Early Stages Of Drug Discovery Research Articles

Paper-based sol-gel thin films immobilized cytochrome P450 for enzyme activity measurement

Cytochrome P450 (CYP450), and in particular CYP3A4, is the most abundantly expressed CYP450 isozyme implicated in many drug-drug and medicinal plant-drug interactions. Therefore, incorporation of CYP3A4 enzyme screening at an early stage of drug discovery is preferable in order to avoid enzymatic interactions. Here we present for the first time a paper-based CYP3A4 immobilized sol-gel-derived a platform using resorufin benzyl ether as a fluorogenic enzyme substrate used to investigate enzyme activity. The fluorescence intensity of the product can be simply quantified by using a handheld digital microscope and an image analysis software. The limit of quantitation was 0.35 μM with good precision (RSDs < 4.1%). Furthermore, the assay of CYP3A4 activity on the developed paper-based device provided comparable results with those obtained from conventional well-plates (p > 0.05), while offering simplicity and lower cost. Kinetic parameters of the immobilized CYP3A4 in sol-gel coated paper were calculated from the Lineweaver-Burk plot, including Michaelis constant (Km) and maximum velocity (Vmax), which were 2.71 ± 0.35 μM and 0.43 ± 0.05 μM/min, respectively. Moreover, a functional test of these devices was conducted by assessments of known CYP3A4 inhibitors (i.e. ketoconazole, itraconazole) and inducers (i.e. phenytoin, carbamazepine). To further demonstrate the broad range of uses, the devices were utilized to assay plant extracts i.e. Areca catechu seeds, Camellia sinensis leaves, Eclipta prostrata aerial part, providing results in good agreement with previous studies. Furthermore, the sol-gel immobilized enzyme stored at 4 °C can increase storage stability, offering the activity of 86.3 ± 0.4% after 3-weeks storage, equivalent to the activity of the free enzyme solution after 1-week storage. The developed paper-based devices offer versatility, portability and low-cost.

A Miniaturized Pump Out Method for Characterizing Molecule Interaction with ABC Transporters.

Characterizing interaction of newly synthetized molecules with efflux pumps remains essential to improve their efficacy and safety. Caco-2 cell line cultivated on inserts is widely used for measuring apparent permeability of drugs across biological barriers, and for estimating their interaction with efflux pumps such as P-gp, BCRP and MRPs. However, this method remains time consuming and expensive. In addition, detection method is required for measuring molecule passage across cell monolayer and false results can be generated if drugs concentrations used are too high as demonstrated with quinidine. For this reason, we developed a new protocol based on the use of Caco-2 cell directly seeded on 96- or 384-well plates and the use of fluorescent substrates for efflux pumps. We clearly observed that the new method reduces costs for molecule screening and leads to higher throughput compared to traditional use of Caco-2 cell model. This accelerated model could provide quick feedback regarding the molecule design during the early stage of drug discovery and therefore reduce the number of compounds to be further evaluated using the traditional Caco-2 insert method.

CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens

Determining chemical carcinogenicity in the early stages of drug discovery is fundamentally important to prevent the adverse effect of carcinogens on human health. There has been a recent surge of interest in developing computational approaches to predict chemical carcinogenicity. However, the predictive power of many existing approaches is limited, and there is plenty of room for improvement. Here, we develop a new deep learning architecture, termed CapsCarcino, to distinguish between carcinogens and noncarcinogens. CapsCarcino is constructed based on a dynamic routing algorithm that requires less data, extracts more comprehensive information, and does not require feature selection. We find that CapsCarcino provides a significantly improved predictive and generalization ability over, and outperforms five other machine learning models. Specifically, the best model of CapsCarcino achieves an accuracy of 85.0% on an external validation dataset. In addition, we discover that the enhanced predictive capability of CapsCarcino over that of the other methods is robust and can be achieved using sparse datasets. Training on merely 20% of the dataset, CapsCarcino performs comparably to the other methods based on the full training dataset. Further mechanism analysis indicates that CapsCarcino could efficiently learn the characteristics of carcinogens even if structural alerts are insufficiently represented. The results indicate that CapsCarcino should be helpful for carcinogen risk assessment.

ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches.

Adverse effects induced by drug-drug interactions may result in early termination of drug development or even withdrawal of drugs from the market, and many drug-drug interactions are caused by the inhibition of cytochrome P450 (CYP450) enzymes. Therefore, the accurate prediction of the inhibition capability of a given compound against a specific CYP450 isoform is highly desirable. In this study, three ensemble learning methods, including random forest, gradient boosting decision tree, and eXtreme gradient boosting (XGBoost), and two deep learning methods, including deep neural networks and convolutional neural networks, were used to develop classification models to discriminate inhibitors and noninhibitors for five major CYP450 isoforms (1A2, 2C9, 2C19, 2D6, and 3A4). The results demonstrate that the ensemble learning models generally give better predictions than the deep learning models for the external test sets. Among all of the models, the XGBoost models achieve the best classification capability (average prediction accuracy of 90.4%) for the test sets, which even outperform the previously reported model developed by the multitask deep autoencoder neural network (88.5%). The Shapley additive explanation method was then used to interpret the models and analyze the misclassified molecules. The important molecular descriptors given by our models are consistent with the structural preferences for inhibitors of different CYP450 isoforms, which may provide valuable clues to detect potential drug-drug interactions in the early stage of drug discovery.

A Quantum-Inspired Method for Three-Dimensional Ligand-Based Virtual Screening.

Measuring similarity between molecules is an important part of virtual screening (VS) experiments deployed during the early stages of drug discovery. Most widely used methods for evaluating the similarity of molecules use molecular fingerprints to encode structural information. While similarity methods using fingerprint encodings are efficient, they do not consider all the relevant aspects of molecular structure. In this paper, we describe a quantum-inspired graph-based molecular similarity (GMS) method for ligand-based VS. The GMS method is formulated as a quadratic unconstrained binary optimization problem that can be solved using a quantum annealer, providing the opportunity to take advantage of this nascent and potentially groundbreaking technology. In this study, we consider various features relevant to ligand-based VS, such as pharmacophore features and three-dimensional atomic coordinates, and include them in the GMS method. We evaluate this approach on various datasets from the DUD_LIB_VS_1.0 library. Our results show that using three-dimensional atomic coordinates as features for comparison yields higher early enrichment values. In addition, we evaluate the performance of the GMS method against conventional fingerprint approaches. The results demonstrate that the GMS method outperforms fingerprint methods for most of the datasets, presenting a new alternative in ligand-based VS with the potential for future enhancement.

Current Progress on the Genomics of Schistosomiasis for Drug Discovery and Diagnostics.

For a number of decades, schistosomiasis has remained a public threat and an economic burden in a number of countries, directly impacting over 200 million people. The past 15 years have seen tremendous progress in the development of high-throughput methods for targeting or compound selection that are vital to early-stage schistosome drug discovery research. Genomewide approaches to analyze gene expression at the transcriptional and other -omic levels have helped immensely for gaining insight into the pathways and mechanisms involved in the schistosomiasis and it is expected to revolutionize the drug discovery as well as related diagnostics. This review discusses the most recent progress of pharmacology and genomics concerning schistosomiasis with a focus on drug discovery and diagnostic tools. It also provides chemical structural insights of promising targets along with available in vitro and/or in vivo data. Although significant research has been done to identify new molecules for the treatment and new methods for diagnosis, the necessity of new options for the sustainable control of schistosomiasis remains a great challenge.

Protein Preparation Automatic Protocol for High-Throughput Inverse Virtual Screening: Accelerating the Target Identification by Computational Methods.

