Biomarkers in pharmacology and drug discovery

Optimal Cut-Point Selection Methods Under Binary Classification When Subclasses Are Involved

In practice, we often encounter binary classification problems where both main classes consist of multiple subclasses. For example, in an ovarian cancer study where biomarkers were evaluated for their accuracy of distinguishing noncancer cases from cancer cases, the noncancer class consists of healthy subjects and benign cases, while the cancer class consists of subjects at both early and late stages. This article aims to provide a large number of optimal cut-point selection methods for such setting. Furthermore, we also study confidence interval estimation of the optimal cut-points. Simulation studies are carried out to explore the performance of the proposed cut-point selection methods as well as confidence interval estimation methods. A real ovarian cancer data set is analyzed using the proposed methods.

Highly Sensitive Wearable Sensor Based on (001)-Orientated TiO2 for Real-Time Electrochemical Detection of Dopamine, Tyrosine, and Paracetamol

The concentration of dopamine (DA) and tyrosine (Tyr) reflects the condition of patients with Parkinson's disease, whereas moderate paracetamol (PA) can help relieve their pain. Therefore, real-time measurements of these bioanalytes have important clinical implications for patients with Parkinson's disease. However, previous sensors suffer from either limited sensitivity or complex fabrication and integration processes. This work introduces a simple and cost-effective method to prepare high-quality, flexible titanium dioxide (TiO2) thin films with highly reactive (001)-facets. The as-fabricated TiO2 film supported by a carbon cloth electrode (i.e., TiO2-CC) allows excellent electrochemical specificity and sensitivity to DA (1.390 µA µM-1 cm-2), Tyr (0.126 µA µM-1 cm-2), and PA (0.0841 µA µM-1 cm-2). More importantly, accurate DA concentration in varied pH conditions can be obtained by decoupling them within a single differential pulse voltammetry measurement without additional sensing units. The TiO2-CC electrochemical sensor can be integrated into a smart diaper to detect the trace amount of DA or an integrated skin-interfaced patch with microfluidic sampling and wireless transmission units for real-time detection of the sweat Try and PA concentration. The wearable sensor based on TiO2-CC prepared by facile manufacturing methods holds great potential in the daily health monitoring and care of patients with neurological disorders.

Experimental Medicine Approaches in Early-Phase CNS Drug Development

Traditionally, Phase 1 clinical trials were largely conducted in healthy normal volunteers and focused on collection of safety, tolerability, and pharmacokinetic data. However, in the CNS therapeutic area, with more drugs failing in later phase development, Phase 1 trials have undergone an evolution that includes incorporation of novel approaches involving novel study designs, inclusion of biomarkers, and early inclusion of patients to improve the pharmacologic understanding of novel CNS-active compounds early in clinical development with the hope of improving success in later phase pivotal trials. In this chapter, the authors will discuss the changing landscape of Phase 1 clinical trials in CNS, including novel trial methodology, inclusion of pharmacodynamic biomarkers, and experimental medicine approaches to inform early decision-making in clinical development.

Insight into Early Diagnosis of Multiple Sclerosis by Targeting Prognostic Biomarkers

Multiple sclerosis (MS) is a central nervous system (CNS) immune-mediated disease that mainly strikes young adults and leaves them disabled. MS is an autoimmune illness that causes the immune system to attack the brain and spinal cord. The myelin sheaths, which insulate the nerve fibers, are harmed by our own immune cells, and this interferes with brain signal transmission. Numbness, tingling, mood swings, memory problems, exhaustion, agony, vision problems, and/or paralysis are just a few of the symptoms. Despite technological advancements and significant research efforts in recent years, diagnosing MS can still be difficult. Each patient's MS is distinct due to a heterogeneous and complex pathophysiology with diverse types of disease courses. There is a pressing need to identify markers that will allow for more rapid and accurate diagnosis and prognosis assessments to choose the best course of treatment for each MS patient. The cerebrospinal fluid (CSF) is an excellent source of particular indicators associated with MS pathology. CSF contains molecules that represent pathological processes such as inflammation, cellular damage, and loss of blood-brain barrier integrity. Oligoclonal bands, neurofilaments, MS-specific miRNA, lncRNA, IgG-index, and anti-aquaporin 4 antibodies are all clinically utilised indicators for CSF in MS diagnosis. In recent years, a slew of new possible biomarkers have been presented. In this review, we look at what we know about CSF molecular markers and how they can aid in the diagnosis and differentiation of different MS forms and treatment options, and monitoring and predicting disease progression, therapy response, and consequences during such opportunistic infections.

