Regulators, health technology assessment (HTA) bodies, academics, bio-pharmaceuticals companies, payers, and patients are presently rethinking the healthcare ecosystem and facing diverse and increasing challenges at different levels. In this conceptual modelling of the healthcare ecosystem, the potential value of Real-World Data (RWD) for decision making has never been more relevant.
Real-World studies seek to provide complementary evidence to that provided by randomized controlled trials. While the latter provide evidence of efficacy, Real-World Data provide information on therapeutic effectiveness in a real-world practice setting.
Pharmaceutical companies need to focus on innovative and targeted therapies both due to the increased competition in the sector, and of the number of better structured and demanding patients’ groups or associations. Pharmaceutical companies are presently discussing on:
- How does RWD contribute to translational research?
- Whether there are any companies optimally leveraging RWD and how its application to biobanks can better advance innovation and discovery of targeted therapies?
- What are the structural and logistical needs for this to be effective?
- What have we already learned about the process?
Regulatory bodies face the challenge of ensuring that only safe and effective medicines reach patients; a decision which so far has only been substantiated upon data from RCTs. They actually need to know:
- Whether other stakeholders can help them in the process and how?
- Where and how do we using RWD in the drug development and approval process?
- What has already been achieved in the field and what the success factors are?
Payers, and HTA bodies want to make sure that the most innovative and novel medicines reach patients who need them most as quickly as possible; and that these medicines provide value for money to all parties: patients and health care systems. They may ask themselves:
- How can sponsors better demonstrate the value of their innovative medicines using RWD?
- How can they work with decision-makers to establish agreed upon standards for RWE research?
- How do recent frameworks (FDA, NICE, etc) help advance RWE research?
What brings all stakeholders at the same level is the need for new paradigms for a more performing healthcare ecosystem that reduce the gap between its representation and its implementation. Emerging e-health models and applications can greatly contribute to it, particularly, if hand-in-hand with Real-World Data Studies. The European Epidemiological Forum will review the progress and perspectives on the value of RWD in the drug life cycle, will discuss what RWD promises, what the barriers may be and what a successful story may look like.
Christian Agboton - Sr Global Brand Medical Director at Takeda
Maurille Feudjo Tepie - Observational Research Director at Amgen
Michele Intorcia - HEOR Senior Director at Apellis Pharmaceuticals
Who should attend?
The conference is addressed to professionals with a deep interest in drug development, healthcare decision making, academia and healthcare research, belonging to department such as: Medical Affairs, R&D, HTA/Market access, Medical Informatics, Clinical Operations, Regulatory Affairs, Pharmacovigilance, Biostatistics and Data Management from Pharmaceutical, Biotechnology and Medical Device companies, CROs, Universities/Hospitals, Academic Research, Patient Associations and Healthcare Organizations.
We’re working on the programme 10:00 AM – 4:00 PM CEST (TBC)
More details available soon
Opportunities and challenges of RWD/E in drug development and commercialisation: The potential roles of some of the advanced tools such as ML/AI
Maurille Feudjo Tepie - Observational Research Director at Amgen
Real world data (RWD) has been described as data that are routinely collected within health systems on a patient’s health status, in a purely observational fashion and; real world evidence (RWE) presented as evidence derived from analysis of RWD. As the healthcare ecosystem around the world continues to face, pressures from an ageing population, an escalating drug development cost, an increased drug prices, limited/restricted reimbursement and/or push toward personalised/stratified medicine; pharmaceutical companies as well as many other stakeholders are looking to capitalise on the increased availability of RWD, powered by advances in information technology and epidemiological methods, to alleviate some of these pressures.
Advances in technology in particular, while increasing the availability of bigger and better data, has also facilitated faster computers with unprecedented storage power. The later has brought to life some of the advanced analytical tools such as ML/AI whose long-recognised promises, have for decades been hampered, among others, by computing power and limited data.
