Translational biomarkers: from preclinical to clinical a report of 2009 AAPS/ACCP Biomarker Workshop

The effect of rivaroxaban on biomarkers in blood and plasma: a review of preclinical and clinical evidence

Rivaroxaban is a direct, oral factor Xa inhibitor that is used for the prevention and treatment of various thromboembolic disorders. Several preclinical and clinical studies have utilized specific molecules as biomarkers to investigate the potential role of rivaroxaban beyond its anticoagulant activity and across a range of biological processes. The aim of this review is to summarize the existing evidence regarding the use of blood-based biomarkers to characterize the effects of rivaroxaban on coagulation and other pathways, including platelet activation, inflammation and endothelial effects. After a literature search using PubMed, almost 100 preclinical and clinical studies were identified that investigated the effects of rivaroxaban using molecular biomarkers. In agreement with the preclinical data, clinical studies reported a trend for reduction in the blood concentrations of D-dimers, thrombin-antithrombin complex and prothrombin fragment 1 + 2 following treatment with rivaroxaban in both healthy individuals and those with various chronic conditions. Preclinical and also some clinical studies have also reported a potential impact of rivaroxaban on the concentrations of platelet activation biomarkers (von Willebrand factor, P-selectin and thrombomodulin), endothelial activation biomarkers (matrix metalloproteinase-9, intercellular adhesion molecule-1 and vascular cell adhesion molecule-1) and inflammation biomarkers (interleukin-6, tumor necrosis factor-α and monocyte chemoattractant protein-1). Based on the results of biomarker studies, molecular biomarkers can be used in addition to traditional coagulation assays to increase the understanding of the anticoagulation effects of rivaroxaban. Moreover, there is preliminary evidence to suggest that rivaroxaban may have an impact on the biological pathways of platelet activation, endothelial activation and inflammation; however, owing to paucity of clinical data to investigate the trends reported in preclinical studies, further investigation is required to clarify these observations.

Collagen Biomarkers Quantify Fibroblast Activity In Vitro and Predict Survival in Patients with Pancreatic Ductal Adenocarcinoma

The use of novel tools to understand tumour-fibrosis in pancreatic ductal adenocarcinoma (PDAC) and novel anti-fibrotic treatments are highly needed. We established a pseudo-3D in vitro model including humane pancreatic fibroblasts (PFs) and pancreatic cancer-associated fibroblasts (CAFs) in combination with clinical collagen biomarkers, as a translational anti-fibrotic drug screening tool. Furthermore, we investigated the prognostic potential of serum collagen biomarkers in 810 patients with PDAC. PFs and CAFs were cultured in Ficoll-media. Cells were treated w/wo TGF-ß1 and the anti-fibrotic compound ALK5i. Biomarkers measuring the formation of type III (PRO-C3) and VI (PRO-C6) collagens were measured by ELISA in supernatant at days 3, 6, 9, and 12. PRO-C3 and PRO-C6, and their association with overall survival (OS), were evaluated in serum with PDAC (n = 810). PRO-C3 and PRO-C6 were upregulated in CAFs compared to PFs (p < 0.0001.). TGF-ß1 increased PRO-C3 in both PFs and CAFs (p < 0.0001). The anti-fibrotic compound ALK5i inhibited both PRO-C3 and PRO-C6 (p < 0.0001). High serum levels of PRO-C3 and PRO-C6 in patients with PDAC were associated with short OS (PRO-C3: HR = 1.48, 95%CI: 1.29-1.71, p < 0.0001 and PRO-C6: HR = 1.31, 95%CI: 1.14-1.50, p = 0.0002). PRO-C3 and PRO-C6 have the potential to be used both pre-clinically and clinically as a measure of tumor fibrosis and CAF activity.

Effectively utilizing the Sponsor Contract Research Organization interaction for successful implementation of critical flow cytometry in the clinic

As the number of therapeutic modalities expand, and the field of scientific research evolves toward finding treatment solutions for complex and rare disease, an ability to demonstrate efficacy through biomarker end points in clinical development studies is becoming increasingly important. Implementing flow cytometry in a clinical setting is challenging and many sponsor organizations take a hybrid approach, developing complex analytical methods internally before identifying and forming partnerships with contract research organizations to conduct the formal analytical method validation and sample bioanalysis. Ensuring that these interactions are effective is critical to the delivery of high-quality, impactful clinical data. This paper provides a review of the recommendations, challenges and solutions for the implementation of decision-making flow cytometry end points effectively utilizing the Sponsor Contract Research Organization interaction.

