Risk Factors for COVID-19 and Respiratory Tract Infections during the Coronavirus Pandemic.

(1) Background: Some individuals are more susceptible to developing respiratory tract infections (RTIs) or coronavirus disease (COVID-19) than others. The aim of this work was to identify risk factors for symptomatic RTIs including COVID-19 and symptomatic COVID-19 during the coronavirus pandemic by using infection incidence, participant baseline, and regional COVID-19 burden data. (2) Methods: Data from a prospective study of 1000 frontline healthcare workers randomized to Bacillus Calmette-Guérin vaccination or placebo, and followed for one year, was analyzed. Parametric time-to-event analysis was performed to identify the risk factors associated with (a) non-specific symptomatic respiratory tract infections including COVID-19 (RTIs+COVID-19) and (b) symptomatic RTIs confirmed as COVID-19 using a polymerase chain reaction or antigen test (COVID-19). (3) Results: Job description of doctor or nurse (median hazard ratio [HR] 1.541 and 95% confidence interval [CI] 1.299-1.822), the reported COVID-19 burden (median HR 1.361 and 95% CI 1.260-1.469 for 1.4 COVID-19 cases per 10,000 capita), or a BMI > 30 kg/m2 (median HR 1.238 and 95% CI 1.132-1.336 for BMI of 35.4 kg/m2) increased the probability of RTIs+COVID-19, while positive SARS-CoV-2 serology at enrollment (median HR 0.583 and 95% CI 0.449-0.764) had the opposite effect. The reported COVID-19 burden (median HR 2.372 and 95% CI 2.116-2.662 for 1.4 COVID-19 cases per 10,000 capita) and a job description of doctor or nurse (median HR 1.679 and 95% CI 1.253-2.256) increased the probability of developing COVID-19, while smoking (median HR 0.428 and 95% CI 0.284-0.648) and positive SARS-CoV-2 serology at enrollment (median HR 0.076 and 95% CI 0.026-0.212) decreased it. (4) Conclusions: Nurses and doctors with obesity had the highest probability of developing RTIs including COVID-19. Non-smoking nurses and doctors had the highest probability of developing COVID-19 specifically. The reported COVID-19 burden increased the event probability, while positive SARS-CoV-2 IgG serology at enrollment decreased the probability of RTIs including COVID-19, and COVID-19 specifically.

Reproducibility in pharmacometrics applied in a phase III trial of BCG-vaccination for COVID-19.

Large clinical trials often generate complex and large datasets which need to be presented frequently throughout the trial for interim analysis or to inform a data safety monitory board (DSMB). In addition, reliable and traceability are required to ensure reproducibility in pharmacometric data analysis. A reproducible pharmacometric analysis workflow was developed during a large clinical trial involving 1000 participants over one year testing Bacillus Calmette-Guérin (BCG) (re)vaccination in coronavirus disease 2019 (COVID-19) morbidity and mortality in frontline health care workers. The workflow was designed to review data iteratively during the trial, compile frequent reports to the DSMB, and prepare for rapid pharmacometric analysis. Clinical trial datasets (n = 41) were transferred iteratively throughout the trial for review. An RMarkdown based pharmacometric processing script was written to automatically generate reports for evaluation by the DSMB. Reports were compiled, reviewed, and sent to the DSMB on average three days after the data cut-off, reflecting the trial progress in real-time. The script was also utilized to prepare for the trial pharmacometric analyses. The same source data was used to create analysis datasets in NONMEM format and to support model script development. The primary endpoint analysis was completed three days after data lock and unblinding, and the secondary endpoint analyses two weeks later. The constructive collaboration between clinical, data management, and pharmacometric teams enabled this efficient, timely, and reproducible pharmacometrics workflow.

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

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

Pharmacokinetic analysis across studies to drive knowledge-integration: A tutorial on individual patient data meta-analysis (IPDMA).

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

Translational predictions of phase 2a first-in-patient efficacy studies for antituberculosis drugs.

