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Early-Stage Drug Discovery: Advances and Challenges

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biology".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 35113

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Dr. Wolfgang Sippl

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Institute of Pharmacy, Martin-Luther University of Halle-Wittenberg, 06120 Halle/Saale, Germany
Interests: epigenetic drug discovery; computer-assisted drug design; histone modifiers; RNA binding proteins; inhibitors for parasitic diseases
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Dear Colleagues,

Developing a new drug from an original hit to the launch of an approved product is a complex process which can take 12–15 years and cost in excess of USD 1–2 billion. The idea for a novel target can come from a variety of sources including clinical research, phenotypic studies, structural biology and from the application of in silico methods. It may take many years to validate a target sufficiently to start a costly drug discovery programme. Once a target has been validated, numerous processes have to be carried out to identify molecules which possess suitable characteristics to make acceptable drug candidates for clinical studies. The application of in silico methods including structure-based design, molecular dynamics simulations, artificial intelligence and machine learning has become an essential part of the early drug-discovery process. A breakthrough in structure-based design is certainly a recent development of the deep-learning approach AlphaFold for the 3D structure prediction of new proteins. However, the development of numerous biophysical and functional assay methods could also help to drive the selection of suitable targets and lead compounds.

Targeted protein degradation in cells by novel chemical compounds such as proteolysis-targeting chimeras (PROTACs) is considered one of the promising techniques in medicinal chemistry and early drug discovery. PROTACs are designed to degrade target proteins by harnessing the ubiquitin-proteasome system, and thus may offer new ways to circumvent some of the limitations associated with traditional small-molecule therapeutics.

Recent examples of medicinal chemistry and early drug discovery include novel targets from cancer epigenetics, tumor microenvironment, cancer immunology, as well as transcription factors, previously underexplored kinases, and RNA. Even proteins previously considered undruggable, such as c-Myc, transcription factors, and RNA/DNA-binding proteins, are now being targeted with novel approaches such as covalent chemical probes and PROTACs. The purpose of this Special Issue is to present current efforts in medicinal chemistry and early drug discovery. Original research articles, review articles, and short communications within (but not limited to) the research areas described are welcome.

Dr. Wolfgang Sippl
Guest Editor

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Keywords

medicinal chemistry
drug discovery
computer-assisted drug design
drug targets
machine learning
epigenetic targets
in vitro assays
biophysical methods
target engagement studies
multi-targeting compounds
PROTAC

Published Papers (11 papers)

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Editorial

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4 pages, 174 KiB

Open AccessEditorial

Editorial for Special Issue—“Early-Stage Drug Discovery: Advances and Challenges”

by Wolfgang Sippl

Int. J. Mol. Sci. 2023, 24(7), 6516; https://doi.org/10.3390/ijms24076516 - 30 Mar 2023

Viewed by 897

Abstract

The development of a new drug from the first hit to the launch of an approved product is a complex process that usually take around 12–15 years and costs more than USD 1–2 billion [...] Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

Research

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11 pages, 1421 KiB

Open AccessCommunication

HyperCys: A Structure- and Sequence-Based Predictor of Hyper-Reactive Druggable Cysteines

by Mingjie Gao and Stefan Günther

Int. J. Mol. Sci. 2023, 24(6), 5960; https://doi.org/10.3390/ijms24065960 - 22 Mar 2023

Cited by 1 | Viewed by 1782

Abstract

The cysteine side chain has a free thiol group, making it the amino acid residue most often covalently modified by small molecules possessing weakly electrophilic warheads, thereby prolonging on-target residence time and reducing the risk of idiosyncratic drug toxicity. However, not all cysteines are equally reactive or accessible. Hence, to identify targetable cysteines, we propose a novel ensemble stacked machine learning (ML) model to predict hyper-reactive druggable cysteines, named HyperCys. First, the pocket, conservation, structural and energy profiles, and physicochemical properties of (non)covalently bound cysteines were collected from both protein sequences and 3D structures of protein–ligand complexes. Then, we established the HyperCys ensemble stacked model by integrating six different ML models, including K-nearest neighbors, support vector machine, light gradient boost machine, multi-layer perceptron classifier, random forest, and the meta-classifier model logistic regression. Finally, based on the hyper-reactive cysteines’ classification accuracy and other metrics, the results for different feature group combinations were compared. The results show that the accuracy, F1 score, recall score, and ROC AUC values of HyperCys are 0.784, 0.754, 0.742, and 0.824, respectively, after performing 10-fold CV with the best window size. Compared to traditional ML models with only sequenced-based features or only 3D structural features, HyperCys is more accurate at predicting hyper-reactive druggable cysteines. It is anticipated that HyperCys will be an effective tool for discovering new potential reactive cysteines in a wide range of nucleophilic proteins and will provide an important contribution to the design of targeted covalent inhibitors with high potency and selectivity. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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17 pages, 2310 KiB

Open AccessArticle

Selectivity of Hydroxamate- and Difluoromethyloxadiazole-Based Inhibitors of Histone Deacetylase 6 In Vitro and in Cells

by Jakub Ptacek, Ivan Snajdr, Jiri Schimer, Zsofia Kutil, Jana Mikesova, Petra Baranova, Barbora Havlinova, Werner Tueckmantel, Pavel Majer, Alan Kozikowski and Cyril Barinka

Int. J. Mol. Sci. 2023, 24(5), 4720; https://doi.org/10.3390/ijms24054720 - 1 Mar 2023

Cited by 11 | Viewed by 2143

Abstract

Histone deacetylase 6 (HDAC6) is a unique member of the HDAC family of enzymes due to its complex domain organization and cytosolic localization. Experimental data point toward the therapeutic use of HDAC6-selective inhibitors (HDAC6is) for use in both neurological and psychiatric disorders. In this article, we provide side-by-side comparisons of hydroxamate-based HDAC6is frequently used in the field and a novel HDAC6 inhibitor containing the difluoromethyl-1,3,4-oxadiazole function as an alternative zinc-binding group (compound 7). In vitro isotype selectivity screening uncovered HDAC10 as a primary off-target for the hydroxamate-based HDAC6is, while compound 7 features exquisite 10,000-fold selectivity over all other HDAC isoforms. Complementary cell-based assays using tubulin acetylation as a surrogate readout revealed approximately 100-fold lower apparent potency for all compounds. Finally, the limited selectivity of a number of these HDAC6is is shown to be linked to cytotoxicity in RPMI-8226 cells. Our results clearly show that off-target effects of HDAC6is must be considered before attributing observed physiological readouts solely to HDAC6 inhibition. Moreover, given their unparalleled specificity, the oxadiazole-based inhibitors would best be employed either as research tools in further probing HDAC6 biology or as leads in the development of truly HDAC6-specific compounds in the treatment of human disease states. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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15 pages, 5355 KiB

Open AccessArticle

Insights into the Transport Cycle of LAT1 and Interaction with the Inhibitor JPH203

by Chiara Brunocilla, Lara Console, Filomena Rovella and Cesare Indiveri

Int. J. Mol. Sci. 2023, 24(4), 4042; https://doi.org/10.3390/ijms24044042 - 17 Feb 2023

Cited by 6 | Viewed by 2147

Abstract

The large Amino Acid Transporter 1 (LAT1) is an interesting target in drug discovery since this transporter is overexpressed in several human cancers. Furthermore, due to its location in the blood-brain barrier (BBB), LAT1 is interesting for delivering pro-drugs to the brain. In this work, we focused on defining the transport cycle of LAT1 using an in silico approach. So far, studies of the interaction of LAT1 with substrates and inhibitors have not considered that the transporter must undergo at least four different conformations to complete the transport cycle. We built outward-open and inward-occluded conformations of LAT1 using an optimized homology modelling procedure. We used these 3D models and the cryo-EM structures in outward-occluded and inward-open conformations to define the substrate/protein interaction during the transport cycle. We found that the binding scores for the substrate depend on the conformation, with the occluded states as the crucial steps affecting the substrate affinity. Finally, we analyzed the interaction of JPH203, a high-affinity inhibitor of LAT1. The results indicate that conformational states must be considered for in silico analyses and early-stage drug discovery. The two built models, together with the available cryo-EM 3D structures, provide important information on the LAT1 transport cycle, which could be used to speed up the identification of potential inhibitors through in silico screening. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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21 pages, 5257 KiB

