Discovery Of Class Research Articles

Abstract A001: Discovery of imidazo [4,5-c]pyridin-2-ones as selective inhibitors of DNA-dependent protein kinase and effective radiosensitizers

Abstract Inhibition of the repair of radiation-induced DNA double strand breaks (DSB) offers the potential to sensitize tumors to radiation therapy. The dominant role of non-homologous end-joining in the repair of radiation-induced DSBs indicates that DNA-dependent protein kinase (DNA-PK) is an excellent target for the development of radiosensitizers. We report the discovery of a new chemical class of kinase inhibitor based on the imidazo[4,5-c]pyridin-2-one scaffold. The class was developed by scaffold-hopping from the pan phosphoinositide 3-kinsase (PI3K) and PI3K-like kinase (PIKK) inhibitor dactolisib. Iterative development culminated in the identification of SN39536 as a low nM DNA-PK inhibitor with excellent selectivity. Further modification led to the discovery of the more potent and more selective SN40905. Both SN39536 and SN40905 were effective inhibitors of DNA-PK kinase activity in a biochemical assay with substantial selectivity for DNA-PK across the PIKK and PI3K families. Both compounds selectively inhibited growth of HAP1 PRKDC wild-type cells when combined with radiation, but not the corresponding PRKDC-/- cells. SN39536 and SN40905 were effective radiosensitizers of colorectal carcinoma (HCT116), non-small cell lung cancer (H460, H1299, and A549), pancreatic (BxPC-3, PANC-1, and MiaPaCa-2) and head and neck squamous cell carcinoma (FaDu and UT-SCC-74B) cells determined using a clonogenic survival endpoint. Both SN39536 and SN40905 displayed high oral bioavailability. When administered PO at a range of non-toxic doses, both SN39536 and SN40905 provided significant additional tumor cell killing of HCT116, UT-SCC-74B, and BxPC-3 tumor xenografts in combination with a single (13 Gy) radiation treatment determined by ex vivo clonogenic survival assays. SN39536 also provided substantial tumor growth inhibition of HCT116 tumor xenografts in combination with RAD (10 Gy). SN39536 and SN40905 represent a new, potent, and selective class of DNA-PK inhibitors with significant potential as radiosensitizers for the treatment of human cancers. Citation Format: Michael P. Hay, Cho R. Hong, Lydia P. Liew, Benjamin D. Dickson, Way W. Wong, Sophia F. O'Brien-Gortner, Rebecca Airey, Jagdish Jaiswal, William R. Wilson, Stephen M. Jamieson. Discovery of imidazo [4,5-c]pyridin-2-ones as selective inhibitors of DNA-dependent protein kinase and effective radiosensitizers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr A001.

Enzymatic degradation of cellulose in soil: A review

Cellulose degradation is a critical process in soil ecosystems, playing a vital role in nutrient cycling and organic matter decomposition. Enzymatic degradation of cellulosic biomass is the most sustainable and green method of producing liquid biofuel. It has gained intensive research interest with future perspective as the majority of terrestrial lignocellulose biomass has a great potential to be used as a source of bioenergy. However, the recalcitrant nature of lignocellulose limits its use as a source of energy. Noteworthy enough, enzymatic conversion of cellulose biomass could be a leading future technology. Fungal enzymes play a central role in cellulose degradation. Our understanding of fungal cellulases has substantially redirected in the past few years with the discovery of a new class of enzymes and Cellulosome. Efforts have been made from time to time to develop an economically viable method of cellulose degradation. This review provides insights into the current state of knowledge regarding cellulose degradation in soil and identifies areas where further research is needed.

Reactivity of 1,3-enyne MIDA boronates: exploration of novel 1,2-alkyne shift via gem-difluorination.

