ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification

Development of scoring-assisted generative exploration (SAGE) and its application to dual inhibitor design for acetylcholinesterase and monoamine oxidase B

De novo molecular design is the process of searching chemical space for drug-like molecules with desired properties, and deep learning has been recognized as a promising solution. In this study, I developed an effective computational method called Scoring-Assisted Generative Exploration (SAGE) to enhance chemical diversity and property optimization through virtual synthesis simulation, the generation of bridged bicyclic rings, and multiple scoring models for drug-likeness. In six protein targets, SAGE generated molecules with high scores within reasonable numbers of steps by optimizing target specificity without a constraint and even with multiple constraints such as synthetic accessibility, solubility, and metabolic stability. Furthermore, I suggested a top-ranked molecule with SAGE as dual inhibitors of acetylcholinesterase and monoamine oxidase B through multiple desired property optimization. Therefore, SAGE can generate molecules with desired properties by optimizing multiple properties simultaneously, indicating the importance of de novo design strategies in the future of drug discovery and development. SCIENTIFIC CONTRIBUTION: The scientific contribution of this study lies in the development of the Scoring-Assisted Generative Exploration (SAGE) method, a novel computational approach that significantly enhances de novo molecular design. SAGE uniquely integrates virtual synthesis simulation, the generation of complex bridged bicyclic rings, and multiple scoring models to optimize drug-like properties comprehensively. By efficiently generating molecules that meet a broad spectrum of pharmacological criteria-including target specificity, synthetic accessibility, solubility, and metabolic stability-within a reasonable number of steps, SAGE represents a substantial advancement over traditional methods. Additionally, the application of SAGE to discover dual inhibitors for acetylcholinesterase and monoamine oxidase B not only demonstrates its potential to streamline and enhance the drug development process but also highlights its capacity to create more effective and precisely targeted therapies. This study emphasizes the critical and evolving role of de novo design strategies in reshaping the future of drug discovery and development, providing promising avenues for innovative therapeutic discoveries.

Dopaminergic Modulation and Computational ADMET Insights for the Antidepressant-like Effect of N-(3-(Phenylselanyl)prop-2-yn-1-yl)benzamide

The compound N-(3-(phenylselanyl)prop-2-yn-1-yl)benzamide (SePB), which combines a selenium atom and a benzamide nucleus in an organic structure, has demonstrated a fast antidepressant-like effect in mice. This action is influenced by the serotonergic system and represents a promising development in the search for novel antidepressant drugs to treat major depressive disorder (MDD), which often resists conventional treatments. This study aimed to further explore the mechanism underlying the antidepressant-like effect of SePB by investigating the involvement of the dopaminergic and noradrenergic systems in the tail suspension test (TST) in mice and evaluating its pharmacokinetic profile in silico. Preadministration of the dopaminergic antagonists haloperidol (0.05 mg/kg, intraperitoneally (i.p.)), a nonselective antagonist of dopamine (DA) receptors, SCH23390 (0.01 mg/kg, subcutaneously (s.c.)), a D1 receptor antagonist, and sulpiride (50 mg/kg, i.p.), a D2/3 receptor antagonist, before SePB (10 mg/kg, intragastrically (i.g.)) prevented the anti-immobility effect of SePB in the TST, demonstrating that these receptors are involved in the antidepressant-like effect of SePB. Administration of the noradrenergic antagonists prazosin (1 mg/kg, i.p.), an α1-adrenergic antagonist, yohimbine (1 mg/kg, i.p.), an α2-adrenergic antagonist, and propranolol (2 mg/kg, i.p.), a β-adrenergic antagonist, did not block the antidepressant-like effect of SePB on TST, indicating that noradrenergic receptors are not involved in this effect. Additionally, the coadministration of SePB and bupropion (a noradrenaline/dopamine reuptake inhibitor) at subeffective doses (0.1 and 3 mg/kg, respectively) produced antidepressant-like effects. SePB also demonstrated good oral bioavailability and low toxicity in computational absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses. These findings suggest that SePB has potential as a new antidepressant drug candidate with a particular focus on the dopaminergic system.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

Anti-leishmanial activity of Eleutherine plicata Herb. and predictions of isoeleutherin and its analogues

Introduction: Leishmaniasis is caused by protozoa of the genus Leishmania, classified as tegumentary and visceral. The disease treatment is still a serious problem, due to the toxic effects of available drugs, the costly treatment and reports of parasitic resistance, making the search for therapeutic alternatives urgent. This study assessed the in vitro anti-leishmanial potential of the extract, fractions, and isoeleutherin from Eleutherine plicata, as well as the in silico interactions of isoeleutherin and its analogs with Trypanothione Reductase (TR), in addition to predicting pharmacokinetic parameters. Methods: From the ethanolic extract of E. plicata (EEEp) the dichloromethane fraction (FDEp) was obtained, and isoeleutherin isolated. All samples were tested against promastigotes, and parasite viability was evaluated. Isoeleutherin analogues were selected based on similarity in databases (ZINK and eMolecules) to verify the impact on structural change. Results and Discussion: The extract and its fractions were not active against the promastigote form (IC50 > 200 μg/mL), while isoeleutherin was active (IC50 = 25 μg/mL). All analogues have high intestinal absorption (HIA), cell permeability was moderate in Caco2 and low to moderate in MDCK. Structural changes interfered with plasma protein binding and blood-brain barrier permeability. Regarding metabolism, all molecules appear to be CYP3A4 metabolized and inhibited 2-3 CYPs. Molecular docking and molecular dynamics assessed the interactions between the most stable configurations of isoeleutherin, analogue compound 17, and quinacrine (control drug). Molecular dynamics simulations demonstrated stability and favorable interactions with TR. In summary, fractionation contributed to antileishmanial activity and isoleutherin seems to be promising. Structural alterations did not contribute to improve pharmacokinetic aspects and analogue 17 proved to be more promising than isoeleutherin, presenting better stabilization in TR.

