Department of Pharmacology, K. V. N. Naik S. P. Sanstha’s, Institute of Pharmaceutical Education & Research, Canada Corner, Nashik, 422002, Maharashtra, India
The advent of precision medicine has instigated a paradigm shift in disease treatment, emphasizing the customization of therapies based on individual molecular profiles. Central to this transformation is the integration of multi-omics data encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and more to elucidate the complex biological networks that influence drug response. This review synthesizes the current landscape of multi-omics approaches in precision pharmacology, concentrating on their application in understanding and predicting variability in therapeutic efficacy and toxicity. We commence by examining the individual omics layers pertinent to pharmacodynamics and pharmacokinetics, highlighting how their integration yields synergistic insights that surpass the limitations of single-omics strategies. The challenges posed by heterogeneous data types, technical variability, and integration complexity are critically assessed. In response, we evaluate computational methodologies developed to manage and analyse multi-omics datasets, including advanced machine learning and AI-driven platforms. Tools such as iCluster, MOFA, and DeepMO are discussed for their role in enhancing the accuracy of drug response predictions. We underscore key case studies demonstrating the clinical impact of multi-omics, such as in EGFR-targeted therapy and BRCA-mutated cancers, and explore emerging areas like the role of the microbiome and single-cell multi-omics in revealing intra-tumour heterogeneity. Finally, we address ethical considerations, data privacy, and the necessity for standardization. This review envisions a future where multi-omics-guided precision medicine optimizes drug efficacy and democratizes personalized care across diverse populations.
Precision medicine, a transformative shift in healthcare, aspires to tailor medical treatment to each patient's individual characteristics. Central to this approach is the understanding that drug responses are not universally predictable but are modulated by a constellation of genetic, molecular, environmental, and lifestyle factors. Despite the promise of “the right drug for the right patient,” inter-individual variability in drug efficacy and adverse reactions continues to challenge clinicians. Bridging this knowledge gap necessitates a deeper, more holistic exploration of the biological determinants of therapeutic response[1]. Enter the era of multi-omics, an integrative framework that harnesses diverse layers of molecular information genomics, transcriptomics, proteomics, metabolomics, and epigenomics to provide a comprehensive view of biological systems. Each omics layer captures a distinct facet of cellular function: DNA mutations and structural variants (genomics), mRNA expression profiles (transcriptomics), protein abundance and modifications (proteomics), metabolite fluxes (metabolomics), and regulatory changes such as DNA methylation or histone modification (epigenomics). Together, they constitute a high-resolution map of the molecular landscape influencing drug behaviour[2]. While each individual omics modality has yielded valuable insights, the integration of these data types holds the greatest promise for understanding drug mechanisms, identifying biomarkers of response or resistance, and uncovering new therapeutic targets. This multidimensional approach enables the dissection of complex drug response phenotypes that single-layer analyses often oversimplify or overlook[3]. In recent years, technological advancements have exponentially increased the throughput and affordability of omics profiling, enabling the generation of massive datasets across populations and disease states. However, these developments have also introduced computational and interpretive challenges. Multi-omics data are inherently high-dimensional, noisy, and heterogeneous, requiring sophisticated algorithms and robust statistical frameworks for effective integration and interpretation[4]. This review aims to critically evaluate the current state of multi-omics in drug response mapping within the context of precision medicine. We begin by dissecting the unique contributions of each omics layer to pharmacological understanding and then delve into the challenges of integrating these diverse data sources. We examine computational strategies—ranging from early data concatenation models to advanced AI and network-based frameworks—that facilitate the extraction of meaningful patterns from complex datasets. In addition, we explore the emerging applications of multi-omics in precision oncology, pharmacogenomics, microbiome research, and single-cell biology, showcasing case studies where integrative omics has led to actionable clinical insights. We also address the ethical, legal, and social considerations intrinsic to handling sensitive multi-omics data, particularly in the context of data sharing and patient privacy. Ultimately, this review aspires to provide a comprehensive, yet critical, examination of how multi-omics reshapes drug development and therapy personalisation. By highlighting current innovations and unmet needs, we aim to chart a roadmap toward a future where multi-omics enhances our mechanistic understanding of drugs and accelerates the delivery of safer, more effective treatments for all patients.
Understanding the molecular determinants of drug response requires dissecting multiple layers of biological regulation. Each omics layer contributes unique insights into how drugs interact with the human system. When integrated, these layers offer a synergistic and mechanistic understanding that can drive personalised treatment strategies.
