What Is Precision Medicine?

Precision medicine personalizes the diagnosis, treatment, and prognosis of individual patients, informed by deep analysis of patient data and relevant population health data for genetic markers, traits, or conditions both today and projected over time. Used to better understand health trends and the specific needs of various groups of individuals, precision medicine offers insights that can help enhance health outcomes, drug development, and care plans.

Types of Precision Medicine

The five P’s of precision medicine are personalized, precise, preventive, predictive, and participatory. To achieve these outcomes, precision medicine uses techniques such as molecular diagnostics and imaging, genetic testing, next-generation genomic sequencing, and molecular dynamics.

Molecular Diagnostics and Genomic Analysis

Molecular diagnostics involves analyzing a patient’s biomarkers through genome sequencing. These genetic tests reveal information that healthcare practitioners can use to provide the most effective treatment or predict which drugs will work best for the patient.

Molecular Imaging

Molecular imaging, specifically a method called cryogenic electron microscopy (cryo-EM), is used extensively in the drug discovery process to provide insights into the 3D structures of proteins and other biological entities. Other forms of molecular imaging fall under medical imaging, including the use of various methods, such as a CT scan, with a contrast agent to visualize, characterize, and quantify biological processes in a patient.

Next-Generation Genomic Sequencing

Advancements such as next-generation sequencing have dramatically impacted genomic research. In the past, traditional sequencing technology required over a decade to deliver results, but next-generation technologies enable an entire human genome to be sequenced in approximately 40 hours. Processing time will become increasingly compressed with further technological advancement.

Molecular Dynamics

Molecular dynamics is a computational method that estimates the possible 3D configurations of chemical and biological structures and simulates their motion. It is often used in conjunction with molecular docking, which is often referred to simply as docking.

Docking is a method used to predict how effectively small molecule drug candidates will interact with a protein of interest for a particular disease. These simulation methods enable the assessment of binding interactions and ultimately allow researchers to rank the effectiveness of various drug candidates.

Quantum Mechanics

Quantum mechanics is a field of physics that can be used in precision medicine to improve the diagnosis and treatment of diseases even before symptoms appear. In treatment, quantum mechanics can model complex biological systems and customize them to an individual patient. Quantum mechanics can also help explain how the human body behaves at nanoscale, leading to the development of ultraprecise tools for diagnostics and treatments.

Benefits of Precision Medicine

The benefits of precision medicine are numerous, from accelerating results and productivity in laboratory testing to automating routine processes, which saves clinician time. Precision medicine democratizes medical knowledge and excellence by enabling diagnosis, treatment, and prognosis of individual patient conditions informed by trends in medically relevant population health data. This, in turn, can help improve aggregate patient outcomes while improving the individual patient care experience.

Precision Medicine Challenges

Given that precision medicine is based on collating and analyzing massive amounts of data, organizations need to prioritize managing diverse data sources and building the analytics infrastructure to interpret complex genetic information. Ensuring the security and anonymization of that data is also paramount in protecting patient information.

In terms of technology, it is critical to have enough compute power to run, store, and analyze massive amounts of data while supporting multiple users concurrently. This can necessitate upgrading inflexible, inefficient, or proprietary legacy infrastructure or technologies.

Additional challenges include properly developing AI to avoid potential bias that results from inadequate training data on underrepresented groups and having in-house IT technical expertise to maintain precision medicine systems.

Artificial Intelligence in Precision Medicine

Increasingly, precision medicine tools are being infused with AI capabilities to accelerate research that can lead to positive health outcomes. Data helps drive scientific discovery, and AI gives researchers more effective ways to transform data into valuable insights.

AI can support precision medicine by:

• Automating screening and laboratory processes.
• Identifying patient phenotypes to support unique healthcare needs.
• Analyzing massive amounts of data from various sources, like medical imaging, wearable devices, and electronic health records.
• Predicting disease progression and the probability of developing chronic diseases by identifying risk factors and patterns.
• Improving diagnoses through data analysis.
• Enabling more targeted and effective personalized care to improve patient outcomes.

Precision Medicine Use Cases

The use cases for precision medicine range from proactive and specific disease prevention to cancer treatment. For example, precision medicine can look at genetic variants in tumors to help inform treatment plans, or it can identify inflammatory drivers in asthma. Precision medicine is also helpful in assessing disease severity and vaccine effectiveness for conditions like COVID-19 and for matching blood transfusion recipients to help minimize potential complications. On a collective social level, public health agencies gain a more complete view of the health of individuals and groups through analyzing precision medicine data.

The Future of Precision Medicine

Several noteworthy trends in precision medicine are setting the pace for patient healthcare and emerging technologies. The proliferation of data from patient devices will continue to expand. AI will be instrumental in helping craft new healthcare models based on identified patterns and correlations. One such example of using AI-enabled precision medicine is immunotherapy, a leading area of cancer research that harnesses the power of natural immune systems to help identify and control diseases.

Clinical genomics will likely become a routine focus for clinicians as well as phenomics, the study of observable patient phenotype characteristics. This will help improve outcomes across diverse patient populations and individual patients.