Computational Cell Cycle Profiling of Cancer Cells for Prioritizing FDA-Approved Drugs with Repurposing Potential

The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

Drug repurposing for cancer therapy

Cancer, a complex and multifactorial disease, presents a significant challenge to global health. Despite significant advances in surgical, radiotherapeutic and immunological approaches, which have improved cancer treatment outcomes, drug therapy continues to serve as a key therapeutic strategy. However, the clinical efficacy of drug therapy is often constrained by drug resistance and severe toxic side effects, and thus there remains a critical need to develop novel cancer therapeutics. One promising strategy that has received widespread attention in recent years is drug repurposing: the identification of new applications for existing, clinically approved drugs. Drug repurposing possesses several inherent advantages in the context of cancer treatment since repurposed drugs are typically cost-effective, proven to be safe, and can significantly expedite the drug development process due to their already established safety profiles. In light of this, the present review offers a comprehensive overview of the various methods employed in drug repurposing, specifically focusing on the repurposing of drugs to treat cancer. We describe the antitumor properties of candidate drugs, and discuss in detail how they target both the hallmarks of cancer in tumor cells and the surrounding tumor microenvironment. In addition, we examine the innovative strategy of integrating drug repurposing with nanotechnology to enhance topical drug delivery. We also emphasize the critical role that repurposed drugs can play when used as part of a combination therapy regimen. To conclude, we outline the challenges associated with repurposing drugs and consider the future prospects of these repurposed drugs transitioning into clinical application.

Parbendazole as a promising drug for inducing differentiation of acute myeloid leukemia cells with various subtypes

Acute myeloid leukemia (AML) is a malignancy characterized by differentiation arrest of hematopoietic precursor cells. Differentiation therapy is effective for patients with acute promyelocytic leukemia; however, only a few effective differentiation therapies have been established for patients with other AML subtypes. In this study, seven benzimidazole anthelmintics were examined to determine the effects of differentiation on AML cells. The expression of monocyte markers (CD11b and CD14) was elevated after treatment with most benzimidazole anthelmintics. Among these drugs, parbendazole (PBZ) induced AML cell differentiation at low concentration. PBZ induced the monocyte marker expression, KLF4/DPYSL2A gene expression, and apoptosis for 21 AML cell lines with various subtypes and a primary AML sample. Finally, an in vivo analysis using an AML patient-derived xenograft mouse model showed a significant decrease in the chimerism level and prolonged survival in PBZ-treated mice. These findings could lead to a more effective differentiation therapy for AML.

Antiproliferation and apoptosis studies of estrone pharmacophores in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer that shows high metastatic capability and poor prognosis. The aggressive behavior of TNBC may involve amplified EGFR expression. Currently, no targeted therapy has been approved for treating TNBC, which urgently needs novel treatment options. In this study, we report that estrone analogs with novel pharmacophores exhibited high potency toward TNBC cells through multiple mechanisms, inhibition of cell proliferation via EGFR receptor, and induction of mitochondrial apoptosis. Molecular docking studies revealed that hit analogs MMA307 and MMA321 were potent against the EGFR receptor (pdb code: 1M17) in silico and were over 10-fold more potent than sorafenib (positive control) when dosed against MDA-MB-468 cells in vitro. MMA307 and MMA321 induced mitochondrial apoptosis as characterized by condensed nuclei with fragmented chromatin, phosphatidylserine flip and modulated expressions of Apaf1, cytochrome c, and caspases 3 and 9. MMA307 and MMA321 inhibited TNBC proliferation through suppression of EGFR and activated EGFR (Y1068) expressions. Similarly, EGFR signaling pathways, RAF/ERK and AKT/mTOR, were inhibited as pARaf, pERK1/2 (characterizes RAF/ERK pathway) and pAKT, pmTOR, p70S6Kα (characterizes AKT/mTOR pathway) were all suppressed. Moreover, MMA307 and MMA321 inhibited TNBC cell growth through downregulation of cyclin D1 expression and arresting TNBC cells in the G1 phase of cell cycle. This study reports for the first time that estrone congeners with novel pharmacophores may be an effective therapy for TNBC. Findings from this research provide a solid foundation for further preclinical and clinical studies in developing estrone derivatives as novel TNBC therapeutics.

