Standardized biomarker and biobanking requirements for personalized psychiatry

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Standardized biomarker and biobanking requirements for personalized psychiatry Catherine Tobena, Victoria K. Arneta, Anita Loa, Pamela H. Saundersb and Bernhard T. Baunec,d,e a

Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia, b SABR Manager, SAHMRI, Adelaide, SA, Australia, c Department of Psychiatry € € and Psychotherapy, University of Munster, Munster, Germany, d Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia, e The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia

1 Personalized psychiatry—from diagnosis to treatment response Worldwide, the increasing phenomenon of mental health disorders has devastating effects on individuals, families, and communities, with depression being projected to be the leading cause of global disability by 2030. As neuropsychiatric disorders are complex and heterogeneous in nature, prognosis, as well as optimum treatment plans, are often ineffective. Currently, patients rely solely on detailed clinical assessments that take time and effort, and that can be incomplete with regard to subtypes (Remick, Sadovnick, Gimbarzevsky, Lam, & Zis, 1993). The emerging field of personalized psychiatry aims to circumvent this by ultimately integrating genetic and bloodbased biomarker panels to assist in diagnosis and design of individualized therapeutic treatment regimes. Blood-based biomarkers require standardized specimen collection and storage protocols. To this end, a biorepository, or biobank, is critical for the accrual of large-scale multimodal data sets containing human biological material and associated clinical data capturing a broad range of commonly encountered clinical psychopathologies. Furthermore, recent innovative methods and technologies have enabled detection and characterization of the underlying molecular and cellular pathophysiology of mental health disorders. In this chapter, we will focus on the large-scale storage of peripheral biomarker material.

2 Fundamentals of biobanking relevant to mental health disorders 2.1 Implementation of a business plan to ensure sustainability Biobanking is a relatively new field within psychiatry. The establishment of a biobank requires implementation of a business plan, and an organizational structure tailored to the jurisdiction of the facility in which it will reside. For example, a privately governed facility will have different governing requirements, compared with a publicly supported and governed institute. Importantly, sustainability of a biobank will be ensured by minimizing risk factors pertaining to negative economic influences that could lead to its demise. To this end, the potential areas for revenue should be identified and actioned. The business plan for a biobank should define the following structures and committees.

2.1.1 Financial provisions Consideration should be given to implementing an itemized financial plan that will account for costs, revenue, and funds received (Gonzalez-Sanchez, Lopez-Valeiras, & Garcı´a-Montero, 2014; McDonald, Velasco, & Ilasi, 2010). Financial support and investment can be achieved via grants from various sources, including philanthropic, government, and institutional. To further support the ongoing maintenance of the biobank, travel grants for further education, as well as equipment grants, are important. In order for the psychiatric biobank to be viable, it is paramount to ensure that any services, including storage/handling and bioanalytical analyses provided to users, are cost recovered. Researchers should include costs for high-quality and standardized sample collection/transport and storage, as well as bioanalytical analyses within their grant applications (Gonzalez-Sanchez et al., 2014).

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2.1.2 Organizational structure and governance model The development of an organizational hierarchy and governance committee for the psychiatric biobank will ensure the aims and services are conducted efficiently. This may include health and medical researchers/professionals, as well as a legal representation (Chalmers et al., 2016). To assure accountability is maintained with stakeholders (researchers, collaborators, general public), processes to allow for transparency and traceability of biospecimen management are required. These include quality control, a defined management plan for access and custodianship of biospecimens, as well as an ethical framework (McDonald et al., 2010). An example includes the access committee that would assess requests for sample usage. Ideally, this committee would include a lay member committed to reflecting the general public perception on sample and associated data usage.

2.1.3 Biobank succession and disassembly Funding cuts or natural or man-made disasters pose a considerable risk to the maintenance and longevity of the psychiatric biobank. In order to prepare for these factors, it is important to construct a detailed action plan for forced biobank closure, and potential loss of irreplaceable biospecimens and data (Stephens & Dimond, 2015). It is also important to define an ownership succession plan. For example, upon closure of a biobank, the samples/data collected by a biobank should be allocated to a nominated custodian within the institution. However, samples/data managed by the biobank would be returned to the owner of the samples.

