Clinical trial simulation to evaluate power to compare the antiviral effectiveness of two hepatitis C protease inhibitors using nonlinear mixed effect models: a viral kinetic approach

Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity

In recent years, virtual drug study, as an emerging research strategy, has become increasingly important in guiding and promoting new drug research and development. Researchers can integrate a variety of technical methods to improve the efficiency of all phases of new drug research and development, including the use of artificial intelligence, modeling and simulation for target identification, compound screening and pharmacokinetic characteristics evaluation, and the application of clinical trial simulation to carry out clinical research. This paper aims to elaborate on the application of virtual drug study in the key stages of new drug research and development and discuss the opportunities and challenges it faces in supporting new drug research and development.

Molnupiravir plus usual care versus usual care alone as early treatment for adults with COVID-19 at increased risk of adverse outcomes (PANORAMIC): an open-label, platform-adaptive randomised controlled trial

BACKGROUND

The safety, effectiveness, and cost-effectiveness of molnupiravir, an oral antiviral medication for SARS-CoV-2, has not been established in vaccinated patients in the community at increased risk of morbidity and mortality from COVID-19. We aimed to establish whether the addition of molnupiravir to usual care reduced hospital admissions and deaths associated with COVID-19 in this population.

METHODS

PANORAMIC was a UK-based, national, multicentre, open-label, multigroup, prospective, platform adaptive randomised controlled trial. Eligible participants were aged 50 years or older-or aged 18 years or older with relevant comorbidities-and had been unwell with confirmed COVID-19 for 5 days or fewer in the community. Participants were randomly assigned (1:1) to receive 800 mg molnupiravir twice daily for 5 days plus usual care or usual care only. A secure, web-based system (Spinnaker) was used for randomisation, which was stratified by age (<50 years vs ≥50 years) and vaccination status (yes vs no). COVID-19 outcomes were tracked via a self-completed online daily diary for 28 days after randomisation. The primary outcome was all-cause hospitalisation or death within 28 days of randomisation, which was analysed using Bayesian models in all eligible participants who were randomly assigned. This trial is registered with ISRCTN, number 30448031.

FINDINGS

Between Dec 8, 2021, and April 27, 2022, 26 411 participants were randomly assigned, 12 821 to molnupiravir plus usual care, 12 962 to usual care alone, and 628 to other treatment groups (which will be reported separately). 12 529 participants from the molnupiravir plus usual care group, and 12 525 from the usual care group were included in the primary analysis population. The mean age of the population was 56·6 years (SD 12·6), and 24 290 (94%) of 25 708 participants had had at least three doses of a SARS-CoV-2 vaccine. Hospitalisations or deaths were recorded in 105 (1%) of 12 529 participants in the molnupiravir plus usual care group versus 98 (1%) of 12 525 in the usual care group (adjusted odds ratio 1·06 [95% Bayesian credible interval 0·81-1·41]; probability of superiority 0·33). There was no evidence of treatment interaction between subgroups. Serious adverse events were recorded for 50 (0·4%) of 12 774 participants in the molnupiravir plus usual care group and for 45 (0·3%) of 12 934 in the usual care group. None of these events were judged to be related to molnupiravir.

INTERPRETATION

Molnupiravir did not reduce the frequency of COVID-19-associated hospitalisations or death among high-risk vaccinated adults in the community.

FUNDING

UK National Institute for Health and Care Research.

Systematic Review and Patient-Level Meta-Analysis of SARS-CoV-2 Viral Dynamics to Model Response to Antiviral Therapies

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) viral loads change rapidly following symptom onset, so to assess antivirals it is important to understand the natural history and patient factors influencing this. We undertook an individual patient-level meta-analysis of SARS-CoV-2 viral dynamics in humans to describe viral dynamics and estimate the effects of antivirals used to date. This systematic review identified case reports, case series, and clinical trial data from publications between January 1, 2020, and May 31, 2020, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A multivariable Cox proportional hazards (Cox-PH) regression model of time to viral clearance was fitted to respiratory and stool samples. A simplified four parameter nonlinear mixed-effects (NLME) model was fitted to viral load trajectories in all sampling sites and covariate modeling of respiratory viral dynamics was performed to quantify time-dependent drug effects. Patient-level data from 645 individuals (age 1 month to 100 years) with 6,316 viral loads were extracted. Model-based simulations of viral load trajectories in samples from the upper and lower respiratory tract, stool, blood, urine, ocular secretions, and breast milk were generated. Cox-PH modeling showed longer time to viral clearance in older patients, men, and those with more severe disease. Remdesivir was associated with faster viral clearance (adjusted hazard ratio (AHR) = 9.19, P < 0.001), as well as interferon, particularly when combined with ribavirin (AHR = 2.2, P = 0.015; AHR = 6.04, P = 0.006). Combination therapy should be further investigated. A viral dynamic dataset and NLME model for designing and analyzing antiviral trials has been established.

