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Westphalen CB, Martins-Branco D, Beal JR, Cardone C, Coleman N, Schram AM, Halabi S, Michiels S, Yap C, André F, Bibeau F, Curigliano G, Garralda E, Kummar S, Kurzrock R, Limaye S, Loges S, Marabelle A, Marchió C, Mateo J, Rodon J, Spanic T, Pentheroudakis G, Subbiah V. The ESMO Tumour-Agnostic Classifier and Screener (ETAC-S): a tool for assessing tumour-agnostic potential of molecularly guided therapies and for steering drug development. Ann Oncol 2024; 35:936-953. [PMID: 39187421 DOI: 10.1016/j.annonc.2024.07.730] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 07/19/2024] [Accepted: 07/29/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Advances in precision oncology led to approval of tumour-agnostic molecularly guided treatment options (MGTOs). The minimum requirements for claiming tumour-agnostic potential remain elusive. METHODS The European Society for Medical Oncology (ESMO) Precision Medicine Working Group (PMWG) coordinated a project to optimise tumour-agnostic drug development. International experts examined and summarised the publicly available data used for regulatory assessment of the tumour-agnostic indications approved by the US Food and Drug Administration and/or the European Medicines Agency as of December 2023. Different scenarios of minimum objective response rate (ORR), number of tumour types investigated, and number of evaluable patients per tumour type were assessed for developing a screening tool for tumour-agnostic potential. This tool was tested using the tumour-agnostic indications approved during the first half of 2024. A taxonomy for MGTOs and a framework for tumour-agnostic drug development were conceptualised. RESULTS Each tumour-agnostic indication had data establishing objective response in at least one out of five patients (ORR ≥ 20%) in two-thirds (≥4) of the investigated tumour types, with at least five evaluable patients in each tumour type. These minimum requirements were met by tested indications and may serve as a screening tool for tumour-agnostic potential, requiring further validation. We propose a conceptual taxonomy classifying MGTOs based on the therapeutic effect obtained by targeting a driver molecular aberration across tumours and its modulation by tumour-specific biology: tumour-agnostic, tumour-modulated, or tumour-restricted. The presence of biology-informed mechanistic rationale, early regulatory advice, and adequate trial design demonstrating signs of biology-driven tumour-agnostic activity, followed by confirmatory evidence, should be the principles for tumour-agnostic drug development. CONCLUSION The ESMO Tumour-Agnostic Classifier (ETAC) focuses on the interplay of targeted driver molecular aberration and tumour-specific biology modulating the therapeutic effect of MGTOs. We propose minimum requirements to screen for tumour-agnostic potential (ETAC-S) as part of tumour-agnostic drug development. Definition of ETAC cut-offs is warranted.
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Affiliation(s)
- C B Westphalen
- Comprehensive Cancer Center Munich & Department of Medicine III, University Hospital, LMU Munich, Munich; German Cancer Consortium (DKTK), partner site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - D Martins-Branco
- Scientific and Medical Division, European Society for Medical Oncology (ESMO), Lugano, Switzerland
| | - J R Beal
- Hospital Israelita Albert Einstein, Sao Paulo, Brazil
| | - C Cardone
- Experimental Clinical Abdominal Oncology Unit, Istituto Nazionale Tumori- IRCCS-Fondazione G. Pascale, Naples, Italy
| | - N Coleman
- School of Medicine, Trinity College Dublin, Dublin; Medical Oncology Department, St. James's Hospital, Dublin; Trinity St. James's Cancer Institute, Dublin, Ireland
| | - A M Schram
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York City; Weill Cornell Medical College, New York City
| | - S Halabi
- Department of Biostatistics and Bioinformatics, Duke University, Durham; Duke Cancer Institute, Duke University, Durham, USA
| | - S Michiels
- Oncostat U1018, Inserm, Université Paris-Saclay, labeled Ligue Contre le Cancer, Villejuif; Service de Biostatistique et Epidémiologie, Gustave Roussy, Villejuif, France
| | - C Yap
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK
| | - F André
- INSERM U981, Gustave Roussy, Villejuif; Department of Cancer Medicine, Gustave Roussy, Villejuif; Faculty of Medicine, Université Paris-Saclay, Kremlin Bicêtre
| | - F Bibeau
- Service d'Anatomie Pathologique, CHU Besançon, Université de Bourgogne Franche-Comté, Besançon, France
| | - G Curigliano
- Istituto Europeo di Oncologia, IRCCS, Milan; Department of Oncology and Hemato-Oncology, University of Milano, Milan, Italy
| | - E Garralda
- Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - S Kummar
- Division of Hematology and Medical Oncology, Department of Medicine, Knight Cancer Institute, Oregon Health and Science University, Portland
| | - R Kurzrock
- Department of Medicine, Medical College of Wisconsin Cancer Center, Milwaukee, USA
| | - S Limaye
- Medical & Precision Oncology, Sir H. N. Reliance Foundation Hospital & Research Centre, Mumbai, India
| | - S Loges