2.1.4 Reporting strategies As part of quality control management, Human Research Ethics Committees (HREC) are an integral part of a sustainable psychiatric biobank. Ongoing consultation and transparent communication of procedures and protocols will help to ensure the viability of the biobank. Furthermore, a comprehensive report may encourage funding from additional stakeholders. As part of the community consultation process, consideration to publicly release the annual report could also be made.

2.1.5 Consumer engagement The discussion in relation to consumer engagement is ongoing. Consumers as “Citizen Scientists” is a growing concept, enabling members of the general public to be involved in psychiatric research. Individuals need no longer be passive human “subjects,” but can be engaged in innovative ways, including digital media platforms over time, and recognized as active, interested, and valued research participants in the field of psychiatry (Kaye et al., 2015).

2.2 Regulatory governance 2.2.1 Ethical considerations Research involving human participants requires some form of ethical review by an independent committee. Informed consent, confidentiality, secondary use of samples, data over time, and return of results are processes that require standardized ethical governance. In the act of ethically acquiring participants, a consent form and information sheet is essential (Participant Information and Consent Forms (PCIF)). The PCIF should be given appropriate thought, as reconsenting is a very disruptive, difficult, costly, and time-consuming process. For example, complications may arise through outright refusal of an updated consent form by the participant, or loss of the participant at follow up (e.g. death). Both broad and dynamic consent forms are flexible, and include unspecified future use of biospecimens and associated data subject to ethical approval (Kaye et al., 2015). This provides researchers with room for future analytical access, while the dynamic consent provides more interaction with the participant, usually via web-based platforms (Chalmers et al., 2016; Graham et al., 2014).

2.2.2 Generation of global participant data networks within ethical and legislative frameworks Participant clinical data is maintained by ethical guidelines and legal frameworks for the individual’s safety and protection. In an era in which biobanks operate within global networks, it is important for researchers and biobanks to abide by the varied legal frameworks within multiple jurisdictions across countries (Chalmers et al., 2016). Added to this is the dynamic nature of legal frameworks that may be reformed within different countries regarding biospecimens and associated data collection, storage, and sharing (Chalmers et al., 2016; Shenkin et al., 2017). The ownership and access of participant data and biospecimens is complicated by disparate digital information dynamic

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legislations. This has the potential to lead to unforeseen risks regarding data access and disclosure of data to third parties (Dove, 2015). Importantly for international collaborations, forethought is required from the beginning to ensure inclusion of adequate information on PCIF, as well as strategies for maintaining data security, participant privacy, and confidentiality on different servers, and meeting data protection law requirements (Bauchner, Golub, & Fontanarosa, 2016; International Compilation of Human Research Standards 2018 Edition, 2018; National Health and Medical Research Council (NHMRC), ARC, & AVCC, 2007). For example, sharing genetic data, as generated from genome-wide association studies (GWAS) (Eiseman, Bloom, Brower, Clancy, & Olmsted, 2003; Zhao & Castellanos, 2016), among international consortia requires comprehensive ethical review. A balance is required among participant privacy, confidentiality, and informed consent with regard to sharing data, especially from publicly funded projects. This is important to maintain research integrity and transparency while maximizing the research value from collected data. This is pertinent to the psychiatric field where a large proportion of collected data is of a highly sensitive nature. Furthermore, participant vulnerability must be taken into account, for example, due to cognitive impairments or research within pediatric cohorts (DuBois, Bante, & Hadley, 2011; Zhao & Castellanos, 2016). Implementation of the preceding safeguards for the potential use of data, including biospecimens, will uphold the participant’s privacy within local and global governance frameworks, regardless of location (Dove, 2015). Striving toward international harmonization requires abiding by minimum ethical and legislative standards for biospecimen collection, data management, and collection to ensure sample quality and strengthen study power across multiple consortia. Minimum normative subject data sets should include standard metadata (Fig. 1), which will facilitate high quality research across consortia within personalized psychiatric medicine.