Open AccessArticle

Inhibition of Neutral Sphingomyelinase 2 by Novel Small Molecule Inhibitors Results in Decreased Release of Extracellular Vesicles by Vascular Smooth Muscle Cells and Attenuated Calcification

by Angelina Pavlic, Hessel Poelman, Grzegorz Wasilewski, Kanin Wichapong, Petra Lux, Cecile Maassen, Esther Lutgens, Leon J. Schurgers, Chris P. Reutelingsperger and Gerry A. F. Nicolaes

Int. J. Mol. Sci. 2023, 24(3), 2027; https://doi.org/10.3390/ijms24032027 - 19 Jan 2023

Cited by 6 | Viewed by 1906

Abstract

Vascular calcification (VC) is an important contributor and prognostic factor in the pathogenesis of cardiovascular diseases. VC is an active process mediated by the release of extracellular vesicles by vascular smooth muscle cells (VSMCs), and the enzyme neutral sphingomyelinase 2 (nSMase2 or SMPD3) plays a key role. Upon activation, the enzyme catalyzes the hydrolysis of sphingomyelin, thereby generating ceramide and phosphocholine. This conversion mediates the release of exosomes, a type of extracellular vesicles (EVs), which ultimately forms the nidus for VC. nSMase2 therefore represents a drug target, the inhibition of which is thought to prevent or halt VC progression. In search of novel druglike small molecule inhibitors of nSMase2, we have used virtual ligand screening to identify potential ligands. From an in-silico collection of 48,6844 small druglike molecules, we selected 996 compounds after application of an in-house multi-step procedure combining different filtering and docking procedures. Selected compounds were functionally tested in vitro; from this, we identified 52 individual hit molecules that inhibited nSMase2 activity by more than 20% at a concentration of 150 µM. Further analysis showed that five compounds presented with IC₅₀s lower than 2 µM. Of these, compounds ID 5728450 and ID 4011505 decreased human primary VSMC EV release and calcification in vitro. The hit molecules identified here represent new classes of nSMase2 inhibitors that may be developed into lead molecules for the therapeutic or prophylactic treatment of VC. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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12 pages, 9299 KiB

Open AccessArticle

XML-CIMT: Explainable Machine Learning (XML) Model for Predicting Chemical-Induced Mitochondrial Toxicity

by Keerthana Jaganathan, Mobeen Ur Rehman, Hilal Tayara and Kil To Chong

Int. J. Mol. Sci. 2022, 23(24), 15655; https://doi.org/10.3390/ijms232415655 - 9 Dec 2022

Cited by 5 | Viewed by 1819

Abstract

Organ toxicity caused by chemicals is a serious problem in the creation and usage of chemicals such as medications, insecticides, chemical products, and cosmetics. In recent decades, the initiation and development of chemical-induced organ damage have been related to mitochondrial dysfunction, among several adverse effects. Recently, many drugs, for example, troglitazone, have been removed from the marketplace because of significant mitochondrial toxicity. As a result, it is an urgent requirement to develop in silico models that can reliably anticipate chemical-induced mitochondrial toxicity. In this paper, we have proposed an explainable machine-learning model to classify mitochondrially toxic and non-toxic compounds. After several experiments, the Mordred feature descriptor was shortlisted to be used after feature selection. The selected features used with the CatBoost learning algorithm achieved a prediction accuracy of 85% in 10-fold cross-validation and 87.1% in independent testing. The proposed model has illustrated improved prediction accuracy when compared with the existing state-of-the-art method available in the literature. The proposed tree-based ensemble model, along with the global model explanation, will aid pharmaceutical chemists in better understanding the prediction of mitochondrial toxicity. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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32 pages, 4655 KiB

