Pub Date : 2024-08-16DOI: 10.1101/2024.08.12.24311432
Girish Harinath, Virginia Lee, Andy Nyquist, Mauricio Moel, Maartje Wouters, Jesper Hagemeier, Brandon Verkennes, Colleen Tacubao, Krister Kauppi, Stefanie L Morgan, Anar Isman, Sajad Zalzala
Rapamycin, also known as sirolimus, has demonstrated great potential for application in longevity medicine. However, the bioavailability of generic and compounded rapamycin at longevity doses in normative aging individuals remains unknown. We conducted a retrospective, real-world study determining the 24-hour blood rapamycin levels to establish the relative bioavailability, dose-to-blood level linearity and inter-individual heterogeneity in a normative aging cohort. Participants received either compounded rapamycin (n = 23, dosages 5, 10, or 15 mg) or generic rapamycin (n= 44, dosages 2, 3, 6, or 8 mg) once per week, and were asked to obtain a sirolimus level blood draw 24 hours after dose self-administration. Similar blood rapamycin levels and a linear dose-to-blood level relationship were observed for both formulations, although a higher bioavailability per milligram of rapamycin was noted for the generic formulation (compounded averaged 0.287 (28.7%) bioavailability relative to generic rapamycin in (ng/mL) / mg rapamycin). While substantial inter-individual heterogeneity in blood rapamycin levels was observed for both formulations, repeat tests for individuals demonstrated high test-retest reliability. As we detected no significant association between bioavailability and measures of body mass index (BMI), sex, age, or length of time taking rapamycin, we suggest that individualized dosing and routine monitoring of blood rapamycin levels should be applied to ensure optimal longevity efficacy. Finally, we contextualize our data with a brief review of the literature on the currently available knowledge of rapamycin's bioavailability in normative aging populations, and provide implications for the clinical use of rapamycin in longevity medicine moving forward.
{"title":"The bioavailability of compounded and generic rapamycin in normative aging individuals: A retrospective study and review with clinical implications","authors":"Girish Harinath, Virginia Lee, Andy Nyquist, Mauricio Moel, Maartje Wouters, Jesper Hagemeier, Brandon Verkennes, Colleen Tacubao, Krister Kauppi, Stefanie L Morgan, Anar Isman, Sajad Zalzala","doi":"10.1101/2024.08.12.24311432","DOIUrl":"https://doi.org/10.1101/2024.08.12.24311432","url":null,"abstract":"Rapamycin, also known as sirolimus, has demonstrated great potential for application in longevity medicine. However, the bioavailability of generic and compounded rapamycin at longevity doses in normative aging individuals remains unknown. We conducted a retrospective, real-world study determining the 24-hour blood rapamycin levels to establish the relative bioavailability, dose-to-blood level linearity and inter-individual heterogeneity in a normative aging cohort. Participants received either compounded rapamycin (n = 23, dosages 5, 10, or 15 mg) or generic rapamycin (n= 44, dosages 2, 3, 6, or 8 mg) once per week, and were asked to obtain a sirolimus level blood draw 24 hours after dose self-administration. Similar blood rapamycin levels and a linear dose-to-blood level relationship were observed for both formulations, although a higher bioavailability per milligram of rapamycin was noted for the generic formulation (compounded averaged 0.287 (28.7%) bioavailability relative to generic rapamycin in (ng/mL) / mg rapamycin). While substantial inter-individual heterogeneity in blood rapamycin levels was observed for both formulations, repeat tests for individuals demonstrated high test-retest reliability. As we detected no significant association between bioavailability and measures of body mass index (BMI), sex, age, or length of time taking rapamycin, we suggest that individualized dosing and routine monitoring of blood rapamycin levels should be applied to ensure optimal longevity efficacy. Finally, we contextualize our data with a brief review of the literature on the currently available knowledge of rapamycin's bioavailability in normative aging populations, and provide implications for the clinical use of rapamycin in longevity medicine moving forward.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-27DOI: 10.1101/2024.07.25.24311039
Stephen Kent, Shiyao Li, Thakshila H Amarasena, Arnold Reynaldi, Wen Shi Lee, Michael G Leeming, David O'Connor, Julie Nguyen, Helen E Kent, Frank Caruso, Jennifer A Juno, Adam K Wheatley, Miles Philip Davenport, Yi Ju
Lipid nanoparticle mRNA vaccines are an exciting but new technology used in humans. There is limited understanding of factors that influence their biodistribution and immunogenicity. Antibodies to polyethylene glycol (PEG), which is on the surface of the lipid nanoparticle, are detectable in humans and boosted by human mRNA vaccination. We hypothesized that PEG-specific antibodies could increase the clearance of mRNA vaccines. We developed methods to quantify both the mRNA and ionizable lipid in frequent serial blood samples from 19 subjects receiving Moderna SPIKEVAX mRNA booster immunization. Both the mRNA and ionizable lipid peaked in blood 1-2 days post vaccination (median peak level 0.19 and 3.22 ng mL-1, respectively). The mRNA was detectable out to 14-28 days post-vaccination in most subjects. We measured the proportion of mRNA that was relatively intact in blood over time and found the decay kinetics of the intact mRNA and ionizable lipid were identical, suggesting the intact lipid nanoparticle recirculates in blood. However, mRNA and ionizable lipid decay rates did not correlate with baseline levels of PEG-specific nor spike-specific antibodies. The magnitude of mRNA and ionizable lipid detected in blood did correlate with the boost in PEG antibodies. Further, the ability of monocytes to phagocytose lipid nanoparticles had an inverse relationship with the rise in PEG antibodies. This suggests circulation of mRNA lipid nanoparticle vaccines into the blood and their ability to be cleared by phagocytes influence PEG immunogenicity of mRNA vaccines. Overall, this work defines the pharmacokinetics of lipid nanoparticle mRNA vaccine components in human blood after intramuscular injection and the factors that influence this. These insights should prove useful in improving the future safety and efficacy of lipid nanoparticle mRNA vaccines and therapeutics.
