Prediction Of Bioavailability Research Articles

Prediction of absolute bioavailability of medicines in children: based on predicted pediatric clearance from adults

Aim: The objective of this study was to evaluate the predictive performance of a proposed method to predict absolute bioavailability of medicines in children (infants to adolescents). Methods: From the literature, systemic and oral clearances as well as absolute bioavailability values for 15 medicines (28 observations across different age groups) from infants to adults were obtained. Systemic and oral clearances of these medicines in children were predicted using age-dependent exponent (ADE) allometric model using observed adult clearance values. Then using the predicted clearance values, absolute bioavailability was predicted in children. The predictive performance of the proposed method was evaluated by comparing the predicted absolute bioavailability of the studied drugs with the observed absolute bioavailability in children. Results: The results of the study indicated that the ADE model provided a good prediction of systemic and oral clearances in children from adult clearance values (89% and 82% observations within 0.5–1.5-fold prediction error, respectively). The predicted absolute bioavailability by the proposed method was within 0.5–1.5-fold prediction error for 93% observations. Conclusions: This study indicated that it was possible to estimate absolute bioavailability of medicines in children with acceptable accuracy (within 0.5–1.5-fold prediction error) by the proposed method. The estimated absolute bioavailability in children could be useful in designing a first-in-children dose during pediatric drug development.

The Particle Drifting Effect: A Combined Function of Colloidal and Drug Properties.

The particle drifting effect, where nanosized colloidal drug particles overcome the diffusional resistance of the aqueous boundary layer adjacent to the intestinal wall and increase drug absorption rates, is drawing increasing attention in pharmaceutical research. However, mechanistic understanding and accurate prediction of the particle drifting effect remain lacking. In this study, we systematically evaluated the extent of the particle drifting effect affected by drug and colloidal properties, including the size, number, and type of the moving species using biphasic diffusion experiments combined with computational fluid dynamics simulations and mass transport analyses. The results showed that the particle drifting effect is a sequential reaction of particle dissolution/dissociation in the diffusional boundary layer, followed by absorption of the free drug. Therefore, factors affecting the rate-limiting step, which can be either process or both under different circumstances, alter the particle drifting effect. Experimental results also agree with the theory that the particle dissolution rate is dependent on particle size, concentration, and drug solubility. In addition, rapid bile micelle dissociation and bile salt absorption facilitated drug absorption by the particle drifting effect. Our findings explain the highly dynamic nature of the particle drifting effect and will contribute to rational formulation development and better bioavailability prediction for formulations containing colloidal particles.

Predicting Human Subcutaneous Bioavailability of Therapeutic Monoclonal Antibodies from Systemic Clearance and Volume of Distribution.

Subcutaneous delivery of monoclonal antibody therapeutics is often preferred to intravenous delivery due to better patient compliance and overall lower cost to the healthcare system. However, the systemic absorption of biologics dosed subcutaneously is often incomplete. The aim of this work was to describe a human bioavailability prediction method for monoclonal antibodies delivered subcutaneously that utilizes intravenous pharmacokinetic parameters as input. A two-compartment pharmacokinetic model featuring a parallel-competitive absorption pathway and a presystemic metabolism pathway was employed. A training data set comprised 19 monoclonal antibodies (geometric mean bioavailability of 68%), with previously reported human pharmacokinetic parameters, while a validation set included data compiled from 5 commercial drug products (geometric mean bioavailability of 69%). A single fitted absorption rate constant, paired with compound-specific estimates of presystemic metabolism rate proportional to compound-specific systemic clearance parameters, resulted in calculations of human subcutaneous bioavailability closely mimicking clinical data in the training data set with a root-mean-square error of 5.5%. Application of the same approach to the validation data set resulted in predictions characterized by 12.6% root-mean-square error. Factors that may have impacted the prediction accuracy include a limited number of validation data set compounds and an uncertainty in the absorption rate, which were subsequently discussed. The predictive method described herein provides an initial estimate of the subcutaneous bioavailability based exclusively on pharmacokinetic parameters available from intravenous dosing.

