Picturing the hidden patient: Overcoming challenges in sizing and segmentation

This report was originally published on decisionresourcesgroup.com, now part of Clarivate™

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Overcoming challenges in sizing and segmentation for rare diseases and niche treatments

A host of lean, agile biotechs has cropped up alongside large and mid-sized pharmas2, pursuing treatments keyed to specific biomarkers, and niche indications are the norm in this emerging ecosystem.

This is good news for patients. For some, it means treatment options where none existed before; for others, more targeted and

Introduction

In recent years advances towards more sophisticated and narrowly targeted medicines1 mean that many new therapies are designed to treat smaller patient populations, whether patients with rare diseases or subsets of patients with more commonplace diseases.

effective treatments, and less precious time lost trying out ineffective treatments.

However, it poses some difficult challenges for the developers of new treatments, who need an accurate picture of hard-to-define patient cohorts to successfully navigate the arduous process of bringing medicines to market.

1 U.S. Food and Drug Administration, 2021. Table of Pharmacogenomic Biomakers in Drug Labeling. [Online] Available at: https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling [Accessed 1 October 2021].

2 UBiopharma Dive, 2021. A record number of biotechs are going public. Here's how they're performing. [Online] Available at: https://www.biopharmadive.com/news/biotech-ipo-performance-tracker/587604/ [Accessed 1 October 2021].

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One size fits all

The mass-market medicines for all

Targeted treatments

Medicines of today

Personalized medicines

Medicines of tomorrow

Drugs for smaller patient groups are increasingly the norm

The proliferation of innovative gene and cell-based therapies comes with an urgent need to define smaller patient cohorts in order to speed medications to market and get them in the hands of undertreated patients.

01Stratification Patient grouped by:

• Disease subtypes• Clinical features• Demographics• Biomarkers

02Personalisation Individual patients:

• Clinical features• Medical history• Environment• Behaviors• Biomarkers

Figure 1: Spectrum of mass to personalized medicines.

"Patients with rare diseases and those that care for them are the thought leaders of their condition. It is critical that companies look at a variety of sources to gain an accurate understanding of these patients and listen to what they have to say at every stage of a medicine’s development."

Mike Ward, Global Head of Thought Leadership, Life Sciences and Healthcare, Clarivate™

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Robust patient analytics is mission-critical

In order to develop and commercialize treatments for smaller patient populations effectively, companies are seeking accurate intelligence on:

Input Needed to Risk of not getting it right

The true size and location of the patient population eligible for a treatment

• Support orphan drug applications

• Define what a reasonable clinical trial size might look like

• Target the right sites of service for physician education

• Uninformed physicians and undiagnosed patients

• Ineffective messaging and poorly informed patients

The true number and character of distinct sub-populations within the patient population, whether measured by severity of disease, comorbidities or other factors

• Segment a patient population according to interplay of severity and comorbidities

• Suboptimal targeting and treatments not reaching the right patients at the right time

Treatment patterns and line of therapy progression

• Identify areas of unmet need by spotlighting points of therapeutic drop-off and drivers

• Map out sites of service through which patients seek care

• Misallocated field resources and uninformed physicians

• Adverse reimbursement negotiations and patients that can’t access treatments

Patient unmet needs, key behavioral triggers and barriers to treatment

• Inform prioritization of indications and patient engagement efforts

• Understand how patients are covered (for example, insured) in order to anticipate market access issues and gain a better picture of the addressable market for therapeutics

Table 1: Patient analytics needed for developing and commercializing treatments for niche populations.

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Defining smaller patient populations poses big challenges

Lack of research

Because these diseases are, by definition, less common, they may be poorly understood and underdiagnosed.

They're not covered in the coding

Often, diseases afflicting smaller patient populations have not been assigned an ICD-10 code or been covered in the extant medical literature, making a conventional approach to understanding incidence and prevalence a nonstarter.

Patient cohorts can vary widely

Subpopulations within more established categories can vary greatly by severity, comorbidities, environmental factors, behaviors and treatment pathways.

Payers and providers require education

Physicians and payers may be unfamiliar with the condition and require education in order to get patients diagnosed with the appropriate condition and enable them to access relevant medications. Identifying potential treatment inflection points can help to increase the speed of potential disease identification and eventual diagnosis, leading to a shrinking undiagnosed population, better health outcomes for patients and, potentially, cost savings for payers.

There are is array of factors that make sizing and segmenting smaller patient audiences particularly difficult:

"A patient population isn’t a single average patient with one set of comorbidities or pill burden. It’s thousands or millions of individuals with their own unique combinations of factors affecting their treatment access and outcomes. We need to reach and engage them as such."

Simon Andrews, Vice President, RWD Engagements and Innovation, Clarivate

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Creative analytics approaches are needed

An emerging arsenal of tools, data sources and patient registries, used in combination with traditional epidemiological tools, can help companies to find the needle in the haystack. This enables teams to understand behavior and needs with more specificity, ultimately enabling the delivery of treatments and support that improves patient outcomes.

Medical science is producing miraculous treatments, and making strides toward a world in which truly

personalized medicines are as commonplace as targeted therapies are today. Getting closer to that goal demands that we get closer to the patient. Fortunately, by combining new and emerging data sources and technologies with traditional methodologies and therapeutic expertise, companies can identify and understand heretofore invisible patient populations—and in doing so, enable them to access new treatments.

Inputs may include: Enabling you to:

Real world and other data sources:

• Medical and pharmacy claims data

• EHR /EMR data

• Social conversation data, search and web behavioral analytics

• Reveal total patient populations

• Enable capture of extremely rare disease patients

• Provide real-time guidance on fast-moving treatment patterns and market dynamics

• Define patient sub-populations not covered in the epidemiological literature

• Inform physician targeting and patient and HCP education and engagement efforts

• Reveal unmet needs and barriers to care

Traditional epidemiology and primary market research

• Validate estimates derived from non-traditional approaches and data sources, either directly if estimates are available, or indirectly if not

• Complement medical and pharmacy claims data or other data sources

Therapeutic expertise • Navigate fine nuances in treatment, care and coding

Machine learning approaches • Enable construction of algorithms capable of identifying tiny patient cohorts within enormous volumes of data

• Predict and size undiagnosed populations by correlating disease-identifying characteristics within the diagnosed population against the wider insured population

Table 2: An integrated approach to sizing and segmentation.

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Contributors

Mike WardGlobal Head of Thought Leadership, Life Sciences and Healthcare, Clarivate

Matthew ArnoldPrincipal Analyst, Life Sciences and Healthcare, Clarivate

Getting an accurate picture of hard-to-define patient cohorts

Understanding small patient populations and generating current and precise trends and forecasts is critical to bring life-saving treatments to patients.

We’re blending real world data with epidemiology so that our customers have the greatest accuracy to enable the right investment and drive the strategic business decisions.

Identify and understand your patient populations with greater precision. Learn more:

https://www.clarivate.com/market-sizing-forecasting

About Clarivate

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