Robust and Comprehensive Targeted Metabolomics Method for Quantification of 50 Different Primary, Secondary, and Sulfated Bile Acids in Multiple Biological Species (Human, Monkey, Rabbit, Dog, and Rat) and Matrices (Plasma and Urine) Using Liquid Chromatography High Resolution Mass Spectrometry (LC-HRMS) Analysis

Bile acids (BAs) are biomolecules synthesized in the liver from cholesterol and are constituents of bile. The in-vivo BA pool includes more than 50 known diverse BAs which are unconjugated, amino acid conjugated, sulfated, and glucuronidated metabolites. Hemostasis of bile acids is known to be highly regulated and an interplay between liver metabolism, gut microbiome function, intestinal absorption, and enterohepatic recirculation. Interruption of BA homeostasis has been attributed to several metabolic diseases and drug induced liver injury (DILI), and their use as potential biomarkers is increasingly becoming important. Speciated quantitative and comprehensive profiling of BAs in various biomatrices from humans and preclinical animal species are important to understand their significance and biological function. Consequently, a versatile one single bioanalytical method for BAs is required to accommodate quantitation in a broad range of biomatrices from human and preclinical animal species. Here we report a versatile, comprehensive, and high throughput liquid chromatography-high resolution mass spectrometry (LC-HRMS) targeted metabolomics method for quantitative analysis of 50 different BAs in multiple matrices including human serum, plasma, and urine and plasma and urine of preclinical animal species (rat, rabbit, dog, and monkey). The method has been sufficiently qualified for accuracy, precision, robustness, and ruggedness and addresses the issue of nonspecific binding of bile acids to plastic for urine samples. Application of this method includes comparison for BA analysis between matched plasma and serum samples, human and animal species differences in BA pools, data analysis, and visualization of complex BA data using BA indices or ratios to understand BA biology, metabolism, and transport.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Healthcare Data for Achieving a More Personalized Treatment of Chronic Kidney Disease

The current concept of healthcare incites a more personalized treatment of diseases. To this aim, biomarkers are needed to improve decision-making facing chronic kidney disease (CKD) patients. Prognostic markers provided by real-world (observational) evidence are proposed in this Special Issue entitled "Biomarkers in Chronic Kidney Disease", with the intention to identify high-risk patients. These markers do not target measurable parameters in patients but clinical endpoints that may be in turn transformed to benefits under the effect of future interventions.

Cholesterol-Lowering Treatment in Chronic Kidney Disease: Multistage Pairwise and Network Meta-Analyses

Pairwise and network meta-analyses on the relationship between the efficacy of the use of statins with or without ezetimibe and reductions in low-density lipoprotein cholesterol (LDLc) and C-reactive protein (CRP) in patients with chronic kidney disease (CKD) are presented. In the pairwise meta-analysis, statins with or without ezetimibe were shown to be efficacious in reducing major adverse cardiovascular events (MACE) in patients with CKD and an estimated glomerular filtration rate (eGFR) of less than 60 ml/min/1.73 m², in the context of both primary prevention [odds ratio (OR)/95% confidence interval (95% CI)/I²/number of studies (n): 0.50/0.40–0.64/0%/6] and primary/secondary prevention (0.66/0.57–0.76/57%/18). However, in the Bayesian network meta-analysis, compared to the placebo, only atorvastatin 80 mg daily and atorvastatin and rosuvastatin at doses equivalent to simvastatin 20 mg daily reduced the odds of MACEs in this patient population. The network meta-analysis for LDLc and CRP treatment objectives also showed that, regardless of eGFR and excluding dialysis patients, the number of MACEs decreased in patients with CKD, with reductions in both LDLc and CRP of less than 50% (surface under the cumulative ranking (SUCRA)/heterogeneity (vague)/n: 0.77/0.14/3). The evaluation of the benefits of drugs may lead to individualized therapy for CKD patients: Cholesterol-lowering treatment for CKD patients with high levels of both LDLc and CRP is suggested.

