Biomarkers In Drug Development Research Articles

Utilising Endogenous Biomarkers in Drug Development to Streamline the Assessment of Drug-Drug Interactions Mediated by Renal Transporters: A Pharmaceutical Industry Perspective.

The renal secretion of many drugs is facilitated by membrane transporters, including organic cation transporter 2, multidrug and toxin extrusion protein 1/2-K and organic anion transporters 1 and 3. Inhibition of these transporters can reduce renal excretion of drugs and thereby pose a safety risk. Assessing the risk of inhibition of these membrane transporters by investigational drugs remains a key focus in the evaluation of drug-drug interactions (DDIs). Current methods to predict DDI risk are based on generating in vitro data followed by a clinical assessment using a recommended exogenous probe substrate for the individual drug transporter. More recently, monitoring plasma-based and urine-based endogenous biomarkers to predict transporter-mediated DDIs in early phase I studies represents a promising approach to facilitate, improve and potentially avoid conventional clinical DDI studies. This perspective reviews the evidence for use of these endogenous biomarkers in the assessment of renal transporter-mediated DDI, evaluates how endogenous biomarkers may help to expand the DDI assessment toolkit and offers some potential knowledge gaps. A conceptual framework for assessment that may complement the current paradigm of predicting the potential for renal transporter-mediated DDIs is outlined.

Decoding disease: Scott Patterson's perspectives on the power of biomarkers in drug development.

Scott Patterson (Gilead Sciences Inc., CA, USA) speaks to Ashling Cannon, Journal Development Editor at BioTechniques, about his career. Patterson is a biochemist and proteomics and biomarker/translational expert with over 30years of industry experience following 13years in an academic setting. Patterson earned his BSc and PhD in Physiology and Pharmacology from the University of Queensland (Australia) while working full time in the Department of Physiology and Pharmacology, rising to a Senior Research Officer. Throughout his career, Patterson has been actively involved in advancing technologies, how they can be applied to address biological questions and the interplay of bioinformatics and large datasets leveraging biomarkers and diagnostics. He has held pivotal roles at renowned institutions and companies such as Cold Spring Harbor Laboratory (NY, USA), Amgen, Inc. (CA, USA), Celera Genomics Group (MD, USA) and Gilead Sciences, Inc. Notably, he served as a Staff Investigator at Cold Spring Harbor Laboratory and was honored with the Long Island Biological Association New Investigator award in addition to being the 2002 Barnett Lecturer at Northeastern University (MA, USA). In early 2015 Patterson joined Gilead Sciences, Inc., bringing his extensive expertise to lead biomarker discovery and development as well as in vitro diagnostics initiatives across all therapeutic domains.

Validation of a suite of ERP and QEEG biomarkers in a pre-competitive, industry-led study in subjects with schizophrenia and healthy volunteers.

Complexity and lack of standardization have mostly limited the use of event-related potentials (ERPs) and quantitative EEG (QEEG) biomarkers in drug development to small early phase trials. We present results from a clinical study on healthy volunteers (HV) and patients with schizophrenia (SZ) that assessed test-retest, group differences, variance, and correlation with functional assessments for ERP and QEEG measures collected at clinical and commercial trial sites with standardized instrumentation and methods, and analyzed through an automated data analysis pipeline. 81 HV and 80 SZ were tested at one of four study sites. Subjects were administered two ERP/EEG testing sessions on separate visits. Sessions included a mismatch negativity paradigm, a 40Hz auditory steady-state response paradigm, an eyes-closed resting state EEG, and an active auditory oddball paradigm. SZ subjects were also tested on the Brief Assessment of Cognition (BAC), Positive and Negative Syndrome Scale (PANSS), and Virtual Reality Functional Capacity Assessment Tool (VRFCAT). Standardized ERP/EEG instrumentation and methods ensured few test failures. The automated data analysis pipeline allowed for near real-time analysis with no human intervention. Test-retest reliability was fair-to-excellent for most of the outcome measures. SZ subjects showed significant deficits in ERP and QEEG measures consistent with published academic literature. A subset of ERP and QEEG measures correlated with functional assessments administered to the SZ subjects. With standardized instrumentation and methods, complex ERP/EEG testing sessions can be reliably performed at clinical and commercial trial sites to produce high-quality data in near real-time.

Biomarkers for drug development in propionic and methylmalonic acidemias.

