Greater humoral EBV response may be associated with choroid plexus inflammation in progressive MS.

Choroid plexus (CP) inflammation can be quantified in vivo with MRI in people with multiple sclerosis (pwMS). It remains unknown whether Epstein Barr Virus (EBV) is related to CP changes. Total of 170 pwMS (116 relapsing-remitting; RRMS and 54 progressive MS; PMS) underwent MRI examination and measurement of humoral anti-EBV response. CP volume and CP pseudo-T2 (pT2), a relaxation time indicative of edema and neuroinflammation, were measured. Moreover, anti-EBV nuclear antigen-1 (EBNA-1) IgG and anti-EBV capsid antigen (VCA) IgG antibodies were measured. The PMS group had greater CP pT2 value when compared to RRMS (1120ms vs. 954ms, p = 0.037). After adjusting for age and therapy effects, higher CP pT2 values were associated with higher anti-EBNA-1 IgG levels only in PMS (r = 0.352, p = 0.015). Higher Anti-EBV humoral response in pwMS may be associated with increased CP neuroinflammatory changes and may be more relevant for the later chronic stage of the disease. Large-scale studies should investigate whether these findings are generalizable to all types of progressive MS.

Evaluating AlphaFold for Clinical Pharmacology and Pharmacogenetics: A Case-Study of Huntingtin Variants Linked to Huntington's Disease.

To evaluate the artificial intelligence (AI)-guided AlphaFold algorithm for studying the binding interactions of human huntingtin and the aggregation of huntingtin peptides. Variants of huntingtin protein implicated in Huntington's disease were used as a model system to evaluate AlphaFold. Variants of huntingtin and huntingtin peptides with polyglutamine tracts (PQT) containing 21, 31, 51, or 78 glutamines were studied. The 3-dimensional structures of huntingtin variants and their interactions with huntingtin-associated protein-40 (HAP40) were obtained. Aggregation experiments were conducted with peptide sequences corresponding to variants of PQT, amino terminal sequence (NTS) plus PQT, NTS plus PQT plus proline rich region (PRR), and the 300 amino acid sequence from the NTS through HEAT3 of huntingtin. Oligomerization experiments with 1, 3, 6, or 12 peptide sequences were used to assess the quaternary structures of aggregates. The PQT and PQT plus NTS peptides formed a helical secondary structure that formed a central core in the quaternary structure of the aggregates The PRR formed an extended type II polyproline helix that did not participate in central core the aggregates. The distance between the amino and carboxyl termini of disease-linked 31Q, 51Q, and 78Q variants of full-length huntingtin was prominently decreased compared to the 21Q huntingtin. The interaction of HAP40 with the 78Q variant increased the distance between the amino and carboxyl termini. AlphaFold identified key tertiary structure changes in human huntingtin that have been independently corroborated in experimental models. The results highlight the utility of AlphaFold for hypothesis generation in pharmaceutical research.

Nuclear-Magnetic-Resonance-Spectroscopy-Derived Serum Biomarkers of Metabolic Vulnerability Are Associated with Disability and Neurodegeneration in Multiple Sclerosis.

PURPOSE: Metabolic vulnerabilities can exacerbate inflammatory injury and inhibit repair in multiple sclerosis (MS). The purpose was to evaluate whether blood biomarkers of inflammatory and metabolic vulnerability are associated with MS disability and neurodegeneration. METHODS: Proton nuclear magnetic resonance spectra were obtained from serum samples from 153 healthy controls, 187 relapsing-remitting, and 91 progressive MS patients. The spectra were analyzed to obtain concentrations of lipoprotein sub-classes, glycated acute-phase proteins, and small-molecule metabolites, including leucine, valine, isoleucine, alanine, and citrate. Composite indices for inflammatory vulnerability, metabolic malnutrition, and metabolic vulnerability were computed. MS disability was measured on the Expanded Disability Status Scale. MRI measures of lesions and whole-brain and tissue-specific volumes were acquired. RESULTS: Valine, leucine, isoleucine, alanine, the Inflammatory Vulnerability Index, the Metabolic Malnutrition Index, and the Metabolic Vulnerability Index differed between healthy control and MS groups in regression analyses adjusted for age, sex, and body mass index. The Expanded Disability Status Scale was associated with small HDL particle levels, inflammatory vulnerability, and metabolic vulnerability. Timed ambulation was associated with inflammatory vulnerability and metabolic vulnerability. Greater metabolic vulnerability and inflammatory vulnerability were associated with lower gray matter, deep gray matter volumes, and greater lateral ventricle volume. CONCLUSIONS: Serum-biomarker-derived indices of inflammatory and metabolic vulnerability are associated with disability and neurodegeneration in MS.

