The emergence of SARS-CoV-2 triggering the COVID-19 pandemic ranks as arguably the greatest medical emergency of the last century. COVID-19 has highlighted health disparities both within and between countries and will leave a lasting impact on global society. Nonetheless, substantial investment in life sciences over recent decades has facilitated a rapid scientific response with innovations in viral characterization, testing, and sequencing. Perhaps most remarkably, this permitted the development of highly effective vaccines, which are being distributed globally at unprecedented speed. In contrast, drug treatments for the established disease have delivered limited benefits so far. Innovative and rapid approaches in the design and execution of large-scale clinical trials and repurposing of existing drugs have saved many lives; however, many more remain at risk. In this review we describe challenges and unmet needs, discuss existing therapeutics, and address future opportunities. Consideration is given to factors that have hindered drug development in order to support planning for the next pandemic challenge and to allow rapid and cost-effective development of new therapeutics with equitable delivery.
1 January 2022 in The American Journal of Medicine
The US Food and Drug Administration (FDA) has recently added a new ‘black box warning’ on all currently approved Janus kinase (JAK) inhibitors indicated for the treatment of arthritis and other inflammatory conditions based on results from the ORAL Surveillance study of tofacitinib versus tumour necrosis factor alpha inhibitors in rheumatoid arthritis. This is a warning difficult to ignore because the data, being from a randomised controlled trial, are of high fidelity and hard to reproach. It is especially problematic because safety data for all the other JAK inhibitors will be pending for several years. So how might we proceed, without being bound by our stasis? The lack of absolute certainty seems to require a pragmatic approach to the routine care use of JAK inhibitors. The patients who were at greatest risk were older and had other risk factors for the corresponding adverse events, in keeping with effect modification. This highlights the need to focus on risk stratification when tailoring therapy. In this viewpoint, we propose a simple illustration to guide clinical decision-making. First, identify general risk factors for venous thromboembolic event (VTE), major adverse cardiac event (MACE) and cancer (age>65 years and smoking) and whether there is a previous history of VTE, MACE or cancer. Then, evaluate risk based on the number of other risk factors for VTE and the number of other risk factors for MACE. Ultimately, ‘treat-to-target’ will in the end always be ‘treat-to-agreement’. As we have done in the past, and will do in the future, the optimal treatment strategy will have to be tailored based on individual patient risk factors and preferences in a shared-decision process.
Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer‐assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems.