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

Evaluation of the Lipophilicity of Angularly Condensed Diquino- and Quinonaphthothiazines as Potential Candidates for New Drugs

Lipophilicity is one of the most important properties of compounds required to estimate the absorption, distribution, and transport in biological systems, in addition to solubility, stability, and acid-base nature. It is crucial in predicting the ADME profile of bioactive compounds. The study assessed the usefulness of computational and chromatographic methods (thin-layer chromatography in a reversed-phase system, RP-TLC) for estimating the lipophilicity of 21 newly synthesized compounds belonging to diquinothiazines and quinonaphthiazines. In order to obtain reliable values of the relative lipophilicities of diquinothiazines and quinonaphthiazines, the partition coefficients obtained using different algorithms such as AlogPs, AClogP, AlogP, MLOGP, XLOGP2, XLOGP3, logP, and ClogP were compared with the chromatographic RM0 values of all the tested compounds measured by the experimental RP-TLC method (logPTLC). Additionally, logPTLC values were also correlated with other descriptors, as well as the predicted ADME and drug safety profiling parameters. The linear correlations of logPTLC values of the tested compounds with other calculated molecular descriptors such as molar refractivity, as well as ADME parameters (Caco-2 substrates, P-gp inhibitors, CYP2C19, and CYP3A4) generally show poor predictive power. Therefore, in silico ADME profiling can only be helpful at the initial step of designing these new candidates for drugs. The compliance of all discussed diquinothiazines and naphthoquinothiazines with the rules of Lipiński, Veber, and Egan suggests that the tested pentacyclic phenothiazine analogs have a chance to become therapeutic drugs, especially orally active drugs.

A new workflow for the effective curation of membrane permeability data from open ADME information

Membrane permeability is an in vitro parameter that represents the apparent permeability (Papp) of a compound, and is a key absorption, distribution, metabolism, and excretion parameter in drug development. Although the Caco-2 cell lines are the most used cell lines to measure Papp, other cell lines, such as the Madin-Darby Canine Kidney (MDCK), LLC-Pig Kidney 1 (LLC-PK1), and Ralph Russ Canine Kidney (RRCK) cell lines, can also be used to estimate Papp. Therefore, constructing in silico models for Papp estimation using the MDCK, LLC-PK1, and RRCK cell lines requires collecting extensive amounts of in vitro Papp data. An open database offers extensive measurements of various compounds covering a vast chemical space; however, concerns were reported on the use of data published in open databases without the appropriate accuracy and quality checks. Ensuring the quality of datasets for training in silico models is critical because artificial intelligence (AI, including deep learning) was used to develop models to predict various pharmacokinetic properties, and data quality affects the performance of these models. Hence, careful curation of the collected data is imperative. Herein, we developed a new workflow that supports automatic curation of Papp data measured in the MDCK, LLC-PK1, and RRCK cell lines collected from ChEMBL using KNIME. The workflow consisted of four main phases. Data were extracted from ChEMBL and filtered to identify the target protocols. A total of 1661 high-quality entries were retained after checking 436 articles. The workflow is freely available, can be updated, and has high reusability. Our study provides a novel approach for data quality analysis and accelerates the development of helpful in silico models for effective drug discovery. Scientific Contribution: The cost of building highly accurate predictive models can be significantly reduced by automating the collection of reliable measurement data. Our tool reduces the time and effort required for data collection and will enable researchers to focus on constructing high-performance in silico models for other types of analysis. To the best of our knowledge, no such tool is available in the literature.

Synthesis, in silico screening, and biological evaluation of novel pyridine congeners as anti-epileptic agents targeting AMPA (α-amino-3-hydroxy-5-methylisoxazole) receptors

