Chungnam National University

A person's hearing can be damaged by loud noise, aging and even certain medications, with little recourse beyond a hearing aid or cochlear implant. Researchers have found a gene that links deafness to cell death in the inner ear in humans -- creating new opportunities for averting hearing loss. A person's hearing can be damaged by loud noise, aging and even certain medications, with little recourse beyond a hearing aid or cochlear implant. But now, UCSF scientists have achieved a breakthrough in understanding what is happening in the inner ear during hearing loss, laying the groundwork for preventing deafness. The research, published on Dec. 22, 2023, in the Journal of Clinical Investigation Insight, links animal studies on hearing loss with a rare type of inherited deafness in humans. In both cases, mutations to the TMTC4 gene trigger a molecular domino effect known as the unfolded protein response (UPR), leading to the death of hair cells in the inner ear. Intriguingly, hearing loss from loud noise exposure or drugs such as cisplatin, a common form of chemotherapy, also stems from activation of the UPR in hair cells, suggesting that the UPR may underly several different forms of deafness. There are several drugs that block the UPR -- and stop hearing loss -- in laboratory animals. The new findings make a stronger case for testing these drugs in people who are at risk of losing their hearing, according to the researchers. "Millions of American adults lose their hearing due to noise exposure or aging each year, but it's been a mystery what was going wrong," said Dylan Chan, MD, PhD, co-senior author on the paper and director of the Children's Communication Center (CCC) in the UCSF Department of Otolaryngology. "We now have solid evidence that TMTC4 is a human deafness gene and that the UPR is a genuine target for preventing deafness." How hair cells in the ear self-destruct In 2014, Elliott Sherr, MD, PhD, director of the UCSF Brain Development Research Program and co-senior author of the paper, noticed that several of his young patients with brain malformations all had mutations to TMTC4. But laboratory studies of this gene soon presented a conundrum. "We expected mice with TMTC4 mutations to have severe brain defects early on, like those pediatric patients, yet to our surprise, they seemed normal at first," Sherr said. "But as those animals grew, we saw that they didn't startle in response to loud noise. They had gone deaf after they had matured." Sherr partnered with Chan, an expert on the inner ear, to look into what was happening to the mice, which looked like an accelerated version of age-related hearing loss in humans. They showed that mutations to TMTC4 primed hair cells in the ear to self-destruct, and loud noise did the same thing. In both cases, hair cells were flooded with excess calcium, throwing off the balance of other cellular signals, including the UPR. But they found there was a way to stop this. ISRIB, a drug developed at UCSF to block the UPR's self-destruct mechanism in traumatic brain injury, prevented animals who were exposed to noise from going deaf. The first adult human deafness gene In 2020, scientists from South Korea, led by Bong Jik Kim, MD, PhD, connected Chan and Sherr's 2018 findings with genetic mutations they found in two siblings who were losing their hearing in their mid-20s. The mutations were in TMTC4 and matched what Chan and Sherr had seen in animals, although they were distinct from those in Sherr's pediatric neurology patients. "It's rare to so quickly connect mouse studies with humans," Sherr said. "Thanks to our Korean collaborators, we could more easily prove the relevance of our work for the many people who go deaf over time." Kim, an otolaryngologist at the Chungnam National University College of Medicine (Korea), facilitated the shipping of cells from those patients to UCSF. Sherr and Chan tested those cells for UPR activity and found that, indeed, this flavor of TMTC4 mutation turned on the destructive UPR pathway in a human context. When Chan and Sherr mutated TMTC4 only in hair cells in mice, the mice went deaf. When they mutated TMTC4 in cells from individuals in the Korean family who hadn't gone deaf, and in laboratory human cell lines, the UPR drove the cells to self-destruct. TMTC4 was more than a deafness gene in mice -- it was a deafness gene in humans, too. Translating a discovery to prevent deafness Understanding TMTC4 mutations gives researchers a new way of studying progressive deafness, since it is critical for maintaining the health of the adult inner ear. The mutations mimic damage from noise, aging or drugs like cisplatin. The researchers envision a future where people who must take cisplatin, or who have to be exposed to loud noises for their jobs, take a drug that dampens the UPR and keeps hair cells from withering away, preserving their hearing. The science also suggests that the UPR could be targeted in other contexts where nerve cells become overwhelmed and die, including diseases long thought to be incurable, like Alzheimer's or Lou Gehrig's disease. "If there's any way that we can get in the way of the hair cells dying, that's how we're going to be able to prevent hearing loss," Chan said.

