Prioritizing target-disease associations with novel safety and efficacy scoring methods

Essential cancer protein identification using graph-based random walk with restart

Protein-protein interaction (PPI) network analysis holds significant promise for cancer diagnosis and drug target identification. This paper introduces a novel random walk-based method called essential cancer protein identification using graph-based random walk with restart (EPI-GBRWR) to address this gap. This proposed method incorporates local and global topological features of proteins, enhancing the accuracy of essential protein identification in PPI networks. Starting with meticulous preprocessing of cancer gene datasets from NCBI, including breast, lung, colorectal, and ovarian cancers, and identifying a core set of common genes. The proposed method constructs PPI networks to capture complex protein interactions from these common cancer genes. Topological analysis, including a centrality measures matrix, is generated to perform the analysis to identify essential nodes. The study revealed that 40 essential proteins among breast, colorectal, lung and ovarian cancer showcase the potency of integrative methodologies in unravelling cancer complexity, signalling a transformative era in cancer research and treatment. The strength of the findings from the study has direct clinical relevance in cancer diseases. It contributes to the field of precision medicine to guide personalized treatment strategies.

Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.

A comprehensive investigation on the receptor BSG expression reveals the potential risk of healthy individuals and cancer patients to 2019-nCoV infection

BACKGROUND

Coronavirus disease-2019 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a newly emerging coronavirus. BSG (basigin) is involved in the tumorigenesis of multiple tumors and recently emerged as a novel viral entry receptor for SARS-CoV-2. However, its expression profile in normal individuals and cancer patients are still unclear.

METHODS

We performed a comprehensive analysis of the expression and distribution of BSG in normal tissues, tumor tissues, and cell lines via bioinformatics analysis and experimental verification. In addition, we investigated the expression of BSG and its isoforms in multiple malignancies and adjacent normal tissues, and explored the prognostic values across pan-cancers. Finally, we conducted function analysis for co-expressed genes with BSG.

RESULTS

We found BSG was highly conserved in different species, and was ubiquitously expressed in almost all normal tissues and significantly increased in some types of cancer tissues. Moreover, BSG at mRNA expression level was higher than ACE2 in normal lung tissues, and lung cancer tissues. High expression of BSG indicated shorter overall survival (OS) in multiple tumors. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that BSG is mostly enriched in genes for mitochondria electron transport, oxidoreduction-driven active transmembrane transporter activity, mitochondrial inner membrane, oxidative phosphorylation, and genes involving COVID-19.

CONCLUSIONS

Our present work emphasized the value of targeting BSG in the treatment of COVID-19 and cancer, and also provided several novel insights for understanding the SARS-CoV-2 pandemic.

In-silico identification and prioritization of therapeutic targets of asthma

Asthma is a "common chronic disorder that affects the lungs causing variable and recurring symptoms like repeated episodes of wheezing, breathlessness, chest tightness and underlying inflammation. The interaction of these features of asthma determines the clinical manifestations and severity of asthma and the response to treatment" [cited from: National Heart, Lung, and Blood Institute. Expert Panel 3 Report. Guidelines for the Diagnosis and Management of Asthma 2007 (EPR-3). Available at: https://www.ncbi.nlm.nih.gov/books/NBK7232/ (accessed on January 3, 2023)]. As per the WHO, 262 million people were affected by asthma in 2019 that leads to 455,000 deaths ( https://www.who.int/news-room/fact-sheets/detail/asthma ). In this current study, our aim was to evaluate thousands of scientific documents and asthma associated omics datasets to identify the most crucial therapeutic target for experimental validation. We leveraged the proprietary tool Ontosight® Discover to annotate asthma associated genes and proteins. Additionally, we also collected and evaluated asthma related patient datasets through bioinformatics and machine learning based approaches to identify most suitable targets. Identified targets were further evaluated based on the various biological parameters to scrutinize their candidature for the ideal therapeutic target. We identified 7237 molecular targets from published scientific documents, 2932 targets from genomic structured databases and 7690 dysregulated genes from the transcriptomics and 560 targets from genomics mutational analysis. In total, 18,419 targets from all the desperate sources were analyzed and evaluated though our approach to identify most promising targets in asthma. Our study revealed IL-13 as one of the most important targets for asthma with approved drugs on the market currently. TNF, VEGFA and IL-18 were the other top targets identified to be explored for therapeutic benefit in asthma but need further clinical testing. HMOX1, ITGAM, DDX58, SFTPD and ADAM17 were the top novel targets identified for asthma which needs to be validated experimentally.