The research involves the synthesis of a series of new pyridine analogs 5(i-x) and their evaluation for anti-epileptic potential using in silico and in vivo models. Synthesis of the compounds was accomplished by using the Vilsmeier-Haack reaction principle. AutoDock 4.2 was used for their in silico screening against AMPA (-amino-3-hydroxy-5-methylisoxazole) receptor (PDB ID:3m3f). For in vivo testing, the maximal electroshock seizure (MES) model was used. The physicochemical, pharmacokinetic, drug-like, and drug-score features of all synthesized compounds were assessed using the online Swiss ADME and Protein Plus software. The in silico results showed that all the synthesized compounds 5(i-x) had 1-3 interactions and affinities ranging from -6.5 to -8.0 kJ/mol with the targeted receptor compared to the binding affinities of the standard drug phenytoin and the original ligand of the target (P99), which were -7.6 and -6.8 kJ/mol, respectively. In vivo study results showed that the compound 5-Carbamoyl-2-formyl-1-[2-(4-nitrophenyl)-2-oxo-ethyl]-pyridinium gave 60% protection against epileptic seizures compared to 59% protection afforded by regular phenytoin. All of them met Lipinski's rule of five and had drug-likeness and drug score values of 0.55 and 0.8, respectively, making them chemically and functionally like phenytoin. According to the findings of the studies, the synthesized derivatives have the potential to be employed as a stepping stone in the development of novel anti-epileptic drugs.

Lipophilic Studies and In Silico ADME Profiling of Biologically Active 2-Aminothiazol-4(5H)-one Derivatives

Pseudothiohydantoin derivatives have a wide range of biological activities and are widely used in the development of new pharmaceuticals. Lipophilicity is a basic, but very important parameter in the design of potential drugs, as it determines solubility in lipids, nonpolar solvents, and makes it possible to predict the ADME profile. The aim of this study was to evaluate the lipophilicity of 28 pseudothiohydantoin derivatives showing the inhibition of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) using chromatographic methods. Experimentally, lipophilicity was determined by reverse phase thin layer chromatography (RP-TLC) and reverse phase high-performance liquid chromatography (RP-HPLC). In both methods, methanol was used as the organic modifier of the mobile phase. For each 2-aminothiazol-4(5H)-one derivative, a relationship was observed between the structure of the compound and the values of the lipophilicity parameters (log kw, RM0). Experimental lipophilicity values were compared with computer calculated partition coefficient (logP) values. A total of 27 of the 28 tested compounds had a lipophilicity value < 5, which therefore met the condition of Lipinski's rule. In addition, the in silico ADME assay showed favorable absorption, distribution, metabolism, and excretion parameters for most of the pseudothiohydantoin derivatives tested. The study of lipophilicity and the ADME analysis indicate that the tested compounds are good potential drug candidates.

Antioxidant evaluation and computational prediction of prospective drug-like compounds from polyphenolic-rich extract of Hibiscus cannabinus L. seed as antidiabetic and neuroprotective targets: assessment through in vitro and in silico studies

BACKGROUND

Reports have implicated diabetes mellitus (DM) and Alzheimer's disease (AD) as some of the global persistent health challenges with no lasting solutions, despite of significant inputs of modern-day pharmaceutical firms. This study therefore, aimed to appraise the in vitro antioxidant potential, enzymes inhibitory activities, and as well carry out in silico study on bioactive compounds from polyphenolic-rich extract of Hibiscus cannabinus seed (PEHc).

METHODS

In vitro antioxidant assays were performed on PEHc using standard methods while the identification of phytoconstituents was carried out with high performance liquid chromatography (HPLC). For the in silico molecular docking using Schrodinger's Grid-based ligand docking with energetics software, seven target proteins were retrieved from the database ( https://www.rcsb.org/ ).

RESULTS

HPLC technique identified twelve chemical compounds in PEHc, while antioxidant quantification revealed higher total phenolic contents (243.5 ± 0.71 mg GAE/g) than total flavonoid contents (54.06 ± 0.09 mg QE/g) with a significant (p < 0.05) inhibition of ABTS (IC₅₀ = 218.30 ± 0.87 µg/ml) and 1, 1-diphenyl-2-picrylhydrazyl free radicals (IC₅₀ = 227.79 ± 0.74 µg/ml). In a similar manner, the extract demonstrated a significant (p < 0.05) inhibitory activity against α-amylase (IC₅₀ = 256.88 ± 6.15 µg/ml) and α-glucosidase (IC₅₀ = 183.19 ± 0.23 µg/ml) as well as acetylcholinesterase (IC₅₀ = 262.95 ± 1.47 µg/ml) and butyrylcholinesterase (IC₅₀ = 189.97 ± 0.82 µg/ml), respectively. Furthermore, In silico study showed that hibiscetin (a lead) revealed a very strong binding affinity energies for DPP-4, (PDB ID: 1RWQ) and α-amylase (PDB ID: 1SMD), gamma-tocopherol ( for peptide-1 receptor; PDB ID: 3C59, AChE; PDB ID: 4EY7 and BChE; PDB ID: 7B04), cianidanol for α-glucosidase; PDB ID: 7KBJ and kaempferol for Poly [ADP-ribose] polymerase 1 (PARP-1); PDB ID: 6BHV, respectively. More so, ADMET scores revealed drug-like potentials of the lead compounds identified in PEHc.

