Integration of PKPD relationships into benefit-risk analysis

Methodological guidelines and publications of benefit-risk assessment for health technology assessment: a scoping review

OBJECTIVES

To map the available methodological guidelines and documents for conducting and reporting benefit-risk assessment (BRA) during health technologies' life cycle; and to identify methodological guidelines for BRA that could serve as the basis for the development of a BRA guideline for the context of health technology assessment (HTA) in Brazil.

DESIGN

Scoping review.

METHODS

Searches were conducted in three main sources up to March 2023: (1) electronic databases; (2) grey literature (48 HTA and regulatory organisations) and (3) manual search and contacting experts. We included methodological guidelines or publications presenting methods for conducting or reporting BRA of any type of health technologies in any context of the technology's life cycle. Selection process and data charting were conducted by independent reviewers. We provided a structured narrative synthesis of the findings.

RESULTS

From the 83 eligible documents, six were produced in the HTA context, 30 in the regulatory and 35 involved guidance for BRA throughout the technology's life cycle. We identified 129 methodological approaches for BRA in the documents. The most commonly referred to descriptive frameworks were the Problem, Objectives, Alternatives, Consequences, Trade-offs, Uncertainty, Risk and Linked decisions and the Benefit-Risk Action Team. Multicriteria decision analysis was the most commonly cited quantitative framework. We also identified the most cited metric indices, estimation and utility survey techniques that could be used for BRA.

CONCLUSIONS

Methods for BRA in HTA are less established. The findings of this review, however, will support and inform the elaboration of the Brazilian methodological guideline on BRA for HTA.

TRIAL REGISTRATION NUMBER

https://doi.org/10.17605/OSF.IO/69T3V.

Potential risk assessment: a model for quality evaluation in fluoroscopy-guided interventional procedures

This study presented a model applied for potential risk assessment in an interventional radiology setting. The model of potential risk assessment (MARP) consisted of the creation of a scale of indicators ranging from 0 to 5. The radiation levels were categorized according to gender, kind of procedure, value of kerma air product (Pka), and accumulated radiation dose (mGy). The MARP model was applied in 121 institutions over 8 y. A total of 201 656 patient radiation doses (Dose-area product and accumulated kerma) data were launched into the system over time, with an average of 22 406 doses per year. In the context of the workers (cardiologists, radiographers, and nurses) monitored during the MARP application, 8007 cases (with an average of 890 per year) of occupational radiation doses were recorded. This study showed a strategy for quality evaluation in fluoroscopy using a model with a compulsory information system for monitoring safety.

Dose rationale for gabapentin and tramadol in pediatric patients with chronic pain

Despite off-label use, the efficacy and safety of gabapentin and tramadol in pediatric patients (3 months to <18 years old) diagnosed with chronic pain has not been characterized. However, generating evidence based on randomized clinical trials in this population has been extremely challenging. The current investigation illustrates the use of clinical trial simulations (CTSs) as a tool for optimizing doses and protocol design for a prospective investigation in pediatric patients with chronic pain. Pharmacokinetic (PK) modeling and CTSs were used to describe the PKs of gabapentin and tramadol in the target population. In the absence of biomarkers of analgesia, systemic exposure (AUC, Css) was used to guide dose selection under the assumption of a comparable exposure-response (PKPD) relationship for either compound between adults and children. Two weight bands were identified for gabapentin, with doses titrated from 5 to 63 mg/kg. This yields gabapentin exposures (AUC0-8 ) of approximately 35 mg/L*h (1200 mg/day adult dose equivalent). For tramadol, median steady state concentrations between 200 and 300 ng/mL were achieved after doses of 2-5 mg/kg, but concentrations showed high interindividual variability. Simulation scenarios showed that titration steps are required to explore therapeutically relevant dose ranges taking into account the safety profile of both drugs. Gabapentin can be used t.i.d. at doses between 7-63 and 5-45 mg/kg for patients receiving gabapentin weighing <15 and ≥15 kg, respectively, whereas a t.i.d. regimen with doses between 1 and 5 mg/kg can be used for tramadol in patients who are not fast metabolisers.

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.

Model-Informed Repurposing of Medicines for SARS-CoV-2: Extrapolation of Antiviral Activity and Dose Rationale for Paediatric Patients