2.2.3 Participant consent form requirements in psychiatry A consent form and information sheet should include: l l l l l

Permission for future use and application of the samples, and all associated data (not sample type or study specific) Assurance in the case of participant withdrawal that medical care will not be compromised Option for participant to withdraw at any time Permission to contact participant in the future State clearly who owns the data (usually the institution)

FIG. 1 Biomarker types, sources, and application in personalized psychiatry.

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Statements to clarify consequences of potential commercialization from the use of samples and/or associated data. For example, study participant or their families will not be the recipient of any monies in relation to commercial discoveries Address strategy about incidental findings

2.2.4 Incidental findings One of the most discussed issues relating to biobanking is the return of incidental findings to the participant. According to NHMRC guidelines, it is a best practice for the biobank to develop a defensible plan for the case in which a researcher may discover a significant genetic finding (National Health and Medical Research Council (NHMRC) et al., 2007 (updated 2015)). The biobanks ethics and governance should determine whether or not the biobank will inform the participant of any incidental findings. A comprehensive procedure involving how the finding will be retested and communicated should be developed if the participant is to be informed.

2.3 Sample management and infrastructure for high quality storage of biospecimens In establishing a biobank with large-scale human biological material and associated data, it is essential that analytical processes and procedures are standardized (Fig. 2). This will ensure sample integrity is maintained for any future analyses, as well as allowing for replication of experiments, and hence, validation of results.

FIG. 2 Management of psychiatric biospecimens for biomarker identification.

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2.3.1 Importance of quality control and standardized protocols for a biobank The development of standard operating procedures (SOPs) needs to include standardization of all aspects, from sample collection to biobanking, with internal, and potentially external, auditing of procedures being important for quality control. Information on method of collection, processing, and accessioning should be included and recorded to account for variations within a cohort. Upholding sample integrity will assist with valid and reproducible biomarker results.

2.3.2 Methods for deidentification of participant data In conjunction with an ethics committee, the type of participant identification will be determined; individually identifiable, reidentifiable, or nonidentifiable. The type of participant sample identification chosen determines the method of designing and assigning the new identifier. Usually, a participant will be assigned a globally unique Personal Participant Identifier (PPID), independent of factors that may distinguish the participant. This process can be enhanced by using a barcode with a human readable format.

2.3.3 Biobanking data management and integration strategies A biobank needs to be able to track, generate, and update data related to the biospecimen efficiently. Many Laboratory Information Management System (LIMS) software options are available to biobanks that allow improved productivity in workflow and data management. Due to funding issues, Excel spreadsheets are commonly used to manage biobanking data. As the number of samples collected grows exponentially, the amount of associated data does also, and comprehensive reporting is required. Furthermore, data security is important to protect participant privacy and information (International Society for Biological and Environmental Repositories (ISBER), 2011). Therefore, spreadsheets would need to be replaced with an appropriate software system. Factors in assessing which LIMS is suitable include: l l l

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Available budget (short- and long-term) Projected size of the biobank (sample quantity, type, and retention period) Envisaged staff usage over the life of the biobank (how many user access licenses are required and how many are available with the selected software) Opportunity for ongoing IT support access Compatibility between software systems to be used by the biorepository and collaborators Accessibility options and security rules around data access Where the data is allowed to be stored; local server, local cloud, or international owned cloud servers

2.3.4 Psychiatric biospecimen storage and handling The choice of sample storage is defined by cost effective usage of available facilities. Factors to consider include: l l l

Infrastructure running and maintenance costs Capacity (volumes of sample  sample quantity) Period of sample storage (short-, medium-, and long-term)

