Human Intestinal Absorption Research Articles

Structure – ADME relationship: still a long way to go?

Background: Theoretical models for predicting absorption, distribution, metabolism and excretion (ADME) properties play increasingly important roles in support of the drug development process. Objective: We briefly review the in silico prediction models for three important ADME properties, namely, aqueous solubility, human intestinal absorption, and oral bioavailability. Methods: Rather than giving detailed descriptions of the ADME prediction models, we focus on the discussions of the prediction accuracies of the in silico models. Results/conclusion: We find that the robustness and predictive capability of the ADME models are directly associated with the complexity of the ADME property. For the ADME properties involving complex phenomena, such as bioavailability, the in silico models usually cannot give satisfactory predictions. Moreover, the lack of large and high-quality data sets also greatly hinder the reliability of ADME predictions. While considerable progress has been achieved in ADME predictions, many challenges remain to be overcome.

Prediction of oral drug absorption in humans – from cultured cell lines and experimental animals

Background: Over the past decade, the use of drug metabolism and pharmacokinetic (DMPK) parameters to optimize selection of candidates for drug development has become one of the primary focuses of research organizations involved in new drug discovery. Objective: This review will focus on the feasibility of predicting human absorption using data from a cultured Caco-2 cell system and non-clinical animals. Methods: In-house Caco-2 permeability data were compared with human absorption data from the literature. Animal absorption data obtained from current literature were also compared with human data. Results/conclusion: Using a combination of rapid in-vivo and in-vitro DMPK screens on a large array of compounds during the lead optimization process has resulted in the streamlined development of compounds that have acceptable DMPK properties. Since it is well recognized that human intestinal absorption cannot be precisely predicted by a single screening assay, it is important to utilize various in-vitro and in-vivo non-clinical studies during lead optimization in drug discovery.

Estimation of ADME Properties in Drug Discovery: Predicting Caco-2 Cell Permeability Using Atom-Based Stochastic and Non-stochastic Linear Indices

The in vitro determination of the permeability through cultured Caco-2 cells is the most often-used in vitro model for drug absorption. In this report, we use the largest data set of measured P(Caco-2), consisting of 157 structurally diverse compounds. Linear discriminant analysis (LDA) was used to obtain quantitative models that discriminate higher absorption compounds from those with moderate-poorer absorption. The best LDA model has an accuracy of 90.58% and 84.21% for training and test set. The percentage of good correlation, in the virtual screening of 241 drugs with the reported values of the percentage of human intestinal absorption (HIA), was greater than 81%. In addition, multiple linear regression models were developed to predict Caco-2 permeability with determination coefficients of 0.71 and 0.72. Our method compares favorably with other approaches implemented in the Dragon software, as well as other methods from the international literature. These results suggest that the proposed method is a good tool for studying the oral absorption of drug candidates.

Intestinal absorption of the acetamiprid neonicotinoid by Caco-2 cells: Transepithelial transport, cellular uptake and efflux

The human intestinal absorption of acetamiprid (AAP) using the Caco-2 cell line reveals that AAP flux was active in a bidirectional mode with an apparent permeability coefficient of 26.10−6 cm·s−1 at 37°C. Apical uptake was concentration-dependent and unsaturated for AAP concentrations up to 200 μ M. AAP cell preloading demonstrated the involvement of active transport mechanisms. Arrhenius plot analysis revealed an unusual profile with two apparent activation energies suggesting two transport processes. Uptake Vi studies indicated the involvement of a sodium-dependent transporter, the presence of a common transporter of AAP and nicotine and the involvement of Ti-sensitive ATP-dependent efflux transporters. Apical efflux investigations showed the involvement of inward active transporter(s). Whereas vincristine had no effect on intracellular accumulation, taxol and daunorubicin treatments unexpectedly led to 10% and 23% reductions respectively, suggesting that the latter shared a common inward transporter with AAP. All these results suggest full and express AAP absorption in vivo with transport involving both inward and outward, passive and active mechanisms. Thus, AAP or its metabolites could be representative of a risk for human health after its ingestion in food.

