Reducing addiction risk from opioid painkillers

Much of the opioid crisis lies in the central nervous system, as the same brain pathways that mediate profound pain relief also drive tolerance and dependence, which increases the risk of addiction. Penn chemist and pharmacologist Dirk Trauner and collaborators have harnessed the pain-relieving power of opioids by developing a platform that allows activity to be turned “on” or “off” with the flick of a light switch. Their findings pave the way for bypassing opioid addiction and other adverse side effects.

Chemical predictions can help prevent unwanted medication reactions or improve efficacy of industrial processes, among other applications, but predicting how chemicals interact can be costly and time-consuming — especially as how chemicals may react in one environment may behave differently in another. A novel experimental modeling approach may help simplify the process by not only increasing the number of chemical predictions, but also the transferability of such foresight.

Two IPC PAS studies show how lignin-derived molecules can be transformed into compounds relevant to pharmaceutical research - at room temperature and atmospheric pressure and, in one case, without a transition-metal catalyst.

Sustainable packaging is only the beginning. What a medicine is made from - and how its ingredients are produced - matters just as much. Yet the synthesis of compounds for pharmaceutical research can still depend on fossil feedstocks, high temperatures, strong oxidants and metals that raise costs and complicate purification. Two recently published studies from Prof. Colmenares' group at IPC PAS demonstrate that cleaner chemistry can begin at the level of a single chemical bond [1][2].

At the heart of both studies is lignin, one of nature’s most abundant polymers and a major by-product of wood processing. Instead of simply burning it, chemists can use its aromatic structures as building blocks for much more valuable compounds. The teams combined this renewable feedstock with light and ultrasound. Light activates the catalyst, while inaudible sound waves produce cavitation: microscopic bubbles form and collapse violently. Energy is generated locally, where the chemistry happens, without exposing the whole system to extreme heat.

Researchers at the Institute of Industrial Science, The University of Tokyo have developed MatterGen-e^-, a machine learning model for the inverse design of inorganic crystals. The model generates crystal structures with electronic-structure descriptors to match a target property, producing more valid, stable, unique, and novel structures than when the crystal structure is considered alone. Control experiments showed that only physically meaningful electronic descriptors improve stability and validity, whereas diversity increased regardless of the descriptors used.