Tubulin inhibitors. Selected scaffolds and main trends in the design of novel anticancer and antiparasitic agents.

Design of tubulin inhibitors as anticancer drugs dynamically developed over the past 20 years. The modern arsenal of potential tubulin-targeting anticancer agents is represented by small molecules, monoclonal antibodies, and antibody-drug conjugates. Moreover, targeting tubulin has been a successful strategy in the development of antiparasitic drugs. In the present review, an overall picture of the research and development of potential tubulin-targeting agents using small molecules between 2018 and 2023 is provided. The data about some most often used and prospective chemotypes of small molecules (privileged heterocycles, moieties of natural molecules) and synthetic methodologies (analogue-based, fragment-based drug design, molecular hybridization) applied for the design of novel agents with an impact on the tubulin system are summarized. The design and prospects of multi-target agents with an impact on the tubulin system were also highlighted. Reported in the review data contribute to the "structure-activity" profile of tubulin-targeting small molecules as anticancer and antiparasitic agents and will be useful for the application by medicinal chemists in further exploration, design, improvement, and optimization of this class of molecules.

Computational fragment-based drug design of potential Glo-I inhibitors.

In this study, a fragment-based drug design approach, particularly de novo drug design, was implemented utilising three different crystal structures in order to discover new privileged scaffolds against glyoxalase-I enzyme as anticancer agents. The fragments were evoluted to indicate potential inhibitors with high receptor affinities. The resulting compounds were served as a benchmark for choosing similar compounds from the ASINEX® database by applying different computational ligand-based drug design techniques. Afterwards, the selection of potential hits was further aided by various structure-based approaches. Then, 14 compounds were purchased, and tested in vitro against Glo-I enzyme. Of the tested 14 hits, the biological screening results showed humble activities where the percentage of Glo-I inhibition ranged from 0-18.70 %. Compound 19 and compound 28, whose percentage of inhibitions are 18.70 and 15.80%, respectively, can be considered as hits that need further optimisation in order to be converted into lead-like compounds.

FRAGMENTISE: A user-friendly, cross-platform tool to create and analyze comprehensive small-molecule fragment databases.

The ongoing COVID-19 pandemic, and constant demand for new therapies in unmet clinical needs, necessitates strategies to identify drug candidates for rapid clinical deployment. Over the years, fragment-based drug design (FBDD) has emerged as a mainstream lead discovery strategy in academia, biotechnology start-ups, and large pharma. Chemical building block libraries are the fundamental component of virtually any FBDD campaign. Current trends focus on smaller and smarter libraries that offer synthetically amenable starting points for rational lead generation. Therefore, there remains an ever-increasing need for new methods to generate fragment libraries to seed early-stage drug discovery programs. Here, we present FRAGMENTISE-a new user-friendly, cross-platform tool for user-tunable retrosynthetic small-molecule fragmentation. FRAGMENTISE allows for visualization, similarity search, annotation, and in-depth analysis of the fragment databases in the medicinal chemistry context. FRAGMENTISE is available as standalone software for Linux, Windows, and macOS users, with a graphical interface or command-line version.

Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores.

Fragment spaces are an efficient way to model large chemical spaces using a handful of small fragments and a few connection rules. The development of Enamine's REAL Space has shown that large spaces of readily available compounds may be created this way. These are several orders of magnitude larger than previous libraries. So far, searching and navigating these spaces is mostly limited to topological approaches. A way to overcome this limitation is optimization via metaheuristics which can be combined with arbitrary scoring functions. Here we present Galileo, a novel Genetic Algorithm to sample fragment spaces. We showcase Galileo in combination with a novel pharmacophore mapping approach, called Phariety, enabling 3D searches in fragment spaces. We estimate the effectiveness of the approach with a small fragment space. Furthermore, we apply Galileo to two pharmacophore searches in the REAL Space, detecting hundreds of compounds fulfilling a HSP90 and a FXIa pharmacophore.

QM assisted ML for 19F NMR chemical shift prediction.

BACKGROUND: Ligand-observed 19F NMR detection is an efficient method for screening libraries of fluorinated molecules in fragment-based drug design campaigns. Screening fluorinated molecules in large mixtures makes 19F NMR a high-throughput method. Typically, these mixtures are generated from pools of well-characterized fragments. By predicting 19F NMR chemical shift, mixtures could be generated for arbitrary fluorinated molecules facilitating for example focused screens. METHODS: In a previous publication, we introduced a method to predict 19F NMR chemical shift using rooted fluorine fingerprints and machine learning (ML) methods. Having observed that the quality of the prediction depends on similarity to the training set, we here propose to assist the prediction with quantum mechanics (QM) based methods in cases where compounds are not well covered by a training set. RESULTS: Beyond similarity, the performance of ML methods could be associated with individual features in compounds. A combination of both could be used as a procedure to split input data sets into those that could be predicted by ML and those that required QM processing. We could show on a proprietary fluorinated fragment library, known as LEF (Local Environment of Fluorine), and a public Enamine data set of 19F NMR chemical shifts that ML and QM methods could synergize to outperform either method individually. Models built on Enamine data, as well as model building and QM workflow tools, can be found at https://github.com/PatrickPenner/lefshift and https://github.com/PatrickPenner/lefqm .