Neuroprotection in neurodegenerations of the brain and eye: Lessons from the past and directions for the future

Background

Neurological and ophthalmological neurodegenerative diseases in large part share underlying biology and pathophysiology. Despite extensive preclinical research on neuroprotection that in many cases bridges and unifies both fields, only a handful of neuroprotective therapies have succeeded clinically in either.

Main body

Understanding the commonalities among brain and neuroretinal neurodegenerations can help develop innovative ways to improve translational success in neuroprotection research and emerging therapies. To do this, analysis of why translational research in neuroprotection fails necessitates addressing roadblocks at basic research and clinical trial levels. These include optimizing translational approaches with respect to biomarkers, therapeutic targets, treatments, animal models, and regulatory pathways.

Conclusion

The common features of neurological and ophthalmological neurodegenerations are useful for outlining a path forward that should increase the likelihood of translational success in neuroprotective therapies.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

4210 Da and 1866 Da polypeptides as potential biomarkers of liver disease progression in hepatitis B virus patients

HBV infection is recognized as a serious global health problem, and hepatitis B virus infection is a complicated chronic disease leading to liver cirrhosis (LC) and hepatocellular carcinoma (HCC). New biochemical serum markers could be used to advance the diagnosis and prognosis of HBV-associated liver diseases during the progression of chronic hepatitis B into cirrhosis and HCC. We determined whether the 4210 Da and 1866 Da polypeptides are serum metabolite biomarkers of hepatopathy with hepatitis B virus. A total of 570 subjects were divided into five groups: healthy controls, those with natural clearance, and patients with CHB, LC, and HCC. The 1866 Da and 4210 Da polypeptides were measured by Clin-ToF II MALDI-TOF-MS. There were significant differences in 4210 Da and 1866 Da levels among the five groups (P < 0.001). For the differential diagnosis of CHB from normal liver, the areas under the receiver operating characteristic (ROC) curve of 4210 Da and 1866 Da and their combination via logistic regression were 0.961, 0.849 and 0.967. For the differential diagnosis of LC from CHB, the areas under the ROC curve were 0.695, 0.841 and 0.826. For the differential diagnosis of HCC from CHB, the areas under the ROC curve were 0.744, 0.710 and 0.761, respectively. For the differential diagnosis of HCC from LC, the areas under the ROC curve of 4210 Da and 1866 Da were 0.580 and 0.654. The positive rate of 1866 Da was 45.5% and 69.0% in AFP-negative HCC patients and that of 4210 Da was 60.6% 58.6% in AFP-negative HCC patients of the study HCC vs. CHB and HCC vs. LC. The 4210 Da and 1866 Da polypeptide levels were positively correlated with HBV DNA levels (P < 0.001, r = 0.269; P < 0.001, r = 0.285). The 4210 Da and 1866 Da polypeptides had good diagnostic value for the occurrence and progression of HBV-related chronic hepatitis, liver cirrhosis and hepatocellular carcinoma and could serve to accurately guide treatment management and predict clinical outcomes.

