In silico prediction of human oral absorption based on QSAR analyses of PAMPA permeability

Anomalous Properties of Cyclodextrins and Their Complexes in Aqueous Solutions

Cyclodextrins (CDs) are cyclic oligosaccharides that emerged as industrial excipients in the early 1970s and are currently found in at least 130 marketed pharmaceutical products, in addition to numerous other consumer products. Although CDs have been the subject of close to 100,000 publications since their discovery, and although their structure and properties appear to be trivial, CDs are constantly surprising investigators by their unique physicochemical properties. In aqueous solutions, CDs are solubilizing complexing agents of poorly soluble drugs while they can also act as organic cosolvents like ethanol. CDs and their complexes self-assemble in aqueous solutions to form both nano- and microparticles. The nanoparticles have diameters that are well below the wavelength of visible light; thus, the solutions appear to be clear. However, the nanoparticles can result in erroneous conclusions and misinterpretations of experimental results. CDs can act as penetration enhancers, increasing drug permeation through lipophilic membranes, but they do so without affecting the membrane barrier. This review is an account of some of the unexpected results the authors have encountered during their studies of CDs as pharmaceutical excipients.

Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability

Membrane permeability plays an important role in oral drug absorption. Caco-2 and Madin-Darby Canine Kidney (MDCK) cell culture systems have been widely used for assessing intestinal permeability. Since most drugs are absorbed passively, Parallel Artificial Membrane Permeability Assay (PAMPA) has gained popularity as a low-cost and high-throughput method in early drug discovery when compared to high-cost, labor intensive cell-based assays. At the National Center for Advancing Translational Sciences (NCATS), PAMPA pH 5 is employed as one of the Tier I absorption, distribution, metabolism, and elimination (ADME) assays. In this study, we have developed a quantitative structure activity relationship (QSAR) model using our ∼6500 compound PAMPA pH 5 permeability dataset. Along with ensemble decision tree-based methods such as Random Forest and eXtreme Gradient Boosting, we employed deep neural network and a graph convolutional neural network to model PAMPA pH 5 permeability. The classification models trained on a balanced training set provided accuracies ranging from 71% to 78% on the external set. Of the four classifiers, the graph convolutional neural network that directly operates on molecular graphs offered the best classification performance. Additionally, an ∼85% correlation was obtained between PAMPA pH 5 permeability and in vivo oral bioavailability in mice and rats. These results suggest that data from this assay (experimental or predicted) can be used to rank-order compounds for preclinical in vivo testing with a high degree of confidence, reducing cost and attrition as well as accelerating the drug discovery process. Additionally, experimental data for 486 compounds (PubChem AID: 1645871) and the best models have been made publicly available (https://opendata.ncats.nih.gov/adme/).

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.

An overview of in vitro, ex vivo and in vivo models for studying the transport of drugs across intestinal barriers

Oral administration is the most commonly used route for drug delivery owing to its cost-effectiveness, ease of administration, and high patient compliance. However, the absorption of orally delivered compounds is a complex process that greatly depends on the interplay between the characteristics of the drug/formulation and the gastrointestinal tract. In this contribution, we review the different preclinical models (in vitro, ex vivo and in vivo) from their development to application for studying the transport of drugs across intestinal barriers. This review also discusses the advantages and disadvantages of each model. Furthermore, the authors have reviewed the selection and validation of these models and how the limitations of the models can be addressed in future investigations. The correlation and predictability of the intestinal transport data from the preclinical models and human data are also explored. With the increasing popularity and prevalence of orally delivered drugs/formulations, sophisticated preclinical models with higher predictive capacity for absorption of oral formulations used in clinical studies will be needed.

Validating ADME QSAR Models Using Marketed Drugs

Problems with drug ADME are responsible for many clinical failures. By understanding the ADME properties of marketed drugs and modeling how chemical structure contributes to these inherent properties, we can help new projects reduce their risk profiles. Kinetic aqueous solubility, the parallel artificial membrane permeability assay (PAMPA), and rat liver microsomal stability constitute the Tier I ADME assays at the National Center for Advancing Translational Sciences (NCATS). Using recent data generated from in-house lead optimization Tier I studies, we update quantitative structure-activity relationship (QSAR) models for these three endpoints and validate in silico performance against a set of marketed drugs (balanced accuracies range between 71% and 85%). Improved models and experimental datasets are of direct relevance to drug discovery projects and, together with the prediction services that have been made publicly available at the ADME@NCATS web portal (https://opendata.ncats.nih.gov/adme/), provide important tools for the drug discovery community. The results are discussed in light of our previously reported ADME models and state-of-the-art models from scientific literature.Graphical Abstract[Figure: see text].