Serum miRNA-based distinct clusters define three groups of breast cancer patients with different clinicopathological and immune characteristics

Breast cancer (BCa) is a heterogeneous disease with different histological, prognostic and clinical aspects. Therefore, the need for identification of novel biomarkers for diagnosis, prognosis and monitoring of disease, as well as treatment outcome prediction remains at the forefront of research. The search for circulating elements, obtainable by simple peripheral blood withdrawal, which may serve as possible biomarkers, constitutes still a challenge. In the present study, we have evaluated the expression of 6 circulating miRNAs, (miR-16, miR-21, miR-23α, miR-146α, miR-155 and miR-181α), in operable BCa patients, with non-metastatic, invasive ductal carcinoma, not receiving neoadjuvant chemotherapy. These miRNAs, known to be involved in both tumor cell progression and immune pathways regulation, were analyzed in relation to circulating cytokines, tumor immune-cell infiltration and established prognostic clinicopathological characteristics. We have identified three different clusters, with overall low (C1), moderate (C2) or high (C3) expression levels of these six circulating miRNAs, which define three distinct groups of non-metastatic BCa patients characterized by different clinicopathological and immune-related characteristics, with possibly different clinical outcomes. Our data provide the proof-of-principle to support the notion that, up- or down-regulation of the same circulating miRNA may reflect different prognosis in BCa. Nonetheless, the prognostic and/or predictive potential of these three "signatures" needs to be further evaluated in larger cohorts of BCa patients with an, at least, 5-year clinical follow-up.

The number of FoxP3 regulatory T cells in the circulation may be a predictive biomarker for kidney transplant recipients: A multistage systematic review

BACKGROUND

The kinetics of the FoxP3 regulatory T-cell (Treg) population in kidney transplant recipients (KTR) are related to the clinical effect of immunosuppression based on mammalian Target Of Rapamycin inhibitors (mTORi) with/without belatacept (predictive biomarker).

METHODS

A multistage systematic review of published and unpublished literature is presented [registration IDs in the International Prospective Register of Systematic Reviews (PROSPERO): CRD42017057570, CRD42018085019, CRD42018084941, CRD42018085186]. A multidisciplinary supervision mechanism for contextualizing of search findings was required. The peripheral blood immune cell phenotypes encompassing all regulatory cells in KTRs were assessed in order to suggest new markers of acute rejection-associated acute allograft dysfunction (AR/AAD) events in KTRs treated with mTORi alone or combined to belatacept. Quantitative estimates and evaluation of the body of evidence are provided.

RESULTS

An increase in Tregs and other regulatory cell types in the circulation in KTRs under mTORi with/without belatacept were observed. Patients with increased Tregs presented a low frequency of AR/AAD events compared to those in which the number of Tregs remained unchanged or even diminished [Odds Ratio (OR)/95% confidence interval (95% CI)/I²/number of studies (n): 0.31/0.10-0.93/0%/6]. Nevertheless, there are too few trials to consider Tregs in the circulation as a predictive biomarker. Inadequate reporting prevents appreciating clinical relevance in such studies.

CONCLUSIONS

Despite advances, clinical qualification of potential predictive biomarkers continues to be difficult. Clinical evidence on Tregs in KTRs needs to be enlarged. Biomarkers should be able to evaluate the effect of medicines targeted to specific patient populations.

Correction of Hyponatremia May Be a Treatment Stratification Biomarker: A Two-Stage Systematic Review and Meta-Analysis

