Benefit–risk assessment: the use of clinical utility index

Derivation and Clinical Utility of Safety Targets for Linezolid-Related Adverse Events in Drug-Resistant Tuberculosis Treatment

Long-term usage of linezolid can result in adverse events such as peripheral neuropathy, anemia and thrombocytopenia. Therapeutic drug monitoring data from 75 drug-resistant tuberculosis patients treated with linezolid were analyzed using a time-to-event (TTE) approach for peripheral neuropathy and anemia and indirect response modelling for thrombocytopenia. Different time-varying linezolid pharmacokinetic exposure indices (AUC0-24h,ss, Cav, Cmax and Cmin) and patient characteristics were investigated as risk factors. A treatment duration shorter than 3 months was considered dropout and was modelled using a TTE approach. An exposure-response relationship between linezolid Cmin and both peripheral neuropathy and anemia was found. The exposure index which best described the development of thrombocytopenia was AUC0-24h. The final TTE dropout model indicated an association between linezolid Cmin and dropout. New safety targets for each adverse event were proposed which can be used for individualized linezolid dosing. According to the model predictions at 6 months of treatment, a Cmin of 0.11 mg/L and 1.4 mg/L should not be exceeded to keep the cumulative probability to develop anemia and peripheral neuropathy below 20%. The AUC0-24h should be below 111 h·mg/L or 270 h·mg/L to prevent thrombocytopenia and severe thrombocytopenia, respectively. A clinical utility assessment showed that the currently recommended dose of 600 mg once daily is safer compared to a 300 mg BID dosing strategy considering all four safety endpoints.

Appropriate design and reporting of superiority, equivalence and non-inferiority clinical trials incorporating a benefit-risk assessment: the BRAINS study including expert workshop

Background

Randomised controlled trials are designed to assess the superiority, equivalence or non-inferiority of a new health technology, but which trial design should be used is not always obvious in practice. In particular, when using equivalence or non-inferiority designs, multiple outcomes of interest may be important for the success of a trial, despite the fact that usually only a single primary outcome is used to design the trial. Benefit-risk methods are used in the regulatory clinical trial setting to assess multiple outcomes and consider the trade-off of the benefits against the risks, but are not regularly implemented in publicly funded trials.

Objectives

The aim of the project is to aid the design of clinical trials with multiple outcomes of interest by defining when each trial design is appropriate to use and identifying when to use benefit-risk methods to assess outcome trade-offs (qualitatively or quantitatively) in a publicly funded trial setting.

Methods

A range of methods was used to elicit expert opinion to answer the project objectives, including a web-based survey of relevant researchers, a rapid review of current literature and a 2-day consensus workshop of experts (in 2019).

Results

We created a list of 19 factors to aid researchers in selecting the most appropriate trial design, containing the following overarching sections: population, intervention, comparator, outcomes, feasibility and perspectives. Six key reasons that indicate a benefit-risk method should be considered within a trial were identified: (1) when the success of the trial depends on more than one outcome; (2) when important outcomes within the trial are in competing directions (i.e. a health technology is better for one outcome, but worse for another); (3) to allow patient preferences to be included and directly influence trial results; (4) to provide transparency on subjective recommendations from a trial; (5) to provide consistency in the approach to presenting results from a trial; and (6) to synthesise multiple outcomes into a single metric. Further information was provided to support the use of benefit-risk methods in appropriate circumstances, including the following: methods identified from the review were collated into different groupings and described to aid the selection of a method; potential implementation of methods throughout the trial process were provided and discussed (with examples); and general considerations were described for those using benefit-risk methods. Finally, a checklist of five pieces of information that should be present when reporting benefit-risk methods was defined, with two additional items specifically for reporting the results.

Conclusions

These recommendations will assist research teams in selecting which trial design to use and deciding whether or not a benefit-risk method could be included to ensure research questions are answered appropriately. Additional information is provided to support consistent use and clear reporting of benefit-risk methods in the future. The recommendations can also be used by funding committees to confirm that appropriate considerations of the trial design have been made.

