Biomarkers as drug development tools: discovery, validation, qualification and use

Defining disease severity in atopic dermatitis and psoriasis for the application to biomarker research: an interdisciplinary perspective

More severe atopic dermatitis and psoriasis are associated with a higher cumulative impact on quality of life, multimorbidity and healthcare costs. Proactive, early intervention in those most at risk of severe disease may reduce this cumulative burden and modify the disease trajectory to limit progression. The lack of reliable biomarkers for this at-risk group represents a barrier to such a paradigm shift in practice. To expedite discovery and validation, the BIOMarkers in Atopic Dermatitis and Psoriasis (BIOMAP) consortium (a large-scale European, interdisciplinary research initiative) has curated clinical and molecular data across diverse study designs and sources including cross-sectional and cohort studies (small-scale studies through to large multicentre registries), clinical trials, electronic health records and large-scale population-based biobanks. We map all dataset disease severity instruments and measures to three key domains (symptoms, inflammatory activity and disease course), and describe important codependencies and relationships across variables and domains. We prioritize definitions for more severe disease with reference to international consensus, reference standards and/or expert opinion. Key factors to consider when analysing datasets across these diverse study types include explicit early consideration of biomarker purpose and clinical context, candidate biomarkers associated with disease severity at a particular point in time and over time and how they are related, taking the stage of biomarker development into account when selecting disease severity measures for analyses, and validating biomarker associations with disease severity outcomes using both physician- and patient-reported measures and across domains. The outputs from this exercise will ensure coherence and focus across the BIOMAP consortium so that mechanistic insights and biomarkers are clinically relevant, patient-centric and more generalizable to current and future research efforts.

CircRNA: An emerging star in plant research: A review

CircRNAs are a class of covalently closed non-coding RNA formed by linking the 5' terminus and the 3' terminus after reverse splicing. CircRNAs are widely found in eukaryotes, and they are highly conserved, with spatio-temporal expression specificity and stability. CircRNAs can act as miRNA sponges to regulate the expression of downstream target genes, regulating the transcription of parental genes and some can even be translated into peptides or proteins. Research on circRNAs in plants is still in its infancy compared to that in animals. With the deepening of research, the results of a variety of plant circRNAs suggest that they play an important role in growth and development, and tolerance towards abiotic stresses such as salt, drought, low temperature, high temperature and other adverse environments. In this review paper, we elaborated the molecular characteristics, mechanism of action, function and bioinformatics databases of plant circRNAs, combined with the progress of circRNA research in animals, discussed the potential mechanism of action of plant circRNAs, and proposed the unsolved problems and prospects for future application of plant circRNAs.

Multiomics Analysis of the PHLDA Gene Family in Different Cancers and Their Clinical Prognostic Value

The PHLDA (pleckstrin homology-like domain family) gene family is popularly known as a potential biomarker for cancer identification, and members of the PHLDA family have become considered potentially viable targets for cancer treatments. The PHLDA gene family consists of PHLDA1, PHLDA2, and PHLDA3. The predictive significance of PHLDA genes in cancer remains unclear. To determine the role of pleckstrin as a prognostic biomarker in human cancers, we conducted a systematic multiomics investigation. Through various survival analyses, pleckstrin expression was evaluated, and their predictive significance in human tumors was discovered using a variety of online platforms. By analyzing the protein-protein interactions, we also chose a collection of well-known functional protein partners for pleckstrin. Investigations were also carried out on the relationship between pleckstrins and other cancers regarding mutations and copy number alterations. The cumulative impact of pleckstrin and their associated genes on various cancers, Gene Ontology (GO), and pathway analyses were used for their evaluation. Thus, the expression profiles of PHLDA family members and their prognosis in various cancers may be revealed by this study. During this multiomics analysis, we found that among the PHLDA family, PHLDA1 may be a therapeutic target for several cancers, including kidney, colon, and brain cancer, while PHLDA2 can be a therapeutic target for cancers of the colon, esophagus, and pancreas. Additionally, PHLDA3 may be a useful therapeutic target for ovarian, renal, and gastric cancer.

Respiratory modulation of the heart rate: A potential biomarker of cardiorespiratory function in human

In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans' physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans' digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (7 days, 4.63 ± 1.52) HR and RR of 89 freely behaving human subjects (Female: 39, age 57.28 ± 5.67, Male: 50, age 58.48 ± 6.32) and analyzed their dynamics using linear models and information theoretic measures. While HR's linear and nonlinear characteristics were expressed within the plane of the HR-RR directed flow of information (HR→RR - RR→HR), their dynamics were determined by its RR→HR axis. More importantly, RR→HR quantified the effect of alcohol consumption on individuals' cardiorespiratory function independent of their consumed amount of alcohol, thereby signifying the presence of this habit in their daily life activities. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals. These results can contribute to humans' phenotyping research by presenting RR→HR as a digital diagnosis/prognosis marker of humans' cardiorespiratory pathology.

Advancing Intelligent Organ-on-a-Chip Systems with Comprehensive In Situ Bioanalysis

In vitro models are essential to a broad range of biomedical research, such as pathological studies, drug development, and personalized medicine. As a potentially transformative paradigm for 3D in vitro models, organ-on-a-chip (OOC) technology has been extensively developed to recapitulate sophisticated architectures and dynamic microenvironments of human organs by applying the principles of life sciences and leveraging micro- and nanoscale engineering capabilities. A pivotal function of OOC devices is to support multifaceted and timely characterization of cultured cells and their microenvironments. However, in-depth analysis of OOC models typically requires biomedical assay procedures that are labor-intensive and interruptive. Herein, the latest advances toward intelligent OOC (iOOC) systems, where sensors integrated with OOC devices continuously report cellular and microenvironmental information for comprehensive in situ bioanalysis, are examined. It is proposed that the multimodal data in iOOC systems can support closed-loop control of the in vitro models and offer holistic biomedical insights for diverse applications. Essential techniques for establishing iOOC systems are surveyed, encompassing in situ sensing, data processing, and dynamic modulation. Eventually, the future development of iOOC systems featuring cross-disciplinary strategies is discussed.

An osteoarthritis pathophysiological continuum revealed by molecular biomarkers

We aimed to identify serum biomarkers that predict knee osteoarthritis (OA) before the appearance of radiographic abnormalities in a cohort of 200 women. As few as six serum peptides, corresponding to six proteins, reached AUC 77% probability to distinguish those who developed OA from age-matched individuals who did not develop OA up to 8 years later. Prediction based on these blood biomarkers was superior to traditional prediction based on age and BMI (AUC 51%) or knee pain (AUC 57%). These results identify a prolonged molecular derangement of joint tissue before the onset of radiographic OA abnormalities consistent with an unresolved acute phase response. Among all 24 protein biomarkers predicting incident knee OA, the majority (58%) also predicted knee OA progression, revealing the existence of a pathophysiological "OA continuum" based on considerable similarity in the molecular pathophysiology of the progression to incident OA and the progression of established OA.

Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity: A Systematic Review

Importance

In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers.

Objective

To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts.

Evidence Review

The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders.

Findings

There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes.

Conclusions and Relevance

Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.

