Discovery of pyrrolo[2,3-d]pyrimidin-4-one derivative YCH3124 as a potent USP7 inhibitor for cancer therapy.

USP7 is one of the most studied deubiquitinating enzymes, which is involved in the regulation of multiple cell signaling pathways and has been shown to be associated with the occurrence and progression of a variety of cancers. Inhibitors targeting USP7 have been studied by several teams, but most of them lack selectivity and have low activities. Herein, we reported a serious of pyrrole[2,3-d]pyrimidin-4-one derivatives through scaffold hopping of recently reported 4-hydroxypiperidine compounds. The representative compound Z33 (YCH3124) exhibited highly potent USP7 inhibition activity as well as anti-proliferative activity against four kinds of cancer cell lines. Further study revealed that YCH3124 effectively inhibited the downstream USP7 pathway and resulted in the accumulation of both p53 and p21 in a dose-dependent manner. Notably, YCH3124 disrupted cell cycle progression through restricting G1 phase and induced significant apoptosis in CHP-212 cells. In summary, our efforts provided a series of novel pyrrole[2,3-d]pyrimidin-4-one analogs as potent USP7 inhibitors with excellent anti-cancer activity.

Modeling multiple-criterion diagnoses by heterogeneous-instance logistic regression.

Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD) that causes a significant burden in caregiving and medical costs. Clinically, the diagnosis of MCI is determined by the impairment statuses of five cognitive domains. If one of these cognitive domains is impaired, the patient is diagnosed with MCI, and if two out of the five domains are impaired, the patient is diagnosed with AD. In medical records, most of the time, the diagnosis of MCI/AD is given, but not the statuses of the five domains. We may treat the domain statuses as missing variables. This diagnostic procedure relates MCI/AD status modeling to multiple-instance learning, where each domain resembles an instance. However, traditional multiple-instance learning assumes common predictors among instances, but in our case, each domain is associated with different predictors. In this article, we generalized the multiple-instance logistic regression to accommodate the heterogeneity in predictors among different instances. The proposed model is dubbed heterogeneous-instance logistic regression and is estimated via the expectation-maximization algorithm because of the presence of the missing variables. We also derived two variants of the proposed model for the MCI and AD diagnoses. The proposed model is validated in terms of its estimation accuracy, latent status prediction, and robustness via extensive simulation studies. Finally, we analyzed the National Alzheimer's Coordinating Center-Uniform Data Set using the proposed model and demonstrated its potential.

An Empirical Bayes Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices.

The James-Stein estimator is an estimator of the multivariate normal mean and dominates the maximum likelihood estimator (MLE) under squared error loss. The original work inspired great interest in developing shrinkage estimators for a variety of problems. Nonetheless, research on shrinkage estimation for manifold-valued data is scarce. In this article, we propose shrinkage estimators for the parameters of the Log-Normal distribution defined on the manifold of N × N symmetric positive-definite matrices. For this manifold, we choose the Log-Euclidean metric as its Riemannian metric since it is easy to compute and has been widely used in a variety of applications. By using the Log-Euclidean distance in the loss function, we derive a shrinkage estimator in an analytic form and show that it is asymptotically optimal within a large class of estimators that includes the MLE, which is the sample Fréchet mean of the data. We demonstrate the performance of the proposed shrinkage estimator via several simulated data experiments. Additionally, we apply the shrinkage estimator to perform statistical inference in both diffusion and functional magnetic resonance imaging problems.

Identification of YCH2823 as a novel USP7 inhibitor for cancer therapy.

Inhibition of the human ubiquitin-specific protease 7 (USP7), the key deubiquitylating enzyme in regulating p53 protein levels, has been considered an attractive anticancer strategy. In order to enhance the cellular activity of FT671, scaffold hopping strategy was employed. This endeavor resulted in the discovery of YCH2823, a novel and potent USP7 inhibitor.YCH2823 demonstrated remarkable efficacy in inhibiting the growth of a specific subset of TP53 wild-type, -mutant, and MYCN-amplified cell lines, surpassing the potency of FT671 by approximately 5-fold. The mechanism of action of YCH2823 involves direct interaction with the catalytic domain of USP7, thereby impeding the cleavage of ubiquitinated substrates. An increase in the expression of p53 and p21, accompanied by G1 phase arrest and apoptosis, was observed upon treatment with YCH2823. Subsequently, the knockdown of p53 or p21 in CHP-212 cells exhibited a substantial reduction in sensitivity to YCH2823, as evidenced by a considerable increase in IC50 values up to 690-fold. Furthermore, YCH2823 treatment specifically enhanced the transcriptional and protein levels of BCL6 in sensitive cells. Moreover, a synergistic effect between USP7 inhibitors and mTOR inhibitors was observed, suggesting the possibility of novel therapeutic strategies for cancer treatment. In conclusion, YCH2823 exhibits potential as an anticancer agent for the treatment of both TP53 wild-type and -mutant tumors.

