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Abstract
Objective個人化藥物(PM)允許治療病人s based on their individual demographic, genomic or biological characteristics for tailoring the ‘right treatment for the right person at the right time’. Robust methodology is required for PM clinical trials, to correctly identify groups of participants and treatments. As an initial step for the development of new recommendations on trial designs for PM, we aimed to present an overview of the study designs that have been used in this field.
DesignScoping review.
MethodsWe searched (April 2020) PubMed, Embase and the Cochrane Library for all reports in English, French, German, Italian and Spanish, describing study designs for clinical trials applied to PM. Study selection and data extraction were performed in duplicate resolving disagreements by consensus or by involving a third expert reviewer. We extracted information on the characteristics of trial designs and examples of current applications of these approaches. The extracted information was used to generate a new classification of trial designs for PM.
ResultsWe identified 21 trial designs, 10 subtypes and 30 variations of trial designs applied to PM, which we classified into four core categories (namely, Master protocol, Randomise-all, Biomarker strategy and Enrichment). We found 131 clinical trials using these designs, of which the great majority were master protocols (86/131, 65.6%). Most of the trials were phase II studies (75/131, 57.2%) in the field of oncology (113/131, 86.3%). We identified 34 main features of trial designs regarding different aspects (eg, framework, control group, randomisation). The four core categories and 34 features were merged into a double-entry table to create a new classification of trial designs for PM.
Conclusions各種試驗設計存在和應用to PM. A new classification of trial designs is proposed to help readers to navigate the complex field of PM clinical trials.
- Precision medicine
- Clinical trial
- Study design
- Scoping review
Data availability statement
Data are available in a public, open access repository. The data set supporting the conclusions of the research reported in this paper is available in the Zenodo repository in the PERMIT community (https://zenodo.org/communities/permit-project/?page=1&size=20). The data set can be accessed via Zenodo athttps://zenodo.org/record/5874552%23.Ye7wJmDEVQMwith doi:10.5281/zenodo.5874552.
This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:https://creativecommons.org/licenses/by/4.0/.
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Strengths and limitations of this study
This is the first review, which systematically searched for all trial designs applied to personalised medicine.
The screening process and data extraction were performed in duplicate.
A new classification of trial designs for personalised medicine has been proposed.
We cannot exclude that we missed some relevant designs since we restricted the search to the last 15 years.
Introduction
Personalised medicine (PM) is an evolving field, which allows treating patients by providing them a specific therapy according to their individual demographic, genomic or biological characteristics.1It was defined by the European Council Conclusion on PM as ‘a medical model using characterisation of individuals’ phenotypes and genotypes (eg, molecular profiling, medical imaging, lifestyle data) for tailoring the right therapeutic strategy for the right person at the right time, and/or to determine the predisposition to disease and/or to deliver timely and targeted prevention’.2
Many trial designs have been used to evaluate personalised treatment or interventions.3The most common design is the enrichment design, whereby only biomarker-positive patients are randomly assigned to the targeted or control arm.4Despite its popularity, the use of enrichment designs is recommended only when the biomarker is a perfect predictor of the response in order not to deny biomarker-negative patients a treatment they would have otherwise benefited from.5Prospective validation of the candidate biomarker is therefore strongly recommended before applying these trials designs.
Over the last years, more complex study designs have been increasingly proposed in the field of PM.4According to the Clinical Trials Facilitation and Coordination Group, a clinical trial is considered as using a complex design ‘if it has separate parts that could constitute individual clinical trials and/or is characterised by extensive prospective adaptations such as planned additions of new Investigational Medicinal Products or new target populations’.6These designs are particularly efficient because they allow answering multiple clinical research questions within a single study.7Examples of common complex designs are the so-called basket, umbrella and platform trials, which are frequently applied in the field of oncology.8Basket trials refer to designs in which patients with heterogeneous diagnoses but with similar disease mechanisms are tested using the same targeted therapy. While, umbrella trials evaluate multiple treatment options in patient groups, which present the same disease, but with different genetic mutations. Finally, platform trials allow testing multiple targeted therapies in patients with the same disease in a perpetual manner, using interim evaluations and allowing therapies to enter or leave the trial.9However, these designs are often challenging6because they often require independent statistical analyses for each subprotocol, including interim analyses driving prospective adaptation with the addition of new interventions or populations, and/or termination of subprotocols based on futility or safety issues.