Limitations

This research was limited in scope and should be considered in conjunction with other trial design methodologies to assess appropriateness. In addition, further research is needed to provide concrete information about which benefit-risk methods are best to use in publicly funded trials, along with recommendations that are specific to each method.

Study registration

The rapid review is registered as PROSPERO CRD42019144882.

Funding

Funded by the Medical Research Council UK and the National Institute for Health and Care Research as part of the Medical Research Council-National Institute for Health and Care Research Methodology Research programme.

Integrated exposure-response analysis of efficacy and safety of lurbinectedin to support the dose regimen in small-cell lung cancer

Purpose

These exposure–response (E–R) analyses integrated lurbinectedin effects on key efficacy and safety variables in relapsed SCLC to determine the adequacy of the dose regimen of 3.2 mg/m² 1-h intravenous infusion every 3 weeks (q3wk).

Methods

Logistic models and Cox regression analyses were applied to correlate lurbinectedin exposure metrics (AUC_tot and AUC_u) with efficacy and safety endpoints: objective response rate (ORR) and overall survival (OS) in SCLC patients (n = 99) treated in study B-005 with 3.2 mg/m² q3wk, and incidence of grade 4 (G4) neutropenia and grade 3–4 (G ≥ 3) thrombocytopenia in a pool of cancer patients from single-agent phase I to III studies (n = 692) treated at a wide range of doses. A clinical utility index was used to assess the appropriateness of the selected dose.

Results

Effect of lurbinectedin AUC_u on ORR best fitted to a sigmoid-maximal response (E_max) logistic model, where E_max was dependent on chemotherapy-free interval (CTFI). Cox regression analysis with OS found relationships with both CTFI and AUC_u. An E_max logistic model for G4 neutropenia and a linear logistic model for G ≥ 3 thrombocytopenia, which retained platelets and albumin at baseline and body surface area, best fitted to AUC_tot and AUC_u. AUC_u between approximately 1000 and 1700 ng·h/L provided the best benefit/risk ratio, and the dose of 3.2 mg/m² provided median AUC_u of 1400 ng·h/L, thus maximizing the proportion of patients within that lurbinectedin target exposure range.

Conclusions

The relationships evidenced in this integrated E–R analysis support a favorable benefit-risk profile for lurbinectedin 3.2 mg/m² q3wk.

Trial registration

Clinicaltrials.gov: NCT02454972; registered May 27, 2015.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-021-04366-3.

Optimal dose-finding for efficacy-safety models

Dose-finding is an important part of the clinical development of a new drug. The purpose of dose-finding studies is to determine a suitable dose for future development based on both efficacy and safety. Optimal experimental designs have already been used to determine the design of this kind of studies, however, often that design is focused on efficacy only. We consider an efficacy-safety model, which is a simplified version of the bivariate Emax model. We use here the clinical utility index concept, which provides the desirable balance between efficacy and safety. By maximizing the utility of the patients, we get the estimated dose. This desire leads us to locally c -optimal designs. An algebraic solution for c -optimal designs is determined for arbitrary c vectors using a multivariate version of Elfving's method. The solution shows that the expected therapeutic index of the drug is a key quantity determining both the number of doses, the doses itself, and their weights in the optimal design. A sequential design is proposed to solve the complication of parameter dependency, and it is illustrated in a simulation study.

Beyond exposure-response: A tutorial on statistical considerations in dose-ranging studies

Dose‐ranging studies are a crucial part of the phase II drug development process. They complement the understanding gained from exposure‐response analyses. However, the statistical design issues related to dose‐ranging studies are not always keenly understood and a poorly designed study can be costly for later development. In this tutorial, we review five key statistical principles in designing such a study. We also describe some popular statistical approaches, including pairwise comparison, modeling, and Multiple Comparison Procedure modeling in the context of principles.