Structure-based virtual screening is highly used in the early stages of drug discovery to identify new putative lead compounds for a given target. However, when a small molecule elicits a biological effect, but its target is unknown, or the side effects it causes arise from its undesired interaction with unknown counterparts, the identification of its interacting targets represents an indispensable task. The computational procedure named inverse virtual screening, which relies on docking a molecule (or a small set of compounds) against panels of target proteins to select the most promising complexes, could be useful to overcome these issues. Panels can contain thousands of proteins, and they must be correctly prepared to assure the best docking performance. Therefore, the preparation of panels of proteins collected in the Protein Data Bank ( www.rcsb.org ), if manually performed, may be costly in terms of time and efforts, and this can limit the applicability of this approach in high-throughput virtual screening workflows. We here show an automated workflow to speed up panel preparation and development, and to test its performance, this protocol was initially applied to a panel of 628 viral proteins and, afterward, to a panel of transferase proteins (2789 entries) to perform a large inverse virtual screening study, testing a small set of compounds synthesized in our laboratory. Tankyrase 2 (PARP 5b) was selected as their preferred target of interaction, and the predicted binding was validated by means of surface plasmon resonance experiments. This protocol is useful for the rapid identification of the interacting target for a bioactive compound; accordingly, it facilitates the re-evaluation of the pharmacological activity of known active compounds, addressing the repurposing and the polypharmacology concepts.

PASS-based prediction of metabolites detection in biological systems

ABSTRACTMetabolite identification is an essential part of the drug discovery and development process. Experimental methods allow identifying metabolites and estimating their relative amount, but they require cost-intensive and time-consuming techniques. Computational methods for metabolite prediction are devoid of these shortcomings and may be applied at the early stage of drug discovery. In this study, we investigated the possibility of creating SAR models for the prediction of the qualitative metabolite yield (‘major’, ‘minor’, ”trace” and ”negligible”) depending on species and biological experimental systems. In addition, we have created models for prediction of xenobiotic excretion depending on its administration route for different species. The prediction is based on an algorithm of naïve Bayes classifier implemented in PASS software. The average accuracy of prediction was 0.91 for qualitative metabolite yield prediction and 0.89 for prediction of xenobiotic excretion. The created models were included as a component of MetaTox web application, which allows predicting the xenobiotic metabolism pathways (http://www.way2drug.com/mg).

Prediction of hERG K+ channel blockage using deep neural networks

Human ether-a-go-go-related gene (hERG) K+ channel blockage may cause severe cardiac side-effects and has become a serious issue in safety evaluation of drug candidates. Therefore, improving the ability to avoid undesirable hERG activity in the early stage of drug discovery is of significant importance. The purpose of this study was to build predictive models of hERG activity by deep neural networks. For each combination of sampling methods and descriptors, deep neural networks with different architectures were implemented to build classification models. The optimal model M15 with three hidden layers, undersampling method, and 2D descriptors yielded the prediction accuracy of 0.78 and F1 score of 0.75 on the test set as well as accuracy of 0.77 and F1 score of 0.34 on the external validation set, outperforming the other 35 models including 9 random forest models. Particularly, the optimal model M15 achieved the highest F1 score and the second highest accuracy when compared with other five methods from four groups using different machine learning algorithms with the same external validation set. It can be believed that this model has powerful capability on prediction of hERG toxicity, which is of great benefit for developing novel drug candidates.

Engineering a surrogate human heteromeric α/β glycine receptor orthosteric site exploiting the structural homology and stability of acetylcholine-binding protein.

Protein-engineering methods have been exploited to produce a surrogate system for the extracellular neurotransmitter-binding site of a heteromeric human ligand-gated ion channel, the glycine receptor. This approach circumvents two major issues: the inherent experimental difficulties in working with a membrane-bound ion channel and the complication that a heteromeric assembly is necessary to create a key, physiologically relevant binding site. Residues that form the orthosteric site in a highly stable ortholog, acetylcholine-binding protein, were selected for substitution. Recombinant proteins were prepared and characterized in stepwise fashion exploiting a range of biophysical techniques, including X-ray crystallography, married to the use of selected chemical probes. The decision making and development of the surrogate, which is termed a glycine-binding protein, are described, and comparisons are provided with wild-type and homomeric systems that establish features of molecular recognition in the binding site and the confidence that the system is suited for use in early-stage drug discovery targeting a heteromeric α/β glycine receptor.

Three-Compartment Model Analysis with Minimal Sampling Points in the Caco-2 Permeability Assay.

