How Multi-Omics is Transforming Drug Development
In the past few decades, the biotechnology field has witnessed remarkable advancements, particularly with the development of multi-omics technologies. These advancements have provided drug development with more comprehensive and systematic research tools. From genomics to proteomics, transcriptomics, and metabolomics, multi-omics technologies have introduced novel perspectives to the field of drug development. These technologies enable researchers to gain deeper and more comprehensive insights into the molecular mechanisms of diseases, identify more precise drug targets, enhance the efficiency of candidate compound screening, and accelerate the entire drug development process.
Multi-Omics in Target Discovery
The initial stages of the drug development process require a clear understanding of molecular/cellular targets and the mechanisms of action of lead compounds. Multi-omics technologies play a crucial role in target discovery by providing a comprehensive view of molecular interactions within biological systems. By integrating datasets from genomics, proteomics, metabolomics, and other omics disciplines, researchers can identify potential drug targets with high precision and accuracy.
Proteomics Approaches
Chemical proteomics is a proteome-wide method that focuses on the identification and quantification of proteins. It involves monitoring specific interactions between molecular probes and active small molecules, thereby discovering target proteins of natural products—a sub-discipline known as sub-proteomics. The construction of molecular probes using synthetic chemistry methods is central to chemical proteomics. Target identification strategies in chemical proteomics can be classified into two categories:
Compound-Centric Chemical Proteomics (CCCP): This strategy is based on the structure of natural products and affinity-based probes, which include traditional immobilized probes, bioorthogonal linkage probes, and photoaffinity labeling (PAL) probes.
Activity-Based Proteomics (ABPP): This approach focuses on the activity or function associated with specific groups in natural products. It employs competitive strategies to screen protein targets and their active sites.
Figure 1. Molecular structure of CCCP used for natural product target discovery.
Biophysical proteomics techniques, such as Drug Affinity Responsive Target Stability (DARTS), Stability of Proteins from Rates of Oxidation (SPROX), Cellular Thermal Shift Assay (CETSA), Thermal Proteome Profiling (TPP), and phage display methods, have been utilized to identify drug targets without requiring chemical modifications of the compounds. These techniques offer robust tools for the discovery of natural product targets, providing critical insights into the molecular interactions within biological systems.
Metabolomics in Drug Target Discovery
Metabolomics serves as an indispensable indirect and auxiliary method in the discovery of drug targets. This field primarily utilizes Nuclear Magnetic Resonance (NMR) spectroscopy or Mass Spectrometry (MS) to simultaneously analyze low molecular weight metabolites (less than 1500 Da, including amino acids, sugars, and lipids) within cells, organs, or entire organisms. The application of metabolomics in drug research has been exemplified by the identification of drug targets for ivosidenib (Tibsovo®) and enasidenib (Idhifa®), two novel clinically approved therapies for the treatment of relapsed or refractory acute myeloid leukemia (AML) associated with isocitrate dehydrogenase 1 (IDH1) and isocitrate dehydrogenase 2 (IDH2) mutations, respectively. These successful drug discoveries underscore the critical role of metabolomics in the identification and validation of drug targets.
Multi-Omics Integration in Complex Disease Analysis
The complexity of pathophysiological processes underlying disease development often necessitates the simultaneous evaluation of multiple biological layers. The analysis of a single omics subset may result in a skewed, biased, and incomplete representation of the underlying biological landscape. Therefore, it is imperative to integrate these complex omics datasets.
Methods for Multi-Omics Data Integration
Several methodologies and tools have been developed for the integration of multi-omics data, including conceptual integration, statistical integration, model-based integration, and network-based integration. Multi-omics data analysis aims to extract meaningful information or knowledge from omics datasets to address specific research questions or hypotheses.
Applications in Drug Target Discovery and Validation
Multi-omics approaches facilitate drug target discovery and validation through the following mechanisms:
Revealing Molecular Signatures: By leveraging omics data at different biomolecular levels, researchers can uncover the molecular signatures or profiles associated with diseases and drug responses.
Constructing Molecular Networks: The integration of omics data enables the construction of molecular networks or pathways that depict disease mechanisms and drug interactions.
Prioritizing Drug Targets: Potential drug targets can be prioritized based on their correlation with or significance to disease and drug response, using omics data across different biomolecular levels.