Turning a Tumor Microenvironment Pitfall into Opportunity: Discovery of Benzamidoxime as PD-L1 Ligand with pH-Dependent Potency.

PD-1/PD-L1 protein complex is attracting a great deal of interest as a drug target for the design of immune therapies able to block its assembly. Although some biologic drugs have entered clinical use, their poor response rate in patients are demanding further efforts to design small molecule inhibitors of PD-1/PD-L1 complex with higher efficacy and optimal physicochemical properties. Dysregulation of pH in the tumor microenvironment is indeed one of the key mechanisms promoting drug resistance and lack of response in cancer therapy. Integrating computational and biophysical approaches, herein we report a screening campaign that has led to identifying VIS310 as a novel ligand of PD-L1, with physicochemical properties enabling a pH-dependent binding potency. Additional optimization efforts by analogue-based screening have been instrumental to disclosing VIS1201, which exhibits improved binding potency against PD-L1 and is able to inhibit PD-1/PD-L1 complex formation in a ligand binding displacement assay. While providing preliminary structure-activity relationships (SARs) of a novel class of PD-L1 ligands, our results lay the foundation for the discovery of immunoregulatory small molecules resilient to tumor microenvironmental conditions for escaping drug-resistance mechanisms.

Targeting HIV-1 Reverse Transcriptase Using a Fragment-Based Approach.

Human immunodeficiency virus type I (HIV-1) is a retrovirus that infects cells of the host's immune system leading to acquired immunodeficiency syndrome and potentially death. Although treatments are available to prevent its progression, HIV-1 remains a major burden on health resources worldwide. Continued emergence of drug-resistance mutations drives the need for novel drugs that can inhibit HIV-1 replication through new pathways. The viral protein reverse transcriptase (RT) plays a fundamental role in the HIV-1 replication cycle, and multiple approved medications target this enzyme. In this study, fragment-based drug discovery was used to optimize a previously identified hit fragment (compound B-1), which bound RT at a novel site. Three series of compounds were synthesized and evaluated for their HIV-1 RT binding and inhibition. These series were designed to investigate different vectors around the initial hit in an attempt to improve inhibitory activity against RT. Our results show that the 4-position of the core scaffold is important for binding of the fragment to RT, and a lead compound with a cyclopropyl substitution was selected and further investigated. Requirements for binding to the NNRTI-binding pocket (NNIBP) and a novel adjacent site were investigated, with lead compound 27-a minimal but efficient NNRTI-offering a starting site for the development of novel dual NNIBP-Adjacent site inhibitors.

Fragment Screening and Fast Micromolar Detection on a Benchtop NMR Spectrometer Boosted by Photoinduced Hyperpolarization.

Fragment-based drug design is a well-established strategy for rational drug design, with nuclear magnetic resonance (NMR) on high-field spectrometers as the method of reference for screening and hit validation. However, high-field NMR spectrometers are not only expensive, but require specialized maintenance, dedicated space, and depend on liquid helium cooling which became critical over the recurring global helium shortages. We propose an alternative to high-field NMR screening by applying the recently developed approach of fragment screening by photoinduced hyperpolarized NMR on a cryogen-free 80 MHz benchtop NMR spectrometer yielding signal enhancements of up to three orders in magnitude. It is demonstrated that it is possible to discover new hits and kick-off drug design using a benchtop NMR spectrometer at low micromolar concentrations of both protein and ligand. The approach presented performs at higher speed than state-of-the-art high-field NMR approaches while exhibiting a limit of detection in the nanomolar range. Photoinduced hyperpolarization is known to be inexpensive and simple to be implemented, which aligns greatly with the philosophy of benchtop NMR spectrometers. These findings open the way for the use of benchtop NMR in near-physiological conditions for drug design and further life science applications.

Crystal-structures-guided design of fragment-based drugs for inhibiting the main protease of SARS-CoV-2.