Research on prediction of human oral bioavailability of drugs based on improved deep forest

Human oral bioavailability is a crucial factor in drug discovery. In recent years, researchers have constructed a variety of different prediction models. However, given the limited size of human oral bioavailability data sets, the challenge of making accurate predictions with small sample sizes has become a critical issue in the field. The deep forest model, with its adaptively determinable number of cascade levels, can perform exceptionally well even on small-scale data. However, the original deep forest suffers unbalanced multi-grained scanning process and premature stopping of cascade forest training. In this paper, we propose a human oral bioavailability predict method based on an improved deep forest, called balanced multi-grained scanning mapping cascade forest (bgmc-forest). Firstly, the mordred descriptor method is selected to feature extraction, then enhanced features are obtained by the improved balanced multi-grained scanning, which solves the problem of missing features at both ends. And finally, the prediction results are obtained by feature mapping cascaded forests, which is based on principal component analysis and cascade forests, ensures the effectiveness of the cascade forest. The superiority of the model constructed in this paper is demonstrated through comparative experiments, while the effectiveness of the improved module is verified through ablation experiments. Finally the decision-making process of the model is explained by the shapley additive explanations interpretation algorithm.

Phytochemical characterization, antimicrobial properties and in silico modeling perspectives of Anacyclus pyrethrum essential oil

Medicinal plants are used widely in the treatment of various infectious diseases. One of these medical plants is Moroccan plants such as Anacyclus pyrethrum. In this study, the essential oil isolated from the leaves of Anacyclus pyrethrum (APEO) by the hydrodistillation method was analyzed using (GC/MS) analysis. A total of forty-four compounds were identified form the oil and the oxygenated monoterpenes were the most abundant class of compounds. The major identified compound is santolina alcohol (40.7 %), followed by germacrene-D (8.9 %). The in-vitro assessment of the antimicrobial efficacy of APEO encompassed an investigation involving six microbial strains, including two Gram-positive bacteria, four Gram-negative bacteria, and three fungal strains. The findings revealed noteworthy antibacterial and antifungal properties against all examined microorganisms, with inhibitory zone diameters ranging from 25.67 ± 0.06 mm to 25.19 ± 0.03 mm for Gram-positive bacteria and from 22.34 ± 0.01 mm to 14.43 ± 0.02 mm for Gram-negative bacteria, as determined through the disc-diffusion assay. In the case of antifungal activity, inhibitory zones ranged from 24.57 ± 0.04 mm to 18.37 ± 0.06 mm. Further evaluation revealed that the MIC values of Gram-positive bacteria were at the concentration 0.25 % v/v, while MBC values ranged from 0.25 % to 1.0 % v/v. The Gram-negative bacteria exhibited MIC values spanning from 0.5 % to 2.0 % v/v, with MBC values in the range of 0.5 %–2.0 % v/v. For the fungal strains, MIC values ranged from 0.5 % to 1.0 % v/v, while the MFC consistently remained at 1.0 % for all tested fungal strains. The assessment of the MBC/MIC and MFC/MIC ratios collectively indicates that A. pyrethrum EO possesses bactericidal and fungicidal attributes. The in silico study of bioavailability predictions for compounds in APEO based on six physicochemical properties show optimal physiochemical properties including size, lipophilicity, solubility, flexibility, and saturation. α-Pinene, limonene, germacrene D, and (E)-β-farnesene are non-polar due to their lack of polar groups, and the ADME profile indicates desirable properties for considering these compounds in drug development. Molecular docking investigation indicates that all the compounds of APEO reside well into the binding site of the DNA gyrase B enzyme of Staphylococcus aureus by mediating a number of significant interactions with the binding site residues. The ADME analysis suggested that the major compounds APEO possess desirable properties for further consideration in drug development. In light of these findings, APEO could serve as a natural source for the elaboration of new and active antimicrobial drugs.