{"title":"Blood Distribution of SARS-CoV-2 Lipid Nanoparticle mRNA Vaccine in Humans","authors":"Stephen Kent, Shiyao Li, Thakshila H Amarasena, Arnold Reynaldi, Wen Shi Lee, Michael G Leeming, David O'Connor, Julie Nguyen, Helen E Kent, Frank Caruso, Jennifer A Juno, Adam K Wheatley, Miles Philip Davenport, Yi Ju","doi":"10.1101/2024.07.25.24311039","DOIUrl":"https://doi.org/10.1101/2024.07.25.24311039","url":null,"abstract":"Lipid nanoparticle mRNA vaccines are an exciting but new technology used in humans. There is limited understanding of factors that influence their biodistribution and immunogenicity. Antibodies to polyethylene glycol (PEG), which is on the surface of the lipid nanoparticle, are detectable in humans and boosted by human mRNA vaccination. We hypothesized that PEG-specific antibodies could increase the clearance of mRNA vaccines. We developed methods to quantify both the mRNA and ionizable lipid in frequent serial blood samples from 19 subjects receiving Moderna SPIKEVAX mRNA booster immunization. Both the mRNA and ionizable lipid peaked in blood 1-2 days post vaccination (median peak level 0.19 and 3.22 ng mL-1, respectively). The mRNA was detectable out to 14-28 days post-vaccination in most subjects. We measured the proportion of mRNA that was relatively intact in blood over time and found the decay kinetics of the intact mRNA and ionizable lipid were identical, suggesting the intact lipid nanoparticle recirculates in blood. However, mRNA and ionizable lipid decay rates did not correlate with baseline levels of PEG-specific nor spike-specific antibodies. The magnitude of mRNA and ionizable lipid detected in blood did correlate with the boost in PEG antibodies. Further, the ability of monocytes to phagocytose lipid nanoparticles had an inverse relationship with the rise in PEG antibodies. This suggests circulation of mRNA lipid nanoparticle vaccines into the blood and their ability to be cleared by phagocytes influence PEG immunogenicity of mRNA vaccines. Overall, this work defines the pharmacokinetics of lipid nanoparticle mRNA vaccine components in human blood after intramuscular injection and the factors that influence this. These insights should prove useful in improving the future safety and efficacy of lipid nanoparticle mRNA vaccines and therapeutics.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1101/2024.07.25.24311015
Jonas Schluter, Fanny Matheis, Wataru Ebina, William Jogia, Alexis P. Sullivan, Kelly Gordon, Elbert Fanega de la Cruz, Mary E. Victory-Hays, Mary Joan Heinly, Catherine S. Diefenbach, Jonathan U. Peled, Kevin R. Foster, Aubrey Levitt, Eric McLaughlin
Antibiotic-induced microbiome injury, defined as a reduction of ecological diversity and obligate anaerobe taxa, is associated with negative health outcomes in hospitalized patients, and healthy individuals who received antibiotics in the past are at higher risk for autoimmune diseases. No interventions are currently available that effectively target the microbial ecosystem in the gut to prevent this negative collateral damage of antibiotics. Here, we present the results from a single-center, randomized placebo-controlled trial involving 32 patients who received an oral, fermentation-derived postbiotic alongside oral antibiotic therapy for gastrointestinal (GI)-unrelated infections. Postbiotics comprise complex mixtures of metabolites produced by bacteria during fermentation and other processes, which can mediate microbial ecology. Bacterial ecosystem alpha diversity, quantified by the inverse Simpson index, during the end of the antibiotic course was significantly higher (+40%) across the 16 postbiotic-treated patients compared with the 16 patients who received a placebo, and the postbiotic was well-tolerated. Secondary analyses of 157 stool samples collected longitudinally revealed that the increased diversity was driven by enrichment in health-associated microbial genera: obligate anaerobe Firmicutes, in particular taxa belonging to the Lachnospiraceae family, were higher in treated patients; conversely, Escherichia/Shigella abundances, which comprise pathobionts and antimicrobial-resistant strains, were reduced in postbiotic-treated patients at the end of their antibiotic course and up to 10 days later. Taken together, these results indicate that postbiotic co-administration during antibiotic therapy could support a health-associated gut microbiome community and may reduce antibiotic-induced microbiome injury.