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.

Intestinal Permeability and Drug Absorption: Predictive Experimental, Computational and In Vivo Approaches

The main objective of this review is to discuss recent advancements in the overall investigation and in vivo prediction of drug absorption. The intestinal permeability of an orally administered drug (given the value P_eff) has been widely used to determine the rate and extent of the drug’s intestinal absorption (F_abs) in humans. Preclinical gastrointestinal (GI) absorption models are currently in demand for the pharmaceutical development of novel dosage forms and new drug products. However, there is a strong need to improve our understanding of the interplay between pharmaceutical, biopharmaceutical, biochemical, and physiological factors when predicting F_abs and bioavailability. Currently, our knowledge of GI secretion, GI motility, and regional intestinal permeability, in both healthy subjects and patients with GI diseases, is limited by the relative inaccessibility of some intestinal segments of the human GI tract. In particular, our understanding of the complex and highly dynamic physiology of the region from the mid-jejunum to the sigmoid colon could be significantly improved. One approach to the assessment of intestinal permeability is to use animal models that allow these intestinal regions to be investigated in detail and then to compare the results with those from simple human permeability models such as cell cultures. Investigation of intestinal drug permeation processes is a crucial biopharmaceutical step in the development of oral pharmaceutical products. The determination of the intestinal P_eff for a specific drug is dependent on the technique, model, and conditions applied, and is influenced by multiple interactions between the drug molecule and the biological membranes.

In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach

Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.

Formation of a Bile Salt-Drug Hydrogel to Predict Human Intestinal Absorption

The unique character of bile salts to self-assemble into hydrogels in the presence of halide salts was exploited in this work to facilitate the prediction of human intestinal absorption (%HIA) for a set of 25 compounds. This was achieved by firstly incorporating each compound separately within the process of gel formation to create a series of gel-drug membranes. Scanning electron microscopy analysis of the freeze-dried samples of the blank bile salt hydrogels and drug-loaded bile salt hydrogels indicated a unique microstructure made of a network of intertwined fibrils. Drug-loaded sodium deoxycholate hydrogels were then utilized as the donor phase to study permeability using flow-through and static diffusion cells. The resulting values of the release-permeability coefficient (K_p) were then analyzed, along with other molecular descriptors, for the %HIA using multiple linear regression. Overall, when comparing predicted values (using the systems presented in this study) with known literature values, it can be seen that both methods (i.e., using static and flow-through cells) had good predictability with R²_PRED values of 79.8% and 79.7%, respectively. This study therefore proposes a novel, accurate, and precise way to predict HIA for compounds of pharmaceutical interest using a simple in vitro permeation system. It is important to develop alternatives to the current methods used in prediction of HIA, which are expensive and time-consuming or include the use of animals. Therefore, the proposed method in this study being economic and time-saving provides superiority over these current methods and suggests the possibility of its use as an alternate to such methods for prediction of HIA.

A new approach to examining the extraction process of Zhishi and Zhiqiao considering the synergistic effect of complex mixtures by PAMPA

Zhishi (ZS) and Zhiqiao (ZQ) are two important traditional Chinese medicines (TCMs) that exert various pharmacological functions due to their active ingredients. However, the oral absorption of these ingredients requires further study. At the early drug discovery stage, the high-throughput parallel artificial membrane permeability assay (PAMPA) is one of the most frequently used to predict transcellular passive absorption in in-vitro models. This study aims to establish a new approach to examine an optimal extraction process that can take into account not only the concentration of active ingredients but also the overall absorption properties of the mixtures extracted from TCMs. A high-performance liquid chromatography triple-quadrupole mass spectrometry (HPLC-QqQ-MS/MS) method was validated for the determination of the effective permeability value (P_e) applied to the above experimental medium. The PAMPA experiment showed that certain active ingredients such as diosmin, rhoifolin, eriocitrin, narirutin, naringin, hesperidin and neohesperidin were not detected in the permeability assay of mono-constituents but were well detected and achieved a better absorption in the permeability assay of the mixture, indicating that certain unknown ingredients may act as cosolvents to improve the solubility or permeability of other ingredients. Furthermore, solid phase extraction (SPE) as an enrichment and purification process enhances absorption. In the present study, a novel in vitro approach was developed to decipher the potential role of TCMs in global absorption, and the extraction process for complex TCMs was described and systematically optimized.

