N-Thiazolylamide-based free fatty-acid 2 receptor agonists: Discovery, lead optimization and demonstration of off-target effect in a diabetes model.

Free fatty acid-2 (FFA2) receptor is a G-protein coupled receptor of interest in the development of therapeutics in metabolic and inflammatory disease areas. The discovery and optimization of an N-thiazolylamide carboxylic acid FFA2 agonist scaffold is described. Dual key objectives were to i) evaluate the potential of this scaffold for lead optimization in particular with respect to safety de-risking physicochemical properties, i.e. lipophilicity and aromatic content, and ii) to demonstrate the utility of selected lead analogues from this scaffold in a pertinent in vivo model such as oral glucose tolerance test (OGTT). As such, a concomitant improvement in bioactivity together with lipophilic ligand efficiency (LLE) and fraction sp3 content (Fsp3) parameters guided these efforts. Compound 10 was advanced into studies in mice on the basis of its optimized profile vs initial lead 1 (ΔLLE = 0.3, ΔFsp3 = 0.24). Although active in OGTT, 10 also displayed similar activity in the FFA2-knockout mice. Given this off-target OGTT effect, we discontinued development of this FFA2 agonist scaffold.

Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth.

We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.

Synergistically applying 1-D modeling and CFD for designing industrial scale bubble column syngas bioreactors.

The reduction of greenhouse gas emissions and future perspectives of circular economy ask for new solutions to produce commodities and fine chemicals. Large-scale bubble columns operated by gaseous substrates such as CO, CO2, and H2 to feed acetogens for product formations could be promising approaches. Valid in silico predictions of large-scale performance are needed to dimension bioreactors properly taking into account biological constraints, too. This contribution deals with the trade-off between sophisticated spatiotemporally resolved large-scale simulations using computationally intensive Euler-Euler and Euler-Lagrange approaches and coarse-grained 1-D models enabling fast performance evaluations. It is shown that proper consideration of gas hold-up is key to predict biological performance. Intrinsic bias of 1-D models can be compensated by reconsideration of Sauter diameters derived from uniquely performed Euler-Lagrange computational fluid dynamics.

Optimization of Novel Antagonists to the Neurokinin-3 Receptor for the Treatment of Sex-Hormone Disorders (Part II).

Further lead optimization on N-acyl-triazolopiperazine antagonists to the neurokinin-3 receptor (NK3R) based on the concurrent improvement in bioactivity and ligand lipophilic efficiency (LLE) is reported. Overall, compound 3 (LLE > 6) emerged as the most efficacious in castrated rat and monkey to lower plasma LH, and it displayed the best off-target safety profile that led to its clinical candidate nomination for the treatment of sex-hormone disorders.

Discovery and optimization of novel antagonists to the human neurokinin-3 receptor for the treatment of sex-hormone disorders (Part I).

Neurokinin-3 receptor (NK3R) has recently emerged as important in modulating the tonic pulsatile gonadotropin-releasing hormone (GnRH) release. We therefore decided to explore NK3R antagonists as therapeutics for sex-hormone disorders that can potentially benefit from lowering GnRH pulsatility with consequent diminished levels of plasma luteinizing hormone (LH) and correspondingly attenuated levels of circulating androgens and estrogens. The discovery and lead optimization of a novel N-acyl-triazolopiperazine NK3R antagonist chemotype achieved through bioisosteric lead change from the high-throughput screening (HTS) hit is described. A concomitant improvement in the antagonist bioactivity and ligand lipophilic efficiency (LLE) parameter were the principal guidelines in the lead optimization efforts. Examples of advanced lead analogues to demonstrate the amenability of this chemotype to achieving a suitable pharmacokinetic (PK) profile are provided as well as pharmacokinetic-pharmacodynamic (PKPD) correlations to analyze the trends observed for LH inhibition in castrated rats and monkeys that served as preliminary in vivo efficacy models.