating gastrointestinal (PAMPA-GIT) and blood-brain barrier (PAMPA-BBB) membranes under consistent conditions to 51 CECs, including some pharmaceuticals. A backward stepwise multivariate linear regression was implemented to explore the correlation between the differences of measured permeabilities from PAMPA-GIT and PAMPA-BBB and Abraham solute descriptors. In addition, a previously reported in silico model was evaluated by comparing predicted and measured permeability results. PAMPA-GIT and PAMPA-BBB experimental permeability results agreed relatively well. The backward stepwise multivariate linear regression identified excess molar refraction and polarizability to be significant at the 0.10 level in predicting the differences between PAMPA-GIT and PAMPA-BBB. The in silico model performed well - with predicted permeability of most compounds within two-fold of experimentally measured values. We found that CECs pose experimental challenges to the PAMPA method in terms of having lower solubility and lower stability compared to most drugs.

A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru

BACKGROUND

Peru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure-property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru.

METHODS

A dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM-RF-GBM model.

RESULTS

An evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R2 = 0.63. In contrast, the SVM, RF, and GBM models showcased an RMSE = 0.39-0.40 and R2 = 0.73-0.74. Notably, the SVM-RF-GBM model demonstrated superior performance, with an RMSE = 0.38 and R2 = 0.76. The model predicted log Papp values for 502 natural products falling within the applicability domain, with 68.9% (n = 346) showing high permeability, suggesting the potential for intestinal absorption. Additionally, we categorized the natural products into six metabolic pathways and assessed their drug-likeness.

CONCLUSIONS

Our results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts.

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.

Hydroxylated Tetramethoxyflavone Affects Intestinal Cell Permeability and Inhibits Cytochrome P450 Enzymes

Tetramethoxyflavones (TMFs) found in the Citrus genus have garnered considerable interest from food scientists and the health food industry because of their promising biological properties. Nonetheless, there are currently limited data available regarding the effectiveness and bioavailability of "hydroxylated TMFs", which are flavones known for their potential in disease prevention through dietary means. This study aims to provide insights into the chemical and biological properties of hydroxylated TMF and evaluates its effects on intestinal cell permeability and cytochrome P450 (CYP) inhibition. Liquid chromatography-mass spectrometry (LC-MS) and microsomes analyze the TMFs and hydroxylated TMFs, elucidating cell penetration and metabolic inhibition potential. 3H7-TMF shows the fastest (1-h) transport efficiency in intestinal cells. The Caco-2 cell model exhibits significant transport and absorption efficiency. Dissolved hydroxyl-TMF with hydrophilicity possibly permeates the gut. 3H7-TMF has higher transport efficiency (46%) 3H6-TMF (39%). IC50 values of TMFs (78-TMF, 57-TMF, 3H7-TMF, 3H6-TMF) against CYP enzymes (CYP1A2, CYP2D6, CYP2C9, CYP2C19, CYP3A4) range from 0.15 to 108 μM, indicating potent inhibition. Hydroxyl groups enhance TMF hydrophilicity and membrane permeability. TMFs display varied inhibitory effects due to hydroxyl and methoxy hindrance. This study underscores the strong CYP inhibitory capabilities in these TMFs, implying potential food-drug interactions if used in medicines or supplements. These findings can also help with food nutrition improvement and pharma food developments through innovative approaches for Citrus waste valorization.

Biological analysis of constituents in Spatholobi Caulis by UFLC-MS/MS: Enhanced quantification and application to permeability properties study in Caco-2 cell monolayer model

Major chemical constituents in medicinal materials are often used as the marker compounds of traditional Chinese medicine (TCM) for treating various diseases. For spatholobi caulis (SPC), it contains a variety of flavones, phenolic acid esters, and lignans which exert many pharmacological effects. However, the absorption and permeability properties of these constituents of SPC are still unclear and require further investigation. Different types and major compounds of SPC were chosen as representative constituents to study their absorption and transepithelial transport characteristics in the human intestinal epithelium-like Caco-2 cell monolayer model. 35 constituents of SPC were evaluated by using ultra fast liquid chromatography combined with electrospray ionization triple quadrupole tandem mass spectrometry (UFLC-MS/MS) method, acetonitrile and water containing with 0.5 mM ammonium acetate were used as mobile phase, these analytes with good linear relationships (R² was within 0.9967-0.9998), precision (CV values were less than 10.23 %, LLOQ was less than 13.69 %), accuracy (Mean of inter- and intra-day were within 85.02 %-111.61 % and 85.50-112.97 %, respectively) and stability (The mean was within 85.07 %-113.93 %), among which 16 analytes showed good permeability, 5 analytes were considered to be poorly permeable compounds, and the other 14 analytes were assigned for the moderately absorbed compounds in Caco-2 cell monolayer model. The further results showed that the absorption mechanism of 7 well absorbed compounds, 8-O-methylretusin (1), genistein (7), spasuberol B (16), naringenin (18), isoliquiritigenin (19), 4-hydroxy-3-methoxy cinnamic acid methyl ester (23) and (+)-epipinoresinol (31) in SPC was mainly passive diffusion, their bidirectional transport rate was correlated with the concentration and transport time. The chemical structures of these compounds could affect the permeability properties on the cell monolayer. This study demonstrated the utility of Caco-2 cell monolayer model for evaluating the absorption properties and initial mechanisms of compounds in SPC in vitro, and provided important basis for predicting oral bioavailability of SPC compounds.