Exploration of the binding pocket of histone deacetylases: the design of potent and isoform-selective inhibitors

Histone deacetylases (HDACs) are enzymes that act on histone proteins to remove the acetyl group and thereby regulate the chromatin state. HDACs act not only on histone protein but also nonhistone proteins that are key players in cellular processes such as the cell cycle, signal transduction, apoptosis, and more. "Classical" HDACs have been shown to be promising targets for anticancer drug design and development. However, the selectivity of HDAC inhibitors for HDAC isoforms remains the motivation of current research in this field. Here, we explored Class I HDACs and HDAC6 by sequence alignment and structural superimposition, catalytic channel extraction, and identification of critical residues involved in HDAC catalysis. Based on the general pharmacophore features of known HDAC inhibitors, we developed a library of compounds by scaffold hopping on a fragment hit identified via structurebased virtual screening of the molecular fragment library retrieved from the Otava database. Molecular docking assay revealed five of these compounds to have increased potency and selectivity for HDACs 1 and 2. Furthermore, their predicted absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties were consistent with those of drug-like compounds. With further modelingbased and experimental investigations, we believe that these findings may offer additional potential HDAC inhibitors with improved selectivity.

Highly predictive and interpretable models for PAMPA permeability

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery.

Computational methods to design cyclic peptides

Cyclic peptides (CPs) are promising modulators of protein-protein interactions (PPIs), but their application remains challenging. It is currently difficult to predict the structures and bioavailability of CPs. The ability to design CPs using computer modeling would greatly facilitate the development of CPs as potent PPI modulators for fundamental studies and as potential therapeutics. Herein, we describe computational methods to generate CP libraries for virtual screening, as well as current efforts to accurately predict the conformations adopted by CPs. These advances are making it possible to envision robust computational design of active CPs. However, unique properties of CPs pose significant challenges associated with sampling CP conformational space and accurately describing CP energetics. These major obstacles to structure prediction likely must be solved before robust design of active CPs can be reliably achieved.

Translating Human Effective Jejunal Intestinal Permeability to Surface-Dependent Intrinsic Permeability: a Pragmatic Method for a More Mechanistic Prediction of Regional Oral Drug Absorption

Regional intestinal effective permeability (P(eff)) values are key for the understanding of drug absorption along the whole length of the human gastrointestinal (GI) tract. The distal regions of the GI tract (i.e. ileum, ascending-transverse colon) represent the main sites for GI absorption when there is incomplete absorption in the upper GI tract, e.g. for modified release formulations. In this work, a new and pragmatic method for the estimation of (passive) intestinal permeability in the different intestinal regions is being proposed, by translating the observed differences in the available mucosal surface area along the human GI tract into corrections of the historical determined jejunal P(eff) values. These new intestinal P(eff) values or "intrinsic" P(eff)(P(eff,int)) were subsequently employed for the prediction of the ileal absorption clearance (CL(abs,ileum)) for a set of structurally diverse compounds. Additionally, the method was combined with a semi-mechanistic absorption PBPK model for the prediction of the fraction absorbed (f(abs)). The results showed that P(eff,int) can successfully be employed for the prediction of the ileal CL(abs) and the f(abs). P(eff,int) also showed to be a robust predictor of the f(abs) when the colonic absorption was allowed in the PBPK model, reducing the overprediction of f(abs) observed for lowly permeable compounds when using the historical P(eff) values. Due to its simplicity, this approach provides a useful alternative for the bottom-up prediction of GI drug absorption, especially when the distal GI tract plays a crucial role for a drug's GI absorption.