There is an unmet need for the development and validation of biomarkers and surrogate endpoints for clinical trials in propionic acidemia (PA) and methylmalonic acidemia (MMA). This review examines the pathophysiology and clinical consequences of PA and MMA that could form the basis for potential biomarkers and surrogate endpoints. Changes in primary metabolites such as methylcitric acid (MCA), MCA:citric acid ratio, oxidation of 13C‐propionate (exhaled 13CO2), and propionylcarnitine (C3) have demonstrated clinical relevance in patients with PA or MMA. Methylmalonic acid, another primary metabolite, is a potential biomarker, but only in patients with MMA. Other potential biomarkers in patients with either PA and MMA include secondary metabolites, such as ammonium, or the mitochondrial disease marker, fibroblast growth factor 21. Additional research is needed to validate these biomarkers as surrogate endpoints, and to determine whether other metabolites or markers of organ damage could also be useful biomarkers for clinical trials of investigational drug treatments in patients with PA or MMA. This review examines the evidence supporting a variety of possible biomarkers for drug development in propionic and methylmalonic acidemias.

Biomarkers in Drug Development

Biomarkers in Drug Development

An Overview of Promising Biomarkers in Cancer Screening and Detection.

Applications of biomarkers have been proved in oncology screening, diagnosis, predicting response to treatment as well as monitoring the progress of the disease. Considering the crucial role played by them during different disease stages, it is extremely important to evaluate, validate, and assess them to incorporate them into routine clinical care. In this review, the role of few most promising and successfully used biomarkers in cancer detection, i.e. PD-L1, E-Cadherin, TP53, Exosomes, cfDNA, EGFR, mTOR with regard to their structure, mode of action, and reports signifying their pathological significance, are addressed. Also, an overview of some successfully used biomarkers for cancer medicine has been presented. The study also summarizes biomarker-driven personalized cancer therapy i.e., approved targets and indications, as per the US FDA. The review also highlights the increasingly prominent role of biomarkers in drug development at all stages, with particular reference to clinical trials. The increasing utility of biomarkers in clinical trials is clearly evident from the trend shown, wherein ~55 percent of all oncology clinical trials in 2019 were seen to involve biomarkers, as opposed to ~ 15 percent in 2001, which clearly proves the essence and applicability of biomarkers for synergizing clinical information with tumor progression. Still, there are significant challenges in the implementation of these possibilities with strong evidence in cost-- effective manner.

Automated, Generic Reagent and Ultratargeted 2D-LC-MS/MS Enabling Quantification of Biotherapeutics and Soluble Targets down to pg/mL Range in Serum.

Mass spectrometry has recently emerged as a powerful analytical tool for the assessment of pharmacokinetics and biomarkers in drug development. Compared with ligand binding assays, a major advantage of mass spectrometry-based assays is that they are less dependent on high quality binding reagents, while a key limitation is the relatively lower sensitivity. To address the sensitivity issue, we have developed a generic reagent, ultratargeted two-dimensional liquid chromatography-tandem mass spectrometry (2D-LC-MS/MS) method which combines commercially available protein A affinity capture, targeted analyte isolation by 2D-LC, and targeted detection by multiple reaction monitoring (MRM). A targeted-2D-with-dilution configuration was designed to automate 2D-LC-MS/MS. This method was systematically evaluated using an anti-CD22 monoclonal antibody spiked into monkey and human serum, where lower limits of quantification (LLOQ) of 0.78 and 1.56 ng/mL were achieved, respectively. This represents an over 100-fold improvement in assay sensitivity compared to the conventional LC-MS/MS method. The performance of the method was further confirmed by analyzing another monoclonal antibody, bevacizumab, as well as a soluble antigen, circulating PD-L1. The results indicate that our method enables quantification of antibody therapeutics and antigen biomarkers in both clinical and nonclinical samples in the pg/mL to low ng/mL range. Protein A affinity capture was employed as a universal sample preparation procedure applicable to both full-length antibody therapeutics and antibody-antigen complexes. This novel method is also fully automated and proven to be highly robust for routine bioanalysis in drug development.

Osteoarthritis: Current Molecular Biomarkers and the Way Forward

The ultimate hope of researchers and patients is a pathway to development of treatments for osteoarthritis to modify the disease process in addition to the symptoms. However, development of disease modifying drugs requires objective endpoints such as measures of joint structure, joint tissue homeostasis and/or joint survival-measures such as provided by imaging biomarkers, molecular biomarkers and joint replacement frequency, respectively. Although biomarkers supporting investigational drug use and drug approval include surrogate endpoints that may not necessarily reflect or directly correlate with the clinical outcome of interest, a formal biomarker qualification process currently exists that is a rigorous three stage process that yields biomarker approvals (or denials) for specific contexts of use. From a cost perspective, biochemical biomarkers are the 'ones to beat'; however, even well-validated biomarkers may not cross the translation gaps for eventual use in healthcare unless they offer an advantage in terms of cost per quality adjusted life year. This review summarizes the case FOR and AGAINST biomarkers in drug development and highlights the current data for a subset of biomarkers in the osteoarthritis research field informing on cartilage homeostasis, joint inflammation and altered subchondral bone remodeling.

