Target discovery

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

Bridging the gap between target-based and phenotypic-based drug discovery

INTRODUCTION

The unparalleled progress in science of the last decades has brought a better understanding of the molecular mechanisms of diseases. This promoted drug discovery processes based on a target approach. However, despite the high promises associated, a critical decrease in the number of first-in-class drugs has been observed.

AREAS COVERED

This review analyses the challenges, advances, and opportunities associated with the main strategies of the drug discovery process, i.e. based on a rational target approach and on an empirical phenotypic approach. This review also evaluates how the gap between these two crossroads can be bridged toward a more efficient drug discovery process.

EXPERT OPINION

The critical lack of knowledge of the complex biological networks is leading to targets not relevant for the clinical context or to drugs that present undesired adverse effects. The phenotypic systems designed by considering available molecular mechanisms can mitigate these knowledge gaps. Associated with the expansion of the chemical space and other technologies, these designs can lead to more efficient drug discoveries. Technological and scientific knowledge should also be applied to identify, as early as possible, both drug targets and mechanisms of action, leading to a more efficient drug discovery pipeline.

Investigating Antiprotozoal Chemotherapies with Novel Proteomic Tools-Chances and Limitations: A Critical Review

Identification of drug targets and biochemical investigations on mechanisms of action are major issues in modern drug development. The present article is a critical review of the classical "one drug"-"one target" paradigm. In fact, novel methods for target deconvolution and for investigation of resistant strains based on protein mass spectrometry have shown that multiple gene products and adaptation mechanisms are involved in the responses of pathogens to xenobiotics rather than one single gene or gene product. Resistance to drugs may be linked to differential expression of other proteins than those interacting with the drug in protein binding studies and result in complex cell physiological adaptation. Consequently, the unraveling of mechanisms of action needs approaches beyond proteomics. This review is focused on protozoan pathogens. The conclusions can, however, be extended to chemotherapies against other pathogens or cancer.

Using Photoreactive Probes to Identify Viable Drug Targets in Non-small Cell Lung Cancer

Recent advancements in chemoproteomics have accelerated new chemical tools for exploring protein ligandability in native biological systems. However, a large fraction of ligandable proteome in cancer cells remains poorly studied. Here, we present a practical and efficient sample processing method for liquid chromatography high-resolution-tandem mass spectrometry (HPLC-MS/MS) analysis. This method uses fully functionalized photoreactive fragment-like probes for profiling protein-ligand interactions in live cancer cells. This method adopts "on-bead" digestion in conjunction with ZipTip desalting prior sample injection to MS. By using this protocol, fragment protein interactions can be visualized using fluorescent imaging, and fragment-associated proteins can be identified via HPLC-MS/MS analysis. Approximately 16 samples would generally expect to be processed within 3 days by following this protocol.

Drug Repositioning Using Computer-aided Drug Design (CADD)

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

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.

CCR8 as a Therapeutic Novel Target: Omics-Integrated Comprehensive Analysis for Systematically Prioritizing Indications

Target identification is a crucial process in drug development, aiming to identify key proteins, genes, and signal pathways involved in disease progression and their relevance in potential therapeutic interventions. While C-C chemokine receptor 8 (CCR8) has been investigated as a candidate anti-cancer target, comprehensive multi-omics analyzes across various indications are limited. In this study, we conducted an extensive bioinformatics analysis integrating genomics, proteomics, and transcriptomics data to establish CCR8 as a promising anti-cancer drug target. Our approach encompassed data collection from diverse knowledge resources, gene function analysis, differential gene expression profiling, immune cell infiltration assessment, and strategic prioritization of target indications. Our findings revealed strong correlations between CCR8 and specific cancers, notably Breast Invasive Carcinoma (BRCA), Colon Adenocarcinoma (COAD), Head and Neck Squamous Cell Carcinoma (HNSC), Rectum adenocarcinoma (READ), Stomach adenocarcinoma (STAD), and Thyroid carcinoma (THCA). This research advances our understanding of CCR8 as a potential target for anti-cancer drug development, bridging the gap between molecular insights and creating opportunities for personalized treatment of solid tumors.

Martynoside rescues 5-fluorouracil-impaired ribosome biogenesis by stabilizing RPL27A

Martynoside (MAR), a bioactive component in several well-known tonic traditional Chinese herbs, exhibits pro-hematopoietic activity during 5-fluorouracil (5-FU) treatment. However, the molecular target and the mechanism of MAR are poorly understood. Here, by adopting the mRNA display with a library of even-distribution (md-LED) method, we systematically examined MAR-protein interactions in vitro and identified the ribosomal protein L27a (RPL27A) as a key cellular target of MAR. Structural and mutational analysis confirmed the specific interaction between MAR and the exon 4,5-encoded region of RPL27A. MAR attenuated 5-FU-induced cytotoxicity in bone marrow nucleated cells, increased RPL27A protein stability, and reduced the ubiquitination of RPL27A at lys92 (K92) and lys94 (K94). Disruption of MAR binding at key residues of RPL27A completely abolished the MAR-induced stabilization. Furthermore, by integrating label-free quantitative ubiquitination proteomics, transcriptomics, and ribosome function assays, we revealed that MAR restored RPL27A protein levels and thus rescued ribosome biogenesis impaired by 5-FU. Specifically, MAR increased mature ribosomal RNA (rRNA) abundance, prevented ribosomal protein degradation, facilitated ribosome assembly, and maintained nucleolar integrity. Collectively, our findings characterize the target of a component of Chinese medicine, reveal the importance of ribosome biogenesis in hematopoiesis, and open up a new direction for improving hematopoiesis by targeting RPL27A.