Detection and Mitigation of SYN Flooding Attacks through SYN/ACK Packets and Black/White Lists.

Software-defined networking (SDN) is a new network architecture that provides programmable networks, more efficient network management, and centralized control than traditional networks. The TCP SYN flooding attack is one of the most aggressive network attacks that can seriously degrade network performance. This paper proposes detection and mitigation modules against SYN flooding attacks in SDN. We combine those modules, which have evolved from the cuckoo hashing method and innovative whitelist, to get better performance compared to current methods Our approach reduces the traffic through the switch and improves detection accuracy, also the required register size is reduced by half for the same accuracy.

Horospherical Decision Boundaries for Large Margin Classification in Hyperbolic Space.

Hyperbolic spaces have been quite popular in the recent past for representing hierarchically organized data. Further, several classification algorithms for data in these spaces have been proposed in the literature. These algorithms mainly use either hyperplanes or geodesics for decision boundaries in a large margin classifiers setting leading to a non-convex optimization problem. In this paper, we propose a novel large margin classifier based on horospherical decision boundaries that leads to a geodesically convex optimization problem that can be optimized using any Riemannian gradient descent technique guaranteeing a globally optimal solution. We present several experiments depicting the competitive performance of our classifier in comparison to SOTA.

Geometric Deep Learning for Unsupervised Registration of Diffusion Magnetic Resonance Images.

Deep learning based models for registration predict a transformation directly from moving and fixed image appearances. These models have revolutionized the field of medical image registration, achieving accuracy on-par with classical registration methods at a fraction of the computation time. Unfortunately, most deep learning based registration methods have focused on scalar imaging modalities such as T1/T2 MRI and CT, with less attention given to more complex modalities such as diffusion MRI. In this paper, to the best of our knowledge, we present the first end-to-end geometric deep learning based model for the non-rigid registration of fiber orientation distribution fields (fODF) derived from diffusion MRI (dMRI). Our method can be trained in a fully-unsupervised fashion using only input fODF image pairs, i.e. without ground truth deformation fields. Our model introduces several novel differentiable layers for local Jacobian estimation and reorientation that can be seamlessly integrated into the recently introduced manifold-valued convolutional network in literature. The results of this work are accurate deformable registration algorithms for dMRI data that can execute in the order of seconds, as opposed to dozens of minutes to hours consumed by their classical counterparts.

Absorption, distribution, metabolism, and excretion of [14C]Mefuparib (CVL218), a novel PARP1/2 inhibitor, in rats.

INTRODUCTION: Mefuparib (CVL218) is a novel second-generation poly-ADP-ribose polymerase (PARP) inhibitor for cancer treatment. CVL218 can easily enter the brain. However, the transport mechanism by which CVL218 crosses the blood-brain barrier (BBB) is unknown. METHODS: (1) [14C] CVL218 metabolism in rats was traced by a liquid scintillation counter and oxidative combustion. (2) Metabolic profiles and metabolites were identified by UHPLC-ß-RAM/UHPLC-Fraction Collector and UHPLC-Q Exactive Plus MS. (3) The partition coefficient Kp,uu,brain value was simulated by two strategies. One strategy was using ACD and GastroPlus Software based on the results of intravenous administration pharmacokinetics and plasma protein-binding studies. The reliability was confirmed by comparison with another strategy (brain/plasma distribution study). RESULTS: (1) Rapid drug elimination was observed 24 h after intragastric administration. The total cumulative excretion in urine and feces within 168 h accounted for 97.15% of the dose. The cumulative radioactive dose recovery in bile was 41.87% within 72 h. The drug-related substances were extensively distributed to the tissues within 48 h. (2) M8 was the major metabolite in plasma, urine, feces and bile. (3) CVL218 exhibited high brain protein-binding rate (88.16%). The Kp,uu,brain value (8.42) simulated by the simple software strategy was similar to that of the brain/plasma distribution study (7.01). CONCLUSIONS: CVL218 is a fast-metabolizing drug and is mainly excreted in feces. The B/P ratio prediction and observation data for CVL218 were consistent. Furthermore, the Kp,uu,brain value indicated that penetration through the BBB might be mediated by uptake transporters.

