Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials

Elucidating Compound Mechanism of Action and Polypharmacology with a Large-scale Perturbational Profile Compendium

The Mechanism of Action (MoA) of a drug is generally represented as a small, non-tissue-specific repertoire of high-affinity binding targets. Yet, drug activity and polypharmacology are increasingly associated with a broad range of off-target and tissue-specific effector proteins. To address this challenge, we have leveraged a microfluidics-based Plate-Seq technology to survey drug perturbational profiles representing >700 FDA-approved and experimental oncology drugs, in cell lines selected as high-fidelity models of 23 aggressive tumor subtypes. Built on this dataset, we implemented an efficient computational framework to define a tissue-specific protein activity landscape of these drugs and reported almost 50 million differential protein activities derived from drug perturbations vs. vehicle controls. These analyses revealed thousands of highly reproducible and novel, drug-mediated modulation of tissue-specific targets, leading to generation of a proteome-wide drug functional network, characterization of MoA-related drug clusters and off-target effects, dramatical expansion of druggable human proteome, and identification and experimental validation of novel, tissue-specific inhibitors of undruggable oncoproteins, most never reported before. The drug perturbation profile resource described here represents the first, large-scale, whole-genome-wide, RNA-Seq based dataset assembled to date, with the proposed framework, which is easily extended to elucidating the MoA of novel small-molecule libraries, facilitates mechanistic exploration of drug functions, supports systematic and quantitative approaches to precision oncology, and serves as a rich data foundation for drug discovery.

Targeting dermatophyte Cdc42 and Rac GTPase signaling to hinder hyphal elongation and virulence

The development of antifungal drugs requires novel molecular targets due to limited treatment options and drug resistance. Through chemical screening and establishment of a novel genetic technique to repress gene expression in Trichophyton rubrum, the primary causal fungus of dermatophytosis, we demonstrated that fungal Cdc42 and Rac GTPases are promising antifungal drug targets. Chemical inhibitors of these GTPases impair hyphal formation, which is crucial for growth and virulence in T. rubrum. Conditional repression of Cdc24, a guanine nucleotide exchange factor for Cdc42 and Rac, led to hyphal growth defects, abnormal cell morphology, and cell death. EHop-016 inhibited the promotion of the guanine nucleotide exchange reaction in Cdc42 and Rac by Cdc24 as well as germination and growth on the nail fragments of T. rubrum and improved animal survival in an invertebrate infection model of T. rubrum. Our results provide a novel antifungal therapeutic target and a potential lead compound.

Comparison of Cyclic and Linear PEG Conjugates

Bioconjugation of polymers to proteins is a method to impart improved stability and pharmacokinetic properties to biologic systems. However, the precise effects of polymer architecture on the resulting bioconjugates are not well understood. Particularly, cyclic polymers are known to possess unique features such as a decreased hydrodynamic radius when compared to their linear counterparts of the same molecular weight, but have not yet been studied. Here, we report the first bioconjugation of a cyclic polymer, poly(ethylene glycol) (PEG), to a model protein, T4 lysozyme, containing a single engineered cysteine residue (V131C). We compare the stability and activity of this conjugate with those of a linear PEG-T4 lysozyme analogue of similar molecular weight. Furthermore, we used molecular dynamics (MD) simulations to determine the behavior of the polymer-protein conjugates in solution. We introduce cyclic polymer-protein conjugates as potential candidates for the improvement of biologic therapeutics.

Down-Regulation of AKT Proteins Slows the Growth of Mutant-KRAS Pancreatic Tumors

Serine/threonine kinase AKT isoforms play a well-established role in cell metabolism and growth. Most pancreatic adenocarcinomas (PDACs) harbor activation mutations of KRAS, which activates the PI3K/AKT signaling pathway. However, AKT inhibitors are not effective in the treatment of pancreatic cancer. To better understand the role of AKT signaling in mutant-KRAS pancreatic tumors, this study utilized proteolysis-targeting chimeras (PROTACs) and CRISPR-Cas9-genome editing to investigate AKT proteins. The PROTAC down-regulation of AKT proteins markedly slowed the growth of three pancreatic tumor cell lines harboring mutant KRAS. In contrast, the inhibition of AKT kinase activity alone had very little effect on the growth of these cell lines. The concurrent genetic deletion of all AKT isoforms (AKT1, AKT2, and AKT3) in the KPC (KrasG12D; Trp53R172H; Pdx1-Cre) pancreatic cancer cell line also dramatically slowed its growth in vitro and when orthotopically implanted in syngeneic mice. Surprisingly, insulin-like growth factor-1 (IGF-1), but not epidermal growth factor (EGF), restored KPC cell growth in serum-deprived conditions, and the IGF-1 growth stimulation effect was AKT-dependent. The RNA-seq analysis of AKT1/2/3-deficient KPC cells suggested that reduced cholesterol synthesis may be responsible for the decreased response to IGF-1 stimulation. These results indicate that the presence of all three AKT isoforms supports pancreatic tumor cell growth, and the pharmacological degradation of AKT proteins may be more effective than AKT catalytic inhibitors for treating pancreatic cancer.

A Novel AMPK Inhibitor Sensitizes Pancreatic Cancer Cells to Ferroptosis Induction

Cancer cells must develop strategies to adapt to the dynamically changing stresses caused by intrinsic or extrinsic processes, or therapeutic agents. Metabolic adaptability is crucial to mitigate such challenges. Considering metabolism as a central node of adaptability, it is focused on an energy sensor, the AMP-activated protein kinase (AMPK). In a subtype of pancreatic ductal adenocarcinoma (PDAC) elevated AMPK expression and phosphorylation is identified. Using drug repurposing that combined screening experiments and chemoproteomic affinity profiling, it is identified and characterized PF-3758309, initially developed as an inhibitor of PAK4, as an AMPK inhibitor. PF-3758309 shows activity in pre-clinical PDAC models, including primary patient-derived organoids. Genetic loss-of-function experiments showed that AMPK limits the induction of ferroptosis, and consequently, PF-3758309 treatment restores the sensitivity toward ferroptosis inducers. The work established a chemical scaffold for the development of specific AMPK-targeting compounds and deciphered the framework for the development of AMPK inhibitor-based combination therapies tailored for PDAC.

An integrative epigenome-based strategy for unbiased functional profiling of clinical kinase inhibitors

More than 500 kinases are implicated in the control of most cellular process in mammals, and deregulation of their activity is linked to cancer and inflammatory disorders. 80 clinical kinase inhibitors (CKIs) have been approved for clinical use and hundreds are in various stages of development. However, CKIs inhibit other kinases in addition to the intended target(s), causing both enhanced clinical effects and undesired side effects that are only partially predictable based on in vitro selectivity profiling. Here, we report an integrative approach grounded on the use of chromatin modifications as unbiased, information-rich readouts of the functional effects of CKIs on macrophage activation. This approach exceeded the performance of transcriptome-based approaches and allowed us to identify similarities and differences among CKIs with identical intended targets, to recognize novel CKI specificities and to pinpoint CKIs that may be repurposed to control inflammation, thus supporting the utility of this strategy to improve selection and use of CKIs in clinical settings.

Technology Readiness Level Roadmap for Developing Innovative Herbal Medicinal Products

Despite the vast global botanical diversity, the pharmaceutical development of herbal medicinal products (HMPs) remains underexploited. Of over 370,000 described plant species, only a few hundred are utilized in HMPs. Most of these have originated from traditional use, and only a minority come from megadiverse countries. Exploiting the pharmacological synergies of the hundreds of compounds found in poorly studied plant species may unlock new therapeutic possibilities, enhance megadiverse countries' scientific and socio-economic development, and help conserve biodiversity. However, extensive constraints in the development process of HMPs pose significant barriers to transforming this unsatisfactory socio-economic landscape. This paper proposes a roadmap to overcome these challenges, based on the technology readiness levels (TRLs) introduced by NASA to assess the maturity of technologies. It aims to assist research entities, manufacturers, and funding agencies from megadiverse countries in the discovery, development, and global market authorization of innovative HMPs that comply with regulatory standards from ANVISA, EMA, and FDA, as well as WHO and ICH guidelines.

Changing the Landscape of Solid Tumor Therapy from Apoptosis-Promoting to Apoptosis-Inhibiting Strategies

The many limitations of implementing anticancer strategies under the term "precision oncology" have been extensively discussed. While some authors propose promising future directions, others are less optimistic and use phrases such as illusion, hype, and false hypotheses. The reality is revealed by practicing clinicians and cancer patients in various online publications, one of which has stated that "in the quest for the next cancer cure, few researchers bother to look back at the graveyard of failed medicines to figure out what went wrong". The message is clear: Novel therapeutic strategies with catchy names (e.g., synthetic "lethality") have not fulfilled their promises despite decades of extensive research and clinical trials. The main purpose of this review is to discuss key challenges in solid tumor therapy that surprisingly continue to be overlooked by the Nomenclature Committee on Cell Death (NCCD) and numerous other authors. These challenges include: The impact of chemotherapy-induced genome chaos (e.g., multinucleation) on resistance and relapse, oncogenic function of caspase 3, cancer cell anastasis (recovery from late stages of apoptosis), and pitfalls of ubiquitously used preclinical chemosensitivity assays (e.g., cell "viability" and tumor growth delay studies in live animals) that score such pro-survival responses as "lethal" events. The studies outlined herein underscore the need for new directions in the management of solid tumors.

Development of Biocompatible Cu(I)-Microdevices for Bioorthogonal Uncaging and Click Reactions

Transition-metal-catalyzed bioorthogonal reactions emerged a decade ago as a novel strategy to implement spatiotemporal control over enzymatic functions and pharmacological interventions. The use of this methodology in experimental therapy is driven by the ambition of improving the tolerability and PK properties of clinically-used therapeutic agents. The preclinical potential of bioorthogonal catalysis has been validated in vitro and in vivo with the in situ generation of a broad range of drugs, including cytotoxic agents, anti-inflammatory drugs and anxiolytics. In this article, we report our investigations towards the preparation of solid-supported Cu(I)-microdevices and their application in bioorthogonal uncaging and click reactions. A range of ligand-functionalized polymeric devices and off-on Cu(I)-sensitive sensors were developed and tested under conditions compatible with life. Last, we present a preliminary exploration of their use for the synthesis of PROTACs through CuAAC assembly of two heterofunctional mating units.

Single Molecular Nanomedicines Based on Macrocyclic Carrier-Drug Conjugates for Concentration-Independent Encapsulation and Precise Activation of Drugs

Nanomedicines often rely on noncovalent self-assembly and encapsulation for drug loading and delivery. However, challenges such as reproducibility issues due to the multicomponent nature, off-target activation caused by premature drug release, and complex pharmacokinetics arising from assembly dissociation have hindered their clinical translation. In this study, we introduce an innovative design concept termed single molecular nanomedicine (SMNM) based on macrocyclic carrier-drug conjugates. Through the covalent linkage of two chemotherapy drugs to a hypoxia-cleavable macrocyclic carrier, azocalix[4]arene, we obtained two self-included complexes to serve as SMNMs. The intramolecular inclusion feature of the SMNMs has not only demonstrated comprehensive shielding and protection for the drugs but also effectively prevented off-target drug leakage, thereby significantly reducing their side effects and enhancing their antitumor therapeutic efficacy. Additionally, the attributes of being a single component and molecularly dispersed confer advantages such as ease of preparation and good reproducibility for SMNMs, which is desirable for clinical applications.