Discovering the mechanism of action of drugs with a sparse explainable network

BACKGROUND

Although Deep Neural Networks (DDNs) have been successful in predicting the efficacy of cancer drugs, the lack of explainability in their decision-making process is a significant challenge. Previous research proposed mimicking the Gene Ontology structure to allow for interpretation of each neuron in the network. However, these previous approaches require huge amount of GPU resources and hinder its extension to genome-wide models.

METHODS

We developed SparseGO, a sparse and interpretable neural network, for predicting drug response in cancer cell lines and their Mechanism of Action (MoA). To ensure model generalization, we trained it on multiple datasets and evaluated its performance using three cross-validation schemes. Its efficiency allows it to be used with gene expression. In addition, SparseGO integrates an eXplainable Artificial Intelligence (XAI) technique, DeepLIFT, with Support Vector Machines to computationally discover the MoA of drugs.

FINDINGS

SparseGO's sparse implementation significantly reduced GPU memory usage and training speed compared to other methods, allowing it to process gene expression instead of mutations as input data. SparseGO using expression improved the accuracy and enabled its use on drug repositioning. Furthermore, gene expression allows the prediction of MoA using 265 drugs to train it. It was validated on understudied drugs such as parbendazole and PD153035.

INTERPRETATION

SparseGO is an effective XAI method for predicting, but more importantly, understanding drug response.

FUNDING

The Accelerator Award Programme funded by Cancer Research UK [C355/A26819], Fundación Científica de la AECC and Fondazione AIRC, Project PIBA_2020_1_0055 funded by the Basque Government and the Synlethal Project (RETOS Investigacion, Spanish Government).

A Novel Ex Vivo Model to Study Therapeutic Treatments for Myelin Repair following Ischemic Damage

Stroke is a major reason for persistent disability due to insufficient treatment strategies beyond reperfusion, leading to oligodendrocyte death and axon demyelination, persistent inflammation and astrogliosis in peri-infarct areas. After injury, oligodendroglial precursor cells (OPCs) have been shown to compensate for myelin loss and prevent axonal loss through the replacement of lost oligodendrocytes, an inefficient process leaving axons chronically demyelinated. Phenotypic screening approaches in demyelinating paradigms revealed substances that promote myelin repair. We established an ex vivo adult organotypic coronal slice culture (OCSC) system to study repair after stroke in a resource-efficient way. Post-photothrombotic OCSCs can be manipulated for 8 d by exposure to pharmacologically active substances testing remyelination activity. OCSCs were isolated from a NG2-CreERT2-td-Tomato knock-in transgenic mouse line to analyze oligodendroglial fate/differentiation and kinetics. Parbendazole boosted differentiation of NG2⁺ cells and stabilized oligodendroglial fate reflected by altered expression of associated markers PDGFR-α, CC1, BCAS1 and Sox10 and GFAP. In vitro scratch assay and chemical ischemia confirmed the observed effects upon parbendazole treatment. Adult OCSCs represent a fast, reproducible, and quantifiable model to study OPC differentiation competence after stroke. Pharmacological stimulation by means of parbendazole promoted OPC differentiation.

Development and validation of an epithelial–mesenchymal transition-related gene signature for predicting prognosis

BACKGROUND

Currently, there are many therapeutic methods for lung adenocarcinoma (LUAD), but the 5-year survival rate is still only 15% at later stages. Epithelial– mesenchymal transition (EMT) has been shown to be closely associated with local dissemination and subsequent metastasis of solid tumors. However, the role of EMT in the occurrence and development of LUAD remains unclear.

AIM

To further elucidate the value of EMT-related genes in LUAD prognosis.

METHODS

Univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were applied to establish and validate a new EMT-related gene signature for predicting LUAD prognosis. The risk model was evaluated by Kaplan–Meier survival analysis, principal component analysis, and functional enrichment analysis and was used for nomogram construction. The potential structures of drugs to which LUAD is sensitive were discussed with respect to EMT-related genes in this model.

