Organoids and cells in organ‐on‐chip platforms replicate higher‐level anatomical, physiological, or pathological states of tissues and organs. These technologies are widely regarded by academia, the pharmacological industry and regulators as key biomedical developments. To map advances in this emerging field, a literature analysis of 16,000 article metadata based on a quality‐controlled text‐mining algorithm is performed. The analysis covers titles, keywords, and abstracts of categorized academic publications in the literature and preprint databases published after 2010. The algorithm identifies and tracks 149 and 107 organs or organ substructures modeled as organoids and organ‐on‐chip, respectively, stem cell sources, as well as 130 diseases, and 16 groups of organisms other than human and mouse in which organoid/organ‐on‐chip technology is applied. The analysis illustrates changing diversity and focus in organoid/organ‐on‐chip research and captures its geographical distribution. The downloadable dataset provided is a robust framework for researchers to interrogate with their own questions.

Organ-on-a-Chip (OoC) systems bring together cell biology, engineering, and material science for creating systems that recapitulate the in vivo microenvironment of tissues and organs. The versatility of OoC systems enables in vitro models for studying physiological processes, drug development, and testing in both academia and industry. This paper evaluates current platforms from the academic end-user perspective, elaborating on usability, complexity, and robustness. We surveyed 187 peers in 35 countries and grouped the responses according to preliminary knowledge and the source of the OoC systems that are used. The survey clearly shows that current commercial OoC platforms provide a substantial level of robustness and usability—which is also indicated by an increasing adaptation of the pharmaceutical industry—but a lack of complexity can challenge their use as a predictive platform. Self-made systems, on the other hand, are less robust and standardized but provide the opportunity to develop customized and more complex models, which are often needed for human disease modeling. This perspective serves as a guide for researchers in the OoC field and encourages the development of next-generation OoCs.

Confocal Raman spectral imaging (RSI) enables high-content, label-free visualization of a wide range of molecules in biological specimens without sample preparation. However, reliable quantification of the deconvoluted spectra is needed. Here we develop an integrated bioanalytical methodology, qRamanomics, to qualify RSI as a tissue phantom calibrated tool for quantitative spatial chemotyping of major classes of biomolecules. Next, we apply qRamanomics to fixed 3D liver organoids generated from stem-cell-derived or primary hepatocytes to assess specimen variation and maturity. We then demonstrate the utility of qRamanomics for identifying biomolecular response signatures from a panel of liver-altering drugs, probing drug-induced compositional changes in 3D organoids followed by in situ monitoring of drug metabolism and accumulation. Quantitative chemometric phenotyping constitutes an important step in developing quantitative label-free interrogation of 3D biological specimens.

Tankyrase 1 and 2 (TNKS1/2) catalyze post-translational modification by poly-ADP-ribosylation of a plethora of target proteins. In this function, TNKS1/2 also impact the WNT/β-catenin and Hippo signaling pathways that are involved in numerous human disease conditions including cancer. Targeting TNKS1/2 with small-molecule inhibitors shows promising potential to modulate the involved pathways, thereby potentiating disease intervention. Based on our 1,2,4-triazole-based lead compound 1 (OM-1700), further structure–activity relationship analyses of East-, South- and West-single-point alterations and hybrids identified compound 24 (OM-153). Compound 24 showed picomolar IC₅₀ inhibition in a cellular (HEK293) WNT/β-catenin signaling reporter assay, no off-target liabilities, overall favorable absorption, distribution, metabolism, and excretion (ADME) properties, and an improved pharmacokinetic profile in mice. Moreover, treatment with compound 24 induced dose-dependent biomarker engagement and reduced cell growth in the colon cancer cell line COLO 320DM.