Advancements in AI have resulted in typically human characteristics like creativity, communication, critical thinking, and learning being replicated by machines for complex tasks like driving vehicles and creating art. With further development, these human-like attributes may develop enough to one day make it possible for robots and AI to be entrusted with nursing, a very ‘human’ practice. But… would it be ethical to entrust the care of humans to machines?
In a step toward answering this question, Japanese researchers recently explored the ethics of such a situation in the journal Nursing Ethics.
The study was conducted by Associate Professor Tomohide Ibuki from Tokyo University of Science, in collaboration with medical ethics researcher Dr Eisuke Nakazawa from The University of Tokyo and nursing researcher Dr Ai Ibuki from Kyoritsu Women’s University.
“This study in applied ethics examines whether robotics, human engineering, and human intelligence technologies can and should replace humans in nursing tasks,” says Dr Ibuki.
Nurses show empathy and establish meaningful connections with their patients, a human touch which is essential in fostering a sense of understanding, trust, and emotional support. The researchers examined whether the current advancements in robotics and AI can implement these human qualities by replicating the ethical concepts attributed to human nurses, including advocacy, accountability, cooperation, and caring.
Advocacy in nursing involves speaking on behalf of patients to ensure that they receive the best possible medical care. This encompasses safeguarding patients from medical errors, providing treatment information, acknowledging the preferences of a patient, and acting as mediators between the hospital and the patient. In this regard, the researchers noted that while AI can inform patients about medical errors and present treatment options, they questioned its ability to truly understand and empathise with patients’ values and to effectively navigate human relationships as mediators.
The researchers also expressed concerns about holding robots accountable for their actions. They suggested the development of explainable AI, which would provide insights into the decision-making process of AI systems, improving accountability.
The study further highlights that nurses are required to collaborate effectively with their colleagues and other healthcare professionals to ensure the best possible care for patients. As humans rely on visual cues to build trust and establish relationships, unfamiliarity with robots might lead to suboptimal interactions. Recognising this issue, the researchers emphasised the importance of conducting further investigations to determine the appropriate appearance of robots for facilitating efficient cooperation with human medical staff.
Lastly, while robots and AI have the potential to understand a patient’s emotions and provide appropriate care, the patient must also be willing to accept robots as care providers.
Having considered the above four ethical concepts in nursing, the researchers acknowledge that while robots may not fully replace human nurses anytime soon, they do not dismiss the possibility. While robots and AI can potentially reduce the shortage of nurses and improve treatment outcomes for patients, their deployment requires careful weighing of the ethical implications and impact on nursing practice.
“While the present analysis does not preclude the possibility of implementing the ethical concepts of nursing in robots and AI in the future, it points out that there are several ethical questions. Further research could not only help solve them but also lead to new discoveries in ethics,” concludes Dr Ibuki.
The Large Language Models (LLM) used in chatbots may appear to offer reliable, persuasive advice in a format which mimics conversation but in they can offer potentially harmful information when prompted with medical questions. Therefore, any LLM-chatbot in a medical setting would require approval as a medical device, argue experts in a paper published in Nature Medicine.
The mistake often made with LLM-chatbots is that they are a true “artificial intelligence” when in fact they are more closely related to the predictive text in a smartphone. They mostly use conversations and text scraped from the internet, and use algorithms to associate words and sentences in a manner that appears meaningful.
“Large Language Models are neural network language models with remarkable conversational skills. They generate human-like responses and engage in interactive conversations. However, they often generate highly convincing statements that are verifiably wrong or provide inappropriate responses. Today there is no way to be certain about the quality, evidence level, or consistency of clinical information or supporting evidence for any response. These chatbots are unsafe tools when it comes to medical advice and it is necessary to develop new frameworks that ensure patient safety,” said Prof Stephen Gilbert at TU Dresden.
Challenges in the regulatory approval of LLMs
Most people research their symptoms online before seeking medical advice. Search engines play a role in decision-making process. The forthcoming integration of LLM-chatbots into search engines may increase users’ confidence in the answers given by a chatbot that mimics conversation. It has been demonstrated that LLMs can provide profoundly dangerous information when prompted with medical questions.