{"title":"A randomized controlled trial of postbiotic administration during antibiotic treatment increases microbiome diversity and enriches health associated taxa","authors":"Jonas Schluter, Fanny Matheis, Wataru Ebina, William Jogia, Alexis P. Sullivan, Kelly Gordon, Elbert Fanega de la Cruz, Mary E. Victory-Hays, Mary Joan Heinly, Catherine S. Diefenbach, Jonathan U. Peled, Kevin R. Foster, Aubrey Levitt, Eric McLaughlin","doi":"10.1101/2024.07.25.24311015","DOIUrl":"https://doi.org/10.1101/2024.07.25.24311015","url":null,"abstract":"Antibiotic-induced microbiome injury, defined as a reduction of ecological diversity and obligate anaerobe taxa, is associated with negative health outcomes in hospitalized patients, and healthy individuals who received antibiotics in the past are at higher risk for autoimmune diseases. No interventions are currently available that effectively target the microbial ecosystem in the gut to prevent this negative collateral damage of antibiotics. Here, we present the results from a single-center, randomized placebo-controlled trial involving 32 patients who received an oral, fermentation-derived postbiotic alongside oral antibiotic therapy for gastrointestinal (GI)-unrelated infections. Postbiotics comprise complex mixtures of metabolites produced by bacteria during fermentation and other processes, which can mediate microbial ecology. Bacterial ecosystem alpha diversity, quantified by the inverse Simpson index, during the end of the antibiotic course was significantly higher (+40%) across the 16 postbiotic-treated patients compared with the 16 patients who received a placebo, and the postbiotic was well-tolerated. Secondary analyses of 157 stool samples collected longitudinally revealed that the increased diversity was driven by enrichment in health-associated microbial genera: obligate anaerobe Firmicutes, in particular taxa belonging to the Lachnospiraceae family, were higher in treated patients; conversely, <em>Escherichia/Shigella</em> abundances, which comprise pathobionts and antimicrobial-resistant strains, were reduced in postbiotic-treated patients at the end of their antibiotic course and up to 10 days later. Taken together, these results indicate that postbiotic co-administration during antibiotic therapy could support a health-associated gut microbiome community and may reduce antibiotic-induced microbiome injury.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.1101/2024.07.22.24310817
MOHAMED IBRAHIM ABDULSAMAD, Ashraf Anass
PBPK/PD modeling is essential in modern drug development. Traditional drug development methods frequently rely on trial and error, which can be time-consuming, costly, and could be risky. Predicting Pharmacokinetics (PK) of drugs in pregnant women, encompassing the intricate aspect of placental drug transfer, remains a complex task. This study was to compare simulated or predicted and observed (previously published approaches) pharmacokinetic parameters among the four antiviral drugs in pregnant and non-pregnant women. In addition, this investigation endeavors to construct and assess physiologically-based pharmacokinetic (PBPK) models specific to maternal-fetal interactions for four antiviral drugs, Acyclovir, Emtricitabine, Dolutegravir (DTG) and Raltegravir (RAL). PBPK models were built with the Open Systems Pharmacology software suite (PK-Sim/MoBi). Different approaches to inform placental drug transfer were applied and compared. Model performance was evaluated using in vivo all 4 aforementioned antiviral maternal plasma concentrations during the 2nd and 3rd trimesters and umbilical vein concentrations at delivery. All clinical in vivo data were obtained from the International Maternal peadiatric and Adolescent AIDS Clinical Trials (IMPAACT) Network P1026s study. The PBPK models successfully predicted plasma concentration-time profiles of four antiviral drugs in the 2nd and 3rd trimesters and most predicted PK parameters fell within a 1.33-fold error range. Predicted umbilical vein concentrations of DTG among others were in reasonable agreement with in vivo data but were sensitive to changes in the placental partition coefficient and transplacental clearance. Maternal-fetal PBPK modeling reliably predicted maternal PK of early mentioned antiviral during pregnancy. For the fetal PK, data on the unbound fraction of highly protein-bound DTG has proven to be important to adequately capture changes in total clearance in silico. More research efforts, along with clinical data, are needed to verify the predictions of fetal PK of antiviral. In conclusion, the findings suggest the feasibility of employing physiologically-based pharmacokinetic (PBPK) models to assess the disposition of antiviral drugs in pregnant women and their fetuses.
