Interdisciplinary pharmacometrics linking oseltamivir pharmacology, influenza epidemiology and health economics to inform antiviral use in pandemics

Evaluating the Public Health and Health Economic Impacts of Baloxavir Marboxil and Oseltamivir for Influenza Pandemic Control in China: A Cost-Effectiveness Analysis Using a Linked Dynamic Transmission-Economic Evaluation Model

BACKGROUND

Pandemic influenza poses a recurring threat to public health. Antiviral drugs are vital in combating influenza pandemics. Baloxavir marboxil (BXM) is a novel agent that provides clinical and public health benefits in influenza treatment.

METHODS

We constructed a linked dynamic transmission-economic evaluation model combining a modified susceptible-exposed-infected-recovered (SEIR) model and a decision tree model to evaluate the cost-effectiveness of adding BXM to oseltamivir in China's influenza pandemic scenario. The cost-effectiveness was evaluated for the general population from the Chinese healthcare system perspective, although the users of BXM and oseltamivir were influenza-infected persons. The SEIR model simulated the transmission dynamics, dividing the population into four compartments: susceptible, exposed, infected, and recovered, while the decision tree model assessed disease severity and costs. We utilized data from clinical trials and observational studies in the literature to parameterize the models. Costs were based on 2021 CN¥ and not discounted due to a short time-frame of one year in the model. One-way, two-way, and probabilistic sensitivity analyses were also conducted.

RESULTS

The integrated model demonstrated that adding BXM to treatment choices reduced the cumulative incidence of infection from 49.49% to 43.26% and increased quality-adjusted life years (QALYs) by 0.00021 per person compared with oseltamivir alone in the base-case scenario. The intervention also amounted to a positive net monetary benefit of CN¥77.85 per person at the willingness to pay of CN¥80,976 per QALY. Sensitivity analysis confirmed the robustness of these findings, with consistent results across varied key parameters and assumptions.

CONCLUSIONS

Adding BXM to treatment choices instead of only treating with oseltamivir for influenza pandemic control in China appears to be cost-effective compared with oseltamivir alone. The dual-agent strategy not only enhances population health outcomes and conserves resources, but also mitigates influenza transmission and alleviates healthcare burden.

A cost-effectiveness analysis of reduced viral transmission with baloxavir marboxil versus oseltamivir or no treatment for seasonal and pandemic influenza management in the United Kingdom

BACKGROUND

Baloxavir marboxil is an oral, single-dose, cap-dependent endonuclease inhibitor that reduces the duration of influenza symptoms and rapidly stops viral shedding. We developed a susceptible, exposed, infected, recovered (SEIR) model to inform a cost-effectiveness model (CEM) of baloxavir versus oseltamivir or no antiviral treatment in the UK.

RESEARCH DESIGN AND METHODS

The SEIR model estimated the attack rates among otherwise healthy and high-risk individuals in seasonal and pandemic settings. The CEM assumed that a proportion of infected patients would receive antiviral treatment. Results were reported at the population level (per 10,000 at risk of infection).

RESULTS

The SEIR model estimated greater reductions in infections with baloxavir. In a seasonal setting, baloxavir provided incremental cost-effectiveness ratios (ICERs) of £1884 per quality-adjusted life-year (QALY) gained versus oseltamivir and a dominant cost-effectiveness position versus no antiviral treatment in the total population; ICERs of £2574/QALY versus oseltamivir and £128/QALY versus no antiviral treatment were seen in the high-risk population. Baloxavir was also cost-effective versus oseltamivir or no antiviral treatment and reduced population-level health system occupancy concerns during a pandemic.

CONCLUSION

Baloxavir treatment resulted in the fewest influenza cases and was cost-effective versus oseltamivir or no antiviral treatment from a UK National Health Service perspective.