Design, synthesis and evaluation of the Brigatinib analogues as potent inhibitors against tertiary EGFR mutants (EGFRdel19/T790M/C797S and EGFRL858R/T790M/C797S).

Although epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have demonstrated encouraging clinical outcomes for patients with EGFR-mutated non-small cell lung cancer, a considerable number of patients will develop drug resistance and eventually undergo disease progression after taking EGFR-TKIs for a period of time. EGFRdel19/T790M/C797S and EGFRL858R/T790M/C797S are two most prevalent tertiary EGFR mutants identified in Osimertinib-resistant tumors and currently there is no therapy approved clinically targeting these mutants. In this study, we designed and synthesized a series of novel 4th generation EGFR inhibitors based on scaffold of Brigatinib. After extensive SAR studies, compound 23, the most promising candidate, exhibited strong biochemical potencies against EGFRdel19/T790M/C797S, EGFRL858R/T790M/C797S and other clinically relevant EGFR mutants while sparing wild type EGFR. In cellular assays, compound 23 potently inhibited proliferation of BaF3EGFR del19/T790M/C797S and PC-9EGFR del19/T790M/C797S. Moreover, compound 23 demonstrated good DMPK profile in mouse PK study.

Nested Grassmannians for Dimensionality Reduction with Applications.

In the recent past, nested structures in Riemannian manifolds has been studied in the context of dimensionality reduction as an alternative to the popular principal geodesic analysis (PGA) technique, for example, the principal nested spheres. In this paper, we propose a novel framework for constructing a nested sequence of homogeneous Riemannian manifolds. Common examples of homogeneous Riemannian manifolds include the n-sphere, the Stiefel manifold, the Grassmann manifold and many others. In particular, we focus on applying the proposed framework to the Grassmann manifold, giving rise to the nested Grassmannians (NG). An important application in which Grassmann manifolds are encountered is planar shape analysis. Specifically, each planar (2D) shape can be represented as a point in the complex projective space which is a complex Grassmann manifold. Some salient features of our framework are: (i) it explicitly exploits the geometry of the homogeneous Riemannian manifolds and (ii) the nested lower-dimensional submanifolds need not be geodesic. With the proposed NG structure, we develop algorithms for the supervised and unsupervised dimensionality reduction problems respectively. The proposed algorithms are compared with PGA via simulation studies and real data experiments and are shown to achieve a higher ratio of expressed variance compared to PGA.

Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design.

Hyperbolic neural networks have been popular in the recent past due to their ability to represent hierarchical data sets effectively and efficiently. The challenge in developing these networks lies in the nonlinearity of the embedding space namely, the Hyperbolic space. Hyperbolic space is a homogeneous Riemannian manifold of the Lorentz group which is a semi-Riemannian manifold, i.e. a manifold equipped with an indefinite metric. Most existing methods (with some exceptions) use local linearization to define a variety of operations paralleling those used in traditional deep neural networks in Euclidean spaces. In this paper, we present a novel fully hyperbolic neural network which uses the concept of projections (embeddings) followed by an intrinsic aggregation and a nonlinearity all within the hyperbolic space. The novelty here lies in the projection which is designed to project data on to a lower-dimensional embedded hyperbolic space and hence leads to a nested hyperbolic space representation independently useful for dimensionality reduction. The main theoretical contribution is that the proposed embedding is proved to be isometric and equivariant under the Lorentz transformations, which are the natural isometric transformations in hyperbolic spaces. This projection is computationally efficient since it can be expressed by simple linear operations, and, due to the aforementioned equivariance property, it allows for weight sharing. The nested hyperbolic space representation is the core component of our network and therefore, we first compare this representation - independent of the network - with other dimensionality reduction methods such as tangent PCA, principal geodesic analysis (PGA) and HoroPCA. Based on this equivariant embedding, we develop a novel fully hyperbolic graph convolutional neural network architecture to learn the parameters of the projection. Finally, we present experiments demonstrating comparative performance of our network on several publicly available data sets.