Predicting ExWAS findings from GWAS data: a shorter path to causal genes

GWAS has identified thousands of loci associated with disease, yet the causal genes within these loci remain largely unknown. Identifying these causal genes would enable deeper understanding of the disease and assist in genetics-based drug development. Exome-wide association studies (ExWAS) are more expensive but can pinpoint causal genes offering high-yield drug targets, yet suffer from a high false-negative rate. Several algorithms have been developed to prioritize genes at GWAS loci, such as the Effector Index (Ei), Locus-2-Gene (L2G), Polygenic Prioritization score (PoPs), and Activity-by-Contact score (ABC) and it is not known if these algorithms can predict ExWAS findings from GWAS data. However, if this were the case, thousands of associated GWAS loci could potentially be resolved to causal genes. Here, we quantified the performance of these algorithms by evaluating their ability to identify ExWAS significant genes for nine traits. We found that Ei, L2G, and PoPs can identify ExWAS significant genes with high areas under the precision recall curve (Ei: 0.52, L2G: 0.37, PoPs: 0.18, ABC: 0.14). Furthermore, we found that for every unit increase in the normalized scores, there was an associated 1.3-4.6-fold increase in the odds of a gene reaching exome-wide significance (Ei: 4.6, L2G: 2.5, PoPs: 2.1, ABC: 1.3). Overall, we found that Ei, L2G, and PoPs can anticipate ExWAS findings from widely available GWAS results. These techniques are therefore promising when well-powered ExWAS data are not readily available and can be used to anticipate ExWAS findings, allowing for prioritization of genes at GWAS loci.

The predictive capacity of in vitro preclinical models to evaluate drugs for the treatment of retinoblastoma

Retinoblastoma is a rare childhood cancer of the eye. Of the small number of drugs are used to treat retinoblastoma, all have been repurposed from drugs developed for other conditions. In order to find drugs or drug combinations better suited to the improved treatment of retinoblastoma, reliable predictive models are required, which facilitate the challenging transition from in vitro studies to clinical trials. In this review, the research performed to date on the development of 2D and 3D in vitro models for retinoblastoma is presented. Most of this research was undertaken with a view to better biological understanding of retinoblastoma, and we discuss the potential for these models to be applied to drug screening. Future research directions for streamlined drug discovery are considered and evaluated, and many promising avenues identified.

A Novel Autoencoder-Based Feature Selection Method for Drug-Target Interaction Prediction with Human-Interpretable Feature Weights

Drug-target interaction prediction provides important information that could be exploited for drug discovery, drug design, and drug repurposing. Chemogenomic approaches for predicting drug-target interaction assume that similar receptors bind to similar ligands. Capturing this similarity in so-called “fingerprints” and combining the target and ligand fingerprints provide an efficient way to search for protein-ligand pairs that are more likely to interact. In this study, we constructed drug and target fingerprints by employing features extracted from the DrugBank. However, the number of extracted features is quite large, necessitating an effective feature selection mechanism since some features can be redundant or irrelevant to drug-target interaction prediction problems. Although such feature selection methods are readily available in the literature, usually they act as black boxes and do not provide any quantitative information about why a specific feature is preferred over another. To alleviate this lack of human interpretability, we proposed a novel feature selection method in which we used an autoencoder as a symmetric learning method and compared the proposed method to some popular feature selection algorithms, such as Kbest, Variance Threshold, and Decision Tree. The results of a detailed performance study, in which we trained six Multi-Layer Perceptron (MLP) Networks of different sizes and configurations for prediction, demonstrate that the proposed method yields superior results compared to the aforementioned methods.

Quantitative Framework for Bench-to-Bedside Cancer Research

Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical-protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets.

Why Do We Care More About Disease than Health?

Modern Western biomedical research and clinical practice are primarily focused on disease. This disease-centric approach has yielded an impressive amount of knowledge around what goes wrong in illness. However, in comparison, researchers and physicians know little about health. What is health? How do we quantify it? And how do we improve it? We currently do not have good answers to these questions. Our lack of fundamental knowledge about health is partly driven by three main factors: (i) a lack of understanding of the dynamic processes that cause variations in health/disease states over time, (ii) an excessive focus on genes, and (iii) a pervasive psychological bias towards additive solutions. Here I briefly discuss potential reasons why scientists and funders have generally adopted a gene- and disease-centric framework, how medicine has ended up practicing "diseasecare" rather than healthcare, and present cursory evidence that points towards an alternative energetic view of health. Understanding the basis of human health with a similar degree of precision that has been deployed towards mapping disease processes could bring us to a point where we can actively support and promote human health across the lifespan, before disease shows up on a scan or in bloodwork.

Human Genomics and Drug Development

Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches.

Improving Small-Molecule Force Field Parameters in Ligand Binding Studies

Small molecules are major players of many chemical processes in diverse fields, from material science to biology. They are made by a combination of carbon and heteroatoms typically organized in system-specific structures of different complexity. This peculiarity hampers the application of standard force field parameters and their in silico study by means of atomistic simulations. Here, we combine quantum-mechanics and atomistic free-energy calculations to achieve an improved parametrization of the ligand torsion angles with respect to the state-of-the-art force fields in the paradigmatic molecular binding system benzamidine/trypsin. Funnel-Metadynamics calculations with the new parameters greatly reproduced the high-resolution crystallographic ligand binding mode and allowed a more accurate description of the binding mechanism, when the ligand might assume specific conformations to cross energy barriers. Our study impacts on future drug design investigations considering that the vast majority of marketed drugs are small-molecules.

Deep_CNN_LSTM_GO: Protein function prediction from amino-acid sequences

Protein amino acid sequences can be used to determine the functions of the protein. However, determining the function of a single protein requires many resources and a tremendous amount of time. Computational Intelligence methods such as Deep learning have been shown to predict the proteins' functions. This paper proposes a hybrid deep neural network model to predict an unknown protein's functions from sequences. The proposed model is named Deep_CNN_LSTM_GO. Deep_CNN_LSTM_GO is an Integration between Convolutional Neural network (CNN) and Long Short-Term Memory (LSTM) Neural Network to learn features from amino acid sequences and outputs the three different Gene Ontology (GO). The gene ontology represents the protein functions in the three sub-ontologies: Molecular Functions (MF), Biological Process (BP), and Cellular Component (CC). The proposed model has been trained and tested using UniProt-SwissProt's dataset. Another test has been done using Computational Assessment of Function Annotation (CAFA) on the three sub-ontologies. The proposed model outperforms different methods proposed in the field with better performance using three different evaluation metrics (Fmax, Smin, and AUPR) in the three sub-ontologies (MF, BP, CC).

