The principles guiding the design and synthesis of bioactive compounds based on natural product (NP) structure, such as biology-oriented synthesis (BIOS), are limited by their partial coverage of the NP-like chemical space of existing NPs and retainment of bioactivity in the corresponding compound collections. Here we propose and validate a concept to overcome these limitations by de novo combination of NP-derived fragments to structurally unprecedented 'pseudo natural products'. Pseudo NPs inherit characteristic elements of NP structure yet enable the efficient exploration of areas of chemical space not covered by NP-derived chemotypes, and may possess novel bioactivities. We provide a proof of principle by designing, synthesizing and investigating the biological properties of chromopynone pseudo NPs that combine biosynthetically unrelated chromane- and tetrahydropyrimidinone NP fragments. We show that chromopynones define a glucose uptake inhibitor chemotype that selectively targets glucose transporters GLUT-1 and -3, inhibits cancer cell growth and promises to inspire new drug discovery programmes aimed at tumour metabolism.
Natural products (NPs) inspire the design and synthesis of novel biologically relevant chemical matter, for instance through biology‐oriented synthesis (BIOS). However, BIOS is limited by the partial coverage of NP‐like chemical space by the guiding NPs. The design and synthesis of “pseudo NPs” overcomes these limitations by combining NP‐inspired strategies with fragment‐based compound design through de novo combination of NP‐derived fragments to unprecedented compound classes not accessible through biosynthesis. We describe the development and biological evaluation of pyrano‐furo‐pyridone (PFP) pseudo NPs, which combine pyridone‐ and dihydropyran NP fragments in three isomeric arrangements. Cheminformatic analysis indicates that the PFPs reside in an area of NP‐like chemical space not covered by existing NPs but rather by drugs and related compounds. Phenotypic profiling in a target‐agnostic “cell painting” assay revealed that PFPs induce formation of reactive oxygen species and are structurally novel inhibitors of mitochondrial complex I.
Bioactive compound design based on natural product (NP) structure may be limited because of partial coverage of NP‐like chemical space and biological target space. These limitations can be overcome by combining NP‐centered strategies with fragment‐based compound design through combination of NP‐derived fragments to afford structurally unprecedented “pseudo‐natural products” (pseudo‐NPs). The design, synthesis, and biological evaluation of a collection of indomorphan pseudo‐NPs that combine biosynthetically unrelated indole‐ and morphan‐alkaloid fragments are described. Indomorphane derivative Glupin was identified as a potent inhibitor of glucose uptake by selectively targeting and upregulating glucose transporters GLUT‐1 and GLUT‐3. Glupin suppresses glycolysis, reduces the levels of glucose‐derived metabolites, and attenuates the growth of various cancer cell lines. Our findings underscore the importance of dual GLUT‐1 and GLUT‐3 inhibition to efficiently suppress tumor cell growth and the cellular rescue mechanism, which counteracts glucose scarcity.
We first review the state-of-the-art in development of log P prediction approaches falling in two major categories: substructure-based and property-based methods. Then, we compare the predictive power of representative methods for one public (N = 266) and two in house datasets from Nycomed (N = 882) and Pfizer (N = 95809). A total of 30 and 18 methods were tested for public and industrial datasets, respectively. Accuracy of models declined with the number of non-hydrogen atoms. The Arithmetic Average Model (AAM), which predicts the same value (the arithmetic mean) for all compounds, was used as a baseline model for comparison. Methods with Root Mean Squared Error (RMSE) greater than RMSE produced by the AAM were considered as unacceptable. The majority of analyzed methods produced reasonable results for the public dataset but only seven methods were successful on the both in house datasets. We proposed a simple equation based on the number of carbon atoms, NC, and the number of hetero-atoms, NHET: log P = 1.46(± 0.02) + 0.11(± 0.001) NC -0.11(± 0.001) NHET. This equation outperformed a large number of programs benchmarked in this study. Factors influencing the accuracy of log P predictions were elucidated and discussed (the article is in press in J. Pharm. Sci.).
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