ISABELA Studies: Plasma Exposure and Target Engagement Do Not Explain the Lack of Efficacy of Ziritaxestat in Patients with Idiopathic Pulmonary Fibrosis

Autotaxin (ATX) contributes to the production of lysophosphatidic acid (LPA), which is associated with fibrosis development in idiopathic pulmonary fibrosis (IPF). The ATX inhibitor ziritaxestat failed to reduce decline in forced vital capacity (FVC) in patients with IPF in ISABELA 1 and 2 (NCT03711162 and NCT03733444), two identically designed phase III studies. In the current analysis, we evaluated pharmacokinetic and pharmacodynamic data from the pooled ISABELA studies to determine whether the lack of efficacy could be attributed to insufficient exposure and/or target engagement. Nonlinear mixed effect modeling was performed to predict ziritaxestat exposure in individual patients and describe its effect on LPA C18:2 levels. We assessed whether there was a correlation between ziritaxestat and ATX concentration and evaluated the relationship between LPA C18:2 reduction and change from baseline in FVC. Ziritaxestat exposure in patients with IPF was numerically lower in those who received ziritaxestat on top of pirfenidone than in those who received ziritaxestat on top of nintedanib or ziritaxestat alone. In most patients, LPA C18:2 reduction was comparable to that reported in healthy volunteers. ATX concentrations increased over time and correlated weakly with ziritaxestat exposure and LPA C18:2 reduction. No correlation between reduction in LPA C18:2 and change from baseline in FVC was apparent. Based on these evaluations, exposure and target engagement are not thought to have contributed to the lack of efficacy observed. We hypothesize that the lack of efficacy of ziritaxestat in the ISABELA program, despite adequate LPA reduction, could be due to the involvement of an alternative pro-fibrotic pathway.

3D-QSAR, docking and molecular dynamics simulations of novel Pyrazolo-pyridazinone derivatives as covalent inhibitors of FGFR1: a scientific approach for possible anticancer agents

Developing highly potent covalent inhibitors of Fibroblast growth factor receptors 1 (FGFR1) has always been a challenging task. In the current study, various computational techniques, such as 3D-QSAR, covalent docking, fingerprinting analysis, MD simulation followed by MMGB/PBSA, and per-residue energy decomposition analysis were used to explore the binding mechanism of pyrazolo[3,4-d]pyridazinone derivatives to FGFR1. The high q2 and r2 values for the CoMFA and CoMSIA models, suggest that the constructed 3D-QSAR models could reliably predict the bioactivities of FGFR1 inhibitors. The structural requirements revealed by the model's contour maps were strategically used to computationally create an in-house library of more than 100 new FGFR1 inhibitors using the R-group exploration technique implemented in the SparkTM software. The compounds from the in-house library were also mapped in the 3D-QSAR model that predicts comparable pIC50 values with the experimental values. A comparison between 3D-QSAR generated contours and molecular docking conformation of ligands was performed to reveal the fundamentals to design potent FGFR1 covalent inhibitors. The estimated binding free energies (MMGB/PBSA) for the selected compounds were in agreement with the experimental value ranking of their binding affinities towards FGFR1. Furthermore, per-residue energy decomposition analysis has identified Arg627 and Glu531 to contribute significantly in improved binding affinity of compound W16. During ADME analysis, the majority of in-house library compounds exhibited pharmacokinetic properties superior to those of experimentally produced compounds. These new compounds may help researchers better understand FGFR1 inhibition and lead to the creation of novel, potent FGFR1 inhibitors.Communicated by Ramaswamy H. Sarma.

Real-World Molecular Out-Of-Distribution: Specification and Investigation

This study presents a rigorous framework for investigating molecular out-of-distribution (MOOD) generalization in drug discovery. The concept of MOOD is first clarified through a problem specification that demonstrates how the covariate shifts encountered during real-world deployment can be characterized by the distribution of sample distances to the training set. We find that these shifts can cause performance to drop by up to 60% and uncertainty calibration by up to 40%. This leads us to propose a splitting protocol that aims to close the gap between the deployment and testing. Then, using this protocol, a thorough investigation is conducted to assess the impact of model design, model selection, and data set characteristics on MOOD performance and uncertainty calibration. We find that appropriate representations and algorithms with built-in uncertainty estimation are crucial to improving performance and uncertainty calibration. This study sets itself apart by its exhaustiveness and opens an exciting avenue to benchmark meaningful algorithmic progress in molecular scoring.

Integrated computational approaches for designing potent pyrimidine-based CDK9 inhibitors: 3D-QSAR, docking, and molecular dynamics simulations

CDK9 is an emerging target for the development of anticancer drugs. The development of CDK9 inhibitors with significant potency had consistently posed a formidable challenge. In the current research, a number of computational methodologies, such as, 3D-QSAR, molecular docking, fingerprint analysis, molecular dynamic (MD) simulations followed by MMGB/PBSA and ADMET studies were used systemically to uncover the binding mechanism of pyrimidine derivatives against CDK9. The CoMFA and CoMSIA models having high q2 (0.53, 0.54) and r2 values (0.96, 0.93) respectively indicating that model could accurately predict the bioactivities of CDK9 inhibitors. Using the R-group exploration technique implemented by the Spark™ by Cresset group, the structural requirements revealed by the contour maps of model were utilized strategically to create an in-house library of 100 new CDK9 inhibitors. Additionally, the compounds from the in-house library were mapped into 3D-QSAR model which predicted pIC50 values comparable to the experimental values. A comparison between 3D-QSAR generated contours and molecular docking conformation of ligands was performed to elucidate the essentials of CDK9 inhibitor design. MD simulations (100 ns) were performed on the selected docked complexes A21, A14 and D98 which contributed in validating the binding interactions. According to the findings of binding free energy analysis (MMGB/PBSA), It was observed that residues CYS106 and GLU107 had a considerable tendency to facilitate ligand-protein interactions via H-bond interactions. The aforementioned findings have the potential to enhance researchers comprehension of the mechanism underlying CDK9 inhibition and may be utilized in the development of innovative and efficacious CDK9 inhibitors.

Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to Consider in QSPR Modeling Approaches

Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical methods to reveal quantitative correlations between the pharmacokinetics of compounds and their molecular structures, as well as their physical and chemical properties. QSPR models have been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties still needs improvement. Therefore, this paper comprehensively reviews the tools employed in various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches to building QSPR models, systematically analyzing the advantages and limitations of each modeling method to ensure their judicious application. We provide an overview of recent advancements in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review explores the inherent challenges in QSPR modeling while also proposing a range of considerations aimed at enhancing model prediction accuracy. The objective is to enhance the predictive capabilities of QSPR models in the field of drug development and provide valuable reference and guidance for researchers in this domain.

