Caco-2 permeability of weakly basic drugs predicted with the Double-Sink PAMPA method

Can membrane permeability of zwitterionic compounds be predicted by the solubility-diffusion model?

Zwitterions contain both positively and negatively charged functional groups, resulting in an overall net neutral charge. Nevertheless, the membrane permeability of the zwitterionic form of a compound is assumed to be much lower than the permeability of the uncharged neutral form. Although a significant proportion of pharmaceuticals are zwitterionic, it has not been clear so far whether their permeability is dominated by the permeation of the zwitterionic or the neutral form, since neutral fractions are often quite low as compared to the zwitterionic fraction. This complicates the in silico prediction of the permeability of zwitterionic compounds. In this work, we re-evaluated existing in vitro permeability data from literature measured with Caco-2/MDCK cell assays, using more strict exclusion criteria for effects like diffusion limitation by the aqueous boundary layers, paracellular transport, active transport and retention. Using this re-evaluated data set, we show that extracted intrinsic permeabilities of the neutral fraction are well predicted by the solubility-diffusion model (RMSE = 1.21; n = 18) if the permeability of the zwitterionic species is assumed negligible. Our work thus suggests that only the neutral species is relevant for the membrane permeability of zwitterionic compounds, and that membrane permeability of zwitterionic compounds is indeed predictable by the solubility-diffusion model.

Unleashing new MTDL AChE and BuChE inhibitors as potential anti-AD therapeutic agents: In vitro, in vivo and in silico studies

Alzheimer's disease (AD) is challenging due to its irreversible declining cognitive symptoms and multifactorial nature. This work tackles targeting both acetylcholinesterase (AChE) and BuChE with a multitarget-directed ligand (MTDL) through design, synthesis, and biological and in silico evaluation of a series of twenty eight new 5-substituted-2-anilino-1,3,4-oxadiazole derivatives 4a-g, 5a-g, 9a-g and 13a-g dual inhibitors of the target biomolecules. In vitro cholinesterases inhibition and selectivity assay of the synthesized derivatives showed excellent nanomolar level inhibitory activities. Compound 5a, the most potent inhibitor, elicited IC50s of 46.9 and 3.5 nM against AChE and BuChE, respectively (SI = 0.07), 5 folds better than the known dual inhibitor Rivastagmine. In vivo and ex vivo investigation showed that 5a significantly inhibited MDA levels and increased GSH contents, thus, attenuating the brain tissue oxidative stress. Additionally, 5a significantly decreased AChE and BuChE levels and inhibited self-mediated β-amyloid aggregation in brains of treated rats. Histopathological and immunohistochemical evaluation demonstrated lessened damage and decreased caspase-3 and VEGF expression levels. In silico prediction of 5a's pharmacokinetics and toxicity profiles reflected promising results. Finally, 5a demonstrated tight binding interactions with the two target biomolecules upon docking along with stable complex formation with its bio-targets throughout the 100 ns MD trajectories.

Predicting the intrinsic membrane permeability of Caco-2/MDCK cells by the solubility-diffusion model

Membrane permeability is one of the main determinants for the absorption, distribution, metabolism and excretion of compounds and is therefore of crucial importance for successful drug development. Experiments with artificial phospholipid membranes have shown that the intrinsic membrane permeability (P0) of compounds is well-predicted by the solubility-diffusion model (SDM). However, using the solubility-diffusion model to predict the P0 of biological Caco-2 and MDCK cell membranes has proven unreliable so far. Recent publications revealed that many published P0 extracted from Caco-2 and MDCK experiments are incorrect. In this work, we therefore used a small self-generated set as well as a large revised set of experimental Caco-2 and MDCK data from literature to compare experimental and predicted P0. The P0 extracted from Caco-2 and MDCK experiments were systematically lower than the P0 predicted by the solubility-diffusion model. However, using the following correlation: log P0,Caco-2/MDCK = 0.84 log P0,SDM - 1.85, P0 of biological Caco-2 and MDCK cell membranes was well-predicted by the solubility-diffusion model.

Pitfalls in evaluating permeability experiments with Caco-2/MDCK cell monolayers

When studying the transport of molecules across biological membranes, intrinsic membrane permeability (P0) is more informative than apparent permeability (Papp), because it eliminates external (setup-specific) factors, provides consistency across experiments and mechanistic insight. It is thus an important building block for modeling the total permeability in any given scenario. However, extracting P0 is often difficult, if not impossible, when the membrane is not the dominant transport resistance. In this work, we set out to analyze Papp values measured with Caco-2/MDCK cell monolayers of 69 literature references. We checked the Papp values for a total of 318 different compounds for the extractability of P0, considering possible limitations by aqueous boundary layers, paracellular transport, recovery issues, active transport, a possible proton flux limitation, and sink conditions. Overall, we were able to extract 77 reliable P0 values, which corresponds to about one quarter of the total compounds analyzed, while about half were limited by the diffusion through the aqueous layers. Compared to an existing data set of P0 values published by Avdeef, our approach resulted in a much higher exclusion of compounds. This is a consequence of stricter compound- and reference-specific exclusion criteria, but also because we considered possible concentration-shift effects due to different pH values in the aqueous layers, an effect only recently described in literature. We thus provide a consistent and reliable set of P0, e.g. as a basis for future modeling.

Impact of the Unstirred Water Layer on the Permeation of Small-Molecule Drugs

Over the last two decades, numerous molecular dynamics (MD) simulation-based investigations have attempted to predict the membrane permeability to small-molecule drugs as indicators of their bioavailability, a majority of which utilize the inhomogeneous solubility diffusion (ISD) model. However, MD-based membrane permeability is routinely 3-4 orders of magnitude larger than the values measured with the intestinal perfusion technique. There have been contentious discussions on the sources of the large discrepancies, and the two indisputable, potentially dominant ones are the fixed protonation state of the permeant and the neglect of the unstirred water layer (UWL). Employing six small-molecule drugs of different biopharmaceutical classification system classes, the current MD study relies on the ISD model but introduces the (de)protonation of the permeant by characterizing the permeation free energy of both neutral and charged states. In addition, the role of the UWL as a potential resistance against permeation is explored. The new MD protocol closely mimics the nature of small-molecule permeation and yields estimates that agree well with in vivo intestinal permeability.