Numerous methodological challenges, covering many aspects of the study design (eg, randomisation, use of control arm, biomarker stratification, biomarker validation), are associated with trial designs applied to PM. The application of robust methodologies is especially important for clinical trials applied to PM to correctly select participants and treatments to be tested. As a starting point for the development of new recommendations on the use of trial designs applied to PM, we aimed to map the landscape of the existing study designs for clinical trials applied to this medical field.
Our specific objectives were to answer to the following five research questions:
What are the available designs for clinical trials applied to PM?
What are the examples of current applications of these approaches?
What are the pros and cons of the different approaches?
How is a PM strategy versus non-personalised strategy evaluated?
What are the gaps in the current research on PM clinical trials?
這個範圍審查許可項目的一部分(PERsonalised MedIcine Trials) aimed at mapping the methods for PM research and building recommendations on robustness and reproducibility of different stages of the development programmes. Although several categorisation may be proposed, the PERMIT project considers four main building blocks of the PM research pipeline: (1) design, building and management of stratification and validation cohorts; (2) application of machine learning methods for patient stratification; (3) use of preclinical methods for translational development, including the use of preclinical models used to assign treatments to patient clusters; (4) evaluation of treatments in randomised clinical trials. This scoping review covers the fourth building block in this framework.
Methods
We conducted a scoping review following the methodological framework suggested by the Joanna Briggs Institute.10The framework consists of six stages: (1) identifying the research questions, (2) identifying relevant studies, (3) selecting the studies, (4) charting the data, (5) collating, summarising and reporting results and (6) pursuing a consultation.
A study protocol was published in Zenodo before conducting the review.11Due to the iterative nature of scoping reviews, deviations from the protocol were expected and duly reported when occurred. We used the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist to report our results.12
Study identification
Relevant studies and documents were identified balancing feasibility with breadth and comprehensiveness of searches. We searched PubMed, Embase and the Cochrane Library (search date: 7–8 April 2020) for all reports describing a study design for clinical trials applied to PM.Online supplemental file 1reports the search strategies applied. We did not restrict the search to any publication type. Because many systematic and narrative reviews on trial designs applied to PM have already been published over the last years, we limited our search from 2005 to April 2020. We restricted inclusion to English, French, German Italian and Spanish languages. We searched for the grey literature on websites of existing projects about innovative clinical trials (eg, EU-PEARL) and by consulting partners of the PERMIT project.
Supplemental material
Eligibility criteria and deviation from the protocol
We included all reports describing a trial design applied to PM. The operational definition of PM used in the present study is reported inbox 1. Because of the extensive volume of literature related to trial designs in PM, we restricted the inclusion criteria to trial designs for phase II, III and IV. We excluded single-arm trials, which are not part of a master protocol, non-adaptive enrichment design and N-of-1 trials. We also excluded publications such as prefaces to a special issue and speaker, symposium and panel abstracts, posters and letters to the editor due to the limited information usually provided. These exclusion criteria were not specified in the protocol, but they were agreed among the authors before starting the screening process. The research question ‘What are the pros and cons of the different approaches?’(ie, objective 3) is not reported in the present paper, and will be subject to a specific study.
Personalised medicine definition
What is Personalised Medicine?
According to the European Council Conclusion on personalised medicine for patients personalised medicine is ‘a medical model using characterisation of individuals’ phenotypes and genotypes (eg, molecular profiling, medical imaging, lifestyle data) for tailoring the right therapeutic strategy for the right person at the right time, and/or to determine the predisposition to disease and/or to deliver timely and targeted prevention’.2
In the context of the PERMIT project, we applied the following common operational definition of personalised medicine research: a set of comprehensive methods, (methodological, statistical, validation or technologies) to be applied in the different phases of the development of a personalised approach to treatment, diagnosis, prognosis or risk prediction. Ideally, robust and reproducible methods should cover all the steps between the generation of the hypothesis (eg, a given stratum of patients could better respond to a treatment), its validation and preclinical development and up to the definition of its value in a clinical setting.11
Study selection
We exported the references retrieved from the searches into the Rayyan online tool.13Duplicates were removed automatically using the reference manager EndNote V.X9 (Clarivate Analytics, Philadelphia, USA) and manually by one author (CS). Eligible reports applying a particular trial design were retrieved from the search strategies and screened by reviewers. Five reviewers independently screened the titles and abstracts: one reviewer (CS) screened all the records and four reviewers (II-I, LMS-G, LSM and PJ) screened 25% of references each. Due to the involvement of many reviewers, we conducted a pilot screening using 56 articles (2.5%), corresponding to the articles published from 1 January 2020 to search date (7–8 April 2020), to verify whether all reviewers used the same inclusion and exclusion criteria. We retrieved full-text copies of potentially eligible reports for further assessment. Six reviewers independently confirmed the eligibility: one reviewer (CS) examined all full-text copies and five reviewers (IB, II-I, LMS-G, MMPS and SLM) assessed 20% of references each. Disagreements were solved by consensus or by involving a third expert reviewer (RP).