Co-encapsulation of hydrophilic and hydrophobic drugs into niosomal nanocarrier for enhanced breast cancer therapy: In silico and in vitro studies

Combination therapy has been considered one of the most promising approaches for improving the therapeutic effects of anticancer drugs. This is the first study that uses two different antioxidants in full-characterized niosomal formulation and thoroughly evaluates their synergistic effects on breast cancer cells. In this study, in-silico studies of hydrophilic and hydrophobic drugs (ascorbic acid: Asc and curcumin: Cur) interactions and release were investigated and validated by a set of in vitro experiments to reveal the significant improvement in breast cancer therapy using a co-delivery approach by niosomal nanocarrier. The niosomal nanoparticles containing surfactants (Span 60 and Tween 60) and cholesterol at 2:1 M ratio were prepared through the film hydration method. A systematic evaluation of nanoniosomes was carried out. The release profile demonstrated two phases (initial burst followed by sustained release) and a pH-dependent release schedule over 72 h. The optimized niosomal preparation displayed superior storage stability for up to 2 months at 4 °C, exhibiting extremely minor changes in pharmaceutical encapsulation efficiency and size. Free dual drugs (Asc + Cur) and dual-drug loaded niosomes (Niosomal (Asc + Cur)) enhanced the apoptotic activity and cytotoxicity and inhibited cell migration which confirmed the synergistic effect of co-encapsulated drugs. Also, significant up-regulation of p53 and Bax genes was observed in cells treated with Asc + Cur and Niosomal (Asc + Cur), while the anti-apoptotic Bcl-2 gene was down-regulated. These results were in correlation with the increase in the enzyme activity of SOD, CAT, and caspase, and the levels of malondialdehyde (MDA) and reactive oxygen species (ROS) upon treatment with the mentioned drugs. Furthermore, these anti-cancer effects were higher when using Niosomal (Asc + Cur) than Asc + Cur. Histopathological examination also revealed that Niosomal (Asc + Cur) had a lower mitosis index, invasion, and pleomorphism than Asc + Cur. These findings indicated that niosomal formulation for co-delivery of Asc and Cur would offer a promising delivery system for an effective breast cancer treatment.

Quantum Machine Learning Predicting ADME-Tox Properties in Drug Discovery

In the drug discovery paradigm, the evaluation of absorption, distribution, metabolism, and excretion (ADME) and toxicity properties of new chemical entities is one of the most critical issues, which is a time-consuming process, immensely expensive, and poses formidable challenges in pharmaceutical R&D. In recent years, emerging technologies like artificial intelligence (AI), big data, and cloud technologies have garnered great attention to predict the ADME and toxicity of molecules. Currently, the blend of quantum computation and machine learning has attracted considerable attention in almost every field ranging from chemistry to biomedicine and several engineering disciplines as well. Quantum computers have the potential to bring advances in high-throughput experimental techniques and in screening billions of molecules by reducing development costs and time associated with the drug discovery process. Motivated by the efficiency of quantum kernel methods, we proposed a quantum machine learning (QML) framework consisting of a classical support vector classifier algorithm with a kernel-based quantum classifier. To demonstrate the feasibility of the proposed QML framework, the simplified molecular input line entry system (SMILES) notation-based string kernel, combined with a quantum support vector classifier, is used for the evaluation of chemical/drug ADME-Tox properties. The proposed quantum machine learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the quantum model achieved the best performance as compared to classical counterparts in terms of the area under the curve of the receiver operating characteristic curve (AUC ROC; 0.80-0.95) for predicting outcomes on ADME-Tox data sets for small molecules, with a different number of features. The deployment of the proposed framework in the pharmaceutical industry would be extremely valuable in making the best decisions possible.

Multifunctional effect of flavonoids from Millettia brandisiana against Alzheimer's disease pathogenesis

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and neuronal death. Fifteen flavonoids from Millettia brandisiana were evaluated for the multifunctional effect against AD pathogenesis, including butyrylcholine esterase (BuChE) inhibition, anti-amyloid beta (Aβ) aggregation and neuroprotection against hydrogen peroxide (H2O2) toxicity in differentiated human neuroblastoma SH-SY5Y cell. To understand the mechanism and structure-activity relationship, binding interactions between flavonoids and the BuChE and Aβ were investigated in silico. Furthermore, drug-likeness properties and ADMET parameters were evaluated in silico using SwissADME and pKCSM tools. All flavonoids exhibit a good drug-likeness profile. Six flavonoids have potency in BuChE inhibition, and four flavonoids show potency in anti-Aβ aggregation. Flavonoids with the 6″,6″-dimethylchromeno- [2″,3″:7,8]-flavone structure show a favorable multifunctional effect. In silico analysis showed that flavonoids can bind in various positions to the catalytic triad, anionic site, and acyl pocket. In Aβ1-42, potential flavonoids can attach to the central hydrophobic region and the C terminal hydrophobic and interfere with Aβ interchain hydrogen binding. When compared together, it can inhibit multifunctional action with a favorable ADMET parameter and drug-likeness profile. In addition, candidine can prevent neuronal damage in differentiated SH-SY5Y neuroblastoma cells induced by H2O2 in a dose-dependent manner.