Passive oral drug absorption can be predicted more reliably by experimental than computational models—Fact or myth

This study assessed the prediction power of experimental and computational models that are widely used to predict human passive intestinal absorption. The models evaluated included two cell lines, three artificial membrane models, in vivo rat experiments, and seven previously described computational quantitative structure property relationship models based on human absorption values. The data sets used in the assessment of the models were carefully chosen from the literature, and different models were compared using the same compounds to ensure objective results. Three of the computational models were found to be significantly more reliable in predicting human passive intestinal absorption than the artificial membrane models that have been developed for the prediction of passive intestinal absorption. Two of these computational models were found to be as reliable as the Caco-2 and the 2/4/A1 cell lines and, furthermore, one of the models was able to predict the absorption of a set of 65 drugs nearly as well as absorption studies in rats. The unexpectedly good prediction power of the simple computational models with high throughput makes them ideal tools in the early screening of drug candidates, whereas laborious cell culture models and animal studies can be useful in the later phases when detailed information about the transport mechanisms is needed.

Statistical Confidence for Variable Selection in QSAR Models via Monte Carlo Cross-Validation

A new variable selection wrapper method named the Monte Carlo variable selection (MCVS) method was developed utilizing the framework of the Monte Carlo cross-validation (MCCV) approach. The MCVS method reports the variable selection results in the most conventional and common measure of statistical hypothesis testing, the P-values, thus allowing for a clear and simple statistical interpretation of the results. The MCVS method is equally applicable to the multiple-linear-regression (MLR)-based or non-MLR-based quantitative structure-activity relationship (QSAR) models. The method was applied to blood-brain barrier (BBB) permeation and human intestinal absorption (HIA) QSAR problems using MLR to demonstrate the workings of the new approach. Starting from more than 1600 molecular descriptors, only two (TPSA(NO) and ALOGP) yielded acceptably low P-values for the BBB and HIA problems, respectively. The new method has been implemented in the QSAR-BENCH v2 program, which is freely available (including its Java source code) from www.dmitrykonovalov.org for academic use.

ChemInform Abstract: ADME Evaluation in Drug Discovery. Part 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine

Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. In silico models for predicting the percentage of HIA based on calculated molecular descriptors are highly needed for the rapid estimation of this property. Here, we have studied the performance of a support vector machine (SVM) to classify compounds with high or low fractional absorption (%FA > 30% or %FA < or = 30%). The analyzed data set consists of 578 structural diverse druglike molecules, which have been divided into a 480-molecule training set and a 98-molecule test set. Ten SVM classification models have been generated to investigate the impact of different individual molecular properties on %FA. Among these studied important molecule descriptors, topological polar surface area (TPSA) and predicted apparent octanol-water distribution coefficient at pH 6.5 (logD6.5) show better classification performance than the others. To obtain the best SVM classifier, the influences of different kernel functions and different combinations of molecular descriptors were investigated using a rigorous training-validation procedure. The best SVM classifier can give satisfactory predictions for the training set (97.8% for the poor-absorption class and 94.5% for the good-absorption class). Moreover, 100% of the poor-absorption class and 97.8% of the good-absorption class in the external test set could be correctly classified. Finally, the influence of the size of the training set and the unbalanced nature of the data set have been studied. The analysis demonstrates that large data set is necessary for the stability of the classification models. Furthermore, the weights for the poor-absorption class and the good-absorption class should be properly balanced to generate unbiased classification models. Our work illustrates that SVMs used in combination with simple molecular descriptors can provide an extremely reliable assessment of intestinal absorption in an early in silico filtering process.

Biopharmaceutics Classification of Selected β-Blockers: Solubility and Permeability Class Membership

