Computer-aided diagnostic screen for Congenital Central Hypoventilation Syndrome with facial phenotype.

BACKGROUND: Congenital Central Hypoventilation Syndrome (CCHS) has devastating consequences if not diagnosed promptly. Despite identification of the disease-defining gene PHOX2B and a facial phenotype, CCHS remains underdiagnosed. This study aimed to incorporate automated techniques on facial photos to screen for CCHS in a diverse pediatric cohort to improve early case identification and assess a facial phenotype-PHOX2B genotype relationship. METHODS: Facial photos of children and young adults with CCHS were control-matched by age, sex, race/ethnicity. After validating landmarks, principal component analysis (PCA) was applied with logistic regression (LR) for feature attribution and machine learning models for subject classification and assessment by PHOX2B pathovariant. RESULTS: Gradient-based feature attribution confirmed a subtle facial phenotype and models were successful in classifying CCHS: neural network performed best (median sensitivity 90% (IQR 84%, 95%)) on 179 clinical photos (versus LR and XGBoost, both 85% (IQR 75-76%, 90%)). Outcomes were comparable stratified by PHOX2B genotype and with the addition of publicly available CCHS photos (n = 104) using PCA and LR (sensitivity 83-89% (IQR 67-76%, 92-100%). CONCLUSIONS: Utilizing facial features, findings suggest an automated, accessible classifier may be used to screen for CCHS in children with the phenotype and support providers to seek PHOX2B testing to improve the diagnostics. IMPACT: Facial landmarking and principal component analysis on a diverse pediatric and young adult cohort with PHOX2B pathovariants delineated a distinct, subtle CCHS facial phenotype. Automated, low-cost machine learning models can detect a CCHS facial phenotype with a high sensitivity in screening to ultimately refer for disease-defining PHOX2B testing, potentially addressing gaps in disease underdiagnosis and allow for critical, timely intervention.

From UK-2A to florylpicoxamid: Active learning to identify a mimic of a macrocyclic natural product.

Scaffold replacement as part of an optimization process that requires maintenance of potency, desirable biodistribution, metabolic stability, and considerations of synthesis at very large scale is a complex challenge. Here, we consider a set of over 1000 time-stamped compounds, beginning with a macrocyclic natural-product lead and ending with a broad-spectrum crop anti-fungal. We demonstrate the application of the QuanSA 3D-QSAR method employing an active learning procedure that combines two types of molecular selection. The first identifies compounds predicted to be most active of those most likely to be well-covered by the model. The second identifies compounds predicted to be most informative based on exhibiting low predicted activity but showing high 3D similarity to a highly active nearest-neighbor training molecule. Beginning with just 100 compounds, using a deterministic and automatic procedure, five rounds of 20-compound selection and model refinement identifies the binding metabolic form of florylpicoxamid. We show how iterative refinement broadens the domain of applicability of the successive models while also enhancing predictive accuracy. We also demonstrate how a simple method requiring very sparse data can be used to generate relevant ideas for synthetic candidates.

The spinosyns, spinosad, spinetoram, and synthetic spinosyn mimics - discovery, exploration, and evolution of a natural product chemistry and the impact of computational tools.

Natural products (NPs) have long been a source of insecticidal crop protection products. Like many macrolide NPs, the spinosyns originated from a soil inhibiting microorganism (Saccharopolyspora spinosa). More than 20 years after initial registration, the spinosyns remain a unique class of NP-based insect control products that presently encompass two insecticidal active ingredients, spinosad, a naturally occurring mixture of spinosyns, and spinetoram, a semi-synthetic spinosyn product. The exploration and exploitation of the spinosyns has, unusually, been tied to an array of computational tools including artificial intelligence (AI)-based quantitative structure activity relationship (QSAR) and most recently computer-aided modeling and design (CAMD). The AI-based QSAR directly lead to the discovery of spinetoram, while the CAMD studies have recently resulted in the discovery and building of a series of synthetic spinosyn mimics. The most recent of these synthetic spinosyn mimics show promise as insecticides targeting lepidopteran insect pests as demonstrated by field studies wherein the efficacy has been shown to be comparable to spinosad and spinetoram. These and a range of other aspects related to the exploration of the spinosyns over the past 30 years are reviewed herein. © 2020 Society of Chemical Industry.

Discovery of highly insecticidal synthetic spinosyn mimics - CAMD enabled de novo design simplifying a complex natural product.

