Phosphoproteomics in drug discovery

Proteomics in decoding cancer: A review

Cancer remains the second leading cause of death worldwide, posing a significant global health challenge. Extensive research has revealed common biological characteristics across cancer cells, forming the foundation for developing innovative diagnostic and therapeutic strategies. To better understand these shared traits, advanced measurement technologies are critical. Proteomics, the large-scale study of proteins and their functions, has emerged as a transformative tool for uncovering the complexities of cancer biology. This approach provides an in-depth view of cellular activities and protein interactions, offering unprecedented insights into cancer progression and treatment. Unlike traditional methods that investigate specific pathways in isolation, proteomics enables simultaneous analysis of thousands of proteins, generating a comprehensive understanding of cancer biology. This review explores the mechanisms underlying proteomics, its application to understanding cancer hallmarks, and its potential to transform clinical approaches. By examining proteomics' role in metastasis, angiogenesis, proliferation, and resistance mechanisms, this study highlights its contributions to cancer diagnosis, treatment, and personalized medicine. Additionally, prospects in integrating proteomics with other -omics fields and advancements in computational analysis will be discussed. This work aims to illuminate the path toward more effective, precise, and individualized cancer care through proteomics innovations.

Multidimensional Proteomic Landscape Reveals Distinct Activated Pathways Between Human Brain Tumors

Brain metastases (BrMs) and gliomas are two typical human brain tumors with high incidence of mortalities and distinct clinical challenges, yet the understanding of these two types of tumors remains incomplete. Here, a multidimensional proteomic landscape of BrMs and gliomas to infer tumor-specific molecular pathophysiology at both tissue and plasma levels is presented. Tissue sample analysis reveals both shared and distinct characteristics of brain tumors, highlighting significant disparities between BrMs and gliomas with differentially activated upstream pathways of the PI3K-Akt signaling pathway that have been scarcely discussed previously. Novel proteins and phosphosites such as NSUN2, TM9SF3, and PRKCG_S330 are also detected, exhibiting a high correlation with reported clinical traits, which may serve as potential immunohistochemistry (IHC) biomarkers. Moreover, tumor-specific altered phosphosites and glycosites on FN1 are highlighted as potential therapeutic targets. Further validation of 110 potential noninvasive biomarkers yields three biomarker panels comprising a total of 19 biomarkers (including DES, VWF, and COL1A1) for accurate discrimination of two types of brain tumors and normal controls. In summary, this is a full-scale dataset of two typical human brain tumors, which serves as a valuable resource for advancing precision medicine in cancer patients through targeted therapy and immunotherapy.

Proteomic and phosphoproteomic landscape of localized prostate cancer unveils distinct molecular subtypes and insights into precision therapeutics

Building upon our previous investigation of genomic, epigenomic, and transcriptomic profiles of prostate cancer in China, we conducted a comprehensive analysis of proteomic and phosphoproteomic profiles of 82 tumor tissues and matched adjacent normal tissues from 41 Chinese patients with localized prostate cancer. We identified three distinct proteomic subtypes with significant difference in both molecular features and clinical prognosis. Notably, these proteomic subtypes exhibited a parallel degree of heterogeneity in the phosphoproteome, featuring unique metabolism, proliferation, and immune infiltration characteristics. We further demonstrated that a combination of proteins and phosphosites serves as the most effective biomarkers in prostate cancer to predict biochemical recurrence. Through an integrated multiomics analysis, we revealed mechanistic differences underlying different proteomic subtypes and highlighted the potential significance of Serine/arginine-rich splicing factor 1 (SRSF1) phosphorylation in promoting the malignant characteristics of prostate cancer cells. Our multiomics data provide valuable resources for understanding the molecular mechanisms of prostate cancer within the Chinese population, which have the potential to inform the development of personalized treatment strategies and enhance prognostic analyses for prostate cancer patients.