The discovery of a new class of heteroatom-rich boron-containing molecules (BCMs) and iterative cross-coupling (ICC) partners created a toolbox for future drug developments using organoboron compounds. Herein, we report the potential utility of 1,3-enyne MIDA boronates to access diverse gem-difluoro MIDA boronates via novel 1,2-alkyne shift. This unique reactivity of 1,3-enyne MIDA boronates offers facile access to previously challenging β-difluorinated alkyl borons. Furthermore, we demonstrated the synthesis of various novel furan-based BCMs via 5-endo-dig cyclization and iterative coupling partners via copper-catalyzed hydroboration and platinum-catalyzed diboration reaction.

A mini review on two dimensional (2D) materials for energy storage applications

The discovery of new class of nanomaterials referred to as two-dimensional (2D) materials create extensive attention due to their unique features such as very small size, high anisotropy, high strength, high young’s modulus and excellent flexibility. The addition of numerous 2D materials, including black phosphorous, transition metal oxides, MXene materials, boron nitrides and others, were discovered following by the graphene. The most common 2D material are widely used in energy storage devices due to its large specific surface area, large lateral size and superior performance and adaptability. Due to the environmental, conductive, and catalytic uses, MXene materials are also attaining great attention. The synthesis and applications of two-dimensional materials like graphene and MXene, are the primary focus of this review paper.

The effects of Dapagliflozin in type 2 diabetes mellitus patients with complications of cardiovascular disease

Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder in which concomitant insulin resistance and beta-cell impairment lead to hyperglycemia. Cardiovascular problems are more common in T2DM but after the discovery of the dapagliflozin class of SGLT2 (Sodium-glucose co-transporter 2) inhibitor, the rate of cardiovascular disease (CVD) reduced slightly including heart failure (HF). Numerous recent clinical studies emphasized that dapagliflozin as monotherapy and combined therapy with another hypoglycemic agent help to improve glycaemic control and decrease blood pressure and body weight. In the present review, the safety and efficacy of dapagliflozin have been highlighted in patients with HFrEF(Heart failure with reduced ejection fraction), after comparison with placebo and dapagliflozin. It decreased the risk of total HF hospitalizations and cardiovascular death with a low-hypoglycaemic effect. Recurrent hospitalizations are a common problem in patients with HFrEF and it is also preventable. This review article also emphasized the comparative effects of drugs and placebo to prove the effectiveness of drugs and a further reduction in the rate of heart failure. The mild adverse effect is observed in clinical trials where urinary tract infection is most common.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Alternative therapeutic strategies to treat antibiotic-resistant pathogens.

Resistance threatens to render antibiotics - which are essential for modern medicine - ineffective, thus posing a threat to human health. The discovery of novel classes of antibiotics able to overcome resistance has been stalled for decades, with the developmental pipeline relying almost entirely on variations of existing chemical scaffolds. Unfortunately, this approach has been unable to keep pace with resistance evolution, necessitating new therapeutic strategies. In this Review, we highlight recent efforts to discover non-traditional antimicrobials, specifically describing the advantages and limitations of antimicrobial peptides and macrocycles, antibodies, bacteriophages and antisense oligonucleotides. These approaches have the potential to stem the tide of resistance by expanding the physicochemical property space and target spectrum occupied by currently approved antibiotics.

High-Mobility Topological Semimetals as Novel Materials for Huge Magnetoresistance Effect and New Type of Quantum Hall Effect.

The quantitative description of electrical and magnetotransport properties of solid-state materials has been a remarkable challenge in materials science over recent decades. Recently, the discovery of a novel class of materials-the topological semimetals-has led to a growing interest in the full understanding of their magnetotransport properties. In this review, the strong interplay among topology, band structure, and carrier mobility in recently discovered high carrier mobility topological semimetals is discussed and their effect on their magnetotransport properties is outlined. Their large magnetoresistance effect, especially in the Hall transverse configuration, and a new version of a three-dimensional quantum Hall effect observed in high-mobility Weyl and Dirac semimetals are reviewed. The possibility of designing novel quantum sensors and devices based on solid-state semimetals is also examined.

