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.

Antigen-presenting autoreactive B cells activate regulatory T cells and suppress autoimmune arthritis in mice.

B cells undergo several rounds of selection to eliminate potentially pathogenic autoreactive clones, but in contrast to T cells, evidence of positive selection of autoreactive B cells remains moot. Using unique tetramers, we traced natural autoreactive B cells (C1-B) specific for a defined triple-helical epitope on collagen type-II (COL2), constituting a sizeable fraction of the physiological B cell repertoire in mice, rats, and humans. Adoptive transfer of C1-B suppressed arthritis independently of IL10, separating them from IL10-secreting regulatory B cells. Single-cell sequencing revealed an antigen processing and presentation signature, including induced expression of CD72 and CCR7 as surface markers. C1-B presented COL2 to T cells and induced the expansion of regulatory T cells in a contact-dependent manner. CD72 blockade impeded this effect suggesting a new downstream suppressor mechanism that regulates antigen-specific T cell tolerization. Thus, our results indicate that autoreactive antigen-specific naïve B cells tolerize infiltrating T cells against self-antigens to impede the development of tissue-specific autoimmune inflammation.

Therapy targeting antigen-specific T cells by a peptide-based tolerizing vaccine against autoimmune arthritis.

A longstanding goal has been to find an antigen-specific preventive therapy, i.e., a vaccine, for autoimmune diseases. It has been difficult to find safe ways to steer the targeting of natural regulatory antigen. Here, we show that the administration of exogenous mouse major histocompatibility complex class II protein bounding a unique galactosylated collagen type II (COL2) peptide (Aq-galCOL2) directly interacts with the antigen-specific TCR through a positively charged tag. This leads to expanding a VISTA-positive nonconventional regulatory T cells, resulting in a potent dominant suppressive effect and protection against arthritis in mice. The therapeutic effect is dominant and tissue specific as the suppression can be transferred with regulatory T cells, which downregulate various autoimmune arthritis models including antibody-induced arthritis. Thus, the tolerogenic approach described here may be a promising dominant antigen-specific therapy for rheumatoid arthritis, and in principle, for autoimmune diseases in general.

Structure-based virtual screening discovers potent and selective adenosine A1 receptor antagonists.

Development of subtype-selective leads is essential in drug discovery campaigns targeting G protein-coupled receptors (GPCRs). Herein, a structure-based virtual screening approach to rationally design subtype-selective ligands was applied to the A1 and A2A adenosine receptors (A1R and A2AR). Crystal structures of these closely related subtypes revealed a non-conserved subpocket in the binding sites that could be exploited to identify A1R selective ligands. A library of 4.6 million compounds was screened computationally against both receptors using molecular docking and 20 A1R selective ligands were predicted. Of these, seven antagonized the A1R with micromolar activities and several compounds displayed slight selectivity for this subtype. Twenty-seven analogs of two discovered scaffolds were designed, resulting in antagonists with nanomolar potency and up to 76-fold A1R-selectivity. Our results show the potential of structure-based virtual screening to guide discovery and optimization of subtype-selective ligands, which could facilitate the development of safer drugs.

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.

Design of Drug Efficacy Guided by Free Energy Simulations of the ß2 -Adrenoceptor.

G-protein-coupled receptors (GPCRs) play important roles in physiological processes and are modulated by drugs that either activate or block signaling. Rational design of the pharmacological efficacy profiles of GPCR ligands could enable the development of more efficient drugs, but is challenging even if high-resolution receptor structures are available. We performed molecular dynamics simulations of the ß2 adrenergic receptor in active and inactive conformations to assess if binding free energy calculations can predict differences in ligand efficacy for closely related compounds. Previously identified ligands were successfully classified into groups with comparable efficacy profiles based on the calculated shift in ligand affinity upon activation. A series of ligands were then predicted and synthesized, leading to the discovery of partial agonists with nanomolar potencies and novel scaffolds. Our results demonstrate that free energy simulations enable design of ligand efficacy and the same approach can be applied to other GPCR drug targets.

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.

Conformational Sampling Deciphers the Chameleonic Properties of a VHL-Based Degrader.

Chameleonicity (the capacity of a molecule to adapt its conformations to the environment) may help to identify orally bioavailable drugs in the beyond-Rule-of-5 chemical space. Computational methods to predict the chameleonic behaviour of degraders have not yet been reported and the identification of molecular chameleons still relies on experimental evidence. Therefore, there is a need to tune predictions with experimental data. Here, we employ PROTAC-1 (a passively cell-permeable degrader), for which NMR and physicochemical data prove the chameleonic behaviour, to benchmark the capacity of two conformational sampling algorithms and selection schemes. To characterize the conformational ensembles in both polar and nonpolar environments, we compute three molecular properties proven to be essential for cell permeability: conformer shape (radius of gyration), polarity (3D PSA), and the number of intramolecular hydrogen bonds. Energetic criteria were also considered. Infographics monitored the simultaneous variation of those properties in computed and NMR conformers. Overall, we provide key points for tuning conformational sampling tools to reproduce PROTAC-1 chameleonicity according to NMR evidence. This study is expected to improve the design of PROTAC drugs and the development of computational sustainable strategies to exploit the potential of new modalities in drug discovery.

Autoantibodies to Disease-Related Proteins in Joints as Novel Biomarkers for the Diagnosis of Rheumatoid Arthritis.

