Predictive ADMET studies, the challenges and the opportunities

PharmaBench: Enhancing ADMET benchmarks with large language models

Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects.

Exploring the inhibitory potential of phytochemicals from Vernonia glaberrima leaves against snake venom toxins through computational simulation and experimental validation

Phospholipase A2 (PLA2) is an enzyme present in appreciable quantity in snake venoms which catalyze the hydrolysis of glycerophospholipids at sn-2 position and promote the release of lysophospholipids and fatty acids. 5-methylcoumarin-4-β-glucoside (5MC4BG) and lupeol were previously isolated from the leaves of V. glaberrima. The aim of this research was to evaluate effect of these compounds as potential inhibitors of snake venom toxins of Naja nigricollis using an in vitro and in silico studies. Antisnake venom studies was conducted using acidimetry while the molecular docking analysis against PLA2 enzyme from N. nigricollis was performed using Auto Dock Vina and ADME-Tox analysis was evaluated using swissADME and ProTox-II online servers. The two compounds (5MC4BG and Lupeol) were able to inhibit the hydrolytic actions of PLA2 enzyme with percentage inhibition ranging from 23.99 to 72.36 % and 21.97-24.82 % at 0.0625-1.00 mg/mL respectively while the standard ASV had 82.63 % at 1.00 mg/mL after 10 min incubation at 37 °C. Similar effects were observed after 30 min incubation, although there was significant increase in percentage inhibition of 5MC4BG and lupeol ranging from 66.51 to 83.73 % and 54.87-59.60 % at similar concentrations. Furthermore, the compounds were able to bind to the active site of PLA2 enzyme with high affinity (-7.7 to -6.3 kcal/mol); the standard ligand, Varespladib had a docking score of -6.9 kcal/mol and they exhibited favorable drug-likeness and pharmacokinetic properties and according to toxicity predictions, the two compounds are toxic. In conclusion, the leaf of V. glaberrima contains phytoconstituents with antisnake activity and thus, validates the hypothesis that, the phytoconstituents of V. glaberrima leaves has antisnake venom activity against N. nigricollis venom and thus, should be studied further for the development as antisnake venom agents.

Modification of ibuprofen to improve the medicinal effect; structural, biological, and toxicological study

Ibuprofen is classified as a non-steroidal anti-inflammatory drug (NSAID) that is employed as an initial treatment option for its non-steroidal anti-inflammatory, pain-relieving, and antipyretic properties. However, Ibuprofen is linked to specific well-known gastrointestinal adverse effects like ulceration and gastrointestinal bleeding. It has been linked to harmful effects on the liver, kidney, and heart. The purpose of the study is to create novel and potential IBU analogue with reduced side effects with the enhancement of their medicinal effects, so as to advance the overall safety profile of the drug. The addition of some novel functional groups including CH3, F, CF3, OCF3, Cl, and OH at various locations in its core structure suggestively boost the chemical as well as biological action. The properties of these newly designed structures were analyzed through chemical, physical, and spectral calculations using Density Functional Theory (DFT) and time-dependent DFT through B3LYP/6-31 g (d,p) basis set for geometry optimization. Molecular docking and non-bonding interaction studies were conducted by means of the human prostaglandin synthase protein (PDB ID: 5F19) to predict binding affinity, interaction patterns, and the stability of the protein-drug complex. Additionally, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) and PASS (Prediction of Activity Spectra for Substances) predictions were employed to evaluate the pharmacokinetic and toxicological properties of these structures. Importantly, most of the analogues displayed reduced hepatotoxicity, nephrotoxicity, and carcinogenicity in comparison to the original drug. Moreover, molecular docking analyses indicated improved medicinal outcomes, which were further supported by pharmacokinetic calculations. Together, these findings suggest that the modified structures have reduced adverse effects along with improved therapeutic action compared to the parent drug.

Novel Thiourea and Oxime Ether Isosteviol-Based Anticoagulants: MD Simulation and ADMET Prediction

Activated blood coagulation factor X (FXa) plays a critical initiation step of the blood-coagulation pathway and is considered a desirable target for anticoagulant drug development. It is reversibly inhibited by nonvitamin K antagonist oral anticoagulants (NOACs) such as apixaban, betrixaban, edoxaban, and rivaroxaban. Thrombosis is extremely common and is one of the leading causes of death in developed countries. In previous studies, novel thiourea and oxime ether isosteviol derivatives as FXa inhibitors were designed through a combination of QSAR studies and molecular docking. In the present contribution, molecular dynamics (MD) simulations were performed for 100 ns to assess binding structures previously predicted by docking and furnish additional information. Moreover, three thiourea- and six oxime ether-designed isosteviol analogs were then examined for their drug-like and ADMET properties. MD simulations demonstrated that four out of the nine investigated isosteviol derivatives, i.e., one thiourea and three oxime ether ISV analogs, form stable complexes with FXa. These derivatives interact with FXa in a manner similar to Food and Drug Administration (FDA)-approved drugs like edoxaban and betrixaban, indicating their potential to inhibit factor Xa activity. One of these derivatives, E24, displays favorable pharmacokinetic properties, positioning it as the most promising drug candidate. This, along with the other three derivatives, can undergo further chemical synthesis and bioassessment.

