Topological Heterogeneity of Protein Kinase C Modulators in Human T-Cells Resolved with In-Cell Dynamic Nuclear Polarization NMR Spectroscopy.

Phorbol ester analogs are a promising class of anticancer therapeutics and HIV latency reversing agents that interact with cellular membranes to recruit and activate protein kinase C (PKC) isoforms. However, it is unclear how these esters interact with membranes and how this might correlate with the biological activity of different phorbol ester analogs. Here, we have employed dynamic nuclear polarization (DNP) NMR to characterize phorbol esters in a native cellular context. The enhanced NMR sensitivity afforded by DNP and cryogenic operation reveals topological heterogeneity of 13C-21,22-phorbol-myristate-acetate (PMA) within T cells utilizing 13C-13C correlation and double quantum filtered NMR spectroscopy. We demonstrate the detection of therapeutically relevant amounts of PMA in T cells down to an upper limit of ∼60.0 pmol per million cells and identify PMA to be primarily localized in cellular membranes. Furthermore, we observe distinct 13C-21,22-PMA chemical shifts under DNP conditions in cells compared to model membrane samples and homogenized cell membranes, that cannot be accounted for by differences in conformation. We provide evidence for distinct membrane topologies of 13C-21,22-PMA in cell membranes that are consistent with shallow binding modes. This is the first of its kind in-cell DNP characterization of small molecules dissolved in the membranes of living cells, establishing in-cell DNP-NMR as an important method for the characterization of drug-membrane interactions within the context of the complex heterogeneous environment of intact cellular membranes. This work sets the stage for the identification of the in-cell structural interactions that govern the biological activity of phorbol esters.

Trimethylene Methane Dianion Equivalent for the Asymmetric Consecutive Allylation of Aldehydes: Applications to Prins-Driven Macrocyclizations for the Synthesis of Bryostatin 1 and Analogues.

We report a one-step (one-flask) generation and reaction of a bifunctional allylating reagent, a trimethylene methane dianion equivalent, that provides a route for the asymmetric 2-(trimethylsilylmethyl) allylation of aldehydes. The product of the first aldehyde allylation process is then set to engage in a second separate aldehyde allylation, providing an improved Prins macrocyclization strategy both for the scalable synthesis of bryostatin 1 and for the total synthesis of a new potent bryostatin analogue.

Practical synthesis of the therapeutic leads tigilanol tiglate and its analogues.

Tigilanol tiglate is a natural product diterpenoid in clinical trials for the treatment of a broad range of cancers. Its unprecedented protein kinase C isoform selectivity make it and its analogues exceptional leads for PKC-related clinical indications, which include human immunodeficiency virus and AIDS eradication, antigen-enhanced cancer immunotherapy, Alzheimer's disease and multiple sclerosis. Currently, the only source of tigilanol tiglate is a rain forest tree, Fontainea picrosperma, whose limited number and restricted distribution (northeastern Australia) has prompted consideration of designed tree plantations to address supply needs. Here we report a practical laboratory synthesis of tigilanol tiglate that proceeds in 12 steps (12% overall yield, >80% average yield per step) and can be used to sustainably supply tigilanol tiglate and its analogues, the latter otherwise inaccessible from the natural source. The success of this synthesis is based on a unique strategy for the installation of an oxidation pattern common to many biologically active tiglianes, daphnanes and their analogues.

Function-Oriented Synthesis: Design, Synthesis, and Evaluation of Highly Simplified Bryostatin Analogues.

Using a function-oriented synthesis strategy, we designed, synthesized, and evaluated the simplest bryostatin 1 analogues reported to date, in which bryostatin's A- and B-rings are replaced by a glutarate linker. These analogues, one without and one with a C26-methyl group, exhibit remarkably different protein kinase C (PKC) isoform affinities. The former exhibited bryostatin-like binding to several PKC isoforms with Ki's < 5 nM, while the latter exhibited PKC affinities that were up to ∼180-fold less potent. The analogue with bryostatin-like PKC affinities also exhibited bryostatin-like PKC translocation kinetics in vitro, indicating rapid cell permeation and engagement of its PKC target. This study exemplifies the power of function-oriented synthesis in reducing structural complexity by activity-informed design, thus enhancing synthetic accessibility, while still maintaining function (biological activity), collectively providing new leads for addressing the growing list of therapeutic indications exhibited by PKC modulators.

Synthesis and evaluation of designed PKC modulators for enhanced cancer immunotherapy.

Bryostatin 1 is a marine natural product under investigation for HIV/AIDS eradication, the treatment of neurological disorders, and enhanced CAR T/NK cell immunotherapy. Despite its promising activity, bryostatin 1 is neither evolved nor optimized for the treatment of human disease. Here we report the design, synthesis, and biological evaluation of several close-in analogs of bryostatin 1. Using a function-oriented synthesis approach, we synthesize a series of bryostatin analogs designed to maintain affinity for bryostatin's target protein kinase C (PKC) while enabling exploration of their divergent biological functions. Our late-stage diversification strategy provides efficient access to a library of bryostatin analogs, which per our design retain affinity for PKC but exhibit variable PKC translocation kinetics. We further demonstrate that select analogs potently increase cell surface expression of CD22, a promising CAR T cell target for the treatment of leukemias, highlighting the clinical potential of bryostatin analogs for enhancing targeted immunotherapies.

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models.

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis.