Overview of biomarkers and surrogate endpoints in drug development

There are numerous factors that recommend the use of biomarkers in drug development including the ability to provide a rational basis for selection of lead compounds, as an aid in determining or refining mechanism of action or pathophysiology, and the ability to work towards qualification and use of a biomarker as a surrogate endpoint. Examples of biomarkers come from many different means of clinical and laboratory measurement. Total cholesterol is an example of a clinically useful biomarker that was successfully qualified for use as a surrogate endpoint. Biomarkers require validation in most circumstances. Validation of biomarker assays is a necessary component to delivery of high-quality research data necessary for effective use of biomarkers. Qualification is necessary for use of a biomarker as a surrogate endpoint. Putative biomarkers are typically identified because of a relationship to known or hypothetical steps in a pathophysiologic cascade. Biomarker discovery can also be effected by expression profiling experiment using a variety of array technologies and related methods. For example, expression profiling experiments enabled the discovery of adipocyte related complement protein of 30 kD (Acrp30 or adiponectin) as a biomarker for in vivo activation of peroxisome proliferator-activated receptors (PPAR) gamma activity.     

Development and use of biomarkers in oncology drug development

Successful development and use of biomarkers will improve the productivity of oncology drug development. Recognition of the importance of biomarkers for speeding drug development is reflected in the precise definitions and concepts proposed by an NIH Working Group to standardize terminology and promote a more coherent and systematic approach to the development and use of biomarkers. Potential clinical biomarkers of drug efficacy are often identified through pre-clinical studies or basic research. Identification of potential biomarkers for use in oncology is moving rapidly forward through continuing advances in clinical imaging technologies, especially molecular and functional imaging. Other rapid advances are a product of the growing availability of new scientific reagents for established technologies and of high-throughput genomic and proteomic technologies that can generate hundreds of potential biomarkers for further evaluation. In certain cases, conventional clinical diagnostic techniques or assays can be adapted for use in pre-clinical models to evaluate their ability to serve as biomarkers for predicting clinical responses to new drug candidates. Evaluation (pre-clinical and clinical) of a potential biomarker is often the longest stage of biomarker development, and standards for evaluation or validation depend on the intended use and stage of clinical development. Biomarkers verified for use in preclinical studies can be used to help select appropriate animal models and lead compounds. Biomarkers verified for use in clinical trials can confirm a drug's pharmacological or biological mechanism of action, guide protocol design, aid patient and dose selection, and help to minimize safety risks. Oncology drug development can be optimized by using a tiered set of clinical biomarkers that predict compound efficacy and safety with increasing confidence at each rise in tier thereby aiding corporate decision-making about advancing compounds. In oncology, a special class of extensively evaluated biomarkers of efficacy (surrogate endpoints) that generally correlate with desired clinical outcomes can be used as a basis for corporate decisions as well as for gaining accelerated provisional regulatory approval of a drug.     

TRARESA: a tissue microarray-based hospital system for biomarker validation and discovery

Formalin fixed and paraffin embedded tissue (FFPE) collections in pathology departments are the largest resource for retrospective biomedical research studies. Based on the literature analysis of FFPE related research, as well as our own technical validation, we present the Translational Research Arrays (TRARESA), a tissue microarray centred, hospital based, translational research conceptual framework for both validation and/or discovery of novel biomarkers. TRARESA incorporates the analysis of protein, DNA and RNA in the same samples, correlating with clinical and pathological parameters from each case, and allowing (a) the confirmation of new biomarkers, disease hypotheses and drug targets, and (b) the postulation of novel hypotheses on disease mechanisms and drug targets based on known biomarkers. While presenting TRARESA, we illustrate the use of such a comprehensive approach. The conceptualisation of the role of FFPE-based studies in translational research allows the utilisation of this commodity, and adds to the hypothesis-generating armamentarium of existing high-throughput technologies.     

