Identification of nicotinamide phosphoribosyltransferase (NAMPT) inhibitors with no evidence of CYP3A4 time-dependent inhibition and improved aqueous solubility

Herein we report the optimization efforts to ameliorate the potent CYP3A4 time-dependent inhibition (TDI) and low aqueous solubility exhibited by a previously identified lead compound from our NAMPT inhibitor program (1, GNE-617). Metabolite identification studies pinpointed the imidazopyridine moiety present in 1 as the likely source of the TDI signal, and replacement with other bicyclic systems was found to reduce or eliminate the TDI finding. A strategy of reducing the number of aromatic rings and/or lowering cLogD7.4 was then employed to significantly improve aqueous solubility. These efforts culminated in the discovery of 42, a compound with no evidence of TDI, improved aqueous solubility, and robust efficacy in tumor xenograft studies.

Identification of amides derived from 1H-pyrazolo[3,4-b]pyridine-5-carboxylic acid as potent inhibitors of human nicotinamide phosphoribosyltransferase (NAMPT)

Potent, 1H-pyrazolo[3,4-b]pyridine-containing inhibitors of the human nicotinamide phosphoribosyltransferase (NAMPT) enzyme were identified using structure-based design techniques. Many of these compounds exhibited nanomolar antiproliferation activities against human tumor lines in in vitro cell culture experiments, and a representative example (compound 26) demonstrated encouraging in vivo efficacy in a mouse xenograft tumor model derived from the A2780 cell line. This molecule also exhibited reduced rat retinal exposures relative to a previously studied imidazo-pyridine-containing NAMPT inhibitor. Somewhat surprisingly, compound 26 was only weakly active in vitro against mouse and monkey tumor cell lines even though it was a potent inhibitor of NAMPT enzymes derived from these species. The compound also exhibited only minimal effects on in vivo NAD levels in mice, and these changes were considerably less profound than those produced by an imidazo-pyridine-containing NAMPT inhibitor. The crystal structures of compound 26 and the corresponding PRPP-derived ribose adduct in complex with NAMPT were also obtained.

Structure-based discovery of novel amide-containing nicotinamide phosphoribosyltransferase (nampt) inhibitors

Crystal structures of several urea- and thiourea-derived compounds in complex with the nicotinamide phosphoribosyltransferase (Nampt) protein were utilized to design a potent amide-containing inhibitor bearing an aza-indole moiety (7, Nampt BC IC50 = 9.0 nM, A2780 cell proliferation IC50 = 10 nM). The Nampt-7 cocrystal structure was subsequently obtained and enabled the design of additional amide-containing inhibitors which incorporated various other fused 6,5-heterocyclic moieties and biaryl sulfone or sulfonamide motifs. Additional modifications of these molecules afforded many potent biaryl sulfone-containing Nampt inhibitors which also exhibited favorable in vitro ADME properties (microsomal and hepatocyte stability, MDCK permeability, plasma protein binding). An optimized compound (58) was a potent inhibitor of multiple cancer cell lines (IC50 <10 nM vs U251, HT1080, PC3, MiaPaCa2, and HCT116 lines), displayed acceptable mouse PK properties (F = 41%, CL = 52.4 mL/min/kg), and exhibited robust efficacy in a U251 mouse xenograft model.

Structure-based identification of ureas as novel nicotinamide phosphoribosyltransferase (Nampt) inhibitors

Nicotinamide phosphoribosyltransferase (Nampt) is a promising anticancer target. Virtual screening identified a thiourea analogue, compound 5, as a novel highly potent Nampt inhibitor. Guided by the cocrystal structure of 5, SAR exploration revealed that the corresponding urea compound 7 exhibited similar potency with an improved solubility profile. These studies also indicated that a 3-pyridyl group was the preferred substituent at one inhibitor terminus and also identified a urea moiety as the optimal linker to the remainder of the inhibitor structure. Further SAR optimization of the other inhibitor terminus ultimately yielded compound 50 as a urea-containing Nampt inhibitor which exhibited excellent biochemical and cellular potency (enzyme IC50 = 0.007 μM; A2780 IC50 = 0.032 μM). Compound 50 also showed excellent in vivo antitumor efficacy when dosed orally in an A2780 ovarian tumor xenograft model (TGI of 97% was observed on day 17).

