Cannabinoid receptors (CB1 and CB2), as part of the endocannabinoid system, play a critical role in numerous human physiological and pathological conditions. Thus, considerable efforts have been made to develop ligands for CB1 and CB2, resulting in hundreds of phyto- and synthetic cannabinoids which have shown varying affinities relevant for the treatment of various diseases. However, only a few of these ligands are clinically used. Recently, more detailed structural information for cannabinoid receptors was revealed thanks to the powerfulness of cryo-electron microscopy, which now can accelerate structure-based drug discovery. At the same time, novel peptide-type cannabinoids from animal sources have arrived at the scene, with their potential in vivo therapeutic effects in relation to cannabinoid receptors. From a natural products perspective, it is expected that more novel cannabinoids will be discovered and forecasted as promising drug leads from diverse natural sources and species, such as animal venoms which constitute a true pharmacopeia of toxins modulating diverse targets, including voltage- and ligand-gated ion channels, G protein-coupled receptors such as CB1 and CB2, with astonishing affinity and selectivity. Therefore, it is believed that discovering novel cannabinoids starting from studying the biodiversity of the species living on planet earth is an uncharted territory.

(1) Background: The voltage-gated potassium channel K_V10.1 (Eag1) is considered a near- universal tumour marker and represents a promising new target for the discovery of novel anticancer drugs. (2) Methods: We utilized the ligand-based drug discovery methodology using 3D pharmacophore modelling and medicinal chemistry approaches to prepare a novel structural class of K_V10.1 inhibitors. Whole-cell patch clamp experiments were used to investigate potency, selectivity, kinetics and mode of inhibition. Anticancer activity was determined using 2D and 3D cell-based models. (3) Results: The virtual screening hit compound ZVS-08 discovered by 3D pharmacophore modelling exhibited an IC₅₀ value of 3.70 µM against K_V10.1 and inhibited the channel in a voltage-dependent manner consistent with the action of a gating modifier. Structural optimization resulted in the most potent K_V10.1 inhibitor of the series with an IC₅₀ value of 740 nM, which was potent on the MCF-7 cell line expressing high K_V10.1 levels and low hERG levels, induced significant apoptosis in tumour spheroids of Colo-357 cells and was not mutagenic. (4) Conclusions: Computational ligand-based drug design methods can be successful in the discovery of new potent K_V10.1 inhibitors. The main problem in the field of K_V10.1 inhibitors remains selectivity against the hERG channel, which needs to be addressed in the future also with target-based drug design methods.

The voltage-gated potassium channel K_V1.3 has been recognized as a tumor marker and represents a promising new target for the discovery of new anticancer drugs. We designed a novel structural class of K_V1.3 inhibitors through structural optimization of benzamide-based hit compounds and structure-activity relationship studies. The potency and selectivity of the new K_V1.3 inhibitors were investigated using whole-cell patch- and voltage-clamp experiments. 2D and 3D cell models were used to determine antiproliferative activity. Structural optimization resulted in the most potent and selective K_V1.3 inhibitor 44 in the series with an IC₅₀ value of 470 nM in oocytes and 950 nM in Ltk⁻ cells. K_V1.3 inhibitor 4 induced significant apoptosis in Colo-357 spheroids, while 14, 37, 43, and 44 significantly inhibited Panc-1 proliferation.

Expression of the voltage-gated potassium channel K_V10.1 (Eag1) has been detected in over 70% of human cancers, making the channel a promising new target for new anticancer drug discovery. A new structural class of K_V10.1 inhibitors was prepared by structural optimisation and exploration of the structure–activity relationship of the previously published hit compound ZVS-08 (1) and its optimised analogue 2. The potency and selectivity of the new inhibitors between K_V10.1 and hERG were investigated using whole-cell patch-clamp experiments. We obtained two new optimised K_V10.1 inhibitors, 17a and 18b, with improved nanomolar IC₅₀ values of 568 nM and 214 nM, respectively. Compound 17a exhibited better ratio between IC₅₀ values for hEAG1 and hERG than previously published diarylamine inhibitors. Compounds 17a and 18b moderately inhibited the growth of the K_V10.1-expressing cell line MCF-7 in two independent assays. In addition, 17a and 18b also inhibited the growth of hERG-expressing Panc-1 cells with higher potency compared with MCF-7 cells. The main obstacle for newly developed diarylamine K_V10.1 inhibitors remains the selectivity toward the hERG channel, which needs to be addressed with targeted drug design strategies in the future.

Effective biodiversity management and policymaking requires timely access to accurate and reliable scientific data on biodiversity status, trends and threats. However, current biodiversity monitoring processes are often time‐consuming, complex and irreproducible. Moreover, the quality and types of biodiversity data are diverse, which challenges their integration and impedes effective monitoring. A major step to overcome such challenges would be the availability of standardized species occurrence data. However, challenges arise in aggregating and integrating these heterogeneous data with environmental and landscape data. By creating standardized biodiversity data cubes and automated workflows for post‐processing, we envision that (1) information from complex datasets will be available in a known format to efficiently communicate biodiversity variables to policymakers; (2) the adoption of repeatable Open Data workflows will make biodiversity data more accessible, efficient and cost‐effective; and (3) cloud computing will make it easier to analyse large datasets, benefit from a broader range of models, share resources and work together on biodiversity projects. This revolution in biodiversity monitoring will rely on community collaboration. By bridging the gap between policymakers' needs, bioinformation specialists' skills and data collectors' motivations, biodiversity monitoring can become a more inclusive and community‐driven effort. As such, we advocate for the development of tools and workflows in close consultation with stakeholders to enhance the impact and use of biodiversity information. Practical implication. The proposed approach faces challenges in maintaining software, data standards and addressing biodiversity data complexity. However, leveraging existing infrastructures like GBIF and Copernicus, and building on the knowledge from GEO and GEO BON offers a feasible path.

The voltage-gated potassium channel KV1.3 has been recognized as a tumor marker and represents a promising new target for the discovery of new anticancer drugs. We designed a novel structural class of KV1.3 inhibitors through structural optimization of benzamide-based hit compounds and structure-activity relationship studies. The potency, and selectivity of the new KV1.3 inhibitors were investigated using whole-cell patch- and voltage-clamp experiments. 2D and 3D cell models were used to determine antiproliferative activity. Structural optimization resulted in the most potent and selective KV1.3 inhibitor 44 in the series with an IC50 value of 470 nM in oocytes and 950 nM in Ltk cells. Kv1.3 inhibitor 4 induced significant apoptosis in Colo-357 spheroids, while 14, 37, 43, and 44 significantly inhibited Panc-1 proliferation.