Dyslipidemias in multiple sclerosis.

PURPOSE: To investigate the frequency of dyslipidemia phenotypes in multiple sclerosis and to assess the associations with lipoprotein particle size distributions. METHODS: This cross-sectional study included 203 healthy controls (HC), 221 relapsing-remitting MS (RRMS), and 126 progressive MS (PMS). A lipid profile with total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, and apolipoprotein B levels were measured. Low density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol, large buoyant LDL-C and small dense LDL-C were calculated using the Sampson-NIH equations method. Dyslipidemia phenotypes were categorized by their nonHDL-C and triglyceride values. The diameters and concentrations of triglyceride-rich lipoprotein particles (TRLP), LDL particles (LDLP), and HDL particles (HDLP) were measured with proton NMR lipoprotein profiling. Serum proprotein convertase subtilisin/kexin type 9 (PCSK9) levels were obtained using immunoassay. RESULTS: The frequencies of normolipidemia, and various dyslipidemia phenotypes were similar in HC, RRMS, and PMS. The size of the TRLP, very large TRLP, large TRLP, and small LDLP concentrations had a decreasing pattern of HC>RR>PMS. The lowest tertile of EDSS was associated with higher concentrations of HDLP and small HDLP in PMS. PCSK9 was associated with concentration of HDL particles, primarily via its effects on the concentration of small HDL particles. CONCLUSIONS: There were no differences in the frequency of dyslipidemias in MS compared to healthy controls. Higher HDLP concentrations are associated with lower disability in PMS.

Performance of the ChatGPT large language model for decision support in community pharmacy.

AIMS: The aim of this study was to assess the ChatGPT-4 (ChatGPT) large language model (LLM) on tasks relevant to community pharmacy. METHODS: ChatGPT was assessed with community pharmacy-relevant test cases involving drug information retrieval, identifying labelling errors, prescription interpretation, decision-making under uncertainty and multidisciplinary consults. Drug information on rituximab, warfarin, and St. John's wort was queried. The decision-support scenarios consisted of a subject with swollen eyelids and a maculopapular rash in a subject on lisinopril and ferrous sulfate. The multidisciplinary scenarios required the integration of medication management with recommendations for healthy eating and physical activity/exercise. RESULTS: The responses from ChatGPT for rituximab, warfarin, and St. John's wort were satisfactory and cited drug databases and drug-specific monographs. ChatGPT identified labeling errors related to incorrect medication strength, form, route of administration, unit conversion, and directions. For the patient with inflamed eyelids, the course of action developed by ChatGPT was comparable to the pharmacist's approach. For the patient with the maculopapular rash, both the pharmacist and ChatGPT placed a drug reaction to either lisinopril or ferrous sulfate at the top of the differential. ChatGPT provided customized vaccination requirements for travel to Brazil, guidance on management of drug allergies and recovery from a knee injury. ChatGPT provided satisfactory medication management and wellness information for a diabetic on metformin and semaglutide. CONCLUSIONS: LLMs have the potential to become a powerful tool in community pharmacy. However, rigorous validation studies across diverse pharmacist queries, drug classes and populations, and engineering to secure patient privacy will be needed to enhance LLM utility.

Variational autoencoders for generative modeling of drug dosing determinants in renal, hepatic, metabolic, and cardiac disease states.