Cost-Effectiveness of Baloxavir Marboxil Versus Oseltamivir or no Treatment for the Management of Influenza in the United States

INTRODUCTION

This study sought to evaluate the cost-effectiveness of baloxavir marboxil compared with oseltamivir or no antiviral treatment from a US payer perspective using data from a real-world US administrative claims study. Given baloxavir's ability to rapidly stop viral shedding, the potential health economic implications of a baloxavir-induced population-level reduction in viral transmission was also explored.

METHODS

A decision tree cost-effectiveness model was developed for seasonal influenza (2018-2020) using a lifetime time horizon with 3.0% discounting for costs and quality-adjusted life-years (QALYs). Patients aged ≥ 12 years could receive baloxavir, oseltamivir or no antiviral treatment. Patient characteristics, complications, and costs were derived from the Merative™ MarketScan® Research Databases including US commercial claims and Medicare and Medicaid Supplemental databases. A scenario analysis explored the impact of reduced viral transmission with baloxavir.

RESULTS

In the base case analysis, baloxavir was cost-effective within a willingness-to-pay threshold of US$100,000/QALY compared with oseltamivir [incremental cost-effectiveness ratio (ICER), $6813/QALY gained] or no antiviral treatment (ICER, $669/QALY gained). The net monetary benefit (NMB) of baloxavir was $1180 and $6208 compared with oseltamivir and no treatment, respectively. The NMB of baloxavir increased linearly with reductions in viral transmission, where a 5% transmission reduction yielded an NMB of $2592 versus oseltamivir and $7621 versus no treatment. Baloxavir became dominant (more effective and less costly, with ICERs < 0) starting with a 12.0% reduction in viral transmission versus oseltamivir and 6.0% versus no antiviral treatment.

CONCLUSION

Baloxavir was cost-effective compared with oseltamivir or no antiviral treatment. The potential of baloxavir to reduce viral transmission offers a substantial economic benefit from a US payer perspective.

Virtual twin for healthcare management

Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.

Seasonal influence on respiratory tract infection severity including COVID-19 quantified through Markov Chain modeling

Respiratory tract infections (RTIs) are a burden to global health, but their characterization is complicated by the influence of seasonality on incidence and severity. The Re-BCG-CoV-19 trial (NCT04379336) assessed BCG (re)vaccination for protection from coronavirus disease 2019 (COVID-19) and recorded 958 RTIs in 574 individuals followed over 1 year. We characterized the probability of RTI occurrence and severity using a Markov model with health scores (HSs) for four states of symptom severity. Covariate analysis on the transition probability between HSs explored the influence of demographics, medical history, severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), or influenza vaccinations, which became available during the trial, SARS-CoV-2 serology, and epidemiology-informed seasonal influence of infection pressure represented as regional COVID-19 pandemic waves, as well as BCG (re)vaccination. The infection pressure reflecting the pandemic waves increased the risk of RTI symptom development, whereas the presence of SARS-CoV-2 antibodies protected against RTI symptom development and increased the probability of symptom relief. Higher probability of symptom relief was also found in participants with African ethnicity and with male biological gender. SARS-CoV-2 or influenza vaccination reduced the probability of transitioning from mild to healthy symptoms. Model diagnostics over calendar-time indicated that COVID-19 cases were under-reported during the first wave by an estimated 2.76-fold. This trial was performed during the initial phase of the COVID-19 pandemic in South Africa and the results reflect that situation. Using this unique clinical dataset of prospectively studied RTIs over the course of 1 year, our Markov Chain model was able to capture risk factors for RTI development and severity, including epidemiology-informed infection pressure.

Interdisciplinary model-informed drug development for extending duration of elagolix treatment in patients with uterine fibroids

AIM

Elagolix, a gonadotropin-releasing hormone receptor antagonist, was recently approved for heavy menstrual bleeding associated with uterine fibroids (UF, Oriahnn) at a dose of 300 mg twice daily (BID) in combination with add-back therapy (oestradiol 1 mg/norethindrone acetate 0.5 mg [E2/NETA] once daily) for 24 months use. The limited duration of treatment is related to elagolix dose- and duration-dependent decrease in oestrogen that is mechanistically linked to changes in bone mineral density (BMD). The work herein supported the extended treatment duration of 24 months.