{"title":"Enhancing Precision Drug Therapy and build Pharmacokinetic model in Pregnant Women: PBPK Modeling of Antiviral drugs","authors":"MOHAMED IBRAHIM ABDULSAMAD, Ashraf Anass","doi":"10.1101/2024.07.22.24310817","DOIUrl":"https://doi.org/10.1101/2024.07.22.24310817","url":null,"abstract":"PBPK/PD modeling is essential in modern drug development. Traditional drug development methods frequently rely on trial and error, which can be time-consuming, costly, and could be risky. Predicting Pharmacokinetics (PK) of drugs in pregnant women, encompassing the intricate aspect of placental drug transfer, remains a complex task. This study was to compare simulated or predicted and observed (previously published approaches) pharmacokinetic parameters among the four antiviral drugs in pregnant and non-pregnant women. In addition, this investigation endeavors to construct and assess physiologically-based pharmacokinetic (PBPK) models specific to maternal-fetal interactions for four antiviral drugs, Acyclovir, Emtricitabine, Dolutegravir (DTG) and Raltegravir (RAL). PBPK models were built with the Open Systems Pharmacology software suite (PK-Sim/MoBi). Different approaches to inform placental drug transfer were applied and compared. Model performance was evaluated using in vivo all 4 aforementioned antiviral maternal plasma concentrations during the 2nd and 3rd trimesters and umbilical vein concentrations at delivery. All clinical in vivo data were obtained from the International Maternal peadiatric and Adolescent AIDS Clinical Trials (IMPAACT) Network P1026s study. The PBPK models successfully predicted plasma concentration-time profiles of four antiviral drugs in the 2nd and 3rd trimesters and most predicted PK parameters fell within a 1.33-fold error range. Predicted umbilical vein concentrations of DTG among others were in reasonable agreement with in vivo data but were sensitive to changes in the placental partition coefficient and transplacental clearance. Maternal-fetal PBPK modeling reliably predicted maternal PK of early mentioned antiviral during pregnancy. For the fetal PK, data on the unbound fraction of highly protein-bound DTG has proven to be important to adequately capture changes in total clearance in silico. More research efforts, along with clinical data, are needed to verify the predictions of fetal PK of antiviral. In conclusion, the findings suggest the feasibility of employing physiologically-based pharmacokinetic (PBPK) models to assess the disposition of antiviral drugs in pregnant women and their fetuses.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-10DOI: 10.1101/2024.07.09.24309334
Susan Philip, Jeesa George, Adusumilli Pramod Kumar, Foujia Begum, Divya Bharathi K
The morbidity, mortality, and diminished quality of life of patients are all impacted by drug related problems. Stroke is one of the leading causes of disability and death in India and since the stroke patients are at an increased risk of DRPs (Drug Related Problems), early identification, prevention and resolution of the same is important for improving the quality of life of stroke patients. The aim of the study was to assess the incidence of DRPs in stroke patients by using Hepler strand classification and the acceptance rate by the multidisciplinary team. The relevant information were documented using a predefined data collection form and was analyzed for drug related problems and categorized according to Hepler Strand classification. A total of 510 DRPs from 130 participants with an average incidence of 3.92 DRPs per patient. Drug drug interaction was found to be 23.53% and ADRs accounted for 12.55 % of total DRPs. The study found a high acceptance rate of recommendations by healthcare professionals at 97.36%, with changes in therapy being made 68.13% of the time. Early detection and resolution of DRP by clinical pharmacist may improve the therapeutic outcomes in stroke patients. This also helps in preventing complications and unnecessary hospitalization, high cost of treatment and deaths among stroke patients.