New Therapeutics for Heart Failure: Focusing on cGMP Signaling

Current drugs for treating heart failure (HF), for example, angiotensin II receptor blockers and β-blockers, possess specific target molecules involved in the regulation of the cardiac circulatory system. However, most clinically approved drugs are effective in the treatment of HF with reduced ejection fraction (HFrEF). Novel drug classes, including angiotensin receptor blocker/neprilysin inhibitor (ARNI), sodium-glucose co-transporter-2 (SGLT2) inhibitor, hyperpolarization-activated cyclic nucleotide-gated (HCN) channel blocker, soluble guanylyl cyclase (sGC) stimulator/activator, and cardiac myosin activator, have recently been introduced for HF intervention based on their proposed novel mechanisms. SGLT2 inhibitors have been shown to be effective not only for HFrEF but also for HF with preserved ejection fraction (HFpEF). In the myocardium, excess cyclic adenosine monophosphate (cAMP) stimulation has detrimental effects on HFrEF, whereas cyclic guanosine monophosphate (cGMP) signaling inhibits cAMP-mediated responses. Thus, molecules participating in cGMP signaling are promising targets of novel drugs for HF. In this review, we summarize molecular pathways of cGMP signaling and clinical trials of emerging drug classes targeting cGMP signaling in the treatment of HF.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

Immune cell profiling in atherosclerosis: role in research and precision medicine

Inflammation is intimately involved at all stages of atherosclerosis and remains a substantial residual cardiovascular risk factor in optimally treated patients. The proof of concept that targeting inflammation reduces cardiovascular events in patients with a history of myocardial infarction has highlighted the urgent need to identify new immunotherapies to treat patients with atherosclerotic cardiovascular disease. Importantly, emerging data from new clinical trials show that successful immunotherapies for atherosclerosis need to be tailored to the specific immune alterations in distinct groups of patients. In this Review, we discuss how single-cell technologies - such as single-cell mass cytometry, single-cell RNA sequencing and cellular indexing of transcriptomes and epitopes by sequencing - are ideal for mapping the cellular and molecular composition of human atherosclerotic plaques and how these data can aid in the discovery of new precise immunotherapies. We also argue that single-cell data from studies in humans need to be rigorously validated in relevant experimental models, including rapidly emerging single-cell CRISPR screening technologies and mouse models of atherosclerosis. Finally, we discuss the importance of implementing single-cell immune monitoring tools in early phases of drug development to aid in the precise selection of the target patient population for data-driven translation into randomized clinical trials and the successful translation of new immunotherapies into the clinic.

Target Discovery for Host-Directed Antiviral Therapies: Application of Proteomics Approaches

Current epidemics, such as AIDS or flu, and the emergence of new threatening pathogens, such as the one causing the current coronavirus disease 2019 (COVID-19) pandemic, represent major global health challenges. While vaccination is an important part of the arsenal to counter the spread of viral diseases, it presents limitations and needs to be complemented by efficient therapeutic solutions. Intricate knowledge of host-pathogen interactions is a powerful tool to identify host-dependent vulnerabilities that can be exploited to dampen viral replication. Such host-directed antiviral therapies are promising and are less prone to the development of drug-resistant viral strains. Here, we first describe proteomics-based strategies that allow the rapid characterization of host-pathogen interactions. We then discuss how such data can be exploited to help prioritize compounds with potential host-directed antiviral activity that can be tested in preclinical models.

Antibiotic Discovery: Where Have We Come from, Where Do We Go?

Given the increase in antibiotic-resistant bacteria, alongside the alarmingly low rate of newly approved antibiotics for clinical usage, we are on the verge of not having effective treatments for many common infectious diseases. Historically, antibiotic discovery has been crucial in outpacing resistance and success is closely related to systematic procedures—platforms—that have catalyzed the antibiotic golden age, namely the Waksman platform, followed by the platforms of semi-synthesis and fully synthetic antibiotics. Said platforms resulted in the major antibiotic classes: aminoglycosides, amphenicols, ansamycins, beta-lactams, lipopeptides, diaminopyrimidines, fosfomycins, imidazoles, macrolides, oxazolidinones, streptogramins, polymyxins, sulphonamides, glycopeptides, quinolones and tetracyclines. During the genomics era came the target-based platform, mostly considered a failure due to limitations in translating drugs to the clinic. Therefore, cell-based platforms were re-instituted, and are still of the utmost importance in the fight against infectious diseases. Although the antibiotic pipeline is still lackluster, especially of new classes and novel mechanisms of action, in the post-genomic era, there is an increasingly large set of information available on microbial metabolism. The translation of such knowledge into novel platforms will hopefully result in the discovery of new and better therapeutics, which can sway the war on infectious diseases back in our favor.

MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis

Dysregulation of signaling pathways in multiple sclerosis (MS) can be analyzed by phosphoproteomics in peripheral blood mononuclear cells (PBMCs). We performed in vitro kinetic assays on PBMCs in 195 MS patients and 60 matched controls and quantified the phosphorylation of 17 kinases using xMAP assays. Phosphoprotein levels were tested for association with genetic susceptibility by typing 112 single-nucleotide polymorphisms (SNPs) associated with MS susceptibility. We found increased phosphorylation of MP2K1 in MS patients relative to the controls. Moreover, we identified one SNP located in the PHDGH gene and another on IRF8 gene that were associated with MP2K1 phosphorylation levels, providing a first clue on how this MS risk gene may act. The analyses in patients treated with disease-modifying drugs identified the phosphorylation of each receptor's downstream kinases. Finally, using flow cytometry, we detected in MS patients increased STAT1, STAT3, TF65, and HSPB1 phosphorylation in CD19⁺ cells. These findings indicate the activation of cell survival and proliferation (MAPK), and proinflammatory (STAT) pathways in the immune cells of MS patients, primarily in B cells. The changes in the activation of these kinases suggest that these pathways may represent therapeutic targets for modulation by kinase inhibitors.

Drug Screening Platforms and RPPA

Since its inception as a scalable and cost-effective method for precise quantification of the abundance of multiple protein analytes and post-translational epitopes across large sample sets, reverse phase protein array (RPPA) has been utilized as a drug discovery tool. Key RPPA drug discovery applications include primary screening of abundance or activation state of nominated protein targets, secondary screening for toxicity and selectivity, mechanism-of-action profiling, biomarker discovery, and drug combination discovery. In recent decades, drug discovery strategies have evolved dramatically in response to continual advances in technology platforms supporting high-throughput screening, structure-based drug design, new therapeutic modalities, and increasingly more complex and disease-relevant cell-based and in vivo preclinical models of disease. Advances in biological laboratory capabilities in drug discovery are complemented by significant developments in bioinformatics and computational approaches for integrating large complex datasets. Bioinformatic and computational analysis of integrated molecular, pathway network and phenotypic datasets enhance multiple stages of the drug discovery process and support more informative drug target hypothesis generation and testing. In this chapter we discuss and present examples demonstrating how the latest advances in RPPA complement and integrate with other emerging drug screening platforms to support a new era of more informative and evidence-led drug discovery strategies.

Chemical Proteomic Approaches Targeting Cancer Stem Cells: A Review of Current Literature

Cancer stem cells (CSCs) have been proposed as central drivers of tumor initiation, progression, recurrence, and therapeutic resistance. Therefore, identifying stem-like cells within cancers and understanding their properties is crucial for the development of effective anticancer therapies. Recently, chemical proteomics has become a powerful tool to efficiently determine protein networks responsible for CSC pathophysiology and comprehensively elucidate molecular mechanisms of drug action against CSCs. This review provides an overview of major methodologies utilized in chemical proteomic approaches. In addition, recent successful chemical proteomic applications targeting CSCs are highlighted. Future direction of potential CSC research by integrating chemical genomic and proteomic data obtained from a single biological sample of CSCs are also suggested in this review.

