Capter 11 Filtering in Drug Discovery

Predicting Cardiotoxicity of Molecules Using Attention-Based Graph Neural Networks

In drug discovery, the search for new and effective medications is often hindered by concerns about toxicity. Numerous promising molecules fail to pass the later phases of drug development due to strict toxicity assessments. This challenge significantly increases the cost, time, and human effort needed to discover new therapeutic molecules. Additionally, a considerable number of drugs already on the market have been withdrawn or re-evaluated because of their unwanted side effects. Among the various types of toxicity, drug-induced heart damage is a severe adverse effect commonly associated with several medications, especially those used in cancer treatments. Although a number of computational approaches have been proposed to identify the cardiotoxicity of molecules, the performance and interpretability of the existing approaches are limited. In our study, we proposed a more effective computational framework to predict the cardiotoxicity of molecules using an attention-based graph neural network. Experimental results indicated that the proposed framework outperformed the other methods. The stability of the model was also confirmed by our experiments. To assist researchers in evaluating the cardiotoxicity of molecules, we have developed an easy-to-use online web server that incorporates our model.

Promising 8-Aminoquinoline-Based Metal Complexes in the Modulation of SIRT1/3-FOXO3a Axis against Oxidative Damage-Induced Preclinical Neurons

The discovery of novel bioactive molecules as potential multifunctional neuroprotective agents has clinically drawn continual interest due to devastating oxidative damage in the pathogenesis and progression of neurodegenerative diseases. Synthetic 8-aminoquinoline antimalarial drug is an attractive pharmacophore in drug development and chemical modification owing to its wide range of biological activities, yet the underlying molecular mechanisms are not fully elucidated in preclinical models for oxidative damage. Herein, the neuroprotective effects of two 8-aminoquinoline-uracil copper complexes were investigated on the hydrogen peroxide-induced human neuroblastoma SH-SY5Y cells. Both metal complexes markedly restored cell survival, alleviated apoptotic cascades, maintained antioxidant defense, and prevented mitochondrial function by upregulating the sirtuin 1 (SIRT1)/3-FOXO3a signaling pathway. Intriguingly, in silico molecular docking and pharmacokinetic prediction suggested that these synthetic compounds acted as SIRT1 activators with potential drug-like properties, wherein the uracil ligands (5-iodoracil and 5-nitrouracil) were essential for effective binding interactions with the target protein SIRT1. Taken together, the synthetic 8-aminoquinoline-based metal complexes are promising brain-targeting drugs for attenuating neurodegenerative diseases.

Exhaustive in silico design and screening of novel antipsychotic compounds with improved pharmacodynamics and blood-brain barrier permeation properties

Antipsychotic drugs or neuroleptics are widely used in the treatment of psychosis as a manifestation of schizophrenia and bipolar disorder. However, their effectiveness largely depends on the blood-brain barrier (BBB) permeation (pharmacokinetics) and drug-receptor pharmacodynamics. Therefore, in this study, we developed and implemented the in silico pipeline to design novel compounds (n = 260) as leads using the standard drug scaffolds with improved PK/PD properties from the standard scaffolds. As a result, the best candidates (n = 3) were evaluated in molecular docking to interact with serotonin and dopamine receptors. Finally, haloperidol (HAL) derivative (1-(4-fluorophenyl)-4-(4-hydroxy-4-{4-[(2-phenyl-1,3-thiazol-4-yl)methyl]phenyl}piperidin-1-yl)butan-1-one) was identified as a "magic shotgun" lead compound with better affinity to the 5-HT2A, 5-HT1D, D2, D3, and 5-HT1B receptors than the control molecule. Additionally, this hit substance was predicted to possess similar BBB permeation properties and much lower toxicological profiles in comparison to HAL. Overall, the proposed rational drug design platform for novel antipsychotic drugs based on the BBB permeation and receptor binding might be an invaluable asset for a medicinal chemist or translational pharmacologist.Communicated by Ramaswamy H. Sarma.

