Kibur Science Spotlight: An Introduction to Precision Medicine

Blog Author: Colin Brenan Published: September 8, 2023

An Introduction to Precision Medicine

The concept of precision medicine is not new. The Canadian-born physician Sir William Osler, physician-in-chief and a founding professor of medicine at the Johns Hopkins School of Medicine in the late 19th century, first articulated the basic idea behind precision medicine: “it is much more important to know what sort of person has a disease than what sort of disease a patient has” (John 2013). Physicians know well patients suffering from what seems to be the same disease will react very differently to the same treatment yet that is not how medicine is mostly practiced in our healthcare systems. The disease or symptoms instead of the patient are treated in “one-size-fits all” approaches where most respond but a significant group remains of non-responders. This leads to successive rounds of different therapies leading to overall poor results, unnecessary suffering, and high costs. Advances in medical technologies, disease understanding, clinical trial design and drug development have begun to swing the pendulum back to more personalized therapies, and in the words of Edward Abrams, President of the Personalized Medicine Coalition, the goal now is “to support the creation and broad dissemination of a therapeutic paradigm where the right drug treats the right patient at the right time” (Abrahams 2008). All health care stakeholders including insurers, drug developers, the FDA, and patients have embraced precision medicine because it will ultimately bring better therapies to market with superior clinical outcomes for patients at a lower cost.

For the practice of precision medicine to deliver its promised value, a healthcare provider ideally has access to the entire spectrum of attributes that define the health of an individual and how disease impacts or changes these attributes. Access to biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors and genome and microbiome sequence data repositories have accelerated the development of advanced multimodal artificial intelligence and machine learning solutions to capture the complexity of human health and disease. These advances are driving key applications in high performance precision medicine as advocated by Dr. Eric Topol at the Scripps Research Translational Institute (Acosta et al. 2022) and the holistic concept of the quantified self from personal -omics profiling pioneered by Professor Michael Snyder at Stanford University (Chen et al. 2012).

In pharmaceutical drug development, the “one-size fits all approach” became increasingly untenable as it became recognized that with so-called blockbuster drugs to treat patient populations with a common condition like high cholesterol, a significant percentage of patients were non-responders that did not benefit from the drug. An early example of precision medicine is in the application of Single Nucleotide Polymorphism (SNP) genotyping to segment patients based on drug metabolism (Lee 2010). Patients for targeted oncology and immuno-oncology drug therapies are selected for treatment based on genetic sequence biomarkers as companion diagnostics to identify the mutational variants targeted by the drug or Microsatellite Instability (MSI) as a biomarker of patient selection for immuno-oncology therapy. The overall goal is to increase the number of patient responders and decrease the non-responder population that would not derive clinical benefit but still be exposed to drug toxicities. Despite these advances the sobering reality is that 15% of US patients with metastatic cancer were eligible for an FDA-approved, genome-guided drug yet only about 7% of those benefited from the prescribed therapy and many relapsed after a couple of years on those drugs (Marquart et al. 2018). This is lower than the number of patients benefiting from standard of care chemotherapy treatment (Kaiser 2018). Because of the early yet limited success of these approaches, nearly all big pharma and most biotechs are actively engaged in research focused on precision medicine, demonstrating its potential for significant advantages to human health.

Function-based oncology diagnostics provides a different yet complementary approach to precision medicine by predicting patient response and even potentially clinical outcomes from direct in situ measurement of tumor-drug response. This is fundamentally different from genome-guided therapy where the genome sequence provides information on genetic vulnerabilities of the tumor that could be matched to an existing FDA-approved drug but nothing on whether the tumor will clinically respond to the treatment. The FDA IDE-approved NanoNail™ from Kibur Medical provides the capability to measure the response of a tumor in its native microenvironment to twenty different drugs or drug combinations at different concentrations simultaneously. Presently the oncologist selects a drug for their patient to which they believe the patient will respond. If there is no response, then the patient has been subjected to the drug’s toxicities with no clinical benefit. Recent clinical research in glioblastoma shows the power of the NanoNail to be at the core of a patient-centric approach to support the oncologist in selecting the best therapy from a range of possibilities to which the patient is likely to respond clinically. In this new world there is no standard of care, or one size fits all treatment, but instead each patient gets the right drug best for their treatment.

As the field of precision medicine continues to advance, it should lead to better health outcomes and a more efficient healthcare system. Providing patients with an evidence-based treatment plan engages the patient and assists them in making the best decisions in collaboration with their oncologist.

References

Abrahams E. Right drug-right patient-right time: personalized medicine coalition. Clin Transl Sci. 2008 May;1(1):11-2.

Acosta JN., Falcone GJ., Rajpurkar P and Topol EJ. Multimodal biomedical AI, Nature Medicine, Volume 28, 2022, 1773-1784.

Chen, R., Mias, G., Li-Pook_Than, J. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes, Cell, 2012, Volume 148, 1293-1307.

Cotler M., Ramadi K. , Hou X., et al. Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors, Translational Oncology, Volume 21, 2022,101427,

Kaiser, J., Is genome-guided cancer treatment hyped?, Science, 2018, Vol 360, Issue 6387, 365.

Jain, S., Pei, L., Spraggins, J.M. et al. Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nat Cell Biol 25, 1089–1100 (2023).

John M., From Osler to the cone technique. HSR Proc Intensive Care Cardiovasc Anesth. 2013, 5(1), 57-58.

Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J, Snowdon JL. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci. 2021 Jan;14(1):86-93.

Lee NH. Pharmacogenetics of drug metabolizing enzymes and transporters: effects on pharmacokinetics and pharmacodynamics of anticancer agents. Anticancer Agents Med Chem. 2010 Oct1;10(8):583-92.

Naithani N, Sinha S, Misra P, Vasudevan B, Sahu R. Precision medicine: Concept and tools. Med J Armed Forces India. 2021 Jul;77(3):249-257.

National Archives and Records Administration. (n.d.). Fact sheet: President Obama’s Precision Medicine initiative. National Archives and Records Administration.