{"title":"THE ROLE OF THE CLINICAL PHARMACIST IN ADDRESSING DRUG-RELATED PROBLEMS IN STROKE PATIENTS IN A TERTIARY CARE CENTRE","authors":"Susan Philip, Jeesa George, Adusumilli Pramod Kumar, Foujia Begum, Divya Bharathi K","doi":"10.1101/2024.07.09.24309334","DOIUrl":"https://doi.org/10.1101/2024.07.09.24309334","url":null,"abstract":"The morbidity, mortality, and diminished quality of life of patients are all impacted by drug related problems. Stroke is one of the leading causes of disability and death in India and since the stroke patients are at an increased risk of DRPs (Drug Related Problems), early identification, prevention and resolution of the same is important for improving the quality of life of stroke patients. The aim of the study was to assess the incidence of DRPs in stroke patients by using Hepler strand classification and the acceptance rate by the multidisciplinary team. The relevant information were documented using a predefined data collection form and was analyzed for drug related problems and categorized according to Hepler Strand classification. A total of 510 DRPs from 130 participants with an average incidence of 3.92 DRPs per patient. Drug drug interaction was found to be 23.53% and ADRs accounted for 12.55 % of total DRPs. The study found a high acceptance rate of recommendations by healthcare professionals at 97.36%, with changes in therapy being made 68.13% of the time. Early detection and resolution of DRP by clinical pharmacist may improve the therapeutic outcomes in stroke patients. This also helps in preventing complications and unnecessary hospitalization, high cost of treatment and deaths among stroke patients.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-09DOI: 10.1101/2024.07.08.24310085
Fung Yau Yu, Irwin K Cheah, Rathi Mahendran, Richard MY Tang, Ru Yuan Chua, Rachel ES Goh, Lei Feng, Jialiang Li, Ee Heok Kua, Christopher Chen, Barry Halliwell
Background and objective: Dementia, particularly Alzheimer's disease, is a major healthcare challenge in ageing societies. Therefore, this study aimed to investigate the efficacy and safety of a dietary compound, ergothioneine, in delaying cognitive decline in elderly individuals. Design, intervention and measurements: Nineteen subjects aged 60 or above with mild cognitive impairment were recruited for this double-blinded, randomized, and placebo-controlled study. Subjects received either ergothioneine (25mg per capsule) or a placebo, taken 3 times a week for one year. The whole blood profile, markers of renal and liver functions, neurocognitive performance, plasma levels of ergothioneine and its metabolites, and plasma biomarkers related to neurodegeneration were measured across the study. Result: Ergothioneine intake did not alter clinical safety markers (blood counts, kidney and liver function) throughout the study, further validating its safety for human consumption. Subjects receiving ergothioneine demonstrated improved performance in assessment of learning ability and stabilized plasma levels of neurofilament light chain, compared with placebo group which saw no improvement in cognitive assessments and a significant increase in neurofilament light chain. Conclusion: Prolonged intake of ergothioneine showed no toxicity in elderly individuals. Enhanced Rey Auditory Verbal Learning Test performance and stabilized neurofilament light chain levels suggest improvements in memory and learning abilities, alongside a deceleration of neuronal damage. Our results add to existing data that ergothioneine is safe for extended consumption and may hold the potential to delay cognitive decline in the elderly.
{"title":"Investigating the Efficacy of Ergothioneine to Delay Cognitive Decline in Mild Cognitively Impaired Subjects: A Pilot Study.","authors":"Fung Yau Yu, Irwin K Cheah, Rathi Mahendran, Richard MY Tang, Ru Yuan Chua, Rachel ES Goh, Lei Feng, Jialiang Li, Ee Heok Kua, Christopher Chen, Barry Halliwell","doi":"10.1101/2024.07.08.24310085","DOIUrl":"https://doi.org/10.1101/2024.07.08.24310085","url":null,"abstract":"Background and objective: Dementia, particularly Alzheimer's disease, is a major healthcare challenge in ageing societies. Therefore, this study aimed to investigate the efficacy and safety of a dietary compound, ergothioneine, in delaying cognitive decline in elderly individuals.\u0000Design, intervention and measurements: Nineteen subjects aged 60 or above with mild cognitive impairment were recruited for this double-blinded, randomized, and placebo-controlled study. Subjects received either ergothioneine (25mg per capsule) or a placebo, taken 3 times a week for one year. The whole blood profile, markers of renal and liver functions, neurocognitive performance, plasma levels of ergothioneine and its metabolites, and plasma biomarkers related to neurodegeneration were measured across the study. Result: Ergothioneine intake did not alter clinical safety markers (blood counts, kidney and liver function) throughout the study, further validating its safety for human consumption. Subjects receiving ergothioneine demonstrated improved performance in assessment of learning ability and stabilized plasma levels of neurofilament light chain, compared with placebo group which saw no improvement in cognitive assessments and a significant increase in neurofilament light chain. Conclusion: Prolonged intake of ergothioneine showed no toxicity in elderly individuals. Enhanced Rey Auditory Verbal Learning Test performance and stabilized neurofilament light chain levels suggest improvements in memory and learning abilities, alongside a deceleration of neuronal damage. Our results add to existing data that ergothioneine is safe for extended consumption and may hold the potential to delay cognitive decline in the elderly.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-08DOI: 10.1101/2024.07.03.24309889
Amoreena Most, Aaron Chase, Steven Xu, Tanner Hedrick, Brian Murray, Kelli Keats, Susan Smith, Erin Barreto, Tianming Liu, Andrea Sikora
Background: Large language models (LLMs) have shown capability in diagnosing complex medical cases and passing medical licensing exams, but to date, only limited evaluations have studied how LLMs interpret, analyze, and optimize complex medication regimens. The purpose of this evaluation was to test four LLMs ability to identify medication errors and appropriate medication interventions on complex patient cases from the intensive care unit (ICU). Methods: A series of eight patient cases were developed by critical care pharmacists including history of present illness, laboratory values, vital signs, and medication regimens. Then, four LLMs (ChatGPT (GPT-3.5), ChatGPT (GPT-4), Claude2, and Llama2-7b) were prompted to develop a medication regimen for the patient. LLM generated medication regimens were then reviewed by a panel of seven critical care pharmacists to assess for presence of medication errors and clinical relevance. For each medication regimen recommended by the LLM, clinicians were asked to assess for if they would continue a medication, identify perceived medication errors in the medications recommended, identify the presence of life-threatening medication choices, and rank overall agreement on a 5-point Likert scale. Results: The clinician panel rated to continue therapies recommended by the LLMs between 55.8-67.9% of the time. Clinicians perceived between 1.57-4.29 medication errors per recommended regimen, and life-threatening recommendations were present between 15.0-55.3% of the time. Level agreement was between 1.85-2.67 for the four LLMs. Conclusions: LLMs demonstrated potential to serve as clinical decision support for the management of complex medication regimens with further domain specific training; however, caution should be used when employing LLMs for medication management given the present capabilities.