In silico methods and tools for drug discovery

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

Determination of Ligand Binding Modes in Hydrated Viral Ion Channels to Foster Drug Design and Repositioning

Target-based design and repositioning are mainstream strategies of drug discovery. Numerous drug design and repositioning projects have been launched to fight the ongoing COVID-19 pandemic. The resulting drug candidates have often failed due to the misprediction of their target-bound structures. The determination of water positions of such structures is particularly challenging due to the large number of possible drugs and the diversity of their hydration patterns. To answer this challenge and help correct predictions, we introduce a new protocol HydroDock, which can build hydrated drug-target complexes from scratch. HydroDock requires only the dry target and drug structures and produces their complexes with appropriately positioned water molecules. As a test application of the protocol, we built the structures of amantadine derivatives in complex with the influenza M2 transmembrane ion channel. The repositioning of amantadine derivatives from this influenza target to the SARS-CoV-2 envelope protein was also investigated. Excellent agreement was observed between experiments and the structures determined by HydroDock. The atomic resolution complex structures showed that water plays a similar role in the binding of amphipathic amantadine derivatives to transmembrane ion channels of both influenza A and SARS-CoV-2. While the hydrophobic regions of the channels capture the bulky hydrocarbon group of the ligand, the surrounding waters direct its orientation parallel with the axes of the channels via bridging interactions with the ionic ligand head. As HydroDock supplied otherwise undetermined structural details, it can be recommended to improve the reliability of future design and repositioning of antiviral drug candidates and many other ligands with an influence of water structure on their mechanism of action.

TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery

Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine coverage of human genes by omic-scale research make it difficult to maintain meaningful overviews from which to identify promising new trends. Here we propose an automated yet flexible pipeline that identifies trends in the scientific corpus which align with the specific interests of a researcher and facilitate an initial prioritisation of opportunities. Using a procedure based on co-citation networks and machine learning, genes and diseases are first parsed from PubMed articles using a novel named entity recognition system together with publication date and supporting information. Then recurrent neural networks are trained to predict the publication dynamics of all human genes. For a user-defined therapeutic focus, genes generating more publications or citations are identified as high-interest targets. We also used topic detection routines to help understand why a gene is trendy and implement a system to propose the most prominent review articles for a potential target. This TrendyGenes pipeline detects emerging targets and pathways and provides a new way to explore the literature for individual researchers, pharmaceutical companies and funding agencies.

Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes

Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.

Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

Resolving cell state in iPSC-derived human neural samples with multiplexed fluorescence imaging

Human induced pluripotent stem cell-derived (iPSC) neural cultures offer clinically relevant models of human diseases, including Amyotrophic Lateral Sclerosis, Alzheimer’s, and Autism Spectrum Disorder. In situ characterization of the spatial-temporal evolution of cell state in 3D culture and subsequent 2D dissociated culture models based on protein expression levels and localizations is essential to understanding neural cell differentiation, disease state phenotypes, and sample-to-sample variability. Here, we apply PRobe-based Imaging for Sequential Multiplexing (PRISM) to facilitate multiplexed imaging with facile, rapid exchange of imaging probes to analyze iPSC-derived cortical and motor neuron cultures that are relevant to psychiatric and neurodegenerative disease models, using over ten protein targets. Our approach permits analysis of cell differentiation, cell composition, and functional marker expression in complex stem-cell derived neural cultures. Furthermore, our approach is amenable to automation, offering in principle the ability to scale-up to dozens of protein targets and samples.

Tomov et al. utilize DNA-PRISM to allow for multiplexed imaging of cultured cells using antibodies modified with oligonucleotide probes. The differentiation of iPSCs to cortical and motor neurons is characterized in model cultures, relevant for use in disease research and drug screening.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Drug target ranking for glioblastoma multiforme

Background

Glioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer.

Results

We use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma.

Conclusions

Given the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients.

Supplementary Information

The online version contains supplementary material available at (10.1186/s42490-021-00052-w).

COVID-19 Vaccination: From Interesting Agent to the Patient

The vaccination for the novel Coronavirus (COVID-19) is undergoing its final stages of analysis and testing. It is an impressive feat under the circumstances that we are on the verge of a potential breakthrough vaccination. This will help reduce the stress for millions of people around the globe, helping to restore worldwide normalcy. In this review, the analysis looks into how the new branch of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) came into the forefront of the world like a pandemic. This review will break down the details of what COVID-19 is, the viral family it belongs to and its background of how this family of viruses alters bodily functions by attacking vital human respiratory organs, the circulatory system, the central nervous system and the gastrointestinal tract. This review also looks at the process a new drug analogue undergoes, from (i) being a promising lead compound to (ii) being released into the market, from the drug development and discovery stage right through to FDA approval and aftermarket research. This review also addresses viable reasoning as to why the SARS-CoV-2 vaccine may have taken much less time than normal in order for it to be released for use.

Proteomics for Target Identification in Psychiatric and Neurodegenerative Disorders

Psychiatric and neurodegenerative disorders such as schizophrenia (SCZ), Parkinson's disease (PD), and Alzheimer's disease (AD) continue to grow around the world with a high impact on health, social, and economic outcomes for the patient and society. Despite efforts, the etiology and pathophysiology of these disorders remain unclear. Omics technologies have contributed to the understanding of the molecular mechanisms that underlie these complex disorders and have suggested novel potential targets for treatment and diagnostics. Here, we have highlighted the unique and common pathways shared between SCZ, PD, and AD and highlight the main proteomic findings over the last 5 years using in vitro models, postmortem brain samples, and cerebrospinal fluid (CSF) or blood of patients. These studies have identified possible therapeutic targets and disease biomarkers. Further studies including target validation, the use of large sample sizes, and the integration of omics findings with bioinformatics tools are required to provide a better comprehension of pharmacological targets.

Study design, result posting and publication of late-stage cardiovascular trials

AIMS

Pre-registration of study protocols in accessible databases is required for publication of study results in high-impact medical journals. Nonetheless, data on characteristics of clinical trials registered in these databases and their outcome, in terms of result reporting and publication are limited.