Changes in serum sodium concentration ([Na⁺]_serum) can permit evaluation of the treatment effect of vasopressin antagonists (vaptans) in patients with worsening heart failure (HF) or cirrhotic ascites; that is, they may act as a treatment stratification biomarker. A two-stage systematic review and meta-analysis were carried out and contextualized by experts in fluid resuscitation and translational pharmacology (registration ID in the International Prospective Register of Systematic Reviews (PROSPERO): CRD42017051440). Meta-analysis of aggregated dichotomous outcomes was performed. Pooled estimates for correction of hyponatremia (normalization or an increase in [Na⁺]_serum of at least 3–5 mEq/L) under treatment with vaptans (Stage 1) and for clinical outcomes in both worsening HF (rehospitalization and/or death) and cirrhotic ascites (ascites worsening) when correction of hyponatremia is achieved (Stage 2) were calculated. The body of evidence was assessed. Correction of hyponatremia was achieved under vaptans (odds ratio (OR)/95% confidence interval (95% CI)/I²/number of studies (n): 7.48/4.95–11.30/58%/15). Clinical outcomes in both worsening HF and cirrhotic ascites improved when correction of hyponatremia was achieved (OR/95% CI/I²/n: 0.51/0.26–0.99/52%/3). Despite the appropriateness of the study design, however, there are too few trials to consider that correction of hyponatremia is a treatment stratification biomarker. Patients with worsening HF or with cirrhotic ascites needing treatment with vaptans, have better clinical outcomes when correction of hyponatremia is achieved. However, the evidence base needs to be enlarged to propose formally correction of hyponatremia as a new treatment stratification biomarker. Markers for use with drugs are needed to improve outcomes related to the use of medicines.

Neuroaging through the Lens of the Resting State Networks

Resting state functional magnetic resonance imaging (rs-fMRI) allows studying spontaneous brain activity in absence of task, recording changes of Blood Oxygenation Level Dependent (BOLD) signal. rs-fMRI enables identification of brain networks also called Resting State Networks (RSNs) including the most studied Default Mode Network (DMN). The simplicity and speed of execution make rs-fMRI applicable in a variety of normal and pathological conditions. Since it does not require any task, rs-fMRI is particularly useful for protocols on patients, children, and elders, increasing participant's compliance and reducing intersubjective variability due to the task performance. rs-fMRI has shown high sensitivity in identification of RSNs modifications in several diseases also in absence of structural modifications. In this narrative review, we provide the state of the art of rs-fMRI studies about physiological and pathological aging processes. First, we introduce the background of resting state; then we review clinical findings provided by rs-fMRI in physiological aging, Mild Cognitive Impairment (MCI), Alzheimer Dementia (AD), and Late Life Depression (LLD). Finally, we suggest future directions in this field of research and its potential clinical applications.

Immunoaffinity LC–MS/MS for quantitative determination of a free and total protein target as a target engagement biomarker

Aim: IP-10 is a protein target for the treatment of Crohn's disease. Inhibition of IP-10 by anti-IP-10 mAbs neutralizes its various biological activities. The measurement of free IP-10 suppression as a target engagement biomarker is required for the assessment of drug effect on the target. Results: The development of highly sensitive immunoaffinity-LC–MS/MS assays for quantifying free and total IP-10 in cynomolgus monkey serum is reported for the first time. This paper details strategies for maximizing assay sensitivity by selecting digestion routes, and optimizing immunocapture to achieve full recovery and minimal matrix effect. For the free IP-10 assay, bioanalytical strategies have been established to minimize drug/ligand dissociation. Conclusion: The assays have been implemented for target engagement measurement, pharmacokinetic-pharmacodynamic correlation, and human dose projections.

A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease

Alzheimer's disease (AD) is the most common dementia with dramatic consequences. The research in structural and functional neuroimaging showed altered brain connectivity in AD. In this study, we investigated the whole-brain resting state functional connectivity (FC) of the subjects with preclinical Alzheimer's disease (PAD), mild cognitive impairment due to AD (MCI) and mild dementia due to Alzheimer's disease (AD), the impact of APOE4 carriership, as well as in relation to variations in core AD CSF biomarkers. The synchronization in the whole-brain was monotonously decreasing during the course of the disease progression. Furthermore, in AD patients we found widespread significant decreases in functional connectivity (FC) strengths particularly in the brain regions with high global connectivity. We employed a whole-brain computational modeling approach to study the mechanisms underlying these alterations. To characterize the causal interactions between brain regions, we estimated the effective connectivity (EC) in the model. We found that the significant EC differences in AD were primarily located in left temporal lobe. Then, we systematically manipulated the underlying dynamics of the model to investigate simulated changes in FC based on the healthy control subjects. Furthermore, we found distinct patterns involving CSF biomarkers of amyloid-beta (Aβ1 - 42) total tau (t-tau) and phosphorylated tau (p-tau). CSF Aβ1 - 42 was associated to the contrast between healthy control subjects and clinical groups. Nevertheless, tau CSF biomarkers were associated to the variability in whole-brain synchronization and sensory integration regions. These associations were robust across clinical groups, unlike the associations that were found for CSF Aβ1 - 42. APOE4 carriership showed no significant correlations with the connectivity measures.