Preclinical side effect prediction through pathway engineering of protein interaction network models

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

Down-regulation of AKT proteins slows the growth of mutant-KRAS pancreatic tumors

Serine/threonine kinase AKT isoforms play a well-established role in cell metabolism and growth. Most pancreatic adenocarcinoma (PDAC) harbors activation mutations of KRAS, which activates the PI3K/AKT signaling pathway. However, AKT inhibitors are not effective in the treatment of pancreatic cancer. To better understand the role of AKT signaling in mutant-KRAS pancreatic tumors, this study utilizes proteolysis-targeting chimeras (PROTACs) and CRISPR-Cas9-genome editing to investigate AKT proteins. PROTAC down-regulation of AKT proteins markedly slowed the growth of three pancreatic tumor cell lines harboring mutant KRAS. In contrast, inhibition of AKT kinase activity alone had very little effect on the growth of these cell lines. Concurrent genetic deletion of all AKT isoforms (AKT1, AKT2, and AKT3) in the KPC (KrasG12D; Trp53R172H; Pdx1-Cre) pancreatic cancer cell line also dramatically slowed its growth in vitro and when orthotopically implanted in syngeneic mice. Surprisingly, insulin-like growth factor-1 (IGF-1), but not epidermal growth factor (EGF), restored KPC cell growth in serum-deprived conditions and the IGF-1 growth stimulation effect was AKT dependent. RNA-seq analysis of AKT1/2/3-deficient KPC cells suggested that reduced cholesterol synthesis may be responsible for the decreased response to IGF-1 stimulation. These results indicate that the presence of all three AKT isoforms supports pancreatic tumor cell growth and pharmacological degradation of AKT proteins may be more effective than AKT catalytic inhibitors for treating pancreatic cancer.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other -omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

Discovery of novel quinolin-2-one derivatives as potential GSK-3β inhibitors for treatment of Alzheimer's disease: Pharmacophore-based design, preliminary SAR, in vitro and in vivo biological evaluation

Recently, glycogen synthase kinase-3β (GSK-3β) has been considered as a critical factor implicated in Alzheimer's disease (AD). In a previous work, a 3D pharmacophore model for GSK-3β inhibitors was created and the results suggested that derivative ZINC67773573, VIII, may provide a promising lead for developing novel GSK-3β inhibitors for the AD's treatment. Consequently, in this work, novel series of quinolin-2-one derivatives were synthesized and assessed for their GSK-3β inhibitory properties. In vitro screening identified three compounds: 7c, 7e and 7f as promising GSK-3β inhibitors. Compounds 7c, 7e and 7f were found to exhibit superior inhibitory effect on GSK-3β with IC50 value ranges between 4.68 ± 0.59 to 8.27 ± 0.60 nM compared to that of staurosporine (IC50 = 6.12 ± 0.74 nM). Considerably, compounds 7c, 7e and 7f effectively lowered tau hyperphosphorylated aggregates and proving their safety towards the SH-SY5Y and THLE2 normal cell lines. The most promising compound 7c alleviated cognitive impairments in the scopolamine-induced model in mice. Compound 7c's activity profile, while not highly selective, may provide a starting point and valuable insights into the design of multi-target inhibitors. According to the ADME prediction results, compounds 7c, 7e and 7f followed Lipinski's rule of five and could almost permeate through the BBB. Molecular docking simulations showed that these compounds are well accommodated in the ATP binding site interacting by its quinoline-2-one ring through hydrogen bonding with the key amino acids Asp133 and Val135 at the hinge region. The findings of this study suggested that these new compounds may have potential as anti-AD drugs targeting GSK-3β.

All eyes on Eya: A unique transcriptional co-activator and phosphatase in cancer

The Eya family of proteins (consisting of Eyas1-4 in mammals) play vital roles in embryogenesis by regulating processes such as proliferation, migration/invasion, cellular survival and pluripotency/plasticity of epithelial and mesenchymal states. Eya proteins carry out such diverse functions through a unique combination of transcriptional co-factor, Tyr phosphatase, and PP2A/B55α-mediated Ser/Thr phosphatase activities. Since their initial discovery, re-expression of Eyas has been observed in numerous tumor types, where they are known to promote tumor progression through a combination of their transcriptional and enzymatic activities. Eya proteins thus reinstate developmental processes during malignancy and represent a compelling class of therapeutic targets for inhibiting tumor progression.

Poly-pharmacology of existing drugs: How to crack the code?

Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.

Exploring the role of histone deacetylase and histone deacetylase inhibitors in the context of multiple myeloma: mechanisms, therapeutic implications, and future perspectives

Histone deacetylase inhibitors (HDACis) are a significant category of pharmaceuticals that have developed in the past two decades to treat multiple myeloma. Four drugs in this category have received approval from the U.S. Food and Drug Administration (FDA) for use: Panobinonstat (though canceled by the FDA in 2022), Vorinostat, Belinostat and Romidepsin. The efficacy of this group of drugs is attributed to the disruption of many processes involved in tumor growth through the inhibition of histone deacetylase, and this mode of action leads to significant anti-multiple myeloma (MM) activity. In MM, inhibition of histone deacetylase has many downstream consequences, including suppression of NF-κB signaling and HSP90, upregulation of cell cycle regulators (p21, p53), and downregulation of antiapoptotic proteins including Bcl-2. Furthermore, HDACis have a variety of direct and indirect oxidative effects on cellular DNA. HDAC inhibitors enhance normal immune function, thereby decreasing the proliferation of malignant plasma cells and promoting autophagy. The various biological effects of inhibiting histone deacetylase have a combined or additional impact when used alongside other chemotherapeutic and targeted drugs for multiple myeloma. This helps to decrease resistance to treatment. Combination treatment regimens that include HDACis have become an essential part of the therapy for multiple myeloma. These regimens incorporate drugs from other important classes of anti-myeloma agents, such as immunomodulatory drugs (IMiDs), conventional chemotherapy, monoclonal antibodies, and proteasome inhibitors. This review provides a comprehensive evaluation of the clinical efficacy and safety data pertaining to the currently approved histone deacetylase inhibitors, as well as an explanation of the crucial function of histone deacetylase in multiple myeloma and the characteristics of the different histone deacetylase inhibitors. Moreover, it provides a concise overview of the most recent developments in the use of histone deacetylase inhibitors for treating multiple myeloma, as well as potential future uses in treatment.

Inference of drug off-target effects on cellular signaling using interactome-based deep learning

Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.