RESULTS

Thirty-three differentially expressed genes related to EMT were found to be highly associated with overall survival (OS) by using univariate Cox regression analysis (log2FC ≥ 1, false discovery rate < 0.001). A prognostic signature of 7 EMT-associated genes was developed to divide patients into two risk groups by high or low risk scores. Kaplan–Meier survival analysis showed that the OS of patients in the high-risk group was significantly poorer than that of patients in the low-risk group (P < 0.05). Multivariate Cox regression analysis showed that the risk score was an independent risk factor for OS (HR > 1, P < 0.05). The results of receiver operator characteristic curve analysis suggested that the 7-gene signature had a perfect ability to predict prognosis (all area under the curves > 0.5).

CONCLUSION

The EMT-associated gene signature classifier could be used as a feasible indicator for predicting OS.

Digitoxin Affects Metabolism, ROS Production and Proliferation in Pancreatic Cancer Cells Differently Depending on the Cell Phenotype

Digitoxin has repeatedly shown to have negative effects on cancer cell viability; however, the actual mechanism is still unknown. In this study, we investigated the effects of digitoxin (1-100 nM) in four pancreatic cancer cell lines, BxPC-3, CFPAC-1, Panc-1, and AsPC-1. The cell lines differ in their KRAS/BRAF mutational status and primary tumor or metastasis origin. We could detect differences in the basal rates of cell proliferation, glycolysis, and ROS production, giving the cell lines different phenotypes. Digitoxin treatment induced apoptosis in all four cell lines, but to different degrees. Cells derived from primary tumors (Panc-1 and BxPC-3) were highly proliferating with a high proportion of cells in the S/G2 phase, and were more sensitive to digitoxin treatment than the cell lines derived from metastases (CFPAC-1 and AsPC-1), with a high proportion of cells in G0/G1. In addition, the effects of digitoxin on the rate of glycolysis, ROS production, and proliferation were dependent on the basal metabolism and origin of the cells. The KRAS downstream signaling pathways were not altered by digitoxin treatment, thus the effects exerted by digitoxin were probably disconnected from these signaling pathways. We conclude that digitoxin is a promising treatment in highly proliferating pancreatic tumors.

Development of a G2/M arrest high-throughput screening method identifies potent radiosensitizers

Radiation is a powerful tool used to control tumor growth and induce an immune response; however, it is limited by damage to surrounding tissue and adverse effects such skin irritation. Breast cancer patients in particular may endure radiation dermatitis, and potentially lymphedema, after a course of radiotherapy. Radio-sensitizing small molecule drugs may enable lower effective doses of both radiation and chemotherapy to minimize toxicity to healthy tissue. In this study, we identified a novel high-throughput method for screening radiosensitizers by image analysis of nuclear size and cell cycle. In vitro assays were performed on cancer cells lines to assess combined therapeutic and radiation effects. In vivo, radiation in combination with proflavine hemisulfate led to enhanced efficacy demonstrated by improved tumor volume control in mice bearing syngeneic breast tumors. This study provides a proof of concept for utilizing G2/M stall as a predictor of radiosensitization and is the first report of a flavin acting as an X-ray radiation enhancer in a breast cancer mouse model.

Cell Cycle-Related Clinical Applications

The cell cycle is a highly regulated and orchestrated mechanism of life that ensures successive division of a cell and precise replication of cellular contents. Cyclins, cyclin-dependent kinases (CDKs), and CDK inhibitors are three of the most critical cell cycle regulatory proteins that enable the smooth progression of cells through the different phases of cell cycle before and after division. The alteration of cell cycle-related proteins causes aberration in the normal cell cycle process, which is one of the pivotal causes of cancer and other diseases. Targeting cell cycle components has proven to be a valuable therapeutic strategy and leads to the development of novel anticancer therapeutic. The purpose of this book chapter is to summarize the literature and discuss the clinical significance of cell cycle-related proteins in cancers and other diseases, with a focus on identifying potential targets as therapeutic interventions for cancer patients.

Identification of novel myelin repair drugs by modulation of oligodendroglial differentiation competence

BACKGROUND

In multiple sclerosis loss of myelin and oligodendrocytes impairs saltatory signal transduction and leads to neuronal loss and functional deficits. Limited capacity of oligodendroglial precursor cells to differentiate into mature cells is the main reason for inefficient myelin repair in the central nervous system. Drug repurposing constitutes a powerful approach for identification of pharmacological compounds promoting this process.