Experimental and Computational Validation: Selected drug targets can be validated using experimental methods or computational models to assess the impact of modulating these targets on disease progression and drug efficacy.
Figures 2: Integration of multi-omics data for drug discovery.
Mechanistic Insights into Drug Actions through Multi-Omics Analysis
In the realm of drug discovery and development, understanding the mechanisms of action (MoA) of therapeutic compounds is of paramount importance. Multi-omics technologies, encompassing genomics, transcriptomics, proteomics, metabolomics, and other omics fields, have revolutionized the ability to elucidate complex molecular pathways influenced by drug compounds. This integrative approach not only aids in the identification of drug targets but also provides a comprehensive understanding of the mechanisms by which these targets modulate cellular processes, thus enhancing the efficacy and safety of pharmacological interventions.
Elucidation of Drug Mechanisms through Multi-Omics Technologies
The application of multi-omics techniques enables a holistic analysis of drug mechanisms, spanning molecular to cellular levels. By integrating data from diverse omics platforms, researchers can uncover intricate molecular pathways impacted by drug compounds, thereby illuminating drug targets, modes of action, and potential side effects.
Mechanistic Studies in Drug Development
Through the comprehensive application of multi-omics technologies, it is possible to thoroughly decipher the mechanisms of action of drugs. This includes analyzing molecular interactions from gene expression to metabolic products, which provides a deeper understanding for both drug development and clinical application. For instance, the study conducted by Shengchuan Bao et al. aimed to elucidate the anti-fibrotic mechanism of action of Ophiopogon D (OP-D), a compound derived from Ophiopogon japonicus, using a correlation analysis between transcriptomics and proteomics data. The results demonstrated that OP-D could inhibit the AKT/GSK3β axis, attenuate epithelial-mesenchymal transition (EMT) and excessive extracellular matrix (ECM) deposition, promote apoptosis of pulmonary fibroblasts, and prevent their differentiation into myofibroblasts.
Implications for Drug Safety and Efficacy
The integration of multi-omics data not only facilitates the identification of drug targets but also aids in the assessment of drug safety and efficacy. By elucidating the molecular mechanisms underlying drug actions, multi-omics approaches enable the prediction and mitigation of potential adverse effects, thereby optimizing therapeutic strategies. The ability to map out detailed molecular networks affected by drugs provides invaluable insights into the modulation of biological systems, ultimately leading to the development of more effective and safer therapeutics.
Figure 3: Correlation analysis between transcriptomics and proteomics.
Mechanistic Studies of Drug Toxicity
The rapid expansion of the pharmaceutical market necessitates the development of viable and effective methods for determining drug toxicity. Omics technologies, which encompass genomics, transcriptomics, proteomics, and metabolomics, offer the capability to capture molecular perturbations within cells and condense these into various types of omics data. Consequently, the analysis of omics data provides a means to decode the underlying mechanisms of drug-induced toxicity.
Elucidation of Drug-Induced Toxicity Mechanisms
Through the application of omics-based analytical techniques, it is possible to gain a deeper understanding of the biological pathways perturbed in the mechanisms of drug-induced liver injury (DILI) compared to traditional in vitro and multiplex assays. These perturbations may involve genetic susceptibility or alterations in gene expression following drug administration, as well as disturbances in biochemical pathways identified from changes in protein and metabolite abundance.
The figure below illustrates the multi-omics workflow using in vitro cell models for the analysis of transcriptomics, proteomics, and metabolomics data, providing insights into the biological pathways affected by drug-induced liver injury (DILI). This integrative approach facilitates the identification of molecular signatures associated with DILI, enabling the dissection of complex biological networks implicated in drug toxicity.
Figure 4: Multi-omics workflow in in vitro cell models.
Multi-Omics Approaches in Biomarker Discovery
Biomarker discovery represents a complex and multifaceted field of research, wherein multi-omics approaches offer powerful tools and technological support. By employing these methodologies, researchers can identify potential biomarkers associated with various aspects of disease, including diagnosis, prognosis, and monitoring of disease progression.
Disease Diagnosis
Ovarian cancer (OC) is the eighth leading cause of cancer-related mortality among women worldwide, necessitating the urgent identification of sensitive biomarkers to detect high-risk populations and facilitate early diagnosis. This early detection is crucial for timely prevention and treatment. Multi-omics approaches provide a comprehensive understanding of OC and contribute to the discovery of biomarkers for early diagnosis.
