Hydrophobic tagging of small molecules: an overview of the literature and future outlook.

INTRODUCTION: Hydrophobic tagging (HyT) technology presents a distinct therapeutic strategy diverging from conventional small molecule drugs, providing an innovative approach to drug design. This review aims to provide an overview of the HyT literature and future outlook to offer guidance for drug design. AREAS COVERED: In this review, the authors introduce the composition, mechanisms and advantages of HyT technology, as well as summarize the detailed applications of HyT technology in anti-cancer, neurodegenerative diseases (NDs), autoimmune disorders, cardiovascular diseases (CVDs), and other fields. Furthermore, this review discusses key aspects of the future development of HyT molecules. EXPERT OPINION: HyT emerges as a highly promising targeted protein degradation (TPD) strategy, following the successful development of proteolysis targeting chimeras (PROTAC) and molecular glue. Based on exploring new avenues, modification of the HyT molecule itself potentially enhances the technology. Improved synthetic pathways and emphasis on pharmacokinetic (PK) properties will facilitate the development of HyT. Furthermore, elucidating the biochemical basis by which the compound's hydrophobic moiety recruits the protein homeostasis network will enable the development of more precise assays that can guide the optimization of the linker and hydrophobic moiety.

Targeting hepatitis B virus cccDNA levels: Recent progress in seeking small molecule drug candidates.

Hepatitis B virus (HBV) infection is a major global health problem that puts people at high risk of death from cirrhosis and liver cancer. The presence of covalently closed circular DNA (cccDNA) in infected cells is considered to be the main obstacle to curing chronic hepatitis B. At present, the cccDNA cannot be completely eliminated by standard treatments. There is an urgent need to develop drugs or therapies that can reduce HBV cccDNA levels in infected cells. We summarize the discovery and optimization of small molecules that target cccDNA synthesis and degradation. These compounds are cccDNA synthesis inhibitors, cccDNA reducers, core protein allosteric modulators, ribonuclease H inhibitors, cccDNA transcriptional modulators, HBx inhibitors and other small molecules that reduce cccDNA levels.

Macrocycles in Drug Discovery─Learning from the Past for the Future.

We have analyzed FDA-approved macrocyclic drugs, clinical candidates, and the recent literature to understand how macrocycles are used in drug discovery. Current drugs are mainly used in infectious disease and oncology, while oncology is the major indication for the clinical candidates and in the literature Most macrocyclic drugs bind to targets that have difficult to drug binding sites. Natural products have provided 80-90% of the drugs and clinical candidates, whereas macrocycles in ChEMBL have less complex structures. Macrocycles usually reside in the beyond the Rule of 5 chemical space, but 30-40% of the drugs and clinical candidates are orally bioavailable. Simple bi-descriptor models, i.e., HBD ≤ 7 in combination with either MW < 1000 Da or cLogP > 2.5, distinguished orals from parenterals and can be used as filters in design. We propose that recent breakthroughs in conformational analysis and inspiration from natural products will further improve the de novo design of macrocycles.

Recent developments in the medicinal chemistry of single boron atom-containing compounds.

Various boron-containing drugs have been approved for clinical use over the past two decades, and more are currently in clinical trials. The increasing interest in boron-containing compounds is due to their unique binding properties to biological targets; for example, boron substitution can be used to modulate biological activity, pharmacokinetic properties, and drug resistance. In this perspective, we aim to comprehensively review the current status of boron compounds in drug discovery, focusing especially on progress from 2015 to December 2020. We classify these compounds into groups showing anticancer, antibacterial, antiviral, antiparasitic and other activities, and discuss the biological targets associated with each activity, as well as potential future developments.

Macrocyclic Peptides Uncover a Novel Binding Mode for Reversible Inhibitors of LSD1.

Lysine-specific demethylase 1 (LSD1) is an epigenetic enzyme which regulates the methylation of Lys4 of histone 3 (H3) and is overexpressed in certain cancers. We used structures of H3 substrate analogues bound to LSD1 to design macrocyclic peptide inhibitors of LSD1. A linear, Lys4 to Met-substituted, 11-mer (4) was identified as the shortest peptide distinctly interacting with LSD1. It was evolved into macrocycle 31, which was >40 fold more potent (K i = 2.3 µM) than 4. Linear and macrocyclic peptides exhibited unexpected differences in structure-activity relationships for interactions with LSD1, indicating that they bind LSD1 differently. This was confirmed by the crystal structure of 31 in complex with LSD1-CoREST1, which revealed a novel binding mode at the outer rim of the LSD1 active site and without a direct interaction with FAD. NMR spectroscopy of 31 suggests that macrocyclization restricts its solution ensemble to conformations that include the one in the crystalline complex. Our results provide a solid basis for the design of optimized reversible LSD1 inhibitors.

Impact of Dynamically Exposed Polarity on Permeability and Solubility of Chameleonic Drugs Beyond the Rule of 5.