Infrared Spectroscopy of Synovial Fluid Shows Accuracy as an Early Biomarker in an Equine Model of Traumatic Osteoarthritis

Osteoarthritis is a leading cause of lameness and joint disease in horses. A simple, economical, and accurate diagnostic test is required for routine screening for OA. This study aimed to evaluate infrared (IR)-based synovial fluid biomarker profiling to detect early changes associated with a traumatically induced model of equine carpal osteoarthritis (OA). Unilateral carpal OA was induced arthroscopically in 9 of 17 healthy thoroughbred fillies; the remainder served as Sham-operated controls. The median age of both groups was 2 years. Synovial fluid (SF) was obtained before surgical induction of OA (Day 0) and weekly until Day 63. IR absorbance spectra were acquired from dried SF films. Following spectral pre-processing, predictive models using random forests were used to differentiate OA, Sham, and Control samples. The accuracy for distinguishing between OA and any other joint group was 80%. The classification accuracy by sampling day was 87%. For paired classification tasks, the accuracies by joint were 75% for OA vs. OA Control and 70% for OA vs. Sham. The accuracy for separating horses by group (OA vs. Sham) was 68%. In conclusion, SF IR spectroscopy accurately discriminates traumatically induced OA joints from controls.

Review of the limitations of current biomarkers in acute kidney injury clinical practices

Acute kidney injury is a prevalent disease in hospitalized patients and is continuously increasing worldwide. Various efforts have been made to define and classify acute kidney injury to understand the progression of this disease. Furthermore, deviations from structure and kidney function and the current diagnostic guidelines are not adequately placed due to baseline serum creatinine values, which are rarely known and estimated based on glomerular function rate, resulting in misclassification of acute kidney injury staging. Hence, the current guidelines are still developing to improve and understand the clinical implications of risk factors and earlier predictive biomarkers of acute kidney injury. Yet, studies have indicated disadvantages and limitations with the current acute kidney injury biomarkers, including lack of sensitivity and specificity. Therefore, the present narrative review brings together the most current evidenced-based practice and literature associated with the limitations of the gold standard for acute kidney injury diagnoses, the need for novel acute kidney injury biomarkers, and the process for biomarkers to be qualified for diagnostic use under the following sections and themes. The introduction section situates the anatomy and normal and abnormal kidney functions related to acute kidney injury disorders. Guidelines in providing acute kidney injury definitions and classification are then considered, followed by a discussion of the disadvantages of standard markers used to diagnose acute kidney injury. Characteristics of an ideal acute kidney injury biomarker are discussed concerning sensitivity, specificity, and anatomic location of injury. A particular focus on the role and function of emerging biomarkers is discussed in relation to their applications and significance to the prognosis and severity of acute kidney injury. Findings show emerging markers are early indicators of acute kidney injury prediction in different clinical settings. Finally, the process required for a biomarker to be applied for diagnostic use is explained.

Prognostic Biomarkers in Kidney Transplantation: A Systematic Review and Critical Appraisal

SIGNIFICANCE STATEMENT

Why are there so few biomarkers accepted by health authorities and implemented in clinical practice, despite the high and growing number of biomaker studies in medical research ? In this meta-epidemiological study, including 804 studies that were critically appraised by expert reviewers, the authors have identified all prognostic kidney transplant biomarkers and showed overall suboptimal study designs, methods, results, interpretation, reproducible research standards, and transparency. The authors also demonstrated for the first time that the limited number of studies challenged the added value of their candidate biomarkers against standard-of-care routine patient monitoring parameters. Most biomarker studies tended to be single-center, retrospective studies with a small number of patients and clinical events. Less than 5% of the studies performed an external validation. The authors also showed the poor transparency reporting and identified a data beautification phenomenon. These findings suggest that there is much wasted research effort in transplant biomarker medical research and highlight the need to produce more rigorous studies so that more biomarkers may be validated and successfully implemented in clinical practice.

BACKGROUND

Despite the increasing number of biomarker studies published in the transplant literature over the past 20 years, demonstrations of their clinical benefit and their implementation in routine clinical practice are lacking. We hypothesized that suboptimal design, data, methodology, and reporting might contribute to this phenomenon.

METHODS

We formed a consortium of experts in systematic reviews, nephrologists, methodologists, and epidemiologists. A systematic literature search was performed in PubMed, Embase, Scopus, Web of Science, and Cochrane Library between January 1, 2005, and November 12, 2022 (PROSPERO ID: CRD42020154747). All English language, original studies investigating the association between a biomarker and kidney allograft outcome were included. The final set of publications was assessed by expert reviewers. After data collection, two independent reviewers randomly evaluated the inconsistencies for 30% of the references for each reviewer. If more than 5% of inconsistencies were observed for one given reviewer, a re-evaluation was conducted for all the references of the reviewer. The biomarkers were categorized according to their type and the biological milieu from which they were measured. The study characteristics related to the design, methods, results, and their interpretation were assessed, as well as reproducible research practices and transparency indicators.

RESULTS

A total of 7372 publications were screened and 804 studies met the inclusion criteria. A total of 1143 biomarkers were assessed among the included studies from blood ( n =821, 71.8%), intragraft ( n =169, 14.8%), or urine ( n =81, 7.1%) compartments. The number of studies significantly increased, with a median, yearly number of 31.5 studies (interquartile range [IQR], 23.8-35.5) between 2005 and 2012 and 57.5 (IQR, 53.3-59.8) between 2013 and 2022 ( P < 0.001). A total of 655 studies (81.5%) were retrospective, while 595 (74.0%) used data from a single center. The median number of patients included was 232 (IQR, 96-629) with a median follow-up post-transplant of 4.8 years (IQR, 3.0-6.2). Only 4.7% of studies were externally validated. A total of 346 studies (43.0%) did not adjust their biomarker for key prognostic factors, while only 3.1% of studies adjusted the biomarker for standard-of-care patient monitoring factors. Data sharing, code sharing, and registration occurred in 8.8%, 1.1%, and 4.6% of studies, respectively. A total of 158 studies (20.0%) emphasized the clinical relevance of the biomarker, despite the reported nonsignificant association of the biomarker with the outcome measure. A total of 288 studies assessed rejection as an outcome. We showed that these rejection studies shared the same characteristics as other studies.

CONCLUSIONS

Biomarker studies in kidney transplantation lack validation, rigorous design and methodology, accurate interpretation, and transparency. Higher standards are needed in biomarker research to prove the clinical utility and support clinical use.

AAAKB: A manually curated database for tracking and predicting genes of Abdominal aortic aneurysm (AAA)

Abdominal aortic aneurysm (AAA), an extremely dangerous vascular disease with high mortality, causes massive internal bleeding due to aneurysm rupture. To boost the research on AAA, efforts should be taken to organize and link the information about AAA-related genes and their functions. Currently, most researchers screen through genetic databases manually, which is cumbersome and time-consuming. Here, we developed "AAAKB" a manually curated knowledgebase containing genes, SNPs and pathways associated with AAA. In order to facilitate researchers to further explore the mechanism network of AAA, AAAKB provides predicted genes that are potentially associated with AAA. The prediction is based on the protein interaction information of genes collected in the database, and the random forest algorithm (RF) is used to build the prediction model. Some of these predicted genes are differentially expressed in patients with AAA, and some have been reported to play a role in other cardiovascular diseases, illustrating the utility of the knowledgebase in predicting novel genes. Also, AAAKB integrates a protein interaction visualization tool to quickly determine the shortest paths between target proteins. As the first knowledgebase to provide a comprehensive catalog of AAA-related genes, AAAKB will be an ideal research platform for AAA. Database URL: http://www.lqlgroup.cn:3838/AAAKB/.

