Discovery of a Novel, Highly Potent, and Selective Thieno[3,2-d]pyrimidinone-Based Cdc7 Inhibitor with a Quinuclidine Moiety (TAK-931) as an Orally Active Investigational Antitumor Agent.

In our pursuit of developing a novel, potent, and selective cell division cycle 7 (Cdc7) inhibitor, we optimized the previously reported thieno[3,2-d]pyrimidinone analogue I showing time-dependent Cdc7 kinase inhibition and slow dissociation kinetics. These medicinal chemistry efforts led to the identification of compound 3d, which exhibited potent cellular activity, excellent kinase selectivity, and antitumor efficacy in a COLO205 xenograft mouse model. However, the issue of formaldehyde adduct formation emerged during a detailed study of 3d, which was deemed an obstacle to further development. A structure-based approach to circumvent the adduct formation culminated in the discovery of compound 11b (TAK-931) possessing a quinuclidine moiety as a preclinical candidate. In this paper, the design, synthesis, and biological evaluation of this series of compounds will be presented.

Small-Scale Panel Comprising Diverse Gene Family Targets To Evaluate Compound Promiscuity.

Despite the recent advances in the life sciences and the remarkable investment in drug discovery research, the success rate of small-molecule drug development remains low. Safety is the second most influential factor of drug attrition in clinical studies; thus, the selection of compounds with fewer toxicity concerns is crucial to increase the success rate of drug discovery. Compounds that promiscuously bind to multiple targets are likely to cause unexpected pharmacological activity that may lead to adverse effects. Therefore, avoiding such compounds during early research stages would contribute to identifying compounds with a higher chance of success in the clinic. To evaluate the interaction profile against a wide variety of targets, we constructed a small-scale promiscuity panel (PP) consisting of eight targets (ROCK1, PDE4D2, GR, PPARγ, 5-HT2B, adenosine A3, M1, and GABAA) that were selected from diverse gene families. The validity of this panel was confirmed by comparison with the promiscuity index evaluated from larger-scale panels. Analysis of data from the PP revealed that both lipophilicity and basicity are likely to increase promiscuity, while the molecular weight does not significantly contribute. Additionally, the promiscuity assessed using our PP correlated with the occurrence of both in vitro cytotoxicity and in vivo toxicity, suggesting that the PP is useful to identify compounds with fewer toxicity concerns. In summary, this small-scale and cost-effective PP can contribute to the identification of safer compounds that would lead to a reduction in drug attrition due to safety issues.

2-Aminomethylthieno[3,2-d]pyrimidin-4(3H)-ones bearing 3-methylpyrazole hinge binding moiety: Highly potent, selective, and time-dependent inhibitors of Cdc7 kinase.

In order to increase the success rate for developing new Cdc7 inhibitors for cancer therapy, we explored a new chemotype which can comply with the previously-constructed pharmacophore model. Substitution of a pyridine ring of a serendipitously-identified Cdc7 inhibitor 2b with a 3-methylpyrazole resulted in a 4-fold increase in potency and acceptable kinase selectivity, leading to the identification of thieno[3,2-d]pyrimidin-4(3H)-one as an alternative scaffold. Structure-activity relationship (SAR) study revealed that incorporation of a substituted aminomethyl group into the 2-position improved kinase selectivity. Indeed, a pyrrolidinylmethyl derivative 10c was a potent Cdc7 inhibitor (IC50=0.70nM) with high selectivity (Cdk2/Cdc7≥14,000, ROCK1/Cdc7=200). It should be noted that 10c exhibited significant time-dependent Cdc7 inhibition with slow dissociation kinetics, cellular pharmacodynamic (PD) effects, and COLO205 growth inhibition. Additionally, molecular basis of high kinase selectivity of 10c is discussed by using the protein structures of Cdc7 and Cdk2.

Identification of a new class of potent Cdc7 inhibitors designed by putative pharmacophore model: Synthesis and biological evaluation of 2,3-dihydrothieno[3,2-d]pyrimidin-4(1H)-ones.