Physiological determinants of drug dosing (PDODD) are a promising approach for precision dosing. This study investigates the alterations of PDODD in diseases and evaluates a variational autoencoder (VAE) artificial intelligence model for PDODD. The PDODD panel contained 20 biomarkers, and 13 renal, hepatic, diabetes, and cardiac disease status variables. Demographic characteristics, anthropometric measurements (body weight, body surface area, waist circumference), blood (plasma volume, albumin), renal (creatinine, glomerular filtration rate, urine flow, and urine albumin to creatinine ratio), and hepatic (R-value, hepatic steatosis index, drug-induced liver injury index), blood cell (systemic inflammation index, red cell, lymphocyte, neutrophils, and platelet counts) biomarkers, and medical questionnaire responses from the National Health and Nutrition Examination Survey (NHANES) were included. The tabular VAE (TVAE) generative model was implemented with the Synthetic Data Vault Python library. The joint distributions of the generated data vs. test data were compared using graphical univariate, bivariate, and multidimensional projection methods and distribution proximity measures. The PDODD biomarkers related to disease progression were altered as expected in renal, hepatic, diabetes, and cardiac diseases. The continuous PDODD panel variables generated by the TVAE satisfactorily approximated the distribution in the test data. The TVAE-generated distributions of some discrete variables deviated from the test data distribution. The age distribution of TVAE-generated continuous variables was similar to the test data. The TVAE algorithm demonstrated potential as an AI model for continuous PDODD and could be useful for generating virtual populations for clinical trial simulations.

Serum Biomarker Signatures of Choroid Plexus Volume Changes in Multiple Sclerosis.

Increased choroid plexus (CP) volume has been recently implicated as a potential predictor of worse multiple sclerosis (MS) outcomes. The biomarker signature of CP changes in MS are currently unknown. To determine the blood-based biomarker characteristics of the cross-sectional and longitudinal MRI-based CP changes in a heterogeneous group of people with MS (pwMS), a total of 202 pwMS (148 pwRRMS and 54 pwPMS) underwent MRI examination at baseline and at a 5-year follow-up. The CP was automatically segmented and subsequently refined manually in order to obtain a normalized CP volume. Serum samples were collected at both timepoints, and the concentration of 21 protein measures relevant to MS pathophysiology were determined using the Olink™ platform. Age-, sex-, and BMI-adjusted linear regression models explored the cross-sectional and longitudinal relationships between MRI CP outcomes and blood-based biomarkers. At baseline, there were no significant proteomic predictors of CP volume, while at follow-up, greater CP volume was significantly associated with higher neurofilament light chain levels, NfL (standardized ß = 0.373, p = 0.001), and lower osteopontin levels (standardized ß = -0.23, p = 0.02). Higher baseline GFAP and lower FLRT2 levels were associated with future 5-year CP % volume expansion (standardized ß = 0.277, p = 0.004 and standardized ß = -0.226, p = 0.014, respectively). The CP volume in pwMS is associated with inflammatory blood-based biomarkers of neuronal injury (neurofilament light chain; NfL) and glial activation such as GFAP, osteopontin, and FLRT2. The expansion of the CP may play a central role in chronic and compartmentalized inflammation and may be driven by glial changes.

Authors' response to letter to editor.

Authors' Response to Letter to Editor from Hinpetch Daungsupawong and Viroj Wiwanitkit.

Glial cell injury and atrophied lesion volume as measures of chronic multiple sclerosis inflammation.

BACKGROUND: Atrophied lesion volume (aLV), a proposed biomarker of disability progression in multiple sclerosis (MS) and transition into progressive MS (PMS), depicts chronic periventricular white matter (WM) pathology. Meningeal infiltrates, imaged as leptomeningeal contrast enhancement (LMCE), are linked with greater cortical pathology. OBJECTIVES: To determine the relationship between serum-derived proteomic data with the development of aLV and LMCE in a heterogeneous group of people with MS (pwMS). METHODS: Proteomic and MRI data for 202 pwMS (148 clinically isolated syndrome /relapsing-remitting MS and 54 progressive MS (PMS)) were acquired at baseline and at 5.4-year follow-up. The concentrations of 21 proteins related to multiple MS pathophysiology pathways were derived using a custom-developed Proximity Extension Assay on the Olink™ platform. The accrual of aLV was determined as the volume of baseline T2-weighted lesions that were replaced by cerebrospinal fluid over the follow-up. Regression models and age-adjusted analysis of covariance (ANCOVA) were used. RESULTS: Older age (standardized beta = 0.176, p = 0.022), higher glial fibrillary acidic protein (standardized beta = 0.312, p = 0.001), and lower myelin oligodendrocyte glycoprotein levels (standardized beta = -0.271, p = 0.002) were associated with accrual of aLV over follow-up. This relationship was driven by the pwPMS population. The presence of LMCE at the follow-up visit was not predicted by any baseline proteomic biomarker nor cross-sectionally associated with any protein concentration. CONCLUSION: Proteomic markers of glial activation are associated with chronic lesional WM pathology (measured as aLV) and may be specific to the progressive MS phenotype. LMCE presence in MS does not appear to relate to proteomic measures.