<i>In silico</i> analysis of anti-inflammatory and anticancer properties of bioactive compounds from <i>Pancratium triflorum</i> (Roxb)

Herbal remedies with medicinal properties have been utilized in Asian countries for centuries. Recently, Pancratium triflorum Roxb., also known as the forest spider lily, has captured the attention of researchers due to its potential therapeutic benefits. This plant produces white flowers and contains several important phytochemicals, including Lycorine, citrate, Gallic acid, Ellagic acid, Quercetin, and Kaempferol. Molecular docking, a computer simulation technique, has been used to identify potential drug candidates that can bind effectively to the active site of a protein. This study explored the anti-inflammatory and anticancer properties of Pancratium triflorum Roxb.'s Phyto-constituents using molecular docking with Auto dock 4.2.6. The study focused on the Vioxx-bound human Cox2 receptor and the Tyrosine kinase receptor bound to Gliteritinib. Standard ligands were used for redocking, and the Swiss ADME software was employed for bioavailability prediction to validate the findings. The results showed that Lycorine and Crinine have excellent anti-inflammatory properties, while Kaempferol and Quercetin have promising anticancer properties. Further research is necessary, but these findings suggest that Pancratium triflorum Roxb. could be a valuable addition to the medical world. However, further research is required to validate these findings, and in vivo studies are needed to confirm the efficacy and safety of these phytochemicals as potential drug candidates.

Construction of An Oral Bioavailability Prediction Model Based on Machine Learning for Evaluating Molecular Modifications

ObjectiveThis study aims to explore the impact of ADME on the Oral Bioavailability (OB) of drugs and to construct a machine learning model for OB prediction. The model is then applied to predict the OB of modified berberine and atenolol molecules to obtain structures with higher OB. MethodsInitially, a drug OB database was established, and corresponding ADME characteristics were obtained. The relationship between ADME and OB was analyzed using machine learning, with Morgan fingerprints serving as molecular descriptors. Compounds from the database were input into Random Forest, XGBoost, CatBoost, and LightGBM machine learning models to train the OB 7prediction model and evaluate its performance. Subsequently, berberine and atenolol were modified using Chemdraw software with ten different substituents for mono-substitution, and chlorine atoms for a full range of double substitutions. The modified molecular structures were converted into the same format as the training set for OB prediction. The predicted OB values of the modified structures of berberine and atenolol were compared. ResultsAn OB database of 386 drugs was obtained. It was found that smaller molecular weight and a higher number of rotatable bonds (ten or less) could potentially lead to higher OB. The four machine learning models were evaluated using MSE, R2 score, MAE, and MFE as metrics, with Random Forest performing the best. The models' predictions for the test set were particularly accurate when OB ranged from 30% to 90%. After mono-substitution and double substitution of berberine and atenolol, the OB of both drugs was significantly improved. ConclusionsThis study found that some ADME properties of molecules do not have an absolute impact on OB. The database played a decisive role in the process of the machine learning OB prediction model, and the performance of the model was evaluated based on predictions within a range of strong generalization ability. In most cases, mono-substitution and double substitution were beneficial for enhancing the OB of berberine and atenolol. In summary, this study successfully constructed a machine learning regression prediction model that can accurately predict drug OB, which can guide drug design to achieve higher OB to some extent.

An Explanation of Why Dose-Corrected Area Under the Curve for Alternate Administration Routes Can Be Greater than for Intravenous Dosing

It is generally believed that bioavailability (F) calculated based on systemic concentration area under the curve (AUC) measurements cannot exceed 1.0, yet some published studies report this inconsistency. We teach and believe, based on differential equation derivations, that rate of absorption has no influence on measured systemic clearance following an oral dose, i.e., determined as available dose divided by AUC. Previously, it was thought that any difference in calculating F from urine data versus that from systemic concentration AUC data was due to the inability to accurately measure urine data. A PubMed literature search for drugs exhibiting F > 1.0 and studies for which F was measured using both AUC and urinary excretion dose-corrected analyses yielded data for 35 drugs. We show and explain, using Kirchhoff’s Laws, that these universally held concepts concerning bioavailability may not be valid in all situations. Bioavailability, determined using systemic concentration measurements, for many drugs may be overestimated since AUC reflects not only systemic elimination but also absorption rate characteristics, which is most easily seen for renal clearance measures. Clearance of drug from the absorption site must be significantly greater than clearance following an iv bolus dose for F(AUC) to correctly correspond with F(urine). The primary purpose of this paper is to demonstrate that studies resulting in F > 1.0 and/or greater systemic vs urine bioavailability predictions may be accurate. Importantly, these explications have no significant impact on current regulatory guidance for bioequivalence testing, nor on the use of exposure (AUC) measures in making drug dosing decisions.Graphical