METHODS

An integrated exposure-response and epidemiological modelling framework of elagolix effects on femoral neck BMD (FN-BMD), informed by real-world data and phase 3 clinical trials data, was developed to predict the time course and magnitude of changes in BMD and its relation to risk of bone fracture in women with UF.

RESULTS

Model results indicated that women treated with elagolix 300 mg BID + E2/NETA in the long term (ie, >24 months) may experience less than 1% loss in FN-BMD per year, relative to placebo. The exposure-response model simulations and clinical risk factors were used to estimate 10-year risk of fractures using the clinically validated Fracture Risk Assessment Tool (FRAX). The impact of elagolix 300 mg BID + E2/NETA treatment on the 10-year risk of hip or major osteoporotic fractures estimated from the FRAX model was minimal compared to that of placebo.

CONCLUSION

The elagolix integrated exposure-BMD analysis and translation to fracture risk provided an interdisciplinary model-informed drug development framework for clinical benefit-risk evaluation and enabled approval of longer treatment duration to benefit the patient.

Economic evaluations of interventions against viral pandemics: a scoping review

OBJECTIVES

The COVID-19 pandemic has led to suggestions that cost-effectiveness analyses should adopt a broader perspective when estimating costs. This review aims to provide an overview of economic evaluations of interventions against viral pandemics in terms of the perspective taken, types of costs included, comparators, type of economic model, data sources and methods for estimating productivity costs.

STUDY DESIGN

Scoping literature review.

METHODS

Publications were eligible if they conducted a cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis or cost-minimisation analysis and evaluated interventions aimed at viral pandemics or for patients infected with viral pandemic disease. We searched PubMed, Embase and Scopus for relevant references and charted data from the selected full-text publications into a predefined spreadsheet based on research sub-questions, summary tables and figures.

RESULTS

From 5410 references, 36 full-text publications fulfilled the inclusion criteria. The economic evaluations were mainly model based and included direct medical costs of hospital treatment. Around half of the studies included productivity costs and the proportion of total costs attributed to productivity costs ranged from 10% to 90%, depending on estimation methods, assumptions about valuation of time, type of intervention, severity of illness and degree of transmission.

CONCLUSIONS

Economic evaluations of interventions against viral pandemics differed in terms of estimation methods and reporting of productivity costs, even for similar interventions. Hence, the literature on economic evaluations for pandemic response would benefit from having standards for conducting and reporting economic evaluations, especially for productivity costs.

Methods for Extrapolating Survival Analyses for the Economic Evaluation of Advanced Therapy Medicinal Products

There are two significant challenges for analysts conducting economic evaluations of advanced therapy medicinal products (ATMPs): (i) estimating long-term treatment effects in the absence of mature clinical data, and (ii) capturing potentially complex hazard functions. This review identifies and critiques a variety of methods that can be used to overcome these challenges. The narrative review is informed by a rapid literature review of methods used for the extrapolation of survival analyses in the economic evaluation of ATMPs. There are several methods that are more suitable than traditional parametric survival modelling approaches for capturing complex hazard functions, including, cure-mixture models and restricted cubic spline models. In the absence of mature clinical data, analysts may augment clinical trial data with data from other sources to aid extrapolation, however, the relative merits of employing methods for including data from different sources is not well understood. Given the high and potentially irrecoverable costs of making incorrect decisions concerning the reimbursement or commissioning of ATMPs, it is important that economic evaluations are correctly specified, and that both parameter and structural uncertainty associated with survival extrapolations are considered. Value of information analyses allow for this uncertainty to be expressed explicitly, and in monetary terms.

