SORC: an integrated spatial omics resource in cancer.

The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell-cell interactions in cancer. Herein, we have developed an integrated spatial omics resource in cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC), which interactively visualizes and analyzes the spatial transcriptomics data in cancer. We manually curated currently available spatial transcriptomics datasets for 17 types of cancer, comprising 722 899 spots across 269 slices. Furthermore, we matched reference single-cell RNA sequencing data in the majority of spatial transcriptomics datasets, involving 334 379 cells and 46 distinct cell types. SORC offers five major analytical modules that address the primary requirements of spatial transcriptomics analysis, including slice annotation, identification of spatially variable genes, co-occurrence of immune cells and tumor cells, functional analysis and cell-cell communications. All these spatial transcriptomics data and in-depth analyses have been integrated into easy-to-browse and explore pages, visualized through intuitive tables and various image formats. In summary, SORC serves as a valuable resource for providing an unprecedented spatially resolved cellular map of cancer and identifying specific genes and functional pathways to enhance our understanding of the tumor microenvironment.

Identification of steroid-induced osteonecrosis of the femoral head biomarkers based on immunization and animal experiments.

BACKGROUND: Steroid-induced osteonecrosis of femoral head (SONFH) is a severe health risk, and this study aims to identify immune-related biomarkers and pathways associated with the disease through bioinformatics analysis and animal experiments. METHOD: Using SONFH-related datasets obtained from the GEO database, we performed differential expression analysis and weighted gene co-expression network analysis (WGCNA) to extract SONFH-related genes. A protein-protein interaction (PPI) network was then constructed, and core sub-network genes were identified. Immune cell infiltration and clustering analysis of SONFH samples were performed to assess differences in immune cell populations. WGCNA analysis was used to identify module genes associated with immune cells, and hub genes were identified using machine learning. Internal and external validation along with animal experiments were conducted to confirm the differential expression of hub genes and infiltration of immune cells in SONFH. RESULTS: Differential expression analysis revealed 502 DEGs. WGCNA analysis identified a blue module closely related to SONFH, containing 1928 module genes. Intersection analysis between DEGs and blue module genes resulted in 453 intersecting genes. The PPI network and MCODE module identified 15 key targets enriched in various signaling pathways. Analysis of immune cell infiltration showed statistically significant differences in CD8 + t cells, monocytes, macrophages M2 and neutrophils between SONFH and control samples. Unsupervised clustering classified SONFH samples into two clusters (C1 and C2), which also exhibited significant differences in immune cell infiltration. The hub genes (ICAM1, NR3C1, and IKBKB) were further identified using WGCNA and machine learning analysis. Based on these hub genes, a clinical prediction model was constructed and validated internally and externally. Animal experiments confirmed the upregulation of hub genes in SONFH, with an associated increase in immune cell infiltration. CONCLUSION: This study identified ICAM1, NR3C1, and IKBKB as potential immune-related biomarkers involved in immune cell infiltration of CD8 + t cells, monocytes, macrophages M2, neutrophils and other immune cells in the pathogenesis of SONFH. These biomarkers act through modulation of the chemokine signaling pathway, Toll-like receptor signaling pathway, and other pathways. These findings provide valuable insights into the disease mechanism of SONFH and may aid in future drug development efforts.

Species differences in plasma protein binding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease inhibitor nirmatrelvir.