Effects of Aqueous Boundary Layers and Paracellular Transport on the Efflux Ratio as a Measure of Active Transport Across Cell Layers

The efflux ratio (ER), determined by Caco-2/MDCK assays, is the standard in vitro metric to establish qualitatively whether a compound is a substrate of an efflux transporter. However, others have also enabled the utilisation of this metric quantitatively by deriving a relationship that expresses the ER as a function of the intrinsic membrane permeability of the membrane (P0) as well as the permeability of carrier-mediated efflux (Ppgp). As of yet, Ppgp cannot be measured directly from transport experiments or otherwise, but the ER relationship provides easy access to this value if P0 is known. However, previous derivations of this relationship failed to consider the influence of additional transport resistances such as the aqueous boundary layers (ABLs) and the filter on which the monolayer is grown. Since single fluxes in either direction can be heavily affected by these experimental artefacts, it is crucial to consider the potential impact on the ER. We present a model that includes these factors and show both mathematically and experimentally that this simple ER relationship also holds for the more realistic scenario that does not neglect the ABLs/filter. Furthermore, we also show mathematically how paracellular transport affects the ER, and we experimentally confirm that paracellular dominance reduces the ER to unity and can mask potential efflux.

Revisiting the pKa-Flux method for determining intrinsic membrane permeability

Intrinsic membrane permeability is one of several factors that critically determine the intestinal absorption of a chemical. The intrinsic membrane permeability of a chemical is usually extracted from transwell experiments with Caco-2 or MDCK cells, preferably by the pKa-Flux method, which is considered the method of choice when aqueous boundary layer effects need to be excluded. The pKa-Flux method has two variants, the iso-pH method, where apical and basolateral pH are equal, and the gradient-pH method, where apical and basolateral pH are different. The most commonly used method is the gradient-pH method, as it is intended to reflect the pH-conditions in the gastrointestinal tract. However, concentration-shift effects caused by the applied pH-difference between apical and basolateral compartment in the gradient-pH method have not been considered in the evaluation of the experimental data in the past. Consequently, incorrect intrinsic membrane permeabilities have been determined. In this work, we present a revised method for extracting the intrinsic membrane permeability from gradient-pH data that considers concentration-shift effects in the basolateral aqueous boundary layer and filter as well as in the cytosol. Furthermore, we propose the use of the iso-pH method, where only concentration-shift effects in the cytosol need to be considered, as an alternative to the gradient-pH method. We use the five lipophilic bases amantadine, chloroquine, propranolol, venlafaxine and verapamil as examples to compare gradient-pH method and iso-pH method with regard to the extractability of the intrinsic membrane permeability. For lipophilic bases, the iso-pH method proves to be advantageous. All intrinsic membrane permeabilities determined in this work were substantially higher than the intrinsic membrane permeabilities reported in literature.

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.

Predictions from First-Principles of Membrane Permeability to Small Molecules: How Useful Are They in Practice?

Predicting from first-principles the rate of passive permeation of small molecules across the biological membrane represents a promising strategy for screening lead compounds upstream in the drug-discovery and development pipeline. One popular avenue for the estimation of permeation rates rests on computer simulations in conjunction with the inhomogeneous solubility-diffusion model, which requires the determination of the free-energy change and position-dependent diffusivity of the substrate along the translocation pathway through the lipid bilayer. In this Perspective, we will clarify the physical meaning of the membrane permeability inferred from such computer simulations, and how theoretical predictions actually relate to what is commonly measured experimentally. We will also examine why these calculations remain both technically challenging and overly computationally expensive, which has hitherto precluded their routine use in nonacademic settings. We finally synopsize possible research directions to meet these challenges, increase the predictive power of physics-based rates of passive permeation, and, by ricochet, improve their practical usefulness.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Plasma Protein-Mediated Uptake and Contradictions to the Free Drug Hypothesis: A Critical Review

According to the free drug hypothesis (FDH), only free, unbound drug is available to interact with biological targets. This hypothesis is the fundamental principle that continues to explain the vast majority of all pharmacokinetic and pharmacodynamic processes. Under the FDH, the free drug concentration at the target site is considered the driver of pharmacodynamic activity and pharmacokinetic processes. However, deviations from the FDH are observed in hepatic uptake and clearance predictions, where observed unbound intrinsic hepatic clearance (CL_int,u) is larger than expected. Such deviations are commonly observed when plasma proteins are present and form the basis of the so-called plasma protein-mediated uptake effect (PMUE). This review will discuss the basis of plasma protein binding as it pertains to hepatic clearance based on the FDH, as well as several hypotheses that may explain the underlying mechanisms of PMUE. Notably, some, but not all, potential mechanisms remained aligned with the FDH. Finally, we will outline possible experimental strategies to elucidate PMUE mechanisms. Understanding the mechanisms of PMUE and its potential contribution to clearance underprediction is vital to improving the drug development process.