CONCLUSION

As a result, the findings of this study point to potential drug-able compounds in PEHc that could be useful for the management of DM and AD.

New Ni(II) and Pd(II) complexes bearing derived sulfa drug ligands: synthesis, characterization, DFT calculations, and in silico and in vitro biological activity studies

In the present study, the synthesis of six new Ni(II) and Pd(II) complexes with three derived sulfamethoxazole drug ligands is reported. The coordination mode, geometry, and chemical formula of all the synthesized compounds have been determined by elemental analysis, mass spectrometry, emission atomic spectroscopy, conductivity measurements, magnetic susceptibility, FTIR, TGA, 1H-NMR, electronic absorption spectroscopy, SEM-EDX along with DFT calculations. The Schiff Base ligands were found to be bidentate and coordinated to the metal ions through sulfonamidic nitrogen and oxazolic nitrogen atoms leading to a square planar geometry for palladium (II) while a distorted octahedral geometry around Nickel (II) ion was suggested. Biological applications of the new complexes including in vitro antimicrobial, antioxidant and anticancer properties were investigated. The results showed that the new metal (II) compounds exhibit remarkable antibacterial inhibition activity against both Gram-positive and Gram-negative bacteria, in addition to noticeable DPPH free radical scavenging activity. The in vitro cytotoxicity assay of the complexes against cell lines of chronic myelogenous leukaemia (K562) showed promising potential for the application of the coordination compounds in antitumor therapy. Subsequently, to evaluate the pharmaceutical potential of the metal-containing compounds, pharmacokinetics and toxicity were studied by ADMET simulations while interactions between the complexes and bacterial proteins were evaluated by molecular docking.

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

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

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

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.

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

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

α-Mangostin and its derivatives against estrogen receptor alpha

Estrogen receptor alpha (ERα) acts as the transcription factor and the main therapeutic target against breast cancer. One of the compounds that has been shown to act as an ERα is α-mangostin. However, it still has weaknesses due to its low solubility and low potent activity. In this study, α-mangostin was modified by substituting -OH group at C6 using benzoyl derivatives through a step by step in silico study, namely pharmacokinetic prediction (https://preadmet.bmdrc.kr/adme/), pharmacophore modeling (LigandScout 4.1), molecular docking simulation (AutoDock 4.2), molecular dynamics simulation (AMBER 16) and a binding free energy analysis using MM-PBSA method. From the computational studies, three compounds which are derived from α-mangostin (AMB-1 (-9.84 kcal/mol), AMB-2 (-6.80 kcal/mol) and AMB-10 (-12.42 kcal/mol)) have lower binding free energy than α-mangostin (-1.77 kcal/mol), as evidenced by the binding free energy calculation using the MM-PBSA method. They can then be predicted to have potent activities as ERα antagonists.Communicated by Ramaswamy H. Sarma.

Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development

In drug development, preclinical safety and pharmacokinetics assessments of candidate drugs to ensure the safety profile are a must. While in vivo and in vitro tests are traditionally used, experimental determinations have disadvantages, as they are usually time-consuming and costly. In silico predictions of these preclinical endpoints have each been developed in the past decades. However, only a few web-based tools have integrated different models to provide a simple one-step platform to help researchers thoroughly evaluate potential drug candidates. To efficiently achieve this approach, a platform for preclinical evaluation must not only predict key ADMET (absorption, distribution, metabolism, excretion and toxicity) properties but also provide some guidance on structural modifications to improve the undesired properties. In this review, we organized and compared several existing integrated web servers that can be adopted in preclinical drug development projects to evaluate the subject of interest. We also introduced our new web server, Virtual Rat, as an alternative choice to profile the properties of drug candidates. In Virtual Rat, we provide not only predictions of important ADMET properties but also possible reasons as to why the model made those structural predictions. Multiple models were implemented into Virtual Rat, including models for predicting human ether-a-go-go-related gene (hERG) inhibition, cytochrome P450 (CYP) inhibition, mutagenicity (Ames test), blood-brain barrier penetration, cytotoxicity and Caco-2 permeability. Virtual Rat is free and has been made publicly available at https://virtualrat.cmdm.tw/.

Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes

Quantitative structure-activity relationships (QSAR) are introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS (acronym for quadratic, bilinear and N-Linear maps based on graph-theoretic electronic-density matrices and atomic weightings) framework for the molecular encoding. Three training sets were employed to build the models: EPA training set (5931 compounds), EPA-full training set (7413 compounds), and Zhu training set (10 152 compounds). Additionally, the EPA test set (1482 compounds) was used for the validation of the QSAR models built on the EPA training set, while the ProTox (425 compounds) and T3DB (284 compounds) external sets were employed for the assessment of all the models. The k-nearest neighbor, multilayer perceptron, random forest, and support vector machine procedures were employed to build several base (individual) models. The base models with R_EPA-training ≥ 0.75 ( R = correlation coefficient) and MAE_EPA-training ≤ 0.5 (MAE = mean absolute error) were retained to build consensus models. As a result, two consensus models based on the minimum operator and denoted as M19 and M22, as well as a consensus model based on the weighted average operator and denoted as M24, were selected as the best ones for each training set considered. According to the applicability domain (AD) analysis performed, model M19 (built on the EPA training set) has MAE_test-AD = 0.4044, MAE_ProTox-AD = 0.4067 and MAE_T3DB-AD = 0.2586 on the EPA test set, ProTox external set, and T3DB external set, respectively; whereas model M22 (built on the EPA-full set) and model M24 (built on the Zhu set) present MAE_ProTox-AD = 0.3992 and MAE_T3DB-AD = 0.2286, and MAE_ProTox-AD = 0.3773 and MAE_T3DB-AD = 0.2471 on the two external sets accounted for, respectively. These outcomes were compared and statistically validated with respect to 14 QSAR methods (e.g., admetSAR, ProTox-II) from the literature. As a result, model M22 presents the best overall performance. In addition, a retrospective study on 261 withdrawn drugs due to their toxic/side effects was performed, to assess the usefulness of prospectively using the QSAR models proposed in the labeling of chemicals. A comparison with regard to the methods from the literature was also made. As a result, model M22 has the best ability of labeling a compound as toxic according to the globally harmonized system of classification and labeling of chemicals. Therefore, it can be concluded that the models proposed, especially model M22, constitute prominent tools for studying AOT, at providing the best results among all the methods examined. A freely available software was also developed to be used in virtual screening tasks ( http://tomocomd.com/apps/ptoxra ).

In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling

One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.

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.

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.

QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations

BACKGROUND

In previous reports, Marrero-Ponce et al. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topological Molecular Computational Design-Computer Aided Rational Drug Design) molecular descriptors. These MDs codify molecular information based on the bilinear, quadratic and linear algebraic forms and the graph-theoretical electronic-density and edge-adjacency matrices in order to consider atom- and bond-based relations, respectively. These MDs have been successfully applied in the screening of chemical compounds of different therapeutic applications ranging from antimalarials, antibacterials, tyrosinase inhibitors and so on. To compute these MDs, a computational program with the same name was initially developed. However, this in house software barely offered the functionalities required in contemporary molecular modeling tasks, in addition to the inherent limitations that made its usability impractical. Therefore, the present manuscript introduces the QuBiLS-MAS (acronym for Quadratic, Bilinear and N-Linear mapS based on graph-theoretic electronic-density Matrices and Atomic weightingS) software designed to compute topological (0-2.5D) molecular descriptors based on bilinear, quadratic and linear algebraic forms for atom- and bond-based relations.