Shifting the Focus from Dissolution to Permeation: Introducing the Meso-fluidic Chip for Permeability Assessment (MCPA)

In response to the growing ethical and environmental concerns associated with animal testing, numerous in vitro tools of varying complexity and biorelevance have been developed and adopted in pharmaceutical research and development. In this work, we present one of these tools, i.e., the Meso-fluidic Chip for Permeability Assessment (MCPA), for the first time. The MCPA combines an artificial barrier (PermeaPad®) with an organ-on-chip device (MIVO®) and real-time automated concentration measurements, to yield a sustainable, yet effortless method for permeation testing. The system offers three major physiological aspects, i.e., a biomimetic membrane, an optimal membrane interfacial area-to-donor-volume-ratio (A/V) and a physiological flow on the acceptor/basolateral side, which makes the MPCA an ideal candidate for mechanistic studies and excellent in vivo bioavailability predictions. We validated the method with a handful of assorted drug compounds in unstirred and stirred donor conditions, before exploring its applicability as a tool for dissolution/permeation testing on a BCS class III/I drug (pyrazinamide) crystalline adducts and BCS class II/IV (hydrocortisone) amorphous solid dispersions. The results were highly reproducible and clearly displayed the method's potential for evaluating the performance of enabling formulations, and possibly even predicting in vivo performance. We believe that, upon further development, the MCPA will serve as a useful in vitro tool that could push sustainability into pharmaceutics by refining, reducing and replacing animal testing in early-stage drug development.

An Algorithm to Assess Calcium Bioavailability from Foods

BackgroundThe recommended calcium intakes to meet calcium requirements at various ages are based on average population absorption values. Absorption is altered by physiology, the calcium load, and type of food. The calcium intake necessary, therefore, to meet requirements depends upon diet composition, through bioavailability. ObjectiveThe objectives of this study was to improve predictions of calcium bioavailability on the basis of the food matrix. MethodsWe modeled calcium absorption data from individual foods, beverages, and fortified foods that were determined with calcium isotopic tracers and compared with milk as a referent to adjust for physiologic differences of the host. ResultsData from 496 observations were modeled to develop a predictive algorithm for calcium bioavailability in adults on the basis of calcium load and oxalate and phytate loads, which represent the 2 main inhibitors of calcium absorption. ConclusionsThis algorithm will be helpful in assessing calcium availability from the food supply, for developing diets for individuals and research cohorts, and for designing policies and interventions to address inadequate calcium intake for populations.

Potential of Secondary Metabolite of Jasminum sambac as Diabetes Mellitus Medicine by Molecular Docking Method

Diabetes mellitus (DM) is a non-infectious disease with a high prevalence in Indonesia. The study explored the potential of the phytochemical component of Jasminum sambac to treat DM. The potential of Jasminum. sambac as a candidate for DM therapy was demonstrated through in silico analysis using several databases and computer-aided drug discovery tools. The bioactive compounds analyzed were obtained from KnapSACK databases. The screening was done to find compounds by estimating bioavailability prediction on the SwissADME. The SwissTargetPrediction tool connects predictions of target proteins from compounds that pass screening to various probable proteins and utilizes the String-DB to show the network between target proteins and associated diseases. After finding the target protein, continue docking the chemical compound to the target protein using PyRx with AutoDock 4.2.6. The search results for the compounds in Jasminum sambac found nine active substances with good bioavailability. The results of the pharmacological network found four proteins associated with Jasminum sambac, among others: GCGR, GSK3B, PPARA, and PPARG. In addition, in-depth analysis was done on the molecular interactions that occurred, how these compounds bind the enzymes found in humans, and their potential to be inhibitors of diabetes mellitus. Proteins such as GCGR, GSK3B, PPARA, and PPARG bind to the compounds found in Jasminum sambac, such as (-)-alpha-cadinol and linalyl benzoate. Thus, it can be said that Jasminum sambac can have anti-diabetic activity.

