Linked Pharmacometric-Pharmacoeconomic Modeling and Simulation in Clinical Drug Development.

Market access and pricing of pharmaceuticals are increasingly contingent on the ability to demonstrate comparative effectiveness and cost-effectiveness. As such, it is widely recognized that predictions of the economic potential of drug candidates in development could inform decisions across the product life cycle. This may be challenging when safety and efficacy profiles in terms of the relevant clinical outcomes are unknown or highly uncertain early in product development. Linking pharmacometrics and pharmacoeconomics, such that outputs from pharmacometric models serve as inputs to pharmacoeconomic models, may provide a framework for extrapolating from early-phase studies to predict economic outcomes and characterize decision uncertainty. This article reviews the published studies that have implemented this methodology and used simulation to inform drug development decisions and/or to optimize the use of drug treatments. Some of the key practical issues involved in linking pharmacometrics and pharmacoeconomics, including the choice of final outcome measures, methods of incorporating evidence on comparator treatments, approaches to handling multiple intermediate end points, approaches to quantifying uncertainty, and issues of model validation are also discussed. Finally, we have considered the potential barriers that may have limited the adoption of this methodology and suggest that closer alignment between the disciplines of clinical pharmacology, pharmacometrics, and pharmacoeconomics, may help to realize the potential benefits associated with linked pharmacometric-pharmacoeconomic modeling and simulation.

Best Practice Approach to Successful Conversion of Fosaprepitant to Aprepitant IV in a Large Multisite Community Oncology Infusion Center: A Retrospective Analysis.

PURPOSE: To evaluate the impact on cost, time, resource use, and clinic workflow of converting the route of drug administration from a neurokinin-1 receptor antagonist (NK-1 RA) 30-min intravenous (IV) infusion to aprepitant IV, and more specifically to IV push, within a multicenter community oncology practice. METHODS: This was a retrospective, multicenter time, motion, and resource/cost evaluation study. Conversion to aprepitant IV was determined by calculating number of doses of aprepitant IV versus fosaprepitant administered in patients receiving moderately or highly emetogenic chemotherapy regimens. Operational advantages (i.e., supply costs, time saved) of switching from fosaprepitant IV infusion to aprepitant administered as a 2-min IV push were assessed. RESULTS: A total of 12,908 doses of aprepitant IV 130 mg were administered at 13 Rocky Mountain Cancer Centers clinics over an 18-month period. Conversion from fosaprepitant to aprepitant IV reached 90% after 9 months of aprepitant IV initiation. Supply costs per administration were reduced ($2.51 to $0.52) when aprepitant was prepared as an IV push versus an NK-1 RA infusion. The overall time savings per administration of aprepitant was reduced by 90% (from 36.5 to 3.5 min, 33 min saved) as an IV push rather than an infusion. Most of the time saved per administration (30 min) pertained to the infusion nurse, and 3 min was saved by the pharmacy technician. CONCLUSION: Successful conversion to aprepitant, and specifically to a 2-min IV push, provides time, cost, and resource savings, improves operational efficiency, and avoids the negative impact of potential future IV fluid shortages.

Chemotherapy-induced nausea and vomiting (CINV) can have a major impact on quality of life for patients receiving chemotherapy. Intravenous (IV) aprepitant is an approved neurokinin-1 receptor antagonist (NK-1 RA) that has been effective and safe when administered as part of a guideline-recommended regimen in patients receiving chemotherapy. In addition to being approved as a 30-min infusion, aprepitant IV is the only NK-1 RA approved for administration as a 2-min injection. These factors contributed to Rocky Mountain Cancer Centers (RMCC), which is a physician-owned community oncology practice, evaluating the impact on cost, time, and resource use of converting from a 30-min infusion of fosaprepitant to aprepitant IV, and more specifically a 2-min injection. Within 9 months of implementing aprepitant IV at RMCC, the percent utilization compared to fosaprepitant reached over 90%, signifying a successful conversion within the practice. Furthermore, a 2-min injection of aprepitant IV resulted in several operational advantages compared to a 30-min infusion. When accounting for all 13 clinics within RMCC, total monthly time savings to the practice would be over 28,000 min, or approximately 60 workdays per month of saved time. This new workflow is more efficient and allows for pharmacy technicians to complete other necessary tasks in the pharmacy such as cleaning, organizing, managing inventory, drug ordering, and charge/documentation corrections. Time saved by the nurses could be used for enhanced patient care, thoroughly reviewing chemotherapy or other orders, and assisting other nurses.