The purpose of this study was to determine the permeability and solubility of seven beta-blockers (acebutolol, atenolol, labetalol, metoprolol, nadolol, sotalol, and timolol) and to classify them according to the Biopharmaceutics Classification System (BCS). Apparent permeability coefficients (Papp) were measured using the Caco-2 cell line, and the solubility was determined at 37 degrees C over a pH range of 1.0-7.5. The permeability coefficients ranged from 1.0x10(-7) to 4.8x10(-5) cm/s. On the basis of the in vitro permeability and solubility data observed in the study, labetolol, metoprolol, and timolol can be categorized as BCS Class I drugs, whereas acebutolol, atenolol, and nadolol belong to BCS Class III. The permeability coefficients in Caco-2 cells were consistent with the reported extent of intestinal absorption in humans for all drugs except sotalol. Sotalol displayed low permeability in the Caco-2 cell line, but the extent of intestinal absorption in humans is over 90%. The low permeability through the Caco-2 monolayers might be largely related to its low lipophilicity. In addition, the difference between the tightness of the intercellular junction in vivo and in vitro may partially contribute to this disparity in the sotalol permeability of in vivo and in vitro.

In Silico ADME Modeling 3: Computational Models to Predict Human Intestinal Absorption Using Sphere Exclusion and kNN QSAR Methods

AbstractModeling of human intestinal absorption (HIA) data of 175 diverse drugs and 336 calculated descriptors is performed to develop global predictive models that are applicable to the whole medicinal chemistry space. With this aim, we employed two automated procedures, (a) Sphere Exclusion Algorithm (SEA) to select members of the training and test sets based on structural dissimilarity and (b) k‐Nearest Neighbors (kNN) method along with Genetic Algorithms (kNN‐QSAR‐GA) to select significant and independent descriptors. This methodology helped us to derive optimal Quantitative Structure–Property Relationship (QSPR) models based on three and four descriptors. The best three descriptor model is based on Delta Chi Index of order 3 (Cluster), Hydrogen type E‐State index ShsOH, AlogP99 ($\rm{ q_{{\rm{LOO}}}^2 }$=0.7401 and $\rm{ q_{{\rm{ext}}}^2 }$=0.7989); the best four variable model is based on auto‐correlation descriptor (Moran) weighted by atomic weights – order 7, AI‐State_Indices_AISssssC, number of hydrogen bond acceptors, AlogP99 ($\rm{ q_{{\rm{LOO}}}^2 }$=0.8196 and $\rm{ q_{{\rm{ext}}}^2 }$=0.6999). Based on extensive validation tests of the models M1–M4, comparison of their overall performance and $\rm{ q_{{\rm{ext}}}^2 }$ statistics with reported models using other approaches, it is shown that: (a) the models have high stability and are robust and (b) for the first time in HIA modeling, the combination of an automated training set selection (SEA) followed by variable selection (kNN‐QSAR_GA) is shown to be a promising methodology to build multiple stable models that are useful in consensus prediction. From the analysis of the physical meaning of the selected descriptors, it is inferred that the HIA of small organic compounds can be accurately predicted using calculated descriptors that code for the following fundamental properties: (1) lipophilicity, (2) hydrogen bonding capacity, (3) size, and (4) shape and further, the role of new calculated descriptors on the HIA profile of small organic compounds is uncovered. Finally, as the models reported herein are based on computed properties, they appear to be a valuable tool in virtual screening, where selection and prioritization of candidates is required.

Regulation of intestinal NPC1L1 expression by dietary fish oil and docosahexaenoic acid

To address the effect of the n-3 fatty acid, docosahexaenoic acid (22:6), on proteins that play a role in cholesterol absorption, CaCo-2 cells were incubated with taurocholate micelles alone or micelles containing 22:6 or oleic acid (18:1). Compared with controls or 18:1, 22:6 did not interfere with the cellular uptake of micellar cholesterol. Apical cholesterol efflux was enhanced in cells incubated with 22:6. Cholesterol trafficking from the plasma membrane to the endoplasmic reticulum was decreased by 22:6. 22:6 decreased Niemann-Pick C1-Like 1 (NPC1L1) protein and mRNA levels without altering gene or protein expression of ACAT2, annexin-2, caveolin-1, or ABCG8. Peroxisome proliferator-activated receptor delta (PPARdelta) activation decreased NPC1L1 mRNA levels and cholesterol trafficking to the endoplasmic reticulum, suggesting that 22:6 may act through PPARdelta. Compared with hamsters fed a control diet or olive oil (enriched 18:1), NPC1L1 mRNA levels were decreased in duodenum and jejunum of hamsters ingesting fish oil (enriched 22:6). In an intestinal cell, independent of changes in ABCG8 expression, 22:6 increases the apical efflux of cholesterol. 22:6 interferes with cholesterol trafficking to the endoplasmic reticulum by the suppression of NPC1L1, perhaps through the activation of PPARdelta. Moreover, a diet enriched in n-3 fatty acids decreases the gene expression of NPC1L1 in duodenum and jejunum of hamster.