Development of artificial neural network models to predict the PAMPA effective permeability of new, orally administered drugs active against the coronavirus SARS-CoV-2

Responding to the pandemic caused by SARS-CoV-2, the scientific community intensified efforts to provide drugs effective against the virus. To strengthen these efforts, the "COVID Moonshot" project has been accepting public suggestions for computationally triaged, synthesized, and tested molecules. The project aimed to identify molecules of low molecular weight with activity against the virus, for oral treatment. The ability of a drug to cross the intestinal cell membranes and enter circulation decisively influences its bioavailability, and hence the need to optimize permeability in the early stages of drug discovery. In our present work, as a contribution to the ongoing scientific efforts, we employed artificial neural network algorithms to develop QSAR tools for modelling the PAMPA effective permeability (passive diffusion) of orally administered drugs. We identified a set of 61 features most relevant in explaining drug cell permeability and used them to develop a stacked regression ensemble model, subsequently used to predict the permeability of molecules included in datasets made available through the COVID Moonshot project. Our model was shown to be robust and may provide a promising framework for predicting the potential permeability of molecules not yet synthesized, thus guiding the process of drug design.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13721-023-00410-9.

In Vivo Toxicity, Redox-Modulating Capacity and Intestinal Permeability of Novel Aroylhydrazone Derivatives as Anti-Tuberculosis Agents

The emergence and spread of Mycobacterium tuberculosis strains resistant to many or all anti-tuberculosis (TB) drugs require the development of new compounds both efficient and with minimal side effects. Structure-activity-toxicity relationships of such novel, structurally diverse compounds must be thoroughly elucidated before further development. Here, we present the aroylhydrazone compounds (3a and 3b) regarding their: (i) acute and subacute toxicity in mice; (ii) redox-modulating in vivo and in vitro capacity; (iii) pathomorphology in the liver, kidney, and small intestine tissue specimens; and (iv) intestinal permeability. The acute toxicity test showed that the two investigated compounds exhibited low toxicity by oral and intraperitoneal administration. Changes in behavior, food amount, and water intake were not observed during 14 days of the oral administration at two doses of 1/10 and 1/20 of the LD₅₀. The histological examination of the different tissue specimens did not show toxic changes. The in vitro antioxidant assays confirmed the ex vivo results. High gastrointestinal tract permeability at all tested pH values were demonstrated for both compounds. To conclude, both compounds 3a and 3b are highly permeable with low toxicity and can be considered for further evaluation and/or lead optimization.

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

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

Impact of cholesterol and sphingomyelin on intrinsic membrane permeability

Transwell experiments with Caco-2 or MDCK cells are the gold standard for determining the intestinal permeability of chemicals. The intrinsic membrane permeability (P₀), that can be extracted from these experiments, might be comparable to P₀ measured in black lipid membrane (BLM) experiments and P₀ predicted by the solubility-diffusion model. Unfortunately, the overlap between experimental P_{0,Caco-2/MDCK} and P_0,BLM data is very small. So far, differences between both approaches have been attributed to the cholesterol and sphingomyelin content of cell membranes, but the database is too sparse to thoroughly test this theory. To create a diverse dataset, we measured P_0,BLM of ten chemicals in BLM experiments using DPhPC and DPhPC/cholesterol/sphingomyelin membranes. The results were compared to predicted BLM data and experimental Caco-2/MDCK data obtained from literature. While P_0,BLM of all chemicals was well predicted by the solubility-diffusion model, P_{0,Caco-2/MDCK} was only predictable for rather hydrophilic compounds with logarithmic hexadecane/water partition coefficients below -0.5. The effect of cholesterol and sphingomyelin on P_0,BLM was negligibly small.

Permeability of Olmesartan Medoxomil from Lipid Based and Suspension Formulations using an Optimized HDM-PAMPA Model

Hexadecane membrane-parallel artificial membrane permeability assay (HDM-PAMPA) is based on an artificial hexadecane membrane that separates the two compartments (donor and acceptor compartment). This model is used to predict the permeability of drugs in gastrointestinal tract and to simulate the passive absorption. In vivo behaviour of the drugs can be estimated with these systems in drug development studies. In our study we optimized HDM-PAMPA model to determine permeability of olmesartan medoxomil (OM) lipid based drug delivery system (OM-LBDDS). In order to prove that LBDDS formulation facilitates the weak permeability of OM, permeation rates were compared with the OM suspension formula (containing 0.25% v/w CMC). The experiment was performed on a 96-well MultiScreen® PAMPA filter plate (MAIPN4510). The permeability of olmesartan formulations from the donor to acceptor compartment separated by a HDM membrane were determined by the previous validated HPLC method. We created positive control series without coating hexadecane membrane to present the LBDDS and suspension formulation permeability from uncoated plates. The effective permeability constant (P_e) was calculated by the formula and improvement of permeability of OM-LBDDS formulation from hexadecane membrane was confirmed. On the contrary there was no permeation of OM-Suspension in the hexadecane coated plates. As a result, the intestinal permeability of OM-LBDDS was calculated to be at least 100 times more than the suspension. OM-Suspension permeation was only observed in the hexadecane uncoated positive control plates. This was also manifestation of HDM-PAMPA mimicking permeability of intestines because of its lipidic construction.

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/).

Assessing the Intestinal Permeability of Small Molecule Drugs via Diffusion Motion on a Multidimensional Free Energy Surface

A protocol that accurately assesses the intestinal permeability of small molecule compounds plays an essential role in decreasing the cost and time in inventing a new drug. This manuscript presents a novel computational method to study the passive permeation of small molecule drugs based on the inhomogeneous solubility-diffusion model. The multidimensional free energy surface of the drug transiting through a lipid bilayer is computed with transition-tempered metadynamics that accurately captures the mechanisms of passive permeation. The permeability is computed by following the diffusion motion of the drug molecules along the minimal free energy path found on the multidimensional free energy surface. This computational method is assessed by studying the permeability of five small molecule drugs (ketoprofen, naproxen, metoprolol, propranolol, and salicylic acid). The results demonstrate a remarkable agreement between the computed permeabilities and those measured with the intestinal assay. The in silico method reported in this manuscript also reproduces the permeability measured from the intestinal assay (in vivo) better than the cell-based assays (e.g., PAMPA and Caco-2) do. In addition, the multidimensional free energy surface reveals the interplay between the structure of the small molecule and its permeability, shedding light on strategies of drug optimization.

