Modeling the local absorption and retention patterns of membrane-permeant small molecules in a cellular context could facilitate development of site-directed chemical agents for bioimaging or therapeutic applications. Here, we present an integrative approach to this problem, combining in silico computational models, in vitro cell based assays and in vivo biodistribution studies. To target small molecule probes to the epithelial cells of the upper airways, a multiscale computational model of the lung was first used as a screening tool, in silico. Following virtual screening, cell monolayers differentiated on microfabricated pore arrays and multilayer cultures of primary human bronchial epithelial cells differentiated in an air-liquid interface were used to test the local absorption and intracellular retention patterns of selected probes, in vitro. Lastly, experiments involving visualization of bioimaging probe distribution in the lungs after local and systemic administration were used to test the relevance of computational models and cell-based assays, in vivo. The results of in vivo experiments were consistent with the results of in silico simulations, indicating that mitochondrial accumulation of membrane permeant, hydrophilic cations can be used to maximize local exposure and retention, specifically in the upper airways after intratracheal administration.
We have developed an integrative, cell-based modeling approach to facilitate the design and discovery of chemical agents directed to specific sites of action within a living organism. Here, a computational, multiscale transport model of the lung was adapted to enable virtual screening of small molecules targeting the epithelial cells of the upper airways. In turn, the transport behaviors of selected candidate probes were evaluated to establish their degree of retention at a site of absorption, using computational simulations as well as two in vitro cell-based assay systems. Lastly, bioimaging experiments were performed to examine candidate molecules' distribution in the lungs of mice after local and systemic administration. Based on computational simulations, the higher mitochondrial density per unit absorption surface area is the key parameter determining the higher retention of small molecule hydrophilic cations in the upper airways, relative to lipophilic weak bases, specifically after intratracheal administration.
In the body, cell monolayers serve as permeability barriers, determining transport of drug molecules from one organ or tissue compartment to another. After oral administration, for example, drug transport across the epithelial cell monolayer lining the lumen of the intestine determines the fraction of drug in the gut that is absorbed by the body. By modeling passive transcellular transport properties in the presence of an apical to basolateral concentration gradient, we demonstrate how a computational, cell-based molecular transport simulator can be used to define a physicochemical property space occupied by molecules with desirable permeability and intracellular retention characteristics. Considering extracellular domains of cell surface receptors located on the opposite side of a cell monolayer as a drug’s desired site-of-action, simulation of transcellular transport can be used to define the physicochemical properties of molecules with maximal transcellular permeability but minimal intracellular retention. Arguably, these molecules would possess very desirable features: least likely to exhibit non-specific toxicity, metabolism and side effects associated with high (undesirable) intracellular accumulation; and, most likely to exhibit favorable bioavailability and efficacy associated with maximal rates of transport across cells and minimal intracellular retention, resulting in (desirable) accumulation at the extracellular site-of-action. Calculated permeability predictions showed good correlations with PAMPA, Caco2, and intestinal permeability measurements, without “training” the model and without resorting to statistical regression techniques to “fit” the data. Therefore, cell-based molecular transport simulators could be useful in silico screening tools for chemical genomics and drug discovery.
Metoprolol; permeability; chemical space; computer aided drug design; virtual screening; chemical genomics; cellular pharmacokinetics; cheminformatics; drug transport; PAMPA; Biopharmaceutics Classification System
Computer-aided drug design (CADD) often involves virtual screening (VS) of large compound datasets and the availability of such is vital for drug discovery protocols. This paper presents an assessment of the “drug-likeness” and pharmacokinetic profile of > 2,400 compounds of natural origin, currently available in the recently published StreptomeDB database.
The evaluation of “drug-likeness” was performed on the basis of Lipinski’s “Rule of Five”, while 46 computed physicochemical properties or molecular descriptors were used to predict the absorption, distribution, metabolism, elimination and toxicity (ADMET) of the compounds.
This survey demonstrated that, of the computed molecular descriptors, about 28% of the compounds within the StreptomeDB database were compliant, having properties which fell within the range of ADMET properties of 95% of currently known drugs, while about 44% of the compounds had ≤ 2 violations. Moreover, about 50% of the compounds within the corresponding “drug-like” subset showed compliance, while >83% of the “drug-like” compounds had ≤ 2 violations.
In addition to the previously verified range of measured biological activities, the compounds in the StreptomeDB database show interesting DMPK profiles and hence could represent an important starting point for hit/lead discovery from natural sources. The generated data are available and could be highly useful for natural product lead generation programs.