METHODS

A phenotypic compound screening using the subcellular distribution of a potent inhibitor of oligodendroglial cell differentiation, namely p57kip2, as differentiation competence marker was conducted. Hit compounds were validated in terms of their impact on developmental cell differentiation and myelination using both rat and human primary cell cultures and organotypic cerebellar slice cultures, respectively. Their effect on spontaneous remyelination was then investigated following cuprizone-mediated demyelination of the corpus callosum.

FINDINGS

A number of novel small molecules able to promote oligodendroglial cell differentiation were identified and a subset was found to foster human oligodendrogenesis as well as myelination ex vivo. Among them the steroid danazol and the anthelminthic parbendazole were found to increase myelin repair.

INTERPRETATION

We provide evidence that early cellular processes involved in differentiation decisions are applicable for the identification of regeneration promoting drugs and we suggest danazol and parbendazole as potent therapeutic candidates for demyelinating diseases.

FUNDING

This work was supported by the Jürgen Manchot Foundation, Düsseldorf; Research Commission of the Medical Faculty of Heinrich-Heine-University Düsseldorf; Christiane and Claudia Hempel Foundation; Stifterverband/Novartisstiftung; James and Elisabeth Cloppenburg, Peek and Cloppenburg Düsseldorf Stiftung and International Progressive MS Alliance (BRAVEinMS).

An Analytical Review of Computational Drug Repurposing

Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or failed drugs. It is cost-effective and reliable in contrast to experimental drug discovery, which is a costly, time-consuming, and risky process and limited to a relatively small number of targets. Accordingly, a plethora of computational methodologies have been propounded to repurpose drugs on a large scale by utilizing available high throughput data. The available literature, however, lacks a contemporary and comprehensive analysis of the current computational drug repurposing methodologies. In this paper, we presented a systematic analysis of computational drug repurposing which consists of three main sections: Initially, we categorize the computational drug repurposing methods based on their technical approach and artificial intelligence perspective and discuss the strengths and weaknesses of various methods. Secondly, some general criteria are recommended to analyze our proposed categorization. In the third and final section, a qualitative comparison is made between each approach which is a guide to understanding their preference to one another. Further, this systematic analysis can help in the efficient selection and improvement of drug repurposing techniques based on the nature of computational methods implemented on biological resources.

A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing

Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.

Phyotochemical candidates repurposing for cancer therapy and their molecular mechanisms

Though limited success through chemotherapy, radiotherapy and surgery has been obtained for efficient cancer therapy for modern decades, cancers are still considered high burden to human health worldwide to date. Recently repurposing drugs are attractive with lower cost and shorter time compared to classical drug discovery, just as Metformin from Galega officinalis, originally approved for treating Type 2 diabetes by FDA, is globally valued at millions of US dollars for cancer therapy. As most previous reviews focused on FDA approved drugs and synthetic agents, current review discussed the anticancer potential of phytochemicals originally approved for treatment of cardiovascular diseases, diabetes, infectious diarrhea, depression and malaria with their molecular mechanisms and efficacies and suggested future research perspectives.

The Benzimidazole-Based Anthelmintic Parbendazole: A Repurposed Drug Candidate That Synergizes with Gemcitabine in Pancreatic Cancer

Pancreatic cancer (PC) is one of the most lethal, chemoresistant malignancies and it is of paramount importance to find more effective therapeutic agents. Repurposing of non-anticancer drugs may expand the repertoire of effective molecules. Studies on repurposing of benzimidazole-based anthelmintics in PC and on their interaction with agents approved for PC therapy are lacking. We analyzed the effects of four Food and Drug Administration (FDA)-approved benzimidazoles on AsPC-1 and Capan-2 pancreatic cancer cell line viability. Notably, parbendazole was the most potent benzimidazole affecting PC cell viability, with half maximal inhibitory concentration (IC₅₀) values in the nanomolar range. The drug markedly inhibited proliferation, clonogenicity and migration of PC cell lines through mechanisms involving alteration of microtubule organization and formation of irregular mitotic spindles. Moreover, parbendazole interfered with cell cycle progression promoting G2/M arrest, followed by the emergence of enlarged, polyploid cells. These abnormalities, suggesting a mitotic catastrophe, culminated in PC cell apoptosis, are also associated with DNA damage in PC cell lines. Remarkably, combinations of parbendazole with gemcitabine, a drug employed as first-line treatment in PC, synergistically decreased PC cell viability. In conclusion, this is the first study providing evidence that parbendazole as a single agent, or in combination with gemcitabine, is a repurposing candidate in the currently dismal PC therapy.