Society of Toxicologic Pathology position paper on pathology image data: compliance with 21 CFR Parts 58 and 11

The Society of Toxicologic Pathology (STP) has developed the following recommendations for the use of pathology images in compliance with the Code of Federal Regulations (CFR), Volume 21, Part 58 (Good Laboratory Practices [GLP]) and Part 11 (Electronic Records/Signatures). These recommendations include: (1) based on current technologies and practices, pathology images (printed, electronic, or digital) used for data generation (e.g., to make a diagnosis or for morphometric analysis) are raw data that must be authenticated and archived; (2) authentication of an image may be done either by initialing and dating a print of the image or by specifically annotating the electronic image file in compliance with Part 11 regulations; (3) images used for raw data are subject to GLP procedures and controls in order to ensure data integrity including written Standard Operating Procedures, testing/validation of equipment, training of personnel, etc.; (4) validation and/or performance qualification of imaging systems used to support GLP studies must be documented and any exceptions to full validation/qualification must be described in the GLP Compliance Statement for the study; (5) images that are not used for data generation are illustrative images, are not raw data, and generally do not have to be archived; 6) illustrative images should not be used to re-evaluate or supersede the pathologist's diagnosis.     

Next-generation sequencing for constitutional variants in the clinical laboratory, 2021 revision: a technical standard of the American College of Medical Genetics and Genomics (ACMG)

Next-generation sequencing (NGS) technologies are now established in clinical laboratories as a primary testing modality in genomic medicine. These technologies have reduced the cost of large-scale sequencing by several orders of magnitude. It is now cost-effective to analyze an individual with disease-targeted gene panels, exome sequencing, or genome sequencing to assist in the diagnosis of a wide array of clinical scenarios. While clinical validation and use of NGS in many settings is established, there are continuing challenges as technologies and the associated informatics evolve. To assist clinical laboratories with the validation of NGS methods and platforms, the ongoing monitoring of NGS testing to ensure quality results, and the interpretation and reporting of variants found using these technologies, the American College of Medical Genetics and Genomics (ACMG) has developed the following technical standards.     

Revisiting the technical validation of tumour biomarker assays: how to open a Pandora's box

A tumour biomarker is a characteristic that is objectively measured and evaluated in tumour samples as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. The development of a biomarker contemplates distinct phases, including discovery by hypothesis-generating preclinical or exploratory studies, development and qualification of the assay for the identification of the biomarker in clinical samples, and validation of its clinical significance. Although guidelines for the development and validation of biomarkers are available, their implementation is challenging, owing to the diversity of biomarkers being developed. The term 'validation' undoubtedly has several meanings; however, in the context of biomarker research, a test may be considered valid if it is 'fit for purpose'. In the process of validation of a biomarker assay, a key point is the validation of the methodology. Here we discuss the challenges for the technical validation of immunohistochemical and gene expression assays to detect tumour biomarkers and provide suggestions of pragmatic solutions to address these challenges.     

Pharmacogenetics or the promise of a personalized medicine: variability in drug metabolism and transport

There is much variability in the manner individuals respond to drugs, such that the management of some drugs is problematic. In France, the incidence of hospital admissions related to adverse drug reactions is estimated to be 3.2 %, at an annual cost of over 300 millions euros. Genetic factors affecting the pharmacokinetics and pharmacodynamics of drugs partly explain interindividual variability in drug response. Pharmacogenetic focuses on the molecular mechanisms involved in drug response, and its ultimate goal is the optimisation of drug treatments, both in terms of efficacy and safety. Numerous polymorphisms in genes encoding drug-metabolising enzymes, transporters and receptors have been described and their consequences on disposition and effect of a substantial number of medications have been elucidated. This review focuses on variability of drug metabolism and transport to define the objectives of pharmacogenetics, the molecular bases of interindividual variation in drug response and the methods used for the evaluation of the individual risk of drug failure or toxicity. Some clinical applications of pharmacogenetics have already been developed in routine medicine resulting in significant improvement in patient treatment. The clinical validation of an increasing number of pharmacogenetic tests, as well as the development of new highly efficient technologies for genotyping (real-time PCR, DNA chips) should further promote pharmacogenetics in clinical practice and lead to the development of a patient-tailored drug therapy.     

Monitoring Proteins and Protein Networks Using Reverse Phase Protein Arrays

Recent advances in high throughput, high content “omic” technologies coupled with clinical information has lead to the expectation that the complexity of the molecular information generated will lead to more robust scientific research as well as the expectation that overarching therapeutic approaches will be patient-tailored to the underlying specific molecular defects of the disease. As disease understanding progresses and more therapeutics, which predominately target proteins, are developed there is a need to more confidently determine the protein signaling events that can be correlated with drug response since the deranged protein signaling networks are often the drug target itself. In this environment, the Reverse Phase Protein Microarray (RPMA) can be utilized to address the needs of both clinical screening and disease understanding through its ability to provide an unmatched functional and highly multiplexed signaling network level mapping of ongoing signaling activation, coupled with the ability of the platform to provide this information reproducibly from a tiny needle biopsy specimen or fine needle aspirate. This platform has now been utilized for biomarker discovery/validation and advancements in disease understanding both in the clinic and at the bench in the fields of cancer, liver disease, immunological disorders, and bacterial infection.     