Since the beginning of the COVID-19 pandemic, scientists across the globe are racing to find a cure for the highly contagious infectious disease caused by the SARS-CoV-2 virus. Despite many promising ongoing progress, there are currently no FDA approved drug to treat infected patients. Recently, the crowdsourcing of drug discovery for inhibiting the main protease (Mpro) of SARS-CoV-2 have yielded a plenty of drug fragments resolved inside the active site of Mpro via the crystallography method. Following the principle of fragment-based drug design (FBDD), we are motivated to design a potent drug candidate (named B19) by merging three fragments JFM, U0P, and HWH. Through extensive all-atom molecular dynamics simulation and molecular docking, we found that B19 among all designed ones is most stable inside the Mpro's active site and the binding free energy of B19 is comparable to or even a little better than that of a native protein ligand processed by Mpro. Our promising results suggest that B19 and its derivatives can potentially be efficacious drug candidates for COVID-19.

Exploration of Ultralarge Compound Collections for Drug Discovery.

Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.

FastGrow: on-the-fly growing and its application to DYRK1A.

Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow's features in practice and its ability to identify active fragments. FastGrow is freely available to the public as a web server at https://fastgrow.plus/ and is part of the SeeSAR 3D software package.

SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction.

A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess the predictive power of methods involved in computer-aided drug design. Application of various methods to fragment molecules are now widely used in the search for new drugs. However, there is little in the way of systematic validation specifically for fragment-based approaches. We have performed a large crystallographic high-throughput fragment screen against the therapeutically relevant second bromodomain of the Pleckstrin-homology domain interacting protein (PHIP2) that revealed 52 different fragments bound across 4 distinct sites, 47 of which were bound to the pharmacologically relevant acetylated lysine (Kac) binding site. These data were used to assess computational screening, binding pose prediction and follow-up enumeration. All submissions performed randomly for screening. Pose prediction success rates (defined as less than 2 Å root mean squared deviation against heavy atom crystal positions) ranged between 0 and 25% and only a very few follow-up compounds were deemed viable candidates from a medicinal-chemistry perspective based on a common molecular descriptors analysis. The tight deadlines imposed during the challenge led to a small number of submissions suggesting that the accuracy of rapidly responsive workflows remains limited. In addition, the application of these methods to reproduce crystallographic fragment data still appears to be very challenging. The results show that there is room for improvement in the development of computational tools particularly when applied to fragment-based drug design.

Synthesis and identification of a novel skeleton of N-(pyridin-3-yl) proline as a selective CDK4/6 inhibitor with anti-breast cancer activities.

CDK4/6 were attractive chemotherapeutic targets for the treatment of malignant tumors, CDK4/6 selective inhibitors have made outstanding contributions in the treatment of breast cancer. However, these inhibitors share a single skeleton of N-(pyridin-2-yl) pyrimidin-2-amine which cannot overcome the side effects in clinical application. In our previous study, an N'- acetylpyrrolidine-1-carbohydrazide was hit as the initial fragment by analyzing the active site characteristics of CDK6. Two series of N-(pyridin-3-yl) proline were obtained by fragment growth method. The QSAR study was carried out according to the in vitro activities data against CDK4/6, and two compounds 7c and 7p with potent inhibitory activities were found to interact with CDK4 in different binding conformation. They showed potential inhibition of cell proliferation against the breast cancer cell, and 7c exhibited promised anti-breast cancer effect in vivo.

Identification of selective homeodomain interacting protein kinase 2 inhibitors, a potential treatment for renal fibrosis.

Homeodomain interacting protein kinase 2 (HIPK2) has emerged as a promising target for the discovery of anti-renal fibrosis drugs. Herein, to develop specific pharmacologic inhibitors of HIPK2, we designed and synthesized a series of compounds containing benzimidazole and pyrimidine scaffolds via fragment-based drug design strategy. Kinase assay was applied to evaluate the inhibitory activity of target compounds against HIPKs enzyme. The molecular docking study suggest the contribution of tyrosine residues beside the active sites of HIPK1-3 to the selectivity of active compounds. Compound 15q displayed good selectivity and potent inhibitory activity against HIPK2 compared to other two subtype enzymes. 15q could downregulate phosphorylated p53, the direct substrate of HIPK2, and decrease the fibrosis-related downstream of HIPK2, such as p-Smad3 and α-SMA in NRK-49F cells. 15q showed no effect on the cell apoptosis in fibrotic or cancer cell lines, suggesting little cancer risk of 15q. Notably, 15q displayed encouraging in vivo anti-fibrotic effects in the unilateral ureteral obstruction mouse model, which could be used as a potential lead for structural optimization and candidate for the development of selective HIPK2 inhibitors.

Drugging an undruggable pocket on KRAS.