cardioToxCSM: A Web Server for Predicting Cardiotoxicity of Small Molecules

The design of novel, safe, and effective drugs to treat human diseases is a challenging venture, with toxicity being one of the main sources of attrition at later stages of development. Failure due to toxicity incurs a significant increase in costs and time to market, with multiple drugs being withdrawn from the market due to their adverse effects. Cardiotoxicity, for instance, was responsible for the failure of drugs such as fenspiride, propoxyphene, and valdecoxib. While significant effort has been dedicated to mitigate this issue by developing computational approaches that aim to identify molecules likely to be toxic, including quantitative structure-activity relationship models and machine learning methods, current approaches present limited performance and interpretability. To overcome these, we propose a new web-based computational method, cardioToxCSM, which can predict six types of cardiac toxicity outcomes, including arrhythmia, cardiac failure, heart block, hERG toxicity, hypertension, and myocardial infarction, efficiently and accurately. cardioToxCSM was developed using the concept of graph-based signatures, molecular descriptors, toxicophore matchings, and molecular fingerprints, leveraging explainable machine learning, and was validated internally via different cross validation schemes and externally via low-redundancy blind sets. The models presented robust performances with areas under ROC curves of up to 0.898 on 5-fold cross-validation, consistent with metrics on blind tests. Additionally, our models provide interpretation of the predictions by identifying whether substructures that are commonly enriched in toxic compounds were present. We believe cardioToxCSM will provide valuable insight into the potential cardiotoxicity of small molecules early on drug screening efforts. The method is made freely available as a web server at https://biosig.lab.uq.edu.au/cardiotoxcsm.

MEMES: Machine learning framework for Enhanced MolEcular Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.

Structural Basis of Selective Human Indoleamine-2,3-dioxygenase 1 (hIDO1) Inhibition

hIDO1 is a heme-dioxygenase overexpressed in the tumor microenvironment and is implicated in the survival of cancer cells. Metabolism of tryptophan to N-formyl-kynurenine by hIDO1 leads to immune suppression to result in cancer cell immune escape. In this article, we discuss the discovery of selective hIDO1 inhibitors for therapeutic intervention that have been promoted to clinical trials and for which crystallographic structural information is available for the respective inhibitor-enzyme complex. The structural insights are based on the complex crystal structures and the relative biological data profiles. The structural basis of selective hIDO1 inhibition, as discussed herein, opens new avenues to the discovery of novel inhibitors with improved activity profiles, selectivity, and distinct structure frameworks.

Identification of potential plant-based inhibitor against viral proteases of SARS-CoV-2 through molecular docking, MM-PBSA binding energy calculations and molecular dynamics simulation

The Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus, SARS-CoV-2, has recently emerged as a pandemic. Here, an attempt has been made through in-silico high throughput screening to explore the antiviral compounds from traditionally used plants for antiviral treatments in India namely, Tea, Neem and Turmeric, as potential inhibitors of two widely studied viral proteases, main protease (Mpro) and papain-like protease (PLpro) of the SARS-CoV-2. Molecular docking study using BIOVIA Discovery Studio 2018 revealed, (−)-epicatechin-3-O-gallate (ECG), a tea polyphenol has a binding affinity toward both the selected receptors, with the lowest CDocker energy − 46.22 kcal mol⁻¹ for SARS-CoV-2 Mpro and CDocker energy − 44.72 kcal mol⁻¹ for SARS-CoV-2 PLpro, respectively. The SARS-CoV-2 Mpro complexed with (−)-epicatechin-3-O-gallate, which had shown the best binding affinity was subjected to molecular dynamics simulations to validate its binding affinity, during which, the root-mean-square-deviation values of SARS-CoV-2 Mpro–Co-crystal ligand (N3) and SARS-CoV-2 Mpro- (−)-epicatechin-3-O-gallate systems were found to be more stable than SARS-CoV-2 Mpro system. Further, (−)-epicatechin-3-O-gallate was subjected to QSAR analysis which predicted IC₅₀ of 0.3281 nM against SARS-CoV-2 Mpro. Overall, (−)-epicatechin-3-O-gallate showed a potential binding affinity with SARS-CoV-2 Mpro and could be proposed as a potential natural compound for COVID-19 treatment.

