Methods and software tools for design evaluation in population pharmacokinetics-pharmacodynamics studies

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

Evaluation of model-based bioequivalence approach for single sample pharmacokinetic studies

In a traditional pharmacokinetic (PK) bioequivalence (BE) study, a two-way crossover study is conducted, PK parameters (namely the area under the time-concentration curve [AUC] and the maximal concentration [C max ]) are obtained by noncompartmental analysis (NCA), and the BE analysis is performed using the two one-sided test (TOST) method. For ophthalmic drugs, however, only one sample of aqueous humor, in one eye, per eye can be obtained in each patient, which precludes the traditional BE analysis. To circumvent this issue, the U.S. Food and Drug Administration (FDA) has proposed an approach coupling NCA with either parametric or nonparametric bootstrap (NCA bootstrap). The model-based TOST (MB-TOST) has previously been proposed and evaluated successfully for various settings of sparse PK BE studies. In this paper, we evaluate, via simulations, MB-TOST in the specific setting of single sample PK BE study and compare its performance to NCA bootstrap. We performed BE study simulations using a published PK model and parameter values and evaluated multiple scenarios, including study design (parallel or crossover), sampling times (5 or 10 spread across the dosing interval), and geometric mean ratio (of 0.8, 0.9, 1, and 1.25). Using the simulated structural PK model, MB-TOST performed similarly to NCA bootstrap for AUC. ForC max , the latter tended to be conservative and less powerful. Our research suggests that MB-TOST may be considered as an alternative BE approach for single sample PK studies, provided that the PK model is correctly specified and the test drug has the same structural model as the reference drug.

NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology

Modeling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data and on how well suited the available data are to a particular modeling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modeling objective. However, implementation of OED is limited by currently available software tools that are not well suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. Here, we present the NLoed software package and demonstrate its use with in vivo data from an optogenetic system in Escherichia coli. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models and facilitates modeling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modeling workflow. NLoed offers an accessible, modular, and flexible OED tool set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLoed's capabilities by applying it to experimental design for characterization of a bacterial optogenetic system.

A case-study of model-informed drug development of a novel PCSK9 anti sense oligonucleotide. Part 1: First time in man to phase II

Here, we show model-informed drug development (MIDD) of a novel antisense oligonucleotide, targeting PCSK9 for treatment of hypocholesteremia. The case study exemplifies use of MIDD to analyze emerging data from an ongoing first-in-human study, utility of the US Food and Drug Administration MIDD pilot program to accelerate timelines, innovative use of competitor data to set biomarker targets, and use of MIDD to optimize sample size and dose selection, as well as to accelerate and de-risk a phase IIb study. The focus of the case-study is on the cross-functional collaboration and other key MIDD enablers that are critical to maximize the value of MIDD, rather than the technical application of MIDD.

Ethnicity evaluation of ferric pyrophosphate citrate among Asian and Non-Asian populations: a population pharmacokinetics analysis

PURPOSE

To evaluate the potential ethnic differences of ferric pyrophosphate citrate (FPC, Triferic) in healthy subjects and patients with hemodialysis-dependent stage 5 chronic kidney disease (CKD-5HD) and identify covariates that may influence pharmacokinetics (PK) of FPC.

METHODS

Data were collected from 2 Asian and 4 non-Asian clinical studies involving healthy subjects and CKD-5HD patients. Three population PK models were developed: M1 for intravenous (IV) administration of FPC in healthy subjects; M2 for dialysate administration of FPC in CKD-5HD patients; M3 for pre-dialyzer administration of FPC in CKD-5HD patients. All the models were fitted to concentration versus time data of FPC using the nonlinear mixed effect approach with the NONMEM® program. All statistical analyses were performed using SAS version 9.4.

RESULTS

In total, 26 Asians and 65 non-Asians were included in the final model analysis database. Forty healthy subjects were administered FPC via intravenous (IV) route and 51 patients with CKD-5HD via dialysate (N = 50) and pre-dialyzer blood circuit administration (N = 51). The PK parameters of FPC IV were similar. The population PK model showed good parameter precision and reliability as shown by model evaluation, and no relevant influence of ethnicity on PK parameters was observed. In healthy subjects, the maximum observed plasma concentration (Cmax) and area under the plasma concentration-time curve (AUC) decreased with increase in lean body mass (LBM) and the average serum total iron at 6 h before the baseline period (Feav), whereas, in both patient populations, Cmax and AUC decreased with increase in LBM and decrease in Febaseline. Other factors such as gender, age, Feav, and ethnicity had no influence on PK exposures in patients. The influence of LBM on PK exposures in patients was smaller than that in healthy subjects (ratio of AUC0-24 for the 5th [68 kg] and 95th [45 kg] patient's LBM was almost 1). The influence of Feav and LBM on PK exposures was < 50%.

CONCLUSION

The population pharmacokinetics model successfully described the PK parameters of FPC in healthy subjects and CKD-5HD patients and were comparable between Asian and non-Asian populations.

Metaheuristics for pharmacometrics

Metaheuristics is a powerful optimization tool that is increasingly used across disciplines to tackle general purpose optimization problems. Nature-inspired metaheuristic algorithms is a subclass of metaheuristic algorithms and have been shown to be particularly flexible and useful in solving complicated optimization problems in computer science and engineering. A common practice with metaheuristics is to hybridize it with another suitably chosen algorithm for enhanced performance. This paper reviews metaheuristic algorithms and demonstrates some of its utility in tackling pharmacometric problems. Specifically, we provide three applications using one of its most celebrated members, particle swarm optimization (PSO), and show that PSO can effectively estimate parameters in complicated nonlinear mixed-effects models and to gain insights into statistical identifiability issues in a complex compartment model. In the third application, we demonstrate how to hybridize PSO with sparse grid, which is an often-used technique to evaluate high dimensional integrals, to search for D -efficient designs for estimating parameters in nonlinear mixed-effects models with a count outcome. We also show the proposed hybrid algorithm outperforms its competitors when sparse grid is replaced by its competitor, adaptive gaussian quadrature to approximate the integral, or when PSO is replaced by three notable nature-inspired metaheuristic algorithms.

Review of the Preanalytical Errors That Impact Therapeutic Drug Monitoring

PURPOSE

Preanalytical errors comprise the majority of testing errors experienced by clinical laboratories and significantly impact the accuracy of therapeutic drug monitoring (TDM).