Model-informed drug repurposing: Viral kinetic modelling to prioritize rational drug combinations for COVID-19

AIM

We hypothesized that viral kinetic modelling could be helpful to prioritize rational drug combinations for COVID-19. The aim of this research was to use a viral cell cycle model of SARS-CoV-2 to explore the potential impact drugs, or combinations of drugs, that act at different stages in the viral life cycle might have on various metrics of infection outcome relevant in the early stages of COVID-19 disease.

METHODS

Using a target-cell limited model structure that has been used to characterize viral load dynamics from COVID-19 patients, we performed simulations to inform on the combinations of therapeutics targeting specific rate constants. The endpoints and metrics included viral load area under the curve (AUC), duration of viral shedding and epithelial cells infected. Based on the known kinetics of the SARS-CoV-2 life cycle, we rank ordered potential targeted approaches involving repurposed, low-potency agents.

RESULTS

Our simulations suggest that targeting multiple points central to viral replication within infected host cells or release from those cells is a viable strategy for reducing both viral load and host cell infection. In addition, we observed that the time-window opportunity for a therapeutic intervention to effect duration of viral shedding exceeds the effect on sparing epithelial cells from infection or impact on viral load AUC. Furthermore, the impact on reduction on duration of shedding may extend further in patients who exhibit a prolonged shedder phenotype.

CONCLUSIONS

Our work highlights the use of model-informed drug repurposing approaches to better rationalize effective treatments for COVID-19.

Reducing influenza virus transmission: the value of antiviral treatment

Prompt antiviral treatment has the potential to reduce influenza virus transmission to close contacts, but rigorous data on the magnitude of treatment effects on transmission are limited. Animal model data indicate that rapid reductions in viral replication after antiviral treatment reduce the risk of transmission. Observational and clinical trial data with oseltamivir and other neuraminidase inhibitors indicate that prompt treatment of household index patients seems to reduce the risk of illness in contacts, although the magnitude of the reported effects has varied widely across studies. In addition, the potential risk of transmitting drug-resistant variants exists with all approved classes of influenza antivirals. A controlled trial examining baloxavir treatment efficacy to reduce transmission, including the risk of transmitting virus with reduced baloxavir susceptibility, is currently in progress. If reduced transmission risk is confirmed, modeling studies indicate that early treatment could have major epidemiologic benefits in seasonal and pandemic influenza.

Blueprint for pandemic response: Focus on translational medicine, clinical pharmacology and pharmacometrics

Perhaps the most important lesson learned from the COVID-19 pandemic is that of preparedness. Enhanced surveillance systems for early threat detection will be crucial to maximizing response time for implementation of public health measures and mobilization of resources in containing an emerging pandemic. Recent outbreaks have been dominated by viral pathogens, with RNA respiratory viruses being the most likely to have pandemic potential. These should therefore be a preparedness priority. Tools in the areas of virology, drug discovery, clinical pharmacology, translational medicine and pharmacometrics should be considered key components in the rapid identification and development of existing and novel interventions for a pandemic response. Prioritization of therapeutics should be based on in vitro activity, likelihood of achieving effective drug concentrations at the site of action, and safety profile at the doses that will be required for clinical efficacy. Deployment strategies must be tailored to the epidemiology of the disease, and the adequacy of the response should be re-evaluated in view of evolving epidemiological factors. An interdisciplinary framework integrating drug pharmacology, viral kinetics, epidemiology and health economics could help optimize the deployment strategy by improving decision-making around who to treat, when to treat, and with what type of intervention for optimal outcomes. Lastly, while an effective vaccine will ultimately end a pandemic, antiviral drug intervention guided by clinical pharmacology principles will continue to play a critical role in any pandemic response.