A flexible approach for predictive biomarker discovery

An endeavor central to precision medicine is predictive biomarker discovery; they define patient subpopulations which stand to benefit most, or least, from a given treatment. The identification of these biomarkers is often the byproduct of the related but fundamentally different task of treatment rule estimation. Using treatment rule estimation methods to identify predictive biomarkers in clinical trials where the number of covariates exceeds the number of participants often results in high false discovery rates. The higher than expected number of false positives translates to wasted resources when conducting follow-up experiments for drug target identification and diagnostic assay development. Patient outcomes are in turn negatively affected. We propose a variable importance parameter for directly assessing the importance of potentially predictive biomarkers and develop a flexible nonparametric inference procedure for this estimand. We prove that our estimator is double robust and asymptotically linear under loose conditions in the data-generating process, permitting valid inference about the importance metric. The statistical guarantees of the method are verified in a thorough simulation study representative of randomized control trials with moderate and high-dimensional covariate vectors. Our procedure is then used to discover predictive biomarkers from among the tumor gene expression data of metastatic renal cell carcinoma patients enrolled in recently completed clinical trials. We find that our approach more readily discerns predictive from nonpredictive biomarkers than procedures whose primary purpose is treatment rule estimation. An open-source software implementation of the methodology, the uniCATE R package, is briefly introduced.

Biomarkers in psychiatric disorders

Psychiatric disorders represent a significant socioeconomic and healthcare burden worldwide. Of these, schizophrenia, bipolar disorder, major depressive disorder and anxiety are among the most prevalent. Unfortunately, diagnosis remains problematic and largely complicated by the lack of disease specific biomarkers. Accordingly, much research has focused on elucidating these conditions to more fully understand underlying pathophysiology and potentially identify biomarkers, especially those of early stage disease. In this chapter, we review current status of this endeavor as well as the potential development of novel biomarkers for clinical applications and future research study.

Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development

With the continuous development of the pharmaceutical industry, people have always paid attention to the safety and effectiveness of drugs, including innovative drugs and generic drugs. For pharmaceutical companies as manufacturers, drug development is a very lengthy process that requires high costs, millions of man-hours, thousands of trials, and the mobilization of hundreds of researchers. Therefore, efforts need to be made to develop drugs with high safety and effectiveness. Drug research and development plays an important role today. Based on this, this article applied computer molecular simulation embedded technology and artificial intelligence technology to drug research and development. First, the problems faced in the research and development of anti-inflammatory disease-dependent tumor drugs were introduced, and then the applications of computer molecular simulation embedded technology and artificial intelligence technology in drug research and development were analyzed. Subsequently, the application of artificial intelligence in drug research and development teaching was analyzed, and a teaching system based on computer molecular simulation embedded technology and artificial intelligence was designed. Finally, the application effects of computer molecular simulation embedded technology and artificial intelligence technology were analyzed, and a feasible conclusion was drawn. The use of computer molecular simulation embedded technology and artificial intelligence technology can greatly improve the efficiency of drug research and development, and the research and development safety of imatinib mesylate has been improved by 7%. On the other hand, it can improve students' learning interest and stimulate their learning interest, and students' drug research and development capabilities have been improved. Drug research and development for inflammatory-dependent tumors has good application prospects.

Pro-Inflammatory Proteins Associated with Frailty and Its Progression-A Longitudinal Study in Community-Dwelling Women

The complex pathophysiology underlying biological aging creates challenges for identifying biomarkers associated with frailty. This longitudinal, nontargeted proteomics study aimed to identify proteins associated with frailty, particularly the change from nonfrail to frail. The population-based Osteoporosis Prospective Risk Assessment cohort includes women all of whom are 75 years old at inclusion (n = 1044) and reassessed at 80 years (n = 715) and 85 years (n = 382). A deficits in health frailty index (FI) and 92 plasma proteins (Olink CVD-II panel) were available at all ages. The identical age facilitated differentiating chronological and biological aging. Bidirectional analyses, performed cross-sectionally and longitudinally, used regression models controlled for false discovery rate (FDR), across 5- and 10-year time windows and longitudinal mixed models. Frailty outcomes were frailty index, frailty status (frail defined as FI ≥ 0.25), change in frailty index, and change in frailty status, together with protein expression or change in protein expression. Elevated levels of 32 proteins were positively associated with the FI, cross-sectionally at all ages (range: β-coefficients 0.22-2.06; FDR 0.021-0.024), of which 18 were also associated with frailty status (range: odds ratios 1.40-5.77; FDR 0.022-0.016). Based on the accrued data, eight core proteins (CD4, FGF23, Gal-9, PAR-1, REN, TNFRSF10A TNFRSF11A, and TNFRSF10B) are proposed. A one-unit change in the FI was additively associated with increased protein expression over 5 and 10 years (range: β-coefficients 0.52-1.59; p < 0.001). Increments in baseline FI consistently associated with a change in protein expression over time (5 years, β-range 0.05-1.35; 10 years, β-range 0.51-1.48; all p < 0.001). A one-unit increase in protein expression was also associated with an increased probability of being frail (FI ≥ 0.25) (β-range: 0.14-0.61). Mirroring the multisystem deterioration that typifies frailty, the proteins and their associated biological pathways reflect pathologies, including the renal system, skeletal homeostasis, and TRAIL-activated apoptotic signaling. The core proteins are compelling candidates for understanding the development and progression of frailty with advancing age, including the intrinsic musculoskeletal component. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

The proteome and phosphoproteome of circulating extracellular vesicle-enriched preparations are associated with characteristic clinical features in type 1 diabetes

Introduction

There are no validated clinical or laboratory biomarkers to identify and differentiate endotypes of type 1 diabetes (T1D) or the risk of progression to chronic complications. Extracellular vesicles (EVs) have been studied as biomarkers in several different disease states but have not been well studied in T1D.

Methods

As the initial step towards circulating biomarker identification in T1D, this pilot study aimed to provide an initial characterization of the proteomic and phosphoproteomic landscape of circulating EV-enriched preparations in participants with established T1D (N=10) and healthy normal volunteers (Controls) (N=7) (NCT03379792) carefully matched by age, race/ethnicity, sex, and BMI. EV-enriched preparations were obtained using EVtrap® technology. Proteins were identified and quantified by LC-MS analysis. Differential abundance and coexpression network (WGCNA), and pathway enrichment analyses were implemented.

Results

The detected proteins and phosphoproteins were enriched (75%) in exosomal proteins cataloged in the ExoCarta database. A total of 181 proteins and 8 phosphoproteins were differentially abundant in participants with T1D compared to controls, including some well-known EVproteins (i.e., CD63, RAB14, BSG, LAMP2, and EZR). Enrichment analyses of differentially abundant proteins and phosphoproteins of EV-enriched preparations identified associations with neutrophil, platelet, and immune response functions, as well as prion protein aggregation. Downregulated proteins were involved in MHC class II signaling and the regulation of monocyte differentiation. Potential key roles in T1D for C1q, plasminogen, IL6ST, CD40, HLA-DQB1, HLA-DRB1, CD74, NUCB1, and SAP, are highlighted. Remarkably, WGCNA uncovered two protein modules significantly associated with pancreas size, which may be implicated in the pathogenesis of T1D. Similarly, these modules showed significant enrichment for membrane compartments, processes associated with inflammation and the immune response, and regulation of viral processes, among others.

Discussion

This study demonstrates the potential of proteomic and phosphoproteomic signatures of EV-enriched preparations to provide insight into the pathobiology of T1D. The WGCNA analysis could be a powerful tool to discriminate signatures associated with different pathobiological components of the disease.