Charting the data
We designed a data extraction form using Google Forms (online supplemental file 1). General study characteristics extracted were as follows: first author name, title of article, contact detail of corresponding author, year of publication and type of publication. In addition, for each trial design referred to in the paper, we collected information on its definition, methodology, statistical considerations, advantages, disadvantages, utility, gaps and examples of actual trials, which adopted the design. A list of trial designs, which were retrieved from two previously conducted systematic reviews,14 15was included in the data extraction form to harmonise the names used to report the same trial design. This initial list of trial designs was used as starting point to classify the identified trial designs and then modified and expanded on based on the results obtained in the present scoping review. When the trial design name reported in the paper did not match any of the trial design names included in the list, reviewers recorded the trial name verbatim.
Two reviewers (CS and FBB) piloted and refined the data extraction form using three reviews (4%). Since many narrative reviews were already published about trial designs applied to PM, the data extraction was conducted in two phases. First, two reviewers (CS and FBB) independently extracted data from the identified systematic and narrative reviews. Second, three reviewers (CS, FBB and MC) working independently extracted data for all the remaining selected records, which were neither a systematic nor narrative review, only if they provided new information, which was not extracted in the previous phase. One reviewer (FBB) extracted data from all records and two reviewers (CS and MC) extracted 60% and 40% of articles, respectively. Differences in terminology were discussed between reviewers to ensure that the same trial designs were included in the same category. Disagreements were solved by consensus or by involving a third expert reviewer (RP).
It was not within the remit of this scoping review to assess the methodological quality of individual studies included in the analysis.
Collating, summarising and reporting results
We summarised the extracted data in tables and figures. Information on the definition, methodology, statistical considerations, advantages, disadvantages, utility and gaps of trial designs was extracted verbatim. Data on the examples of clinical trials adopting the different approaches were summarised using frequencies and percentages.
研究員(CS)列出所有的研究設計和identified the central feature(s) for each of them, which were grouped into feature domains. The initial list was reviewed by a senior statistician with expertise in designing clinical trials (RP). A final list was created and agreed on with members of the PERMIT steering committee and coauthors of the present study. The list of features was therefore based on the identified study designs and also the expertise of members of the PERMIT project.
New classification of trial designs in PM
Based on the identified trial designs and features, we proposed a new classification of trial designs for PM. Other attempts in classifying trial designs applied to PM have been proposed in the literature. However, they were limited to classifying the designs into categories3 4 8or identifying the design based on a specific feature (eg, adaptive or non-adaptive trials).14 15This new classification goes a step further, proposing a new approach in classifying the trial designs considering two variables, which are core designs and design features, into a double-entry table.
Consultation exercise
The members of the PERMIT consortium, associated partners and the PERMIT project Scientific Advisory Board discussed the preliminary findings of the scoping review in a 2-hour online workshop. A first version of the classification of the trial designs in PM was presented and discussed.
Patient and public involvement
The European Patients Forum is a member of PERMIT project. Although not directly involved in the conduction of the scoping review, they received the draft review protocol for collecting comments and feedback.
Results
Study selection and general characteristics of reports
We retrieved 2350 citations from the electronic search and after removing the duplicates, 2301 remained. We excluded 1841 records based on titles and abstracts. After full-text assessment, 323 publications were excluded, and 163 met the inclusion criteria (see flow chart infigure 1andonline supplemental file 1;數據提取包括信息general study characteristics and definition, methodology, statistical considerations and examples of each study design referred to in each included paper, is available on the online platform Zenodo16). From these 163 publications, we identified 5 systematic reviews, 66 narrative reviews, 8 original research articles, 26 methodological studies, 4 study protocols, 37 conference abstracts, 4 commentaries, 2 discussion papers, 3 reports, 1 book chapter, 1 editorial, 1 guidance document and 5 links about trial registration (eg, ClinicalTrials.gov).