Therapeutic Potential of Bioactive Components from Scutellaria baicalensis Georgi in Inflammatory Bowel Disease and Colorectal Cancer: A Review

Scutellaria baicalensis Georgi (SBG), an herbal medicine with various biological activities, including anti-inflammatory, anticancer, antiviral, antibacterial, and antioxidant activities, is effective in treatment of colitis, hepatitis, pneumonia, respiratory infections, and allergic diseases. This herbal medicine consists of major active substances, such as baicalin, baicalein, wogonoside, and wogonin. Inflammatory bowel disease (IBD) comprises a group of inflammatory conditions of the colon and small intestine, with Crohn's disease and ulcerative colitis being the main types. IBD can lead to serious complications, such as increased risk of colorectal cancer (CRC), one of the most common cancers worldwide. Currently, there is no cure for IBD, and its incidence has been increasing over the past few decades. This review comprehensively summarizes the efficacy of SBG in IBD and CRC and may serve as a reference for future research and development of drugs for IBD and cancer treatment.

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, Pharmacokinetic Characterization and Antioxidant Capacity of Carotenoid Succinates and Their Melatonin Conjugates

Carotenoid succinates were synthesized from hydroxy carotenoids and were coupled to a commercially available derivative of melatonin via amide bond for producing more powerful anti-oxidants and yet new hybrid lipophilic bifunctional molecules with additional therapeutic effects. The coupling reactions produced conjugates in acceptable to good yields. Succinylation increased the water solubility of the carotenoids, while the conjugation with melatonin resulted in more lipophilic derivatives. The conjugates showed self-assembly in aqueous medium and yielded relatively stable colloidal solutions in phosphate-buffered saline. Antioxidant behavior was measured with ABTS and the FRAP methods for the carotenoids, the carotenoid succinates, and the conjugates with melatonin. A strong dependence on the quality of the solvent was observed. TEAC values of the new derivatives in phosphate-buffered saline were found to be comparable to or higher than those of parent carotenoids, however, synergism was observed only in FRAP assays.

In vitro models and ex vivo systems used in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic, relapsing gastrointestinal condition. Ulcerative colitis and Crohn’s disease are types of inflammatory bowel disease. Over many decades, the disease has been a topic of study, with experts still trying to figure out its cause and pathology. Researchers have established many in vivo animal models, in vitro cell lines, and ex vivo systems to understand its cause ultimately and adequately identify a therapy. However, in vivo animal models cannot be regarded as good models for studying IBD since they cannot completely simulate the disease. Furthermore, because species differences are a crucial subject of concern, in vitro cell lines and ex vivo systems can be employed to recreate the condition properly. In vitro models serve as the starting point for biological and medical research. Ex vivo and in vitro models for replicating gut physiology have been developed. This review aims to present a clear understanding of several in vitro and ex vivo models of IBD and provide insights into their benefits and limits and their value in understanding intestinal physiology.

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.

Global Analysis of Models for Predicting Human Absorption: QSAR, In Vitro, and Preclinical Models

Models intended to predict intestinal absorption are an essential part of the drug development process. Although many models exist for capturing intestinal absorption, many questions still exist around the applicability of these models to drug types like "beyond rule of 5" (bRo5) and low absorption compounds. This presents a challenge as current models have not been rigorously tested to understand intestinal absorption. Here, we assembled a large, structurally diverse dataset of ∼1000 compounds with known in vitro, preclinical, and human permeability and/or absorption data. In silico (quantitative structure-activity relationship), in vitro (Caco-2), and in vivo (rat) models were statistically evaluated for predictive performance against this human intestinal absorption dataset. We expect this evaluation to serve as a resource for DMPK scientists and medicinal/computational chemists to increase their understanding of permeability and absorption model utility and applications for academia and industry.

Investigating the phosphinic acid tripeptide mimetic DG013A as a tool compound inhibitor of the M1-aminopeptidase ERAP1

ERAP1 is a zinc-dependent M1-aminopeptidase that trims lipophilic amino acids from the N-terminus of peptides. Owing to its importance in the processing of antigens and regulation of the adaptive immune response, dysregulation of the highly polymorphic ERAP1 has been implicated in autoimmune disease and cancer. To test this hypothesis and establish the role of ERAP1 in these disease areas, high affinity, cell permeable and selective chemical probes are essential. DG013A 1, is a phosphinic acid tripeptide mimetic inhibitor with reported low nanomolar affinity for ERAP1. However, this chemotype is a privileged structure for binding to various metal-dependent peptidases and contains a highly charged phosphinic acid moiety, so it was unclear whether it would display the high selectivity and passive permeability required for a chemical probe. Therefore, we designed a new stereoselective route to synthesize a library of DG013A 1 analogues to determine the suitability of this compound as a cellular chemical probe to validate ERAP1 as a drug discovery target.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

Determination and prediction of permeability across intestinal epithelial cell monolayer of a diverse range of industrial chemicals/drugs for estimation of oral absorption as a putative marker of hepatotoxicity

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Permeability values of 90 industry chemicals were measured by a Caco-2 system.

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A multivariate prediction equation for permeability of chemicals was proposed.

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Chemical permeability coefficients were inversely associated with hepatic NOELs.

Apparent permeability coefficients (P_app) across a human intestinal epithelial Caco-2 cell monolayer were measured for a range of industrial/drug chemicals. A predictive equation for determining in vitro P_app values of fifty-six substances was set up using multivariate regression analysis based on in silico-estimated physicochemical properties (molecular weights and water distribution coefficients for apical and basal pH environments) (r = 0.77, p <  0.01). Predicted logP_app values of a secondary set of 34 compounds were correlated with the measured values. Under the medicinal logP_app values associated with their reported fraction absorbed, a significant inverse non-linear correlation was found between the logarithmic transformed values of observed P_app values and reported hepatic no-observed-effect levels of industrial chemicals (r = –0.55, p <  0.01, n = 29). In vitro determination and/or in silico prediction of permeability across intestinal cells could be effective for estimating oral absorption as a putative indicator for hepatotoxicity.