Reproducibility of drug-induced effects on the contractility of an engineered heart tissue derived from human pluripotent stem cells

Introduction: Engineered heart tissues (EHTs) are three-dimensional culture platforms with cardiomyocytes differentiated from human pluripotent stem cells (hPSCs) and were designed for assaying cardiac contractility. For drug development applications, EHTs must have a stable function and provide reproducible results. We investigated these properties with EHTs made with different tissue casting batches and lines of differentiated hPSC-cardiomyocytes and analyzed them at different times after being fabricated. Methods: A video-optical assay was used for measuring EHT contractile outputs, and these results were compared with results from motion traction analysis of beating hPSC-cardiomyocytes cultured as monolayers in two-dimensional cultures. The reproducibility of induced contractile variations was tested using compounds with known mechanistic cardiac effects (isoproterenol, EMD-57033, omecamtiv mecarbil, verapamil, ranolazine, and mavacamten), or known to be clinically cardiotoxic (doxorubicin, sunitinib). These drug-induced variations were characterized at different electrical pacing rates and variations in intracellular calcium transients were also assessed in EHTs. Results: To ensure reproducibility in experiments, we established EHT quality control criteria based on excitation-contraction coupling and contractile sensitivity to extracellular calcium concentration. In summary, a baseline contractile force of 0.2 mN and excitation-contraction coupling of EHTs were used as quality control criteria to select suitable EHTs for analysis. Overall, drug-induced contractile responses were similar between monolayers and EHTs, where a close relationship was observed between contractile output and calcium kinetics. Contractile variations at multiple time points after adding cardiotoxic compounds were also detectable in EHTs. Discussion: Reproducibility of drug-induced effects in EHTs between experiments and relative to published work on these cellular models was generally observed. Future applications for EHTs may require additional mechanistic criteria related to drug effects and cardiac functional outputs to be measured in regard to specific contexts of use.

Orthogonal proteomics methods warrant the development of Duchenne muscular dystrophy biomarkers

BACKGROUND

Molecular components in blood, such as proteins, are used as biomarkers to detect or predict disease states, guide clinical interventions and aid in the development of therapies. While multiplexing proteomics methods promote discovery of such biomarkers, their translation to clinical use is difficult due to the lack of substantial evidence regarding their reliability as quantifiable indicators of disease state or outcome. To overcome this challenge, a novel orthogonal strategy was developed and used to assess the reliability of biomarkers and analytically corroborate already identified serum biomarkers for Duchenne muscular dystrophy (DMD). DMD is a monogenic incurable disease characterized by progressive muscle damage that currently lacks reliable and specific disease monitoring tools.

METHODS

Two technological platforms are used to detect and quantify the biomarkers in 72 longitudinally collected serum samples from DMD patients at 3 to 5 timepoints. Quantification of the biomarkers is achieved by detection of the same biomarker fragment either through interaction with validated antibodies in immuno-assays or through quantification of peptides by Parallel Reaction Monitoring Mass Spectrometry assay (PRM-MS).

RESULTS

Five, out of ten biomarkers previously identified by affinity-based proteomics methods, were confirmed to be associated with DMD using the mass spectrometry-based method. Two biomarkers, carbonic anhydrase III and lactate dehydrogenase B, were quantified with two independent methods, sandwich immunoassays and PRM-MS, with Pearson correlations of 0.92 and 0.946 respectively. The median concentrations of CA3 and LDHB in DMD patients was elevated in comparison to those in healthy individuals by 35- and 3-fold, respectively. Levels of CA3 vary between 10.26 and 0.36 ng/ml in DMD patients whereas those of LDHB vary between 15.1 and 0.8 ng/ml.

CONCLUSIONS

These results demonstrate that orthogonal assays can be used to assess the analytical reliability of biomarker quantification assays, providing a means to facilitate the translation of biomarkers to clinical practice. This strategy also warrants the development of the most relevant biomarkers, markers that can be reliably quantified with different proteomics methods.

UPO Biobank: The Challenge of Integrating Biobanking into the Academic Environment to Support Translational Research

Biobanks are driving motors of precision and personalized medicine by providing high-quality biological material/data through the standardization and harmonization of their collection, preservation, and distribution. UPO Biobank was established in 2020 as an institutional, disease, and population biobank within the University of Piemonte Orientale (UPO) for the promotion and support of high-quality, multidisciplinary studies. UPO Biobank collaborates with UPO researchers, sustaining academic translational research, and supports the Novara Cohort Study, a longitudinal cohort study involving the population in the Novara area that will collect data and biological specimens that will be available for epidemiological, public health, and biological studies on aging. UPO Biobank has been developed by implementing the quality standards for the field and the ethical and legal issues and normative about privacy protection, data collection, and sharing. As a member of the "Biobanking and Biomolecular Resources Research Infrastructure" (BBMRI) network, UPO Biobank aims to expand its activity worldwide and launch cooperation with new national and international partners and researchers. The objective of this manuscript is to report an institutional and operational experience through the description of the technical and procedural solutions and ethical and scientific implications associated with the establishment of this university research biobank.

Salivary and serum periostin in kidney transplant recipients

INTRODUCTION

End-stage renal disease (ESRD) treatment includes dialysis and kidney transplantation. Transplant rejection is a major barrier to transplant success. One of the markers mentioned in previous studies on renal function in patients with renal failure for various reasons is periostin (POSTN). The expression of POSTN correlates with interstitial fibrosis and reduced renal function. One of the limitations in this regard is the effect of oral lesions on the POSTN level. This study was conducted aimed to measure the relationship between salivary and serum POSTN and renal function in patients with a history of a kidney transplant, taking into account all the conditions affecting POSTN.

METHODS

In this study, serum and saliva samples were taken from 23 transplant patients with normal function (NF) and 29 transplant patients with graft failure (GF). At least one year had passed since the transplant. Before sampling, a complete oral examination was performed. Salivary and serum POSTN was examined by ELISA. The results were analyzed by SPSS software.

RESULTS

The POSTN level in the serum of the NF group (191.00 ± 33.42) was higher than GF patients (178.71 ± 25.68), but the difference was not significant (P = 0.30). Salivary POSTN in NF patients (2.76 ± 0.35) was significantly higher than GF patients (2.44 ± 0.60) (P = 0.01).

CONCLUSIONS

The superiority of saliva as a diagnostic fluid includes ease of collection and storage, and non-invasiveness, all of which can lead to the replacement of blood with this bio-fluid. The significant results of salivary POSTN may be due to the lack of serum disturbing factors. Saliva is an ultra-filtered fluid from serum and therefore there are fewer proteins and polysaccharides attached to biomarkers in saliva and the accuracy of measuring these biomarkers in the saliva is more valuable than serum.

Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU

Introduction

Millions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients.

Methods

In our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool.

Results

We isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients.

Discussion

In conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.

Consortium-based approach to receiving an EMA qualification opinion on the use of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies

The development of medical products that can delay or prevent progression to stage 3 type 1 diabetes faces many challenges. Of note, optimising patient selection for type 1 diabetes prevention clinical trials is hindered by significant patient heterogeneity and a lack of characterisation of the time-varying probability of progression to stage 3 type 1 diabetes in individuals positive for two or more islet autoantibodies. To meet these needs, the Critical Path Institute's Type 1 Diabetes Consortium was launched in 2017 as a pre-competitive public-private partnership between stakeholders from the pharmaceutical industry, patient advocacy groups, philanthropic organisations, clinical researchers, the National Institutes of Health and the Food and Drug Administration. The Type 1 Diabetes Consortium acquired and aggregated data from three longitudinal observational studies, Environmental Determinants of Diabetes in the Young (TEDDY), Diabetes Autoimmunity Study in the Young (DAISY) and TrialNet Pathway to Prevention (TN01), and used analysis subsets of these data to support the model-based qualification of islet autoantibodies as enrichment biomarkers for patient selection in type 1 diabetes prevention trials, including registration studies. The Type 1 Diabetes Consortium has now received a qualification opinion from the European Medicines Agency for the use of these biomarkers, a major success for the field of type 1 diabetes. This endorsement will improve product developers' ability to design clinical trials of agents intended to prevent or delay type 1 diabetes that are reduced in size and/or length, while being adequately powered.

