Stephanie LaPointe, PharmD, CSPO, FMPA

Pharmacy practice and the pharmaceutical industry are undergoing dramatic change. One area on the rise is pharmacogenomics (PGx), which is a part of the broader term “precision medicine.”

Numerous authors have noted that clinical pharmacists are well-positioned, both by training and placement, within the health care delivery system to play an essential role managing these innovative treatments.

Given that many of the recent therapy advancements are centered around patients living with complex, chronic diseases often managed with specialty treatments, the placement of clinical pharmacists at the nexus of functional PGx and in specialty pharmacy care makes good sense. 

Pharmacogenomics is a growing and innovative area of health care that is influencing specialty pharmacy practice. This form of precision medicine and related treatments is being driven by advances in genomic testing to inform pretreatment decisions about a given patient’s expected treatment response and adverse event likelihood.

With such rapid innovation in PGx diagnostic and treatment approaches across a widening expanse of conditions, there is a level of uncertainty for how clinical pharmacists in specialty pharmacy can adapt and add value. Although there are numerous ways in which pharmacists can bring value, we will specifically discuss their role as a “trusted counselor” to minimize certain barriers associated with optimizing precision medicine within the specialty space.

Clinical pharmacists serve to fill certain care and information gaps that are currently acting as barriers to optimizing precision medicine technology and evidence advancement.

Traditional Medicine and the Advent of Precision Medicine

Historically, clinical evidence rested upon the fundamental principles inherent to the randomized clinical trial (RCT), in which promising treatments get compared with a standard (placebo or current standard of care) under controlled conditions and evaluated using deductive statistical inference to evaluate outcomes.

As the gold standard, RCT has associated advances in the development of effective clinical treatments that help the most people the most.

 However, the need to still treat individuals uniquely is never fully addressed using group statistics, no matter how rigorously derived.

Precision medicine changes this dynamic by aligning pharmaceutical development and testing toward acknowledgement and consideration of the individual differences

inherent in every person and every treatment response (or nonresponse).

Targeted therapies associated with precision medicine represent an important advancement in drug development, with oncology being the area of fastest growth.

 Additionally, rare diseases are a popular target for newer gene and cell therapies.

Although cell and gene therapies might have the potential to treat a wide scope of diseases, it is estimated that approximately half of these approved gene and cell therapies will be indicated for the treatment of hematological cancers, particularly B-cell lymphomas and leukemias.

These targeted cancer treatments inhibit the progression of cancer by acting on a more selective range of molecules. Previously available chemotherapies were less selective and, therefore, resulted in killing a much wider range of cancerous 

Targeted therapies, such as these, help reduce the potential secondary damage to healthy tissue, while more precisely attacking cancerous ones.

Although only a few gene and cell therapies are currently available, by 2030, it is projected that 30—60 of these products will have attained FDA approval, with approximately 350,000 to 500,000 patients being treated with this form of precision medicine.

 According to the American Society of Gene & Cell Therapy, there are 546 clinical trials involving cell and gene therapies across various stages of development, as of April 15, 2020.

Because precision medicines have very specific targets, diagnostic testing for specific mutations is key to identifying genetically optimal patients. Without a way to accurately match a patient’s specific genetic abnormality with the correct targeted treatment, even the most detailed will struggle to reach the right patients.

Unfortunately, these diagnostic tests are not often part of routine laboratory tests. For this reason, some precision medicine developers have also become involved with the development of diagnostic tests to identify the genetic mutation being targeted by their product.

In many cases, the FDA has approved specific diagnostics for use with individual precision medicine products. The prescribing information for precision medicine products often references confirmation of a specific genetic mutation via a companion diagnostic.

A list of currently approved diagnostic tests and their corresponding precision medicine products can be found on the 

. Failure to confirm genetic mutation by an FDA-approved test may result in insurance denying coverage for the precision medicine product.

Specialty Pharmacy and the Pharmacist as a Trusted Counselor

Specialty pharmacy is where much of the precision medicine and PGx innovation are being delivered.  These types of pharmacies and the services they provide are well-positioned to address several key obstacles to more comprehensive implementation and application of effective cell and gene therapies.

