Unlocking the Full Impact of Breakthrough Therapies in Breast Cancer Through Earlier Detection
By Léon Van Wouwe, Clinical Innovation Director, Volv Global New data from Roche’s Itovebi (inavolisib) combined with Pfizer’s Ibrance (palbociclib) and Faslodex (fulvestrant) has delivered a major milestone in breast cancer care, showing a remarkable 33% reduction in mortality for patients with HR-positive, HER2-negative, PIK3CA-mutated advanced breast cancer. This breakthrough offers significant hope in managing one of the most common and challenging breast cancer subtypes, especially benefiting patients who have progressed after hormone therapy. Yet, as we acknowledge this clinical achievement, a critical and pressing question arises: How many more lives could we significantly improve, or even save, if these patients were diagnosed earlier? The Challenge: Too Many Diagnoses Still Come Too Late While targeted therapies are revolutionising cancer care, their full therapeutic potential often remains unrealised due to the persistent and complex issue of late diagnosis. Particularly in HR+/HER2- breast cancer, the diagnostic journey is often protracted and challenging. Symptoms can be subtle and misleading, resistance to treatment evolves gradually and silently, and crucial genomic testing access may be limited by systemic barriers. For patients with PIK3CA mutations, every delay translates directly into worsened outcomes, significantly limiting their therapeutic options. These diagnostic delays result in considerable missed opportunities – not only for patients whose lives depend on timely intervention but also for pharmaceutical innovations whose efficacy diminishes as disease stages advance. The Opportunity: Detecting Risk Signals Earlier Using EMR and AI Emerging advancements in AI and sophisticated data analytics now offer a promising pathway to closing the critical gap in early breast cancer diagnosis. By harnessing patterns within electronic medical records (EMRs), it is possible to identify early, subtle risk indicators well before a formal breast cancer diagnosis. These early indicators can manifest as seemingly benign, recurrent symptoms, medication prescription patterns, comorbid conditions, or specific referral behaviours. When accurately captured and analysed, these subtle signals facilitate the predictive identification of at-risk patients, potentially years ahead of current clinical diagnosis timelines. Similar methodologies have already proven successful in rare, hard-to-diagnose cancers like Gastroenteropancreatic Neuroendocrine Tumours (GEP-NETs), where diagnostic delays have been significantly reduced by up to seven years through predictive analytics. Extending these methodologies to more prevalent cancers, such as HR+/HER2-/PIK3CA-mutated breast cancer, presents an immense opportunity to substantially extend therapeutic windows and improve patient prognoses. The Shift: From Precision Medicine to Precision Diagnosis Modern oncology has seen extraordinary advancements in precision therapies, but these advancements must be complemented by equivalent progress in precision diagnosis. The shift must start significantly earlier, ideally within primary care settings, as waiting until oncology referral often proves too late. With robust AI-driven healthcare infrastructure, we can empower clinicians, researchers, and pharmaceutical companies to: Identify patient segments at risk much earlier, enabling healthcare providers to initiate preventive and proactive care strategies at a point when interventions can be most impactful, thereby reducing the long-term burden on healthcare systems and improving individual patient outcomes. Enhance patient selection precision for clinical trials, ensuring that trials are better designed, more efficiently executed, and specifically targeted to patient populations most likely to benefit, thereby improving the speed and effectiveness of new treatment validations. Improve access and adoption of groundbreaking therapies, significantly expanding their clinical impact by accurately targeting therapy to patients who will derive the greatest benefit, which increases therapy effectiveness, reduces wastage, and maximises healthcare resources. Enable clinicians to navigate and address the most critical unmet needs in cancer care today by providing comprehensive, data-driven insights that facilitate timely diagnosis and personalised treatment decisions, ultimately closing critical gaps in patient care and improving survival rates. What’s Next? The oncology pipeline is brimming with innovative therapies, and the clinical and pharmaceutical sectors have never been more prepared to harness these transformative possibilities. However, maximising patient outcomes requires a concerted parallel investment: in early detection mechanisms, actionable data intelligence, and strategic collaborations. Leading pharmaceutical companies, healthcare providers, and technology innovators must collaborate to leverage AI-driven predictive analytics, transforming early detection from aspiration to operational reality. By accelerating this integration, we ensure more patients benefit fully from therapeutic breakthroughs like Itovebi and Ibrance. At Volv Global, we’re already working with forward-thinking teams to make this vision a reality — helping to identify the patients who need access to personalised therapies, earlier and more precisely than ever before. Let us shape a future where oncology care is defined not merely by the potency of treatments available but by the precision with which we ensure they reach patients exactly when they can make the greatest difference. About the author Léon van Wouwe has 20+ years’ global experience in clinical development and operations, uniting data science with pharma and research. He drives cross-functional collaboration to advance innovative treatments.
