Biomarkers in early-phase trials: fundamental issues

Logistic regression and other statistical tools in diagnostic biomarker studies

A biomarker is a measured indicator of a variety of processes, and is often used as a clinical tool for the diagnosis of diseases. While the developmental process of biomarkers from lab to clinic is complex, initial exploratory stages often focus on characterizing the potential of biomarkers through utilizing various statistical methods that can be used to assess their discriminatory performance, establish an appropriate cut-off that transforms continuous data to apt binary responses of confirming or excluding a diagnosis, or establish a robust association when tested against confounders. This review aims to provide a gentle introduction to the most common tools found in diagnostic biomarker studies used to assess the performance of biomarkers with an emphasis on logistic regression.

Interlaboratory evaluation of LC-MS-based biomarker assays

Validation of biomarker assays is crucial for effective drug development and clinical applications. Interlaboratory reproducibility is vital for reliable comparison and combination of data from different centers. This review summarizes interlaboratory studies of quantitative LC-MS-based biomarker assays using reference standards for calibration curves. The following points are discussed: trends in reports, reference and internal standards, evaluation of analytical validation parameters, study sample analysis and normalization of biomarker assay data. Full evaluation of these parameters in interlaboratory studies is limited, necessitating further research. Some reports suggest methods to address variations in biomarker assay data among laboratories, facilitating organized studies and data combination. Method validation across laboratories is crucial for reducing interlaboratory differences and reflecting target biomarker responses.

Real-time detection of bone-invasive oral cancer with laser-induced breakdown spectroscopy: A proof-of-principle study

OBJECTIVES

Intraoperative definition of resection margin status in bone-invasive oral cancer is a fundamental problem in oncologic surgery due to the lack of rapid bone analysis methods. Laser-induced breakdown spectroscopy (LIBS) provides direct measurement with real-time examination of a minimal tissue sample. This proof-of-principle study aimed to evaluate the possibility of distinguishing tumorous and healthy areas with LIBS.

MATERIALS AND METHODS

LIBS experiments were executed on native segmental mandibulectomy specimens from 15 patients with bone-invasive oral cancer. Normalized and intensity-matched spectra were compared. Under biological derivation, peak area calculation and principal component analysis (PCA) were applied. The discriminatory power of the PCAs was correlated with the architectural and cytological characteristics of the lasered tumor tissue. Receiver operating characteristics analysis was used to evaluate the performance of LIBS in the real-time detection of bone-invasive cancer.

RESULTS

Calcium (Ca), which is high in healthy bone, is replaced by potassium (K) and sodium (Na) in bone-invasive cancer. The degree of stromal induction is significantly correlated with the discriminatory power between healthy and tumorous spectra. In this study, LIBS ensured an overall sensitivity of 95.51% and a specificity of 98.64% via the intracellular detection of K and Na.

CONCLUSION

This study demonstrated robust real-time detection of bone-invasive oral cancer with LIBS, which may lay the foundation for establishing LIBS as a rapid bone analysis method. Further development of a LIBS-guided assessment of bone tumor resection margins might reduce the extent of bony resection without compromising oncologic safety.

A natural history study to track brain and spinal cord changes in individuals with Friedreich's ataxia: TRACK-FA study protocol

INTRODUCTION

Drug development for neurodegenerative diseases such as Friedreich's ataxia (FRDA) is limited by a lack of validated, sensitive biomarkers of pharmacodynamic response in affected tissue and disease progression. Studies employing neuroimaging measures to track FRDA have thus far been limited by their small sample sizes and limited follow up. TRACK-FA, a longitudinal, multi-site, and multi-modal neuroimaging natural history study, aims to address these shortcomings by enabling better understanding of underlying pathology and identifying sensitive, clinical trial ready, neuroimaging biomarkers for FRDA.

METHODS

200 individuals with FRDA and 104 control participants will be recruited across seven international study sites. Inclusion criteria for participants with genetically confirmed FRDA involves, age of disease onset ≤ 25 years, Friedreich's Ataxia Rating Scale (FARS) functional staging score of ≤ 5, and a total modified FARS (mFARS) score of ≤ 65 upon enrolment. The control cohort is matched to the FRDA cohort for age, sex, handedness, and years of education. Participants will be evaluated at three study visits over two years. Each visit comprises of a harmonized multimodal Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) scan of the brain and spinal cord; clinical, cognitive, mood and speech assessments and collection of a blood sample. Primary outcome measures, informed by previous neuroimaging studies, include measures of: spinal cord and brain morphometry, spinal cord and brain microstructure (measured using diffusion MRI), brain iron accumulation (using Quantitative Susceptibility Mapping) and spinal cord biochemistry (using MRS). Secondary and exploratory outcome measures include clinical, cognitive assessments and blood biomarkers.

DISCUSSION

Prioritising immediate areas of need, TRACK-FA aims to deliver a set of sensitive, clinical trial-ready neuroimaging biomarkers to accelerate drug discovery efforts and better understand disease trajectory. Once validated, these potential pharmacodynamic biomarkers can be used to measure the efficacy of new therapeutics in forestalling disease progression.

CLINICAL TRIAL REGISTRATION

ClinicalTrails.gov Identifier: NCT04349514.

Evaluation of electrolyte element composition in human tissue by laser-induced breakdown spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) enables the direct measurement of cell electrolyte concentrations. The utility of LIBS spectra in biomarker studies is limited because these studies rarely consider basic physical principles. The aim of this study was to test the suitability of LIBS spectra as an analytical method for biomarker assays and to evaluate the composition of electrolyte elements in human biomaterial. LIBS as an analytical method was evaluated by establishing KCl calibration curves to demonstrate linearity, by the correct identification of emission lines with corresponding reference spectra, and by the feasibility to use LIBS in human biomaterial, analyzing striated muscle tissues from the oral regions of two patients. Lorentzian peak fit and peak area calculations resulted in better linearity and reduced shot-to-shot variance. Correct quantitative measurement allowed for differentiation of human biomaterial between patients, and determination of the concentration ratios of main electrolytes within human tissue. The clinical significance of LIBS spectra should be evaluated using peak area rather than peak intensity. LIBS might be a promising tool for analyzing a small group of living cells. Due to linearity, specificity and robustness of the proposed analytical method, LIBS could be a component of future biomarker studies.

Biomarkers in oral immunotherapy

Food allergy (FA) is a global health problem that affects a large population, and thus effective treatment is highly desirable. Oral immunotherapy (OIT) has been showing reasonable efficacy and favorable safety in most FA subjects. Dependable biomarkers are needed for treatment assessment and outcome prediction during OIT. Several immunological indicators have been used as biomarkers in OIT, such as skin prick tests, basophil and mast cell reactivity, T cell and B cell responses, allergen-specific antibody levels, and cytokines. Other novel indicators also could be potential biomarkers. In this review, we discuss and assess the application of various immunological indicators as biomarkers for OIT.

Biomarker development: bedside to bench

This commentary complements the report from Nixon and colleagues by addressing the critical definitions, assay and analytical quality control and interpretation, and resources available to advance similar fit-for-purpose biomarker development.

A multilaboratory validation study of LC/MS biomarker assays for three lysophosphatidylcholines

Aim: Although the fit-for-purpose approach has been proposed for validation procedures and acceptance criteria for biomarker assays, practical biomarker assays to facilitate clinical application and regulatory documents on biomarker assays remain limited. Materials & methods: We assigned six independent laboratories and selected three lysophosphatidylcholines (LPCs): LPC(16:0), LPC(18:0) and LPC(18:1) as model biomarkers. Using LC-MS, the following key validation parameters were evaluated: calibration curve, carryover, parallelism, precision and relative accuracy and these values were similar among all laboratories. Further, we determined LPC levels in six lots of rat plasma at unknown concentrations and compared them among the laboratories. Conclusion: Our multilaboratory validation and reproducibility data are useful for the development of future biomarker assay validation procedures, as well as regulatory documents.

