Holmusk’s Nawal Roy highlights the increasing recognition of the value of real-world evidence (RWE) by both innovative pharma companies and drug regulators and its potential to increase market capture and profitability.
RWE has the potential to expand the market size of a disease and accelerate market capture of a given therapeutic agent
Following over ten years of development, significant R&D investment of potentially hundreds of millions of dollars, and the successful navigation of clinical and regulatory hurdles, a new drug is launched into the marketplace. However, increasingly, commercial success does not necessarily follow, as revenues fall short of expectations.
According to one report, drugs launched in 2005-09 on average barely broke even compared to the high profitability experienced in drug launches of the 14 years prior.1 Furthermore, two-thirds of new drug launches fail to meet pre-launch sales estimates in the first year, with this trend continuing over subsequent years.2 The reasons for the dearth of revenues are myriad and include issues of higher R&D costs, market competitiveness, reimbursement, market penetration, and patient access among others.
Drug regulatory agencies such as the FDA and EMA have recently provided provisions for the use of high-grade (clinical and regulatory grade) real-world evidence (RWE) generated from real-world data (RWD) to enhance or accelerate the delivery of medical products to patients. RWE is patient healthcare information derived from sources outside of clinical trials. These may include electronic health records, claims and billing data, product and disease registries, and even data gathered by personal devices and health applications. Effective use of RWE may assist pharmaceutical companies in greater market capture and increased profitability, because of better translation of clinical trial results to real-world patients.
Real-world treatment effectiveness
Clinical trials are conducted under highly “sanitized” and controlled conditions with a narrowly defined patient pool that meets high thresholds for selection and exclusion criteria. Real-life patients are never as “clean” as those tested within clinical trials, as they often suffer from multiple comorbidities and resulting polypharmacy. RWE can confirm the effectiveness of treatments beyond the controlled conditions of clinical trials, generating the evidence for the prescribing clinicians. RWE is the best path forward to show efficacy and safety that is meaningful to prescribers.
Continuous evidence generation
Under conventional settings, post-approval evidence is generated from Phase 4 studies. RWE is a scalable option for generating evidence of efficacy and safety on a wider range of patients. Increasingly, clinicians prefer RWE to randomized clinical trial (RCT) evidence to make decisions about choice of drugs. Comparative efficacy from RWE allows easier product differentiation against competitor products on the same type of patients, something is normally quite challenging based on RCT alone. Additionally, RWE collection can efficiently fulfil post-marketing regulatory approval requirements and ongoing pharmacovigilance.
Patient identification and access
A major impact of RWE is clear identification of patients with unmet need. With the advent of sophisticated artificial intelligence (AI) and machine learning (ML)-based engines, RWE is enabling the development of tools for identification of high-risk patients allowing better targeting of new drugs. Evidence collected from drug usage and resultant clinical observations, can help identify those patients best suited for the drug. Such data may be used to access broader patient populations not tested during the clinical trials and even for usage in new countries. In fact, evidence from incidental use cases can be used for extensions of label or follow-on indications of the same drug using only RWE.
Care pathway positioning
Evidence of product effectiveness can appropriately position the product within the care pathway, and perhaps even as the new standard of care, and thus inform clinical practice guidelines. RWE can also be used in supportive evidence for pharmacoeconomic justifications.
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These opportunities point to the general trend within healthcare towards evidence-based treatment, and away from the sales-force driven model for acceptance of treatments by physicians. They illustrate how following approval and launch of a new drug, quality RWE can be gathered and used to increase drug use and adoption by clinicians. However, the case can be made to start utilizing RWE prior to a new drug launch to further increase competitiveness. For example, mental health disorders are complex and plagued with comorbidities and polypharmacy. Diagnosis is subjective in nature and can be delayed due to a multitude of reasons. Combining large sources of evidence with advanced analytical techniques can significantly improve early and appropriate diagnosis and treatment. Use of such digital tools can expand the overall market and provide deeper penetration for new therapeutic agents.
RWE paired with AI is highly suitable for establishing effectiveness of treatment in complex diseases plagued with comorbidities and polypharmacy
It is rather timely that in April 2019, the FDA published the following: “Proposed Regulatory Framework for Modifications to AI/ML-Based Software as a Medical Device (SaMD) – Discussion Paper and Request for Feedback”.3 This highlights the fact that the FDA recognizes the importance of AI and ML as part of the repertoire of tools and techniques to aid it in its mandate to help accelerate the delivery of medical products to patients in need.
References
The Golden Era Of Pharmaceutical Profits Over? Forbes, 2016.
McKinsey & Company. The Secret of Successful Drug Launches, 2014.
FDA. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) – Discussion Paper and Request for Feedback, 2019.
https://www.fda.gov/MedicalDevices/DigitalHealth/SoftwareasaMedicalDevice/ucm634612.htm
Nawal Roy is founder and CEO of Holmusk, a firm aiming to build the world’s largest real-world evidence platform for neurosciences. Holmusk leverages its technology to work with real-world data across the whole spectrum of chronic diseases.