The purpose of this study was to establish a modified method of three-compartment model analysis that minimized the sampling frequency. A Caco-2 permeability assay was performed on ten structurally diverse compounds with passive diffusion. A three-compartment model was analyzed by a conventional method and a method with fewer sampling points, called the simplified method, using concentration-time profiles in the donor, intracellular, and receiver compartments. The concentration-time profiles in all compartments were well described by the conventional method. The calculated unbound fraction of intracellular (fu2) and apparent permeability coefficient (Papp) were 0.0107-1.22 and 0.886-146 × 10-6 cm/s, respectively. The simplified method also described the concentration profiles in the compartments of all ten compounds except one, ibuprofen. The difference in values calculated by the simplified method compared to the conventional method was between -7 and 7% for fu2 and between -6 and 42% for Papp. These results suggested that the parameter values from the simplified method were comparable with those from the conventional method. The simplified method may be a promising approach to improve the throughput of three-compartment model analyses of Caco-2 permeability assays in the early stages of drug discovery.

Metabolomics, lipidomics and proteomics profiling of myoblasts infected with Trypanosoma cruzi after treatment with different drugs against Chagas disease.

Chagas disease, the most important parasitic infection in Latin America, is caused by the intracellular protozoan Trypanosoma cruzi. To treat this disease, only two nitroheterocyclic compounds with toxic side effects exist and frequent treatment failures are reported. Hence there is an urgent need to develop new drugs. Recently, metabolomics has become an efficient and cost-effective strategy for dissecting drug mode of action, which has been applied to bacteria as well as parasites, such as different Trypanosome species and forms. We assessed if the metabolomics approach can be applied to study drug action of the intracellular amastigote form of T. cruzi in a parasite-host cell system. We applied a metabolic fingerprinting approach (DI-MS and NMR) to evaluate metabolic changes induced by six different (candidate) drugs in a parasite-host cell system. In a second part of our study, we analyzed the impact of two drugs on polar metabolites, lipid and proteins to evaluate if affected pathways can be identified. Metabolic signatures, obtained by the fingerprinting approach, resulted in three different clusters. Two can be explained by already known of mode actions, whereas the three experimental drugs formed a separate cluster. Significant changes induced by drug action were observed in all the three metabolic fractions (polar metabolites, lipids and proteins). We identified a general impact on the TCA cycle, but no specific pathways could be attributed to drug action, which might be caused by a high percentage of common metabolome between a eukaryotic host cell and a eukaryotic parasite. Additionally, ion suppression effects due to differences in abundance between host cells and parasites may have occurred. We validated the metabolic fingerprinting approach to a complex host-cell parasite system. This technique can potentially be applied in the early stage of drug discovery and could help to prioritize early leads or reconfirmed hits for further development.

Proposal of a Parameter for OATP1B1 Inhibition Screening at the Early Drug Discovery Stage

It is known that potent inhibition of organic-anion-transporting polypeptide (OATP)1B1 increases exposure to statins, leading to severe adverse effects. The aim of this study was to propose a parameter and its criteria in OATP1B1 inhibition assay at the early drug discovery stage to avoid compounds with the risk of statin-related adverse effects. According to drug label information, most compounds classified as “contraindicated” or “should be avoided” when administered concomitantly with statins increased their AUCs more than 4-fold. Generally, R values where R = 1 + plasma unbound fraction (fu) × maximum inhibitor concentration at the inlet to the liver/IC50 are used to evaluate the extent of clinical drug interaction. However, clinical doses and Cmax cannot be determined at the screening stage. Therefore, we estimated the correlations between change in AUC of statins concomitantly administered with OATP1B1 inhibitors and various parameters including fu/IC50. Cyclosporin A, rifampicin, and telaprevir increased the AUC of statins more than 4-fold and fu/IC50 of these compounds was >0.1 L/μmol. On the other hand, fu/IC50 of other compounds was ≤0.03 L/μmol. This study indicates that fu/IC50 is a useful parameter to avoid compounds that seriously affect statin potency through interaction with OATP1B1 at the screening stage.