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.

Statistical modelling of data showing pandemic H1N1 2009 swine influenza a virus infection kinetics in vaccinated pigs

A swine influenza A pandemic 2009 H1N1 (pH1N1) virus was used in a pig challenge model to investigate the efficacy of whole inactivated vaccines homologous or heterologous to the challenge virus as well as a commercial vaccine. Nasal shedding of viral RNA was monitored daily by real-time, quantitative RT-PCR (RRT-qPCR) as detailed (1). Here we report the statistical modelling of the viral RNA shedding kinetics.

A pharmacokinetic/viral kinetic model to evaluate treatment of chronic HCV infection with a non-nucleoside polymerase inhibitor

BACKGROUND

Viral kinetic models have proven useful in characterizing treatment effectiveness during HCV therapy with interferon (IFN) as well as with direct-acting antivirals (DAAs).

METHODS

Here we use a pharmacokinetic/viral kinetic (PK/VK) model to describe HCV RNA kinetics during treatment with setrobuvir, a non-nucleosidic inhibitor of the HCV NS5B polymerase enzyme. Using PK data from three studies in healthy volunteers and PK and VK data from a Phase I study, where setrobuvir was administered for 3 days, we fitted a two-compartment PK model with first-order absorption and lag-time, an Emax pharmacodynamics model and a standard biphasic VK model.

RESULTS

Setrobuvir's EC₅₀ and Hill coefficient and the viral clearance rate were significantly different (P=0.014, P<0.001 and P=0.004, respectively) between patients infected with HCV subtypes 1b and 1a, leading to an increased viral load decline in patients infected with genotype 1b virus.

CONCLUSIONS

Understanding the combined effects of PK/VK on the performance of a non-nucleoside polymerase inhibitor such as setrobuvir could provide valuable insights into their use in combination with other DAAs as well as to optimize future therapy. Further, our work suggests that patients infected with subtype 1a would need higher doses than those infected with subtype 1b to achieve the same effectiveness. Whether this is true for other non-nucleoside polymerase inhibitors needs to be examined.

A Regression Approach to Visual Predictive Checks for Population Pharmacometric Models

A visual predictive check (VPC) is a common diagnostic procedure for population pharmacometric models. Typically, VPCs are generated by specifying intervals, or “bins”, of an independent variable (e.g., time). However, bin specification is not always straightforward and the choice of bins may affect the appearance, and possibly conclusions, of VPCs. The objective of this work was to demonstrate how regression techniques can be used to derive VPCs and prediction‐corrected VPCs (pcVPCs) for population pharmacometric models. This alternative approach negates the need for empirical bin selection. The proposed method utilizes local and additive quantile regression. Implementation is straightforward and computationally acceptable. This work provides support for deriving VPCs and pcVPCs via regression techniques.

Integrated pharmacokinetic/viral dynamic model for daclatasvir/asunaprevir in treatment of patients with genotype 1 chronic hepatitis C

In order to develop an integrated pharmacokinetic/viral dynamic (PK/VD) model to predict long-term virological response rates to daclatasvir (DCV) and asunaprevir (ASV) combination therapy in patients infected with genotype 1 (GT1) chronic hepatitis C virus (HCV), a systematic publication search was conducted for DCV and ASV administered alone and/or in combination in healthy subjects or patients with GT1 HCV infection. On the basis of a constructed meta-database, an integrated PK/VD model was developed, which adequately described both DCV and ASV PK profiles and viral load time curves. The IC₅₀ values of DCV and ASV were estimated to be 0.041 and 2.45 μg/L, respectively, in GT1A patients. A sigmoid E_max function was applied to describe the antiviral effects of DCV and ASV, depending on the drug concentrations in the effect compartment. An empirical exponential function revealed that IC₅₀ changing over time described drug resistance in HCV GT1A patients during DCV or ASV monotherapy. Finally, the PK/VD model was evaluated externally by comparing the expected and observed virological response rates during and post-treatment with DCV and ASV combination therapy in HCV GT1B patients. Both the rates were in general agreement. Our PK/VD model provides a useful platform for the characterization of pharmacokinetic/pharmacodynamic relationships and the prediction of long-term virological response rates to aid future development of direct acting antiviral drugs.