BACKGROUND: Phase 2a trials in tuberculosis typically use early bactericidal activity (EBA), the decline in sputum CFU over 14 days, as the primary end-point for testing the efficacy of drugs as monotherapy. However, the cost of phase 2a trials can range from USD 7 million to USD 19.6 million on average, while >30% of drugs fail to progress to phase 3. Better utilising pre-clinical data to predict and prioritise the most likely drugs to succeed will thus help to accelerate drug development and reduce costs. We aim to predict clinical EBA using pre-clinical in vivo pharmacokinetic (PK)-pharmacodynamic (PD) data and a model-based translational pharmacology approach. METHODS AND FINDINGS: First, mouse PK, PD and clinical PK models were compiled. Second, mouse PK-PD models were built to derive an exposure-response relationship. Third, translational prediction of clinical EBA studies was performed using mouse PK-PD relationships and informed by clinical PK models and species-specific protein binding. Presence or absence of clinical efficacy was accurately predicted from the mouse model. Predicted daily decreases of CFU in the first 2 days of treatment and between day 2 and day 14 were consistent with clinical observations. CONCLUSION: This platform provides an innovative solution to inform or even replace phase 2a EBA trials, to bridge the gap between mouse efficacy studies and phase 2b and phase 3 trials, and to substantially accelerate drug development.

Standards for model-based early bactericidal activity analysis and sample size determination in tuberculosis drug development.

Background: A critical step in tuberculosis (TB) drug development is the Phase 2a early bactericidal activity (EBA) study which informs if a new drug or treatment has short-term activity in humans. The aim of this work was to present a standardized pharmacometric model-based early bactericidal activity analysis workflow and determine sample sizes needed to detect early bactericidal activity or a difference between treatment arms. Methods: Seven different steps were identified and developed for a standardized pharmacometric model-based early bactericidal activity analysis approach. Non-linear mixed effects modeling was applied and different scenarios were explored for the sample size calculations. The sample sizes needed to detect early bactericidal activity given different TTP slopes and associated variability was assessed. In addition, the sample sizes needed to detect effect differences between two treatments given the impact of different TTP slopes, variability in TTP slope and effect differences were evaluated. Results: The presented early bactericidal activity analysis approach incorporates estimate of early bactericidal activity with uncertainty through the model-based estimate of TTP slope, variability in TTP slope, impact of covariates and pharmacokinetics on drug efficacy. Further it allows for treatment comparison or dose optimization in Phase 2a. To detect early bactericidal activity with 80% power and at a 5% significance level, 13 and 8 participants/arm were required for a treatment with a TTP-EBA0-14 as low as 11 h when accounting for variability in pharmacokinetics and when variability in TTP slope was 104% [coefficient of variation (CV)] and 22%, respectively. Higher sample sizes are required for smaller early bactericidal activity and when pharmacokinetics is not accounted for. Based on sample size determinations to detect a difference between two groups, TTP slope, variability in TTP slope and effect difference between two treatment arms needs to be considered. Conclusion: In conclusion, a robust standardized pharmacometric model-based EBA analysis approach was established in close collaboration between microbiologists, clinicians and pharmacometricians. The work illustrates the importance of accounting for covariates and drug exposure in EBA analysis in order to increase the power of detecting early bactericidal activity for a single treatment arm as well as differences in EBA between treatments arms in Phase 2a trials of TB drug development.

Implementing best practices on data generation and reporting of Mycobacterium tuberculosis in vitro assays within the ERA4TB consortium.

Tuberculosis (TB) is the historical leading cause of death by a single infectious agent. The European Regimen Accelerator for Tuberculosis (ERA4TB) is a public-private partnership of 30+ institutions with the objective to progress new anti-TB regimens into the clinic. Thus, robust and replicable results across independent laboratories are essential for reliable interpretation of treatment efficacy. A standardization workgroup unified in vitro protocols and data reporting templates. Time-kill assays provide essential input data for pharmacometric model-informed translation of single agents and regimens activity from in vitro to in vivo and the clinic. Five conditions were assessed by time-kill assays in six independent laboratories using four bacterial plating methods. Baseline bacterial burden varied between laboratories but variability was limited in net drug effect, confirming 2.5 µL equally robust as 100 µL plating. This exercise establishes the foundations of collaborative data generation, reporting, and integration within the overarching Antimicrobial Resistance Accelerator program.