Use of biomarkers to improve immunosuppressive drug development and outcomes in renal organ transplantation: A meeting report.

On September 27-28, 2018 the Food and Drug Administration (FDA) and the Critical Path Institute's Transplant Therapeutics Consortium convened a public workshop titled "Evidence-Based Treatment Decisions in Transplantation: The Right Dose & Regimen for the Right Patient/Individualized Treatment." The workshop facilitated cooperative engagement of transplant community stakeholders, including pharmaceutical industry, academic researchers, clinicians, patients, and regulators to discuss methods to advance the development of novel immunosuppressive drugs for use in solid organ transplantation. Day 1 focused on the utility of biomarkers in drug development, with considerations for seeking regulatory endorsement for use in clinical trials. Biomarkers add value to drug development by improving patient selection criteria, safety monitoring, endpoint selection, and more. Regulatory endorsement through the FDA Biomarker Qualification Program encourages the use of biomarkers in drug development by instilling confidence and consistency in biomarker interpretation across trials. Public-private partnerships or consortia allow stakeholders to share expertise, resources, and data in pursuit of biomarker qualification. Biomarkers relevant to pretransplant risk assessment, early posttransplant care, and assessment of immune response, immunosuppressive drug efficacy, and graft function as discussed on day 1 of the workshop are described.

Lessons learned from using fMRI in the early clinical development of a mu-opioid receptor antagonist for disorders of compulsive consumption.

Functional magnetic resonance imaging (fMRI) has been widely used to gain a greater understanding of brain circuitry abnormalities in CNS disorders. fMRI has also been used to examine pharmacological modulation of brain circuity and is increasingly being used in early clinical drug development as functional pharmacodynamic index of target engagement, and to provide early indication of clinical efficacy. In this short review, we summarize data from experimental medicine and early clinical development studies of a mu-opioid receptor antagonist, GSK1521498 developed fordisorders of compulsive consumption including binge eating in obesity. We demonstrate how fMRI can be used to answer important questions of early clinical drug development relating to; (1) target engagement, (2) dose response relationships, (3) differential efficacy and (4) prediction of behavioural and clinically relevant outcomes. We also highlight important methodological factors that need to be considered when conducting fMRI studies in drug development given the challenges faced with small sample sizes in Phase 1 and early proof of mechanism studies. While these data highlight the value of fMRI as a biomarker in drug development, its use for making Go/No-go decisions is still faced with challenges given the variability of responses, interpretation of brain activation changes and the limited data linking drug induced changes in brain activity to clinical or behavioural outcome. These challenges need to be addressed to fulfil the promise of fMRI as a tool in clinical drug development.

Concordance of Genomic Variants in Matched Primary Breast Cancer, Metastatic Tumor, and Circulating Tumor DNA: The MIRROR Study.

Genetic heterogeneity between primary tumors and their metastatic lesions has been documented in several breast cancer studies. However, the selection of therapy for patients with metastatic breast cancer and the search for biomarkers for targeted therapy are often based on findings from the primary tumor, mainly because of the difficulty of distant metastasis core biopsies. New methods for monitoring genomic changes in metastatic breast cancer are needed (ie, circulating tumor DNA [ctDNA] genomic analysis). The objectives of this study were to assess the concordance of genomic variants between primary and metastatic tumor tissues and the sensitivity of plasma ctDNA analysis to identify variants detected in tumor biopsies. Next-generation sequencing technology was used to assess the genomic mutation profile of a panel of 54 cancer genes in matched samples of primary tumor, metastatic tumor, and plasma from 40 patients with metastatic breast cancer. Using Ion Torrent technology (ThermoFisher Scientific, Waltham, MA), we identified 110 variants that were common to the primary and metastatic tumors. ctDNA analysis had a sensitivity of 0.972 in detecting variants present in both primary and metastatic tissues. In addition, we identified 13 variants in metastatic tissue and ctDNA not present in primary tumor. We identified genomic variants present in metastatic biopsies and plasma ctDNA that were not present in the primary tumor. Deep sequencing of plasma ctDNA detected most DNA variants previously identified in matched primary and metastatic tissues. ctDNA might aid in therapy selection and in the search for biomarkers for drug development in metastatic breast cancer.