Back to the Future - Part 2. Post-mortem assessment and evolutionary role of the bio-medicolegal sciences

Part 2 of the review "Back to the Future" is dedicated to the evolutionary role of the bio-medicolegal sciences, reporting the historical profiles, the state of the art, and prospects for future development of the main related techniques and methods of the ancillary disciplines that have risen to the role of "autonomous" sciences, namely, Genetics and Genomics, Toxicology, Radiology, and Imaging, involved in historic synergy in the "post-mortem assessment," together with the mother discipline Legal Medicine, by way of its primary fundament, universally denominated as Forensic Pathology. The evolution of the scientific research and the increased accuracy of the various disciplines will be oriented towards the elaboration of an "algorithm," able to weigh the value of "evidence" placed at the disposal of the "justice system" as real truth and proof.

Proteomics insights into DNA damage response and translating this knowledge to clinical strategies

Genomic instability is a critical driver in the process of cancer formation. At the same time, inducing DNA damage by irradiation or genotoxic compounds constitutes a key therapeutic strategy to kill fast-dividing cancer cells. Sensing of DNA lesions initiates a complex set of signalling pathways, collectively known as the DNA damage response (DDR). Deciphering DDR signalling pathways with high-throughput technologies could provide insights into oncogenic transformation, metastasis formation and therapy responses, and could build a basis for better therapeutic interventions in cancer treatment. Mass spectrometry (MS)-based proteomics emerged as a method of choice for global studies of proteins and their posttranslational modifications (PTMs). MS-based studies of the DDR have aided in delineating DNA damage-induced signalling responses. Those studies identified changes in abundance, interactions and modification of proteins in the context of genotoxic stress. Here we review ground-breaking MS-based proteomics studies, which analysed changes in protein abundance, protein-protein and protein-DNA interactions, phosphorylation, acetylation, ubiquitylation, SUMOylation and Poly(ADP-ribose)ylation (PARylation) in the DDR. Finally, we provide an outlook on how proteomics studies of the DDR could aid clinical developments on multiple levels.

Utilizing the Luminex Magnetic Bead-Based Suspension Array for Rapid Multiplexed Phosphoprotein Quantification

The study of protein phosphorylation is critical for the advancement of our understanding of cellular responses to external and internal stimuli. Phosphorylation, the addition of phosphate groups, most often occurs on serine, threonine, or tyrosine residues due to the action of protein kinases. This structural change causes the protein to become activated (or deactivated) and enables it in turn to initiate the phosphorylation of other proteins in a cascade, eventually causing cell-wide changes such as apoptosis, cell differentiation, and growth (among others). Cellular phosphoprotein pathway dysregulation by mutation or chromosomal instability can often give the cell a selective advantage and lead to cancer. Obviously the understanding of these systems is of huge importance to the field of oncology.This chapter aims to provide a "how to" manual for one such technology, the 96-well plate-based xMAP® platform from Luminex. The system utilizes antibody-bound free-floating magnetic spheres which can easily be removed from suspension via magnetization. There are 100 unique bead sets (moving up to 500 bead sets for the most recent system) identified by the ratio of two dyes coating the microsphere. Each bead set is conjugated to a specific antibody which allows targeted protein extraction from low-concentration lysate solution. Biotinylated secondary antibodies/streptavidin-R-phycoerythrin (SAPE) complexes provide the quantification mechanism for the phosphoprotein of interest.

Semiconductor technology in protein kinase research and drug discovery: sensing a revolution

Since the discovery of protein kinase activity in 1954, close to 600 kinases have been discovered that have crucial roles in cell physiology. In several pathological conditions, aberrant protein kinase activity leads to abnormal cell and tissue physiology. Therefore, protein kinase inhibitors are investigated as potential treatments for several diseases, including dementia, diabetes, cancer and autoimmune and cardiovascular disease. Modern semiconductor technology has recently been applied to accelerate the discovery of novel protein kinase inhibitors that could become the standard-of-care drugs of tomorrow. Here, we describe current techniques and novel applications of semiconductor technologies in protein kinase inhibitor drug discovery.