Lower arterial cerebral blood flow is associated with worse neuroinflammation and immunomodulation composite proteomic scores.

BACKGROUND: Brain hypoperfusion is linked with worse physical, cognitive and MRI outcomes in multiple sclerosis (MS). Understanding the proteomic signatures related to hypoperfusion could provide insights into the pathophysiological mechanism. METHODS: 140 people with MS (pwMS; 86 clinically isolated syndrome (CIS)/relapsing-remitting (RRMS) and 54 progressive (PMS)) were included. Cerebral arterial blood flow (CABF) was determined using ultrasound Doppler measurement as the sum of blood flow in the bilateral common carotid arteries and vertebral arteries. Proteomic analysis was performed using the Multiple Sclerosis Disease Activity (MSDA) test assay panel performed on the Olink™ platform. The MSDA test measures the concentrations of 18 proteins that are age and sex-adjusted. It utilizes a stacked classifier logistic regression model to determine 4 disease pathway scores (immunomodulation, neuroinflammation, myelin biology, and neuroaxonal integrity) as well as an overall disease activity score (1 to 10). MRI measures of T2 lesion volume (LV) and whole brain volume (WBV) were derived. RESULTS: The pwMS were on average 54 years old and had an average CABF of 951 mL/min. There were no differences in CABF between CIS/RRMS vs. PMS groups. Lower CABF levels were correlated with the overall disease activity score (r = -0.26, p = 0.003) and with the neuroinflammation (r = -0.29, p = 0.001), immunomodulation (r = -0.26, p = 0.003) and neuroaxonal integrity (r = -0.23, p = 0.007) pathway scores. After age and body mass index (BMI)-adjustment, lower CABF remained associated with the neuroinflammatory (r = -0.23, p = 0.011) and immunomodulation (r = -0.20, p = 0.024) pathway scores. The relationship between CABF and the neuroinflammation pathway score remained significant after adjusting for T2-LV and WBV (p = 0.038). Individual analyses identified neurofilament light chain, CCL-20 and TNFSF13B as contributors. When compared to the highest quartile (>1133.5 mL/min), the pwMS in the lowest CABF quartile (<764 mL/min) had greater overall disease activity score (p = 0.003), neuroinflammation (p = 0.001), immunomodulation (p = 0.004) and neuroaxonal integrity pathway scores (p = 0.007). CONCLUSION: Lower cerebral arterial perfusion in MS is associated with changes in neuroinflammatory/immunomodulation pathways and their respective proteomic biomarkers. These findings may suggest a relationship between the hypoperfusion and pro-inflammatory MS changes rather than being merely an epiphenomenon subsequent to lower energy demands.

Neuroimaging assessment of facility-bound severely-affected MS reveals the critical role of cortical gray matter pathology: results from the CASA-MS case-controlled study.

