MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.

In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. However, the compound-protein interaction is complicated and the features extracted by most deep models are not comprehensive, which limits the performance to a certain extent. In this paper, we proposed a multiscale convolutional network that extracted the local and global features of the protein and the topological feature of the compound using different types of convolutional networks. The results showed that our model obtained the best performance compared with the existing deep learning methods.

Appropriate in vivo follow-up assays to an in vitro bacterial reverse mutation (Ames) test positive investigational drug candidate (active pharmaceutical ingredient), drug-related metabolite, or drug-related impurity.

Kirkland et al. [Mutation Research/Genetic Toxicology and Environmental Mutagenesis 847 (2019) 403035, https://doi.org/10.1016/j.mrgentox.2019.03.008; Mutation Research/Genetic Toxicology and Environmental Mutagenesis 839 (2019): 21-35, https://doi.org/10.1016/j.mrgentox.2019.01.007] made recommendations on the use of the in vivo comet and transgenic rodent (TGR) gene mutation assays to screen for in vivo mutagenicity. Although it is not directly stated in either of these publications, we are concerned that the reports could potentially be used to support assertions that it is equally acceptable to follow up a positive bacterial reverse mutation (Ames) finding for an investigational drug with either the in vivo TGR mutation assay or an in vivo comet assay. For regulatory genotoxicity assessment, the in vivo follow-up for an in vitro bacterial mutation-positive drug, drug-related metabolite, or impurity should be based upon evaluating a similar endpoint (i.e., mutagenicity) as the intent is to determine if the findings of in vitro gene mutation correlate with findings of in vivo gene mutation (i.e., biologically relevant to the in vitro results). Thus, the most scientifically appropriate in vivo assays would be the TGR mutation assay or, in some circumstances, the in vivo Pig-a assay. An in vivo rodent comet assay or combination of the in vivo micronucleus and in vivo rodent comet assays would generally not be an appropriate follow-up test.

New machine learning and physics-based scoring functions for drug discovery.

Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein-ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein-protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein-protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br .

Explainable drug sensitivity prediction through cancer pathway enrichment.

Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose PathDSP: a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer dataset. Using a deep neural network, we outperform state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine.

Reldesemtiv in Patients with Spinal Muscular Atrophy: a Phase 2 Hypothesis-Generating Study.

This phase 2, double-blind, placebo-controlled, hypothesis-generating study evaluated the effects of oral reldesemtiv, a fast skeletal muscle troponin activator, in patients with spinal muscular atrophy (SMA). Patients ≥ 12 years of age with type II, III, or IV SMA were randomized into 2 sequential, ascending reldesemtiv dosing cohorts (cohort 1: 150 mg bid or placebo [2:1]; cohort 2: 450 mg bid or placebo [2:1]). The primary objective was to determine potential pharmacodynamic effects of reldesemtiv on 8 outcome measures in SMA, including 6-minute walk distance (6MWD) and maximum expiratory pressure (MEP). Changes from baseline to weeks 4 and 8 were determined. Pharmacokinetics and safety were also evaluated. Patients were randomized to reldesemtiv 150 mg, 450 mg, or placebo (24, 20, and 26, respectively). The change from baseline in 6MWD was greater for reldesemtiv 450 mg than for placebo at weeks 4 and 8 (least squares [LS] mean difference, 35.6 m [p = 0.0037] and 24.9 m [p = 0.058], respectively). Changes from baseline in MEP at week 8 on reldesemtiv 150 and 450 mg were significantly greater than those on placebo (LS mean differences, 11.7 [p = 0.038] and 13.2 cm H2O [p = 0.03], respectively). For 6MWD and MEP, significant changes from placebo were seen in the highest reldesemtiv peak plasma concentration quartile (Cmax > 3.29 µg/mL; LS mean differences, 43.3 m [p = 0.010] and 28.8 cm H2O [p = 0.0002], respectively). Both dose levels of reldesemtiv were well tolerated. Results suggest reldesemtiv may offer clinical benefit and support evaluation in larger SMA patient populations.

Machine learning identifies candidates for drug repurposing in Alzheimer's disease.

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

Molecular structure, NBO analysis of the hydrogen-bonded interactions, spectroscopic (FT-IR, FT-Raman), drug likeness and molecular docking of the novel anti COVID-2 molecule (2E)-N-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide (Dimer) - quantum chemical approach.