The basis of LLMs do not have any medical “ground truth,” which is inherently dangerous. Chat-interfaced LLMs have already provided harmful medical responses and have already been used unethically in ‘experiments’ on patients without consent. Almost every medical LLM use case requires regulatory control in the EU and US. In the US their lack of explainability disqualifies them from being ‘non devices’. LLMs with explainability, low bias, predictability, correctness, and verifiable outputs do not currently exist and they are not exempted from current (or future) governance approaches.
The authors describe in their paper the limited scenarios in which LLMs could find application under current frameworks. They also describe how developers can seek to create LLM-based tools that could be approved as medical devices, and they explore the development of new frameworks that preserve patient safety. “Current LLM-chatbots do not meet key principles for AI in healthcare, like bias control, explainability, systems of oversight, validation and transparency. To earn their place in medical armamentarium, chatbots must be designed for better accuracy, with safety and clinical efficacy demonstrated and approved by regulators,” concludes Prof Gilbert.
Technical work on the discovery of new medicines is not commonly done in Africa, but Kelly Chibale, a professor in organic chemistry and founder of H3D at the University of Cape Town is changing this. PHOTO: Nasief Manie/Spotlight
Inside Professor Kelly Chibale’s office the bookshelves are packed with awards. On the walls, framed photographs include his class photo at Cambridge University in the United Kingdom, dated 1989.
Chibale is a professor of organic chemistry and founder of the pioneering Holistic Drug Discovery and Development Centre – H3D – at the University of Cape Town. While many important clinical trials have been conducted by Africans in Africa, the kind of drug discovery work that Chibale is doing is rare on the continent.
Chibale relays how he sees molecules everywhere – in hair, in clothes, in all of life around us. His animated voice fills the space as he speaks. “With organic chemistry, we are very visual. We look at chemical structures. If you give me a chemical structure, oh my goodness, my head starts racing about what I can do with it, or how I can change it to create new properties or new materials.”
H3D has 76 staff members investigating novel chemical compounds that could become new lifesaving medicines, with a focus on malaria, tuberculosis, and antibiotic-resistant microbial diseases.
Effectively a small biotech company embedded within the university, to date, H3D’s most notable discovery was a compound in 2012 which they named MMV390048, which had the potential to become a single-dose cure for malaria. Phase I clinical trials saw MMV390048 tested on human volunteers in South Africa and in Australia.
“In Australia, the testing model used is a volunteer infection study where human beings volunteer to be injected with the malaria parasite, which they know can be treated using available medicines,” says Chibale. “And then a section of those are given the experimental drug. And it worked beautifully there.”
‘Fail your way to success’
He adds, “People don’t realise this – there’s no medicine that will be given to people if it wasn’t tested on people first. Even me as an African. Oh man, I suffered from malaria as a child in Zambia many times. Thanks to our government then I’d be taken to a health facility and get malaria tablets, which I took and got well again. Otherwise, I would have died. Malaria kills very quickly. Now this is something I didn’t know then, something I took for granted. Only much later in life did I realise, goodness the medicine I took – someone somewhere invested in its research and development. And someone, somewhere, another human being, volunteered for that drug to be tested on them for my benefit.”
In 2017, the compound made it to Phase II clinical trials in patients with the disease, but further development was halted in 2020 when extensive further tests showed toxicity signals in rats – not rabbits though, Chibale says, adding that they had to err on the side of caution.
“In drug discovery, you have to kiss many frogs before you meet the prince,” he says. “Many drugs fail to progress. People focus on one product that makes it onto the market, right? But there are many failures that don’t even see the light of day. In this industry, you fail your way to success.”
H3D’s most notable discovery was a compound in 2012 which they named MMV390048, which had the potential to become a single-dose cure for malaria. PHOTO: Nasief Manie/Spotlight
Their work continues. In April last year at a function at Cape Town’s Vineyard Hotel, multinational pharmaceutical company Johnson & Johnson announced H3D as one of its three satellite centres for global health discovery. The other centres are in London and Singapore. At the time, Johnson & Johnson stated, “Driven by some of the leading researchers in Africa and discovery science, the satellite center [H3D] is focused on outpacing the rising threat of antimicrobial resistance by accelerating innovation against multidrug-resistance gram-negative bacteria.”