Relationship between Invasive and Non-Invasive Hemodynamic Measures in Experimental Pulmonary Hypertension

Introduction:

Animal models have been used to understand the pathophysiology of pulmonary hypertension, to describe the mechanisms of action and to evaluate promising active ingredients. The monocrotaline-induced pulmonary hypertension model is the most used animal model. In this model, invasive and non-invasive hemodynamic variables that resemble human measurements have been used. Aim: To define if non-invasive variables can predict hemodynamic measures in the monocrotaline-induced pulmonary hypertension model.

Materials and Methods:

Twenty 6-week old male Wistar rats weighing between 250-300g from the bioterium of the Universidad del Valle (Cali - Colombia) were used in order to establish that the relationships between invasive and non-invasive variables are sustained in different conditions (healthy, hypertrophy and treated). The animals were organized into three groups, a control group who was given 0.9% saline solution subcutaneously (sc), a group with pulmonary hypertension induced with a single subcutaneous dose of Monocrotaline 30 mg/kg, and a group with pulmonary hypertension with 30 mg/kg of monocrotaline treated with Sildenafil. Right ventricle ejection fraction, heart rate, right ventricle systolic pressure and the extent of hypertrophy were measured. The functional relation between any two variables was evaluated by the Pearson correlation coefficient.

Results:

It was found that all correlations were statistically significant (p <0.01). The strongest correlation was the inverse one between the RVEF and the Fulton index (r = -0.82). The Fulton index also had a strong correlation with the RVSP (r = 0.79). The Pearson correlation coefficient between the RVEF and the RVSP was -0.81, meaning that the higher the systolic pressure in the right ventricle, the lower the ejection fraction value. Heart rate was significantly correlated to the other three variables studied, although with relatively low correlation.

Conclusion:

The correlations obtained in this study indicate that the parameters evaluated in the research related to experimental pulmonary hypertension correlate adequately and that the measurements that are currently made are adequate and consistent with each other, that is, they have good predictive capacity.

Tracking the potential involvement of metabolic disease in Alzheimer's disease-Biomarkers and beyond

There is a vast literature linking systemic metabolic conditions to dementia due to Alzheimer's disease (AD). Advances in in vivo measurements of AD neuropathology using brain imaging, cerebrospinal fluid (CSF), and/or blood biomarkers have led to research in AD that uses in vivo biomarkers as outcomes, focusing primarily on amyloid, tau, and neurodegeneration as constructs. Studies of Type 2 Diabetes Mellitus (T2DM) and AD biomarkers seem to show that T2DM is not related to amyloid deposition, but is related to neurodegeneration and tau deposition. There is a dearth of studies examining adiposity, insulin resistance, and metabolic syndrome in relation to AD biomarkers and the associations in these studies are inconsistent. Metabolomics studies have reported associations of unsaturated fatty acids with AD neuropathology at autopsy, and sphingolipids and glycerophospholipids in relation to neurodegeneration and amyloid and tau. There are other neurodegenerative diseases, such as Lewy body disease that may overlap with AD, and specific biomarkers for these pathologies are being developed and should be integrated into AD biomarker research. More longitudinal studies are needed with concurrent assessment of metabolic factors and AD biomarkers in order to improve the opportunity to assess causality. Ideally, AD biomarkers should be integrated into clinical trials of interventions that affect metabolic factors. Advances in blood-based AD biomarkers, which are less costly and invasive compared with CSF and brain imaging biomarkers, could facilitate widespread implementation of AD biomarkers in studies examining the metabolic contribution to AD.

Mass spectrometry: A platform for biomarker discovery and validation for Alzheimer's and Parkinson's diseases

Accurate, reliable, and objective biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), and related age-associated neurodegenerative disorders are urgently needed to assist in both diagnosis, particularly at early stages, and monitoring of disease progression. Technological advancements in protein detection platforms over the last few decades have resulted in a plethora of reported molecular biomarker candidates for both AD and PD; however, very few of these candidates are developed beyond the discovery phase of the biomarker development pipeline, a reflection of the current bottleneck within the field. In this review, the expanded use of selected reaction monitoring (SRM) targeted mass spectrometry will be discussed in detail as a platform for systematic verification of large panels of protein biomarker candidates prior to costly validation testing. We also advocate for the coupling of discovery-based proteomics with modern targeted MS-based approaches (e.g., SRM) within a single study in future workflows to expedite biomarker development and validation for AD and PD. It is our hope that improving the efficiency within the biomarker development process by use of an SRM pipeline may ultimately hasten the development of biomarkers that both decrease misdiagnosis of AD and PD and ultimately lead to detection at early stages of disease and objective assessment of disease progression. This article is part of the special issue "Proteomics".

