# Internal Medicine/Precision Medicine

Introduction to Precision Medicine
Precision medicine represents a paradigm shift in healthcare, aimed at tailoring medical interventions to the unique characteristics of each individual patient. Unlike the traditional one-size-fits-all approach, precision medicine recognizes that genetic makeup, molecular profiles, environmental factors, and clinical presentation vary among individuals. By integrating these diverse aspects, clinicians can make more informed decisions regarding diagnosis, prognosis, and treatment.
Phenotyping and Genomic Profiling
Phenotyping forms the foundation of precision medicine, involving the meticulous examination of a patient's clinical symptoms, disease patterns, and physiological responses. Complementing this clinical information, molecular tests and functional studies provide a more comprehensive understanding, known as the endophenotype or endotype. A recent concept gaining momentum is orthogonal phenotyping, which considers multiple dimensions of patient data, including clinical, molecular, imaging, and functional features. This holistic approach enhances the ability to distinguish subtle sub-phenotypes and identify underlying disease mechanisms.
Genomic Profiling and Comprehensive Genomics
Genomic profiling plays a pivotal role in precision medicine by analyzing the genetic information encoded in an individual's DNA. Techniques like DNA sequencing, gene expression analysis, metabolomics, epigenetics, and metagenomics provide insights into various biological layers. While whole-blood DNA sequencing is versatile, many genomic features are tissue-specific. Integrating whole-blood profiles with organ-specific expression patterns bridges the gap between blood-based testing and organ-specific disease manifestations. Induced pluripotent stem cells have emerged as a strategy to study gene expression and metabolism, though challenges remain in faithfully reproducing native cell characteristics.
Time Trajectories and Precision Genotyping
Precision genotyping takes advantage of temporal changes in gene expression patterns and phenotypes over time. Monitoring these trajectories provides dynamic insights into disease progression and treatment responses. Although acquiring timed samples from patients poses challenges, advancements in genomics technology are gradually mitigating these limitations. In the context of cancer, frequent sampling and genomic analysis have revolutionized treatment strategies. However, interpreting the complex data generated by these analyses remains a significant challenge, particularly in solid tumors with intricate interactions between malignant and stromal cells.
Therapeutic Target Identification and Precision Drug Repurposing
Identifying suitable therapeutic targets is a central objective of precision medicine. This involves analyzing gene expression, binding partner proteins, and functional pathways. Constructing protein-protein interactomes and disease modules aids in pinpointing drug targets with precision. Network-based approaches enable the strategic repurposing of existing drugs by assessing their proximity to disease modules. Notably, multicentric Castleman's disease serves as an example of precision medicine's potential, as the activation of the PI3K/Akt/mTOR pathway led to successful trials with the drug sirolimus.
Examples of Precision Medicine Applications
The evolution of precision medicine can be observed through historical examples. In the pregenomic era, heart failure treatment exemplified precision medicine by tailoring therapies based on pathophysiologic phenotypes. Genomic characterization has redefined disease classifications, as seen in conditions like pulmonary arterial hypertension and dementias. The identification of autoantibodies has refined therapeutic approaches in diseases such as neuromyelitis optica and myasthenia gravis. In the context of cancer, the discovery of common oncogenic pathways has paved the way for targeted therapies that revolutionize treatment outcomes.
Challenges and the Future of Precision Medicine
The success of precision medicine relies on addressing several challenges. Handling vast amounts of phenotypic and genomic data demands robust storage and analytical capabilities. Additionally, integrating diverse phenotypic data sources, including personal device data, environmental exposures, and social interactions, presents a complex task that requires standardization and harmonization efforts. One critical challenge is determining the minimal dataset required for effective prediction of treatment outcomes and responses. Machine learning and artificial intelligence play an indispensable role in deriving insights from complex data sets. As precision medicine advances, the boundaries for what is considered "precise" may not be rigid, but the predictions should be practically applicable in real-world clinical settings while remaining cost-effective.