Simplifying complex natural products: Computer modeling-based design leads to highly insecticidal, chemically simpler synthetic mimics of the spinosyn natural products that are active in the field. © 2018 Society of Chemical Industry.

De Novo Design of Potent, Insecticidal Synthetic Mimics of the Spinosyn Macrolide Natural Products.

New insect pest control agents are needed to meet the demands to feed an expanding global population, to address the desire for more environmentally-friendly insecticide tools, and to fill the loss of control options in some crop-pest complexes due to development of insecticide resistance. The spinosyns are a highly effective class of naturally occurring, fermentation derived insecticides, possessing a very favorable environmental profile. Chemically, the spinosyns are composed of a large complex macrolide tetracycle coupled to two sugars. As a means to further exploit this novel class of natural product-based insecticides, molecular modeling studies coupled with bioactivity-directed chemical modifications were used to define a less complex, synthetically accessible replacement for the spinosyn tetracycle. These studies lead to the discovery of highly insecticidal analogs, possessing a simple tri-aryl ring system as a replacement for the complex macrolide tetracycle.

Synthesis and Insecticidal Activity of Spinosyns with C9-O-Benzyl Bioisosteres in Place of the 2',3',4'-Tri-O-methyl Rhamnose.

The spinosyns are fermentation-derived natural products active against a wide range of insect pests. They are structurally complex, consisting of two sugars (forosamine and rhamnose) coupled to a macrocyclic tetracycle. Removal of the rhamnose sugar results in a >100-fold reduction in insecticidal activity. C9-O-benzyl analogues of spinosyn D were synthesized to determine if the 2',3',4'-tri-O-methyl rhamnose moiety could be replaced with a simpler, synthetic bioisostere. Insecticidal activity was evaluated against larvae of Spodoptera exigua (beet armyworm) and Helicoverpa zea (corn earworm). Whereas most analogues were far less active than spinosyn D, a few of the C9-O-benzyl analogues, such as 4-CN, 4-Cl, 2-isopropyl, and 3,5-diOMe, were within 3-15 times the activity of spinosyn D for larvae of S. exigua and H. zea. Thus, although not yet quite as effective, synthetic bioisosteres can substitute for the naturally occurring 2',3',4'-tri-O-methyl rhamnose moiety.

Dihydropiperazine neonicotinoid compounds. Synthesis and insecticidal activity.

Syntheses of various isomeric dihydropiperazines can be approached successfully by taking advantage of the regioselective monothionation of their respective diones. Preparation of the precursor unsymmetrical N-substituted piperazinediones from readily available diamines is key to this selectivity. The dihydropiperazine ring system, as exemplified in 1-[(6-chloropyridin-3-yl)methyl]-4-methyl-3-oxopiperazin-2-ylidenecyanamide (4) and 1-[(2-chloro-1,3-thiazol-5-yl)methyl]-4-methyl-3-oxopiperazin-2-ylidenecyanamide (25), has been shown to be a suitable bioisosteric replacement for the imidazolidine ring system contained in neonicotinoid compounds. However, placement of the cyanoimino electron-withdrawing group further removed from the pyridine ring, as in 4-[(6-chloropyridin-3-yl)methyl]-3-oxopiperazin-2-ylidenecyanamide (3a), or relocation of the carbonyl group, as in 1-[(6-chloropyridin-3-yl)methyl]-4-methyl-5-oxopiperazin-2-ylidenecyanamide (5), results in significantly decreased bioisosterism. The dihydropiperazine ring system of 4 and 25 also lends a degree of rigidity to the molecule that is not offered by the inactive acyclic counterpart 2-[(6-chloropyridin-3-yl)-methyl-(methyl)amino]-2-(cyanoimino)-N,N-dimethylacetamide (6). A pharmacophore model is proposed that qualitatively explains the results on the basis of good overlap of the key pharmacophore elements of 4 and imidacloprid (1); the less active regioisomers of 4 (3a, 5, and 6) feature a smaller degree of overlap.

Adenosine A1 Receptor and Ligand Molecular Modeling.

This symposium provided a forum for presentations by the relevant groups on ligand design and ligand binding on the adenosine A1, receptor. Agreement appears to exist that the "N6-C8" model of ligand binding to the receptor is the preferred mode. A consensus has not yet been reached on the actual placement of the ligand in the receptor and the exact amino acids which interact in its binding. Two viable models exist at present. Both can be tested with selective site-directed mutagenic studies on the A1 receptor as well as with additional designed ligands.