OBJECTIVE: This study was undertaken to develop and characterize a multiplex immunoassay for detection of autoantibodies against peptides derived from proteins known to play a role in development of arthritis and that are also expressed in joints. METHODS: We selected peptides from the human counterpart of proteins expressed in the joints, based on mouse models that showed these to be targeted by pathogenic or regulatory antibodies in vivo. Using bead-based flow immunoassays measuring IgG antibodies, we selected triple helical or cyclic peptides, containing the epitopes, to avoid collinear reactivity. We characterized the analytical performance of the immunoassay and then validated it in 3 independent rheumatoid arthritis (RA) cohorts (n = 2,110), Swedish age- and sex-matched healthy controls, and patients with osteoarthritis (OA), patients with psoriatic arthritis (PsA), and patients with systemic lupus erythematosus (SLE). RESULTS: Screening assays showed 5 peptide antigens that discriminated RA patients from healthy controls with 99% specificity (95% confidence interval [CI] 98-100%). In our validation studies, we reproduced the discriminatory capacity of the autoantibodies in 2 other RA cohorts, showing that the autoantibodies had high discriminatory capacity for RA versus OA, PsA, and SLE. The novel biomarkers identified 22.5% (95% CI 19-26%) of early RA patients seronegative for anti-cyclic citrullinated peptide and rheumatoid factor. The usefulness of the biomarkers in identifying seronegative RA patients was confirmed in validation studies using 2 independent cohorts of RA patients and cohorts of patients with OA, PsA, and SLE. CONCLUSION: A multiplex immunoassay with peptides from disease-related proteins in joints was found to be useful for detection of specific autoantibodies in RA serum. Of note, this immunoassay had high discriminatory capacity for early seronegative RA.

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.

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.

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.

Ultralarge Virtual Screening Identifies SARS-CoV-2 Main Protease Inhibitors with Broad-Spectrum Activity against Coronaviruses.

Drugs targeting SARS-CoV-2 could have saved millions of lives during the COVID-19 pandemic, and it is now crucial to develop inhibitors of coronavirus replication in preparation for future outbreaks. We explored two virtual screening strategies to find inhibitors of the SARS-CoV-2 main protease in ultralarge chemical libraries. First, structure-based docking was used to screen a diverse library of 235 million virtual compounds against the active site. One hundred top-ranked compounds were tested in binding and enzymatic assays. Second, a fragment discovered by crystallographic screening was optimized guided by docking of millions of elaborated molecules and experimental testing of 93 compounds. Three inhibitors were identified in the first library screen, and five of the selected fragment elaborations showed inhibitory effects. Crystal structures of target-inhibitor complexes confirmed docking predictions and guided hit-to-lead optimization, resulting in a noncovalent main protease inhibitor with nanomolar affinity, a promising in vitro pharmacokinetic profile, and broad-spectrum antiviral effect in infected cells.

Importance of Binding Site Hydration and Flexibility Revealed When Optimizing a Macrocyclic Inhibitor of the Keap1-Nrf2 Protein-Protein Interaction.

Upregulation of the transcription factor Nrf2 by inhibition of the interaction with its negative regulator Keap1 constitutes an opportunity for the treatment of disease caused by oxidative stress. We report a structurally unique series of nanomolar Keap1 inhibitors obtained from a natural product-derived macrocyclic lead. Initial exploration of the structure-activity relationship of the lead, followed by structure-guided optimization, resulted in a 100-fold improvement in inhibitory potency. The macrocyclic core of the nanomolar inhibitors positions three pharmacophore units for productive interactions with key residues of Keap1, including R415, R483, and Y572. Ligand optimization resulted in the displacement of a coordinated water molecule from the Keap1 binding site and a significantly altered thermodynamic profile. In addition, minor reorganizations of R415 and R483 were accompanied by major differences in affinity between ligands. This study therefore indicates the importance of accounting both for the hydration and flexibility of the Keap1 binding site when designing high-affinity ligands.

Key interactions in the trimolecular complex consisting of the rheumatoid arthritis-associated DRB1*04:01 molecule, the major glycosylated collagen II peptide and the T-cell receptor.

OBJECTIVES: Rheumatoid arthritis (RA) is an autoimmune disease strongly associated with the major histocompatibility complex (MHC) class II allele DRB1*04:01, which encodes a protein that binds self-peptides for presentation to T cells. This study characterises the autoantigen-presenting function of DRB1*04:01 (HLA-DRA*01:01/HLA-DRB1*04:01) at a molecular level for prototypic T-cell determinants, focusing on a post-translationally modified collagen type II (Col2)-derived peptide. METHODS: The crystal structures of DRB1*04:01 molecules in complex with the peptides HSP70289-306, citrullinated CILP982-996 and galactosylated Col2259-273 were determined on cocrystallisation. T cells specific for Col2259-273 were investigated in peripheral blood mononuclear cells from patients with DRB1*04:01-positive RA by cytofluorometric detection of the activation marker CD154 on peptide stimulation and binding of fluorescent DRB1*0401/Col2259-273 tetramer complexes. The cDNAs encoding the T-cell receptor (TCR) α-chains and ß-chains were cloned from single-cell sorted tetramer-positive T cells and transferred via a lentiviral vector into TCR-deficient Jurkat 76 cells. RESULTS: The crystal structures identified peptide binding to DRB1*04:01 and potential side chain exposure to T cells. The main TCR recognition sites in Col2259-273 were lysine residues that can be galactosylated. RA T-cell responses to DRB1*04:01-presented Col2259-273 were dependent on peptide galactosylation at lysine 264. Dynamic molecular modelling of a functionally characterised Col2259-273-specific TCR complexed with DRB1*04:01/Col2259-273 provided evidence for differential allosteric T-cell recognition of glycosylated lysine 264. CONCLUSIONS: The MHC-peptide-TCR interactions elucidated in our study provide new molecular insights into recognition of a post-translationally modified RA T-cell determinant with a known dominant role in arthritogenic and tolerogenic responses in murine Col2-induced arthritis.

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.