Delocalized quinolinium-macrocyclic peptides, an atypical chemotype for CNS penetration.

Macrocyclic drugs can address an increasing range of molecular targets but enabling central nervous system (CNS) access to these drugs has been viewed as an intractable problem. We designed and synthesized a series of quinolinium-modified cyclosporine derivatives targeted to the mitochondrial cyclophilin D protein. Modification of the cation to enable greater delocalization was confirmed by x-ray crystallography of the cations. Critically, greater delocalization improved brain concentrations. Assessment of the compounds in preclinical assays and for pharmacokinetics identified a molecule JP1-138 with at least 20 times the brain levels of a non-delocalized compound or those reported for cyclosporine. Levels were maintained over 24 hours together with low hERG potential. The paradigm outlined here could have widespread utility in the treatment of CNS diseases.

Multi-State Gene Cluster Switches Determine the Adaptive Mitochondrial And Metabolic Landscape of Breast Cancer.

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering (MCbiclust) and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labelled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states.