Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles

A novel in vitro high-content imaging assay for the prediction of drug-induced lung toxicity

The development of inhaled drugs for respiratory diseases is frequently impacted by lung pathology in non-clinical safety studies. To enable design of novel candidate drugs with the right safety profile, predictive in vitro lung toxicity assays are required that can be applied during drug discovery for early hazard identification and mitigation. Here, we describe a novel high-content imaging-based screening assay that allows for quantification of the tight junction protein occludin in A549 cells, as a model for lung epithelial barrier integrity. We assessed a set of compounds with a known lung safety profile, defined by clinical safety or non-clinical in vivo toxicology data, and were able to correctly identify 9 of 10 compounds with a respiratory safety risk and 9 of 9 compounds without a respiratory safety risk (90% sensitivity, 100% specificity). The assay was sensitive at relevant compound concentrations to influence medicinal chemistry optimization programs and, with an accessible cell model in a 96-well plate format, short protocol and application of automated imaging analysis algorithms, this assay can be readily integrated in routine discovery safety screening to identify and mitigate respiratory toxicity early during drug discovery. Interestingly, when we applied physiologically-based pharmacokinetic (PBPK) modelling to predict epithelial lining fluid exposures of the respiratory tract after inhalation, we found a robust correlation between in vitro occludin assay data and lung pathology in vivo, suggesting the assay can inform translational risk assessment for inhaled small molecules.

Photophysical evaluation on the electronic properties of synthesized biologically significant pyrido fused imidazo[4,5-c]quinolines

A single pot microwave assisted method was employed to synthesize a series of novel pyrido fused imidazo[4,5-c]quinolines. The electronic properties of these derivatives were investigated by following their photophysical behaviour under isolated and solvated conditions via computational and experimental approaches. The solvatochromic effect of these derivatives was investigated in the ground and excited singlet states by following the absorption and fluorescence emission and excitation spectra. Further the effect of general and specific solvent effects were also investigated by plotting Stokes shift against Lippert-Mataga, ET(30) and Kamlet-Taft polarity parameters respectively. The deviation from linearity in ET(30) plot indicates that formation of different species in polar protic solvents. The biological applications of these derivatives as potential drug candidates were evaluated by in silico computational methods followed by pharmacokinetic properties predictions. The ability of these derivatives to inhibit human casein kinase 2 (CK2) was evaluated. The structure activity relationships were correlated by evaluating the electronic properties through experimental photophysical investigations including solvatochromic effect and computational electronic structure calculations. Of the various derivatives, p-nitro phenyl substituted pyrido fused imidazo[4,5-c]quinoline exhibited good inhibitory activity against CK2 enzyme and hence could serve as a promising drug candidate.

Multitask CapsNet: An Imbalanced Data Deep Learning Method for Predicting Toxicants

Drug development has a high failure rate, with safety properties constituting a considerable challenge. To reduce risk, in silico tools, including various machine learning methods, have been applied for toxicity prediction. However, these approaches often confront a serious problem: the training data sets are usually biased (imbalanced positive and negative samples), which would result in model training difficulty and unsatisfactory prediction accuracy. Multitask networks obtained significantly better predictive accuracies than single-task methods, and capsule neural networks showed excellent performance in sparse data sets in previous studies. In this study, we developed a new multitask framework based on a capsule neural network (multitask CapsNet) to measure 12 different toxic effects simultaneously. We found that multitask CapsNet excelled in toxicity prediction and outperformed many other computational approaches using the multitask strategy. Only after training on biased data sets did multitask CapsNet achieve significantly improved prediction accuracy on the Tox21 Data Challenge, which gave the largest ratio of highest accuracy (8/12) among compared models. Our model gave a prediction accuracy of 96.6% for the target NR.PPAR.gamma, whose ratio of negative to positive samples was up to 36:1. These results suggested that multitask CapsNet could overcome the bias problems and would provide a novel, accurate, and efficient approach for predicting the toxicities of compounds.

Computationally approached inhibition potential of Tinospora cordifolia towards COVID-19 targets

The recent emergence of novel coronavirus (SARS-CoV-2) has been a major threat to human society, as the challenge of finding suitable drug or vaccine is not met till date. With increasing morbidity and mortality, the need for novel drug candidates is under great demand. The investigations are progressing towards COVID-19 therapeutics. Among the various strategies employed, the use of repurposed drugs is competing along with novel drug inventions. Based on the therapeutic significance, the chemical constituents from the extract of Tinospora cordifolia belonging to various classes like alkaloids, lignans, steroids and terpenoids are investigated as potential drug candidates for COVID-19. The inhibition potential of the proposed compounds against viral spike protein and human receptor ACE2 were evaluated by computational molecular modeling (Auto dock), along with their ADME/T properties. Prior to docking, the initial geometry of the compounds were optimized by Density functional theory (DFT) method employing B3LYP hybrid functional and 6-311 +  + G (d,p) basis set. The results of molecular docking and ADME/T studies have revealed 6 constituents as potential drug candidates that can inhibit the binding of SARS-CoV-2 spike protein with the human receptor ACE2 protein. The narrowed down list of constituents from Tinospora cordifolia paved way for further tuning their ability to inhibit COVID-19 by modifying the chemical structures and by employing computational geometry optimization and docking methods.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-021-00666-7.

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction

Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios, whose characteristics differ from a random split of conventional training data sets. We developed a pharmacological data set augmentation procedure, Stochastic Negative Addition (SNA), which randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, drug-screening benchmark performance increases from R² = 0.1926 ± 0.0186 to 0.4269 ± 0.0272 (122%). This gain was accompanied by a modest decrease in the temporal benchmark (13%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed y-randomized controls. Our results highlight where data and feature uncertainty may be problematic and how leveraging uncertainty into training improves predictions of drug-target relationships.

Predicting drug properties with parameter-free machine learning: pareto-optimal embedded modeling (POEM)

The prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) of small molecules from their molecular structure is a central problem in medicinal chemistry with great practical importance in drug discovery. Creating predictive models conventionally requires substantial trial-and-error for the selection of molecular representations, machine learning (ML) algorithms, and hyperparameter tuning. A generally applicable method that performs well on all datasets without tuning would be of great value but is currently lacking. Here, we describe pareto-optimal embedded modeling (POEM), a similarity-based method for predicting molecular properties. POEM is a non-parametric, supervised ML algorithm developed to generate reliable predictive models without need for optimization. POEM’s predictive strength is obtained by combining multiple different representations of molecular structures in a context-specific manner, while maintaining low dimensionality. We benchmark POEM relative to industry-standard ML algorithms and published results across 17 classifications tasks. POEM performs well in all cases and reduces the risk of overfitting.

Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.

A Selective Synthesis of 2,2-Difluorobicyclo[1.1.1]pentane Analogues: "BCP-F2"

The bicyclo[1.1.1]pentane (BCP) motif has been utilized as bioisosteres in drug candidates to replace phenyl, tert-butyl, and alkynyl fragments in order to improve physicochemical properties. However, bceause of the difficulty of synthesis, most BCP analogues prepared only bear 1,3-"para"-substituents. We report the first selective synthesis of 2,2-difluorobicyclo[1.1.1]pentanes via difluorocarbene insertion into bicyclo[1.1.0]butanes. Moreover, this methodology should inspire future studies on synthesis of other "ortho/meta-substituted" BCPs via similar mechanisms.

Expanding the Chemical Space of Withaferin A by Incorporating Silicon To Improve Its Clinical Potential on Human Ovarian Carcinoma Cells

Ovarian cancer represents the seventh most commonly diagnosed cancer worldwide. Herein, we report on the development of a withaferin A (WA)-silyl ether library with 30 analogues reported for the first time. Cytotoxicity assays on human epithelial ovarian carcinoma cisplatin-sensitive and -resistant cell lines identified eight analogues displaying nanomolar potency (IC₅₀ ranging from 1 to 32 nM), higher than that of the lead compound and reference drug. This cytotoxic potency is also coupled with a good selectivity index on a nontumoral cell line. Cell cycle analysis of two potent analogues revealed cell death by apoptosis without indication of cell cycle arrest in G0/G1 phase. The structure-activity relationship and in silico absorption, distribution, metabolism, and excretion studies demonstrated that the incorporation of silicon and a carbonyl group at C-4 in the WA framework enhances potency, selectivity, and drug likeness. These findings reveal analogues 22, 23, and 25 as potential candidates for clinical translation in patients with relapsed ovarian cancer.