Progressive Pulmonary Fibrosis: Solving the Puzzle of Poor Disease Control
By Léon Van Wouwe, Clinical Innovation Director, Volv Global Introduction Progressive Pulmonary Fibrosis (PPF) is a severe clinical condition emerging from various fibrosing interstitial lung diseases (ILDs). PPF is characterised by ongoing fibrosis and relentless functional decline despite treatment efforts. Although increasingly recognised, patients continue to encounter significant challenges, including delayed diagnoses, limited therapeutic interventions, and inadequate monitoring. By enhancing patient identification and refining targeted interventions, we can significantly shift outcomes and improve lives. 1. Diagnostic Challenges: An Elusive Phenotype Diagnosing PPF remains complex. The condition can originate from multiple ILD subtypes, including idiopathic pulmonary fibrosis (IPF), hypersensitivity pneumonitis, and autoimmune-related lung diseases. Also, overlapping clinical presentations often mask the specific diagnosis, delaying referrals to specialist respiratory care. Patients frequently experience prolonged cycles within primary and general pulmonary care, with breathlessness worsening progressively before ILD suspicion is raised. Additionally, inconsistent access to multidisciplinary teams (MDTs) and variability in adherence to guideline-driven evaluations further extend diagnostic uncertainty, during which time fibrotic processes continue to progress. 2. Treatment Challenges: When Disease Control Falls Short Even following a correct diagnosis, optimal disease management for PPF often remains difficult to achieve. Immunomodulatory therapies frequently fall short of effectively halting fibrosis. Antifibrotic agents, despite availability in certain healthcare systems, are frequently underutilised due to complex regulatory environments, prohibitive costs, clinical uncertainty, or conservative clinical decision-making. Treatment inertia, driven by hesitation to escalate interventions despite documented disease progression, presents an additional barrier. Poorly defined follow-up protocols and inconsistent lung function monitoring further add to these challenges, leading to missed opportunities for timely therapeutic adjustments. 3. Mapping Challenges to Solutions Challenge Impact Example Solutions Diagnostic Delay Missed early intervention Education on ILD indicators, streamlined referral algorithms Overlap with ILDs Diagnostic ambiguity Wider MDT access, AI-powered pattern recognition tools Treatment Inertia Disease progression despite therapy Explicit escalation guidelines, antifibrotic initiation frameworks Poor Monitoring Unnoticed disease progression Automated lung function trend alerts, digital symptom diaries Case-Finding Deficits Hospitalisation-based detection AI-driven risk scoring and patient identification 4. Solutions: Precision Tools to Drive Better Care Advances in data science, particularly AI-driven healthcare intelligence, present groundbreaking opportunities, for both pharmaceutical companies and healthcare providers alike, helping them address strategic gaps and patient-care challenges in PPF. Specifically: Phenotypic Clustering and Trajectory Modelling: Enables early identification by doctors, of patients at high risk of progression by analysing complex real-world data to reveal subtle patterns indicative of disease pathways. Pharma companies can leverage these insights to optimise trial recruitment and personalise treatment plans, directly addressing gaps in trial precision and patient targeting. High-Risk Case Flagging: Accelerates referrals and intervention through AI-enhanced alert systems for healthcare practitioners, ensuring patients most likely to benefit from timely, targeted interventions are identified earlier, thereby maximising therapeutic effectiveness and resource allocation efficiency. Real-time Disease Activity Monitoring: Utilises advanced digital tools for continuous tracking of disease progression, alerting clinicians promptly to suboptimal disease control. This approach supports timely escalation and proactive disease management, addressing treatment inertia by offering clear, actionable data to healthcare providers. Real-World Evidence Generation: Facilitates comprehensive retrospective and prospective analyses of patient outcomes and treatment efficacies. This not only supports regulatory submissions and reimbursement decisions but also provides robust evidence for therapeutic efficacy and economic value, bridging critical gaps in clinical and economic understanding. By moving from reactive to predictive care, such tools can help transform