Early signaling dynamics of the epidermal growth factor receptor

Despite extensive study of the EGF receptor (EGFR) signaling network, the immediate posttranslational changes that occur in response to growth factor stimulation remain poorly characterized; as a result, the biological mechanisms underlying signaling initiation remain obscured. To address this deficiency, we have used a mass spectrometry-based approach to measure system-wide phosphorylation changes throughout the network with 10-s resolution in the 80 s after stimulation in response to a range of eight growth factor concentrations. Significant changes were observed on proteins far downstream in the network as early as 10 s after stimulation, indicating a system capable of transmitting information quickly. Meanwhile, canonical members of the EGFR signaling network fall into clusters with distinct activation patterns. Src homology 2 domain containing transforming protein (Shc) and phosphoinositol 3-kinase (PI3K) phosphorylation levels increase rapidly, but equilibrate within 20 s, whereas proteins such as Grb2-associated binder-1 (Gab1) and SH2-containing tyrosine phosphatase (SHP2) show slower, sustained increases. Proximity ligation assays reveal that Shc and Gab1 phosphorylation patterns are representative of separate timescales for physical association with the receptor. Inhibition of phosphatases with vanadate reveals site-specific regulatory mechanisms and also uncovers primed activating components in the network, including Src family kinases, whose inhibition affects only a subset of proteins within the network. The results presented highlight the complexity of signaling initiation and provide a window into exploring mechanistic hypotheses about receptor tyrosine kinase (RTK) biology.

Drafting the proteome landscape of myeloid-derived suppressor cells

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of cells that are defined by their myeloid origin, immature state, and ability to potently suppress T-cell responses. They regulate immune responses and the population significantly increases in the tumor microenvironment of patients with glioma and other malignant tumors. For their study, MDSCs are usually isolated from the spleen or directly of tumors from a large number of tumor-bearing mice although promising ex vivo differentiated MDSC production systems have been recently developed. During the last years, proteomics has emerged as a powerful approach to analyze MDSCs proteomes using shotgun-based mass spectrometry (MS), providing functional information about cellular homeostasis and metabolic state at a global level. Here, we will revise recent proteome profiling studies performed in MDSCs from different origins. Moreover, we will perform an integrative functional analysis of the protein compilation derived from these large-scale proteomic studies in order to obtain a comprehensive view of MDSCs biology. Finally, we will also discuss the potential application of high-throughput proteomic approaches to study global proteome dynamics and post-translational modifications (PTMs) during the differentiation process of MDSCs that will greatly boost the identification of novel MDSC-specific therapeutic targets to apply in cancer immunotherapy.

Direct Interface between Digital Microfluidics and High Performance Liquid Chromatography-Mass Spectrometry

We introduce an automated method to facilitate in-line coupling of digital microfluidics (DMF) with HPLC-MS, using a custom, 3D-printed manifold and a custom plugin to the popular open-source control system, DropBot. The method was designed to interface directly with commercial autosamplers (with no prior modification), suggesting that it will be widely accessible for end-users. The system was demonstrated to be compatible with samples dissolved in aqueous buffers and neat methanol and was validated by application to a common steroid-labeling derivatization reaction. We propose that the methods described here will be useful for a wide range of applications, combining the automated sample processing power of DMF with the resolving and analytical capacity of HPLC-MS.

Proteomics in pharmaceutical research and development

In the 20 years since its inception, the evolution of proteomics in pharmaceutical industry has mirrored the developments within academia and indeed other industries. From initial enthusiasm and subsequent disappointment in global protein expression profiling, pharma research saw the biggest impact when relating to more focused approaches, such as those exploring the interaction between proteins and drugs. Nowadays, proteomics technologies have been integrated in many areas of pharmaceutical R&D, ranging from the analysis of therapeutic proteins to the monitoring of clinical trials. Here, we review the development of proteomics in the drug discovery process, placing it in a historical context as well as reviewing the current status in light of the contributions to this special issue, which reflect some of the diverse demands of the drug and biomarker pipelines.