Seated at a boardroom table in his office, Chibale laughs deep from his belly. “We associate Johnson & Johnson with baby powder, but there’s much more…”
His left arm is in a sling following shoulder surgery – an injury stemming from lockdown when he slipped and fell while hiking on Table Mountain. “It happened just here, above the university,” he gestures, with his other arm.
Chibale and his wife Bertha live on the university’s campus, where he has served as warden of student residence Upper Campus Residence, formerly Smuts Hall, since 2015. Here he weathered the #rhodesmustfall and #feesmustfall protests, which saw students torch vehicles and police deploy stun grenades a stone’s throw away from his home.
Referring to his injured arm, he says at least his writing arm wasn’t hurt and that he can still type with one hand.
From a village in Zambia
Mentions of gratitude underpin the story of his journey, which starts in a village without electricity or running water in Zambia’s Mpika district. His father died when he was two months old. Laughing, he relays how hearing in his one ear is still impaired after being ambushed as a kid while stealing mangoes.
“This was a township,” he says. “So I’m climbing up a tree to steal mangoes and I was coming down. This gang, or well guys who were playful, had surrounded us. There were only about four of us, of who three managed to escape. And I was the only one left. Oh my goodness. And they took a big rock and smashed it to my ear. And then, when they saw me bleeding, they actually ran away. They were so scared of the damage they had done. Oh, that day! Anyway, so I went home and lied to my mother and said, no I went to school and tripped over a hole.”
During high school classes, thanks to an excellent teacher, he became fascinated with chemistry experiments. He went on to study organic chemistry at the University of Zambia, where he fell in love with the logical nature of organic molecules. “These things cannot be planned. I simply fell in love with organic chemistry, in the same way I fell in love with my wife Bertha,” he says.
From early on he realised education was a way out of poverty. “To get out of poverty, you either play sport or you follow education,” he says. “So I started applying for scholarships, writing letters to universities around the world. And I got rejected. I kept applying and kept on being rejected. But I didn’t give up. I kept applying.”
His first job was at Kafironda Explosives in the mining town Mufulira, on Zambia’s Copperbelt, where he made detonators, dynamite, and other explosives for use in Zambian mines. Laughing, he says this would come to haunt him later while applying for a visa to enter the United States. “There was a section on the form where you had to declare whether you’ve worked with explosives,” he says. “Of course, I said ‘yes’, and fortunately nothing happened.”
During two years at Kafironda, he continued applying for scholarships. “And I remember this,” he says. “It was January of 1989. I got a letter saying you have been shortlisted for a Cambridge Livingstone Trust Scholarship. Please present yourself for an interview on the 26th of January at the Anglo-American Corporation offices in Harare, Zimbabwe… So that was my first time out of Zambia. The first time to fly on an aeroplane.”
‘This was my turn’
Competition for the scholarship was tight, with shortlisted candidates from several African countries. “So in that year, there were six of us from Zambia, from different disciplines. I was the only scientist. And of course, I’d been failing all this time, getting rejected. But this was my turn. It was God’s appointed time for me. Actually, I was the only successful candidate.”
At Cambridge, without having completed an honours or master’s degree, Chibale enrolled for a PhD under the late organic chemist Professor Stuart Warren. “So Stuart, this amazing, incredible man, just gave me a chance. I mean there was such a gap between me and my colleagues who had all done their undergraduates at Cambridge. But in life, you can moan and complain about a disadvantage, or you can turn it into a challenge. I mean, the first three to six months were rough. Stuart would recommend to me that I sneak into first-year undergraduate classes to catch up. Stuart, he saw something in me that I didn’t even see in myself, and really gave me a chance.”
Chibale’s work at Warren’s lab, developing new synthetic methods for optically active molecules, helped secure his first post-doctoral position at the University of Liverpool, in the United Kingdom, after which he joined the Scripps Research Institute in La Jolla, California, funded through a Wellcome Trust International Prize Travelling Research Fellowship.
“That was another miracle,” he says. “I was eligible for this fellowship only because I had lived in England for three years, which was a minimum requirement. And the scholarship was so good, it even gave me an allowance for my family. I haven’t forgotten. It was 1 000 pounds per month. In those days, the pound was much stronger than the US dollar. So I went from rags to riches. In Liverpool, I was walking most of the time while in California, I actually had a car!”