Pretomanid dose selection for pulmonary tuberculosis: An application of multi-objective optimization to dosage regimen design

Clinical development of combination chemotherapies for tuberculosis (TB) is complicated by partial or restricted phase II dose‐finding. Barriers include a propensity for drug resistance with monotherapy, practical limits on numbers of treatment arms for component dose combinations, and limited application of current dose selection methods to multidrug regimens. A multi‐objective optimization approach to dose selection was developed as a conceptual and computational framework for currently evolving approaches to clinical testing of novel TB regimens. Pharmacokinetic‐pharmacodynamic (PK‐PD) modeling was combined with an evolutionary algorithm to identify dosage regimens that yield optimal trade‐offs between multiple conflicting therapeutic objectives. The phase IIa studies for pretomanid, a newly approved nitroimidazole for specific cases of highly drug‐resistant pulmonary TB, were used to demonstrate the approach with Pareto optimized dosing that best minimized sputum bacillary load and the probability of drug‐related adverse events. Results include a population‐typical characterization of the recommended 200 mg once daily dosage, the optimality of time‐dependent dosing, examples of individualized therapy, and the determination of optimal loading doses. The approach generalizes conventional PK‐PD target attainment to a design problem that scales to drug combinations, and provides a benefit‐risk context for clinical testing of complex drug regimens.

A Personalized Medicine Approach Using Clinical Utility Index and Exposure-Response Modeling Informed by Patient Preferences Data

Selection of a personalized dose for an individual patient can be informed by the patient's preferences, translated as weights on each of the clinically relevant safety and efficacy drug attributes, based on results from a brief patient preference elicitation questionnaire. In this analysis, the weighted attributes were simulated to represent various endometriosis patient profiles. Exposure-response simulations were performed for elagolix, a drug approved for management of moderate to severe pain associated with endometriosis, across a range of plasma exposures corresponding to a range of doses. The results were combined to calculate a personalized clinical utility index. An interactive user-friendly online application was developed and envisioned as a physician's desk tool to personalize the dose selection process based on individual patient preferences. This demonstration should serve as an example of how patient/physician conversation can be facilitated with quantitative tools for personalizing the dose.

The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman.

The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.

Exposure-Response-Based Product Profile-Driven Clinical Utility Index for Ipatasertib Dose Selection in Prostate Cancer

The aims of this work were to characterize ipatasertib exposure–response (E‐R) relationships in a phase II study and to quantitatively assess benefit‐risk using a clinical utility index approach to support ipatasertib phase III dose selection in patients with metastatic castration‐resistant prostate cancer. Logistic regression and Cox proportional‐hazards models characterized E‐R relationships for safety and efficacy endpoints, respectively. Exposure metrics with and without considering dose interruptions/reductions (modifications) were tested in the E‐R models. Despite a steeper E‐R relationship when accounting for dose modifications, similar dose‐response projections were generated. The clinical utility index analysis assessed important attributes, weights, and clinically meaningful cutoff/tradeoff values based on predefined minimal, target, and optimistic product profiles. Ipatasertib 400 mg daily, showing the highest probability of achieving the minimal product profiles and better benefit‐risk balance than other doses (200–500 mg daily), was selected for further development in metastatic castration‐resistant prostate cancer.

The Potential Role of Individual-Level Benefit-Risk Assessment in Treatment Decision Making: A DIA Study Endpoints Community Workstream

Benefit-risk assessment is the cornerstone of decision making in medical care, playing a critical role in bringing treatments to market by informing decisions regarding drug development, licensing and reimbursement, and informing treatment decisions made by health care professionals and patients in clinical practice. In regulatory approval decision making, benefit and risk attributes are identified and defined based on available, aggregated clinical data from registration trials. In the context of major developments in recent years for involvement of patients as partners in all phases of drug development and in health care improvement, decision makers increasingly recognize the importance of informing treatment decisions by patient needs, values, experiences, and preferences. Using this as a basis, a DIA workstream was convened to explore the potential of individual-level benefit-risk assessment as a supplement to traditional group-level benefit-risk assessment for evaluating treatment. Various approaches as to how this information could be collected, including via patient-reported outcome measures, open-ended questioning, and stated-preference methods are presented. The utility of this information for various stakeholders is discussed.