Molecular modeling study of pyrrolidine derivatives as novel myeloid cell leukemia-1 inhibitors through combined 3D-QSAR, molecular docking, ADME/Tox and MD simulation techniques

A series of pyrrolidine derivatives have been used to study the main structural requirements for designing novel Mcl-1 inhibitors. For this purpose, three models CoMSIA, CoMFA and HQSAR were generated using QSAR molecular modeling techniques. The statistical results of the CoMFA (Q2 = 0.689; R = 0.999; R2pred = 0.986), CoMSIA (Q2 = 0.614; R2 = 0.923; R2pred = 0.815) and HQSAR (Q2= 0.603; R2 = 0.662; R2pred = 0.743) models showed good stability and predictability. The results of the models were presented as contours and colored fragments indicating the favorable and unfavorable contribution to the inhibitory activity of Mcl-1. Based on the obtained results, four new compounds were designed with more potent predicted pIC50 inhibitory activity. The ADME/Tox results and the pharmacokinetic properties revealed that these four compounds are orally bioavailable and show good permeability. In addition the four compounds showing non-inhibitors of CYP3A4 and CYP2D6 with the exception of Pred03. At the level of toxicity profile, the compounds Pred01, Pred02 and Pred03 showed interesting results and showed no AMES toxicity, no hERG inhibition and no skin sensitization. Molecular docking results were used to uncover the mode of interaction between the ligand and key residues of protein binding site. Molecular docking results were supported by molecular simulation and binding free energy estimation (MMPBSA). These results demonstrate the stability of the analyzed compounds in the target protein binding site during a 100 ns trajectory. Finally, all these results create a strong lead to develop promising new Pyrrolidine-based inhibitors against Mcl-1.Communicated by Ramaswamy H. Sarma.

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling

Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorption, distribution, metabolism, and excretion (ADME) parameters, which can be a time-consuming and expensive process. This raises a need to create computational models capable of predicting input parameter values for PBPK models, especially for new compounds. In this review, we summarize an emerging paradigm for integrating PBPK modeling with machine learning (ML) or artificial intelligence (AI)-based computational methods. This paradigm includes 3 steps (1) obtain time-concentration PK data and/or ADME parameters from publicly available databases, (2) develop ML/AI-based approaches to predict ADME parameters, and (3) incorporate the ML/AI models into PBPK models to predict PK summary statistics (eg, area under the curve and maximum plasma concentration). We also discuss a neural network architecture "neural ordinary differential equation (Neural-ODE)" that is capable of providing better predictive capabilities than other ML methods when used to directly predict time-series PK profiles. In order to support applications of ML/AI methods for PBPK model development, several challenges should be addressed (1) as more data become available, it is important to expand the training set by including the structural diversity of compounds to improve the prediction accuracy of ML/AI models; (2) due to the black box nature of many ML models, lack of sufficient interpretability is a limitation; (3) Neural-ODE has great potential to be used to generate time-series PK profiles for new compounds with limited ADME information, but its application remains to be explored. Despite existing challenges, ML/AI approaches will continue to facilitate the efficient development of robust PBPK models for a large number of chemicals.

Discovery of pulmonary fibrosis inhibitor targeting TGF-β RI in Polygonum cuspidatum by high resolution mass spectrometry with in silico strategy

Pulmonary fibrosis (PF) is an irreversible lung disease that is characterized by excessive scar tissue with a poor median survival rate of 2-3 years. The inhibition of transforming growth factor-β receptor type-I (TGF-β RI) by an appropriate drug may provide a promising strategy for the treatment of this disease. Polygonum cuspidatum (PC) is a well-known traditional Chinese herbal medicine which has an anti-PF effect. Accordingly, a combination of high resolution mass spectrometry with an in silico strategy was developed as a new method to search for potential chemical ingredients of PC that target the TGF-β RI. Based on this strategy, a total of 24 ingredients were identified. Then, absorption, distribution, metabolism, and excretion (ADME)-related properties were subsequently predicted to exclude compounds with potentially undesirable pharmacokinetics behaviour. Molecular docking studies on TGF-β RI were adopted to discover new PF inhibitors. Eventually, a compound that exists in PC known as resveratrol was proven to have excellent biological activity on TGF-β RI, with an IC₅₀ of 2.211 μM in vitro. Furthermore, the complex formed through molecular docking was tested via molecular dynamics simulations, which revealed that resveratrol had strong interactions with residues of TGF-β RI. This study revealed that resveratrol has significant potential as a treatment for PF due to its ability to target TGF-β RI. In addition, this research demonstrated the exploration of natural products with excellent biological activities toward specific targets via high resolution mass spectrometry in combination with in silico technology is a promising strategy for the discovery of novel drugs.

Synthesis and Anticancer Activity of Indole-Functionalized Derivatives of Betulin

Pentacyclic triterpenes, including betulin, are widespread natural products with various pharmacological effects. These compounds are the starting material for the synthesis of substances with promising anticancer activity. The chemical modification of the betulin scaffold that was carried out as part of the research consisted of introducing the indole moiety at the C-28 position. The synthesized new 28-indole-betulin derivatives were evaluated for anticancer activity against seven human cancer lines (A549, MDA-MB-231, MCF-7, DLD-1, HT-29, A375, and C32). It was observed that MCF-7 breast cancer cells were most sensitive to the action of the 28-indole-betulin derivatives. The study shows that the lup-20(29)-ene-3-ol-28-yl 2-(1H-indol-3-yl)acetate caused the MCF-7 cells to arrest in the G1 phase, preventing the cells from entering the S phase. The performed cytometric analysis of DNA fragmentation indicates that the mechanism of EB355A action on the MCF-7 cell line is related to the induction of apoptosis. An in silico ADMET profile analysis of EB355A and EB365 showed that both compounds are bioactive molecules characterized by good intestinal absorption. In addition, the in silico studies indicate that the 28-indole-betulin derivatives are substances of relatively low toxicity.

In Silico Evaluation of Sesquiterpenes and Benzoxazinoids Phytotoxins against Mpro, RNA Replicase and Spike Protein of SARS-CoV-2 by Molecular Dynamics. Inspired by Nature

In the work described here, a number of sesquiterpenes and benzoxazinoids from natural sources, along with their easily accessible derivatives, were evaluated against the main protease, RNA replicase and spike glycoprotein of SARS-CoV-2 by molecular docking. These natural products and their derivatives have previously shown remarkable antiviral activities. The most relevant compounds were the 4-fluoro derivatives of santamarine, reynosin and 2-amino-3H-phenoxazin-3-one in terms of the docking score. Those compounds fulfill the Lipinski’s rule, so they were selected for the analysis by molecular dynamics, and the kinetic stabilities of the complexes were assessed. The addition of the 4-fluorobenzoate fragment to the natural products enhances their potential against all of the proteins tested, and the complex stability after 50 ns validates the inhibition calculated. The derivatives prepared from reynosin and 2-amino-3H-phenoxazin-3-one are able to generate more hydrogen bonds with the M^pro, thus enhancing the stability of the protein–ligand and generating a long-term complex for inhibition. The 4-fluoro derivate of santamarine and reynosin shows to be really active against the spike protein, with the RMSD site fluctuation lower than 1.5 Å. Stabilization is mainly achieved by the hydrogen-bond interactions, and the stabilization is improved by the 4-fluorobenzoate fragment being added. Those compounds tested in silico reach as candidates from natural sources to fight this virus, and the results concluded that the addition of the 4-fluorobenzoate fragment to the natural products enhances their inhibition potential against the main protease, RNA replicase and spike protein of SARS-CoV-2.

