ABSTRACT:Thirty-two structurally diverse drugs used for the treatment of various conditions of the central nervous system (CNS), along with two active metabolites, and eight non-CNS drugs were measured in brain, plasma, and cerebrospinal fluid in the P-glycoprotein (P-gp) knockout mouse model after subcutaneous administration, and the data were compared with corresponding data obtained in wild-type mice. Total brain-to-plasma (B/P) ratios for the CNS agents ranged from 0.060 to 24. Of the 34 CNS-active agents, only 7 demonstrated B/P area under the plasma concentration curve ratios between P-gp knockout and wild-type mice that did not differ significantly from unity. Most of the remaining drugs demonstrated 1.1-to 2.6-fold greater B/P ratios in P-gp knockout mice versus wild-type mice. Three, risperidone, its active metabolite 9-hydroxyrisperidone, and metoclopramide, showed marked differences in B/P ratios between knockout and wild-type mice (6.6-to 17-fold). Differences in B/P ratios and cerebrospinal fluid/ plasma ratios between wild-type and knockout animals were correlated. Through the use of this model, it appears that most CNSactive agents demonstrate at least some P-gp-mediated transport that can affect brain concentrations. However, the impact for the majority of agents is probably minor. The example of risperidone illustrates that even good P-gp substrates can still be clinically useful CNS-active agents. However, for such agents, unbound plasma concentrations may need to be greater than values projected using receptor affinity data to achieve adequate receptor occupancy for effect.Active transport mechanisms as determinants of drug absorption, distribution, and clearance have been the focus of considerable research effort over the past decade. Of the numerous transporter proteins recently investigated, the one for which the greatest amount of knowledge exists is P-glycoprotein (MDR1). Originally described as a transporter involved in imparting drug resistance to tumor cells, P-glycoprotein has been demonstrated to be important in reducing absorption of drugs from the intestinal lumen, in active secretion of drugs into urine and bile, and in extrusion of drugs from vital organs such as the brain and reproductive tissues (Troutman et al., 2002). As such, P-glycoprotein-mediated transport has become an important issue in the discovery and development of new drugs. For example, new compounds that are promising with regard to target receptor/ enzyme activity can be severely hampered in their ability to elicit pharmacological effects in vivo should they be good substrates for P-glycoprotein, especially if the route of administration is intended to be oral or the target tissues is one rich in P-glycoprotein activity. Furthermore, the potential for drug-drug interactions arises in the event that the P-glycoprotein substrate is coadministered with another agent that can inhibit P-glycoprotein.Several models have been developed to assess drugs as P-glycoprotein substrates. In vitro models have included the Caco...

This article is available online at http://dmd.aspetjournals.org ABSTRACT:The objectives of this study were to generate a data set of bloodbrain barrier (BBB) permeability values for drug-like compounds and to develop a computational model to predict BBB permeability from structure. The BBB permeability, expressed as permeabilitysurface area product (PS, quantified as logPS), was determined for 28 structurally diverse drug-like compounds using the in situ rat brain perfusion technique. A linear model containing three descriptors, logD, van der Waals surface area of basic atoms, and polar surface area, was developed based on 23 compounds in our data set, where the penetration across the BBB was assumed to occur primarily by passive diffusion The blood-brain barrier (BBB 1 ) consists of a continuous layer of endothelial cells joined by tight junctions at the cerebral vasculature. It represents a physical and enzymatic barrier to restrict and regulate the penetration of compounds into and out of the brain and maintain the homeostasis of the brain microenvironment (Davson and Segal, 1995). Brain penetration is essential for compounds where the site of action is within the central nervous system, whereas BBB penetration needs to be minimized for compounds that target peripheral sites to reduce potential central nervous system-related side effects. Therefore, it is critical during the drug discovery phase to select compounds that have appropriate brain penetration properties. Brain penetration is commonly assessed by two experimental approaches, namely equilibrium distribution between brain and blood and BBB permeability. The equilibrium distribution is defined as the ratio of concentrations in brain and blood (BB, quantified as logBB). LogBB is determined at

Adenosine monophosphate-activated protein kinase (AMPK) is a protein kinase involved in maintaining energy homeostasis within cells. On the basis of human genetic association data, AMPK activators were pursued for the treatment of diabetic nephropathy. Identification of an indazole amide high throughput screening (HTS) hit followed by truncation to its minimal pharmacophore provided an indazole acid lead compound. Optimization of the core and aryl appendage improved oral absorption and culminated in the identification of indole acid, PF-06409577 (7). Compound 7 was advanced to first-in-human trials for the treatment of diabetic nephropathy.

Glucokinase is a key regulator of glucose homeostasis, and small molecule allosteric activators of this enzyme represent a promising opportunity for the treatment of type 2 diabetes. Systemically acting glucokinase activators (liver and pancreas) have been reported to be efficacious but in many cases present hypoglycaemia risk due to activation of the enzyme at low glucose levels in the pancreas, leading to inappropriately excessive insulin secretion. It was therefore postulated that a liver selective activator may offer effective glycemic control with reduced hypoglycemia risk. Herein, we report structure-activity studies on a carboxylic acid containing series of glucokinase activators with preferential activity in hepatocytes versus pancreatic β-cells. These activators were designed to have low passive permeability thereby minimizing distribution into extrahepatic tissues; concurrently, they were also optimized as substrates for active liver uptake via members of the organic anion transporting polypeptide (OATP) family. These studies lead to the identification of 19 as a potent glucokinase activator with a greater than 50-fold liver-to-pancreas ratio of tissue distribution in rodent and non-rodent species. In preclinical diabetic animals, 19 was found to robustly lower fasting and postprandial glucose with no hypoglycemia, leading to its selection as a clinical development candidate for treating type 2 diabetes.