How COVID-19 has fundamentally changed clinical research in global health

COVID-19 has had negative repercussions on the entire global population. Despite there being a common goal that should have unified resources and efforts, there have been an overwhelmingly large number of clinical trials that have been registered that are of questionable methodological quality. As the final paper of this Series, we discuss how the medical research community has responded to COVID-19. We recognise the incredible pressure that this pandemic has put on researchers, regulators, and policy makers, all of whom were doing their best to move quickly but safely in a time of tremendous uncertainty. However, the research community's response to the COVID-19 pandemic has prominently highlighted many fundamental issues that exist in clinical trial research under the current system and its incentive structures. The COVID-19 pandemic has not only re-emphasised the importance of well designed randomised clinical trials but also highlighted the need for large-scale clinical trials structured according to a master protocol in a coordinated and collaborative manner. There is also a need for structures and incentives to enable faster data sharing of anonymised datasets, and a need to provide similar opportunities to those in high-income countries for clinical trial research in low-resource regions where clinical trial research receives considerably less research funding.

Model-Informed Drug Development for Anti-Infectives: State of the Art and Future

Model-informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti-infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug-drug interactions. Examples are presented for state-of-the-art, empiric, mechanistic, interdisciplinary, and real-world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration-based models, mechanism-based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired-resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model-centered approaches to anti-infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

Model-informed drug repurposing: A pharmacometric approach to novel pathogen preparedness, response and retrospection

During a pandemic caused by a novel pathogen (NP), drug repurposing offers the potential of a rapid treatment response via a repurposed drug (RD) while more targeted treatments are developed. Five steps of model‐informed drug repurposing (MIDR) are discussed: (i) utilize RD product label and in vitro NP data to determine initial proof of potential, (ii) optimize potential posology using clinical pharmacokinetics (PK) considering both efficacy and safety, (iii) link events in the viral life cycle to RD PK, (iv) link RD PK to clinical and virologic outcomes, and optimize clinical trial design, and (v) assess RD treatment effects from trials using model‐based meta‐analysis. Activities which fall under these five steps are categorized into three stages: what can be accomplished prior to an NP emergence (preparatory stage), during the NP pandemic (responsive stage) and once the crisis has subsided (retrospective stage). MIDR allows for extraction of a greater amount of information from emerging data and integration of disparate data into actionable insight.

Using in silico viral kinetic models to guide therapeutic strategies during a pandemic: An example in SARS-CoV-2

AIMS

We propose the use of in silico mathematical models to provide insights that optimize therapeutic interventions designed to effectively treat respiratory infection during a pandemic. A modelling and simulation framework is provided using SARS-CoV-2 as an example, considering applications for both treatment and prophylaxis.

METHODS

A target cell-limited model was used to quantify the viral infection dynamics of SARS-CoV-2 in a pooled population of 105 infected patients. Parameter estimates from the resulting model were used to simulate and compare the impact of various interventions against meaningful viral load endpoints.

RESULTS

Robust parameter estimates were obtained for the basic reproduction number, viral release rate and infected-cell mortality from the infection model. These estimates were informed by the largest dataset currently available for SARS-CoV-2 viral time course. The utility of this model was demonstrated using simulations, which hypothetically introduced inhibitory or stimulatory drug mechanisms at various target sites within the viral life-cycle. We show that early intervention is crucial to achieving therapeutic benefit when monotherapy is administered. In contrast, combination regimens of two or three drugs may provide improved outcomes if treatment is initiated late. The latter is relevant to SARS-CoV-2, where the period between infection and symptom onset is relatively long.

CONCLUSIONS

The use of in silico models can provide viral load predictions that can rationalize therapeutic strategies against an emerging viral pathogen.

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.