Computational analysis of kinase inhibitor selectivity using structural knowledge

Motivation

Kinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling.

Results

Here, we present a KinomeFEATURE database for kinase binding site similarity search by comparing protein microenvironments characterized using diverse physiochemical descriptors. Initial selectivity prediction of 15 known kinase inhibitors achieved an >90% accuracy and demonstrated improved performance in comparison to commonly used kinase inhibitor selectivity prediction methods. Additional kinase ATP binding site similarity assessment (120 binding sites) identified 55 kinases with significant promiscuity and revealed unexpected inhibitor cross-activities between PKR and FGFR2 kinases. Kinome-wide selectivity profiling of 11 kinase drug candidates predicted novel as well as experimentally validated off-targets and suggested structural mechanisms of kinase cross-activities. Our study demonstrated potential utilities of our approach for large-scale kinase inhibitor selectivity profiling that could contribute to kinase drug development and safety assessment.

Availability and implementation

The KinomeFEATURE database and the associated scripts for performing kinase pocket similarity search can be downloaded from the Stanford SimTK website (https://simtk.org/projects/kdb).

Supplementary information

Supplementary data are available at Bioinformatics online.

Systems biology based drug repositioning for development of cancer therapy

Drug repositioning is a powerful method that can assists the conventional drug discovery process by using existing drugs for treatment of a disease rather than its original indication. The first examples of repurposed drugs were discovered serendipitously, however data accumulated by high-throughput screenings and advancements in computational biology methods have paved the way for rational drug repositioning methods. As chemotherapeutic agents have notorious side effects that significantly reduce quality of life, drug repositioning promises repurposed noncancer drugs with little or tolerable adverse effects for cancer patients. Here, we review current drug-related data types and databases including some examples of web-based drug repositioning tools. Next, we describe systems biology approaches to be used in drug repositioning for effective cancer therapy. Finally, we highlight examples of mostly repurposed drugs for cancer treatment and provide an overview of future expectations in the field for development of effective treatment strategies.

How Does SUMO Participate in Spindle Organization?

The ubiquitin-like protein SUMO is a regulator involved in most cellular mechanisms. Recent studies have discovered new modes of function for this protein. Of particular interest is the ability of SUMO to organize proteins in larger assemblies, as well as the role of SUMO-dependent ubiquitylation in their disassembly. These mechanisms have been largely described in the context of DNA repair, transcriptional regulation, or signaling, while much less is known on how SUMO facilitates organization of microtubule-dependent processes during mitosis. Remarkably however, SUMO has been known for a long time to modify kinetochore proteins, while more recently, extensive proteomic screens have identified a large number of microtubule- and spindle-associated proteins that are SUMOylated. The aim of this review is to focus on the possible role of SUMOylation in organization of the spindle and kinetochore complexes. We summarize mitotic and microtubule/spindle-associated proteins that have been identified as SUMO conjugates and present examples regarding their regulation by SUMO. Moreover, we discuss the possible contribution of SUMOylation in organization of larger protein assemblies on the spindle, as well as the role of SUMO-targeted ubiquitylation in control of kinetochore assembly and function. Finally, we propose future directions regarding the study of SUMOylation in regulation of spindle organization and examine the potential of SUMO and SUMO-mediated degradation as target for antimitotic-based therapies.

Dissecting the mechanisms of cell division

Cell division is a highly regulated and carefully orchestrated process. Understanding the mechanisms that promote proper cell division is an important step toward unraveling important questions in cell biology and human health. Early studies seeking to dissect the mechanisms of cell division used classical genetics approaches to identify genes involved in mitosis and deployed biochemical approaches to isolate and identify proteins critical for cell division. These studies underscored that post-translational modifications and cyclin-kinase complexes play roles at the heart of the cell division program. Modern approaches for examining the mechanisms of cell division, including the use of high-throughput methods to study the effects of RNAi, cDNA, and chemical libraries, have evolved to encompass a larger biological and chemical space. Here, we outline some of the classical studies that established a foundation for the field and provide an overview of recent approaches that have advanced the study of cell division.