Prospective antiviral molecule (2E)-N-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide has been probed using Fourier transform infrared (FTIR), FT-Raman and quantum chemical computations. The geometry equilibrium and natural bond orbital analysis have been carried out with density functional theory employing Becke, 3-parameter, Lee-Yang-Parr method with the 6-311G++(d,p) basis set. The vibrational assignments pertaining to different modes of vibrations have been augmented by normal coordinate analysis, force constant and potential energy distributions. Drug likeness and oral activity have been carried out based on Lipinski's rule of five. The inhibiting potency of 2(2E)-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide has been investigated by docking simulation against SARS-CoV-2 protein. The optimized geometry shows a planar structure between the chromone and the side chain. Differences in the geometries due to the substitution of the electronegative atom and intermolecular contacts due to the chromone and hydrazinecarbothioamide were analyzed. NBO analysis confirms the presence of two strong stable hydrogen bonded NH⋯O intermolecular interactions and two weak hydrogen bonded CH⋯O interactions. The red shift in NH stretching frequency exposed from IR substantiates the formation of NH⋯O intermolecular hydrogen bond and the blue shift in CH stretching frequency substantiates the formation of CH⋯O intermolecular hydrogen bond. Drug likeness, absorption, distribution, metabolism, excretion and toxicity property gives an idea about the pharmacokinetic properties of the title molecule. The binding energy of the nonbonding interaction with Histidine 41 and Cysteine 145, present a clear view that 2(2E)-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide can irreversibly interact with SARS-CoV-2 protease.

Inhibition of the ß-carbonic anhydrase from the protozoan pathogen Trichomonas vaginalis with sulphonamides.

Sulphonamides and their isosteres are classical inhibitors of the carbonic anhydrase (CAs, EC 4.2.1.1) metalloenzymes. The protozoan pathogen Trichomonas vaginalis encodes two such enzymes belonging to the ß-class, TvaCA1 and TvaCA2. Here we report the first sulphonamide inhibition study of TvaCA1, with a series of simple aromatic/heterocyclic primary sulphonamides as well as with clinically approved/investigational drugs for a range of pathologies (diuretics, antiglaucoma, antiepileptic, antiobesity, and antitumor drugs). TvaCA1 was effectively inhibited by acetazolamide and ethoxzolamide, with KIs of 391 and 283 nM, respectively, whereas many other simple or clinically used sulphonamides were micromolar inhibitors or did not efficiently inhibit the enzyme. Finding more effective TvaCA1 inhibitors may constitute an innovative approach for fighting trichomoniasis, a sexually transmitted infection, caused by T. vaginalis.

Open Targets Platform: supporting systematic drug-target identification and prioritisation.

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target-disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors.

Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein-ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/.

The future of cerebral organoids in drug discovery.

Until the discovery of human embryonic stem cells and human induced pluripotent stem cells, biotechnology companies were severely limited in the number of human tissues that they could model in large-scale in vitro studies. Until this point, companies have been limited to immortalized cancer lines or a small number of primary cell types that could be extracted and expanded. Nowadays, protocols continue to be developed in the stem cell field, enabling researchers to model an ever-growing library of cell types in controlled, large-scale screens. One differentiation method in particular- cerebral organoids- shows substantial potential in the field of neuroscience and developmental neurobiology. Cerebral organoid technology is still in an early phase of development, and there are several challenges that are currently being addressed by academic and industrial researchers alike. Here we briefly describe some of the early adopters of cerebral organoids, several of the challenges that they are likely facing, and various technologies that are currently being implemented to overcome them.

Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.

Global Substance Registration System: consistent scientific descriptions for substances related to health.