A heterogeneous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase

Over-activation of the epidermal growth factor receptor (EGFR) is a hallmark of glioblastoma. However, EGFR-targeted therapies have led to minimal clinical response. While delivery of EGFR inhibitors (EGFRis) to the brain constitutes a major challenge, how additional drug-specific features alter efficacy remains poorly understood. We apply highly multiplex single-cell chemical genomics to define the molecular response of glioblastoma to EGFRis. Using a deep generative framework, we identify shared and drug-specific transcriptional programs that group EGFRis into distinct molecular classes. We identify programs that differ by the chemical properties of EGFRis, including induction of adaptive transcription and modulation of immunogenic gene expression. Finally, we demonstrate that pro-immunogenic expression changes associated with a subset of tyrphostin family EGFRis increase the ability of T-cells to target glioblastoma cells.

A comprehensive review on role of Aurora kinase inhibitors (AKIs) in cancer therapeutics

Aurora kinases (AURKs) are a family of serine /threonine protein kinases that have a crucial role in cell cycle process mainly in the event of chromosomal segregation, centrosome maturation and cytokinesis. The family consists of three members including Aurora kinase A (AURK-A), Aurora kinase B (AURK-B) and Aurora kinase C (AURK-C). All AURKs contain a conserved kinase domain for their activity but differ in their cellular localization and functions. AURK-A and AURK-B are expressed mainly in somatic cells while the expression of AURK-C is limited to germ cells. AURK-A promotes G2 to M transition of cell cycle by controlling centrosome maturation and mitotic spindle assembly. AURK-B and AURK-C form the chromosome passenger complex (CPC) that ensures proper chromosomal alignments and segregation. Aberrant expression of AURK-A and AURK-B has been detected in several solid tumours and malignancies. Hence, they have become an attractive therapeutic target against cancer. The first part of this review focuses on AURKs structure, functions, subcellular localization, and their role in tumorigenesis. The review also highlights the functional and clinical impact of selective as well as pan kinase inhibitors. Currently, >60 compounds that target AURKs are in preclinical and clinical studies. The drawbacks of existing inhibitors like selectivity, drug resistance and toxicity have also been addressed. Since, majority of inhibitors are Aurora kinase inhibitor (AKI) type-1 that bind to the active (DFGin and Cin) conformation of the kinase, this information may be utilized to design highly selective kinase inhibitors that can be combined with other therapeutic agents for better clinical outcomes.

Redefining cancer research for therapeutic breakthroughs

Cancer research has played a pivotal role in improving patient outcomes. However, despite the significant investment in fundamental cancer research over the past few decades, the translation of funding into substantial advancements in cancer treatment has been limited. This perspective article employs a detailed analysis to outline strategies for promoting innovation and facilitating discoveries within the field of cancer research.

Engineering a hyaluronic acid-encapsulated tumor-targeted nanoplatform with sensitized chemotherapy and a photothermal effect for enhancing tumor therapy

Chemotherapy remains one of the most widely used cancer treatment modalities in clinical practice. However, the characteristic microenvironment of solid tumors severely limits the anticancer efficacy of chemotherapy. In addition, a single treatment modality or one death pathway reduces the antitumor outcome. Herein, tumor-targeting O2 self-supplied nanomodules (CuS@DOX/CaO2-HA) are proposed that not only alleviate tumor microenvironmental hypoxia to promote the accumulation of chemotherapeutic drugs in tumors but also exert photothermal effects to boost drug release, penetration and combination therapy. CuS@DOX/CaO2-HA consists of copper sulfide (CuS)-loaded calcium peroxide (CaO2) and doxorubicin (DOX), and its surface is further modified with HA. CuS@DOX/CaO2-HA underwent photothermal treatment to release DOX and CaO2. Hyperthermia accelerates drug penetration to enhance chemotherapeutic efficacy. The exposed CaO2 reacts with water to produce Ca2+, H2O2 and O2, which sensitizes cells to chemotherapy through mitochondrial damage caused by calcium overload and a reduction in drug efflux via the alleviation of hypoxia. Moreover, under near infrared (NIR) irradiation, CuS@DOX/CaO2-HA initiates a pyroptosis-like cell death process in addition to apoptosis. In vivo, CuS@DOX/CaO2-HA demonstrated high-performance antitumor effects. This study provides a new strategy for synergistic enhancement of chemotherapy in hypoxic tumor therapy via combination therapy and multiple death pathways.

Application of artificial intelligence and machine learning in drug repurposing

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.

Select EZH2 inhibitors enhance viral mimicry effects of DNMT inhibition through a mechanism involving NFAT:AP-1 signaling

DNA methyltransferase inhibitor (DNMTi) efficacy in solid tumors is limited. Colon cancer cells exposed to DNMTi accumulate lysine-27 trimethylation on histone H3 (H3K27me3). We propose this Enhancer of Zeste Homolog 2 (EZH2)-dependent repressive modification limits DNMTi efficacy. Here, we show that low-dose DNMTi treatment sensitizes colon cancer cells to select EZH2 inhibitors (EZH2is). Integrative epigenomic analysis reveals that DNMTi-induced H3K27me3 accumulates at genomic regions poised with EZH2. Notably, combined EZH2i and DNMTi alters the epigenomic landscape to transcriptionally up-regulate the calcium-induced nuclear factor of activated T cells (NFAT):activating protein 1 (AP-1) signaling pathway. Blocking this pathway limits transcriptional activating effects of these drugs, including transposable element and innate immune response gene expression involved in viral defense. Analysis of primary human colon cancer specimens reveals positive correlations between DNMTi-, innate immune response-, and calcium signaling-associated transcription profiles. Collectively, we show that compensatory EZH2 activity limits DNMTi efficacy in colon cancer and link NFAT:AP-1 signaling to epigenetic therapy-induced viral mimicry.

