Tobias Noeske

Ligand-Based Target Prediction with Signature Fingerprints

When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.

Design and optimization of pyrazinecarboxamide-based inhibitors of diacylglycerol acyltransferase 1 (DGAT1) leading to a clinical candidate dimethylpyrazinecarboxamide phenylcyclohexylacetic acid (AZD7687).

A new series of pyrazinecarboxamide DGAT1 inhibitors was designed to address the need for a candidate drug with good potency, selectivity, and physical and DMPK properties combined with a low predicted dose in man. Rational design and optimization of this series led to the discovery of compound 30 (AZD7687), which met the project objectives for potency, selectivity, in particular over ACAT1, solubility, and preclinical PK profiles. This compound showed the anticipated excellent pharmacokinetic properties in human volunteers.

Design of Small Molecule Inhibitors of Acetyl-Coa Carboxylase 1 and 2 Showing Reduction of Hepatic Malonyl-Coa Levels in Vivo in Obese Zucker Rats.

Inhibition of acetyl-CoA carboxylases has the potential for modulating long chain fatty acid biosynthesis and mitochondrial fatty acid oxidation. Hybridization of weak inhibitors of ACC2 provided a novel, moderately potent but lipophilic series. Optimization led to compounds 33 and 37, which exhibit potent inhibition of human ACC2, 10-fold selectivity over inhibition of human ACC1, good physical and in vitro ADME properties and good bioavailability. X-ray crystallography has shown this series binding in the CT-domain of ACC2 and revealed two key hydrogen bonding interactions. Both 33 and 37 lower levels of hepatic malonyl-CoA in vivo in obese Zucker rats.

Flagging Drugs That Inhibit the Bile Salt Export Pump

The bile salt export pump (BSEP) is an ABC-transporter expressed at the canalicular membrane of hepatocytes. Its physiological role is to expel bile salts into the canaliculi from where they drain into the bile duct. Inhibition of this transporter may lead to intrahepatic cholestasis. Predictive computational models of BSEP inhibition may allow for fast identification of potentially harmful compounds in large databases. This article presents a predictive in silico model based on physicochemical descriptors that is able to flag compounds as potential BSEP inhibitors. This model was built using a training set of 670 compounds with available BSEP inhibition potencies. It successfully predicted BSEP inhibition for two independent test sets and was in a further step used for a virtual screening experiment. After in vitro testing of selected candidates, a marketed drug, bromocriptin, was identified for the first time as BSEP inhibitor. This demonstrates the usefulness of the model to identify new BSEP inhibitors and therefore potential cholestasis perpetrators.

Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen

The drug-induced accumulation of phospholipids in lysosomes of various tissues is predominantly observed in regular repeat dose studies, often after prolonged exposure, and further investigated in mechanistic studies prior to candidate nomination. The finding can cause delays in the discovery process inflicting high costs to the affected projects. This article presents an in vitro imaging-based method for early detection of phospholipidosis liability and a hybrid approach for early detection and risk mitigation of phospolipidosis utilizing the in vitro readout with in silico model prediction. A set of reference compounds with phospolipidosis annotation was used as an external validation set yielding accuracies between 77.6% and 85.3% for various in vitro and in silico models, respectively. By means of a small set of chemically diverse known drugs with in vivo phospholipidosis annotation, the advantages of combining different prediction methods to reach an overall improved phospholipidosis prediction will be discussed.

Strategies for Minimisation of the Cholestatic Liver Injury Liability Posed by Drug- Induced Bile Salt Export Pump (BSEP) Inhibition

Hepatobiliary uptake and efflux transporter proteins play key roles in the formation of bile, which is a vital function of the liver. The ATP-dependent bile salt export pump (BSEP) excretes bile salts from hepatocytes into bile. Inherited BSEP mutations in humans cause intrahepatic accumulation of bile salts, which results in cholestatic liver injury. Furthermore, inhibition of BSEP activity is considered one of a number of key initiating mechanisms by which drugs may cause liver injury (drug-induced liver injury, DILI) in the human population. DILI is an important cause of serious drug-induced illness and is a leading cause of drug attrition during development and of drug withdrawal and restrictive labelling post-marketing. In this chapter we summarise the evidence that BSEP inhibition is a drug-related DILI risk factor, we describe experimental approaches (in silico, in vitro and in vivo) which may be used to predict and quantify this process during drug discovery and development and we discuss data interpretation. We also outline an approach by which assessment of BSEP inhibition in drug discovery can be used to reduce the likelihood that DILI may arise during development. In addition, we consider the current state of computational predictive modelling of BSEP inhibition and discuss the influence of physicochemical parameters.