Synthesis and Biological Evaluation of ß-Lactam Derivatives Targeting Speckle-Type POZ Protein (SPOP).

Speckle-type POZ protein (SPOP) acts as a cullin3-RING ubiquitin ligase adaptor, which facilitates the recognition and ubiquitination of substrate proteins. Previous research suggests that targeting SPOP holds promise in the treatment of clear cell renal cell carcinoma (ccRCC). On the basis of the reported SPOP inhibitor 230D7, a series of ß-lactam derivatives were synthesized in this study. The biological activity assessment of these compounds revealed E1 as the most potent inhibitor, which can disrupt the SPOP-substrate interactions in vitro and suppress the colony formation of ccRCC cells. Taken together, this study provided compound E1 as a potent inhibitor against ccRCC and offered insight into the development of the ß-lactam SPOP inhibitor.

Design, Synthesis, and Biological Evaluation of Potent and Selective PROTAC Degraders of Oncogenic KRASG12D.

KRASG12D, the most frequent KRAS oncogenic mutation, is a promising target for cancer therapy. Herein, we report the design, synthesis, and biological evaluation of a series of KRASG12D PROTACs by connecting the analogues of MRTX1133 and the VHL ligand. Structural modifications of the linker moiety and KRAS inhibitor part suggested a critical role of membrane permeability in the degradation activity of the KRASG12D PROTACs. Mechanism studies with the representative compound 8o demonstrated that the potent, rapid, and selective degradation of KRASG12D induced by 8o was via a VHL- and proteasome-dependent manner. This compound selectively and potently suppressed the growth of multiple KRASG12D mutant cancer cells, displayed favorable pharmacokinetic and pharmacodynamic properties in mice, and showed significant antitumor efficacy in the AsPC-1 xenograft mouse model. Further optimization of 8o appears to be promising for the development of a new chemotherapy for KRASG12D-driven cancers as the complementary therapeutic strategy to KRAS inhibition.

Sequence-based drug design as a concept in computational drug design.

Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.

Discovery of a Potent, Cooperative, and Selective SOS1 PROTAC ZZ151 with In Vivo Antitumor Efficacy in KRAS-Mutant Cancers.

The linker moiety of a proteolysis-targeting chimera (PROTAC) molecule plays a critical role in modulating the degradation activity, target selectivity, and physico-chemical properties. However, the basics and underlying mechanisms of chemical modifications of the linker structure causing dramatic changes in the PROTAC degradation activity warrant further investigation. Herein, we report the design and characterization of a highly potent and selective SOS1 PROTAC ZZ151. After systematically modifying the linker length and composition, we observed that subtle modification of just one atom of the linker moiety of ZZ151 resulted in remarkable changes in the formation of the ternary complex and thus dramatically affected the degradation activities. ZZ151 quickly, specifically, and effectively induced SOS1 degradation; displayed potent antiproliferation activities against a broad panel of KRAS mutant-driven cancer cells; and showed superior anticancer activities in the KRASG12D- and G12V-mutant xenografts in mice. ZZ151 is a promising lead for developing new chemotherapies targeting KRAS mutants.

Discovery and characterization of a novel cGAS covalent inhibitor for the treatment of inflammatory bowel disease.

Cyclic GMP-AMP synthase (cGAS), a cytosolic DNA sensor, acts as a nucleotidyl transferase that catalyzes ATP and GTP to form cyclic GMP-AMP (cGAMP) and plays a critical role in innate immunity. Hyperactivation of cGAS-STING signaling contributes to hyperinflammatory responses. Therefore, cGAS is considered a promising target for the treatment of inflammatory diseases. Herein, we report the discovery and identification of several novel types of cGAS inhibitors by pyrophosphatase (PPiase)-coupled activity assays. Among these inhibitors, 1-(1-phenyl-3,4-dihydro-1H-pyrrolo[1,2-a]pyrazin-2-yl)prop-2-yn-1-one (compound 3) displayed the highest potency and selectivity at the cellular level. Compound 3 exhibited better inhibitory activity and pathway selectivity than RU.521, which is a selective cGAS inhibitor with anti-inflammatory effects in vitro and in vivo. Thermostability analysis, nuclear magnetic resonance and isothermal titration calorimetry assays confirmed that compound 3 directly binds to the cGAS protein. Mass spectrometry and mutation analysis revealed that compound 3 covalently binds to Cys419 of cGAS. Notably, compound 3 demonstrated promising therapeutic efficacy in a dextran sulfate sodium (DSS)-induced mouse colitis model. These results collectively suggest that compound 3 will be useful for understanding the biological function of cGAS and has the potential to be further developed for inflammatory disease therapies.