Leukemia Cell Cycle Chemical Profiling Identifies the G2-Phase Leukemia Specific Inhibitor Leusin-1

Targeting the leukemia proliferation cycle has been a successful approach to developing antileukemic therapies. However, drug screening efforts to identify novel antileukemic agents have been hampered by the lack of a suitable high-throughput screening platform for suspension cells that does not rely on flow-cytometry analyses. We report the development of a novel leukemia cell-based high-throughput chemical screening platform for the discovery of cell cycle phase specific inhibitors that utilizes chemical cell cycle profiling. We have used this approach to analyze the cell cycle response of acute lymphoblastic leukemia CCRF-CEM cells to each of 181420 druglike compounds. This approach yielded cell cycle phase specific inhibitors of leukemia cell proliferation. Further analyses of the top G2-phase and M-phase inhibitors identified the leukemia specific inhibitor 1 (Leusin-1). Leusin-1 arrests cells in G2 phase and triggers an apoptotic cell death. Most importantly, Leusin-1 was more active in acute lymphoblastic leukemia cells than other types of leukemias, non-blood cancers, or normal cells and represents a lead molecule for developing antileukemic drugs.

Pocket similarity identifies selective estrogen receptor modulators as microtubule modulators at the taxane site

Taxanes are a family of natural products with a broad spectrum of anticancer activity. This activity is mediated by interaction with the taxane site of beta-tubulin, leading to microtubule stabilization and cell death. Although widely used in the treatment of breast cancer and other malignancies, existing taxane-based therapies including paclitaxel and the second-generation docetaxel are currently limited by severe adverse effects and dose-limiting toxicity. To discover taxane site modulators, we employ a computational binding site similarity screen of > 14,000 drug-like pockets from PDB, revealing an unexpected similarity between the estrogen receptor and the beta-tubulin taxane binding pocket. Evaluation of nine selective estrogen receptor modulators (SERMs) via cellular and biochemical assays confirms taxane site interaction, microtubule stabilization, and cell proliferation inhibition. Our study demonstrates that SERMs can modulate microtubule assembly and raises the possibility of an estrogen receptor-independent mechanism for inhibiting cell proliferation.

Taxanes are natural products which bind beta-tubulin, stabilize microtubules and have a broad spectrum of anticancer activity. Here authors employ a computational binding site similarity screen and cell-based assays to reveal a SERM cross-reactivity between the estrogen receptor and the beta-tubulin taxane binding pocket.

Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma

Image-based analysis is an increasingly important tool to characterize the effect of drugs in large-scale chemical screens. Herein, we present image and data analysis methods to investigate population cell-cycle dynamics in patient-derived brain tumor cells. Images of glioblastoma cells grown in multiwell plates were used to extract per-cell descriptors, including nuclear DNA content. We reduced the DNA content data from per-cell descriptors to per-well frequency distributions, which were used to identify compounds affecting cell-cycle phase distribution. We analyzed cells from 15 patient cases representing multiple subtypes of glioblastoma and searched for clusters of cell-cycle phase distributions characterizing similarities in response to 249 compounds at 11 doses. We show that this approach applied in a blind analysis with unlabeled substances identified drugs that are commonly used for treating solid tumors as well as other compounds that are well known for inducing cell-cycle arrest. Redistribution of nuclear DNA content signals is thus a robust metric of cell-cycle arrest in patient-derived glioblastoma cells.

Machine learning in chemoinformatics and drug discovery

Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.

Fanconi Anemia Signaling and Cancer

The extremely high cancer incidence associated with patients suffering from a rare human genetic disease, Fanconi anemia (FA), demonstrates the importance of FA genes. Over the course of human tumor development, FA genes perform critical tumor-suppression roles. In doing so, FA provides researchers with a unique genetic model system to study cancer etiology. Here, we review how aberrant function of the 22 FA genes and their signaling network contributes to malignancy. From this perspective, we will also discuss how the knowledge discovered from FA research serves basic and translational cancer research.