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The online version of this article (doi:10.1186/2193-1801-2-353) contains supplementary material, which is available to authorized users.
3D structures; Streptomyces; Database; In silico; Natural products; Virtual screening
The present study was aimed to predict the absorption profile of a risperidone immediate release tablet (IR) and to develop the level A in vitro–in vivo correlation (IVIVC) of the drug using the gastrointestinal simulation based on the advanced compartmental absorption and transit model implemented in GastroPlus™. Plasma concentration data, physicochemical, and pharmacokinetic properties of the drug were used in building its absorption profile in the gastrointestinal tract. Since the fraction absorbed of risperidone in simulation was more than 90% with low water solubility, the drug met the criteria of class II of the Biopharmaceutics Classification System. The IVIVC was developed based on the model built using the plasma data and the in vitro dissolution data in several dissolution media based on the Japanese Guideline for Bioequivalence Studies of Generic Products. The gastrointestinal absorption profile of risperidone was successfully predicted. A level A IVIVC was also successfully developed in all dissolution media with percent prediction error for Cmax and the area under the curve less than 10% for both reference and test drug.
GastroPlus™; immediate release tablet; in vitro–in vivo correlation; risperidone
As part of our effort to increase survival of drug candidates and to move our medicinal chemistry design to higher probability space for success in the Neuroscience therapeutic area, we embarked on a detailed study of the property space for a collection of central nervous system (CNS) molecules. We carried out a thorough analysis of properties for 119 marketed CNS drugs and a set of 108 Pfizer CNS candidates. In particular, we focused on understanding the relationships between physicochemical properties, in vitro ADME (absorption, distribution, metabolism, and elimination) attributes, primary pharmacology binding efficiencies, and in vitro safety data for these two sets of compounds. This scholarship provides guidance for the design of CNS molecules in a property space with increased probability of success and may lead to the identification of druglike candidates with favorable safety profiles that can successfully test hypotheses in the clinic.
Central nervous system (CNS); CNS drugs; CNS candidates; lipophilicity; topological polar surface area; polarity; molecular weight; hydrogen bond donor; most basic pKa; high-throughput screening; passive permeability; Madin−Darby canine kidney; P-glycoprotein; human liver microsome stability; unbound intrinsic clearance; ligand efficiency; ligand-lipophilicity efficiency; ligand-efficiency-dependent lipophilicity; drug−drug interactions; dofetilide binding; transformed human liver epithelial cells; cellular toxicity
Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration–time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1–4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound’s BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions.
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The online version of this article (doi:10.1208/s12248-012-9419-5) contains supplementary material, which is available to authorized users.
absorption modeling; BCS/BDDCS; food effect prediction; human PBPK model; oral bioavailability
The absorption, distribution, metabolism and excretion (ADME) and pharmacokinetic (PK) properties of inorganic nanoparticles with hydrodynamic diameters between 2 and 20 nm are presently unpredictable. It is unclear whether unpredictable in vivo properties and effects arise from a subset of molecules in a nanomaterials preparation, or if the ADME/PK properties are ensemble properties of an entire preparation. Here we characterize the ADME/PK properties of atomically precise preparations of ligand protected gold nanoclusters in a murine model system. We constructed atomistic models and tested in vivo properties for five well defined compounds, based on crystallographically resolved Au25(SR)18 and Au102(SR)44 nanoclusters with different (SR) ligand shells. To rationalize unexpected distribution and excretion properties observed for several clusters in this study and others, we defined a set of atomistic structure–activity relationships (SAR) for nanoparticles, which includes previously investigated parameters such as particle hydrodynamic diameter and net charge, and new parameters such as hydrophobic surface area and surface charge density. Overall we find that small changes in particle formulation can provoke dramatic yet potentially predictable changes in ADME/PK.