Mass spectrometry in leukemia research and treatment

Mass spectrometry (MS) is a complex analytical chemistry tool that allows qualitative and quantitative assessments of the components of complex chemical compounds. Applications of MS in medicine include the identification and quantification of drugs and metabolites; identification of proteins, biopolymers and disease markers and investigation of differential protein expression and proteins altered by mutations and/or post-translational changes. A variety of MS methods and technologies now play valuable and expanding roles in the diagnosis and monitoring of acute leukemia, as well as in identification of therapeutic targets and biomarkers, drug discovery, and other important areas of leukemia research. The objective of this review is to present a clinically oriented review of the roles of MS in the research, diagnosis and therapy of acute leukemia.

Analytical challenges translating mass spectrometry-based phosphoproteomics from discovery to clinical applications

Phosphoproteomics is the systematic study of one of the most common protein modifications in high throughput with the aim of providing detailed information of the control, response, and communication of biological systems in health and disease. Advances in analytical technologies and strategies, in particular the contributions of high-resolution mass spectrometers, efficient enrichments of phosphopeptides, and fast data acquisition and annotation, have catalyzed dramatic expansion of signaling landscapes in multiple systems during the past decade. While phosphoproteomics is an essential inquiry to map high-resolution signaling networks and to find relevant events among the apparently ubiquitous and widespread modifications of proteome, it presents tremendous challenges in separation sciences to translate it from discovery to clinical practice. In this mini-review, we summarize the analytical tools currently utilized for phosphoproteomic analysis (with focus on MS), progresses made on deciphering clinically relevant kinase-substrate networks, MS uses for biomarker discovery and validation, and the potential of phosphoproteomics for disease diagnostics and personalized medicine.

Signaling networks in MS: a systems-based approach to developing new pharmacological therapies

The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.

Integrating phosphoproteomics in systems biology

Phosphorylation of serine, threonine and tyrosine plays significant roles in cellular signal transduction and in modifying multiple protein functions. Phosphoproteins are coordinated and regulated by a network of kinases, phosphatases and phospho-binding proteins, which modify the phosphorylation states, recognize unique phosphopeptides, or target proteins for degradation. Detailed and complete information on the structure and dynamics of these networks is required to better understand fundamental mechanisms of cellular processes and diseases. High-throughput technologies have been developed to investigate phosphoproteomes in model organisms and human diseases. Among them, mass spectrometry (MS)-based technologies are the major platforms and have been widely applied, which has led to explosive growth of phosphoproteomic data in recent years. New bioinformatics tools are needed to analyze and make sense of these data. Moreover, most research has focused on individual phosphoproteins and kinases. To gain a more complete knowledge of cellular processes, systems biology approaches, including pathways and networks modeling, have to be applied to integrate all components of the phosphorylation machinery, including kinases, phosphatases, their substrates, and phospho-binding proteins. This review presents the latest developments of bioinformatics methods and attempts to apply systems biology to analyze phosphoproteomics data generated by MS-based technologies. Challenges and future directions in this field will be also discussed.

Systems toxicology: from basic research to risk assessment

Systems Toxicology is the integration of classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Society demands increasingly close scrutiny of the potential health risks associated with exposure to chemicals present in our everyday life, leading to an increasing need for more predictive and accurate risk-assessment approaches. Developing such approaches requires a detailed mechanistic understanding of the ways in which xenobiotic substances perturb biological systems and lead to adverse outcomes. Thus, Systems Toxicology approaches offer modern strategies for gaining such mechanistic knowledge by combining advanced analytical and computational tools. Furthermore, Systems Toxicology is a means for the identification and application of biomarkers for improved safety assessments. In Systems Toxicology, quantitative systems-wide molecular changes in the context of an exposure are measured, and a causal chain of molecular events linking exposures with adverse outcomes (i.e., functional and apical end points) is deciphered. Mathematical models are then built to describe these processes in a quantitative manner. The integrated data analysis leads to the identification of how biological networks are perturbed by the exposure and enables the development of predictive mathematical models of toxicological processes. This perspective integrates current knowledge regarding bioanalytical approaches, computational analysis, and the potential for improved risk assessment.