The US Food and Drug Administration (FDA) and the National Center for Advancing Translational Sciences (NCATS) have collaborated to publish rigorous scientific descriptions of substances relevant to regulated products. The FDA has adopted the global ISO 11238 data standard for the identification of substances in medicinal products and has populated a database to organize the agency's regulatory submissions and marketed products data. NCATS has worked with FDA to develop the Global Substance Registration System (GSRS) and produce a non-proprietary version of the database for public benefit. In 2019, more than half of all new drugs in clinical development were proteins, nucleic acid therapeutics, polymer products, structurally diverse natural products or cellular therapies. While multiple databases of small molecule chemical structures are available, this resource is unique in its application of regulatory standards for the identification of medicinal substances and its robust support for other substances in addition to small molecules. This public, manually curated dataset provides unique ingredient identifiers (UNIIs) and detailed descriptions for over 100 000 substances that are particularly relevant to medicine and translational research. The dataset can be accessed and queried at https://gsrs.ncats.nih.gov/app/substances.

DrugSpaceX: a large screenable and synthetically tractable database extending drug space.

One of the most prominent topics in drug discovery is efficient exploration of the vast drug-like chemical space to find synthesizable and novel chemical structures with desired biological properties. To address this challenge, we created the DrugSpaceX (https://drugspacex.simm.ac.cn/) database based on expert-defined transformations of approved drug molecules. The current version of DrugSpaceX contains >100 million transformed chemical products for virtual screening, with outstanding characteristics in terms of structural novelty, diversity and large three-dimensional chemical space coverage. To illustrate its practical application in drug discovery, we used a case study of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, to show DrugSpaceX performing a quick search of initial hit compounds. Additionally, for ligand identification and optimization purposes, DrugSpaceX also provides several subsets for download, including a 10% diversity subset, an extended drug-like subset, a drug-like subset, a lead-like subset, and a fragment-like subset. In addition to chemical properties and transformation instructions, DrugSpaceX can locate the position of transformation, which will enable medicinal chemists to easily integrate strategy planning and protection design.

ConjuPepDB: a database of peptide-drug conjugates.

Peptide-drug conjugates are organic molecules composed of (i) a small drug molecule, (ii) a peptide and (iii) a linker. The drug molecule is mandatory for the biological action, however, its efficacy can be enhanced by targeted delivery, which often also reduces unwanted side effects. For site-specificity the peptide part is mainly responsible. The linker attaches chemically the drug to the peptide, but it could also be biodegradable which ensures controlled liberation of the small drug. Despite the importance of the field, there is no public comprehensive database on these species. Herein we describe ConjuPepBD, a freely available, fully annotated and manually curated database of peptide drug conjugates. ConjuPepDB contains basic information about the entries, e.g. CAS number. Furthermore, it also implies their biomedical application and the type of chemical conjugation employed. It covers more than 1600 conjugates from ∼230 publications. The web-interface is user-friendly, intuitive, and useable on several devices, e.g. phones, tablets, PCs. The webpage allows the user to search for content using numerous criteria, chemical structure and a help page is also provided. Besides giving quick insight for newcomers, ConjuPepDB is hoped to be also helpful for researchers from various related fields. The database is accessible at: https://conjupepdb.ttk.hu/.

Dual-Inhibitors of N-Myc and AURKA as Potential Therapy for Neuroendocrine Prostate Cancer.

Resistance to androgen-receptor (AR) directed therapies is, among other factors, associated with Myc transcription factors that are involved in development and progression of many cancers. Overexpression of N-Myc protein in prostate cancer (PCa) leads to its transformation to advanced neuroendocrine prostate cancer (NEPC) that currently has no approved treatments. N-Myc has a short half-life but acts as an NEPC stimulator when it is stabilized by forming a protective complex with Aurora A kinase (AURKA). Therefore, dual-inhibition of N-Myc and AURKA would be an attractive therapeutic avenue for NEPC. Following our computer-aided drug discovery approach, compounds exhibiting potent N-Myc specific inhibition and strong anti-proliferative activity against several N-Myc driven cell lines, were identified. Thereafter, we have developed dual inhibitors of N-Myc and AURKA through structure-based drug design approach by merging our novel N-Myc specific chemical scaffolds with fragments of known AURKA inhibitors. Favorable binding modes of the designed compounds to both N-Myc and AURKA target sites have been predicted by docking. A promising lead compound, 70812, demonstrated low-micromolar potency against both N-Myc and AURKA in vitro assays and effectively suppressed NEPC cell growth.

Design of Novel 4-Aminobenzofuroxans and Evaluation of Their Antimicrobial and Anticancer Activity.