Clinical utility of a molecular signature in inflammatory demyelinating disease

Objective

We sought to develop molecular biomarkers of intrathecal inflammation to assist neurologists in identifying patients most likely to benefit from a range of immune therapies.

Methods

We used Luminex technology and index determination to search for an inflammatory activity molecular signature (IAMS) in patients with inflammatory demyelinating disease (IDD), other neuroinflammatory diagnoses, and noninflammatory controls. We then followed the clinical characteristics of these patients to find how the presence of the signature might assist in diagnosis and prognosis.

Results

A CSF molecular signature consisting of elevated CXCL13, elevated immunoglobulins, normal albumin CSF/serum ratio (Q_albumin), and minimal elevation of cytokines other than CXCL13 provided diagnostic and prognostic value; absence of the signature in IDD predicted lack of subsequent inflammatory events. The signature outperformed oligoclonal bands, which were frequently false positive for active neuroinflammation.

Conclusions

A CSF IAMS may prove useful in the diagnosis and management of patients with IDD and other neuroinflammatory syndromes.

Classification of evidence

This study provides Class IV evidence that a CSF IAMS identifies patients with IDD.

Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.

Did the reporting of prognostic studies of tumour markers improve since the introduction of REMARK guideline? A comparison of reporting in published articles

Although biomarkers are perceived as highly relevant for future clinical practice, few biomarkers reach clinical utility for several reasons. Among them, poor reporting of studies is one of the major problems. To aid improvement, reporting guidelines like REMARK for tumour marker prognostic (TMP) studies were introduced several years ago. The aims of this project were to assess whether reporting quality of TMP-studies improved in comparison to a previously conducted study assessing reporting quality of TMP-studies (PRE-study) and to assess whether articles citing REMARK (citing group) are better reported, in comparison to articles not citing REMARK (not-citing group). For the POST-study, recent articles citing and not citing REMARK (53 each) were identified in selected journals through systematic literature search and evaluated in same way as in the PRE-study. Ten of the 20 items of the REMARK checklist were evaluated and used to define an overall score of reporting quality. The observed overall scores were 53.4% (range: 10%-90%) for the PRE-study, 57.7% (range: 20%-100%) for the not-citing group and 58.1% (range: 30%-100%) for the citing group of the POST-study. While there is no difference between the two groups of the POST-study, the POST-study shows a slight but not relevant improvement in reporting relative to the PRE-study. Not all the articles of the citing group, cited REMARK appropriately. Irrespective of whether REMARK was cited, the overall score was slightly higher for articles published in journals requesting adherence to REMARK than for those published in journals not requesting it: 59.9% versus 51.9%, respectively. Several years after the introduction of REMARK, many key items of TMP-studies are still very poorly reported. A combined effort is needed from authors, editors, reviewers and methodologists to improve the current situation. Good reporting is not just nice to have but is essential for any research to be useful.

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

METHODS

We used standard searches to find publications using ADNI data.

RESULTS

(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.