Synthesis of novel pyrazolone candidates with studying some biological activities and in-silico studies

Pyranopyrazole derivatives have a vital role in the class of organic compounds because of their broad spectrum of biological and pharmacological importance. Our current goal is the [3 + 3] cycloaddition of benzoyl isothiocyanate and pyrazolone 1 to undergo oxidation cyclization, producing pyrazoloxadiazine 3. The diol 5 was obtained as a condensation of two equivalents of 1 with thiophene-2-carboxaldehyde in acetic acid above the sodium acetate mixture. When the condensation was carried out in piperidine under fusion, unsaturated ketone 4 was obtained. The pyrazolo pyran derivative 11 resulted from the [3 + 3] cycloaddition of 1 and cinnamic acid, while the Pyrone derivative was prepared by acylation of 12 with two equivalents of acetic anhydride. Phthalic anhydride undergoes arylation using zinc chloride as a catalyst. The cyclic keto acid 23 was synthesized by the action of succinic anhydride on 12 in the acetic medium, while the latter reacted with cinnamic acid, leading to pyrazole derivative 24. All of these reactions were through the Michael reaction mechanism. All the tested compounds showed good antimicrobial activity against pathogenic microorganisms; newly synthesized compounds were also screened for their antioxidant activity. Rational studies were carried out by the ABTs method to allow a broader choice of activities. In addition, similar off-compounds were conducted. Molecular docking studies with the CB-Dock server and MD simulations were created with the default settings of the Solution Builder on the CHARMM-GUI server at 150 nm. A good correlation was obtained between the experimental results and the theoretical bioavailability predictions using POM theory.

BIOAVAILABILITY AND MOLECULAR DOCKING PREDICTION OF SECONDARY METABOLITE OF Curcuma zedoaria AS POTENTIAL MPRO SARS COV-2 INHIBITOR

Almost all countries worldwide have been exposed to the COVID-19 pandemic, which can cause respiratory diseases. Therefore, it is necessary to identify and develop drugs for the treatment of COVID-19. White turmeric (Curcuma zedoaria) is very popular because it is effective as an anticancer and antiviral agent. This study used the in silico method to predict the secondary metabolite compounds contained in C. zedoaria as inhibitors of Mpro (main protease) SARS-CoV-2. Of the 30 secondary metabolite compounds identified, 26 compounds had good bioavailability prediction, and three compounds (curcumadiol, curcumin, and desmethoxycurcumin) were predicted to have potential as SARS-COV-2 Mpro inhibitors because they have low inhibition constants and numerous ligand-protein interactions. Curcumadiol have binding energy: -8.34 kcal/mol; pKi: 0.77 µM; hydrogen bond: Glu166, Thr190, and Gln192; and hydrophobic bond: Met165 and Gln189. Curcumin have binding energy: -8.28 kcal/mol; pKi: 0.85 µM; hydrogen bond: Leu141, Gly143, Ser144, and Thr190; and hydrophobic bond: Cys145, Met165, and Gln189. Desmethoxycurcumin have binding energy: -7.83 kcal/mol; pKi: 1.84 µM; hydrogen bond: Asp187, Thr190, and Gln192; and hydrophobic bond: Met165. Keywords: Curcuma zedoaria, SARS-CoV-2, bioavailability, molecular docking

Bioavailability predictions, pharmacokinetics and drug-likeness of bioactive compounds from Andrographis paniculata using Swiss ADME