Exploring experimental and computational markers of cyclic peptides: Charting islands of permeability

An increasing number of macrocyclic peptides that cross biological membranes are being reported, suggesting that it might be possible to develop peptides into orally bioavailable therapeutics; however, current understanding of what makes macrocyclic peptides cell permeable is still limited. Here, we synthesized 62 cyclic hexapeptides and characterized their permeability using in vitro assays commonly used to predict in vivo absorption rates, i.e. the Caco-2 and PAMPA assays. We correlated permeability with experimentally measured parameters of peptide conformation obtained using rapid methods based on chromatography and nuclear magnetic resonance spectroscopy. Based on these correlations, we propose a model describing the interplay between peptide permeability, lipophilicity and hydrogen bonding potential. Specifically, peptides with very high permeability have high lipophilicity and few solvent hydrogen bond interactions, whereas peptides with very low permeability have low lipophilicity or many solvent interactions. Our model is supported by molecular dynamics simulations of the cyclic peptides calculated in explicit solvent, providing a structural basis for the observed correlations. This prospective exploration into biomarkers of peptide permeability has the potential to unlock wider opportunities for development of peptides into drugs.

Direct In Vivo Human Intestinal Permeability (Peff ) Determined with Different Clinical Perfusion and Intubation Methods

Regional in vivo human intestinal effective permeability (Peff ) is calculated by measuring the disappearance rate of substances during intestinal perfusion. Peff is the most relevant parameter in the prediction of rate and extent of drug absorption from all parts of the intestine. Today, human intestinal perfusions are not performed on a routine basis in drug development. Therefore, it would be beneficial to increase the accuracy of the in vitro and in silico tools used to evaluate the intestinal Peff of novel drugs. This review compiles historical Peff data from 273 individual measurements of 80 substances from 61 studies performed in all parts of the human intestinal tract. These substances include: drugs, monosaccharaides, amino acids, dipeptides, vitamins, steroids, bile acids, ions, fatty acids, and water. The review also discusses the determination and prediction of Peff using in vitro and in silico methods such as quantitative structure-activity relationship, Caco-2, Ussing chamber, animal intestinal perfusion, and physiologically based pharmacokinetic (PBPK) modeling. Finally, we briefly outline how to acquire accurate human intestinal Peff data by deconvolution of plasma concentration-time profiles following regional intestinal bolus dosing.

Drug discovery and regulatory considerations for improving in silico and in vitro predictions that use Caco-2 as a surrogate for human intestinal permeability measurements

There is a growing need for highly accurate in silico and in vitro predictive models to facilitate drug discovery and development. Results from in vitro permeation studies across the Caco-2 cell monolayer are commonly used for drug permeability screening in industry and are also accepted as a surrogate for human intestinal permeability measurements by the US FDA to support new drug applications. Countless studies carried out in this cell line with published permeability measurements have enabled the development of many in silico prediction models. We identify several common cases that illustrate how using Caco-2 permeability measurements in these in silico and in vitro predictive models will not correlate with human intestinal permeability and will further lead to inaccuracies in these models. We provide guidelines and recommendations for improving these models to more accurately predict clinically relevant information, thereby enhancing the drug discovery, development, and regulatory approval processes.

Intestinal absorption and bioavailability of traditional Chinese medicines: a review of recent experimental progress and implication for quality control

Objectives

Experimental studies on the pharmacokinetics of traditional Chinese medicines (TCMs) have achieved great progress in recent years. This review aims to summarize the progress made on intestinal absorption and bioavailability of TCMs, and proposes the application of intestinal absorption assays as new tools for the quality and safety control of these medicines.

Key findings

Since only the absorbed constituents may produce possible therapeutic effect (except those that directly target the digestive tract), intestinal absorption is of utmost importance for the drug action of TCMs, which are usually taken orally. Meanwhile, complicated drug interactions may occur among the multiple ingredients in a herbal mixture. In this regard, the intestinal permeability assays not only provide useful pharmacokinetic data of TCMs, but have potential applications for quality and safety control. Moreover, knockout animals, 2/4/A1 in-vitro cell model and physiologically-based in-silico models based on the online TCM database can be quite useful for the prediction of absorption and bioavailability of TCMs.

Summary

A variety of in-vivo, in-vitro, in-situ and in-silico models for predicting the intestinal absorption and bioavailability can be applied to study the herbal interactions and screen appropriate biomarkers for the quality and safety control of TCMs.