Given the Obama administration’s definition of precision medicine as an area that encompasses genomic, environmental, and lifestyle factors, the need to address the environmental and lifestyle factors in unison, with the changing PGx evidence, will be an increasingly important pharmacist function to fill the information and service gaps for consumers.

Although the focus of nearly all conversations regarding PGx and precision medicine has been on the genomic data and its promised value, some authors have cautioned against an over reliance on the promise of that domain alone for precision medicine. 

 displays the 3 domains consistent with the aforementioned definition, with the patient and clinical pharmacist consultation at the center of the intersection. 

Figure 1: Precision Medicine Domains and Examples

Little has been discussed on how to address the other 2 spheres of social/lifestyle and environment. Precision medicine is still an ongoing project and no clear models for how to integrate the 3 relevant domains with regards to assessing and intervening and the junction of genomics, social/lifestyle, and environment.

Furthermore, it is unclear who will bring and how to bring these domains together within a practical, patient encounter that is consumer friendly. One proposed solution is viewing the pharmacist as a trusted counselor to address the range of topics that might be of patient or even practitioner concern.

Although opportunities for pharmacists to address environmental risks may be limited, they can certainly play a significant role in addressing some of the social risks, as well as serving as a quality control for the appropriate application of genetic evidence and companion diagnostics. Probably the most obvious social intervention the pharmacists in specialty pharmacy can facilitate is prescription access.

Let us consider 3 overlapping roles in which the pharmacist might function as a trusted counselor:

 This is the provision of support through active services of “doing” for others. This is exemplified by services designed to ease the friction that is almost always a part of the administrative aspect of accessing and interacting with health care, including gaining access to specialty medications. Ostensibly bureaucratic processes and costs that limit access to care and services can be a significant dissatisfier and stressor for patients and families. In the context of specialty pharmacy, this includes services such as benefits investigations and verifications, patient assistance qualifications, and home delivery, among others.

 Many patients requiring specialty pharmacy services find themselves in unfamiliar territory in terms of their condition and its treatment. In many cases, they are also new to the role of a specialty pharmacy and how it will affect their care. Given the clinical context and complex nature of the treatments and conditions, specialty pharmacies provide substantial educational and instructional content to patients. This typically includes collateral assets such as instructions, tip cards, etc, and programs such as nurse-led adherence and 24/7 clinical pharmacist access.

 Coping with a chronic illness is, at best, stressful and often associated with various negative emotions (eg, depression, anxiety, fear, grief).

 Emotional support can be complex, and yet, simple. However, given the prevalence of negative emotions as a comorbidity and complicating factor, not addressing it would be a clinically significant omission from a comprehensive patient support experience. It is, however, an area in which formal training and practice is spotty, at best, and many clinical pharmacists might be most uncomfortable in this supportive role. The use of validated assessment tools can help operationalize patient need, and guide pharmacists to capture the right clinical information for triage. The use of such measures can also contribute to more rigorous collection of real-world data in this setting and of this type.

The American Society of Health-System Pharmacists (ASHP), a national organization focused on optimizing medication use that represents more than 55,000 patient care providers, which include pharmacists, student pharmacists, and pharmacy technicians, has developed guidelines for effective education and counseling.

: Develop a trusted and reciprocal relationship with patients based on a strong working alliance, in which the patient sees and uses the pharmacist as an integral part of their health and treatment team.

: This can be a complex set of activities considering patient-specific issues such as attitude (ie, motivation), their confidence assuming the role of condition and treatment self-manager, and related tasks.

Physical Capabilities (Limitations) and Barriers

: Most significant illnesses, almost by definition, create some level of physical limitation. These might require consultation for physical aids, tools, and devices, as well as helping patients set more reasonable self-expectations during this time.

Mental Capabilities (Limitations) and Barriers

: Many things can affect the cognitive abilities of a patient, and therefore, their understanding of health information. Considerations should include health literacy/numeracy. Certain conditions and treatments can also affect cognitive functions, such as the ability to pay attention, concentration, memory, etc.