Discovery and identification of a novel small molecule BCL-2 inhibitor that binds to the BH4 domain.

The B-cell lymphoma 2 (BCL-2) protein family plays a pivotal role in regulating the apoptosis process. BCL-2, as an antiapoptotic protein in this family, mediates apoptosis resistance and is an ideal target for cell death strategies in cancer therapy. Traditional treatment modalities target BCL-2 by occupying the hydrophobic pocket formed by BCL-2 homology (BH) domains 1-3, while in recent years, the BH4 domain of BCL-2 has also been considered an attractive novel target. Herein, we describe the discovery and identification of DC-B01, a novel BCL-2 inhibitor targeting the BH4 domain, through virtual screening combined with biophysical and biochemical methods. Our results from surface plasmon resonance and cellular thermal shift assay confirmed that the BH4 domain is responsible for the interaction between BCL-2 and DC-B01. As evidenced by further cell-based experiments, DC-B01 induced cell killing in a BCL-2-dependent manner and triggered apoptosis via the mitochondria-mediated pathway. DC-B01 disrupted the BCL-2/c-Myc interaction and consequently suppressed the transcriptional activity of c-Myc. Moreover, DC-B01 inhibited tumor growth in vivo in a BCL­2­dependent manner. Collectively, these results indicate that DC-B01 is a promising BCL-2 BH4 domain inhibitor with the potential for further development.

Discovery of ARF1-targeting inhibitor demethylzeylasteral as a potential agent against breast cancer.

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Discovery of the First-in-Class Agonist-Based SOS1 PROTACs Effective in Human Cancer Cells Harboring Various KRAS Mutations.

Regulating SOS1 functions may result in targeted pan-KRAS therapies. Small-molecule SOS1 inhibitors showed promising anticancer potential, and the most advanced inhibitor BI 1701963 is currently under phase I clinical studies. SOS1 agonists provide new opportunities to treat cancer; however, the underlying mechanisms still warrant investigation. We here report the discovery of the first SOS1 PROTACs designed uniquely by connecting a VHL ligand to the reported SOS1 agonist, ensuring that the observed inhibitory activity results from degraders. The best compound 9d induced SOS1 degradation in various KRAS-driven cancer cells and displayed superior antiproliferation activity compared to the agonist itself. Tumor xenograft study clearly showed the promising antitumor potency of 9d against human lung cancer. This study provides good evidence of using agonists to design SOS1 PROTACs and demonstrates that targeted SOS1 degradation represents an effective therapeutic strategy for overcoming KRAS-driven cancers.

Drug target inference by mining transcriptional data using a novel graph convolutional network framework.

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.

Active Learning for Drug Design: A Case Study on the Plasma Exposure of Orally Administered Drugs.

The success of artificial intelligence (AI) models has been limited by the requirement of large amounts of high-quality training data, which is just the opposite of the situation in most drug discovery pipelines. Active learning (AL) is a subfield of AI that focuses on algorithms that select the data they need to improve their models. Here, we propose a two-phase AL pipeline and apply it to the prediction of drug oral plasma exposure. In phase I, the AL-based model demonstrated a remarkable capability to sample informative data from a noisy data set, which used only 30% of the training data to yield a prediction capability with an accuracy of 0.856 on an independent test set. In phase II, the AL-based model explored a large diverse chemical space (855K samples) for experimental testing and feedback. Improved accuracy and new highly confident predictions (50K samples) were observed, which suggest that the model's applicability domain has been significantly expanded.

[Studies on identification of Phragmitis Rhizoma and its counterfeits].

The study aims to explore the main differential characteristics of Phragmites Rhizoma and its counterfeits (rhizomes of Arundo donax, Triarrhena lutarioriparia and Miscanthus sinensis) and provide experimental basis for the reasonable applications of gramineous plants through system research and comparison of plant morphogenesis, character, transverse organization characteristics and powder microscopic characteristics.