METHODS

Specific preanalytical factors in sample timing, collection, transport, processing, and storage that lead to errors in TDM were reviewed. We performed a literature search using several scientific databases including PubMed, ScienceDirect, Scopus, Web of Science, and ResearchGate for human studies published in the English language from January 1980 to February 2021, reporting on TDM and the preanalytical phase.

RESULTS

Blood collection errors (ie, wrong anticoagulant/clot activator used, via an intravenous line, incorrect time after dosing) delay testing, cause inaccurate results, and adversely impact patient care. Blood collected in lithium heparin tubes instead of heparin sodium tubes produce supertoxic lithium concentrations, which can compromise care. Specimens collected in serum separator gel tubes cause falsely decreased concentrations due to passive absorption into the gel when samples are not processed and analyzed quickly. Dried blood spots are popular for TDM as they are minimally invasive, allowing for self-sampling and direct shipping to a clinical laboratory using regular mail. However, blood collection techniques, such as trauma to the collection site, filter paper fragility, and hematocrit (Hct) bias, can adversely affect the accuracy of the results. Volumetric absorptive microsampling is a potential alternative to dried blood spot that offers fast, volume-fixed sampling, low pain tolerance, and is not susceptible to Hct concentrations.

CONCLUSIONS

The identification of preanalytical factors that may negatively impact TDM is critical. Developing workflows that can standardize TDM practices, align appropriate timing and blood collection techniques, and specimen processing will eliminate errors.

Tutorial for $DESIGN in NONMEM: Clinical trial evaluation and optimization

This NONMEM tutorial shows how to evaluate and optimize clinical trial designs, using algorithms developed in design software, such as PopED and PFIM 4.0. Parameter precision and model parameter estimability is obtained by assessing the Fisher Information Matrix (FIM), providing expected model parameter uncertainty. Model parameter identifiability may be uncovered by very large standard errors or inability to invert an FIM. Because evaluation of FIM is more efficient than clinical trial simulation, more designs can be investigated, and the design of a clinical trial can be optimized. This tutorial provides simple and complex pharmacokinetic/pharmacodynamic examples on obtaining optimal sample times, doses, or best division of subjects among design groups. Robust design techniques accounting for likely variability among subjects are also shown. A design evaluator and optimizer within NONMEM allows any control stream first developed for trial design exploration to be subsequently used for estimation of parameters of simulated or clinical data, without transferring the model to another software. Conversely, a model developed in NONMEM could be used for design optimization. In addition, the $DESIGN feature can be used on any model file and dataset combination to retrospectively evaluate the model parameter uncertainty one would expect given that the model generated the data, particularly if outliers of the actual data prevent a reasonable assessment of the variance‐covariance. The NONMEM trial design feature is suitable for standard continuous data, whereas more elaborate trial designs or with noncontinuous data‐types can still be accomplished in optimal design dedicated software like PopED and PFIM.

Finding optimal design in nonlinear mixed effect models using multiplicative algorithms

BACKGROUND AND OBJECTIVES

To optimize designs for longitudinal studies analyzed by nonlinear mixed effect models (NLMEMs), the Fisher information matrix (FIM) can be used. In this work, we focused on the multiplicative algorithms, previously applied in standard individual regression, to find optimal designs for NLMEMs.

METHODS

We extended multiplicative algorithms to mixed models and implemented the algorithm both in R and in C. Then, we applied the algorithm to find D-optimal designs in two longitudinal data examples, one with continuous and one with binary outcome.

RESULTS

For these examples, we quantified the improved speed when C is used instead of R. Design optimization using the multiplicative algorithm led to designs with D-efficiency gains between 13% and 25% compared to non-optimized designs.

CONCLUSION

We found that the multiplicative algorithm can be used efficiently to design longitudinal studies.

Flexibility in Drug Product Development: A Perspective

The process of bringing a drug to market involves innumerable decisions to refine a concept into a final product. The final product goes through extensive research and development to meet the target product profile and to obtain a product that is manufacturable at scale. Historically, this process often feels inflexible and linear, as ideas and development paths are eliminated early on to allow focus on the workstream with the highest probability of success. Carrying multiple options early in development is both time-consuming and resource-intensive. Similarly, changing development pathways after significant investment carries a high "penalty of change" (PoC), which makes pivoting to a new concept late in development inhibitory. Can drug product (DP) development be made more flexible? The authors believe that combining a nonlinear DP development approach, leveraging state-of-the art data sciences, and using emerging process and measurement technologies will offer enhanced flexibility and should become the new normal. Through the use of iterative DP evaluation, "smart" clinical studies, artificial intelligence, novel characterization techniques, automation, and data collection/modeling/interpretation, it should be possible to significantly reduce the PoC during development. In this Perspective, a review of ideas/techniques along with supporting technologies that can be applied at each stage of DP development is shared. It is further discussed how these contribute to an improved and flexible DP development through the acceleration of the iterative build-measure-learn cycle in laboratories and clinical trials.

Pharmacometric estimation methods for aggregate data, including data simulated from other pharmacometric models

Lack of data is an obvious limitation to what can be modelled. However, aggregate data in the form of means and possibly (co)variances, as well as previously published pharmacometric models, are often available. Being able to use all available data is desirable, and therefore this paper will outline several methods for using aggregate data as the basis of parameter estimation. The presented methods can be used for estimation of parameters from aggregate data, and as a computationally efficient alternative for the stochastic simulation and estimation procedure. They also allow for population PK/PD optimal design in the case when the data-generating model is different from the data-analytic model, a scenario for which no solutions have previously been available. Mathematical analysis and computational results confirm that the aggregate-data FO algorithm converges to the same estimates as the individual-data FO and yields near-identical standard errors when used in optimal design. The aggregate-data MC algorithm will asymptotically converge to the exactly correct parameter estimates if the data-generating model is the same as the data-analytic model. The performance of the aggregate-data methods were also compared to stochastic simulations and estimations (SSEs) when the data-generating model is different from the data-analytic model. The aggregate-data FO optimal design correctly predicted the sampling distributions of 200 models fitted to simulated datasets with the individual-data FO method.