Constructing an In Silico Three-Class Predictor of Human Intestinal Absorption With Caco-2 Permeability and Dried-DMSO Solubility

Absorption of drugs is the first step after dosing, and it largely affects drug bioavailability. Hence, estimating the fraction of absorption (Fa) in humans is important in the early stages of drug discovery. To achieve correct exclusion of low Fa compounds and retention of potential compounds, we developed a freely available model to classify compounds into 3 levels of Fa capacity using only the chemical structure. To improve Fa prediction, we added predicted binary classification results of membrane permeability measured using Caco-2 cell line (Papp) and dried–dimethyl sulfoxide solubility (accuracy, 0.836; kappa, 0.560). The constructed models can be accessed via a web application.

A nanodroplet cell processing platform facilitating drug synergy evaluations for anti-cancer treatments

Therapeutic drug synergism intervened in cancer treatments has been demonstrated to be more effective than using a single effector. However, it remains inherently challenging, with a limited cell count from tumor samples, to achieve potent personalized drug cocktails. To address the issue above, we herein present a nanodroplet cell processing platform. The platform incorporates an automatic nanodroplet dispenser with cell array ParaStamp chips, which were fabricated by a new wax stamping approach derived from laser direct writing. Such approach enables not only the on-demand de-wetting with hydrophobic wax films on substrates but also the mask-less fabrication of non-planar microstructures (i.e. no photolithography process). The ParaStamp chip was pre-occupied with anti-cancer drugs and their associate mixtures, enabling for the spatially addressable screening of optimal drug combinations simultaneously. Each droplet with a critical volume of 200 nl containing with 100 cells was utilized. Results revealed that the optimal combination reduces approximate 28-folds of conducted doses compared with single drugs. Tumor inhibition with the optimally selected drug combination was further confirmed by using PC-3 tumor-bearing mouse models. Together, the nanodroplet cell processing platform could therefore offer new opportunities to power the personalized cancer medicine at early-stage drug screening and discovery.

Is prodrug design an approach to increase water solubility?

Water solubility has been identified as a critical parameter and the main responsible by affecting poor performance of oral drug delivery. Poorly soluble drugs can originate unsatisfactory ADME properties leading to low oral bioavailability, insufficient chemical stability, low half-life, fast pre-systemic metabolism and difficulties in formulation. In this context, the prodrug design is an alternative in order to improve physicochemical, biopharmaceutical and pharmacokinetic properties such as permeability, solubility, bioavailability, chemical stability and metabolism of molecules presenting poor drug-like properties. In this article we highlight the importance of the prodrug design in the early stages of drug discovery and development process, in an attempt to diminish the attrition rate and end up falling into the valley of death. Selected examples of this strategy are provided in this review and they are classified by some basic functional groups that are amenable to the prodrug approach with the aim of increasing aqueous solubility of poorly water-soluble compounds. Over the past decade, the number of approved prodrugs is considerable among all drugs launched in the market, emphasizing the importance of this tool on drug design. It is reported that 10% of all marketed drug worldwide can be classified as prodrugs. Furthermore, prodrugs designed to be more water soluble launched in the past decade are summarized in a table to have a closer look and finally state that the prodrug design is an amenable approach to increase water solubility.

The Role Change of Drug Metabolism and Pharmacokinetics Research in the Drug Development

Global big pharmaceutical companies identify Absorption-Distribution-Metabolism-Excretion-Toxicity (ADMET) properties in early drug discovery stage to improve poor pharmacokinetics (PK) and bioavailability (BA) that are the major cause of attrition rate in drug development in 1990s. Attrition rate due to poor PK and BA was around 40% among total attrition rate. Hence, they intend to assign in-vitro ADME assay evaluations at the early drug discovery stage for finding optimal ADME chemicals to reduce attrition rate. As a result, attrition rate in 2000s due to poor PK & BA greatly decreased to less than 8% and the role of Drug-Metabolism and Pharmacokinetics (DMPK) research has changed importantly during this process. According to this trend, canonical role of DMPK is moving fast to convergence area for optimization of new drugs. We expressed typical nonclinical work-flow in the 1990s and 2000s and tried to investigate how non-clinical work-flow changed in early drug discovery stage. The biggest changes are that in-vitro ADME assays were performed during the early drug discovery stage. In this review, we tried to inform to Korean pharmaceutical company about the world wide paradigm shift in DMPK research. We discussed drug discovery strategies to increase the success rate and the future direction of DMPK research scope.