A biophysical, computational model of cell pharmacokinetics (1CellPK) is being developed to enable prediction of the intracellular accumulation and transcellular transport properties of small molecules using their calculated physicochemical properties as input. To test if 1CellPK can generate accurate, quantitative hypotheses and guide experimental analysis of the transcellular transport kinetics of small molecules, epithelial cells were grown on impermeable polyester membranes with cylindrical pores and chloroquine (CQ) was used as a transport probe. The effect of the number of pores and their diameter on transcellular transport of CQ was measured in apical-to-basolateral or basolateral-to-apical directions, at pH 7.4 and 6.5 in the donor compartment. Experimental and simulation results were consistent with a phospholipid bilayer-limited, passive diffusion transport mechanism. In experiments and 1CellPK simulations, intracellular CQ mass and the net rate of mass transport varied <2-fold although total pore area per cell varied >10-fold, so by normalizing the net rate of mass transport by the pore area available for transport, cell permeability on 3µm pore diameter membranes was more than an order of magnitude less than on 0.4µm pore diameter membranes. The results of simulations of transcellular transport were accurate for the first four hours of drug exposure, but those of CQ mass accumulation were accurate only for the first five minutes. Upon prolonged incubation, changes in cellular parameters such as lysosome pH rise, lysosome volume expansion, and nuclear shrinkage were associated with excess CQ accumulation. Based on the simulations, lysosome volume expansion alone can partly account for the measured, total intracellular CQ mass increase, while adding the intracellular binding of the protonated, ionized forms of CQ (as reflected in the measured partition coefficient of CQ in detergent-permeabilized cells at physiological pH) can further improve the intracellular CQ mass accumulation prediction.
Systems Biology; Epithelial Cells; Membrane Transport; Mathematical Models; Pharmacokinetics; Cell Permeability
Peptides are of great therapeutic potential as vaccines and drugs. Knowledge
of physicochemical descriptors, including the partition coefficient P (commonly
expressed in logarithm form: logP), is useful for screening out unsuitable
molecules and also for the development of predictive Quantitative
Structure-Activity Relationships (QSARs). In this paper we develop a new
approach to the prediction of LogP values for peptides based on an empirical
relationship between global molecular properties and measured physical
properties. Our method was successful in terms of peptide prediction (total
r2 = 0.641). The final model consisted of 5 physicochemical
descriptors (molecular weight, number of single bonds, 2D-VDW volume, 2D-VSA
hydrophobic and 2D-VSA polar). The approach is peptide specific and its
predictive accuracy was high. Overall, 67% of the peptides were able to
be predicted within +/-0.5 log units from the experimental values. Our method
thus represents a novel prediction method with proven predictive ability.
peptide; log P; partition coefficient; octanol-water; regression; physicochemical descriptor; hydrophobicity
Polyphenols, a group of complex naturally occurring compounds, are widely distributed throughout the plant kingdom and are therefore readily consumed by humans. The relationship between their chemical structure and intestinal absorption, transport, and first-pass metabolism remains unresolved, however.
Here, we investigated the intestinal absorption and first-pass metabolism of four polyphenol compounds, apigenin, resveratrol, emodin and chrysophanol, using the in vitro Caco-2 cell monolayer model system and in situ intestinal perfusion and in vivo pharmacokinetic studies in rats, so as to better understand the relationship between the chemical structure and biological fate of the dietary polyphenols.
After oral administration, emodin and chrysophanol exhibited different absorptive and metabolic behaviours compared to apigenin and resveratrol. The differences in their chemical structures presumably resulted in differing affinities for drug-metabolizing enzymes, such as glucuronidase and sulphatase, and transporters, such as MRP2, SGLT1, and P-glycoprotein, which are found in intestinal epithelial cells.
Synthetic combinatorial methods now make it practical to readily produce hundreds of thousands of individual compounds, but it is clearly impractical to screen each separately in vivo. We theorized that the direct in vivo testing of mixture-based combinatorial libraries during the discovery phase would enable the identification of novel individual compounds with desirable antinociceptive profiles while simultaneously eliminating many compounds with poor absorption, distribution, metabolism, or pharmacokinetic properties. The TPI 1346 small-molecule combinatorial library is grouped in 120 mixtures derived from 26 functionalities at the first three positions and 42 functionalities at the fourth position of a pyrrolidine bis-cyclic guanidine core scaffold, totaling 738,192 compounds. These 120 mixtures were screened in vivo using the mouse 55°C warm water tail-withdrawal assay to identify mixtures producing antinociception. From these data, two fully defined individual compounds (TPI 1818-101 and TPI 1818-109) were synthesized. These were examined for antinociceptive, respiratory, locomotor, and conditioned place preference effects. The tail-withdrawal assay consistently demonstrated distinctly active mixtures with analgesic activity that was blocked by pretreatment with the non-selective opioid antagonist, naloxone. Based on these results, synthesis and testing of TPI 1818-101 and 1818-109 demonstrated a dose-dependent antinociceptive effect three to five times greater than morphine that was antagonized by mu- or mu- and kappa-opioid receptor selective antagonists, respectively. Neither 1818-101 nor 1818-109 produced significant respiratory depression, hyperlocomotion, or conditioned place preference. Large, highly diverse mixture-based libraries can be screened directly in vivo to identify individual compounds, potentially accelerating the development of promising therapeutics.