Cycling as a sport and leisure practice during the Covid-19 Pandemic

Maingoal: to identify the factors of the Covid-19 pandemic influencing the adoption of bicycle in sports and leisure practices in regions of Paraíba-Brazil.Methodology: it uses a qualitative approach by performing a lexical analysis of the data through Iramuteq.Originality/relevance: It discusses contexts of relevance about the pandemic, the impacts on urban mobility and the increase in the practice of cycling as a safe mobility for a sustainable city model, during the contagion of covid-19. Main results: our main findings revealed that the negative effects of the lockdown, the home office, the perception of a sedentary lifestyle and psychological pressures motivated new adepts to use the bicycle for sports and leisure.Theoretical/methodological contributions: the research advances in the discovery of classes that confirm the increase in bicycle use in the pandemic and calls attention to the implementation of public policies that make cycling viable in the cities studied, especially in the face of current health uncertainties.

More Reliable Neighborhood Contrastive Learning for Novel Class Discovery in Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) systems have made significant progress in recognizing and classifying human activities using sensor data from a variety of sensors. Nevertheless, they have struggled to automatically discover novel activity classes within massive amounts of unlabeled sensor data without external supervision. This restricts their ability to classify new activities of unlabeled sensor data in real-world deployments where fully supervised settings are not applicable. To address this limitation, this paper presents the Novel Class Discovery (NCD) problem, which aims to classify new class activities of unlabeled sensor data by fully utilizing existing activities of labeled data. To address this problem, we propose a new end-to-end framework called More Reliable Neighborhood Contrastive Learning (MRNCL), which is a variant of the Neighborhood Contrastive Learning (NCL) framework commonly used in visual domain. Compared to NCL, our proposed MRNCL framework is more lightweight and introduces an effective similarity measure that can find more reliable k-nearest neighbors of an unlabeled query sample in the embedding space. These neighbors contribute to contrastive learning to facilitate the model. Extensive experiments on three public sensor datasets demonstrate that the proposed model outperforms existing methods in the NCD task in sensor-based HAR, as indicated by the fact that our model performs better in clustering performance of new activity class instances.

Aplastic Anemia Patients Have Diverse T Cell Repertoires and Flexible T Cell Responses Similar to Healthy Individuals