Open AccessArticle

Discovery of 3-Amino-1H-pyrazole-Based Kinase Inhibitors to Illuminate the Understudied PCTAIRE Family

by Jennifer Alisa Amrhein, Lena Marie Berger, Amelie Tjaden, Andreas Krämer, Lewis Elson, Tuomas Tolvanen, Daniel Martinez-Molina, Astrid Kaiser, Manfred Schubert-Zsilavecz, Susanne Müller, Stefan Knapp and Thomas Hanke

Int. J. Mol. Sci. 2022, 23(23), 14834; https://doi.org/10.3390/ijms232314834 - 27 Nov 2022

Cited by 3 | Viewed by 1954

Abstract

The PCTAIRE subfamily belongs to the CDK (cyclin-dependent kinase) family and represents an understudied class of kinases of the dark kinome. They exhibit a highly conserved binding pocket and are activated by cyclin Y binding. CDK16 is targeted to the plasma membrane after binding to N-myristoylated cyclin Y and is highly expressed in post-mitotic tissues, such as the brain and testis. Dysregulation is associated with several diseases, including breast, prostate, and cervical cancer. Here, we used the N-(1H-pyrazol-3-yl)pyrimidin-4-amine moiety from the promiscuous inhibitor 1 to target CDK16, by varying different residues. Further optimization steps led to 43d, which exhibited high cellular potency for CDK16 (EC₅₀ = 33 nM) and the other members of the PCTAIRE and PFTAIRE family with 20–120 nM and 50–180 nM, respectively. A DSF screen against a representative panel of approximately 100 kinases exhibited a selective inhibition over the other kinases. In a viability assessment, 43d decreased the cell count in a dose-dependent manner. A FUCCI cell cycle assay revealed a G2/M phase cell cycle arrest at all tested concentrations for 43d, caused by inhibition of CDK16. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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9 pages, 2666 KiB

Open AccessArticle

Quinolizidines as Novel SARS-CoV-2 Entry Inhibitors

by Li Huang, Lei Zhu, Hua Xie, Jeffery Shawn Goodwin, Tanu Rana, Lan Xie and Chin-Ho Chen

Int. J. Mol. Sci. 2022, 23(17), 9659; https://doi.org/10.3390/ijms23179659 - 25 Aug 2022

Cited by 3 | Viewed by 1735

Abstract

COVID-19, caused by the highly transmissible severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has rapidly spread and become a pandemic since its outbreak in 2019. We have previously discovered that aloperine is a new privileged scaffold that can be modified to become a specific antiviral compound with markedly improved potency against different viruses, such as the influenza virus. In this study, we have identified a collection of aloperine derivatives that can inhibit the entry of SARS-CoV-2 into host cells. Compound 5 is the most potent tested aloperine derivative that inhibited the entry of SARS-CoV-2 (D614G variant) spike protein-pseudotyped virus with an IC₅₀ of 0.5 µM. The compound was also active against several other SARS-CoV-2 variants including Delta and Omicron. Results of a confocal microscopy study suggest that compound 5 inhibited the viral entry before fusion to the cell or endosomal membrane. The results are consistent with the notion that aloperine is a privileged scaffold that can be used to develop potent anti-SARS-CoV-2 entry inhibitors. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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34 pages, 8840 KiB

Open AccessArticle

Synthesis, Molecular Docking and Biological Characterization of Pyrazine Linked 2-Aminobenzamides as New Class I Selective Histone Deacetylase (HDAC) Inhibitors with Anti-Leukemic Activity

by Hany S. Ibrahim, Mohamed Abdelsalam, Yanira Zeyn, Matthes Zessin, Al-Hassan M. Mustafa, Marten A. Fischer, Patrik Zeyen, Ping Sun, Emre F. Bülbül, Anita Vecchio, Frank Erdmann, Matthias Schmidt, Dina Robaa, Cyril Barinka, Christophe Romier, Mike Schutkowski, Oliver H. Krämer and Wolfgang Sippl