Clinical Pharmacokinetics of Oral Sodium Selenite and Dosing Implications in the Treatment of Patients with Metastatic Cancer

Background

Selenite is a radiosensitizer and inhibitor of androgen receptor expression and function. In a phase I study (NCT02184533) in 15 subjects with metastatic cancer receiving daily oral sodium selenite with palliative radiation therapy, disease stabilization was observed, as evidenced by tumor regression, marked reduction in pain symptoms, and decreased prostate-specific antigen levels (only patients with castrate-resistant prostate cancer).

Objective

The aim of this work was to characterize the pharmacokinetics of selenite to suggest dosing strategies and to propose a study design for further investigation.

Methods

With selenium plasma concentrations obtained from five dosing cohorts (5.5, 11, 16.5, 33, and 49.5 mg), a population pharmacokinetic model was constructed using NONMEM. The model described externally administered selenite (inorganic) with a baseline component for endogenous selenium levels. Using the pharmacokinetic model, simulations were performed to suggest dosing regimens that achieved in vitro target selenite levels, and optimal pharmacokinetic sampling times for a subsequent study were proposed using PopED.

Results

A one-compartment model characterized selenite pharmacokinetics. Parameter estimates were absorption rate constant (0.64 h⁻¹), apparent clearance (1.58 L/h), apparent volume of distribution (42.3 L), and baseline selenium amount (5270 μg). A logarithmic function characterized the inverse relationship between dose level and bioavailability. Four regimens to reach in vitro target selenite levels were proposed: 33 mg daily, 16.5 mg twice daily (BID), 11 mg BID, and 5.5 mg thrice daily (TID). Optimal sampling times were 1, 2, 6, and 24 h.

Discussion

The population model described the pharmacokinetic data well. Three regimens (33 mg daily, 11 mg BID, 5.5 mg TID) achieved in vitro target selenite levels after one dose. The model and optimal sampling times may inform future studies evaluating the efficacy of selenite for metastatic cancer treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40268-021-00340-9.

Model-based approach to sampling optimization in studies of antibacterial drugs for infants and young children

Clinical trials for pediatric indications and new pediatric drugs face challenges, including the limited blood volume due to the patients’ small bodies. In Japan, the Evaluation Committee on Unapproved or Off‐labeled Drugs with High Medical Needs has discussed the necessity of pediatric indications against the background of a lack of Japanese pediatric data. The limited treatment options regarding antibiotics for pediatric patients are associated with the emergence of antibiotic‐resistant bacteria. Regulatory guidelines promote the use of model‐based drug development to reduce practical and ethical constraints for pediatric patients. Sampling optimization is one of the key study designs for pediatric drug development. In this simulation study, we evaluated the precision of the empirical Bayes estimates of pharmacokinetic (PK) parameters based on the sampling times optimized by published pediatric population PK models. We selected three previous PK studies of cefepime and ciprofloxacin in infants and young children as paradigms. The number of sampling times was reduced from original full sampling times to two to four sampling times based on the Fisher information matrix. We observed that the precision of empirical Bayes estimates of the key PK parameters and the predicted efficacy based on the reduced sampling times were generally comparable to those based on the original full sampling times. The model‐based approach to sampling optimization provided a maximization of PK information with a minimum burden on infants and young children for the future development of pediatric drugs.

Dose prediction for repurposing nitazoxanide in SARS-CoV-2 treatment or chemoprophylaxis

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared a global pandemic and urgent treatment and prevention strategies are needed. Nitazoxanide, an anthelmintic drug, has been shown to exhibit in vitro activity against SARS-CoV-2. The present study used physiologically based pharmacokinetic (PBPK) modelling to inform optimal doses of nitazoxanide capable of maintaining plasma and lung tizoxanide exposures above the reported SARS-CoV-2 EC90 .

METHODS

A whole-body PBPK model was validated against available pharmacokinetic data for healthy individuals receiving single and multiple doses between 500 and 4000 mg with and without food. The validated model was used to predict doses expected to maintain tizoxanide plasma and lung concentrations above the EC90 in >90% of the simulated population. PopDes was used to estimate an optimal sparse sampling strategy for future clinical trials.

RESULTS

The PBPK model was successfully validated against the reported human pharmacokinetics. The model predicted optimal doses of 1200 mg QID, 1600 mg TID and 2900 mg BID in the fasted state and 700 mg QID, 900 mg TID and 1400 mg BID when given with food. For BID regimens an optimal sparse sampling strategy of 0.25, 1, 3 and 12 hours post dose was estimated.

CONCLUSION

The PBPK model predicted tizoxanide concentrations within doses of nitazoxanide already given to humans previously. The reported dosing strategies provide a rational basis for design of clinical trials with nitazoxanide for the treatment or prevention of SARS-CoV-2 infection. A concordant higher dose of nitazoxanide is now planned for investigation in the seamless phase I/IIa AGILE trial.

Therapeutic drug monitoring of anti-tumour necrosis factor-α agents in inflammatory bowel disease: Limits and improvements

AIMS

Since the publication of the American Gastroenterological Association's recommendations in 2017, there have been no significant changes in the biological monitoring recommendations in inflammatory bowel disease. Possible limitations are the lack of evidence to recommend proactive therapeutic drug monitoring (pTDM) over reactive TDM (rTDM), and the limited information about individualized dosing methods. This article aims to review the TDM strategy updates and the use of individualized dosing methods.

METHODS

For the analysis of the TDM strategies and individualized dosing method, a search was carried out in PubMed and Cochrane Central. In the TDM case, since August 2017.

RESULTS

A total of 263 publications were found, but only 7 related to proactive TDM. Five of these publications directly compared pTDM vs rTDM and 2 were randomized clinical trials. Six studies found benefits of pTDM and 1 found no differences. Regarding the individualized dosing method, 229 distinct results were found. Population pharmacokinetics was the most widely used method to develop individual dosage models and to analyse the influence of factors on drug concentrations (albumin concentration, weight, presence of anti-drug antibodies etc).

CONCLUSION

We have found no major changes in TDM strategies. There is a growing trend towards the use of pTDM because it has shown a longer duration of treatment response, lower rates of discontinuation and relapses. However, the available evidence is limited and of low quality. Despite the common use of population pharmacokinetic methods to analyse pharmacokinetic factors, they are not commonly used for personalized dosing.

