Comparisons of Analysis Methods for Proof-of-Concept Trials.
ABSTRACT: Drug development struggles with high costs and time consuming processes. Hence, a need for new strategies has been accentuated by many stakeholders in drug development. This study proposes the use of pharmacometric models to rationalize drug development. Two simulated examples, within the therapeutic areas of acute stroke and type 2 diabetes, are utilized to compare a pharmacometric model-based analysis to a t-test with respect to study power of proof-of-concept (POC) trials. In all investigated examples and scenarios, the conventional statistical analysis resulted in several fold larger study sizes to achieve 80% power. For a scenario with a parallel design of one placebo group and one active dose arm, the difference between the conventional and pharmacometric approach was 4.3- and 8.4-fold, for the stroke and diabetes example, respectively. Although the model-based power depend on the model assumptions, in these scenarios, the pharmacometric model-based approach was demonstrated to permit drastic streamlining of POC trials.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e23; doi:10.1038/psp.2012.24; advance online publication 16 January 2013.
Project description:Reliance on modeling and simulation in drug discovery and development has dramatically increased over the past decade. Two disciplines at the forefront of this activity, pharmacometrics and systems pharmacology (SP), emerged independently from different fields; consequently, a perception exists that only few examples integrate these approaches. Herein, we review the state of pharmacometrics and SP integration and describe benefits of combining these approaches in a model-informed drug discovery and development framework.
Project description:AIMS:Recombinant tissue plasminogen activator (rt-PA) is the only first-line agent approved by the US Food and Drug Administration to treat acute ischaemic stroke. However, it often causes the serious adverse event (AE) of haemorrhagic transformation. The present study developed a pharmacometric model for the rt-PA treatment effect and AE and, using the developed model, proposed a benefit-to-risk ratio assessment scheme as a supportive tool to optimize treatment outcome. METHODS:The data from 336 acute ischaemic stroke patients were used. The treatment effect was assessed based on an improvement in National Institutes of Health Stroke Scale (NIHSS) scores, which were described using an item response theory (IRT)-based disease progression model. Treatment failure and AE probabilities, and their occurrence times, were described by incidence and time-to-event models. Using the developed model, benefit-to-risk ratios were simulated under various scenarios using the global benefit-to-risk trade-off ratio (GBR). RESULTS:High initial NIHSS score and middle cerebral artery (MCA) stroke were risk factors for treatment failure, where the failure rate with MCA stroke was 2.87-fold higher than with non-MCA stroke. The haemorrhagic transformation time was associated with longitudinal changes in NIHSS scores. The benefit-to-risk ratio simulated was highest in minor stroke severity, with GBR >1 in all scenarios, and the ratio with non-MCA stroke was 2-3 fold higher than with MCA stroke. CONCLUSIONS:The study demonstrated the feasibility of applying an IRT model to describing the time course of the rt-PA treatment effect and AE. Benefit-to-risk ratio analyses showed that the treatment was optimal in non-MCA stroke with minor stroke severity.
Project description:Pharmacometrics is an emerging science that interprets drug, disease, and trial information in a mathematical fashion to inform and facilitate efficient drug development and/or regulatory decisions. Pharmacometrics study is increasingly adopted in the regulatory review of new antimicrobial agents. We summarized the 31 antimicrobial agents approved by the US Food and Drug Administration (FDA) and the 26 antimicrobial agents approved by European Medicines Agency (EMA) from January 2001 to May 2019. We also reviewed recent examples of utilizing pharmacometrics to support antimicrobial agent's registration in China, including modeling and simulation methods, effects of internal/external factors on pharmacokinetic (PK) parameters, safety and efficacy evaluation in terms of exposure-response analysis, refinement of the wording of product labeling and package leaflet, and possible postmarketing clinical trial. Ongoing communication among regulator, academia, and industry regarding pharmacometrics is encouraged to streamline and facilitate the development of new antimicrobial agents. The industry can maximize its benefit in drug development through continued pharmacometrics education/training.