As the costs of genetic testing decline and it becomes more commonplace, the consistent application of companion diagnostics will change how medicine is prescribed, delivered, assessed and paid for.

As research and development of genetically-targeted medications increase, organizations must develop clear plans for how to successfully evaluate and stay current on this aspect of drug development.

They must also implement processes within the patient-care workflow that assesses and addresses instrumental, informational, and emotional

support. Clinical pharmacists with primary counseling functions must maintain their understanding of how to interpret the growing number of genetic tests and their treatment evidence to confidently make recommendations on how this information informs treatment in a manner readily understandable by a range of patients.

Where health care data and care systems allow for the identification of gaps in application of precision practices (eg, companion diagnostics) the clinical pharmacist can serve as a liaison between patient and provider to ensure proper application of the technology. In some cases, this will be to assure application and documentation of the appropriate practice and might include education around the most up-to-date evidence, recommendations, and guidelines. 

To meet this need, certain organizations such as PharmGKB and the National Institutes of Health Pharmacogenomics Research Network, are available to provide evidence-based information. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has designed guidelines to support clinicians in optimizing medication therapy based on knowledge of patient genotyping. Numerous CPIC guidelines have been endorsed by various professional societies, including the ASHP.

 provides a selection of useful resources for remaining current with evidence and practice related to PGx.

Table 1: Resources for PGx Information and Guidelines

eurekalert.org/pub_releases/2019-09/afmp-arc090419.php

Canadian Pharmacogenomic Network for Drug Safety

Articles

The placement of clinical pharmacists at the nexus of functional pharmacogenomics and in specialty pharmacy care makes good sense. 

Precision Medicine and the Pharmacist’s Role as a Trusted Counselor in Specialty Pharmacy

Specialty drug utilization is increasing, as are the associated challenges around adherence, cost, and delivery of optimal value. Specialty drugs are reported to represent 39% of the overall pharmacy benefit spend, with projections to increase to 48% by 2020.

 The prices for specialty drugs had a reported average increase of 9.6% from 2014 to 2015, and are expected to continuously rise, with the largest growth expected within oncology.

 The specialty medication pipeline is also expanding, comprising 60% of all new drug approvals in 2017,

This growing landscape offers patients with complex and chronic conditions more treatment options; however, increased utilization is often associated with certain challenges. Health care stakeholders face increasing pressure to address the rising costs of these specialty medications while continuing to provide affordable and accessible care.

A 2017 Express Scripts report documented that 118 specialty drugs entered the market over the preceding 5 years, with predictions outlining another 25 expected annually until 2022.

 This indicates that health care stakeholders—payers in particular&mdash;must prepare strategies to address the expanding number and costs of specialty medications that this large influx of new drug developments might preface.

Payers are joining forces with pharmacy benefit managers (PBMs) to enhance their ability to combat the increase in drug prices.

 As more specialty medications enter the market, payers and PBMs are being more selective in their formulary management, and negotiating better pricing structures. Manufacturers want to be included on the larger plans’ formularies; therefore, price negotiations often lead to offering payers and PBMs a larger discount or rebate.

 Within formulary management, dose optimization and utilization management are 2 proactive strategies that payers and PBMs might use to address the increasing costs.

For the purposes of one program—implemented at Diplomat Specialty Pharmacy in Flint, Michigan&mdash;dose optimization is defined as consolidating a patient's regimen into fewer units without a change in their dosing frequency. Dose optimization strategies offer a potentially valid, clinically based intervention in which payers can realize a direct drug cost savings, and indirect medical cost avoidance. Dose optimization programs have been evaluated with once-daily, oral maintenance medications using various methods that produced varied results.

 These studies were conducted in medication classes such as gastroesophageal reflux disease (GERD), anxiety and depression, and hypercholesterolemia.

While the current literature describes dose optimization in the nonspecialty space, there is limited literature on dose optimization strategies used for specialty products.

A specialty pharmacy developing a dose optimization program could evaluate the implications and viability for specialty products, since they work closely with payers and providers. A successful dose optimization program within a specialty pharmacy could contribute a significant cost savings for payers, further mitigating the rising costs of specialty medications. Therefore, the goal of this pilot program was to evaluate the scenarios and opportunities for dose optimization within a selected group of oncolytics.