Importance of the Choice of a Recombinant System to Produce Large Amounts of Functional Membrane Protein hERG.

Human ether-a-gogo related gene (hERG) product is the membrane potassium channel Kv11.1, which is involved in the electrical activity of the heart. As such, it is a key player in the toxicity of many drug candidates. Therefore, having this protein at hand during earlier stages of drug discovery is important for preventing later toxicity. Furthermore, having a fair quantity of functional channels may help in the development of the necessary techniques for gaining insight in this channel structure. Thus, we performed a comparative study of methods for over-expressing a mutated but functional, hERG in different orthologous hosts, such as yeast, bacteria, insect and human cell lines. We also engineered the protein to test various constructs of a functional channel. We obtained a significant amount of a functional mutant channel from HEK cells that we thoroughly characterized. The present work paves the way for the expression of large amounts of this protein, with which protein crystallization or cryo-electronic microscopy will be attempted. This will be a way to gain information on the structure of the hERG active site and its modelization to obtain data on the pauses of various reference compounds from the pharmacopeia, as well as to gain information about the thermodynamics of the hERG/ligand relationship.

A Simple Procedure for Creating Scalable Phenotypic Screening Assays in Human Neurons

Neurons created from human induced pluripotent stem cells (hiPSCs) provide the capability of identifying biological mechanisms that underlie brain disorders. IPSC-derived human neurons, or iNs, hold promise for advancing precision medicine through drug screening, though it remains unclear to what extent iNs can support early-stage drug discovery efforts in industrial-scale screening centers. Despite several reported approaches to generate iNs from iPSCs, each suffer from technological limitations that challenge their scalability and reproducibility, both requirements for successful screening assays. We addressed these challenges by initially removing the roadblocks related to scaling of iNs for high throughput screening (HTS)-ready assays. We accomplished this by simplifying the production and plating of iNs and adapting them to a freezer-ready format. We then tested the performance of freezer-ready iNs in an HTS-amenable phenotypic assay that measured neurite outgrowth. This assay successfully identified small molecule inhibitors of neurite outgrowth. Importantly, we provide evidence that this scalable iN-based assay was both robust and highly reproducible across different laboratories. These streamlined approaches are compatible with any iPSC line that can produce iNs. Thus, our findings indicate that current methods for producing iPSCs are appropriate for large-scale drug-discovery campaigns (i.e. >10e5 compounds) that read out simple neuronal phenotypes. However, due to the inherent limitations of currently available iN differentiation protocols, technological advances are required to achieve similar scalability for screens that require more complex phenotypes related to neuronal function.

Carboxylesterase catalyzed 18O-labeling of carboxylic acid and its potential application in LC-MS/MS based quantification of drug metabolites

LC-MS quantification of drug metabolites is sometimes impeded by the availability of internal standards that often requires customized synthesis and/or extensive purification. Although isotopically labeled internal standards are considered ideal for LC-MS/MS based quantification, de novo synthesis using costly isotope-enriched starting materials makes it impractical for early stage of drug discovery. Therefore, quick access to these isotope-enriched compounds without chemical derivatization and purification will greatly facilitate LC-MS/MS based quantification. Herein, we report a novel 18O-labeling technique using metabolizing enzyme carboxylesterase (CES) and its potential application in metabolites quantification study. Substrates of CES typically undergo a two-step oxygen exchange with H218O in the presence of the enzyme, generating singly- and doubly-18O-labeled carboxylic acids; however, unexpected hydrolytic behavior was observed for three of the test compounds – indomethacin, piperacillin and clopidogrel. These unusual observations led to the discovery of several novel hydrolytic mechanisms. Finally, when used as internal standard for LC-MS/MS based quantification, these in situ labeled compounds generated accurate quantitation comparable to the conventional standard curve method. The preliminary results suggest that this method has potential to eliminate laborious chemical synthesis of isotope-labeled internal standards for carboxylic acid-containing compounds, and can be developed to facilitate quantitative analysis in early-stage drug discovery.