Abstract Background: Earlier research on Andrographis paniculata focused on documenting their bioactive compounds profiles and traditional use. Before making a drug-like substance prediction using information from in silico experimental models, the current work aimed to examine and analyze the ADMET properties. This study assessed the drug-likeness and ADMET characteristics of bioactive compounds from A. paniculata. Materials and Methods: The current study will be the first to use the free online tool Swiss ADME to report the ADME characteristics of A. paniculata. The ADME properties of 10 bioactive compounds from A. paniculata were screened, and the results were evaluated. Results: Six bioactive compounds were identified to have good gastrointestinal absorption and can penetrate the brain. These compounds include andrographolide, 14-Acetylandrographolide, Drf3188, Neoandrographolide, Isoandrographolide, and 3,19-O-diacetyl andrographolide. On the other hand, three of the bioactive compounds were found to show Ames mutagenicity: 19-O-Acetylandrographolide, 3,19-isopropylideneandrographolide, and 14-glycinylandrographolide hydrochloride. Except 5-Hydroxy-7,8-dimethoxyflavone, most of these substances do not serve as substrates for both P-glycoprotein (P-gp) and Cytochrome P-450 isoenzymes (CYP). Furthermore, all the compounds were found to pass the Lipinski rule of five, indicating their potential suitability as drug candidates. Conclusion: Swiss ADME has proven to be an effective, dependable, and straightforward method for determining the ADME characteristics of the bioactive compounds in A. paniculata. Based on the information, it was predicted that A. panicluata would be effective in managing the disease. To validate these findings, it is advisable to conduct further controlled experimental research exploring the bioactive compounds’ pharmacological effects.

Improving on in-silico prediction of oral drug bioavailability

ABSTRACT Introduction Although significant development has been made in high-throughput screening of oral drug absorption and oral bioavailability, in silico prediction continues to play an important role in prediction of oral bioavailability and assisting in the proper selection of potential drug candidates. Areas covered This review describes the improvements and latest modeling methods and algorithms available for the prediction of this important parameter. We performed a PubMed database search with a focus on the literature published in the last 15 years. Expert opinion A tremendous efforts have been done in the past several years to develop reliable prediction tools that can provide accurate prediction for oral bioavailability. Several studies demonstrated new methodologies and techniques to develop either web-based in silico predictive tools or integrated PBPK models to predict oral bioavailability for new molecules. Improvements in the databases and the computational power will enhance the in silico prediction accuracy and reliability. Finally, introducing artificial intelligence to the drug development process will help improve the prediction tools.

Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction.

Oral bioavailability is a pharmacokinetic property that plays an important role in drug discovery. Recently developed computational models involve the use of molecular descriptors, fingerprints, and conventional machine-learning models. However, determining the type of molecular descriptors requires domain expert knowledge and time for feature selection. With the emergence of the graph neural network (GNN), models can be trained to automatically extract features that they deem important. In this article, we exploited the automatic feature selection of GNN to predict oral bioavailability. To enhance the prediction performance of GNN, we utilized transfer learning by pre-training a model to predict solubility and obtained a final average accuracy of 0.797, an F1 score of 0.840, and an AUC-ROC of 0.867, which outperformed previous studies on predicting oral bioavailability with the same test data set.

Insights on Prospects of Prediction of Drug Bioavailability from in Vitro Models

Insights on Prospects of Prediction of Drug Bioavailability from in Vitro Models

Synthesis by microwave irradiation of new pyrazole-imidazoline-pyrimidine analogs: Physicochemical and photophysical properties and their biological activity against Trypanosoma cruzi

Pyrazole derivatives are pharmacologically important agents that possess a wide spectrum of biological activities. Thus, the optimization of their synthetic approaches is of great interest for medicinal and pharmaceutical chemistry. In this work, eleven new 7-aryl-2,3-dihydro-7H-imidazo[1,2-c]pyrazolo[4,3-e]pyrimidines 1(a-k) were synthesized employing microwave irradiation conditions (150 W, 15–45 min). All compounds were completely characterized through Fourier Transform Infrared Spectroscopy (FT-IR), Nuclear Magnetic Resonance (NMR), and High-resolution Mass Spectrometry (HRMS) analyses. The in silico prediction of physicochemical properties showed that all analogs do not violate Lipinki's rule with prediction of good oral bioavailability. Analogs showed low activity against T. cruzi. Correlated experimental methods have been applied to investigate the photophysical properties of 1(a-k), describing their absorption properties in function of substituents. The molecular set exhibits a cyan emission, enabling it to investigate the basic fluorescence spectroscopy in solution.