Non-Linear Pharmacokinetics of Oral Roscovitine (Seliciclib) in Cystic Fibrosis Patients Chronically Infected with Pseudomonas aeruginosa: A Study on Population Pharmacokinetics with Monte Carlo Simulations

Roscovitine (Seliciclib), a new protein kinase inhibitor, was administered orally to adult patients with cystic fibrosis for the first time in the ROSCO-CF trial, a dose-escalation, phase IIa, randomized, controlled trial. Extensive pharmacokinetic sampling was performed up to 12 h after the first oral dose. Roscovitine and its main metabolite M3 were quantified by liquid chromatography coupled with tandem mass spectrometry. The pharmacokinetics analyses were performed by non-linear mixed effects modelling. Monte Carlo simulations were performed to assess the impact of dose on the pharmacokinetics of oral roscovitine. Twenty-three patients received oral doses ranging from 200 to 800 mg of roscovitine and 138 data points were available for both roscovitine and M3 concentrations. The pharmacokinetics was best described by a two-compartment parent-metabolite model, with a complex saturable absorption process modelled as the sum of Gaussian inverse density functions. The Monte Carlo simulations showed a dose-dependent and saturable first-pass effect leading to pre-systemic formation of M3. The treatment with proton-pump inhibitors reduced the rate of absorption of oral roscovitine. The pharmacokinetics of oral roscovitine in adult patients with cystic fibrosis was non-linear and showed significant inter-individual variability. A repeat-dose study will be required to assess the inter-occasional variability of its pharmacokinetics.

Population pharmacokinetic study of caffeine citrate in Chinese premature infants with apnea

WHAT IS KNOWN AND OBJECTIVES

Caffeine citrate is a commonly used methylxanthine for pharmacologic treatment of apnea of prematurity. The aim of this study was to develop and verify a population pharmacokinetic (PPK) model, which can provide a reference for individualized caffeine citrate treatment of apnea in Chinese premature infants.

METHODS

A total of 88 serum concentration measurements from 46 preterm patients (median gestational age 29 weeks) were retrospectively collected and the relevant clinical data of patients were recorded. The PPK analysis was performed by non-linear mixed-effect modelling method using NONMEM. Allometric scaling was applied in the PPK analysis, and the final model was evaluated by graphic and statistical methods, including goodness-of-fit plots, normalized prediction distribution errors plots and bootstrap procedures.

RESULTS

A one-compartment model with first-order elimination was successfully fitted to the data. The typical scaled values for the parameters clearance and volume of distribution (V) were 0.268 L/h and 109 L per 70 kg, respectively. The weight at the time of blood collection (CW) and post-natal age were identified as important predictors for pharmacokinetic parameters of caffeine. The evaluation process showed good stability and predictability of the final PPK model.

WHAT IS NEW AND CONCLUSION

This is a complete PPK study of caffeine citrate in Chinese premature infants with apnea, which complements caffeine pharmacokinetic data of the premature from China. A final PPK model was developed which may serve as a beneficial tool for the use of caffeine citrate in the treatment of apnea in Chinese preterm infants.

Pharmacokinetic/pharmacodynamic data extrapolation models for improved pediatric efficacy and toxicity estimation, with application to secondary hyperparathyroidism

In most drug development settings, the regulatory approval process is accompanied by extensive studies performed to understand the drug's pharmacokinetic (PK) and pharmacodynamic (PD) properties. In this article, we attempt to utilize the rich PK/PD data to inform the borrowing of information from adults during pediatric drug development. In pediatric settings, it is especially crucial that we are parsimonious with the patients recruited for experimentation. We illustrate our approaches in the context of clinical trials of cinacalcet for treating secondary hyperparathyroidism in pediatric and adult patients with chronic kidney disease, where we model both parathyroid hormone (efficacy endpoint) and corrected calcium levels (safety endpoint). We use population PK/PD modeling of the cinacalcet data to quantitatively assess the similarity between adults and children, and use this information in various hierarchical Bayesian adult borrowing rules whose statistical properties can then be evaluated. In particular, we simulate the bias and mean square error performance of our approaches in settings where borrowing is and is not warranted to inform guidelines for the future use of our methods.

Dose prediction for repurposing nitazoxanide in SARS-CoV-2 treatment or chemoprophylaxis

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared a global pandemic by the World Health Organisation and urgent treatment and prevention strategies are needed. Many clinical trials have been initiated with existing medications, but assessments of the expected plasma and lung exposures at the selected doses have not featured in the prioritisation process. Although no antiviral data is currently available for the major phenolic circulating metabolite of nitazoxanide (known as tizoxanide), the parent ester drug has been shown to exhibit in vitro activity against SARS-CoV-2. Nitazoxanide is an anthelmintic drug and its metabolite tizoxanide has been described to have broad antiviral activity against influenza and other coronaviruses. The present study used physiologically-based pharmacokinetic (PBPK) modelling to inform optimal doses of nitazoxanide capable of maintaining plasma and lung tizoxanide exposures above the reported nitazoxanide 90% effective concentration (EC ₉₀ ) against SARS-CoV-2.

METHODS

A whole-body PBPK model was constructed for oral administration of nitazoxanide and validated against available tizoxanide pharmacokinetic data for healthy individuals receiving single doses between 500 mg SARS-CoV-2 4000 mg with and without food. Additional validation against multiple-dose pharmacokinetic data when given with food was conducted. The validated model was then used to predict alternative doses expected to maintain tizoxanide plasma and lung concentrations over the reported nitazoxanide EC ₉₀ in >90% of the simulated population. Optimal design software PopDes was used to estimate an optimal sparse sampling strategy for future clinical trials.

RESULTS

The PBPK model was validated with AAFE values between 1.01 SARS-CoV-2 1.58 and a difference less than 2-fold between observed and simulated values for all the reported clinical doses. The model predicted optimal doses of 1200 mg QID, 1600 mg TID, 2900 mg BID in the fasted state and 700 mg QID, 900 mg TID and 1400 mg BID when given with food, to provide tizoxanide plasma and lung concentrations over the reported in vitro EC ₉₀ of nitazoxanide against SARS-CoV-2. For BID regimens an optimal sparse sampling strategy of 0.25, 1, 3 and 12h post dose was estimated.

CONCLUSION

The PBPK model predicted that it was possible to achieve plasma and lung tizoxanide concentrations, using proven safe doses of nitazoxanide, that exceed the EC ₉₀ for SARS-CoV-2. The PBPK model describing tizoxanide plasma pharmacokinetics after oral administration of nitazoxanide was successfully validated against clinical data. This dose prediction assumes that the tizoxanide metabolite has activity against SARS-CoV-2 similar to that reported for nitazoxanide, as has been reported for other viruses. The model and the reported dosing strategies provide a rational basis for the design (optimising plasma and lung exposures) of future clinical trials of nitazoxanide in the treatment or prevention of SARS-CoV-2 infection.