On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach

A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.

Simulation Models for Prediction of Bioavailability of Medicinal Drugs-the Interface Between Experiment and Computation

The oral drug bioavailability (BA) problems have remained inevitable over the years, impairing drug efficacy and indirectly leading to eventual human morbidity and mortality. However, some conventional lab-based methods improve drug absorption leading to enhanced BA, and the recent experimental techniques are up-and-coming. Nevertheless, some have inherent drawbacks in improving the efficacy of poorly insoluble and low impermeable drugs. Drug BA and strategies to overcome these challenges were briefly highlighted. This review has significantly unravelled the different computational models for studying and predicting drug bioavailability. Several computational approaches provide mechanistic insights into the oral drug delivery system simulation of descriptors like solubility, permeability, transport protein-ligand interactions, and molecular structures. The in silico techniques have long been known still are just being applied to unravel drug bioavailability issues. Many publications have reported novel applications of the computational models towards achieving improved drug BA, including predicting gastrointestinal tract (GIT) drug absorption properties and passive intestinal membrane permeability, thus maximizing time and resources. Also, the classical molecular simulation models for free solvation energies of soluble-related processes such as solubilization, dissolutions, supersaturation, and precipitation have been used in virtual screening studies. A few of the tools are GastroPlus^TM that supports biowaiver for drugs, mainly BCS class III and predicts drug compounds' absorption and pharmacokinetic process; SimCyp® simulator for mechanistic modelling and simulation of drug formulation processes; pharmacodynamics analysis on non-linear mixed-effects modelling; and mathematical models, predicting absorption potential/maximum absorption dose. This review provides in silico-experiment annexation in the drug bioavailability enhancement, possible insights that lead to critical opinion on the applications and reliability of the various in silico models as a growing tool for drug development and discovery, thus accelerating drug development processes.

Prodrugs of E-selectin Antagonists with Enhanced Pharmacokinetic Properties

Because of their large polar surface area, carbohydrates often exhibit insufficient pharmacokinetic properties. Specifically, the carboxylic acid function of the tetrasaccharide sialyl Lewis^x , a pharmacophore crucial for the formation of a salt bridge with selectins, prevents oral availability. A common approach is the transfer of carboxylic acid into ester prodrugs. Once the prodrug is either actively or passively absorbed, the active principle is released by hydrolysis. In the present study, ester prodrugs of selectin antagonists with aliphatic promoieties were synthesized and their potential for oral availability was investigated in vitro and in vivo. The addition of lipophilic ester moieties to overcome insufficient lipophilicity improved passive permeation into enterocytes, however at the same time supported efflux back to the small intestines as well as oxidation into non-hydrolysable metabolites. In summary, our examples demonstrate that different modifications of carbohydrates can result in opposing effects and have to be studied in their entirety.

Design, synthesis and evaluation of heteroaryldihydropyrimidine analogues bearing spiro ring as hepatitis B virus capsid protein inhibitors

GLS4, a potent antiviral drug candidate, has been widely studied and entered into phase II clinical trials. Nevertheless, the therapeutic application of GLS4 is limited due to poor water solubility, short half-life, and low bioavailability. In order to improve the hydrophilicity and pharmacokinetic (PK) properties of GLS4, herein, we retained the dominant fragments, and used a scaffold hopping strategy to replace the easily metabolized morpholine ring of GLS4 with diverse sizes of spiro rings consisting of hydrogen bond donor and acceptor substituents. Potent in vitroanti-HBV activity and low cytotoxicity were observed for compound 4r (EC50 = 0.20 ± 0.00 μM, CC50 > 87.03 μM), which was more potent than the positive control lamivudine (EC50 = 0.37 ± 0.04 μM, CC50 > 100.00 μM) in this assay and was about a quarter as effective as GLS4 (EC50 = 0.045 ± 0.01 μM, CC50 > 99.20 μM). Preliminary structure-activity relationship (SAR) analysis and molecular docking studies were carried out to explore potential interactions and binding mode between compounds and target protein. In terms of the physicochemical properties, 4r was predicted to be consistent with the rule-of-five, which means 4r may have favourable absorption and permeation. Finally, ADMET and PK characteristics of 4r and GLS4 were predicted to be comparable in most aspects, implying that the two compounds may have similar profiles in vivo.

Global Analysis of Models for Predicting Human Absorption: QSAR, In Vitro, and Preclinical Models

Models intended to predict intestinal absorption are an essential part of the drug development process. Although many models exist for capturing intestinal absorption, many questions still exist around the applicability of these models to drug types like "beyond rule of 5" (bRo5) and low absorption compounds. This presents a challenge as current models have not been rigorously tested to understand intestinal absorption. Here, we assembled a large, structurally diverse dataset of ∼1000 compounds with known in vitro, preclinical, and human permeability and/or absorption data. In silico (quantitative structure-activity relationship), in vitro (Caco-2), and in vivo (rat) models were statistically evaluated for predictive performance against this human intestinal absorption dataset. We expect this evaluation to serve as a resource for DMPK scientists and medicinal/computational chemists to increase their understanding of permeability and absorption model utility and applications for academia and industry.

PSC-db: A Structured and Searchable 3D-Database for Plant Secondary Compounds

Plants synthesize a large number of natural products, many of which are bioactive and have practical values as well as commercial potential. To explore this vast structural diversity, we present PSC-db, a unique plant metabolite database aimed to categorize the diverse phytochemical space by providing 3D-structural information along with physicochemical and pharmaceutical properties of the most relevant natural products. PSC-db may be utilized, for example, in qualitative estimation of biological activities (Quantitative Structure-Activity Relationship, QSAR) or massive docking campaigns to identify new bioactive compounds, as well as potential binding sites in target proteins. PSC-db has been implemented using the open-source PostgreSQL database platform where all compounds with their complementary and calculated information (classification, redundant names, unique IDs, physicochemical properties, etc.) were hierarchically organized. The source organism for each compound, as well as its biological activities against protein targets, cell lines and different organism were also included. PSC-db is freely available for public use and is hosted at the Universidad de Talca.