Bayesian joint modelling of benefit and risk in drug development

To gain regulatory approval, a new medicine must demonstrate that its benefits outweigh any potential risks, ie, that the benefit-risk balance is favourable towards the new medicine. For transparency and clarity of the decision, a structured and consistent approach to benefit-risk assessment that quantifies uncertainties and accounts for underlying dependencies is desirable. This paper proposes two approaches to benefit-risk evaluation, both based on the idea of joint modelling of mixed outcomes that are potentially dependent at the subject level. Using Bayesian inference, the two approaches offer interpretability and efficiency to enhance qualitative frameworks. Simulation studies show that accounting for correlation leads to a more accurate assessment of the strength of evidence to support benefit-risk profiles of interest. Several graphical approaches are proposed that can be used to communicate the benefit-risk balance to project teams. Finally, the two approaches are illustrated in a case study using real clinical trial data.

Impact of FRAX-based osteoporosis intervention using real world data

OBJECTIVES

To estimate the implications and accuracy of the most common fracture prevention strategies: (1) fixed threshold by the National Osteoporosis Foundation (NOF), (2) age-dependent threshold by the National Osteoporosis Guideline Group (NOGG) and (3) osteoporotic bone mineral density (BMD).

METHODS

A retrospective cohort of all 50-90years old female members in a nationally representative payer provider healthcare organization in Israel, with 10years of follow-up on incident events of major osteoporotic fractures. Since events are less frequent than non-events, balanced accuracy (the average between the accuracy obtained for patients with and without events) was used to measure performance.

RESULTS

Overall among 141,320 women NOF and NOGG would recommend therapy for 17.3% and 2.8% respectively, with NOF exhibiting higher balanced accuracy: 74.1% vs. 54.2% for incident hip fractures detection and 60.0% vs. 51.6% for a composite outcome of major osteoporotic fractures. In patients with available BMD (n=16,578) the treatment intervention criteria of NOF, NOGG, osteoporotic femur neck or vertebral BMD were met by 30.5%, 9.3% and 24.6% of the population, with balanced accuracy of 70.1%, 56.5% and 62.3% respectively for hip fractures and 61.4%, 52.8%, 58.1% for major osteoporotic fractures. At the age of 75years or older the NOF hip fracture risk threshold (3%) was exceeded in most women regardless of risk factors other than age.

CONCLUSIONS

In this large population-based study, detection of patients at high risk of sustaining a major osteoporotic fracture within 10 years was more accurate with the NOF fixed threshold criteria as compared with the age-varying NOGG or BMD-only. However, special consideration and further studies are warranted in patients aged 75years or older with preserved bone density, which may benefit from non-medicinal interventions.

Moving Beyond Maximum Tolerated Dose for Targeted Oncology Drugs: Use of Clinical Utility Index to Optimize Venetoclax Dosage in Multiple Myeloma Patients

Exposure-response analyses of venetoclax in combination with bortezomib and dexamethasone in previously treated patients with multiple myeloma (MM) were performed on a phase Ib venetoclax dose-ranging study. Logistic regression models were utilized to determine relationships, identify subpopulations with different responses, and optimize the venetoclax dosage that balanced both efficacy and safety. Bortezomib refractory status and number of prior treatments were identified to impact the efficacy response to venetoclax treatment. Higher venetoclax exposures were estimated to increase the probability of achieving a very good partial response (VGPR) or better through venetoclax doses of 1,200 mg. However, the probability of neutropenia (grade ≥3) was estimated to increase at doses >800 mg. Using a clinical utility index, a venetoclax dosage of 800 mg daily was selected to optimally balance the VGPR or better rates and neutropenia rates in MM patients administered 1-3 prior lines of therapy and nonrefractory to bortezomib.