Plasma protein binding (PPB) studies on the SARS-CoV-2 main protease inhibitor nirmatrelvir revealed considerable species differences primarily in dog and rabbit, which prompted further investigations into the biochemical basis for these differences.The unbound fraction (fu) of nirmatrelvir in dog and rabbit plasma was concentration (2-200 µM)-dependent (dog fu,p 0.024-0.69, rabbit fu,p 0.010-0.82). Concentration (0.1-100 µM)-dependent binding in serum albumin (SA) (fu,SA 0.040-0.82) and alpha-1-acid glycoprotein (AAG) (fu,AAG 0.050-0.64) was observed in dogs. Nirmatrelvir showed minimal binding to rabbit SA (1-100 µM: fu,SA 0.70-0.79), while binding to rabbit AAG was concentration-dependent (0.1-100 µM: fu,AAG 0.024-0.66). In contrast, nirmatrelvir (2 µM) revealed minimal binding (fu,AAG 0.79-0.88) to AAG from rat and monkeys. Nirmatrelvir showed minimal-to-moderate binding to SA (1-100 µM; fu,SA 0.70-1.0) and AAG (0.1-100 µM; fu,AAG 0.48-0.58) from humans across tested concentrations.Nirmatrelvir molecular docking studies using published crystal structures and homology models of human and preclinical species SA and AAG were used to rationalise the species differences to plasma proteins. This suggested that species differences in PPB are primarily driven by molecular differences in albumin and AAG resulting in differences in binding affinity.

TorsionNet: A Deep Neural Network to Rapidly Predict Small-Molecule Torsional Energy Profiles with the Accuracy of Quantum Mechanics.

Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM) methods are limited by insufficient coverage of drug-like chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small-molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate compound library and leveraged massively parallel cloud computing resources for density functional theory (DFT) torsion scans of these fragments, generating a training data set of 1.2 million DFT energies. After training TorsionNet on this data set, we obtain a model that can rapidly predict the torsion energy profile of typical drug-like fragments with DFT-level accuracy. Importantly, our method also provides an uncertainty estimate for the predicted profiles without any additional calculations. In this report, we show that TorsionNet can accurately identify the preferred dihedral geometries observed in crystal structures. Our TorsionNet-based analysis of a diverse set of protein-ligand complexes with measured binding affinity shows a strong association between high ligand strain and low potency. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. TorsionNet500, a benchmark data set comprising 500 chemically diverse fragments with DFT torsion profiles (12k MM- and DFT-optimized geometries and energies), has been created and is made publicly available.

Ligand biological activity predicted by cleaning positive and negative chemical correlations.

Predicting ligand biological activity is a key challenge in drug discovery. Ligand-based statistical approaches are often hampered by noise due to undersampling: The number of molecules known to be active or inactive is vastly less than the number of possible chemical features that might determine binding. We derive a statistical framework inspired by random matrix theory and combine the framework with high-quality negative data to discover important chemical differences between active and inactive molecules by disentangling undersampling noise. Our model outperforms standard benchmarks when tested against a set of challenging retrospective tests. We prospectively apply our model to the human muscarinic acetylcholine receptor M1, finding four experimentally confirmed agonists that are chemically dissimilar to all known ligands. The hit rate of our model is significantly higher than the state of the art. Our model can be interpreted and visualized to offer chemical insights about the molecular motifs that are synergistic or antagonistic to M1 agonism, which we have prospectively experimentally verified.

Optimal designs for pairwise calculation: An application to free energy perturbation in minimizing prediction variability.

Pairwise-based methods such as the free energy perturbation (FEP) method have been widely deployed to compute the binding free energy differences between two similar host-guest complexes. The calculated pairwise free energy difference is either directly adopted or transformed to absolute binding free energy for molecule rank ordering. We investigated, through both analytic derivations and simulations, how the selection of pairs in the experiment could impact the overall prediction precision. Our studies showed that (1) the estimated absolute binding free energy ( ΔG^ ) derived from calculated pairwise differences (ΔΔG) through weighted least squares fitting is more precise in prediction than the pairwise difference values when the number of pairs is more than the number of ligands and (2) prediction precision is influenced by both the total number of pairs and the specifically selected pairs, the latter being critically important when the number of calculated pairs is limited. Furthermore, we applied optimal experimental design in pair selection and found that the optimally selected pairs can outperform randomly selected pairs in prediction precision. In an illustrative example, we showed that, upon weighing ligand structure similarity into design optimization, the weighted optimal designs are more efficient than the literature reported designs. This work provides a new approach to assess retrospective pairwise-based prediction results, and a method to design new prospective pairwise-based experiments for molecular lead optimization. © 2019 Wiley Periodicals, Inc.