In silico analysis of Phyllanthus amarus phytochemicals as potent drugs against SARS-CoV-2 main protease

Phyllanthus amarus, also known as Bhui Korma in India, is well known for its medicinal properties and is used to treat several diseases worldwide. This study aims to identify phytochemicals from P. amarus and assess their anti-viral activity through in silico methods against the main protease (3CL^Pro/M^Pro) enzyme of the novel coronavirus. 190 compounds were obtained from literature and docked against 3CLPro and 16 compounds showed higher binding affinity with 3CL^Pro with their values lying between -8.9 ​kcal/mol to -9.6 ​kcal/mol. The top two compounds, Myricitrin (CID: 5352000) and Quercetin-3-O-glucuronide (CID: 12004528) gave high binding affinity values of -9.6 ​kcal/mol and -9.4 ​kcal/mol respectively and also display favourable binding interactions with the 3CL^Pro. Both the compounds were further subjected to molecular dynamics simulation and MM-PBSA based binding free energy calculations. ADMET and drug-likeness properties were studied to assess the pharmacokinetic properties of the compounds. Favourable pharmacokinetic results reinforced the applicability of the compounds assessed. Along with continuous studies being carried out with chemical compounds, research needs to expand into all areas, including the use of natural compounds as drug compounds. The identified hits from this study can be taken further for in vitro and in vivo studies to examine their efficacy against COVID-19.

PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties

Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility

Advances reported over the last few years and the increasing availability of protein crystal structure data have greatly improved structure-based druggability approaches. However, in practice, nearly all druggability estimation methods are applied to protein crystal structures as rigid proteins, with protein flexibility often not directly addressed. The inclusion of protein flexibility is important in correctly identifying the druggability of pockets that would be missed by methods based solely on the rigid crystal structure. These include cryptic pockets and flexible pockets often found at protein-protein interaction interfaces. Here, we apply an approach that uses protein modeling in concert with druggability estimation to account for light protein backbone movement and protein side-chain flexibility in protein binding sites. We assess the advantages and limitations of this approach on widely-used protein druggability sets. Applying the approach to all mammalian protein crystal structures in the PDB results in identification of 69 proteins with potential druggable cryptic pockets.

Chemical predictive modelling to improve compound quality

The 'quality' of small-molecule drug candidates, encompassing aspects including their potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics, is a key factor influencing the chances of success in clinical trials. Importantly, such characteristics are under the control of chemists during the identification and optimization of lead compounds. Here, we discuss the application of computational methods, particularly quantitative structure-activity relationships (QSARs), in guiding the selection of higher-quality drug candidates, as well as cultural factors that may have affected their use and impact.

BioProfile--extract knowledge from corporate databases to assess cross-reactivities of compounds

In the last 10-15 years, many new technologies and approaches have been implemented in research in the pharmaceutical industry; these include high-throughput screening or combinatorial chemistry, which result in a rapidly growing amount of biological assay and structural data in the corporate databases. Efficient use of the data from this growing data mountain is a key success factor; 'provide as much knowledge as possible as early as possible and therefore enable research teams to make the best possible decision whenever this decision can be supported by stored data'. Here, an approach which started several years ago to obtain as much information as possible out of historical assay data stored in the corporate database is described. It will be shown how important a careful preprocessing of the stored data is to enhance its information. Different possibilities for accessing and to analyzing the preconditioned data are in place. Some of will be described in the examples.

