The COVID-19 explorer-An integrated, whole patient knowledge model of COVID-19 disease

A brief reference to AI-driven audible reality (AuRa) in open world: potential, applications, and evaluation

Recent developments on artificial intelligence (AI) and machine learning (ML) techniques are expected to have significant impact on public health in several ways. Indeed, modern AI/ML methods have been applied on multiple occasions on topics ranging from drug discovery and disease diagnostics to personalized medicine, medical imaging, and healthcare operations. While such developments may improve several quality-of-life aspects (such as access to health services and education), it is important considering that some individuals may face more challenges, particularly in extreme or emergency situations. In this work, we focus on utilizing AI/ML components to support scenarios when visual impairment or other limitations hinder the ability to interpret the world in this way. Specifically, we discuss the potential and the feasibility of automatically transferring key visual information into audio communication, in different languages and in real-time-a setting which we name 'audible reality' (AuRa). We provide a short guide to practical options currently available for implementing similar solutions and summarize key aspects for evaluating their scope. Finally, we discuss diverse settings and functionalities that AuRA applications could have in terms of broader impact, from a social and public health context, and invite the community to further such digital solutions and perspectives soon.

Rewiring Drug Research and Development through Human Data-Driven Discovery (HD3)

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

Whole patient knowledge modeling of COVID-19 symptomatology reveals common molecular mechanisms

Infection with SARS-CoV-2 coronavirus causes systemic, multi-faceted COVID-19 disease. However, knowledge connecting its intricate clinical manifestations with molecular mechanisms remains fragmented. Deciphering the molecular basis of COVID-19 at the whole-patient level is paramount to the development of effective therapeutic approaches. With this goal in mind, we followed an iterative, expert-driven process to compile data published prior to and during the early stages of the pandemic into a comprehensive COVID-19 knowledge model. Recent updates to this model have also validated multiple earlier predictions, suggesting the importance of such knowledge frameworks in hypothesis generation and testing. Overall, our findings suggest that SARS-CoV-2 perturbs several specific mechanisms, unleashing a pathogenesis spectrum, ranging from "a perfect storm" triggered by acute hyper-inflammation, to accelerated aging in protracted "long COVID-19" syndromes. In this work, we shortly report on these findings that we share with the community via 1) a synopsis of key evidence associating COVID-19 symptoms and plausible mechanisms, with details presented within 2) the accompanying "COVID-19 Explorer" webserver, developed specifically for this purpose (found at https://covid19.molecularhealth.com). We anticipate that our model will continue to facilitate clinico-molecular insights across organ systems together with hypothesis generation for the testing of potential repurposing drug candidates, new pharmacological targets and clinically relevant biomarkers. Our work suggests that whole patient knowledge models of human disease can potentially expedite the development of new therapeutic strategies and support evidence-driven clinical hypothesis generation and decision making.