Tailor-Made Medicine: An Evolution in Precision Cures

PRECISION MEDICINE, offering pinpoint accuracy in tailored healthcare treatments, is the latest iteration in evidenced-based health. Powered by a rapidly expanding knowledge of genes, genomes and their variabilities, the ability to identify a condition’s underlying genetic cause in combination with environmental and lifestyle factors can help to determine the best opportunity for targeted treatments. The pace of genetic research, enhanced by the ability to collect, manipulate and study data for the benefit of both individual care and the larger public health, is opening new doors and making early and positive improvements in patient health.

Efficiency and cost-effective methodologies for assessing disease markers, both static and dynamic, are improving every day, making this evolution to tailored care a reality, particularly in cancer care, where it has been most widely used to date. Costs of genome-wide and partial sequencing have come down significantly, as has the cost of data and data storage. The ability to churn through that data for rapid and accurate assessments will have profound impacts on the success of treatments and long-term health outcomes. Precision medicine must link and learn from all source data, including genetic, behavioral and environmental data,¹ so prediction modeling of health status in combination with treatment options, both pharmacologic and behavioral, can identify outcomes most likely to have greatest impact.

The greater our understanding of disease and its mechanics, including genetics, the greater likelihood patients will have better prescribed treatments, with a high expectation of efficacy and low risk of adverse events. By reaching further and digging deeper to understand etiology and drug targets, science is building opportunity for more proactive and effective treatments based on individualized makeup.

Pharmacogenomics

Most studies in tailor-made medicine have so far focused on pharmacogenomics, or the development of medicines and drug delivery systems based on patients’ specific genetic markers. But, the road to tailor-made medicines in this “golden age” of drug discovery has not been without its speed bumps. As genomics became the focus of drug development in the 1990s due to significant advancements in biotechnology and computational capabilities, the discovery of expressed sequencing tags, which enabled a shift from alleviating symptoms of disease to attacking the mechanics of disease, led to a massive increase in research spending but with a subsequent increase in clinical trial failures.

In 2003, the publication of the human genome sequence significantly increased understanding of disease etiology, including how genomic variations interact with complex diseases. But this massive potential has, so far, not met expectations for targeted drug development. Genome-wide association studies (GWAS), for example, enabled a better understanding of diseases, but offered little insight into potential causes. With nearly three million single nucleotide polymorphisms (SNPs) for many human traits and diseases, there are as many as 100,000 contributing SNPs throughout the genome. In fact, between the years 2007 and 2012, nearly 2,000 potential drug leads for almost 2,000 associated loci of complex diseases and traits were identified, some of which were associated with multiple diseases and several autoimmune conditions, including psoriasis, inflammatory bowel disease and ankylosing spondylitis. However, despite these findings, a lack of biological explanation still exists because GWAS most often point to a group of genes close to the signal rather than identifying specifics. This lack of a strong cause-and-effect relationship between a gene and disease means GWAS have, as yet, had little impact on new drug development.²

Yet, despite these setbacks, evolution away from medicine for the masses in favor of targeted treatments that focus on patients’ underlying disease continues to advance, particularly for disorders with strong genetic components such as neurological diseases and cancer. In fact, the disruption of pathways and introduction of immunotherapy has coincided with rapid advancements in companion diagnostics, enabling matching of patients and specific targeted therapies.²

Metabolomics

Of course, variability in drug response is not solely genetic and, therefore, cannot be predicted by the genome alone. Patients’ medical history, the environmental health of their surroundings, their gut and other lifestyle factors must also be considered to determine which drug will be most effective and at what dose. This is made possible with metabolomics, which is an understanding of the metabolome, or the biochemicals present in cells, tissues and body fluids, and their influence on the genome. By collecting metabolome and genome data, creating a map of underlying molecular disease mechanisms, and identifying biomarkers for drug response phenotypes, better diagnostic capabilities are possible, allowing for more accurate treatments and toxicity risk predictions.