Discovery of two clinical histamine H(3) receptor antagonists: trans-N-ethyl-3-fluoro-3-[3-fluoro-4-(pyrrolidinylmethyl)phenyl]cyclobutanecarboxamide (PF-03654746) and trans-3-fluoro-3-[3-fluoro-4-(pyrrolidin-1-ylmethyl)phenyl]-N-(2-methylpropyl)cyclobutanecarboxamide (PF-03654764)

The discovery of two histamine H(3) antagonist clinical candidates is disclosed. The pathway to identification of the two clinical candidates, 6 (PF-03654746) and 7 (PF-03654764) required five hypothesis driven design cycles. The key to success in identifying these clinical candidates was the development of a compound design strategy that leveraged medicinal chemistry knowledge and traditional assays in conjunction with computational and in vitro safety tools. Overall, clinical compounds 6 and 7 exceeded conservative safety margins and possessed optimal pharmacological and pharmacokinetic profiles, thus achieving our initial goal of identifying compounds with fully aligned oral drug attributes, "best-in-class" molecules.

Nanosizing for oral and parenteral drug delivery: a perspective on formulating poorly-water soluble compounds using wet media milling technology

A significant percentage of active pharmaceutical ingredients identified through discovery screening programs is poorly soluble in water. These molecules are often difficult to formulate using conventional approaches and are associated with innumerable formulation-related performance issues, e.g. poor bioavailability, lack of dose proportionality, slow onset of action and other attributes leading to poor patient compliance. In addition, for parenteral products, these molecules are generally administered with co-solvents and thus have many undesirable side effects. Wet media milling is one of the leading particle size reduction approaches that have been successfully used to formulate these problematic compounds. The approach is a water-based media milling process where micron-sized drug particles are shear-fractured into nanometer-sized particles. Nanoparticle dispersions are stable and typically have a mean diameter of less than 200 nm with 90% of the particles being less than 400 nm. The formulation consists only of water, drug and one or more GRAS excipients. Drug concentrations approaching 300-400mg/g can be targeted with the use of minimal amounts stabilizer. Typically, on average, the drug to stabilizer ratio on a weight basis ranges from 2:1 to 20:1. These liquid nanodispersions exhibit acceptable shelf-life and can be post-processed into various types of solid dosage forms. Nanoparticulate-based drug products have been shown to improve bioavailability and enhance drug exposure for oral and parenteral dosage forms. Suitable formulations for the most commonly used routes of administration can be identified with milligram quantities of drug substance providing the discovery scientist an alternate avenue for screening and identifying superior leads. In the last few years, formulating poorly water soluble compounds as nanosuspensions has evolved from a conception to a realization. The versatility and applicability of this drug delivery platform are just beginning to be realized.

Defining desirable central nervous system drug space through the alignment of molecular properties, in vitro ADME, and safety attributes

As part of our effort to increase survival of drug candidates and to move our medicinal chemistry design to higher probability space for success in the Neuroscience therapeutic area, we embarked on a detailed study of the property space for a collection of central nervous system (CNS) molecules. We carried out a thorough analysis of properties for 119 marketed CNS drugs and a set of 108 Pfizer CNS candidates. In particular, we focused on understanding the relationships between physicochemical properties, in vitro ADME (absorption, distribution, metabolism, and elimination) attributes, primary pharmacology binding efficiencies, and in vitro safety data for these two sets of compounds. This scholarship provides guidance for the design of CNS molecules in a property space with increased probability of success and may lead to the identification of druglike candidates with favorable safety profiles that can successfully test hypotheses in the clinic.

Turning down tau phosphorylation

Phosphorylation and glycosylation of the tau protein, which is implicated in neurodegenerative diseases, are intimately linked. In vivo pharmacological inhibition of tau deglycosylation may be a new way to suppress abnormal tau phosphorylation, known to be involved in the formation of neurofibrillary tangles in the brain.

