We recently sat down with senior pharma leader, Mr. Eiichi Naka, to discuss patient-centricity and the impact of AI in driving outcomes in rare disease. Recently retired after 40 years of dedicated service, Mr. Naka began his career in 1983 at Sandoz and navigated through various roles in clinical development and marketing, notably at Novartis, following a merger. Throughout his tenure, he held leadership positions in Japan, specializing in specialty marketing and rare diseases. With over three decades at Novartis and a decade at Biogen, Mr. Naka made significant contributions to therapeutic advancements in areas such as organ transplantation, severe asthma, autoimmune diseases, and groundbreaking work in rare diseases including hemophilia, spinal muscular atrophy, multiple sclerosis, and Alzheimer’s disease. His legacy reflects a commitment to innovation and improving patient outcomes, leaving a lasting impact on the rare disease landscape.
Prospection: Hello, today we have Mr. Naka, a senior pharma leader with 40 years experience with Novartis and Biogen, who will discuss patient-centric approaches to address unmet needs for new treatment and challenges when establishing new standards of care in rare disease. Mr. Naka, what do you see as the key pillars and challenges that pharma commercial executives need to be considering here?
Mr. Naka: Thank you. I would like to share my experience in bringing new treatments to market to address unmet medical needs in rare disease. In particular, the challenge of incorporating new treatments into standard of care through a patient-centric approach and strategies to stay at the forefront of patient-centric solutions.
The key pillars to addressing unmet need in rare disease tend to be:
- Patient identification
- Treatment activation (how to start therapy)
- Retention (ensuring patients stay on treatment to benefit)
Preparing for new entrants is crucial for long-term business success as other treatments become available. Hence, capabilities beyond just having an efficacious medicine are important. Capabilities to establish a core treatment as the first treatment option and to build an ecosystem by aligning communities and stakeholders around the treatment.
The main challenges faced at launch include the applicability of externally generated data to Japanese patients and generating timely Japanese data. For example, in organ transplantation, therapeutic drug monitoring to determine accurate dosing was extremely important between treatments. Applying techniques like blood drug concentration measurement was challenging under insurance, so generating supporting data through academia was essential.
In autoimmune disease and other therapies new to Japanese patients, creating evidence in Japan in a timely manner with physicians was also a consistent challenge. Another example is the introduction of biologics for severe asthma. Some patients experienced remarkable effects beyond what was seen before. But with asthma treatment otherwise inexpensive, generating cost-effectiveness data to support uptake of expensive biologics was difficult and time-consuming.
In summary, the challenges introducing new disease treatments are:
- Timely data generation, including Japanese data
- Building mutual understanding among patients, physicians and patient groups regarding new treatment options
Prospection: Thank you for sharing the challenges faced in launching a therapy in rare disease. To address unmet needs once in-market, could you share your experience of patient support programs in rare disease?
Mr. Naka: Let me focus on recent experience in Japan, especially personalized support. The standard digital approaches, like disease education websites, customized patient sites and email services are primarily push-based information channels.
In contrast, new initiatives are more personalized phone consultations for patients and families with counselors to address concerns about therapy, life impacts and so on. It is provided for members who register for support programs. Counselors can send materials in advance, then advise on therapy choices, how to use apps, and try to alleviate anxieties. This phone support aims to provide individualized support versus existing digital tools.
An analysis of a support program for patients with designated intractable diseases also showed that phone or video consultations have higher perceived value and satisfaction versus digital methods like apps and email. Over half of users found in-person methods very satisfactory. This suggests the greater potential of personalized human support.
Furthermore, analysis in multiple sclerosis showed significantly higher adherence for patients provided in-person support programs. So, while we have various app-based support, the ability to work through individual issues and provide reminders and encouragement directly can be impactful.
In summary, disease education and patient support focuses on adherence programs to keep patients on therapy after initiation. While apps and other digital tools can improve adherence, personal interaction like phone counseling that addresses individual concerns and provides a human touch can have a greater impact. Ongoing efforts in improving outcomes from patient support programs center on how to provide more tailored support based on individual needs of patients, families and healthcare professionals within compliance.
Prospection: It sounds like patient support programs will be more personalised and patient-centric in the future. What are the key elements to enable this?
Mr. Naka: I believe it’s crucial that these programs can meet the needs of patients, their families, and healthcare professionals in a personalized manner. Utilization of data and enabling communication among stakeholders is key.