Over the years, he was gaining insight into the pharmaceutical sector – the science but also the entrepreneurial side that pushes innovation, all the while longing to bring this knowledge to Africa. Peers suggested he consider South Africa, and particularly the University of Cape Town [UCT]. Around 1994, then UCT Department of Chemistry head, Professor James Bull actually made Chibale an offer to pursue postdoctoral research – which he declined. “Because I thought there was going to be a civil war in South Africa! I remember watching the release of Nelson Mandela on TV in England, quiet, just watching.”
Towards the end of 1995, inside a copy of the British scientific journal Nature, Chibale found an advertisement for a position as a lecturer in organic chemistry at UCT and applied. “It was a calling,” he says. The family moved to Cape Town.
Then in 2010 at UCT, with five post-doctoral staff, Chibale founded H3D. At the time his mentors included Dr Anthony Wood, former Pfizer senior vice-president, now head of GlaxoSmithKline’s Research and Development, who arranged for Chibale to have a four-month sabbatical with Pfizer in the United Kingdom to learn about the practicalities of innovative pharma. Thirteen years later, H3D has blossomed.
Chibale says he is a Christian as well as a soccer and boxing fan. His wife Bertha runs a Cape Town catering business called Hearts and Tarts. They have three sons.
As the interview draws to a close, he looks up at his 1989 Cambridge class photo. “You won’t believe it,” he says. “Last year I visited my college at Cambridge with my wife and second son and they pulled out a copy of my handwritten scholarship application letter, written to them from Zambia all those years back.”
This precious relic of Chibale’s journey is not in his office. He keeps it on his desk at home.
Image created using an AI art program, Craiyon, with the prompt “An AI monitoring a patient in an ICU ward”.
In the future, artificial intelligence will play an important role in medicine. In diagnostics, successful tests have already been performed with AI, such as accurately categorising images according to whether they show pathological changes or not. But training an AI run in real time to examine the time-varying conditions of patients in an ICU and to calculate treatment suggestions has remained a challenge. Now, University of Vienna Researchers report in the Journal of Clinical Medicine that they have accomplished such a feat.
With the help of extensive data from ICUs of various hospitals, an AI was developed that provides suggestions for the treatment of people who require intensive care due to sepsis. Analyses show that AI already surpasses the quality of human decisions making it important to also discuss the legal aspects of such methods.
Making optimal use of existing data
“In an intensive care unit, a lot of different data is collected around the clock. The patients are constantly monitored medically. We wanted to investigate whether these data could be used even better than before,” says Prof Clemens Heitzinger from the Institute for Analysis and Scientific Computing at TU Wien (Vienna).
Medical staff make their decisions on the basis of well-founded rules. Most of the time, they know very well which parameters they have to take into account in order to provide the best care. But now, a computer can easily take many more parameters than a human into account – sometimes leading to even better decisions.
The computer as planning agent
“In our project, we used a form of machine learning called reinforcement learning,” says Clemens Heitzinger. “This is not just about simple categorisation – for example, separating a large number of images into those that show a tumour and those that do not – but about a temporally changing progression, about the development that a certain patient is likely to go through. Mathematically, this is something quite different. There has been little research in this regard in the medical field.”
The computer becomes an agent that makes its own decisions: if the patient is well, the computer is “rewarded”. If the condition deteriorates or death occurs, the computer is “punished”. The computer programme has the task of maximising its virtual “reward” by taking actions. In this way, extensive medical data can be used to automatically determine a strategy which achieves a particularly high probability of success.
Already better than a human
“Sepsis is one of the most common causes of death in intensive care medicine and poses an enormous challenge for doctors and hospitals, as early detection and treatment is crucial for patient survival,” says Prof Oliver Kimberger from the Medical University of Vienna. “So far, there have been few medical breakthroughs in this field, which makes the search for new treatments and approaches all the more urgent. For this reason, it is particularly interesting to investigate the extent to which artificial intelligence can contribute to improve medical care here. Using machine learning models and other AI technologies are an opportunity to improve the diagnosis and treatment of sepsis, ultimately increasing the chances of patient survival.”
Analysis shows that AI capabilities are already outperforming humans: “Cure rates are now higher with an AI strategy than with purely human decisions. In one of our studies, the cure rate in terms of 90-day mortality was increased by about 3% to about 88%,” says Clemens Heitzinger.