Repurposing of remdesivir and other drugs with potential antiviral activity has been the basis of numerous clinical trials aimed at SARS-CoV-2 infection in adults. However, expeditiously designed trials without careful consideration of dose rationale have often resulted in treatment failure and toxicity in the target patient population, which includes not only adults but also children. Here we show how paediatric regimens can be identified using pharmacokinetic-pharmacodynamic (PKPD) principles to establish the target exposure and evaluate the implications of dose selection for early and late intervention. Using in vitro data describing the antiviral activity and published pharmacokinetic data for the agents of interest, we apply a model-based approach to assess the exposure range required for adequate viral clearance and eradication. Pharmacokinetic parameter estimates were subsequently used with clinical trial simulations to characterise the probability target attainment (PTA) associated with enhanced antiviral activity in the lungs. Our analysis shows that neither remdesivir, nor anti-malarial drugs can achieve the desirable target exposure range based on a mg/kg dosing regimen, due to a limited safety margin and high concentrations needed to ensure the required PTA. To date, there has been limited focus on suitable interventions for children affected by COVID-19. Most clinical trials have defined doses selection criteria empirically, without thorough evaluation of the PTA. The current results illustrate how model-based approaches can be used for the integration of clinical and nonclinical data, providing a robust framework for assessing the probability of pharmacological success and consequently the dose rationale for antiviral drugs for the treatment of SARS-CoV-2 infection in children.

Concept and utility of population pharmacokinetic and pharmacokinetic/pharmacodynamic models in drug development and clinical practice

Due to frequent clinical trial failures and consequently fewer new drug approvals, the need for improvement in drug development has, to a certain extent, been met using model-based drug development. Pharmacometrics is a part of pharmacology that quantifies drug behaviour, treatment response and disease progression based on different models (pharmacokinetic - PK, pharmacodynamic - PD, PK/PD models, etc.) and simulations. Regulatory bodies (European Medicines Agency, Food and Drug Administration) encourage the use of modelling and simulations to facilitate decision-making throughout all drug development phases. Moreover, the identification of factors that contribute to variability provides a basis for dose individualisation in routine clinical practice. This review summarises current knowledge regarding the application of pharmacometrics in drug development and clinical practice with emphasis on the population modelling approach.

Combining Model-Based Clinical Trial Simulation, Pharmacoeconomics, and Value of Information to Optimize Trial Design

The Bayesian decision‐analytic approach to trial design uses prior distributions for treatment effects, updated with likelihoods for proposed trial data. Prior distributions for treatment effects based on previous trial results risks sample selection bias and difficulties when a proposed trial differs in terms of patient characteristics, medication adherence, or treatment doses and regimens. The aim of this study was to demonstrate the utility of using pharmacometric‐based clinical trial simulation (CTS) to generate prior distributions for use in Bayesian decision‐theoretic trial design. The methods consisted of four principal stages: a CTS to predict the distribution of treatment response for a range of trial designs; Bayesian updating for a proposed sample size; a pharmacoeconomic model to represent the perspective of a reimbursement authority in which price is contingent on trial outcome; and a model of the pharmaceutical company return on investment linking drug prices to sales revenue. We used a case study of febuxostat versus allopurinol for the treatment of hyperuricemia in patients with gout. Trial design scenarios studied included alternative treatment doses, inclusion criteria, input uncertainty, and sample size. Optimal trial sample sizes varied depending on the uncertainty of model inputs, trial inclusion criteria, and treatment doses. This interdisciplinary framework for trial design and sample size calculation may have value in supporting decisions during later phases of drug development and in identifying costly sources of uncertainty, and thus inform future research and development strategies.

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real-world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo-responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.

Distributions of the National Early Warning Score (NEWS) across a healthcare system following a large-scale roll-out

Background

Early warning scores (EWS) were developed in acute hospital settings to improve recognition and response to patient deterioration. In 2012, the UK Royal College of Physicians developed the National Early Warning Score (NEWS) to standardise EWS across the NHS. Its use was also recommended outside acute hospital settings; however, there is limited information about NEWS in these settings. From March 2015, NEWS was implemented across the healthcare system in the West of England, with the aim that NEWS would be calculated for all patients prior to referral into acute care.

Aim

To describe the distribution and use of NEWS in out-of-hospital settings for patients with acute illness or long-term conditions, following system wide implementation.

Method

Anonymised data were obtained from 115 030 emergency department (ED) attendances, 1 137 734 ambulance electronic records, 31 063 community attendances and 15 160 general practitioner (GP) referrals into secondary care, in the West of England. Descriptive statistics are presented.

Results

Most attendance records had NEWS=0–2: 80% in ED, 67% of ambulance attendances and 72% in the community. In contrast, only 8%, 18% and 11% of attendances had NEWS ≥5 (the trigger for escalation of care in-hospital), respectively. Referrals by a GP had higher NEWS on average (46% NEWS=0–2 and 30% NEWS ≥5). By April 2016, the use of NEWS was reasonably stable in ED, ambulance and community populations, and still increasing for GP referrals.

Conclusions

NEWS ≥5 occurred in less than 20% of ED, ambulance and community populations studied and 30% of GP referrals. This suggests that in most out-of-hospital settings studied, high scores are reasonably uncommon.

