Transformation of a Dopamine D2 Receptor Agonist to Partial Agonists as Novel Antipsychotic Agents.

Designed ligands of G protein-coupled receptors can exert a spectrum of modulating effects, varying from full agonists and partial agonists to antagonists and inverse agonists. For the dopamine D2 receptor (D2R), partial agonist activity is the pharmacological feature of the third-generation antipsychotics, including aripiprazole, brexpiprazole, and cariprazine. Started from a benzofuran-derived D2R full agonist O4LE6 (4), which was identified using a structure-based method by us in previous studies, a series of D2R partial agonists were designed and synthesized by introducing different tail groups. Among them, compound 10b showed excellent activity in D2R pharmacological assays. Further optimizations using a structural rigidification approach led to the discovery of brain-penetrant compounds 29c and 29d, which exhibited potent antipsychotic effects in the mouse hyperlocomotion model. Compound 29c also showed excellent drug-like pharmacokinetic properties in rats and qualifies as an antipsychotic agent that is worth further evaluations.

Structural insights into constitutive activity of 5-HT6 receptor.

While most therapeutic research on G-protein-coupled receptors (GPCRs) focuses on receptor activation by (endogenous) agonists, significant therapeutic potential exists through agonist-independent intrinsic constitutive activity that can occur in various physiological and pathophysiological settings. For example, inhibiting the constitutive activity of 5-HT6R-a receptor that is found almost exclusively in the brain and mediates excitatory neurotransmission-has demonstrated a therapeutic effect on cognitive/memory impairment associated with several neuropsychiatric disorders. However, the structural basis of such constitutive activity remains unclear. Here, we present a cryo-EM structure of serotonin-bound human 5-HT6R-Gs heterotrimer at 3.0-Å resolution. Detailed analyses of the structure complemented by comprehensive interrogation of signaling illuminate key structural determinants essential for constitutive 5-HT6R activity. Additional structure-guided mutagenesis leads to a nanobody mimic Gαs for 5-HT6R that can reduce its constitutive activity. Given the importance of 5-HT6R for a large number of neuropsychiatric disorders, insights derived from these studies will accelerate the design of more effective medications, and shed light on the molecular basis of constitutive activity.

Structure-based discovery of nonhallucinogenic psychedelic analogs.

Drugs that target the human serotonin 2A receptor (5-HT2AR) are used to treat neuropsychiatric diseases; however, many have hallucinogenic effects, hampering their use. Here, we present structures of 5-HT2AR complexed with the psychedelic drugs psilocin (the active metabolite of psilocybin) and d-lysergic acid diethylamide (LSD), as well as the endogenous neurotransmitter serotonin and the nonhallucinogenic psychedelic analog lisuride. Serotonin and psilocin display a second binding mode in addition to the canonical mode, which enabled the design of the psychedelic IHCH-7113 (a substructure of antipsychotic lumateperone) and several 5-HT2AR ß-arrestin-biased agonists that displayed antidepressant-like activity in mice but without hallucinogenic effects. The 5-HT2AR complex structures presented herein and the resulting insights provide a solid foundation for the structure-based design of safe and effective nonhallucinogenic psychedelic analogs with therapeutic effects.

Structure-based design of a novel third-generation antipsychotic drug lead with potential antidepressant properties.

Partial agonist activity at the dopamine D2 receptor (DRD2) is a key feature of third-generation antipsychotics (TGAs). However, TGAs also act as antagonists or weak partial agonists to the serotonin (5-hydroxytryptamine; 5-HT) 2A receptor (5-HT2AR). Here we present the crystal structures of aripiprazole- and cariprazine-bound human 5-HT2AR. Both TGAs adopt an unexpected 'upside-down' pose in the 5-HT2AR binding pocket, with secondary pharmacophores inserted in a similar way to a 'bolt'. This insight into the binding modes of TGAs offered a structural mechanism underlying their varied partial efficacies at 5-HT2AR and DRD2. These structures enabled the design of a partial agonist at DRD2/3 and 5-HT1AR with negligible 5-HT2AR binding that displayed potent antipsychotic-like activity without motor side effects in mice. This TGA lead also had antidepressant-like effects and improved cognitive performance in mouse models via 5-HT1AR. This work indicates that 5-HT2AR affinity is a dispensable contributor to the therapeutic actions of TGAs.

GCP: Graph Encoder With Content-Planning for Sentence Generation From Knowledge Bases.

