Linking ATP and allosteric sites to achieve superadditive binding with bivalent EGFR kinase inhibitors

Bivalent molecules consisting of groups connected through bridging linkers often exhibit strong target binding and unique biological effects. However, developing bivalent inhibitors with the desired activity is challenging due to the dual motif architecture of these molecules and the variability that can be introduced through differing linker structures and geometries. We report a set of alternatively linked bivalent EGFR inhibitors that simultaneously occupy the ATP substrate and allosteric pockets. Crystal structures show that initial and redesigned linkers bridging a trisubstituted imidazole ATP-site inhibitor and dibenzodiazepinone allosteric-site inhibitor proved successful in spanning these sites. The re-engineered linker yielded a compound that exhibited significantly higher potency (~60 pM) against the drug-resistant EGFR L858R/T790M and L858R/T790M/C797S, which was superadditive as compared with the parent molecules. The enhanced potency is attributed to factors stemming from the linker connection to the allosteric-site group and informs strategies to engineer linkers in bivalent agent design.

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning

Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in late-stage functionalization, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4-5%, while the reactivity of novel reactions with known and unknown substrates was classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured with a classifier F-score of 67%. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and electronic information on model performance was quantified, and a comprehensive simple user-friendly reaction format was introduced that proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation for late-stage functionalization.

A synthetic agent ameliorates polycystic kidney disease by promoting apoptosis of cystic cells through increased oxidative stress

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic cause of chronic kidney disease and the fourth leading cause of end-stage kidney disease, accounting for over 50% of prevalent cases requiring renal replacement therapy. There is a pressing need for improved therapy for ADPKD. Recent insights into the pathophysiology of ADPKD revealed that cyst cells undergo metabolic changes that up-regulate aerobic glycolysis in lieu of mitochondrial respiration for energy production, a process that ostensibly fuels their increased proliferation. The present work leverages this metabolic disruption as a way to selectively target cyst cells for apoptosis. This small-molecule therapeutic strategy utilizes 11beta-dichloro, a repurposed DNA-damaging anti-tumor agent that induces apoptosis by exacerbating mitochondrial oxidative stress. Here, we demonstrate that 11beta-dichloro is effective in delaying cyst growth and its associated inflammatory and fibrotic events, thus preserving kidney function in perinatal and adult mouse models of ADPKD. In both models, the cyst cells with homozygous inactivation of Pkd1 show enhanced oxidative stress following treatment with 11beta-dichloro and undergo apoptosis. Co-administration of the antioxidant vitamin E negated the therapeutic benefit of 11beta-dichloro in vivo, supporting the conclusion that oxidative stress is a key component of the mechanism of action. As a preclinical development primer, we also synthesized and tested an 11beta-dichloro derivative that cannot directly alkylate DNA, while retaining pro-oxidant features. This derivative nonetheless maintains excellent anti-cystic properties in vivo and emerges as the lead candidate for development.

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Identification of Novel EGFR Inhibitors for the Targeted Therapy of Colorectal Cancer Using Pharmacophore Modelling, Docking, Molecular Dynamic Simulation and Biological Activity Prediction

BACKGROUND

Colorectal cancer (CRC) is considered the second deadliest cancer in the world. One of the reasons for the occurrence of this cancer is the deregulation of the Epidermal Growth Factor Receptor (EGFR), which plays a critical role in regulating cell division, persistence, differentiation, and migration. The overexpression of the EGFR protein leads to its dysregulation and causes CRC.

OBJECTIVE

Hence, this work aims to identify and validate novel EGFR inhibitors for the treatment of colorectal cancer employing various computer aided techniques such as pharmacophore modeling, docking, molecular dynamic simulation and Quantitative Structure-Activity Relationship (QSAR) analysis.

METHODS

In this work, a shared-featured ligand-based pharmacophore model was generated using the known inhibitors of EGFR. The best model was validated and screened against ZincPharmer and Maybridge databases, and 143 hits were obtained. Pharmacokinetic and toxicological properties of these hits were studied, and the acceptable ligands were docked against EGFR. The best five protein-ligand complexes with binding energy less than -5 kcal/mol were selected. The molecular dynamic simulation studies of these complexes were conducted for 100 nanoseconds (ns), and the results were analyzed. The biological activity of this ligand was calculated using QSAR analysis.

RESULTS

The best complex with Root Mean Square Deviation (RMSD) 3.429 A0 and Radius of Gyration (RoG) 20.181 A0 was selected. The Root Mean Square Fluctuations (RMSF) results were also found to be satisfactory. The biological activity of this ligand was found to be 1.38 µM.

CONCLUSION

This work hereby proposes the ligand 2-((1,6-dimethyl-4-oxo-1,4-dihydropyridin-3-yl)oxy)-N- (1H-indol-4-yl)acetamide as a potential EGFR inhibitor for the treatment of colorectal cancer. The wet lab analysis must be conducted, however, to confirm this hypothesis.

The Structure-property Relationships of Clinically Approved Protease Inhibitors

BACKGROUND

Proteases play important roles in the regulation of many physiological processes, and protease inhibitors have become one of the important drug classes. Especially because the development of protease inhibitors often starts from a substrate- based peptidomimetic strategy, many of the initial lead compounds suffer from pharmacokinetic liabilities.

OBJECTIVE

To reduce drug attrition rates, drug metabolism and pharmacokinetics studies are fully integrated into modern drug discovery research, and the structure-property relationship illustrates how the modification of the chemical structure influences the pharmacokinetic and toxicological properties of drug compounds. Understanding the structure- property relationships of clinically approved protease inhibitor drugs and their analogues could provide useful information on the lead-to-candidate optimization strategies.

METHODS

About 70 inhibitors against human or pathogenic viral proteases have been approved until the end of 2021. In this review, 17 inhibitors are chosen for the structure- property relationship analysis because detailed pharmacological and/or physicochemical data have been disclosed in the medicinal chemistry literature for these inhibitors and their close analogues.

RESULTS

The compiled data are analyzed primarily focusing on the pharmacokinetic or toxicological deficiencies found in lead compounds and the structural modification strategies used to generate candidate compounds.

CONCLUSION

The structure-property relationships hereby summarized how the overall druglike properties could be successfully improved by modifying the structure of protease inhibitors. These specific examples are expected to serve as useful references and guidance for developing new protease inhibitor drugs in the future.

