Precision Medicine: Investments and Outcomes Expected

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Customization might have launched with coffee orders, but precision medicine is the future of individualized healthcare, targeted treatments, and overall improved care plans. Understanding patient care cannot be “built to order,” as it is impractical and deeply expensive, change was inevitable. It is necessary to understand the blend of technologies, including software, artificial intelligence (AI), and analytics, that will enable the adoption of precision medicine for each patient.

Precision medicine is based on specific genetic and demographic factors, meaning the tests are more accurate, and patients are spared unnecessary procedures, prescriptions, and adverse events. For example, a patient being treated for anxiety may try various prescription strengths and drug types before landing on the “right” therapy.

With precision medicine, this back-and-forth is reduced tenfold; before prescribing, providers could order a genetic test to see whether the individual has a genetic makeup that would reduce the effectiveness of a specific drug, thus removing the need to find the correct fit and allowing the patient a faster path to the most appropriate medication.

Variables from environment, lifestyle, genetics, and medical data feed into biomarker research, bioinformatics, and genomics, thereby allowing clinicians to find the most appropriate drug and treatment plan for each individual patient. Through advanced analytics, big data, deep learning, and statistical models, clinicians can explore and solve for a patient’s distinct ailment and their respective demographic and genetic information.

With the US government’s unveiling of the Precision Medicine Initiative in 2015 and investing $215 million in the National Institutes of Health (NIH)’s 2016 budget—with an additional $54 million announced in 2023—precision medicine is now seen as a healthcare approach for both the present and the future, particularly with the explosion of personalized care delivery.

Customized Patient Care – Who are the Stakeholders?

Beyond the investments from governments around the globe, precision medicine is becoming standard practice in healthcare with the objective of treating patients effectively, quickly, and without unnecessary cost. Stakeholders include clinicians, hospitals, pharmacists, payers, and, of course, patients.

Healthcare providers—as well as pharmaceutical, biotech, and medical device companies—must cater to this to succeed and stay in business. Pharma companies are also recognizing the significance of precision medicine in drug development.

Historically, drug development was a one-size-fits-all approach, resulting in poor outcomes for a larger percentage of patients. Before commercialization can even happen, clinical studies fail because there is no consideration of genetic makeup or unique characteristics.

Precision medicine helps clinicians reduce educated guesses by using big data as a primary diagnostic and decision-making tool. It also helps physicians better understand the underlying mechanisms by which these diseases occur and improve their approach to preventing, diagnosing, and treating these diseases.

Payers are in tune with this approach and are critical in enforcing tests to ensure patients can genetically match for the more appropriate medication, thus significantly reducing the overall healthcare burden. The next phase has loftier goals to target cancers, chronic conditions, rare diseases, and neurodegenerative diseases, emphasizing the need for personalized dosing.

Precision Medicine and Dosing

Naturally, precision medicine plays into the notion of patient-centricity as clinicians and providers develop a personalized care plan based on an individual’s genetic and demographic information. Genomic testing arms physicians with an unprecedented level of insight into a patient’s condition.

One factor for this is pharmacogenetics, which refers to the variability in response to drug therapies in humans. A trend to watch as it is a fast-growing area in molecular biology and clinical medicine.

To date, obstacles persist stemming from disparate data, a one-size-fits-all approach to therapeutic drugs, and the lack of a comprehensive collection of results to understand both problems and patient outcomes. Over and above this, education is lacking in how to apply pharmacogenomics at the bedside.

Dosing represents the “last mile” of precision medicine, in which individualized treatment plans truly come to fruition. Matching the patient with the most appropriate medication but then underdosing or overdosing that patient would effectively render all the tools used upstream useless and costly.

With a proliferation of diagnostic testing, particularly biomarker-driven approaches, healthcare providers are equipped with unprecedented insights into patients' unique physiological profiles. Moreover, advancements in therapeutic drug monitoring (TDM) have streamlined the process, enabling faster delivery of medications tailored to each patient's individual needs. This convergence of diagnostic precision and rapid medication delivery not only enhances therapeutic efficacy, but also exemplifies the transformative power of precision medicine in optimizing patient outcomes.