Model-informed pediatric dose selection of marzeptacog alfa (activated): An exposure matching strategy.

Marzeptacog alfa (activated) (MarzAA) is an activated recombinant human rFVII variant intended for subcutaneous (s.c.) administration to treat or prevent bleeding in individuals with hemophilia A (HA) or B (HB) with inhibitors, and other rare bleeding disorders. The s.c. administration provides benefits over i.v. injections. The objective of the study was to support the first-in-pediatric dose selection for s.c. MarzAA to treat episodic bleeding episodes in children up through 11 years in a registrational phase III trial. Assuming the same exposure-response relationship as in adults, an exposure matching strategy was used with a population pharmacokinetics model. A sensitivity analysis evaluating the impact of doubling in absorption rate and age-dependent allometric exponents on dose selection was performed. Subsequently, the probability of trial success, defined as the number of successful trials for a given pediatric dose divided by the number of simulated trials (n = 1000) was studied. A successful trial was defined as outcome where four, three, or two out of 24 pediatric subjects per trial were allowed to fall outside the adult exposures after s.c. administration of 60 µg/kg. A dose of 60 µg/kg in children with HA/HB was supported by the clinical trial simulations to match exposures in adults. The sensitivity analyses further supported selection of the 60 µg/kg dose level in all age groups. Moreover, the probability of trial success evaluations given a plausible design confirmed the potential of a 60 µg/kg dose level. Taken together, this work demonstrates the utility of model-informed drug development and could be helpful for other pediatric development programs for rare diseases.

Translational predictions of phase 2a first-in-patient efficacy studies for antituberculosis drugs.

Background: Phase 2a trials in tuberculosis typically use early bactericidal activity (EBA), the decline in sputum colony forming units (CFU) over 14 days, as the primary outcome for testing the efficacy of drugs as monotherapy. However, the cost of phase 2a trials can range from 7 to 19.6 million dollars on average, while more than 30% of drugs fail to progress to phase 3. Better utilizing preclinical data to predict and prioritize the most likely drugs to succeed will thus help accelerate drug development and reduce costs. We aim to predict clinical EBA using preclinical in vivo pharmacokinetic-pharmacodynamic (PKPD) data and a model-based translational pharmacology approach. Methods and Findings: First, mouse PK, PD and clinical PK models were compiled. Second, mouse PKPD models were built to derive an exposure response relationship. Third, translational prediction of clinical EBA studies was performed using mouse PKPD relationships and informed by clinical PK models and species-specific protein binding. Presence or absence of clinical efficacy was accurately predicted from the mouse model. Predicted daily decreases of CFU in the first 2 days of treatment and between day 2 and day 14 were consistent with clinical observations. Conclusion: This platform provides an innovative solution to inform or even replace phase 2a EBA trials, to bridge the gap between mouse efficacy studies and phase 2b and phase 3 trials, and to substantially accelerate drug development.

Safety and efficacy of BCG re-vaccination in relation to COVID-19 morbidity in healthcare workers: A double-blind, randomised, controlled, phase 3 trial.

