Towards a new age of virtual ADME/TOX and multidimensional drug discovery

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.

Machine Learning Models Identify New Inhibitors for Human OATP1B1

The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [3H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug-drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.

Evaluation of Quantitative Structure Property Relationship Algorithms for Predicting Plasma Protein Binding in Humans

The extent of plasma protein binding is an important compound-specific property that influences a compound's pharmacokinetic behavior and is a critical input parameter for predicting exposure in physiologically based pharmacokinetic (PBPK) modeling. When experimentally determined fraction unbound in plasma (fup) data are not available, quantitative structure-property relationship (QSPR) models can be used for prediction. Because available QSPR models were developed based on training sets containing pharmaceutical-like compounds, we compared their prediction accuracy for environmentally relevant and pharmaceutical compounds. Fup values were calculated using Ingle et al., Watanabe et al. and ADMET Predictor (Simulation Plus). The test set included 818 pharmaceutical and environmentally relevant compounds with fup values ranging from 0.01 to 1. Overall, the three QSPR models resulted in over-prediction of fup for highly binding compounds and under-prediction for low or moderately binding compounds. For highly binding compounds (0.01≤ fup ≤ 0.25), Watanabe et al. performed better with a lower mean absolute error (MAE) of 6.7% and a lower mean absolute relative prediction error (RPE) of 171.7 % than other methods. For low to moderately binding compounds, both Ingle et al. and ADMET Predictor performed better than Watanabe et al. with superior MAE and RPE values. The positive polar surface area, the number of basic functional groups and lipophilicity were the most important chemical descriptors for predicting fup. This study demonstrated that the prediction of fup was the most uncertain for highly binding compounds. This suggested that QSPR-predicted fup values should be used with caution in PBPK modeling.

Big Data and Artificial Intelligence Modeling for Drug Discovery

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

Computational insights into factor affecting the potency of diaryl sulfone analogs as Escherichia coli dihydropteroate synthase inhibitors

Dihydropteroate synthase (DHPS) is an alluring target for designing novel drug candidates to prevent infections caused by pathogenic Escherichia coli strains. Diaryl Sulfone (SO) compounds are found to inhibit DHPS competitively with respect to the substrate pABA (p-aminobenzoate). The extra aromatic ring of diaryl sulfone compounds found to stabilize them in highly flexible pABA binding loops. In this present study, a statistically significant 3D-QSAR model was developed using a data set of diaryl sulfone compounds. The favourable and unfavourable contributions of substitutions in sulfone compounds were illustrated by contour plot obtained from the developed 3D-QSAR model. Molecular docking calculations were performed to investigate the putative binding mode of diaryl sulfone compounds at the catalytic pocket. DFT calculations were carried out using SCF approach, B3LYP- 6-31 G (d) basis set to compute the HOMO, LUMO energies and their respective location at pABA binding pocket. Further, the developed model was validated by FEP (Free Energy Perturbation) calculations. The calculated relative free energy of binding between the highly potent and less potent sulfone compound was found to be -3.78 kcal/ mol which is comparable to the experimental value of -5.85 kcal/mol. A 10 ns molecular dynamics simulation of inhibitor and DHPS confirmed its stability at pABA catalytic site. Outcomes of the present work provide deeper insight in designing novel drug candidates for pathogenic Escherichia coli strains.

Data Mining and Computational Modeling of High-Throughput Screening Datasets

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

Molecular properties associated with transporter-mediated drug disposition

Membrane transporters play a key role in the absorption, distribution, clearance, elimination, and transport of drugs. Understanding the drug properties and structure activity relationships (SAR) for affinity to membrane transporters is critical to optimize clearance and pharmacokinetics during drug design. To facilitate the early identification of clearance mechanism, a framework named the extended clearance classification system (ECCS) was recently introduced. Using in vitro and physicochemical properties that are readily available in early drug discovery, ECCS has been successfully applied to identify major clearance mechanism and to implicate the role of membrane transporters in determining pharmacokinetics. While the crystal structures for most of the drug transporters are currently not available, ligand-based modeling approaches that use information obtained from the structure and molecular properties of the ligands have been applied to associate the drug-related properties and transporter-mediated disposition. The approach allows prospective prediction of transporter both substrate and/or inhibitor affinity and build quantitative structure-activity relationship (QSAR) to enable early optimization of pharmacokinetics, tissue distribution and drug-drug interaction risk. Drug design applications can be further improved through uncovering transporter protein crystal structure and generation of quality data to refine and develop viable predictive models.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

The unbinding studies of vascular endothelial growth factor receptor-2 protein tyrosine kinase type II inhibitors

Vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase has two conformations, active and inactive conformations. Type II inhibitors bind to inactive conformation. It has two possible binding/unbinding paths. To explore the unbinding path of inhibitor 01-435 that was generated by fragment build in the binding pocket of VEGFR-2, molecular dynamics (MD) simulation was performed on the crystal structure of VEGFR-2 in complex with 01-435, then steered molecular dynamics (SMD) simulation was executed on the crystal structure of VEGFR-2 in complex with 01-435. Pull force, van der Waals and electrostatic interaction along the two paths were calculated by using SMD simulation. The SMD simulation results indicate that the more favorable path for inhibitor dissociation is along with the traditional ATP-channel rather than the allosteric-pocket-channel, which is mainly due to the less electrostatic interaction that the ligand suffers during dissociation process along the traditional ATP-channel.

Computational ligand-based rational design: Role of conformational sampling and force fields in model development

A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

Integrated Decision-tree Testing Strategies for Acute Systemic Toxicity and Toxicokinetics with Respect to the Requirements of the EU REACH Legislation

Liverpool John Moores University and FRAME conducted a joint research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with REACH. This paper focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for acute systemic toxicity and toxicokinetic testing. The paper reviews in vitro tests based on basal cytotoxicity and target organ toxicity, along with QSAR models and expert systems available for this endpoint. The use of PBPK modelling for the prediction of ADME properties is also discussed. These tests are then incorporated into a decision-tree style, integrated testing strategy, which also includes the use of refined in vivo acute toxicity tests, as a last resort. The implementation of the strategy is intended to minimise the use of animals in the testing of acute systemic toxicity and toxicokinetics, whilst satisfying the scientific and logistical demands of the EU REACH legislation.

Integrated decision-tree testing strategies for acute systemic toxicity and toxicokinetics with respect to the requirements of the EU REACH legislation

Liverpool John Moores University and FRAME conducted a joint research project, sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with REACH. This paper focuses on the use of alternative (non-animal) methods (both in vitro and in silico) for acute systemic toxicity and toxicokinetic testing. The paper reviews in vitro tests based on basal cytotoxicity and target organ toxicity, along with QSAR models and expert systems available for this endpoint. The use of PBPK modelling for the prediction of ADME properties is also discussed. These tests are then incorporated into a decision-tree style, integrated testing strategy, which also includes the use of refined in vivo acute toxicity tests, as a last resort. The implementation of the strategy is intended to minimise the use of animals in the testing of acute systemic toxicity and toxicokinetics, whilst satisfying the scientific and logistical demands of the EU REACH legislation.

Selection of heterocycles for drug design

A method has been devised to obtain heterocyclic ring systems suitable for use in drug design and library design, with an emphasis on the selection of systems with good absorption, distribution, metabolism, excretion and toxicity (ADMET) properties in man. This has been achieved by extraction of the ring systems found in drugs that have reached Phase II or later stages of drug development and launch. Properties have been calculated for these ring systems to enable them to be rationally selected from the database, including descriptors based on molecular size, shape, hydrogen bonding and orbital properties. In many cases, the properties have been calculated for different attachment points of the same heterocycle. Principal components analysis has been used to enable visualization of the set of heterocycles in a useful "chemical space". Using this space, it is possible to select heterocycles for drug design to explore specific aspects of the properties of the heterocycle, such as size or hydrogen bonding, while maintaining other parameters near constant, or to select heterocycles with extreme values of these properties but which are nonetheless likely to be acceptable in a drug. The differences between the properties calculated for the most- and least-frequently used heterocycles from the late-phase drug set have been analyzed, and may suggest that heterocycles in successful drugs are more likely to have calculated quantities associated with lower chemical reactivity.

History, Evolution, and Trends in Compound Management for High Throughput Screening

Growth in compound library size with increasing levels of high throughput screening has resulted in the complementary development of automated hardware and software systems for compound management. The history of this technology from its emergence in the early 1990s, its current status, and future trends are all described. The perspective is primarily a personal one, based on the author's involvement with compound management from its outset to the present day.

ADME-Tox in drug discovery: integration of experimental and computational technologies

Over the past ten years, in vitro experimental tools to characterize ADME-Tox profiles of compounds have been applied in early stages of the drug discovery process to increase the success rate of discovery programmes and to progress better candidates into drug development. Application of in silico ADME-Tox models has further enhanced discovery support, enabling virtual screening of compounds and thus, application of ADME-Tox at every stage of the discovery process. Ultimately, effective and efficient ADME-Tox support of discovery will depend on a complementary and synergistic use of experimental and in silico ADME-Tox.