A knowledge base is a large repository of facts usually represented as triples, each consisting of a subject, a predicate, and an object. The triples together form a graph, i.e., a knowledge graph. The triple representation in a knowledge graph offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of triples (i.e., a graph) into natural sentences. We take an encoder-decoder based approach. Specifically, we propose a Graph encoder with Content-Planning capability (GCP) to encode an input graph. GCP not only works as an encoder but also serves as a content-planner by using an entity-order aware topological traversal to encode a graph. This way, GCP can capture the relationships between entities in a knowledge graph as well as providing information regarding the proper entity order for the decoder. Hence, the decoder can generate sentences with a proper entity mention ordering. Experimental results show that GCP achieves improvements over state-of-the-art models by up to 3.6%, 4.1%, and 3.8% in three common metrics BLEU, METEOR, and TER, respectively. The code is available at (https://github.com/ruizhang-ai/GCP/).

Adversarial Distillation for Learning with Privileged Provisions.

Knowledge distillation aims to train a student (model) for accurate inference in a resource-constrained environment. Traditionally, the student is trained by a high-capacity teacher (model) whose training is resource-intensive. The student trained this way is suboptimal because it is difficult to learn the real data distribution from the teacher. To address this issue, we propose to train the student against a discriminator in a minimax game. Such a minimax game has an issue that it can take an excessively long time for the training to converge. To address this issue, we propose adversarial distillation consisting of a student, a teacher, and a discriminator. The discriminator is now a multi-class classifier that distinguishes among the real data, the student, and the teacher. The student and the teacher aim to fool the discriminator via adversarial losses, while they learn from each other via distillation losses. By optimizing the adversarial and the distillation losses simultaneously, the student and the teacher can learn the real data distribution. To accelerate the training, we propose to obtain low-variance gradient updates from the discriminator using a Gumbel-Softmax trick. We conduct extensive experiments to demonstrate the superiority of the proposed adversarial distillation under both accuracy and training speed.

Haloperidol bound D2 dopamine receptor structure inspired the discovery of subtype selective ligands.

The D2 dopamine receptor (DRD2) is one of the most well-established therapeutic targets for neuropsychiatric and endocrine disorders. Most clinically approved and investigational drugs that target this receptor are known to be subfamily-selective for all three D2-like receptors, rather than subtype-selective for only DRD2. Here, we report the crystal structure of DRD2 bound to the most commonly used antipsychotic drug, haloperidol. The structures suggest an extended binding pocket for DRD2 that distinguishes it from other D2-like subtypes. A detailed analysis of the structures illuminates key structural determinants essential for DRD2 activation and subtype selectivity. A structure-based and mechanism-driven screening combined with a lead optimization approach yield DRD2 highly selective agonists, which could be used as chemical probes for studying the physiological and pathological functions of DRD2 as well as promising therapeutic leads devoid of promiscuity.

Quality Evaluation of Ilex asprella Based on Simultaneous Determination of Five Bioactive Components, Chlorogenic Acid, Luteoloside, Quercitrin, Quercetin, and Kaempferol, Using UPLC-Q-TOF MS Study.

Background: Ilex asprella (Hook. Et Arn.) Champ. Ex Benth. is one of the representative medicinal plants that naturally grows in South China. It serves as a major component of herbal tea as an aid for sore throat, toothache, and acne, and it is a folk medicine for treating upper respiratory tract inflammation resulting from fever, infectious hepatitis, and enteritis. Objective: To evaluate the quality of Ilex asprella, the bioactive components were identified comprehensively using quadruple time-of-flight (Q-TOF) MS, and the HPLC method for quality evaluation was established for the first time. Methods: Detection was conducted under the positive electrospray ionization mode with the 110 V fragment voltage and 4.0 kV capillary voltage for the ultra-performance LC-Q-TOF MS study. A Thermo Fisher C18 column (4.6 × 150 mm, 5 µm) associated with the 0.10% formic acid and acetonitrile as mobile phase and gradient elution was carried out for separation process, and the HPLC quality evaluation was detected at a wavelength of 340 nm. Results: The method was validated according to the International Conference on Harmonization regulation including LOQ, LOD, recovery, replication, precision, and linearity. The contents of five components were important for quality evaluation of Ilex asprella. Moreover, luteoloside and quercitrin had more significant impact than others. Conclusions: A specific accurate method has been proposed for the identification of the bioactive components and applied to simultaneous quantification analysis of five components in Ilex asprella. Highlights: The quality evaluation of Ilex asprella established based on its bioactive components can provide a solid promotion for applications of Ilex asprella in food and drug fields.