Computational chemistry approaches to drug discovery in signal transduction

The advent of therapeutic strategies aimed at targeting specific macromolecular components of deregulated signaling pathways associated with particular disease states has given rise to the idea that it should be possible to design ligands as drug candidates to these targets from first principles. This concept has been beckoning for a long time but structure-based ligand design only became feasible once it was possible to determine the 3-D structures of molecular targets at atomic resolution. However, structure-based design turned out to be difficult, chiefly because under physiological conditions both receptors and ligands are not static but they behave dynamically. While it is possible to design ligands with high steric and electronic complementarity to a receptor site, it is always uncertain how biologically relevant the assumed conformations of both ligand and receptor actually are. The fact that it remains beyond our current abilities to predict with sufficient accuracy the affinity between hypothetical ligand and receptor poses is in part connected with this problem and continues to confound the reliable prediction of drug-like ligands for therapeutic targets. Nevertheless, significant progress has been made and so-called virtual screening methods that use computational methods to dock candidate ligands into receptor sites and to score the resulting complexes are now used routinely as one of the components in drug discovery screening campaigns. Here an overview is given of the underlying principles, implementations, and applications of structure-guided computational design technologies. Although the emphasis is on receptor-based strategies, mention will also be made of some of the more established ligand-based approaches, such as similarity analyses and quantitative structure-activity relationship methods.

Untangling tau hyperphosphorylation in drug design for neurodegenerative diseases

Aggregation of hyperphosphorylated tau is one of the characteristic neuropathological lesions of Alzheimer's disease and other neurodegenerative disorders. Pharmacological modulation of tau hyperphosphorylation might represent a valid and feasible therapeutic strategy for such disorders. Here, we consider recent evidence supporting the validity of the three most relevant kinases affecting tau hyperphosphorylation - GSK3beta, CDK5 and ERK2 - as drug targets and describe progress in the design of inhibitors for these kinases.

Application of data mining approaches to drug delivery

Computational approaches play a key role in all areas of the pharmaceutical industry from data mining, experimental and clinical data capture to pharmacoeconomics and adverse events monitoring. They will likely continue to be indispensable assets along with a growing library of software applications. This is primarily due to the increasingly massive amount of biology, chemistry and clinical data, which is now entering the public domain mainly as a result of NIH and commercially funded projects. We are therefore in need of new methods for mining this mountain of data in order to enable new hypothesis generation. The computational approaches include, but are not limited to, database compilation, quantitative structure activity relationships (QSAR), pharmacophores, network visualization models, decision trees, machine learning algorithms and multidimensional data visualization software that could be used to improve drug delivery after mining public and/or proprietary data. We will discuss some areas of unmet needs in the area of data mining for drug delivery that can be addressed with new software tools or databases of relevance to future pharmaceutical projects.

Knowledge-based chemoinformatic approaches to drug discovery

The modern drug discovery process is steadily becoming more information driven. Structural, physicochemical and ADME-Tox property profiles of reference (successful) ligands, along with structural information of their target proteins, have been extremely useful for early-stage drug discovery. Recently, databases of known biologically active ligands (knowledge bases) have become more focused toward different protein-target classes. The number of new chemoinformatics tools used to analyze structures and properties of successful molecules has also increased enormously. Scientists in this area are exploring new physicochemical properties and appropriate drug sets to understand druglike properties. In this review, the various uses of the ligand knowledge bases in the drug discovery process have been critically reviewed.

A practical view of ‘druggability’

The introduction of Lipinski's 'Rule of Five' has initiated a profound shift in the thinking paradigm of medicinal chemists. Understanding the difference between biologically active small molecules and drugs became a priority in the drug discovery process, and the importance of addressing pharmacokinetic properties early during lead optimization is a clear result. These concepts of 'drug-likeness' and 'druggability' have been extended to proteins and genes for target identification and selection. How should these concepts be integrated practically into the drug discovery process? This review summarizes the recent advances in the field and examines the usefulness of 'the rules of the game' in practice from a medicinal chemist's standpoint.