Structure-property Relationships Reported for the New Drugs Approved in 2022

BACKGROUND

The structure-property relationship illustrates how modifying the chemical structure of a pharmaceutical compound influences its absorption, distribution, metabolism, excretion, and other related properties. Understanding structure-property relationships of clinically approved drugs could provide useful information for drug design and optimization strategies.

METHOD

Among new drugs approved around the world in 2022, including 37 in the US, structure- property relationships of seven drugs were compiled from medicinal chemistry literature, in which detailed pharmacokinetic and/or physicochemical properties were disclosed not only for the final drug but also for its key analogues generated during drug development.

RESULTS

The discovery campaigns for these seven drugs demonstrate extensive design and optimization efforts to identify suitable candidates for clinical development. Several strategies have been successfully employed, such as attaching a solubilizing group, bioisosteric replacement, and deuterium incorporation, resulting in new compounds with enhanced physicochemical and pharmacokinetic properties.

CONCLUSION

The structure-property relationships hereby summarized illustrate how proper structural modifications could successfully improve the overall drug-like properties. The structure-property relationships of clinically approved drugs are expected to continue to provide valuable references and guides for the development of future drugs.

compounds for PTP1B inhibition in diabetes treatment: in silico molecular docking and dynamics simulation

Protein tyrosine phosphatase 1B (PTP1B) has been identified as a promising drug target for the development of diabetes medications via an inhibition mechanism. Using a computational approach, this study investigates the binding mechanism of lead optimized natural compounds from Allium sativum against the human PTP1B. The molecular docking, induced-fit docking, and binding free energy calculations were analyzed using Schrödinger Suite 2021-2. MD simulation, and gene enrichment analysis was achieved via the Desmond module of Schrödinger to identify best compounds as inhibitors against PTP1B in diabetes management. The docking scores of the lead optimized compounds were good; 5280443_121 from apigenin had the best binding score of -9.345 kcal/mol, followed by 5280443_129 with a binding score of -9.200 kcal/mol, and 5280863_177 from kaempferol had a binding score of -8.528 kcal/mol, followed by 5280863_462 with a binding score of -8.338 kcal/mol. The top two lead optimized compounds, docked better than the standard PTP1B inhibitor (-7.155 kcal/mol), suggesting them as potent inhibitors than the standard PTP1B inhibitor. The outcomes of the induced-fit docking were consistent with the increased binding affinity used in the Glide computation of the five conformed poses between the derivatives (5280443_121, 5280443_129, 5280863_177, and 5280863_462) and the protein (PTP1B). Based on the binding fee energies (MM-GBSA), the lead optimized compounds from kaempferol exhibited more stability than those from apigenin. In the pharmacophore development, all the models exhibit good results across the different metrics. The best performing model with five of five matches on a 1.34 and 1.33 phase score was DDRRR_1, DDRRR_2, and DDDRR_1. The average BEDROC value (= 160.9) was 1, while the average EF 1% value across all models was 101. There were no substantial conformational modifications during the MD simulation process, indicating that the apigenin derivatives (5280443_121) was stable in the protein's active site in 100 ns. IGF1R, EGFR, INSR, PTPN1, SRC, JAK2, GRB2, BCAR1, and IRS1 are among the 11 potential targets found in the protein-protein interaction (PPI) of A. sativum against PTP1B that may be important in A. sativum's defense against PTP1B. Sixty-four (64) pathways were found by KEGG pathway enrichment analysis to be potentially involved in the anti-PTP1B of A. sativum. Consequently, data obtained indicates the effectiveness of the in silico studies in identifying potential lead compounds in A. sativum against PTP1B target.Communicated by Ramaswamy H. Sarma.

A peptide derived from interleukin-10 exhibits potential anticancer activity and can facilitate cell targeting of gold nanoparticles loaded with anticancer therapeutics

Human interleukin-10 (IL-10) is an immunosuppressive and anti-inflammatory cytokine, and its expression is upregulated in tumor tissues and serum samples of patients with various cancers. Because of its immunosuppressive nature, IL-10 has also been suggested to be a factor leading to tumor cells' evasion of immune surveillance and clearance by the host immune system. In this study, we refined a peptide with 20 amino acids, named NK20a, derived from the binding region of IL-10 on the basis of in silico analysis of the complex structure of IL-10 with IL-10Ra, the ligand binding subunit of the IL-10 receptor. The binding ability of the peptide was confirmed through in vitro biophysical biolayer interferometry and cellular experiments. The IL-10 inhibitory peptide exerted anticancer effects on lymphoma B cells and could abolish the suppression effect of IL-10 on macrophages. NK20a was also conjugated with gold nanoparticles to target the chemotherapeutic 5-fluorouracil (5-FU)-loaded nanoparticles to enhance the anticancer efficacy of 5-FU against the breast cancer cell line BT-474. Our study demonstrated that NK20a designed in silico with improved binding affinity to the IL-10 receptor can be used as a tool in developing anticancer strategies.

Modern Alchemical Free Energy Methods for Drug Discovery Explained

This Perspective provides a contextual explanation of the current state-of-the-art alchemical free energy methods and their role in drug discovery as well as highlights select emerging technologies. The narrative attempts to answer basic questions about what goes on "under the hood" in free energy simulations and provide general guidelines for how to run simulations and analyze the results. It is the hope that this work will provide a valuable introduction to students and scientists in the field.

Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening

Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp3-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening.

Lead optimization study on indoline-2,3-dione derivatives as potential fatty acid amide hydrolase inhibitors

Based on the known isatin-based fatty acid amide hydrolase (FAAH) inhibitor BSS-7, we designed and synthesized two small sets (6-13 and 17-20) of N-1 and C-3 substituted isatin derivatives and evaluated them for their in vitro FAAH inhibition properties. The lead simplification by modification of bulky aryl moiety at N-1 with a flexible allyl group produced a nanomolar (IC50 = 6.7 nM, Ki = 5 nM) inhibitor 11 (Z)-3-((1H-benzo[d]imidazol-2-yl)imino)-1-allylindolin-2-one which exhibited a reversible and competitive FAAH inhibition with 1500 times more potency to BSS-7 (1.49 ± 0.03 µM). The lead compound 11 also showed a high blood-brain permeability and a significant antioxidant profile with no neurotoxicity. Docking results suggested that the inhibitor molecules occupied the active site of FAAH and offered optimal binding interactions. A molecular dynamics simulation study ascertained the stability of the lead inhibitor 11-FAAH complex. In silico ADMET profiling studies unveiled that compound 11 possesses good drug-like properties and merits further evaluation.Communicated by Ramaswamy H. Sarma.