Acquired aplastic anemia (AA) is a bone marrow failure disease caused by T cell-mediated autoimmune attack on hematopoietic stem and progenitor cells (HSPCs). Identifying autoreactive T cells in AA has been challenging. Advances in T cell receptor (TCR) sequencing and the discovery of Human Leukocyte Antigen Class I (HLA-I) risk alleles linked to autoantigen presentation in AA have rekindled interest in exploring T cell repertoires in AA. We hypothesized that autoreactive CD8 + T cells in AA may have convergent TCR structures allowing them to recognize AA autoantigens presented by specific HLA-I risk alleles. To test our hypothesis, we analyzed TCR repertoires and HLA-restricted CD8 + T cell reactivity in AA patients. Using TCR Vβ (TRB) sequencing, we analyzed 83 patient samples obtained at AA diagnosis or relapse. High-risk alleles HLA-B*14:02 and B*40:02 were present in 26% and 10% of patients, respectively. HLA-DR*15 was present in 24%. Patient samples were largely sorted bone marrow (BM) CD8 + T lymphocytes. Patient TRB repertoires were compared to those in 709 control samples, largely of unsorted peripheral blood (PB). To account for sampling differences, we downsampled sequences to the minimum number of clones in the matching tissue. Normalized clone size distributions were visualized on rank plots (Fig 1). TRB repertoires in both patients and controls were diverse. Highly expanded clones (>5%) were rare in either group. Patients had 1,415,069 unique CDR3 amino acid sequences (CDR3AA), of which 106,446 (7.5%) were shared by ≥2 patients. Among the 106,446 shared CDR3AA, 95.4% were found in controls, and 4.3% TCRs had reported specificity to exogenous (mostly viral) or endogenous antigens in VDJdb. 4,920 (4.6%) of the shared CDR3AA were detected only in patients (median 2 patients (2-25)). Of these, 190 exhibited TCR convergence in ≥1 patient, such that the same CDR3AA was encoded by ≥2 distinct nucleotide sequences. Clustering 4,920 patient-associated CDR3AA using GLIPH (Grouping of lymphocyte interactions by paratope hotspots) identified 43 TCR antigen convergence groups. CDR3AA in each cluster were not significantly expanded in the majority of patients and were not enriched for the high-risk alleles HLA-B*14:02 or B*40:02. Our data suggest that most CDR3AA in AA are private (unique to individual patients). Shared autoreactive CDR3AA may exist in patients but are likely to also be present in healthy individuals. As the next step towards identifying autoreactive T cells, we focused on AA patients with known HLA-I risk alleles. We tested their CD8 + T cells for reactivity with endogenously expressed antigens by stimulating T cells with hematopoietic cell lines engineered to express single HLA-I alleles matching those of the patients (Fig 2). TRB sequencing showed increased clonality and significant expansion of a subset of clonotypes after T cell stimulation. Interferon γ enzyme-linked immunospot assays confirmed the expanded T cells' responsiveness to antigens presented by HLA alleles used for T cell stimulation. Additionally, these expanded T cells demonstrated variable reactivity to cells expressing other HLA alleles from the same patient. Among the 1% of CDR3AA clonotypes most highly expanded with single HLA-I alleles (63-3000-fold), we identified several that were expanded in an HLA-restricted manner. However, several highly expanded CDR3AA were highly expanded with multiple HLA alleles. Potential reasons for expansion of some CDR3AA after stimulation with cells expressing different HLA alleles include 1) binding of the same CDR3AA to different antigens presented by distinct HLA alleles, 2) expansion in response to the same antigens presented by distinct HLA alleles, or 3) expansion in response to non-HLA antigens. In conclusion, our data show diverse and mostly private TRB repertoires in AA patients. Our in vitro findings suggest that much like T cell responses in non-AA individuals, T cell responses in AA are diverse and exhibit flexibility with respect to activation in response to various endogenous antigens in the context of different self HLA-I alleles. Given the diversity of TCR responses observed in AA patients, the tissue specificity of autoimmune attack and established association with risk HLA-I alleles in AA remain a mystery and likely require HSPC-specific autoantigen expression and efficient autoantigen presentation by individual patients' HLA-I alleles.

Machine-learning applications for cataclysmic variable discovery in the ZTF alert stream

ABSTRACT Cataclysmic variables (CV) encompass a diverse array of accreting white dwarf binary systems. Each class of CV represents a snapshot along an evolutionary journey, one with the potential to trigger a type Ia supernova event. The study of CVs offers valuable insights into binary evolution and accretion physics, with the rarest examples potentially providing the deepest insights. However, the escalating number of detected transients, coupled with our limited capacity to investigate them all, poses challenges in identifying such rarities. Machine learning (ML) plays a pivotal role in addressing this issue by facilitating the categorization of each detected transient into its respective transient class. Leveraging these techniques, we have developed a two-stage pipeline tailored to the Zwicky Transient Facility transient alert stream. The first stage is alerts filter aimed at removing non-CVs, while the latter is an ML classifier produced using Extreme Gradient Boosting, achieving a macro average area under the curve score of 0.92 for distinguishing between CV classes. By utilizing the generative topographic mapping algorithm with classifier posterior probabilities as input, we obtain representations indicating that CV evolutionary factors play a role in classifier performance, while the associated feature maps present a potent tool for identifying the features deemed most relevant for distinguishing between classes. Implementation of the pipeline in 2023 June yielded 51 intriguing candidates that are yet to be reported as CVs or classified with further granularity. Our classifier represents a significant step in the discovery and classification of different CV classes, a domain of research still in its infancy.

