A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas

This paper provides a comprehensive tutorial for Bayesian practitioners in pharmacometrics using Pumas workflows. We start by giving a brief motivation of Bayesian inference for pharmacometrics highlighting limitations in existing software that Pumas addresses. We then follow by a description of all the steps of a standard Bayesian workflow for pharmacometrics using code snippets and examples. This includes: model definition, prior selection, sampling from the posterior, prior and posterior simulations and predictions, counter-factual simulations and predictions, convergence diagnostics, visual predictive checks, and finally model comparison with cross-validation. Finally, the background and intuition behind many advanced concepts in Bayesian statistics are explained in simple language. This includes many important ideas and precautions that users need to keep in mind when performing Bayesian analysis. Many of the algorithms, codes, and ideas presented in this paper are highly applicable to clinical research and statistical learning at large but we chose to focus our discussions on pharmacometrics in this paper to have a narrower scope in mind and given the nature of Pumas as a software primarily for pharmacometricians

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationship between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. Recent technological advances rendered collecting real-time and detailed data easy. However, the pharmacometric tools have not been designed to handle heterogeneous, big data and complex models. The estimation methods are outdated to solve modern healthcare challenges. We set out to design a platform that facilitates domain specific modeling and its integration with modern analytics to foster innovation and readiness to data deluge in healthcare. New specialized estimation methodologies have been developed that allow dramatic performance advances in areas that have not seen major improvements in decades. New ODE solver algorithms, such as coefficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to up to 4x performance improvements across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques and further specialize the solution process on the individual systems, allowing statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME parameter estimation see run times halved while retaining the same accuracy. Meanwhile in areas with less prior optimization of software, like optimal experimental design, we see orders of magnitude performance enhancements. Together we show a fast and modern domain specific modeling framework which lays a platform for innovation via upcoming integrations with modern analytics

Intranasal and rectal diazepam for rescue therapy: assessment of pharmacokinetics and tolerability.

University of Minnesota Ph.D. dissertation. December 2010. Major: Social, Administrative, and Clinical Pharmacy. Advisor: James C. Cloyd. 1 computer file (PDF); xvi, 185 pages, appendix p. 181-185.The use of rectal diazepam has improved the management of acute repetitive seizures (ARS) outside a health care facility. Two placebo controlled trials have shown that rectal administration of diazepam is safe and effective for treatment of this condition. Diastat® is the only FDA approved treatment for ARS in the United States. Although some older children and adults are willing to use Diastat®, many patients in these age groups as well as physicians and caregivers object to the route of administration and instead use other therapies not approved for this purpose, receive no treatment, or use emergency medical services or acute care systems. We developed and evaluated three nasal spray formulations of diazepam which can be easily administered with rapid absorption characteristics intended as an alternative to rectal administration. One formulation used a supersaturated glycofurol based co-solvent system while the remaining two (Nas-A & Nas-B) used microemulsion based co-solvent systems. These formulations were studied for their pharmacokinetics and tolerability in healthy adult volunteers. Data from these studies were then compared to the pharmacokinetics after rectal administration using both model-based analysis (NONMEM) and graphical methods. The primary finding from this work was that, only the microemulsion-based formulations, particularly Nas-B could be used for further development as the glycofurol formulation was not well tolerated by subjects. The pharmacokinetic profiles after intranasal administration were associated with high variability. However, we are able to show that the dose-normalized partial area under the curve (AUC - an exposure parameter) after nasal administration, at times when the drug concentrations are most important, are 60-80 % of that when given via the rectal route. Given the ease and social acceptability of nasal administration compared to rectal, equivalent exposures can be easily attained by giving a second nasal dose, and we thus conclude that intranasal diazepam is a feasible and preferable alternative to rectal diazepam in the management of ARS outside a hospital. This work also provides some recommendations for future studies in the development of an intranasal product.Ivaturi, Vijay Deep. (2010). Intranasal and rectal diazepam for rescue therapy: assessment of pharmacokinetics and tolerability.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/100044