Design, Synthesis, and Bioactivity of Novel Ester-Substituted Cyclohexenone Derivatives as Safeners

Tembotrione, a 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitor, has been widely used in many types of plants. Tembotrione has been reported for its likelihood of causing injury and plant death to certain corn hybrids. Safeners are co-applied with herbicides to protect certain crops without compromising weed control efficacy. Alternatively, herbicide safeners may effectively improve herbicide selectivity. To address tembotrione-induced Zea mays injury, a series of novel ester-substituted cyclohexenone derivatives were designed using the fragment splicing method. In total, 35 title compounds were synthesized via acylation reactions. All the compounds were characterized using infrared spectroscopy, ¹H and ¹³C nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry. The configuration of compound II-15 was confirmed using single-crystal X-ray diffraction. The bioactivity assay proved that tembotrione phytotoxicity to maize could be reduced by most title compounds. In particular, compound II-14 exhibited the highest activity against tembotrione. The molecular structure comparisons as well as absorption, distribution, metabolism, excretion, and toxicity predictions demonstrated that compound II-14 exhibited pharmacokinetic properties similar to those of the commercial safener isoxadifen-ethyl. The molecular docking model indicated that compound II-14 could prevent tembotrione from reaching or acting with Z. mays HPPD (PDB: 1SP8). Molecular dynamics simulations showed that compound II-14 maintained satisfactory stability with Z. mays HPPD. This research revealed that ester-substituted cyclohexenone derivatives can be developed as potential candidates for discovering novel herbicide safeners in the future.

Boesenbergia Pandurata as an Anti-Breast Cancer Agent: Molecular Docking and ADMET Study

Background:

Boesenbergia pandurata or fingerroot is known to have various pharmacological activities, including anticancer properties. Extracts from these plants are known to inhibit the growth of cancer cells, including breast cancer. Anti-breast cancer activity is significantly influenced by the inhibition of two receptors: ER-α and HER2. However, it is unknown which metabolites of B. pandurata play the most crucial role in exerting anticancer activity.

Objective:

This study aimed to determine the metabolites of B. pandurata with the best potential as ER-α and HER2 inhibitors.

Method:

The method used was molecular docking of several B. pandurata metabolites to ER-α and HER2 receptors, followed by an ADMET study of several metabolites with the best docking results.

Results:

The docking results showed eight metabolites with the best docking results for the two receptors based on the docking score and ligand-receptor interactions. Of these eight compounds, compounds 11 ((2S)-7,8-dihydro-5-hydroxy-2-methyl-2-(4''-methyl-3''-pentenyl)-8-phenyl-2H,6H-benzo(1,2-b-5,4- b')dipyran-6-one) and 34 (geranyl-2,4-dihydroxy-6-phenethylbenzoate) showed the potential to inhibit both receptors. Both ADMET profiles also showed mixed results; however, there is a possibility of further development.

Conclusion:

In conclusion, the metabolites of B. pandurata, especially compounds 11 and 34, can be developed as anti-breast cancer agents by inhibiting ER-α and HER2.

Identifying the Novel Inhibitors Against the Mycolic Acid Biosynthesis Pathway Target "mtFabH" of Mycobacterium tuberculosis

Mycolic acids are the key constituents of mycobacterial cell wall, which protect the bacteria from antibiotic susceptibility, helping to subvert and escape from the host immune system. Thus, the enzymes involved in regulating and biosynthesis of mycolic acids can be explored as potential drug targets to kill Mycobacterium tuberculosis (Mtb). Herein, Kyoto Encyclopedia of Genes and Genomes is used to understand the fatty acid metabolism signaling pathway and integrative computational approach to identify the novel lead molecules against the mtFabH (β-ketoacyl-acyl carrier protein synthase III), the key regulatory enzyme of the mycolic acid pathway. The structure-based virtual screening of antimycobacterial compounds from ChEMBL library against mtFabH results in the selection of 10 lead molecules. Molecular binding and drug-likeness properties of lead molecules compared with mtFabH inhibitor suggest that only two compounds, ChEMBL414848 (C1) and ChEMBL363794 (C2), may be explored as potential lead molecules. However, the spatial stability and binding free energy estimation of thiolactomycin (TLM) and compounds C1 and C2 with mtFabH using molecular dynamics simulation, followed by molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) indicate the better activity of C2 (ΔG = -14.18 kcal/mol) as compared with TLM (ΔG = -9.21 kcal/mol) and C1 (ΔG = -13.50 kcal/mol). Thus, compound C1 may be explored as promising drug candidate for the structure-based drug designing of mtFabH inhibitors in the therapy of Mtb.