Discrepancies between multicriteria decision analysis-based ranking and intuitive ranking for pharmaceutical benefit-risk profiles in a hypothetical setting

WHAT IS KNOWN AND OBJECTIVE

Multicriteria decision analysis (MCDA) has been generally considered a promising decision-making methodology for the assessment of drug benefit-risk profiles. There have been many discussions in both public and private sectors on its feasibility and applicability, but it has not been employed in official decision-makings. For the purpose of examining to what extent MCDA would reflect the first-hand, intuitive preference of evaluators in practical pharmaceutical assessments, we conducted a questionnaire survey involving the participation of employees of pharmaceutical companies.

METHODS

Showing profiles of the efficacy and safety of four hypothetical drugs, each respondent was asked to rank them following the standard MCDA process and then to rank them intuitively (i.e. without applying any analytical framework).

RESULTS AND DISCUSSION

These two approaches resulted in substantially different ranking patterns from the same individuals, and the concordance rate was surprisingly low (17%). Although many respondents intuitively showed a preference for mild, balanced risk-benefit profiles over profiles with a conspicuous advantage in either risk or benefit, the ranking orders based on MCDA scores did not reflect the intuitive preference.

WHAT IS NEW AND CONCLUSION

Observed discrepancies between the rankings seemed to be primarily attributed to the structural characteristics of MCDA, which assumes that evaluation on each benefit and risk component should have monotonic impact on final scores. It would be difficult for MCDA to reflect commonly observed non-monotonic preferences for risk and benefit profiles. Possible drawbacks of MCDA should be further investigated prior to the real-world application of its benefit-risk assessment.

BEACON: A Summary Framework to Overcome Potential Reimbursement Hurdles

OBJECTIVE

To provide a framework for addressing payers' criteria during the development of pharmaceuticals.

METHODS

A conceptual framework was presented to an international health economic expert panel for discussion. A structured literature search (from 2010 to May 2015), using the following databases in Ovid: Medline(®) and Medline(®) In-Process (PubMed), Embase (Ovid), EconLit (EBSCOhost) and the National Health Service Economic Evaluation Database (NHS EED), and a 'grey literature' search, were conducted to identify existing criteria from the payer perspective. The criteria assessed by existing frameworks and guidelines were collated; the most commonly reported criteria were considered for inclusion in the framework. A mnemonic was conceived as a memory aide to summarise these criteria.

RESULTS

Overall, 41 publications were identified as potentially relevant to the objective. Following further screening, 26 were excluded upon full-text review on the basis of no framework presented (n = 13), redundancy (n = 11) or abstract only (n = 2). Frameworks that captured criteria developed for or utilised by the pharmaceutical industry (n = 5) and reimbursement guidance (n = 10) were reviewed. The most commonly identified criteria-unmet need/patient burden, safety, efficacy, quality-of-life outcomes, environment, evidence quality, budget impact and comparator-were incorporated into the summary framework. For ease of communication, the following mnemonic was developed: BEACON (Burden/target population, Environment, Affordability/value, Comparator, Outcomes, Number of studies/quality of evidence).

CONCLUSIONS

The BEACON framework aims to capture the 'essence' of payer requirements by addressing the most commonly described criteria requested by payers regarding the introduction of a new pharmaceutical.

Using a patient-centered approach to benefit-harm assessment in treatment decision-making: a case study in uveitis

BACKGROUND

Synthesizing evidence from comparative effectiveness trials can be difficult because multiple outcomes of different importance are to be considered. The goal of this study was to demonstrate an approach to conducting quantitative benefit-harm assessment that considers patient preferences.