- DKFZ-Hector Cancer Institute at the University Medical Center Mannheim, Department of Personalized Oncology, University Hospital Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim; Division of Personalized Medical Oncology (A420), German Cancer Research Center (DKFZ), German Center for Lung Research (DZL), German Cancer Consortium (DKTK), Heidelberg, Germany
| | - A Marabelle
- Drug Development Department (DITEP) and Laboratory for Translational Research in Immunotherapy (LRTI), Gustave Roussy, INSERM U1015 & CIC1428, Université Paris-Saclay, Villejuif, France
| | - C Marchió
- Department of Medical Sciences, University of Turin, Turin; Division of Pathology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - J Mateo
- Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - J Rodon
- Department of Investigational Cancer Therapeutics, UT MD Anderson, Houston, USA
| | - T Spanic
- Europa Donna Slovenia, Ljubljana, Slovenia
| | - G Pentheroudakis
- Scientific and Medical Division, European Society for Medical Oncology (ESMO), Lugano, Switzerland
| | - V Subbiah
- Early-Phase Drug Development, Sarah Cannon Research Institute (SCRI), Nashville, USA
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Hogervorst MA, van Hattem CC, Sonke GS, Mantel-Teeuwisse AK, Goettsch WG, Bloem LT. Healthcare decision-making for tumour-agnostic therapies in Europe: lessons learned. Drug Discov Today 2024; 29:104031. [PMID: 38796096 DOI: 10.1016/j.drudis.2024.104031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 05/28/2024]
Abstract
The tumour-agnostic authorisations of larotrectinib and entrectinib shifted the paradigm for indication setting. European healthcare decision-makers agreed on their therapeutic potential but diverged primarily in identified uncertainties concerning basket trial designs and endpoints, prognostic value of neurotrophic tropomyosin receptor kinase (NTRK) gene fusions, and resistance mechanisms. In addition, assessments of relevant comparators, unmet medical needs (UMNs), and implementation of NTRK-testing strategies diverged. In particular, the tumour-specific reimbursement recommendations and guidelines do not reflect tumour-agnostic thinking. These differences indicate difficulties experienced in these assessments and provide valuable lessons for future disruptive therapies. As we discuss here, early multistakeholder dialogues concerning minimum evidence requirements and involving clinicians are essential.
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Affiliation(s)
- Milou A Hogervorst
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, the Netherlands
| | - Christine C van Hattem
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, the Netherlands
| | - Gabe S Sonke
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Medical Oncology, University of Amsterdam, Amsterdam, the Netherlands
| | - Aukje K Mantel-Teeuwisse
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, the Netherlands
| | - Wim G Goettsch
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, the Netherlands; National Health Care Institute (ZIN), Diemen, the Netherlands
| | - Lourens T Bloem
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, the Netherlands.
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Gao W, Liu J, Shtylla B, Venkatakrishnan K, Yin D, Shah M, Nicholas T, Cao Y. Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development. CPT Pharmacometrics Syst Pharmacol 2024; 13:691-709. [PMID: 37969061 PMCID: PMC11098159 DOI: 10.1002/psp4.13079] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/20/2023] [Accepted: 10/30/2023] [Indexed: 11/17/2023] Open
Abstract
Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.
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Affiliation(s)
- Wei Gao
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Jiang Liu
- Food and Drug AdministrationSilver SpringMarylandUSA
| | - Blerta Shtylla
- Quantitative Systems PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Karthik Venkatakrishnan
- Quantitative PharmacologyEMD Serono Research & Development Institute, Inc.BillericaMassachusettsUSA
| | - Donghua Yin
- Clinical PharmacologyPfizerSan DiegoCaliforniaUSA
| | - Mirat Shah
- Food and Drug AdministrationSilver SpringMarylandUSA
| | | | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of PharmacyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
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Collignon O, Schiel A, Burman C, Rufibach K, Posch M, Bretz F. Estimands and Complex Innovative Designs. Clin Pharmacol Ther 2022; 112:1183-1190. [PMID: 35253205 PMCID: PMC9790227 DOI: 10.1002/cpt.2575] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/01/2022] [Indexed: 01/31/2023]
Abstract
Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data-driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.
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Affiliation(s)
| | | | - Carl‐Fredrik Burman
- Statistical Innovation, Data Science & Artificial IntelligenceAstraZeneca Research & DevelopmentGothenburgSweden
| | - Kaspar Rufibach
- Methods, Collaboration, and Outreach Group, Product Development Data SciencesF.Hoffmann‐La RocheBaselSwitzerland
| | - Martin Posch
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria
| | - Frank Bretz
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria
- NovartisBaselSwitzerland
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