Aspirin in Primary Prevention: Looking for Those Who Enjoy It.

Based on a wealth of evidence, aspirin is one of the cornerstones of secondary prevention of cardiovascular disease. However, despite several studies showing efficacy also in primary prevention, an unopposed excess risk of bleeding leading to a very thin safety margin is evident in subjects without a clear acute cardiovascular event. Overall, the variability in recommendations from different scientific societies for aspirin use in primary prevention is a classic example of failure of simple risk stratification models based on competing risks (atherothrombosis vs. bleeding), perceived to be opposed but intertwined at the pathophysiological level. Notably, cardiovascular risk is dynamic in nature and cannot be accurately captured by scores, which do not always consider risk enhancers. Furthermore, the widespread use of other potent medications in primary prevention, such as lipid-lowering and anti-hypertensive drugs, might be reducing the benefit of aspirin in recent trials. Some authors, drawing from specific pathophysiological data, have suggested that specific subgroups might benefit more from aspirin. This includes patients with diabetes and those with obesity; sex-based differences are considered as well. Moreover, molecular analysis of platelet reactivity has been proposed. A beneficial effect of aspirin has also been demonstrated for the prevention of cancer, especially colorectal. This review explores evidence and controversies concerning the use of aspirin in primary prevention, considering new perspectives in order to provide a comprehensive individualized approach.

MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery.

We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.

High-throughput synthesis provides data for predicting molecular properties and reaction success.

The generation of attractive scaffolds for drug discovery efforts requires the expeditious synthesis of diverse analogues from readily available building blocks. This endeavor necessitates a trade-off between diversity and ease of access and is further complicated by uncertainty about the synthesizability and pharmacokinetic properties of the resulting compounds. Here, we document a platform that leverages photocatalytic N-heterocycle synthesis, high-throughput experimentation, automated purification, and physicochemical assays on 1152 discrete reactions. Together, the data generated allow rational predictions of the synthesizability of stereochemically diverse C-substituted N-saturated heterocycles with deep learning and reveal unexpected trends on the relationship between structure and properties. This study exemplifies how organic chemists can exploit state-of-the-art technologies to markedly increase throughput and confidence in the preparation of drug-like molecules.

Integrated and automated high-throughput purification of libraries on microscale.

We herein report the development of an automation platform for rapid purification and quantification of chemical libraries including reformatting of chemical matter to 10 mM DMSO stock solutions. This fully integrated workflow features tailored conditions for preparative reversed-phase (RP) HPLC-MS on microscale based on analytical data, online fraction QC and CAD-based quantification as well as automated reformatting to enable rapid purification of chemical libraries. This automated workflow is entirely solution-based, eliminating the need to weigh or handle solids. This increases process efficiency and creates a link between high-throughput synthesis and profiling of novel chemical matter with respect to biological and physicochemical properties in relevant assays.

Managing chronic pain with spinal cord stimulation.

Since its introduction as a procedure of last resort in a terminally ill patient with intractable cancer-related pain, spinal cord stimulation has been used to effectively treat chronic pain of varied origins. Spinal cord stimulation is commonly used for control of pain secondary to failed back surgery syndrome and complex regional pain syndrome, as well as pain from angina pectoris, peripheral vascular disease, and other causes. By stimulating one or more electrodes implanted in the posterior epidural space, the patient feels paresthesias in their areas of pain, which reduces the level of pain. Pain is reduced without the side effects associated with analgesic medications. Patients have improved quality of life and improved function, with many returning to work. Spinal cord stimulation has been shown to be cost effective as compared with conservative management alone. There is strong evidence for efficacy and cost effectiveness of spinal cord stimulation in the treatment of pain associated with intractable angina, failed back surgery syndrome, and complex regional pain syndrome. In this article, we review the history and pathophysiology of spinal cord stimulation, and the evidence (or lack thereof) for efficacy in common clinical practice.