An Integrative Approach for Improved Assessment of Cardiovascular Safety Data.

Cardiovascular adverse effects in drug development are a major source of compound attrition. Characterization of blood pressure (BP), heart rate (HR), stroke volume (SV), and QT-interval prolongation are therefore necessary in early discovery. It is, however, common practice to analyze these effects independently of each other. High-resolution time courses are collected via telemetric techniques, but only low-resolution data are analyzed and reported. This ignores codependencies among responses (HR, BP, SV, and QT-interval) and separation of system (turnover properties) and drug-specific properties (potencies, efficacies). An analysis of drug exposure-time and high-resolution response-time data of HR and mean arterial blood pressure was performed after acute oral dosing of ivabradine, sildenafil, dofetilide, and pimobendan in Han-Wistar rats. All data were modeled jointly, including different compounds and exposure and response time courses, using a nonlinear mixed-effects approach. Estimated fractional turnover rates [h-1, relative standard error (%RSE) within parentheses] were 9.45 (15), 30.7 (7.8), 3.8 (13), and 0.115 (1.7) for QT, HR, total peripheral resistance, and SV, respectively. Potencies (nM, %RSE within parentheses) were IC 50 = 475 (11), IC 50 = 4.01 (5.4), EC 50 = 50.6 (93), and IC 50 = 47.8 (16), and efficacies (%RSE within parentheses) were I max = 0.944 (1.7), Imax = 1.00 (1.3), E max = 0.195 (9.9), and Imax = 0.745 (4.6) for ivabradine, sildenafil, dofetilide, and pimobendan. Hill parameters were estimated with good precision and below unity, indicating a shallow concentration-response relationship. An equilibrium concentration-biomarker response relationship was predicted and displayed graphically. This analysis demonstrates the utility of a model-based approach integrating data from different studies and compounds for refined preclinical safety margin assessment. SIGNIFICANCE STATEMENT: A model-based approach was proposed utilizing biomarker data on heart rate, blood pressure, and QT-interval. A pharmacodynamic model was developed to improve assessment of high-resolution telemetric cardiovascular safety data driven by different drugs (ivabradine, sildenafil, dofetilide, and pimobondan), wherein system- (turnover rates) and drug-specific parameters (e.g., potencies and efficacies) were sought. The model-predicted equilibrium concentration-biomarker response relationships and was used for safety assessment (predictions of 20% effective concentration, for example) of heart rate, blood pressure, and QT-interval.

NMDA receptor antagonists traxoprodil and lanicemine improve hippocampal-prefrontal coupling and reward-related networks in rats.

RATIONALE: The N-methyl-D-aspartate receptor (NMDAR) antagonist ketamine is known to have not only a rapid antidepressant effect but also dissociative side effects. Traxoprodil and lanicemine, also NMDA antagonists, are candidate antidepressant drugs with fewer side effects. OBJECTIVES: In order to understand their mechanism of action, we investigated the acute effects of traxoprodil and lanicemine on brain connectivity using resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: Functional connectivity (FC) alterations were examined using interregional correlation networks. Graph theoretical methods were used for whole brain network analysis. As interest in NMDAR antagonists as potential antidepressants was triggered by the antidepressant effect of ketamine, results were compared to previous findings from our ketamine studies. RESULTS: Similar to ketamine but to a smaller extent, traxoprodil increased hippocampal-prefrontal (Hc-PFC) coupling. Unlike ketamine, traxoprodil decreased connectivity within the PFC. Lanicemine had no effect on these properties. The improvement of Hc-PFC coupling corresponds well to clinical result, showing ketamine to have a greater antidepressant effect than traxoprodil, while lanicemine has a weak and transient effect. Connectivity changes overlapping between the drugs as well as alterations of local network properties occurred mostly in reward-related regions. CONCLUSION: The antidepressant effect of NMDA antagonists appears to be associated with enhanced Hc-PFC coupling. The effects on local network properties and regional connectivity suggest that improvement of reward processing might also be important for understanding the mechanisms underlying the antidepressant effects of these drugs.

Modeling energy intake and body weight effects of a long-acting amylin analogue.

The inhibitory effect of anti-obesity drugs on energy intake (EI) is counter-acted by feedback regulation of the appetite control circuit leading to drug tolerance. This complicates the design and interpretation of EI studies in rodents that are used for anti-obesity drug development. Here, we investigated a synthetic long-acting analogue of the appetite-suppressing peptide hormone amylin (LAMY) in lean and diet-induced obese (DIO) rats. EI and body weight (BW) were measured daily and LAMY concentrations in plasma were assessed using defined time points following subcutaneous administration of the LAMY at different dosing regimens. Overall, 6 pharmacodynamic (PD) studies including a total of 173 rats were considered in this evaluation. Treatment caused a dose-dependent reduction in EI and BW, although multiple dosing indicated the development of tolerance over time. This behavior could be adequately described by a population model including homeostatic feedback of EI and a turnover model describing the relationship between EI and BW. The model was evaluated by testing its ability to predict BW loss in a toxicology study and was utilized to improve the understanding of dosing regimens for obesity therapy. As such, the model proved to be a valuable tool for the design and interpretation of rodent studies used in anti-obesity drug development.

Discovery of BI 99179, a potent and selective inhibitor of type I fatty acid synthase with central exposure.

Based on a high-throughput screen, cyclopentanecarboxanilides were identified as a new chemotype of non-covalent inhibitors of type I fatty acid synthase (FAS). Starting from initial hits we aimed at generating a tool compound suitable for the in vivo validation of FAS as a therapeutic target. Optimisation yielded BI 99179 which is characterised by high potency, remarkably high selectivity and significant exposure (both peripheral and central) upon oral administration in rats.

Novel 6,7,8,9-tetrahydro-5H-1,4,7,10a-tetraaza-cyclohepta[f]indene analogues as potent and selective 5-HT(2C) agonists for the treatment of metabolic disorders.

The discovery of a novel series of 5-HT(2C) agonists based on a tricyclic pyrazolopyrimidine scaffold is described. Compounds with good levels of in vitro potency and moderate to good levels of selectivity with respect to the 5-HT(2A) and 5-HT(2B) receptors were identified. One of the analogues (7 g) was found to be efficacious in a sub-chronic weight loss model. A key limitation of the series of compounds was that they were found to be potent inhibitors of the hERG ion channel. Some compounds, bearing polar side chains were identified which showed a much reduced hERG liability however these compounds were sub-optimal in terms of their in vitro potency or selectivity.

Computational approaches to predict drug metabolism.

BACKGROUND: Metabolism is one of the key parameters to be investigated and optimized in drug discovery projects. Metabolically unstable compounds or potential inhibitors of important enzymes should be detected as early as possible. As more compounds are synthesized than can be investigated experimentally, powerful computational methods are needed. OBJECTIVE: We give an overview of state-of-the-art in-silico methods to predict experimental metabolic endpoints with a focus on the applicability in pharmaceutical industry. A macroscopic as well as a microscopic view of the metabolic fate and the interaction with metabolizing enzymes are given. METHODS: Ligand-, protein- and rule-based approaches are presented. CONCLUSION: Although considerable progress has been made, the results of the calculations still need careful inspection. The domain of applicability of the models as well as methodological limitations have to be taken into account.