DAIKON: A Data Acquisition, Integration, and Knowledge Capture Web Application for Target-Based Drug Discovery

Primitive data organization practices struggle to deliver at the scale and consistency required to meet multidisciplinary collaborations in drug discovery. For effective data sharing and coordination, a unified platform that can collect and analyze scientific information is essential. We present DAIKON, an open-source framework that integrates targets, screens, hits, and manages projects within a target-based drug discovery portfolio. Its knowledge capture components enable teams to record subsequent molecules as their properties improve, facilitate team collaboration through discussion threads, and include modules that visually illustrate the progress of each target as it advances through the pipeline. It serves as a repository for scientists sourcing data from Mycobrowser, UniProt, PDB. The goal is to globalize several variations of the drug-discovery program without compromising local aspects of specific workflows. DAIKON is modularized by abstracting the database and creating separate layers for entities, business logic, infrastructure, APIs, and frontend, with each tier allowing for extensions. Using Docker, the framework is packaged into two solutions: daikon-server-core and daikon-client. Organizations may deploy the project to on-premises servers or VPC. Active-Directory/SSO is supported for user administration. End users can access the application with a web browser. Currently, DAIKON is implemented in the TB Drug Accelerator program (TBDA).

Optimizing the Safety and Efficacy of Bio-Radiopharmaceuticals for Cancer Therapy

The precise delivery of cytotoxic radiation to cancer cells through the combination of a specific targeting vector with a radionuclide for targeted radionuclide therapy (TRT) has proven valuable for cancer care. TRT is increasingly being considered a relevant treatment method in fighting micro-metastases in the case of relapsed and disseminated disease. While antibodies were the first vectors applied in TRT, increasing research data has cited antibody fragments and peptides with superior properties and thus a growing interest in application. As further studies are completed and the need for novel radiopharmaceuticals nurtures, rigorous considerations in the design, laboratory analysis, pre-clinical evaluation, and clinical translation must be considered to ensure improved safety and effectiveness. Here, we assess the status and recent development of biological-based radiopharmaceuticals, with a focus on peptides and antibody fragments. Challenges in radiopharmaceutical design range from target selection, vector design, choice of radionuclides and associated radiochemistry. Dosimetry estimation, and the assessment of mechanisms to increase tumor uptake while reducing off-target exposure are discussed.

Industrializing AI/ML during the end-to-end drug discovery process

Drug discovery aims to select proper targets and drug candidates to address unmet clinical needs. The end-to-end drug discovery process includes all stages of drug discovery from target identification to drug candidate selection. Recently, several artificial intelligence and machine learning (AI/ML)-based drug discovery companies have attempted to build data-driven platforms spanning the end-to-end drug discovery process. The ability to identify elusive targets essentially leads to the diversification of discovery pipelines, thereby increasing the ability to address unmet needs. Modern ML technologies are complementing traditional computer-aided drug discovery by accelerating candidate optimization in innovative ways. This review summarizes recent developments in AI/ML methods from target identification to molecule optimization, and concludes with an overview of current industrial trends in end-to-end AI/ML platforms.

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022

The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the European Molecular Biology Laboratory (EMBL), Europe's only intergovernmental life sciences organisation. This overview summarises the status of services that EMBL-EBI data resources provide to scientific communities globally. The scale, openness, rich metadata and extensive curation of EMBL-EBI added-value databases makes them particularly well-suited as training sets for deep learning, machine learning and artificial intelligence applications, a selection of which are described here. The data resources at EMBL-EBI can catalyse such developments because they offer sustainable, high-quality data, collected in some cases over decades and made openly availability to any researcher, globally. Our aim is for EMBL-EBI data resources to keep providing the foundations for tools and research insights that transform fields across the life sciences.

