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17β-HSD14 belongs to the SDR family and oxidizes the hydroxyl group at position 17 of estradiol and 5-androstenediol using NAD as cofactor. The goal of this study was to identify and optimize 17β-HSD14 nonsteroidal inhibitors as well as to disclose their structure-activity relationship. In a first screen, a library of 17β-HSD1 and 17β-HSD2 inhibitors, selected with respect to scaffold diversity, was tested for 17β-HSD14 inhibition. The most interesting hit was taken as starting point for chemical modification applying a ligand-based approach. The designed compounds were synthesized and tested for 17β-HSD14 inhibitory activity. The two best inhibitors identified in this study have a very high affinity to the enzyme with a K equal to 7 nM. The strong affinity of these inhibitors to the enzyme active site could be explained by crystallographic structure analysis, which highlighted the role of an extended H-bonding network in the stabilization process. The selectivity of the most potent compounds with respect to 17β-HSD1 and 17β-HSD2 is also addressed.

17β-HSD14 is a SDR enzyme able to oxidize estradiol and 5-androstenediol using NAD(+). We determined the crystal structure of this human enzyme as the holo form and as ternary complexes with estrone and with the first potent, nonsteroidal inhibitor. The structures reveal a conical, rather large and lipophilic binding site and are the starting point for structure-based inhibitor design. The two natural variants (S205 and T205) were characterized and adopt a similar structure.

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Human embryonic stem cells (hESCs) differentiated into cardiomyocytes (CM) often develop into complex 3D structures that are composed of various cardiac cell types. Conventional methods to study the electrophysiology of cardiac cells are patch clamp and microelectrode array (MEAs) analyses. However, these methods are not suitable to investigate the contractile features of 3D cardiac clusters that detach from the surface of the culture dishes during differentiation. To overcome this problem, we developed a video-based motion detection software relying on the optical flow by Farnebäck that we call cBRA (cardiac beat rate analyzer). The beating characteristics of the differentiated cardiac clusters were calculated based on the local displacement between two subsequent images. Two differentiation protocols, which profoundly differ in the morphology of cardiac clusters generated and in the expression of cardiac markers, were used and the resulting CM were characterized. Despite these differences, beat rates and beating variabilities could be reliably determined using cBRA. Likewise, stimulation of β-adrenoreceptors by isoproterenol could easily be identified in the hESC-derived CM. Since even subtle changes in the beating features are detectable, this method is suitable for high throughput cardiotoxicity screenings.

Within the Mobility Choices (MC) project we have developed an app that allows users to record their travel behavior and encourages them to try out new means of transportation that may better fit their preferences. Tracks explicitly released by the users are anonymized and can be analyzed by authorized institutions. For recorded tracks, the freely available app automatically determines the segments with their transportation mode; analyzes the track according to the criteria environment, health, costs, and time; and indicates alternative connections that better fit the criteria, which can individually be configured by the user. In the second step, the users can edit their tracks and release them for further analysis by authorized institutions. The system is complemented by a Web-based analysis program that helps authorized institutions carry out specific evaluations of traffic flows based on the released tracks of the app users. The automatic transportation mode detection of the system reaches an accuracy of 97%. This requires only minimal corrections by the user, which can easily be done directly in the app before releasing a track. All this enables significantly more accurate surveys of transport behavior than the usual time-consuming manual (non-automated) approaches, based on questionnaires.

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