analgesia; in vivo; mixture-based libraries; opioid; testing
The purpose of this study was to examine absorption of basic drugs as a function of the composite solubility curve and intestinally relevant pH by using a gastrointestinal tract (GIT) absorption simulation based on the advanced compartmental absorption and transit model. Absorption simulations were carried out for virtual monobasic drugs having a range of pKa, log D, and dose values as a function of presumed solubility and permeability. Results were normally expressed as the combination that resulted in 25% absorption. Absorption of basic drugs was found to be a function of the whole solubility/pH relationship rather than a single solubility value at pH 7. In addition, the parameter spaces of greatest sensitivity were identified. We compared 3 theoretical scenarios: the GIT pH range overlapping (1) only the salt solubility curve, (2) the salt and base solubility curves, or (3) only the base curve. Experimental solubilities of 32 compounds were determined at pHs of 2.2 and 7.4, and they nearly all fitted into 2 of the postulated scenarios. Typically, base solubilities can be simulated in silico, but salt solubilities at low pH can only be measured. We concluded that quality absorption simulations of candidate drugs in most cases require experimental solubility determination at 2 pHs, to permit calculation of the whole solubility/pH profile.
GIT; absorption simulation; pH solubility curve; BCS; solid-state properties; solubility screening
Cell-based molecular transport simulations are being developed to facilitate exploratory cheminformatic analysis of virtual libraries of small drug-like molecules. For this purpose, mathematical models of single cells are built from equations capturing the transport of small molecules across membranes. In turn, physicochemical properties of small molecules can be used as input to simulate intracellular drug distribution, through time. Here, with mathematical equations and biological parameters adjusted so as to mimic a leukocyte in the blood, simulations were performed to analyze steady state, relative accumulation of small molecules in lysosomes, mitochondria, and cytosol of this target cell, in the presence of a homogenous extracellular drug concentration. Similarly, with equations and parameters set to mimic an intestinal epithelial cell, simulations were also performed to analyze steady state, relative distribution and transcellular permeability in this non-target cell, in the presence of an apical-to-basolateral concentration gradient. With a test set of ninety-nine monobasic amines gathered from the scientific literature, simulation results helped analyze relationships between the chemical diversity of these molecules and their intracellular distributions.
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The online version of this article (doi:10.1007/s10822-008-9194-7) contains supplementary material, which is available to authorized users.
Cheminformatics; Lysosomotropic; Cellular pharmacokinetics; Drug transport; Small molecule permeability; Subcellular localization; Simulation; Rational drug design
The goal of this study was to apply gastrointestinal simulation technology and integration of physiological parameters to predict biopharmaceutical drug classification. GastroPlus® was used with experimentally determined physicochemical and pharmacokinetic drug properties to simulate the absorption of several weak acid and weak base BCS class II compounds. Simulation of oral drug absorption given physicochemical drug properties and physicochemical parameters will aid justification of biowaivers for selected BCS class II compounds.
absorption; bioavailability; bioequivalence; biowaiver; permeability; solubility
While drug discovery scientists take heed of various guidelines concerning drug-like character, the influence of acid/base properties often remains under-scrutinised. Ionisation constants (pKa values) are fundamental to the variability of the biopharmaceutical characteristics of drugs and to underlying parameters such as logD and solubility. pKa values affect physicochemical properties such as aqueous solubility, which in turn influences drug formulation approaches. More importantly, absorption, distribution, metabolism, excretion and toxicity (ADMET) are profoundly affected by the charge state of compounds under varying pH conditions. Consideration of pKa values in conjunction with other molecular properties is of great significance and has the potential to be used to further improve the efficiency of drug discovery. Given the recent low annual output of new drugs from pharmaceutical companies, this review will provide a timely reminder of an important molecular property that influences clinical success.