Integration of Pharmacometrics and Pharmacoeconomics to Quantify the Value of Improved Forgiveness to Nonadherence: A Case Study of Novel Xanthine Oxidase Inhibitors for Gout.

Linked pharmacometric and pharmacoeconomic models provide a structured approach for assessing the value of candidate drugs in development. The aim of this study was to assess the utility of such an approach for identifying the properties of xanthine oxidase inhibitors (XOi) providing improved forgiveness to nonadherence and estimate the maximum reimbursement price. The pharmacometric and pharmacoeconomic models were used to simulate the time course of serum uric acid concentrations and estimate quality-adjusted life years and costs for the XOi febuxostat and a range of hypothetical analogues. Compounds with reduced clearance or increased potency were more forgiving to missed doses, however, even following relatively large changes in these properties the predicted maximum reimbursement prices represented an increase of only 19% above febuxostat 80 mg. Linked pharmacometric and pharmacoeconomic modeling methods have the potential to inform early drug development by providing an indication of pricing options that may permit reimbursement.

Model-Informed Drug Discovery and Development: Current Industry Good Practice and Regulatory Expectations and Future Perspectives.

Good practices around model-informed drug discovery and development (MID3) aim to improve the implementation, standardization, and acceptance of these approaches within drug development and regulatory review. A survey targeted to clinical pharmacology and pharmacometric colleagues across industry, the US Food and Drug Administration (FDA), and the European Medicines Agency (EMA) was conducted to understand current and future roles of MID3. The documented standards were generally affirmed as a "good match" to current industry practice and regulatory expectations, with some identified gaps that are discussed. All have seen at least a "modest" step forward in MID3 implementation associated with greater organizational awareness and share the expectation for a future wider use and impact. The priority within organizations was identified as a limitation with respect to the future of MID3. Finally, potential solutions, including a global overarching MID3 regulatory guideline, to facilitate greater acceptance by industry and regulatory decision makers are discussed.

Impact of Non-Adherence and Flare Resolution on the Cost-Effectiveness of Treatments for Gout: Application of a Linked Pharmacometric/Pharmacoeconomic Model.

BACKGROUND: Dual urate-lowering therapy (ULT) with lesinurad in combination with either allopurinol or febuxostat is an option for patients with gout unsuccessfully treated on either monotherapy. Treatment failure is often a result of poor medication adherence. Imperfect adherence in clinical trials may lead to biased estimates of treatment effect and confound the results of cost-effectiveness analyses. OBJECTIVES: To estimate the impact of varying medication adherence on the cost effectiveness of lesinurad dual therapy and estimate the value-based price of lesinurad at which the incremental cost-effectiveness ratio is equal to £20,000 per quality-adjusted life-year (QALY). METHODS: Treatment effect was simulated using published pharmacokinetic-pharmacodynamic models and scenarios representing adherence in clinical trials, routine practice, and perfect use. The subsequent cost and health impacts, over the lifetime of a patient cohort, were estimated using a bespoke pharmacoeconomic model. RESULTS: The base-case incremental cost-effectiveness ratios comparing lesinurad dual ULT with monotherapy ranged from £39,184 to £78,350/QALY gained using allopurinol and £31,901 to £124,212/QALY gained using febuxostat, depending on the assumed medication adherence. Results assuming perfect medication adherence imply a per-quarter value-based price of lesinurad of £45.14 when used in dual ULT compared with allopurinol alone and £57.75 compared with febuxostat alone, falling to £25.41 and £3.49, respectively, in simulations of worsening medication adherence. CONCLUSIONS: The estimated value-based prices of lesinurad only exceeded that which has been proposed in the United Kingdom when assuming both perfect drug adherence and the eradication of gout flares in sustained treatment responders.

Predictors of the placebo analgesia response in randomized controlled trials of chronic pain: a meta-analysis of the individual data from nine industrially sponsored trials.