Pharmacogenetics of anticancer drugs

Much progress has been made in treating human malignancies and there are now multiple treatment options with similar efficacy for nearly every type of cancer. However, the narrow therapeutic index of most chemotherapeutic agents and the severe consequences of undertreatment or overdosing have led to research molecular predictive factors of the toxicity and efficacy of cancer treatments. Genetic factors affecting drug metabolism and transport partly explain interindividual variability in drug response. Pharmacogenetic focuses on the molecular mechanisms involved in drug response, and its ultimate goal is the optimisation of the treatments, that combines the optimal efficacy and the minimal risk of severe side effects. Polymorphisms in genes encoding specific drug-metabolising enzymes can result in individuals in the general population being characterised as low, rapid or even ultra-rapid metabolisers. Phenotyping and genotyping tests are now available that determine or predict the metabolic status of an individual and, thus, enable the evaluation of risk of drug failure or toxicity. Some clinical applications of pharmacogenetics (5-FU, irinotecan, thiopurines) have already been developed in routine medicine resulting in significant improvement in patient treatment. The clinical validation of an increasing number of pharmacogenetic tests, as well as the development of new highly efficient technologies for genotyping (real-time PCR, DNA chips...) should further promote pharmacogenetics in clinical practice and lead to the development of a patient-tailored drug therapy.     

Application of the principles of good manufacturing practices (GMP) to the design and operation of a tissue culture laboratory

Summary Recommendations are presented for the design and operation of a tissue culture laboratory to reduce microbial contamination of cultures. These recommendations are based on practices currently used in the manufacturing of sterile drug products, certain medical devices, and in vitro diagnostic products. The recommendations depend on qualification and validation of equipment, systems, and procedures and on the control and monitoring of the laboratory environment and procedures.     

The process chain for peptidomic biomarker discovery

Over the last few years the interest in diagnostic markers for specific diseases has increased continuously. It is expected that they not only improve a patient's medical treatment but also contribute to accelerating the process of drug development. This demand for new biomarkers is caused by a lack of specific and sensitive diagnosis in many diseases. Moreover, diseases usually occur in different types or stages which may need different diagnostic and therapeutic measures. Their differentiation has to be considered in clinical studies as well. Therefore, it is important to translate a macroscopic pathological or physiological finding into a microscopic view of molecular processes and vice versa, though it is a difficult and tedious task. Peptides play a central role in many physiological processes and are of importance in several areas of drug research. Exploration of endogenous peptides in biologically relevant sources may directly lead to new drug substances, serve as key information on a new target and can as well result in relevant biomarker candidates. A comprehensive analysis of peptides and small proteins of a biological system corresponding to the respective genomic information (peptidomics(R)methods) was a missing link in proteomics. A new peptidomic technology platform addressing peptides was recently presented, developed by adaptation of the striving proteomic technologies. Here, concepts of using peptidomics technologies for biomarker discovery are presented and illustrated with examples. It is discussed how the biological hypothesis and sample quality determine the result of the study. A detailed study design, appropriate choice and application of technology as well as thorough data interpretation can lead to significant results which have to be interpreted in the context of the underlying disease. The identified biomarker candidates will be characterised in validation studies before use. This approach for discovery of peptide biomarkes has potential for improving clinical studies.     

Technologies for measuring HIV-1 drug resistance

Drug resistance testing significantly improves response to antiretroviral treatment in HIV-1-infected patients, therefore it has recently been implemented into current guidelines for the management of antiretroviral therapy. Knowledge about technologies for measuring drug resistance is important for several reasons: (a) differences exist between different technologies and also between assays based on the same technology; (b) the results of resistance testing are strongly dependent on the reliability and precision of the technology used; and (c) technical aspects have to be considered for a clinically relevant interpretation of drug resistance. The spectrum of genotypic and phenotypic technologies as well as the technical quality is increasing, which shifts the emphasis to the interpretation of resistance profiles. The interpretation is based on the knowledge of drug resistance-associated mutations as well as correlations between genotype and phenotype and clinical response, which are incorporated into rules-based systems. Bioinformatic techniques are used to generate mathematical models for the prediction of drug resistance from genotype. Both approaches are converging toward the prediction of clinical response. Because therapy response is dependent on many additional variables, further efforts are required for the generation of a large clinical database. This will be the basis of a prediction system that will optimize the antiretroviral therapy for each individual patient.     