METHODS AND RESULTS

We searched for interventional, late-phase cardiovascular disease (CVD) studies in adults registered in Clinicaltrials.gov. first posted after 1/1/2013 and completed up to 31/12/2018. Data on study design, result reporting and publication were collected, and potential associations with a pre-defined set of explanatory factors were examined.In total, 250 CVD trials were included in the analysis. Of these, 193 (77.2%) were randomized studies, 99 (39.6%) open label designs, and 126 (50.4%) had industry as main sponsor. 179 trials (71.6%) evaluated the effect of drugs and 27 (10.8%) evaluated devices. The most common primary outcomes were non-clinical endpoints (76.0%), with only 17% of studies evaluating clinical endpoints. Industry-funded trials focused on patent-protected drugs and devices more often than non-industry-funded trials (72.0% vs. 30.6%, P < 0.001 and 55.0% vs. 26.3%, P = 0.033, respectively). Sixty three studies (25.2%) had results posted on clinicaltrials.gov, and 116 (46.4%) had results published in the scientific literature. In multivariate analysis, industry sponsorship was statistically significantly associated with results posting (OR: 3.38; 95% CI: 1.56-7.30, P = 0.002) and publication (OR: 0.41; 95% CI: 0.23-0.75, P = 0.004).

CONCLUSION

Among late-stage cardiovascular trials only 1/4 had results posted on clinicaltrials.gov and <50% had results published. Industry sponsors were more likely to invest in research on patent-protected drugs and devices than were non-industry sponsors. Industry-sponsored studies were more likely to have their results posted, but less likely to have their results published in the scientific literature.

Integrating bioassay data for improved prediction of drug-target interaction

Identifying drug targets is one of the major tasks in drug discovery. As experimental identification of targets is rather challenging, development of computational methods is necessary for efficient identification of drug-target interaction. Traditional computational method, such as docking, is based solely on the chemical structure, which is not available for most of the targets. On the other hand, bioassay data might contain information helpful for prediction of drug-target interaction. In this study, a feature enrichment method integrating bioassay and chemical structure data was developed to predict drug-target interaction. Using a large-scale benchmark on the datasets, we demonstrated that the model adopting integrated fingerprint outperformed the one using chemical fingerprint. Influence of the false positive hits in bioassays and algorithm-related factors on the model performance were also investigated. The results suggested that prediction by using integrated fingerprint was robust to false positive hits, the choice of classifiers, and different random splits of the datasets.

Targeting Casein Kinase 1 Delta Sensitizes Pancreatic and Bladder Cancer Cells to Gemcitabine Treatment by Upregulating Deoxycytidine Kinase

Although gemcitabine is the cornerstone of care for pancreatic ductal adenocarcinoma (PDA), patients lack durable responses and relapse is inevitable. While the underlying mechanisms leading to gemcitabine resistance are likely to be multifactorial, there is a strong association between activating gemcitabine metabolism pathways and clinical outcome. This study evaluated casein kinase 1 delta (CK1δ) as a potential therapeutic target for PDA and bladder cancer, in which CK1δ is frequently overexpressed. We assessed the antitumor effects of genetically silencing or pharmacologically inhibiting CK1δ using our in-house CK1δ small-molecule inhibitor SR-3029, either alone or in combination with gemcitabine, on the proliferation and survival of pancreatic and bladder cancer cell lines and orthotopic mouse models. Genetic studies confirmed that silencing CK1δ or treatment with SR-3029 induced a significant upregulation of deoxycytidine kinase (dCK), a rate-limiting enzyme in gemcitabine metabolite activation. The combination of SR-3029 with gemcitabine induced synergistic antiproliferative activity and enhanced apoptosis in both pancreatic and bladder cancer cells. Furthermore, in an orthotopic pancreatic tumor model, we observed improved efficacy with combination treatment concomitant with increased dCK expression. This study demonstrates that CK1δ plays a role in gemcitabine metabolism, and that the combination of CK1δ inhibition with gemcitabine holds promise as a future therapeutic option for metastatic PDA as well as other cancers with upregulated CK1δ expression.

Exploring different approaches to improve the success of drug discovery and development projects: a review

Background

There has been a significant increase in the cost and timeline of delivering new drugs for clinical use over the last three decades. Despite the increased investments in research infrastructure by pharmaceutical companies and technological advances in the scientific tools available, efforts to increase the number of molecules coming through the drug development pipeline have largely been unfruitful.

Main body

A non-systematic review of the current literature was undertaken to enumerate the various strategies employed to improve the success rates in the pharmaceutical research and development. The review covers the exploitation of genomics and proteomics, complementarity of target-based and phenotypic efficacy screening platforms, drug repurposing and repositioning, collaborative research, focusing on underserved therapeutic fields, outsourcing strategy, and pharmaceutical modeling and artificial intelligence. Examples of successful drug discoveries achieved through application of these strategies are highlighted and discussed herein.

Conclusions

Genomics and proteomics have uncovered a wide array of potential drug targets and are facilitative of enhanced scrupulous target identification and validation thus reducing efficacy-related drug attrition. When used complementarily, phenotypic and target-based screening platforms would likely allow serendipitous drug discovery while increasing rationality in drug design. Drug repurposing and repositioning reduces financial risks in drug development accompanied by cost and time savings, while prolonging patent exclusivity hence increased returns on investment to the innovator company. Equally important, collaborative research is facilitative of cross-fertilization and refinement of ideas, while sharing resources and expertise, hence reducing overhead costs in the early stages of drug discovery. Underserved therapeutic fields are niche drug discovery areas that may be used to experiment and launch novel drug targets, while exploiting incentivized benefits afforded by drug regulatory authorities. Outsourcing allows the pharma industries to focus on their core competencies while deriving greater efficiency of specialist contract research organizations. The existing and emerging pharmaceutical modeling and artificial intelligence softwares and tools allow for in silico computation enabling more efficient computer-aided drug design. Careful selection and application of these strategies, singly or in combination, may potentially harness pharmaceutical research and innovation.