A ROADMAP Plan to Address Research Needs for Gonococcal Antimicrobial Resistance in China

Gonococcal antimicrobial resistance (AMR) has become a global threat significantly hampering the control of gonorrhea. Many socioeconomic, biological, behavioral, and programmatic factors have played an important role in driving the emergence, transmission and spread of gonococcal AMR. However, research to address these scientific and programmatic questions is limited in China. Here we propose a ROADMAP (acronym for resistance surveillance, outcomes due to AMR, antibiotic stewardship and application, diagnostic tools, mechanisms of AMR, antimicrobial assessment, and population pharmacokinetics and pharmacodynamics) plan for expanding interdisciplinary collaborations to address the research needs in China.

Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance

Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies.

Population pharmacokinetics of piperacillin in febrile children receiving cancer chemotherapy: the impact of body weight and target on an optimal dosing regimen

BACKGROUND

The β-lactam antibiotic piperacillin (in combination with tazobactam) is commonly chosen for empirical treatment of suspected bacterial infections. However, pharmacokinetic variability among patient populations and across ages leads to uncertainty when selecting a dosing regimen to achieve an appropriate pharmacodynamic target.

OBJECTIVES

To guide dosing by establishing a population pharmacokinetic model for unbound piperacillin in febrile children receiving cancer chemotherapy, and to assess pharmacokinetic/pharmacodynamic target attainment (100% fT > 1×MIC and 50% fT > 4×MIC) and resultant exposure, across body weights.

METHODS

Forty-three children admitted for 89 febrile episodes contributed 482 samples to the pharmacokinetic analysis. The typical doses required for target attainment were compared for various dosing regimens, in particular prolonged infusions, across MICs and body weights.

RESULTS

A two-compartment model with inter-fever-episode variability in CL, and body weight included through allometry, described the data. A high CL of 15.4 L/h (70 kg) combined with high glomerular filtration rate (GFR) values indicated rapid elimination and hyperfiltration. The target of 50% fT > 4×MIC was achieved for an MIC of 4.0 mg/L in a typical patient with extended infusions of 2-3 (q6h) or 3-4 (q8h) h, at or below the standard adult dose (75 and 100 mg/kg/dose for q6h and q8h, respectively). Higher doses or continuous infusion were needed to achieve 100% fT > 1×MIC due to the rapid piperacillin elimination.

CONCLUSIONS

The licensed dose for children with febrile neutropenia (80 mg/kg q6h as a 30 min infusion) performs poorly for attainment of fT>MIC pharmacokinetic/pharmacodynamic targets. Given the population pharmacokinetic profile, feasible dosing regimens with reasonable exposure are continuous infusion (100% fT > 1×MIC) or prolonged infusions (50% fT > 4×MIC).

Optimizing drug development in oncology by clinical trial simulation: Why and how?

In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after an extensive and expensive preclinical development period. Methodologies such as computer modeling and clinical trial simulation (CTS) might represent a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the preclinical phase to postmarketing. However, they are barely used and are poorly regarded for drug approval, despite Food and Drug Administration and European Medicines Agency recommendations. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by clinical trial studies and hospital databases. Data sharing and data merging raise legal, policy and technical issues that will need to be addressed. Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increased computing speed should allow this leap forward. The realization of CTS requires not only bioinformatics tools to allow interconnection and global integration of all clinical data but also a universal legal framework to protect the privacy of every patient. While recognizing that CTS can never replace 'real-life' trials, they should be implemented in future drug development schemes to provide quantitative support for decision-making. This in silico medicine opens the way to the P4 medicine: predictive, preventive, personalized and participatory.

Development of a Parsimonious Design for Optimal Classification of Exclusive Breastfeeding

A deuterium oxide dose‐to‐mother (DTM) technique is used to determine if an infant is exclusive breastfeeding (EBF). However, the DTM method is intensive, requiring seven paired mother–infant samples during a 14‐day study period. The purpose of this study was to develop a field‐friendly protocol. Data from 790 mother–infant pairs from nine countries were analyzed using a Markov chain Monte Carlo method with Stan. The data were split into (i) model building (565 pairs) and (ii) design evaluation (225 pairs). EBF classification was based on a previously published cut‐off for nonmilk water intake. Classification based on the full design was the reference (gold standard classification). The receiver operating characteristics of parsimonious designs were used to determine an optimal parsimonious classification method. The best two postdose windows (days 7–9 and 13–14) yielded optimal categorization with similar performance in the design evaluation data. This postdose two‐sample design provided 95% sensitivity and specificity when compared with the full design.

Robust designs accounting for model uncertainty in longitudinal studies with binary outcomes

To optimize designs for longitudinal studies analyzed by mixed-effect models with binary outcomes, the Fisher information matrix can be used. Optimal design approaches, however, require a priori knowledge of the model. We aim to propose, for the first time, a robust design approach accounting for model uncertainty in longitudinal trials with two treatment groups, assuming mixed-effect logistic models. To optimize designs given one model, we compute several optimality criteria based on Fisher information matrix evaluated by the new approach based on Monte-Carlo/Hamiltonian Monte-Carlo. We propose to use the DDS-optimality criterion, as it ensures a compromise between the precision of estimation of the parameters, and hence the Wald test power, and the overall precision of parameter estimation. To account for model uncertainty, we assume candidate models with their respective weights. We compute robust design across these models using compound DDS-optimality. Using the Fisher information matrix, we propose to predict the average power over these models. Evaluating this approach by clinical trial simulations, we show that the robust design is efficient across all models, allowing one to achieve good power of test. The proposed design strategy is a new and relevant approach to design longitudinal studies with binary outcomes, accounting for model uncertainty.

Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory

PURPOSE

Physiologically-based pharmacokinetic (PBPK) models are essential in drug development, but require parameters that are not always obtainable. We developed a methodology to investigate the feasibility and requirements for precise and accurate estimation of PBPK parameters using population modelling of clinical data and illustrate this for two key PBPK parameters for hepatic metabolic clearance, namely whole liver unbound intrinsic clearance (CLint_,u,WL) and hepatic blood flow (Qh) in children.