Adequate bioavailability is one of the essential properties for an orally administered drug. Lipinski and others have formulated simplified rules in which compounds that satisfy selected physiochemical properties, for example, molecular weight (MW)≤500, or logarithm of octanol-water partition coefficient logP(o/w)<5, are anticipated to likely have pharmacokinetic properties appropriate for oral administration. However, these schemes do not simultaneously consider the combination of the physiochemical properties, complicating their application in a more automated fashion. To overcome this we present a novel method to select compounds with a combination of physicochemical properties that maximize bioavailability and druglikeness, based on compounds in the World Drug Index (WDI) database. In the study four properties, MW, logP(o/w), number of hydrogen bond donors and number of hydrogen acceptors were combined into a 4-D histogram, from which a scoring function was defined based on a 4D dependent multivariate Gaussian model. The resulting equation allows for assigning compounds a bioavailability score, termed 4D-BA, such that chemicals with higher 4D-BA scores are more likely to have oral drug-like characteristics. The descriptor is validated by applying the function to drugs previously categorized in the Biopharmaceutics Classification System and examples of application of the descriptor are given in the context of previously published studies targeting heme oxygenase and SHP2 phosphatase. The approach is anticipated to be useful in early lead identification studies in combination with clustering methods to maximize chemical and structural diversity when selecting compounds for biological assays from large database screens. It may also be applied to prioritize synthetically feasible chemical modifications during lead compound optimization.
Drug metabolism and pharmacokinetic (DMPK) assessment has come to occupy a place of interest during the early stages of drug discovery today. Computer-based methods are slowly gaining ground in this area and are often used as initial tools to eliminate compounds likely to present uninteresting pharmacokinetic profiles and unacceptable levels of toxicity from the list of potential drug candidates, hence cutting down the cost of the discovery of a drug.
In the present study, we present an in silico assessment of the DMPK profile of our recently published natural products database of 1,859 unique compounds derived from 224 species of medicinal plants from the Cameroonian forest. In this analysis, we have used 46 computed physico-chemical properties or molecular descriptors to predict the absorption, distribution, metabolism and elimination (ADME) of the compounds. This survey demonstrated that about 50% of the compounds within the Cameroonian medicinal plant and natural products (CamMedNP) database are compliant, having properties which fall within the range of ADME properties of >95% of currently known drugs, while >73% of the compounds have ≤2 violations. Moreover, about 72% of the compounds within the corresponding ‘drug-like’ subset showed compliance.
In addition to the previously verified levels of ‘drug-likeness’ and the diversity and the wide range of measured biological activities, the compounds in the CamMedNP database show interesting DMPK profiles and, hence, could represent an important starting point for hit/lead discovery from medicinal plants in Africa.
ADMET; Database collection; Descriptors; In silico; Medicinal plants; Natural products
Chemogenomics methods seek to characterize the interaction between drugs and biological systems and are an important guide for the selection of screening compounds. The acid/base character of drugs has a profound influence on their affinity for the receptor, on their absorption, distribution, metabolism, excretion and toxicity (ADMET) profile and the way the drug can be formulated. In particular, the charge state of a molecule greatly influences its lipophilicity and biopharmaceutical characteristics.
This study investigates the acid/base profile of human small molecule drugs, chemogenomics datasets and screening compounds including a natural products set. We estimate the ionization constants (pKa values) of these compounds and determine the identity of the ionizable functional groups in each set. We find substantial differences in acid/base profiles of the chemogenomic classes. In many cases, these differences can be linked to the nature of the target binding site and the corresponding functional groups needed for recognition of the ligand. Clear differences are also observed between the acid/base characteristics of drugs and screening compounds. For example, the proportion of drugs containing a carboxylic acid was 20%, in stark contrast to a value of 2.4% for the screening set sample. The proportion of aliphatic amines was 27% for drugs and only 3.4% for screening compounds. This suggests that there is a mismatch between commercially available screening compounds and the compounds that are likely to interact with a given chemogenomic target family. Our analysis provides a guide for the selection of screening compounds to better target specific chemogenomic families with regard to the overall balance of acids, bases and pKa distributions.
Acid; Acidity; Base; Basicity; Chemogenomics; Drug discovery; Functional groups; GPCR; Ion channels; Ionization constant; Kinases; pKa
In silico screening based on the structures of the ligands or of the receptors has become an essential tool to facilitate the drug discovery process but compound collections are needed to carry out such in silico experiments. It has been recognized that absorption, distribution, metabolism, excretion and toxicity (ADME/tox) are key properties that need to be considered early on, even during the database preparation stage. FAF-Drugs is an online service based on Frowns (a chemoinformatics toolkit) that allows users to process their own compound collections via simple ADME/Tox filtering rules such as molecular weight, polar surface area, logP or number of rotatable bonds. SMILES (Simplified Molecular Input Line Entry System), CANSMILES (canonical smiles) or SDF (structure data file) files are required as input and molecules that pass or do not pass the filters are sent back in CANSMILES format. This service should thus help scientists engaging in drug discovery campaigns. Other utilities and several compound collections suitable for in silico screening are available at our site. FAF-Drugs can be accessed at .