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014)

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late‐stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well‐established and regulatory‐acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4–5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP‐Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)‐based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well‐designed dose‐finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

Predicting CD4 T-Cell Reconstitution Following Pediatric Hematopoietic Stem Cell Transplantation

Hematopoietic stem cell transplantation (HSCT) is an increasingly common treatment for children with a range of hematological disorders. Conditioning with cytotoxic chemotherapy and total body irradiation leaves patients severely immunocompromised. T‐cell reconstitution can take several years due to delayed restoration of thymic output. Understanding T‐cell reconstitution in children is complicated by normal immune system maturation, heterogeneous diagnoses, and sparse uneven sampling due to the long time spans involved. We describe here a mechanistic mathematical model for CD4 T‐cell immune reconstitution following pediatric transplantation. Including relevant biology and using mixed‐effects modeling allowed the factors affecting reconstitution to be identified. Bayesian predictions for the long‐term reconstitution trajectories of individual children were then obtained using early post‐transplant data. The model was developed using data from 288 children; its predictive ability validated on data from a further 75 children, with long‐term reconstitution predicted accurately in 81% of the patients.

Understanding and applying pharmacometric modelling and simulation in clinical practice and research

Understanding the dose–concentration–effect relationship is a fundamental component of clinical pharmacology. Interpreting data arising from observations of this relationship requires the use of mathematical models; i.e. pharmacokinetic (PK) models to describe the relationship between dose and concentration and pharmacodynamic (PD) models describing the relationship between concentration and effect. Drug development requires several iterations of pharmacometric model‐informed learning and confirming. This includes modelling to understand the dose–response in preclinical studies, deriving a safe dose for first‐in‐man, and the overall analysis of Phase I/II data to optimise the dose for safety and efficacy in Phase III pivotal trials. However, drug development is not the boundary at which PKPD understanding and application stops. PKPD concepts will be useful to anyone involved in the prescribing and administration of medicines for purposes such as determining off‐label dosing in special populations, individualising dosing based on a measured biomarker (personalised medicine) and in determining whether lack of efficacy or unexpected toxicity maybe solved by adjusting the dose rather than the drug. In clinical investigator‐led study design, PKPD can be used to ensure the optimal dose is used, and crucially to define the expected effect size, thereby ensuring power calculations are based on sound prior information. In the clinical setting the most likely people to hold sufficient expertise to advise on PKPD matters will be the pharmacists and clinical pharmacologists. This paper reviews fundamental PKPD principles and provides some real‐world examples of PKPD use in clinical practice and applied clinical research.

Evaluation of Approaches to Deal with Low-Frequency Nuisance Covariates in Population Pharmacokinetic Analyses

Clinical studies include occurrences of rare variables, like genotypes, which due to their frequency and strength render their effects difficult to estimate from a dataset. Variables that influence the estimated value of a model-based parameter are termed covariates. It is often difficult to determine if such an effect is significant, since type I error can be inflated when the covariate is rare. Their presence may have either an insubstantial effect on the parameters of interest, hence are ignorable, or conversely they may be influential and therefore non-ignorable. In the case that these covariate effects cannot be estimated due to power and are non-ignorable, then these are considered nuisance, in that they have to be considered but due to type 1 error are of limited interest. This study assesses methods of handling nuisance covariate effects. The specific objectives include (1) calibrating the frequency of a covariate that is associated with type 1 error inflation, (2) calibrating its strength that renders it non-ignorable and (3) evaluating methods for handling these non-ignorable covariates in a nonlinear mixed effects model setting. Type 1 error was determined for the Wald test. Methods considered for handling the nuisance covariate effects were case deletion, Box-Cox transformation and inclusion of a specific fixed effects parameter. Non-ignorable nuisance covariates were found to be effectively handled through addition of a fixed effect parameter.