Temperature controlled environments, while handling samples outside of storage, should also be incorporated into SOPs, such as dry ice and cryocarts. High infrastructure costs associated with biobanking not only come from the purchase of the specialized equipment, but also from the high ongoing operational costs, including scheduled maintenance, uninterrupted power supply, generator back-up power supply, and monitoring systems. Accessibility to the storage facility within the biobank should be limited and applied through physical security systems. Ideally, access to individual freezers should be restricted to “owner” users and centrally monitored. Other aspects of sample management that need to be considered and standardized in both procedure and compliance within the facility are oxygen sensors with alarms and associated rescue equipment, and also ambient air temperature monitors with alarms to reduce equipment compressors’ stress. For audit purposes, a backup monitoring system for individual equipment needs to be a system independent of the manufacturer’s self-monitoring system. Backup storage availability needs to be predetermined in the event of equipment failure with a detailed SOP for monitoring sample movement. Variations in temperature need to be recorded in cold chain management (from the time the sample is first stabilized to the time the sample is used and depleted, as well as time spent in transportation) (Moore et al., 2011). Regular internal and external testing and auditing can be carried out to assess degradation of samples over specific time frames. Other measures to ensure quality of biospecimens include reducing freeze-thaw cycles by using minimal volume aliquots for 1–3

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freeze-thaw cycles. Details of freeze-thaw cycles, including frequency and methods, are to be recorded. Quality control of biospecimens can also be audited independently by groups such as the Integrated Biobank of Luxembourg (IBBL).

2.3.5 Recording of biospecimen information To enable future replication of results, the recording of all aspects of biobanking requires strict documentation of all data generated through the total management of the samples. This commences at the time of collection of the parent sample, transportation, processing, initial stabilization, long-term cold chain management, shipping, and distribution. When retrieving samples for shipping, depletion of an individual’s original stock of samples is not recommended. To assist in standardizing annotation of biospecimen types and collection methods, the Standard Pre-analytical Coding for Biospecimens (SPREC) codes can be used (Lehmann et al., 2012).

2.3.6 Sharing biospecimens An inherent aspect of biobanking is the sharing of stored samples. In fact “specimen locators,” otherwise known as “specimen catalogues,” are now commonly available on biobank websites. These assist researchers in locating specific specimens related to their field of research. Consequently, sharing of samples and/or associated clinical data includes both data recently collected with collaborators, as well as retrospectively collected samples. Samples that have been permitted for sharing will need to follow a process whereby a letter of intent (LOI) would be submitted to the biobank access committee. If approved for sharing, the next step would include ratification of a material transfer agreement (MTA).

2.3.7 Transfer of biospecimens to internal and external sites Standardized transportation protocols are to be derived from local regulatory bodies, such as the International Air Transport Association (IATA), which is, for example, Australia’s aviation transport authority. These protocols will stipulate that distribution of biospecimens and associated clinical data locally, nationally, or internationally will require current import/ export permit requirements specific to the biospecimen. To further ensure standardization in transport, it is recommended that biobanking staff be trained in accredited transport of specimen courses such as those offered by the Civil Aviation Safety Authority (CASA). The chosen courier company is required to provide total quality of control for biospecimens in transit from time of collection to receipt of samples. In the case of frozen samples, a best practice is the use of a dry shipper with a temperature probe, a dry ice monitoring and retrieval transportation audit log (Moore et al., 2011), as well as real time sample location acknowledgement via email. Costs of biospecimen transportation to/from the biobank should be recovered from the researcher (McDonald et al., 2010). Any events that may compromise sample integrity, and thereby future outcome of analyses, need to be recorded and communicated with all stakeholders.

2.3.8 Associated clinical data To allow for comprehensive clinical data collection, the PCIF annotation needs to include the different sources of data collection, including the health department, data registries, and any specific health professionals. The methodology for collection of clinical data may be through paper records that require digitalization, manual data entry, or via data downloads from health department records, pathology departments, data registries, and other authorized sources. Patient data collection from patient medical records has, in the past, been a time-consuming process, however with more modern and streamlined patient electronic health records, platforms such as South Australia’s Enterprise Patient Administration System (EPAS) clinical data collection will become less time consuming and complicated, and more cost effective. Access to clinical data can be provided in association with biospecimen assay results, or independently of biospecimen distribution. In the latter case, the biobank can consider charging for a separate service.