outcomes for patients often lost in the complexity of ILD management. Conclusion: A New Collaborative Frontier Addressing the complexities of PPF demands a shift from reactive to proactive care models, supported by advanced predictive and analytical capabilities. For healthcare providers, adopting AI-driven insights into clinical practice means recognising subtle indicators of progression early and confidently escalating care. Pharmaceutical companies must also engage deeply with these precision tools, supporting their development and integration to ensure timely patient access to targeted therapies and personalised care plans. By fostering collaboration and integrating advanced AI tools into clinical pathways, we can substantially alter the trajectory for PPF patients, creating a new standard of care that prioritises early diagnosis, targeted intervention, and improved quality of life. About the author Léon van Wouwe has 20+ years’ global experience in clinical development and operations, uniting data science with pharma and research. He drives cross-functional collaboration to advance innovative treatments.
Case Study: Detecting signs of Fabry and Pompe disease in UK clinical data
Volv, supported by Sanofi, and working with Optimum Patient Care, and collaborating with a specialist Consultant Clinician, is performing research to build algorithms in the UK which are aimed at finding ways to better identify people living with Fabry or Pompe disease. This novel and innovative methodology, inTrigue, is highlighting ways in which we can be much more precise in detecting people living with either disease much earlier. Are you a Fabry or Pompe specialist in the UK and want to know more, or collaborate? Please contact us. inTrigue: helping people living with disease get better outcomes In the sections below you will find an overview of how we create models to help predict which people might be at risk of disease, some of the current performance metrics, and also some background information on both Fabry and Pompe disease. By using the inTrigue methodology in collaboration with Optimum Patient Care (OPC) in the UK and the OPC Research Database and supported by Sanofi, we are learning novel patterns of disease, we do this because using published medical criteria does not help find the patients that remain undiagnosed and in fact highlights many more patients that do not in fact have disease (false positives). The inTrigue approach looks for people that cannot be found using those methods. inTrigue is designed to help clinicians detect the people who are living with a rare or difficult-to-diagnose disease and help uncover those people who are therefore otherwise unlikely to get a diagnosis. Importantly, this is a research project that focusses on a limited population at first works with a population of clinicians that have signed up for the OPC quality improvement (QI) programme to improve the quality of care for patients in general practice aims to use the feedback from clinicians to improve the approach This is a completely different level of performance that promises to reduce the time to a diagnosis, and also importantly, uncover the undiagnosed patients. OPC quality improvement (QI) programme: (https://www.primescholars.com/articles/strategies-that-promote-sustainability-in-quality-improvement-activities-for-chronic-disease-management-in-healthcare-se-100520.html) Volv, Sanofi and OPC: collaborating for people living with disease Volv, supported by Sanofi, and leveraging the data from OPC in the UK, is creating a unique collaboration that does not stop here. Introduction The first phase of this project was to collaborate to build new types of models for two rare diseases: Fabry and Pompe. To do this, we focussed on primary health care records, i.e. the records that general practitioners use. Both diseases are difficult to diagnose for primary care clinicians, and as a result, remain underdiagnosed. For Pompe disease in the UK, it is estimated that 50% of people with the disease are not being diagnosed, leading to a longer delay until they eventually do get diagnosed. This data is managed by Optimum Patient Care, which provides de-identified data, of around 8.5 million patient records, for research purposes. Data security and protection are paramount. This means that the data remains anonymous and secure during the disease model development process. The data complies with: GDPR/ DPA 2018 compliant Secured EHR data extraction Data is de-identified (no PID) Data is pseudonymised SHA256 Secure data encryption AES256 Secure data transfer via HSCN NHS DSP Toolkit (ref: 