What is systems medicine?
Advancements in systems medicine
Application of systems medicine in human diseases
Application of systems medicine in drug development
References
Further reading
Systems medicine is a novel approach that involves generating, storing, and retrieving clinical and basic research information to improve patient care. This interdisciplinary approach has become an integral part of personalized or precision medicine.
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What is systems medicine?
Systems medicine is an emerging biomedical approach wherein the computational analysis of a wide variety of clinical information is performed to improve disease diagnosis, treatment, and prognosis. It helps pave the way toward personalized medicine.
In the biomedical field, systems refer to the networks of interacting molecules or cells. For any systems medicine approach investigating a specific problem or condition, the initial step would be identifying relevant system variables, including molecules and cell types, followed by characterizing the interactions between these variables at the molecular, cellular, or physiological level.
The ultimate goal of a systems medicine approach is to evaluate the system's behavior induced by the interactions between specific variables. To better understand a specific problem or condition, it is vital to design suitable approaches that can utilize clinical and biological data to create and validate a model system and predict its behavior. Various statistical, mathematical, and computational tools are used to analyze and interpret the data generated by systems medicine approaches.
In any health condition, systems medicine approaches help to identify and understand changes at the genomic, transcriptomic, proteomic, and metabolic levels. In other words, systems medicine aims to decode dynamic interaction networks vital for manipulating a disease's clinical course.
Lee Hood on Systems Biology and Systems Medicine
Advancements in systems medicine
Systems medicine has dramatically changed the healthcare system by improving the perception of disease etiology, diagnostic accuracy, and treatment efficacy.
Mechanistic models are the central hub of systems medicine that utilize clinical data of individual patients to provide personalized predictions of outcomes in different situations. These predictions are made by systematically characterizing the systems of individual patients and, thus, cannot be generalized. These computational models can also predict the impact of environmental or lifestyle factors on disease prognosis and treatment efficacy.
In targeted therapy, mechanistic models help identify a combination of drugs, where one drug inhibits the escape routes of the other drug to maximize therapeutic efficacy.
Application of systems medicine in human diseases
Systems medicine approaches have been applied to various health conditions, including cancer and infectious diseases. Mathematical models have been constructed to evaluate how disruptions in oncogene regulatory networks may lead to neoplastic transformation and how anti-cancer drugs can perturb these networks to prevent cancer progression.
Systems-based computational analysis has been conducted on pre-existing experimental data of specific molecular interactions to identify key molecular drivers of disease development and progression.
The pathogenesis of human immunodeficiency virus (HIV) infection has been studied by developing motif discovery algorithms that specifically identify statistically enriched motifs in viral protein sequences that bind to targeted host proteins. Such sequence-based predictions are useful in identifying viral protein hotspots that target host proteins.
Systems-based computational analysis of high-throughput metabolic profiling data has made constructing metabolite networks that regulate various metabolic pathways possible. A model regulatory pathway involving 38 key metabolites has been established to understand the metabolism of serum urate, which is a strong risk factor for gout, metabolic syndrome, and cardiovascular diseases.
Application of systems medicine in drug development
One of the major challenges in drug discovery and development is drug-induced toxicities. In this area, systems medicine approaches make useful contributions by predicting drug-induced adverse events during the early phase of drug development. Such approaches help identify the factors that are collectively responsible for drug-related adversities.
The antidiabetic drug rosiglitazone is known to increase the risk of myocardial infarction in patients receiving the treatment. Using systems-based approaches, scientists have identified a secondary drug, exenatide, that regulates blood clotting processes to reduce the cardiac side effects of rosiglitazone.
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Systems medicine approaches have also shown promising outcomes in predicting drug-target interactions. Scientists have analyzed the drug-induced gene expression database using systems approaches to predict novel inhibitors against a human gene that encodes the alpha subunit of the potassium ion channel.
Drug repositioning or drug repurposing is another area of drug research wherein systems medicine has provided useful insights. Drug repositioning is a process of reanalyzing clinically approved or rejected drugs to use in different diseases.
Scientists have used systems-based analytical approaches together with novel cancer-signaling bridge network components to predict the clinical response of a wide range of clinically-approved drugs in different cancer types, including breast cancer, prostate cancer, and leukemia. Such efforts are particularly effective in minimizing the off-target effects of anti-cancer drugs.
Another potential application of systems medicine approaches is to identify novel disease networks. This research is performed by analyzing the interactions between pathogenic molecules that are aberrantly expressed in various diseases.
MicroRNA (miRNA)-mediated gene expression is considered to be the major focus area in this context. Many bioinformatics web tools have been developed to understand the degree of molecular interactions directly regulated by miRNAs.
References
Revolutionary AI predicts aging and disease from DNA patterns