“…Attempts have been made to recognize this liability early in a lead optimization stage and mitigate the potential of AO catalyzed metabolism at this stage. Several reports describing medicinal chemistry strategies to mitigate AO liabilities have been published recently (Magee et al, 2009;Jones et al, 2011;Linton et al, 2011;Tran et al, 2011;Pryde et al, 2012). …”
ABSTRACT:Current studies explored the effect of structural changes on the aldehyde oxidase (AO)-mediated metabolism of zoniporide (1). Zoniporide analogs with modifications of the acylguanidine moiety, the cyclopropyl group on the pyrazole ring, and the quinoline ring were studied for their AO-catalyzed metabolism using the human S9 fraction. Analysis of the half-lives suggested that subtle changes in the structure of 1 influenced its metabolism and that the guanidine and the quinoline moieties were prerequisites for AO-catalyzed oxidation to 2-oxozoniporide (M1). In contrast, replacement of the cyclopropyl group with other alkyl groups was tolerated. The effect of structural variation on AO properties was rationalized by docking 1 and its analogs into the human AO homology model. These studies indicated the importance of electrostatic, -stacking and hydrophobic interactions of the three motifs with residues in the active site. Differences in substrate properties were also rationalized by comparing their half-lives with cLogD, electrophilicity parameters [electrostatic potential (ESP) charges and energy of lowest unoccupied molecular orbitals (E LUMO )], and the energies of formation of tetrahedral intermediates (J Med Chem 50:4642-4647, 2007). Whereas the success of energetics in predicting the AO substrate properties of analogs was 87%, the predictive ability of other descriptors was none (cLogD) to 60% (ESP charges and E LUMO ). Overall, the structuremetabolism relationship could be rationalized using a combination of both the energy calculations and docking studies. This combination method can be incorporated into a strategy for mitigating AO liabilities observed in the lead candidate or studying structuremetabolism relationships of other AO substrates.
“…Attempts have been made to recognize this liability early in a lead optimization stage and mitigate the potential of AO catalyzed metabolism at this stage. Several reports describing medicinal chemistry strategies to mitigate AO liabilities have been published recently (Magee et al, 2009;Jones et al, 2011;Linton et al, 2011;Tran et al, 2011;Pryde et al, 2012). …”
ABSTRACT:Current studies explored the effect of structural changes on the aldehyde oxidase (AO)-mediated metabolism of zoniporide (1). Zoniporide analogs with modifications of the acylguanidine moiety, the cyclopropyl group on the pyrazole ring, and the quinoline ring were studied for their AO-catalyzed metabolism using the human S9 fraction. Analysis of the half-lives suggested that subtle changes in the structure of 1 influenced its metabolism and that the guanidine and the quinoline moieties were prerequisites for AO-catalyzed oxidation to 2-oxozoniporide (M1). In contrast, replacement of the cyclopropyl group with other alkyl groups was tolerated. The effect of structural variation on AO properties was rationalized by docking 1 and its analogs into the human AO homology model. These studies indicated the importance of electrostatic, -stacking and hydrophobic interactions of the three motifs with residues in the active site. Differences in substrate properties were also rationalized by comparing their half-lives with cLogD, electrophilicity parameters [electrostatic potential (ESP) charges and energy of lowest unoccupied molecular orbitals (E LUMO )], and the energies of formation of tetrahedral intermediates (J Med Chem 50:4642-4647, 2007). Whereas the success of energetics in predicting the AO substrate properties of analogs was 87%, the predictive ability of other descriptors was none (cLogD) to 60% (ESP charges and E LUMO ). Overall, the structuremetabolism relationship could be rationalized using a combination of both the energy calculations and docking studies. This combination method can be incorporated into a strategy for mitigating AO liabilities observed in the lead candidate or studying structuremetabolism relationships of other AO substrates.
“…C) Effect of the exhaustion protocol on CAR T cell expression of exhaustion markers, PD‐1, LAG‐3 and Tim‐3. D) Structure of the TLR7‐22 agonist [26] used for rejuvenation of the exhausted CAR T cells in panels E and F. E) Effect of 12 hour exposure to increasing concentrations of TLR7‐22 on the ability of the exhausted CAR T cells to lyse CD19 + Raji cells (left panel) and produce IFN‐γ (right panel). F) Change in expression of a CAR T cell exhaustion marker (Tim‐3) following 12 hour exposure to TLR7‐22.…”
Although chimeric antigen receptor (CAR) T cells have demonstrated significant promise in suppressing hematopoietic cancers, their applications in treating solid tumors have been limited by onset of CAR T cell exhaustion that accompanies continuous CAR T cell exposure to tumor antigen. To address this limitation, we have exploited the abilities of recently designed universal CARs to bind fluorescein and internalize a fluorescein-TLR7 agonist conjugate by CAR-mediated endocytosis. We demonstrate here that anti-fluorescein CAR-mediated uptake of a fluorescein-TLR7-3 conjugate can reactivate exhausted CAR T cells, leading to dramatic reduction in T cell exhaustion markers (PD-1 + Tim-3 + ) and shrinkage of otherwise resistant tumors without inducing systemic activation of the immune system. We conclude that CAR T cell exhaustion can be reversed by administration of a CAR-targeted TLR7 agonist, thereby enabling the CAR T cells to successfully treat solid tumors without incurring the systemic toxicity that commonly accompanies administration of nontargeted TLR7 agonists.
“…A dataset of 256 TLR7 ligands with their EC 50 and structures was generated from the literature [ 10 , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] ]. This dataset was generated using a set of search criteria in Scopus database using these three keywords “TLR7” AND “Agonist” AND “EC 50 ”.…”
Section: Methodsmentioning
confidence: 99%
“… Fig. 1 An overview of seven previously characterized N9-pyridinylmethyl analogues with their EC 50 values and some other well-known TLR7 agonists [ 10 ]. Also, the EC 50 values for imiquimod [ 11 ], resiquimod [ 12 ], and gardiquimod [ 11 ] have been deduced from the references in the BindingDB database [ 13 ].…”
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