The 3 human RAS genes, KRAS, NRAS, and HRAS, encode 4 different RAS proteins which belong to the protein family of small GTPases that function as binary molecular switches involved in cell signaling. Activating mutations in RAS are among the most common oncogenic drivers in human cancers, with KRAS being the most frequently mutated oncogene. Although KRAS is an excellent drug discovery target for many cancers, and despite decades of research, no therapeutic agent directly targeting RAS has been clinically approved. Using structure-based drug design, we have discovered BI-2852 (1), a KRAS inhibitor that binds with nanomolar affinity to a pocket, thus far perceived to be "undruggable," between switch I and II on RAS; 1 is mechanistically distinct from covalent KRASG12C inhibitors because it binds to a different pocket present in both the active and inactive forms of KRAS. In doing so, it blocks all GEF, GAP, and effector interactions with KRAS, leading to inhibition of downstream signaling and an antiproliferative effect in the low micromolar range in KRAS mutant cells. These findings clearly demonstrate that this so-called switch I/II pocket is indeed druggable and provide the scientific community with a chemical probe that simultaneously targets the active and inactive forms of KRAS.

Critical Assessment of a Structure-Based Screening Campaign for IDO1 Inhibitors: Tips and Pitfalls.

Over the last two decades, indoleamine 2,3-dioxygenase 1 (IDO1) has attracted wide interest as a key player in immune regulation, fostering the design and development of small molecule inhibitors to restore immune response in tumor immunity. In this framework, biochemical, structural, and pharmacological studies have unveiled peculiar structural plasticity of IDO1, with different conformations and functional states that are coupled to fine regulation of its catalytic activity and non-enzymic functions. The large plasticity of IDO1 may affect its ligand recognition process, generating bias in structure-based drug design campaigns. In this work, we report a screening campaign of a fragment library of compounds, grounding on the use of three distinct conformations of IDO1 that recapitulate its structural plasticity to some extent. Results are instrumental to discuss tips and pitfalls that, due to the large plasticity of the enzyme, may influence the identification of novel and differentiated chemical scaffolds of IDO1 ligands in structure-based screening campaigns.

Druggable Allosteric Sites in ß-Propeller Lectins.

Carbohydrate-binding proteins (lectins) are auspicious targets in drug discovery to combat antimicrobial resistance; however, their non-carbohydrate drug-like inhibitors are still unavailable. Here, we present a druggable pocket in a ß-propeller lectin BambL from Burkholderia ambifaria as a potential target for allosteric inhibitors. This site was identified employing 19 F NMR fragment screening and a computational pocket prediction algorithm SiteMap. The structure-activity relationship study revealed the most promising fragment with a dissociation constant of 0.3±0.1 mM and a ligand efficiency of 0.3 kcal mol-1 HA-1 that affected the orthosteric site. This effect was substantiated by site-directed mutagenesis in the orthosteric and secondary pockets. Future drug-discovery campaigns that aim to develop small molecule inhibitors can benefit from allosteric sites in lectins as a new therapeutic approach against antibiotic-resistant pathogens.

Novel 1-(prop-2-yn-1-ylamino)-2,3-dihydro-1H-indene-4-thiol derivatives as potent selective human monoamine oxidase B inhibitors: Design, SAR development, and biological evaluation.

Successes have been achieved in developing human monoamine oxidase B (hMAO-B) inhibitors as anti-Parkinson's disease (PD) drugs. However, low efficiency and unwanted side effects of the marketed hMAO-B inhibitors hamper their medical applications, therefore, novel potent selective hMAO-B inhibitors are still of great interest. Herein we report 1-(prop-2-yn-1-ylamino)-2,3-dihydro-1H-indene-4-thiol derivatives as hMAO-B inhibitors, which were designed by employing a fragment-based drug design strategy to link rasagiline to hydrophobic fragments. Among the synthesized 31 compounds, K8 and K24 demonstrated very encouraging hMAO-B inhibitory activities and selectivity over hMAO-A, better than rasagiline and safinamide. In vitro studies indicated that K8 and K24 are nontoxic to nervous tissue cells and they have considerable effects against ROS formation and potential neuroprotective activity. Further mice behavioral tests demonstrated these two compounds have good therapeutic effects on MPTP-induced PD model mice. All these experiment results suggest that compounds K8 and K24 can be promising candidates for further research for treatment of PD.

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities.

The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

Progress in Free Energy Perturbation: Options for Evolving Fragments.

One of the remaining bottlenecks in fragment-based drug design (FBDD) is the initial exploration and optimization of the identified hit fragments. There is a growing interest in computational approaches that can guide these efforts by predicting the binding affinity of newly designed analogues. Among others, alchemical free energy (AFE) calculations promise high accuracy at a computational cost that allows their application during lead optimization campaigns. In this review, we discuss how AFE could have a strong impact in fragment evolution, and we raise awareness on the challenges that could be encountered applying this methodology in FBDD studies.