Repressing MYC by targeting BET synergizes with selective inhibition of PI3Kα against B cell lymphoma.

Phosphatidylinositol 3-kinase (PI3K) δ-specific inhibitors have been approved for the therapy of certain types of B cell lymphoma (BCL). However, their clinical use is limited by the substantial toxicity and lack of efficacy in other types of BCL. Emerging evidence indicates that PI3Kα plays important roles in the progression of B cell lymphoma. In this study, we revealed that PI3Kα was important for the PI3K signaling and proliferation in BCL cells. A novel clinical PI3Kα-selective inhibitor CYH33 possessed superior activity against BCL compared to the marketed PI3Kα-selective inhibitor Alpelisib and PI3Kδ-selective inhibitor Idelalisib. Though CYH33 was able to inhibit PI3K/AKT signaling in tested BCL cells, differential activity against proliferation was observed. Transcriptome profiling revealed that CYH33 down-regulated "MYC-targets" gene set in sensitive but not resistant cells. CYH33 inhibited c-MYC transcription in sensitive cells, which was attributed to a decrease in acetylated H3 bound to the promoter and super-enhancer region of c-MYC. Accordingly, CYH33 treatment resulted in phosphorylation and proteasomal degradation of the histone acetyltransferase p300. An unbiased screening with drugs approved or in clinical trials for the therapy of BCL identified that the clinical BET (Bromodomain and Extra Terminal domain) inhibitor OTX015 significantly potentiated the activity of CYH33 against BCL in vitro and in vivo, which was associated with enhanced inhibition on c-MYC expression and induction of cell cycle arrest and apoptosis. Our findings provide the rationale of combined CYH33 with BET inhibitors for the therapy of B cell lymphoma.

Adaptive resistance to PI3Kα-selective inhibitor CYH33 is mediated by genomic and transcriptomic alterations in ESCC cells.

Phosphoinositide-3 kinase alpha-specific inhibitors (PI3Kαi) displayed promising potential for the treatment of esophageal squamous cell carcinoma (ESCC) with frequent activation in PI3K signaling. However, acquired resistance is likely to develop and limit the efficacy of PI3Kαi like other targeted therapies. To identify genomic adaptation to PI3Kαi, we applied whole-genome sequencing and detected gene mutation and amplification in four lines of ESCC cells established with adapted resistance to a novel PI3Kαi CYH33. Particularly, HRASG12S mutation was found in KYSE180C cells. Overexpression of HRASG12S in ESCC parental cells rendered resistance to CYH33. By contrast, down-regulation of HRASG12S restored the sensitivity of KYSE180C1 cells to CYH33, and combination of CYH33 and MEK162 displayed synergistic effect against KYSE180C1 cells and xenografts. Furthermore, elevated mTORC1, mitogen-activated protein kinase (MAPK), and c-Myc signaling pathways were found in resistant cells by RNA sequencing and combination of CYH33 and RAD001, MEK162, or OTX015 overcame the resistance to CYH33, which was accompanied with enhanced inhibition on S6, extracellular signal-regulated kinase 1 (ERK), or c-Myc, respectively. Overall, we characterized the adaptations to PI3Kαi in ESCC cells and identified combinatorial regimens that may circumvent resistance.

Identification of methyl (5-(6-((4-(methylsulfonyl)piperazin-1-yl)methyl)-4-morpholinopyrrolo[2,1-f][1,2,4]triazin-2-yl)-4-(trifluoromethyl)pyridin-2-yl)carbamate (CYH33) as an orally bioavailable, highly potent, PI3K alpha inhibitor for the treatment of advanced solid tumors.

In various human cancers, PI3Ks pathway is ubiquitously dysregulated and thus become a promising anti-cancer target. To discover new potent and selective PI3K inhibitors as potential anticancer drugs, new pyrrolo[2,1-f][1,2,4]triazines were designed, leading to the discovery of compound 37 (CYH33), a selective PI3Kα inhibitor (IC50 = 5.9 nM, ß/α, δ/α,γ/α = 101-, 13-, 38-fold). Western blot analysis confirmed that compound 37 could inhibit phosphorylation of AKT in human cancer cells to modulate the cellular PI3K/AKT/mTOR pathway. And further evaluation in vivo against SKOV-3 xenograft models demonstrated that a dose-dependent antitumor efficacy was achieved.