Project description:Item response theory (IRT) was used to characterize the time course of lower urinary tract symptoms due to benign prostatic hyperplasia (BPH-LUTS) measured by item-level International Prostate Symptom Scores (IPSS). The Fisher information content of IPSS items was determined and the power to detect a drug effect using the IRT approach was examined. Data from 403 patients with moderate-to-severe BPH-LUTS in a placebo-controlled phase II trial studying the effect of degarelix over 6 months were used for modeling. Three pharmacometric models were developed: a model for total IPSS, a unidimensional IRT model, and a bidimensional IRT model, the latter separating voiding and storage items. The population-level time course of BPH-LUTS in all models was described by initial improvement followed by worsening. In the unidimensional IRT model, the combined information content of IPSS voiding items represented 72% of the total information content, indicating that the voiding subscore may be more sensitive to changes in BPH-LUTS compared with the storage subscore. The pharmacometric models showed considerably higher power to detect a drug effect compared with a cross-sectional and while-on-treatment analysis of covariance, respectively. Compared with the sample size required to detect a drug effect at 80% power with the total IPSS model, a reduction of 5.9% and 11.7% was obtained with the unidimensional and bidimensional IPSS IRT model, respectively. Pharmacometric IRT analysis of the IPSS within BPH-LUTS may increase the precision and efficiency of treatment effect assessment, albeit to a more limited extent compared with applications in other therapeutic areas.
Project description:The aim of this study was to investigate pharmacodynamic (PD) interactions in mice infected with Mycobacterium tuberculosis using population pharmacokinetics (PKs), the Multistate Tuberculosis Pharmacometric (MTP) model, and the General Pharmacodynamic Interaction (GPDI) model. Rifampicin, isoniazid, ethambutol, or pyrazinamide were administered in monotherapy for 4 weeks. Rifampicin and isoniazid showed effects in monotherapy, whereas the animals became moribund after 7 days with ethambutol or pyrazinamide alone. No PD interactions were observed against fast-multiplying bacteria. Interactions between rifampicin and isoniazid on killing slow and non-multiplying bacteria were identified, which led to an increase of 0.86 log10 colony-forming unit (CFU)/lungs at 28 days after treatment compared to expected additivity (i.e., antagonism). An interaction between rifampicin and ethambutol on killing non-multiplying bacteria was quantified, which led to a decrease of 2.84 log10 CFU/lungs at 28 days after treatment (i.e., synergism). These results show the value of pharmacometrics to quantitatively assess PD interactions in preclinical tuberculosis drug development.
Project description:<h4>Purpose</h4>This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT).<h4>Methods</h4>A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model.<h4>Results</h4>IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis.<h4>Conclusion</h4>A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data.
Project description:Population or other pharmacometric models are a useful means to describe, succinctly, the relationships between drug administration, exposure (concentration), and downstream changes in pharmacodynamic (PD) biomarkers and clinical endpoints, including the mixed effects of patient factors and random interpatient variation (fixed and random effects). However, showing a set of covariate equations to a drug development team is perhaps not the best way to get a message across. Visualization of the consequences of the knowledge encapsulated within the model is the key component. Yet in many instances, it can take hours, perhaps days, to collect ideas from teams, write scripts, and run simulations before presenting the results-by which time they have moved on. How much better, then, to seize the moment and work interactively to decide on a course of action, guided by the model. We exemplify here the visualization of pharmacometric models using the Berkeley Madonna software with a particular focus on interactive sessions. The examples are provided as Supplementary Material.
Project description:In antihyperglycemic drug development, drug effects are usually characterized using glucose provocations. Analyzing provocation data using pharmacometrics has shown powerful, enabling small studies. In preclinical drug development, high power is attractive due to the experiment sizes; however, insulin is not always available, which potentially impacts power and predictive performance. This simulation study was performed to investigate the implications of performing model-based drug characterization without insulin. The integrated glucose-insulin model was used to simulate and re-estimated oral glucose tolerance tests using a crossover design of placebo and study compound. Drug effects were implemented on seven different mechanisms of action (MOA); one by one or in two-drug combinations. This study showed that exclusion of insulin may severely reduce the power to distinguish the correct from competing drug effect, and to detect a primary or secondary drug effect, however, it did not affect the predictive performance of the model.
Project description:<h4>Purpose</h4>Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity.<h4>Methods</h4>Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CL<sub>SN-38</sub>: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m<sup>2</sup> (-30%)). Study power was assessed given diverse scenarios (n = 50-400 patients/arm, parallel/crossover, varying magnitude of CL<sub>SN-38</sub>, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing.<h4>Results</h4>The magnitude of CL<sub>SN-38</sub> reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·10<sup>9</sup> cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ<sup>2</sup>/McNemar's test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power.<h4>Conclusions</h4>The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.
Project description:The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.