In the pharma industry, there is a call for increased use of RWE to help define, differentiate and deliver value across the drug life cycle. During this talk we will recall some of the key elements why the scientific community now believe ML/AI potentially has a critical role to play in drug development and drug commercialisation, this from the discovery phase with a better leverage of bigger and better omics and imagine data, right through the clinical development phase with better and innovative trial designs; to the commercialisation phase with an optimally customised healthcare, treatment and practices.
Challenges of AI in Clinical Research: seeing beyond the algorithms
Christian Agboton - Sr Global Brand Medical Director at Takeda
Observational studies and real world evidence (RWE) in general are a unique resource that is difficult to ignore in our current approaches to better understand the advanced medications that we are developing. So far, statistical approaches and interpretation of observational data were largely based on techniques developed for traditional randomized clinical trials; and could be considered as an extension of the set of tools used in registrations trials.
Some RWE statistical methods such as propensity score matching were even developed in order to “simulate” a cohort randomization.
With the advent of AI/ML, for the first time, a set of tools never used in any other clinical research is coming on us, with a lot of promises but also some challenges that we will describe. AI/ML has the potential of being transformative, offering insights not accessible before with traditional analytical techniques.
However, the lack of transparency of algorithms and the “black box” ethos might impede the trust researchers could have in AI/ML.
This aspect coupled with the quality of data (garbage in, garbage out) is a barrier to further adoption.
Phenotype libraries as means to portability and transparency of patient definitions
Vasa Curcin - Reader in Health Informatics at King’s College London
A phenotype definition specifies cohorts of patients that exhibit certain phenotypic traits, such as the same diseases, characteristics, or set of co-morbidities. Electronic health record (EHR)-based phenotype definitions (also referred to as phenotyping algorithms) express the logic required to identify such a cohort from patient health records. Phenotype definitions can be represented in many forms, including narrative descriptions, pseudo-code, or in a standard structured format.
A high-quality definition is one that can be easily implemented across multiple organisations and datasets (portable); one that can be implemented accurately, to facilitate complementary research or validate existing results (reproducible); and one that is shown to effectively capture the disease or condition modelled (valid).
Phenotype libraries provide a platform through which a phenotype definition can be uploaded, stored, indexed, retrieved, and downloaded by users. Rather than just passively cataloguing definitions, these platforms can also impact the quality of the phenotype definitions they host.
One way to do this is for a library to facilitate parts of the phenotype development lifecycle often considered extraneous, including modelling, implementation and validation. For example, libraries that host definitions structured according to some set of standard models, and track the evolution of definitions under these models, often contain phenotypes that are clearer to understand and thus have the potential to be more reproducible.
We shall discuss a number of desiderata for the development of phenotype libraries, to ensure that future libraries are designed to maximise the quality of the phenotypes they contain.
Real-World Evidence and Digital Innovation in Healthcare
Kelly H. Zou - Head, Global Medical Analytics and Real World Evidence at Viatris
Digital endpoints can be used based on data through various sources, such as apps, sensors, and wearables such as smart watches, for example, under the bring your own device model.
Digital therapeutics may be evaluated through randomized controlled trials (RCTs), pragmatic clinical trials (PCTs), or real-world data (RWD). The corresponding endpoints may be collected continuous in routine daily living and real-time activities, rather than settings such as doctors’ offices or hospitals. Digital endpoints (DTx) may be useful for remote patient monitoring and continuous tracking of patient responses and wellbeing through digitalization of patient diaries, electronic patient-reported outcomes (ePROs), mobility, medication use, and activities to generate real-world evidence (RWE) beyond RCTs. Therefore, they are useful in medical research via de-centralizing RCTs.
This presentation aims to: review the uses of digital endpoints and digital therapeutics in various types of studies; examine how cutting-edge digital innovation can potentially improve medication adherence; share some best practices by study type, RCTs, PCTs or RWD, for evidence-generation; discuss advantages and limitations.
