Precision medicine: a new era of treating patients, not diseases
Precision medicine: a new era of treating patients, not diseases
“The observation that patients with the same clinical diagnosis or symptoms respond differently to the same treatment has led to the development of Precision Medicine (PM), a novel therapeutic approach that relies on biological information and health data from patient tiers to develop tier-specific treatments that lead to better health outcomes.”
PM is the evolution of healthcare from a “one-cure-fits-all” strategy to the tailored development of precise medications targeted at specific individuals. It is a holistic approach to diagnosis and treatment in which traditional healthcare plays only a minor role in a patient’s health, treating each patient as an individual and using his or her unique clinical data, genomic profile, family history, environmental factors, and lifestyle to more efficiently guide diagnosis, treatment, and prognosis
Advancements in PM have been driven by the growing understanding of the biological pathways of diseases at the molecular level and the identification of novel biomarkers (a signature component detected in the blood, body fluids, or tissues, such as genes, proteins, etc.) that signal a normal or abnormal cascade of biological processes within the body. These biomarkers act as specific targets for more accurate diagnosis or more efficient treatment.
The concept of PM is not new; oncology has been the main early adopter of this approach in treatment, such as with Xalkori from Pfizer. Wider adoption of PM in other therapeutic areas was limited, owing to the high associated costs and technological limitations in data access and utilization. However, the picture is changing due to rising demands to reduce escalating healthcare costs by reducing reliance on:
- Unnecessary, non-effective medications, especially for diseases (e.g., psychiatry, oncology, and rheumatology) that are commonly associated with high costs of prescription drugs and low response rates.
- Traditional diagnostic tools, which are characterized by low accuracy and limited detection of biomarkers, increasing the likelihood of subsequent medical interventions to treat complications. On the other hand, advanced diagnostics can screen millions of circulating biomarkers and detect early signs of diseases.
A paradigm shift in the way drugs are developed and manufactured
Pharmaceutical companies are under increasing pressure to justify the return on their R&D investments. A few years ago, they were reluctant to invest in R&D for PM due to the compromised commercial values associated with targeting limited populations. Nowadays, pharmaceutical companies are shifting their profit focus to price, not volume, as new drugs targeting niche populations can achieve higher selling prices with much lower marketing expenditures and more guaranteed sales.
Big pharma companies can technically rely on their in-house manufacturing capabilities to produce precision therapies, but this is not economically viable because small batches of precision therapies will result in underutilized time, machinery, and resources. The traditional pharmaceutical manufacturing process relies on the production of several batches of high volumes of products to control costs and benefit from economies of scale. This cannot meet the complex needs of PM to produce a wider variety of batches of temperature-sensitive, complex products at lower volumes to serve a wider variety of patient populations. Flexible manufacturing and single-use technologies are emerging to provide companies and CDMOs (Contract Development and Manufacturing Organizations) with greater flexibility to manage the production of a variety of products in smaller batches by allowing companies to replace disposable single-use reactors for each medication. This not only reduces cross-contamination but also improves operational efficiency by significantly reducing the time needed to clean the reactors between different product lines.
Advances in digital technology are key enablers for realizing the potential of PM
For healthcare organizations to realize the full potential of PM, they must be able to collect enormous amounts of genomic, social, and physical data. They must also leverage modern technologies to transform this complex data into structured datasets that generate accurate insights regarding the best treatments while reducing time and errors. The following are key examples of high-potential technologies.
Data analytics and AI
Advances in computational power enable the processing of huge amounts of data from various sources and provide valuable insights about the human body’s interactions with drugs.
NLP created new opportunities for hospitals to leverage their data, an opportunity that was unattainable with humans alone. NLP can learn and understand the human language within the healthcare context more effectively and rapidly than humans. NLP then extracts valuable information from this vast unstructured data and translates it into more structured data sets ready for analysis.
They are physiological and behavioral data collected via digital devices such as wearables and portables. The widespread use of smartphones, along with the rapid development of sensor technologies, has enabled the accurate collection of health and wellness data in real-time. Digital biomarkers can disrupt traditional clinical assessments because objective and specific data is collected in real-life settings without any external bias. This increases the statistical power and increases the accuracy and sensitivity of the clinical results.
A huge promise with challenges ahead
Despite the unique potential PM can bring to public health, and how technology is making it more feasible than before, PM is not yet broadly integrated within healthcare systems due to some challenges such as:
Quality of data
An average hospital produces around 50 petabytes of data annually (1 petabyte is equivalent to 11,000 4K movies). Most of this data is non-standardized and comes from multiple EHRs (Electronic Health Records) and disparate data repositories, making it very challenging and time-consuming to process and use.
Building an economic case for PM is not an easy task because advanced diagnostics and screening tests have much higher costs than traditional tests. Still, PM has strong potential to increase the efficiency of treatments, produce better outcomes, and thus reduce the overall costs of care. This is because PM eliminates the need for repeated diagnostic tests and the traditional trial-and-error approaches, which are more costly and less accurate.
Physicians lack technological expertise and need to be trained and qualified to be able to interpret the data models built from genetic and biological markers via modern data analytics tools and technologies.
PM involves the flow of enormous amounts of data among different stakeholders, and it is very critical to ensure the protection of such sensitive data and maintain patients’ privacy.
Over the past decade, at least 14 countries have launched genomics-based medicine initiatives
According to GlobeNewswire, the global PM market size was estimated at USD 65.89 billion in 2021 and is forecast to increase by a CAGR of 12.1% during 2022-2028, reaching USD 146.57 billion in 2028. Governments and insurance companies are strong advocates of PM to bring healthcare costs down and improve the quality of care, which are the essences of value-based healthcare models. In that sense, they have an important role in developing policies, regulatory reforms, and novel reimbursement plans to accelerate the transition of PM from research to clinical application. Over the past decade, at least 14 countries (Australia, Japan, the USA, the UK, Qatar, KSA, etc.) have collectively invested billions of dollars in large-scale projects to collect genomic and demographic data from thousands or even millions of citizens. These initiatives have great potential to accelerate the integration of genomics within healthcare systems and support the development of PM.
Also, big pharma and technology companies are fostering strategic collaborations with strong investments to support the development of precision therapies, for example, in 2022:
Google participated in a USD 65 million series A investment round for Vicinitas Therapeutics, which is a precision medicine startup for cancer and genetic disorders.
Sanofi has entered into a research collaboration with Exscientia to leverage its AI-based capabilities and personalized medicine platform to develop a pipeline of precision-engineered therapies.
Like any new disruptive technology, PM still has many hurdles to overcome, and the key to its success is to get all the ecosystem stakeholders (governments, insurers, pharma and biotech companies, technology providers, etc.) working in silos to collaborate, share resources, and establish standardization frameworks for the diverse data out there. Collaboration is also crucial to reducing costs and driving the development of a sustainable PM-based ecosystem.