In Vitro and Computational Studies of Perezone and Perezone Angelate as Potential Anti-Glioblastoma Multiforme Agents

Glioblastoma multiforme (GBM) represents the most malignant type of astrocytoma, with a life expectancy of two years. It has been shown that Poly (ADP-ribose) polymerase 1 (PARP-1) protein is over-expressed in GBM cells, while its expression in healthy tissue is low. In addition, perezone, a phyto-compound, is a PARP-1 inhibitor with anti-neoplastic activity. As a consequence, in the present study, both in vitro and computational evaluations of perezone and its chemically related compound, perezone angelate, as anti-GBM agents were performed. Hence, the anti-proliferative assay showed that perezone angelate induces higher cytotoxicity in the GBM cell line (U373 IC50 = 6.44 μM) than perezone (U373 IC50 = 51.20 μM) by induction of apoptosis. In addition, perezone angelate showed low cytotoxic activity in rat glial cells (IC50 = 173.66 μM). PARP-1 inhibitory activity (IC50 = 5.25 μM) and oxidative stress induction by perezone angelate were corroborated employing in vitro studies. In the other hand, the performed docking studies allowed explaining the PARP-1 inhibitory activity of perezone angelate, and ADMET studies showed its probability to permeate cell membranes and the blood–brain barrier, which is an essential characteristic of drugs to treat neurological diseases. Finally, it is essential to highlight that the results confirm perezone angelate as a potential anti-GBM agent.

Fiber-Reinforced-Phospholipid Vehicle-Based Delivery of l-Ascorbic Acid: Development, Characterization, ADMET Profiling, and Efficacy by a Randomized, Single-Dose, Crossover Oral Bioavailability Study

l-ascorbic acid (AA) or vitamin C is a crucial nutrient needed for optimal health. However, being unable to be synthesized by the body, it is thus necessary to be included in health care products. Moreover, AA is one of the antioxidants that occur naturally, which is used in pharmaceutical and food products as an antioxidant additive. However, AA is vulnerable to environmental settings and undergoes oxidative degradation to dehydroascorbic acid and further to inactive products. Therefore, new research strategies and approaches are required to augment its stability. The objective of this study is to develop and characterize a fiber-reinforced-phospholipid (FRP) matrix-based vehicle, Zeal-AA, for the delivery of AA and optimize the oral bioavailability of the obtained AA powder using an efficacy study by open-label, randomized, single-dose, two-treatment, two-sequence, two-period, two-way crossover. The structural and surface morphologies were analyzed by Fourier transform infrared spectroscopy, transmission electron microscopy, scanning electron microscopy, and differential scanning calorimetry studies. Encapsulation efficiency, mean particle size, size distribution, ζ-potential measurements, and ADMET profiling revealed the potential delivery system for AA. AUC0-t was found to be 55.23 (mg/dL) for Zeal-AA, whereas it was 9.38 (mg/dL) for AA, and C max was found to be 6.69 (mg/dL) for Zeal-AA, whereas it was 1.23 (mg/dL) for AA, with a fold difference of bioavailability in terms of AUC found to be 5.9 fold. The results show that a single oral dose of Zeal-AA is capable of rising the AA levels in the body relative to the control up to 24 h.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Theoretical Study of Monoamine Oxidase B Inhibitors as Drug Candidates for Treatment of Parkinson's Disease

BACKGROUND

Drugs used for Parkinson's disease (PD) are mainly responsible for only relieving major symptoms, but may present several side effects that are typical of such pharmacological treatment.

METHODS

This study aimed to use in silico methods for drug designing inhibitors of the PD therapeutic target, monoamine oxidase B (MAO-B). Thus, 20 MAO-B inhibitors from the BindingDB database were selected followed by a calculation of their descriptors at DFT B3LYP/6-31G** level of theory.

RESULTS

Statistical analysis considering a Pearson correlation matrix led to the selection of electrophilicity index as a descriptor related to the biological activity of inhibitors. Furthermore, based on the prediction of suitable ADME/Tox properties, the molecule CID 54583085 was selected as a template to carry out structural modifications to obtain 3 analogues, whereas molecules B and C showed significant improvement in mutagenicity and carcinogenicity, in relation to the template.

CONCLUSION

Thus, it is concluded that the proposed modifications led us to satisfactory results, since there was an improvement in the toxicological properties of molecules, however, further studies must be carried out to evaluate their biological activities as possible MAO-B inhibitors for PD treatment.