Early Treatment of COVID-19 Disease: A Missed Opportunity

Antivirals have demonstrated efficacy in treating other infectious diseases in early stages of disease, reducing morbidity, mortality, and the likelihood of onward transmission. At the time of writing, more than 1900 clinical trials are registered globally to assess the efficacy and safety of candidate therapeutics for COVID-19. The majority of these trials are designed to evaluate the comparative efficacy and safety of candidate therapeutics for the treatment of COVID-19 to prevent death among populations of hospitalized patients with advanced disease. Yet, emerging epidemiological evidence now indicates that the majority of those infected with the SARS-CoV-2, while still infectious, experience minimal or mild disease symptomology. Like HIV and hepatitis C that pioneered treatment as prevention, there is a missed opportunity for trials of early pharmaceutical intervention for COVID-19 disease evaluating not only reductions in morbidity and mortality but also transmissibility. We discuss this clinical research gap within an historical context of viral treatment as prevention for HIV and hepatitis C, and comment on the challenges and opportunities for clinical research of candidate therapeutics for early COVID-19 disease.

Cost-Effectiveness of Antiviral Treatments for Pandemics and Outbreaks of Respiratory Illnesses, Including COVID-19: A Systematic Review of Published Economic Evaluations

OBJECTIVE

To review published economic evaluations of antiviral treatment for pandemics and outbreaks of respiratory illnesses.

METHODS

We conducted a systematic review to identify economic evaluations of antiviral treatment for pandemics and outbreaks of respiratory illnesses, including coronavirus disease 2019 (COVID-19). We searched Medline (EBSCOhost), EMBASE (Ovid), EconLit (Ovid), National Health Service Economic Evaluation Database (Ovid), and Health Technology Assessment (Ovid). The search was last rerun on July 5, 2020. Citation tracking and reference checking were used. Only full economic evaluations published as peer-reviewed articles in the last 10 years were included. Studies were quality assessed using the National Institute for Health and Care Excellence economic evaluation checklist.

RESULTS

Overall, 782 records were identified, of which 14 studies met the inclusion criteria. The studies were mostly conducted in high-income countries. All were model-based. Seven (50%) were cost-utility analyses, 4 (28.6%) were cost-effectiveness analyses, 2 (14.3%) were cost-consequences analyses, and 1 (7.1%) was a cost-benefit analysis. Strategies including antiviral treatment were found to be either cost-saving or cost-effective, at the study-specific willingness-to-pay thresholds. Empirical treatment was more cost-effective than test-guided treatment for young adults but less so for older adults.

CONCLUSIONS

Antiviral treatment for managing pandemics and outbreaks of respiratory illnesses that have very high case fatality rate, similar to COVID-19 pandemic, are likely to be cost-effective either as a standalone intervention or part of a multifaceted strategy. Investing in the development of such curative treatments and promptly evaluating their cost-effectiveness, relative to other strategies in use at the time of their introduction should be the focus going forward to inform resource allocation decisions particularly in low- and middle-income countries.

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.

Incorporating Pharmacometrics into Pharmacoeconomic Models: Applications from Drug Development

Pharmacometrics is the science of quantifying the relationship between the pharmacokinetics and pharmacodynamics of drugs in combination with disease models and trial information to aid in drug development and dosing optimization for clinical practice. Considering the variability in the dose-concentration-effect relationship of drugs, an opportunity exists in linking pharmacokinetic and pharmacodynamic model-based estimates with pharmacoeconomic models. This link may provide early estimates of the cost effectiveness of drug therapies, thus informing late-stage drug development, pricing, and reimbursement decisions. Published case studies have demonstrated how integrated pharmacokinetic-pharmacodynamic-pharmacoeconomic models can complement traditional pharmacoeconomic analyses by identifying the impact of specific patient sub-groups, dose, dosing schedules, and adherence on the cost effectiveness of drugs, thus providing a mechanistic basis to predict the economic value of new drugs. Greater collaboration between the pharmacoeconomics and pharmacometrics community can enable methodological improvements in pharmacokinetic-pharmacodynamic-pharmacoeconomic models to support drug development.