RESULTS

The QuBiLS-MAS module was designed as standalone software, in which extensions and generalizations of the former ToMoCoMD-CARDD 2D-algebraic indices are implemented, considering the following aspects: (a) two new matrix normalization approaches based on double-stochastic and mutual probability formalisms; (b) topological constraints (cut-offs) to take into account particular inter-atomic relations; (c) six additional atomic properties to be used as weighting schemes in the calculation of the molecular vectors; (d) four new local-fragments to consider molecular regions of interest; (e) number of lone-pair electrons in chemical structure defined by diagonal coefficients in matrix representations; and (f) several aggregation operators (invariants) applied over atom/bond-level descriptors in order to compute global indices. This software permits the parallel computation of the indices, contains a batch processing module and data curation functionalities. This program was developed in Java v1.7 using the Chemistry Development Kit library (version 1.4.19). The QuBiLS-MAS software consists of two components: a desktop interface (GUI) and an API library allowing for the easy integration of the latter in chemoinformatics applications. The relevance of the novel extensions and generalizations implemented in this software is demonstrated through three studies. Firstly, a comparative Shannon's entropy based variability study for the proposed QuBiLS-MAS and the DRAGON indices demonstrates superior performance for the former. A principal component analysis reveals that the QuBiLS-MAS approach captures chemical information orthogonal to that codified by the DRAGON descriptors. Lastly, a QSAR study for the binding affinity to the corticosteroid-binding globulin using Cramer's steroid dataset is carried out.

CONCLUSIONS

From these analyses, it is revealed that the QuBiLS-MAS approach for atom-pair relations yields similar-to-superior performance with regard to other QSAR methodologies reported in the literature. Therefore, the QuBiLS-MAS approach constitutes a useful tool for the diversity analysis of chemical compound datasets and high-throughput screening of structure-activity data.

Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins

Breast cancer is the most frequent cancer reported in women, being responsible for hundreds of thousands of deaths. Chemotherapy has proven to be effective against this malignant neoplasm depending on different biological factors such as the histopathology, grade, and stage, among others. However, breast cancer cells have become resistant to current chemotherapeutic regimens, urging the discovery of new anti-breast cancer drugs. Computational approaches have the potential to offer promising alternatives to accelerate the search for potent and versatile anti-breast cancer agents. In the present work, we introduce the first multitasking (mtk) computational model devoted to the in silico fragment-based design of new molecules with high inhibitory activity against 19 different proteins involved in breast cancer. The mtk-computational model was created from a dataset formed by 24,285 cases, and it exhibited accuracy around 93% in both training and prediction (test) sets. Several molecular fragments were extracted from the molecules present in the dataset, and their quantitative contributions to the inhibitory activities against all the proteins under study were calculated. The combined use of the fragment contributions and the physicochemical interpretations of the different molecular descriptors in the mtk-computational model allowed the design of eight new molecular entities not reported in our dataset. These molecules were predicted as potent multi-target inhibitors against all the proteins, and they exhibited a desirable druglikeness according to the Lipinski's rule of five and its variants.

Toxicokinetics of Polar Chemicals in Zebrafish Embryo (Danio rerio): Influence of Physicochemical Properties and of Biological Processes

The time-resolved uptake of 17 nonionic and ionic polar compounds (logD ≤ 2) with a diversity of functional groups into zebrafish embryos (ZFE) was studied over 96 h of exposure. Among them were pharmaceuticals, pesticides and plant active ingredients. Uptake rates for the diffusion controlled passive uptake through the ZFE membrane ranged from 0.02 to 24 h(-1) for the nonionic compounds and were slower for ionic compounds (<0.008-0.08 h(-1)). The study compounds did not enrich much in the ZFE (median bioconcentration factor of 1, max. 7). Biotransformation significantly influenced the internal concentration of some of the test compounds over time (benzocaine, phenacetin, metribuzin, phenytoin, thiacloprid, valproic acid). For benzocaine, valproic acid and phenacetin several transformation products (TPs) were observed by LC-MS already at early life-stages (before 28 hpf); for benzocaine the TPs comprised >90% of the initial amount taken up into the ZFE. For six compounds internal concentrations remained very low (rel. int. conc. < 0.2). Besides biotransformation (sulfamethoxazole), poor membrane permeability (cimetidine, colchicine) and also affinity to efflux transporters (atropine and chloramphenicol) are the likely reasons for these low internal concentrations. This study outlines that the uptake of polar compounds into ZFE is influenced by their physicochemical properties. However, biological processes, biotransformation and, likely, efflux can strongly affect the internal concentrations already in early developmental stages of the ZFE. This should be considered in future toxicokinetic modeling. The evaluation of the toxicity of chemicals by ZFE requires toxicokinetic studies of the test compounds and their TPs to increase comparability to effects in fish.