PK/PD studies on non-selective PDE inhibitors in rats using cAMP as a marker of pharmacological response

In recent years, phosphodiesterase (PDE) inhibitors have been frequently tested for the treatment of experimental inflammatory and immune disorders. It is suggested that anti-inflammatory properties of PDE inhibitors are related to their ability to increase cAMP levels. The aim of this study was to verify the hypothesis that cAMP may be a useful marker of pharmacological response following administration of non-selective PDE inhibitors (pentoxifylline and (±)-lisofylline) to endotoxemic rats. Male Wistar rats were administered LPS (1 mg kg⁻¹, i.v.) simultaneously with either compound given at two doses (40 and 80 mg kg⁻¹, i.v.). Levels of cAMP and both compounds in animal plasma were measured by the validated HPLC methods. Pharmacokinetic-pharmacodynamic analysis was performed using basic and modified indirect response (IDR) models II in Phoenix WinNonlin. The results of this study indicate that, in contrast to pentoxifylline, (±)-lisofylline demonstrates a non-linear pharmacokinetics in rats with endotoxemia. In vitro study using human recombinant PDE4B and PDE7A revealed the occurrence of additive interaction between studied compounds. Moreover, (±)-lisofylline is a more potent inhibitor of PDEs compared to pentoxifylline, as evidenced by lower IC₅₀ values. Following administration of both compounds, levels of cAMP in rat plasma increased in a dose-dependent manner. The modified IDR model II better described cAMP levels over time profiles. The validity of the proposed marker was confirmed by measuring plasma TNF-α levels in the studied animals. In conclusion, cAMP may be used in future preclinical and clinical studies of some PDE inhibitors to evaluate the drug concentration–effect relationship.

Human tear proteomics and peptidomics in ophthalmology: Toward the translation of proteomic biomarkers into clinical practice

Tears are a complex biological mixture containing electrolytes, metabolites, lipids, mucins, some small organic molecules, and proteins. The tear film has various roles in the lubrication, protection from the external environment, and nutrition of the cornea; it is also involved in the modulation of the optical properties of the eye. Tear composition reflects the physiological condition of the underlying tissues. Therefore, the tear fluid is useful in the evaluation of health and disease states and it is a valuable source of biomarkers for objective analysis of ocular and systemic diseases. The relatively high protein concentration of this fluid and the ease of noninvasive sample collection make it suitable for diagnostic and prognostic purposes. Efforts in proteomics research have positively affected to the field of ophthalmology, and the knowledge on the tear proteome has expanded considerably in the last few years. Nevertheless, despite a large amount of available data and the many biomarkers proposed for several eye and systemic diseases, the extent of translation to well-characterized and clinically useful tools has been largely insufficient. As for most of other biofluids, the road from discovery to clinical application is still long and full of pitfalls. In this review, we discuss the proteomic approaches used in the characterization of tear protein and peptide content, recapitulating the main studies and the progress done. We also present a brief summary of the path from discovery to clinical application of tear protein markers, with some representative examples of translation from the bench to the bedside.

SIGNIFICANCE

In this review we cover the most relevant proteomic approaches used in the characterization of the tear proteome, and for the first time we also focus in advances performed in the nowadays emerging peptide content characterization. In this context, we recapitulate on the main studies and the progresses done in this field. We also present a concise overview of the course that may be happen from discovery to clinical application for tear protein markers. Finally we include some representative examples of translation from the bench to the bedside.

Analysis of a urinary biomarker panel for clinical outcomes assessment in cirrhosis

BACKGROUND

Biomarkers are potentially useful in assessment of outcomes in patients with cirrhosis, but information is very limited. Given the large number of biomarkers, adequate choice of which biomarker(s) to investigate first is important.