METHODS

First, structural identifiability was enabled through re-parametrization and the definition of essential trial design components. Subsequently, requirements for the trial components to yield precise estimation of the PBPK parameters and their inter-individual variability were established using a novel application of population optimal design theory. Finally, the performance of the proposed trial design was assessed using stochastic simulation and estimation.

RESULTS

Precise estimation of CLint_,u,WL and Qh and their inter-individual variability was found to require a trial with two drugs, of which one has an extraction ratio (ER) ≤ 0.27 and the other has an ER ≥ 0.93. The proposed clinical trial design was found to lead to precise and accurate parameter estimates and was robust to parameter uncertainty.

CONCLUSION

The proposed framework can be applied to other PBPK parameters and facilitate the development of PBPK models.

Pharmacokinetic studies in children: recommendations for practice and research

Optimising the dosing of medicines for neonates and children remains a challenge. The importance of pharmacokinetic (PK) and pharmacodynamic (PD) research is recognised both in medicines regulation and paediatric clinical pharmacology, yet there remain barriers to undertaking high-quality PK and PD studies. While these studies are essential in understanding the dose-concentration-effect relationship and should underpin dosing recommendations, this review examines how challenges affecting the design and conduct of paediatric pharmacological studies can be overcome using targeted pharmacometric strategies. Model-based approaches confer benefits at all stages of the drug life-cycle, from identifying the first dose to be used in children, to clinical trial design, and optimising the dosing regimens of older, off-patent medications. To benefit patients, strategies to ensure that new PK, PD and trial data are incorporated into evidence-based dosing recommendations are needed. This review summarises practical strategies to address current challenges, particularly the use of model-based (pharmacometric) approaches in study design and analysis. Recommendations for practice and directions for future paediatric pharmacological research are given, based on current literature and our joint international experience. Success of PK research in children requires a robust infrastructure, with sustainable funding mechanisms at its core, supported by political and regulatory initiatives, and international collaborations. There is a unique opportunity to advance paediatric medicines research at an unprecedented pace, bringing the age of evidence-based paediatric pharmacotherapy into sight.

Reduced and optimized trial designs for drugs described by a target mediated drug disposition model

Monoclonal antibodies against soluble targets are often rich and include the sampling of multiple analytes over a lengthy period of time. Predictive models built on data obtained in such studies can be useful in all drug development phases. If adequate model predictions can be maintained with a reduced design (e.g. fewer samples or shorter duration) the use of such designs may be advocated. The effect of reducing and optimizing a rich design based on a published study for Omalizumab (OMA) was evaluated as an example. OMA pharmacokinetics were characterized using a target-mediated drug disposition model considering the binding of OMA to free IgE and the subsequent formation of an OMA–IgE complex. The performance of the reduced and optimized designs was evaluated with respect to: efficiency, parameter uncertainty and predictions of free target. It was possible to reduce the number of samples in the study by 30% while still maintaining an efficiency of almost 90%. A reduction in sampling duration by two-thirds resulted in an efficiency of 75%. Omission of any analyte measurement or a reduction of the number of dose levels was detrimental to the efficiency of the designs (efficiency ≤ 51%). However, other metrics were, in some cases, relatively unaffected, showing that multiple metrics may be needed to obtain balanced assessments of design performance.

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models

BACKGROUND AND OBJECTIVE

Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features.

METHODS

Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization.

RESULTS

The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters.

CONCLUSION

PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr.

Assessing robustness of designs for random effects parameters for nonlinear mixed-effects models

Optimal designs for nonlinear models are dependent on the choice of parameter values. Various methods have been proposed to provide designs that are robust to uncertainty in the prior choice of parameter values. These methods are generally based on estimating the expectation of the determinant (or a transformation of the determinant) of the information matrix over the prior distribution of the parameter values. For high dimensional models this can be computationally challenging. For nonlinear mixed-effects models the question arises as to the importance of accounting for uncertainty in the prior value of the variances of the random effects parameters. In this work we explore the influence of the variance of the random effects parameters on the optimal design. We find that the method for approximating the expectation and variance of the likelihood is of potential importance for considering the influence of random effects. The most common approximation to the likelihood, based on a first-order Taylor series approximation, yields designs that are relatively insensitive to the prior value of the variance of the random effects parameters and under these conditions it appears to be sufficient to consider uncertainty on the fixed-effects parameters only.

PharmML in Action: an Interoperable Language for Modeling and Simulation

PharmML is an XML-based exchange format created with a focus on nonlinear mixed-effect (NLME) models used in pharmacometrics, but providing a very general framework that also allows describing mathematical and statistical models such as single-subject or nonlinear and multivariate regression models. This tutorial provides an overview of the structure of this language, brief suggestions on how to work with it, and use cases demonstrating its power and flexibility.

The effect of using a robust optimality criterion in model based adaptive optimization

Optimizing designs using robust (global) optimality criteria has been shown to be a more flexible approach compared to using local optimality criteria. Additionally, model based adaptive optimal design (MBAOD) may be less sensitive to misspecification in the prior information available at the design stage. In this work, we investigate the influence of using a local (lnD) or a robust (ELD) optimality criterion for a MBAOD of a simulated dose optimization study, for rich and sparse sampling schedules. A stopping criterion for accurate effect prediction is constructed to determine the endpoint of the MBAOD by minimizing the expected uncertainty in the effect response of the typical individual. 50 iterations of the MBAODs were run using the MBAOD R-package, with the concentration from a one-compartment first-order absorption pharmacokinetic model driving the population effect response in a sigmoidal EMAX pharmacodynamics model. The initial cohort consisted of eight individuals in two groups and each additional cohort added two individuals receiving a dose optimized as a discrete covariate. The MBAOD designs using lnD and ELD optimality with misspecified initial model parameters were compared by evaluating the efficiency relative to an lnD-optimal design based on the true parameter values. For the explored example model, the MBAOD using ELD-optimal designs converged quicker to the theoretically optimal lnD-optimal design based on the true parameters for both sampling schedules. Thus, using a robust optimality criterion in MBAODs could reduce the number of adaptations required and improve the practicality of adaptive trials using optimal design.