In vitro blood-brain barrier models: current and perspective technologies

Even in the 21st century, studies aimed at characterizing the pathological paradigms associated with the development and progression of central nervous system diseases are primarily performed in laboratory animals. However, limited translational significance, high cost, and labor to develop the appropriate model (e.g., transgenic or inbred strains) have favored parallel in vitro approaches. In vitro models are of particular interest for cerebrovascular studies of the blood-brain barrier (BBB), which plays a critical role in maintaining the brain homeostasis and neuronal functions. Because the BBB dynamically responds to many events associated with rheological and systemic impairments (e.g., hypoperfusion), including the exposure of potentially harmful xenobiotics, the development of more sophisticated artificial systems capable of replicating the vascular properties of the brain microcapillaries are becoming a major focus in basic, translational, and pharmaceutical research. In vitro BBB models are valuable and easy to use supporting tools that can precede and complement animal and human studies. In this article, we provide a detailed review and analysis of currently available in vitro BBB models ranging from static culture systems to the most advanced flow-based and three-dimensional coculture apparatus. We also discuss recent and perspective developments in this ever expanding research field.

Modeling the pharmacodynamics of passive membrane permeability

Small molecule permeability through cellular membranes is critical to a better understanding of pharmacodynamics and the drug discovery endeavor. Such permeability may be estimated as a function of the free energy change of barrier crossing by invoking the barrier domain model, which posits that permeation is limited by passage through a single “barrier domain” and assumes diffusivity differences among compounds of similar structure are negligible. Inspired by the work of Rezai and co-workers (JACS 128:14073–14080, 2006), we estimate this free energy change as the difference in implicit solvation free energies in chloroform and water, but extend their model to include solute conformational affects. Using a set of eleven structurally diverse FDA approved compounds and a set of thirteen congeneric molecules, we show that the solvation free energies are dominated by the global minima, which allows solute conformational distributions to be effectively neglected. For the set of tested compounds, the best correlation with experiment is obtained when the implicit chloroform global minimum is used to evaluate the solvation free energy difference.

Importance of physicochemical properties for the design of new pesticides

The physicochemical properties of candidate compounds play important roles in the design of new pesticides. Pesticides must be absorbed by pests, be transported to the target site, and then interact with proteins. Hydrophobicity is very important for these processes. Log P, where P is the partition coefficient in the 1-octanol/water system, is commonly used as a hydrophobic descriptor and correlates with membrane permeation and transport. It was recently reported that permeability by the parallel artificial membrane permeation assay (PAMPA) could be used to predict human oral absorption of passively transported compounds. PAMPA, which is a rapid high-throughput screening system, may be useful to predict pesticide absorption because PAMPA permeability can be calculated using log P and other parameters. Electronic and structural properties as well as hydrophobicity are important factors for protein-ligand interaction. To show the importance of physicochemical properties, the classic QSAR and CoMFA of neonicotinoids and prediction of bioavailability of pesticides in terms of membrane permeability in comparison with drugs are described.

The future of toxicity testing

In 2007, the U.S. National Research Council (NRC) released a report, "Toxicity Testing in the 21st Century: A Vision and a Strategy," that proposes a paradigm shift for toxicity testing of environmental agents. The vision is based on the notion that exposure to environmental agents leads to adverse health outcomes through the perturbation of toxicity pathways that are operative in humans. Implementation of the NRC vision will involve a fundamental change in the assessment of toxicity of environmental agents, moving away from adverse health outcomes observed in experimental animals to the identification of critical perturbations of toxicity pathways. Pathway perturbations will be identified using in vitro assays and quantified for dose response using methods in computational toxicology and other recent scientific advances in basic biology. Implementation of the NRC vision will require a major research effort, not unlike that required to successfully map the human genome, extending over 10 to 20 years, involving the broad scientific community to map important toxicity pathways operative in humans. This article provides an overview of the scientific tools and technologies that will form the core of the NRC vision for toxicity testing. Of particular importance will be the development of rapidly performed in vitro screening assays using human cells and cell lines or human tissue surrogates to efficiently identify environmental agents producing critical pathway perturbations. In addition to the overview of the NRC vision, this study documents the reaction by a number of stakeholder groups since 2007, including the scientific, risk assessment, regulatory, and animal welfare communities.