Article | September 1, 2021
Generating insights about patients on specialty therapies through data aggregation and integration can improve patient outcomes.
As the number of specialty drugs expands in complex disease areas, such as autoimmune, multiple sclerosis, oncology, and respiratory illnesses, pharmaceutical companies have shown an increased interest in tracking and understanding real world and clinical data throughout the patient journey to support more targeted decision-making. There are significant challenges in the journeys that patients with complex diseases experience, including gaps, lapses, or delays in therapy. This often includes the initial start of a treatment, as well as when switching to a next-line therapy. These delays or gaps can not only lead to negative impacts for the patients but can affect the manufacturers as well. When specialty product pharmaceutical companies enrich their understanding of the patient journey through aggregated and integrated data, they will be more apt in improving treatment outcomes and making smarter decisions.
Complexities with specialty products
Over the last several decades, the production and use of specialty drugs has increased as there has been a wider global share of spending in higher-income countries.In the 10 largest developed countries and other high- and upper-middle-income countries,the use of specialty therapies has reached 47% and 37% respectively in 2020, up from 24% and 21% 10 years earlier. Specialty therapies will represent nearly half of global spending in 2025 and almost 60% of total spending in developed markets.
The growing number of specialty products in any given market has generated improved patient outcomes, but the stakes are high for manufacturers when it comes to new patient starts and adherence. Specialty products typically demand more complex distribution and more hands-on administration to patients. A wealth of data is available from limited network specialty providers, but to get a broader view of the patients’ entire healthcare experience, integration with third-party claims data is necessary. With access to a greater depth and breadth of data, specialty product manufacturers have opportunities to advance their analytics capabilities and generate greater insights across vital patient touch points.
For patients with complex diseases who are being treated with specialty products, there are multiple challenges they may face throughout their journeys:
Fragmentation: Patients often need to interact with multiple sites of care, which results in fragmented data. A mix of pharmacy and medical claims can result in an incomplete view of the patient, prescriber, or payer, as well as create distorted views of administrations or doses across treatments which may need to be normalized for analysis.
Data gaps: There could also be differences in the various data elements reported by each source, which can vary significantly, whether these are prescription data, laboratory data, claims databases, electronic health records, health registries, or patient reported outcomes data. Furthermore, there can be data lags, varying frequencies across datasets. Finally, multiple pieces of patient data can result in elevated privacy risks.
These complexities directly affect manufacturers’ ability to gain insights to support their commercial activities:
Finding and understanding potential patients, or tracking patients enrolled on a treatment, but subsequently dropped, can be a challenge as well as understanding comorbidities or concomitant therapies.
Targeting HCPs is difficult as prescriber volume across datasets can be unclear and can limit the ability to prioritize healthcare professionals by patient mix, practice, specialty, and treatment dynamics.
Aligning pricing, contracting and access, and determining incentives and return on investment (ROI)promotional metrics represent challenges.
There is only a partial picture of market access and payer dynamics. Incentive compensation is complex due to duplicate or missing data which causes inequities in geographic or target-based plans. ROI metrics for promotional effectiveness may be over or understated due to inaccuracies in prescriber counts and inability to bridge to associated data.
Defining the patient journey
Specialty drugs are high-cost, high-touch products with complex manufacturing, dosing, and handling requirements. Often, the patient populations who utilize these drugs are small and dispersed, and may experience late diagnoses, misdiagnoses, lack of treatment options, and limited access to relevant specialists. This makes it difficult to find them and requires more customized market strategies to ensure their physicians have the right data they need at the point of treatment initiation.
To generate effective forecasting and commercial strategies, pharmaceutical companies need specialty data tactics which can define every step to help them answer key questions, including:
When in the patient journey is treatment prescribed?
Who is prescribing it?
Which tests, physician appointments, or medical events precede diagnosis?
How long are patients typically adherent and what typically complicates adherence or causes a stop?
Is the product being used as part of a combination therapy?
What triggers the transition to a new line of therapy?
What impact do copay assistance, nurse educator services, and other value-added programs have on the patient and physician experience?
When pharmaceutical companies can combine the answers to these questions, they gain clarity into the critical milestones that define the patient experience.
This enables them to find patients and their physicians more quickly and to be more targeted in their engagement strategies which, ultimately, generates more successful marketing campaigns.
Difficulty accessing the data
Unfortunately, these datasets can be difficult to access. Many pharmaceutical manufacturers partner with individual specialty pharma service providers to try to meet their data needs. But these resources tend to be limited and often fail to provide the comprehensive view needed to make meaningful decisions.