Comparative QSAR studies on PAMPA/modified PAMPA for high throughput profiling of drug absorption potential with respect to Caco-2 cells and human intestinal absorption

Despite the dramatic increase in speed of synthesis and biological evaluation of new chemical entities, the number of compounds that survive the rigorous processes associated with drug development is low. Thus, an increased emphasis on thorough ADMET (absorption, distribution, metabolism, excretion and toxicity) studies based on in vitro and in silico approaches allows for early evaluation of new drugs in the development phase. Artificial membrane permeability measurements afford a high throughput, relatively low cost but labor intensive alternative for in vitro determination of drug absorption potential; parallel artificial membrane permeability assays have been extensively utilized to determine drug absorption potentials. The present study provides comparative QSAR analysis on PAMPA/modified PAMPA for high throughput profiling of drugs with respect to Caco-2 cells and human intestinal absorption.

Oral drug absorption and the Biopharmaceutics Classification System

Bioavailability (BA) and bioequivalence (BE) play a central role in pharmaceutical product development and BE studies are presently being conducted for New Drug Application (NDAs) of new compounds, in supplementary NDAsfor new medical indications and product line extensions, in Abbreviated New Drug Applications (ANDAs) of generic products and in applications for scale-up and post-approval changes. The Biopharmaceutics Classification System (BCS) has been developed to provide a scientific approach for classifying drug compounds based on solubility as related to dose and intestinal permeability in combination with the dissolution properties of the oral immediate release (IR) dosage form. The aim of BCS is to provide a regulatory tool for replacing certain BE studies by accurate in vitro dissolution tests. The aim of the present review is to present the status of BCS and discuss its future application in pharmaceutical product development. This will be discussed in relation to novel findings in human intestinal absorption, permeability and solubility. The future application of BCS is likely to be increasingly important if the BCS borders for certain Class II and III drugs are extended. The BCS is also a simple tool in early drug development to determine the rate-limiting step in the oral absorption process, which has facilitated the information between different experts involved in the overall drug development process. In the future, this increased awareness of a proper biopharmaceutical characterization of new drugs may result in drug molecules with a sufficiently high permeability, solubility and dissolution rate that will automatically increase the importance of BCS as a regulatory tool over time.

Human Small Intestinal Absorption Following Gastric Bypass Surgery

Gastric bypass surgery (GBS) produces a relatively achlorhydric state by formation of a small cardia-fundal pouch with a gastro-jejunal anastomosis. Small intestinal bacterial overgrowth (SIBO) is known to alter vitamin B12 absorption. It has been proposed that gastric acid secretion prevents SIBO. We hypothesized that GBS alters small intestinal micronutrient absorption. This is a retrospective review of patients who underwent Roux-en-Y GBS from 1999-2005. Postoperative patients underwent glucose-hydrogen breath testing to examine upper intestinal (jejunal) bacterial production of methane and hydrogen. Patients’ sex, age, and whole blood thiamine diphosphate levels were obtained 6 months after GBS. Abnormal glucose-hydrogen breath test was defined by fasting breath hydrogen/methane >10 ppm or increased hydrogen/methane of >20 ppm within 60 minutes after 50 grams oral glucose. There were 32 females & 4 males (mean age 47 years; range 22 to 65). All 36 had abnormal glucose-hydrogen breath tests. Thirty-two patients (89%) were hydrogen producers & 13 patients (36%) produced methane; 26 patients (72%) had whole blood thiamine levels below the lower limit of normal. CONCLUSION: This study supports the idea that gastric acid secretion prevents SIBO. Since 72% of GBS patients had low thiamine levels, micronutrient absorption is altered after GPS. Present physiological evidence supports the presence of a saturable, carrier-mediated exchange mechanism for thiamine transport across human jejunum; therefore, SIBO may alter this carrier-mediated mechanism.