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Reproducible identification of biomarker signatures within mental health disorders

Biomarkers are objectively measured characteristics providing an indication of a biological process (normal or pathological) or response to an intervention. In the field of psychiatry, investigation into biomarkers has focused on biological specimens including peripheral blood, saliva, urine, and hair analyses. In this chapter, we will focus on the identification of those biomarkers from peripheral blood that are associated with inflammation, oxidative stress, and blood brain barrier integrity. For biomarkers to ultimately be applied in clinical practice, certain conditions need to be fulfilled. This includes the use of high sensitivity and specific biomarker assays, as well as output of accurate and reproducible results.

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The importance of a multicombination biomarker panel or signature will improve the specificity and sensitivity required for stratification of heterogeneous psychiatric disorders. Discovery of novel psychiatric biomarkers requires an integrated approach among the clinic, biobank, and researchers. As the realm of psychiatry presents complexity in terms of a heterogeneous phenotype, it is important to standardize collection of biospecimens and biomarker analyses. As in any experimental design, there are a number of factors that must be addressed in order to establish a robust plan of research. All stages of the biomarker discovery workflow are to be considered, especially from initial concept, to sample collection, and biomarker validation (Fig. 3). The scope of this chapter will focus on describing key aspects within the first three stages of the biomarker discovery workflow, and noting factors pertinent to psychiatry. Technological progress has increased the variety of biomarker types and sources that may be assessed and analyzed. This includes molecular (DNA, RNA, and protein) and cellular, as well as other metabolites. Each type of biomarker has its own strengths and limitations, with many methodologies still under development. As proteins appear to be easily detectable in point-of-care devices, proteomic studies remain one of the most promising sources of biomarker discovery within psychiatry. It is important to note that automation within the discovery process should be included where possible and practical to: l l l

Minimize introduction of errors, including type I and II Increase efficiency of handling large numbers of samples Minimize missing and/or misplacement of samples

3.1 Biomarker discovery in psychiatry—considerations, stage I Aim: Determine type of biomarker(s) to be identified, as well as the application (e.g. diagnostic, prognostic, predictive of therapeutic outcome, etc.) and disorder of interest. Some of the factors necessary to identify within the first stage, experimental design, include; clinical case, potential biomarker application, overall biomarker identification approach (global or hypothesis driven), details about the type of biomarker to be identified, and the number of participants required for statistical power.

FIG. 3 Biomarker discovery workflow toward precision psychiatry (stages I to IV).

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Other considerations include: l l l l l

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Have the samples already been collected? Is collected sample integrity appropriate for the type of biomarkers to be investigated? Is there sufficient amount of sample for the biomarker assessment techniques available? Are the available biomarker assessment techniques specific and sensitive? If following a hypothesis driven approach, is there enough evidence to suggest the biomarker candidate could sufficiently stratify participants? Are there strategies to minimize bias, such as blindness and randomization?

Securing these details will help to direct, define, streamline, and standardize downstream protocols and procedures.

3.2 Biomarker discovery in psychiatry—considerations stage II Aim: (a) Appropriate participant characterization (b) sample collection, processing, and storage maintains integrity of biomarkers of interest while ensuring sample and participant details are recorded appropriately.

3.2.1 Participant recruitment In the field of psychiatry, despite an increasing prevalence in mental illness, there are inherent challenges associated with securing participants for a study. For example, participants with psychoses can demonstrate characteristics of unreliability as part of their presentation, and are therefore less likely to attend follow-up time points. Establishing bidirectional lines of communication with the researchers and the whole clinical team, including receptionists, is a useful strategy to gain and maintain access to more eligible participants, and mitigate effects of participant dropout. In addition, media and digital platforms may be used within an ethical framework to also raise awareness and widen exposure to increase recruitment opportunities. Appropriate subject groups, including healthy controls and matched controls, as well as calculated required sample sizes defined in the study design of Stage I, should be standardized. Strategies should be included to address the potential of failing to meet predetermined sample size.