Study selection flow diagram. PERMIT, PERsonalised MedIcine Trials.
Trial designs and core designs in PM
We identified 21 trial designs, 10 subtypes and 30 variations of trial designs applied to PM (online supplemental file 1). Information on the definition, methodology and statistical considerations of identified trial designs are reported on theonline supplemental file 1.
We classified the trial designs into four core categories named asMaster protocols,Randomise-all, Biomarker-strategyandEnrichment. Building on the definitions provided by Tajiket al3and Parket al,8we defined the four core categories as:
Master protocols: trial design, which includes multiple parallel substudies under a common infrastructure.
Randomise-all: trial design where patients meeting the eligibility criteria, irrespective of their biomarker status, are randomised to either an experimental or control treatment. This category also includes those hybrid designs, which first use aRandomise-alldesign, and then only a specific biomarker defined subgroup is randomised to either an experimental or control treatment.
Biomarker-strategy: trial design where eligible patients are randomised to either a marker-based treatment strategy or non-marker-based treatment strategy.
Enrichment: trial design where eligibility is determined according to the biomarker status and patients are then randomised to either an experimental or control treatment. A specific biomarker defined subgroup (usually biomarker positives) is believed to benefit more from a treatment compared with the other subgroup (usually biomarker negatives).
An example of a study design for each core category, including its definition and methodology used, is shown intable 1. Overall, we identified 5 trial designs, 6 subtypes and 7 variations forMaster protocols, and 10 trial designs, 2 subtypes and 22 variations forRandomise-all, 5 trial designs forBiomarker-strategyand 1 trial design, 2 subtypes and 1 variation forEnrichment.
Examples of core categories
From the identified designs, we found 34 main features of trial designs in PM, which were clustered into 11 features domains (table 2). The feature domains include the key design features that characterise a trial design for PM such as framework, model, control group, randomisation, biomarker assessment and adaptive aspects, and that should be carefully considered when designing a trial. A new classification of the trials designs for PM has been proposed and is reported intable 3. The classification is presented in a double-entry table, which includes the main trial features on the y-axis and core categories of the trial designs on the x-axis.
Main features of trial designs applied to personalised medicine
Trial designs classification
General characteristics of clinical trials in PM
We found 131 clinical trials, which used the identified designs (online supplemental file 1).Table 4presents the general characteristics of the identified trials.
General characteristics of clinical trials in personalised medicine
Most trials used a basket (35/131, 26.7%), umbrella (30/131, 22.9%), platform (18/131, 13.7%) or marker stratified (15/131, 11.5%) design. The great majority of the trials were in the field of oncology (113/131, 86.3%). At the time of writing (March 2021), the recruitment status was ongoing for 48.1% (63/131) of the trials. A trial (0.8%) was not registered and seven (5.3%) presented an unknown status (meaning that the trial status has not been verified within the past 2 years on the ClinicalTrials.gov website). Out of 131, 75 (57.3%) trials were phase II studies. For four trial designs, we did not find any examples of current applications.
Trial designs for assessing personalised versus non-personalised strategy
We identified 16 trials (16/131, 12.2%) evaluating a PM versus a non-PM strategy, which used nine different study designs (online supplemental file 1).
Three trials used a biomarker design with a biomarker assessment in the control group.14 17 18This study design consists of first testing the marker status of the entire study population and then randomises the patients either to a biomarker-based strategy arm or a non-biomarker strategy arm.14In the GILT docetaxel trial (NCT00174629), patients with advanced non-small-cell lung cancer (NSCLC) were randomly assigned to either the control arm receiving a standard therapy of docetaxel plus cisplatin or the genotypic arm in which patients with low ERCC1 levels received docetaxel plus cisplatin and those with high levels received docetaxel plus gemcitabine. In the LIFT trial (NCT02498977), liver transplant recipients were randomised to either non-biomarker-based immunosuppression (IS) weaning or a biomarker-based IS weaning. ERCC1 gene expression was assessed in patients with NSCLC, which were then randomised to either to platinum therapy or non-platinum therapy in the ERCC1 trial (NCT00801736).