Omics for Bioprospecting and Drug Discovery from Bacteria and Microalgae

"Omics" represent a combinatorial approach to high-throughput analysis of biological entities for various purposes. It broadly encompasses genomics, transcriptomics, proteomics, lipidomics, and metabolomics. Bacteria and microalgae exhibit a wide range of genetic, biochemical and concomitantly, physiological variations owing to their exposure to biotic and abiotic dynamics in their ecosystem conditions. Consequently, optimal conditions for adequate growth and production of useful bacterial or microalgal metabolites are critically unpredictable. Traditional methods employ microbe isolation and 'blind'-culture optimization with numerous chemical analyses making the bioprospecting process laborious, strenuous, and costly. Advances in the next generation sequencing (NGS) technologies have offered a platform for the pan-genomic analysis of microbes from community and strain downstream to the gene level. Changing conditions in nature or laboratory accompany epigenetic modulation, variation in gene expression, and subsequent biochemical profiles defining an organism's inherent metabolic repertoire. Proteome and metabolome analysis could further our understanding of the molecular and biochemical attributes of the microbes under research. This review provides an overview of recent studies that have employed omics as a robust, broad-spectrum approach for screening bacteria and microalgae to exploit their potential as sources of drug leads by focusing on their genomes, secondary metabolite biosynthetic pathway genes, transcriptomes, and metabolomes. We also highlight how recent studies have combined molecular biology with analytical chemistry methods, which further underscore the need for advances in bioinformatics and chemoinformatics as vital instruments in the discovery of novel bacterial and microalgal strains as well as new drug leads.

Synthesis of (1,3,4-thiadiazol-2-yl)-acrylamide derivatives as potential antitumor agents against acute leukemia cells

A lead compound with the (1,3,4-thiadiazol-2-yl)-acrylamide scaffold was discovered to have significant cytotoxicity on several tumor cell lines in an in-house cell-based screening. A total of 60 derivative compounds were then synthesized and tested in a CCK-8 cell viability assay. Some of them exhibited improved cytotoxic activities. The most potent compounds had IC₅₀ values of 1-5 μM on two acute leukemia tumor cell lines, i.e. RS4;11 and HL-60. Flow cytometry analysis of several active compounds and detection of caspase activation indicated that they induced caspase-dependent apoptosis. It was also encouraging to observe that these compounds did not have obvious cytotoxicity on normal cells, i.e. IC₅₀ > 50 μM on HEK-293T cells. Although the molecular targets of this class of compound are yet to be revealed, our current results suggest that this class of compound represents a new possibility for developing drug candidates against acute leukemia.

Self-assembly of cholesterol end-capped polymer micelles for controlled drug delivery

BACKGROUND

During the past few decades, drug delivery system (DDS) has attracted many interests because it could enhance the therapeutic effects of drugs and reduce their side effects. The advent of nanotechnology has promoted the development of nanosized DDSs, which could promote drug cellular uptake as well as prolong the half-life in blood circulation. Novel polymer micelles formed by self-assembly of amphiphilic polymers in aqueous solution have emerged as meaningful nanosystems for controlled drug release due to the reversible destabilization of hydrophobic domains under different conditions.

RESULTS

The amphiphilic polymers presented here were composed of cholesterol groups end capped and poly (poly (ethylene glycol) methyl ether methacrylate) (poly (OEGMA)) as tailed segments by the synthesis of cholesterol-based initiator, followed by atom transfer radical polymerization (ATRP) with OEGMA monomer. FT-IR and NMR confirmed the successfully synthesis of products including initiator and polymers as well as the Mw of the polymers were from 33,233 to 89,088 g/mol and their corresponding PDI were from 1.25 to 1.55 by GPC. The average diameter of assembled polymer micelles was in hundreds nanometers demonstrated by DLS, AFM and SEM. The behavior of the amphiphilic polymers as micelles was investigated using pyrene probing to explore their critical micelle concentration (CMC) ranging from 2.53 × 10-4 to 4.33 × 10-4 mg/ml, decided by the balance between cholesterol and poly (OEGMA). Besides, the CMC of amphiphilic polymers, the quercetin (QC) feeding ratio and polarity of solvents determined the QC loading ratio maximized reaching 29.2% certified by UV spectrum, together with the corresponding size and stability changes by DLS and Zeta potential, and thermodynamic changes by TGA and DSC. More significantly, cholesterol end-capped polymer micelles were used as nanosized systems for controlled drug release, not only alleviated the cytotoxicity of QC from 8.6 to 49.9% live cells and also achieved the QC release in control under different conditions, such as the presence of cyclodextrin (CD) and change of pH in aqueous solution.

CONCLUSIONS

The results observed in this study offered a strong foundation for the design of favorable polymer micelles as nanosized systems for controlled drug release, and the molecular weight adjustable amphiphilic polymer micelles held potential for use as controlled drug release system in practical application.

Interpretable machine learning as a tool for scientific discovery in chemistry

There has been an upsurge of interest in applying machine learning to chemistry, and impressive predictive accuracies have been achieved, but this has been done without providing any insight into what has been learnt from the training data.

Application of Bioactive Thermal Proteome Profiling to Decipher the Mechanism of Action of the Lipid Lowering 132-Hydroxy-pheophytin Isolated from a Marine Cyanobacteria

The acceleration of the process of understanding the pharmacological application of new marine bioactive compounds requires identifying the compound protein targets leading the molecular mechanisms in a living cell. The thermal proteome profiling (TPP) methodology does not fulfill the requirements for its application to any bioactive compound lacking chemical and functional characterization. Here, we present a modified method that we called bTPP for bioactive thermal proteome profiling that guarantees target specificity from a soluble subproteome. We showed that the precipitation of the microsomal fraction before the thermal shift assay is crucial to accurately calculate the melting points of the protein targets. As a probe of concept, the protein targets of 13²-hydroxy-pheophytin, a compound previously isolated from a marine cyanobacteria for its lipid reducing activity, were analyzed on the hepatic cell line HepG2. Our improved method identified 9 protein targets out of 2500 proteins, including 3 targets (isocitrate dehydrogenase, aldehyde dehydrogenase, phosphoserine aminotransferase) that could be related to obesity and diabetes, as they are involved in the regulation of insulin sensitivity and energy metabolism. This study demonstrated that the bTPP method can accelerate the field of biodiscovery, revealing protein targets involved in mechanisms of action (MOA) connected with future applications of bioactive compounds.