Lack of predictive tools for conventional and targeted cancer therapy: Barriers to biomarker development and clinical translation

Predictive tools, utilising biomarkers, aim to objectively assessthe potentialresponse toa particular clinical intervention in order to direct treatment.Conventional cancer therapy remains poorly served by predictive biomarkers, despite being the mainstay of treatment for most patients. In contrast, targeted therapy benefits from a clearly defined protein target for potential biomarker assessment. We discuss potential data sources of predictive biomarkers for conventional and targeted therapy, including patient clinical data andmulti-omicbiomarkers (genomic, transcriptomic and protein expression).Key examples, either clinically adopted or demonstrating promise for clinical translation, are highlighted. Following this, we provide an outline of potential barriers to predictive biomarker development; broadly discussing themes of approaches to translational research and study/trial design, and the impact of cellular and molecular tumor heterogeneity. Future avenues of research are also highlighted.

Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients

Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8-13 Hz) relative to a wide band of 0.5-40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.

Targeting for Success: Demonstrating Proof-of-Concept with Mechanistic Early Phase Clinical Pharmacology Studies for Disease-Modification in Neurodegenerative Disorders

The clinical failure rate for disease-modifying treatments (DMTs) that slow or stop disease progression has been nearly 100% for the major neurodegenerative disorders (NDDs), with many compounds failing in expensive and time-consuming phase 2 and 3 trials for lack of efficacy. Here, we critically review the use of pharmacological and mechanistic biomarkers in early phase clinical trials of DMTs in NDDs, and propose a roadmap for providing early proof-of-concept to increase R&D productivity in this field of high unmet medical need. A literature search was performed on published early phase clinical trials aimed at the evaluation of NDD DMT compounds using MESH terms in PubMed. Publications were selected that reported an early phase clinical trial with NDD DMT compounds between 2010 and November 2020. Attention was given to the reported use of pharmacodynamic (mechanistic and physiological response) biomarkers. A total of 121 early phase clinical trials were identified, of which 89 trials (74%) incorporated one or multiple pharmacodynamic biomarkers. However, only 65 trials (54%) used mechanistic (target occupancy or activation) biomarkers to demonstrate target engagement in humans. The most important categories of early phase mechanistic and response biomarkers are discussed and a roadmap for incorporation of a robust biomarker strategy for early phase NDD DMT clinical trials is proposed. As our understanding of NDDs is improving, there is a rise in potentially disease-modifying treatments being brought to the clinic. Further increasing the rational use of mechanistic biomarkers in early phase trials for these (targeted) therapies can increase R&D productivity with a quick win/fast fail approach in an area that has seen a nearly 100% failure rate to date.

Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management

Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990′s, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3–5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

Developmental Pharmacodynamics and Modeling in Pediatric Drug Development

Challenges in pediatric drug development include small patient numbers, limited outcomes research, ethical barriers, and sparse biosamples. Increasingly, pediatric drug development is focusing on extrapolation: leveraging knowledge about adult disease and drug responses to inform projections of drug and clinical trial performance in pediatric subpopulations. Pharmacokinetic-pharmacodynamic (PK-PD) modeling and extrapolation aim to reduce the numbers of patients and data points needed to establish efficacy. Planning for PK-PD and biomarker studies should begin early in the adult drug development program. Extrapolation relies on the assumption that both the underlying disease and the mechanism of action of the drug used to treat that disease are similar in adults and pediatric subpopulations. Clearly, developmental changes in PK and PD need to be considered to enhance the quality of PK-PD modeling and, therefore, increase the success of extrapolation. This article focuses on the influence of differences in PD between adults and pediatric subpopulations that are highly relevant for the use of extrapolation.

