Bayesian model-guided antimicrobial therapy in pediatrics

Antimicrobials have transformed the practice of medicine, making life-threatening infections treatable, but determining optimal dosing, particularly in pediatric patients, remains a challenge. The lack of pediatric data can largely be traced back to pharmaceutical companies, which, until recently, were not required to perform clinical testing in pediatrics. As a result, most antimicrobial use in pediatrics is off-label. In recent years, a concerted effort (e.g., Pediatric Research Equality Act) has been made to fill these knowledge gaps, but progress is slow and better strategies are needed. Model-based techniques have been used by pharmaceutical companies and regulatory agencies for decades to derive rational individualized dosing guidelines. Historically, these techniques have been unavailable in a clinical setting, but the advent of Bayesian-model-driven, integrated clinical decision support platforms has made model-informed precision dosing more accessible. Unfortunately, the rollout of these systems remains slow despite their increasingly well documented contributions to patient-centered care. The primary goals of this work are to 1) provide a succinct, easy-to-follow description of the challenges associated with designing and implementing dose-optimization strategies; and 2) provide supporting evidence that Bayesian-model informed precision dosing can meet those challenges. There are numerous stakeholders in a hospital setting, and our intention is for this work to serve as a starting point for clinicians who recognize that these techniques are the future of modern pharmacotherapy and wish to become champions of that movement

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 The estimation methods are outdated to solve modern healthcare challenges.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

Optimal use of intravenous tranexamic acid for hemorrhage prevention in pregnant women.

CTSI FUNDED. Funding text #1 The project was funded by the National Institutes of Health (NIH) (K23HL141640 and KL2TR001877/UL1TR001876 to H.K.A., R61HL141791 to A.S.W., and T32HD087969 to J.V.D.A.). Of note, this publication was supported by award numbers UL1TR001876 and KL2TR001877 from the NIH National Center for Advancing Translational Sciences

Population pharmacokinetics and pharmacodynamics of Tranexamic acid in women undergoing caesarean delivery.

AIMS: The population pharmacokinetics (PK) and pharmacodynamics (PD) of tranexamic acid (TXA) have not been studied to prevent postpartum haemorrhage (PPH) in pregnant women. It is unclear which TXA dose assures sufficient PPH prevention. This study investigated population PK/PD of TXA in pregnant women who underwent caesarean delivery to determine the optimal prophylactic doses of TXA for future studies. METHODS: We analysed concentration (PK) and maximum lysis (PD) data from 30 pregnant women scheduled for caesarean delivery who received 5, 10 or 15 mg/kg of TXA intravenously using population approach. RESULTS: TXA PK was best described by a two-compartment model with first-order elimination and the following parameters: clearance (between-subject variability) of 9.4 L/h (27.7%), central volume of 10.1 L (47.4%), intercompartmental clearance of 22.4 L/h (66.7%), peripheral volume of 14.0 L (13.1%) and additive error of 1.4 mg/L. The relationship between TXA concentration and maximum lysis was characterized by a sigmoid Emax model with baseline lysis of 97%, maximum inhibition of 89%, IC CONCLUSION: This is the first population PK and PD study of TXA in pregnant women undergoing caesarean delivery. Our analysis suggests that a 650 mg dose provides adequate PPH prophylaxis up to 1 hour, which is less than the currently used 1000 mg of TXA in pregnant women

Correction: Preservation of myocardial contractility during acute hypoxia with OMX-CV, a novel oxygen delivery biotherapeutic.

[This corrects the article DOI: 10.1371/journal.pbio.2005924.]