Prediction and Mechanistic Interpretation of Human Oral Drug Absorption Using MI-QSAR Analysis

Membrane-interaction [MI]-QSAR analysis, which includes descriptors explicitly derived from simulations of solutes [drugs] interacting with phospholipid membrane models, was used to construct QSAR models for human oral intestinal drug absorption. A data set of 188 compounds, which are mainly drugs, was divided into a parent training set of 164 compounds and a test set of 24 compounds. Stable, but not highly fit [R2 = 0.68] MI-QSAR models could be built for all 188 compounds. However, the relatively large number [47] of drugs having 100% absorption, as well as all zwitterionic compounds [11], had to be eliminated from the training set in order to construct a linear five-term oral absorption diffusion model for 106 compounds which was both stable [R2 = 0.82, Q2 = 0.79] and predictive given the test set compounds were predicted with nearly the same average accuracy as the compounds of the training set. Intermolecular membrane-solute descriptors are essential to building good oral absorption models, and these intermolecular descriptors are displaced in model optimizations and intramolecular solute descriptors found in published oral absorption QSAR models. A general form for all of the oral intestinal absorption MI-QSAR models has three classes of descriptors indicative of three thermodynamic processes: (1) solubility and partitioning, (2) membrane-solute interactions, and (3) flexibility of the solute and/or membrane. The intestinal oral absorption MI-QSAR models were compared to MI-QSAR models previously developed for Caco-2 cell permeation and for blood-brain barrier penetration. The MI-QSAR models for all three of these ADME endpoints share several common descriptors, and suggest a common mechanism of transport across all three barriers. A further analysis of these three types of MI-QSAR models has been done to identify descriptor-term differences across these three models, and the corresponding differences in thermodynamic transport behavior of the three barriers.

ADME Evaluation in Drug Discovery. 7. Prediction of Oral Absorption by Correlation and Classification

A critically evaluated database of human intestinal absorption for 648 chemical compounds is reported in this study, among which 579 are believed to be transported by passive diffusion. The correlation analysis between the intestinal absorption and several important molecular properties demonstrated that no single molecular property could be used as a good discriminator to efficiently distinguish the poorly absorbed compounds from those that are well absorbed. The theoretical correlation models for a training set of 455 compounds were proposed by using the genetic function approximation technique. The best prediction model contains four molecular descriptors: topological polar surface area, the predicted distribution coefficient at pH = 6.5, the number of violations of the Lipinski's rule-of-five, and the square of the number of hydrogen-bond donors. The model was able to predict the fractional absorption with an r = 0.84 and a prediction error (absolute mean error) of 11.2% for the training set. Moreover, it achieves an r = 0.90 and a prediction error of 7.8% for a 98-compound test set. The recursive partitioning technique was applied to find the simple hierarchical rules to classify the compounds into poor (%FA < or = 30%) and good (%FA > 30%) intestinal absorption classes. The high quality of the classification model was validated by the satisfactory predictions on the training set (correctly identifying 95.9% of the compounds in the poor-absorption class and 96.1% of the compounds in the good-absorption class) and on the test set (correctly identifying 100% of the compounds in the poor-absorption class and 96.8% of the compounds in the good-absorption class). We expect that, in the future, the rules for the prediction of carrier-mediated transporting and first pass metabolism can be integrated into the current hierarchical rules, and the classification model may become more powerful in the prediction of intestinal absorption or even human bioavailability. The databases of human intestinal absorption reported here are available for download from the supporting Web site: http://modem.ucsd.edu/adme.

