The early economic evaluation of novel biomarkers to accelerate their translation into clinical applications

Quality Assessment of Long-Term Cryopreserved Human Bone-Derived Marrow Mesenchymal Stromal Cell Samples: Experience from the Texas Heart Institute Biorepository and Biospecimen Profiling Core

In biomedical research, biorepositories are pivotal resources that safeguard and supply clinical samples for scientific investigators. Proper long-term cryopreservation conditions are essential to maintain biospecimen quality. In this study, we analyzed the efficacy of sample cryopreservation at the Texas Heart Institute Biorepository and Biospecimen Profiling Core (THI-BRC). Our assessments included a thorough review of internal processes, quality reports, and both internal and external audit outcomes. We examined the integrity of human bone marrow-derived multipotent mesenchymal stromal cells (BM-MSCs) that were cryopreserved for over 5 years. These samples originated from randomly selected clinical trial participants or commercially sourced cell lines. Parameters such as cell viability, DNA and RNA integrity, population doubling time, sterility, and BM-MSC-specific attributes such as surface antigen expression and differentiation potential were studied. BM-MSC samples cryopreserved for ∼6 months served as our control. Our results demonstrated that the 5-year cryopreserved samples maintained their integrity compared with the shorter-term stored control samples. Moreover, THI-BRC has met accreditation agency standards and has not received any repeated deficiencies over 7 years. Collectively, our findings affirm that THI-BRC's biospecimen storage protocols align with accepted standards as confirmed by the quality assessment of long-term stored clinical samples.

Headroom Analysis for Early Economic Evaluation: A Systematic Review

OBJECTIVES

The headroom analysis is an early economic evaluation that quantifies the highest price at which an intervention may still be cost effective. Currently, there is no comprehensive review on how it is applied. This study investigated the application of the headroom analysis, specifically (1) how the headroom analysis is framed (2) the analytical approach and sources of evidence used, and (3) how expert judgement is used and reported.

METHODS

A systematic search was conducted in PubMed, Embase, Web of Science, EconLit, and Google Scholar on 28 April 2022. Studies were eligible if they reported an application of the headroom analysis. Data were presented in tabular form and summarised descriptively.

RESULTS

We identified 42 relevant papers. The headroom analysis was applied to medicines (29%), diagnostic or screening tests (29%), procedures, programmes and systems (21%), medical devices (19%), and a combined test and device (2%). All studies used model-based analyses, with 40% using simple models and 60% using more comprehensive models. Thirty-three percent of the studies assumed perfect effectiveness of the health technology, while 67% adopted realistic assumptions. Ten percent of the studies calculated an effectiveness-seeking headroom instead of a cost-seeking headroom. Expert judgement was used in 71% of the studies; 23 studies (55%) used expert opinion, 6 studies (14%) used expert elicitation, and 1 study (2%) used both.

CONCLUSIONS

Because the application of the headroom analysis varies considerably, we recommend its appropriate use and clear reporting of analytical approaches, level of evidence available, and the use of expert judgement.

Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

OBJECTIVE

To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.

DESIGN

Scoping review.

METHODS

We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests.

RESULTS

Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.

CONCLUSIONS

While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.

A toolkit of methods of development-focused health technology assessment

Health technology assessment conducted to inform decisions during technology development (development-focused or DF-HTA) has a number of distinct features compared with HTA conducted to inform reimbursement and usage decisions. In particular, there are a broad range of decisions to be informed related to the development of a technology; multiple markets and decision makers to be considered; a limited (and developing) evidence base; and constrained resources for analysis. These features impact upon methods adopted by analysts. In this paper, we (i) set out methods of DF-HTA against a timeline of technology development; (ii) provide examples of the methods’ use; and (iii) explain how they have been adapted as a result of the features of DF-HTA. We present a toolkit of methods for analysts working with developers of medical technologies. Three categories of methods are described: literature review, stakeholder consultation, and decision analytic modeling. Literature review and stakeholder consultation are often used to fill evidence gaps. Decision analytic modeling is used to synthesize available evidence alongside plausible assumptions to inform developers about price or performance requirements. Methods increase in formality and complexity as the development and evidence base progresses and more resources are available for assessment. We hope this toolkit will be used in conjunction with the framework of features of DF-HTA presented in our earlier article in order to improve the clarity and appropriateness of methods of HTA used in DF-HTA. We also seek to contribute to a continuing dialogue about the nature of, and the best approach to, DF-HTA.

Readily available biomarkers predict poor survival in metastatic pancreatic cancer

BACKGROUND

Identification of metastatic pancreatic cancer (mPC) patients with the worst prognosis could help to tailor therapy. We evaluated readily available biomarkers for the prediction of 90-day mortality in a nationwide cohort of mPC patients.

METHODS

Patients with synchronous mPC were included from the Netherlands Cancer Registry (2015-2017). Baseline CA19-9, albumin, CRP, LDH, CRP/albumin ratio, and (modified) Glasgow Prognostic Score ((m)GPS composed of albumin and CRP) were evaluated. Multivariable logistic regression analyses were performed to identify predictors of 90-day mortality. Prognostic value per predictor was quantified by Nagelkerke's partial R².

RESULTS

Overall, 4248 patients were included. Median overall survival was 2.2 months and 90-day mortality was 59.4% (n = 1629). All biomarkers predicted 90-day mortality in univariable analysis, and remained statistically significant after adjustment for clinically relevant factors and all other biomarkers (all p < 0.001). The prognostic value of the biomarkers combined was similar to WHO performance status. Patients who received chemotherapy had better outcomes than those who did not, regardless of biomarker levels.

CONCLUSIONS

In mPC patients, albumin, CA19-9, CRP, LDH, CRP/albumin ratio, and (m)GPS are prognostic for poor survival. Biomarkers did not predict response to chemotherapy. These readily available biomarkers can be used to better inform patients and to stratify in clinical trials.

A different animal? Identifying the features of health technology assessment for developers of medical technologies

Health technology assessment (HTA) conducted to inform developers of health technologies (development-focused HTA, DF-HTA) has a number of distinct features when compared to HTA conducted to inform usage decisions (use-focused HTA). To conduct effective DF-HTA, it is important that analysts are aware of its distinct features as analyses are often not published. We set out a framework of ten features, drawn from the literature and our own experience: a target audience of developers and investors; an underlying user objective to maximize return on investment; a broad range of decisions to inform; wide decision space; reduced evidence available; earlier timing of analysis; fluid business model; constrained resources for analysis; a positive stance of analysis; and a "consumer"-specific burden of proof. This paper presents a framework of ten features of DF-HTA intended to initiate debate as well as provide an introduction for analysts unfamiliar with the field.