Comprehensive Assessment of Torsional Strain in Crystal Structures of Small Molecules and Protein-Ligand Complexes using ab Initio Calculations.

The energetics of rotation around single bonds (torsions) is a key determinant of the three-dimensional shape that druglike molecules adopt in solution, the solid state, and in different biological environments, which in turn defines their unique physical and pharmacological properties. Therefore, accurate characterization of torsion angle preference and energetics is essential for the success of computational drug discovery and design. Here, we analyze torsional strain in crystal structures of druglike molecules in Cambridge structure database (CSD) and bioactive ligand conformations in protein data bank (PDB), expressing the total strain energy as a sum of strain energy from constituent rotatable bonds. We utilized cloud computing to generate torsion scan profiles of a very large collection of chemically diverse neutral fragments at DFT(B3LYP)/6-31G*//6-31G** or DFT(B3LYP)/6-31+G*//6-31+G** (for sulfur-containing molecule). With the data generated from these ab initio calculations, we performed rigorous analysis of strain due to deviation of observed torsion angles relative to their ideal gas-phase geometries. Contrary to the previous studies based on molecular mechanics, we find that in the crystalline state, molecules generally adopt low-strain conformations, with median per-torsion strain energy in CSD and PDB under one-tenth and one-third of a kcal/mol, respectively. However, for a small fraction (<5%) of motifs, external effects such as steric hindrance and hydrogen bonds result in strain penalty exceeding 2.5 kcal/mol. We find that due to poor quality of PDB structures in general, bioactive structures tend to have higher torsional strain compared to small-molecule crystal conformations. However, in the absence of structural fitting artifacts in PDB structures, protein-induced strain in bioactive conformations is quantitatively similar to those due to the packing forces in small-molecule crystal structures. This analysis allows us to establish strain energy thresholds to help identify biologically relevant conformers in a given ensemble. The work presented here is the most comprehensive study to date that demonstrates the utility and feasibility of gas-phase quantum mechanics (QM) calculations to study conformational preference and energetics of drug-size molecules. Potential applications of this study in computational lead discovery and structure-based design are discussed.

Fragment-based discovery of DNA gyrase inhibitors targeting the ATPase subunit of GyrB.

Inhibitors of the ATPase function of bacterial DNA gyrase, located in the GyrB subunit and its related ParE subunit in topoisomerase IV, have demonstrated antibacterial activity. In this study we describe an NMR fragment-based screening effort targeting Staphylococcus aureus GyrB that identified several attractive and novel starting points with good ligand efficiency. Fragment hits were further characterized using NMR binding studies against full-length S. aureus GyrB and Escherichia coli ParE. X-ray co-crystal structures of select fragment hits confirmed binding and suggested a path for medicinal chemistry optimization. The identification, characterization, and elaboration of one of these fragment series to a 0.265 µM inhibitor is described herein.

Advancing dentin remineralization: Exploring amorphous calcium phosphate and its stabilizers in biomimetic approaches.

OBJECTIVE: This review elucidates the mechanisms underpinning intrafibrillar mineralization, examines various amorphous calcium phosphate (ACP) stabilizers employed in dentin's intrafibrillar mineralization, and addresses the challenges encountered in clinical applications of ACP-based bioactive materials. METHODS: The literature search for this review was conducted using three electronic databases: PubMed, Web of Science, and Google Scholar, with specific keywords. Articles were selected based on inclusion and exclusion criteria, allowing for a detailed examination and summary of current research on dentin remineralization facilitated by ACP under the influence of various types of stabilizers. RESULTS: This review underscores the latest advancements in the role of ACP in promoting dentin remineralization, particularly intrafibrillar mineralization, under the regulation of various stabilizers. These stabilizers predominantly comprise non-collagenous proteins, their analogs, and polymers. Despite the diversity of stabilizers, the mechanisms they employ to enhance intrafibrillar remineralization are found to be interrelated, indicating multiple driving forces behind this process. However, challenges remain in effectively designing clinically viable products using stabilized ACP and maximizing intrafibrillar mineralization with limited materials in practical applications. SIGNIFICANCE: The role of ACP in remineralization has gained significant attention in dental research, with substantial progress made in the study of dentin biomimetic mineralization. Given ACP's instability without additives, the presence of ACP stabilizers is crucial for achieving in vitro intrafibrillar mineralization. However, there is a lack of comprehensive and exhaustive reviews on ACP bioactive materials under the regulation of stabilizers. A detailed summary of these stabilizers is also instrumental in better understanding the complex process of intrafibrillar mineralization. Compared to traditional remineralization methods, bioactive materials capable of regulating ACP stability and controlling release demonstrate immense potential in enhancing clinical treatment standards.

