Infraestructura de Medicina de Precisión asociada a la Ciencia y la Tecnología
The treatment of patients with RA poses a significant economic and social burden for the National Health System (NHS) and society, mainly due to the cost of new therapies that include biological treatments (TB) such as monoclonal antibodies aimed at blocking cytokines (TNF-alpha and IL-6) and small molecules such as JAK kinase inhibitors. Although leading clinical practice guidelines advise optimizing TB in patients in whom inflammatory manifestations of RA have been controlled, tools are needed to help identify those that can be safely optimized. Based on a Personalized Medicine Project funded by the ISCIII (PMP15/00032), the OPTIBIO algorithm has been developed to predict the probability of reactivation (flare) of RA in patients in remission treated with TB. The hypotheses of this project are: 1. The use of the OPTIBIO algorithm that combines clinical data and molecular biomarkers allows to identify with high precision which patients are good candidates for TB optimization with a low risk of reactivation. 2. The management of these patients with the OPTIBIO algorithm will offer a positive cost-effectiveness and cost-utility ratio for the NHS and society, compared to current clinical management. 3. Combining 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 physicians’ decision-making in relation to patients with RA 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 a 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 that decides the optimization of BT (see figure 2 in the Annex). The recruitment period will be 12 months. Each patient will be followed for 12 months (for clinical evaluation and budget impact analysis at 12 months). In addition, two sub-studies will be carried out with the aim of improving OPTIBIO: validation of molecular biomarkers related to the AR outbreak (PRIME cells); Development and validation of a system based on Deep-Learning to detect synovial inflammation in ultrasound images (image biomarkers).
The main objective of this proposal is to evaluate the efficacy, safety and cost-effectiveness/cost-utility ratio of a tool (OPTIBIO algorithm) to help clinicians make decisions 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.
In addition, in the context of this proposal, we propose two secondary objectives:
1.1. Evaluate the tool from the point of view of a Health Technology Assessment, in order to compile all the necessary documentation so that it can be evaluated by the NHS for its incorporation into its portfolio of services;
1.2. 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 PRIME cells as molecular biomarkers of relapse after BT optimization, based on their genomic and epigenomic analysis.
2.2. To validate joint ultrasound (US) image biomarkers, evaluated by CNN models, generated through federated analysis of US images previously obtained during clinical practice.
For the patient and their caregivers: With the use of OPTIBIO, TNFi optimization will be carried out only in those subjects with the lowest probability of disease reactivation. In this way, we will maximize the time in remission or in low disease activity for RA patients who have achieved a controlled state of the disease, thus preventing them from suffering relapses and a deterioration in their quality of life (main objective of the project). For the patient and their informal caregivers, this means less or no disability, the possibility of continuing to fulfill their social roles, keeping their jobs and, in general, being able to continue living a “normal” life. In addition, patients on optimization will be less likely to suffer adverse events associated with optimized medication.
For health professionals: OPTIBIO will offer support in decision-making. Currently, optimization is done through a “trial and error” process, reducing the dose of the drug until the patient relapses or treatment is suspended. Although there are some recommendations from national and international professional societies, there is a lack of a specific optimization protocol for biological therapy (BT) in patients with RA once the patient is in remission. With the use of OPTIBIO, the decision to optimize or not will be supported by the solid evidence generated by this project (primary project objective and secondary objective 2). On the other hand, the development of an automatic system to assess the presence of synovial inflammation on ultrasound (US) images will facilitate the assessment of RA patients in daily practice, saving time and accelerating the learning curve (Secondary Objective 2.2).
For the health system: Rheumatic diseases account for a significant percentage of hospital pharmacy costs due to the growing number of patients and their need for long-term treatment. BT represents more than 30% of total expenditure on hospital pharmacy in Spain, with 50% coming from Rheumatology Services. OPTIBIO will enable a cost-effective use of BT, leading to significant savings in the annual cost per patient, which will be quantified in the project (Secondary Objective 1). On the other hand, health systems are data powerhouses that generate an enormous amount of information, which is often not exploited or accessed properly. By deploying a federated architecture, we will be able to leverage and reuse the US images generated during the delivery of care, which are currently very limited in use. In addition, this architecture will allow us to exploit more data in the future, as most of the participating clinical sites have a history of past and most likely future collaborations. This secondary use of data directly benefits the health system, allowing it to develop and exploit innovative solutions driven by data technologies and artificial intelligence (Secondary Objective 2.2).
For 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, three times more frequent in women (Silva-Fernández et al., 2020)]; disabling (a significant impairment in 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 [increased mortality from cardiovascular disease, infections and lymphoid neoplasms (Lajas et al., 2003)]. Globally, in 2017 there were nearly 20 million prevalent cases, 1.2 million incident cases, and 3.4 billion disability-adjusted life years (DALYs), highlighting the significant, yet under-recognized, burden of RA in the world (Safiri et al., 2019). RA is a major cause of job loss in Europe, and its impact on labour participation generates substantial costs on labour productivity. This project can reduce the population burden of these conditions, understood as both “biomedical burden” and “economic burden”: By preventing disease reactivation during iTNFi optimization, we expect a reduction in disease morbidity and an improvement in patient quality of life and well-being. On the other hand, the cost-effectiveness/cost-utility analysis and health technology assessment will assess whether this reduction in morbidity translates into a decrease in the economic burden of RA, providing the health system, policymakers, and regulators with the necessary evidence to include OPTBIO in the NHS portfolio (Secondary Objective 1).