Computing the relative binding affinity of ligands based on a pairwise binding comparison network

Structure-based lead optimization is an open challenge in drug discovery, which is still largely driven by hypotheses and depends on the experience of medicinal chemists. Here we propose a pairwise binding comparison network (PBCNet) based on a physics-informed graph attention mechanism, specifically tailored for ranking the relative binding affinity among congeneric ligands. Benchmarking on two held-out sets (provided by Schrödinger and Merck) containing over 460 ligands and 16 targets, PBCNet demonstrated substantial advantages in terms of both prediction accuracy and computational efficiency. Equipped with a fine-tuning operation, the performance of PBCNet reaches that of Schrödinger's FEP+, which is much more computationally intensive and requires substantial expert intervention. A further simulation-based experiment showed that active learning-optimized PBCNet may accelerate lead optimization campaigns by 473%. Finally, for the convenience of users, a web service for PBCNet is established to facilitate complex relative binding affinity prediction through an easy-to-operate graphical interface.

All-round catalytic and atroposelective strategy via dynamic kinetic resolution for N-/2-/3-arylindoles

As the complexity of organic molecules utilized by mankind increases, the phenomenon of atropisomerism is more frequently encountered. While a variety of well-established methods enable the control of a stereogenic center, a catalytic method for controlling a stereogenic axis in one substrate is typically unavailable for controlling axial chirality in other substrates with a similar structure. Herein, we report o-amidobiaryl as a flexible platform for chiral phosphoric acid-catalyzed atroposelective dynamic kinetic resolution. To demonstrate our strategy, three distinct types of arylindoles were utilized and reacted intermolecularly with ketomalonate in the presence of chiral phosphoric acid. An investigation of 46 substrates having an aromatic ring in different positions yields the desired products with excellent enantioselectivities. Computational investigation into the origin of enantioselectivity highlights the importance of the NH group. Given the biological significance of indoles, antiproliferative effects have been investigated; our scaffold exhibits good efficacy in this regard.

Stand Up to Stand Out: Natural Dietary Polyphenols Curcumin, Resveratrol, and Gossypol as Potential Therapeutic Candidates against Severe Acute Respiratory Syndrome Coronavirus 2 Infection

The COVID-19 pandemic has stimulated collaborative drug discovery efforts in academia and the industry with the aim of developing therapies and vaccines that target SARS-CoV-2. Several novel therapies have been approved and deployed in the last three years. However, their clinical application has revealed limitations due to the rapid emergence of viral variants. Therefore, the development of next-generation SARS-CoV-2 therapeutic agents with a high potency and safety profile remains a high priority for global health. Increasing awareness of the "back to nature" approach for improving human health has prompted renewed interest in natural products, especially dietary polyphenols, as an additional therapeutic strategy to treat SARS-CoV-2 patients, owing to its good safety profile, exceptional nutritional value, health-promoting benefits (including potential antiviral properties), affordability, and availability. Herein, we describe the biological properties and pleiotropic molecular mechanisms of dietary polyphenols curcumin, resveratrol, and gossypol as inhibitors against SARS-CoV-2 and its variants as observed in in vitro and in vivo studies. Based on the advantages and disadvantages of dietary polyphenols and to obtain maximal benefits, several strategies such as nanotechnology (e.g., curcumin-incorporated nanofibrous membranes with antibacterial-antiviral ability), lead optimization (e.g., a methylated analog of curcumin), combination therapies (e.g., a specific combination of plant extracts and micronutrients), and broad-spectrum activities (e.g., gossypol broadly inhibits coronaviruses) have also been emphasized as positive factors in the facilitation of anti-SARS-CoV-2 drug development to support effective long-term pandemic management and control.

Substituted salicylic acid analogs offer improved potency against multidrug-resistant Neisseria gonorrhoeae and good selectivity against commensal vaginal bacteria

Drug-resistant Neisseria gonorrhoeae represents a major threat to public health; without new effective antibiotics, untreatable gonococcal infections loom as a real possibility. In a previous drug-repurposing study, we reported that salicylic acid had good potency against azithromycin-resistant N. gonorrhoeae. We now report that the anti-gonococcal activity in this scaffold is easily lost by inopportune substitution, but that select substituted naphthyl analogs (3b, 3o and 3p) have superior activity to salicylic acid itself. Furthermore, these compounds retained potency against multiple ceftriaxone- and azithromycin-resistant strains, exhibited rapid bactericidal activity against N. gonorrhoeae, and showed high tolerability to mammalian cells (CC50 > 128 µg/mL). Promisingly, these compounds also show very weak growth inhibition of commensal vaginal bacteria.

In silico targeting of colony-stimulating factor-1 receptor: delineating immunotherapy in cancer

Aim

Delineate structure-based inhibition of colony-stimulating factor-1 receptor (CSF1R) by small molecule CSF1R inhibitors in clinical development for target identification and potential lead optimization in cancer therapeutics since CSF1R is a novel predictive biomarker for immunotherapy in cancer.

Methods

Compounds were in silico modelled by induced fit docking protocol in a molecular operating environment (MOE, MOE.v.2015). The 3-dimensional (3D) X-ray crystallized structure of CSF1R kinase (Protein Databank, ID 4R7H) was obtained from Research Collaboratory for Structural Bioinformatics (RSCB) Protein Databank. The 3D conformers of edicotinib, DCC-3014, ARRY-382, BLZ-945, chiauranib, dovitinib, and sorafenib were obtained from PubChem Database. These structures were modelled in Amber10:EHT molecular force field, and quick prep application was used to correct and optimize the structures for missing residues, H-counts, termini capping, and alternates. The binding site was defined within the vicinity of the co-crystallized ligand of CSF1R kinase. The compounds were docked by the triangular matcher placement method and ranked by the London dG scoring function. The docked poses were further refined by the induced fit method. The pose with the lowest binding score (ΔG) was used to model the ligand interaction profile in Discovery Studio Visualizer v17.2. The co-crystallized ligand was docked in its apo conformation, and root-mean-square deviation was computed to validate the docking protocol.

Results

All 7 CSF1R inhibitors interact with residue Met637 exhibiting selectivity except for edicotinib. The inhibitors maintain CSF1R in an auto-inhibitory conformation by interacting with Asp797 of the Asp-Phe-Gly (DFG) motif and/or hindering the conserved salt bridge formed between Glu633 and Lys616 thus stabilizing the activation loop, or interacting with tryptophan residue (Trp550) in the juxtamembrane domain. DCC-3014, ARRY-382, BLZ-945, and sorafenib bind with the lowest binding energy with CSF1R kinase.

