Improved survival with enasidenib versus standard of care in relapsed/refractory acute myeloid leukemia associated with IDH2 mutations using historical data and propensity score matching analysis.

BACKGROUND: The present study evaluated the relative survival benefits associated with enasidenib and current standard of care (SoC) therapies for patients with relapsed/refractory (R/R) acute myeloid leukemia (AML) and an isocitrate dehydrogenase 2 (IDH2) mutation who are ineligible for hematopoietic stem cell transplantation (HSCT). METHODS: Propensity score matching (PSM) analysis compared survival outcomes observed with enasidenib 100 mg daily in the phase I/II AG221-C-001 trial and SoC outcomes obtained from a real-world chart review of patients in France. RESULTS: Before matching, enasidenib (n = 195) was associated with numerically improved overall survival (OS) relative to SoC (n = 80; hazard ratio [HR], 0.82; 95% confidence interval [CI], 0.61-1.11). After matching and adjusting for covariates (n = 78 per group), mortality risk was significantly lower with enasidenib than with SoC (HR, 0.67; 95% CI, 0.47-0.97). The median OS was 9.26 months for enasidenib (95% CI, 7.72-13.24) and 4.76 months for SoC (95% CI, 3.81-8.21). Results remained robust across all sensitivity analyses conducted. CONCLUSIONS: PSM analyses indicate that enasidenib significantly prolongs survival relative to SoC among patients with R/R AML and an IDH2 mutation who are ineligible for HSCT. Future prospective studies are needed to validate these findings using other data sources and to assess the comparative efficacy of enasidenib for other treatment outcomes.

Toxicity and antitumor activity of novel agents in elderly patients with cancer included in phase 1 studies.

Introduction The number of cancer cases among the elderly continue to increase as the worldwide population ages. This patient subset is underrepresented in clinical trials, partly because of unresolved uncertainties about age-associated tolerabilities and antitumor activities. We reviewed phase 1 trial data to study tolerance and efficacy of novel agents used for treatment of elderly patients with cancer. Methods Data from 773 consecutive evaluable patients in 85 phase 1 clinical trials (2008-2016) at START Madrid-CIOCC were analyzed according to age, with respect to objective response, survival, and toxicity. Results The mean age was 58.7 (range: 18-87) years; 260 (33.6%) patients were >65 y (elderly group). One hundred thirty-seven (17.8%) patients received immunotherapy drugs, 308 (39.8%) received targeted agents, and 328 (42.4%) received chemotherapy. No statistically significant differences in overall survival, objective response, or severe toxicity rates were found according to treatment type. Similar toxicities and clinical activities were found between the two age subgroups; 18.8% of the elderly and 20.7% of the younger patients experienced severe hematological toxicity (p=0.5), and 30.2% and 32.7%, respectively, experienced severe non-hematological toxicity (p=0.4). Regarding antitumor activity, 12.4% of the elderly and 15% of the younger patients achieved objective responses (p=0.41). There were no significant between-group differences in overall survival (9.7 versus 11.5 months, respectively, p=0.1) or progression-free survival (2.3 versus 2.2 months, respectively, p=0.7). Conclusions This retrospective study found that elderly and younger populations had comparable antitumor activities and toxicity profiles. These results support including elderly patients with cancer in early-phase trials.

Designing Dose-Finding Phase I Clinical Trials: Top 10 Questions That Should Be Discussed With Your Statistician.

In recent years, the landscape in clinical trial development has changed to involve many molecularly targeted agents, immunotherapies, or radiotherapy, as a single agent or in combination. Given their different mechanisms of action and lengths of administration, these agents have different toxicity profiles, which has resulted in numerous challenges when applying traditional designs such as the 3 + 3 design in dose-finding clinical trials. Novel methods have been proposed to address these design challenges such as combinations of therapies or late-onset toxicities. However, their design and implementation require close collaboration between clinicians and statisticians to ensure that the appropriate design is selected to address the aims of the study and that the design assumptions are pertinent to the study drug. The goal of this paper is to provide guidelines for appropriate questions that should be considered early in the design stage to facilitate the interactions between clinical and statistical teams and to improve the design of dose-finding clinical trials for novel anticancer agents.