Screening Chemicals for Receptor-Mediated Toxicological and Pharmacological Endpoints: Using Public Data to Build Screening Tools within a KNIME Workflow

Assessing compounds for their pharmacological and toxicological properties is of great importance for industry and regulatory agencies. In this study an approach using open source software and open access databases to build screening tools for receptor-mediated effects is presented. The retinoic acid receptor (RAR), as a pharmacologically and toxicologically relevant target, was chosen for this study. RAR agonists are used in the treatment of a number of dermal conditions and specific types of cancer, such as acute promyelocytic leukemia. However, when administered chronically, there is strong evidence that RAR agonists cause hepatosteatosis and liver injury. After compiling information on ligand-protein-interactions, common substructures and physico-chemical properties of ligands were identified manually and coded into SMARTS strings. Based on these SMARTS strings and calculated physico-chemical features, a rule-based screening workflow was built within the KNIME platform. The workflow was evaluated on two datasets: one with RAR agonists exclusively and another large, chemically diverse dataset containing only a few RAR agonists. Possible modifications and applications of screening workflows, dependent on their purpose, are presented.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

From natural products to drugs for epimutation computer-aided drug design

The epimutational event, i.e., ectopic methylation in tumor suppressor genes, can lead to gene silencing, thus promoting prognosis of cancer. The progression of DNA methylation is a cycle of demethylation, de novo methylation, and maintenance methylation. The enzyme responsible for maintenance of methylation status is DNA methyltransferase 1 (DNMT1), the continuous activity of which is required to maintain the pattern of epimutation; thus, its inhibition is a promising strategy for the treatment of cancer. To the best of our knowledge, this study is the first to focus on the recently developed crystal structure of the catalytic site of DNMT1. Here in this study, we have used the crystal structure for the development of non-nucleoside DNMT1 inhibitors using virtual screening (VS), absorption, distribution, metabolism, elimination/toxicology analysis, and molecular docking studies. In this study, VS was carried out on 48,531 natural products to create a subset of lead-like natural products. Three of them were found to form hydrogen bonds with the catalytic site of the DNMT1 (Cys 1226). Thus, this study adumbrates potential lead compounds for treatment of epimutation.

Application of Machine Leaning in Drug Discovery and Development

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.

Pharmacokinetic study with computational tools in the medicinal chemistry course

To improve the teaching-learning process in the Medicinal Chemistry course, new strategies have been incorporated into practical classes of this fundamental discipline of the pharmaceutical curriculum. Many changes and improvements have been made in the area of medicinal chemistry so far, and students should be prepared for these new approaches with the use of technological resources in this field. Practical activities using computational techniques have been directed to the evaluation of chemical and physicochemical properties that affect the pharmacokinetics of drugs. Their objectives were to allow students to know these tools, to learn how to access them, to search for the structures of drugs and to analyze results. To the best of our knowledge, this is the first study in Brazil to demonstrate the use of computational practices in teaching pharmacokinetics. Practical classes using Osiris and Molinspiration were attractive to students, who developed the activities easily and acquired better theoretical knowledge.

QSARs for PBPK modelling of environmental contaminants

Physiologically-based pharmacokinetic (PBPK) models are increasingly finding use in risk assessment applications of data-rich compounds. However, it is a challenge to determine the chemical-specific parameters for these models, particularly in time- and resource-limiting situations. In this regard, SARs, QSARs and QPPRs are potentially useful for computing the chemical-specific input parameters of PBPK models. Based on the frequency of occurrence of molecular fragments (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure) and exposure conditions, the available QSAR-PBPK models facilitate the simulation of tissue and blood concentrations for some inhaled volatile organic chemicals. The application domain of existing QSARs for developing PBPK models is limited, due to lack of relevant data for diverse chemicals and mechanisms. Even though this approach is conceptually applicable to non-volatile and high molecular weight organics as well, it is more challenging to predict the other PBPK model parameters required for modelling the kinetics of these chemicals (particularly tissue diffusion coefficients, association constants for binding and oral absorption rates). As the level of our understanding of the mechanistic basis of toxicokinetic processes improves, QSARs to provide a priori predictions of key chemical-specific PBPK parameters can be developed to expedite the internal dose-based health risk assessments in data-poor situations.

Computer-aided drug discovery and development

Computer-aided approaches have been widely used in pharmaceutical research to improve the efficiency of the drug discovery and development pipeline. To identify and design small molecules as clinically effective therapeutics, various computational methods have been evaluated as promising strategies, depending on the purpose and systems of interest. Both ligand and structure-based drug design approaches are powerful technologies, which can be applied to virtual screening for lead identification and optimization. Here, we review the progress in this field and summarize the application of some new technologies we developed. These state-of-the-art tools have been used for the discovery and development of active agents for various diseases, in particular for cancer therapies. The described protocols are appropriate for all drug discovery stages, but expertise is still needed to perform the studies based on the targets of interest.