AutoEpiCollect, a Novel Machine Learning-Based GUI Software for Vaccine Design: Application to Pan-Cancer Vaccine Design Targeting PIK3CA Neoantigens

Previous epitope-based cancer vaccines have focused on analyzing a limited number of mutated epitopes and clinical variables preliminarily to experimental trials. As a result, relatively few positive clinical outcomes have been observed in epitope-based cancer vaccines. Further efforts are required to diversify the selection of mutated epitopes tailored to cancers with different genetic signatures. To address this, we developed the first version of AutoEpiCollect, a user-friendly GUI software, capable of generating safe and immunogenic epitopes from missense mutations in any oncogene of interest. This software incorporates a novel, machine learning-driven epitope ranking method, leveraging a probabilistic logistic regression model that is trained on experimental T-cell assay data. Users can freely download AutoEpiCollectGUI with its user guide for installing and running the software on GitHub. We used AutoEpiCollect to design a pan-cancer vaccine targeting missense mutations found in the proto-oncogene PIK3CA, which encodes the p110ɑ catalytic subunit of the PI3K kinase protein. We selected PIK3CA as our gene target due to its widespread prevalence as an oncokinase across various cancer types and its lack of presence as a gene target in clinical trials. After entering 49 distinct point mutations into AutoEpiCollect, we acquired 361 MHC Class I epitope/HLA pairs and 219 MHC Class II epitope/HLA pairs. From the 49 input point mutations, we identified MHC Class I epitopes targeting 34 of these mutations and MHC Class II epitopes targeting 11 mutations. Furthermore, to assess the potential impact of our pan-cancer vaccine, we employed PCOptim and PCOptim-CD to streamline our epitope list and attain optimized vaccine population coverage. We achieved a world population coverage of 98.09% for MHC Class I data and 81.81% for MHC Class II data. We used three of our predicted immunogenic epitopes to further construct 3D models of peptide-HLA and peptide-HLA-TCR complexes to analyze the epitope binding potential and TCR interactions. Future studies could aim to validate AutoEpiCollect's vaccine design in murine models affected by PIK3CA-mutated or other mutated tumor cells located in various tissue types. AutoEpiCollect streamlines the preclinical vaccine development process, saving time for thorough testing of vaccinations in experimental trials.

The importance of preclinical models in cholangiocarcinoma

Cholangiocarcinoma (CCA) is an adenocarcinoma of the hepatobiliary system with a grim prognosis. Incidence is rising globally and surgery is currently the only curative treatment, but is only available for patients who are fit and diagnosed in an early-stage of disease progression. Great importance has been placed on developing preclinical models to help further our understanding of CCA and potential treatments to improve therapeutic outcomes. Preclinical models of varying complexity and cost have been established, ranging from more simplistic in vitro 2D CCA cell lines in culture, to more complex in vivo genetically engineered mouse models. Currently there is no single model that faithfully recaptures the complexities of human CCA and the in vivo tumour microenvironment. Instead a multi-model approach should be used when designing preclinical trials to study CCA and potential therapies.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Comparative investigation into the anticancer activity of analogs of marine coelenterazine and coelenteramine

Cancer is still one of the most challenging diseases to treat, making the pursuit for novel molecules with potential anticancer activity an important research topic. Herein, we have performed a comparative investigation into the anticancer activity of analogs of marine coelenterazine and coelenteramine. The former is a well-known bioluminescent substrate, while the latter is a metabolic product of the resulting bioluminescent reaction. While both types of analogs showed anticancer activity toward lung and gastric cancer cell lines, we have obtained data that highlight relevant differences between the activity of these two types of compounds. More specifically, we observed relevant differences in structure-activity relationships between these types of compounds. Also, coelenteramine analogs showed time-dependent activity, while coelenterazine-based compounds usually present time-independent activity. Coelenterazine analogs also appear to be relatively safer toward noncancer cells than coelenteramine analogs. There was also seen a correlation between the activity of the coelenterazine-based compounds and their light-emission properties. Thus, these results further indicate the potential of the marine coelenterazine chemi-/bioluminescent system as a source of new molecules with anticancer activity, while providing more insight into their modes of action.

Creative approaches using proximity labeling to gain new biological insights

At its most fundamental level, life is a collection of synchronized cellular processes driven by interactions among biomolecules. Proximity labeling has emerged as a powerful technique to capture these interactions in native settings, revealing previously unexplored elements of biology. This review highlights recent developments in proximity labeling, focusing on methods that push the fundamental technologies beyond the classic bait-prey paradigm, such as RNA-protein interactions, ligand/small-molecule-protein interactions, cell surface protein interactions, and subcellular protein trafficking. The advancement of proximity labeling methods to address different biological problems will accelerate our understanding of the complex biological systems that make up life.

Drugging the entire human proteome: Are we there yet?

Each of the ∼20,000 proteins in the human proteome is a potential target for compounds that bind to it and modify its function. The 3D structures of most of these proteins are now available. Here, we discuss the prospects for using these structures to perform proteome-wide virtual HTS (VHTS). We compare physics-based (docking) and AI VHTS approaches, some of which are now being applied with large databases of compounds to thousands of targets. Although preliminary proteome-wide screens are now within our grasp, further methodological developments are expected to improve the accuracy of the results.

Forward Genetic Screens Identify Mechanisms of Resistance to Small-Molecule Lactate Dehydrogenase Inhibitors

Altered metabolism is a hallmark of cancer; however, it has been difficult to specifically target metabolism in cancer for therapeutic benefit. Cancers with genetically defined defects in metabolic enzymes constitute a subset of cancers where targeting metabolism is potentially accessible. Hürthle cell carcinoma of the thyroid (HTC) tumors frequently harbor deleterious mitochondrial DNA (mtDNA) mutations in subunits of complex I of the mitochondrial electron transport chain (ETC). Previous work has shown that HTC models with deleterious mtDNA mutations exhibit mitochondrial ETC defects that expose lactate dehydrogenase (LDH) as a therapeutic vulnerability. Here, we performed forward genetic screens to identify mechanisms of resistance to small-molecule LDH inhibitors. We identified two distinct mechanisms of resistance: upregulation of an LDH isoform and a compound-specific resistance mutation. Using these tools, we demonstrate that the anticancer activity of LDH inhibitors in cell line and xenograft models of complex I mutant HTC is through on-target LDH inhibition.

A Tandem-Affinity Purification Method for Identification of Primary Intracellular Drug-Binding Proteins

In the field of drug discovery, understanding how small molecule drugs interact with cellular components is crucial. Our study introduces a novel methodology to uncover primary drug targets using Tandem Affinity Purification for identification of Drug-Binding Proteins (TAP-DBP). Central to our approach is the generation of a FLAG-hemagglutinin (HA)-tagged chimeric protein featuring the FKBP12(F36V) adaptor protein and the TurboID enzyme. Conjugation of drug molecules with the FKBP12(F36V) ligand allows for the coordinated recruitment of drug-binding partners effectively enabling in-cell TurboID-mediated biotinylation. By employing a tandem affinity purification protocol based on FLAG-immunoprecipitation and streptavidin pulldown, alongside mass spectrometry analysis, TAP-DBP allows for the precise identification of drug-primary binding partners. Overall, this study introduces a systematic, unbiased method for identification of drug-protein interactions, contributing a clear understanding of target engagement and drug selectivity to advance the mode of action of a drug in cells.