Serendipitous discovery of Class I HDAC inhibitors from rational design of molecular glue degraders targeting HDAC

Zinc-dependent histone deacetylases (HDACs) play an essential role as epigenetic regulators and are becoming increasingly important drug targets for the treatment of cancer. Although five HDAC inhibitors have been approved for treating several cancers, only one of them is a Class I HDAC inhibitor, which may have advantages over pan-HDAC inhibitors due to the various side effects associated with the latter. On the other hand, the emerging strategy of molecular glue degraders offers a unique advantage for targeting therapeutic proteins. In this study, we synthesized a series of candidate compounds based on the molecule glue, pomalidomide, using a “merger principle”, initially aiming to obtain molecular glue degraders that can target HDAC degradation. However, we serendipitously discovered that compounds 2f and 3f may be potent Class I HDAC selective inhibitors. After further evaluation, we found that compounds 2f and 3f exhibit selective inhibitory effects on Class I HDAC in cancer cells. Moreover, they showed the robust antiproliferative activities against various hematological tumor cells, comparable to that of the approved Class I HDAC inhibitor, Chidamide. These results suggest that pomalidomide-derivatized compounds have promising potential as Class I HDAC inhibitors with therapeutic applications in cancer treatment.

Discovery of SARS-CoV-2 Inhibitors Featuring Novel Histidine α-Nitrile Motif.

As COVID-19 infection caused severe public health concerns recently, the development of novel antivirals has become the need of the hour. Main protease (Mpro ) has been an attractive target for antiviral drugs since it plays a vital role in polyprotein processing and virus maturation. Herein we report the discovery of a novel class of inhibitors against the SARS-CoV-2, bearing histidine α-nitrile motif embedded on a simple dipeptide framework. In-vitro and in-silico studies revealed that the histidine α-nitrile motif envisioned to target the Mpro contributes to the inhibitory activity. Among a series of dipeptides synthesized featuring this novel structural motif, some dipeptides displayed strong viral reduction (EC50 =0.48 μM) with a high selectivity index, SI>454.54. These compounds also exhibit strong binding energies in the range of -28.7 to -34.2 Kcal/mol. The simple dipeptide structural framework, amenable to quick structural variations, coupled with ease of synthesis from readily available commercial starting materials are the major attractive features of this novel class of SARS-CoV-2 inhibitors. The histidine α-nitrile dipeptides raise the hope of discovering potent drug candidates based on this motif to fight the dreaded SARS-CoV-2.

Discovery of Novel NLRP3 Inflammasome Inhibitors Composed of an Oxazole Scaffold Bearing an Acylsulfamide.

The NLRP3 inflammasome plays an important role in the defense mechanism of the innate immune system and has recently attracted much attention as a drug target for various inflammatory disorders. Among the strategies for generating the novel chemotype in current drug discovery, scaffold hopping and bioisosteric replacement are known to be attractive approaches. As the results of our medicinal chemistry campaign, which involved exploration of core motifs using a ring closing approach, a five-membered oxazole-based scaffold was identified, and subsequent implementation of bioisosteric replacement led to discovery of a novel chemical class of NLRP3 inflammasome inhibitor bearing the acylsulfamide group. Further optimization of aniline and sulfamide moieties to improve potency in human whole blood assay led to the identification of the orally bioactive compound 32 in the LPS challenge model. Furthermore, compound 32 attenuated kidney injury in adriamycin-induced glomerulonephritis in mice. These investigations indicated that the NLRP3 inhibitor could be a potential therapeutic agent for glomerulonephritis.

Two-dimensional phase-change chalcohalides

Two-dimensional (2D) materials exhibiting structural phase transitions have a wide range of potential applications, yet 2D phase-change materials have been mostly restricted to binary chalcogenides. Here, we report the computational discovery of a new class of mixed-anion 2D phase-change Janus chalcohalides InSeX (X = Cl, Br, I), in which the lateral displacements of the halogen atoms can be induced by external stimuli such as biaxial strain, electric field, or chemical vacancies, leading to a structural phase transition from an octahedrally (O) coordinated phase to a unique tetrahedrally (T) coordinated phase. The O-to-T phase transition not only leads to an indirect-to-direct bandgap transition, but also results in a drastic reduction of the electron effective mass from 0.31 to 0.81 m0 to ∼0.19 m0. These results highlight the potential for discovering novel phase-change materials in mixed-anion compounds.