BACKGROUND: A subgroup of people with multiple sclerosis (pwMS) will develop severe disability. The pathophysiology underlying severe MS is unknown. The comprehensive assessment of severely affected MS (CASA-MS) was a case-controlled study that compared severely disabled in skilled nursing (SD/SN) (EDSS ≥ 7.0) to less-disabled (EDSS 3.0-6.5) community dwelling (CD) progressive pwMS, matched on age-, sex- and disease-duration (DDM). OBJECTIVES: To identify neuroimaging and molecular biomarker characteristics that distinguish SD/SN from DDM-CD progressive pwMS. METHODS: This study was carried at SN facility and at a tertiary MS center. The study collected clinical, molecular (serum neurofilament light chain, sNfL and glial acidic fibrillary protein, sGFAP) and MRI quantitative lesion-, brain volume-, and tissue integrity-derived measures. Statistical analyses were controlled for multiple comparisons. RESULTS: 42 SD/SN and 42 DDM-CD were enrolled. SD/SN pwMS showed significantly lower cortical volume (CV) (p < 0.001, d = 1.375) and thalamic volume (p < 0.001, d = 0.972) compared to DDM-CD pwMS. In a logistic stepwise regression model, the SD/SN pwMS were best differentiated from the DDM-CD pwMS by lower CV (p < 0.001) as the only significant predictor, with the accuracy of 82.3%. No significant differences between the two groups were observed for medulla oblongata volume, a proxy for spinal cord atrophy and white matter lesion burden, while there was a statistical trend for numerically higher sGFAP in SD/SN pwMS. CONCLUSIONS: The CASA-MS study showed significantly more gray matter atrophy in severe compared to less-severe progressive MS.

Human restricted CHRFAM7A gene increases brain efficiency.

Introduction: CHRFAM7A, a uniquely human fusion gene, has been associated with neuropsychiatric disorders including Alzheimer's disease, schizophrenia, anxiety, and attention deficit disorder. Understanding the physiological function of CHRFAM7A in the human brain is the first step to uncovering its role in disease. CHRFAM7A was identified as a potent modulator of intracellular calcium and an upstream regulator of Rac1 leading to actin cytoskeleton reorganization and a switch from filopodia to lamellipodia implicating a more efficient neuronal structure. We performed a neurocognitive-MRI correlation exploratory study on 46 normal human subjects to explore the effect of CHRFAM7A on human brain. Methods: Dual locus specific genotyping of CHRFAM7A was performed on genomic DNA to determine copy number (TaqMan assay) and orientation (capillary sequencing) of the CHRFAM7A alleles. As only the direct allele is expressed at the protein level and affects α7 nAChR function, direct allele carriers and non-carriers are compared for neuropsychological and MRI measures. Subjects underwent neuropsychological testing to measure motor (Timed 25-foot walk test, 9-hole peg test), cognitive processing speed (Symbol Digit Modalities Test), Learning and memory (California Verbal Learning Test immediate and delayed recall, Brief Visuospatial Memory Test-Revised immediate and delayed recall) and Beck Depression Inventory-Fast Screen, Fatigue Severity Scale. All subjects underwent MRI scanning on the same 3 T GE scanner using the same protocol. Global and tissue-specific volumes were determined using validated cross-sectional algorithms including FSL's Structural Image Evaluation, using Normalization, of Atrophy (SIENAX) and FSL's Integrated Registration and Segmentation Tool (FIRST) on lesion-inpainted images. The cognitive tests were age and years of education-adjusted using analysis of covariance (ANCOVA). Age-adjusted analysis of covariance (ANCOVA) was performed on the MRI data. Results: CHRFAM7A direct allele carrier and non-carrier groups included 33 and 13 individuals, respectively. Demographic variables (age and years of education) were comparable. CHRFAM7A direct allele carriers demonstrated an upward shift in cognitive performance including cognitive processing speed, learning and memory, reaching statistical significance in visual immediate recall (FDR corrected p = 0.018). The shift in cognitive performance was associated with smaller whole brain volume (uncorrected p = 0.046) and lower connectivity by resting state functional MRI in the visual network (FDR corrected p = 0.027) accentuating the cognitive findings. Conclusion: These data suggest that direct allele carriers harbor a more efficient brain consistent with the cellular biology of actin cytoskeleton and synaptic gain of function. Further larger human studies of cognitive measures correlated with MRI and functional imaging are needed to decipher the impact of CHRFAM7A on brain function.

Evaluation of ChatGPT and Gemini large language models for pharmacometrics with NONMEM.