A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit-Risk Ratio of a Treatment Using a Convolution-Based Modeling Approach

The net benefit of a treatment can be defined by the relationship between clinical improvement and risk of adverse events: the benefit‐risk ratio. The optimization of the benefit‐risk ratio can be achieved by identifying the most adequate dose (and/or dosage regimen) jointly with the best‐performing in vivo release properties of a drug. A general in silico tool is presented for identifying the dose, the in vitro and the in vivo release properties that maximize the benefit‐risk ratio using convolution‐based modeling, an exposure‐response model, and a surface response analysis. A case study is presented to illustrate how the benefit‐risk ratio of methylphenidate for the treatment of attention deficit hyperactivity disorder can be maximized using the proposed strategy. The results of the analysis identified the characteristics of an optimized dose and in vitro/in vivo release suitable to provide a sustained clinical response with respect to the conventional dosage regimen and formulations.

Model-Informed Drug Development for Ixazomib, an Oral Proteasome Inhibitor

Model-informed drug development (MIDD) was central to the development of the oral proteasome inhibitor ixazomib, facilitating internal decisions (switch from body surface area (BSA)-based to fixed dosing, inclusive phase III trials, portfolio prioritization of ixazomib-based combinations, phase III dose for maintenance treatment), regulatory review (model-informed QT analysis, benefit-risk of 4 mg dose), and product labeling (absolute bioavailability and intrinsic/extrinsic factors). This review discusses the impact of MIDD in enabling patient-centric therapeutic optimization during the development of ixazomib.

Biomarkers in epilepsy-A modelling perspective

Biomarkers can be categorised from type 0 (genotype or phenotype), through 6 (clinical scales), each level representing a part of the processes involved in the biological system and drug treatment. This classification facilitates the identification and connection of information required to fully (mathematically) model a disease and its treatment using integrated information from biomarkers. Two recent reviews thoroughly discussed the current status and development of biomarkers for epilepsy, but a path towards the integration of such biomarkers for the personalisation of anti-epileptic drug treatment is lacking. Here we aim to 1) briefly categorise the available epilepsy biomarkers and identify gaps, and 2) provide a modelling perspective on approaches to fill such gaps. There is mainly a lack of biomarker types 2 (target occupancy) and 3 (target activation). Current literature typically focuses on qualitative biomarkers for diagnosis and prediction of treatment response or failure, leaving a need for biomarkers that help to quantitatively understand the overall system to explain and predict differences in disease and treatment outcome. Due to the complexity of epilepsy, filling the biomarker gaps will require collaboration and expertise from the fields of systems biology and systems pharmacology.

How to optimise drug study design: pharmacokinetics and pharmacodynamics studies introduced to paediatricians

OBJECTIVES

In children, there is often lack of sufficient information concerning the pharmacokinetics (PK) and pharmacodynamics (PD) of a study drug to support dose selection and effective evaluation of efficacy in a randomised clinical trial (RCT). Therefore, one should consider the relevance of relatively small PKPD studies, which can provide the appropriate data to optimise the design of an RCT.

METHODS

Based on the experience of experts collaborating in the EU-funded Global Research in Paediatrics consortium, we aimed to inform clinician-scientists working with children on the design of investigator-initiated PKPD studies.

KEY FINDINGS

The importance of the identification of an optimal dose for the paediatric population is explained, followed by the differences and similarities of dose-ranging and efficacy studies. The input of clinical pharmacologists with modelling expertise is essential for an efficient dose-finding study.

CONCLUSIONS

The emergence of new laboratory techniques and statistical tools allows for the collection and analysis of sparse and unbalanced data, enabling the implementation of (observational) PKPD studies in the paediatric clinic. Understanding of the principles and methods discussed in this study is essential to improve the quality of paediatric PKPD investigations, and to prevent the conduct of paediatric RCTs that fail because of inadequate dosing.

Pharmacotherapy in pediatric epilepsy: from trial and error to rational drug and dose selection - a long way to go

INTRODUCTION

Whereas ongoing efforts in epilepsy research focus on the underlying disease processes, the lack of a physiologically based rationale for drug and dose selection contributes to inadequate treatment response in children. In fact, limited information on the interindividual variation in pharmacokinetics and pharmacodynamics of anti-epileptic drugs (AEDs) in children drive prescription practice, which relies primarily on dose regimens according to a mg/kg basis. Such practice has evolved despite advancements in pediatric pharmacology showing that growth and maturation processes do not correlate linearly with changes in body size.

AREAS COVERED

In this review we aim to provide 1) a comprehensive overview of the sources of variability in the response to AEDs, 2) insight into novel methodologies to characterise such variation and 3) recommendations for treatment personalisation.

EXPERT OPINION

The use of pharmacokinetic-pharmacodynamic principles in clinical practice is hindered by the lack of biomarkers and by practical constraints in the evaluation of polytherapy. The identification of biomarkers and their validation as tools for drug development and therapeutics will require some time. Meanwhile, one should not miss the opportunity to integrate the available pharmacokinetic data with modeling and simulation concepts to prevent further delays in the development of personalised treatments for pediatric patients.