Recommendations for Classification of Commercial LBA Kits for Biomarkers in Drug Development From the GCC for Bioanalysis

Over the last decade, the use of biomarker data has become integral to drug development. Biomarkers are not only utilized for internal decision-making by sponsors; they are increasingly utilized to make critical decisions for drug safety and efficacy. As the regulatory agencies are routinely making decisions based on biomarker data, there has been significant scrutiny on the validation of biomarker methods. Contract research organizations regularly use commercially available immunoassay kits to validate biomarker methods. However, adaptation of such kits in a regulated environment presents significant challenges and was one of the key topics discussed during the 12th Global Contract Research Organization Council for Bioanalysis (GCC) meeting. This White Paper reports the GCC members' opinion on the challenges facing the industry and the GCC recommendations on the classification of commercial kits that can be a win-win for commercial kit vendors and end users.

Mass Spectrometry-Based Biomarkers in Drug Development.

Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increases the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.

Adaptation of commercial biomarker kits and proposal for 'drug development kits' to support bioanalysis: call for action.

There has been an increased use of commercial kits for biomarker measurement, commensurate with the increased demand for biomarkers in drug development. However, in most cases these kits do not meet the quality attributes for use in regulated environment. The process for adaptation of these kits can be frustrating, time consuming and resource intensive. In addition, a lack of harmonized guidance for the validation of biomarker poses a significant challenge in the adaptation of kits in a regulated environment. The purpose of this perspective is to propose a tiered approach to commercial drug development kits with clearly defined quality attributes and to demonstrate how these kits can be adapted to perform analytical validation in a regulated environment.

Two approaches for estimating the lower limit of quantitation (LLOQ) of microRNA levels assayed as exploratory biomarkers by RT-qPCR

BackgroundCirculating microRNAs are undergoing exploratory use as safety biomarkers in drug development. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) is one common approach used to quantitate levels of microRNAs in samples that includes the use of a standard curve of calibrators fit to a regression model. Guidelines are needed for setting assay quantitation thresholds that are appropriate for this method and to biomarker pre-validation.ResultsIn this report, we develop two workflows for determining a lower limit of quantitation (LLOQ) for RT-qPCR assays of microRNAs in exploratory studies. One workflow is based on an error threshold calculated by a logistic model of the calibration curve data. The second workflow is based on a threshold set by the sample blank, which is the no template control for RT-qPCR. The two workflows are used to set lower thresholds of reportable microRNA levels for an example dataset in which miR-208a levels in biofluids are quantitated in a cardiac injury model. LLOQ thresholds set by either workflow are effective in filtering out microRNA values with large uncertainty estimates.ConclusionsTwo workflows for LLOQ determinations are presented in this report that provide methods that are easy to implement in investigational studies of microRNA safety biomarkers and offer choices in levels of conservatism in setting lower limits of acceptable values that facilitate interpretation of results.

Machine-Learning Prediction of Drug-Induced Cardiac Arrhythmia: Analysis of Gene Expression and Clustering.

A marked delay in the electrical repolarization of heart ventricles is characterized by prolongation of the Q-T wave (QT) interval on a surface electrocardiogram. Such a delay can lead to potentially life-threatening cardiac arrhythmia (torsades de pointes). Such prolongation is also a widely accepted cardiac safety biomarker in drug development. Current preclinical drug-safety assays include patch clamp analysis to evaluate drug-related blockade of cardiac repolarizing ion currents. Recently reported patch clamp assay results have shown predictive sensitivities and specificities in the ranges of 64%-82% and 75%-88%, respectively. In this project, we use a support vector machine classifier to find mean sensitivities and specificities of 85% and 90%, respectively, across 77 drug subclassifications. Clustering by gene expression profile similarities shows that drugs known to prolong the QT interval do not always form distinct groups, but the number of groups is limited. The most common biological network links associated with these groups involve genes linked with fatty acid metabolism, G proteins, intracellular glutathione, immune responses, apoptosis, mitochondrial function, electron transport, and mitogen-activated protein kinases. These results suggest that machine-learning analysis of gene expression and clustering may augment cardiac safety predictions for improving drug-safety assessments.

What evidence do we need for biomarker qualification?

Biomarkers can facilitate all aspects of the drug development process. However, biomarker qualification-the use of a biomarker that is accepted by the U.S. Food and Drug Administration-needs a clear, predictable process. We describe a multistakeholder effort including government, industry, and academia that proposes a framework for defining the amount of evidence needed for biomarker qualification. This framework is intended for broad applications across multiple biomarker categories and uses.

Molecular-based diagnosis of multiple sclerosis and its progressive stage.