Time to Step Up: A Call to Action For the Clinical and Quantitative Pharmacology Community to Accelerate Therapeutics for COVID-19

The global response to finding therapeutics for coronavirus disease 2019 (COVID‐19) is chaotic even if well intentioned. There is an opportunity, but more importantly, an obligation for the global clinical and quantitative pharmacology community to come together and use our state‐of‐the‐art tools and expertise to help society accelerate therapeutics to fight COVID‐19. This brief commentary is a call to action and highlights how the global pharmacology community should contribute to the COVID‐19 pandemic and prepare for future pandemics.

Modeling mitigation of influenza epidemics by baloxavir

Influenza viruses annually kill 290,000-650,000 people worldwide. Antivirals can reduce death tolls. Baloxavir, the recently approved influenza antiviral, inhibits initiation of viral mRNA synthesis, whereas oseltamivir, an older drug, inhibits release of virus progeny. Baloxavir blocks virus replication more rapidly and completely than oseltamivir, reducing the duration of infectiousness. Hence, early baloxavir treatment may indirectly prevent transmission. Here, we estimate impacts of ramping up and accelerating baloxavir treatment on population-level incidence using a new model that links viral load dynamics from clinical trial data to between-host transmission. We estimate that ~22 million infections and >6,000 deaths would have been averted in the 2017-2018 epidemic season by administering baloxavir to 30% of infected cases within 48 h after symptom onset. Treatment within 24 h would almost double the impact. Consequently, scaling up early baloxavir treatment would substantially reduce influenza morbidity and mortality every year. The development of antivirals against the SARS-CoV2 virus that function like baloxavir might similarly curtail transmission and save lives.

Recent advances in taste transduction and signaling

In the last few years, single-cell profiling of taste cells and ganglion cells has advanced our understanding of transduction, encoding, and transmission of information from taste buds as relayed to the central nervous system. This review focuses on new knowledge from these molecular approaches and attempts to place this in the context of previous questions and findings in the field. The individual taste cells within a taste bud are molecularly specialized for detection of one of the primary taste qualities: salt, sour, sweet, umami, and bitter. Transduction and transmitter release mechanisms differ substantially for taste cells transducing sour (Type  III cells) compared with those transducing the qualities of sweet, umami, or bitter (Type II cells), although ultimately all transmission of taste relies on activation of purinergic P2X receptors on the afferent nerves. The ganglion cells providing innervation to the taste buds also appear divisible into functional and molecular subtypes, and each ganglion cell is primarily but not exclusively responsive to one taste quality.

Precision medicine technology hype or reality? The example of computer-guided dosing

Novel technologies labelled as ‘precision medicine’ are targeting all aspects of clinical care. Whilst some technological advances are undeniably exciting, many doctors at the frontline of healthcare view precision medicine as being out of reach for their patients. Computer-guided dosing is a precision medicine technology that predicts drug concentrations and drug responses based on individual patient characteristics. In this opinion piece, the example of computer-guided dosing is used to illustrate eight features of a precision medicine technology less likely to be hyperbole and more likely to improve patient care. Positive features in this regard include: (1) fitting the definition of ‘precision medicine’; (2) addressing a major clinical problem that negatively impacts patient care; (3) a track record of high-quality medical science published via peer-reviewed literature; (4) well-defined clinical cases for application; (5) quality evidence of benefits measured by various clinical, patient and health economic endpoints; (6) strong economic drivers; (7) user friendliness, including easy integration into clinical workflow, and (8) recognition of importance by patients and their endorsement for broader clinical use. Barriers raised by critics of the approach are given to balance the view. The value of computer-guided dosing will be decided ultimately by the extent to which it can improve cost-effective patient care.

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.