AIM

Analysis of potential usefulness of a panel of urinary biomarkers in outcome assessment in cirrhosis.

PATIENTS AND METHODS

Fifty-five patients with acute decompensation of cirrhosis were studied: 39 had Acute Kidney Injury (AKI) (Prerenal 12, type-1 HRS (hepatorenal syndrome) 15 and Acute Tubular Necrosis (ATN) 12) and 16 acute decompensation without AKI. Thirty-four patients had Acute-on-chronic liver failure (ACLF). A panel of 12 urinary biomarkers was assessed, using a multiplex assay, for their relationship with ATN, ACLF and mortality.

RESULTS

Biomarker with best accuracy for ATN diagnosis was NGAL (neutrophil-gelatinase associated lipocalin): 36 [26-125], 104 [58-208] and 1807 [494-3,716] μg/g creatinine in Prerenal-AKI, type-1 HRS and ATN, respectively; p<0.0001 (AUROC 0.957). Other attractive biomarkers for ATN diagnosis were IL-18, albumin, trefoil-factor-3 (TFF-3) and glutathione-S-transferase-π (GST-π) Biomarkers with less accuracy for ATN AUCROC<0.8 were β2-microglobulin, calbindin, cystatin-C, clusterin and KIM-1 (kidney injury molecule-1). For ACLF, the biomarker with the best accuracy was NGAL (ACLF vs. No-ACLF: 165 [67-676] and 32 [19-40] μg/g creatinine; respectively; p<0.0001; AUROC 0.878). Interestingly, other biomarkers with high accuracy for ACLF were osteopontin, albumin, and TFF-3. Biomarkers with best accuracy for prognosis were those associated with ACLF.

CONCLUSIONS

A number of biomarkers appear promising for differential diagnosis between ATN and other types of AKI. The most interesting biomarkers for ACLF and prognosis are NGAL, osteopontin, albumin, and TFF-3. These results support the role of major inflammatory reaction in the pathogenesis of ACLF.

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

BACKGROUND

Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions.

METHODS

This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified.

RESULTS

BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein.

CONCLUSIONS

Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation.

Resolving breast cancer heterogeneity by searching reliable protein cancer biomarkers in the breast fluid secretome

BACKGROUND

One of the major goals in cancer research is to find and evaluate the early presence of biomarkers in human fluids and tissues. To resolve the complex cell heterogeneity of a tumor mass, it will be useful to characterize the intricate biomolecular composition of tumor microenvironment (the so called cancer secretome), validating secreted proteins as early biomarkers of cancer initiation and progression. This approach is not broadly applicable because of the paucity of well validated and FDA-approved biomarkers and because most of the candidate biomarkers are mainly organ-specific rather than tumor-specific. For these reasons, there is an urgent need to identify and validate a panel of biomarker combinations for early detection of human tumors. This is especially important for breast cancer, the cancer spread most worldwide among women. It is well known that patients with early diagnosed breast cancer live longer, require less extensive treatment and fare better than patients with more aggressive and/or advanced disease.

RESULTS

In the frame of searching breast cancer biomarkers (especially using nipple aspirate fluid mirroring breast microenvironment), studies have highlighted an optimal combination of well-known biomarkers: uPA + PAI-1 + TF. When individually investigated they did not show perfect accuracy in predicting the presence of breast cancer, whereas the triple combination has been demonstrated to be highly predictive of pre-cancer and/or cancerous conditions, approaching 97-100% accuracy.

CONCLUSION

Despite the heterogeneous composition of breast cancer and the difficulties to find specific breast cancer biomolecules, the noninvasive analysis of the nipple aspirate fluid secretome may significantly improve the discovery of promising biomarkers, helping also the differentiation among benign and invasive breast diseases, opening new frontiers in early oncoproteomics.

Strategic applications of gene expression: from drug discovery/development to bedside

Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to clinically meaningful phenotypic characteristics and requires a biologically meaningful mechanism combined with a stringent statistical rigor. Much of the success in clinical drug development is hinged on predefining the signature genes for their fitness for purposes of application. Specific examples are highlighted to illustrate the breadth and depth of the potential utility of gene-expression signatures in drug discovery and clinical development to targeted therapeutics at the bedside.