{"title":"Large language models management of complex medication regimens: a case-based evaluation","authors":"Amoreena Most, Aaron Chase, Steven Xu, Tanner Hedrick, Brian Murray, Kelli Keats, Susan Smith, Erin Barreto, Tianming Liu, Andrea Sikora","doi":"10.1101/2024.07.03.24309889","DOIUrl":"https://doi.org/10.1101/2024.07.03.24309889","url":null,"abstract":"Background: Large language models (LLMs) have shown capability in diagnosing complex medical cases and passing medical licensing exams, but to date, only limited evaluations have studied how LLMs interpret, analyze, and optimize complex medication regimens. The purpose of this evaluation was to test four LLMs ability to identify medication errors and appropriate medication interventions on complex patient cases from the intensive care unit (ICU). Methods: A series of eight patient cases were developed by critical care pharmacists including history of present illness, laboratory values, vital signs, and medication regimens. Then, four LLMs (ChatGPT (GPT-3.5), ChatGPT (GPT-4), Claude2, and Llama2-7b) were prompted to develop a medication regimen for the patient. LLM generated medication regimens were then reviewed by a panel of seven critical care pharmacists to assess for presence of medication errors and clinical relevance. For each medication regimen recommended by the LLM, clinicians were asked to assess for if they would continue a medication, identify perceived medication errors in the medications recommended, identify the presence of life-threatening medication choices, and rank overall agreement on a 5-point Likert scale. Results: The clinician panel rated to continue therapies recommended by the LLMs between 55.8-67.9% of the time. Clinicians perceived between 1.57-4.29 medication errors per recommended regimen, and life-threatening recommendations were present between 15.0-55.3% of the time. Level agreement was between 1.85-2.67 for the four LLMs. Conclusions: LLMs demonstrated potential to serve as clinical decision support for the management of complex medication regimens with further domain specific training; however, caution should be used when employing LLMs for medication management given the present capabilities.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-07DOI: 10.1101/2024.07.06.24309990
Victor O.K. Li, Yang Han, Tushar Kaistha, Qi Zhang, Jocelyn Downey, Illana Gozes, Jacqueline C.K. Lam
Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.