8HR5) Non-identifiable data is contributed to OPCCRD for ethically approved research NHS IHRA REC (ref: 20/EM/0148) Phase 1: Learn an algorithm/model for the diseases and validate with expert clinicians The first phase of the inTrigue methodology involved an iterative process of finding a way to determine what makes patients with Fabry and Pompe disease stand out from all other patients. We used a combination of data science (or AI) approaches to get to a list of patients that plausibly have a disease. Within this phase, crucially and differentiatingly, we also needed to validate whether the approach has worked by checking the inTrigue results with an expert clinician. We did this with a consultant in a specialist Fabry and Pompe department in a UK teaching hospital. The results of this evaluation can be seen in the results section. Once the clinician’s validation was complete, we then take those inputs and optimise the algorithm, which will again boost the performance. Once this is done, we are ready to move to Phase 2. Phase 2: Clinical follow-up on plausible patients, more accurately and earlier In this second phase, the algorithm is applied to the data, and clinicians are asked if they want to participate in the model deployment programme. The clinicians need to give their consent to be part of this quality improvement programme. Several QI programmes are already in place and if they agree, they can then check to see if any of the patients in their practice are at risk of these diseases. This is done through the remote installation of reports in the GP system. We can then monitor to see if there is an improvement in terms of quality of clinical care. More results on this aspect of the deployment of the models will be published at a later stage, but the optimisation steps post clinician validation shows significant improvement on these results presented here. Later phases After this programme, consideration is being given to deploying the models more widely by embedding them into GP systems nationwide. Initial metrics on model performance Model performance: Fabry disease in UK Task Use model learned via Algorithm SLSL to find undiagnosed FD patients in OPCRD EHR database GP-EHR-DB-UK (18M patients). Evaluation procedure Request that FD specialist practicing in UK review EHRs of top 50 candidate patients (candidates have predicted probabilities exceeding FD threshold FD). Evaluation outcome Results are very promising showing that out of 50 patients the top 25 have a precision of 88%, and when the total 50 patients are considered the precision remains high at 76% using the precision@k metric. Model performance: Pompe disease in UK Task Use model learned via Algorithm SLSL to find undiagnosed PD patients in OPCRD EHR database GP-EHR-DB-UK (18M patients). Evaluation procedure Request that PD specialist practicing in UK review
White Paper: The Path to Rare Disease Clinical Trial Innovation
By Volv Global SA and WODC EU contributors Executive Summary For decades, the pharmaceutical industry has faced the same recurring problems with clinical development: the struggle to fully recruit and retain enough patients, meet target timelines, and have trials conclude on time. Certainly, the industry does overestimate its ability to recruit, but a bigger issue is that study designs and protocol development seemingly fail to truly reflect patients’ lives, or account for the reality in the clinic. In fact, data shows the probability of success for any clinical development effort is 6.2% for orphan drug trials, compared with 13.8% overall, which translates to a 93.8% failure rate for orphan drug development efforts. Given the often progressive and irreversible nature of rare diseases, there is a need to increase efforts to find those undiagnosed patients, diagnose them earlier, and bring them into the frame when developing new treatment options. To achieve this, collectively as an industry, we must do more research into the rare disease patient population to characterise and better understand both the already diagnosed and the undiagnosed. We need this deeper understanding before deciding on the best clinical development strategy, finalising clinical trial design, and starting the enrolment of the patient population in a clinical study. To do that, clinical researchers and drug developers need to include much more knowledge and understanding of those people who are unknowingly living with the disease in the design of clinical development plans and study protocols. To find those people, there is a need to consult more extensively on the design of protocols, not just with the key opinion leaders, but