Design, synthesis and anticonvulsant activity of new imidazolidindione and imidazole derivatives

New imidazolidindiones and tetra-substituted imidazole derivatives were designed, synthesized, and evaluated for the anticonvulsant activity through pentylenetetrazole (PTZ)-induced seizures and maximal electroshock (MES) tests using valproate sodium and phenytoin sodium as reference drugs, respectively. Most of the target compounds showed excellent activity against pentylenetetrazole (PTZ)-induced seizures with fair to no-activity against MES. Compounds 3d, 4e, 11b, and 11e showed higher activity (120%) than that of valproate sodium in PTZ model. Almost all compounds showed no neurotoxicity, as indicated by the rotarod test. Estimation of physicochemical properties and pharmacokinetic profiles of the target compounds were studied. The chemical structures of the target compounds were characterized by different spectrometric methods and elemental analysis.

Probing the Open Global Health Chemical Diversity Library for Multistage-Active Starting Points for Next-Generation Antimalarials

Most phenotypic screens aiming to discover new antimalarial chemotypes begin with low cost, high-throughput tests against the asexual blood stage (ABS) of the malaria parasite life cycle. Compounds active against the ABS are then sequentially tested in more difficult assays that predict whether a compound has other beneficial attributes. Although applying this strategy to new chemical libraries may yield new leads, repeated iterations may lead to diminishing returns and the rediscovery of chemotypes hitting well-known targets. Here, we adopted a different strategy to find starting points, testing ∼70,000 open source small molecules from the Global Health Chemical Diversity Library for activity against the liver stage, mature sexual stage, and asexual blood stage malaria parasites in parallel. In addition, instead of using an asexual assay that measures accumulated parasite DNA in the presence of compound (SYBR green), a real time luciferase-dependent parasite viability assay was used that distinguishes slow-acting (delayed death) from fast-acting compounds. Among 382 scaffolds with the activity confirmed by dose response (<10 μM), we discovered 68 novel delayed-death, 84 liver stage, and 68 stage V gametocyte inhibitors as well. Although 89% of the evaluated compounds had activity in only a single life cycle stage, we discovered six potent (half-maximal inhibitory concentration of <1 μM) multistage scaffolds, including a novel cytochrome bc1 chemotype. Our data further show the luciferase-based assays have higher sensitivity. Chemoinformatic analysis of positive and negative compounds identified scaffold families with a strong enrichment for activity against specific or multiple stages.

Adenosine Derivates as Antioxidant Agents: Synthesis, Characterization, in Vitro Activity, and Theoretical Insights

In this work, we present results about the synthesis and the antioxidant properties of seven adenosine derivatives. Four of these compounds were synthesized by substituting the N6-position of adenosine with aliphatic amines, and three were obtained by modification of the ribose ring. All compounds were obtained in pure form using column chromatography, and their structures were elucidated by infrared spectroscopy (IR) and Nuclear Magnetic Resonance (NMR). All adenosine derivatives were further evaluated in vitro as free radical scavengers. Our results show that compounds 1c, 3, and 5 display a potent antioxidant effect compared with the reference compound ascorbic acid. In addition, the absorption, distribution, metabolism and excretion (ADME) calculations show favorable pharmacokinetic parameters for the set of compounds analyzed, which guarantees their suitability as potential antioxidant drugs. Furthermore, theoretical analyses using Molecular Quantum Similarity and reactivity indices were performed in order to discriminate the different reactive sites involved in oxidative processes.

Design and synthesis of 1,2,4-triazolo[1,5-a]pyrimidine derivatives as PDE 4B inhibitors endowed with bronchodilator activity

A series of 1,2,4-triazolo[1,5-a]pyrimidine derivatives was designed, synthesized, and screened for their phosphodiesterase (PDE 4B) inhibitory activity and bronchodilation ability. Compound 7e showed 41.80% PDE 4B inhibition at 10 µM. Eight compounds were screened for their bronchodilator activity, where compounds 7f and 7e elicited promising bronchodilator activity with EC₅₀ values of 18.6 and 57.1 µM, respectively, compared to theophylline (EC₅₀  = 425 µM). Molecular docking at the PDE 4B active site revealed a binding mode and docking scores comparable to those of a reference ligand, consistent with their PDE 4B inhibition activity.

Ultrasound-assisted synthesis of novel chalcone, heterochalcone and bis-chalcone derivatives and the evaluation of their antioxidant properties and as acetylcholinesterase inhibitors

The chalcone and bis-chalcone derivatives have been synthesized under sonication conditions via Claisen-Schmidt condensation with KOH in ethanol at room temperature (20-89%). The structures were established on the basis of NMR, IR, Single-crystal XRD, and MS. The best compound 3u had inhibitory activity (IC₅₀ = 7.50 µM). The synthesis, the antioxidative properties, chemical reactivity descriptors supported in Density Functional Theory (DFT), acetylcholinesterase (AChE) inhibition and their potential binding modes, and affinity were predicted by molecular docking of a number of morpholine-chalcones and quinoline-chalcone. A series of bis-chalcones are also reported. Molecular docking and an enzyme kinetic study on compound 3u suggested that it simultaneously binds to the catalytic active site (CAS) and peripheral anionic site (PAS) of AChE. Moreover, the pharmacokinetic profile of these compounds was investigated using a computational method.

Prediction of human intestinal absorption using micellar liquid chromatography with an aminopropyl stationary phase

The extent of human intestinal absorption (HIA) for a drug is considered to be an important pharmacokinetic parameter which must be determined for orally administered drugs. Traditional experimental methods relied upon animal testing and are renowned for being time consuming and expensive as well as being ethically unfavourable. As a result, the development of alternative methods to evaluate a drug's pharmacokinetics is crucial. Micellar liquid chromatography is considered to be one of these methods that can replace the use of animals in the prediction of HIA. In this study, the combination of an aminopropyl column with the biosurfactant sodium deoxycholate bile salt was used in the experimental determination of micelle-water partition coefficients (log P_mw ) for a group of compounds. Multiple linear regression was then used for the prediction of HIA using the experimentally determined log P_mw along with other molecular descriptors, leading to the construction of a model equation of R²  = 85% and a prediction power represented by R² _Pred. = 72%. The use of micellar liquid chromatography with an aminopropyl column in combination with sodium deoxycholate was found to be a good method for the prediction of human intestinal absorption, providing data for a far wider range of compounds compared with previous studies.