Efficacy and safety of acupuncture for painful diabetic neuropathy: a systematic review and meta-analysis.

Background: Painful diabetic neuropathy (PDN) is a common chronic neurological complication of diabetes mellitus. Medications are often used to relieve pain, but with significant side effects. Acupuncture is now a component of pragmatic and integrative treatment for PDN. An increasing number of relevant randomized controlled trials have been published in recent years, but a comprehensive meta-analysis has not yet been performed. The aim of this paper is to verify the effectiveness and safety of acupuncture for PDN by meta-analysis and trial sequential analysis (TSA). Methods: All participants in this study should have had a PDN diagnosis and the trial group was treated with acupuncture. Eight databases, including EMbase, PubMed, Web of science, Cochrane Library, China Biology Medicine disc (CBM), China National Knowledge Infrastructure (CNKI), Wanfang and Chongqing VIP (CQVIP) were retrieved from inception to 5 April 2023. Meta-analysis was conducted utilizing RevMan 5.3 and Stata 15.0. TSA was performed to assess the adequacy of sample size for the outcomes. Results: A total of 36 studies, comprising 2,739 PDN patients, were included. Among them, 1,393 patients were assigned to the trial group and 1,346 patients were treated in the control group. Outcomes covers the primary indicator Total effective rate (RR = 1.42, 95%CI [1.34, 1.52], p < 0.00001), with 21 studies reported, Pain intensity (SMD = -1.27, 95%CI [-1.58, -0.95], p < 0.00001), with 23 studies reported, and other outcomes, including motor nerve conduction velocity (MCV; MD = 3.58, 95%CI [2.77, 4.38], p < 0.00001), sensory nerve conduction velocity (SCV; MD = 3.62, 95%CI [2.75, 4.49], p < 0.00001), Depression score (SMD = -1.02, 95%CI [1.58, 0.46]), Toronto clinical scoring system (TCSS; MD = -2.41, 95%CI [-3.37, -1.45], p < 0.00001), Quality of life (SMD = 1.06, 95%CI [0.66, 1.46]), traditional Chinese medicine (TCM) syndrome score (MD = -4.99, 95%CI [-6.79, -3.18], p < 0.00001), suggesting that acupuncture have an ameliorating effect on PDN in various respect. Egger's test revealed publication bias for four outcomes. TSA showed that as for Total effective rate, Pain Intensity, MCV and SCV, the number of included studies was sufficient to support the conclusions. Conclusion: Acupuncture demonstrates significant effectiveness in improving PDN outcomes, including Total effective rate, Pain intensity, MCV, SCV, Depression score, TCSS, Quality of life, TCM syndrome score. But the Adverse events rate is no different in trail group and control group. The publication bias presented in Total effective rate, Pain intensity, MCV and SCV can be remedied by Trim and filling method. Systematic review registration: Prospero, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=477295.

Predictive Minisci late stage functionalization with transfer learning.

Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms.

Probing the chemical 'reactome' with high-throughput experimentation data.