Genomics: Blueprint of Drug Response
Genomic variation plays a foundational role in drug efficacy and toxicity. Germline polymorphisms in drug-metabolising enzymes, such as CYP2C9, CYP2D6, and TPMT, can significantly affect pharmacokinetics, leading to overexposure or underexposure to therapeutic agents. For example, polymorphisms in CYP2C19 influence the activation of clopidogrel, an antiplatelet agent, resulting in poor therapeutic outcomes in specific genotypes[5]. In oncology, somatic mutations in genes like EGFR, KRAS, or BRAF have emerged as predictive biomarkers for targeted therapies. For instance, non-small cell lung cancer patients harbouring EGFR mutations show robust responses to tyrosine kinase inhibitors such as erlotinib and gefitinib. Thus, genomic profiling has become a clinical cornerstone in cancer precision medicine[6].
Transcriptomics: Functional Readout of Gene Activity
Transcriptomics provides a dynamic snapshot of gene expression under specific physiological or pathological conditions. Unlike the static nature of the genome, transcriptomic changes can reflect immediate cellular responses to drugs. Pharmacotranscriptomics studies have revealed how expression changes in transporter or efflux pump genes, like ABCB1 or SLCO1B1, can modulate drug distribution and clearance[7]. Gene expression profiles have also been used to classify responders versus non-responders to chemotherapy. The development of the Oncotype DX and MammaPrint assays, which predict breast cancer recurrence risk and chemotherapy benefit, underscores the translational power of transcriptomic data[8].
Proteomics: Bridging Genotype and Phenotype
Proteins, the primary executors of cellular function, represent direct drug targets and mediators of pharmacologic effects. Quantitative proteomics measures protein abundance, turnover, and post-translational modifications (PTMs) such as phosphorylation or ubiquitination—key drug activity and resistance regulators[9]. For example, phosphorylation of the estrogen receptor can influence sensitivity to hormonal therapies in breast cancer. Mass spectrometry-based proteomics has also identified differential protein expression patterns that correlate with cisplatin resistance in ovarian cancer, facilitating predictive biomarker discovery[10].
Metabolomics: Snapshot of Biochemical Activity
Metabolomics captures the small-molecule metabolites that serve as substrates, intermediates, and products of cellular metabolism. Since drugs are metabolised into active or inactive compounds, metabolomics directly informs on drug bioavailability and toxicity[11]. For instance, metabolomic profiling has identified specific metabolic signatures associated with doxorubicin-induced cardiotoxicity, potentially allowing for early risk stratification. Additionally, changes in tryptophan metabolism have been implicated in immune checkpoint therapy responses, highlighting metabolomics as a tool for immunotherapy optimisation[12].
Epigenomics: Regulatory Modulators of Drug Response
Epigenetic modifications such as DNA methylation and histone acetylation dynamically regulate gene expression without altering the DNA sequence. Aberrant methylation of promoter regions can silence tumour suppressor genes, contributing to drug resistance. For example, hypermethylation of the MGMT gene is associated with increased sensitivity to temozolomide in glioblastoma[13]. Epigenomic drugs like DNA methyltransferase inhibitors (e.g., azacitidine) are now used to reverse resistant phenotypes in hematologic malignancies, demonstrating the therapeutic potential of targeting the epigenome[14].
Synergy Across Omics Layers:
Individually, each omics domain offers partial insights into drug response. However, these data layers can reveal complex regulatory networks that control cellular fate under pharmacologic intervention. For instance, integrating epigenomic and transcriptomic data can clarify whether gene silencing arises from DNA methylation or transcriptional repression. Similarly, linking proteomic and metabolomic data can illuminate how altered enzyme levels affect drug metabolism[15]. Thus, multi-omics integration represents a paradigm shift from single-biomarker models to systems-level therapeutic efficacy and safety predictors.
Gargi Bachhav*, Akshat Bhamare, Sakshi Ahirrao, Samruddhi Avhad, Janhavi Bhalerao, Mukund Pache, Mapping Drug Responses Through Multi-Omics: A New Era of Bioinformatics in Precision Medicine, Int. J. Sci. R. Tech., 2025, 2 (8), 319-335. https://doi.org/10.5281/zenodo.16914606
10.5281/zenodo.16914606