Deterministic identifiability of population pharmacokinetic and pharmacokinetic-pharmacodynamic models

Identifiability is an important component of pharmacokinetic-pharmacodynamic (PKPD) model development. Structural identifiability is concerned with the uniqueness of the model parameters for a set of perfect input-output data and deterministic identifiability with the precision of parameter estimation given imperfect input-output data. We introduce two subcategories of deterministic identifiability, external and internal, and consider factors that distinguish between these forms. We define external deterministic identifiability as a function of externally controllable variables, i.e., the design, and internal deterministic identifiability as a function of the model and its parameter values. The concepts are explored using three common PK and PKPD models, and verified for their precision for the selected set of parameter values under optimal design.

Evidence-based drug treatment for special patient populations through model-based approaches

The majority of marketed drugs remain understudied in some patient populations such as pregnant women, paediatrics, the obese, the critically-ill, and the elderly. As a consequence, currently used dosing regimens may not assure optimal efficacy or minimal toxicity in these patients. Given the vulnerability of some subpopulations and the challenges and costs of performing clinical studies in these populations, cutting-edge approaches are needed to effectively develop evidence-based and individualized drug dosing regimens. Five key issues are presented that are essential to support and expedite the development of drug dosing regimens in these populations using model-based approaches: 1) model development combined with proper validation procedures to extract as much valid information from available study data as possible, with limited burden to patients and costs; 2) integration of existing data and the use of prior pharmacological and physiological knowledge in study design and data analysis, to further develop knowledge and avoid unnecessary or unrealistic (large) studies in vulnerable populations; 3) clinical proof-of-principle in a prospective evaluation of a developed drug dosing regimen, to confirm that a newly proposed regimen indeed results in the desired outcomes in terms of drug concentrations, efficacy, and/or safety; 4) pharmacodynamics studies in addition to pharmacokinetics studies for drugs for which a difference in disease progression and/or in exposure-response relation is anticipated compared to the reference population; 5) additional efforts to implement developed dosing regimens in clinical practice once drug pharmacokinetics and pharmacodynamics have been characterized in special patient populations. The latter remains an important bottleneck, but this is essential to truly realize evidence-based and individualized drug dosing for special patient populations. As all tools required for this purpose are available, we have the moral and societal obligation to make safe and effective pharmacotherapy available for these patients too.

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.

Optimum designs for non-linear mixed effects models in the presence of covariates

In this article, we present a new method for optimizing designs of experiments for non-linear mixed effects models, where a categorical factor with covariate information is a design variable combined with another design factor. The work is motivated by the need to efficiently design preclinical experiments in enzyme kinetics for a set of Human Liver Microsomes. However, the results are general and can be applied to other experimental situations where the variation in the response due to a categorical factor can be partially accounted for by a covariate. The covariate included in the model explains some systematic variability in a random model parameter. This approach allows better understanding of the population variation as well as estimation of the model parameters with higher precision.

Pharmacokinetic/pharmacodynamic integration and modelling of oxytetracycline for the porcine pneumonia pathogens Actinobacillus pleuropneumoniae and Pasteurella multocida

Pharmacokinetic–pharmacodynamic (PK/PD) integration and modelling were used to predict dosage schedules of oxytetracycline for two pig pneumonia pathogens, Actinobacillus pleuropneumoniae and Pasteurella multocida. Minimum inhibitory concentration (MIC) and mutant prevention concentration (MPC) were determined in broth and porcine serum. PK/PD integration established ratios of average concentration over 48 h (C_av0–48 h)/MIC of 5.87 and 0.27 μg/mL (P. multocida) and 0.70 and 0.85 μg/mL (A. pleuropneumoniae) for broth and serum MICs, respectively. PK/PD modelling of in vitro time–kill curves established broth and serum breakpoint values for area under curve (AUC_0–24 h)/MIC for three levels of inhibition of growth, bacteriostasis and 3 and 4 log₁₀ reductions in bacterial count. Doses were then predicted for each pathogen, based on Monte Carlo simulations, for: (i) bacteriostatic and bactericidal levels of kill; (ii) 50% and 90% target attainment rates (TAR); and (iii) single dosing and daily dosing at steady‐state. For 90% TAR, predicted daily doses at steady‐state for bactericidal actions were 1123 mg/kg (P. multocida) and 43 mg/kg (A. pleuropneumoniae) based on serum MICs. Lower TARs were predicted from broth MIC data; corresponding dose estimates were 95 mg/kg (P. multocida) and 34 mg/kg (A. pleuropneumoniae).

The effect of Fisher information matrix approximation methods in population optimal design calculations

With the increasing popularity of optimal design in drug development it is important to understand how the approximations and implementations of the Fisher information matrix (FIM) affect the resulting optimal designs. The aim of this work was to investigate the impact on design performance when using two common approximations to the population model and the full or block-diagonal FIM implementations for optimization of sampling points. Sampling schedules for two example experiments based on population models were optimized using the FO and FOCE approximations and the full and block-diagonal FIM implementations. The number of support points was compared between the designs for each example experiment. The performance of these designs based on simulation/estimations was investigated by computing bias of the parameters as well as through the use of an empirical D-criterion confidence interval. Simulations were performed when the design was computed with the true parameter values as well as with misspecified parameter values. The FOCE approximation and the Full FIM implementation yielded designs with more support points and less clustering of sample points than designs optimized with the FO approximation and the block-diagonal implementation. The D-criterion confidence intervals showed no performance differences between the full and block diagonal FIM optimal designs when assuming true parameter values. However, the FO approximated block-reduced FIM designs had higher bias than the other designs. When assuming parameter misspecification in the design evaluation, the FO Full FIM optimal design was superior to the FO block-diagonal FIM design in both of the examples.