HiRAND: A novel GCN semi-supervised deep learning-based framework for classification and feature selection in drug research and development

The prediction of response to drugs before initiating therapy based on transcriptome data is a major challenge. However, identifying effective drug response label data costs time and resources. Methods available often predict poorly and fail to identify robust biomarkers due to the curse of dimensionality: high dimensionality and low sample size. Therefore, this necessitates the development of predictive models to effectively predict the response to drugs using limited labeled data while being interpretable. In this study, we report a novel Hierarchical Graph Random Neural Networks (HiRAND) framework to predict the drug response using transcriptome data of few labeled data and additional unlabeled data. HiRAND completes the information integration of the gene graph and sample graph by graph convolutional network (GCN). The innovation of our model is leveraging data augmentation strategy to solve the dilemma of limited labeled data and using consistency regularization to optimize the prediction consistency of unlabeled data across different data augmentations. The results showed that HiRAND achieved better performance than competitive methods in various prediction scenarios, including both simulation data and multiple drug response data. We found that the prediction ability of HiRAND in the drug vorinostat showed the best results across all 62 drugs. In addition, HiRAND was interpreted to identify the key genes most important to vorinostat response, highlighting critical roles for ribosomal protein-related genes in the response to histone deacetylase inhibition. Our HiRAND could be utilized as an efficient framework for improving the drug response prediction performance using few labeled data.

A "best-in-class" systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression

We aimed to identify markers in blood (serum) to predict clinically relevant knee osteoarthritis (OA) progression defined as the combination of both joint structure and pain worsening over 48 months. A set of 15 serum proteomic markers corresponding to 13 total proteins reached an area under the receiver operating characteristic curve (AUC) of 73% for distinguishing progressors from nonprogressors in a cohort of 596 individuals with knee OA. Prediction based on these blood markers was far better than traditional prediction based on baseline structural OA and pain severity (59%) or the current "best-in-class" biomarker for predicting OA progression, urinary carboxyl-terminal cross-linked telopeptide of type II collagen (58%). The generalizability of the marker set was confirmed in a second cohort of 86 individuals that yielded an AUC of 70% for distinguishing joint structural progressors. Blood is a readily accessible biospecimen whose analysis for these biomarkers could facilitate identification of individuals for clinical trial enrollment and those most in need of treatment.

An artificial neural network classification method employing longitudinally monitored immune biomarkers to predict the clinical outcome of critically ill COVID-19 patients

Background

The severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death. To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients.

Methods

The longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured. We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability. The biomarkers were used as input of artificial neural network, random forest, classification and regression trees, k-nearest neighbors and support vector machines. Two different clinical cohorts were used to grant validity to the findings.

Results

We benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that artificial neural networks was the best classifier. From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients. First, all the biomarkers available yielded a satisfactory classification. Next, we assessed the prediction capacity of each protein separated. With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data. We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained.

Conclusions

In this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients. The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases.

Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement-the Mobilise-D study protocol

BACKGROUND

The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions.

METHODS/DESIGN

The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson's Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability.

DISCUSSION

The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility.

TRIAL REGISTRATION

ISRCTN12051706.

Identification of Biomarkers in Intracranial Aneurysm and Their Immune Infiltration Characteristics

BACKGROUND

Intracranial aneurysm (IA), known as the intracranial "unscheduled bomb," is one of the most dangerous cerebrovascular diseases, with unclear pathogenesis. This study aimed to show the mechanisms and identify the new biological targets by applying bioinformatics analysis.

METHODS

Expression profiling for control superficial temporal artery and IA walls in GSE26969 and GSE75436 datasets were downloaded. By executing the LIMMA package in R software, the differentially expressed genes (DEGs) were filtered, and the functional enrichments were consequently performed. Further cross-linking with the 2483 immune-related genes (IRGs) from the ImmPort database, the differentially expressed IRGs were identified. Based on them, the least absolute shrinkage and selection operator logistic regression and support vector machine-recursive feature elimination algorithms were used to screen the biomarkers, which were validated in the GSE54083 datasets. The CIBERSORT algorithm was applied to evaluate the infiltration of immune cells in tissues.

RESULTS

A total of 668 DEGs were obtained, and the functional enrichment suggested that they were closely related to the immune process. After intersecting them with the IRGs, 90 differentially expressed IRGs emerged, and ADIPOQ and ESM1 were identified as the biomarkers. Besides, we found that the infiltrated immune cells, such as the mast cells resting, might be associated with them.

CONCLUSIONS

We explored the contributing factors involving IA, which may generate a better understanding of the complex interactions among them and inspire a promising strategy for clinical works.

Biomarkers of disease progression in people with psoriasis: a scoping review

BACKGROUND

Identification of those at risk of more severe psoriasis and/or associated morbidities offers opportunity for early intervention, reduced disease burden and more cost-effective healthcare. Prognostic biomarkers of disease progression have thus been the focus of intense research, but none are part of routine practice.

OBJECTIVES

To identify and catalogue candidate biomarkers of disease progression in psoriasis for the translational research community.

METHODS

A systematic search of CENTRAL, Embase, LILACS and MEDLINE was performed for relevant articles published between 1990 and December 2021. Eligibility criteria were studies involving patients with psoriasis (any age, n ≥ 50) reporting biomarkers associated with disease progression. The main outcomes were any measure of skin severity or any prespecified psoriasis comorbidity. Data were extracted by one reviewer and checked by a second; studies meeting minimal quality criteria (longitudinal design and/or use of methods to control for confounding) were formally assessed for bias. Candidate biomarkers were identified by an expert multistakeholder group using a majority voting consensus exercise, and mapped to relevant cellular and molecular pathways.

RESULTS

Of 181 included studies, most investigated genomic or proteomic biomarkers associated with disease severity (n = 145) or psoriatic arthritis (n = 30). Methodological and reporting limitations compromised interpretation of findings, most notably a lack of longitudinal studies, and inadequate control for key prognostic factors. The following candidate biomarkers with future potential utility were identified for predicting disease severity: LCE3D, interleukin (IL)23R, IL23A, NFKBIL1 loci, HLA-C*06:02 (genomic), IL-17A, IgG aHDL, GlycA, I-FABP and kallikrein 8 (proteomic), tyramine (metabolomic); psoriatic arthritis: HLA-C*06:02, HLA-B*27, HLA-B*38, HLA-B*08, and variation at the IL23R and IL13 loci (genomic); IL-17A, CXCL10, Mac-2 binding protein, integrin b5, matrix metalloproteinase-3 and macrophage-colony stimulating factor (proteomic) and tyramine and mucic acid (metabolomic); and type 2 diabetes mellitus: variation in IL12B and IL23R loci (genomic). No biomarkers were supported by sufficient evidence for clinical use without further validation.

CONCLUSIONS

This review provides a comprehensive catalogue of investigated biomarkers of disease progression in psoriasis. Future studies must address the common methodological limitations identified herein to expedite discovery and validation of biomarkers for clinical use. What is already known about this topic? The current treatment paradigm in psoriasis is reactive. There is a need to develop effective risk-stratified management approaches that can proactively attenuate the substantial burden of disease. Prognostic biomarkers of disease progression have therefore been the focus of intense research. What does this study add? This review is the first to scope, collate and catalogue research investigating biomarkers of disease progression in psoriasis. The review identifies potentially promising candidate biomarkers for further investigation and highlights common important limitations that should be considered when designing and conducting future studies in this area.