Background: BCG vaccination prevents severe childhood tuberculosis (TB) and was introduced in South Africa in the 1950s. It is hypothesised that BCG trains the innate immune system by inducing epigenetic and functional reprogramming, thus providing non-specific protection from respiratory tract infections. We evaluated BCG for reduction of morbidity and mortality due to COVID-19 in healthcare workers in South Africa. Methods: This randomised, double-blind, placebo-controlled trial recruited healthcare workers at three facilities in the Western Cape, South Africa, unless unwell, pregnant, breastfeeding, immunocompromised, hypersensitivity to BCG, or undergoing experimental COVID-19 treatment. Participants received BCG or saline intradermally (1:1) and were contacted once every 4 weeks for 1 year. COVID-19 testing was guided by symptoms. Hospitalisation, COVID-19, and respiratory tract infections were assessed with Cox proportional hazard modelling and time-to-event analyses, and event severity with post hoc Markovian analysis. This study is registered with ClinicalTrials.gov, NCT04379336. Findings: Between May 4 and Oct 23, 2020, we enrolled 1000 healthcare workers with a median age of 39 years (IQR 30-49), 70·4% were female, 16·5% nurses, 14·4% medical doctors, 48·5% had latent TB, and 15·3% had evidence of prior SARS-CoV-2 exposure. Hospitalisation due to COVID-19 occurred in 15 participants (1·5%); ten (66·7%) in the BCG group and five (33·3%) in the placebo group, hazard ratio (HR) 2·0 (95% CI 0·69-5·9, p = 0·20), indicating no statistically significant protection. Similarly, BCG had no statistically significant effect on COVID-19 (p = 0·63, HR = 1·08, 95% CI 0·82-1·42). Two participants (0·2%) died from COVID-19 and two (0·2%) from other reasons, all in the placebo group. Interpretation: BCG did not protect healthcare workers from SARS-CoV-2 infection or related severe COVID-19 disease and hospitalisation. Funding: Funding provided by EDCTP, grant number RIA2020EF-2968. Additional funding provided by private donors including: Mediclinic, Calavera Capital (Pty) Ltd, Thys Du Toit, Louis Stassen, The Ryan Foundation, and Dream World Investments 401 (Pty) Ltd. The computations were enabled by resources in project SNIC 2020-5-524 provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant agreement No. 2018-05,973.

Finding the right hazard function for time-to-event modeling: A tutorial and Shiny application.

Parametric time-to-event analysis is an important pharmacometric method to predict the probability of an event up until a certain time as a function of covariates and/or drug exposure. Modeling is performed at the level of the hazard function describing the instantaneous rate of an event occurring at that timepoint. We give an overview of the parametric time-to-event analysis starting with graphical exploration by Kaplan-Meier plotting for the event data including censoring and nonparametric hazard estimators such as the kernel-based visual hazard comparison for the underlying hazard. The most common hazard functions including the exponential, Gompertz, Weibull, log-normal, log-logistic, and circadian functions are described in detail. A Shiny application was developed to graphically guide the modeler which of the most common hazard functions presents a similar shape compared to the data in order to guide which hazard functions to test in the parametric time-to-event analysis. For the chosen hazard function(s), the Shiny application can additionally be used to explore corresponding parameter values to inform on suitable initial estimates for parametric modeling as well as on possible covariate or treatment relationships to certain parameters. Moreover, it can be used for the dissemination of results as well as communication, training, and workshops on time-to-event analysis. By guiding the modeler on which functions and what parameter values to test and compare as well as to assist in dissemination, the Shiny application developed here greatly supports the modeler in complicated parametric time-to-event modeling.

Anti-tuberculosis effect of isoniazid scales accurately from zebrafish to humans.