Four trials used a biomarker strategy design without biomarker assessment in the control arm.14 19–21This design only evaluates the biomarker status in patients who are assigned to the biomarker-based strategy.14Patients were randomised to either the NT-pro-BNP-guided therapy or usual care in the GUIDE-IT trial (NCT01685840) and either an algorithm driven individualised haemodynamic goal-directed therapy or standard care in the iPEGASUS trial (NCT03021525). Patients with mild head injury were randomly assigned to computed tomography or observation in the hospital in the OCTOPUS trial (ISRCTN81464462) and children with a doctor’s diagnosis of asthma were randomised to a PM genotype-guided treatment arm or to usual care, non-genotype-guided, control arm in the PUFFIN trial (NCT03654508).
A modified strategy design, which differs from the previous strategy designs in including multiple targeted molecular profiles,22was used in two trials.22–25Patients with refractory cancer in the SHIVA trial (NCT01771458) were randomised to receive a molecularly targeted therapy based on metastasis molecular profiling or a conventional chemotherapy. In the NCI-MPACT trial (NCT01827384), patients with an actionable mutation of interest (aMOI) were assigned to a targeted therapy based on mutation status or a therapy, chosen from the four regimes, not targeting the aMOI. We found that these two trials were also labelled as basket trials26–28as well as platform trial in the case of the SHIVA trial.29
One trial used an adaptive strategy design for biomarkers with measurement error.25這個設計時使用第二個biomarke便宜r exists and may be concordant with a more expensive one, which is considered the gold standard. This design was used with some modifications in the OPTIMA trial (ISRCTN42400492). Oestrogen receptor-positive, human epidermal growth factor receptor 2 (HER-2) negative breast cancer patients were randomised to be either in the control arm receiving the standard care (ie, chemotherapy and endocrine therapy) or in the treatment arm receiving the marker-guided therapy (ie, endocrine therapy). Patients in the treatment arm, which obtained a high-risk test, also received chemotherapy.
The Siyaphambili Study (NCT03500172) used a sequential multiple assignment randomised (SMART) design to compare an individualised intervention (ie, peer-led, individualised case management) or non-individualised intervention (ie, nurse-led mobile decentralised treatment programmes) to standard care (ie, South African standard of care) or combination of both interventions in women living with HIV.30The SMART design allows comparing adaptive treatment strategies, which consist of a series of tailored therapies during the course of a treatment.31
ProBio (NCT03903835) used an outcome-randomisation adaptive design to investigate whether a treatment based on molecular biomarker signature is more effective than standard care in men with metastatic castrate-resistant prostate cancer.
Finally, we found four trials, which evaluated a personalised versus a non-personalised strategy using a master protocol design.32–35IMPACT II (NCT02152254) used a basket design and UPSTREAM (NCT03088059), SAFIR02_Breast (NCT02299999) and SAFIR02_Lung (NCT02117167) an umbrella design.
Gaps in the current research on clinical trials applied to PM
The results of this scoping review also allowed us to identify some gaps in the current research on clinical trials in PM. We identified three main gaps, which concern (1) the terminology used in labelling trial designs applied to PM, (2) the applications of complex innovative trial designs to fields outside of oncology and (3) the implementation of trials for evaluating PM strategy versus non-personalised strategy.
We found that trial designs are often labelled in different ways or mislabelled, despite this gap having been identified previously.3 4 14 15An example is theMarker stratified design, which was named using 18 different labels (online supplemental file 1). We also found that a study design adopted in a clinical trial was defined differently across the literature. For instance, the I-SPY 2 trial (NCT01042379) has been labelled as outcome-based adaptive randomisation,15platform36or umbrella design.37The I-SPY 2 is an ongoing platform trial, which studies multiple therapies in the context of breast cancer in a perpetual manner with arms being added or dropped based on current knowledge and collected data. Moreover, the study design adopted in the I-SPY 2 trial includes Bayesian adaptation algorithms in order to make decisions on estimated posterior probabilities, which are calculated at frequent interim-analysis points and response-adaptive randomisation.9According to the new proposed classification, I-SPY 2 trial would be classified asMaster protocolbecause it includes multiple substudies under the same framework, with common/shared control group, early stopping, interim analysis and outcome-based adaptive randomisation as main design features.
Moreover, another gap in the current research on PM is the lack of application of novel complex study designs to fields outside of oncology. We found that 94% (81/86) of the clinical trials which used a master protocol design were in the field of oncology.
Finally, a strong need exists for clinical trials evaluating the effectiveness of a PM strategy versus non-personalised strategy. This constitutes the third gap that we identified by mapping the evidence on clinical trials applied to PM. We found only 16 trials using nine different trial designs, which compared the two strategies.