This Diplomat program was designed as a proof of concept to illustrate the implications and barriers associated with dose optimization in a specialty pharmacy setting. This setting offers access to many specialty medications, and uses a high-touch model of therapy management for patients with complex conditions, such as cancer, multiple sclerosis, psoriasis, human immunodeficiency virus (HIV), and others. With more than 1100 products currently in development, oncology is one of the fastest-growing specialty segment on the market.

 This growth is what led the specialty pharmacy to focus on dose optimization opportunities within this population.

Diplomat’s oncology program delivers comprehensive care management to help patients address complex aspects of their treatment and condition. The crossfunctional oncology team is composed of specialized clinicians, and nonclinicians leveraging evidence-based care for treatment optimization, improved care coordination, and therapeutic cost management. The steps the team followed are displayed in the Figure below.

The retrospective prescription evaluation of oral oncology medications identified 58 initial opportunities for dose optimization. Financial contingencies varied for each stakeholder, and did not always align among the patient, the payer or PBM, and the pharmacy, which hindered dose optimization recommendations. During the evaluation phase, it was imperative to assess the impact on these stakeholders, including patient, payer, and pharmacy, to ensure a win for all. As shown in Table below, several circumstances would have resulted in a therapy delay that could potentially have adversely affected patient outcomes and adherence.

Table — Scenarios Impeding Dose Optimization

Additional Prior Authorization Requirement

Specialty pharmacy is uniquely positioned to ensure proper patient care while optimizing medication adherence and outcomes. If we anticipated that the dose optimization program would negatively impact patients, as shown in Table 1, we excluded those patients from the dose optimization program. Specifically, we excluded:

Patients who had a change in therapy to a different medication during the evaluation time period

Patients whose providers initiated a dose-optimized prescription without any intervention from the pharmacy

Patients who would face increased financial responsibility, such as a copay, or a change in financial assistance	

One patient would have been required to resubmit a grant application for financial assistance if a dose-optimized prescription were pursued. The outcome of the application would be unknown until the prescription was processed and could potentially compromise the patient’s current financial assistance.

Overall, we successfully optimized dosing for 9 of 58 patients.

For payers and PBMs, this optimization produced a cost savings of approximately $135,000 that was not payer- or medication-specific.

of qualifying prescriptions via phone, and received responses within 5 days of initial contact. Of the 9 potential prescriptions, we received 6 and successfully filled 5. The sixth prescription received a payer mandate rejection that necessitated it be transferred to a different pharmacy.	

For the 3 patients for whom we did not receive a dose-optimized prescription, the provider indicated either that the patient was not interested in dose optimization at this time or that the patient had swallowing issues and preferred the smaller formulation.

This proof-of-concept study of dose optimization in a specialty pharmacy setting evaluated various scenarios and identified significant barriers. Importantly, this program illustrated how stakeholders might influence one another, creating competing financial and service contingencies.

Dose optimization strategies offer a potentially valid, clinically based intervention in which payers can realize a direct drug cost savings, and indirect medical cost avoidance.

Viability of a Dose Optimization Program Within a Specialty Pharmacy

All industries rely on the flow of information in formats ranging from well-groomed surveys and sales reports to inconsistent fax images and call logs. These data create an opportunity for advanced analysis, leading to the processing and examining of historical data (also known as real world data) to unlock the information hidden within and better explain current behavior.

Taken a step further, these analyses can provide an opportunity for predicting future behavior (ie, predictive modeling). Predictive modeling is the process of assessing future outcomes by detecting relationships or patterns in existing data via mining/wrangling, advanced statistics, or machine learning techniques.

As the industry focuses more on value-based care, such techniques allow pharmacies to shift their services from a one-size-fits-all approach to a personalized, high-touch strategy. This is especially true within specialty pharmacy, where there is a greater emphasis on patient support that targets complex pharmaceutical uses and outcomes. Through predictive modeling, personalized treatment and adherence expectations can be better integrated with the patient’s unique attributes and contextual factors.