Conclusions

Pyrimidines are potent inhibitors that interact with CSF1R residues. DCC-3014 and ARRY-382 exhibit exceptional pharmaceutical potential exhibiting great structural stability and affinity.

Lenalidomide derivatives and proteolysis-targeting chimeras for controlling neosubstrate degradation

Lenalidomide, an immunomodulatory drug (IMiD), is commonly used as a first-line therapy in many haematological cancers, such as multiple myeloma (MM) and 5q myelodysplastic syndromes (5q MDS), and it functions as a molecular glue for the protein degradation of neosubstrates by CRL4CRBN. Proteolysis-targeting chimeras (PROTACs) using IMiDs with a target protein binder also induce the degradation of target proteins. The targeted protein degradation (TPD) of neosubstrates is crucial for IMiD therapy. However, current IMiDs and IMiD-based PROTACs also break down neosubstrates involved in embryonic development and disease progression. Here, we show that 6-position modifications of lenalidomide are essential for controlling neosubstrate selectivity; 6-fluoro lenalidomide induced the selective degradation of IKZF1, IKZF3, and CK1α, which are involved in anti-haematological cancer activity, and showed stronger anti-proliferative effects on MM and 5q MDS cell lines than lenalidomide. PROTACs using these lenalidomide derivatives for BET proteins induce the selective degradation of BET proteins with the same neosubstrate selectivity. PROTACs also exert anti-proliferative effects in all examined cell lines. Thus, 6-position-modified lenalidomide is a key molecule for selective TPD using thalidomide derivatives and PROTACs.

The Therapeutic Potential of Natural Dietary Flavonoids against SARS-CoV-2 Infection

The exploration of non-toxic and cost-effective dietary components, such as epigallocatechin 3-gallate and myricetin, for health improvement and disease treatment has recently attracted substantial research attention. The recent COVID-19 pandemic has provided a unique opportunity for the investigation and identification of dietary components capable of treating viral infections, as well as gathering the evidence needed to address the major challenges presented by public health emergencies. Dietary components hold great potential as a starting point for further drug development for the treatment and prevention of SARS-CoV-2 infection owing to their good safety, broad-spectrum antiviral activities, and multi-organ protective capacity. Here, we review current knowledge of the characteristics-chemical composition, bioactive properties, and putative mechanisms of action-of natural bioactive dietary flavonoids with the potential for targeting SARS-CoV-2 and its variants. Notably, we present promising strategies (combination therapy, lead optimization, and drug delivery) to overcome the inherent deficiencies of natural dietary flavonoids, such as limited bioavailability and poor stability.

Development and Characterization of Novel Selective, Non-Basic Dopamine D2 Receptor Antagonists for the Treatment of Schizophrenia

The dopamine D₂ receptor, which belongs to the family of G protein-coupled receptors (GPCR), is an important and well-validated drug target in the field of medicinal chemistry due to its wide distribution, particularly in the central nervous system, and involvement in the pathomechanism of many disorders thereof. Schizophrenia is one of the most frequent diseases associated with disorders in dopaminergic neurotransmission, and in which the D₂ receptor is the main target for the drugs used. In this work, we aimed at discovering new selective D₂ receptor antagonists with potential antipsychotic activity. Twenty-three compounds were synthesized, based on the scaffold represented by the D2AAK2 compound, which was discovered by our group. This compound is an interesting example of a D₂ receptor ligand because of its non-classical binding to this target. Radioligand binding assays and SAR analysis indicated structural modifications of D2AAK2 that are possible to maintain its activity. These findings were further rationalized using molecular modeling. Three active derivatives were identified as D₂ receptor antagonists in cAMP signaling assays, and the selected most active compound 17 was subjected to X-ray studies to investigate its stable conformation in the solid state. Finally, effects of 17 assessed in animal models confirmed its antipsychotic activity in vivo.

Rational Design of Highly Potent and Selective Covalent MAP2K7 Inhibitors

The mitogen-activated protein kinase signaling cascade is conserved across eukaryotes, where it plays a critical role in the regulation of activities including proliferation, differentiation, and stress responses. This pathway propagates external stimuli through a series of phosphorylation events, which allows external signals to influence metabolic and transcriptional activities. Within the cascade, MEK, or MAP2K, enzymes occupy a molecular crossroads immediately upstream to significant signal divergence and cross-talk. One such kinase, MAP2K7, also known as MEK7 and MKK7, is a protein of great interest in the molecular pathophysiology underlying pediatric T cell acute lymphoblastic leukemia (T-ALL). Herein, we describe the rational design, synthesis, evaluation, and optimization of a novel class of irreversible MAP2K7 inhibitors. With a streamlined one-pot synthesis, favorable in vitro potency and selectivity, and promising cellular activity, this novel class of compounds wields promise as a powerful tool in the study of pediatric T-ALL.

Discovery of diarylpyrimidine derivatives bearing piperazine sulfonyl as potent HIV-1 nonnucleoside reverse transcriptase inhibitors

HIV-1 reverse transcriptase is one of the most attractive targets for the treatment of AIDS. However, the rapid emergence of drug-resistant strains and unsatisfactory drug-like properties seriously limit the clinical application of HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs). Here we show that a series of piperazine sulfonyl-bearing diarylpyrimidine-based NNRTIs were designed to improve the potency against wild-type and NNRTI-resistant strains by enhancing backbone-binding interactions. Among them, compound 18b1 demonstrates single-digit nanomolar potency against the wild-type and five mutant HIV-1 strains, which is significantly better than the approved drug etravirine. The co-crystal structure analysis and molecular dynamics simulation studies were conducted to explain the broad-spectrum inhibitory activity of 18b1 against reverse transcriptase variants. Besides, compound 18b1 demonstrates improved water solubility, cytochrome P450 liability, and other pharmacokinetic properties compared to the currently approved diarylpyrimidine (DAPY) NNRTIs. Therefore, we consider compound 18b1 a potential lead compound worthy of further study.

