Background: Increased blood brain barrier (BBB) permeability, CNS inflammation and neuroaxonal damage are pathological hallmarks in early multiple sclerosis (MS). Objective: To investigate the associations of neurofilament light chain (NfL) levels with measures of BBB integrity and central nervous system (CNS) inflammation in MS during the first demyelinating event. Methods: Blood and cerebrospinal fluid (CSF) were obtained from 142 MS (McDonald 2017) treatment-naive patients from the SET study (63% female; age: 29.7 ± 7.9 years) following the disease onset. NfL, albumin, immunoglobulin G (IgG), and immunoglobulin M (IgM) levels were measured in CSF and blood samples. Albumin quotient was computed as a marker of BBB integrity. Immune cell subset counts in CSF were measured using flow cytometry. MS risk factors, such as Human leukocyte antigen DRB1 locus gene ( HLA DRB1)*1501, anti-Epstein–Barr virus (EBV) antibodies, and 25-hydroxy vitamin D3, were also measured. Results: Higher serum NfL (sNfL) levels were associated with higher albumin quotient ( p < 0.001), CSF CD80+ ( p = 0.012), and CD80+ CD19+ ( p = 0.015) cell frequency. sNfL levels were also associated with contrast-enhancing and T2 lesions on brain magnetic resonance imaging (MRI; all p ⩽ 0.001). Albumin quotient was not associated with any of the MS risk factors assessed. sNfL levels were associated with anti-EBV viral capsid antigen (VCA) IgG levels ( p = 0.0026). Conclusion: sNfL levels during the first demyelinating event of MS are associated with greater impairment of BBB integrity, immune cell extravasation, and brain lesion activity on MRI.

The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.

To develop a portable point-of-care system based on biosensors for common infectious diseases such as urinary tract infection, the sensing process needs to be implemented within an enclosed fluidic system. On chip sample preparation of clinical samples remains a significant obstacle to achieve robust sensor performance. Herein AC electrokinetics is applied in an electrochemical biosensor cassette to enhance molecular convection and hybridization efficiency though electrokinetic induced fluid motion and Joule heating induced temperature elevation. Using E. coli as an exemplary pathogen, we determined the optimal electrokinetic parameters for detecting bacterial 16S rRNA in the biosensor cassette based on the current output, signal-to-noise ratio, and limit of detection. In addition, a panel of six probe sets targeting common uropathogenic bacteria was demonstrated. The optimized parameters were also validated using patient-derived clinical urine samples. The effectiveness of electrokinetic for on chip sample preparation will facilitate the implementation of point-of-care diagnosis of urinary tract infection in the future.

There is an unmet need for identifying innovative machine learning (ML) strategies to improve drug treatment regimens and therapeutic outcomes. We investigate Generalized Pharmacometric Modeling (GPM), a novel paradigm that integrates ML algorithms with pharmacokinetic and pharmacodynamic structural models, population covariate modeling, and “big data,” and enables identification of patient‐specific factors contributing to drug disposition. We hypothesize that GPM will enhance forecasting of drug outcomes in diverse populations. We assessed random forest regression in conjunction with Bayesian networks as the ML methods within GPM and used the National Health and Nutrition Examination Survey population‐based study database. GPM was utilized to identify subject‐specific factors associated with cholesterol dynamics. Our results demonstrate the utility of GPM to enhance pharmacometrics modeling and its potential for modeling drug outcomes in diverse populations.

No abstract