Addressing the potential toxicities of the non-specific P-glycoprotein modulation by amalgamation with targeted approach in MDR tumors

Multidrug resistance (MDR) is an area of concern for the drug developers as well as clinical practitioners. This phenomenon is putting an emance pressure on treatment modalities of many diseases as well as posing a grave danger over clinically accepted drugs as well as molecules under clinical development. P-glycoprotein (P-gp) mediated efflux of xenobiotics is one of the major mechanisms involved in MDR. Hence, an effective modulation of P-gp may restore the potential of many substrate drugs. However, the non-specific P-gp modulation may be associated with many unwanted toxic effects. Therefore, an approach involving simultaneous exploitation of P-gp modulation as well as targeted delivery in a particulate carrier system may result in a more effective MDR reversal, accompanying a safer drug profile.     

US Biosimilar Pathway Unlikely to be Used

In March 2010, the US passed the healthcare reform bill, including The Biologics Price Competition and Innovation Act of 2009, which established an abbreviated Biologic License Application (aBLA) pathway for the approval of biosimilars. The aBLA pathway may never be used. At the “Business of Biosimilars” meeting in Boston in September, developers of both innovator and generic biologics as well as representatives from the scientific, regulatory, and legal communities noted that, because of unclear requirements for clinical data and the need for public disclosure of proprietary data, manufacturers of generic biologics are unlikely to take advantage of the aBLA process, opting instead for a standard Biologic License Application (BLA). The implications of an unusable biosimilars pathway in the US dampen our already soft outlook for biosimilars. Companies will still develop follow-on biologics, but approved compounds will behave as new branded drugs. Biosimilars in the US are therefore not likely to lead to aggressive pricing, but will more likely mirror current situations where several similar biologics are available. For example, the interferon (IFN) β-1a products Avonex® and Rebif®, and Betaseron® (IFN β-1b) have all enjoyed >10% price increases for the last several years in spite of their clinical similarities. inThought reiterates its outlook for generic erosion of a typical biologic that projects a loss of revenue of 30% over 5 years compared to the 90% revenue loss for a typical branded small molecule.     

Technologies for measuring HIV-1 drug resistance

Abstract

Drug resistance testing significantly improves response to antiretroviral treatment in HIV-1-infected patients, therefore it has recently been implemented into current guidelines for the management of antiretroviral therapy. Knowledge about technologies for measuring drug resistance is important for several reasons: (a) differences exist between different technologies and also between assays based on the same technology; (b) the results of resistance testing are strongly dependent on the reliability and precision of the technology used; and (c) technical aspects have to be considered for a clinically relevant interpretation of drug resistance. The spectrum of genotypic and phenotypic technologies as well as the technical quality is increasing, which shifts the emphasis to the interpretation of resistance profiles. The interpretation is based on the knowledge of drug resistance-associated mutations as well as correlations between genotype and phenotype and clinical response, which are incorporated into rules-based systems. Bioinformatic techiques are used to generate mathematical models for the prediction of drug resistance from genotype. Both approaches are converging toward the prediction of clinical response. Because therapy response is dependent on many additional variables, further efforts are required for the generation of a large clinical database. This will be the basis of a prediction system that will optimize the antiretroviral therapy for each individual patient.     

A field guide for cancer diagnostics using cell‐free DNA: From principles to practice and clinical applications

Recently, many genome‐wide profiling studies provided insights into the molecular make‐up of major cancer types. The deeper understanding of these genetic alterations and their functional consequences led to the discovery of novel therapeutic opportunities improving clinical management of cancer patients. While tissue‐based molecular patient stratification is the gold standard for precision medicine, it has certain limitations: Tissue biopsies are invasive sampling procedures carrying the risk of complications and may not represent the entire tumor due to underlying genetic heterogeneity. In this context, complementary characterization of genetic information in the blood of cancer patients can serve as minimal‐invasive ‘liquid biopsy’. Fragments of circulating cell‐free DNA (cfDNA) are released from tissues of healthy individuals as well as cancer patients. The fraction of cfDNA that is released from primary tumors or metastases (i.e. circulating tumor DNA, ctDNA) represents genetic aberrations in cancer cells, which are a potential source for diagnostic, prognostic, and predictive biomarkers. Recent studies have demonstrated technical feasibility and clinical applications including detection of drug targets and resistance mutations as well as longitudinal monitoring of tumors under therapy. To this end, a variety of pre‐analytical procedures for blood processing, isolation and quantification of cfDNA are being employed and several analytical methods and technologies ranging from PCR‐based single locus assays to genome‐wide approaches are available, which considerably differ in sensitivity, specificity, and throughput. However, broad implementation of ctDNA analysis in daily clinical practice requires a thorough understanding of theoretical, technical, and biological concepts and necessitates standardization and validation of pre‐analytical and analytical procedures across different technologies. Here, we review the pertinent literature and discuss the advantages and limitations of available methodologies and their potential applications in molecular diagnostics.     