With more input comes more complicated assessment due to integration challenges. So, the next step in research is merging multiple domain risk studies with anonymized data in its original context and meaning with other inputs, while preserving all multivariate statistical properties.1 For example, assessments of patients’ metabotypes prior, during and posttreatment may give powerful clues to pharmaceutical intervention response variability. Metabolomics is a valuable tool and is complementary to precision medicine with the potential to identify factors fundamental to disease, facilitate the discovery of new biomarkers and, thus, identify new targets for clinical intervention.³

Inherent Biases of Big Data

The promise of tailor-made medicine runs across the continuum of care, from dynamic disease risk and prediction modeling to diagnosis and optimized treatments. But, for disease prediction to be most effective, it must include not only static factors (genes, age and race), but dynamic factors (behavioral, social, environmental). This holistic, multi-domain approach offers the best opportunity for meaningful intervention as disease etiology, phenotypes and their subclasses, etc., have profound impacts on treatment choices. The inclusion of patients’ own-generated data through wearables and apps that capture blood pressure, activity level, dietary intake and more can further enhance valuable clinical data.

Particularly as technologies advance, the ability to integrate genomic data with electronic health record (EHR) and personal health record data provides opportunity, albeit with its own set of challenges. As the capability for capturing and using data grows, the opportunities of precision medicine grow with it. Enter artificial intelligence (AI) and big data, which many believe are key to advancing precision medicine through their ability to understand diseases, their causes and in what patients they are most likely to occur before showing symptoms.

Moving beyond the significant barriers of competing data systems — with differing coding, access protocols, semantic integration and bias — these vast networks can facilitate multidomain studies, sequencing large numbers of genes simultaneously. For instance, the bias introduced through EHR data is important to recognize and mitigate to the extent possible, as patient populations, the frequency of healthcare visits, diagnostics and care, including prescriptions, may inherently flaw outputs, making big data prediction modeling, in some respects, of limited value. Examples of bias that could be addressed are the use of gender, race and ethnicity as risk modifiers, with the replacement of environmental categories such as lifestyle and diet.

With so much available data that is linkable and comes from a variety of sources, a real challenge is parsing through that which has the greatest statistical significance and clinical relevance to gain actionable knowledge. Thus far, genomic and other “omic” studies have, understandably, been limited by small and heterogeneous sample sizes.¹

Even so, it is largely thought that without big data and AI, the ability to grow tailor-made healthcare would be curtailed. For example, biomarker identification through digitally enabled technologies is expected to benefit new product label extensions and rationale for reimbursement of diagnostic tests and targeted therapies. More than a third of healthcare settings are expected to invest in personalizing clinical care recommendations, including drug therapies, with the use of AI as a clinical decision support tool.⁴

Advancement of Precision Public Health Criticisms of precision medicine, based on a perceived lack of broader benefit, include locationally driven concerns, economic segmentation and ethics such as religious and political views. Another challenge is diversity. With the majority of genomic data obtained from those of European descent, research is missing key markers of minority and understudied populations, according to the Population Architecture using Genomics and Epidemiology study funded by the National Human Genome Research Institute and National Institute on Minority Health and Health Disparities. As precision medicine grows, the gap widens to pinpoint care for these racially and ethnically diverse populations that have been understudied. Case in point: Sixty-five new or previously unknown genetic associations along a chromosome where genetic variants are located were recently uncovered in a study of nonwhite Americans. With the potential for information coded in biomarkers to be transferable to additional groups who share genetic lineage, the risk for missing important information and misunderstanding how this information may be attributed in the larger population is an oversight, but one that can be corrected.⁵

There are many examples of drugs that are effective in treating disease, but not for all, and in some cases not for the population with the highest risk for the disease. One example is asthma, which is predominantly seen in African-Americans and Latinos, yet both groups do not respond as readily as others to the most common drugs used in inhalers.

The 2018 launch of the National Institutes of Health’s “All of Us” is working to correct the underrepresentation of minorities in research through partnership and sharing of health data of more than one million participants of all races and ethnicities. Prior to All of Us, for example, just 3 percent of African-Americans and less than one percent of Hispanics were represented in genome databases, even though they make up 13 percent and 18 percent of the U.S. population. In All of Us, to date, 21.5 percent of participants are African-American and 17.6 percent are Hispanic, offering an opportunity to better understand complex traits and disease causes and effects.⁶

The opportunity to expand precision medicine to a larger construct of public health is growing by incorporating not only genetic and genomic data, but by combining it with other healthrelated factors that can help to develop a more complete picture of disease and targeted therapies that best suit the individual. There is much work still to be done, but the future looks promising for this collaborative world of health research.