Target-mediated pharmacokinetic/pharmacodynamic model based meta-analysis and dosing regimen optimization of a long-acting release formulation of exenatide in patients with type 2 diabetes mellitus

A hybrid pharmacokinetic/pharmacodynamic (PK/PD) model with extended-release (ER) process and target mediated drug disposition (TMDD) was developed for exenatide ER to account for its complex absorption process and glucagon-like peptide 1 receptor (GLP-1R)-mediated non-linear PK behaviors along with its influences to fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c). Using hybrid PK/PD model, simulations were done to explore the potential dosing regimens which could achieve likelihood of more pharmacodynamic exposure with respect to FPG and HbA1c over a much shorter period compared with the currently used treatment protocol. The mean PK/PD data about exenatide ER for type 2 diabetes mellitus (T2DM) were digitized from the publications, and the hybrid PK/PD model was performed using the Monolix 4.3 program. The plasma concentration-time and FPG/HbA1c-time profiles for exenatide ER subcutaneously administrated to patients with T2DM were well described by this hybrid model. Monte Carlo simulation was applied to mimic the PK profiles when higher loading dose 7.5 and 5.0 mg exenatide ER were subcutaneously administrated with different dosing intervals at the first 3 weeks of 30-week treatment. Two potentially optimizing schedules could improve the likelihood of achieving much more FPG and HbA1c exposures than currently used clinical treatment protocol.

Utilization of model-based meta-analysis to delineate the net efficacy of taspoglutide from the response of placebo in clinical trials

The objective of this study was to develop quantitative models to delineate the net efficacy of taspoglutide on fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c) from the response of placebo in type 2 diabetes patients, and further find pharmacodynamic potency of taspoglutide and FPG for half of maximum reduction responses of FPG and HbA1c, respectively. Several PD data about taspoglutide treatments for type 2 diabetes patients were digitalized from the published papers related with the clinical development of taspoglutide. The model based meta-analysis (MBMA) studies for FPG and HbA1c were performed with Monolix 4.2 software. The MBMA successfully described the effects of placebo and taspoglutide on pharmacological indexes of FPG and HbA1c through mono and multiple combination therapies in clinical trials. The pharmacodynamic potency (25.3 pmol/l) produced 50% of maximum responses of FPG (−2.39 mmol/l) from the responses of placebo for FPG (−0.371 mmol/l); the response change of FPG (−1.81 mmol/l) affected 50% of maximum response change (−1.74%) for HbA1c from the response of placebo (−0.253%). The leveraging prior knowledge from the longitudinal MBMA will be utilized to guide clinical development of taspoglutide and further support study designs including optimization of dose and duration of therapy.

Using pharmacokinetic and viral kinetic modeling to estimate the antiviral effectiveness of telaprevir, boceprevir, and pegylated interferon during triple therapy in treatment-experienced hepatitis C virus-infected cirrhotic patients

Triple therapy combining a protease inhibitor (PI) (telaprevir or boceprevir), pegylated interferon (PEG-IFN), and ribavirin (RBV) has dramatically increased the chance of eradicating hepatitis C virus (HCV). However, the efficacy of this treatment remains suboptimal in cirrhotic treatment-experienced patients. Here, we aimed to better understand the origin of this impaired response by estimating the antiviral effectiveness of each drug. Fifteen HCV genotype 1-infected patients with compensated cirrhosis, who were nonresponders to prior PEG-IFN/RBV therapy, were enrolled in a nonrandomized study. HCV RNA and concentrations of PIs, PEG-IFN, and RBV were frequently assessed in the first 12 weeks of treatment and were analyzed using a pharmacokinetic/viral kinetic model. The two PIs achieved similar levels of molar concentrations (P=0.5), but there was a significant difference in the 50% effective concentrations (EC50) (P=0.008), leading to greater effectiveness for telaprevir than for boceprevir in blocking viral production (99.8% versus 99.0%, respectively, P=0.002). In all patients, the antiviral effectiveness of PEG-IFN was modest (43.4%), and there was no significant contribution of RBV exposure to the total antiviral effectiveness. The second phase of viral decline, which is attributed to the loss rate of infected cells, was slow (0.19 day(-1)) and was higher in patients who subsequently eradicated HCV (P=0.03). The two PIs achieved high levels of antiviral effectiveness. However, the suboptimal antiviral effectiveness of PEG-IFN/RBV and the low loss of infected cells suggest that a longer treatment duration might be needed in cirrhotic treatment-experienced patients and that a future IFN-free regimen may be particularly beneficial in these patients.