Proteomics and metabolomics in renal transplantation-quo vadis?

The improvement of long-term transplant organ and patient survival remains a critical challenge following kidney transplantation. Proteomics and biochemical profiling (metabolomics) may allow for the detection of early changes in cell signal transduction regulation and biochemistry with high sensitivity and specificity. Hence, these analytical strategies hold the promise to detect and monitor disease processes and drug effects before histopathological and pathophysiological changes occur. In addition, they will identify enriched populations and enable individualized drug therapy. However, proteomics and metabolomics have not yet lived up to such high expectations. Renal transplant patients are highly complex, making it difficult to establish cause-effect relationships between surrogate markers and disease processes. Appropriate study design, adequate sample handling, storage and processing, quality and reproducibility of bioanalytical multi-analyte assays, data analysis and interpretation, mechanistic verification, and clinical qualification (=establishment of sensitivity and specificity in adequately powered prospective clinical trials) are important factors for the success of molecular marker discovery and development in renal transplantation. However, a newly developed and appropriately qualified molecular marker can only be successful if it is realistic that it can be implemented in a clinical setting. The development of combinatorial markers with supporting software tools is an attractive goal.

Induction of osteoarthritis and metabolic inflammation by a very high-fat diet in mice: effects of short-term exercise

OBJECTIVE

To test the hypotheses that obesity due to a very high-fat diet induces knee osteoarthritis (OA), and that short-term wheel-running exercise protects against obesity-induced knee OA by reducing systemic inflammation and metabolic dysregulation.

METHODS

Male C57BL/6J mice were fed either a control diet (13.5% kcal from fat) or a very high-fat diet (60% kcal from fat) from age 12 weeks to age 24 weeks. From 20 to 24 weeks of age, half of the mice were housed with running wheels. The severity of knee OA was determined by assessing histopathologic features, and serum cytokines were measured using a multiplex bead immunoassay and enzyme-linked immunosorbent assays. Body composition was quantified by dual-energy x-ray absorptiometry, and insulin resistance was assessed by glucose tolerance testing.

RESULTS

Feeding mice with a very high-fat diet increased knee OA scores and levels of serum leptin, adiponectin, KC (mouse analog of interleukin-8 [IL-8]), monokine induced by interferon-γ (CXCL9), and IL-1 receptor antagonist to an extent in proportion to the gain in body fat (3-fold increase in percent body fat compared to controls). Wheel-running exercise reduced progression of OA in the medial femur of obese mice. In addition, exercise disrupted the clustering of cytokine expression and improved glucose tolerance, without reducing body fat or cytokine levels.

CONCLUSION

Obesity induced by a very high-fat diet in mice causes OA and systemic inflammation in proportion to body fat. Increased joint loading is not sufficient to explain the increased incidence of knee OA with obesity, as wheel running is protective rather than damaging. Exercise improves glucose tolerance and disrupts the coexpression of proinflammatory cytokines, suggesting that increased aerobic exercise may act independently of weight loss in promoting joint health.

Pharmacokinetic/pharmacodynamic modeling in inflammation

Inflammation is an array of immune responses to infection and injury. It results from a complex immune cascade and is the basis of many chronic diseases such as arthritis, diabetes, and cancer. Numerous mathematical models have been developed to describe the disease progression and effects of anti-inflammatory drugs. This review illustrates the state of the art in modeling the effects of diverse drugs for treating inflammation, describes relevant biomarkers amenable to modeling, and summarizes major advantages and limitations of the published pharmacokinetic/ pharmacodynamic (PK/PD) models. Simple direct inhibitory models are often used to describe in vitro effects of anti-inflammatory drugs. Indirect response models are more mechanism based and have been widely applied to the turnover of symptoms and biomarkers. These, along with target-mediated and transduction models, have been successfully applied to capture the PK/PD of many anti-inflammatory drugs and describe disease progression of inflammation. Biologics have offered opportunities to address specific mechanisms of action, and evolve small systems models to quantitatively capture the underlying physiological processes. More advanced mechanistic models should allow evaluation of the roles of some key mediators in disease progression, assess drug interactions, and better translate drug properties from in vitro and animal data to patients.