DISCUSSION

Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

Advantages of Array-Based Technologies for Pre-Emptive Pharmacogenomics Testing

As recognised by the National Institutes of Health (NIH) Precision Medicine Initiative (PMI), microarray technology currently provides a rapid, inexpensive means of identifying large numbers of known genomic variants or gene transcripts in experimental and clinical settings. However new generation sequencing techniques are now being introduced in many clinical genetic contexts, particularly where novel mutations are involved. While these methods can be valuable for screening a restricted set of genes for known or novel mutations, implementation of whole genome sequencing in clinical practice continues to present challenges. Even very accurate high-throughput methods with small error rates can generate large numbers of false negative or false positive errors due to the high numbers of simultaneous readings. Additional validation is likely to be required for safe use of any such methods in clinical settings. Custom-designed arrays can offer advantages for screening for common, known mutations and, in this context, may currently be better suited for accredited, quality-controlled clinical genetic screening services, as illustrated by their successful application in several large-scale pre-emptive pharmacogenomics programs now underway. Excessive, inappropriate use of next-generation sequencing may waste scarce research funds and other resources. Microarrays presently remain the technology of choice in applications that require fast, cost-effective genome-wide screening of variants of known importance, particularly for large sample sizes. This commentary considers some of the applications where microarrays continue to offer advantages over next-generation sequencing technologies.

Comparison of the response using ICR mice derived from three different sources to ethanol/hydrochloric acid-induced gastric injury

Animal models for gastric ulcers produced by physical, pharmacological and surgical methods have been widely employed to evaluate therapeutic drugs and investigate the mechanism of action of this disease. ICR mice were selected to produce this model, even though several mice and rats have been widely used in studies of gastric ulcers. To compare the responses of ICR mice obtained from three different sources to gastric ulcer inducers, alterations in gastric injury, histopathological structure, and inflammation were measured in Korl:ICR (Korea NIFDS source), A:ICR (USA source) and B:ICR (Japan source) treated with three concentrations of ethanol (EtOH) (50, 70, and 90%) in 150 mM hydrochloric acid (HCl) solution. Firstly, the stomach lesion index gradually increased as the EtOH concentration increased in three ICR groups. Moreover, a significant increase in the level of mucosal injury, edema and the number of inflammatory cells was similarly detected in the EtOH/HCl treated group compared with the vehicle treated group in three ICR groups. Furthermore, the number of infiltrated mast cells and IL-1β expression were very similar in the ICR group derived from three different sources, although some differences in IL-1β expression were detected. Especially, the level of IL-1β mRNA in 50 and 90EtOH/HCl treated group was higher in Korl:ICR and A:ICR than B:ICR. Overall, the results of this study suggest that Korl:ICR, A:ICR and B:ICR derived from different sources have an overall similar response to gastric ulcer induced by EtOH/HCl administration, although there were some differences in the magnitude of their responses.

Recent advances in quantitative high throughput and high content data analysis

INTRODUCTION

High throughput screening has become a basic technique with which to explore biological systems. Advances in technology, including increased screening capacity, as well as methods that generate multiparametric readouts, are driving the need for improvements in the analysis of data sets derived from such screens.

AREAS COVERED

This article covers the recent advances in the analysis of high throughput screening data sets from arrayed samples, as well as the recent advances in the analysis of cell-by-cell data sets derived from image or flow cytometry application. Screening multiple genomic reagents targeting any given gene creates additional challenges and so methods that prioritize individual gene targets have been developed. The article reviews many of the open source data analysis methods that are now available and which are helping to define a consensus on the best practices to use when analyzing screening data.

EXPERT OPINION

As data sets become larger, and more complex, the need for easily accessible data analysis tools will continue to grow. The presentation of such complex data sets, to facilitate quality control monitoring and interpretation of the results will require the development of novel visualizations. In addition, advanced statistical and machine learning algorithms that can help identify patterns, correlations and the best features in massive data sets will be required. The ease of use for these tools will be important, as they will need to be used iteratively by laboratory scientists to improve the outcomes of complex analyses.