3.2.2 Biospecimen collection and processing Safety concerns should be addressed with standard operating procedures (SOPs), including appropriate personal protective equipment (PPE), such as mandatory eye protection, laboratory gowns, gloves, and use of biohazard cabinets (e.g. infectious diseases may be transmitted via particular biological samples, such as peripheral blood). Appropriate action plans should also be developed in conjunction with health, safety, and wellbeing (HSW) committees within facilities where samples will be collected, processed, and analyzed. Other factors relating to sample collection and processing to define and standardize include: l l

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Appropriate time point(s) of collection are established for the study Ensure any participant requirements prior to sample collection have been met (examples include fasting status, taking medication, etc.) Appropriate consumables chosen; collection materials, preservatives, and buffers to enhance biomarker measurement while minimizing potential artefacts and errors downstream Certain factors may need to be controlled for, as the stability of some biomarkers are low. This includes time and other environmental factors, such as temperature. Additionally, there may be factors that may affect biomarker detection, and should be controlled for at sample collection, processing, and storage stages. Examples include participant fasting status, deproteination of sample (e.g. when observing levels of oxidative stress marker), quality of collected biospecimen (hemolysis of blood may interfere with certain biomarker detection), tube design, and sterility may compromise sample integrity or ability for accurate detection, and so forth.

3.2.3 Biospecimen shipping and storage for preservation of biomarker integrity Important factors of shipping and storage requirements of samples should be defined and standardized. Most factors relate to maintaining biomarker integrity, and therefore sample integrity. Storage requirements are sample specific, and depend on how long the samples may need to be stored before use. These items include: l l

Control critical environmental factors, such as temperature, pH, use of RNase/DNase free tubes Distance and time are important factors when samples must be transferred to multiple sites (collection and/or processing and/or biomarker detection). Specialized couriers may be required, especially to deliver biological and

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temperature sensitive samples in a safe and timely manner (e.g. when dry ice is required to maintain –80°C frozen state of samples). To minimize biomarker degradation within liquid biopsy samples induced by multiple freeze-thaw cycles, it is recommended to divide the collected and processed sample into aliquots at the point of processing. The volume chosen is based on types and number of biomarker detection technique(s) planned to be used. Strategies to maintain security and stability of sample until use in biomarker validation (Stage III). This includes backup freezers, back-up energy generation, and secure long-term and short-term storage security (e.g. card access).

