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.

Exploring the chemical space of orally bioavailable PROTACs.

A principal challenge in the discovery of proteolysis targeting chimeras (PROTACs) as oral medications is their bioavailability. To facilitate drug design, it is therefore essential to identify the chemical space where orally bioavailable PROTACs are more likely to be situated. To this aim, we extracted structure-bioavailability insights from published data using traditional 2D descriptors, thereby shedding light on their potential and limitations as drug design tools. Subsequently, we describe cutting-edge experimental, computational and hybrid design strategies based on 3D descriptors, which show promise for enhancing the probability of discovering PROTACs with high oral bioavailability.

Molecular chameleons in drug discovery.

Molecular chameleons possess a flexibility that allows them to dynamically shield or expose polar functionalities in response to the properties of the environment. Although the concept of molecular chameleons was introduced already in 1970, interest in them has grown considerably since the 2010s, when drug discovery has focused to an increased extent on new chemical modalities. Such modalities include cyclic peptides, macrocycles and proteolysis-targeting chimeras, all of which reside in a chemical space far from that of traditional small-molecule drugs. Both cell permeability and aqueous solubility are required for the oral absorption of drugs. Engineering these properties, and potent target binding, into the larger new modalities is a more daunting task than for traditional small-molecule drugs. The ability of chameleons to adapt to different environments may be essential for success. In this Review, we provide both general and theoretical insights into the realm of molecular chameleons. We discuss why chameleons have come into fashion and provide a do-it-yourself toolbox for their design; we then provide a glimpse of how advanced in silico methods can support molecular chameleon design.

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.

Predictive Modeling of PROTAC Cell Permeability with Machine Learning.

Approaches for predicting proteolysis targeting chimera (PROTAC) cell permeability are of major interest to reduce resource-demanding synthesis and testing of low-permeable PROTACs. We report a comprehensive investigation of the scope and limitations of machine learning-based binary classification models developed using 17 simple descriptors for large and structurally diverse sets of cereblon (CRBN) and von Hippel-Lindau (VHL) PROTACs. For the VHL PROTAC set, kappa nearest neighbor and random forest models performed best and predicted the permeability of a blinded test set with >80% accuracy (k ≥ 0.57). Models retrained by combining the original training and the blinded test set performed equally well for a second blinded VHL set. However, models for CRBN PROTACs were less successful, mainly due to the imbalanced nature of the CRBN datasets. All descriptors contributed to the models, but size and lipophilicity were the most important. We conclude that properly trained machine learning models can be integrated as effective filters in the PROTAC design process.

Simulation Reveals the Chameleonic Behavior of Macrocycles.

Conformational analysis is central to the design of bioactive molecules. It is particularly challenging for macrocycles due to noncovalent transannular interactions, steric interactions, and ring strain that are often coupled. Herein, we simulated the conformations of five macrocycles designed to express a progression of increasing complexity in environment-dependent intramolecular interactions and verified the results against NMR measurements in chloroform and dimethyl sulfoxide. Molecular dynamics using an explicit solvent model, but not the Monte Carlo method with implicit solvation, handled both solvents correctly. Refinement of conformations at the ab initio level was fundamental to reproducing the experimental observations─standard state-of-the-art molecular mechanics force fields were insufficient. Our simulations correctly predicted the intramolecular interactions between side chains and the macrocycle and revealed an unprecedented solvent-induced conformational switch of the macrocyclic ring. Our results provide a platform for the rational, prospective design of molecular chameleons that adapt to the properties of the environment.

Going Viral: An Investigation into the Chameleonic Behaviour of Antiviral Compounds.