Screening drug target combinations in disease-related molecular networks

Background

For treating a complex disease such as cancer, some effective means are needed to control biological networks that underlies the disease. The one-target one-drug paradigm has been the dominating drug discovery approach in the past decades. Compared to single target-based drugs, combination drug targets may overcome many limitations of single drug target and achieve a more effective and safer control of the disease. Most of existing combination drug targets are developed based on clinical experience or text-and-trial strategy, which cannot provide theoretical guidelines for designing and screening effective drug combinations. Therefore, systematic identification of multiple drug targets and optimal intervention strategy needs to be developed.

Results

We developed a strategy to screen the synergistic combinations of two drug targets in disease networks based on the classification of single drug targets. The method tried to identify the sensitivity of single intervention and then the combination of multiple interventions that can restore the disease network to a desired normal state. In our strategy of screening drug target combinations, we first classified all drug targets into sensitive and insensitive single drug targets. Then, we identified the synergistic and antagonistic of drug target combinations, including the combinations of sensitive drug targets, the combinations of insensitive drug target and the combination of sensitive and insensitive targets. Finally, we applied our strategy to Arachidonic Acid (AA) metabolic network and found 18 pairs of synergistic drug target combinations, five of which have been proven to be viable by biological or medical experiments.

Conclusions

Different from traditional methods for judging drug synergy and antagonism, we propose the framework of how to enhance the efficiency by perturbing two sensitive targets in a combinatorial way, how to decrease the drug dose and therefore its side effect and cost by perturbing combinatorially a main sensitive target and an auxiliary insensitive target, and how to perturb two insensitive targets to realize the transition from a disease state to a healthy one which cannot be realized by perturbing each insensitive target alone. Although the idea is mainly applied to an AA metabolic network, the strategy holds for more general molecular networks such as combinatorial regulation in gene regulatory networks.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2730-8) contains supplementary material, which is available to authorized users.

Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects

Only a small fraction of early drug programs progress to the market, due to safety and efficacy failures, despite extensive efforts to predict safety. Characterizing the effect of natural variation in the genes encoding drug targets should present a powerful approach to predict side effects arising from drugging particular proteins. In this retrospective analysis, we report a correlation between the organ systems affected by genetic variation in drug targets and the organ systems in which side effects are observed. Across 1819 drugs and 21 phenotype categories analyzed, drug side effects are more likely to occur in organ systems where there is genetic evidence of a link between the drug target and a phenotype involving that organ system, compared to when there is no such genetic evidence (30.0 vs 19.2%; OR = 1.80). This result suggests that human genetic data should be used to predict safety issues associated with drug targets.

Safety issues including side effects are one of the major factors causing failure of clinical trials in drug development. Here, the authors leverage information about phenotypes associated with variation in genes encoding drug targets to predict drug-treatment-related side effects.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Application of the Transcriptional Disease Signature (TDSs) to Screen Melanoma-Effective Compounds in a Small Fish Model

Cell culture and protein target-based compound screening strategies, though broadly utilized in selecting candidate compounds, often fail to eliminate candidate compounds with non-target effects and/or safety concerns until late in the drug developmental process. Phenotype screening using intact research animals is attractive because it can help identify small molecule candidate compounds that have a high probability of proceeding to clinical use. Most FDA approved, first-in-class small molecules were identified from phenotypic screening. However, phenotypic screening using rodent models is labor intensive, low-throughput, and very expensive. As a novel alternative for small molecule screening, we have been developing gene expression disease profiles, termed the Transcriptional Disease Signature (TDS), as readout of small molecule screens for therapeutic molecules. In this concept, compounds that can reverse, or otherwise affect known disease-associated gene expression patterns in whole animals may be rapidly identified for more detailed downstream direct testing of their efficacy and mode of action. To establish proof of concept for this screening strategy, we employed a transgenic strain of a small aquarium fish, medaka (Oryzias latipes), that overexpresses the malignant melanoma driver gene xmrk, a mutant egfr gene, that is driven by a pigment cell-specific mitf promoter. In this model, melanoma develops with 100% penetrance. Using the transgenic medaka malignant melanoma model, we established a screening system that employs the NanoString nCounter platform to quantify gene expression within custom sets of TDS gene targets that we had previously shown to exhibit differential transcription among xmrk-transgenic and wild-type medaka. Compound-modulated gene expression was identified using an internet-accessible custom-built data processing pipeline. The effect of a given drug on the entire TDS profile was estimated by comparing compound-modulated genes in the TDS using an activation Z-score and Kolmogorov-Smirnov statistics. TDS gene probes were designed that target common signaling pathways that include proliferation, development, toxicity, immune function, metabolism and detoxification. These pathways may be utilized to evaluate candidate compounds for potential favorable, or unfavorable, effects on melanoma-associated gene expression. Here we present the logistics of using medaka to screen compounds, as well as, the development of a user-friendly NanoString data analysis pipeline to support feasibility of this novel TDS drug-screening strategy.

Gallic Acid Potentiates the Antimicrobial Activity of Tulathromycin Against Two Key Bovine Respiratory Disease (BRD) Causing-Pathogens

Bovine respiratory disease (BRD) is the most common infectious disease in dairy and beef cattle. It is associated with significant morbidity and mortality and causes a huge economic loss each year. In western Canada, a one-time injection of tulathromycin is commonly used as a metaphylactic procedure to reduce BRD incidence and eliminate potential BRD outbreak. With increased global concern on antimicrobial usage in dairy and beef products and bacterial resistance to antimicrobials, it is important to develop a novel strategy to eliminate the usage or decrease the dosage of antimicrobials. In this study, we showed that gallic acid was active against both Mannheimia haemolytica and Pasteurella multocida, two key BRD associated-pathogens, with the minimum inhibitory concentration (MIC) measured at 250 and 500 μg/mL, respectively. Co-administration of tulathromycin and gallic acid exhibited a strong additive or weak synergistic effect toward both M. haemolytic and P. multocida. Tulathromycin, gallic acid and their combination were also effective against the mixed culture of M. haemolytic and P. multocida. Furthermore, we showed that pre-exposure to tulathromycin generated bacterial resistance to the antimicrobial in M. haemolytica but not in P. multocida.