A series of novel 4-aminobenzofuroxan derivatives containing aromatic/aliphatic amines fragments was achieved via aromatic nucleophilic substitution reaction of 4,6-dichloro-5-nitrobenzofuroxan. The quantum chemistry calculations were performed to identify the factors affecting the regioselectivity of the reaction. The formation of 4-substituted isomer is favored both by its greater stability and the lower activation barrier. Antimicrobial activity of the obtained compounds has been evaluated and some of them were found to suppress effectively bacterial biofilm growth. Fungistatic activity of 4-aminobenzofuroxans were tested on two genetically distinct isolates of M. nivale. The effect of some benzofuroxan derivatives is likely to be more universal against different varieties of M. nivale compared with benzimidazole and carbendazim. Additionally, their anti-cancer activity in vitro has been tested. 4-aminofuroxans possessing aniline moiety showed a high selectivity towards MCF-7 and M-HeLa tumor cell lines. Moreover, they exhibit a significantly lower toxicity towards normal liver cells compared to Doxorubicin and Tamoxifen. Thus, benzofuroxans containing aromatic amines fragments in their structure are promising candidates for further development both as anti-cancer and anti-microbial agents.

Latest trends in structure based drug design with protein targets.

Structure based drug designing is an important endeavor in the field of structural bioinformatics. Previously the entire process was dependent on the wet-lab experiments to build libraries of ligand molecules. And the molecules used to be tested to determine their binding efficacies with protein target. However, the entire process is very lengthy and above all highly expensive. With the advent of supercomputers and increasing computational powers, the search process for finding suitable ligand molecules against target proteins have become more streamlined and cost-effective. Now the entire ligand search process is performed in-silico with the help of the techniques of virtual screening, molecular docking simulations and molecular dynamics studies. In the present chapter, a brief overview of the computational techniques involved in structure based drug designing is presented with a special emphasis on the thermodynamic principles behind the molecular interactions.

The Pharmaceutical Industry in 2019. An Analysis of FDA Drug Approvals from the Perspective of Molecules.

During 2019, the US Food and Drug Administration (FDA) approved 48 new drugs (38 New Chemical Entities and 10 Biologics). Although this figure is slightly lower than that registered in 2018 (59 divided between 42 New Chemical Entities and 17 Biologics), a year that broke a record with respect to new drugs approved by this agency, it builds on the trend initiated in 2017, when 46 drugs were approved. Of note, three antibody drug conjugates, three peptides, and two oligonucleotides were approved in 2019. This report analyzes the 48 new drugs of the class of 2019 from a strictly chemical perspective. The classification, which was carried out on the basis of chemical structure, includes the following: Biologics (antibody drug conjugates, antibodies, and proteins); TIDES (peptide and oligonucleotides); drug combinations; natural products; and small molecules.

Characterizing and Minimizing Aggregation and Particle Formation of Three Recombinant Fusion-Protein Bulk Antigens for Use in a Candidate Trivalent Rotavirus Vaccine.

In a companion paper, the structural integrity, conformational stability, and degradation mechanisms of 3 recombinant fusion-protein antigens comprising a non-replicating rotavirus (NRRV) vaccine candidate (currently being evaluated in early-stage clinical trials) are described. In this work, we focus on the aggregation propensity of the 3 NRRV antigens coupled to formulation development studies to identify common frozen bulk candidate formulations. The P2-VP8-P[8] antigen was most susceptible to shaking and freeze-thaw-induced aggregation and particle formation. Each NRRV antigen formed aggregates with structurally altered protein (with exposed apolar regions and intermolecular ß-sheet) and dimers containing a non-native disulfide bond. From excipient screening studies with P2-VP8-P[8], sugars or polyols (e.g., sucrose, trehalose, mannitol, sorbitol) and various detergents (e.g., Pluronic F-68, polysorbate 20 and 80, PEG-3350) were identified as stabilizers against aggregation. By combining promising additives, candidate bulk formulations were optimized to not only minimize agitation-induced aggregation, but also particle formation due to freeze-thaw stress of P2-VP8-P[8] antigen. Owing to limited material availability, stabilization of the P2-VP8-P[4] and P2-VP8-P[6] was confirmed with the lead candidate P2-VP8-P[8] formulations. The optimization of these bulk NRRV candidate formulations is discussed in the context of subsequent drug product formulations in the presence of aluminum adjuvants.