A rapid, non-invasive method for fatigue detection based on voice information

Fatigue results from a series of physiological and psychological changes due to continuous energy consumption. It can affect the physiological states of operators, thereby reducing their labor capacity. Fatigue can also reduce efficiency and, in serious cases, cause severe accidents. In addition, it can trigger pathological-related changes. By establishing appropriate methods to closely monitor the fatigue status of personnel and relieve the fatigue on time, operation-related injuries can be reduced. Existing fatigue detection methods mostly include subjective methods, such as fatigue scales, or those involving the use of professional instruments, which are more demanding for operators and cannot detect fatigue levels in real time. Speech contains information that can be used as acoustic biomarkers to monitor physiological and psychological statuses. In this study, we constructed a fatigue model based on the method of sleep deprivation by collecting various physiological indexes, such as P300 and glucocorticoid level in saliva, as well as fatigue questionnaires filled by 15 participants under different fatigue procedures and graded the fatigue levels accordingly. We then extracted the speech features at different instances and constructed a model to match the speech features and the degree of fatigue using a machine learning algorithm. Thus, we established a method to rapidly judge the degree of fatigue based on speech. The accuracy of the judgment based on unitary voice could reach 94%, whereas that based on long speech could reach 81%. Our fatigue detection method based on acoustic information can easily and rapidly determine the fatigue levels of the participants. This method can operate in real time and is non-invasive and efficient. Moreover, it can be combined with the advantages of information technology and big data to expand its applicability.

One small step in time, one giant leap for DMPK kind - A CRO perspective of the evolving core discipline of drug development

As the Space Race or Formula 1 drives innovation, efficiency and progress in home technology and home car markets, so Drug Metabolism and Pharmacokinetics (DMPK) drives scientific innovation and value for drug development companies. Stand still and fall behind as the saying goes, and these analogies are true as much in the design and conduct of DMPK studies as they are in technology and manufacturing sectors.This short review showcases the impact that DMPK has had on drug development and how it has changed in the last 10 years, illustrating the value added scientific benefit, cost and time saving, that innovative DMPK program design and study conduct have. Examples and case studies spanning novel in vitro alternatives such as organ-on-a-chip (OOAC) developments; use of in vivo microsampling across small and large animal species; to how challenging historical paradigms in Absorption, Distribution, Metabolism and Excretion (ADME) studies; and embracing new technologies to address regulatory concerns, are presented.The continual pace of change has kept DMPK at the core of pharmaceutical, crop and chemical evaluation, and this is set to continue as regulators use this discipline to inform decision making. With new modalities and new scientific questions, DMPK will continue to evolve, with the likes of new in vitro, in vivo and in silico models becoming central to candidate selection and progression.

Clinical monitoring in osteoarthritis: Biomarkers

OBJECTIVE

The purpose of this overview of osteoarthritis (OA) biomarkers is to provide the non-specialist with a toolbox, based on experience acquired by biomarker researchers over many years, to understand biomarkers in general and their use in the OA field.

METHODS

We provide an update on this subject since the OARSI Primer on osteoarthritis (OA) nearly a decade ago.

RESULTS

Since the last update, the importance of molecular biomarkers has been increasingly recognized in the field, but no OA-related biomarkers have been adopted for routine use in clinical practice. The current lack of chondroprotective treatments for OA impairs the assessment, validation and qualification of the potential role of biomarkers as tools for monitoring disease status and patient responses to treatment of OA. Yet there is no lack of an evolving compendium of OA-related biomarkers, ever more fit-for-purpose, that could currently facilitate drug development for OA. We provide an abbreviated update and overview of specific soluble OA-related biomarkers for this new OARSI Primer on OA with OA-relevant examples encompassing the concepts of biomarker nomenclature, qualification, interpretation, measurement, reporting requirements, application to research, drug discovery and clinical care, and future needs for biomarker advancement.

CONCLUSION

Appropriate biomarkers should play a role at all stages of OA diagnosis, prognosis, drug development, and treatment. The future of OA biomarker research and development holds great promise as its foundation is increasingly robust.

Clinical trial design in the era of precision medicine

Recent rapid biotechnological breakthroughs have led to the identification of complex and unique molecular features that drive malignancies. Precision medicine has exploited next-generation sequencing and matched targeted therapy/immunotherapy deployment to successfully transform the outlook for several fatal cancers. Tumor and liquid biopsy genomic profiling and transcriptomic, immunomic, and proteomic interrogation can now all be leveraged to optimize therapy. Multiple new trial designs, including basket and umbrella trials, master platform trials, and N-of-1 patient-centric studies, are beginning to supplant standard phase I, II, and III protocols, allowing for accelerated drug evaluation and approval and molecular-based individualized treatment. Furthermore, real-world data, as well as exploitation of digital apps and structured observational registries, and the utilization of machine learning and/or artificial intelligence, may further accelerate knowledge acquisition. Overall, clinical trials have evolved, shifting from tumor type-centered to gene-directed and histology-agnostic trials, with innovative adaptive designs and personalized combination treatment strategies tailored to individual biomarker profiles. Some, but not all, novel trials now demonstrate that matched therapy correlates with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To further improve the precision medicine paradigm, the strategy of matching drugs to patients based on molecular features should be implemented earlier in the disease course, and cancers should have comprehensive multi-omic (genomics, transcriptomics, proteomics, immunomic) tumor profiling. To overcome cancer complexity, moving from drug-centric to patient-centric individualized combination therapy is critical. This review focuses on the design, advantages, limitations, and challenges of a spectrum of clinical trial designs in the era of precision oncology.

New Drug Development and Clinical Trial Design by Applying Genomic Information Management

Depending on the patients’ genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design.

Biomarkers for Alzheimer's Disease: Context of Use, Qualification, and Roadmap for Clinical Implementation

Background and Objectives: The US Food and Drug Administration (FDA) defines a biomarker as a characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention. Biomarkers may be used in clinical care or as drug development tools (DDTs) in clinical trials. The goal of this review and perspective is to provide insight into the regulatory guidance for the use of biomarkers in clinical trials and clinical care. Materials and Methods: We reviewed FDA guidances relevant to biomarker use in clinical trials and their transition to use in clinical care. We identified instructive examples of these biomarkers in Alzheimer's disease (AD) drug development and their application in clinical practice. Results: For use in clinical trials, biomarkers must have a defined context of use (COU) as a risk/susceptibility, diagnostic, monitoring, predictive, prognostic, pharmacodynamic, or safety biomarker. A four-stage process defines the pathway to establish the regulatory acceptance of the COU for a biomarker including submission of a letter of intent, description of the qualification plan, submission of a full qualification package, and acceptance through a qualification recommendation. Biomarkers used in clinical care may be companion biomarkers, in vitro diagnostic devices (IVDs), or laboratory developed tests (LDTs). A five-phase biomarker development process has been proposed to structure the biomarker development process. Conclusions: Biomarkers are increasingly important in drug development and clinical care. Adherence to regulatory guidance for biomarkers used in clinical trials and patient care is required to advance these important drug development and clinical tools.