Regulatory Forum Opinion Piece*: Veterinary Pathologists in Translational Pharmacology and Biomarker Integration in Drug Discovery and Development

This article highlights emerging roles for veterinary pathologists outside of traditional functions and in line with the translational research (TR) approach. Veterinary pathologists offer unique and valuable expertise toward addressing particular TR and associated translational pharmacology questions, identifying gaps and risks in biomarker and pathology strategies, and advancing TR team decision making. Veterinary pathologists' attributes that are integral to the TR approach include (i) well-developed understanding of comparative physiology, pathology, and disease; (ii) extensive experience in interpretation and integration of complex data sets on whole-body responses and utilizing this for deciphering pathogenesis and translating events between laboratory species and man; (iii) proficiency in recognizing differences in disease end points among individuals, animal species and strains, and assessing correlations between these differences and other investigative (including biomarker) findings; and (iv) strong background in a wide spectrum of research technologies that can address pathomechanistic questions and biomarker needs. Some of the more evident roles in which veterinary pathologists can offer their greatest contributions to address questions and strategies of TR and biomarker integration will be emphasized.

The preclinical data forum network: A new ECNP initiative to improve data quality and robustness for (preclinical) neuroscience

Current limitations impeding on data reproducibility are often poor statistical design, underpowered studies, lack of robust data, lack of methodological detail, biased reporting and lack of open data sharing, coupled with wrong research incentives. To improve data reproducibility, robustness and quality for brain disease research, a Preclinical Data Forum Network was formed under the umbrella of the European College of Neuropsychopharmacology (ECNP). The goal of this network, members of which met for the first time in October 2014, is to establish a forum to collaborate in precompetitive space, to exchange and develop best practices, and to bring together the members from academia, pharmaceutical industry, publishers, journal editors, funding organizations, public/private partnerships and non-profit advocacy organizations. To address the most pertinent issues identified by the Network, it was decided to establish a data sharing platform that allows open exchange of information in the area of preclinical neuroscience and to develop an educational scientific program. It is also planned to reach out to other organizations to align initiatives to enhance efficiency, and to initiate activities to improve the clinical relevance of preclinical data. Those Network activities should contribute to scientific rigor and lead to robust and relevant translational data. Here we provide a synopsis of the proceedings from the inaugural meeting.

A Novel Method for the Synthesis of (99m)Tc-Ofloxacin Kits Using D-Penicillamine as Coligand and Their Application as Infection Imaging Agent

The employment of radiopharmaceuticals is increasing nowadays for infection imaging and early execution of patients having infectious or inflammatory complaints. The main aim of this study was to discover a novel method for the labeling of ofloxacin with ^99mTc, optimization of labelling conditions to get higher percent yield, to assess kits radiochemical purity, in vitro stability, partition coefficient, protein binding, and intracellular accumulation in Pseudomonas aeruginosa, Salmonella typhi, and Escherichia coli in infected rabbits. Maximum labeling efficiency was achieved when 1.5 mg ofloxacin was labeled with 10–20 mCi sodium pertechnetate in the presence of 3 mg D-penicillamine, 75 μg SnCl₂. In vitro binding and biodistribution in Pseudomonas aeruginosa, Salmonella typhi, and Escherichia coli showed good results. This new complex is efficient for the imaging of infections caused by Gram-positive and Gram-negative bacteria.

Parafilm-assisted microdissection: a sampling method for mass spectrometry-based identification of differentially expressed prostate cancer protein biomarkers

Profiling of tumors using a combined MALDI MS imaging and parafilm-assisted microdissection (PAM) workflow identified candidate biomarkers of prostate cancer.

A comparative analysis on the binding characteristics of various mammalian albumins towards a multitherapeutic agent, pinostrobin

The interaction of pinostrobin (PS), a multitherapeutic agent with serum albumins of various mammalian species namely, goat, bovine, human, porcine, rabbit, sheep and dog was investigated using fluorescence quench titration and competitive drug displacement experiments. Analysis of the intrinsic fluorescence quenching data revealed values of the association constant, K_a in the range of 1.49 – 6.12 × 10⁴ M⁻¹, with 1:1 binding stoichiometry. Based on the PS–albumin binding characteristics, these albumins were grouped into two classes. Ligand displacement studies using warfarin as the site I marker ligand correlated well with the binding data. Albumins from goat and bovine were found to be closely similar to human albumin on the basis of PS binding characteristics.