3.3 Biomarker discovery in psychiatry—considerations, stage III Aim: (a) Confidently measure, analyze, and identify biomarker(s) of interest within collected samples, and (b) integrate observed biological results with associated participant data to form clinically relevant inferences (specified at Stage I). (1) Data Acquisition A variety of data and information may be acquired through data mining and literature searches. However, biological analysis and quantification of biomarkers based on collected and processed samples is essential in identifying reproducible and clinically relevant biomarkers. When investigating molecular biomarkers, appropriate bioanalytical infrastructure access and maintenance is required. This includes equipment such as calibrated pipettes, centrifuge, microplate readers, and temperature-controlled storage facilities, and so forth. If multiple sites of analysis are involved, it is important to conserve critical equipment parameters and standards, such as regularly serviced pipettes, types of pipettes used, and centrifuge spinning variables. Collaborations with equipment providers can provide valuable advice and access to additional resources. Biological assays are a popular method of molecular biomarker detection and quantification. Depending on the biomarker of interest, bioanalytical assays may be sourced in validated kit formats. Regardless of format, quality assurance is extremely important, and in addition to the studies’ experimental controls (i.e. healthy controls), other appropriate internal quality control(s) (QCs) within the assay must be selected (i.e. sample spiked with positive and negative QC). Addition of QCs also offers a means to assess intra- and inter-assay reproducibility. Specificity and sensitivity (minimizes high background noise within a cohort) are additional performance characteristics that must be considered when deciding which assay to select. Appropriate replication of samples must also be confirmed. For example, triplicates are a common minimum in immunoassays. However, if samples are extremely limited, duplicates are adequate. In other techniques, such as genomic sequencing, replicates are not usually required. Further validation for the use of a manufactured bioanalytical kit requires measurements for dilution linearity and recovery. Further parameters, including limit of quantification, limit of detection, specificity, and selectivity are generally assessed by the manufacturer (Andreasson et al., 2015). Error and variation thresholds need to be determined in the context of study design and biomarker detection method. The co-efficient of variation (CV) under 20% is adequate for immunoassays in the proteomic biomarker validation phase (Guidance for Industry: Bioanalytical Method Validation, 2001). However, biomarker detection methods require a tighter CV limit for clinical implementation. Removal of bias at the assay level is another item to consider and standardize. In order for this to be achieved, digital randomization of sample orders for biomarker analysis is a recommended strategy. Statistical methods must also be evaluated in reference to the selected assay before establishment of the study protocol. Application for the appropriate regression model for an assay’s standard curve can be further confirmed by a curve fit of R2 > 0.95. Establishing standardized formatting and location of associated sample data generated by the assay, as well as potentially useful sample information, saves time in data collation, analysis, result reporting, and future reference. Potential sample information of note could include sample integrity and unusual clinical information. (2) Data processing, Integration, and Bioinformatics and (3) Data interpretation and Analysis Data processing, integration, and interpretation are the final stages of initial biomarker discovery validation. It is an area where there are challenges and demands with increasingly high-dimensional, complex, and large data sets. However, coding experts, software packages (including statistical and network modeling packages), and emerging trends toward machine learning (“method of data analysis that automates analytical model building”) are helping to efficiently and effectively identify and perform the required correlative and mapping models to aid in the identification of clinically relevant biomarker signatures (Alawieh et al., 2012). Some variables affecting biomarker research are impractical to standardize at the earlier recruitment stages. These include confounding variables, such as medication and comorbidities that can, however, be controlled for using statistical techniques. Sound and robust inferential statistical analyses are required within biomarker research. Methodologies are relatively standardized in the field, with traditional null hypothesis significance testing most commonly used. Specific tests employed are based on the question at hand, and the types of variables at play. Sample size is also important, however, the

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statistical power required to ensure that the clinical use of the biomarkers is powerful enough to influence decision-making processes is yet to be agreed upon within the medical community. Interestingly, other methods, such as Bayesian statistics, may more easily communicate confidence and percentages of likelihood than traditional P-values. Additionally, the size of effect is slowly gaining use in the field of psychiatry (McDermott et al., 2013; Robotti, Manfredi, & Marengo, 2014). Candidate biomarkers require further validation before commercialization and clinical use (Fig. 3, stage IV). These processes progressing toward clinical implementation are highly regulated and entail a series of preclinical phases. They include functional in vitro assays and external and reproducible verification (DeSilva et al., 2003; Guidance for Industry: Bioanalytical Method Validation, 2001; Marini et al., 2014).

3.3.1 Reporting and sharing results Standardized reporting is beneficial as it ensures consistency and reproducibility, and all required details are included. Internal reporting procedures for future reference and audits are useful for both laboratory and biobank records, as well as updating collaborators. Sharing results externally, for example, by publication and/or potential commercialization, requires consideration throughout the biomarker discovery process (Bauchner et al., 2016). Increasingly, fund providers will request analyzed data to be published via open source, and prior to subsequent funding (Shenkin et al., 2017). There are individual requirements specific to each avenue, beyond the scope of this chapter.

3.4 Future vision for biomarker discovery and validation in psychiatry A multimodal biomarker assessment-based approach will enable clinicians to better stratify the heterogeneous course of psychiatric disorders into clinical subgroups, and help advance the development of personalized psychiatric diagnostic and treatment strategies. Research in the field of clinically relevant psychiatric biomarkers is growing. However, standardization in this field is important to reduce bias and errors, as well as increase sensitivity and specificity, which consequently removes barriers toward biomarker signatures gaining clinical implementation. Approval of an appropriate panel of biomarkers is still in its infancy due to the relatively long lag time between translational validation and commercialization. For this to be realized, integration among biobanking staff, clinicians, basic science researchers, and bioinformaticians is required. Psychiatric-focused biobanks are the cornerstone to supporting significant neuropsychiatric translational research, and moving toward precision-based medical care.

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