We developed highly predictive classification models for human liver microsomal (HLM) stability using the apparent intrinsic clearance (CL(int, app)) as the end point. HLM stability has been shown to be an important factor related to the metabolic clearance of a compound. Robust in silico models that predict metabolic clearance are very useful in early drug discovery stages to optimize the compound structure and to select promising leads to avoid costly drug development failures in later stages. Using Random Forest and Bayesian classification methods with MOE, E-state descriptors, ADME Keys, and ECFP_6 fingerprints, various highly predictive models were developed. The best performance of the models shows 80 and 75% prediction accuracy for the test and validation sets, respectively. A detailed analysis of results will be shown, including an assessment of the prediction confidence, the significant descriptors, and the application of these models to drug discovery projects.

The aqueous solvation free energies and the average solute-solvent interaction energies of a diverse set of organic molecules were calculated by the XRISM (extended reference interaction-site model) method for two related potential-energy functions based upon the optimized potentials for liquid simulation (OPLS) parameter set.The results are compared with available data obtained from experimental and other theoretical methods. The XRISM method with the given potential-energy function parameter sets produces reasonable free-energy values for a number of molecules but not for all of the molecules studied. For some molecules, the results were also quite sensitive to the potential-energy function parameters used.

Most pharmacologically active molecules contain one or more ionizing groups, and it is well-known that knowledge of the ionization state of a drug, indicated by the pKa value, is critical for understanding many properties important to the drug discovery and development process. The ionization state of a compound directly influences such important pharmaceutical characteristics as aqueous solubility, permeability, crystal structure, etc. Tremendous advances have been made in the field of experimental determination of pKa, in terms of both quantity/speed and quality/accuracy. However, there still remains a need for accurate in silico predictions of pKa both to estimate this parameter for virtual compounds and to focus screening efforts of real compounds. The computer program SPARC (SPARC Performs Automated Reasoning in Chemistry) was used to predict the ionization state of a drug. This program has been developed based on the solid physical chemistry of reactivity models and applied to successfully predict numerous physical properties as well as chemical reactivity parameters. SPARC predicts both macroscopic and microscopic pKa values strictly from molecular structure. In this paper, we describe the details of the SPARC reactivity computational methods and its performance on predicting the pKa values of known drugs as well as Pfizer internal discovery/development compounds. A high correlation (r2=0.92) between experimental and the SPARC calculated pKa values was obtained with root-mean-square error (RMSE) of 0.78 log unit for a set of 123 compounds including many known drugs. For a set of 537 compounds from the Pfizer internal dataset, correlation coefficient r2=0.80 and RMSE=1.05 were obtained.

The adipose tissue-derived hormone leptin can drive decreases in food intake while increasing energy expenditure. In diet-induced obesity, circulating leptin levels rise proportionally to adiposity. Despite this hyperleptinemia, rodents and humans with obesity maintain increased adiposity and are resistant to leptin’s actions. Here we show that inhibitors of the cytosolic enzyme histone deacetylase 6 (HDAC6) act as potent leptin sensitizers and anti-obesity agents in diet-induced obese mice. Specifically, HDAC6 inhibitors, such as tubastatin A, reduce food intake, fat mass, hepatic steatosis and improve systemic glucose homeostasis in an HDAC6-dependent manner. Mechanistically, peripheral, but not central, inhibition of HDAC6 confers central leptin sensitivity. Additionally, the anti-obesity effect of tubastatin A is attenuated in animals with a defective central leptin-melanocortin circuitry, including db/db and MC4R-KO mice. Our results suggest the existence of an HDAC6-regulated adipokine that serves as a leptin-sensitizing agent, and reveals HDAC6 as a potential target for the treatment of obesity.

Bone morphogenetic protein (BMP) signaling is critical in renal development and disease. In animal models of chronic kidney disease (CKD), re-activation of BMP signaling is reported to be protective by promoting renal repair and regeneration. Clinical use of recombinant BMPs, however, requires harmful doses to achieve efficacy and is costly because of BMPs' complex synthesis. Therefore, alternative strategies are needed to harness the beneficial effects of BMP signaling in CKD. Key aspects of the BMP signaling pathway can be regulated by both extracellular and intracellular molecules. In particular, secreted proteins like noggin and chordin inhibit BMP activity, whereas kielin/chordin-like proteins (KCP) enhance it and attenuate kidney fibrosis or CKD. Clinical development of KCP, however, is precluded by its size and complexity. Therefore, we propose an alternative strategy to enhance BMP signaling by using small molecules, which are simpler to synthesize and more cost-effective. To address our objective, here we developed a small-molecule high-throughput screen (HTS) with human renal cells having an integrated luciferase construct highly responsive to BMPs. We demonstrate the activity of a potent benzoxazole compound, sb4, that rapidly stimulated BMP signaling in these cells. Activation of BMP signaling by sb4 increased the phosphorylation of key second messengers (SMAD-1/5/9) and also increased expression of direct target genes (inhibitors of DNA binding, Id1 and Id3) in canonical BMP signaling. Our results underscore the feasibility of utilizing HTS to identify compounds that mimic key downstream events of BMP signaling in renal cells and have yielded a lead BMP agonist. Figure 3. Dose responses of the top 12 potential BMP agonists.A, 12 compounds were tested for BRE-Luc responses at 2-fold increasing concentrations from 0.5 nM to 10 M in triplicate. B, effective concentrations at 50% maximum response (EC 50 ), and the Hill slopes were calculated for each compound. Maximum -fold induction (FI) above DMSO controls is reported. C, chemical structures of the 12 candidate BMP agonists are shown schematically.