Role of STAT5a in regulation of sex-specific gene expression in female but not male mouse liver revealed by microarray analysis

Sexual dimorphism in mammalian liver impacts genes affecting hepatic physiology, including inflammatory responses, diseased states, and the metabolism of steroids and foreign compounds. Liver sex specificity is dictated by sex differences in pituitary growth hormone (GH) secretion, with the transcription factor signal transducer and activator of transcription (STAT)5b required for intracellular signaling initiated by the pulsatile male plasma GH profile. STAT5a, a minor liver STAT5 form >90% identical to STAT5b, also responds to sexually dimorphic plasma GH stimulation but is unable to compensate for the loss of STAT5b and the associated loss of sex-specific liver gene expression. A large-scale gene expression study was conducted using 23,574-feature oligonucleotide microarrays and livers of male and female mice, both wild-type and Stat5a-inactivated mice, to elucidate any dependence of liver gene expression on STAT5a. Significant sex differences in expression were found for 2,482 mouse genes, 1,045 showing higher expression in males and 1,437 showing higher expression in females. In contrast to the widespread effects of the loss of STAT5b, STAT5a deficiency had a limited but well-defined impact on liver sex specificity, with 219 of 1,437 female-predominant genes (15%) specifically decreased in expression in STAT5a-deficient female liver. Analysis of liver RNAs from wild-type mice representing three mixed or outbred strains identified 1,028 sexually dimorphic genes across the strains, including 393 female-predominant genes, of which 89 (23%) required STAT5a for normal expression in female liver. These findings highlight the importance of STAT5a for regulation of sex-specific gene expression specifically in female liver, in striking contrast to STAT5b, whose major effects are restricted to male liver.

Computational Solvent Mapping Reveals the Importance of Local Conformational Changes for Broad Substrate Specificity in Mammalian Cytochromes P450†

Computational solvent mapping moves small organic molecules as probes around a protein surface, finds favorable binding positions, clusters the conformations, and ranks the clusters on the basis of their average free energy. Prior mapping studies of enzymes, crystallized in either substrate-free or substrate-bound form, have shown that the largest number of solvent probe clusters invariably overlaps in the active site. We have applied this method to five cytochromes P450. As expected, the mapping of two bacterial P450s, P450 cam (CYP101) and P450 BM-3 (CYP102), identified the substrate-binding sites in both ligand-bound and ligand-free P450 structures. However, the mapping finds the active site only in the ligand-bound structures of the three mammalian P450s, 2C5, 2C9, and 2B4. Thus, despite the large cavities seen in the unbound structures of these enzymes, the features required for binding small molecules are formed only in the process of substrate binding. The ability of adjusting their binding sites to substrates that differ in size, shape, and polarity is likely to be responsible for the broad substrate specificity of these mammalian P450s. Similar behavior was seen at "hot spots" of protein-protein interfaces that can also bind small molecules in grooves created by induced fit. In addition, the binding of S-warfarin to P450 2C9 creates a high-affinity site for a second ligand, which may help to explain the prevalence of drug-drug interactions involving this and other mammalian P450s.

Sex-Dependent Liver Gene Expression Is Extensive and Largely Dependent upon Signal Transducer and Activator of Transcription 5b (STAT5b): STAT5b-Dependent Activation of Male Genes and Repression of Female Genes Revealed by Microarray Analysis