Inhibiting Pneumococcal Surface Antigen A (PsaA) with Small Molecules Discovered through Virtual Screening: Steps toward Validating a Potential Target for Streptococcus pneumoniae

The pneumococcal surface antigen A (PsaA) metal transporter protein provides manganese to bacterial cells. The X-ray crystal structures of PsaA, in both closed (Mn bound) and open (metal free) conformations, were explored with virtual screening to identify potential inhibitors of manganese transport. We pursued three strategies for inhibition: i) targeting a cavity close to the bound Mn to keep the metal in place; ii) targeting the metal-free Mn site to prevent metal uptake; and iii) targeting a potentially druggable allosteric site involving loops that translate between the conformations. Tiered assays were used to test the resulting 170 acquired hits: i) assay 1 tested the compounds' growth inhibition of the TIGR4 S. pneumoniae strain (ΔPsaA mutant control), yielding 80 compounds (MIC≤250 μm); ii) assay 2 tested if the addition of 20 μm Mn to inhibited cell cultures restored growth, yielding 21 compounds; and iii) assay 3 confirmed that the restored bacterial growth was Mn concentration dependent, as was the restoration of ΔPsaA growth, yielding 12 compounds with MICs of 125 μm or greater. It may be possible for a small molecule to inhibit PsaA, but we have not yet identified a compound with exemplary properties.

Synthesis and Evaluation of 2-Azetidinone and 1H-Pyrrole-2,5-dione Derivatives as Cholesterol Absorption Inhibitors for Reducing Inflammation Response and Oxidative Stress

Excess lipid accumulation can initiate the development and progression of atherosclerotic lesions, thus eventually leading to cardiovascular disease. Lipid-lowering medication therapy is one of the cornerstones of cardiovascular disease therapy. On the basis of the cholesterol absorption inhibitor ezetimibe, we successfully synthesized seven 2-azetidinone derivatives and eighteen 1H-pyrrole-2,5-dione derivatives. Most of the new compounds significantly inhibited cholesterol uptake in vitro. In addition, one of the most active inhibitors, 3-(4-fluorophenyl)-1-[(3S)-3-hydroxy-3-(4-hydroxyphenyl)propyl]-4-(4-hydroxyphenyl)-1H-pyrrole-2,5-dione (14q), showed no cytotoxicity in L02 and HEK293T cell lines. Further evaluation indicated that 14q inhibited considerably the amount of TNF-α, ROS, MDA, and LDH in vitro. Therefore, 14q might be a novel cholesterol absorption inhibitor.

Atomistic Picture of Fluorescent Probes with Hydrocarbon Tails in Lipid Bilayer Membranes: An Investigation of Selective Affinities and Fluorescent Anisotropies in Different Environmental Phases

By reverting to spectroscopy, changes in the biological environment of a fluorescent probe can be monitored and the presence of various phases of the surrounding lipid bilayer membranes can be detected. However, it is currently not always clear in which phase the probe resides. The well-known orange 1,1'-dioctadecyl-3,3,3',3'-tetramethylindodicarbo-cyanine perchlorate (DiI-C18(5)) fluorophore, for instance, and the new, blue BODIPY (4,4-difluoro-4-bora-3 a,4 a-diaza- s-indacene) derivative were experimentally seen to target and highlight identical parts of giant unilamellar vesicles of various compositions, comprising mixtures of dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylcholine (DOPC), sphingomyelin (SM), and cholesterol (Chol). However, it was not clear which of the coexisting membrane phases were visualized (Bacalum et al., Langmuir. 2016, 32, 3495). The present study addresses this issue by utilizing large-scale molecular dynamics simulations and the z-constraint method, which allows evaluating Gibbs free-energy profiles. The current calculations give an indication why, at room temperature, both BODIPY and DiI-C18(5) probes prefer the gel (S_o) phase in DOPC/DPPC (2:3 molar ratio) and the liquid-ordered (L_o) phase in DOPC/SM/Chol (1:2:1 molar ratio) mixtures. This study highlights the important differences in orientation and location and therefore in efficiency between the probes when they are used in fluorescence microscopy to screen various lipid bilayer membrane phases. Dependent on the lipid composition, the angle between the transition-state dipole moments of both probes and the normal to the membrane is found to deviate clearly from 90°. It is seen that the DiI-C18(5) probe is located in the headgroup region of the SM/Chol mixture, in close contact with water molecules. A fluorescence anisotropy study also indicates that DiI-C18(5) gives rise to a distinctive behavior in the SM/Chol membrane compared to the other considered membranes. The latter behavior has not been seen for the studied BODIPY probe, which is located deeper in the membrane.

Clinical trial design and dissemination: comprehensive analysis of clinicaltrials.gov and PubMed data since 2005

OBJECTIVE

To investigate the distribution, design characteristics, and dissemination of clinical trials by funding organisation and medical specialty.

DESIGN

Cross sectional descriptive analysis.

DATA SOURCES

Trial protocol information from clinicaltrials.gov, metadata of journal articles in which trial results were published (PubMed), and quality metrics of associated journals from SCImago Journal and Country Rank database.

SELECTION CRITERIA

All 45 620 clinical trials evaluating small molecule therapeutics, biological drugs, adjuvants, and vaccines, completed after January 2006 and before July 2015, including randomised controlled trials and non-randomised studies across all clinical phases.

RESULTS

Industry was more likely than non-profit funders to fund large international randomised controlled trials, although methodological differences have been decreasing with time. Among 27 835 completed efficacy trials (phase II-IV), 15 084 (54.2%) had disclosed their findings publicly. Industry was more likely than non-profit trial funders to disseminate trial results (59.3% (10 444/17 627) v 45.3% (4555/10 066)), and large drug companies had higher disclosure rates than small ones (66.7% (7681/11 508) v 45.2% (2763/6119)). Trials funded by the National Institutes of Health (NIH) were disseminated more often than those of other non-profit institutions (60.0% (1451/2417) v 40.6% (3104/7649)). Results of studies funded by large drug companies and NIH were more likely to appear on clinicaltrials.gov than were those from non-profit funders, which were published mainly as journal articles. Trials reporting the use of randomisation were more likely than non-randomised studies to be published in a journal article (6895/19 711 (34.9%) v 1408/7748 (18.2%)), and journal publication rates varied across disease areas, ranging from 42% for autoimmune diseases to 20% for oncology.