METHODS

We conducted a benefit-harm assessment using data from the Multicenter Uveitis Steroid Treatment Trial that compared corticosteroid implant versus systemic corticosteroids and immunosuppression in non-infectious intermediate, posterior, and panuveitis. We focused on clinical outcomes considered important to patients, including visual acuity, development of cataracts/glaucoma, need for eye surgery, prescription-requiring hypertension, hyperlipidemia, and infections. Patient preferences elicited in a recent survey were then incorporated into our assessment of the benefit-harm balance.

RESULTS

Benefit-harm metrics were calculated for each time point that summarized the numbers of outcomes, caused or prevented by implant therapy versus systemic therapy if 1000 patients were treated. The benefit-harm metric was -129 (95% confidence interval: -242 to -14), -317 (-436 to -196), -390 (-514 to -264), and -526 (-687 to -368) at 6, 12, 18, and 24 months follow up, respectively, suggesting that systemic therapy may have a better benefit-harm balance. However, measures of quality of life for patients treated with implant therapy were found to be better than patients treated with systemic therapy over the same time period.

CONCLUSIONS

Results of benefit-harm assessment were different from the prospectively collected quality of life data during trial follow up. Future studies should explore the reasons for such discrepancies and the strength and weakness of each method to assess treatment benefits and harms.

A quantitative benefit-risk assessment approach to improve decision making in drug development: Application of a multicriteria decision analysis model in the development of combination therapy for overactive bladder

A multicriteria decision analysis (MCDA) approach was developed and used to estimate the benefit-risk of solifenacin and mirabegron and their combination in the treatment of overactive bladder (OAB). The objectives were 1) to develop an MCDA tool to compare drug effects in OAB quantitatively, 2) to establish transparency in the evaluation of the benefit-risk profile of various dose combinations, and 3) to quantify the added value of combination use compared to monotherapies. The MCDA model was developed using efficacy, safety, and tolerability attributes and the results of a phase II factorial design combination study were evaluated. Combinations of solifenacin 5 mg and mirabegron 25 mg and mirabegron 50 (5+25 and 5+50) scored the highest clinical utility and supported combination therapy development of solifenacin and mirabegron for phase III clinical development at these dose regimens. This case study underlines the benefit of using a quantitative approach in clinical drug development programs.

Integration of PKPD relationships into benefit-risk analysis

AIM

Despite the continuous endeavour to achieve high standards in medical care through effectiveness measures, a quantitative framework for the assessment of the benefit-risk balance of new medicines is lacking prior to regulatory approval. The aim of this short review is to summarise the approaches currently available for benefit-risk assessment. In addition, we propose the use of pharmacokinetic-pharmacodynamic (PKPD) modelling as the pharmacological basis for evidence synthesis and evaluation of novel therapeutic agents.

METHODS

A comprehensive literature search has been performed using MESH terms in PubMed, in which articles describing benefit-risk assessment and modelling and simulation were identified. In parallel, a critical review of multi-criteria decision analysis (MCDA) is presented as a tool for characterising a drug's safety and efficacy profile.

RESULTS

A definition of benefits and risks has been proposed by the European Medicines Agency (EMA), in which qualitative and quantitative elements are included. However, in spite of the value of MCDA as a quantitative method, decisions about benefit-risk balance continue to rely on subjective expert opinion. By contrast, a model-informed approach offers the opportunity for a more comprehensive evaluation of benefit-risk balance before extensive evidence is generated in clinical practice.

CONCLUSIONS

Benefit-risk balance should be an integral part of the risk management plan and as such considered before marketing authorisation. Modelling and simulation can be incorporated into MCDA to support the evidence synthesis as well evidence generation taking into account the underlying correlations between favourable and unfavourable effects. In addition, it represents a valuable tool for the optimization of protocol design in effectiveness trials.

Balancing benefit and risk of medicines: a systematic review and classification of available methodologies

BACKGROUND

The need for formal and structured approaches for benefit-risk assessment of medicines is increasing, as is the complexity of the scientific questions addressed before making decisions on the benefit-risk balance of medicines. We systematically collected, appraised and classified available benefit-risk methodologies to facilitate and inform their future use.