Cell division cycle 7 (Cdc7) is a serine/threonine kinase that plays important roles in the regulation of DNA replication process. A genetic study indicates that Cdc7 inhibition can induce selective tumor-cell death in a p53-dependent manner, suggesting that Cdc7 is an attractive target for the treatment of cancers. In order to identify a new class of potent Cdc7 inhibitors, we generated a putative pharmacophore model based on in silico docking analysis of a known inhibitor with Cdc7 homology model. The pharmacophore model provided a minimum structural motif of Cdc7 inhibitor, by which preliminary medicinal chemistry efforts identified a dihydrothieno[3,2-d]-pyrimidin-4(1H)-one scaffold having a heteroaromatic hinge-binding moiety. The structure-activity relationship (SAR) studies resulted in the discovery of new, potent, and selective Cdc7 inhibitors 14a, c, e. Furthermore, the high selectivity of 14c, e for Cdc7 over Rho-associated protein kinase 1 (ROCK1) is discussed by utilizing a docking study with Cdc7 and ROCK2 crystal structures.

Novel hierarchical classification and visualization method for multiobjective optimization of drug properties: application to structure-activity relationship analysis of cytochrome P450 metabolism.

In the lead optimization process, medicinal chemists must consider various chemical properties of active compounds, including ADME/Tox properties, and find the best compromise among these. This study presents a novel data mining method for multiobjective optimization of chemical properties, which consists of the hierarchical classification and visualization of multidimensional data. A hierarchical classification tree model is generated by an extension of recursive partitioning that utilizes averaged information gains for multiple objective variables as a quality-of-split criterion. All the hierarchically structured data objects are represented using a large-scale data visualization technique. The technique is an extension of HeiankyoView, which displays data objects as colored icons and group nodes as rectangular borders. Each icon is divided into subregions with different colors, so that it can present multidimensional data according to brightness of the colors. The proposed method was applied to the structure-activity relationship analysis for cytochrome P450 (CYP) substrates. The substrate specificity of six CYP isoforms was successfully delineated: e.g., CYP2C9 substrates are anionic compounds, while CYP2D6 substrates are cationic; and CYP2E1 substrates are smaller compounds, while CYP3A4 substrates are larger compounds.

Visualization of large-scale aqueous solubility data using a novel hierarchical data visualization technique.

It is a difficult task to recognize the trends in molecular physical properties relevant to a specific chemical class and find a way to optimize potential compounds. We present here a novel hierarchical data visualization technique, named "HeiankyoView", to visualize large-scale multidimensional chemical information. HeiankyoView represents hierarchically organized data objects by mapping leaf nodes as colored square icons and nonleaf nodes as rectangular borders. In this way, data objects can be expressed as equishaped icons without overlapping one another in the two-dimensional display space. HeiankyoView has been applied to visualize aqueous solubility data for 908 compounds collected from the published literature. When the results of a recursive partitioning analysis and hierarchical clustering analysis were visualized, the trends hidden in the solubility data could be effectively displayed as intuitively understandable visual images. Most interestingly, the data visualization technique, without any statistical computations, was able to assist us in extracting from such large-scale data meaningful information establishing that ClogP and the molecular weight are critical factors in determining aqueous solubility. Thus, HeiankyoView is a powerful tool to help us understand structure-activity relationships intuitively from a large-scale data set.

Two- and three-dimensional QSAR of carrier-mediated transport of beta-lactam antibiotics in Caco-2 cells.

In this study, we investigated whether such a topological descriptor-based approach is suitable for predicting the carrier-mediated transport of 20 beta-lactam antibiotics that are substrates of peptide transporters. To select the molecular descriptors that can effectively predict a targeted property in QSAR analysis, the genetic algorithm-combined partial least squares approach was used. The feasibility of the two-dimensional (2D)-QSAR approach was compared with that of comparative molecular field analysis (CoMFA). The logarithm of the uptake values of 20 beta-lactam antibiotics in Caco-2 cells obtained from the literature ranged from -1.15 to 1.09 (nmol/cm2/2 h). When preliminary leave-one-out cross-validated partial least squares analyses implemented in the SYBYL/CoMFA program were conducted, the r2pred was 0.759 and the standard error of prediction (s) was 0.373. However, the 2D-QSAR approach based on Molconn-Z descriptors gave a better predictability (r2pred = 0.923, s = 0.211), where 14 descriptors were selected and the optimal number of principal components was 4. Considering that the 2D-topological descriptors are less computationally intensive and practically completely automated, the simple 2D-QSAR model is also of great importance in drug discovery settings.