The goal of predictive modeling is to identify actionable insights from a known set of metrics that accurately anticipate future events. Those actionable insights that most strongly predict an event can then be the target for earlier intervention and potentially greater efficacy.

 displays a data science framework for understanding key areas of overlapping function applicable to most organizations.

 As can be seen, effective use of a data strategy, including predictive modeling, is an interdisciplinary, cross functional process.

Figure 1. Data Science Conceptual Framework 

The 2 main statistical methods commonly used in creating predictive models are distinguished by how the model “learns” about the data being analyzed. These methods are referred to as “supervised” and “unsupervised” learning.

 These system-state and result relationships allow the predictive model to infer the expected result of a new state.

In supervised learning, input variables (ie, factors) are mapped to a single numerical output (regression) or sorted into buckets of similar results (classification) with the aim of being capable of deriving an output from new future inputs.

 By doing so, new factors can be tested against the model to predict an outcome.

In contrast, unsupervised learning uses only input variables with the aim of detecting hidden patterns or underlying structure in the data.

As such, unsupervised learning is more about discovery, and supervised learning is better suited to confirmation.

While the methods of implementing a predictive model are numerous and outside the scope of this article, creating a strong predictive model follows a general set of steps, including:

5. Evaluation of model predictions using new input data  

6. Monitoring of model results and modification as necessary

Examples of predictive modeling in the healthcare industry can be found in the provider, payer/insurance, pharmaceutical manufacturer, and public health settings.

 In medicine, predictive models have been used to predict mortality and hospital readmission rates, determine the likelihood of disease onset and progression, help physicians with diagnoses based on certain patient characteristics, and much more.

Medical insurance companies are using these techniques to predict future medical costs based on patient characteristics included in the predictive model.

 Pharmaceutical companies can use predictive models to more closely match targeted, low-production medicines to those who will benefit most.

The change in care from a “shotgun-style” or one-size-fits-all approach to a more targeted one is helping drive the expanding concept of precision medicine and can be used to help justify a drug’s clinical and economic viability. Similarly, there are growing efforts within the specialty pharmacy industry to embrace and leverage these new technologies to better meet patient support needs, optimize business and clinical operations, and reduce costs.

The definition of big data varies by industry and even organization but can loosely be described as consolidating a company’s (often very large amount of) data into a system in which it can be more effectively leveraged. As computing power has become more accessible and analytical tools more user friendly, companies have pushed for more data-driven solutions.

The amount of data being acquired and stored has grown exponentially as a result of the data revolution. For perspective, 90% of the data generated in the history of the world have been produced in the last 2 years.

Similarly, by collecting and moving as much data as possible into a single system, inferences from normally disparate systems can be drawn without disrupting normal operations. Classic examples of unconventional relationships include the 7-fold surge in purchasing of Pop-tarts before a hurricane or relating a person’s credit to their correct use of capitalization.

This sort of indiscriminate data collection reveals novel relationships that would previously only be found if specifically sought (ie, a priori), but these relationships are counter-intuitive and unlikely to be sought prospectively. Lee and Yoon summarize this idea well, referring to big data as “hypothesis-generating, rather than hypothesis-testing.”

 In this sense, it becomes the organization’s data laboratory where various ideas can be modeled, tested, and validated before implementation.

Patient Adherence-Need Triaging: An Example Predictive Model

Opportunities to benefit patients and the specialty pharmacy arise when:

Costs can be lowered without impacting care.

Additional services can be offered without impacting cost.

Services can be improved while reducing costs.

These scenarios all represent win-win solutions for the patient and the specialty pharmacy.

One example of predictive analytics within specialty pharmacy is evaluating a patient’s expected adherence to a new therapy. Adherence is generally defined as how compliant a patient is with the treatment plan given by their health care provider.

Better adherence leads to better outcomes, which is the shared objective of the patient, the specialty pharmacy, and the health care system. Adherence is often measured after a set period (eg, first 3 prescription fills), with the initial period often representing the period most at risk for non-adherence.

To help solve this catch-22, a predictive model was developed out of available real world data to accurately identify those patients most at risk to struggle with adherence or prematurely discontinue therapy. 

displays the model’s general approach to triage.