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

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

The Whole Is Bigger than the Sum of Its Parts: Drug Transport in the Context of Two Membranes with Active Efflux

Cell envelope plays a dual role in the life of bacteria by simultaneously protecting it from a hostile environment and facilitating access to beneficial molecules. At the heart of this ability lie the restrictive properties of the cellular membrane augmented by efflux transporters, which preclude intracellular penetration of most molecules except with the help of specialized uptake mediators. Recently, kinetic properties of the cell envelope came into focus driven on one hand by the urgent need in new antibiotics and, on the other hand, by experimental and theoretical advances in studies of transmembrane transport. A notable result from these studies is the development of a kinetic formalism that integrates the Michaelis-Menten behavior of individual transporters with transmembrane diffusion and offers a quantitative basis for the analysis of intracellular penetration of bioactive compounds. This review surveys key experimental and computational approaches to the investigation of transport by individual translocators and in whole cells, summarizes key findings from these studies and outlines implications for antibiotic discovery. Special emphasis is placed on Gram-negative bacteria, whose envelope contains two separate membranes. This feature sets these organisms apart from Gram-positive bacteria and eukaryotic cells by providing them with full benefits of the synergy between slow transmembrane diffusion and active efflux.

Drug-Like Small Molecule HSP27 Functional Inhibitor Sensitizes Lung Cancer Cells to Gefitinib or Cisplatin by Inducing Altered Cross-Linked Hsp27 Dimers

Relationships between heat shock protein 27 (HSP27) and cancer aggressiveness, metastasis, drug resistance, and poor patient outcomes in various cancer types including non-small cell lung cancer (NSCLC) were reported, and inhibition of HSP27 expression is suggested to be a possible strategy for cancer therapy. Unlike HSP90 or HSP70, HSP27 does not have an ATP-binding pocket, and no effective HSP27 inhibitors have been identified. Previously, NSCLC cancer cells were sensitized to radiation and chemotherapy when co-treated with small molecule HSP27 functional inhibitors such as zerumbone (ZER), SW15, and J2 that can induce abnormal cross-linked HSP27 dimer. In this study, cancer inhibition effects of NA49, a chromenone compound with better solubility, longer circulation time, and less toxicity than J2, were examined in combination with anticancer drugs such as cisplatin and gefitinib in NSCLC cell lines. When the cytotoxic drug cisplatin was treated in combination with NA49 in epidermal growth factor receptors (EGFRs) WT cell lines, sensitization was induced in an HSP27 expression-dependent manner. With gefitinib treatment, NA49 showed increased combination effects in both EGFR WT and Mut cell lines, also with HSP27 expression-dependent patterns. Moreover, NA49 induced sensitization in EGFR Mut cells with a secondary mutation of T790M when combined with gefitinib. Augmented tumor growth inhibition was shown with the combination of cisplatin or gefitinib and NA49 in nude mouse xenograft models. These results suggest the combination of HSP27 inhibitor NA49 and anticancer agents as a candidate for overcoming HSP27-mediated drug resistance in NSCLC patients.

Exatecan Antibody Drug Conjugates Based on a Hydrophilic Polysarcosine Drug-Linker Platform

We herein report the development and evaluation of a novel HER2-targeting antibody-drug conjugate (ADC) based on the topoisomerase I inhibitor payload exatecan, using our hydrophilic monodisperse polysarcosine (PSAR) drug-linker platform (PSARlink). In vitro and in vivo experiments were conducted in breast and gastric cancer models to characterize this original ADC and gain insight about the drug-linker structure-activity relationship. The inclusion of the PSAR hydrophobicity masking entity efficiently reduced the overall hydrophobicity of the conjugate and yielded an ADC sharing the same pharmacokinetic profile as the unconjugated antibody despite the high drug-load of the camptothecin-derived payload (drug-antibody ratio of 8). Tra-Exa-PSAR10 demonstrated strong anti-tumor activity at 1 mg/kg in an NCI-N87 xenograft model, outperforming the FDA-approved ADC DS-8201a (Enhertu), while being well tolerated in mice at a dose of 100 mg/kg. In vitro experiments showed that this exatecan-based ADC demonstrated higher bystander killing effect than DS-8201a and overcame resistance to T-DM1 (Kadcyla) in preclinical HER2+ breast and esophageal models, suggesting potential activity in heterogeneous and resistant tumors. In summary, the polysarcosine-based hydrophobicity masking approach allowsfor the generation of highly conjugated exatecan-based ADCs having excellent physicochemical properties, an improved pharmacokinetic profile, and potent in vivo anti-tumor activity.

Predicting PAMPA permeability using the 3D-RISM-KH theory: are we there yet?

The parallel artificial membrane permeability assay (PAMPA), a non-cellular lab-based assay, is extensively used to measure the permeability of pharmaceutical compounds. PAMPA experiments provide a working mimic of a molecule passing through cells and PAMPA values are widely used to estimate drug absorption parameters. There is an increased interest in developing computational methods to predict PAMPA permeability values. We developed an in silico model to predict the permeability of compounds based on the PAMPA assay. We used the three-dimensional reference interaction site model (3D-RISM) theory with the Kovalenko-Hirata (KH) closure to calculate the excess chemical potentials of a large set of compounds and predicted their apparent permeability with good accuracy (mean absolute error or MAE = 0.69 units) when compared to a published experimental data set. Furthermore, our in silico PAMPA protocol performed very well in the binary prediction of 288 compounds as being permeable or impermeable (precision = 94%, accuracy = 93%). This suggests that our in silico protocol can mimic the PAMPA assay and could aid in the rapid discovery or screening of potentially therapeutic drug leads that can be delivered to a desired tissue.