The ability to adjust conformations in response to the polarity of the environment, i.e. molecular chameleonicity, is considered to be important for conferring both high aqueous solubility and high cell permeability to drugs in chemical space beyond Lipinski's rule of 5. We determined the conformational ensembles populated by the antiviral drugs asunaprevir, simeprevir, atazanavir and daclatasvir in polar (DMSO-d6 ) and non-polar (chloroform) environments with NMR spectroscopy. Daclatasvir was fairly rigid, whereas the first three showed large flexibility in both environments, that translated into major differences in solvent accessible 3D polar surface area within each conformational ensemble. No significant differences in size and polar surface area were observed between the DMSO-d6 and chloroform ensembles of these three drugs. We propose that such flexible compounds are characterized as "partial molecular chameleons" and hypothesize that their ability to adopt conformations with low polar surface area contributes to their membrane permeability and oral absorption.

Linker-Dependent Folding Rationalizes PROTAC Cell Permeability.

Proteolysis-targeting chimeras (PROTACs) must be cell permeable to reach their target proteins. This is challenging as the bivalent structure of PROTACs puts them in chemical space at, or beyond, the outer limits of oral druggable space. We used NMR spectroscopy and molecular dynamics (MD) simulations independently to gain insights into the origin of the differences in cell permeability displayed by three flexible cereblon PROTACs having closely related structures. Both methods revealed that the propensity of the PROTACs to adopt folded conformations with a low solvent-accessible 3D polar surface area in an apolar environment is correlated to high cell permeability. The chemical nature and the flexibility of the linker were essential for the PROTACs to populate folded conformations stabilized by intramolecular hydrogen bonds, π-π interactions, and van der Waals interactions. We conclude that MD simulations may be used for the prospective ranking of cell permeability in the design of cereblon PROTACs.

Enhancing preclinical drug discovery with artificial intelligence.

Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.

Identification of C5-NH2 Modified Oseltamivir Derivatives as Novel Influenza Neuraminidase Inhibitors with Highly Improved Antiviral Activities and Favorable Druggability.

Our previous efforts have proved that modifications targeting the 150-cavity of influenza neuraminidase can achieve more potent and more selective inhibitors. In this work, four subseries of C5-NH2 modified oseltamivir derivatives were designed and synthesized to explore every region inside the 150-cavity. Among them, compound 23d was exceptionally potent against the whole panel of Group-1 NAs with IC50 values ranging from 0.26 to 0.73 nM, being 15-53 times better than oseltamivir carboxylate (OSC) and 7-11 times better than zanamivir. In cellular assays, 23d showed more potent or equipotent antiviral activities against corresponding virus strains compared to OSC with no cytotoxicity. Furthermore, 23d exhibited high metabolic stability in human liver microsomes (HLM) and low inhibitory effect on main cytochrome P450 enzymes. Notably, 23d displayed favorable druggability in vivo and potent antiviral efficacy in the embryonated egg model and mice model. Overall, 23d appears to be a promising candidate for the treatment of influenza virus infection.

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.

Search, Identification, and Design of Effective Antiviral Drugs Against Pandemic Human Coronaviruses.

Recent coronavirus outbreaks of SARS-CoV-1 (2002-2003), MERS-CoV (since 2012), and SARS-CoV-2 (since the end of 2019) are examples of how viruses can damage health care and generate havoc all over the world. Coronavirus can spread quickly from person to person causing high morbidity and mortality. Unfortunately, the antiviral armamentarium is insufficient to fight these infections. In this chapter, we provide a detailed summary of the current situation in the development of drugs directed against pandemic human coronaviruses. Apart from the recently licensed remdesivir, other antiviral agents discussed in this review include molecules targeting viral components (e.g., RNA polymerase inhibitors, entry inhibitors, or protease inhibitors), compounds interfering with virus-host interactions, and drugs identified in large screening assays, effective against coronavirus replication, but with an uncertain mechanism of action.

Cell Permeability of Isomeric Macrocycles: Predictions and NMR Studies.

Conformation-dependent 3D descriptors have been shown to provide better predictions of the physicochemical properties of macrocycles than 2D descriptors. However, the computational identification of relevant conformations for macrocycles is nontrivial. Herein, we report that the Caco-2 cell permeability difference between a pair of diastereomeric macrocycles correlated with their solvent accessible 3D polar surface area and radius of gyration. The descriptors were calculated from the macrocycles' solution-phase conformational ensembles and independently from ensembles obtained by conformational sampling. Calculation of the two descriptors for three other stereo- and regioisomeric macrocycles also allowed the correct ranking of their cell permeability. Methods for conformational sampling may thus allow ranking of passive permeability for moderately flexible macrocycles, thereby contributing to the prioritization of macrocycles for synthesis in lead optimization.