Patients at high risk of non-adherence or therapy discontinuation can then be targeted for additional preemptive support services, whereas those at low risk require less support. The predictive model is used to select a risk-appropriate intervention type and program intensity based on a person’s profile of modifiable risk factors.

Similarly, to balance these new services and manage costs and labor, those with lower risk profiles may be offered more self-service options (eg, a patient portal) for managing their therapy. Increasing the odds of keeping at-risk patients on their treatment plan during the initial at-risk evaluation period—while offering convenience of resources for those at less risk&mdash;creates a win-win scenario for both the patient and specialty pharmacy.

Figure 2 - High-level Overview of Predicting Patient Adherence

Big data have created opportunities in many industries, including specialty pharmacy, to build predictive models that create actionable insights by anticipating future outcomes. Specialty pharmacy’s primary goal is patient care: balancing operational efficiencies and affordability, while offering various support services to help patients manage health conditions and optimize treatment.

In the example of adherence triage, large data sets of hundreds of thousands or even millions of records were needed to create a model accurate enough to be actionable. The application of predictive modeling in specialty pharmacy will undoubtedly increase as well.

Our example illustrates the use of predictive modeling to accurately identify those patients most at risk to discontinue or not strictly adhere to treatment, allowing more targeted interventions for the at-risk populations. Predictive modeling is a critical analytical tool that will continue to provide practical solutions in the future as more industries begin to implement them into the workflow and recognize their benefits to clients and their businesses. 

As the application of predictive modeling continues to expand, it will be increasingly important to consider the type and quality of the available real world data. If a specialty pharmacy or any organization is going to get the most out of predictive strategies, it will need to specifically review (and in some cases test) the predictive quality of the available data.

Organizations should consider data gaps that could be filled to enhance prediction, personalization, and intervention. The resources needed to build the predictive model might not be available, which can delay the use of big data.

Even if a predictive model is built, the cost of system integration might be a limiting factor, as making amendments to a current operating system can be difficult and costly. Despite these challenges, predictive modeling and the use of big data will be beneficial throughout all industries, including the specialty pharmacy space, providing actionable insights by anticipating future outcomes.

Data Analytics Vs Predictive Analytics - Which One is Useful. (2019). Retrieved 22 November 2019, from https://www.educba.com/data-analytics-vs-predictive-analytics/.

Hernandez, I & Zhang, Y. (2017).  Using predictive analytics and big data to optimize pharmaceuitcal outcomes. 

Predictive Modeling: The Only Guide You Need. (2019). Retrieved 22 November 2019, from https://www.microstrategy.com/us/resources/introductory-guides/predictive-modeling-the-only-guide-you-need#medical.

Ballard, B. G. (2017, April 1). Retrieved from bengballard.com /uncategorized/data-science/.

 Supervised Learning vs Unsupervised Learning - Best 7 Useful Comparison. (2019). Retrieved 22 November 2019, from https://www.educba.com/supervised-learning-vs-unsupervised-learning/.

Seven ways predictive analytics can improve healthcare. (2019). Retrieved 22 November 2019, from https://www.elsevier.com/connect/seven-ways-predictive-analytics-can-improve-healthcare.

Jimmy, B., & Jose, J. (2011). Patient Medication Adherence: Measures in Daily Practice. 

Big Data, for better or worse: 90% of world's data generated over last two years. (2019). Retrieved 22 November 2019, from https://www.sciencedaily.com/releases/2013/05/130522085217.htm.

Guest Blog: 9 Bizarre and Surprising Insights from Data Science. (2019). Retrieved 22 November 2019, from https://blogs.scientificamerican.com/guest-blog/9-bizarre-and-surprising-insights-from-data-science/.

Lee, C., & Yoon, H. (2017). Medical big data: promise and challenges. 

Kidney Research And Clinical Practice, 36

(1), 3-11. doi: 10.23876/j.krcp.2017.36.1.3.

As the industry focuses more on value-based care, predictive modeling allows pharmacies to shift their services from a one-size-fits-all approach to a personalized, high-touch strategy.