Sexual dimorphism in mammalian liver contributes to sex differences in physiology, homeostasis, and steroid and foreign compound metabolism. Many sex-dependent liver genes are regulated by sex differences in pituitary GH secretion, with the transcription factor, signal transducer and activator of transcription (STAT5b), proposed to mediate signaling by the pulsatile, male plasma GH profile. Presently, a large-scale gene expression study was conducted using male and female mice, wild type and Stat5b inactivated, to characterize sex differences in liver gene expression and their dependence on STAT5b. The relative abundance of individual liver RNAs was determined for each sex-genotype combination by competitive hybridization to 23,574-feature oligonucleotide microarrays. Significant sex differences in hepatic expression were seen for 1603 mouse genes. Of 850 genes showing higher expression in males, 767 (90%) were down-regulated in STAT5b-deficient males. Moreover, of 753 genes showing female-predominant expression, 461 (61%) were up-regulated in STAT5b-deficient males. In contrast, approximately 90% of the sex-dependent genes were unaffected by STAT5b deficiency in females. Thus: 1) STAT5b is essential for sex-dependent liver gene expression, a characteristic of approximately 1600 mouse genes (4% of the genome); 2) male-predominant liver gene expression requires STAT5b, or STAT5b-dependent factors, which act in a positive manner; and 3) many female-predominant liver genes are repressed in males in a STAT5b-dependent manner. Several of the STAT5b-dependent male genes encode transcriptional repressors; these may include direct STAT5b targets that repress female-predominant genes in male liver. Several female-predominant repressors are elevated in STAT5b-deficient males; these may contribute to the major loss of male gene expression seen in the absence of STAT5b.

Clustering of domains of functionally related enzymes in the interaction database PRECISE by the generation of primary sequence patterns

The PRECISE database was developed by our laboratory to allow for the systematic study of the ligand interactions common to a set of functionally related enzymes, where an interaction site is defined broadly as any residue(s) that interact with a ligand. During the construction of PRECISE, enzyme chains are extracted from the protein data bank (PDB) and clustered according to functional homology as defined by the enzyme commission (EC) nomenclature system. A sequence representative is chosen from each cluster based on the criterion set forth by the non-redundant PDB set, and pair-wise alignments of each cluster member to the representative are performed. Atom-based residue-ligand interactions are calculated for each cluster member, and the summation of ligand interactions for all cluster members at each aligned position is determined. Although we were able to successfully align most clusters using a simple dynamic programming algorithm, several cluster created exhibited poor pair-wise alignments of each cluster member to its sequence representative. We hypothesized that the observed alignment problems were, in most cases, due to the incorrect separation and alignment of different domains in multi-domain proteins, a mistake that frequently causes error proliferation in functional annotation. Here we present the results of generating primary sequence patterns for each poorly aligned cluster in PRECISE to assess the extent to which multi-domain proteins that are incorrectly aligned contributes to poor pair-wise alignments of each cluster member to its representative. This requires the use of an iterative locally optimal pair-wise alignment algorithm to build a hierarchical similarity-based sequence pattern for a set of functionally related enzymes. Our results show that poor alignments in PRECISE are caused most frequently by the misalignment of multi-domain proteins, and that the generation of primary sequence patterns for the assignment of sequence family membership yields better alignments for the functionally related enzyme clusters in PRECISE than our original alignment algorithm.

Optimal clustering for detecting near-native conformations in protein docking

Clustering is one of the most powerful tools in computational biology. The conventional wisdom is that events that occur in clusters are probably not random. In protein docking, the underlying principle is that clustering occurs because long-range electrostatic and/or desolvation forces steer the proteins to a low free-energy attractor at the binding region. Something similar occurs in the docking of small molecules, although in this case shorter-range van der Waals forces play a more critical role. Based on the above, we have developed two different clustering strategies to predict docked conformations based on the clustering properties of a uniform sampling of low free-energy protein-protein and protein-small molecule complexes. We report on significant improvements in the automated prediction and discrimination of docked conformations by using the cluster size and consensus as a ranking criterion. We show that the success of clustering depends on identifying the appropriate clustering radius of the system. The clustering radius for protein-protein complexes is consistent with the range of the electrostatics and desolvation free energies (i.e., between 4 and 9 Angstroms); for protein-small molecule docking, the radius is set by van der Waals interactions (i.e., at approximately 2 Angstroms). Without any a priori information, a simple analysis of the histogram of distance separations between the set of docked conformations can evaluate the clustering properties of the data set. Clustering is observed when the histogram is bimodal. Data clustering is optimal if one chooses the clustering radius to be the minimum after the first peak of the bimodal distribution. We show that using this optimal radius further improves the discrimination of near-native complex structures.