Synthesis of diN-Substituted Glycyl-Phenylalanine Derivatives by Using Ugi Four Component Reaction and Their Potential as Acetylcholinesterase Inhibitors

Ugi four component reaction (Ugi-4CR) isocyanide-based multicomponent reactions were used to synthesize diN-substituted glycyl-phenylalanine (diNsGF) derivatives. All of the synthesized compounds were characterized by spectroscopic and spectrometric techniques. In order to evaluate potential biological applications, the synthesized compounds were tested in computational models that predict the bioactivity of organic molecules by using only bi-dimensional molecular information. The diNsGF derivatives were predicted as cholinesterase inhibitors. Experimentally, all of the synthesized diNsGF derivatives showed moderate inhibitory activities against acetylcholinesterase (AChE) and poor activities against butyrylcholinesterase (BuChE). Compound 7a has significant activity and selectivity against AChE, which reveals that the diNsGF scaffold could be improved to reach novel candidates by combining other chemical components of the Ugi-4CR in a high-throughput combinatorial screening experiment. Molecular docking experiments of diNsGF derivatives inside AChE suggest that these compounds placed the phenylalanine group at the peripheral site of AChE. The orientations and chemical interactions of diNsGF derivatives were analyzed, and the changeable groups were identified for future exploration of novel candidates that could lead to the improvement of diNsGF derivative inhibitory activities.

Development and Testing of Druglike Screening Libraries

Although significant advances in experimental high throughput screening (HTS) have been made for drug lead identification, in silico virtual screening (VS) is indispensable owing to its unique advantage over experimental HTS, target-focused, cheap, and efficient, albeit its disadvantage of producing false positive hits. For both experimental HTS and VS, the quality of screening libraries is crucial and determines the outcome of those studies. In this paper, we first reviewed the recent progress on screening library construction. We realized the urgent need for compiling high-quality screening libraries in drug discovery. Then we compiled a set of screening libraries from about 20 million druglike ZINC molecules by running fingerprint-based similarity searches against known drug molecules. Lastly, the screening libraries were objectively evaluated using 5847 external actives covering more than 2000 drug targets. The result of the assessment is very encouraging. For example, with the Tanimoto coefficient being set to 0.75, 36% of external actives were retrieved and the enrichment factor was 13. Additionally, drug target family specific screening libraries were also constructed and evaluated. The druglike screening libraries are available for download from https://mulan.pharmacy.pitt.edu .

Incorporating physiologically relevant mobile phases in micellar liquid chromatography for the prediction of human intestinal absorption

Micellar liquid chromatography is a popular method used in the determination of a compound's lipophilicity. This study describes the use of the obtained micelle-water partition coefficient (log Pmw ) by such a method in the prediction of human intestinal absorption (HIA). As a result of the close resemblance of the novel composition of the micellar mobile phase to that of physiological intestinal fluid, prediction was deemed to be highly successful. The unique micellar mobile phase consisted of a mixed micellar mixture of lecithin and six bile salts, i.e. a composition matching that found in the human intestinal environment, prepared in ratios resembling those in the intestine. This is considered to be the first method to use a physiological mixture of biosurfactants in the prediction of HIA. As a result, a mathematical model with high predictive ability (R2 PRED  = 81%) was obtained using multiple linear regression. The micelle-water partition coefficient (log Pmw ) obtained from micellar liquid chromatography was found to be a successful tool for prediction where the final optimum model included log Pmw and polar surface area as key descriptors with high statistical significance for the prediction of HIA. This can be attributed to the nature of the mobile phase used in this study which contains the lecithin-bile salt complex, thus forming a bilayer system and therefore mimicking absorption across the intestinal membrane.

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

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

Integrating theoretical and experimental permeability estimations for provisional biopharmaceutical classification: Application to the WHO essential medicines

The accuracy of the provisional estimation of the Biopharmaceutics Classification System (BCS) is heavily influenced by the permeability measurement. In this study, several theoretical and experimental models currently employed for BCS permeability classification have been analysed. The experimental models included the in situ rat intestinal perfusion, the ex vivo rat intestinal tissue in an Ussing chamber, the MDCK and Caco-2 cell monolayers, and the parallel artificial membrane (PAMPA). The theoretical models included the octanol-water partition coefficient and the QSPeR (Quantitative Structure-Permeability Relationship) model recently developed. For model validation, a dataset of 43 compounds has been recompiled and analysed for the suitability for BCS permeability classification in comparison with the use of human intestinal absorption and oral bioavailability values. The application of the final model, based on a majority voting system showed a 95.3% accuracy for predicting human permeability. Finally, the present approach was applied to the 186 orally administered drugs in immediate-release dosage forms of the WHO Model List of Essential Medicines. The percentages of the drugs that were provisionally classified as BCS Class I and Class III was 62.4%, suggesting that in vivo bioequivalence (BE) may potentially be assured with a less expensive and more easily implemented in vitro dissolution test, ensuring the efficiency and quality of pharmaceutical products. The results of the current study improve the accuracy of provisional BCS classification by combining different permeability models.

Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products

This is a new golden age for drug discovery based on natural products derived from both marine and terrestrial sources. Herein, a straightforward but important question is "what are the major structural differences between marine natural products (MNPs) and terrestrial natural products (TNPs)?" To answer this question, we analyzed the important physicochemical properties, structural features, and drug-likeness of the two types of natural products and discussed their differences from the perspective of evolution. In general, MNPs have lower solubility and are often larger than TNPs. On average, particularly from the perspective of unique fragments and scaffolds, MNPs usually possess more long chains and large rings, especially 8- to 10-membered rings. MNPs also have more nitrogen atoms and halogens, notably bromines, and fewer oxygen atoms, suggesting that MNPs may be synthesized by more diverse biosynthetic pathways than TNPs. Analysis of the frequently occurring Murcko frameworks in MNPs and TNPS also reveals a striking difference between MNPs and TNPs. The scaffolds of the former tend to be longer and often contain ester bonds connected to 10-membered rings, while the scaffolds of the latter tend to be shorter and often bear more stable ring systems and bond types. Besides, the prediction from the naïve Bayesian drug-likeness classification model suggests that most compounds in MNPs and TNPs are drug-like, although MNPs are slightly more drug-like than TNPs. We believe that MNPs and TNPs with novel drug-like scaffolds have great potential to be drug leads or drug candidates in drug discovery campaigns.

Biowaiver or Bioequivalence: Ambiguity in Sildenafil Citrate BCS Classification

The aim of the present study is to contribute to the scientific characterization of sildenafil citrate according to the Biopharmaceutics Classification System, following the World Health Organization (WHO) guidelines for biowaivers. The solubility and intestinal permeability data of sildenafil citrate were collected from literature; however, the experimental solubility studies are inconclusive and its "high permeability" suggests an API in the borderline of BCS Class I and Class II. The pH-solubility profile was determined using the saturation shake-flask method over the pH range of 1.2-6.8 at a temperature of 37 °C in aqueous media. The intestinal permeability was determined in rat by a closed-loop in situ perfusion method (the Doluisio technique). The solubility of sildenafil citrate is pH-dependent and at pH 6.8 the dose/solubility ratio obtained does not meet the WHO criteria for "high solubility." The high permeability values obtained by in situ intestinal perfusion in rat reinforce the published permeability data for sildenafil citrate. The experimental results obtained and the data available in the literature suggest that sildenafil citrate is clearly a Class II of BCS, according to the current biopharmaceutics classification system and WHO guidance.

Computational modeling of human oral bioavailability: what will be next?

INTRODUCTION

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

Predicting the Absorption Potential of Chemical Compounds Through a Deep Learning Approach

The human colorectal carcinoma cell line (Caco-2) is a commonly used in-vitro test that predicts the absorption potential of orally administered drugs. In-silico prediction methods, based on the Caco-2 assay data, may increase the effectiveness of the high-throughput screening of new drug candidates. However, previously developed in-silico models that predict the Caco-2 cellular permeability of chemical compounds use handcrafted features that may be dataset-specific and induce over-fitting problems. Deep Neural Network (DNN) generates high-level features based on non-linear transformations for raw features, which provides high discriminant power and, therefore, creates a good generalized model. We present a DNN-based binary Caco-2 permeability classifier. Our model was constructed based on 663 chemical compounds with in-vitro Caco-2 apparent permeability data. Two hundred nine molecular descriptors are used for generating the high-level features during DNN model generation. Dropout regularization is applied to solve the over-fitting problem and the non-linear activation. The Rectified Linear Unit (ReLU) is adopted to reduce the vanishing gradient problem. The results demonstrate that the high-level features generated by the DNN are more robust than handcrafted features for predicting the cellular permeability of structurally diverse chemical compounds in Caco-2 cell lines.

Selective Inhibitors of Human Neuraminidase 3

Human neuraminidases (NEU) are associated with human diseases including cancer, atherosclerosis, and diabetes. To obtain small molecule inhibitors as research tools for the study of their biological functions, we designed a library of 2-deoxy-2,3-didehydro- N-acetylneuraminic acid (DANA) analogues with modifications at C4 and C9 positions. This library allowed us to discover selective inhibitors targeting the human NEU3 isoenzyme. Our most selective inhibitor for NEU3 has a K_i of 320 ± 40 nM and a 15-fold selectivity over other human neuraminidase isoenzymes. This inhibitor blocks glycolipid processing by NEU3 in vitro. To improve their pharmacokinetic properties, various esters of the best inhibitors were synthesized and evaluated. Finally, we confirmed that our best compounds exhibited selective inhibition of NEU orthologues from murine brain.

A novel adaptive ensemble classification framework for ADME prediction

AECF is a GA based ensemble method. It includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble.

Modeling of the hERG K+ Channel Blockage Using Online Chemical Database and Modeling Environment (OCHEM)

Human ether-a-go-go related gene (hERG) K+ channel plays an important role in cardiac action potential. Blockage of hERG channel may result in long QT syndrome (LQTS), even cause sudden cardiac death. Many drugs have been withdrawn from the market because of the serious hERG-related cardiotoxicity. Therefore, it is quite essential to estimate the chemical blockage of hERG in the early stage of drug discovery. In this study, a diverse set of 3721 compounds with hERG inhibition data was assembled from literature. Then, we make full use of the Online Chemical Modeling Environment (OCHEM), which supplies rich machine learning methods and descriptor sets, to build a series of classification models for hERG blockage. We also generated two consensus models based on the top-performing individual models. The consensus models performed much better than the individual models both on 5-fold cross validation and external validation. Especially, consensus model II yielded the prediction accuracy of 89.5 % and MCC of 0.670 on external validation. This result indicated that the predictive power of consensus model II should be stronger than most of the previously reported models. The 17 top-performing individual models and the consensus models and the data sets used for model development are available at https://ochem.eu/article/103592.

Efficacy and cytotoxicity in cell culture of novel α-hydroxytropolone inhibitors of hepatitis B virus ribonuclease H

Chronic Hepatitis B virus (HBV) infection is a major worldwide public health problem. Current direct-acting anti-HBV drugs target the HBV DNA polymerase activity, but the equally essential viral ribonuclease H (RNaseH) activity is unexploited as a drug target. Previously, we reported that α-hydroxytropolone compounds can inhibit the HBV RNaseH and block viral replication. Subsequently, we found that our biochemical RNaseH assay underreports efficacy of the α-hydroxytropolones against HBV replication. Therefore, we conducted a structure-activity analysis of 59 troponoids against HBV replication in cell culture. These studies revealed that antiviral efficacy is diminished by larger substitutions on the tropolone ring, identified key components in the substitutions needed for high efficacy, and revealed that cytotoxicity correlates with increased lipophilicity of the α-hydroxytropolones. These data provide key guidance for further optimization of the α-hydroxytropolone scaffold as novel HBV RNaseH inhibitors.