Loss-of-Function but Not Gain-of-Function Properties of Mutant TP53 Are Critical for the Proliferation, Survival, and Metastasis of a Broad Range of Cancer Cells

Mutations in the tumor suppressor TP53 cause cancer and impart poor chemotherapeutic responses, reportedly through loss-of-function, dominant-negative effects and gain-of-function (GOF) activities. The relative contributions of these attributes is unknown. We found that removal of 12 different TP53 mutants with reported GOFs by CRISPR/Cas9 did not impact proliferation and response to chemotherapeutics of 15 human cancer cell lines and colon cancer-derived organoids in culture. Moreover, removal of mutant TP53/TRP53 did not impair growth or metastasis of human cancers in immune-deficient mice or growth of murine cancers in immune-competent mice. DepMap mining revealed that removal of 158 different TP53 mutants had no impact on the growth of 391 human cancer cell lines. In contrast, CRISPR-mediated restoration of wild-type TP53 extinguished the growth of human cancer cells in vitro. These findings demonstrate that LOF but not GOF effects of mutant TP53/TRP53 are critical to sustain expansion of many tumor types.

SIGNIFICANCE

This study provides evidence that removal of mutant TP53, thereby deleting its reported GOF activities, does not impact the survival, proliferation, metastasis, or chemotherapy responses of cancer cells. Thus, approaches that abrogate expression of mutant TP53 or target its reported GOF activities are unlikely to exert therapeutic impact in cancer. See related commentary by Lane, p. 211 . This article is featured in Selected Articles from This Issue, p. 201.

AI for targeted polypharmacology: The next frontier in drug discovery

In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such drugs were discovered serendipitously, limiting predictability and efficacy, but currently artificial intelligence (AI) offers a transformative solution. Machine learning and deep learning techniques enable modeling protein structures, generating novel compounds, and decoding their polypharmacological effects, opening an avenue for more systematic and predictive multi-target drug design. This review explores the use of AI in identifying synergistic co-targets and delineating them from anti-targets that lead to adverse effects, and then discusses advances in AI-enabled docking, generative chemistry, and proteochemometric modeling of proteome-wide compound interactions, in the context of polypharmacology. We also provide insights into challenges ahead.

Quantification of Binding of Small Molecules to Native Proteins Overexpressed in Living Cells

The affinity and selectivity of small molecules for proteins drive drug discovery and development. We report a fluorescent probe cellular binding assay (FPCBA) for determination of these values for native (untagged) proteins overexpressed in living cells. This method uses fluorophores such as Pacific Blue (PB) linked to cell-permeable protein ligands to generate probes that rapidly and reversibly equilibrate with intracellular targets, as established by kinetic assays of cellular uptake and efflux. To analyze binding to untagged proteins, an internal ribosomal entry site (IRES) vector was employed that allows a single mRNA to encode both the protein target and a separate orthogonal fluorescent protein (mVenus). This enabled cellular uptake of the probe to be correlated with protein expression by flow cytometry, allowing measurement of cellular dissociation constants (Kd) of the probe. This approach was validated by studies of the binding of allosteric activators to eight different Protein Kinase C (PKC) isozymes. Full-length PKCs expressed in transiently transfected HEK293T cells were used to measure cellular Kd values of a probe comprising PB linked to the natural product phorbol via a carbamate. These values were further used to determine competitive binding constants (cellular Ki values) of the nonfluorescent phorbol ester PDBu and the anticancer agent bryostatin 1 for each isozyme. For some PKC-small molecule pairs, these cellular Ki values matched known biochemical Ki values, but for others, altered selectivity was observed in cells. This approach can facilitate quantification of interactions of small molecules with physiologically relevant native proteins.

EMID2 is a novel biotherapeutic for aggressive cancers identified by in vivo screening

BACKGROUND

New drugs to tackle the next pathway or mutation fueling cancer are constantly proposed, but 97% of them are doomed to fail in clinical trials, largely because they are identified by cellular or in silico screens that cannot predict their in vivo effect.

METHODS

We screened an Adeno-Associated Vector secretome library (> 1000 clones) directly in vivo in a mouse model of cancer and validated the therapeutic effect of the first hit, EMID2, in both orthotopic and genetic models of lung and pancreatic cancer.

RESULTS

EMID2 overexpression inhibited both tumor growth and metastatic dissemination, consistent with prolonged survival of patients with high levels of EMID2 expression in the most aggressive human cancers. Mechanistically, EMID2 inhibited TGFβ maturation and activation of cancer-associated fibroblasts, resulting in more elastic ECM and reduced levels of YAP in the nuclei of cancer cells.

CONCLUSION

This is the first in vivo screening, precisely designed to identify proteins able to interfere with cancer cell invasiveness. EMID2 was selected as the most potent protein, in line with the emerging relevance of the tumor extracellular matrix in controlling cancer cell invasiveness and dissemination, which kills most of cancer patients.

Breaking the stromal barrier in pancreatic cancer: Advances and challenges

Pancreatic cancer (PC) remains a leading cause of mortality worldwide due to the absence of early detection methods and the low success rates of traditional therapeutic strategies. Drug resistance in PC is driven by its desmoplastic stroma, which creates a barrier that shields cancer niches and prevents the penetration of drugs. The PC stroma comprises heterogeneous cellular populations and non-cellular components involved in aberrant ECM deposition, immunosuppression, and drug resistance. These components can influence PC development through intricate and complex crosstalk with the PC cells. Understanding how stromal components and cells interact with and influence the invasiveness and refractoriness of PC cells is thus a prerequisite for developing successful stroma-modulating strategies capable of remodeling the PC stroma to alleviate drug resistance and enhance therapeutic outcomes. In this review, we explore how non-cellular and cellular stromal components, including cancer-associated fibroblasts and tumor-associated macrophages, contribute to the immunosuppressive and tumor-promoting effects of the stroma. We also examine the signaling pathways underlying their activation, tumorigenic effects, and interactions with PC cells. Finally, we discuss recent pre-clinical and clinical work aimed at developing and testing novel stroma-modulating agents to alleviate drug resistance and improve therapeutic outcomes in PC.