To assess ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on NONMEM coding tasks relevant to pharmacometrics and clinical pharmacology. ChatGPT and Gemini were assessed on tasks mimicking real-world applications of NONMEM. The tasks ranged from providing a curriculum for learning NONMEM, an overview of NONMEM code structure to generating code. Prompts in lay language to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex model with two parallel first-order absorption mechanisms were investigated. Reproducibility and the impact of "temperature" hyperparameter settings were assessed. The code was reviewed by two NONMEM experts. ChatGPT and Gemini provided NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills including NONMEM code structure and syntax. ChatGPT provided an informative summary of the NONMEM control stream structure and outlined the key NONMEM Translator (NM-TRAN) records needed. ChatGPT and Gemini were able to generate code blocks for the NONMEM control stream from the lay language prompts for the two coding tasks. The control streams contained focal structural and syntax errors that required revision before they could be executed without errors and warnings. The code output from ChatGPT and Gemini was not reproducible, and varying the temperature hyperparameter did not reduce the errors and omissions substantively. Large language models may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors and omissions that require correction.

Non-neurological factors associated with serum neurofilament levels in the United States population.

OBJECTIVE: To model interdependencies of serum neurofilament light chain (sNfL), a clinically useful biomarker of axonal injury in neurological diseases, with demographic, anthropometric, physiological, and disease biomarkers in the United States population. METHODS: sNfL and 80 biomarkers were obtained from the National Health and Nutrition Examination Survey (n = 2071, age: 20-75 years). Body habitus and composition, electrolytes, blood cell, metabolic, liver, and kidney function biomarkers, and common diseases were assessed with weighted regression adjusted for age, sex, and race/ethnicity. Salient biomarkers were modeled with ensemble learning; a Bayesian network structure was obtained for interdependencies. RESULTS: Age was strongly associated with sNfL. sNfL levels were 13% higher in men versus women. Mexican Americans had 18.5% lower sNfL versus Non-Hispanic Whites. sNfL was similar in pregnant versus nonpregnant women. Lymphocyte, and neutrophil numbers, and phosphorus, and chloride levels were associated with sNfL. Multiple liver function (e.g., albumin and gamma-glutamyltransferase), renal function (e.g., creatinine and urea), and carbohydrate/lipid metabolism markers (e.g., glucose and triglycerides) were associated with sNfL. A 50% greater creatinine was associated with 26.8% greater sNfL. Diabetes, kidney disease, congestive heart failure, and stroke were associated with sNfL. The ensemble learning algorithm predicted high sNfL outliers with 5.06%-9.16% test error. Bayesian network modeling indicated sNfL had neighbor dependencies with age, creatinine, albumin, and chloride. INTERPRETATION: sNfL is associated with age, kidney and liver function, diabetes, blood cell subsets, and electrolytes. sNfL may be a useful biomarker for biological age of the whole body and major organ systems including the brain.

Dynamic disability measures decrease the clinico-radiological gap in people with severely affected multiple sclerosis.

BACKGROUND: Expanded Disability Status Scale (EDSS) is limited when utilized in highly disabled people with multiple sclerosis (pwMS). OBJETIVE: To explore the relationship between disability measures and MRI outcomes in severely-affected pwMS. METHODS: PwMS recruited from The Boston Home (TBH), a specialized residential facility for severly-affected pwMS and University at Buffalo (UB) MS Center were assessed using EDSS, MS Severity Scale, age-related MSS, Scripps Neurological Rating Scale (SNRS) and Combinatorial Weight-Adjusted Disability Score (CombiWISE). In all scores except SNRS, higher score indicates greater disability. MRI measures of T1, T2-lesion volume (LV), whole brain, gray matter, medulla oblongata and thalamic volumes (WBV, GMV, MOV, TV) and thalamic dysconnectivity were obtained. RESULTS: Greatest disability differences between the TBH and UB pwMS were in SNRS (24.4 vs 71.9, p < 0.001, Cohen's d = 4.05) and CombiWISE (82.3 vs. 38.9, p < 0.001, Cohen's d = 4.02). In combined analysis of all pwMS, worse SNRS scores were correlated with worse MRI pathology in 8 out of 9 outcomes. EDSS only with 3 measures (GMV, MOV and TV). In severely-affected pwMS, SNRS was associated with T1-LV, T2-LV and WBV (not surviving false discovery rate (FDR) correction for multiple comparisons) whereas EDSS did not. CONCLUSION: Granular and dynamic disability measures may bridge the clinico-radiologcal gap present in severely affected pwMS.

