Mapping the interactions between active ingredients in drugs and the full range of proteins produced by an individual patient's genome lays the foundation for systematically identifying unknown effects of existing medications and enables the advancement of personalised pharmacological therapies.
«Some drugs have the potential to do far more than we currently realise. One of the most well-known examples is aspirin, whose pain-relieving properties have long been established. However, it was later discovered that its active ingredient, acetylsalicylic acid, also thins the blood, and it is now routinely prescribed to patients with cardiovascular issues. We believe that many widely-used drugs still hold unknown benefits. One of our key objectives is to systematically uncover these advantages, rather than relying on accidental discoveries»: these are the words of a group of scientists from the School of Life Sciences at the Technical University of Munich, as featured in their article, “Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics” (Nature Biotechnology). The article details their recent work on the application of proteomics to the study of drug mechanisms, specifically focusing on the dose-response relationship that governs the «changes in protein expression induced by specific medications».
The importance of this research is rooted in a branch of medicine that investigates the side effects of active drug ingredients to identify potential adverse reactions in patients. Based on this information, healthcare providers can determine the most appropriate and personalised treatment for each individual.
This research demands an in-depth and meticulous understanding of the cellular processes involved in drug response. But let’s break it down step by step.
TAKEAWAYS
What is proteomics and how does it contribute to diagnostics and drug research?
A branch of molecular biology, “proteomics” explores the structure, functions, and interactions of proteins, as well as their expression changes, providing a comprehensive understanding of the human body. «It is estimated that nearly one million proteins exist in the body, with the human genome coding for approximately 26,000-31,000 proteins» [source: “Proteomics: Concepts and applications in human medicine” – National Library of Medicine, 2021].
About protein coding by the genome, the term “proteome” «refers to the complete set of proteins encoded by the genome».
«As with other “omic” sciences, such as genomics (studying genes) or transcriptomics (studying RNA), proteomics examines the entirety of the proteins synthesised by cells» [source: “Proteomics: The story of cells told by biomarkers” – Italian Society of General Medicine, 2018].
Together, “genomics, proteomics, transcriptomics, and metabolomics” aim to «identify, characterise, and quantify all biological molecules involved in the structure, function, and dynamics of a cell, tissue, or organism» [source: University of Illinois Urbana-Champaign].
Proteomics has many applications, the foremost being the identification of biological markers (biomarkers), which are «indicators of physiological processes, pathological conditions, or biological responses to therapeutic interventions». Through protein analysis, it is possible to detect bacterial antigens or tumour markers associated with certain cancers. «Cancer cells often produce altered proteins or proteins that are not usually expressed in those cells under normal conditions, making them valuable biomarkers» [source: Italian Society of General Medicine, 2018].
Another key application of proteomics is in drug discovery, where specific proteins associated with particular diseases are studied as potential therapeutic targets. By studying individual proteomes, personalised drugs can be developed based on a patient’s clinical history, protein structure, and protein function alterations due to the type and dosage of treatment received. «This tailored approach could significantly enhance the precision of future therapies».
Proteomics and dose-response dynamics in pharmacology
«It has long been known that drugs exert their effects in a dose-dependent manner and that most of them act on proteins» notes the team from the School of Life Sciences at the Technical University of Munich.
While the analysis of dose-response dynamics in relation to an organism’s characteristics (phenotype) is not new in pharmacology, the application of proteomics to study drug dosing is less common.
According to the team, «As far as we are aware, a systematic review of the dose-response characteristics of proteome expression changes induced by drugs has not yet been undertaken. This limits our understanding of the molecular foundations that drive and explain the phenotypic changes observed».
The novelty of their research lies in the introduction of a method to «measure the dose-response relationship in protein expression changes» triggered by specific drugs, which could serve as a new tool for precision medicine.
The decryptE method
In the field of proteomics, the team has developed a method called “decryptE”, which utilised a model based on Jurkat T cells («T-lymphoblasts typically used in medical research to study acute leukaemia»). The model was tested on 144 drugs from 16 different classes of anti-cancer treatments, most of which are already in use in oncology or awaiting approval.
«The cells were cultivated in 48-well plates and treated for eighteen hours with five doses of each drug,” the authors explain. “Cell activity and morphology were observed and assessed in parallel for all 144 drugs, using the same time intervals and doses».
After extracting the proteins, the entire proteome was measured using high-field asymmetric ion mobility spectrometry. The comprehensive drug screening took 768 hours (5.3 hours per drug) and led to the identification and quantification of 8,892 new proteins and the classification of1,133,847 dose-response curves (which are now available online for the global scientific community). These curves illustrate the mechanisms underlying the effects of active ingredients during treatment.