Mapping drug-target interaction networks

Molecular polypharmacological studies have gained more and more attention as they are important in predicting drug off-target properties and potential toxicity/side effect. The explosive growth of biomedical data provides us an opportunity to develop novel strategies to conduct such studies by analyzing molecular interaction networks. In this paper, we present an integrated web application that is implemented based on more than 5,000 drugs and 56,000 biological macromolecule structures. With efficient search of drug information (biological targets, pharmacology, side effect, etc.) and chemical similarity, molecular maps can be constructed to demonstrate the relationships among multiple drugs and receptors. In addition, receptor information can also be employed to map the interaction network. The 3D structures of available drug-receptor complexes can be visualized via our web server, and the query results will be used to identify similar structures for any given drugs as well as their cross interactions with other biological targets. Our implementation provides an efficient way to evaluate the safety and polypharmacological properties of chemical compounds.

Prediction of human pharmacokinetics—evaluation of methods for prediction of hepatic metabolic clearance

Methods for prediction of hepatic clearance (CLH) in man have been evaluated. A physiologically-based in-vitro to in-vivo (PB-IVIV) method with human unbound fraction in blood (fu,bl) and hepatocyte intrinsic clearance (CLint)-data has a good rationale and appears to give the best predictions (maximum ∼2-fold errors; < 25% errors for half of CL-predictions; appropriate ranking). Inclusion of an empirical scaling factor is, however, needed, and reasons include the use of cryopreserved hepatocytes with low activity, and inappropriate CLint- and fu,bl-estimation methods. Thus, an improvement of this methodology is possible and required. Neglect of fu,bl or incorporation of incubation binding does not seem appropriate. When microsome CLint-data are used with this approach, the CLH is underpredicted by 5- to 9-fold on average, and a 106-fold underprediction (attrition potential) has been observed. The poor performance could probably be related to permeation, binding and low metabolic activity. Inclusion of scaling factors and neglect of fu,bl for basic and neutral compounds improve microsome predictions. The performance is, however, still not satisfactory. Allometry incorrectly assumes that the determinants for CLH relate to body weight and overpredicts human liver blood flow rate. Consequently, allometric methods have poor predictability. Simple allometry has an average overprediction potential, > 2-fold errors for ∼1/3 of predictions, and 140-fold underprediction to 5800-fold overprediction (potential safety risk) range. In-silico methodologies are available, but these need further development. Acceptable prediction errors for compounds with low and high CLH should be ∼50 and ∼10%, respectively. In conclusion, it is recommended that PB-IVIV with human hepatocyte CLint and fu,bl is applied and improved, limits for acceptable errors are decreased, and that animal CLH-studies and allometry are avoided.

Human radiolabeled mass balance studies: objectives, utilities and limitations

The determination of metabolic pathways of a drug candidate through the identification of circulating and excreted metabolites is vitally important to understanding its physical and biological effects. Knowledge of metabolite profiles of a drug candidate in animals and humans is essential to ensure that animal species used in toxicological evaluations of new drug candidates are appropriate models of humans. The recent FDA final guidance recommends that human oxidative metabolites whose exposure exceeds 10% of the parent AUC at steady-state should be assessed in at least one of the preclinical animal species used in toxicological assessment. Additional toxicological testing on metabolites that have higher exposure in humans than in preclinical species may be required. The metabolite profiles in laboratory animals and humans are generally accomplished by mass balance and excretion studies in which radiolabeled drugs are administered to these species. The biological fluids are collected, analysed for total radioactivity and evaluated for a quantitative profile of metabolites. Thus, these studies not only determine the rates and routes of excretion but also provide very critical information on the metabolic pathways of drugs in preclinical species and humans. In addition, these studies are required by regulatory agencies for the new drug approval process. Despite the usefulness of these radiolabeled mass balance studies, there is little concrete guidance on how to perform or assess these complex studies. This article examines the objectives, utilities and limitations of these studies and how these studies could be used for the determination of the metabolite exposure in animals and humans.

Prediction of human intestinal absorption by GA feature selection and support vector machine regression

QSAR (Quantitative Structure Activity Relationships) models for the prediction of human intestinal absorption (HIA) were built with molecular descriptors calculated by ADRIANA.Code, Cerius² and a combination of them. A dataset of 552 compounds covering a wide range of current drugs with experimental HIA values was investigated. A Genetic Algorithm feature selection method was applied to select proper descriptors. A Kohonen's self-organizing Neural Network (KohNN) map was used to split the whole dataset into a training set including 380 compounds and a test set consisting of 172 compounds. First, the six selected descriptors from ADRIANA.Code and the six selected descriptors from Cerius² were used as the input descriptors for building quantitative models using Partial Least Square (PLS) analysis and Support Vector Machine (SVM) Regression. Then, another two models were built based on nine descriptors selected by a combination of ADRIANA.Code and Cerius² descriptors using PLS and SVM, respectively. For the three SVM models, correlation coefficients (r) of 0.87, 0.89 and 0.88 were achieved; and standard deviations (s) of 10.98, 9.72 and 9.14 were obtained for the test set.