An ultra-sensitive and high-throughput trapping-micro-LC-MS method for quantification of circulating vitamin D metabolites and application in multiple sclerosis patients.

Quantitative analysis of the biologically-active metabolites of vitamin D (VitD), which are crucial in regulating various physiological and pathological processes, is important for clinical investigations. Liquid chromatography-tandem mass spectrometry (LC-MS) has been widely used for this purpose but existing LC-MS methods face challenges in achieving highly sensitive and accurate quantification of low-abundance VitD metabolites while maintaining high throughput and robustness. Here we developed a novel pipeline that combines a trapping-micro-LC-(T-µLC) with narrow-window-isolation selected-reaction monitoring MS(NWI-SRM) for ultra-sensitive, robust and high-throughput quantification of VitD metabolites in serum samples after derivatization. The selective-trapping and delivery approach efficiently removes matrix components, enabling high-capacity sample loading and enhancing sensitivity, throughput, and robustness. The NWI-SRM further improves the sensitivity by providing high selectivity. The lower limits of quantification (LOQs) achieved were markedly lower than any existing LC-MS methods: 1.0 pg/mL for 1,25(OH)2D3, 5.0 pg/mL for 24,25(OH)2D3, 30 pg/mL for both 25(OH)D2 and 25(OH)D3, all within a 9-min cycle. The method is applied to quantify VitD metabolites from 218 patients with multiple sclerosis. This study revealed negative correlations(r=- 0.44 to - 0.51) between the levels of 25(OH)D2 and all the three D3 metabolites in multiple sclerosis patients.

Proteomic signatures of physical, cognitive, and imaging outcomes in multiple sclerosis.

BACKGROUND: A quantitative measurement of serum proteome biomarkers that would associate with disease progression endpoints can provide risk stratification for persons with multiple sclerosis (PwMS) and supplement the clinical decision-making process. MATERIALS AND METHODS: In total, 202 PwMS were enrolled in a longitudinal study with measurements at two time points with an average follow-up time of 5.4 years. Clinical measures included the Expanded Disability Status Scale, Timed 25-foot Walk, 9-Hole Peg, and Symbol Digit Modalities Tests. Subjects underwent magnetic resonance imaging to determine the volumetric measures of the whole brain, gray matter, deep gray matter, and lateral ventricles. Serum samples were analyzed using a custom immunoassay panel on the Olink™ platform, and concentrations of 18 protein biomarkers were measured. Linear mixed-effects models and adjustment for multiple comparisons were performed. RESULTS: Subjects had a significant 55.6% increase in chemokine ligand 20 (9.7 pg/mL vs. 15.1 pg/mL, p < 0.001) and neurofilament light polypeptide (10.5 pg/mL vs. 11.5 pg/mL, p = 0.003) at the follow-up time point. Additional changes in CUB domain-containing protein 1, Contactin 2, Glial fibrillary acidic protein, Myelin oligodendrocyte glycoprotein, and Osteopontin were noted but did not survive multiple comparison correction. Worse clinical performance in the 9-HPT was associated with neurofilament light polypeptide (p = 0.001). Increases in several biomarker candidates were correlated with greater neurodegenerative changes as measured by different brain volumes. CONCLUSION: Multiple proteins, selected from a disease activity test that represent diverse biological pathways, are associated with physical, cognitive, and radiographic outcomes. Future studies should determine the utility of multiple protein assays in routine clinical care.

Modelling the progression of illicit substance use patterns from real-world evidence.