Of course, this does not mean that one should leave medical decisions in an ICU to the computer alone. But the artificial intelligence may run along as an additional device at the bedside – and the medical staff can consult it and compare their own assessment with the AI’s suggestions. Such AIs can also be highly useful in education.
Discussion about legal issues is necessary
“However, this raises important questions, especially legal ones,” says Clemens Heitzinger. “One probably thinks of the question who will be held liable for any mistakes made by the artificial intelligence first. But there is also the converse problem: what if the artificial intelligence had made the right decision, but the human chose a different treatment option and the patient suffered harm as a result?” Does the doctor then face the accusation that it would have been better to trust the artificial intelligence because it comes with a huge wealth of experience? Or should it be the human’s right to ignore the computer’s advice at all times?
“The research project shows: artificial intelligence can already be used successfully in clinical practice with today’s technology – but a discussion about the social framework and clear legal rules are still urgently needed,” Clemens Heitzinger is convinced.
Model of a testosterone molecule. Source: Wikimedia CC0
The androgen receptor is a key transcriptional factor for proper sex development, especially in males and the physiological balance of all the tissues that express this receptor. The androgen receptor is involved in several pathologies and syndromes, such as spinal and bulbar muscular atrophy or androgen insensitivity syndrome, for which there is no specific treatment. Regarded as the main initial and progression factor in prostate cancer, this receptor has been the main therapeutic target for the treatment against this disease for decades.
Now, a study published in Science Advances describes the structural and functional effects of mutations on the androgen receptor, as well as how these changes lead to the development of prostate cancer.
Point mutations in the androgen receptor
The human androgen receptor is a key protein in the development and functioning of the prostate in response to male hormones, such as testosterone. Point mutations in the androgen receptor – specifically, one amino acid swapped for another – are one of the main mechanisms than can lead to structural and functional alterations in the receptor, which result in the development of diseases.
The results of the University of Barcelona-led study show that the analysed mutations affect several functional regions of the union domain of the androgen receptor to testosterone. In particular, these are mutations that alter a region of the receptor which is the target for posttranscriptional modifications (that is, modifications in the protein once this is produced).
This type of chemical alterations affect specific amino acids of the androgen receptor and are executed by regulating proteins which are critical for the proper functioning of the receptor. If this receptor’s regulation pathway is altered, such as the case of the presence of mutations described by the team, its function is deregulated and it can be dysfunctional and cause pathologies.
“In our study, we experimentally checked that these mutations deregulate a specific mutation, known as arginine methylation, which is one of the posttranscriptional modifications, due to the structural changes these alterations produce in a functional area of the receptor. Also, we could observe that the deregulation of the androgen receptor methylation involves relevant changes in its function within the cell,” the team concludes.
A trial published in the International Journal of Gynecology & Obstetrics lends support to the idea that 3D virtual reality lessons can improve medical students’ retention of knowledge and understanding of complex topics in obstetrics and gynaecology.
For the study, 21 students took part in a 15-minute virtual reality learning environment (VRLE) experience on the stages of foetal development, while 20 students received a PowerPoint tutorial on the same topic, serving as a control.
While the students’ level of knowledge increased after both learning experiences, it was only retained in the VRLE group at one-week follow up. Questionnaires completed by participants reflected a high degree of satisfaction with the VRLE tool compared with the traditional tutorial.
“Virtual reality learning tools hold potential to enhance student learning and are very well received by students,” said corresponding author Fionnuala McAuliffe, MD, of University College Dublin National Maternity Hospital, in Ireland.
A new biosensor engineered by Penn State researchers offers scientists the first dynamic glimpses of the elusive – and vital – manganese ion. The researchers engineered the sensor from a natural protein called lanmodulin, which binds rare earth elements with high selectivity and was discovered five years ago by some of this study’s researchers. Their findings are published in the Proceedings of the National Academy of Sciences.
The researchers genetically reprogrammed the protein to favour manganese over other common transition metals like iron and copper, unlike most transition metal-binding molecules. The sensor could have broad applications in biotechnology to advance understanding of photosynthesis, host-pathogen interactions and neurobiology.