Huntington disease: new insights into molecular pathogenesis and therapeutic opportunities

Huntington disease (HD) is a neurodegenerative disease caused by CAG repeat expansion in the huntingtin gene (HTT) and involves a complex web of pathogenic mechanisms. Mutant HTT (mHTT) disrupts transcription, interferes with immune and mitochondrial function, and is aberrantly modified post-translationally. Evidence suggests that the mHTT RNA is toxic, and at the DNA level, somatic CAG repeat expansion in vulnerable cells influences the disease course. Genome-wide association studies have identified DNA repair pathways as modifiers of somatic instability and disease course in HD and other repeat expansion diseases. In animal models of HD, nucleocytoplasmic transport is disrupted and its restoration is neuroprotective. Novel cerebrospinal fluid (CSF) and plasma biomarkers are among the earliest detectable changes in individuals with premanifest HD and have the sensitivity to detect therapeutic benefit. Therapeutically, the first human trial of an HTT-lowering antisense oligonucleotide successfully, and safely, reduced the CSF concentration of mHTT in individuals with HD. A larger trial, powered to detect clinical efficacy, is underway, along with trials of other HTT-lowering approaches. In this Review, we discuss new insights into the molecular pathogenesis of HD and future therapeutic strategies, including the modulation of DNA repair and targeting the DNA mutation itself.

Biostatistical and Logistical Considerations in the Development of Basket and Umbrella Clinical Trials

Oncology clinical trials are undergoing transformation to evaluate targeted therapies addressing a wider variety of biologically defined cancer subgroups. Multiarm basket and umbrella trials conducted under master protocols have become more prominent mechanisms for the clinical evaluation of promising new biologically driven anticancer therapies that are integral to precision oncology medicine. These new trial designs permit efficient clinical evaluation of multiple therapies in a variety of histologically and biologically defined cancers. These complex trials require extensive planning and attention to many factors, including choice of biomarker assay platform, mechanism for processing clinicopathologic and biomarker data to assign patients to substudies, and statistical design, monitoring, and analysis of substudies. Trial teams have expanded to include expertise in the interface between biology, clinical oncology, bioinformatics, and statistics. Strategies for the design, conduct, and analysis of these complex trials will continue to evolve to meet new challenges and opportunities in precision oncology medicine.

Evidence to Support Inclusion of Pharmacogenetic Biomarkers in Randomised Controlled Trials

Pharmacogenetics and biomarkers are becoming normalised as important technologies to improve drug efficacy rates, reduce the incidence of adverse drug reactions, and make informed choices for targeted therapies. However, their wider clinical implementation has been limited by a lack of robust evidence. Suitable evidence is required before a biomarker’s clinical use, and also before its use in a clinical trial. We have undertaken a review of five pharmacogenetic biomarker-guided randomised controlled trials (RCTs) and evaluated the evidence used by these trials to justify biomarker inclusion. We assessed and quantified the evidence cited in published rationale papers, or where these were not available, obtained protocols from trial authors. Very different levels of evidence were provided by the trials. We used these observations to write recommendations for future justifications of biomarker use in RCTs and encourage regulatory authorities to write clear guidelines.

Methodological and statistical issues in developing an External Quality Assessment scheme in laboratory medicine: Focus on biomarker research

External Quality Assessment (EQA) schemes are well-established tools with which to evaluate, monitor and improve the output quality of clinical laboratories, recognising that high quality laboratory medicine is essential for patient care. EQA programs involve the testing of multiple laboratories and the statistical comparison of their results, according to a multistep workflow. New clinical laboratory activities, such as biomarker research, require new EQA schemes. Critical elements in designing EQA programs are choosing the statistical methods and defining reference values and control limits. This article summarizes the key features of an EQA scheme, including designing the study, identifying reference values and control limits for qualitative and quantitative data, and graphically reporting laboratory performance statistics. These steps are illustrated with examples taken from the authors' experience in national and international quality assessment schemes for biomarker research.