Artefenomel Regioisomer RLA-3107 Is a Promising Lead for the Discovery of Next-Generation Endoperoxide Antimalarials

Clinical development of the antimalarial artefenomel was recently halted due to formulation challenges stemming from the drug's lipophilicity and low aqueous solubility. The symmetry of organic molecules is known to influence crystal packing energies and by extension solubility and dissolution rates. Here we evaluate RLA-3107, a desymmetrized, regioisomeric form of artefenomel in vitro and in vivo, finding that the regioisomer retains potent antiplasmodial activity while offering improved human microsome stability and aqueous solubility as compared to artefenomel. We also report in vivo efficacy data for artefenomel and its regioisomer across 12 different dosing regimens.

Recent Advances in Alchemical Binding Free Energy Calculations for Drug Discovery

Rigorous physics-based methods to calculate binding free energies of protein-ligand complexes have become a valued component of structure-based drug design. Relative and absolute binding free energy calculations have been deployed prospectively in support of solving diverse drug discovery challenges. Here we review recent applications of binding free energy calculations to fragment growing and linking, scaffold hopping, binding pose validation, virtual screening, covalent enzyme inhibition, and positional analogue scanning. Furthermore, we discuss the merits of using protein models and highlight recent efforts to replace costly binding free energy calculations with predictions from machine learning models trained on a limited number of free energy perturbation or thermodynamic integration calculations thereby allowing for extended chemical space exploration.

Lead optimization, pharmacophore development and scaffold design of protein kinase CK2 inhibitors as potential COVID-19 therapeutics

Therapeutic agents being designed against COVID-19 have targeted either the virus directly or the host cellular machinery. A particularly attractive host target is the ubiquitous and constitutively active serine-threonine kinase, Protein kinase CK2 (CK2). CK2 enhances viral protein synthesis by inhibiting the sequestration of host translational machinery as stress granules and assists in viral egression via association with the N-protein at filopodial protrusions of the infected cell. CK2 inhibitors such as Silmitasertib have been proposed as possible therapeutic candidates in COVID-19 infections. The present study aims to optimize Silmitasertib, develop pharmacophore models and design unique scaffolds to modulate CK2. The lead optimization phase involved the generation of compounds structurally similar to Silmitasertib via bioisostere replacement followed by a multi-stage docking approach to identify drug-like candidates. Molecular dynamics (MD) simulations were performed for two promising candidates (ZINC-43206125 and PC-57664175) to estimate their binding stability and interaction. Top scoring candidates from the lead optimization phase were utilized to build ligand-based pharmacophore models. These models were then merged with structure-based pharmacophores (e-pharmacophores) to build a hybrid hypothesis. This hybrid hypothesis was validated against a decoy set and used to screen a diverse kinase inhibitors library to identify favored chemical features in the retrieved actives. These chemical features include; an anion, an aromatic ring and an H-bond acceptor. Based on the knowledge of these features; de-novo scaffold design was carried out which identified phenindiones, carboxylated steroids, macrocycles and peptides as novel scaffolds with the potential to modulate CK2.Communicated by Ramaswamy H. Sarma.

Corrigendum: Assessing potency and binding kinetics of soluble adenylyl cyclase (sAC) inhibitors to maximize therapeutic potential

[This corrects the article DOI: 10.3389/fphys.2022.1013845.].

[2]-Ladderanes as isosteres for meta-substituted aromatic rings and rigidified cyclohexanes

Aromatic ring isosteres and rigidified saturated hydrocarbons are important motifs to enable drug discovery. Herein we disclose [2]-ladderanes as a class of meta-substituted aromatic ring isosteres and rigidified cyclohexanes. A straightforward synthesis of the building blocks is presented along with representative derivatization. Preliminary studies reveal that the [2]-ladderanes offer similar metabolic and physicochemical properties thus establishing this class of molecules as interesting motifs.

Assessing potency and binding kinetics of soluble adenylyl cyclase (sAC) inhibitors to maximize therapeutic potential

In mammalian cells, 10 different adenylyl cyclases produce the ubiquitous second messenger, cyclic adenosine monophosphate (cAMP). Amongst these cAMP-generating enzymes, bicarbonate (HCO3 -)-regulated soluble adenylyl cyclase (sAC; ADCY10) is uniquely essential in sperm for reproduction. For this reason, sAC has been proposed as a potential therapeutic target for non-hormonal contraceptives for men. Here, we describe key sAC-focused in vitro assays to identify and characterize sAC inhibitors for therapeutic use. The affinity and binding kinetics of an inhibitor can greatly influence in vivo efficacy, therefore, we developed improved assays for assessing these efficacy defining features.

Recent advances in structural modification strategies for lead optimization of tyrosine kinase inhibitors to explore novel anticancer agents

Lead optimization as a bottleneck in the process of drug discovery is conducted to tackle problems associated with poor pharmacokinetics, continuous emergence of drug-resistance, adverse side effects and drug-drug interactions of known pharmaceuticals. Due to the intensive application of multi-targeted tyrosine kinase inhibitors (MTKI) in various pathological conditions, optimization of their structures has always been the focus of intensive medicinal chemistry research efforts. Current review portrays the application of scaffold hopping, bioisosterism, structure-based, and hybrid-based drug design methods in optimization of lead compounds aiming to enhance their usefulness as novel drugs. Then, the review proceeds with examples of structural modifications carried out, particularly, on multi-targeted drugs already available on the market. The demonstrated examples cover structural modifications on 7 well known drugs during last twenty years where the application of above mentioned strategies has led to generation of 52 new multi-targeted tyrosine kinase inhibitors. Most of the optimized compounds showed improved properties compared to their parent lead compound. The rationales behind the applied modifications and the achieved outcomes were discussed with the hope of presenting practical examples to the researchers engaged in the area.

Evaluating the use of absolute binding free energy in the fragment optimisation process

Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely N_wat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.

Synthesis of aryldifluoromethyl aryl ethers via nickel-catalyzed suzuki cross-coupling between aryloxydifluoromethyl bromides and boronic acids

As a unique organofluorine fragment, gem-difluoromethylated motifs have received widespread attention. Here, a convenient and efficient synthesis of aryldifluoromethyl aryl ethers (ArCF₂OAr') was established via Nickel-catalyzed aryloxydifluoromethylation with arylboronic acids. This approach features easily accessible starting materials, good tolerance of functionalities, and mild reaction conditions. Diverse late-stage difluoromethylation of many pharmaceuticals and natural products were readily realized. Notably, a new difluoromethylated PD-1/PD-L1 immune checkpoint inhibitor was conveniently synthesized and showed both improved metabolic stability and enhanced antitumor efficacy. Preliminary mechanistic studies suggested the involvement of a Ni(I/III) catalytic cycle.