Discussion
The present study provides a broad overview and proposes a new classification of the trial designs applied to PM.
被認為是t範圍評估方法he most suitable to respond to the extensive scope of the field. Compared with systematic reviews that aim to answer specific questions, scoping reviews are used to present a broad overview of the evidence pertaining to a topic and they are useful to examine areas that are emerging, to clarify key concepts and identify gaps.38 39
To our knowledge, this is the first study, which systematically reviews all trial designs, including complex innovative designs (ie, basket, umbrella and platform), applied to PM. Other systematic reviews have been performed on specific trial designs such as biomarker-guided adaptive trial designs,15biomarker-guided non-adaptive trials designs14and master protocols8在海裏或不考慮主協議rch strategy.3
We identified 21 trial designs, 10 subtypes, and 30 variations of trial designs applied to PM, which have been classified into four core categories:Master protocols,Randomise-all,Biomarker strategyandEnrichment. Randomise-allencompasses the largest number of trial designs (ie, 10 trial designs, 2 subtypes and 22 variations) andMaster protocolsincludes those study designs which are more frequently used in clinical trials (86/131, 65.6%). A variation of the enrichment design calledmultistage adaptive biomarker-directed targeted (MAT) design,40which combines some features of both targeted and adaptive designs, was included in the present review because does not present the standard characteristics of a classical enrichment design but not in our classification. In the MAT design, biomarker-positive patients are first randomised to either treatment or standard of care and interim analyses are then conducted to monitor if the primary study objectives can be achieved.
From the different approaches applied to PM, we identified 34 central features, which were combined with the four core categories in a double-entry table. The proposed table constitutes a novel manner to classify trial designs applied to PM, considering its corresponding core category and main features (eg, PM specific or generic adaptive aspects). The classification only includes features, which are strictly related to trial designs. Methods for stratification and validation of clusters in a clinical trial (eg, data-driven subgroup identification) were considered not eligible and therefore were not included. In particular, those methods were identified and described in another recent scoping review (2021).41Due to the variety and diversity of trial designs currently available, this classification provides a clearer and more accessible picture of the different trial designs available in PM, helping the readers to navigate this complex field. In addition, it could be particularly helpful for researchers as a first step for understanding the different methodological approaches available for their trials.
Also, it permits to consider all the relevant features associated with a trial design reducing confusion in reporting and labelling. We believe that this classification is more accurate and appropriate for describing a trial design applied to PM in its complexity. Moreover, it could help researchers and clinicians in using a harmonised terminology for labelling a trial.
Based on the results obtained, we identified three main gaps in the current research on clinical trials applied to PM. We found that more research is needed to evaluate the efficiency of PM approach versus non-personalised standard of care. A few clinical trials (16/131, 12.2%), using nine different study designs, were found evaluating these different strategies. In addition, these trials would be particularly relevant for health technologies assessment (HTA) bodies to evaluate the incremental benefit of PM over that of non-personalised approaches, from both a clinical and economical perspective, in those situations in which a non-personalised strategy is considered standard practice. We also need more research to apply trial designs to fields outside of oncology. This last result was consistent with what was found in a recent systematic review of master protocols.8The review showed that the great majority of basket, umbrella and platform studies (76/83, 91.6%) were conducted in the field of oncology. In particular, no umbrella trials were found outside of oncology. Finally, in line with two previous systematic reviews,3 4we found that a harmonised terminology was required because it would permit increase clarity among the variety of trial designs applied to PM.
Furthermore, current applications of the identified trial designs, together with the input of some experts in the field, helped us to identify four typologies of PM. Fortargeted or precision medicine, a targeted treatment, which is specific for one disease, is identified and used to treat patients with heterogeneous diagnoses but similar disease mechanisms (eg, basket trials).Stratified medicineincludes trials in which patients are stratified in different clusters based on the collection of data characterised by the genotype or phenotype of the individuals (eg, adaptive signature trials). The treatment is tailored to each patient in theindividualised medicine(eg, trials using pharmacokinetic models). Finally, inindividualised medicine with a dynamic regime, the treatment tailored to each patient is adjusted over time based on the patient’s response (eg, SMART trials).