LBD: a manually curated database of experimentally validated lymphoma biomarkers

Lymphoma is a heterogeneous disease caused by malignant proliferation of lymphocytes, resulting in significant mortality worldwide. While more and more lymphoma biomarkers have been identified with the advent and development of precision medicine, there are currently no databases dedicated to systematically gathering these scattered treasures. Therefore, we developed a lymphoma biomarker database (LBD) to curate experimentally validated lymphoma biomarkers in this study. LBD consists of 793 biomarkers extracted from 978 articles covering diverse subtypes of lymphomas, including 715 single and 78 combined biomarkers. These biomarkers can be categorized into molecular, cellular, image, histopathological, physiological and other biomarkers with various functions such as prognosis, diagnosis and treatment. As a manually curated database that provides comprehensive information about lymphoma biomarkers, LBD is helpful for personalized diagnosis and treatment of lymphoma.

Database URL

http://bis.zju.edu.cn/LBD

Rejuvenation of neutrophils and their extracellular vesicles is associated with enhanced aged fracture healing

Tissue repair is negatively affected by advanced age. Recent evidence indicates that hematopoietic cell-derived extracellular vesicles (EVs) are modulators of regenerative capacity. Here, we report that plasma EVs carrying specific surface markers indicate the degree of age-associated immunosenescence; moreover, this immunosenescence phenotype was accentuated by fracture injury. The number of CD11b+ Ly6Cintermediate Ly6Ghigh neutrophils significantly decreased with age in association with defective tissue regeneration. In response to fracture injury, the frequencies of neutrophils and associated plasma EVs were significantly higher in fracture calluses than in peripheral blood. Exposure of aged mice to youthful circulation through heterochronic parabiosis increased the number of neutrophils and their correlated Ly6G+ plasma EVs, which were associated with improved fracture healing in aged mice of heterochronic parabiosis pairs. Our findings create a foundation for utilizing specific immune cells and EV subsets as potential biomarkers and therapeutic strategies to promote resilience to stressors during aging.

Corticosteroid response predicts bronchopulmonary dysplasia status at 36 weeks in preterm infants treated with dexamethasone: A pilot study

IMPORTANCE

A major barrier to therapeutic development in neonates is a lack of standardized drug response measures that can be used as clinical trial endpoints. The ability to quantify treatment response in a way that aligns with relevant downstream outcomes may be useful as a surrogate marker for new therapies, such as those for bronchopulmonary dysplasia (BPD).

OBJECTIVE

To construct a measure of clinical response to dexamethasone that was well aligned with the incidence of severe BPD or death at 36 weeks' postmenstrual age.

DESIGN

Retrospective cohort study.

SETTING

Level IV Neonatal Intensive Care Unit.

PARTICIPANTS

Infants treated with dexamethasone for developing BPD between 2010 and 2020.

MAIN OUTCOME(S) AND MEASURE(S)

Two models were built based on demographics, changes in ventilatory support, and partial pressure of carbon dioxide (pCO₂ ) after dexamethasone administration. An ordinal logistic regression and regularized binary logistic model for the composite outcome were used to associate response level to BPD outcomes defined by both the 2017 BPD Collaborative and 2018 Neonatal Research Network definitions.

RESULTS

Ninety-five infants were treated with dexamethasone before 36 weeks. Compared to the baseline support and demographic data at the time of treatment, changes in ventilatory support improved ordinal model sensitivity and specificity. For the binary classification, BPD incidence was well aligned with risk levels, increasing from 16% to 59%.

CONCLUSIONS AND RELEVANCE

Incorporation of response variables as measured by changes in ventilatory parameters and pCO₂ following dexamethasone administration were associated with downstream outcomes. Incorporating drug response phenotype into a BPD model may enable more rapid development of future therapeutics.

An antibody-free platform for multiplexed, sensitive quantification of protein biomarkers in complex biomatrices

Sensitive, multiplexed protein quantification remains challenging despite recent advancements in LC-MS assays for targeted protein biomarker quantification. High-sensitivity protein biomarker measurements usually require immuno-affinity enrichment of target protein; a process which is highly dependent on capture reagent and limited in capability to measure multiple analytes. Herein, we report a novel antibody-free platform, which measures multiple biomarkers from complex matrices employing a strategically optimized solid-phase extraction cleanup and orthogonal multidimensional LC-MS. Eight human protein biomarkers with different specifications were spiked into canine plasma as a model investigation system. The developed strategy achieved the desired sensitivity, robustness, and throughput via the following steps: (1) post digestion mixed-mode cation exchange-reverse phase SPE enrichment cleaned up the sample initially; (2) rapid, high-pH peptide fractionation further eliminated background components efficiently while selectively enriched signature peptides (SP) to provide sufficient sensitivity for multiple targets; and (3) trapping-micro-LC-MS analysis delivered high sensitivity comparable to a nano-LC-MS method but with much better robustness and throughput for the final analysis. Compared with a conventional LC-MS assay with direct protein digestion and limited clean-up, analysis with this antibody-free platform improved the LLOQ by 1-2 orders of magnitude for the eight protein biomarkers, reaching as low as 5 ng/mL in plasma, with feasible robustness and throughput. This platform was applied for the quantification of biomarkers of respiratory conditions in patients with various lung diseases, demonstrating real-world applicability.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Confronting Racism in All Forms of Pain Research: Reframing Study Designs

This second paper in a 3-part series on antiracism in pain research across the translational spectrum focuses on study design factors. Although objectivity is a cornerstone value of science, subjectivity is embedded in every step of the research process as investigators make choices about who they collaborate with, which research questions they ask, how they recruit participants, which research tools they use, and how they analyze and interpret data. We present theory and evidence from disciplines such as sociology, medical anthropology, statistics, and public health to discuss 4 common study design factors, including 1) the dominant biomedical narrative of pain that restricts funding and exploration of social indicators of pain, 2) low diversity and inclusion in pain research enrollment that restricts generalizability to racialized groups, 3) the use of "race" or "ethnicity" as a statistical variable and proxy for lived experiences (eg, racism, resilience), and 4) limited modeling in preclinical research for the impact of social factors on pain physiology. The information presented in this article is intended to start conversations across stakeholders in the pain field to explore how we can come together to adopt antiracism practices in our work at large to achieve equity for racialized groups. PERSPECTIVE: This is the second paper in a 3-part series on antiracism in pain research. This part identifies common study design factors that risk hindering progress toward pain care equity. We suggest reframes using an antiracism framework for these factors to encourage all pain investigators to collectively make strides toward equity.

Evaluation of Companion Diagnostics in Scientific Advice and Drug Marketing Authorization Applications by the European Medicines Agency

With the implementation of the new EU regulation on in vitro diagnostics (IVDR) in May 2022, notified bodies will be required to assess Companion Diagnostics (CDx). The EMA and national medicines agencies will be consulted on the performance and safety of CDx. In this paper, we report on our systematic review on how the EMA has dealt with CDx in dossiers for marketing authorization procedures, in 2017–2019, and in scientific advice procedures in 2016–2020, prior to the implementation of the new IVDR. Out of 167 medicines approved or refused by the EMA, CDx played a role for 20 medicines during assessment. Both European public assessment reports (EPARs) and the internal day 80 and day 120 assessment reports (ARs) of the EMA centralized marketing authorization procedures for these 20 medicines were analyzed in detail to determine how CDx were assessed. Likewise, in 46 of 159 cases in which scientific advice was provided, CDx were mentioned in the question-and-answer section of the scientific advice, and these were analyzed in an analogous manner. Our analysis indicates that clinical performance and analytical performance of the CDx were the most-discussed topics, being discussed 11 and seven times in the 20 EPARs and 59 and 29 times in the ARs, respectively. For scientific advice, clinical and analytical performance was discussed 65 and 22 times in the 46 retrieved mentions of scientific advice. Other aspects in relation to CDx were discussed as well, although at a lower frequency, in assessment reports and scientific advice. Overall, our analysis demonstrates that, despite the absence of an obligation from a legal point of view, EMA has gained experience on the assessment of CDx, most notably regarding its analytical and clinical performance. This experience may be useful in situations in which the EMA and national agencies of EU member states will formally be consulted by notified bodies regarding the performance and safety of CDx. In addition, the issues raised in the EPARs, ARs and scientific advice reports provide insight for applicants on aspects of CDx that need careful consideration.