Because most specialty products have such small and dispersed patient populations, patients are rarely served by a single pharmacy, healthcare professional, or healthcare organization. Each time a patient switches a product or line of therapy, it can result in disparate data collected through several channels, including physicians, clinics, hospitals, alternate sites of care, pharmacies, labs, payers, and digital health assets.
Additionally, much of the patient journey data never passes through the limited distribution specialty pharmacy network, nor is it collected in a consistent, accessible format, making it difficult to analyze. As more specialty pharmacy service providers launch, it will become more difficult for manufacturers to compile a global view of the product from any single source, or to capture patient data with any degree of granularity.
Expanding the view of the patient journey with data aggregation and integration
Data aggregation and integration can help specialty pharmaceutical companies obtain an expanded view of the patient journey.
The best data aggregation solutions provide pharmaceutical companies with broad sourcesof data as well as integration services that deliver high-level perspectives across patient populations or geographies. The data can be linked back to an individual prescriber, healthcare organization, insurer’s practice, or de-identified patient.
By leveraging data from a limited distribution network and integrating with third-party healthcare data assets, including digital patient data, sponsors can identify a greater majority of patients likely to be prescribed or considered for their treatment.
Oncology example: One manufacturer might choose three specialty pharmacies for their third-line oral oncology product, but the entire category, which includes first- and second-line therapies, may span well over 10 specialty pharmacies and a multitude of medical claims processors. An experienced data partner will be able to recommend and access data from most of these providers either directly and/or via use of syndicated data assets, to deliver a more complete picture of the patient journey and competitive landscape.
The best partners offer more than data aggregation and integration; they provide deep analytics capabilities which empower commercial teams to deliver precise physician, organization, and patient engagements. The best partners will prioritize their efforts based on individual needs. Predictive analytics via artificial intelligence and machine learning capabilities are a must for a patient journey analysis and timely decision-making.
Ultimately, good data aggregation and integration will lead to enriched data and insights to generate personalized treatment plans for the right patient at the right time to enhance outcomes. Access to the right data, analytics, and insights drive tangible results for patients and physicians while also ensuring that pharmaceutical companies generate the best ROIs.
Article | August 18, 2021
In a way, getting through the initial stages of a complex pharmaceutical project that is being outsourced to a contract development and manufacturing organization is like getting a rocket off the ground. Many drug developers express frustration with the time it often takes during the initial stages of working with a CDMO — from the time they first reach out to a CDMO for help until they receive a proposal. Some have described it as months of silence from when they send a request for proposal (RFP) until they have a proposal in hand.
The initial stages of a relationship between drug sponsor and CDMO often do not get the attention it deserves, and valuable time is lost, delaying projects and delaying delivery of therapeutics to patients. The quick scheduling of the ACT meeting with the right attendees can deliver immediate answers to key questions needed by the drug sponsor for effective planning and can help propel projects to a successful launch.
Article | May 25, 2021
Technological innovations disrupt many industries, but the speeds of their adoption in the pharma industry have become more rampant than ever. A report from global market advisory firm ABI Research predicts that by 2030, the pharma industry will have spent over $4.5 billion on digital transformation. This is due to many things, from the need to optimize production lines to patent protection.
A decade from the forecasted market peak there have already been many applications of these rising tech trends. So let’s have a look at some of them:
Digital monitoring system
Pharma businesses need to comply with certain regulatory requirements before their drugs can be sold on the consumer market. For example, they need to be stored at a certain temperature. The state of the drugs manufactured and used during clinical trials also needs to be monitored. Fortunately, digital monitoring systems created by companies like Aptar Pharma, Primex, and Monnit, have made it easier to provide the reports regulatory boards such as the Food and Drug Administration and Central require. Aptar Pharma, for instance, offers sensors that can monitor and record the patients’ adherence level during ophthalmic clinical trials. Meanwhile, Monnit’s freezer monitoring solution provides data logs that can be filed as proof of compliance.
It doesn’t matter what kind of pharma data you need — there will be a digital monitoring system that can help you collect it.
Extended reality (XR) is used to describe all real-like virtual environments that are generated by computer programs. The two most common types of XR are augmented reality (AR), where digital graphics are overlaid onto the real world, and virtual reality (VR) where the user is “transported” to a digital world through headsets. To create realistic projections, VR and AR technologies are built with complex and densely packed electrical PCB designs. From wiring the schematic to comparing physical validation rules, all of this is carefully done to ensure that the technology has all the 3D features it needs. Pharma has many uses for this kind of technology. For example, one of Augray’s solutions is to allow researchers to better visualize human models using XR.