Conformational flexibility has been proposed to significantly affect drug properties outside rule-of-5 (Ro5) chemical space. Here, we investigated the influence of dynamically exposed polarity on cell permeability and aqueous solubility for a structurally diverse set of drugs and clinical candidates far beyond the Ro5, all of which populated multiple distinct conformations as revealed by X-ray crystallography. Efflux-inhibited (passive) Caco-2 cell permeability correlated strongly with the compounds' minimum solvent-accessible 3D polar surface areas (PSA), whereas aqueous solubility depended less on the specific 3D conformation. Inspection of the crystal structures highlighted flexibly linked aromatic side chains and dynamically forming intramolecular hydrogen bonds as particularly effective in providing "chameleonic" properties that allow compounds to display both high cell permeability and aqueous solubility. These structural features, in combination with permeability predictions based on the correlation to solvent-accessible 3D PSA, should inspire drug design in the challenging chemical space far beyond the Ro5.

Binding affinity models for Falcipain inhibition based on the Linear Interaction Energy method.

The high rate of drug resistance as well as the complex biochemical process of the parasite reproduction cycle makes development of new drugs for malaria a very important but challenging task. Falcipain 2 (FL2) and Falcipain 3 (FL3) are the major cysteine protease enzymes that play a central role in providing essential amino acids for the parasite's protein biosynthesis through the hemoglobin hydrolysis process. Selective inhibition of these enzymes is considered as a promising chemotherapeutic target. In the present investigation, the highly efficient linear interaction energy (LIE) method has been parameterized for binding affinity predictions and assessed with a set of 244 compounds for FL2 and FL3 inhibition. The results revealed that the van der Waals energy is very important for ligands binding to Falcipain proteins and that, overall, the electrostatic energy contribution is minor. The best models obtained for FL2 and FL3 give root mean square errors (RMSE) of 1.82 and 1.33kcal/mol respectively, for the test set. In this study, we also investigate how the choice of initial protein-ligand confirmation (pose) impacts the overall quality of the LIE models. Moreover, the transferability of LIE parameters is further discussed.

Targeting VEGF with LNA-stabilized G-rich oligonucleotide for efficient breast cancer inhibition.

In this study, we investigated the efficacy of an LNA (locked nucleic acid)-modified DNA aptamer named RNV66 targeting VEGF against various breast cancer cell lines. Our results demonstrate that RNV66 efficiently inhibits breast cancer cell proliferation both in vitro and in vivo. Introduction of LNA nucleotides were crucial for higher efficacy. Furthermore, the binding interaction of RNV66 with VEGF was investigated using molecular dynamic simulations leading to the first computational model of the LNA aptamer-VEGF complex blocking its interaction with VEGF-receptor.

Computational investigation of locked nucleic acid (LNA) nucleotides in the active sites of DNA polymerases by molecular docking simulations.

Aptamers constitute a potential class of therapeutic molecules typically selected from a large pool of oligonucleotides against a specific target. With a scope of developing unique shorter aptamers with very high biostability and affinity, locked nucleic acid (LNA) nucleotides have been investigated as a substrate for various polymerases. Various reports showed that some thermophilic B-family DNA polymerases, particularly KOD and Phusion DNA polymerases, accepted LNA-nucleoside 5'-triphosphates as substrates. In this study, we investigated the docking of LNA nucleotides in the active sites of RB69 and KOD DNA polymerases by molecular docking simulations. The study revealed that the incoming LNA-TTP is bound in the active site of the RB69 and KOD DNA polymerases in a manner similar to that seen in the case of dTTP, and with LNA structure, there is no other option than the locked C3'-endo conformation which in fact helps better orienting within the active site.

Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors.

P-Glycoprotein (P-gp, ABCB1) plays a significant role in determining the ADMET properties of drugs and drug candidates. Substrates of P-gp are not only subject to multidrug resistance (MDR) in tumor therapy, they are also associated with poor pharmacokinetic profiles. In contrast, inhibitors of P-gp have been advocated as modulators of MDR. However, due to the polyspecificity of P-gp, knowledge on the molecular basis of ligand-transporter interaction is still poor, which renders the prediction of whether a compound is a P-gp substrate/non-substrate or an inhibitor/non-inhibitor quite challenging. In the present investigation, we used a set of fingerprints representing the presence/absence of various functional groups for machine learning based classification of a set of 484 substrates/non-substrates and a set of 1935 inhibitors/non-inhibitors. Best models were obtained using a combination of a wrapper subset evaluator (WSE) with random forest (RF), kappa nearest neighbor (kNN) and support vector machine (SVM), showing accuracies >70%. Best P-gp substrate models were further validated with three sets of external P-gp substrate sources, which include Drug Bank (n = 134), TP Search (n = 90) and a set compiled from literature (n = 76). Association rule analysis explores the various structural feature requirements for P-gp substrates and inhibitors.