Preterm birth buccal cell epigenetic biomarkers to facilitate preventative medicine

Preterm birth is the major cause of newborn and infant mortality affecting nearly one in every ten live births. The current study was designed to develop an epigenetic biomarker for susceptibility of preterm birth using buccal cells from the mother, father, and child (triads). An epigenome-wide association study (EWAS) was used to identify differential DNA methylation regions (DMRs) using a comparison of control term birth versus preterm birth triads. Epigenetic DMR associations with preterm birth were identified for both the mother and father that were distinct and suggest potential epigenetic contributions from both parents. The mother (165 DMRs) and female child (136 DMRs) at p < 1e-04 had the highest number of DMRs and were highly similar suggesting potential epigenetic inheritance of the epimutations. The male child had negligible DMR associations. The DMR associated genes for each group involve previously identified preterm birth associated genes. Observations identify a potential paternal germline contribution for preterm birth and identify the potential epigenetic inheritance of preterm birth susceptibility for the female child later in life. Although expanded clinical trials and preconception trials are required to optimize the potential epigenetic biomarkers, such epigenetic biomarkers may allow preventative medicine strategies to reduce the incidence of preterm birth.

Reproducibility, relevance and reliability as barriers to efficient and credible biomedical technology translation

Biomedical research accuracy and relevance for improving healthcare are increasingly identified as costly problems. Basic research data quality, reporting and methodology, and reproducibility are common factors implicated in this challenge. Preclinical models of disease and therapy, largely conducted in rodents, have known deficiencies in replicating most human conditions. Their translation to human results is acknowledged to be poor for decades. Clinical data quality and quantity is also recognized as deficient; gold standard randomized clinical trials are expensive. Few solid conclusions from clinical studies are replicable and many remain unpublished. The translational pathway from fundamental biomedical research through to innovative solutions handed to clinical practitioners is therefore highly inefficient and costly in terms of wasted resources, early claims from fundamental discoveries never witnessed in humans, and few new, improved solutions available clinically for myriad diseases. Improving this biomedical research strategy and resourcing for reliability, translational relevance, reproducibility and clinical impact requires careful analysis and consistent enforcement at both funding and peer review levels.

Significance of Cardiac Troponins as an Identification Tool in COVID-19 Patients Using Biosensors: An Update

Coronavirus disease 2019 (COVID-19) has rapidly developed as a global health emergency. Respiratory diseases are significant causes of morbidity and mortality in these patients with a spectrum of different diseases, from asymptomatic subclinical infection to the progression of severe pneumonia and subsequent acute respiratory distress syndrome. Individuals with cardiovascular disease are more likely to become infected with SARS-CoV-2 and develop severe symptoms. Hence, patients with underlying cardiovascular disease mortality rate are over three times. Furthermore, note that patients with a history of cardiovascular disease are more likely to have higher cardiac biomarkers, especially cardiac troponins, than infected patients, especially those with severe disease, making these patients more susceptible to cardiac damage caused by SARS-2-CoV. Biomarkers are important in decision-making to facilitate the efficient allocation of resources. Viral replication in the heart muscle can lead to a cascade of inflammatory processes that lead to fibrosis and, ultimately, cardiac necrosis. Elevated troponin may indicate damage to the heart muscle and may predict death. After the first Chinese analysis, increased cardiac troponin value was observed in a significant proportion of patients, suggesting that myocardial damage is a possible pathogenic mechanism leading to severe disease and death. However, the prognostic performance of troponin and whether its value is affected by different comorbidities present in COVID-19 patients are not known. This review aimed to assess the diagnostic value of troponin to offer insight into pathophysiological mechanisms and reported new assessment methods, including new biosensors for troponin in patients with COVID-19.

Lights and Shadows of Cerebrospinal Fluid Biomarkers in the Current Alzheimer's Disease Framework

BACKGROUND

The most significant biomarkers that are included in the Alzheimer's disease (AD) research framework are amyloid-β plaques deposition, p-tau, t-tau, and neurodegeneration.Although cerebrospinal fluid (CSF) biomarkers are included in the most recent AD research criteria, their use is increasing in the routine clinical practice and is applied also to the preclinical phases of AD, including mild cognitive impairment. The role of these biomarkers is still unclear concerning the preclinical stage of AD diagnosis, the CSF methodology, and the costs-benefits of the biomarkers' tests. The controversies regarding the use of biomarkers in the clinical practice are related to the concepts of analytical validity, clinical validity, and clinical utility and to the question of whether they are able to diagnose AD without the support of AD clinical phenotypes.

OBJECTIVE

The objective of the present work is to expose the strengths and weaknesses of the use of CSF biomarkers in the diagnosis of AD in a clinical context.

METHODS

We used PubMed as main source for articles published and the final reference list was generated on the basis of relevance to the topics covered in this work.

RESULTS

The use of CSF biomarkers for AD diagnosis is certainly important but its indication in routine clinical practice, especially for prodromal conditions, needs to be regulated and also contextualized considering the variety of possible clinical AD phenotypes.

CONCLUSION

We suggest that the diagnosis of AD should be understood both as clinical and pathological.

High-grade glioma therapy: adding flexibility in trial design to improve patient outcomes

INTRODUCTION

Outcomes for patients with high grade gliomas have changed little over the past thirty years. This realization prompted renewed efforts to increase flexibility in the design and conduct of clinical brain tumor trials.

AREAS COVERED

This manuscript reviews the development of clinical trial methods, challenges and considerations of flexible clinical trial designs, approaches to improve identification and testing of active agents for high grade gliomas, and evaluation of their delivery to the central nervous system.

EXPERT OPINION

Flexibility can be introduced in clinical trials in several ways. Flexible designs tout smaller sample sizes, adaptive modifications, fewer control arms, and inclusion of multiple arms in one study. Unfortunately, modifications in study designs cannot address two challenges that are largely responsible for the lack of progress in treating high grade gliomas: 1) the identification of active pharmaceutical agents and 2) the delivery of these agents to brain tumor tissue in therapeutic concentrations. To improve the outcomes of patients with high grade gliomas efforts must be focused on the pre-clinical screening of drugs for activity, the ability of these agents to achieve therapeutic concentrations in non-enhancing tumors, and a willingness to introduce novel compounds in minimally pre-treated patient populations.

Differentiating between UCTD and early-stage SLE: from definitions to clinical approach

Systemic lupus erythematosus (SLE) is an autoimmune disease with heterogeneous clinical manifestations that can potentially affect every organ and system. SLE is usually identified on the basis of clinical or serological manifestations; however, some individuals can present with signs and symptoms that are consistent with SLE but are not sufficient for a definite diagnosis. Disease in these individuals can either progress over time to definite SLE or remain stable, in which case their disease is often described as intermediate, possible or probable SLE. Alternatively, such individuals might have undifferentiated connective tissue disease (UCTD). Being able to differentiate between those with stable UCTD and those with SLE at an early stage is important to avoid irreversible target-organ damage from occurring. This Review provides insight into existing and evolving perceptions of the early stages of SLE, including clinical and mechanistic considerations, as well as potential paths towards early identification and intervention. Further research into the earliest phases of SLE will be important for the development of targeted diagnostic approaches and biomarkers for the identification of individuals with early disease who are likely to progress to definite SLE.