Study design and population pharmacokinetic analysis of a phase II dose-ranging study of interleukin-1 receptor antagonist

Interleukin-1 receptor antagonist, a naturally-occurring antagonist to the pro-inflammatory cytokine Interleukin-1, is already in clinical use. In experimental models of stroke, Interleukin-1 receptor antagonist in cerebrospinal fluid has been associated with cerebral neuroprotection and in a phase I clinical trial in patients with subarachnoid haemorrhage it crosses the blood-cerebrospinal fluid barrier. The aims of the current work were to design a dose-ranging clinical study in patients and to analyse the plasma and cerebrospinal fluid data obtained using a population pharmacokinetic modelling approach. The study was designed using prior information: a published population pharmacokinetic model and associated parameter estimates. Simulations were carried out to identify combinations of intravenous bolus and 4 h infusion doses that could achieve a concentration of 100 ng/ml in cerebrospinal fluid within approximately 30 min. The most informative time points for plasma and cerebrospinal fluid were obtained prospectively; optimisation identified five sampling time points that were included in the 15 time points in the present study design. All plasma and cerebrospinal fluid concentration data from previous and current studies were combined for updated analysis. The result of the simulations showed that a dosage regimen of 500 mg intravenous bolus and 10 mg/kg/h could achieve the target concentration, however four other regimens that represent a stepwise increase in maximum concentration were also selected. Analysis of the updated data showed improvement in parameter accuracy and predictive performance of the model; the percentage relative standard errors for fixed and random-effects parameters were <15 and 35% respectively. A dose-ranging study was successfully designed using modelling and simulation.

Children in clinical trials: towards evidence-based pediatric pharmacotherapy using pharmacokinetic-pharmacodynamic modeling

INTRODUCTION

In pediatric pharmacotherapy, many drugs are still used off-label, and their efficacy and safety is not well characterized. Different efficacy and safety profiles in children of varying ages may be anticipated, due to developmental changes occurring across pediatric life.

AREAS COVERED

Beside pharmacokinetic (PK) studies, pharmacodynamic (PD) studies are urgently needed. Validated PKPD models can be used to derive optimal dosing regimens for children of different ages, which can be evaluated in a prospective study before implementation in clinical practice. Strategies should be developed to ensure that formularies update their drug dosing guidelines regularly according to the most recent advances in research, allowing for clinicians to integrate these guidelines in daily practice. Expert commentary: We anticipate a trend towards a systems-level approach in pediatric modeling to optimally use the information gained in pediatric trials. For this approach, properly designed clinical PKPD studies will remain the backbone of pediatric research.

Optimal Design for Informative Protocols in Xenograft Tumor Growth Inhibition Experiments in Mice

Tumor growth inhibition (TGI) models are increasingly used during preclinical drug development in oncology for the in vivo evaluation of antitumor effect. Tumor sizes are measured in xenografted mice, often only during and shortly after treatment, thus preventing correct identification of some TGI model parameters. Our aims were (i) to evaluate the importance of including measurements during tumor regrowth and (ii) to investigate the proportions of mice included in each arm. For these purposes, optimal design theory based on the Fisher information matrix implemented in PFIM4.0 was applied. Published xenograft experiments, involving different drugs, schedules, and cell lines, were used to help optimize experimental settings and parameters using the Simeoni TGI model. For each experiment, a two-arm design, i.e., control versus treatment, was optimized with or without the constraint of not sampling during tumor regrowth, i.e., "short" and "long" studies, respectively. In long studies, measurements could be taken up to 6 g of tumor weight, whereas in short studies the experiment was stopped 3 days after the end of treatment. Predicted relative standard errors were smaller in long studies than in corresponding short studies. Some optimal measurement times were located in the regrowth phase, highlighting the importance of continuing the experiment after the end of treatment. In the four-arm designs, the results showed that the proportions of control and treated mice can differ. To conclude, making measurements during tumor regrowth should become a general rule for informative preclinical studies in oncology, especially when a delayed drug effect is suspected.

An MCMC method for the evaluation of the Fisher information matrix for non-linear mixed effect models

Non-linear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal design based on the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. In recent years, estimation algorithms for NLMEMs have transitioned from linearization toward more exact higher-order methods. Optimal design, on the other hand, has mainly relied on first-order (FO) linearization to calculate the FIM. Although efficient in general, FO cannot be applied to complex non-linear models and with difficulty in studies with discrete data. We propose an approach to evaluate the expected FIM in NLMEMs for both discrete and continuous outcomes. We used Markov Chain Monte Carlo (MCMC) to integrate the derivatives of the log-likelihood over the random effects, and Monte Carlo to evaluate its expectation w.r.t. the observations. Our method was implemented in R using Stan, which efficiently draws MCMC samples and calculates partial derivatives of the log-likelihood. Evaluated on several examples, our approach showed good performance with relative standard errors (RSEs) close to those obtained by simulations. We studied the influence of the number of MC and MCMC samples and computed the uncertainty of the FIM evaluation. We also compared our approach to Adaptive Gaussian Quadrature, Laplace approximation, and FO. Our method is available in R-package MIXFIM and can be used to evaluate the FIM, its determinant with confidence intervals (CIs), and RSEs with CIs.

Sampling Optimization in Pharmacokinetic Bridging Studies: Example of the Use of Deferiprone in Children With β-Thalassemia

Despite wide clinical experience with deferiprone, the optimum dosage in children younger than 6 years remains to be established. This analysis aimed to optimize the design of a prospective clinical study for the evaluation of deferiprone pharmacokinetics in children. A 1-compartment model with first-order oral absorption was used for the purposes of the analysis. Different sampling schemes were evaluated under the assumption of a constrained population size. A sampling scheme with 5 samples per subject was found to be sufficient to ensure accurate characterization of the pharmacokinetics of deferiprone. Whereas the accuracy of parameters estimates was high, precision was slightly reduced because of the small sample size (CV% >30% for Vd/F and KA). Mean AUC ± SD was found to be 33.4 ± 19.2 and 35.6 ± 20.2 mg · h/mL, and mean Cmax ± SD was found to be 10.2 ± 6.1 and 10.9 ± 6.7 mg/L based on sparse and frequent sampling, respectively. The results showed that typical frequent sampling schemes and sample sizes do not warrant accurate model and parameter identifiability. Expectation of the determinant (ED) optimality and simulation-based optimization concepts can be used to support pharmacokinetic bridging studies. Of importance is the accurate estimation of the magnitude of the covariate effects, as they partly determine the dose recommendation for the population of interest.

Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation

This document was developed to enable greater consistency in the practice, application, and documentation of Model-Informed Drug Discovery and Development (MID3) across the pharmaceutical industry. A collection of "good practice" recommendations are assembled here in order to minimize the heterogeneity in both the quality and content of MID3 implementation and documentation. The three major objectives of this white paper are to: i) inform company decision makers how the strategic integration of MID3 can benefit R&D efficiency; ii) provide MID3 analysts with sufficient material to enhance the planning, rigor, and consistency of the application of MID3; and iii) provide regulatory authorities with substrate to develop MID3 related and/or MID3 enabled guidelines.