Oral delivery of protein-based therapeutics: Gastroprotective strategies, physiological barriers and in vitro permeability prediction

The number of biological molecules emerging as therapeutics is growing exponentially due to their higher specificity and tolerability profiles compared to small molecules. Despite this, their traditionally parenteral delivery often results in poor patient compliance and incomplete treatment. Current research is focussed on developing effective oral delivery strategies to facilitate administration of these biomolecules, however no universal method exists to simultaneously provide gastric protection as well as enhance transport across the gastrointestinal epithelium. Furthermore, for efficient formulation development it is imperative that we can reliably analyse permeability of biomolecules through the gastrointestinal tract, highlighting the importance of the continual development and ongoing evaluation of in vitro predictive permeability tools. Here, we review the physiological obstacles associated with peptide and protein delivery throughout the gastrointestinal tract. Furthermore, we highlight methods utilised to circumvent these barriers and promote improved intestinal permeability. Lastly, we explore in vitro models employed to predict epithelial transport. Key findings highlight the need to carefully understand gastrointestinal physiology, allowing specific engineering of oral delivery systems for biomolecules. Significant importance is placed upon understanding enzymatic degradation susceptibility as well as uptake mechanisms for particulate and protein-based therapeutics for the development of successful oral protein delivery platforms.

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

•

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

•

A multivariate prediction equation for permeability of chemicals was proposed.

•

Chemical permeability coefficients were inversely associated with hepatic NOELs.

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

MolMeDB: Molecules on Membranes Database

Biological membranes act as barriers or reservoirs for many compounds within the human body. As such, they play an important role in pharmacokinetics and pharmacodynamics of drugs and other molecular species. Until now, most membrane/drug interactions have been inferred from simple partitioning between octanol and water phases. However, the observed variability in membrane composition and among compounds themselves stretches beyond such simplification as there are multiple drug–membrane interactions. Numerous experimental and theoretical approaches are used to determine the molecule–membrane interactions with variable accuracy, but there is no open resource for their critical comparison. For this reason, we have built Molecules on Membranes Database (MolMeDB), which gathers data about over 3600 compound–membrane interactions including partitioning, penetration and positioning. The data have been collected from scientific articles published in peer-reviewed journals and complemented by in-house calculations from high-throughput COSMOmic approach to set up a baseline for further comparison. The data in MolMeDB are fully searchable and browsable by means of name, SMILES, membrane, method or dataset and we offer the collected data openly for further reuse and we are open to further additions. MolMeDB can be a powerful tool that could help researchers better understand the role of membranes and to compare individual approaches used for the study of molecule/membrane interactions.

QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network

The aim of this study is to establish a promising QSPR model for the Caco-2 permeability prediction.

Prediction of Passive Membrane Permeability by Semi-Empirical Method Considering Viscous and Inertial Resistances and Different Rates of Conformational Change and Diffusion

Membrane permeability is an important property of drugs in adsorption. Many prediction methods work well for small molecules, but the prediction of middle-molecule permeability is still difficult. In the present study, we modified a classical permeability model based on Fick's law to study passive membrane permeability. The model consisted of the distribution of solute from water to membrane and the diffusion of solute in each solvent. The diffusion coefficient is the inverse of the resistance, and we examined the inertial resistance in addition to the viscous resistance, the latter of which has been widely used in permeability prediction. Also, we examined three models changing the balance between the diffusion of solute in membrane and the conformational change of solute. The inertial resistance improved the prediction results in addition to the viscous resistance. The models worked well not only for small molecules but also for middle molecules, whose structures have more conformational freedom.

Prediction Characteristics of Oral Absorption Simulation Software Evaluated Using Structurally Diverse Low-Solubility Drugs

The purpose of the present study was to characterize current biopharmaceutics modeling and simulation software regarding the prediction of the fraction of a dose absorbed (Fa) in humans. As commercial software products, GastroPlus™ and Simcyp® were used. In addition, the gastrointestinal unified theoretical framework, a simple and publicly accessible model, was used as a benchmark. The Fa prediction characteristics for a total of 96 clinical Fa data of 27 model drugs were systematically evaluated using the default settings of each software product. The molecular weight, dissociation constant, octanol-water partition coefficient, solubility in biorelevant media, dose, and particle size of model drugs were used as input data. Although the same input parameters were used, GastroPlus™, Simcyp®, and the gastrointestinal unified theoretical framework showed different Fa prediction characteristics depending on the rate-limiting steps of oral drug absorption. The results of the present study would be of great help for the overall progression of physiologically based absorption models.

Synthesis, Physicochemical Properties, and Biological Activities of 4-(S-Methyl-N-(2,2,2-Trifluoroacetyl)Sulfilimidoyl) Anthranilic Diamide

Novel anthranilic diamides with sulfilimidoyl and sulfoximidoyl functionalities were successfully prepared. Among newly-prepared organosulfur compounds, 3-bromo-1-(3-chloropyridin-2-yl)-N-(2-methyl-6-(methylcarbamoyl)-4-(methylthio)phenyl)-1H-pyrazole-5-carboxamide and (S,E)-3-bromo-1-(3-chloropyridin-2-yl)-N-(2-methyl-4-(S-methyl-N-(2,2,2-trifluoroacetyl)sulfinimidoyl)-6-(methylcarbamoyl)phenyl)-1H-pyrazole-5-carboxamide showed good levels of efficacy and a strong correlation between insecticidal activities and physical properties, respectively. In particular, available data indicated that the N-trifluoroacetyl sulfilimine moiety could be an appealing structural scaffold for the discovery of a new crop-protecting agent.