The FDA is stepping up also to enforce the future of better healthcare. The goal of Project Optimus is to educate, innovate, and collaborate with companies, academia, professional societies, international regulatory authorities, and patients to move forward with a dose-finding and dose optimization paradigm across oncology that emphasizes the selection of a dose or doses that maximizes not only the efficacy of a drug, but the safety and tolerability as well.

The burgeoning aging population—coupled with complex variables such as obesity and the widespread use of prescription drugs—accentuates the critical need for tools and methodologies that can swiftly optimize diagnosis, treatment, and cure across a spectrum of diseases and conditions. In an era in which healthcare is increasingly personalized to meet individual patient needs, innovative approaches are imperative to address the multifaceted challenges posed by aging demographics and prevalent chronic conditions.

AI Impact

AI is significantly democratizing precision medicine, making personalized healthcare accessible and affordable for all. AI’s integration with real-time data and large language models (LLM) to enhance computational capabilities cultivates a system that not only generates insightful inferences but also evolves through iterative learning.

This strengthens the decision-making of clinicians with data-driven analytical support. Precision medicine can now better identify unique patient phenotypes, especially those who have atypical responses to treatments or specific health needs.

Precision dosing remains a fundamental aspect of emerging therapeutic interventions. While AI significantly contributes to this field, it is not equipped to handle full complexity of tasks, such as Bayesian dosing independently. The need for clinician oversight remains particularly important due to the complexity of patient responses and the current lack of data in certain patient populations.

The FDA’s Project Optimus is reshaping the expectations for clinical trial design, pushing the pharmaceutical industry to fundamentally rethink how they determine and evaluate dosing regimens. Rather than adhering to a traditional, static model, trial designs are now being challenged to incorporate adaptive methodologies that can respond to emerging data in real-time. This means that pharmaceutical companies must be able to implement designs that allow for dose adjustments within the trial itself, utilizing advanced algorithms and continuous patient monitoring to ensure optimal dosing.

Furthermore, pharmaceutical companies are encouraged to adopt a more holistic view of data collections, integrating biomarkers, patient characteristics, and other relevant data points to refine dosing strategies. This is not a mere enhancement but a necessary pivot to align with a regulatory environment that increasingly prioritizes patient-specific outcomes over broad-brush approaches.

This signifies a shift toward trials that are inherently flexible, able to incorporate interim analyses, and capable of evolving as patient responses become clearer. It calls for investment in technologies and expertise that enable not just data collection, but data synthesis and interpretation at a level of complexity and speed previously unrequired. Such adaptability is not just a regulatory requirement, it’s a commitment to patient safety and efficacy that ultimately drives therapeutic success in the real world.

Incorporating these principles into trial designs will require cross-functional collaboration, employing clinical insights and computational power to navigate the nuances to dose optimization. With the patient at the center, the path from trial to treatment is in position to become more direct, individualized, and effective.

Future Patient Care and Therapies

The future of medicine lies in the precise matching of patients to medicines based on their unique characteristics. Cancer remains one of the primary areas of focus, with advancements extending into all facets of healthcare.

The field of oncology serves as a testament to this progress, with the FDA increasingly approving more effective and personalized drugs. Clinical workflow encompasses a series of interconnected tasks performed by individuals across various environments to deliver optimal care.

Each task may necessitate coordination between individuals, teams, or organizations, occurring both sequentially and simultaneously. As we navigate this evolving landscape of personalized medicine and streamlined clinical practices, it becomes evident that the future of healthcare rests on the seamless integration of tailored treatments and efficient workflows to ensure the best possible outcomes, one patient at a time.

About the Authors

Paul Edwards, CEO, DoseMe.

Sharmeen Roy, PharmD, BCPS, Chief Science and Strategy Officer, DoseMe.