Network approaches for modeling the effect of drugs and diseases

The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).

Translational Scoring of Candidate Treatments for Alzheimer's Disease: A Systematic Approach

BACKGROUND

There are many failures in treatment development for Alzheimer's disease (AD). Some of these failures are the result of development programs that lacked critical information about candidate drugs as these were advanced from one phase of development to the next. Translational scoring (TS) has been proposed as a means of increasing the rigor with which treatment development programs are executed. Previously, these approaches were not specific to AD or to the phase of drug development. Detailed information on the characteristics needed to advance a candidate agent from one phase to the next is the basis for success in subsequent phases.

SUMMARY

The TS approach is presented with a score range of 0-25 for agents entering phases 1, 2, and 3 of development and those that have completed phase 3 and are being considered for regulatory review. Each phase has 5 essential categories scored from 0-5 indicating the completeness of the data available when the agent is being considered for promotion to the next phase. Lower scores suggest that the development program should be reexamined for missing information while higher scores increase the confidence that the agent has the potential to succeed in the next phase. Scoring guidelines are provided and examples of scores for drugs in recent development programs are provided to illustrate the principles of TS. Key Messages: Successful development of drugs for AD treatment requires disciplined informed decision-making at each phase of development. TS is a methodology for more rigorous drug development to help ensure that inadequately characterized drugs are not advanced and that the development platform at each phase is optimal to support success at the next phase.

TREAP: A New Topological Approach to Drug Target Inference

Over 50% of drugs fail in stage 3 clinical trials, many because of a poor understanding of the drug's mechanisms of action (MoA). A better comprehension of drug MoA will significantly improve research and development (R&D). Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug-induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA's accuracy. We find that network topology predominantly determines the accuracy of ProTINA's predictions. We further show that the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. We then demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP, that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous because it is not computationally demanding, provides easy-to-interpret results, and is often more accurate at predicting drug targets than current state-of-the-art approaches.

Large-scale production, purification, and function of a tumor multi-epitope vaccine: Peptibody with bFGF/VEGFA

In tumor tissue, basic fibroblast growth factor (bFGF) and vascular endothelial growth factor A (VEGFA) promote tumorigenesis by activating angiogenesis, but targeting single factor may produce drug resistance and compensatory angiogenesis. The Peptibody with bFGF/VEGFA was designed to simultaneously blockade these two factors. We were aiming to produce this Fc fusion protein in a large scale. The biological characterizations of Peptibody strains were identified as Escherichia coli and the fermentation mode was optimized in the shake flasks and 10-L bioreactor. The fermentation was scaled up to 100 L, with wet cell weight (WCW) 126 g/L, production 1.41 g/L, and productivity 0.35 g/(L·h) of IPTG induction. The target protein was isolated by cation-exchange, hydrophobic and Protein A chromatography, with total recovery of 60.28% and HPLC purity of 86.71%. The host cells protein, DNA, and endotoxin residues were within the threshold. In mouse model, immunization of Peptibody vaccine could significantly suppressed the tumor growth and angiogenesis, with inhibition rate of 57.73 and 39.34%. The Peptibody vaccine could elicit high-titer anti-bFGF and anti-VEGFA antibodies, which inhibited the proliferation and migration of Lewis lung cancer cell cells by decreasing the Akt/MAPK signal pathways. Therefore, the Peptibody with bFGF/VEGFA might be used as a therapeutic tumor vaccine. The large-scale process we developed could support its industrial production and pre-clinical study in the future.

ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery

Summary

Estimating efficacy of gene–target-disease associations is a fundamental step in drug discovery. An important data source for this laborious task is RNA expression, which can provide gene–disease associations on the basis of expression fold change and statistical significance. However, the simply use of the log-fold change can lead to numerous false-positive associations. On the other hand, more sophisticated methods that utilize gene co-expression networks do not consider tissue specificity. Here, we introduce Transcriptome-driven Efficacy estimates for gene-based TArget discovery (ThETA), an R package that enables non-expert users to use novel efficacy scoring methods for drug–target discovery. In particular, ThETA allows users to search for gene perturbation (therapeutics) that reverse disease-gene expression and genes that are closely related to disease-genes in tissue-specific networks. ThETA also provides functions to integrate efficacy evaluations obtained with different approaches and to build an overall efficacy score, which can be used to identify and prioritize gene(target)–disease associations. Finally, ThETA implements visualizations to show tissue-specific interconnections between target and disease-genes, and to indicate biological annotations associated with the top selected genes.

Availability and implementation

ThETA is freely available for academic use at https://github.com/vittoriofortino84/ThETA.

Contact

vittorio.fortino@uef.fi

Supplementary information

Supplementary data are available at Bioinformatics online.

Drug efficacy and toxicity prediction: an innovative application of transcriptomic data

Drug toxicity and efficacy are difficult to predict partly because they are both poorly defined, which I aim to remedy here from a transcriptomic perspective. There are two major categories of drugs: (1) restorative drugs aiming to restore an abnormal cell, tissue, or organ to normal function (e.g., restoring normal membrane function of epithelial cells in cystic fibrosis), and (2) disruptive drugs aiming to kill pathogens or malignant cells. These two types of drugs require different definition of efficacy and toxicity. I outlined rationales for defining transcriptomic efficacy and toxicity and illustrated numerically their application with two sets of transcriptomic data, one for restorative drugs (treating cystic fibrosis with lumacaftor/ivacaftor aiming to restore the cellular function of epithelial cells) and the other for disruptive drugs (treating acute myeloid leukemia with prexasertib). The conceptual framework presented will help and sensitize researchers to collect data required for determining drug toxicity.

The Design, Characterizations, and Tumor Angiogenesis Inhibition of a Multi-Epitope Peptibody With bFGF/VEGFA

Tumor angiogenesis is dependent on growth factors, and inhibition of their pathways is one of the promising strategies in cancer therapy. However, resistance to single pathway has been a great concern in clinical trials so that it necessitates multiple targetable factors for developing tumor angiogenesis inhibitors. Moreover, the strategy of Fc fusion protein is an attractive platform for novel peptide agents, which gains increasing importance with FDA approval because of better immunogenicity and stability. Here, we applied the Fc fusion protein concept to bFGF/VEGFA pathways and designed a multi-epitope Peptibody with immunogenic peptides derived from human bFGF and VEGFA sequences. Immunization with Peptibody could elicit high-titer anti-bFGF and anti-VEGFA antibodies, activate T cells, and induce Th1/Th2-type cytokines. In in vitro experiments, the isolated anti-Peptibody antibody inhibited the proliferation and migration of A549 cells and human umbilical vein endothelial cells (HUVECs) by decreasing the MAPK/Akt/mTOR signal pathways. In the murine tumor model, pre-immunization with Peptibody suppressed the tumor growth and neovascularization of lung cancer by decreasing the production of bFGF/VEGFA/PDGF, the MAPK/Akt/mTOR signal pathways, and the activation of suppressive cells in tumor sites. Further, the biological characterizations of the recombinant Peptibody were investigated systematically, including protein primary structure, secondary structure, stability, and toxicity. Collectively, the results highlighted the strategy of bFGF/VEGFA pathways and Fc fusion protein in suppressing tumor progression and angiogenesis, which emphasized the potential of multiple targetable factors for producing enduring clinical responses in tumor patients.

Epilepsy Benchmarks Area II: Prevent Epilepsy and Its Progression

Area II of the 2014 Epilepsy Research Benchmarks aims to establish goals for preventing the development and progression of epilepsy. In this review, we will highlight key advances in Area II since the last summary of research progress and opportunities was published in 2016. We also highlight areas of investigation that began to develop before 2016 and in which additional progress has been made more recently.