阿尔茨海默病（AD）严重损害人类尊严和生活质量。虽然新批准的淀粉样蛋白免疫疗法已有报道，但有效的阿兹海默病药物仍有待确定。在此，我们提出了一种新型人工智能驱动的药物再利用方法--DeepDrug，以确定治疗AD患者的已获批准药物的先导组合。DeepDrug 从四个方面推进了药物再利用方法。首先，它结合了专家知识，将候选靶点扩展到包括长基因、免疫和衰老途径以及与AD相关的体细胞突变标记。其次，它纳入了一个有符号的有向异质生物医学图谱，其中包含丰富的节点和边，并对节点/边进行加权，以捕捉与艾滋病相关的关键通路。第三，它通过图神经网络将加权生物医学图编码到一个新的嵌入空间，以捕捉不同节点之间的细微关系。第四，它通过基于收益递减的阈值系统地选择高阶药物组合。根据DeepDrug的评分，从顶级候选药物中选出了由托法替尼、尼拉帕利、巴瑞替尼、恩帕格列嗪和多西卡西醇组成的五种药物先导组合，以实现最大的协同效应。这五种药物针对的是神经炎症、线粒体功能障碍和糖代谢，而这些都与注意力缺失症的病理相关。DeepDrug 提供了一种新颖的人工智能和大数据、专家指导机制，用于发现新的药物组合，并对 AD 和其他神经退行性疾病进行药物再利用，可立即应用于临床。
{"title":"DeepDrug: An Expert-led Domain-specific AI-Driven Drug-Repurposing Mechanism for Selecting the Lead Combination of Drugs for Alzheimer's Disease","authors":"Victor O.K. Li, Yang Han, Tushar Kaistha, Qi Zhang, Jocelyn Downey, Illana Gozes, Jacqueline C.K. Lam","doi":"10.1101/2024.07.06.24309990","DOIUrl":"https://doi.org/10.1101/2024.07.06.24309990","url":null,"abstract":"Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-04DOI: 10.1101/2024.07.03.24309892
Laura Katharina Schnider, Marta Ratajczak, Rafael Wespi, Jacqueline Kientsch, Francesco Bavato, Laurenz Marten, Jonas Kost, Maxim Puchkov, Corinne Eicher, Martina Boxler, Clarissa Voegel, Oliver Gero Bosch, Eus van Someren, Dario Dornbierer, Hans-Peter Landolt
Background: Insomnia is common and causes immense disease burden. Current sleep medications show suboptimal clinical features, leaving an unmet need for pharmacological innovation to improve both sleep and waking functions. The locus coeruleus noradrenergic (LC-NA) system may provide a potential new target for pharmacological insomnia treatment, particularly in patients with elevated stress. The selective alpha-2 noradrenergic agonist dexmedetomidine (DEX) attenuates LC-NA activity. In current use as intravenous anesthetic, DEX could thus be a promising repurposing candidate for stress-related insomnia. Methods: We developed two distinct, fast-disintegrating, oro-mucosal - one sublingual and one buccal - DEX formulas tailored for self-administration and established their pharmacokinetic and pharmacodynamic (PK-PD) profiles. In two separate studies, the first comprising 8 healthy male good sleepers and the second including 17 men with subclinical insomnia, we administered sub-anesthetic doses (20 & 40 microgram) of the two formulas. Both studies followed a randomized, double-blind, placebo-controlled, cross-over design. We complemented the PK assessments during sleep by all-night polysomnography, nocturnal cortisol and melatonin measurements, assessments of cardiovascular functions during and after sleep, cortisol awakening response, and examination of post-awakening subjective state and vigilance. Results: Particularly buccal DEX was rapidly absorbed and exhibited excellent dose-proportionality with minimal between-subject variation in exposure. Additionally, DEX shortened the latency to fall asleep, increased the time spent in non-rapid-eye-movement (NREM) sleep, and elevated electroencephalographic slow wave energy (0.75-4.0 Hz), a marker of NREM sleep depth. Rapid-eye-movement (REM) sleep latency was dose-dependently prolonged. Nocturnal cortisol, melatonin and heart rate, as well as morning cortisol, were not significantly affected by DEX nor did post-awakening orthostatic regulation, subjective sleepiness and mood, and psychomotor vigilance differ among the conditions. Conclusions: The favorable PK-PD profile of oro-mucosal delivery of sub-anesthetic DEX doses warrants further dose-finding and clinical studies, to establish the exact roles of the LA-NA system in pharmacological sleep enhancement and alpha-2 receptor agonism as novel mode of action in alleviating stress-related insomnia.
{"title":"EFFECTS OF LOW-DOSE ORO-MUCOSAL DEXMEDETOMIDINE ON SLEEP AND THE SLEEP EEG IN HUMANS: A PHARMACOKINETICS-PHARMACODYNAMICS STUDY","authors":"Laura Katharina Schnider, Marta Ratajczak, Rafael Wespi, Jacqueline Kientsch, Francesco Bavato, Laurenz Marten, Jonas Kost, Maxim Puchkov, Corinne Eicher, Martina Boxler, Clarissa Voegel, Oliver Gero Bosch, Eus van Someren, Dario Dornbierer, Hans-Peter Landolt","doi":"10.1101/2024.07.03.24309892","DOIUrl":"https://doi.org/10.1101/2024.07.03.24309892","url":null,"abstract":"Background: Insomnia is common and causes immense disease burden. Current sleep medications show suboptimal clinical features, leaving an unmet need for pharmacological innovation to improve both sleep and waking functions. The locus coeruleus noradrenergic (LC-NA) system may provide a potential new target for pharmacological insomnia treatment, particularly in patients with elevated stress. The selective alpha-2 noradrenergic agonist dexmedetomidine (DEX) attenuates LC-NA activity. In current use as intravenous anesthetic, DEX could thus be a promising repurposing candidate for stress-related insomnia.\u0000Methods: We developed two distinct, fast-disintegrating, oro-mucosal - one sublingual and one buccal - DEX formulas tailored for self-administration and established their pharmacokinetic and pharmacodynamic (PK-PD) profiles. In two separate studies, the first comprising 8 healthy male good sleepers and the second including 17 men with subclinical insomnia, we administered sub-anesthetic doses (20 & 40 microgram) of the two formulas. Both studies followed a randomized, double-blind, placebo-controlled, cross-over design. We complemented the PK assessments during sleep by all-night polysomnography, nocturnal cortisol and melatonin measurements, assessments of cardiovascular functions during and after sleep, cortisol awakening response, and examination of post-awakening subjective state and vigilance.