Prediction models of human plasma protein binding rate and oral bioavailability derived by using GA-CG-SVM method

In this study, support vector machine (SVM) method combined with genetic algorithm (GA) for feature selection and conjugate gradient (CG) method for parameter optimization (GA-CG-SVM), has been employed to develop prediction models of human plasma protein binding rate (PPBR) and oral bioavailability (BIO). The advantage of the GA-CG-SVM is that it can deal with feature selection and SVM parameter optimization simultaneously. Five-fold cross-validation as well as independent test set method were used to validate the prediction models. For the PPBR, a total of 692 compounds were used to train and test the prediction model. The prediction accuracy by means of 5-fold cross-validation is 86% and that for the independent test set (161 compounds) is 81%. These accuracies are markedly higher over that of the best model currently available in literature. The number of descriptors selected is 29. For the BIO, the training set is composed of 690 compounds and external 76 compounds form an independent validation set. The prediction accuracy for the training set by using 5-fold cross-validation and that for the independent test set are 80% and 86%, respectively, which are better than or comparable to those of other classification models in literature. The number of descriptors selected is 25. For both the PPBR and BIO, the descriptors selected by GA-CG method cover a large range of molecular properties which imply that the PPBR and BIO of a drug might be affected by many complicated factors.

Utility of imaging mass spectrometry (IMS) by matrix-assisted laser desorption ionization (MALDI) on an ion trap mass spectrometer in the analysis of drugs and metabolites in biological tissues

INTRODUCTION

The properties and potential liabilities of drug candidate are investigated in detailed ADME assays and in toxicity studies, where findings are placed in context of exposure to dosed drug and metabolites. The complex nature of biological samples may necessitate work-up procedures prior to high performance liquid chromatography-mass spectrometric (HPLC-MS) analysis of endogenous or xenobiotic compounds. This concept can readily be applied to biological fluids such as blood or urine, but in localized samples such as organs and tissues potentially important spatial, thus anatomical, information is lost during sample preparation as the result of homogenization and extraction procedures. However, the localization of test article or spatial identification of metabolites may be critical to the understanding of the mechanism of target-organ toxicity and its relevance to clinical safety.

METHODS

Tissue imaging mass spectrometry (IMS) by matrix-assisted laser desorption ionization (MALDI) and ion trap mass spectrometry (MS) with higher order mass spectrometric scanning functions was utilized for localization of dosed drug or metabolite in tissue. Laser capture microscopy (LCM) was used to obtain related samples from tissue for analyses by standard MALDI-MS and HPLC-MS.

RESULTS

In a toxicology study, rats were administered with a high dosage of a prodrug for 2 weeks. Birefringent microcrystalline material (10-25 microm) was observed in histopathologic formalin-fixed tissue samples. Direct analysis by IMS provided the identity of material in the microcrystals as circulating active drug while maintaining spatial orientation. Complementary data from visual cross-polarized light microscopy as well as standard MALDI-MS and HPLC-MS experiments on LCM samples validated the qualitative results obtained by IMS. Furthermore, the HPLC-MS analysis on the LCM samples afforded a semi-quantitative assessment of the crystalline material in the tissue samples.

DISCUSSION

IMS by MALDI ion trap MS proved sensitive, specific, and highly amenable to the image analysis of traditional small molecule drug candidates directly in tissue.

Toxicogenomics in Drug Discovery and Drug Development: Potential Applications and Future Challenges

Despite the massive investments made by pharmaceutical companies on drug research and development, the number of new drug approvals has remained stagnant in the past decades. It is well known that developing safe and effective new drugs is a long, difficult, and expensive process. While the cost of developing new drugs is increasing rapidly, withdrawals of drugs from the marketplace due to adverse drug reactions (ADR) and/or toxicity is increasing concurrently. The recent advent of high-throughput in silico (computer softwares) and in vitro (cell cultures) screenings have somewhat alleviated some, but not all, of these challenges by providing an efficient and effective way for developing safer and better drugs. This emerging technology, known as toxicogenomics, has great potential to facilitate the development of methodologies that could predict the long-term toxic effects of compounds using relatively short-term bioassays. This review is aimed at discussing the potential applications and future challenges of toxicogenomics in drug discovery and drug development.

