ATTECs and several other emerging degrader technologies hijacking the lysosomal pathways greatly expand the spectrum of degradable targets and provide new opportunities for targeted drug discovery.

The detection of small molecules has increasingly attracted the attention of researchers because of its important physiological function. In this manuscript, we propose a novel optical sensor which uses an optofluidic microbubble resonator (OFMBR) for the highly sensitive detection of small molecules. This paper demonstrates the binding of the small molecule biotin to surface-immobilized streptavidin with a detection limit reduced to 0.41 pM. Furthermore, binding specificity of four additional small molecules to surface-immobilized streptavidin is shown. A label-free OFMBR-based optical sensor has great potential in small molecule detection and drug screening because of its high sensitivity, low detection limit, and minimal sample consumption.

Aberrant activation of stimulator of interferon genes (STING) is tightly associated with multiple types of disease, including cancer, infection, and autoimmune diseases. However, the development of STING modulators for the therapy of STING‐related diseases is still an unmet clinical need. We employed a high‐throughput screening approach based on the interaction of small‐molecule chemical compounds with recombinant STING protein to identify functional STING modulators. Intriguingly, the cyclin‐dependent protein kinase (CDK) inhibitor Palbociclib was found to directly bind STING and inhibit its activation in both mouse and human cells. Mechanistically, Palbociclib targets Y167 of STING to block its dimerization, its binding with cyclic dinucleotides, and its trafficking. Importantly, Palbociclib alleviates autoimmune disease features induced by dextran sulphate sodium or genetic ablation of three prime repair exonuclease 1 (Trex1) in mice in a STING‐dependent manner. Our work identifies Palbociclib as a novel pharmacological inhibitor of STING that abrogates its homodimerization and provides a basis for the fast repurposing of this Food and Drug Administration‐approved drug for the therapy of autoinflammatory diseases.

Significance Classical drug discovery identifies inhibitors that block the activities of pathogenic proteins. This typically relies on a measurable biochemical readout and accessible binding sites whose occupancy influences the activity of the target protein. These requirements make many pathogenic proteins “undruggable.” Here, we report a strategy to target these undruggable proteins: screening for compounds that directly bind to the undruggable target and rescue disease-relevant phenotypes. These compounds may suppress the target’s pathogenic functions via direct binding to it. We applied this strategy to the mutant HTT protein, which is an undruggable protein that causes Huntington’s disease (HD). We revealed desonide, an FDAapproved drug, as a possible lead compound for HD drug discovery.

Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.

Guided-mode resonance (GMR) sensors are widely used as biosensors with the advantages of simple structure, easy detection schemes, high efficiency, and narrow linewidth. However, their applications are limited by their relatively low sensitivity (<200 nm/RIU) and in turn low figure of merit (FOM, <100 1/RIU). Many efforts have been made to enhance the sensitivity or FOM, separately. To enhance the sensitivity and FOM simultaneously for more sensitive sensing, we proposed a metal layer-assisted double-grating (MADG) structure with the evanescent field extending to the sensing region enabled by the metal reflector layer underneath the double-grating. The influence of structural parameters was systematically investigated. Bulk sensitivity of 550.0 nm/RIU and FOM of 1571.4 1/RIU were obtained after numerical optimization. Compared with a single-grating structure, the surface sensitivity of the double-grating structure for protein adsorption increases by a factor of 2.4 times. The as-proposed MADG has a great potential to be a biosensor with high sensitivity and high accuracy.

Subvisible particles are an ongoing problem in biotherapeutic injectable pharmaceutical formulations, and their identification is an important prerequisite for tracing them back to their source and optimizing the process. Flow imaging microscopy (FIM) is a favored imaging technique, mainly because of its ability to achieve rapid batch imaging of subvisible particles in solution with excellent imaging quality. This study used VGG16 after transfer learning to identify subvisible particle images acquired using FlowCam. We manually prepared standards for seven classes of particles, acquired the image information through FlowCam, and fed the images over 5 µm into VGG16 consisting of a convolutional base of VGG16 pre-trained with ImageNet data and a custom classifier for training. An accuracy of 97.51% was obtained for the test set data. The study also demonstrated that the recognition method using transfer learning outperforms machine learning methods based on morphological parameters in terms of accuracy, and has a significant training speed advantage over scratch-trained CNN. The combination of transfer learning and FIM images is expected to provide a general and accurate data-analysis method for identifying subvisible particles.

Autophagy is a powerful protein degradation pathway with limited specificity. Our recent study proposed and demonstrated a potential strategy to harness autophagy to selectively degrade a specific pathogenic protein using autophagosome tethering compounds (ATTEC). ATTEC interact with both the target protein and the autophagosome protein LC3, and thus tether the target protein to the autophagosomes for subsequent degradation.The concentration-dependent curve of the target protein is U-shaped, but there has been lack of both kinetic and steady-state modeling of the degradation effects of ATTEC. Here we established a simplified model describing the kinetics and steady-state level of target protein, and characterized how compounds' properties, especially binding affinities to LC3 and to the target protein, may influence their degradation effects.