Population Pharmacokinetic Modeling of Gentamicin in Pediatrics

The primary objective of this work was to characterize the pharmacokinetics (PK) of gentamicin across the whole pediatric age spectrum from premature neonates to young adults with a single model by identifying significant clinical predictors. A nonlinear mixed‐effect population PK model was developed with retrospective therapeutic drug‐monitoring data. A total of 6459 drug concentration measurements from 3370 hospitalized patients were collected for model building (n = 2357) and evaluation (n = 1013). In agreement with previously reported models, a 2‐compartment model with first‐order elimination best described the drug PK. Patient‐specific factors significantly impacting gentamicin clearance included fat‐free mass, postmenstrual age, and serum creatinine (SCr). Based on our model, the deviation of the individual SCr from the age‐dependent expected mean SCr value (SCrM) can result in a 40% lower clearance in a patient with renal impairment than that in a patient with normal kidney function, with SCrM:SCr ratios between 0.16 and 3.2 in this study. Consistent with the known age‐dependent changes of the proportion of extracellular water in body weight, the inclusion of the impact of extracellular water maturation on the central volume of distribution was found to improve the model fitting significantly. In comparison with other published models, model evaluation suggested the developed model was the least biased and physiologically most representative. These results will be used to inform individualized initial dosing strategies and serve as a prior PK model for Bayesian updating and forecasting as individual clinical observations become availabl

Population Pharmacokinetic Modeling of Gentamicin in Pediatrics

The primary objective of this work was to characterize the pharmacokinetics (PK) of gentamicin across the whole pediatric age spectrum from premature neonates to young adults with a single model by identifying significant clinical predictors. A nonlinear mixed‐effect population PK model was developed with retrospective therapeutic drug‐monitoring data. A total of 6459 drug concentration measurements from 3370 hospitalized patients were collected for model building (n = 2357) and evaluation (n = 1013). In agreement with previously reported models, a 2‐compartment model with first‐order elimination best described the drug PK. Patient‐specific factors significantly impacting gentamicin clearance included fat‐free mass, postmenstrual age, and serum creatinine (SCr). Based on our model, the deviation of the individual SCr from the age‐dependent expected mean SCr value (SCrM) can result in a 40% lower clearance in a patient with renal impairment than that in a patient with normal kidney function, with SCrM:SCr ratios between 0.16 and 3.2 in this study. Consistent with the known age‐dependent changes of the proportion of extracellular water in body weight, the inclusion of the impact of extracellular water maturation on the central volume of distribution was found to improve the model fitting significantly. In comparison with other published models, model evaluation suggested the developed model was the least biased and physiologically most representative. These results will be used to inform individualized initial dosing strategies and serve as a prior PK model for Bayesian updating and forecasting as individual clinical observations become availabl

Population Pharmacokinetic Modeling of Gentamicin in Pediatrics

The primary objective of this work was to characterize the pharmacokinetics (PK) of gentamicin across the whole pediatric age spectrum from premature neonates to young adults with a single model by identifying significant clinical predictors. A nonlinear mixed‐effect population PK model was developed with retrospective therapeutic drug‐monitoring data. A total of 6459 drug concentration measurements from 3370 hospitalized patients were collected for model building (n = 2357) and evaluation (n = 1013). In agreement with previously reported models, a 2‐compartment model with first‐order elimination best described the drug PK. Patient‐specific factors significantly impacting gentamicin clearance included fat‐free mass, postmenstrual age, and serum creatinine (SCr). Based on our model, the deviation of the individual SCr from the age‐dependent expected mean SCr value (SCrM) can result in a 40% lower clearance in a patient with renal impairment than that in a patient with normal kidney function, with SCrM:SCr ratios between 0.16 and 3.2 in this study. Consistent with the known age‐dependent changes of the proportion of extracellular water in body weight, the inclusion of the impact of extracellular water maturation on the central volume of distribution was found to improve the model fitting significantly. In comparison with other published models, model evaluation suggested the developed model was the least biased and physiologically most representative. These results will be used to inform individualized initial dosing strategies and serve as a prior PK model for Bayesian updating and forecasting as individual clinical observations become availabl