Dynamic Protonation Dramatically Affects the Membrane Permeability of Drug-like Molecules

Permeability (P_m) across biological membranes is of fundamental importance and a key factor in drug absorption, distribution, and development. Although the majority of drugs will be charged at some point during oral delivery, our understanding of membrane permeation by charged species is limited. The canonical model assumes that only neutral molecules partition into and passively permeate across membranes, but there is mounting evidence that these processes are also facile for certain charged species. However, it is unknown whether such ionizable permeants dynamically neutralize at the membrane surface or permeate in their charged form. To probe protonation-coupled permeation in atomic detail, we herein apply continuous constant-pH molecular dynamics along with free energy sampling to study the permeation of a weak base propranolol (PPL), and evaluate the impact of including dynamic protonation on P_m. The simulations reveal that PPL dynamically neutralizes at the lipid-tail interface, which dramatically influences the permeation free energy landscape and explains why the conventional model overestimates the assigned intrinsic permeability. We demonstrate how fixed-charge-state simulations can account for this effect, and propose a revised model that better describes pH-coupled partitioning and permeation. Our results demonstrate how dynamic changes in protonation state may play a critical role in the permeation of ionizable molecules, including pharmaceuticals and drug-like molecules, thus requiring a revision of the standard picture.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning

Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA), and effects of partial charge sets, CGenFF, AM1-BCC, and OPLS, on the performance of the predictions were discussed. (D)HDGB-based models improved the predictions over the two-state implicit membrane models, and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions, although it could not outperform the physics-based approach in terms of correlations.

Physicochemical QSAR Analysis of Passive Permeability Across Caco-2 Monolayers

Caco-2 cell line is frequently used as a simplified in vitro model of intestinal absorption. In this study, a database of 1366 Caco-2 permeability coefficients (P_e) for 768 diverse drugs and drug-like compounds was compiled from public sources. The collected data represent permeation rates measured at varying experimental conditions (pH from 4.0 to 8.0, and stirring rates from 0 to >1000 rpm) that presumably account for passive diffusion across mucosal epithelium. These data were subjected to multistep nonlinear regression analysis using a minimal set of physicochemical descriptors (octanol-water log D, pKa, hydrogen bonding potential, and molecular size). The model was constructed in a mechanistic manner incorporating the following components: (i) a hydrodynamic equation of size- and charge-specific along with nonspecific diffusion across the paracellular pathway; (ii) transcellular diffusion represented by thermodynamic membrane/water partitioning ratio; (iii) stirring-dependent limit of maximum achievable permeability due to the presence of unstirred water layer. The obtained model demonstrates good accuracy of log P_e predictions with a residual mean square error <0.5 log units for all training and validation sets. Given its robust performance and straightforward interpretation in terms of simple physicochemical properties, the proposed model may serve as a valuable tool to guide drug discovery efforts toward readily absorbable compounds.

Using pH Gradient Dissolution with In-Situ Flux Measurement to Evaluate Bioavailability and DDI for Formulated Poorly Soluble Drug Products

This study described a pH-gradient dissolution method combined with flux measurements as an in vitro tool for assessing the risk of bioavailability reduction due to drug-drug interactions (DDI) caused by acid reducing agents (ARAs). The device incorporates absorption chambers into USP II dissolution vessels, with fiber optic UV-probes monitoring concentration in situ. Dosage forms of Genentech BCS class II drugs, GDC-0810, GDC-0941, and compound A, were tested by starting the dissolution in either pH 1.6 or pH 4.0 media then converting to FaSSIF after 30 min. GDC-0810 showed no significant difference in flux between the two conversion experiments. A supersaturation phase was observed for GDC-0941 in the pH 1.6 experiments after media conversion to FaSSIF; however, it did not appear to occur in the pH 4.0 experiment due to low drug solubility at pH 4.0, resulting in a 95% decrease in flux compared to pH 1.6 experiment. The extent of flux reduction and the total accumulated API mass in the absorption chamber agreed well with the 89% reduction in mean Cmax and the 82% reduction in mean AUC from dog PK study between animals treated with pentagastrin and famotidine. Testing of the compound A optimized formulation tablets showed a 25% reduction in flux and in vitro absorbed amount by changing pH 1.6 to 4.0, correlating well with the AUC decrease in clinical studies. Good correlation between in vitro data and in vivo PK data demonstrated the applicability of the method for formulators to develop drug products mitigating DDI from ARAs.

MDCKpred: a web-tool to calculate MDCK permeability coefficient of small molecule using membrane-interaction chemical features

Structure-based models to understand the transport of small molecules through biological membrane can be developed by enumerating intermolecular interactions of the small molecule with a biological membrane, usually a dimyristoylphosphatidylcholine (DMPC) monolayer. This ADME (absorption, distribution, metabolism, and excretion) property based on Madin-Darby Canine Kidney (MDCK) cell line demonstrates intestinal drug absorption of small molecules and correlated to human intestinal absorption which acts as a determining factor to forecast small-molecule prioritization in drug-discovery projects. We present here the development of MDCKpred web-tool which calculates MDCK permeability coefficient of small molecule based on the regression model, developed using membrane-interaction chemical features. The web-tool allows users to calculate the MDCK permeability coefficient (nm/s) instantly by providing simple descriptor inputs. The chemical-interaction features are derived from different parts of the DMPC molecule viz. head, middle, and tail regions and accounts overall intermolecular contacts of the small molecule when passively diffused through the phospholipid-rich biological membrane. The MDCKpred model is both internally (R² = .76; [Formula: see text]= .68; R_train = .87; R_test = .69) and externally (R_ext = .55) validated. Furthermore, we used natural molecules as application examples to demonstrate its utility in lead exploration and optimization projects. The MDCKpred web-tool can be accessed freely at http://www.mdckpred.in . This web-tool is designed to offer an intuitive way of prioritizing small molecules based on calculated MDCK permeabilities.

