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Basket versus umbrella trial design. a In a basket trial, a targeted therapy (purple pill) is tested on patients with a specific genetic mutation (purple diamond) across a variety of tumor types. b In an umbrella trial, different tumor types (organ icon linked with patient color) are tested for specific genetic mutations (purple diamond, brown triangle, yellow star). These mutations are then sorted into independent groups and treated with a matched inhibitor (purple, brown, yellow pill)

Basket versus umbrella trial design. a In a basket trial, a targeted therapy (purple pill) is tested on patients with a specific genetic mutation (purple diamond) across a variety of tumor types. b In an umbrella trial, different tumor types (organ icon linked with patient color) are tested for specific genetic mutations (purple diamond, brown triangle, yellow star). These mutations are then sorted into independent groups and treated with a matched inhibitor (purple, brown, yellow pill)

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The therapeutic armamentarium for the treatment of cancer has rapidly evolved with the advent of molecularly targeted and immuno-oncology agents. Dramatic and prolonged responses observed in patients with advanced cancers have created excitement and promise for expedited development of effective new treatments. However, this has also necessitated a...

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Immunotherapies have shown long‐lasting and unparalleled responses for cancer patients compared to conventional therapy. However, they seem to only be effective in a subset of patients. Therefore, it has become evident that a greater understanding of the tumor microenvironment (TME) is required to understand the nuances which may be at play for a f...

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... (© Used with permission of Springer Verlag). Description of three different dose escalation designs[24]. Green box represents a patient that does not experience any toxicity. Yellow box represents mild toxicity. ...
... The primary objectives of phase I clinical trials are to determine the safety and tolerability of an investigational drug or combination and to define the maximum tolerated dose (MTD) and recommended dose to be used in phase II clinical trials (Garralda et al., 2019). In contrast, the primary objective of phase II clinical trials is often to evaluate the preliminary efficacy of the treatment in a well-defined population of patients (Bui & Kummar, 2018;Garralda et al., 2019). The primary objective of phase III clinical trials is to evaluate the efficacy of the investigational drug compared with standard-of-care treatment (Garralda et al., 2019). ...
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Background: Advanced practice providers (APPs) play important roles in enrolling, educating, and caring for patients in clinical trials. However, much remains unknown about the role of APPs in managing adverse events (AEs) in early (phase I to II) clinical trials. In this study, we assessed the outpatient management of grade 3 to 4 AEs by APPs in early trials and characterized the workflow of our APP Phase I to II Fast Track (FT) Clinic. Patients and methods: We retrospectively reviewed records of patients with advanced or metastatic solid tumors enrolled in phase I to II clinical trials who were seen by APPs from September 2017 to August 2018 in the APP phase I to II FT clinic in the Department of Investigational Cancer Therapeutics. Results: A total of 808 patients enrolled in 159 clinical trials were seen in 2,697 visits (median 3 visits per patient; range 1-28) by 10 APPs. Treatment was interrupted in 6.9% of visits, and grade 3 to 4 AEs were seen in 5.4% of visits; however, patients from 1.4% of visits were sent to the emergency center (EC) and/or admitted. Patients referred to the EC and/or admitted were more likely to have baseline hypoalbuminemia, high lactate dehydrogenase, and poor Eastern Cooperative Oncology Group performance status (i.e., ECOG > 1; p < .001). There were no associations between EC referral and gender, APP years of experience, or type of treatment. Conclusions: The APP Phase I to II FT Clinic has an important role in the management of AEs by APPs in early clinical trials in the outpatient setting, potentially avoiding EC visits and admissions.
... Patients with advanced cancer without standard-of-care treatment (SOC) options can opt for experimental treatment in early-phase clinical trials (Phase I/II clinical trials). Early-phase clinical trials can be divided into three categories; all-comer design trials, enrichment design trials, i.e. targeted or biomarker-guided trials, and master protocol trials, i.e. basket and umbrella trials [1]. Traditionally, an allcomer design strategy has minimal inclusion criteria and low response rates [2,3]. ...
... Still, more than half of the patients with an (potentially) actionable event did not receive a matched drug, mostly due to the unavailability of a matching trial. In addition, most patients had more than one driver variant and therefore combination therapies would be favourable [1,22,23]. This demonstrates an unmet need for accessible targeted treatment for patients with certain actionable targets. ...