Steering New Drug Discovery Campaigns: Permeability, Solubility, and Physicochemical Properties in the bRo5 Chemical Space.

An increasing number of drug discovery programs concern compounds in the beyond rule of 5 (bRo5) chemical space, such as cyclic peptides, macrocycles, and degraders. Recent results show that common paradigms of property-based drug design need revision to be applied to larger and more flexible compounds. A virtual event entitled "Solubility, permeability and physico-chemical properties in the bRo5 chemical space" was organized to provide preliminary guidance on how to make the discovery of oral drugs in the bRo5 space more effective. The four speakers emphasized the importance of the bRo5 space as a source of new oral drugs and provided examples of experimental and computational methods specifically tailored for design and optimization in this chemical space.

Solution Conformations Shed Light on PROTAC Cell Permeability.

Proteolysis targeting chimeras (PROTACs) induce intracellular degradation of target proteins. Their bifunctional structure puts degraders in a chemical space where ADME properties often complicate drug discovery. Herein we provide the first structural insight into PROTAC cell permeability obtained by NMR studies of a VHL-based PROTAC (1), which is cell permeable despite having a high molecular weight and polarity and a large number of rotatable bonds. We found that 1 populates elongated and polar conformations in solutions that mimic extra- and intracellular compartments. Conformations were folded and had a smaller polar surface area in chloroform, mimicking a cell membrane interior. Formation of intramolecular and nonclassical hydrogen bonds, π-π interactions, and shielding of amide groups from solvent all facilitate cell permeability by minimization of size and polarity. We conclude that molecular chameleonicity appears to be of major importance for 1 to enter into target cells.

Mining Natural Products for Macrocycles to Drug Difficult Targets.

Lead generation for difficult-to-drug targets that have large, featureless, and highly lipophilic or highly polar and/or flexible binding sites is highly challenging. Here, we describe how cores of macrocyclic natural products can serve as a high-quality in silico screening library that provides leads for difficult-to-drug targets. Two iterative rounds of docking of a carefully selected set of natural-product-derived cores led to the discovery of an uncharged macrocyclic inhibitor of the Keap1-Nrf2 protein-protein interaction, a particularly challenging target due to its highly polar binding site. The inhibitor displays cellular efficacy and is well-positioned for further optimization based on the structure of its complex with Keap1 and synthetic access. We believe that our work will spur interest in using macrocyclic cores for in silico-based lead generation and also inspire the design of future macrocycle screening collections.

Predicting the Permeability of Macrocycles from Conformational Sampling - Limitations of Molecular Flexibility.

Macrocycles constitute superior ligands for targets that have flat binding sites but often require long synthetic routes, emphasizing the need for property prediction prior to synthesis. We have investigated the scope and limitations of machine learning classification models and of regression models for predicting the cell permeability of a set of denovo-designed, drug-like macrocycles. 2D-Based classification models, which are fast to calculate, discriminated between macrocycles that had low-medium and high permeability and may be used as virtual filters in early drug discovery projects. Importantly, stereo- and regioisomer were correctly classified. QSPR studies of two small sets of comparator drugs suggested that use of 3D descriptors, calculated from biologically relevant conformations, would allow development of more precise regression models for late phase drug projects. However, a 3D permeability model could only be developed for a rigid series of macrocycles. Comparison of NMR based conformational analysis with in silico conformational sampling indicated that this shortcoming originates from the inability of the molecular mechanics force field to identify the relevant conformations for flexible macrocycles. We speculate that a Kier flexibility index of ≤10 constitutes a current upper limit for reasonably accurate 3D prediction of macrocycle cell permeability.

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.