METHODS

A systematic review of publications identified benefit-risk assessment methodologies. Methodologies were appraised on their fundamental principles, features, graphical representations, assessability and accessibility. We created a taxonomy of methodologies to facilitate understanding and choice.

RESULTS

We identified 49 methodologies, critically appraised and classified them into four categories: frameworks, metrics, estimation techniques and utility survey techniques. Eight frameworks describe qualitative steps in benefit-risk assessment and eight quantify benefit-risk balance. Nine metric indices include threshold indices to measure either benefit or risk; health indices measure quality-of-life over time; and trade-off indices integrate benefits and risks. Six estimation techniques support benefit-risk modelling and evidence synthesis. Four utility survey techniques elicit robust value preferences from relevant stakeholders to the benefit-risk decisions.

CONCLUSIONS

Methodologies to help benefit-risk assessments of medicines are diverse and each is associated with different limitations and strengths. There is not a 'one-size-fits-all' method, and a combination of methods may be needed for each benefit-risk assessment. The taxonomy introduced herein may guide choice of adequate methodologies. Finally, we recommend 13 of 49 methodologies for further appraisal for use in the real-life benefit-risk assessment of medicines.

Modeling of human tumor xenografts and dose rationale in oncology

Xenograft models are commonly used in oncology drug development. Although there are discussions about their ability to generate meaningful data for the translation from animal to humans, it appears that better data quality and better design of the preclinical experiments, together with appropriate data analysis approaches could make these data more informative for clinical development. An approach based on mathematical modeling is necessary to derive experiment-independent parameters which can be linked with clinically relevant endpoints. Moreover, the inclusion of biomarkers as predictors of efficacy is a key step towards a more general mechanism-based strategy.

Effective visualization of integrated knowledge and data to enable informed decisions in drug development and translational medicine

Integrative understanding of preclinical and clinical data is imperative to enable informed decisions and reduce the attrition rate during drug development. The volume and variety of data generated during drug development have increased tremendously. A new information model and visualization tool was developed to effectively utilize all available data and current knowledge. The Knowledge Plot integrates preclinical, clinical, efficacy and safety data by adding two concepts: knowledge from the different disciplines and protein binding.

Internal and public available data were gathered and processed to allow flexible and interactive visualizations. The exposure was expressed as the unbound concentration of the compound and the treatment effect was normalized and scaled by including expert opinion on what a biologically meaningful treatment effect would be.

The Knowledge Plot has been applied both retrospectively and prospectively in project teams in a number of different therapeutic areas, resulting in closer collaboration between multiple disciplines discussing both preclinical and clinical data. The Plot allows head to head comparisons of compounds and was used to support Candidate Drug selections and differentiation from comparators and competitors, back translation of clinical data, understanding the predictability of preclinical models and assays, reviewing drift in primary endpoints over the years, and evaluate or benchmark compounds in due diligence comparing multiple attributes.

The Knowledge Plot concept allows flexible integration and visualization of relevant data for interpretation in order to enable scientific and informed decision-making in various stages of drug development. The concept can be used for communication, decision-making, knowledge management, and as a forward and back translational tool, that will result in an improved understanding of the competitive edge for a particular project or disease area portfolio. In addition, it also builds up a knowledge and translational continuum, which in turn will reduce the attrition rate and costs of clinical development by identifying poor candidates early.

Payers and the assessment of clinical utility for companion diagnostics

The decision makers who approve or deny payment for health-care services review many new technologies. Reimbursement for companion diagnostics for expensive drugs ("personalized medicine") is already under close policy scrutiny, in line with long-standing concerns about overuse of diagnostic tests. Evaluation of diagnostic tests adds some complexities to the payer's comparative-effectiveness evaluation for drugs alone. Currently, decision-making frameworks suitable for companion diagnostics are being developed for practical application by payer policy makers.