PRECISE: a Database of Predicted and Consensus Interaction Sites in Enzymes

PRECISE (Predicted and Consensus Interaction Sites in Enzymes) is a database of interactions between the amino acid residues of an enzyme and its ligands (substrate and transition state analogs, cofactors, inhibitors and products). It is available online at http://precise.bu.edu/. In the current version, all information on interactions is extracted from the enzyme–ligand complexes in the Protein Data Bank (PDB) by performing the following steps: (i) clustering homologous enzyme chains such that, in each cluster, the proteins have the same EC number and all sequences are similar; (ii) selecting a representative chain for each cluster; (iii) selecting ligand types; (iv) finding non-bonded interactions and hydrogen bonds; and (v) summing the interactions for all chains within the cluster. The output of the search is the color-coded sequence of the representative. The colors indicate the total number of interactions found at each amino acid position in all chains of the cluster. Clicking on a residue displays a detailed list of interactions for that residue. Optional filters allow restricting the output to selected chains in the cluster, to non-bonded or hydrogen bonding interactions, and to selected ligand types. The binding site information is essential for understanding and altering substrate specificity and for the design of enzyme inhibitors.

Sexual Dimorphism of Rat Liver Gene Expression: Regulatory Role of Growth Hormone Revealed by Deoxyribonucleic Acid Microarray Analysis

GH has diverse physiological actions and regulates the tissue-specific expression of numerous genes involved in growth, metabolism, and differentiation. Several of the effects of GH on somatic growth and gene expression are sex dependent and are regulated by pituitary GH secretory patterns, which are sexually differentiated. The resultant sex differences in plasma GH profiles are particularly striking in rodents and are the major determinant of sex differences in pubertal body growth rates and the expression in liver of several cytochrome P450 (CYP) enzymes that metabolize steroids, drugs, and environmental chemicals of importance to endocrinology, pharmacology, and toxicology. DNA microarray analysis was used to identify rat liver-expressed genes that show sexual dimorphism, and to ascertain the role of GH as a regulator of their sexually dimorphic expression. Adult male and female rats were untreated or were treated with GH by 7-d continuous infusion using an Alzet osmotic minipump. Poly(A) RNA was purified from individual livers and Cy3- and Cy5-labeled cDNA probes cohybridized to Pan Rat Liver and 5K Rat Oligonucleotide microarrays representing 5889 unique rat genes. Analysis of differential gene expression profiles identified 37 liver-expressed, female-predominant genes; of these, 27 (73%) were induced by continuous GH treatment of male rats. Moreover, only three of 30 genes up-regulated in male rat liver by continuous GH treatment did not display female-dominant expression. Further analysis revealed that 44 of 49 male-predominant genes (90%) were down-regulated in the livers of continuous GH-treated male rats compared with untreated male rats, whereas only five of 49 genes that were down-regulated in male rats by continuous GH treatment were not male dominant in their expression. Real-time PCR analysis applied to a sampling of 10 of the sexually dimorphic genes identified in the microarray analysis verified their sex- and GH-dependent patterns of regulation. Taken together, these studies establish that GH-regulated gene expression is the major mechanistic determinant of sexually dimorphic gene expression in the rat liver model.

Predictome: a database of putative functional links between proteins

The current deluge of genomic sequences has spawned the creation of tools capable of making sense of the data. Computational and high-throughput experimental methods for generating links between proteins have recently been emerging. These methods effectively act as hypothesis machines, allowing researchers to screen large sets of data to detect interesting patterns that can then be studied in greater detail. Although the potential use of these putative links in predicting gene function has been demonstrated, a central repository for all such links for many genomes would maximize their usefulness. Here we present Predictome, a database of predicted links between the proteins of 44 genomes based on the implementation of three computational methods--chromosomal proximity, phylogenetic profiling and domain fusion--and large-scale experimental screenings of protein-protein interaction data. The combination of data from various predictive methods in one database allows for their comparison with each other, as well as visualization of their correlation with known pathway information. As a repository for such data, Predictome is an ongoing resource for the community, providing functional relationships among proteins as new genomic data emerges. Predictome is available at http://predictome.bu.edu.