How good is your screening library?

Efficient library design is an ongoing challenge for investigators seeking novel ligands for proteins, whether for drug discovery or chemical biology. Strategies that add neglected chemistry or exclude unproductive compounds are two dominant recent themes, as is a growing awareness of molecular complexity and its implications. The choice of how complex molecules in screening libraries should be often amounts to how big they should be. Small, simple molecules have lower affinities and must be screened at high concentration, but they will also have higher hit rates. Larger compounds, on the other hand, will often more closely resemble final drugs, but because they are more highly functionalized and specific, they will have much lower hit rates. The best general-purpose screening libraries may well be those of intermediate complexity that are free of artifact-causing nuisance compounds.

Which hydroxy? Evidence for species differences in the regioselectivity of glucuronidation in rat, dog, and human in vitro systems and dog in vivo

The glucuronidation of (1S,2R,3R,5R)-3-(hydroxymethyl)-5-[7-{[(1R,2S)-2-phenylcyclopropyl]amino}-5-(propylthio)-3H-[1,2,3]triazolo[4,5-d]pyrimidin-3-yl]cyclopentane-1,2-diol (AZ11939714) was studied in UDP-glucuronic acid (UDPGA)-supplemented hepatic microsomes from rat, dog, and human liver. The major biliary metabolite of this compound after intraduodenal administration to a beagle dog was also studied. The techniques of HPLC, HPLC-MS and HPLC-NMR were used to characterize the glucuronides. An analysis of the proton NMR chemical shift differences between parent and metabolites was sufficient to deduce the sites of glucuronidation, although these were confirmed by 2D ROESY experiments. In dog microsomes, AZ11939714 was O-glucuronidated exclusively at the 1-position of the cyclopentanediol. This glucuronide was also the major metabolite in dog bile. In human microsomes, AZ11939714 was O-glucuronidated almost exclusively at the 3-hydroxymethyl position. Rat microsomes produced a mixture of glucuronides at the 2-position of the cyclopentanediol (major) and at the 3-hydroxymethyl position (minor). A clear qualitative species difference in the glucuronidation of AZ11939714 has been demonstrated in vitro. This may have implications for the choice of laboratory species to study the pharmacokinetics and safety of this compound.

Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery

The success of any drug will depend on how closely it achieves an ideal combination of potency, selectivity, pharmacokinetics and safety. The key to achieving this success efficiently is to consider the overall balance of molecular properties of compounds against the ideal profile for the therapeutic indication from the earliest stages of a drug discovery project. The use of in silico predictive models of absorption, distribution, metabolism and elimination (ADME) and physicochemical properties is a major aid in this exercise, as it enables virtual molecules to be assessed across a broad range of properties from initial library generation, through to candidate selection. Of course, no measurement, whether in silico, in vitro or in vivo, is perfect and the uncertainties in any data should be explicitly taken into account when basing conclusions on test results. In addition, in the early stages of drug discovery, when designing a library that is lead seeking or building compound structure-activity relationships, the quality of any set of molecules should also be balanced against the chemical diversity covered. Here, a scheme is presented for achieving these goals based on a suite of predictive ADME models, probabilistic scoring and multiobjective optimisation for library design. The use of this platform for applications in lead identification and optimisation is illustrated.

The Molecular Retention Mechanism in Reversed-Phase Liquid Chromatography of Meso-ionic Compounds by Quantitative Structure-Retention Relationships (QSRR)

Absorption, distribution, metabolism, and excretion (ADME) properties are of invaluable importance to a bioactive compound. Permeation process is one of the most widely studied by many different techniques. Among them, reversed-phase liquid chromatography (RP-LC) is proving to be of great interest, in which retention time can provide some insights on the hydrophobic interaction behavior. A series of antiprotozoal meso-ionic 1,3,4-thiadiazolium-3-aminides were studied in order to obtain the retention-time data through two different organic modifiers using a commercially available Supelcosil ABZ(+) Plus column. MeOH/buffer mobile phase provided the best results in assessing the hydrophobicity of these compounds as compared to MeCN. Quantitative Structure-Retention Relationships (QSRR) models were built to identify the physicochemical properties that govern the process by using PLS analysis. VolSurf descriptors extracted from water (H(2)O), hydrophobic (DRY), amide N-atom (N(1)) and carbonyl O-atom (O) probes showed that the retention is positively correlated to the molecules size and shape, hydrophobic pattern, and dispersion forces. Hydrogen bonding depicts the solvent force considering the donor and acceptor differences between MeOH and MeCN organic modifiers.

High throughput P450 inhibition screens in early drug discovery

This review of high throughput (HT) P450 inhibition technologies and their impact on early drug discovery finds the field at a mature stage. The relationship between P450 inhibition and drug-drug interactions is well understood. A wide variety of P450 inhibition detection technologies are readily available off-the-shelf, but what seems still to be missing is a general agreement on how much weight one should give to the various types of early discovery HT P450 inhibition data. Method-dependent potency differences are a cause of concern, and to resolve this issue the authors advocate calibration of the HT methods with a large set of marketed drugs.

CHARACTERIZATION OF RAT SMALL INTESTINAL AND COLON PRECISION-CUT SLICES AS AN IN VITRO SYSTEM FOR DRUG METABOLISM AND INDUCTION STUDIES

The aim of this study was to characterize rat small intestinal and colon tissue slices as a tool to study intestinal metabolism and to investigate gradients of drug metabolism along the intestinal tract as well as drug-induced inhibition and induction of biotransformation. Tissue morphology and the intestinal mucus layer remained intact in small intestinal and colon slices during 3 h of incubation, while alkaline phosphatase was retained and the rate of metabolism of three model compounds (7-hydroxycoumarin, 7-ethoxycoumarin, and testosterone) appeared constant. Phase I and phase II metabolic gradients, decreasing from stomach toward colon were shown to be clearly different for the model compounds used. Furthermore, the observed slice activities were similar or even higher compared with the literature data concerning metabolism of in vitro intestinal systems. Preincubation with beta-naphthoflavone for 24 h induced the O-deethylation of 7-ethoxycoumarin from nearly undetectable to 140 pmol/min/mg protein in small intestine (fresh slices, 43 pmol/min/mg protein) and to 100 pmol/min/mg protein in colon slices (fresh slices, undetectable). Ketoconazole inhibited metabolism of testosterone by 40% and that of 7-ethoxycoumarin by 100%. In conclusion, we showed that the intestinal slice model is an excellent model to study drug metabolism in the intestine in vitro, since we found that the viability parameters remain constant and the measured enzyme activities are relevant, sensitive to inhibitors, and inducible. Therefore, it is a promising tool to study intestinal drug metabolism in human intestine in vitro in the future.

A UNIFIED MODEL FOR PREDICTING HUMAN HEPATIC, METABOLIC CLEARANCE FROM IN VITRO INTRINSIC CLEARANCE DATA IN HEPATOCYTES AND MICROSOMES

The aim of this study was to evaluate a unified method for predicting human in vivo intrinsic clearance (CL(int, in vivo)) and hepatic clearance (CL(h)) from in vitro data in hepatocytes and microsomes by applying the unbound fraction in blood (fu(b)) and in vitro incubations (fu(inc)). Human CL(int, in vivo) was projected using in vitro data together with biological scaling factors and compared with the unbound intrinsic clearance (CL(int, ub, in vivo)) estimated from clinical data using liver models with and without the various fu terms. For incubations conducted with fetal calf serum (n=14), the observed CL(int, in vivo) was modeled well assuming fu(inc) and fu(b) were equivalent. CL(int, ub, in vivo) was predicted best using both fu(b) and fu(inc) for other hepatocyte data (n=56; r(2)=0.78, p=3.3 x 10(-19), average fold error=5.2). A similar model for CL(int, ub, in vivo) was established for microsomal data (n=37; r(2)=0.77, p=1.2 x 10(-12), average fold error=6.1). Using the model for CL(int, ub, in vivo) (including a further empirical scaling factor), the CL(h) in humans was also calculated according to the well stirred liver model for the most extensive dataset. CL(int, in vivo) and CL(h) were both predicted well using in vitro human data from several laboratories for acidic, basic, and neutral drugs. The direct use of this model using only in vitro human data to predict the metabolic component of CL(h) is attractive, as it does not require extra information from preclinical studies in animals.

Chemical approaches to the discovery and development of cancer therapies

The chemical sciences are essential for the process of anticancer-drug discovery, and a range of chemical research techniques is needed to develop clinically effective drugs. Improved understanding of the cellular, molecular and genetic basis of cancer has increased the number of drug targets available. What chemical approaches are used to develop agents that target specific features of cancer cells and make these therapeutics more effective? We outline the roles that chemical synthesis and understanding of drug uptake have had in drug discovery over the past 100 years, as well as the chemical insights derived from knowledge of the three-dimensional structure of targets.

Integrating virtual screening in lead discovery

Target- and ligand-based virtual screening have emerged as resource-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Eight confirmed virtual screening hits have recently been described and are discussed in this review, with focus on the workflow. These are then evaluated in the light of pharmacokinetics prediction (e.g. Caco-2 permeability, cytochrome P450 inhibition and hERG binding). We anticipate problems for five of these hits (e.g. cardiac toxicity), which warrant further experiments. Future challenges include dynamic tautomer/protonation treatment for both ligands and targets and improved pre- and post- virtual screening filters.