Localization and role of NPC1L1 in cholesterol absorption in human intestine

Recent studies have documented the presence of Niemann-Pick C1-Like 1 (NPC1L1) in the small intestine and its capacity to transport cholesterol in mice and rats. The current investigation was undertaken to explore the localization and function of NPC1L1 in human enterocytes. Cell fractionation experiments revealed an NPC1L1 association with apical membrane of the enterocyte in human jejunum. Signal was also detected in lysosomes, endosomes, and mitochondria. Confirmation of cellular NPC1L1 distribution was obtained by immunocytochemistry. Knockdown of NPC1L1 caused a decline in the ability of Caco-2 cells to capture micellar [(14)C]free cholesterol. Furthermore, this NPC1L1 suppression resulted in increased and decreased mRNA levels and activity of HMG-CoA reductase, the rate-limiting step in cholesterol synthesis, and of ACAT, the key enzyme in cholesterol esterification, respectively. An increase was also noted in the transcriptional factor sterol-regulatory element binding protein that modulates cholesterol homeostasis. Efforts were devoted to define the impact of NPC1L1 knockdown on other mediators of cholesterol uptake. RT-PCR evidence is presented to show the significant decrease in the levels of scavenger receptor class B type I (SR-BI) with no changes in ABCA1, ABCG5, and cluster determinant 36 in NPC1L1-deficient Caco-2 cells. Together, our data suggest that NPC1L1 contributes to intestinal cholesterol homeostasis and possibly cooperates with SR-BI to mediate cholesterol absorption in humans.

A ‘Rule of Unity’ for Human Intestinal Absorption

The ability to predict the passive intestinal absorption of organic compounds can be a valuable tool in drug design. Although Lipinski's 'rule of 5' is commonly used for this purpose, it does not routinely give reliable results. An alternative 'rule of unity' is proposed to predict the absorption efficiency of orally administered drugs that are passively transported. The rule of unity based upon the theoretical principals that govern passive transport. The 'rule of 5' and the 'rule of unity' are compared using experimentally determined passive human intestinal absorption data for 155 drugs. Absorption values which are >50% of the dose are classified as well absorbed and absorption values which are </=50% of the dose are classified as classified as poorly absorbed. Comparison of the two models using a receiver operating characteristic (ROC) plot and McNemar's test reveal striking differences in absorption predictability. The 'rule of 5' gives twice as many false predictions than the 'rule of unity.'

Kernel Functions for Attributed Molecular Graphs – A New Similarity‐Based Approach to ADME Prediction in Classification and Regression

AbstractKernel methods, like the well‐known Support Vector Machine (SVM), have received growing attention in recent years for designing QSAR models that have a high predictive strength. One of the key concepts of SVMs is the usage of a so‐called kernel function, which can be thought of as a special similarity measure. In this paper we consider kernels for molecular structures, which are based on a graph representation of chemical compounds. The similarity score is calculated by computing an optimal assignment of the atoms from one molecule to those of another one, including information on specific chemical properties, membership to a substructure (e.g., aromatic ring, carbonyl group, etc.) and neighborhood for each atom. We show that by using this kernel we can achieve a generalization performance comparable to a classical model with a few descriptors, which are a‐priori known to be relevant for the problem, and significantly better results than with and without performing an automatic descriptor selection. For this purpose we investigate ADME classification and regression datasets for predicting bioavailability (Yoshida), Human Intestinal Absorption (HIA), Blood‐Brain‐Barrier (BBB) penetration and a dataset consisting of four different inhibitor classes (SOL). We further explore the effect of combining our kernel with a problem‐dependent descriptor set. We also demonstrate the usefulness of an extension of our method to a reduced graph representation of molecules, in which certain structural features, like, e.g., rings, donors or acceptors, are represented as a single node in the molecular graph.

Computational approaches for modeling human intestinal absorption and permeability.

Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. Computational models are needed for the rapid estimation of this property. The measurements are determined via in vivo experiments or in vitro permeability studies. We present several computational models that are able to predict the absorption of drugs by the human intestine and the permeability through human Caco-2 cells. The training and prediction sets were derived from literature sources and carefully examined to eliminate compounds that are actively transported. We compare our results to models derived by other methods and find that the statistical quality is similar. We believe that models derived from both sources of experimental data would provide greater consistency in predictions. The performance of several QSPR models that we investigated to predict outside the training set for either experimental property clearly indicates that caution should be exercised while applying any of the models for quantitative predictions. However, we are able to show that the qualitative predictions can be obtained with close to a 70% success rate.Electronic Supplementary MaterialSupplementary material is available for this article at http://dx.doi.org/10.1007/s00894-005-0065-z.