Preclinical characterization of danatinib as a novel FLT3 inhibitor with excellent efficacy against resistant acute myeloid leukemia

The therapeutic benefits of available FLT3 inhibitors for AML are limited by drug resistance, which is related to mutations, as well toxicity caused by off-target effects. In this study, we introduce a new small molecule FLT3 inhibitor called danatinib, which was designed to overcome the limitations of currently approved agents. Danatinib demonstrated greater potency and selectivity, resulting in cytotoxic activity specific to FLT3-ITD and/or FLT3-TKD mutated models. It also showed a superior kinome inhibition profile compared to several currently approved FLT3 inhibitors. In diverse FLT3-TKD models, danatinib exhibited substantially improved activity at clinically relevant doses, outperforming approved FLT3 inhibitors. In vivo safety evaluations performed on the granulopoiesis of transgenic myeloperoxidase (MPO) zebrafish and mice models proved danatinib to have an acceptable safety profile. Danatinib holds promise as a new and improved FLT3 inhibitor for the treatment of AML, offering long-lasting remissions and improved overall survival rates.

PIM kinases regulate early human Th17 cell differentiation

The serine/threonine-specific Moloney murine leukemia virus (PIM) kinase family (i.e., PIM1, PIM2, and PIM3) has been extensively studied in tumorigenesis. PIM kinases are downstream of several cytokine signaling pathways that drive immune-mediated diseases. Uncontrolled T helper 17 (Th17) cell activation has been associated with the pathogenesis of autoimmunity. However, the detailed molecular function of PIMs in human Th17 cell regulation has yet to be studied. In the present study, we comprehensively investigated how the three PIMs simultaneously alter transcriptional gene regulation during early human Th17 cell differentiation. By combining PIM triple knockdown with bulk and scRNA-seq approaches, we found that PIM deficiency promotes the early expression of key Th17-related genes while suppressing Th1-lineage genes. Further, PIMs modulate Th cell signaling, potentially via STAT1 and STAT3. Overall, our study highlights the inhibitory role of PIMs in human Th17 cell differentiation, thereby suggesting their association with autoimmune phenotypes.

Computational analysis of potential drug-like compounds from Solanum torvum - A promising phytotherapeutics approach for the treatment of diabetes

Diabetes mellitus (DM) is a global pandemic that is characterized by high blood glucose levels. Conventional treatments have limitations, leading to the search for natural alternatives. This study focused on Solanum torvum (STV), a medicinal plant, to identify potential anti-diabetic compounds using molecular docking and molecular dynamics simulations. We focused on identifying natural inhibitors of two key enzymes involved in glucose metabolism: α-amylase (1HNY) and α-glucosidase (4J5T). In our preliminary docking study, rutin showed the highest binding affinity (-11.58 kcal/mol) to α-amylase, followed by chlorogenin (-7.58 kcal/mol) and myricetin (-5.82 kcal/mol). For α-glucosidase, rutin had the highest binding affinity (-11.78 kcal/mol), followed by chlorogenin (-7.11 kcal/mol) and fisetin (-6.44 kcal/mol). Hence, chlorogenin and rutin were selected for further analysis and compared with acarbose, an FDA-approved antidiabetic drug. Comparative docking revealed that chlorogenin had the highest binding affinity of (-9.9 kcal/mol) > rutin (-8.7 kcal/mol) and > acarbose (-7.7 kcal/mol) for α-amylase. While docking with α-glucosidase, chlorogenin again had the highest binding affinity of (-9.8 kcal/mol) > compared to rutin (-9.5 kcal/mol) and acarbose (-7.9 kcal/mol). Molecular dynamics (MD) simulations were conducted to assess their stability. We simulated 100 nanoseconds (ns) trajectories to analyze their stability on various parameters, including RMSD, RMSF, RG, SASA, H-bond analysis, PCA, FEL, and MM-PBSA on the six docked proteins. In conclusion, our study suggests that chlorogenin and rutin derived from STV may be effective natural therapeutic agents for diabetes management because of their strong binding affinities for the α-amylase and α-glucosidase enzymes.Communicated by Ramaswamy H. Sarma.

Equilibrium Thermodynamics of Macropa Complexes with Selected Metal Isotopes of Radiopharmaceutical Interest

To pursue the design of in vivo stable chelating systems for radiometals, a concise and straightforward method toolbox was developed combining NMR, isothermal titration calorimetry (ITC), and europium time-resolved laser-induced fluorescence spectroscopy (Eu-TRLFS). For this purpose, the macropa chelator was chosen, and Lu3+, La3+, Pb2+, Ra2+, and Ba2+ were chosen as radiopharmaceutically relevant metal ions. They differ in charge (2+ and 3+) and coordination properties (main group vs lanthanides). 1H NMR was used to determine four pKa values (±0.15; carboxylate functions, 2.40 and 3.13; amino functions, 6.80 and 7.73). Eu-TRLFS was used to validate the exclusive existence of the 1:1 Mn+/ligand complex in the chosen pH range at tracer level concentrations. ITC measurements were accomplished to determine the resulting stability constants of the desired complexes, with log K values ranging from 18.5 for the Pb-mcp complex to 7.3 for the Lu-mcp complex. Density-functional-theory-calculated structures nicely mirror the complexes' order of stabilities by bonding features. Radiolabeling with macropa using ligand concentrations from 10-3 to 10-6 M was accomplished by pointing out the complex formation and stability (212Pb > 133La > 131Ba ≈ 224Ra > 177Lu) by means of normal-phase thin-layer chromatography analyses.