Through the expansion and utilization of data, it’s possible to provide timely, accurate information that supports the patients’ specific conditions. And the importance of properly supporting both healthcare professionals and patients in achieving better communication cannot be understated.
Prospection: Thank you for your insights on patient support programs in rare disease, how a patient-centric approach is the future, and the importance of data. At Prospection we focus on AI-driven insights from patient data to improve patient outcomes. I would now like to explore with you the potential impact of rapidly advancing AI in this field.
First, a major challenge in rare diseases is identifying patients. AI may help diagnose patients earlier or with greater accuracy. But in your experience, what are some of the challenges in finding these patients, and how are you addressing this?
Mr. Naka: It varies by disease, so there’s no single answer. But when new treatment options emerge, they draw attention and motivate specialists to identify and treat more patients. So the availability of new therapies is a major motivator. At the same time, there are diseases where patients struggle to access specialty care and diagnosis. Beyond specialists who may be skilled at finding patients, improving information and education of physicians can help identify patients who otherwise may be missed.
While AI may help physicians who don’t regularly see certain patients diagnose them more accurately if provided with comprehensive information, patient identification ultimately requires continued effort across stakeholders to help patients access appropriate care and therapies for their disease.
Prospection: Given the need for personalization and the fact that treatment options are limited and highly variable in rare diseases, tailored treatment planning and individualized management become even more critical. We believe AI-enabled large-scale data analysis can contribute to developing personalized treatment plans but there are limitations and physicians may resist adopting it. What are your thoughts on this?
Mr. Naka: AI could logically analyze data to create standardized models and case examples of optimal treatment pathways. But practical implementation needs to account for differences across regions, hospitals, and individual physician approaches that AI alone may miss.
While an AI-created pathway may be logical, adjusting it based on real-world data collection and input from on-site doctors is likely needed, especially for complex, previously untreated diseases. So while AI can be useful, human input is likely still required to finalize personalized approaches tailored to local needs.
In terms of adoption by physicians, as long as the plans aim to improve patient care, I think acceptance would be relatively high, though it depends on the disease and proposals. It may even be welcomed by physicians who don’t routinely see certain patients, since AI could comprehensively cover aspects they may miss. But for true experts, while they may identify things systematically themselves, AI could still help avoid oversight and ensure thorough treatment proposals. So I think it would generally be received positively rather than negatively.
Prospection: What are your thoughts on how real-time data needs to be to bring value?
Mr. Naka: It does depend on whether it is a chronic versus progressive disease. For rapidly progressing conditions, minimizing time to appropriate treatment is critical. So more real-time data would be highly valuable to inform care. But for chronic stable conditions, timeliness may be less of a factor. Either way, being able to leverage real-world data for patient care is important and has value now.
Prospection: If AI could help predict patients at higher risk of side effects or events requiring hospitalization, it may significantly improve persistence. I would be interested in your thoughts on the potential importance and value of improving persistence by predicting risks for individual patients.
Mr. Naka: You’re absolutely right that persistence – ensuring patients stay on therapy – is critical, and side effects or hospitalization that disrupt treatment negatively impact outcomes. So if risks could be predicted or mitigated to enable better side effect management and reduce risks proactively, it could have a big impact in supporting persistence. While the goal shouldn’t necessarily be to minimize hospitalization, improved side effect management would enable earlier intervention and continuity of therapy. So from a persistence standpoint, being able to proactively address side effect risks is very important and could have a significant impact.
Prospection: In closing, it is clear that predictive AI models have the potential to significantly impact healthcare by improving treatment persistence, managing side effects, and addressing issues related to hospitalization. Could you comment on the impact this will have on patient outcomes?
Mr. Naka: The impact could certainly be huge. From a side effect management perspective, if risks can be well-managed, it would support keeping patients on therapy and achieving desired outcomes. For hospitalization, while the goal isn’t necessarily shorter or longer stays, better side effect management would enable closer monitoring and earlier intervention. Rather than the length of hospitalization, the key would be maximizing the ability to proactively address side effect risks to maintain persistence. So the impact on appropriate side effect management could be very large.
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Prospection has been providing solutions and services to pharma analytics and commercial teams for more than 10 years. Its proprietary Patient-centric Intelligence Core transforms RWD into intelligence, and powers a range of solutions including the flagship brand management platform, Prospection AI.
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