A Deep Learning-Based Novel Class Discovery Approach for Automatic Modulation Classification

A Deep Learning-Based Novel Class Discovery Approach for Automatic Modulation Classification

Using alternative SMILES representations to identify novel functional analogues in chemical similarity vector searches

Chemical similarity searches are a widely used family of in silico methods for identifying pharmaceutical leads. These methods historically relied on structure-based comparisons to compute similarity. Here, we use a chemical language model to create a vector-based chemical search. We extend previous implementations by creating a prompt engineering strategy that utilizes two different chemical string representation algorithms: one for the query and the other for the database. We explore this method by reviewing search results from nine queries with diverse targets. We find that the method identifies molecules with similar patent-derived functionality to the query, as determined by our validated LLM-assisted patent summarization pipeline. Further, many of these functionally similar molecules have different structures and scaffolds from the query, making them unlikely to be found with traditional chemical similarity searches. This method may serve as a new tool for the discovery of novel molecular structural classes that achieve target functionality.

A reliable adaptive prototype-based learning for evolving data streams with limited labels

Data stream mining presents notable challenges in the form of concept drift and evolution. Existing learning algorithms, typically designed within a supervised learning framework, require class labels for all data points. However, this is an impractical requirement given the rapid pace of data streams, which often results in label scarcity. Recognizing the realistic necessity of learning from data streams with limited labels, we propose an adaptive, data-driven, prototype-based semi-supervised learning framework specifically tailored to handle evolving data streams. Our method employs a prototype-based data representation, summarizing the continuous flow of streaming data using dynamic prototypes at varying levels of granularity. This technique enables improved data abstraction, capturing the underlying local data distributions more accurately. The model also incorporates reliability modeling and efficient emerging class discovery, dynamically updating the significance of prototypes over time and swiftly adapting to local concept drift. We further leverage these adaptive prototypes to intuitively detect concept evolution, i.e., identifying novel classes from a local density perspective. To minimize the need for manual labeling while optimizing performance, we incorporate active learning into our method. This method employs a dual-criteria approach for data point selection, considering both uncertainty and local density. These manually labeled data points, together with unlabeled data, serve to update the model efficiently and robustly. Empirical validation using several benchmark datasets demonstrates promising performance in comparison to existing state-of-the-art techniques.

Plights of Tenured Teachers in Integrating Technology in Classroom Instruction - A Phenomenological Inquiry

Technology integration for tenured teachers improves and enhances their skills in the teaching process. The study aims to explore the lived experiences, coping mechanism, and insights of tenured teachers in integrating technology. The researcher collected data from 17 Araling Panlipunan Tenured Teachers in high school from the Division of Tagum City using purposive sampling. This study employed a phenomenological approach and utilized an interview guide for in-depth interview and focus group discussion. The results of the study were analyzed using thematic analysis. In the lived experiences of tenured teachers, there are three emerging themes; facing challenges brought by ICT innovations; experiencing lack of know-how and interest; experiencing difficulty in access to resources and learning materials. Further, on tenured teachers coping mechanism there are also three themes; consultation with peers, class discovery and experiential learning; self-determination and perseverance. Lastly, on the insights of tenured teachers, three themes emerged; improving skills and adapting to change, best to share and learn from peers; never lose hope and determination. Results revealed that it is a challenge for tenured teachers to innovate strategies due to lack of know-how and difficulty or limited access to technology infrastructures. However, teachers were able to overcome these challenges through peer support, positive attitude and discovering classroom strategies which promotes experiential learning. Furthermore, tenured teachers see technology integration valuable since it improves their skills and adaptability to the technological changes and enable them to cope and never lose hope despite their tenure. This implies that on-going professional development plays a relevant role in improving tenured teachers’ skills and delivery of classroom instruction with technology integration.