AIMS: To investigate an innovative pharmacometrics approach that addresses the challenges of using real-world evidence to model the progression of illicit substance use. METHODS: The modelling strategy analysed real-world data from the National Longitudinal Study of Adolescent to Adult Health (AddHealth) survey using survival analyses and differential equations. Respondents were categorized into drug-naïve, active users and nonusers. The transitions between categories were modelled using interval-censored parametric survival analysis. The resulting hazard rate functions were used as time-dependent rate constants in a differential equation system. Covariate models for sex and depression status were assessed. RESULTS: AddHealth enrolled 6504 American teenagers (median age 16 years, range 11-21 years); this cohort was followed with five interviews over a 22-year period; the median age at the last interview was 38 years (range 34-45 years). The percentages of illicit drug users at Interviews 1-5 were 7.7%, 5.9%, 15.8%, 21.4% and 0.98%, respectively. The generalized gamma distribution emerged as the preferred model for the survival functions for transitions between categories. Age-dependent prevalence was obtained from the differential equation system. Active drug use was more prevalent in males, increased in adolescence and college years, peaked at 24 years, and decreased to low levels by 35 years. Depression, which was more frequent in females, increased the drug-naïve-active user transition rates but not the active user-nonuser and nonuser-active user transition rates. The evidence did not support an interaction between sex and depression. CONCLUSIONS: The model provided a satisfactory approximation for the age-dependent progression of illicit substance use from preadolescence to early middle age.

Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model.

To systematically assess the ChatGPT large language model on diverse tasks relevant to pharmacokinetic data analysis. ChatGPT was evaluated with prototypical tasks related to report writing, code generation, non-compartmental analysis, and pharmacokinetic word problems. The writing task consisted of writing an introduction for this paper from a draft title. The coding tasks consisted of generating R code for semi-logarithmic graphing of concentration-time profiles and calculating area under the curve and area under the moment curve from time zero to infinity. Pharmacokinetics word problems on single intravenous, extravascular bolus, and multiple dosing were taken from a pharmacokinetics textbook. Chain-of-thought and problem separation were assessed as prompt engineering strategies when errors occurred. ChatGPT showed satisfactory performance on the report writing, code generation tasks and provided accurate information on the principles and methods underlying pharmacokinetic data analysis. However, ChatGPT had high error rates in numerical calculations involving exponential functions. The outputs generated by ChatGPT were not reproducible: the precise content of the output was variable albeit not necessarily erroneous for different instances of the same prompt. Incorporation of prompt engineering strategies reduced but did not eliminate errors in numerical calculations. ChatGPT has the potential to become a powerful productivity tool for writing, knowledge encapsulation, and coding tasks in pharmacokinetic data analysis. The poor accuracy of ChatGPT in numerical calculations require resolution before it can be reliably used for PK and pharmacometrics data analysis.

Exploratory 5-year follow-up study of retinol, tocopherols, and carotenoids in multiple sclerosis.

BACKGROUND: Retinol, tocopherols, and carotenoids (RTC) have physiological roles as vitamins, pro-vitamins, and antioxidants, and provide biomarkers of dietary vegetable and fruit intake. The goal was to investigate RTC in multiple sclerosis (MS). METHODS: This exploratory study included 106 people with MS (71 relapsing-remitting MS or RR-MS; and 35 progressive MS or PMS) and 31 healthy controls (HC) at baseline and 5-year follow-up (5YFU). Serum retinol, α-carotene, ß-carotene, α-tocopherol, δ-tocopherol, γ-tocopherol, ß-cryptoxanthin, lutein/zeaxanthin, and lycopene were measured using high performance liquid chromatography. Serum neurofilament light chain (sNfL) levels were measured using the single molecule array method. Expanded Disability Status Scale (EDSS) and low contrast letter acuity (LCLA) were used as disability measures. RESULTS: Retinol in MS was positively correlated with α-carotene, ß-carotene, ß-cryptoxanthin, lutein/zeaxanthin, and α-tocopherol but negatively correlated with δ-tocopherol. EDSS was associated with α-tocopherol, δ-tocopherol, and lycopene. Greater retinol levels were associated with greater LCLA in RR-MS and PMS; high contrast visual acuity was not associated. Greater γ-tocopherol levels were associated with lower LCLA and high contrast visual acuity in PMS. CONCLUSIONS: RTC exhibit distinctive associations with LCLA and EDSS in MS.