The new classification and the four typologies of PM clinical trials provide the basis for the future recommendations on the use of trial designs applied to PM and on trials assessing personalised versus non-PM strategy. These recommendations are strongly needed to conduct new studies within the context of PM and, consequently, have new direct high-quality evidence in the evaluation of co-dependent PM technologies.42
The present study has strengths but also limitations. This is the first scoping review, which presents an overview of all trial designs applied to PM. We followed a systematic approach to map the evidence and described the process using the PRISMA-ScR guideline. However, we restricted the search strategy to the last 15 years proving a comprehensive overview rather than an exhaustive list of trial designs used in PM. In addition, by excluding single-arm trials, which are not part of a master protocol, non-adaptive enrichment design and N-of-1 trials, we might misrepresent certain study designs used for PM. Moreover, although we conducted a pilot screening for verifying the use of the same inclusion and exclusion criteria among reviewers, we cannot exclude that we did not identify some relevant publications. The information on the definition, methodology, statistical considerations, advantages, disadvantages, utility and gaps of trial designs was extracted verbatim from the included records. However, the selection of this information could be affected by the perception of the three reviewers who conducted the data extraction. Also, even if we built on existing reviews14 15and carefully developed a comprehensive classification, all attempts at categorisation are reductive in nature, and different classification schemes could be proposed. We believe that all classifications are based on decisions, some of which are inevitably arbitrary. Nonetheless, our proposal allows separating between core design features that characterise the main objective of the trial and the patient flow, important aspects of the trial, and more accessory design features. It may form the basis of the evaluation of which design, and which features would be best suited for a given situation. For instance, HTA representatives could use our classification as a first step to better understand the design choice taken by the researchers and successively evaluate it.
The information extracted on the pros and cons of each approach (ie, objective 3) will be subject of further analysis and will be publish in a separate study due to considerable volume of information collected. We will also explore the pros and cons of each approach in more detail, together with experts from academia and regulatory agencies, when preparing the recommendations on the use of trial designs applied to PM.
Conclusions
The findings of this scoping review show that several existing trial designs are applied to PM, which can be grouped into four core categories. A new classification has been proposed that allows describing trial designs taking into account their corresponding core category and main features. It can be used by readers to explore and better understand the complex field of PM clinical trials.
Data availability statement
Data are available in a public, open access repository. The data set supporting the conclusions of the research reported in this paper is available in the Zenodo repository in the PERMIT community (https://zenodo.org/communities/permit-project/?page=1&size=20). The data set can be accessed via Zenodo athttps://zenodo.org/record/5874552%23.Ye7wJmDEVQMwith doi:10.5281/zenodo.5874552.
Ethics statements
Patient consent for publication
Acknowledgments
The authors thank Vanna Pistotti for her assistance with search strategy development and conduction, Ines Bouajila for collaborating to the screening process and Frank Bretz, Frank Petavy and Stephen Senn for their excellent inputs and feedback.
References
Supplementary materials
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Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Collaborators允許組:安東尼奧L安德魯,佛羅倫薩Bietrix,Maria del Mar Polo-de Santos, Maddalena Fratelli, Vibeke Fosse, Enrico Glaab, Rainer Girgenrath, Alexander Grundmann, Josep Maria Haro, Frank Hulstaert, Pascale Jonckheer, Setefilla Luengo Matos, Emmet McCormack, Anna Monistrol Mula, Albert Sanchez Niubo, Emanuela Oldoni, Teresa Torres.
ContributorsStudy conception and design: CG, CS, II-I, JD, LSM, LMS-G, PG, RB and RP. Methodology: CG, CS, RB and RP. Data collection and analysis: CS, FBB, MC, II-I, LSM, LMS-G and RP. Trial design classification: CS and RP. Original draft preparation: CS. Review and editing: CG, II-I, LSM, LMS-G, MC, PG, RB and RP. Responsible for the overall content as guarantor: CS. All authors read and approved the final version of the manuscript. The members of the PERMIT group were involved in the preparation or revision of the joint protocol of the four scoping reviews of the PERMIT series, attended the joint workshop (consultation exercise) or contributed to one of the other scoping reviews of the PERMIT series. PG and JD coordinate the PERMIT project. JD obtained funding.
Funding這個項目已經收到了來自歐洲的資金n Union’s Horizon 2020 research and innovation programme under grant agreement No. 874825.
Competing interestsNone declared.
Patient and public involvementThe European Patients’ Forum is a member of PERMIT project. Although not directly involved in the conduction of the scoping review, they received the draft review protocol for collecting comments and feedback.
Ethics ApprovalThis study was based entirely on a scoping review of relevant published literature and did not require an ethics approval.
出處和同行reviewNot commissioned; externally peer reviewed.
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