BACKGROUND AND PURPOSE: There is a clear need for innovation in anti-tuberculosis drug development. The zebrafish larva is an attractive disease model in tuberculosis research. To translate pharmacological findings to higher vertebrates, including humans, the internal exposure of drugs needs to be quantified and linked to observed response. EXPERIMENTAL APPROACH: In zebrafish studies, drugs are usually dissolved in the external water, posing a challenge to quantify internal exposure. We developed experimental methods to quantify internal exposure, including nanoscale blood sampling, and to quantify the bacterial burden, using automated fluorescence imaging analysis, with isoniazid as the test compound. We used pharmacokinetic-pharmacodynamic modelling to quantify the exposure-response relationship responsible for the antibiotic response. To translate isoniazid response to humans, quantitative exposure-response relationships in zebrafish were linked to simulated concentration-time profiles in humans, and two quantitative translational factors on sensitivity to isoniazid and stage of infection were included. KEY RESULTS: Blood concentration was only 20% of the external drug concentration. The bacterial burden increased exponentially, and an isoniazid dose corresponding to 15 mg·L-1 internal concentration (minimum inhibitory concentration) leads to bacteriostasis of the mycobacterial infection in the zebrafish. The concentration-effect relationship was quantified, and based on that relationship and the translational factors, the isoniazid response was translated to humans, which correlated well with observed data. CONCLUSIONS AND IMPLICATIONS: This proof of concept study confirmed the potential of zebrafish larvae as tuberculosis disease models in translational pharmacology and contributes to innovative anti-tuberculosis drug development, which is very clearly needed.

Quantification of Natural Growth of Two Strains of Mycobacterium Marinum for Translational Antituberculosis Drug Development.

The zebrafish infected with Mycobacterium marinum (M. marinum) is an attractive tuberculosis disease model, showing similar pathogenesis to Mycobacterium tuberculosis (M. tuberculosis) infections in humans. To translate pharmacological findings from this disease model to higher vertebrates, a quantitative understanding of the natural growth of M. marinum in comparison to the natural growth of M. tuberculosis is essential. Here, the natural growth of two strains of M. marinum, E11 and MUSA , is studied over an extended period using an established model-based approach, the multistate tuberculosis pharmacometric (MTP) model, for comparison to that of M. tuberculosis. Poikilotherm-derived strain E11 and human-derived strain MUSA were grown undisturbed up to 221 days and viability of cultures (colony forming unit (CFU)/mL) was determined by plating at different time points. Nonlinear mixed effects modeling using the MTP model quantified the bacterial growth, the transfer among fast, slow, and non-multiplying states, and the inoculi. Both strains showed initial logistic growth, reaching a maximum after 20-25 days for E11 and MUSA , respectively, followed by a decrease to a new plateau. Natural growth of both E11 and MUSA was best described with Gompertz growth functions. For E11, the inoculum was best described in the slow-multiplying state, for MUSA in the fast-multiplying state. Natural growth of E11 was most similar to that of M. tuberculosis, whereas MUSA showed more aggressive growth behavior. Characterization of natural growth of M. marinum and quantitative comparison with M. tuberculosis brings the zebrafish tuberculosis disease model closer to the quantitative translational pipeline of antituberculosis drug development.

Mechanistic and Quantitative Understanding of Pharmacokinetics in Zebrafish Larvae through Nanoscale Blood Sampling and Metabolite Modeling of Paracetamol.

Zebrafish larvae are increasingly used for pharmacological research, but internal drug exposure is often not measured. Understanding pharmacokinetics is necessary for reliable translation of pharmacological results to higher vertebrates, including humans. Quantification of drug clearance and distribution requires measurements of blood concentrations. Additionally, measuring drug metabolites is of importance to understand clearance in this model organism mechanistically. We therefore mechanistically studied and quantified pharmacokinetics in zebrafish larvae, and compared this to higher vertebrates, using paracetamol (acetaminophen) as a paradigm compound. A method was developed to sample blood from zebrafish larvae 5 days post fertilization. Blood concentrations of paracetamol and its major metabolites, paracetamol-glucuronide and paracetamol-sulfate, were measured. Blood concentration data were combined with measured amounts in larval homogenates and excreted amounts and simultaneously analyzed through nonlinear mixed-effects modeling, quantifying absolute clearance and distribution volume. Blood sampling from zebrafish larvae was most successful from the posterior cardinal vein, with a median volume (interquartile range) of 1.12 nl (0.676-1.66 nl) per blood sample. Samples were pooled (n = 15-35) to reach measurable levels. Paracetamol blood concentrations at steady state were only 10% of the external paracetamol concentration. Paracetamol-sulfate was the major metabolite, and its formation was quantified using a time-dependent metabolic formation rate. Absolute clearance and distribution volume correlated well with reported values in higher vertebrates, including humans. Based on blood concentrations and advanced data analysis, the mechanistic and quantitative understanding of paracetamol pharmacokinetics in zebrafish larvae has been established. This will improve the translational value of this vertebrate model organism in drug discovery and development. SIGNIFICANCE STATEMENT: In early phases of drug development, new compounds are increasingly screened in zebrafish larvae, but the internal drug exposure is often not taken into consideration. We developed innovative experimental and computational methods, including a blood-sampling technique, to measure the paradigm drug paracetamol (acetaminophen) and its major metabolites and quantify pharmacokinetics (absorption, distribution, elimination) in zebrafish larvae of 5 days post fertilization with a total volume of only 300 nl. These parameter values were scaled to higher vertebrates, including humans.