\u0000Results: Particularly buccal DEX was rapidly absorbed and exhibited excellent dose-proportionality with minimal between-subject variation in exposure. Additionally, DEX shortened the latency to fall asleep, increased the time spent in non-rapid-eye-movement (NREM) sleep, and elevated electroencephalographic slow wave energy (0.75-4.0 Hz), a marker of NREM sleep depth. Rapid-eye-movement (REM) sleep latency was dose-dependently prolonged. Nocturnal cortisol, melatonin and heart rate, as well as morning cortisol, were not significantly affected by DEX nor did post-awakening orthostatic regulation, subjective sleepiness and mood, and psychomotor vigilance differ among the conditions.\u0000Conclusions: The favorable PK-PD profile of oro-mucosal delivery of sub-anesthetic DEX doses warrants further dose-finding and clinical studies, to establish the exact roles of the LA-NA system in pharmacological sleep enhancement and alpha-2 receptor agonism as novel mode of action in alleviating stress-related insomnia.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141549224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-30DOI: 10.1101/2024.06.29.24309701
Amoreena Most, Aaron Chase, Andrea Sikora
Background: Large language models (LLMs) such as ChatGPT have emerged as promising artificial intelligence tools to support clinical decision making. The ability of ChatGPT to evaluate medication regimens, identify drug-drug interactions (DDIs), and provide clinical recommendations is unknown. The purpose of this study is to examine the performance of GPT-4 to identify clinically relevant DDIs and assess accuracy of recommendations provided. Methods: A total of 15 medication regimens were created containing commonly encountered DDIs that were considered either clinically significant or clinically unimportant. Two separate prompts were developed for medication regimen evaluation. The primary outcome was if GPT-4 identified the most relevant DDI within the medication regimen. Secondary outcomes included rating GPT-4s interaction rationale, clinical relevance ranking, and overall clinical recommendations. Interrater reliability was determined using kappa statistic. Results: GPT-4 identified the intended DDI in 90% of medication regimens provided (27/30). GPT-4 categorized 86% as highly clinically relevant compared to 53% being categorized as highly clinically relevant by expert opinion. Inappropriate clinical recommendations potentially causing patient harm were provided in 14% of responses provided by GPT-4 (2/14), and 63% of responses contained accurate information but incomplete recommendations (19/30). Conclusions: While GPT-4 demonstrated promise in its ability to identify clinically relevant DDIs, application to clinical cases remains an area of investigation. Findings from this study may assist in future development and refinement of LLMs for drug-drug interaction queries to assist in clinical decision-making.
{"title":"Assessing the potential of ChatGPT-4 to accurately identify drug-drug interactions and provide clinical pharmacotherapy recommendations","authors":"Amoreena Most, Aaron Chase, Andrea Sikora","doi":"10.1101/2024.06.29.24309701","DOIUrl":"https://doi.org/10.1101/2024.06.29.24309701","url":null,"abstract":"Background: Large language models (LLMs) such as ChatGPT have emerged as promising artificial intelligence tools to support clinical decision making. The ability of ChatGPT to evaluate medication regimens, identify drug-drug interactions (DDIs), and provide clinical recommendations is unknown. The purpose of this study is to examine the performance of GPT-4 to identify clinically relevant DDIs and assess accuracy of recommendations provided. Methods: A total of 15 medication regimens were created containing commonly encountered DDIs that were considered either clinically significant or clinically unimportant. Two separate prompts were developed for medication regimen evaluation. The primary outcome was if GPT-4 identified the most relevant DDI within the medication regimen. Secondary outcomes included rating GPT-4s interaction rationale, clinical relevance ranking, and overall clinical recommendations. Interrater reliability was determined using kappa statistic. Results: GPT-4 identified the intended DDI in 90% of medication regimens provided (27/30). GPT-4 categorized 86% as highly clinically relevant compared to 53% being categorized as highly clinically relevant by expert opinion. Inappropriate clinical recommendations potentially causing patient harm were provided in 14% of responses provided by GPT-4 (2/14), and 63% of responses contained accurate information but incomplete recommendations (19/30). Conclusions: While GPT-4 demonstrated promise in its ability to identify clinically relevant DDIs, application to clinical cases remains an area of investigation. Findings from this study may assist in future development and refinement of LLMs for drug-drug interaction queries to assist in clinical decision-making.","PeriodicalId":501447,"journal":{"name":"medRxiv - Pharmacology and Therapeutics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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