CDK11 requires a critical activator SAP30BP to regulate pre-mRNA splicing

CDK11 is an emerging druggable target for cancer therapy due to its prevalent roles in phosphorylating critical transcription and splicing factors and in facilitating cell cycle progression in cancer cells. Like other cyclin-dependent kinases, CDK11 requires its cognate cyclin, cyclin L1 or cyclin L2, for activation. However, little is known about how CDK11 activities might be modulated by other regulators. In this study, we show that CDK11 forms a tight complex with cyclins L1/L2 and SAP30BP, the latter of which is a poorly characterized factor. Acute degradation of SAP30BP mirrors that of CDK11 in causing widespread and strong defects in pre-mRNA splicing. Furthermore, we demonstrate that SAP30BP facilitates CDK11 kinase activities in vitro and in vivo, through ensuring the stabilities and the assembly of cyclins L1/L2 with CDK11. Together, these findings uncover SAP30BP as a critical CDK11 activator that regulates global pre-mRNA splicing.

Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

Steering and controlling evolution - from bioengineering to fighting pathogens

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

The combination of PAC-1 and entrectinib for the treatment of metastatic uveal melanoma

The treatment of metastatic uveal melanoma remains a major clinical challenge. Procaspase-3, a proapoptotic protein and precursor to the key apoptotic executioner caspase-3, is overexpressed in a wide range of malignancies, and the drug PAC-1 leverages this overexpression to selectively kill cancer cells. Herein, we investigate the efficacy of PAC-1 against uveal melanoma cell lines and report the synergistic combination of PAC-1 and entrectinib. This preclinical activity, tolerability data in mice, and the known clinical effectiveness of these drugs in human cancer patients led to a small Phase 1b study in patients with metastatic uveal melanoma. The combination of PAC-1 and entrectinib was tolerated with no treatment-related grade ≥3 toxicities in these patients. The pharmacokinetics of entrectinib were not affected by PAC-1 treatment. In this small and heavily pretreated initial cohort, stable disease was observed in four out of six patients, with a median progression-free survival of 3.38 months (95% CI 1.6-6.5 months). This study is an initial demonstration that the combination of PAC-1 and entrectinib may warrant further clinical investigation. Clinical trial registration: Clinical Trials.gov: NCT04589832.

Patient-derived bladder cancer organoid model to predict sensitivity and feasibility of tailored precision therapy

Background

Bladder cancer is a common and highly heterogeneous malignant tumor with a relatively poor prognosis. Thus, personalized treatment strategies for bladder cancer are essential for improving patient outcomes.

Materials and methods

We developed an efficient 3-dimensional in vitro organoid culture system for bladder cancer organoids (BCOs), which maintains the homology with the original patient tumors and the heterogeneity between different individuals. In addition, we constructed chimeric antigen receptor (CAR)-T cells targeting B7H3 and evaluated the antitumor function of CAR-T cells by coculturing them with BCOs.

Results

The BCOs closely resembled the characteristics of human tumors and were used to test individual sensitivity to platinum-based drugs and olaparib therapy. Coculture with CAR-T cells demonstrated specific antigen recognition and immune activation, indicating their potential in immunotherapy.

Conclusions

Our study highlights the potential of BCOs to facilitate the development of personalized medicine for bladder cancer and improve the efficiency of drug discovery for bladder cancer therapy.

Virus-like Iron-Gold Heterogeneous Nanoparticles for Drug Target Screening

Drug-target recognition has great impacts on revealing mechanisms of pharmacological activities, especially drug resistance and off-target effects. In recent years, chemoproteomics has been widely used for drug target screening and discovery due to its high-throughput, high accuracy, and sensitivity. However, there still remain challenges on how to efficiently and unambiguously track target proteins from complex biological matrices. Herein, we report a drug target screening method based on virus-like iron-gold heterogeneous nanoparticles (Au@Fe3O4 NPs). The unique structure of Au@Fe3O4 NPs not only maintains the magnetism of Fe3O4 NPs to facilitate protein enrichment and purification, but also increases drug modification by introducing more active sites on the surface of Au NPs. After coincubating the drug modified NPs with the cell lysate, the high loading of drug on the surface of Au@Fe3O4 NPs was beneficial for capturing target proteins with low abundance. This well-designed heterogeneous nanomaterial provides a novel strategy for improving the efficiency and accuracy of affinity-based proteomics.

Transcriptomic and genomic identification of spliceosomal genes from Euglena gracilis

Diverse splicing types in nuclear and chloroplast genes of protist Euglena gracilis have been recognized for decades. However, the splicing machinery responsible for processing nuclear precursor messenger RNA introns, including trans-splicing of the 5' terminal outron and spliced leader (SL) RNA, remains elusive. Here, we identify 166 spliceosomal protein genes and two snRNA genes from E. gracilis by performing bioinformatics analysis from a combination of next-generation and full-length transcriptomic RNA sequencing (RNAseq) data as well as draft genomic data. With the spliceosomal proteins we identified in hand, the insensitivity of E. gracilis to some splicing modulators is revealed at the sequence level. The prevalence of SL RNA-mediated trans-splicing is estimated to be more than 70% from our full-length RNAseq data. Finally, the splicing proteomes between E. gracilis and its three evolutionary cousins within the same Excavata group are compared. In conclusion, our study characterizes the spliceosomal components in E. gracilis and provides the molecular basis for further exploration of underlying splicing mechanisms.

Target deconvolution with matrix-augmented pooling strategy reveals cell-specific drug-protein interactions

Target deconvolution is a crucial but costly and time-consuming task that hinders large-scale profiling for drug discovery. We present a matrix-augmented pooling strategy (MAPS) which mixes multiple drugs into samples with optimized permutation and delineates targets of each drug simultaneously with mathematical processing. We validated this strategy with thermal proteome profiling (TPP) testing of 15 drugs concurrently, increasing experimental throughput by 60x while maintaining high sensitivity and specificity. Benefiting from the lower cost and higher throughput of MAPS, we performed target deconvolution of the 15 drugs across 5 cell lines. Our profiling revealed that drug-target interactions can differ vastly in targets and binding affinity across cell lines. We further validated BRAF and CSNK2A2 as potential off-targets of bafetinib and abemaciclib, respectively. This work represents the largest thermal profiling of structurally diverse drugs across multiple cell lines to date.