Race and ethnicity representation in clinical trials: findings from a literature review of Phase I oncology trials.

Aim: To provide an assessment of published literature on the demographic representation in Phase I trials of biopharmaceutical oncology agents. Materials & methods: We conducted a rapid evidence assessment to identify demographic representation reported in Phase I clinical trials for biopharmaceutical oncology agents published in 2019. Results: Globally, the population was predominantly White/Caucasian (62.2%). In the USA, the distribution was heavily skewed toward White/Caucasian (84.2%), with minimal representation of Blacks/African-Americans (7.3%), Asians (3.4%), Hispanics/Latinos (2.8%) or other race/ethnicity groups. Conclusion: Our data highlight that Phase I oncology trials do not reflect the population at large, which may perpetuate health disparities. Further research is needed to understand and address barriers to participation, particularly among under-represented groups.

Lay abstract A plain language version of this article is available and is published alongside the paper online: www.futuremedicine.com/doi/suppl/10.2217/fon-2020-1262.

Systematic review of phase-I/II trials enrolling refractory and recurrent Ewing sarcoma: Actual knowledge and future directions to optimize the research.

BACKGROUND: Optimal Phase-II design to evaluate new therapies in refractory/relapsed Ewing sarcomas (ES) remains imperfectly defined. OBJECTIVES: Recurrent/refractory ES phase-I/II trials analysis to improve trials design. METHODS: Comprehensive review of therapeutic trials registered on five databases (who.int/trialsearch, clinicaltrials.gov, clinicaltrialsregister.eu, e-cancer.fr, and umin.ac.jp) and/or published in PubMed/ASCO/ESMO websites, between 2005 and 2018, using the criterion: (Ewing sarcoma OR bone sarcoma OR sarcoma) AND (Phase-I or Phase-II). RESULTS: The 146 trials identified (77 phase-I/II, 67 phase-II, and 2 phase-II/III) tested targeted (34%), chemo- (23%), immune therapies (19%), or combined therapies (24%). Twenty-three trials were ES specific and 48 had a specific ES stratum. Usually multicentric (88%), few trials were international (30%). Inclusion criteria cover the recurrent ES age range for only 12% of trials and allowed only accrual of measurable diseases (RECIST criteria). Single-arm design was the most frequent (88%) testing mainly single drugs (61%), only 5% were randomized. Primary efficacy outcome was response rate (RR=CR+PR; Complete+Partial response) (n = 116/146; 79%), rarely progression-free or overall survival (16% PFS and 3% OS). H0 and H1 hypotheses were variable (3%-25% and 20%-50%, respectively). The 62 published trials enrolled 827 ES patients. RR was poor (10%; 15 CR=1.7%, 68 PR=8.3%). Stable disease was the best response for 186 patients (25%). Median PFS/OS was of 1.9 (range 1.3-14.7) and 7.6 months (5-30), respectively. Eleven (18%) published trials were considered positive, with median RR/PFS/OS of 15% (7%-30%), 4.5 (1.3-10), and 16.6 months (6.9-30), respectively. CONCLUSION: This review supports the need to develop the international randomized phase-II trials across all age ranges with PFS as primary endpoint.

Early 3+3 Trial Dose-Escalation Phase I Clinical Trial Design and Suitability for Immune Checkpoint Inhibitors.

PURPOSE: Despite the expansion of immune checkpoint inhibitor (ICI) indications, the relationship between ICI dose and toxicity or response is not well established. To understand this correlation, we performed a meta-analysis of ICI trials that used dose escalation. EXPERIMENTAL DESIGN: We searched PubMed and abstracts presented at (inter)national meetings for trials using FDA-approved ICIs. The reported rates of grade 3-5 adverse events (G3-5 AE), immune-related adverse events (irAE), and response were correlated with doses within each ICI using marginal exact generalized linear models. RESULTS: A total of 74 trials (7,469 patients) published between January 2010 and January 2017 were included. For ipilimumab, the incidence of G3-5 AEs was 34% with a significant 27% reduced risk in lower doses (P = 0.002). However, no relationship was observed between dose and irAEs or response. For nivolumab, the incidence of G3-5 AEs was 20.1% which was lower in non-small cell lung cancer (NSCLC) compared with renal cell carcinoma (RCC) or melanoma (P ≤ 0.05) with no dose-toxicity relationship. In melanoma and NSCLC, a dose-response association was observed, which was not observed in RCC. For pembrolizumab, the incidence of G3-5 AEs was 13.3%, which was lower in melanoma compared with NSCLC (P = 0.03) with no dose-toxicity relationship. In melanoma, lower dose levels correlated with decreased odds of response (P = 0.01), a relationship that was not observed in NSCLC. CONCLUSIONS: Our analysis shows a lack of consistent dose-toxicity or dose-response correlation with ICIs. Therefore, dose escalation is not an appropriate design to conduct ICI studies. Here we present an innovative trial design for immune-modulating agents.

Radiological evaluation of malignant pleural mesothelioma - defining distant metastatic disease.

BACKGROUND: Malignant pleural mesothelioma (MPM) is traditionally characterized by local destructive spread of the pleura and surrounding tissues. Patient outcomes in MPM with distant metastatic dissemination are lacking. METHODS: In this retrospective study, we reviewed a cohort of 164 MPM patients referred to a Phase I trials unit, aiming to describe identified metastatic sites, and correlate with clinical outcomes. RESULTS: 67% of patients were diagnosed with distant metastatic disease with a high incidence of bone (19%), visceral (14%), contralateral lung (35%) and peritoneal metastases (22%). Peritoneal metastases were more likely in epithelioid versus biphasic/ sarcomatoid MPM (p = 0.015). Overall survival was 23.8 months with no statistical difference in survival between those with distant metastases and those without. CONCLUSIONS: This report highlights the frequency of distant metastases and encourages further radiological investigations in the presence of symptoms. In particular, given the relatively high incidence of bone metastases, bone imaging should be considered in advanced MPM clinical workflow and trial protocols. The presence of distant metastases does not appear to have prognostic implications under existing treatment paradigms. This cohort of MPM patients gives an indication of patterns of metastatic spread that are likely to become prevalent as prognosis improves with emerging treatment paradigms.

Clinical Development Times for Biosimilars in the United States.

Biosimilars are versions of biologic drugs made by different manufacturers that can help lower spending by promoting competition. However, few biosimilars are currently available in the US. To assess the role of testing requirements in this outcome, we investigated clinical development times for 40 biosimilars that initiated phase I testing between 2012 and 2015. We found that most biosimilars underwent phase III testing with an average trial length of 22 months. Of 20 biosimilars that had been approved by October 2019, the median time from initiation of phase I testing to approval was 69.9 months. These findings reveal a high testing bar for approval that likely contributed to limited market entry.

Impact of COVID-19 in paediatric early-phase cancer clinical trials in Europe: A report from the Innovative Therapies for Children with Cancer (ITCC) consortium.

INTRODUCTION: Data regarding real-world impact on cancer clinical research during COVID-19 are scarce. We analysed the impact of the COVID-19 pandemic on the conduct of paediatric cancer phase I-II trials in Europe through the experience of the Innovative Therapies for Children with Cancer (ITCC). METHODS: A survey was sent to all ITCC-accredited early-phase clinical trial hospitals including questions about impact on staff activities, recruitment, patient care, supply of investigational products and legal aspects, between 1st March and 30th April 2020. RESULTS: Thirty-one of 53 hospitals from 12 countries participated. Challenges reported included staff constraints (30% drop), reduction in planned monitoring activity (67% drop of site initiation visits and 64% of monitoring visits) and patient recruitment (61% drop compared with that in 2019). The percentage of phase I, phase II trials and molecular platforms closing to recruitment in at least one site was 48.5%, 61.3% and 64.3%, respectively. In addition, 26% of sites had restrictions on performing trial assessments because of local contingency plans. Almost half of the units suffered impact upon pending contracts. Most hospitals (65%) are planning on improving organisational and structural changes. CONCLUSION: The study reveals a profound disruption of paediatric cancer early-phase clinical research due to the COVID-19 pandemic across Europe. Reported difficulties affected both patient care and monitoring activity. Efforts should be made to reallocate resources to avoid lost opportunities for patients and to allow the continued advancement of oncology research. Identified adaptations to clinical trial procedures may be integrated to increase preparedness of clinical research to futures crises.

Pre-clinical animal models are poor predictors of human toxicities in phase 1 oncology clinical trials.

BACKGROUND: Our objective was to determine the correlation between preclinical toxicity found in animal models (mouse, rat, dog and monkey) and clinical toxicity reported in patients participating in Phase 1 oncology clinical trials. METHODS: We obtained from two major early-Phase clinical trial centres, preclinical toxicities from investigational brochures and clinical toxicities from published Phase 1 trials for 108 drugs, including small molecules, biologics and conjugates. Toxicities were categorised according to Common Terminology Criteria for Adverse Events version 4.0. Human toxicities were also categorised based on their reported clinical grade (severity). Positive predictive values (PPV) and negative predictive values (NPV) were calculated to determine the probability that clinical studies would/would not show a particular toxicity category given that it was seen in preclinical toxicology analysis. Statistical analyses also included kappa statistics, and Matthews (MCC) and Spearman correlation coefficients. RESULTS: Overall, animal toxicity did not show strong correlation with human toxicity, with a median PPV of 0.65 and NPV of 0.50. Similar results were obtained based on kappa statistics and MCC. CONCLUSIONS: There is an urgent need to assess more novel approaches to the type and conduct of preclinical toxicity studies in an effort to provide better predictive value for human investigation.

A comparison of phase I dose-finding designs in clinical trials with monotonicity assumption violation.

BACKGROUND/AIMS: In oncology, new combined treatments make it difficult to order dose levels according to monotonically increasing toxicity. New flexible dose-finding designs that take into account uncertainty in dose levels ordering were compared with classical designs through simulations in the setting of the monotonicity assumption violation. We give recommendations for the choice of dose-finding design. METHODS: Motivated by a clinical trial for patients with high-risk neuroblastoma, we considered designs that require a monotonicity assumption, the Bayesian Continual Reassessment Method, the modified Toxicity Probability Interval, the Bayesian Optimal Interval design, and designs that relax monotonicity assumption, the Bayesian Partial Ordering Continual Reassessment Method and the No Monotonicity Assumption design. We considered 15 scenarios including monotonic and non-monotonic dose-toxicity relationships among six dose levels. RESULTS: The No Monotonicity Assumption and Partial Ordering Continual Reassessment Method designs were robust to the violation of the monotonicity assumption. Under non-monotonic scenarios, the No Monotonicity Assumption design selected the correct dose level more often than alternative methods on average. Under the majority of monotonic scenarios, the Partial Ordering Continual Reassessment Method selected the correct dose level more often than the No Monotonicity Assumption design. Other designs were impacted by the violation of the monotonicity assumption with a proportion of correct selections below 20% in most scenarios. Under monotonic scenarios, the highest proportions of correct selections were achieved using the Continual Reassessment Method and the Bayesian Optimal Interval design (between 52.8% and 73.1%). The costs of relaxing the monotonicity assumption by the No Monotonicity Assumption design and Partial Ordering Continual Reassessment Method were decreases in the proportions of correct selections under monotonic scenarios ranging from 5.3% to 20.7% and from 1.4% to 16.1%, respectively, compared with the best performing design and were higher proportions of patients allocated to toxic dose levels during the trial. CONCLUSIONS: Innovative oncology treatments may no longer follow monotonic dose levels ordering which makes standard phase I methods fail. In such a setting, appropriate designs, as the No Monotonicity Assumption or Partial Ordering Continual Reassessment Method designs, should be used to safely determine recommended for phase II dose.

A dose-finding approach for genomic patterns in phase I trials.

Precision medicine is an emerging approach for disease treatment and prevention that accounts for individual variability in genes, environment, and lifestyle. Cancer is a genomic disease; therefore, the dose-efficacy and dose-toxicity relationships for molecularly targeted agents in cancer most likely differ, based on the genomic mutation pattern. The individualized optimal dose - the maximal efficacious dose with a clinically acceptable safety profile - may vary depending on the genomic mutation patterns and should be determined prior to the use of these agents in precision medicine. In addition, genes that influence the individualized optimal doses should be identified in early-phase development. In this study, we propose a novel dose-finding approach to identify the individualized optimal dose for molecularly targeted agents in phase I cancer trials. Individualized optimal dose determination and gene selection were conducted simultaneously based on L1 and L2 penalized regression. Similar to most reported dose-finding approaches, this study considers non-monotonic patterns for dose-efficacy and dose-toxicity relationships, as well as correlations between efficacy and toxicity outcomes based on multinomial distribution. Our dose-finding algorithm is based on the predictive probability calculated with an estimated penalized regression model. We compare the operating characteristics between the proposed and existing methods by simulation studies under various scenarios.

Proportion of Patients in Phase I Oncology Trials Receiving Treatments That Are Ultimately Approved.

BACKGROUND: Phase I oncology trials are often regarded as a therapeutic option for patients. However, such claims have relied on surrogate measures of benefit, such as objective response. METHODS: Using a systematic search of publications, we assessed the therapeutic value of phase I cancer trial participation by determining the probability that patients will receive active doses of treatments that eventually receive FDA approval or a National Comprehensive Cancer Network (NCCN) guideline recommendation for their indication. ClinicalTrials.gov, PubMed, American Society of Clinical Oncology reports, NCCN guidelines, and Drugs@FDA were searched between May 1, 2018, and July 31, 2018. All statistical tests were 2-sided. RESULTS: A total of 1000 phase I oncology trials initiated between 2005 and 2010 and enrolling 32 582 patients were randomly sampled from 3229 eligible trials on ClinicalTrials.gov. A total of 386 (1.2%) patients received a treatment that was approved by the US Food and Drug Administration for their malignancy at a dose delivered in the trial; including NCCN guideline recommendations, the number and proportion are 1168 (3.6%). Meta-regression showed a statistically significantly greater proportion of patients receiving a drug that was ultimately FDA approved in biomarker trials (rate ratio = 4.49, 95% confidence interval [CI] = 1.53 to 13.23; P = .006) and single-indication trials (rate ratio = 3.32, 95% CI = 1.21 to 9.15; P = .02); proportions were statistically significantly lower for combination vs monotherapy trials (rate ratio = 0.09, 95% CI = 0.01 to 0.68; P = .02). CONCLUSIONS: One in 83 patients in phase I cancer trials received a treatment that was approved for their indication at the doses received. Given published estimates of serious adverse event rates of 10%-19%, this represents low therapeutic value for phase I trial participation.

An adaptive trial design to optimize dose-schedule regimes with delayed outcomes.

This paper proposes a two-stage phase I-II clinical trial design to optimize dose-schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decision-making is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design's performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.

The i3+3 design for phase I clinical trials.

The traditional rule-based design, 3 + 3, has been shown to be less likely to achieve the objectives of dose-finding trials when compared with model-based designs. We propose a new rule-based design called i3 + 3, which is based on simple but more advanced rules that account for the variabilities in the observed data. We compare the operating characteristics for the proposed i3 + 3 design with other popular phase I designs by simulation. The i3 + 3 design is far superior than the 3 + 3 design in trial safety and the ability to identify the true MTD. Compared with model-based phase I designs, i3 + 3 also demonstrates comparable performances.

Lack of Availability and Efficacy of Phase I and Basket Trials for Patients With Gastrointestinal Cancers.

In the modern era of targeted and immune-based therapies, investigator and patient expectations of availability and efficacy in phase I trials have increased. We assessed availability of, and benefit from, early drug development trials, specifically in patients with gastrointestinal cancers. We reviewed computerized referral records of the Early Drug Development Service at our institution to identify patients internally referred from our Gastrointestinal Oncology Service in calendar year 2018. End points were treatment on a trial, 3- and 6-month progression-free survival (PFS), and any tumor shrinkage. Of 394 gastrointestinal cancer patients referred in 2018, 54 enrolled on a trial and 53 (13.5%) were treated (1 withdrew before treatment): 34 on immune-based and 19 on targeted (3 to phase II basket) studies. None of the 52 patients who had exhausted standard therapy achieved 6-month PFS, two (3.8%) met 3-month PFS with tumor growth below Response Evaluation Criteria in Solid Tumors progression at 3 months, and both came off study for progression at 4 months. One patient who was to receive an irinotecan-based regimen as standard therapy instead received irinotecan plus an investigational targeted agent and remained stable for 8 months. No patients achieved any degree of tumor shrinkage. The most common reasons for nonaccrual were lack of available protocol treatment openings and failure to meet eligibility criteria for specific trials. Thus, availability and benefit from investigational treatment in this treatment-refractory gastrointestinal cancer patient population was extremely modest. Expectations regarding both availability and efficacy of phase I investigational therapy in gastrointestinal cancer patients likely exceed what our experience suggests.

TITE-BOIN-ET: Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes.

One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.

A utility-based Bayesian optimal interval (U-BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies.

In the era of targeted therapy and immunotherapy, the objective of dose finding is often to identify the optimal biological dose (OBD), rather than the maximum tolerated dose. We develop a utility-based Bayesian optimal interval (U-BOIN) phase I/II design to find the OBD. We jointly model toxicity and efficacy using a multinomial-Dirichlet model, and employ a utility function to measure dose risk-benefit trade-off. The U-BOIN design consists of two seamless stages. In stage I, the Bayesian optimal interval design is used to quickly explore the dose space and collect preliminary toxicity and efficacy data. In stage II, we continuously update the posterior estimate of the utility for each dose after each cohort, using accumulating efficacy and toxicity from both stages I and II, and then use the posterior estimate to direct the dose assignment and selection. Compared to existing phase I/II designs, one prominent advantage of the U-BOIN design is its simplicity for implementation. Once the trial is designed, it can be easily applied using predetermined decision tables, without complex model fitting and estimation. Our simulation study shows that, despite its simplicity, the U-BOIN design is robust and has high accuracy to identify the OBD. We extend the design to accommodate delayed efficacy by leveraging the short-term endpoint (eg, immune activity or other biological activity of targeted agents), and using it to predict the delayed efficacy outcome to facilitate real-time decision making. A user-friendly software to implement the U-BOIN is freely available at www.trialdesign.org.

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions.

OBJECTIVES: We develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FISs), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of the risk. METHODS AND MATERIALS: Three FISs, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to their strategic choices. Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base are agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and later validated the model. RESULTS: In an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008-2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret our results. We have also run regressions to better understand the implications of our results. CONCLUSIONS: The main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of our system, which also may be easily applied to other settings.