also with physicians that are typically seeing and treating larger numbers of patients. One crucial factor with rare diseases is that the diagnostic journey is arduous and lengthy, often with many patients not being correctly diagnosed. As an example, a study found that 58% of Ehlers-Danlos syndrome (EDS) patients consulted more than five doctors, and 20% consulted more than 20[i]. So, when designing and recruiting for clinical trials, drug developers must first learn where the “as yet undiagnosed patients” are “hidden” – in other words, where they may be in the healthcare system, and which specialists they are seeing. It is those specialisms that need to be brought along in the diagnostic journey, so they can learn to identify rare disease patients within their practice. This is very well illustrated in the case of acute hepatic porphyria (AHP), where the view is that patients reside in the gastroenterology world, but, in fact, an even larger group is residing in other specialties. Another example is cited in Chapter 2. With novel approaches, such as the use of Machine Learning (ML), we can now highlight people who are not yet diagnosed as patients but are likely to be living with a disease, for their clinicians’ attention. Subtle indicators are derived from health care records by using ML, which would be difficult or nigh impossible for a doctor to recognise amidst the wealth of data already in front of them. Conducting thorough natural history studies of patients living with disease, but also including those wider populations of people suspected of living with disease but currently undiagnosed, can help to uncover sentinel events or detectable physiologic changes that are key predictors of disease progression or that are clinically important. These can provide an understanding of which subgroups of people living with the disease might benefit from a drug in development and should therefore be targeted for inclusion in the clinical trial. And, importantly, clinical researchers need to scrutinise the data and adopt insights gained by using ML models which will enable better clinical development strategy, design, and patient stratification. First, though, we need to understand the barriers and misconceptions about the art of the possible and address those directly. This paper explores the changing expectations of the regulators, the challenges the health industry continues to face, and the ways in which we can rethink the entire clinical development process – from development strategy to protocol design, to patient identification and recruitment – to achieve real breakthroughs in rare disease research and development. Chapter 2: Misconceptions and industry challenges The path to rare disease innovation begins with a better understanding of the complexity of each disease – a point well understood by the health authorities. As the US Federal Food and Drug Administration (FDA) has identified in its guidance on natural history studies, rare diseases can have substantial genotypic and/or phenotypic heterogeneity. As such, the natural history of each subtype, if it exists at all, may be poorly understood or inadequately characterised. Above all, a typical natural history study certainly does not include those people living with the disease that – in rare – often remain undiagnosed. There are two levels of undiagnosed patients: those who have had no diagnosis at all and have therefore not been matched with a disease, and those who have had a partial diagnosis but whose symptoms are not well characterised and therefore do not belong in a defined subgroup. As researchers learn more about rare diseases, they are starting to understand that different phenotypes may present with the involvement of different organ systems, with varying degrees of severity or rate of deterioration. As noted earlier, ML can help to elicit subtle indicators from electronic health records or claims data. However, during panel debates at recent orphan drug conferences, there seemed a strong bias towards the use of registries for research and patient characterisation, and there were clear misconceptions from both industry and regulators about the usability of primary care electronic medical records (or electronic claims data) for the purpose of early disease detection, be it in a traditional manner, or ML assisted. The limitations of registries While disease registries have a clear purpose, they are constrained by the fact that they tend only to contain data on patients that are known to have a given disease. By focusing only on rare disease data that already exists in patient registries, research