Like iron, copper and zinc, manganese is an essential metal for plants and animals. Its function is to activate enzymes. In humans, manganese is linked to neural development. Accumulation of excess manganese in the brain induces Parkinsonian-like motor disease, whereas reduced manganese levels have been observed in association with Huntington’s disease, the researchers explained.
“We believe that this is the first sensor that is selective enough for manganese for detailed studies of this metal in biological systems,” said Jennifer Park, a graduate student at Penn State and lead author on the paper. “We’ve used it – and seen the dynamics of how manganese comes and goes in a living system, which hasn’t been possible before.”
She explained that the team was able to monitor the behaviour of manganese within bacteria and are now working to engineer even tighter binding sensors to potentially study how the metal works in mammalian systems.
Scientific understanding of manganese has lagged behind that of other essential metals, in part because of a lack of techniques to visualise its concentration, localisation and movement within cells. The new sensor opens the door for all kinds of new research, explained Joseph Cotruvo, associate professor of chemistry at Penn State and senior author on the paper.
“There are so many potential applications for this sensor,” said Cotruvo. “Personally, I am particularly interested in seeing how manganese interacts with pathogens.”
He explained that the body works hard to restrict the iron that most bacterial pathogens need for survival, and so those pathogens instead turn to manganese.
“We know there is this tug-of-war for vital metals between the immune system and these invading pathogens, but we haven’t been able to fully understand these dynamics, because we couldn’t see them in real time,” he said, adding that with new capabilities to visualise the process, researchers have tools to potentially develop new drug targets for a range of infections for which resistance has emerged to common antibiotics, like staph (MRSA).
Designing proteins to bind to particular metals is an intrinsically difficult problem, Cotruvo explained, because there are so many similarities between the transition metals present in cells. As a result, there has been a lack of chemical biology tools with which to study manganese physiology in live cells.
“The question for us was, can we engineer a protein to only bind to one thing, a manganese ion, even in the presence of a huge excess of other very similar-looking things, like calcium, magnesium, iron, and zinc ions?” Cotruvo said. “What we had to do was create a binding site arranged in just the right way, so that this protein bond was more stable in manganese than any other metal.”
Having successfully demonstrated lanmodulin is capable of such a task, the team is now planning to use it as a scaffold from which to evolve other types of biological tools for sensing and recovering many different metal ions that have biological and technological importance.
“If you can figure out ways of discriminating between very similar metals, that’s really powerful,” said Cotruvo. “If we can take lanmodulin and turn it into a manganese-binding protein, then what else can we do?”
ChatGPT can score at or around the approximately 60% pass mark for the United States Medical Licensing Exam (USMLE), with responses that make coherent, internal sense and contain frequent insights, according to a study published in PLOS Digital Health by Tiffany Kung, Victor Tseng, and colleagues at AnsibleHealth.
ChatGPT is a new artificial intelligence (AI) system, known as a large language model (LLM), designed to generate human-like writing by predicting upcoming word sequences. Unlike most chatbots, ChatGPT cannot search the internet. Instead, it generates text using word relationships predicted by its internal processes.
Kung and colleagues tested ChatGPT’s performance on the USMLE, a highly standardised and regulated series of three exams (Steps 1, 2CK, and 3) required for medical licensure in the United States. Taken by medical students and physicians-in-training, the USMLE assesses knowledge spanning most medical disciplines, ranging from biochemistry, to diagnostic reasoning, to bioethics.
After screening to remove image-based questions, the authors tested the software on 350 of the 376 public questions available from the June 2022 USMLE release.
After indeterminate responses were removed, ChatGPT scored between 52.4% and 75.0% across the three USMLE exams. The passing threshold each year is approximately 60%. ChatGPT also demonstrated 94.6% concordance across all its responses and produced at least one significant insight (something that was new, non-obvious, and clinically valid) for 88.9% of its responses. Notably, ChatGPT exceeded the performance of PubMedGPT, a counterpart model trained exclusively on biomedical domain literature, which scored 50.8% on an older dataset of USMLE-style questions.
While the relatively small input size restricted the depth and range of analyses, the authors note their findings provide a glimpse of ChatGPT’s potential to enhance medical education, and eventually, clinical practice. For example, they add, clinicians at AnsibleHealth already use ChatGPT to rewrite jargon-heavy reports for easier patient comprehension.