CONCLUSIONS

Trial design and dissemination of results vary substantially depending on the type and size of funding institution as well as the disease area under study.

The Current Landscape of 3D In Vitro Tumor Models: What Cancer Hallmarks Are Accessible for Drug Discovery?

Cancer prognosis remains a lottery dependent on cancer type, disease stage at diagnosis, and personal genetics. While investment in research is at an all-time high, new drugs are more likely to fail in clinical trials today than in the 1970s. In this review, a summary of current survival statistics in North America is provided, followed by an overview of the modern drug discovery process, classes of models used throughout different stages, and challenges associated with drug development efficiency are highlighted. Then, an overview of the cancer hallmarks that drive clinical progression is provided, and the range of available clinical therapies within the context of these hallmarks is categorized. Specifically, it is found that historically, the development of therapies is limited to a subset of possible targets. This provides evidence for the opportunities offered by novel disease-relevant in vitro models that enable identification of novel targets that facilitate interactions between the tumor cells and their surrounding microenvironment. Next, an overview of the models currently reported in literature is provided, and the cancer biology they have been used to explore is highlighted. Finally, four priority areas are suggested for the field to accelerate adoption of in vitro tumour models for cancer drug discovery.

Biomaterials-Based Approaches to Tumor Spheroid and Organoid Modeling

Evolving understanding of structural and biological complexity of tumors has stimulated development of physiologically relevant tumor models for cancer research and drug discovery. A major motivation for developing new tumor models is to recreate the 3D environment of tumors and context-mediated functional regulation of cancer cells. Such models overcome many limitations of standard monolayer cancer cell cultures. Under defined culture conditions, cancer cells self-assemble into 3D constructs known as spheroids. Additionally, cancer cells may recapitulate steps in embryonic development to self-organize into 3D cultures known as organoids. Importantly, spheroids and organoids reproduce morphology and biologic properties of tumors, providing valuable new tools for research, drug discovery, and precision medicine in cancer. This Progress Report discusses uses of both natural and synthetic biomaterials to culture cancer cells as spheroids or organoids, specifically highlighting studies that demonstrate how these models recapitulate key properties of native tumors. The report concludes with the perspectives on the utility of these models and areas of need for future developments to more closely mimic pathologic events in tumors.

Identification of novel drug targets in bovine respiratory disease: an essential step in applying biotechnologic techniques to develop more effective therapeutic treatments

BACKGROUND

Bovine Respiratory Disease (BRD) is a major problem in cattle production which causes substantial economic loss. BRD has multifactorial aetiologies, is multi-microbial, and several of the causative pathogens are unknown. Consequently, primary management practices such as metaphylactic antimicrobial injections for BRD prevention are used to reduce the incidence of BRD in feedlot cattle. However, this poses a serious threat in the form of development of antimicrobial resistance and demands an urgent need to find novel interventions that could reduce the effects of BRD drastically and also delay/prevent bacterial resistance.

MATERIALS AND METHODS

We have employed a subtractive genomics approach that helps delineate essential, host-specific, and druggable targets in pathogens responsible for BRD. We also proposed antimicrobials from FDA green and orange book that could be repositioned for BRD.

RESULTS

We have identified 107 putative targets that are essential, selective and druggable. We have also confirmed the susceptibility of two BRD pathogens to one of the proposed antimicrobials - oxytetracycline.

CONCLUSION

This approach allows for repositioning drugs known for other infections to BRD, predicting novel druggable targets for BRD infection, and providing a new direction in developing more effective therapeutic treatments for BRD.

Direct and indirect approaches to identify drug modes of action

Phenotypic assays are becoming increasingly more common among drug discovery practices, expanding drug target diversity as lead compounds identified through such screens are not limited to known targets. While increasing diversity is beneficial to the drug discovery process and the fight against disease, the unknown modes of action of new lead compounds can hamper drug discovery as, in most cases, the process of lead compound optimization is made difficult due to the unknown nature of the target; blindly changing substituents can prove fruitless due to the inexhaustible number of potential combinations, and it is therefore desirable to rapidly identify the targets of lead compounds developed through phenotypic screening. In addition, leads identified through target-based screening often have off-target effects that contribute towards drug toxicity, and by identifying those secondary targets, the drugs can be improved. However, the identification of a leads mode of action is far from trivial and now represents a major bottleneck in the drug discovery pipeline. This review looks at some of the recent developments in the identification of drug modes of action, focusing on phenotype-based methods using metabolomics, proteomics, transcriptomics, and genomics to detect changes in phenotype in response to the presence of the drug, and affinity-based methods using modified/unmodified drug as bait to capture and identify targets. © 2017 IUBMB Life, 70(1):9-22, 2018.

Dual protein kinase and nucleoside kinase modulators for rationally designed polypharmacology

Masitinib, a highly selective protein kinase inhibitor, can sensitise gemcitabine-refractory cancer cell lines when used in combination with gemcitabine. Here we report a reverse proteomic approach that identifies the target responsible for this sensitisation: the deoxycytidine kinase (dCK). Masitinib, as well as other protein kinase inhibitors, such as imatinib, interact with dCK and provoke an unforeseen conformational-dependent activation of this nucleoside kinase, modulating phosphorylation of nucleoside analogue drugs. This phenomenon leads to an increase of prodrug phosphorylation of most of the chemotherapeutic drugs activated by this nucleoside kinase. The unforeseen dual activity of protein kinase inhibition/nucleoside kinase activation could be of great therapeutic benefit, through either reducing toxicity of therapeutic agents by maintaining effectiveness at lower doses or by counteracting drug resistance initiated via down modulation of dCK target.

Masitinib is a protein kinase inhibitor that sensitises refractory pancreatic adenocarcinoma cells to treatment with the nucleoside analog gemcitabine. Here the authors show that Masitinib activates deoxycytidine kinase to enhance phosphorylation of nucleoside analogue pro-drugs, increasing their potency.