Int. J. Mol. Sci. 2022, 23(1), 369; https://doi.org/10.3390/ijms23010369 - 29 Dec 2021

Cited by 25 | Viewed by 4352

Abstract

Class I histone deacetylases (HDACs) are key regulators of cell proliferation and they are frequently dysregulated in cancer cells. We report here the synthesis of a novel series of class-I selective HDAC inhibitors (HDACi) containing a 2-aminobenzamide moiety as a zinc-binding group connected with a central (piperazin-1-yl)pyrazine or (piperazin-1-yl)pyrimidine moiety. Some of the compounds were additionally substituted with an aromatic capping group. Compounds were tested in vitro against human HDAC1, 2, 3, and 8 enzymes and compared to reference class I HDACi (Entinostat (MS-275), Mocetinostat, CI994 and RGFP-966). The most promising compounds were found to be highly selective against HDAC1, 2 and 3 over the remaining HDAC subtypes from other classes. Molecular docking studies and MD simulations were performed to rationalize the in vitro data and to deduce a complete structure activity relationship (SAR) analysis of this novel series of class-I HDACi. The most potent compounds, including 19f, which blocks HDAC1, HDAC2, and HDAC3, as well as the selective HDAC1/HDAC2 inhibitors 21a and 29b, were selected for further cellular testing against human acute myeloid leukemia (AML) and erythroleukemic cancer (HEL) cells, taking into consideration their low toxicity against human embryonic HEK293 cells. We found that 19f is superior to the clinically tested class-I HDACi Entinostat (MS-275). Thus, 19f is a new and specific HDACi with the potential to eliminate blood cancer cells of various origins. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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Review

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40 pages, 1466 KiB

Open AccessReview

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

by Chayna Sarkar, Biswadeep Das, Vikram Singh Rawat, Julie Birdie Wahlang, Arvind Nongpiur, Iadarilang Tiewsoh, Nari M. Lyngdoh, Debasmita Das, Manjunath Bidarolli and Hannah Theresa Sony

Int. J. Mol. Sci. 2023, 24(3), 2026; https://doi.org/10.3390/ijms24032026 - 19 Jan 2023

Cited by 35 | Viewed by 10465

Abstract

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as “message-passing paradigms”, “spatial-symmetry-preserving networks”, “hybrid de novo design”, and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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30 pages, 24391 KiB

Open AccessReview

MDM2-Based Proteolysis-Targeting Chimeras (PROTACs): An Innovative Drug Strategy for Cancer Treatment

by André T. S. Vicente and Jorge A. R. Salvador

Int. J. Mol. Sci. 2022, 23(19), 11068; https://doi.org/10.3390/ijms231911068 - 21 Sep 2022

Cited by 10 | Viewed by 4191

Abstract

Proteolysis-targeting chimeras (PROTACs) are molecules that selectively degrade a protein of interest (POI). The incorporation of ligands that recruit mouse double minute 2 (MDM2) into PROTACs, forming the so-called MDM2-based PROTACs, has shown promise in cancer treatment due to its dual mechanism of action: a PROTAC that recruits MDM2 prevents its binding to p53, resulting not only in the degradation of POI but also in the increase of intracellular levels of the p53 suppressor, with the activation of a whole set of biological processes, such as cell cycle arrest or apoptosis. In addition, these PROTACs, in certain cases, allow for the degradation of the target, with nanomolar potency, in a rapid and sustained manner over time, with less susceptibility to the development of resistance and tolerance, without causing changes in protein expression, and with selectivity to the target, including the respective isoforms or mutations, and to the cell type, overcoming some limitations associated with the use of inhibitors for the same therapeutic target. Therefore, the aim of this review is to analyze and discuss the characteristics of MDM2-based PROTACs developed for the degradation of oncogenic proteins and to understand what potential they have as future anticancer drugs. Full article

(This article belongs to the Special Issue Early-Stage Drug Discovery: Advances and Challenges)

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