Kinetic solubility measurements using prototypical assay buffer conditions are presented for a ~58,000 member library of small molecules. Analyses of the data based upon physical and calculated properties of each individual molecule were performed and resulting trends were considered in the context of commonly held opinions of how physicochemical properties influence aqueous solubility. We further analyze the data using a decision tree model for solubility prediction and via a multi-dimensional assessment of physicochemical relationships to solubility in the context of specific ‘rule-breakers’ relative to common dogma. The role of solubility as a determinant of assay outcome is also considered based upon each compound’s cross-assay activity score for a collection of publicly available screening results. Further, the role of solubility as a governing factor for colloidal aggregation formation within a specified assay setting is examined and considered as a possible cause of a high cross-assay activity score. The results of this solubility profile should aid chemists during library design and optimization efforts and represents a useful training set for computational solubility prediction.
Gold nanocages have recently emerged as a novel class of photothermal transducers and drug carriers for cancer treatment. However, their pharmacokinetics and tumor targeting capability remain to be largely unexplored due to the lack of an imaging modality for quick and reliable mapping of their distributions in vivo. Herein, Au nanocages were prepared with controlled physicochemical properties and radiolabeled with 64Cu in high specific activities for in vivo evaluation using positron emission tomography (PET). Our pharmacokinetic studies with femtomolar administrations suggest that nanocages of 30 nm in size had a greatly improved biodistribution profile than nanocages of 55 nm in size, together with higher blood retention and lower hepatic and splenic uptakes. In a murine EMT-6 breast cancer model, the small cages also showed a significantly higher level of tumor uptake and a greater tumor-to-muscle ratio than the large cages. Quantitative PET imaging confirmed rapid accumulation and retention of Au nanocages inside the tumors. The ability to directly and quickly image the distribution of Au nanocages in vivo allows us to further optimize their physicochemical properties for a range of theranostic applications.
gold nanocage; radiolabeling; positron emission tomography (PET); biodistribution; cancer targeting
Nutrient absorption in the small intestine cannot occur until molecules are presented to the epithelial cells that line intestinal villi, finger-like protrusions under enteric control. Using a two-dimensional multiscale lattice Boltzmann model of a lid-driven cavity flow with ‘villi’ at the lower surface, we analyse the hypothesis that muscle-induced oscillatory motions of the villi generate a controlled ‘micro-mixing layer’ (MML) that couples with the macro-scale flow to enhance absorption. Nutrient molecules are modelled as passive scalar concentrations at high Schmidt number. Molecular concentration supplied at the cavity lid is advected to the lower surface by a lid-driven macro-scale eddy. We find that micro-scale eddying motions enhance the macro-scale advective flux by creating an MML that couples with the macro-scale flow to increase absorption rate. We show that the MML is modulated by its interactions with the outer flow through a diffusion-dominated layer that separates advection-dominated macro-scale and micro-scale mixed layers. The structure and strength of the MML is sensitive to villus length and oscillation frequency. Our model suggests that the classical explanation for the existence of villi—increased absorptive surface area—is probably incorrect. The model provides support for the potential importance of villus motility in the absorptive function of the small intestine.
gastrointestinal; intestine; gut; absorption; villi; lattice Boltzmann method
Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties.
The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/−bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction.
The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.
Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making.
This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries.
We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.
Prodrugs are chemistry-enabled drug delivery modifications of active molecules designed to enhance their pharmacokinetic, pharmacodynamic and/or biopharmaceutical properties. Ideally, prodrugs are efficiently converted in vivo, through chemical or enzymatic transformations, to the active parent molecule. The goal of this work is to enhance the colonic absorption of a drug molecule with a short half-life via a prodrug approach to deliver sustained plasma exposure and enable once daily (QD) dosing. The compound has poor absorption in the colon and by the addition of a promoiety to block the ionization of the molecule as well as increase lipophilicity, the relative colonic absorption increased from 9% to 40% in the retrograde dog colonic model. A combination of acceptable solubility and stability in the gastrointestinal tract (GI) as well as permeability was used to select suitable prodrugs to optimize colonic absorption.
prodrugs; colonic absorption; dog colonic studies; enabling QD dosing; chemistry-enabled drug delivery; enhancing lipophilicity