A large number of analgesics have failed to prove superiority over placebo in randomized controlled trials (RCTs), and as this has been related to increasing placebo responses, there is currently an interest in specifying predictors of the placebo response. The literature on placebo mechanisms suggests that factors related to patients' expectations of treatment efficacy are pivotal for the placebo response. Also, general characteristics of RCTs have been suggested to influence the placebo response. Yet, only few meta-analyses have directly tested these hypotheses. Placebo data from 9 industrially sponsored, randomized, double-blind, placebo-controlled, multicenter phase III trials in 2017 adult patients suffering from chronic painful osteoarthritis (hip or knee) or low back pain were included. The primary outcome was pain intensity. Based on previous studies, we chose 3 expectancy-related primary predictors: type of active medication, randomization ratio, and number of planned face-to-face visits. In addition, explorative analyses tested whether RCT and patients' characteristics predicted the placebo response. Opioid trials, a high number of planned face-to-face visits, and randomization ratio predicted the magnitude of the placebo response, thereby supporting the expectancy hypothesis. Exploratory models with baseline pain intensity, age, washout length, and discontinuation because of adverse events accounted for approximately 10% of the variability in the placebo response. Based on these results and previous mechanisms studies, we think that patients' perception of treatment allocation and expectations toward treatment efficacy could potently predict outcomes of RCTs.

The application of a Bayesian approach to the analysis of a complex, mechanistically based model.

The Bayesian approach has been suggested as a suitable method in the context of mechanistic pharmacokinetic-pharmacodynamic (PK-PD) modeling, as it allows for efficient use of both data and prior knowledge regarding the drug or disease state. However, to this day, published examples of its application to real PK-PD problems have been scarce. We present an example of a fully Bayesian re-analysis of a previously published mechanistic model describing the time course of circulating neutrophils in stroke patients and healthy individuals. While priors could be established for all population parameters in the model, not all variability terms were known with any degree of precision. A sensitivity analysis around the assigned priors used was performed by testing three different sets of prior values for the population variance terms for which no data were available in the literature: "informative", "semi-informative", and "noninformative", respectively. For all variability terms, inverse gamma distributions were used. It was possible to fit the model to the data using the "informative" priors. However, when the "semi-informative" and "noninformative" priors were used, it was impossible to accomplish convergence due to severe correlations between parameters. In addition, due to the complexity of the model, the process of defining priors and running the Markov chains was very time-consuming. We conclude that the present analysis represents a first example of the fully transparent application of Bayesian methods to a complex, mechanistic PK-PD problem with real data. The approach is time-consuming, but enables us to make use of all available information from data and scientific evidence. Thereby, it shows potential both for detection of data gaps and for more reliable predictions of various outcomes and "what if" scenarios.

A longitudinal model for non-monotonic clinical assessment scale data.

Clinical assessment scales, where subitem ratings are added and summarized as a total score, are convenient tools for monitoring disease progression and often used to measure the effect of drug treatment in clinical trials. Statistical evaluation of any beneficial treatment effects tends to focus on single-valued summary measures, for example, the difference between the score at the end of treatment and the score at baseline. Such analyses ignore potentially important features of the data, e.g. early vs. late recoveries. It is therefore of interest to develop longitudinal models that make more efficient use of the information present in non-monotonic clinical assessment scale data. We propose a two-part modeling approach for the modeling of this type of data. Non-monotonicity is managed by regarding score changes as Markovian transition events. A set of probabilistic models are used to describe the occurrences of the transitions. Continuous models are used to describe the magnitude of the scale score change, given the observed transition. In this manner, a non-monotonic disease progression is handled more efficiently than if other available methods are used. We illustrate this approach using data from a recent phase II study of a drug used in the treatment of stroke, where stroke severity was measured on the Scandinavian Stroke Scale (SSS). This scale consists of nine subitems: consciousness, eye movements, hand/arm/leg motor performance, orientation, speech, facial palsy, and gait. The data were non-monotonic, since there was at any time a risk of a score decline, despite a general tendency towards healing. The two-part probabilistic/continuous model fit the data well and proved to be robust in model-checking procedures such as posterior predictive checks and bootstrapping. The models derived using this approach could potentially accommodate drug effects, not only in terms of score improvement at end of study, but also on the onset of recovery, on dropout and on the probability of unfavorable progression patterns. In addition, it is possible to use the resulting for simulation of the prospective outcome of future studies. We conclude that this approach has considerable potential for more efficient use of information in longitudinal modeling of non-monotonic clinical assessment scale data.