Quantitative Prediction of Intestinal Absorption of Drugs from In Vitro Study: Utilization of Differentiated Intestinal Epithelial Cells Derived from Intestinal Stem Cells at Crypts.

Prediction of intestinal absorption of drugs in humans is one of the critical elements in the development process for oral drugs. However, it remains challenging, because intestinal absorption of drugs is influenced by multiple factors, including the function of various metabolic enzymes and transporters, and large species differences in drug bioavailability hinder the prediction of human bioavailability directly from in vivo animal experiments. For the screening of intestinal absorption properties of drugs, a transcellular transport assay with Caco-2 cells is still routinely used by pharmaceutical companies because of its convenience, but the predictability of the fraction of the oral dose that goes to the portal vein of metabolic enzyme/transporter substrate drugs was not always good because the cellular expression of metabolic enzymes and transporters is different from that in the human intestine. Recently, various novel in vitro experimental systems have been proposed such as the use of human-derived intestinal samples, transcellular transport assay with induced pluripotent stem-derived enterocyte-like cells, or differentiated intestinal epithelial cells derived from intestinal stem cells at crypts. Crypt-derived differentiated epithelial cells have an excellent potential to characterize species differences and regional differences in intestinal absorption of drugs because a unified protocol can be used for the proliferation of intestinal stem cells and their differentiation into intestinal absorptive epithelial cells regardless of the animal species and the gene expression pattern of differentiated cells is maintained at the site of original crypts. The advantages and disadvantages of novel in vitro experimental systems for characterizing intestinal absorption of drugs are also discussed. SIGNIFICANCE STATEMENT: Among novel in vitro tools for the prediction of human intestinal absorption of drugs, crypt-derived differentiated epithelial cells have many advantages. Cultured intestinal stem cells are rapidly proliferated and easily differentiated into intestinal absorptive epithelial cells simply by changing the culture media. A unified protocol can be used for the establishment of intestinal stem cell culture from preclinical species and humans. Region-specific gene expression at the collection site of crypts can be reproduced in differentiated cells.

Assessment of the bioaccessibility and bioavailability prediction of omega 3 and conjugated fatty acids by in vitro standardized digestion model (INFOGEST) and cell model

Omega 3 EPA and DHA are polyunsaturated fatty acids with relevant health benefits. Conjugated linoleic and linolenic acids are known for their anti-carcinogenic effect, anti-inflammatory properties and body weight reduction. To achieve therapeutical doses, high amounts of these fatty acids’ food sources must be consumed. Thus, the intake of enriched oils with a high concentration of these fatty acids is often used. But several factors influence their bioavailability. Here, by using the INFOGEST static in vitro protocol of gastrointestinal tract digestion it was studied the bioaccessibility of these fatty acids in different matrixes: Pomegranate and Fish oil and omega 3, CLA and CLNA soft-gel enriched capsules. After digestion, the Recovery Index for the major bioactive PUFAs are very low: Pomegranate oil is 2%, Fish oil 11–13%, CLNA 17%, CLA 6% and Omega 3 capsules 3%. Higher initial concentrations of these PUFAs seem to be related to higher degrees of oxidation. In Pomegranate oil, CLNA and Omega 3 capsules, the digestion process negatively influenced the antioxidant potential. The opposite was verified for the Fish oil and CLA capsules. Importantly, bioaccessibility studies of similar matrixes are very scarce and intestinal permeability is absent in most of the studies. Intestinal permeability studies were performed using a Caco-2/HT29-MTX co-culture: there is significative incorporation of the bioactive fatty acids into the intestinal cells, which may affect their permeability performance. Interestingly, most fatty acids remain in the non-bioaccessible fraction which may be relevant when designing oral routes of administration and in gut microbiota modulation.