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Background Biomarker-guided therapy in an experimental setting has been suggested to improve patient outcomes. However, trial-specific pre-screening tests are time and tissue consuming and complicate the personalised treatment of patients eligible for early-phase clinical trials. In this study the feasibility of whole-genome sequencing (WGS) as a one-test-for-all for guided inclusion in early-phase trials was investigated. Methods Phase I Molecular Tumor Board (MTB) at the Erasmus MC Cancer Institute reviewed patients with advanced cancer without standard-of-care treatment (SOC) options for a ‘fresh-frozen’ (FF) tumour biopsy for WGS based on clinical-pathological features. Clinical grade WGS was performed by Hartwig Medical Foundation. MTB matched the patient with a trial, if available. Results From September 2019–March 2021, 31 patients with highly diverse tumour types underwent a tumour biopsy for WGS. The median turnaround time (TAT) was 15 days [10–42 days]. At least one actionable event was found in 84% of the patients (26/31). One-third of the patients (11/31) received matched experimental treatment. Conclusions WGS on fresh FF biopsies is a feasible tool for the selection of personalised experimental therapy in patients with advanced cancer without SOC options. WGS is now possible in an acceptable TAT and thus could fulfil the role of a universal genomic pre-screening test.
... The notion of tailoring therapies based on genomic characteristics is not new (10). "Basket trials," in which a targeted therapy is matched to patients with a specific genetic lesion across a variety of tumor types, have led to FDA approval of treatments such as pembrolizumab in mismatch repair-deficient cancers and BRAF/MEK inhibition in metastatic BRAF V600E-mutated non-small cell lung cancer. ...
... "Basket trials," in which a targeted therapy is matched to patients with a specific genetic lesion across a variety of tumor types, have led to FDA approval of treatments such as pembrolizumab in mismatch repair-deficient cancers and BRAF/MEK inhibition in metastatic BRAF V600E-mutated non-small cell lung cancer. However, heterogeneity in overall response rate has been observed on the basis of tumor type or histology (10). "Umbrella trials" such as NCI's Molecular Analysis for Therapy of Choice (MATCH) trial include multiple targeted therapies, and assign patients to a therapy based on the presence of specific genetic lesions in their tumors (11). ...
... "Umbrella trials" such as NCI's Molecular Analysis for Therapy of Choice (MATCH) trial include multiple targeted therapies, and assign patients to a therapy based on the presence of specific genetic lesions in their tumors (11). Although umbrella trials have shown some promise (12), adequate enrollment of patients into each therapeutic arm, heterogeneity of patients within each arm, and efficacy of single-agent therapy in highly pretreated patients, have been major challenges (10). "N of 1" trials use genomic and molecular characteristics to guide therapy on an individualized basis without a predetermined set of therapies but rather a host of therapies that may be chosen based on molecular predictors (13). ...
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... Dose escalation followed a standard 3 + 3 design. According to a 3 + 3 design at least 3 patients are treated at each dose level, by applying the following rules; 1. dose escalation to the next dose level if no dose limiting toxicity (DLT) occurs, 2. if 1 out of 3 patients experiences a DLT, treat 3 additional patients at this dose level, 3. stop dose escalation if ≥2 out of 3 or ≥ 2 out of 6 patients experience a DLT [14]. In part 1 and in part 2 CPC634 was administered without premedication. ...
... In our study grade ≥ 3 neutropenia only occurred in two patients and only, short lasting, at the highest dose level of 100 mg/m 2 . This is in contrast with other phase 1 studies with docetaxel-containing nanoparticles where neutropenia was an important DLT [5,14,15]. In a pharmacodynamics analysis of conventional docetaxel monotherapy, greater C max of unbound plasma docetaxel was correlated with higher risk of grade 4 neutropenia [24]. ...
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... The modeling applications discussed to this point emphasize the importance of addressing multi-pronged questions, e.g., not only around dose finding, but also on the identification of an adequate time window for maximizing therapeutic benefits (98). This problem is particularly challenging in the development of combination therapies, where multiple options around which cancer indication, which combination agents, which scheduling per agent, and which sequencing of the agents make trial design enormously complex (99,100). In recent years, platform design of clinical studies, driven by one master protocol, has gained momentum (101, 102)-a format which, in fact, benefits even further from a supportive quantitative mechanistic modeling approach (103). ...
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Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment—with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.