A scientist engineer's contribution to therapeutic discovery and development

An engineering perspective views cells as complex circuits that process inputs – drugs, environmental cues – to create complex outcomes – disease, growth, death – and this perspective has immense potential for drug development. Logical rules can describe the features of cells and reductionist approaches have exploited these rules for drug development. In contrast, the reductionist approach serially characterizes cellular components and develops a deep understanding of each component’s specific role. This approach underutilizes the full system of biomolecules relevant to disease pathology and drug effects. An engineering perspective provides the tools to understand and leverage the full extent of biological systems; applying both reverse and forward engineering, a strength of the engineering approach has demonstrated progress in advancing understanding of disease and drug mechanisms. Drug development lacks sufficient engineering specifications, or empirical models, of drug pharmacodynamic effects and future efforts to derive empirical models of drug effects will streamline this development. At this stage of progress, the scientist engineer is uniquely poised to solve problems in therapeutics related to modulating multiple diseases with a single or multiple therapeutic agents and identifying pharmacodynamics biomarkers with knowledge of drug pathways. This article underscores the value of these principles in an age where drug development costs are soaring and finding efficacious therapies is challenging.

Synthesis, Pharmacological Evaluation, and Docking Studies of Novel Pyridazinone-Based Cannabinoid Receptor Type 2 Ligands

In recent years, cannabinoid type 2 receptors (CB₂ R) have emerged as promising therapeutic targets in a wide variety of diseases. Selective ligands of CB₂ R are devoid of the psychoactive effects typically observed for CB₁ R ligands. Based on our recent studies on a class of pyridazinone 4-carboxamides, further structural modifications of the pyridazinone core were made to better investigate the structure-activity relationships for this promising scaffold with the aim to develop potent CB₂ R ligands. In binding assays, two of the new synthesized compounds [6-(3,4-dichlorophenyl)-2-(4-fluorobenzyl)-cis-N-(4-methylcyclohexyl)-3-oxo-2,3-dihydropyridazine-4-carboxamide (2) and 6-(4-chloro-3-methylphenyl)-cis-N-(4-methylcyclohexyl)-3-oxo-2-pentyl-2,3-dihydropyridazine-4-carboxamide (22)] showed high CB₂ R affinity, with K_i values of 2.1 and 1.6 nm, respectively. In addition, functional assays of these compounds and other new active related derivatives revealed their pharmacological profiles as CB₂ R inverse agonists. Compound 22 displayed the highest CB₂ R selectivity and potency, presenting a favorable in silico pharmacokinetic profile. Furthermore, a molecular modeling study revealed how 22 produces inverse agonism through blocking the movement of the toggle-switch residue, W6.48.

QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities

Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium, affecting more than 200 million people worldwide every year and leading to about a half million deaths. Malaria parasites of humans have evolved resistance to all current antimalarial drugs, urging for the discovery of new effective compounds. Given that the inhibition of deoxyuridine triphosphatase of Plasmodium falciparum (PfdUTPase) induces wrong insertions in plasmodial DNA and consequently leading the parasite to death, this enzyme is considered an attractive antimalarial drug target. Using a combi-QSAR (quantitative structure-activity relationship) approach followed by virtual screening and in vitro experimental evaluation, we report herein the discovery of novel chemical scaffolds with in vitro potency against asexual blood stages of both P. falciparum multidrug-resistant and sensitive strains and against sporogonic development of P. berghei. We developed 2D- and 3D-QSAR models using a series of nucleosides reported in the literature as PfdUTPase inhibitors. The best models were combined in a consensus approach and used for virtual screening of the ChemBridge database, leading to the identification of five new virtual PfdUTPase inhibitors. Further in vitro testing on P. falciparum multidrug-resistant (W2) and sensitive (3D7) parasites showed that compounds LabMol-144 and LabMol-146 demonstrated fair activity against both strains and presented good selectivity versus mammalian cells. In addition, LabMol-144 showed good in vitro inhibition of P. berghei ookinete formation, demonstrating that hit-to-lead optimization based on this compound may also lead to new antimalarials with transmission blocking activity.