Researchers develop a model to describe the interaction between a rocket plume and the surface of a planetary body in near-vacuum conditions. The computational framework takes in information about the rocket, its engines, and the surface composition and topography, as well as the atmospheric conditions and gravitational forces at the landing site, and the results can be used to evaluate the safety and feasibility of a proposed landing site and to optimize the design of spacecraft and rocket engines for planetary landings. When a lander descends toward the moon -- or a rocky planet, asteroid, or comet -- the exhaust plume of the rocket interacts with the surface, causing erosion and kicking up regolith particles. The resulting blanket of dusty debris can create a dangerous brownout effect, limiting visibility and potentially damaging the spacecraft or nearby equipment. In Physics of Fluids, by AIP Publishing, researchers from Chungnam National University, the University of Edinburgh, Gyeongsang National University, and the Korea Institute of Science and Technology Information developed a model to describe the interaction between a rocket plume and the surface of a planetary body in near-vacuum conditions. The results can be used to evaluate the safety and feasibility of a proposed landing site and to optimize the design of spacecraft and rocket engines for planetary landings. "Understanding the interaction between the rocket plume and the surface is important for the safety and success of space missions in terms of contamination and erosion, landing accuracy, planetary protection, and engineering design, as well as for scientific understanding and future exploration," said author Byoung Jae Kim of Chungnam National University. The computational framework takes in information about the rocket, its engines, and the surface composition and topography, as well as the atmospheric conditions and gravitational forces at the landing site. By considering the interaction of the gas with solid particles as a system of equations, the simulation estimates the shape and size of the plume, the temperature and pressure of the plume and surface, and the amount of material eroded or displaced. It does so in a way that is more computationally efficient than previous methods. "Our tool can simulate the plume surface interaction problem at the fundamental level (e.g., scour pattern formation and development of erosion models) and for practical engineering applications (e.g., predicting particle trajectories to avoid damage to the lander and previously established sites and planning descend/ascend scenarios)," said Kim. In the model, small regolith particles reached high altitudes and caused severe brownout effects during ascent and descent. In contrast, larger particles with increased bed height led to a more favorable brownout status. "The insights gained from this study of the effects of different parameters on plume-surface interaction can inform the development of more effective and efficient landing technologies," said Kim. "The study also sheds light on the festooned scour patterns that can be observed on planetary surfaces, which can provide valuable information for future scientific investigations of planetary bodies." The researchers plan to improve the capabilities of the framework to include more complex physics, such as chemical reactions and solid particle collisions. They believe the model can be applied to other physics scenarios including needle-free drug delivery systems.

A team of mathematicians and pharmaceutical scientists collaborated to identify the major cause of the low accuracy of the equation in the FDA guidance used to predict drug-drug interaction through an enzyme (cytochrome P450, CYP) induction mediation. To overcome the fundamental limit of the conventional equation, the scientists derived a new equation based on a mathematical theory. This dramatically improved the accuracy of the prediction to determine potential drug-drug interactions, which is critical in preventing drug failure and bringing about new drug development. Drugs absorbed into the body are metabolized and thus removed by enzymes from several organs like the liver. The how fast the drug is cleared out of the system can be increased by other drugs that increase the amount of enzyme secretion in the body. This dramatically decreases the concentration of a drug, reducing its efficacy, often leading to the failure of having any effect at all. Therefore, accurately predicting the clearance rate in the presence of drug-drug interaction* is critical in the process of drug prescription and development of a new drug. *Drug-drug interaction: In terms of metabolism, drug-drug interaction is a phenomenon in which one drug changes the metabolism of another drug to promote or inhibit its excretion from the body when two or more drugs are taken together. As a result, it increases the toxicity of medicines or causes loss of efficacy. Since it is practically impossible to evaluate all interactions between new drug candidates and all marketed drugs during the development process, the FDA recommends indirect evaluation of drug interactions using a formula suggested in their guidance first published in 1997, revised in January of 2020, in order to evaluate drug interactions and minimize side effects of having to use more than one type of drugs at once. The formula relies on the 110-year-old Michaelis-Menten (MM) model which has a fundamental limit of making a very broad and groundless assumption on the part of the presence of the enzymes that metabolizes the drug. While MM equation has been one of the most widely known equations in biochemistry used in more than 220,000 published papers, the MM equation is accurate only when the concentration of the enzyme that metabolizes the drug is almost non-existent, causing the accuracy of the equation highly unsatisfactory -- only 38 percent of the predictions had less than two-fold errors. "To make up for the gap, researcher resorted to plugging in scientifically unjustified constants into the equation," Professor Jung-woo Chae of Chungnam National Univeristy College of Pharmacy said. "This is comparable to having to have the epicyclic orbits introduced to explain the motion of the planets back in the days in order to explain the now-defunct Ptolemaic theory, because it was THE theory back then." A joint research team composed of mathematicians from the Biomedical Mathematics Group within the Institute for Basic Science (IBS) and the Korea Advanced Institute of Science and Technology (KAIST) and pharmacological scientists from the Chungnam National University reported that they identified the major causes of the FDA-recommended equation's inaccuracies and presented a solution. When estimating the gut bioavailability (Fg), which is the key parameter of the equation, the fraction absorbed from the gut lumen (Fa) is usually assumed to be 1. However, many experiments have shown that Fa is less than 1, obviously since it can't be expected that all of the orally taken drugs to be completely absorbed by the intestines. To solve this problem, the research team used an "estimated Fa" value based on factors such as the drug's transit time, intestine radius, and permeability values and used it to re-calculate Fg. Also, taking a different approach from the MM equation, the team used an alternative model they derived in a previous study back in 2020, which can more accurately predict the drug metabolism rate regardless of the enzyme concentration. Combining these changes, the modified equation with re-calculated Fg had a dramatically increased accuracy of the resulting estimate. The existing FDA formula predicted drug interactions within a 2-fold margin of error at the rate of 38%, whereas the accuracy rate of the revised formula reached 80%. "Such drastic improvement in drug-drug interaction prediction accuracy is expected to make great contribution to increasing the success rate of new drug development and drug efficacy in clinical trials. As the results of this study were published in one of the top clinical pharmacology journal, it is expected that the FDA guidance will be revised according to the results of this study." said Professor Sang Kyum Kim from Chungnam National University College of Pharmacy. Furthermore, this study highlights the importance of collaborative research between research groups in vastly different disciplines, in a field that is as dynamic as drug interactions. "Thanks to the collaborative research between mathematics and pharmacy, we were able to recify the formula that we have accepted to be the right answer for so long to finally grasp on the leads toward healthier life for mankind.," said Professor Jae Kyung Kim. He continued, "I hope seeing a 'K-formula' entered into the US FDA guidance one day."