Molecular transport through membranes: Accurate permeability coefficients from multidimensional potentials of mean force and local diffusion constants

Estimating the permeability coefficient of small molecules through lipid bilayer membranes plays an important role in the development of effective drug candidates. In silico simulations can produce acceptable relative permeability coefficients for a series of small molecules; however, the absolute permeability coefficients from simulations are usually off by orders of magnitude. In addition to differences between the lipid bilayers used in vitro and in silico, the poor convergence of permeation free energy profiles and over-simplified diffusion models have contributed to these discrepancies. In this paper, we present a multidimensional inhomogeneous solubility-diffusion model to study the permeability of a small molecule drug (trimethoprim) passing through a POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) lipid bilayer. Our approach improves the permeation model in three ways: First, the free energy profile (potential of mean force, PMF) is two-dimensional in two key coordinates rather than simply one-dimensional along the direction normal to the bilayer. Second, the 2-D PMF calculation has improved convergence due to application of the recently developed transition-tempered metadynamics with randomly initialized replicas, while third, the local diffusivity coefficient was calculated along the direction of the minimum free energy path on the two-dimensional PMF. The permeability is then calculated as a line integral along the minimum free energy path of the PMF. With this approach, we report a considerably more accurate permeability coefficient (only 2-5 times larger than the experimental value). We also compare our approach with the common practice of computing permeability coefficients based only on the translation of the center of mass of the drug molecule. Our paper concludes with a discussion of approaches for minimizing the computational cost for the purpose of more rapidly screening a large number of drug candidate molecules.

Digging into Lipid Membrane Permeation for Cardiac Ion Channel Blocker d-Sotalol with All-Atom Simulations

Interactions of drug molecules with lipid membranes play crucial role in their accessibility of cellular targets and can be an important predictor of their therapeutic and safety profiles. Very little is known about spatial localization of various drugs in the lipid bilayers, their active form (ionization state) or translocation rates and therefore potency to bind to different sites in membrane proteins. All-atom molecular simulations may help to map drug partitioning kinetics and thermodynamics, thus providing in-depth assessment of drug lipophilicity. As a proof of principle, we evaluated extensively lipid membrane partitioning of d-sotalol, well-known blocker of a cardiac potassium channel K_v11.1 encoded by the hERG gene, with reported substantial proclivity for arrhythmogenesis. We developed the positively charged (cationic) and neutral d-sotalol models, compatible with the biomolecular CHARMM force field, and subjected them to all-atom molecular dynamics (MD) simulations of drug partitioning through hydrated lipid membranes, aiming to elucidate thermodynamics and kinetics of their translocation and thus putative propensities for hydrophobic and aqueous hERG access. We found that only a neutral form of d-sotalol accumulates in the membrane interior and can move across the bilayer within millisecond time scale, and can be relevant to a lipophilic channel access. The computed water-membrane partitioning coefficient for this form is in good agreement with experiment. There is a large energetic barrier for a cationic form of the drug, dominant in water, to cross the membrane, resulting in slow membrane translocation kinetics. However, this form of the drug can be important for an aqueous access pathway through the intracellular gate of hERG. This route will likely occur after a neutral form of a drug crosses the membrane and subsequently re-protonates. Our study serves to demonstrate a first step toward a framework for multi-scale in silico safety pharmacology, and identifies some of the challenges that lie therein.

Closed-Loop Doluisio (Colon, Small Intestine) and Single-Pass Intestinal Perfusion (Colon, Jejunum) in Rat-Biophysical Model and Predictions Based on Caco-2

PURPOSE

The effective rat intestinal permeability (P _eff ) was deconvolved using a biophysical model based on parameterized paracellular, aqueous boundary layer, transcellular permeabilities, and the villus-fold surface area expansion factor.

METHODS

Four types of rat intestinal perfusion data were considered: single-pass intestinal perfusion (SPIP) in the jejunum (n = 40), and colon (n = 15), closed-loop (Doluisio type) in the small intestine (n = 78), and colon (n = 74). Moreover, in vitro Caco-2 permeability values were used to predict rat in vivo values in the rat data studied.

RESULTS

Comparable number of molecules permeate via paracellular water channels as by the lipoidal transcellular route in the SPIP method, although in the closed-loop method, the paracellular route appears dominant in the colon. The aqueous boundary layer thickness in the small intestine is comparable to that found in unstirred in vitro monolayer assays; it is thinner in the colon. The mucosal surface area in anaesthetized rats is 0.96-1.4 times the smooth cylinder calculated value in the colon, and it is 3.1-3.6 times in the small intestine. The paracellular permeability of the intestine appeared to be greater in rat than human, with the colon showing more leakiness (higher P _para ) than the small intestine.

CONCLUSION

Based on log intrinsic permeability values, the correlations between the in vitro and in vivo models ranged from r² 0.82 to 0.92. The SPIP-Doluisio method comparison indicated identical log permeability selectivity trend with negligible bias.

Predicting apparent passive permeability of Caco-2 and MDCK cell-monolayers: A mechanistic model

Experimentally derived apparent permeabilities, Papp, through cell monolayers such as Caco-2 and MDCK are considered to be an in-vitro gold standard for assessing the uptake efficiency of drugs. Here, we present a mechanistic model that describes 'passive' Papp values (i.e., neglecting active transport) by accounting for the different resistances solutes encounter when permeating a cell monolayer. We described three parallel permeation pathways, namely a cytosolic-, paracellular-, and lateral route, each of which consists of a number of serial resistances. These resistances were accounted for via a mechanistic depiction of the underlying processes that are largely based on literature work. For the present Papp dataset, about as much chemicals are dominated by the cytosolic route as were dominated by the paracellular route, while the lateral route was negligible. For the cytosolic route by far the most chemicals found their main resistance in the various water layers and not in the membrane. Although correlations within the subclasses of chemicals dominated by a specific permeation route were rather poor, we could overall satisfyingly predict Papp for 151 chemicals at a pH of 7.4 (R2 = 0.77, RMSE = 0.48). For a specific evaluation of the intrinsic membrane permeability, Pm, a second experimental dataset based on experiments with black lipid membranes, BLM, was evaluated. Pm could be predicted for 37 chemicals with R2 = 0.91 and RMSE = 0.64 log units.