Impact of post-hatching maturation on the pharmacokinetics of paracetamol in zebrafish larvae.

Zebrafish larvae are increasingly used in pharmacological and toxicological studies, but it is often overlooked that internal exposure to exogenous compounds, rather than the incubation medium concentration, is driving observed effects. Moreover, as the zebrafish larva is a developing organism, continuous physiological changes impact pharmacokinetic or toxicokinetic processes like the absorption and elimination of exogenous compounds, influencing the interpretation of observations and conclusions drawn from experiments at different larval ages. Here, using paracetamol as paradigm compound, mathematical modelling is used to quantify absorption and elimination rates from internal exposure over time profiles after waterborne treatment, as well as changes in these parameters in post-hatching larvae of 3, 4, and 5 days post fertilisation (dpf). An increase of 106% in absorption rate was observed between 3 and 4 dpf, but no further increase at 5 dpf, and an increase of 17.5% in elimination rate for each dpf. Paracetamol clearance, determined from elimination rate constants and reported total larval volumes of 253, 263, and 300 nL at 3, 4, and 5 dpf respectively, correlates best with higher vertebrates at 5 dpf. This suggests that when studying direct effects of exogenous compounds, experiments with zebrafish larvae are best performed at 5 dpf.

Outside-In Systems Pharmacology Combines Innovative Computational Methods With High-Throughput Whole Vertebrate Studies.

To advance the systems approach in pharmacology, experimental models and computational methods need to be integrated from early drug discovery onward. Here, we propose outside-in model development, a model identification technique to understand and predict the dynamics of a system without requiring prior biological and/or pharmacological knowledge. The advanced data required could be obtained by whole vertebrate, high-throughput, low-resource dose-exposure-effect experimentation with the zebrafish larva. Combinations of these innovative techniques could improve early drug discovery.

Development of a population pharmacokinetic model to predict brain distribution and dopamine D2 receptor occupancy of raclopride in non-anesthetized rat.

BACKGROUND: Raclopride is a selective antagonist of the dopamine D2 receptor. It is one of the most frequently used in vivo D2 tracers (at low doses) for assessing drug-induced receptor occupancy (RO) in animals and humans. It is also commonly used as a pharmacological blocker (at high doses) to occupy the available D2 receptors and antagonize the action of dopamine or drugs on D2 in preclinical studies. The aims of this study were to comprehensively evaluate its pharmacokinetic (PK) profiles in different brain compartments and to establish a PK-RO model that could predict the brain distribution and RO of raclopride in the freely moving rat using a LC-MS based approach. METHODS: Rats (n=24) received a 10-min IV infusion of non-radiolabeled raclopride (1.61µmol/kg, i.e. 0.56mg/kg). Plasma and the brain tissues of striatum (with high density of D2 receptors) and cerebellum (with negligible amount of D2 receptors) were collected. Additional microdialysis experiments were performed in some rats (n=7) to measure the free drug concentration in the extracellular fluid of the striatum and cerebellum. Raclopride concentrations in all samples were analyzed by LC-MS. A population PK-RO model was constructed in NONMEM to describe the concentration-time profiles in the unbound plasma, brain extracellular fluid and brain tissue compartments and to estimate the RO based on raclopride-D2 receptor binding kinetics. RESULTS: In plasma raclopride showed a rapid distribution phase followed by a slower elimination phase. The striatum tissue concentrations were consistently higher than that of cerebellum tissue throughout the whole experimental period (10-h) due to higher non-specific tissue binding and D2 receptor binding in the striatum. Model-based simulations accurately predicted the literature data on rat plasma PK, brain tissue PK and D2 RO at different time points after intravenous or subcutaneous administration of raclopride at tracer dose (RO <10%), sub-pharmacological dose (RO 10%-30%) and pharmacological dose (RO >30%). CONCLUSION: For the first time a predictive model that could describe the quantitative in vivo relationship between dose, PK and D2 RO of raclopride in non-anesthetized rat was established. The PK-RO model could facilitate the selection of optimal dose and dosing time when raclopride is used as tracer or as pharmacological blocker in various rat studies. The LC-MS based approach, which doses and quantifies a non-radiolabeled tracer, could be useful in evaluating the systemic disposition and brain kinetics of tracers.

