Infraestructura de Medicina de Precisión asociada a la Ciencia y la Tecnología
Treatment of patients with RA carries a significant economic and social burden on the National Health System (NHS) and society, mainly due to the cost of novel therapies that include biological treatments (BT) such as monoclonal antibodies aimed at blockingcytokines (TNF-alpha and IL-6) and small molecules such as JAK kinase inhibitors. Although the main clinical practice guidelines advise to optimize BT in patients in whom the inflammatory manifestations of RA have been controlled, tools are needed to help identify those who can be optimized safely. Based on a Personalized Medicine Project funded by the ISCIII (PMP15/00032) the OPTIBIO algorithm has been developed to predict the likelihood of reactivation (flare) of RA in patients in remission treated with BT. The hypotheses of this project are: 1. The use of the OPTIBIO algorithm that combines clinical data and molecular biomarkers makes it possible to identify with high accuracy which patients are good candidates for BT optimisation with a low risk of reactivation. 2. The management of these patients with the OPTIBIO algorithm will offer positive cost-effectiveness and cost-utility for the NHS and society, compared to the current clinical management. 3. The combination of the current OPTIBIO algorithm with other complementary biomarkers will improve its accuracy. The main objective of this proposal is to evaluate the efficacy, safety and cost effectiveness/ cost-utility of the OPTIBIO algorithm to help decision-making for clinicians regarding patients with RA who are candidates for BT optimization treated with TNF-inhibitors (TNFi). To achieve this objective, we will carry out a multicenter, prospective, controlled and randomized clinical trial with medical device and two parallel groups, (Group 1, dose reduction of TNFi decided with the OPTIBIO algorithm and Group 2, dose reduction decided with current practice), patient and evaluator blinded to the method deciding BT optimisation (see figure 2 in the Annex). The recruitment period will be 12 months. Each patient will be followed up for 12 months (for clinical evaluation and budget impact analysis at 12 months). In addition, two substudies will be carried out with the aim of improving OPTIBIO: validation of molecular biomarkers related with flare in RA (PRIME cells); development and validation of a deep-learning based system to detect synovial inflammation in ultrasound images (imaging biomarkers).
The main objective of this proposal is to evaluate the efficacy, safety and cost-effectiveness/cost-utility of a tool (OPTIBIO algorithm) to help decision-making for clinicians regarding patients with RA who are candidates for BT optimization treated with TNF-inhibitors. This tool uses clinical data and information from genetic and proteomic biomarkers.
Furthermore, in the context of this proposal we propose two secondary objectives:
1.1. To evaluate the tool from the point of view of a Health Technology Assessment, in order to gather all the necessary documentation so that it can be evaluated by the NHS for its incorporation into its portfolio of services;
1.2. To prepare a business plan that analyzes the commercial possibilities of the tool and the best way to reach the market, including the creation of a start-up/spin-off.
2.1. To validate the PRIME cells as molecular biomarkers of flare after BT optimization, based on their genomic and epigenomic analysis.
2.2. To validate articular ultrasound (US) imaging biomarkers, assessed using CNN models, generated through the federated analysis of US images previously obtained during clinical practice.
For the patient and caregivers: With the use of OPTIBIO, TNFi optimization will only be carried out in those subjects with the lowest chances of disease reactivation. Therefore we will maximize the time in remission or low disease activity for RA patients that were able to achieve a controlled disease state, so they will be spared of suffering disease flares and a deterioration in their quality of life (main objective of the Project) For the patient and informal caregivers that means no/less disability, being able to keep fulfilling their social roles, maintaining their jobs, and overall, being able to keep living a “regular” life. Furthermore, those patients undergoing optimization will less likely suffer from adverse events associated with the optimized medication.
– For the Health Professionals: OPTIBIO will offer support in their decision making. Currently, the optimization is carried out using a “trial-an-error” process, lowering the drug dose until the patient flares or the treatment is discontinued. Although some recommendations by national and international professional societies exist, there is a lack of specific optimization protocols for biological therapy (BT) in patients with RA once the patient is in remission. With the use of OPTIBIO, the decision of whether to optimize or not will be supported by the robust evidence generated by this Project (main objective of the Project, and secondary objective 2). On the other hand, the development of an automatic system to assess the presence of synovial inflammation in ultrasound (US) images will facilitate the assessment of RA patients in daily practice, saving time, and steeping the learning curve (Secondary objective 2.2).
– For the Healthcare System: Rheumatic diseases represent a significant percentage of the invoice of hospital pharmacies due to the increasing number of these patients and their need for prolonged treatment. BT represents >30% of total expenditure in hospital pharmacy in Spain, with 50% coming from the Rheumatology Services. OPTIBIO will allow a cost-effective use of BT, leading to significant savings in the annual cost per patient, that will be quantified in the Project (Secondary objective 1). On the other hand, healthcare systems are data powerhouses that generate an enormous amount of data, which is often unexploited and not (correctly) accessible. By deploying a federated architecture, we will be able to harness and re-use US images that were generated during the provision of care, but currently have a very limited use. Moreover, this architecture will allow us to exploit further data in the future, as most participating clinical centers have a long history of past and most likely future collaborations. This secondary use of data directly benefits the healthcare system, allowing it to develop and exploit ground-breaking solutions powered by data and Artificial Intelligence technologies (Secondary objective2.2).
– For the Society: RA imposes a significant burden on society. It is a common chronic disease [prevalence in Spain of 0.82%, affecting 220,000-430,000 people, x3 more prevalent in women (Silva-Fernández et al., 2020)]; disabling (a significant impairment on physical function in up to 38% of patients, reduced social functioning, mental distress, pain and fatigue in 27–50% (Uhlig et al. 1998), 30–40% of patients stop working early (Uhlig et al., 2014)], and associated with reduced life expectancy [increasing mortality from cardiovascular, infectious and lymphoid neoplasms (Lajas et al., 2003)]. Worldwide, there were (2017) globally almost 20 million prevalent cases, 1.2 million incident cases and 3.4 million disability-adjusted life years (DALYs), which serves to highlight the significant, yet under-recognised, global burden of RA (Safiri et al., 2019). RA is a major cause of work loss in Europe and their effect on work participation gives rise to substantial work productivity costs. This project can reduce the population burden of these conditions, both understood as “biomedical” and “economic” burden: By preventing disease reactivation during TNFi optimization, we expect a reduction in disease morbidity and an improvement of patient’s QoL and wellbeing. On the other hand, the cost-effectiveness/cost-utility and HTA analysis will assess if this reduced morbidity is translated into a reduction of the economic burden of RA, providing the Health Care System, Policymakers, and Regulators with the evidence needed to include OPTBIO in the NHS portfolio (Secondary objective 1).