Pharmacogenomics and its implications for autoimmune disease

A striking failure of modern medicine is the debilitating and lethal consequences of adverse drug reactions (ADRs) which rank as one of the top ten leading causes of death and illness in the developed world with direct medical costs of 137-177 billion US dollars annually in the USA. Although many factors influence the effect of medications (i.e. age, organ function, drug interactions), genetic factors account for 20-95% of drug response variability and play a significant role in the incidence and severity of ADRs. The field of pharmacogenomics seeks to identify genetic factors responsible for individual differences in drug efficacy and adverse drug reactions. Pharmacogenomics has led to several genetic tests that provide clinical dosing recommendations. For autoimmune disease, pharmacogenomics has led to several DNA-based tests to improve drug selection, optimize dosing, and minimize the risk of toxicity. The 'GATC' project is a nation-wide project established in Canada to identify novel predictive genomic markers of severe ADRs in children. An ADR surveillance network has been established in all of Canada's major children's hospitals, serving up to 75% of all Canadian children. The goal of the project is to identify patients experiencing specific ADRs, collect DNA samples, and apply genomics-based technologies to identify ADR-associated genetic markers.     

Intraoperative ultrasound for guidance and tissue shift correction in image-guided neurosurgery

We present a surgical guidance system that incorporates pre-operative image information (e.g., MRI) with intraoperative ultrasound (US) imaging to detect and correct for brain tissue deformation during image-guided neurosurgery (IGNS). Many interactive IGNS implementations employ pre-operative images as a guide to the surgeons throughout the procedure. However, when a craniotomy is involved, tissue movement during a procedure can be a significant source of error in these systems. By incorporating intraoperative US imaging, the target volume can be scanned at any time, and two-dimensional US images may be compared directly to the corresponding slice from the pre-operative image. Homologous points may be mapped from the intraoperative to the pre-operative image space with an accuracy of better than 2 mm, enabling the surgeon to use this information to assess the accuracy of the guidance system along with the progress of the procedure (e.g., extent of lesion removal) at any time during the operation. Anatomical features may be identified on both the pre-operative and intraoperative images and used to generate a deformation map, which can be used to warp the pre-operative image to match the intraoperative US image. System validation is achieved using a deformable multi-modality imaging phantom, and preliminary clinical results are presented.     

Validation of a flow cytometry based G(2)M delay cell cycle assay for use in evaluating the pharmacodynamic response to Aurora A inhibition

Pharmacodynamic assays are important aspects for understanding molecularly targeted anticancer agents to investigate the relationship between drug concentration (pharmacokinetics) and drug “effect” or biological activity. As new drug entities are developed that affect DNA cell cycle, a pharmacodynamic assay which measures cell cycle perturbation would be a valuable clinical trial tool. During recent years, flow cytometry has established itself as a useful method to determine the relative nuclear DNA content and percentage of cycling cells of biological specimens. However to date, the analytical validation of cytometry based assays is limited and there is no suitable guidance for method validation of flow cytometry based cell cycle assays. Here we report the validation of a flow cytometry based cell cycle G₂/M delay assay for use in evaluating the effect of investigational drug MLN8237, a small molecule inhibitor of a mitotic kinase Aurora A, for clinical trial use. The assay method was validated by examining assay robustness, repeatability, reproducibility, precision, and determining the cutoff for a true drug effect based on biostatistical analysis models. Experimental results show that the intra-assay repeatability was less than 20% with an intra-donor variability of less than 40%. The robustness of the assay was less than 30%. Since this is an ex-vivo stimulation assay, variability parameters were expected to be higher. Based on biostatistical modeling, an absolute change in %G₂M of 5.2% (95% CI) was needed in order to detect a true drug effect. Overall, the assay demonstrated acceptable variability to warrant further in vivo testing.