Drug-induced protein expression changes: key to oncology
The first model of the decryptE system deliberately reproduced a type of cancer – more specifically, blood cancer – providing a clear example of how crucial it is to understand the dose-response relationship in drug-induced proteome alterations. These processes are pivotal in oncology, as the research team points out:
«Depending on the type of cancer, the protein landscape can vary significantly. This is critical for choosing the most appropriate treatments and can also offer clues for the development of new drugs»
For example, the data gathered from the initial drug screening using this method revealed how a class of drugs known as HDAC inhibitors (Histone Deacetylase inhibitors, proteins involved in cell division), which are commonly used in cancer treatment, can weaken the human immune system. This discovery has significant implications, as it highlights how HDAC inhibitors could negatively impact cancer treatments that rely on immune system activation.
The team admits that this finding emerged serendipitously during the system’s first application, showcasing its unique capabilities compared to traditional pharmacological experiments, which are generally «designed to answer a specific, well-defined question». decryptE method, however, allows for a broader exploration of drug effects, potentially revealing unknown impacts.
Glimpses of Futures
All pharmacological therapies impact the extensive protein repertoire present in the human body. How and to what extent they do so – whether at low, moderate, or high doses, by stimulating the cell to produce new proteins or by inhibiting its activity – is not always fully understood.
The significance of the study lies in shedding light on aspects of this issue and providing a tool that enables “proteomics-based-dose-response” tests across all classes of drugs, not just anti-cancer medications.
Let us now anticipate potential future scenarios by analysing, through the STEPS matrix, the impacts that the evolution of this method (which measures the “drug dose”-“proteome response” relationship) could have from various perspectives.
S – SOCIAL: in future scenarios, the evolution of this method – moving beyond phenotypic dose-response tests based on broad organismal traits such as morphology and physiology – could be applied to other classes of drugs, including those for autoimmune or neurodegenerative diseases. In just over 5 hours of analysis, it could lead to the discovery of thousands of new molecular data points, such as protein structure, functions, interactions, and alterations. Moreover, it could yield vast amounts of quantitative information on dose-response curves. This new approach opens the door to large-scale proteome analysis, with the potential to become a standard tool in both pharmacological and clinical settings. It could support the identification of unknown benefits in existing drugs and the development of personalised therapies tailored to individual patients’ specific needs.
T – TECHNOLOGICAL: in the future, the evolution of the approach developed by the University of Munich could contribute to bringing genomics and proteomics closer together. While these fields focus on different aspects (genes and proteins, respectively), they are both integral parts of the “omic sciences”, sharing the goal of understanding the biological systems at play. By intertwining knowledge and techniques from both disciplines, this approach could help the research community refine its understanding of the connections between pathological conditions, genes, and proteins. This may lead to the discovery of new treatments, offering a holistic approach where every element is interconnected. In particular, while genomics deals with the structure, function, and mapping of genomes, with a focus on protein production by ribosomes, proteomics could in future zoom in on how proteins change, especially in response to the type and dosage of drugs.
E – ECONOMIC: on one hand, personalised medicine – towards which the decryptE method is aimed – seeks to reduce the financial waste associated with standardised treatments that are ineffective or even harmful to many patients. On the other hand, the rise of personalised treatments presents economic challenges for national healthcare systems. If, in the future, the approach developed by the German research team enables not only the exclusion of certain existing drugs based on the proteomic profile and dose-response curve of individual patients, but also the development of tailor-made therapies, financial considerations will become crucial. Healthcare systems will need to establish budget caps and find ways to make personalised therapies both accessible and affordable for everyone, without creating disparities.
P – POLITICAL: if the tool introduced by the research team gains widespread adoption in the scientific community and eventually becomes an accredited method, it could play a significant role in the European Union’s policy landscape. Since 2016, the International Consortium for Personalised Medicine (ICPerMed)has been active, established by the European Commission alongside international research funders, with the aim of «supporting the scientific basis of personalised medicine through a coordinated research approach and paving the way for personalised medicine approaches for citizens». More recently, in October 2023, the European Partnership for Personalised Medicine (EP PerMed) was launched by the Council of the European Union. This initiative aims to accelerate precision medicine research across the EU, with 49 partners participating in its development. The adoption of the decryptE method could thus align with and support these EU initiatives, further advancing the research and implementation of personalised healthcare across Europe.
S – SUSTAINABILITY: the future sustainability of the system proposed by the authors lies not only in economic sustainability, by helping avoid waste from ineffective standard therapies, but also in environmental sustainability. The use of unsuitable drugs – those not aligned with the individual patient’s proteome and therefore incapable of triggering positive and functional changes in protein expression – adds to the waste of unused medications. The management and disposal of these drugs are not always transparent or compliant with environmental regulations. In this sense, a methodology that facilitates the advancement of precision medicine could lead to a cleaner ecosystem in the long term, reducing the environmental burden caused by discarded standard medications that fail to deliver effective outcomes.