Development of New Potential Inhibitors of β1 Integrins through In Silico Methods-Screening and Computational Validation

Integrins are transmembrane receptors that play a critical role in many biological processes which can be therapeutically modulated using integrin blockers, such as peptidomimetic ligands. This work aimed to develop new potential β1 integrin antagonists using modeled receptors based on the aligned crystallographic structures and docked with three lead compounds (BIO1211, BIO5192, and TCS2314), widely known as α4β1 antagonists. Lead-compound complex optimization was performed by keeping intact the carboxylate moiety of the ligand, adding substituents in two other regions of the molecule to increase the affinity with the target. Additionally, pharmacokinetic predictions were performed for the ten best ligands generated, with the lowest docking interaction energy obtained for α4β1 and BIO5192. Results revealed an essential salt bridge between the BIO5192 carboxylate group and the Mg²⁺ MIDAS ion of the integrin. We then generated more than 200 new BIO5192 derivatives, some with a greater predicted affinity to α4β1. Furthermore, the significance of retaining the pyrrolidine core of the ligand and increasing the therapeutic potential of the new compounds is emphasized. Finally, one novel molecule (1592) was identified as a potential drug candidate, with appropriate pharmacokinetic profiles, similar dynamic behavior at the integrin interaction site compared with BIO5192, and a higher predicted affinity to VLA-4.

Conversion of a False Virtual Screen Hit into Selective JAK2 JH2 Domain Binders Using Convergent Design Strategies

The Janus kinase 2 (JAK2) pseudokinase domain (JH2) is an ATP-binding domain that regulates the activity of the catalytic tyrosine kinase domain (JH1). Dysregulation of JAK2 JH1 signaling caused by the V617F mutation in JH2 is implicated in various myeloproliferative neoplasms. To explore if JAK2 activity can be modulated by a small molecule binding to the ATP site in JH2, we have developed several ligand series aimed at selectively targeting the JAK2 JH2 domain. We report here the evolution of a false virtual screen hit into a new JAK2 JH2 series. Optimization guided by computational modeling has yielded analogues with nanomolar affinity for the JAK2 JH2 domain and >100-fold selectivity for the JH2 domain over the JH1 domain. A crystal structure for one of the potent compounds bound to JAK2 JH2 clarifies the origins of the strong binding and selectivity. The compounds expand the platform for seeking molecules to regulate JAK2 signaling, including V617F JAK2 hyperactivation.

Alchemical Design of Pharmacological Chaperones with Higher Affinity for Phenylalanine Hydroxylase

Phenylketonuria (PKU) is a rare metabolic disease caused by variations in a human gene, PAH, encoding phenylalanine hydroxylase (PAH), and the enzyme converting the essential amino acid phenylalanine into tyrosine. Many PKU-causing variations compromise the conformational stability of the encoded enzyme, decreasing or abolishing its catalytic activity, and leading to an elevated concentration of phenylalanine in the blood, which is neurotoxic. Several therapeutic approaches have been developed to treat the more severe manifestations of the disorder, but they are either not entirely effective or difficult to adhere to throughout life. In a search for novel pharmacological chaperones to treat PKU, a lead compound was discovered (compound IV) that exhibited promising in vitro and in vivo chaperoning activity on PAH. The structure of the PAH-IV complex has been reported. Here, using alchemical free energy calculations (AFEC) on the structure of the PAH-IV complex, we design a new generation of compound IV-analogues with a higher affinity for the enzyme. Seventeen novel analogues were synthesized, and thermal shift and isothermal titration calorimetry (ITC) assays were performed to experimentally evaluate their stabilizing effect and their affinity for the enzyme. Most of the new derivatives bind to PAH tighter than lead compound IV and induce a greater thermostabilization of the enzyme upon binding. Importantly, the correspondence between the calculated alchemical binding free energies and the experimentally determined ΔΔG_b values is excellent, which supports the use of AFEC to design pharmacological chaperones to treat PKU using the X-ray structure of their complexes with the target PAH enzyme.

Second-Generation Prostaglandin Receptor EP2 Antagonist, TG8-260, with High Potency, Selectivity, Oral Bioavailability, and Anti-Inflammatory Properties

EP2, a G-protein-coupled prostaglandin-E₂ receptor, has emerged as a seminal biological target for drug discovery. EP2 receptor activation is typically proinflammatory; therefore, the development of EP2 antagonists to mitigate the severity and disease pathology in a variety of inflammation-driven central nervous system and peripheral disorders would be a novel strategy. We have recently developed a second-generation EP2 antagonist TG8-260 and shown that it reduces hippocampal neuroinflammation and gliosis after pilocarpine-induced status epilepticus in rats. Here, we present details of synthesis, lead optimization on earlier leads that resulted in TG8-260, potency and selectivity evaluations using cAMP-driven time-resolved fluorescence resonance energy-transfer (TR-FRET) assays and [H³]-PGE2-binding assays, absorption, distribution, metabolism, and excretion (ADME), and pharmacokinetics. TG8-260 (2f) showed Schild K _B = 13.2 nM (3.6-fold more potent than the previous lead TG8-69 (1c)) and 500-fold selectivity to EP2 against other prostanoid receptors. Pharmacokinetic data indicated that TG8-260 has a plasma half-life of 2.14 h (PO) and excellent oral bioavailability (77.3%). Extensive ADME tests indicated that TG8-260 is a potent inhibitor of CYP450 enzymes. Further, we show that TG8-260 displays antagonistic activity on the induction of EP2 receptor-mediated inflammatory gene expression in microglia BV2-hEP2 cells; therefore, it can serve as a tool for investigating anti-inflammatory pathways in peripheral inflammatory disease animal models.

Erratum: Assessing potency and binding kinetics of soluble adenylyl cyclase (sAC) inhibitors to maximize therapeutic potential

[This corrects the article DOI: 10.3389/fphys.2022.1013845.].

Chagas Disease Drug Discovery in Latin America-A Mini Review of Antiparasitic Agents Explored Between 2010 and 2021

Chagas disease is a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi that endangers almost 70 million people worldwide. The only two drugs that are currently approved for its treatment, benznidazole and nifurtimox, have controversial efficacy in adults and restricting safety issues, leaving thousands of patients without a suitable treatment. The neglect of Chagas disease is further illustrated by the lack of a robust and diverse drug discovery and development portfolio of new chemical entities, and it is of paramount importance to build a strong research and development network for antichagasic drugs. Focusing on drug discovery programs led by scientists based in Latin America, the main endemic region for this disease, we discuss herein what has been published in the last decade in terms of identification of new antiparasitic drugs to treat Chagas disease, shining a spotlight on the origin, chemical diversity, level of characterization of hits, and strategies used for optimization of lead compounds. Finally, we identify strengths and weaknesses in these drug discovery campaigns and highlight the importance of multidisciplinary collaboration and knowledge sharing.