Impact of capillary flow hydrodynamics on carrier-mediated transport of opioid derivatives at the blood-brain barrier, based on pH-dependent Michaelis-Menten and Crone-Renkin analyses

Most studies of blood-brain barrier (BBB) permeability and transport are conducted at a single pH, but more detailed information can be revealed by using multiple pH values. A pH-dependent biophysical model was applied to the mechanistic analysis of published pH-dependent BBB luminal uptake data from three opioid derivatives in rat: pentazocine (Suzuki et al., 2002a, 2002b), naloxone (Suzuki et al., 2010a), and oxycodone (Okura et al., 2008). Two types of data were processed: in situ brain perfusion (ISBP) and brain uptake index (BUI). The published perfusion data were converted to apparent luminal permeability values, P_app, and analyzed by the pCEL-X program (Yusof et al., 2014), using the pH-dependent Crone-Renkin equation (pH-CRE) to determine the impact of cerebrovascular flow on the Michaelis-Menten transport parameters (Avdeef and Sun, 2011). For oxycodone, the ISBP data had been measured at pH7.4 and 8.4. The present analysis indicates a 7-fold lower value of the cerebrovascular flow velocity, F_pf, than that expected in the original study. From the pyrilamine-inhibited data, the flow-corrected passive intrinsic permeability value was determined to be P₀=398×10^-6cm·s^-1. The uptake data indicate that the neutral form of oxycodone is affected by a transporter at pH8.4. The extent of the cation uptake was less certain from the available data. For pentazocine, the brain uptake by the BUI method had been measured at pH5.5, 6.5, and 7.4, in a concentration range 0.1-40mM. Under similar conditions, ISBP data were also available. The pH-CRE determined values of F_pf from both methods were nearly the same, and were smaller than the expected value in the original publication. The transport of the cationic pentazocine was not fully saturated at pH5.5 at 40mM. The transport of the neutral species at pH7.4 appeared to reach saturation at 40mM pentazocine concentration, but not at 12mM. In the case of naloxone, a pH-dependent Michaelis-Menten equation (pH-MME) analysis of the data indicated a smooth sigmoidal transition from a higher capacity uptake process affecting cationic naloxone (pH5.0-7.0) to a lower capacity uptake process affecting the neutral drug (pH8.0-8.5), with cross-over point near pH7.4. Evidently, measurements at multiple pH values can reveal important information about both cerebrovascular flow and BBB transport kinetics.

Continuous Intestinal Absorption Model Based on the Convection-Diffusion Equation

Prediction of the rate and extent of drug absorption upon oral dosing needs models that capture the complexities of both the drug molecule and intestinal physiology. We report here the development of a continuous intestinal absorption model based on the convection-diffusion equation. The model includes explicit enterocyte apical membrane and intracellular lipid radial compartments along the length of the intestine. Physiologic functions along length x are built into the model and include velocity, diffusion, surface areas, and pH of the intestine. Also included are expression levels of the intestinal active uptake transporter OATP2B1 and efflux transporter P-gp. Oral dosing of solution as well as solid (with a dissolution function) was modeled for several drugs. The fraction absorbed (FA) and concentration-time (C-t) profiles were predicted and compared with clinical data. Overall, FA was well predicted upon oral (n = 21) or colonic dosing (n = 11), with four outliers. The overall accuracy (prediction of the correct bin) was 81% with outliers and 90% without outliers. Of the nine solution dosing data sets, six drugs were very well predicted with an exposure overlap coefficient (EOC) > 0.9 and predicted C_max and T_max values similar to those observed. Of the six solid dose formulations evaluated, the EOC values were > 0.9 for all drugs except budesonide. The observed precipitation of nifedipine at high doses was predicted by the model. Most of the poor predictions were for drugs that are known to be transporter substrates. As proof of concept, incorporating OATP2B1 and P-gp markedly improved the EOC and predicted C_max and T_max for fexofenadine. Finally, the continuous intestinal model accurately recapitulated the known relationships between drug absorption and permeability, solubility, and particle size. Together, these results indicate that this preliminary intestinal absorption model offers a simple and straightforward framework to build in complexities such as drug permeability, lipid partitioning, solubility, metabolism, and transport for improved prediction of the rate and extent of drug absorption.

Development of Cassette PAMPA for Permeability Screening

The parallel artificial membrane permeability assay (PAMPA) is widely used in early-stage drug discovery to discriminate compounds by intestinal permeability. The purpose of the current study was to establish a cassette (n-in-1) PAMPA to enable permeability screening of lipophilic compounds. A double-sink PAMPA consisting of a pH gradient (i.e., pH 6.5 and 7.4 for the donor and receiver compartments, respectively) and a lipophilic sink (i.e., a surfactant in the receiver solution) was utilized with cassette incubation of 10 reference compounds. Sample analysis was conducted using selected reaction monitoring (SRM) with a triple quadrupole LC-MS/MS system. Correlation between PAMPA permeability and human intestinal absorption (HIA) of the reference compounds yielded two false negatives, namely propranolol (PPN) and verapamil (VER); these two compounds showed a substantially lower recovery (ca. 10%) than other reference compounds (>69%). This cassette PAMPA was repeated subsequently with polysorbate 80 added to the donor compartments, which resulted in a significant increase in both the recovery and the permeability of the false negatives. Accordingly, the permeability class of all reference compounds could be unambiguously differentiated using this cassette PAMPA. Also, a strong linear correlation (r=0.9845) was observed between the cassette and discrete permeability of all reference compounds.