Biomarkers in Alzheimer's disease analysis by mass spectrometry-based proteomics

Alzheimer’s disease (AD) is a common chronic and destructive disease. The early diagnosis of AD is difficult, thus the need for clinically applicable biomarkers development is growing rapidly. There are many methods to biomarker discovery and identification. In this review, we aim to summarize Mass spectrometry (MS)-based proteomics studies on AD and discuss thoroughly the methods to identify candidate biomarkers in cerebrospinal fluid (CSF) and blood. This review will also discuss the potential research areas on biomarkers.

A knowledge-driven approach to extract disease-related biomarkers from the literature

The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that a knowledge-driven text mining approach can exploit a large literature database to extract a dataset of biomarkers related to diseases covering all therapeutic areas. Our methodology takes advantage of the annotation of MEDLINE publications pertaining to biomarkers with MeSH terms, narrowing the search to specific publications and, therefore, minimizing the false positive ratio. It is based on a dictionary-based named entity recognition system and a relation extraction module. The application of this methodology resulted in the identification of 131,012 disease-biomarker associations between 2,803 genes and 2,751 diseases, and represents a valuable knowledge base for those interested in disease-related biomarkers. Additionally, we present a bibliometric analysis of the journals reporting biomarker related information during the last 40 years.

The end of Alzheimer's disease--from biochemical pharmacology to ecopsychosociology: a personal perspective

The future of the Alzheimer's disease (AD) field involves a more complete understanding not only the state of current scientific approaches, but also the linguistic and cultural context of preclinical and clinical research and policy activities. The challenges surrounding dementia are large and growing but are only part of broader social and health concerns. In this latter context, the current state of research in the AD area is reviewed together with necessary priorities in moving forward. Creating a more optimistic future will depend less on genetic and reductionist approaches and more on environmental and intergenerative approaches that will aid in recalibrating the study of AD from an almost exclusive focus on biochemical, molecular and genetic aspects to better encompass "real world" ecological and psychosocial models of health.

Replicated, replicable and relevant-target engagement and pharmacological experimentation in the 21st century

A pharmacological experiment is typically conducted to: i) test or expand a hypothesis regarding the potential role of a target in the mechanism(s) underlying a disease state using an existing drug or tool compound in normal and/or diseased tissue or animals; or ii) characterize and optimize a new chemical entity (NCE) targeted to modulate a specific disease-associated target to restore homeostasis as a potential drug candidate. Hypothesis testing necessitates an intellectually rigorous, null hypothesis approach that is distinct from a high throughput fishing expedition in search of a hypothesis. In conducting an experiment, the protocol should be transparently defined along with its powering, design, appropriate statistical analysis and consideration of the anticipated outcome (s) before it is initiated. Compound-target interactions often involve the direct study of phenotype(s) unique to the target at the cell, tissue or animal/human level. However, in vivo studies are often compromised by a lack of sufficient information on the compound pharmacokinetics necessary to ensure target engagement and also by the context-free analysis of ubiquitous cellular signaling pathways downstream from the target. The use of single tool compounds/drugs at one concentration in engineered cell lines frequently results in reductionistic data that have no physiologically relevance. This overview, focused on trends in the peer-reviewed literature, discusses the execution and reporting of experiments and the criteria recommended for the physiologically-relevant assessment of target engagement to identify viable new drug targets and facilitate the advancement of translational studies.