Cell permeability beyond the rule of 5

Drug discovery for difficult targets that have large and flat binding sites is often better suited to compounds beyond the "rule of 5" (bRo5). However, such compounds carry higher pharmacokinetic risks, such as low solubility and permeability, and increased efflux and metabolism. Interestingly, recent drug approvals and studies suggest that cell permeable and orally bioavailable drugs can be discovered far into bRo5 space. Tactics such as reduction or shielding of polarity by N-methylation, bulky side chains and intramolecular hydrogen bonds may be used to increase cell permeability in this space, but often results in decreased solubility. Conformationally flexible compounds can, however, combine high permeability and solubility, properties that are keys for cell permeability and intestinal absorption. Recent developments in computational conformational analysis will aid design of such compounds and hence prediction of cell permeability. Transporter mediated efflux occurs for most investigated drugs in bRo5 space, however it is commonly overcome by high local intestinal concentrations on oral administration. In contrast, there is little data to support significant impact of transporter-mediated intestinal absorption in bRo5 space. Current knowledge of compound properties that govern transporter effects of bRo5 drugs is limited and requires further fundamental and comprehensive studies.

Prediction of the permeability of neutral drugs inferred from their solvation properties

Motivation: Determination of drug absorption is an important component of the drug discovery and development process in that it plays a key role in the decision to promote drug candidates to clinical trials. We have developed a method that, on the basis of an analysis of the dynamic distribution of water molecules around a compound obtained by molecular dynamics simulations, can compute a parameter-free value that correlates very well with the compound permeability measured using the human colon adenocarcinoma (Caco-2) cell line assay.

Results: The method has been tested on twenty-three neutral drugs for which a consistent set of experimental data is available. We show here that our method reproduces the experimental data better than other existing tools. Furthermore it provides a detailed view of the relationship between the hydration and the permeability properties of molecules.

Contact:anna.tramontano@uniroma1.it

Supplementary information:Supplementary data are available at Bioinformatics online.

Gastrointestinal localization of metronidazole by a lactobacilli-inspired tetramic acid motif improves treatment outcomes in the hamster model of Clostridium difficile infection

OBJECTIVES

Metronidazole, a mainstay treatment for Clostridium difficile infection (CDI), is often ineffective for severe CDI. Whilst this is thought to arise from suboptimal levels of metronidazole in the colon due to rapid absorption, empirical validation is lacking. In contrast, reutericyclin, an antibacterial tetramic acid from Lactobacillus reuteri, concentrates in the gastrointestinal tract. In this study, we modified metronidazole with reutericyclin's tetramic acid motif to obtain non-absorbed compounds, enabling assessment of the impact of pharmacokinetics on treatment outcomes.

METHODS

A series of metronidazole-bearing tetramic acid substituents were synthesized and evaluated in terms of anti-C. difficile activities, gastric permeability, in vivo pharmacokinetics, efficacy in the hamster model of CDI and mode of action.

RESULTS

Most compounds were absorbed less than metronidazole in cell-based Caco-2 permeability assays. In hamsters, lead compounds compartmentalized in the colon rather than the bloodstream with negligible levels detected in the blood, in direct contrast with metronidazole, which was rapidly absorbed into the blood and was undetectable in caecum. Accordingly, four leads were more efficacious (P < 0.05) than metronidazole in C. difficile-infected animals. Improved efficacy was not due to an alternative mode of action, as the leads retained the mode of action of metronidazole.