“Reaching the passing score for this notoriously difficult expert exam, and doing so without any human reinforcement, marks a notable milestone in clinical AI maturation,” say the authors.
Author Dr Tiffany Kung added that ChatGPT’s role in this research went beyond being the study subject: “ChatGPT contributed substantially to the writing of [our] manuscript… We interacted with ChatGPT much like a colleague, asking it to synthesise, simplify, and offer counterpoints to drafts in progress…All of the co-authors valued ChatGPT’s input.”
Photo by Reproductive Health Supplies Coalition on Unsplash
Thus far, very few contraceptive options being developed target the sperm cells. Researchers are now developing approaches that target testosterone or otherwise interrupt the sperm’s ability to fertilise an egg, yet these may not work for everyone. But now, researchers publishing in ACS’ Journal of Medicinal Chemistry have identified a new candidate molecule that could become an effective non-hormonal contraceptive for males.
Previously, Gunda I. Georg and colleagues investigated non-hormonal contraceptive options, as approaches targeting testosterone produced unwanted side effects. They developed a drug targeted at a specific vitamin A receptor and found that it worked as a highly effective contraceptive with no side effects. But numerous proteins are involved in forming sperm, and exploring multiple options would maximise chances for a drug that would eventually make it to market.
Another set of proteins involved in the cell cycle are the cyclin-dependent kinases, or CDKs, which play a role in sperm cell production and tumour development. Mice without the CDK2 receptor are sterile, so a drug that targets this protein could serve as an effective contraceptive. It also has potential as a cancer therapeutic because inhibiting the enzyme slowed tumour growth in previous studies. However, CDK2 has a very similar shape to other enzymes in its family, and currently available inhibitors tend to produce undesirable off-target effects by accidentally binding the others as well. So, Georg and her team wanted to develop a drug that could selectively inhibit CDK2 to serve as another contraceptive option.
The team previously discovered an unknown binding site in CDK2 and a commercially available dye molecule that successfully bound to it. Using the dye as a starting point, the researched screened tens of thousands of different compounds in their current work to find ones that also bound the pocket well. They narrowed the list down to just three, picking one to further optimize. The best version, named EF-4-177, demonstrated a long half-life and good diffusion into the testes of mice. After a 28-day exposure, the animals’ sperm counts decreased by about 45%. Additionally, EF-4-117 bound much more strongly to the CDK2 pocket than the dye, making it the highest affinity inhibitor for this site reported to date. The researchers say that this work proves the potential of this inhibitor for future therapeutic applications.
Potassium is essential to normal cellular function, helping the cardiac muscle work correctly and aids in the transmission of electrical signals within cells. A new mathematical model published in PLOS Computational Biology sheds light on the often mysterious process of potassium homeostasis.
Using existing biological data, researchers at the University of Waterloo built a mathematical model that simulates how an average person’s body regulates potassium, both in times of potassium depletion and during potassium intake. Because so many foods contain abundant potassium, the body is continually storing, deploying, and disposing of potassium to keep it in a healthy range, ie the process of potassium homeostasis. Understanding potassium homeostasis is essential in helping diagnose the source of the problem when something goes wrong, for example, when kidney disease or medication leads to dysregulation.
“Too much potassium in the body, or hyperkalaemia, can be just as dangerous as hypokalaemia, or too little,” said study lead author Melissa M. Stadt, a PhD student in applied mathematics. “Dysregulation of potassium can lead to dangerous and potentially fatal consequences.”
The model could be used for a virtual patient trial, allowing researchers to generate dozens of patients and then predict which ones would have hyper- or hypokalaemia based on different controls.
“A lot of our models are pieces of a bigger picture,” said Anita Layton, professor of applied mathematics and Canada 150 Research Chair in mathematical biology and medicine. “This model is one new and exciting piece in helping us understand how our incredibly complex internal systems work.”
The model is especially exciting because it allows scientists to test the muscle-kidney cross-talk signal hypothesis. Scientists have hypothesised that skeletal muscles, which store most of the body’s potassium, can directly signal to the kidneys to dump potassium when there’s too much stores, and vice versa. When the mathematical researchers tested the hypothesis in their model, it more accurately reflected existing biological data regarding potassium homeostasis, suggesting that muscle-kidney cross talk might be an essential piece in the puzzle of potassium regulation.