Investigation of NPB Analogs That Target Phosphorylation of BAD-Ser99 in Human Mammary Carcinoma Cells

The design and development of a small molecule named NPB [3-{(4(2,3-dichlorophenyl)piperazin-1-yl}{2-hydroxyphenyl)methyl}-N-cyclopentylbenzamide], which specifically inhibited the phosphorylation of BAD at Ser99 in human carcinoma cells has been previously reported. Herein, the synthesis, characterization, and effect on cancer cell viability of NPB analogs, and the single-crystal X-ray crystallographic studies of an example compound (4r), which was grown via slow-solvent evaporation technique is reported. Screening for loss of viability in mammary carcinoma cells revealed that compounds such as 2[(4(2,3-dichlorophenyl)piperazin-1-yl][naphthalen-1-yl]methyl)phenol (4e), 5[(4(2,3-dichlorophenyl)piperazin-1-yl][2-hydroxyphenyl)methyl)uran-2-carbaldehyde (4f), 3[(2-hydroxyphenyl][4(p-tolyl)piperazin-1-yl)methyl)benzaldehyde (4i), and NPB inhibited the viability of MCF-7 cells with IC₅₀ values of 5.90, 3.11, 7.68, and 6.5 µM, respectively. The loss of cell viability was enhanced by the NPB analogs synthesized by adding newer rings such as naphthalene and furan-2-carbaldehyde in place of N-cyclopentyl-benzamide of NPB. Furthermore, these compounds decreased Ser99 phosphorylation of hBAD. Additional in silico density functional theory calculations suggested possibilities for other analogs of NPB that may be more suitable for further development.

Novel Marine Secondary Metabolites Worthy of Development as Anticancer Agents: A Review

Secondary metabolites from marine sources have a wide range of biological activity. Marine natural products are promising candidates for lead pharmacological compounds to treat diseases that plague humans, including cancer. Cancer is a life-threatening disorder that has been difficult to overcome. It is a long-term illness that affects both young and old people. In recent years, significant attempts have been made to identify new anticancer drugs, as the existing drugs have been useless due to resistance of the malignant cells. Natural products derived from marine sources have been tested for their anticancer activity using a variety of cancer cell lines derived from humans and other sources, some of which have already been approved for clinical use, while some others are still being tested. These compounds can assault cancer cells via a variety of mechanisms, but certain cancer cells are resistant to them. As a result, the goal of this review was to look into the anticancer potential of marine natural products or their derivatives that were isolated from January 2019 to March 2020, in cancer cell lines, with a focus on the class and type of isolated compounds, source and location of isolation, cancer cell line type, and potency (IC₅₀ values) of the isolated compounds that could be a guide for drug development.

GSK-3 Inhibitors as New Leads to Treat Type-II Diabetes

In India as well as globally, diabetes is in a state of high risk and next to cardiovascular disease. As per the WHO, the risk of diabetes is expected to rise about 511 million by 2030. In quest of novel targets for type-2 diabetes, many targets were elucidated, such as Glycogen Synthase Kinase-3 (GSK-3), Dipeptidyl Peptidase (DPP-IV), PPAR-γ, α-Glucosidase, α-Amylase, GLP-1, and SGLT. Among the targets, GSK-3 was reported to be an effective target for the treatment of diabetes. In the metabolism of glycogen, GSK is a regulatory enzyme for the biosynthesis of glycogen (glycogenesis). It catalyzes the synthesis of a linear unbranched molecule with 1,4-α-glycosidic linkages. GSK-3 family has two isoenzymes, namely α and β, which differ in their Nand C- terminal sequences and are semi-conservative multifunctional serine/threonine kinase enzymes. In this chapter, we discuss an overview of general diabetic mechanisms and how GSK-3 modulation may influence these processes. Mutation in the GSK-3 complex causes diabetes. Synthetic and natural scaffolds modulate GSK-3 against diabetes and leading to its optimization for the development of GSK-3 inhibitors. This review mainly focuses on the development of GSK-3 inhibitors and highlights current and potential future therapeutic approaches that support the notion of targeting glucose metabolism with novel antidiabetic agents.

Optimization of Triarylpyridinone Inhibitors of the Main Protease of SARS-CoV-2 to Low-Nanomolar Antiviral Potency

Non-covalent inhibitors of the main protease (Mpro) of SARS-CoV-2 having a pyridinone core were previously reported with IC50 values as low as 0.018 μM for inhibition of enzymatic activity and EC50 values as low as 0.8 μM for inhibition of viral replication in Vero E6 cells. The series has now been further advanced by consideration of placement of substituted five-membered-ring heterocycles in the S4 pocket of Mpro and N-methylation of a uracil ring. Free energy perturbation calculations provided guidance on the choice of the heterocycles, and protein crystallography confirmed the desired S4 placement. Here we report inhibitors with EC50 values as low as 0.080 μM, while remdesivir yields values of 0.5-2 μM in side-by-side testing with infectious SARS-CoV-2. A key factor in the improvement is enhanced cell permeability, as reflected in PAMPA measurements. Compounds 19 and 21 are particularly promising as potential therapies for COVID-19, featuring IC50 values of 0.044-0.061 μM, EC50 values of ca. 0.1 μM, good aqueous solubility, and no cytotoxicity.

MolOpt: A Web Server for Drug Design using Bioisosteric Transformation

BACKGROUND

Bioisosteric replacement is widely used in drug design for lead optimization. However, the identification of a suitable bioisosteric group is not an easy task.

METHODS

In this work, we present MolOpt, a web server for in silico drug design using bioisosteric transformation. Potential bioisosteric transformation rules were derived from data mining, deep generative machine learning and similarity comparison. MolOpt tries to assist the medicinal chemist in his/her search for what to make next.

RESULTS AND DISCUSSION

By replacing molecular substructures with similar chemical groups, MolOpt automatically generates lists of analogues. MolOpt also evaluates forty important pharmacokinetic and toxic properties for each newly designed molecule. The transformed analogues can be assessed for possible future study.