CONCLUSIONS

This study provides the clearest empirical evidence that the high absorption of metronidazole lowers treatment outcomes for CDI and suggests a role for the tetramic acid motif for colon-specific drug delivery. This approach also has the potential to lower systemic toxicity and drug interactions of nitroheterocyclic drugs for treating gastrointestine-specific diseases.

Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC50 values for epimastigote elimination (AE) lower than 50 μM, while for the benznidazole the IC50 = 54.7 μM which was used as reference compound. The value of IC50 for cytotoxicity of these compounds is at least 5 times greater than their value of IC50 for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds.

A Sensitive Medium-Throughput Method to Predict Intestinal Absorption in Humans Using Rat Intestinal Tissue Segments

A range of in vitro, ex vivo, and in vivo approaches are currently used for drug development. Highly predictive human intestinal absorption models remain lagging behind the times because of numerous variables concerning permeability through gastrointestinal tract in humans. However, there is a clear need for a drug permeability model early in the drug development process that can balance the requirements for high throughput and effective predictive potential. The present study developed a medium throughput screening Snapwell (MTS-Snapwell) ex vivo model to provide an alternative method to classify drug permeability. Rat small intestine tissue segments were mounted in commercial Snapwell™ inserts. Unidirectional drug transport (A-B) was measured by collecting samples at different time points. Viability of intestinal tissue segments was measured by examining transepithelial electric resistance (TEER) and phenol red and caffeine transport. As a result, the apparent permeability (Papp; ×10(-6) cm/s) was determined for atenolol (10.7 ± 1.2), caffeine (17.6 ± 3.1), cimetidine (6.9 ± 0.1), metoprolol (12.6 ± 0.7), theophylline (15.3 ± 1.6) and, ranitidine (3.8 ± 0.4). All drugs were classified in high/low permeability according to Biopharmaceutics Classification System showing high correlation with human data (r = 0.89). These findings showed a high correlation with human data (r = 0.89), suggesting that this model has potential predictive capacity for paracellular and transcellular passively absorbed molecules.

Comparative study to predict toxic modes of action of phenols from molecular structures

Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.

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

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

Classification models for safe drug molecules

Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.

Early phase drug discovery: cheminformatics and computational techniques in identifying lead series

Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration.

Drug-permeability and transporter assays in Caco-2 and MDCK cell lines

The human colon adenocarcinoma Caco-2 and Madin–Darby canine kidney epithelial cell lines provide in vitro tools to assess a drug’s permeability and transporter interactions during discovery and development. The cells, when cultured on semiporous filters, form confluent monolayers that model the intestinal epithelial barrier for permeability, transporter and drug-interaction assays. The applications of these assays in pharmaceutical research include qualitative prediction and ranking of absorption, determining mechanism(s) of permeability, formulation effects on drug permeability, and the potential for transporter-mediated drug–drug interactions. This review focuses on recent examples of Caco-2 and Madin–Darby canine kidney cells assays for drug permeability including transfected and knock-down cells, miniaturization and automation, and assay combinations to better understand and predict intestinal drug absorption.

Vanilloid derivatives as tyrosinase inhibitors driven by virtual screening-based QSAR models

A number of vanilloids have been tested as tyrosinase inhibitors using Ligand-Based Virtual Screening (LBVS) driven by QSAR (Quantitative Structure-Activity Relationship) models as the multi-agent classification system. A total of 81 models were used to screen this family. Then, a preliminary cluster analysis of the selected chemicals was carried out based on their bioactivity to detect possible similar substructural features among these compounds and the active database used in the QSAR model construction. The compounds identified were tested in vitro to corroborate the results obtained in silico. Among them, two chemicals, isovanillin (K(M) (app) = 1.08 mM) near to kojic acid (reference drug) in one cluster and isovanillyl alcohol (K(M) (app) = 0.88 mM) at the same distance as hydroquinone (reference drug) in another cluster showed inhibitory activity against tyrosinase. The algorithm proposed here could result in a suitable approach for faster and more effective identification of hit and/or lead compounds with tyrosinase inhibitory activity, helping to shorten the long pipeline in the research of novel depigmenting agents to treat skin disorders.