Jörg Bentzien

5-Aminomethylbenzimidazoles as potent ITK antagonists

Benzamide 1 demonstrated good potency as a selective ITK inhibitor, however the amide moiety was found to be hydrolytically labile in vivo, resulting in low oral exposure and the generation of mutagenic aromatic amine metabolites. Replacing the benzamide with a benzylamine linker not only addressed the toxicity issue, but also improved the cellular and functional potency as well as the drug-like properties. SAR studies around the benzylamines and the identification of 10n and 10o as excellent tools for proof-of-concept studies are described.

Discovery of potent inhibitors of interleukin-2 inducible T-cell kinase (ITK) through structure-based drug design

Interleukin-2 inducible T-cell kinase (ITK) is a member of the Tec kinase family and is involved with T-cell activation and proliferation. Due to its critical role in acting as a modulator of T-cells, ITK inhibitors could provide a novel route to anti-inflammatory therapy. This work describes the discovery of ITK inhibitors through structure-based design where high-resolution crystal structural information was used to optimize interactions within the kinase specificity pocket of the enzyme to improve both potency and selectivity.

2-Aminobenzimidazoles as potent ITK antagonists: trans-stilbene-like moieties targeting the kinase specificity pocket.

Based on the information from molecular modeling and X-ray crystal structures, the kinase specificity pocket of ITK could be occupied upon extension of the right-hand-side of the 2-benzimidazole core of the inhibitors. 2-Aminobenzimidazoles with a trans-stilbene-like extension were designed and synthesized as novel ITK antagonists. Significant improvement on binding affinity and cellular activity were obtained through the trans-stilbene-like antagonists. Several compounds showed inhibitory activity in an IL-2 functional assay.

DemQSAR: predicting human volume of distribution and clearance of drugs

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure–activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VDss) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VDss and CL is available on the webpage of DemPRED: http://agknapp.chemie.fu-berlin.de/dempred/.

Identification of a Potent Sodium Hydrogen Exchanger Isoform 1 (NHE1) Inhibitor with a Suitable Profile for Chronic Dosing and Demonstrated Cardioprotective Effects in a Preclinical Model of Myocardial Infarction in the Rat

Sodium-hydrogen exchanger isoform 1 (NHE1) is a ubiquitously expressed transmembrane ion channel responsible for intracellular pH regulation. During myocardial ischemia, low pH activates NHE1 and causes increased intracellular calcium levels and aberrant cellular processes, leading to myocardial stunning, arrhythmias, and ultimately cell damage and death. The role of NHE1 in cardiac injury has prompted interest in the development of NHE1 inhibitors for the treatment of heart failure. This report outlines our efforts to identify a compound suitable for once daily, oral administration with low drug-drug interaction potential starting from NHE1 inhibitor sabiporide. Substitution of a piperidine for the piperazine of sabiporide followed by replacement of the pyrrole moiety and subsequent optimization to improve potency and eliminate off-target activities resulted in the identification of N-[4-(1-acetyl-piperidin-4-yl)-3-trifluoromethyl-benzoyl]-guanidine (60). Pharmacological evaluation of 60 revealed a remarkable ability to prevent ischemic damage in an ex vivo model of ischemia reperfusion injury in isolated rat hearts.

Non-steroidal dissociated glucocorticoid agonists: indoles as A-ring mimetics and function-regulating pharmacophores

We report a SAR of non-steroidal glucocorticoid mimetics that utilize indoles as A-ring mimetics. Detailed SAR is discussed with a focus on improving PR and MR selectivity, GR agonism, and in vitro dissociation profile. SAR analysis led to compound (R)-33 which showed high PR and MR selectivity, potent agonist activity, and reduced transactivation activity in the MMTV and aromatase assays. The compound is equipotent to prednisolone in the LPS-TNF model of inflammation. In mouse CIA, at 30 mg/kg compound (R)-33 inhibited disease progression with an efficacy similar to the 3 mg/kg dose of prednisolone.

Discovery of a potent and dissociated non-steroidal glucocorticoid receptor agonist containing an alkyl carbinol pharmacophore.

Synthesis and structure-activity relationship (SAR) of a series of alkyl and cycloalkyl containing non-steroidal dissociated glucocorticoid receptor (GR) agonists is reported. This series of compounds was identified as part of an effort to replace the CF3 group in a scaffold represented by 1a. The study culminated in the identification of compound 14, a t-butyl containing derivative, which has shown potent activity for GR, selectivity against the progesterone receptor (PR) and the mineralocorticoid receptor (MR), in vitro anti-inflammatory activity in an IL-6 transrepression assay, and dissociation in a MMTV transactivation counter-screen. In a collagen-induced arthritis mouse model, 14 displayed prednisolone-like efficacy, and lower impact on body fat and free fatty acids than prednisolone at an equivalent anti-inflammatory dose.

Function-regulating pharmacophores in a sulfonamide class of glucocorticoid receptor agonists

A class of α-methyltryptamine sulfonamide glucocorticoid receptor (GR) modulators was optimized for agonist activity. The design of ligands was aided by molecular modeling, and key function-regulating pharmacophoric points were identified that are critical in achieving the desired agonist effect in cell based assays. Compound 27 was profiled in vitro and in vivo in models of inflammation. Analogs could be rapidly prepared in a parallel approach from aziridine building blocks.

Automatically updating predictive modeling workflows support decision-making in drug design

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

Substituted phenyl as a steroid A-ring mimetic: Providing agonist activity to a class of arylsulfonamide nonsteroidal glucocorticoid ligands

A class of arylsulfonamide glucocorticoid receptor agonists that contains a substituted phenyl group as a steroid A-ring mimetic is reported. The structural design and SAR that provide the functional switching of a GR antagonist to an agonist is described. A combination of specific hydrogen bonding and lipophilic elements on the A-ring moiety is required to achieve potent GR agonist activity. This study culminated in the identification of compound 23 as a potent GR agonist with selectivity over the PR and MR nuclear hormone receptors.