Key words: Digital Endpoint; Digital Therapeutics; Randomized Controlled Trial; Pragmatic Clinical Trial; Real-World Data and Evidence; Electronic Patient-Reported Outcome.
A brief overview of methodology
1. Review the uses of digital endpoints and digital therapeutics in various types of studies
Use of AI/ML by Payers in Assessing Value and Integrating RWE Innovative Reimbursement Models
Omar Ali - Visiting Lecturer Value Based Pricing at University of Portsmouth & Former Adviser to NICE
During the presentation the speaker with deal with the following topics:
CAncer PAtients Better Life Experience (CAPABLE): Real World Data and novel methodologies in the service of cancer patients’ quality of life
Matteo Gabetta - R&D Manager at BIOMERIS
The CAPABLE (CAncer PAtient Better Life Experience) project (https://capable-project.eu/), funded in H2020 program, is realizing a novel system to improve the quality of life of cancer patients managed at home.
Project team is formed by experts in complementary fields, such as data- and knowledge-driven AI, data integration, telemedicine, and decision support, with the shared goal of fully exploiting Artificial Intelligence for cancer care and bringing the benefits right to patients’ homes. Within the CAPABLE framework, predictive models, based on both retrospective and prospective data and computer interpretable guidelines, are made available to oncologists.
Furthermore, CAPABLE’s Virtual Coach provides patients with decision support and lifestyle guidance to improve mental and physical wellbeing.
CAPABLE project offers the opportunity to show different aspects of the Real World Data lifecycle because:
Use of AI/ML to improve Secondary Prevention in ACS Patients: the PRAISE model
Fabrizio D'Ascenzo - MD at Division of Cardiology, Department of Medical Science, Città della Salute e della Scienza, Turin
Risk prediction for Acute Coronary Syndromes (ACSs) represents an unmeet need in medicine. In particular, prediction of risk of death may be of help to tailor aggressive lifestyle interventions and close clinical and with imaging follow up. Moreover, these patients are themselves at increased risk of recurrent thrombotic events, but in the mean time, due to use of antiaggregant therapy they are exposed to elevated risk of bleedings. “Traditional” risk scores, derived with mainly with logistic regression, are derived from patients suffering both ACS and stable angina, consequently with limited reproducibility in every day clinical practice. Moreover, Artificial Intelligence (AI) and Machine Learning (ML), through exploration of non-linear complex relationships may help to capture other kind of associations.
In the PRAISE (PRedicting with Artificial Intelligence riSk aftEr ACS) study, 19826 patients were exploited to predict 1-year mortality, recurrent infarctions and major bleedings. Trials of four ML classifiers [Adaptive Boosting , Naive Bayes, KNN, Random Forest] were employed to generate four models for the prediction of each study outcome. The Adaptive Boosting, which performed superior to others, offered for 1-year all-cause death an AUC of 0.82 (95%CI 0.78-0.85) and 0.92 (95%CI 0.90-0.93). in the evaluation and the validation cohort, respectively. The PRAISE score for MI showed an AUC of 0.74 (95%CI 0.70-0.78) and 0.81 (95%CI 0.76-0.85) in the evaluation and the validation cohort, respectively. The PRAISE score for major bleeding showed an AUC of 0.70 (95%CI 0.66-0.75) in the evaluation cohort and 0.86 (95%CI 0.82-0.89). in the evaluation cohort and 0.86 (95%CI 0.82-0.89). in the validation cohort.
Maurille Feudjo Tepie
Kelly H. Zou
Quote di iscrizione
€ 410,00* Super Early Bird fee until 30 June 2021
€ 490,00* Early Bird fee until 05 September 2021
€ 630,00* Ordinary fee
€ 290,00* Freelance, Academy, Public Administration
* for Italian companies: +22% VAT
Fee includes: access to the virtual conference, organizational support, certificate of attendance, slide presentations in pdf format provided post-conference.