High-throughput experimentation (HTE) has the potential to improve our understanding of organic chemistry by systematically interrogating reactivity across diverse chemical spaces. Notable bottlenecks include few publicly available large-scale datasets and the need for facile interpretation of these data's hidden chemical insights. Here we report the development of a high-throughput experimentation analyser, a robust and statistically rigorous framework, which is applicable to any HTE dataset regardless of size, scope or target reaction outcome, which yields interpretable correlations between starting material(s), reagents and outcomes. We improve the HTE data landscape with the disclosure of 39,000+ previously proprietary HTE reactions that cover a breadth of chemistry, including cross-coupling reactions and chiral salt resolutions. The high-throughput experimentation analyser was validated on cross-coupling and hydrogenation datasets, showcasing the elucidation of statistically significant hidden relationships between reaction components and outcomes, as well as highlighting areas of dataset bias and the specific reaction spaces that necessitate further investigation.

In vitro selection and analysis of SARS-CoV-2 nirmatrelvir resistance mutations contributing to clinical virus resistance surveillance.

To facilitate the detection and management of potential clinical antiviral resistance, in vitro selection of drug-resistant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) against the virus Mpro inhibitor nirmatrelvir (Paxlovid active component) was conducted. Six Mpro mutation patterns containing T304I alone or in combination with T21I, L50F, T135I, S144A, or A173V emerged, with A173V+T304I and T21I+S144A+T304I mutations showing >20-fold resistance each. Biochemical analyses indicated inhibition constant shifts aligned to antiviral results, with S144A and A173V each markedly reducing nirmatrelvir inhibition and Mpro activity. SARS-CoV-2 surveillance revealed that in vitro resistance-associated mutations from our studies and those reported in the literature were rarely detected in the Global Initiative on Sharing All Influenza Data database. In the Paxlovid Evaluation of Protease Inhibition for COVID-19 in High-Risk Patients trial, E166V was the only emergent resistance mutation, observed in three Paxlovid-treated patients, none of whom experienced COVID-19-related hospitalization or death.

Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation.

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein-ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.

A Second-Generation Oral SARS-CoV-2 Main Protease Inhibitor Clinical Candidate for the Treatment of COVID-19.

Despite the record-breaking discovery, development and approval of vaccines and antiviral therapeutics such as Paxlovid, coronavirus disease 2019 (COVID-19) remained the fourth leading cause of death in the world and third highest in the United States in 2022. Here, we report the discovery and characterization of PF-07817883, a second-generation, orally bioavailable, SARS-CoV-2 main protease inhibitor with improved metabolic stability versus nirmatrelvir, the antiviral component of the ritonavir-boosted therapy Paxlovid. We demonstrate the in vitro pan-human coronavirus antiviral activity and off-target selectivity profile of PF-07817883. PF-07817883 also demonstrated oral efficacy in a mouse-adapted SARS-CoV-2 model at plasma concentrations equivalent to nirmatrelvir. The preclinical in vivo pharmacokinetics and metabolism studies in human matrices are suggestive of improved oral pharmacokinetics for PF-07817883 in humans, relative to nirmatrelvir. In vitro inhibition/induction studies against major human drug metabolizing enzymes/transporters suggest a low potential for perpetrator drug-drug interactions upon single-agent use of PF-07817883.

Report of intersex individuals from a southeastern Chinese Choroterpes facialis (Gillies, 1951) population (Ephemeroptera: Leptophlebiidae).

Previously there were only three cases of mayfly gynandromorphism reported from Asia, occurring in the families Baetidae and Heptageniidae. Here, we report two intersex individuals of Choroterpes facialis (Gillies, 1951) (Ephemeroptera: Leptophlebiidae) from southeastern China. They have similar external morphologies (each having two different eyes, two shortened penes, and female sternum IX) but with one being predominately male and the other being predominately female (one with eggs in the abdomen, but the other apparently with sperm). We believe this to be the first report of a feminized male individual. This phenomenon implies their intersexuality is caused by some similar reasons, such as temperature or parasitism. Remarkably, two intersex specimens found among 1,736 normal individuals shows that gynandromorphism does occur rarely, and only six normal males in the sampling suggest the species C. facialis is partially parthenogenetic at least.

A new Neoleptophlebia Kluge, 1997 species from eastern China (Ephemeroptera: Leptophlebiidae).