Identification of 2-substituted pyrrolo[1,2-b]pyridazine derivatives as new PARP-1 inhibitors.

A library of new 2-substituted pyrrolo[1,2-b]pyridazine derivatives were rapidly assembled and identified as PARP inhibitors. Structure-activity relationship for this class of inhibitor resulted in the discovery of most potent compounds 15a and 15b that exhibited about 29- and 5- fold selective activity against PARP-1 over PARP-2 respectively. The antiproliferative activity of the as-prepared compounds were demonstrated by further celluar assay in BRCA2-deficient V-C8 and BRCA1-deficient MDA-MB-436 cell lines, displaying that compound 15b could robustly reduce the corresponding cell proliferation and growth with CC50s of 340 and 106 nM respectively. The PK property of 15b was also investigated here.

Design, synthesis and antiproliferative activity evaluation of a series of pyrrolo[2,1-f][1,2,4]triazine derivatives.

A series of 6-aminocarbonyl pyrrolo[2,1-f][1,2,4]triazine derivatives were designed by scaffold hopping strategy. The IC50 values of compound 14a against PI3Ks were measured, showing selective activity against p110α and p110δ with IC50s of 122 nM and 119 nM respectively. All the synthesized compounds were evaluated for their antiproliferative activity against human cancer cells by SRB assay. Compounds 14a, 14p and 14q exhibited potent antiproliferative activity against five types of human cancer cells and the PK property of 14q was also investigated here.

An Aptamer-Based Capacitive Sensing Platform for Specific Detection of Lung Carcinoma Cells in the Microfluidic Chip.

Improvement of methods for reliable and early diagnosis of the cellular diseases is necessary. A biological selectivity probe, such as an aptamer, is one of the candidate recognition layers that can be used to detect important biomolecules. Lung cancer is currently a typical cause of cancer-related deaths. In this work, an electrical sensing platform is built based on amine-terminated aptamer modified-gold electrodes for the specific, label-free detection of a human lung carcinoma cell line (A549). The microdevice, that includes a coplanar electrodes configuration and a simple microfluidic channel on a glass substrate, is fabricated using standard photolithography and cast molding techniques. A procedure of self-assembly onto the gold surface is proposed. Optical microscope observations and electrical impedance spectroscopy measurements confirm that the fabricated microchip can specifically and effectively identify A549 cells. In the experiments, the capacitance element that is dominant in the change of the impedance is calculated at the appropriate frequency for evaluation of the sensitivity of the biosensor. Therefore, a simple, inexpensive, biocompatible, and selective biosensor that has the potential to detect early-stage lung cancer would be developed.

Decrease in phosphorylated ERK indicates the therapeutic efficacy of a clinical PI3Kα-selective inhibitor CYH33 in breast cancer.

PI3Ks are frequently hyper-activated in breast cancer and targeting PI3Kα has exhibited promising but variable response in preclinical and clinical settings. CYH33 is a novel PI3Kα-selective inhibitor in phase I clinical trial. We investigated the efficacy of CYH33 against breast cancer and explored potential predictive biomarkers. CYH33 potently restrained tumor growth in mice bearing human breast cancer cell xenografts and in R26-Pik3caH1047R;MMTV-Cre transgenic mice. CYH33 significantly inhibited proliferation of a panel of human breast cancer cells, while diversity in sensitivity has been observed. Cells harboring activating PIK3CA mutation, amplified HER2 were more responsive to CYH33 than their counterparts. Besides, cells in HER2-enriched or luminal subtype were more sensitive to CYH33 than basal-like breast cancer. Sensitivity to CYH33 has been further revealed to be associated with induction of G1 phase arrest and simultaneous inhibition of Akt and ERK. Sensitivity of patient-derived xenograft to CYH33 was also positively correlated with decrease in phosphorylated ERK. Taken together, CYH33 is a promising PI3Kα inhibitor for breast cancer treatment and decrease in ERK phosphorylation may indicate its efficacy, which provides useful clues for rational design of the ongoing clinical trials.