CONCLUSION

MolOpt is useful for the identification of suitable lead optimization ideas. The MolOpt Server is freely available for use on the web at http://xundrug.cn/molopt.

Semisynthetic modifications of antitubercular lanostane triterpenoids from Ganoderma

Antitubercular lanostane triterpenoids isolated from mycelial cultures of the basidiomycete Ganoderma australe were structurally modified by semisynthesis. One of the synthetic compounds, named GA003 (9), showed more potent activity against Mycobacterium tuberculosis H37Ra than the lead natural lanostane (1). GA003 was also significantly active against the virulent strain (H37Rv) as well as extensively drug-resistant tuberculosis strains.

A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria

Multiple different screening tests for candidate leads in drug development may often yield conflicting or ambiguous results, sometimes making the selection of leads a nontrivial maximum-likelihood ranking problem. Here, we employ methods from the field of multiple criteria decision making (MCDM) to the problem of screening candidate antibody therapeutics. We employ the SMAA-TOPSIS method to rank a large cohort of antibodies using up to eight weighted screening criteria, in order to find lead candidate therapeutics for Alzheimer's disease, and determine their robustness to both uncertainty in screening measurements, as well as uncertainty in the user-defined weights of importance attributed to each screening criterion. To choose lead candidates and measure the confidence in their ranking, we propose two new quantities, the Retention Probability and the Topness, as robust measures for ranking. This method may enable more systematic screening of candidate therapeutics when it becomes difficult intuitively to process multi-variate screening data that distinguishes candidates, so that additional candidates may be exposed as potential leads, increasing the likelihood of success in downstream clinical trials. The method properly identifies true positives and true negatives from synthetic data, its predictions correlate well with known clinically approved antibodies vs. those still in trials, and it allows for ranking analyses using antibody developability profiles in the literature. We provide a webserver where users can apply the method to their own data: http://bjork.phas.ubc.ca.

Development of potent and selective inhibitors targeting the papain-like protease of SARS-CoV-2

The COVID-19 pandemic has been disastrous to society and effective drugs are urgently needed. The papain-like protease domain (PLpro) of SARS-CoV-2 (SCoV2) is indispensable for viral replication and represents a putative target for pharmacological intervention. In this work, we describe the development of a potent and selective SCoV2 PLpro inhibitor, 19. The inhibitor not only effectively blocks substrate cleavage and immunosuppressive function imparted by PLpro, but also markedly mitigates SCoV2 replication in human cells, with a submicromolar IC₅₀. We further present a convenient and sensitive activity probe, 7, and complementary assays to readily evaluate SCoV2 PLpro inhibitors in vitro or in cells. In addition, we disclose the co-crystal structure of SCoV2 PLpro in complex with a prototype inhibitor, which illuminates their detailed binding mode. Overall, these findings provide promising leads and important tools for drug discovery aiming to target SCoV2 PLpro.

In vivo uptake of antisense oligonucleotide drugs predicted by ab initio quantum mechanical calculations

Liver and kidney uptake and antisense activity is studied for a series of Locked Nucleic Acid (LNA) oligonucleotides with fully stereo-defined, internucleoside linkages. These stereo-specific phosphorothioates are made with a newly developed synthesis method and are being analyzed both theoretically and experimentally. Their structures are obtained theoretically by using many-body Schrödinger equations applied to a group of 11 stereo-defined LNA antisense oligonucleotides selected for biological experiments. The fully converged electronic structures were obtained from ab initio quantum calculations providing the specific electronic structures. One important result was the observation that the calculated electronic structure, represented by the iso-surface area of the electron density in Å2, correlated linearly with LNA oligonucleotide uptake in the liver and kidney. This study also shows that more complex biological phenomena, such as drug activity, will require more molecular and cellular identifiers than used here before a correlation can be found. Establishing biological correlations between quantum mechanical (QM) calculated structures and antisense oligonucleotides is novel, and this method may constitute new tools in drug discovery.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

BACKGROUND

Enhancing a compound's biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process.

METHODS

Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold.

RESULTS

Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563).

CONCLUSION

An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Structure-Guided Discovery of Silicon-Containing Subnanomolar Inhibitor of Hydroxyphenylpyruvate Dioxygenase as a Potential Herbicide

4-Hydroxyphenylpyruvate dioxygenase (HPPD, EC 1.13.11.27) has been recognized as one of the most promising targets in the field of herbicide innovation considering the severity of weed resistance currently. In a persistent effort to develop effective HPPD-inhibiting herbicides, a structure-guided strategy was carried out to perform the structural optimization for triketone-quinazoline-2,4-diones, a novel HPPD inhibitor scaffold first discovered in our lab. Herein, starting from the crystal structure of Arabidopsis thaliana (At)HPPD complexed with 6-(2-hydroxy-6-oxocyclohex-1-ene-1-carbonyl)-1,5-dimethyl-3-(o-tolyl)quinazoline-2,4(1H,3H)-dione (MBQ), three subseries of quinazoline-2,4-dione derivatives were designed and prepared by optimizing the hydrophobic interactions between the side chain of the core structure at the R¹ position and the hydrophobic pocket at the active site entrance of AtHPPD. 6-(2-Hydroxy-6-oxocyclohex-1-ene-1-carbonyl)-1,5-dimethyl-3-(3-(trimethylsilyl)prop-2-yn-1-yl)quinazoline-2,4(1H,3H)-dione (60) with the best inhibitory activity against AtHPPD was identified to be the first subnanomolar-range AtHPPD inhibitor (K_i = 0.86 nM), which significantly outperformed that of the lead compound MBQ (K_i = 8.2 nM). Further determination of the crystal structure of AtHPPD in complex with compound 60 (1.85 Å) and the binding energy calculation provided a molecular basis for the understanding of its high efficiency. Additionally, the greenhouse assay indicated that 6-(2-hydroxy-6-oxocyclohex-1-ene-1-carbonyl)-1,5-dimethyl-3-propylquinazoline-2,4(1H,3H)-dione (28) and compound 60 showed acceptable crop safety against peanut and good herbicidal activity with a broad spectrum. Moreover, compound 28 also showed superior selectivity for wheat at the dosage of 120 g ai/ha and favorable herbicidal efficacy toward the gramineous weeds at the dosage of as low as 30 g ai/ha. We believe that compounds 28 and 60 have promising prospects as new herbicide candidates for wheat and peanut fields.

QSAR-assisted-MMPA to expand chemical transformation space for lead optimization

Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.