BackgroundDigital innovation, introduced across many industries, is a strong force of transformation. Some industries have seen faster transformation, whereas the health care sector only recently came into focus. A context where digital corporations move into health care, payers strive to keep rising costs at bay, and longer-living patients desire continuously improved quality of care points to a digital and value-based transformation with drastic implications for the health care sector.ObjectiveWe tried to operationalize the discussion within the health care sector around digital and disruptive innovation to identify what type of technological enablers, business models, and value networks seem to be emerging from different groups of innovators with respect to their digital transformational efforts.MethodsFrom the Forbes 2000 and CBinsights databases, we identified 100 leading technology, life science, and start-up companies active in the health care sector. Further analysis identified projects from these companies within a digital context that were subsequently evaluated using the following criteria: delivery of patient value, presence of a comprehensive and distinctive underlying business model, solutions provided, and customer needs addressed.ResultsOur methodological approach recorded more than 400 projects and collaborations. We identified patterns that show established corporations rely more on incremental innovation that supports their current business models, while start-ups engage their flexibility to explore new market segments with notable transformations of established business models. Thereby, start-ups offer higher promises of disruptive innovation. Additionally, start-ups offer more diversified value propositions addressing broader areas of the health care sector.ConclusionsDigital transformation is an opportunity to accelerate health care performance by lowering cost and improving quality of care. At an economic scale, business models can be strengthened and disruptive innovation models enabled. Corporations should look for collaborations with start-up companies to keep investment costs at bay and off the balance sheet. At the same time, the regulatory knowledge of established corporations might help start-ups to kick off digital disruption in the health care sector.

Herein we describe the discovery, mode of action, and preclinical characterization of the soluble guanylate cyclase (sGC) activator runcaciguat. The sGC enzyme, via the formation of cyclic guanosine monophoshphate, is a key regulator of body and tissue homeostasis. sGC activators with their unique mode of action are activating the oxidized and heme-free and therefore NO-unresponsive form of sGC, which is formed under oxidative stress. The first generation of sGC activators like cinaciguat or ataciguat exhibited limitations and were discontinued. We overcame limitations of firstgeneration sGC activators and identified a new chemical class via highthroughput screening. The investigation of the structure−activity relationship allowed to improve potency and multiple solubility, permeability, metabolism, and drug-drug interactions parameters. This program resulted in the discovery of the oral sGC activator runcaciguat (compound 45, BAY 1101042). Runcaciguat is currently investigated in clinical phase 2 studies for the treatment of patients with chronic kidney disease and nonproliferative diabetic retinopathy.

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

Sickle cell disease (SCD) is associated with intravascular hemolysis and oxidative inhibition of nitric oxide (NO) signaling. BAY 54-6544 is a small-molecule activator of oxidized soluble guanylate cyclase (sGC), which, unlike endogenous NO and the sGC stimulator, BAY 41-8543, preferentially binds and activates heme-free, NO-insensitive sGC to restore enzymatic cGMP production. We tested orally delivered sGC activator, BAY 54-6544 (17 mg/kg/d), sGC stimulator, BAY 41-8543, sildenafil, and placebo for 4-12 weeks in the Berkeley transgenic mouse model of SCD (BERK-SCD) and their hemizygous (Hemi) littermate controls (BERK-Hemi). Right ventricular (RV) maximum systolic pressure (RVmaxSP) was measured using micro right-heart catheterization. RV hypertrophy (RVH) was determined using Fulton's index and RV corrected weight (ratio of RV to tibia). Pulmonary artery vasoreactivity was tested for endothelium-dependent and -independent vessel relaxation. Right-heart catheterization revealed higher RVmaxSP and RVH in BERK-SCD versus BERK-Hemi, which worsened with age. Treatment with the sGC activator more effectively lowered RVmaxSP and RVH, with 90-day treatment delivering superior results, when compared with other treatments and placebo groups. In myography experiments, acetylcholine-induced (endothelium-dependent) and sodium-nitroprusside-induced (endothelium-independent NO donor) relaxation of the pulmonary artery harvested from placebo-treated BERK-SCD was impaired relative to BERK-Hemi but improved after therapy with sGC activator. By contrast, no significant effect for sGC stimulator or sildenafil was observed in BERK-SCD. These findings suggest that sGC is oxidized in the pulmonary arteries of transgenic SCD mice, leading to blunted responses to NO, and that the sGC activator, BAY 54-6544, may represent a novel therapy for SCD-associated pulmonary arterial hypertension and cardiac remodeling.