Pharmacokinetic Modeling of Paracetamol Uptake and Clearance in Zebrafish Larvae: Expanding the Allometric Scale in Vertebrates with Five Orders of Magnitude.

Zebrafish larvae (Danio rerio) are increasingly used to translate findings regarding drug efficacy and safety from in vitro-based assays to vertebrate species, including humans. However, the limited understanding of drug exposure in this species hampers its implementation in translational research. Using paracetamol as a paradigm compound, we present a novel method to characterize pharmacokinetic processes in zebrafish larvae, by combining sensitive bioanalytical methods and nonlinear mixed effects modeling. The developed method allowed quantification of paracetamol and its two major metabolites, paracetamol-sulfate and paracetamol-glucuronide in pooled samples of five lysed zebrafish larvae of 3 days post-fertilization. Paracetamol drug uptake was quantified to be 0.289 pmole/min and paracetamol clearance was quantified to be 1.7% of the total value of the larvae. With an average volume determined to be 0.290 µL, this yields an absolute clearance of 2.96 × 107 L/h, which scales reasonably well with clearance rates in higher vertebrates. The developed methodology will improve the success rate of drug screens in zebrafish larvae and the translation potential of findings, by allowing the establishment of accurate exposure profiles and thereby also the establishment of concentration-effect relationships.

Integration of PKPD relationships into benefit-risk analysis.

AIM: Despite the continuous endeavour to achieve high standards in medical care through effectiveness measures, a quantitative framework for the assessment of the benefit-risk balance of new medicines is lacking prior to regulatory approval. The aim of this short review is to summarise the approaches currently available for benefit-risk assessment. In addition, we propose the use of pharmacokinetic-pharmacodynamic (PKPD) modelling as the pharmacological basis for evidence synthesis and evaluation of novel therapeutic agents. METHODS: A comprehensive literature search has been performed using MESH terms in PubMed, in which articles describing benefit-risk assessment and modelling and simulation were identified. In parallel, a critical review of multi-criteria decision analysis (MCDA) is presented as a tool for characterising a drug's safety and efficacy profile. RESULTS: A definition of benefits and risks has been proposed by the European Medicines Agency (EMA), in which qualitative and quantitative elements are included. However, in spite of the value of MCDA as a quantitative method, decisions about benefit-risk balance continue to rely on subjective expert opinion. By contrast, a model-informed approach offers the opportunity for a more comprehensive evaluation of benefit-risk balance before extensive evidence is generated in clinical practice. CONCLUSIONS: Benefit-risk balance should be an integral part of the risk management plan and as such considered before marketing authorisation. Modelling and simulation can be incorporated into MCDA to support the evidence synthesis as well evidence generation taking into account the underlying correlations between favourable and unfavourable effects. In addition, it represents a valuable tool for the optimization of protocol design in effectiveness trials.