Innovation at the Intersection of Clinical Trials and Real-World Data Science to Advance Patient Care

While efficacy and safety data collected from randomized clinical trials are the evidentiary standard for determining market authorization, this alone may no longer be sufficient to address the needs of key stakeholders (regulators, providers, and payers) and guarantee long-term success of pharmaceutical products. There is a heightened interest from stakeholders on understanding the use of real-world evidence (RWE) to substantiate benefit-risk assessment and support the value of a new drug. This review provides an overview of real-world data (RWD) and related advances in the regulatory framework, and discusses their impact on clinical research and development. A framework for linking drug development decisions with the value proposition of the drug, utilizing pharmacokinetic-pharmacodynamic-pharmacoeconomic models, is introduced. The summary presented here is based on the presentations and discussion at the symposium entitled Innovation at the Intersection of Clinical Trials and Real-World Data to Advance Patient Care at the American Society for Clinical Pharmacology and Therapeutics (ASCPT) 2017 Annual Meeting.

Cost-utility analysis of antiviral use under pandemic influenza using a novel approach - linking pharmacology, epidemiology and heath economics

Simulation models are used widely in pharmacology, epidemiology and health economics (HEs). However, there have been no attempts to incorporate models from these disciplines into a single integrated model. Accordingly, we explored this linkage to evaluate the epidemiological and economic impact of oseltamivir dose optimisation in supporting pandemic influenza planning in the USA. An HE decision analytic model was linked to a pharmacokinetic/pharmacodynamics (PK/PD) - dynamic transmission model simulating the impact of pandemic influenza with low virulence and low transmissibility and, high virulence and high transmissibility. The cost-utility analysis was from the payer and societal perspectives, comparing oseltamivir 75 and 150 mg twice daily (BID) to no treatment over a 1-year time horizon. Model parameters were derived from published studies. Outcomes were measured as cost per quality-adjusted life year (QALY) gained. Sensitivity analyses were performed to examine the integrated model's robustness. Under both pandemic scenarios, compared to no treatment, the use of oseltamivir 75 or 150 mg BID led to a significant reduction of influenza episodes and influenza-related deaths, translating to substantial savings of QALYs. Overall drug costs were offset by the reduction of both direct and indirect costs, making these two interventions cost-saving from both perspectives. The results were sensitive to the proportion of inpatient presentation at the emergency visit and patients' quality of life. Integrating PK/PD-EPI/HE models is achievable. Whilst further refinement of this novel linkage model to more closely mimic the reality is needed, the current study has generated useful insights to support influenza pandemic planning.

Interdisciplinary pharmacometrics linking oseltamivir pharmacology, influenza epidemiology and health economics to inform antiviral use in pandemics

AIMS

A modular interdisciplinary platform was developed to investigate the economic impact of oseltamivir treatment by dosage regimen under simulated influenza pandemic scenarios.

METHODS

The pharmacology module consisted of a pharmacokinetic distribution of oseltamivir carboxylate daily area under the concentration-time curve at steady state (simulated for 75 mg and 150 mg twice daily regimens for 5 days) and a pharmacodynamic distribution of viral shedding duration obtained from phase II influenza inoculation data. The epidemiological module comprised a susceptible, exposed, infected, recovered (SEIR) model to which drug effect on the basic reproductive number (R₀ ), a measure of transmissibility, was linked by reduction of viral shedding duration. The number of infected patients per population of 100 000 susceptible individuals was simulated for a series of pandemic scenarios, varying oseltamivir dose, R₀ (1.9 vs. 2.7), and drug uptake (25%, 50%, and 80%). The number of infected patients for each scenario was entered into the health economics module, a decision analytic model populated with branch probabilities, disease utility, costs of hospitalized patients developing complications, and case-fatality rates. Change in quality-adjusted life years was determined relative to base case.

RESULTS

Oseltamivir 75 mg relative to no treatment reduced the median number of infected patients, increased change in quality-adjusted life years by deaths averted, and was cost-saving under all scenarios; 150 mg relative to 75 mg was not cost effective in low transmissibility scenarios but was cost saving in high transmissibility scenarios.

CONCLUSION

This methodological study demonstrates proof of concept that the disciplines of pharmacology, disease epidemiology and health economics can be linked in a single quantitative framework.