HLA-B*40 allele plays a role in the development of acute leukemia in Mexican population: a case-control study

Among oncohematological diseases, acute lymphoid leukemia (ALL) and acute myeloid leukemia (AML) are characterized by the uncontrolled production and accumulation of blasts that can lead to death. Although the physiopathology of these diseases is multifactorial, a genetic factor seems to be at play. Several studies worldwide have shown association of ALL and AML with several alleles of the major histocompatibility complex (MHC). Objective. To determine gene frequencies of HLA-B alleles in Mexicans (individuals with Native American genetic background admixed with European descent) with ALL and AML. Methods. We compared the HLA-B alleles in 213 patients with ALL and 85 patients with AML to those present in 731 umbilical cord blood (UCB) samples as a control group; this was done by means of the PCR-SSP technique. Results. We found an increased frequency of the HLA-B*40 allele in ALL patients as compared to the control group (14.5% versus 9.84%, P = 0.003, OR = 1.67); this was particularly evident in a subgroup of young (less than 18 years old) ALL patients (P = 0.002, OR = 1.76); likewise, a decreased frequency of HLA-B*40 allele in AML patients was observed as compared to the control group (4.70% versus 9.84%, P = 0.02, OR = 0.42). Conclusions. These results might suggest opposing effects of the HLA-B*40 in the genetic susceptibility to develop ALL or AML and offer the possibility to study further the molecular mechanisms of cell differentiation within the bone marrow lineage.

Translational paradigms in pharmacology and drug discovery

The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization.

The fall and rise of pharmacology--(re-)defining the discipline?

Pharmacology is an integrative discipline that originated from activities, now nearly 7000 years old, to identify therapeutics from natural product sources. Research in the 19th Century that focused on the Law of Mass Action (LMA) demonstrated that compound effects were dose-/concentration-dependent eventually leading to the receptor concept, now a century old, that remains the key to understanding disease causality and drug action. As pharmacology evolved in the 20th Century through successive biochemical, molecular and genomic eras, the precision in understanding receptor function at the molecular level increased and while providing important insights, led to an overtly reductionistic emphasis. This resulted in the generation of data lacking physiological context that ignored the LMA and was not integrated at the tissue/whole organism level. As reductionism became a primary focus in biomedical research, it led to the fall of pharmacology. However, concerns regarding the disconnect between basic research efforts and the approval of new drugs to treat 21st Century disease tsunamis, e.g., neurodegeneration, metabolic syndrome, etc. has led to the reemergence of pharmacology, its rise, often in the semantic guise of systems biology. Against a background of limited training in pharmacology, this has resulted in issues in experimental replication with a bioinformatics emphasis that often has a limited relationship to reality. The integration of newer technologies within a pharmacological context where research is driven by testable hypotheses rather than technology, together with renewed efforts in teaching pharmacology, is anticipated to improve the focus and relevance of biomedical research and lead to novel therapeutics that will contain health care costs.

Animal models of human disease: challenges in enabling translation

Animal models have historically played a critical role in the exploration and characterization of disease pathophysiology, target identification, and in the in vivo evaluation of novel therapeutic agents and treatments. In the wake of numerous clinical trial failures of new chemical entities (NCEs) with promising preclinical profiles, animal models in all therapeutic areas have been increasingly criticized for their limited ability to predict NCE efficacy, safety and toxicity in humans. The present review discusses some of the challenges associated with the evaluation and predictive validation of animal models, as well as methodological flaws in both preclinical and clinical study designs that may contribute to the current translational failure rate. The testing of disease hypotheses and NCEs in multiple disease models necessitates evaluation of pharmacokinetic/pharmacodynamic (PK/PD) relationships and the earlier development of validated disease-associated biomarkers to assess target engagement and NCE efficacy. Additionally, the transparent integration of efficacy and safety data derived from animal models into the hierarchical data sets generated preclinically is essential in order to derive a level of predictive utility consistent with the degree of validation and inherent limitations of current animal models. The predictive value of an animal model is thus only as useful as the context in which it is interpreted. Finally, rather than dismissing animal models as not very useful in the drug discovery process, additional resources, like those successfully used in the preclinical PK assessment used for the selection of lead NCEs, must be focused on improving existing and developing new animal models.