CRISPR-Cas RNA-guided endonucleases hold great promise for disrupting or correcting genomic sequences through site-specific DNA cleavage and repair. However, the lack of methods for cell- and tissue-selective delivery currently limits both research and clinical uses of these enzymes. We report the design and in vitro evaluation of S. pyogenes Cas9 proteins harboring asialoglycoprotein receptor ligands (ASGPrL). In particular, we demonstrate that the resulting ribonucleoproteins (Cas9-ASGPrL RNP) can be engineered to be preferentially internalized into cells expressing the corresponding receptor on their surface. Uptake of such fluorescently labeled proteins in liver-derived cell lines HEPG2 (ASGPr+) and SKHEP (control; diminished ASGPr) was studied by live cell imaging and demonstrates increased accumulation of Cas9-ASGPrL RNP in HEPG2 cells as a result of effective ASGPr-mediated endocytosis. When uptake occurred in the presence of a peptide with endosomolytic properties, we observed receptor-facilitated and cell-type specific gene editing that did not rely on electroporation or the use of transfection reagents. Overall, these in vitro results validate the receptor-mediated delivery of genome-editing enzymes as an approach for cell-selective gene editing and provide a framework for future potential applications to hepatoselective gene editing in vivo.

Current drug discovery is expensive and time-consuming. It remains a challenging task to create a wide variety of novel compounds that not only have desirable pharmacological properties but also are cheaply available to low-income people. In this work, we develop a generative network complex (GNC) to generate new drug-like molecules based on the multiproperty optimization via the gradient descent in the latent space of an autoencoder. In our GNC, both multiple chemical properties and similarity scores are optimized to generate drug-like molecules with desired chemical properties. To further validate the reliability of the predictions, these molecules are reevaluated and screened by independent 2D fingerprint-based predictors to come up with a few hundreds of new drug candidates. As a demonstration, we apply our GNC to generate a large number of new BACE1 inhibitors, as well as thousands of novel alternative drug candidates for eight existing market drugs, including Ceritinib, Ribociclib, Acalabrutinib, Idelalisib, Dabrafenib, Macimorelin, Enzalutamide, and Panobinostat.

Recently, molecular fingerprints extracted from three-dimensional (3D) structures using advanced mathematics, such as algebraic topology, differential geometry, and graph theory have been paired with efficient machine learning, especially deep learning algorithms to outperform other methods in drug discovery applications and competitions. This raises the question of whether classical 2D fingerprints are still valuable in computer-aided drug discovery. This work considers 23 datasets associated with four typical problems, namely protein-ligand binding, toxicity, solubility and partition coefficient to assess the performance of eight 2D fingerprints. Advanced machine learning algorithms including random forest, gradient boosted decision tree, single-task deep neural network and multitask deep neural network are employed to construct efficient 2D-fingerprint based models. Additionally, appropriate consensus models are built to further enhance the performance of 2D-fingerprintbased methods. It is demonstrated that 2D-fingerprint-based models perform as well as the state-of-the-art 3D structure-based models for the predictions of toxicity, solubility, partition coefficient and protein-ligand binding affinity based on only ligand information. However, 3D structure-based models outperform 2D fingerprint-based methods in complex-based protein-ligand binding affinity predictions. * Corresponding to Guo-Wei Wei.Molecular fingerprints are one way of encoding the structural features of a molecule. They play a fundamental role in QSAR/QSPR analysis, virtual screening, similarity-based compound search, target molecule ranking, drug ADMET prediction, and other drug discovery processes. Molecular fingerprints are property profiles of a molecule, usually in the form of vectors with each vector element indicating the existence, the degree or the frequency of one particular structure feature. [12][13][14] Various fingerprints have been developed for molecular feature encoding in the past few decades. [15][16][17] Most fingerprints are 2D fingerprints which can be extracted from molecular connection tables without 3D structure information. However, high dimensional fingerprints have also been developed to utilize 3D molecular structure and other information. 18 There are four main categories of 2D fingerprints, namely substructure key-based fingerprints, topological or path-based fingerprints, circular fingerprints, and pharmacophore fingerprints. Substructure key-based fingerprints are bit strings representing the presence of certain substructures or fragments from a given list of structural keys in the compound. Molecular access system (MACCS) 19 is one of the most popular substructure keybased fingerprint methods. Topological or path-based fingerprints are based on analyzing all the fragments of a molecule following a (usually linear) path up to a certain number of bonds, and then hashing every one of these paths to create one fingerprint. The most prominent ones in this category are FP2, 20 Daylight 21 and electrotopological sta...

A compact and stable bicyclic bridged ketal was developed as a ligand for the asialoglycoprotein receptor (ASGPR). This compound showed excellent ligand efficiency, and the molecular details of binding were revealed by the first X-ray crystal structures of ligand-bound ASGPR. This analogue was used to make potent di- and trivalent binders of ASGPR. Extensive characterization of the function of these compounds showed rapid ASGPR-dependent cellular uptake in vitro and high levels of liver/plasma selectivity in vivo. Assessment of the biodistribution in rodents of a prototypical Alexa647-labeled trivalent conjugate showed selective hepatocyte targeting with no detectable distribution in nonparenchymal cells. This molecule also exhibited increased ASGPR-directed hepatocellular uptake and prolonged retention compared to a similar GalNAc derived trimer conjugate. Selective release in the liver of a passively permeable small-molecule cargo was achieved by retro-Diels-Alder cleavage of an oxanorbornadiene linkage, presumably upon encountering intracellular thiol. Therefore, the multicomponent construct described here represents a highly efficient delivery vehicle to hepatocytes.