... At the same time, during these last years the arrival of immune therapy to the oncology therapeutic armamentarium has marked a groundbreaking milestone for the treatment of cancer patients, and the number of immune-oncology agents entering drug development continues to rise (Martin-Liberal et al., 2017). These factors, added to the strong collaboration with the regulatory agencies, approving novel agents based on data obtained from phase 1/2 trials, have led to an unprecedented evolution in the design of early-stage clinical trials (Bui and Kummar, 2018). In this regard, the US Food and Drug Administration (FDA) has approved 10 anticancer drugs matched to companion diagnostic biomarkers based on data obtained from nonrandomized trials in the last 2 years. ...
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Cancer treatment has made significant strides towards the promise of personalized medicine. Recent scientific advances have shown that there are numerous genetic deregulations that are common in multiple cancer types, raising the possibility of developing drugs targeting those deregulations irrespective of the tumour type. Precision cancer medicine was born out of accumulated evidence matching targeted agents with these tumour molecular deregulations. At the same time, the therapeutic armamentarium is rapidly increasing and the number of new drugs (including immune‐oncology agents) entering drug development continues to rise. These factors, added to strong collaboration with regulatory agencies which have approved novel agents based on data obtained from Phase 1/2 trials, have led to unprecedented evolution in the design of early stage clinical trials. Currently, we have seen rapid Phase 1 dose escalation trials followed by remarkably large expansion cohorts, and are witnessing the emergence of new trials, such as adaptive studies with basket and umbrella designs aimed at optimizing the biomarker‐drug co‐development process. Alongside the growing complexity of these clinical trials, new frameworks for stronger and faster collaboration between all stakeholders in drug development, including academic institutions and frameworks, clinicians, pharma companies and regulatory agencies, have been established. In this review article, we describe the main challenges and opportunities that these new trial designs may provide for a more efficient drug development process, which may ultimately help ensure that precision cancer medicine becomes a reality for patients.
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Introduction/Objective During the 1150 days of COVID-19 pandemic there were great efforts to develop efficient treatments for the disease. After this long time, some drugs emerged as treatment for COVID-19. Some of them are new drugs, most of them, known drugs. These developments were triggered by information already available in patent documents. Pharmaceutical companies, therefore, rushed to conduct drugs evaluations and trials in order to deliver to the world a reasonable treatment that could reach the majority of its population. However, it is not immediately clear how companies operated to reach their goals. The ability of open innovation to achieve results assertively and faster than closed innovation strategies is questioned and therefore, it is questioned whether pharmaceutical companies use open innovation to face COVID-19. Methods In this work, data available on patent databases were mined to inform about the scientific and technological panorama of selected drugs tested for COVID-19 treatment and to understand the perspectives of such developments during the pandemic. Results This study evidenced that most treatments were based on known drugs, that some of the initially promising drugs were abandoned during the pandemic, and that it was able to inform if open innovation and collaborations were explored strategies. Conclusion This study evidenced that the developments during COVID-19 were not based on open innovation by revealing a patent race towards the treatment development, but with practically no collaborations or information exchange between companies, universities, and research facilities.
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Background: Phase I trials historically involved heavily pretreated patients (pts) with no more effective therapeutic options available and with poor expected outcomes. There is scare data regarding profile and outcomes of pts enrolled into modern phase I trials. Here, we sought to provide an overview of pts’ profile and outcome into phase I trials at Gustave Roussy (GR). Methods: This is a monocentric retrospective study, including all pts enrolled into phase I trials at GR from 2017 to 2021. Data regarding pts’ demographics, tumor types, investigational treatments and survival outcomes were collected. Results: In total, 9482 pts were referred for early phase trials. 2478 pts were screened, among which 449 (18·1%) failed screening. 1693 pts finally received at least one treatment dose as part of a phase I trial. Median age of patients was 59 years old (range, 18-88) and most common tumor types included gastrointestinal (25·3%), hematological (15%), lung (13·6%), genitourinary (10·5%) and gynecologic cancers (9·4%). Amongst all pts treated and evaluable for response (1634 pts), objective response rate was 15.9% and disease control rate was 45.4%. median progression-free survival and overall survival were respectively of 2·6 months (95% CI, 2·3-2·8) and 12·4 months (95%CI, 11·7;13·6). Conclusion: As compared with historical data, our study shows that outcomes of patients included into modern phase I trials have improved and that these trials constitute nowadays a valid and safe therapeutic option. These updated data provide facts for adapting the methodology, role and place of phase I trials over the next years.