XR can also be used in lab and manufacturing training. Before letting people train onsite, XR solution providers like SoftCover VR and Labrodex Studios can create simulations that let them familiarize themselves with the equipment virtually. This is very important in the pharma industry, as one error can easily contaminate the drugs.
Whether it’s for drug discovery research or clinical trials, artificial intelligence (AI) can help accelerate the process. AI is a technology that “learns.” AI programs, after they’re made, are immediately trained to detect patterns and features in the data to help collect insights. British startup Pangaea Data helps global pharma companies identify patient cohorts and trials using AI algorithms.
AI can also be trained to perform mundane tasks more efficiently, like arrange clinical data for researchers or gather studies. An AI program called Atomwise does this by analyzing thousands of existing medicines and picking out the ones that can be repurposed to treat diseases it wasn’t initially made for. This was even the AI that identified two drugs that could mitigate Ebola’s effects in 2015, saving multiple lives. In the future, AI can be taught more things that will allow them to aid medical research.
Additive manufacturing, commonly known as 3D printing, is an industrial production process that lets businesses create 3D products using successive layers of a specific material. Since 3D printers will literally print any object with the right blueprints, additive manufacturing has been a big help in the mass production of drugs. However, researchers are now finding more uses for additive manufacturing — one of which is in the field of precision medicine. Precision medicine takes into account the patient’s lifestyle, history record, and even genetics. Eventually, they're given medicine that’s specially tailored for their body. Since blueprints can easily be edited, combining drugs can be done faster and with more accuracy. Of course, additive manufacturing’s application in this field is still at its testing phases, but researchers are hopeful about the results.
New discoveries are made in the pharma industry thanks to technology, and more will continue to do so as long as breakthroughs are made. Businesses should always be updated on these emerging trends, lest they want to be left behind by the competition.
VIEWS AND ANALYSIS
Article | April 20, 2021
For much of the past three decades, even as methodologies for clinical trial design have advanced and refined, the idea of the optimized clinical trial has centered on optimal patient samples, target enrollment rates, and generally the most efficient uses of scarce resources in the form of patients. Yet anyone who has had to design and optimize a clinical trial, knows that trial optimization occurs within an ecosystem of choices; a series of choices that stretch from the time it takes to implement a clinical trial and submit clinical data for analysis, to general concerns about the cost and power of a clinical trial. A true clinical trial optimization process would try to unify a number of these choices into a single framework for trial optimization.
The complexity of clinical trial optimization comes from the need to align priorities on the one hand, and to understand opportunities on the other. We know that at a very general level, clinical operations specialists benefit from simplicity in clinical trial design, and that commercial teams prefer shorter clinical trials to longer ones. We also know that the statistical design of a clinical trial can influence both simplicity and duration. Yet how many sponsors have their clinical operations and commercial teams, sit with their R&D teams to review various statistically nuanced design options?
For many sponsors, the reason this process does not occur as often as it should, is because the nuanced statistical parameters of a clinical trial design are very difficult to communicate to non-statisticians. Yet a trial optimization tool like Solara, equipped with data visualizations and the ability to see tradeoffs intuitively, can overcome this challenge. The real challenge is often convincing the non-statistician that they have a stake in clinical trial design.
Cytel recently had a client that thought it needed a sample size re-estimation design, because it had a very strict limit on the number of patients it could enroll. After a few hours of working with Solara, though, a statistician discovered that a much simpler Group Sequential Design would deliver comparable power using about the same number of patients. The gains from the more complex design were minimal from the optimization perspective, when understood as the eco-system of choices.
Similarly, most commercial teams pressure their clinical trial designers to have the most accelerated clinical trial imaginable, but as we all know, the longer the clinical trial the more likely there will be a higher number of events that demonstrate the effectiveness of a new medicine. So commercialization teams have a stake in longer clinical trials, even when their rule of thumb is to shorten them.
Therefore, it is absolutely essential to communicate the benefits of various statistical designs to multiple stakeholders in a way that makes tradeoffs clear. Aligning on priorities early during the clinical trial design process is essential to selecting the optimal clinical trial. Yet for this statisticians need to be equipped for both a strategic and communicative role in the R&D process.