Biomarkers aid diagnosis, allow inexpensive screening of therapies, and guide selection of patient-specific therapeutic regimens in most internal medicine disciplines. In contrast, neurology lacks validated measurements of the physiological status, or dysfunction(s) of cells of the central nervous system (CNS). Accordingly, patients with chronic neurological diseases are often treated with a single disease-modifying therapy without understanding patient-specific drivers of disability. Therefore, using multiple sclerosis (MS) as an example of a complex polygenic neurological disease, we sought to determine whether cerebrospinal fluid (CSF) biomarkers are intraindividually stable, cell type-, disease- and/or process-specific, and responsive to therapeutic intervention. We used statistical learning in a modeling cohort (n = 225) to develop diagnostic classifiers from DNA-aptamer-based measurements of 1,128 CSF proteins. An independent validation cohort (n = 85) assessed the reliability of derived classifiers. The biological interpretation resulted from in vitro modeling of primary or stem cell-derived human CNS cells and cell lines. The classifier that differentiates MS from CNS diseases that mimic MS clinically, pathophysiologically, and on imaging achieved a validated area under the receiver operating characteristic curve (AUROC) of 0.98, whereas the classifier that differentiates relapsing-remitting from progressive MS achieved a validated AUROC of 0.91. No classifiers could differentiate primary progressive from secondary progressive MS better than random guessing. Treatment-induced changes in biomarkers greatly exceeded intraindividual and technical variabilities of the assay. CNS biological processes reflected by CSF biomarkers are robust, stable, disease specific, or even disease stage specific. This opens opportunities for broad utilization of CSF biomarkers in drug development and precision medicine for CNS disorders. Ann Neurol 2017;82:795-812.

Imaging of glia activation in people with primary lateral sclerosis.

BackgroundGlia activation is thought to contribute to neuronal damage in several neurodegenerative diseases based on preclinical and human post-mortem studies, but its role in primary lateral sclerosis (PLS) is unknown.ObjectivesTo localize and measure glia activation in people with PLS compared to healthy controls (HC).MethodsTen participants with PLS and ten age-matched HCs underwent simultaneous magnetic resonance (MR) and proton emission tomography (PET). The radiotracer [11C]-PBR28 was used to obtain PET-based measures of 18 kDa translocator protein (TSPO) expression, a marker of activated glial cells. MR techniques included a structural sequence to measure cortical thickness and diffusion tensor imaging (DTI) to assess white matter integrity.ResultsPET data showed increased [11C]-PBR28 uptake in anatomically-relevant motor regions which co-localized with areas of regional gray matter atrophy and decreased subcortical fractional anisotropy.ConclusionsThis study supports a link between glia activation and neuronal degeneration in PLS, and suggests that these disease mechanisms can be measured in vivo in PLS. Future studies are needed to determine the longitudinal changes of these imaging measures and to clarify if MR-PET with [11C]-PBR28 can be used as a biomarker for drug development in the context of clinical trials for PLS.

Success, Failure, and Transparency in Biomarker-Based Drug Development: A Case Study of Cholesteryl Ester Transfer Protein Inhibitors.

Although biomarkers are used as surrogate measures for drug targeting and approval and are generally based on plausible biological hypotheses, some are found to not correlate well with clinical outcomes. Over-reliance on inadequately validated biomarkers in drug development can lead to harm to trial subjects and patients and to research waste. To shed greater light on the process and ethics of biomarker-based drug development, we conducted a systematic portfolio analysis of cholesterol ester transfer protein inhibitors, a drug class designed to improve lipid profiles and prevent cardiovascular events. Despite years of development, no cholesterol ester transfer protein inhibitor has yet been approved for clinical use. We searched PubMed and Clinicaltrials.gov for clinical studies of 5 known cholesterol ester transfer protein inhibitors: anacetrapib, dalcetrapib, evacetrapib, TA-8995, and torcetrapib. Published reports and registration records were extracted for patient demographic characteristics and study authors' recommendations of clinical usage or further testing. We used Accumulating Evidence and Research Organization graphing to depict the portfolio of research activities and a Poisson model to examine trends. We identified 100 studies for analysis that involved 96 944 human subjects. The data from only 41 201 (42%) of the human subjects had been presented in a published report. For the 3 discontinued cholesterol ester transfer protein inhibitors, we found a pattern of consistently positive results on lipid-modification end points followed by negative results using clinical end points. Inefficiencies and harms can arise if a biomarker hypothesis continues to drive trials despite successive failures. Regulators, research funding bodies, and public policy makers may need to play a greater role in evaluating and coordinating biomarker-driven research programs.