Pharmacokinetic–Pharmacodynamic–Efficacy Analysis of Efalizumab in Patients with Moderate to Severe Psoriasis

Ng, Chee M.; Joshi, Amita; Dedrick, Russell L.; Garovoy, Marvin R.; Bauer, Robert J.

doi:10.1007/s11095-005-5642-4

Cited by 83 publications

(87 citation statements)

References 23 publications

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“…The availability of more powerful estimation algorithms, software packages, and computers enabled scientists to incorporate more sophisticated mechanisms in PK/PD models for clinical and experimental studies (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38). Whenever true biological variability between individuals is present and significant, population PK/PD modeling is a powerful concept to describe and predict the behavior of such systems.…”

Section: Discussionmentioning

confidence: 99%

Performance and Robustness of the Monte Carlo Importance Sampling Algorithm Using Parallelized S-ADAPT for Basic and Complex Mechanistic Models

Bulitta

Landersdorfer

2011

AAPS J

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Abstract. The Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm can approximate the true log-likelihood as precisely as needed and is efficiently parallelizable. Our objectives were to evaluate an importance sampling version of the MC-PEM algorithm for mechanistic models and to qualify the default estimation settings in SADAPT-TRAN. We assessed bias, imprecision and robustness of this algorithm in S-ADAPT for mechanistic models with up to 45 simultaneously estimated structural parameters, 14 differential equations, and 10 dependent variables (one drug concentration and nine pharmacodynamic effects). Simpler models comprising 15 parameters were estimated using three of the ten dependent variables. We set initial estimates to 0.1 or 10 times the true value and evaluated 30 bootstrap replicates with frequent or sparse sampling. Datasets comprised three dose levels with 16 subjects each. Parallelized estimation was 23-fold (6.9-fold) faster using 48 threads (eight threads) relative to one thread. The MC-PEM algorithm was robust and provided unbiased and adequately precise means and variances during simultaneous estimation of complex, mechanistic models in a 45 dimensional parameter space with rich or sparse data using poor initial estimates.

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Section: Discussionmentioning

confidence: 99%

Performance and Robustness of the Monte Carlo Importance Sampling Algorithm Using Parallelized S-ADAPT for Basic and Complex Mechanistic Models

Bulitta

Landersdorfer

2011

AAPS J

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“…This benefit is greatest for mechanistic models that may contain 20 and more random model parameters (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27), as the likelihood for model specification errors increases with model complexity. The bi-directional and hierarchical error checks of the SADAPT-TRAN pre-processor (Fig.…”

Section: Discussionmentioning

confidence: 99%

Development of a New Pre- and Post-Processing Tool (SADAPT-TRAN) for Nonlinear Mixed-Effects Modeling in S-ADAPT

Bulitta

Bingölbali

Shin

et al. 2011

AAPS J

111

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Abstract. Mechanistic modeling greatly benefits from automated pre-and post-processing of model code and modeling results. While S-ADAPT provides many state-of-the-art parametric population estimation methods, its pre-and post-processing capabilities are limited. Our objective was to develop a fully automated, open-source pre-and post-processor for nonlinear mixed-effects modeling in S-ADAPT. We developed a new translator tool (SADAPT-TRAN) based on Perl scripts. These scripts (a) automatically translate the core model components into robust Fortran code, (b) perform extensive mutual error checks across all input files and the raw dataset, (c) extend the options of the Monte Carlo Parametric Expectation Maximization (MC-PEM) algorithm, and (d) improve the numerical robustness of the model code. The post-processing scripts automatically summarize the results of one or multiple models as tables and, by generating problem specific R scripts, provide an extended series of standard and covariatestratified diagnostic plots. The SADAPT-TRAN package substantially improved the efficiency to specify, debug, and evaluate models and enhanced the flexibility of using the MC-PEM algorithm for parallelized estimation in S-ADAPT. The parameter variability model can take any combination of normally, lognormally, or logistically distributed parameters and the SADAPT-TRAN package can automatically generate the Fortran code required to specify between occasion variability. Extended estimation features are available to avoid local minima, estimate means with negligible variances, and estimate variances for fixed means. The SADAPT-TRAN package significantly facilitated model development in S-ADAPT, reduced model specification errors, and provided useful error messages for beginner and advanced users. This benefit was greatest for complex mechanistic models.

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“…Only a few examples of therapeutic proteins have been well described with quantitative PK-PD approaches, and one of the hallmark molecules utilizing quantitative approaches is efalizumab. 18 The efalizumab model described the PK, PD, and efficacy data in psoriasis patients, and they utilized the simulations from the final model to help with justifying less frequent dosing, which maintained similar efficacy and made the compound more convenient to use. The understanding of soluble targets and PK-PD of molecules binding to soluble targets have been described in many binding analyses, 1 of which was done with omalizumab on IgE.…”

Section: Pharmacokinetic-pharmacodynamic Relationshipsmentioning

confidence: 99%

Clinical Pharmacology Tools and Evaluations to Facilitate Comprehensive Dose Finding in Oncology: A Continuous Risk‐Benefit Approach

Bullock

Lin

Bilic

2017

The Journal of Clinical Pharma

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Targeted therapies are now considered an integral component in the treatment armamentarium for many malignancies, and the approach to developing these drugs needs to be refined from the previous cytotoxic paradigm of toxicity-guided dose finding and identification of maximum tolerated dose to a paradigm driven by target activity. Moving away from the toxicity-driven dose finding and justification model requires an integrated approach in order to adequately characterize the risk-benefit of a drug. This approach starts with understanding the importance of collecting samples for pharmacokinetic and pharmacodynamic assessments in all phases of clinical development to fully characterize the pharmacokinetics and identify covariates and then correlating exposure to key markers of safety and efficacy in pharmacometric analyses to perform a robust risk-benefit assessment and establish the right dose. In addition, for oral agents, decisions on administering the drug with respect to food can impact dose among other clinical trial outcomes such as tolerability and patient compliance. Understanding the importance of model-based drug development as a decision-making tool to support drug development through incorporation of all relevant data allows for a robust risk-benefit assessment at key decision points. Utilization of clinical pharmacology tools and assessments throughout development will provide the key components of a successful oncology development program.

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Pharmacokinetic–Pharmacodynamic–Efficacy Analysis of Efalizumab in Patients with Moderate to Severe Psoriasis

Abstract: The final model described the PK/PD/E data in psoriasis patients reasonably well. In addition, simulations using the final model suggested that efalizumab administered less frequently could possibly be more convenient with similar efficacy.

Cited by 83 publications

References 23 publications

Performance and Robustness of the Monte Carlo Importance Sampling Algorithm Using Parallelized S-ADAPT for Basic and Complex Mechanistic Models

Performance and Robustness of the Monte Carlo Importance Sampling Algorithm Using Parallelized S-ADAPT for Basic and Complex Mechanistic Models

Development of a New Pre- and Post-Processing Tool (SADAPT-TRAN) for Nonlinear Mixed-Effects Modeling in S-ADAPT

Clinical Pharmacology Tools and Evaluations to Facilitate Comprehensive Dose Finding in Oncology: A Continuous Risk‐Benefit Approach

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