Why Precision Medicine Is Replacing One-Size-Fits-All Treatment — and What It Takes to Get There

While pharma has produced some incredible blockbusters, it has largely remained unremarkable at the individual patient level, with only an estimated 30% to 50% of patients fully responding to many leading conventional drugs. One size does not, in fact, fit all. Some treatments just don’t work for some patients. This leads to a lengthy and costly cycle of trial-and-error, which can have grave consequences for some patients. Additionally, a one-size-fits-all approach is thought to waste tens of billions of dollars on ineffective treatments annually.

Moving Away from One-Size-Fits-All Medicine

A gradual shift toward stratified medicine has been happening for years. With this approach, both prophylactic care and disease treatment are guided by grouping patients into broad categories, such as by disease subtype, biomarker presence, responders versus non-responders, clinical features, and the presence of single-gene mutations linked to monogenic disease. Precision medicine aims to move patient treatment to the next level.

What is Precision Medicine?

Precision medicine is rooted in the reality that the way patients respond to treatment is heavily affected by their individual genetic variability, their complex biology, and external factors. Precision medicine personalizes each patient’s treatment using accurate diagnosis, a thorough understanding of disease mechanisms and treatment options, and a multitude of individual patient factors, such as genetic and metabolic makeup, lab and test results, environmental conditions, lifestyle, and treatment preferences. It's about delivering “the right treatment to the right patient at the right time.”

In some instances, this means narrowing down which existing treatments are best suited for a patient based on their profile and known data on how certain patients respond to certain drugs. In other cases, it might mean creating an individualized medicine, such as a personalized cell therapy or replacement tissue created just for that patient. Achieving either requires remarkable feats of data collection, analysis, predictive modeling, and clinical application.

The ultimate goal of precision medicine is to fully leverage complex multimodal data and advanced technologies so that patients receive the exact care they need—a marked difference from the historical paradigm in which only about 60% of care is deemed warranted, 30% is considered wasteful, and 10% is labeled harmful. Only by accepting and working with the personal expression of disease, taking into account the unique genotype (gene) and phenotype (physical expression) of every person, can we effectively treat diseases such as cancer.

Precision Medicine Advances

While there is still a long road ahead for precision medicine, some promising advances have been made in recent years. Here are a few highlights:

Biomarkers

There is growing development and use of biomarkers—informative biological measurements that have a proven link to specific factors, such as a patient’s disease risk, potential drug response, or likelihood of experiencing adverse reactions. These include genetic mutation markers, blood pressure and glucose levels, lab measurements, imaging results, microbial presence, and more. The US Food and Drug Administration (FDA) categorizes biomarkers into molecular, histologic, radiographic, or physiologic groups. Biomarkers help inform treatment decisions across a range of conditions, such as cancer, HIV, thromboembolism, psychiatric disorders, neurologic conditions, and infectious diseases. They are also used in early drug development and clinical trial design to reduce timelines and increase the chances of success by better targeting treatments to those patients most likely to respond.

Wearables

Wearable devices, such as smart watches, shoe inserts, wrist cuffs, chest straps, glucose monitors, and pulse oxygen meters, are increasingly used to continuously monitor patients’ biochemical, physiological, and movement endpoints outside the clinic. Data from these devices can help inform and monitor treatment choices, as well as influence preventative care measures. Collective data from wearables can also be used in the development of predictive algorithms that help guide more personalized treatment. Applications span metabolic, cardiovascular, gastrointestinal, neurologic, pulmonary and mental health; sleep and movement disorders; maternal and pre- and neonatal care; and monitoring of environmental exposures.

Rare Diseases

Precision medicine first gained ground in oncology, where genetic knowledge about specific cancers is helping to target treatments. Its use is rapidly expanding to other treatment areas—including notoriously difficult-to-treat rare diseases—thanks to better access to and application of big data, as well as increased accessibility of advanced technologies like genetic sequencing, artificial intelligence, and spatial genomics.

“TuPro” Multi-omics Tumor Profiler

Genetic characterization of tumors has become increasingly common in cancer diagnosis and treatment. The TuPro integrated treatment-recommendation protocol, developed by a Swiss research consortium, goes a step further. TuPro analyzes multiple additional “omics” data points, including a tumor’s high-resolution molecular profile and its ex vivo drug response—all within clinically relevant turnaround times. This is an example of leveraging multiple data types to inform personalized treatment decisions.

Undoubtedly, the expectations for precision medicine are high. Many believe that in addition to improving individual patient outcomes, precision medicine also holds promise to reduce overall treatment costs by eliminating ineffective or unnecessary medical care and by identifying high-risk patients who need early targeted care. A Harvard Business Review analysis projected that the elimination of unwarranted variations in medical care through use of precision medicine could potentially reduce the cost of patient management by at least 35%. However, shifting paradigms to precision medicine will necessitate overcoming many hurdles, from technology and data integration challenges, to racial bias in genetic population data, to privacy, cost and accessibility concerns, to policy change, oversight, and adoption logistics.

Multi-omic Data and Predictive Analysis in Precision Medicine

The high expectations for precision medicine were recently underscored in a report published by the US White House’s Office of Science and Technology Policy. The report is a follow-on to President Biden’s 2022 Executive Order on Advancing National Biotechnology and Biomanufacturing Innovation. Among the many “bold goals” presented in the report are two directed toward precision medicine:

• ​Collect Multi-Omic Data: In 5 years, collect multi-omic measures in large cohorts with participants from diverse populations and identify which measures are most relevant to the diagnosis and management of at least 50 diseases with high incidence and impact.
• ​Enable Personal Multi-Ome: In 20 years, develop molecular classifications for diagnosis, prevention, and treatment to address leading causes of disease-related mortality in the U.S. and make these actionable with development of the $1,000 multi-ome.

The report’s focus on multi-omics data highlights that making precision medicine a reality is not just a matter of adopting advanced technologies, like genome sequencing and biomarker screening, or performing deep assessment of clinical data, immune response, medical history, environmental factors, or behavioral traits. Instead, it’s about making sense of all that diverse data as a whole, not as individual parts. This means collating, analyzing, and connecting genomic and clinical data across huge population data sets, leveraging predictive tools like machine learning and artificial intelligence to identify trends, and then tying those insights back to the lab, where treatments are developed, and to individual patient-care settings, where treatment decisions are personalized. This is an incredibly challenging endeavor given the volume and variety of both data and experts involved in these processes.

Next week, the discussion will dive deeper into iterative multi-omic data analysis, the challenges it presents, and why adopting an end-to-end R&D data platform that integrates diverse data types is an essential precursor to success.

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