To identify biomarkers for the early detection of ovarian cancer, researchers can utilize an integrative approach involving genomics/transcriptomics sequencing and proteomics/metabolomics mass spectrometry. These techniques enable the analysis of tissues and body fluids—such as blood, ascites, uterine lavage, cervical smears, and urine—across multiple omics platforms. Through this multi-platform analysis, potential biomarkers can be identified, offering new avenues for the early diagnosis of ovarian cancer.
Figure 5: Multi-omics approaches for biomarker discovery in the early diagnosis of ovarian cancer.
Disease Prognosis and Biomarker Discovery
Prognostic biomarkers are critical tools for predicting the likelihood of disease recurrence or progression in patients. By integrating diverse technologies, such as proteomics, metabolomics, and genomics, multi-omics approaches offer a comprehensive and systematic analysis of molecular changes occurring during disease progression. This enables the identification of biomarkers that are associated with disease prognosis.
Integration of Multi-Omics Approaches for Prognostic Biomarker Discovery
To address the challenges of accurately identifying prognostic biomarkers for cancer, a methodology was proposed by Ning Zhao and colleagues, which integrates multi-omics data, including DNA methylation (DM), gene expression (GE), somatic copy number variations, and microRNA expression (ME). This approach employs a ranking method based on an "expectation score" to prioritize genes.
Application to Cancer Prognosis
Using this integrative approach, a list of prognostic genes was identified across 13 types of cancer. This methodology provides a theoretical foundation and reliable prognostic biomarkers that are essential for cancer diagnosis, prognosis, and therapeutic research.
In summary, the integration of multi-omics data facilitates a more accurate and comprehensive identification of prognostic biomarkers. The approach developed by Zhao et al. exemplifies the power of multi-omics methodologies in overcoming the challenges of cancer prognosis. These biomarkers serve as invaluable tools in clinical practice, aiding in the prediction of disease outcomes and the development of personalized therapeutic strategies.
Comprehensive Multi-Omics Solutions for Cutting-Edge Drug Development
Multi-omics technologies have revolutionized target discovery, elucidation of drug mechanisms, and the advancement of biomarker identification, establishing themselves at the forefront of modern drug development. Creative Proteomics is dedicated to providing industry-leading multi-omics services in the fields of proteomics and metabolomics, offering comprehensive solutions tailored to support your research endeavors. Our expertise spans a wide array of research areas, including biomarker discovery, gut microbiome studies, exosome research, and disease-related investigations, ensuring that we meet the multidisciplinary demands of your work. Our comprehensive suite of multi-omics services includes:
Our Strengths:
- Comprehensive Data Integration: We integrate various omics datasets to provide a holistic view of biological processes.
- Diverse Solution Options: Advanced technology platforms and multiple proteomics strategies such as SILAC, iTRAQ, and label-free approaches.
- Broad Metabolomics Targeting: Coverage of a wide range of metabolite classes, including lipidomics, amino acid profiling, bile acids, and more.
- Efficient One-Stop Service: A streamlined solution encompassing everything from experimental design to data delivery.
References
- Zhang H W, Lv C, Zhang L J, et al. Application of omics-and multi-omics-based techniques for natural product target discovery. Biomedicine & Pharmacotherapy, 2021, 141: 111833.2.
- Ivanisevic T, Sewduth R N. Multi-omics integration for the design of novel therapies and the identification of novel biomarkers. Proteomes, 2023, 11(4): 34.3.
- Bao S, Chen T, Chen J, et al. Multi-omics analysis reveals the mechanism of action of ophiopogonin D against pulmonary fibrosis. Phytomedicine, 2023, 121: 155078.4.
- Nguyen N, Jennen D, Kleinjans J. Omics technologies to understand drug toxicity mechanisms. Drug Discovery Today, 2022, 27(11): 103348.5.
- Kralj T, Brouwer K L R, Creek D J. Analytical and omics-based advances in the study of drug-induced liver injury. Toxicological Sciences, 2021, 183(1): 1-13.6.
- Xiao Y, Bi M, Guo H, et al. Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis. EBioMedicine, 2022, 79.7.
- Zhao N, Guo M, Wang K, et al. Identification of pan-cancer prognostic biomarkers through integration of multi-omics data. Frontiers in Bioengineering and Biotechnology, 2020, 8: 268.
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