The genus Neoleptophlebia Kluge, 1997 includes five Asian species. Three of them were reported from northeastern Asia and two were found from Chinese Taiwan Island, leaving a huge geographic gap on the Chinese mainland. Here we find a new one, which is named N. uncinata Zhou sp. nov., from Nanjing municipality, eastern China. Via field collecting and indoor rearing, all life stages were obtained, and its nymphs are found living in small creeks (with a width less than 1 m) and shallow waters (with a depth less than 30 cm). Diagnostically, the imago of this new species has larger lateral penial appendages than its congeners, and its nymph has subequal broadened segments II and III of maxillary and labial palpi. Biogeographically, this species bridges two northern and southern groups of the genus.

Structural basis of the acyl-transfer mechanism of human GPAT1.

Glycerol-3-phosphate acyltransferase (GPAT)1 is a mitochondrial outer membrane protein that catalyzes the first step of de novo glycerolipid biosynthesis. Hepatic expression of GPAT1 is linked to liver fat accumulation and the severity of nonalcoholic fatty liver diseases. Here we present the cryo-EM structures of human GPAT1 in substrate analog-bound and product-bound states. The structures reveal an N-terminal acyltransferase domain that harbors important catalytic motifs and a tightly associated C-terminal domain that is critical for proper protein folding. Unexpectedly, GPAT1 has no transmembrane regions as previously proposed but instead associates with the membrane via an amphipathic surface patch and an N-terminal loop-helix region that contains a mitochondrial-targeting signal. Combined structural, computational and functional studies uncover a hydrophobic pathway within GPAT1 for lipid trafficking. The results presented herein lay a framework for rational inhibitor development for GPAT1.

Discovery of the Potent and Selective MC4R Antagonist PF-07258669 for the Potential Treatment of Appetite Loss.

The melanocortin-4 receptor (MC4R) is a centrally expressed, class A GPCR that plays a key role in the regulation of appetite and food intake. Deficiencies in MC4R signaling result in hyperphagia and increased body mass in humans. Antagonism of MC4R signaling has the potential to mitigate decreased appetite and body weight loss in the setting of anorexia or cachexia due to underlying disease. Herein, we report on the identification of a series of orally bioavailable, small-molecule MC4R antagonists using a focused hit identification effort and the optimization of these antagonists to provide clinical candidate 23. Introduction of a spirocyclic conformational constraint allowed for simultaneous optimization of MC4R potency and ADME attributes while avoiding the production of hERG active metabolites observed in early series leads. Compound 23 is a potent and selective MC4R antagonist with robust efficacy in an aged rat model of cachexia and has progressed into clinical trials.

Characterization of Field-Evolved Resistance to Afidopyropen, a Novel Insecticidal Toxin Developed from Microbial Secondary Metabolites, in Bemisia tabaci.

Afidopyropen, a newly identified chemical, is a derivative of pyripyropene A, which is produced by the filamentous fungus Penicillium coprobium. It is a promising novel pesticide applied against whiteflies in agriculture. In this study, the reversion and selection, cross-resistance patterns, synergistic effects, and fitness costs of afidopyropen resistance were studied in a field-developed resistant population of B. tabaci. Compared to a reference MED-S strain, the field-developed resistant Haidian (HD) population showed 36.5-fold resistance to afidopyropen. Significant reversion of resistance to afidopyropen was found in the HD population when it was kept with no selective pressure of the insecticide. The HD-Afi strain, developed from the HD population with afidopyropen pressure, developed 104.3-fold resistance to afidopyropen and significant cross-resistance to sulfoxaflor. Piperonyl butoxide (PBO) largely inhibited afidopyropen resistance in the HD-Afi strain, which indicates that P450 monooxygenase could be involved in the resistance. Significant fitness costs associated with afidopyropen resistance were observed in HD-Afi. This study indicates that a rotation of afidopyropen with other chemical control agents could be useful for impeding afidopyropen resistance in B. tabaci. In addition, we expanded upon the understanding of resistance to afidopyropen, offering evidence suggesting the importance of devising better strategies for the management of whiteflies.